From 245f5d35211af4badafa4a373fd9f678f1cf33e6 Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Sat, 17 Oct 2020 17:24:13 -0400 Subject: [PATCH 01/25] adding coal dataset --- datasets/2018_coal/burner_0.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_1.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_10.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_11.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_2.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_3.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_4.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_5.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_6.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_7.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_8.csv | 14402 +++++++++++++++++++++++++++++ datasets/2018_coal/burner_9.csv | 14402 +++++++++++++++++++++++++++++ multithreaded/examm_mt_nlp.cxx | 2 + word_series/word_series.cxx | 10 +- word_series/word_series.hxx | 1 + 15 files changed, 172835 insertions(+), 2 deletions(-) create mode 100644 datasets/2018_coal/burner_0.csv create mode 100644 datasets/2018_coal/burner_1.csv create mode 100644 datasets/2018_coal/burner_10.csv create mode 100644 datasets/2018_coal/burner_11.csv create mode 100644 datasets/2018_coal/burner_2.csv create mode 100644 datasets/2018_coal/burner_3.csv create mode 100644 datasets/2018_coal/burner_4.csv create mode 100644 datasets/2018_coal/burner_5.csv create mode 100644 datasets/2018_coal/burner_6.csv create mode 100644 datasets/2018_coal/burner_7.csv create mode 100644 datasets/2018_coal/burner_8.csv create mode 100644 datasets/2018_coal/burner_9.csv diff --git a/datasets/2018_coal/burner_0.csv b/datasets/2018_coal/burner_0.csv new file mode 100644 index 00000000..4472aaea --- /dev/null +++ b/datasets/2018_coal/burner_0.csv @@ -0,0 +1,14402 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+0.520752,0.398967,0.96105,0.728382,0.988845,0.655314,0.579152,0.012547,0.00917791,0.702652,0.000144735,0.346609 +0.540237,0.401273,0.955099,0.730177,0.989047,0.649513,0.574959,0.0123836,0.0087497,0.703517,0.000144735,0.388068 +0.53537,0.400812,0.958932,0.732658,0.988983,0.656374,0.579919,0.0124063,0.00906876,0.706942,0.000144735,0.455339 +0.540611,0.399428,0.952326,0.728603,0.989006,0.647378,0.573416,0.0123986,0.00894953,0.701875,0.000144735,0.398233 +0.549071,0.399428,0.948169,0.718295,0.988645,0.641671,0.569291,0.0126653,0.00986862,0.691682,0.000144735,0.402902 +0.544715,0.401273,0.958687,0.713253,0.988587,0.637052,0.565953,0.0127874,0.00952289,0.68592,0.000144735,0.404834 diff --git a/multithreaded/examm_mt_nlp.cxx b/multithreaded/examm_mt_nlp.cxx index 04efd7b7..2b1b921d 100644 --- a/multithreaded/examm_mt_nlp.cxx +++ b/multithreaded/examm_mt_nlp.cxx @@ -94,6 +94,8 @@ int main(int argc, char **argv) int32_t word_offset = 1; get_argument(arguments,"--word_offset",true,word_offset); + uint32_t batch_size = 64; + get_argument(arguments,"--batch_size",true,batch_size); Corpus* corpus_sets = Corpus::generate_from_arguments(arguments); diff --git a/word_series/word_series.cxx b/word_series/word_series.cxx index fd761773..5a865c2f 100644 --- a/word_series/word_series.cxx +++ b/word_series/word_series.cxx @@ -527,6 +527,7 @@ Corpus* Corpus::generate_from_arguments(const vector &arguments){ cs->filenames.clear(); cs->word_index.clear(); cs->vocab.clear(); + uint32_t _batch_size; if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { vector training_filenames; @@ -554,6 +555,11 @@ Corpus* Corpus::generate_from_arguments(const vector &arguments){ exit(1); } + + + get_argument(arguments,"--batch_size", true,_batch_size); + + cs->batch_size =_batch_size; cs->all_parameter_names.clear(); cs->input_parameter_names.clear(); cs->output_parameter_names.clear(); @@ -908,8 +914,8 @@ void Corpus::export_sent_series(const vector &series_indexes, int word_offs sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); } - inputs = batchify(64,temp_inputs); - outputs = batchify(64,temp_outputs); + inputs = batchify(batch_size,temp_inputs); + outputs = batchify(batch_size,temp_outputs); } diff --git a/word_series/word_series.hxx b/word_series/word_series.hxx index 5b9fd667..ca244813 100644 --- a/word_series/word_series.hxx +++ b/word_series/word_series.hxx @@ -117,6 +117,7 @@ class Corpus { private: + uint32_t batch_size; string normalize_type; vector filenames; From 4924bc2f65f015f925906d479685622a435e0258 Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Sat, 17 Oct 2020 18:42:51 -0400 Subject: [PATCH 02/25] removed backup files --- word_series/word_series.cxx.orig | 1040 --------------------- word_series/word_series_BACKUP_133016.cxx | 1040 --------------------- word_series/word_series_BACKUP_133226.cxx | 1040 --------------------- word_series/word_series_BACKUP_133300.cxx | 1040 --------------------- word_series/word_series_BACKUP_133388.cxx | 1040 --------------------- word_series/word_series_BACKUP_133937.cxx | 1040 --------------------- word_series/word_series_BACKUP_134984.cxx | 1040 --------------------- word_series/word_series_BASE_133016.cxx | 1031 -------------------- word_series/word_series_BASE_133226.cxx | 1031 -------------------- word_series/word_series_BASE_133300.cxx | 1031 -------------------- word_series/word_series_BASE_133388.cxx | 1031 -------------------- word_series/word_series_BASE_133937.cxx | 1031 -------------------- word_series/word_series_BASE_134984.cxx | 1031 -------------------- word_series/word_series_LOCAL_133016.cxx | 1037 -------------------- word_series/word_series_LOCAL_133226.cxx | 1037 -------------------- word_series/word_series_LOCAL_133300.cxx | 1037 -------------------- word_series/word_series_LOCAL_133388.cxx | 1037 -------------------- word_series/word_series_LOCAL_133937.cxx | 1037 -------------------- word_series/word_series_LOCAL_134984.cxx | 1037 -------------------- word_series/word_series_REMOTE_133016.cxx | 1029 -------------------- word_series/word_series_REMOTE_133226.cxx | 1029 -------------------- word_series/word_series_REMOTE_133300.cxx | 1029 -------------------- word_series/word_series_REMOTE_133388.cxx | 1029 -------------------- word_series/word_series_REMOTE_133937.cxx | 1029 -------------------- word_series/word_series_REMOTE_134984.cxx | 1029 -------------------- 25 files changed, 25862 deletions(-) delete mode 100644 word_series/word_series.cxx.orig delete mode 100644 word_series/word_series_BACKUP_133016.cxx delete mode 100644 word_series/word_series_BACKUP_133226.cxx delete mode 100644 word_series/word_series_BACKUP_133300.cxx delete mode 100644 word_series/word_series_BACKUP_133388.cxx delete mode 100644 word_series/word_series_BACKUP_133937.cxx delete mode 100644 word_series/word_series_BACKUP_134984.cxx delete mode 100644 word_series/word_series_BASE_133016.cxx delete mode 100644 word_series/word_series_BASE_133226.cxx delete mode 100644 word_series/word_series_BASE_133300.cxx delete mode 100644 word_series/word_series_BASE_133388.cxx delete mode 100644 word_series/word_series_BASE_133937.cxx delete mode 100644 word_series/word_series_BASE_134984.cxx delete mode 100644 word_series/word_series_LOCAL_133016.cxx delete mode 100644 word_series/word_series_LOCAL_133226.cxx delete mode 100644 word_series/word_series_LOCAL_133300.cxx delete mode 100644 word_series/word_series_LOCAL_133388.cxx delete mode 100644 word_series/word_series_LOCAL_133937.cxx delete mode 100644 word_series/word_series_LOCAL_134984.cxx delete mode 100644 word_series/word_series_REMOTE_133016.cxx delete mode 100644 word_series/word_series_REMOTE_133226.cxx delete mode 100644 word_series/word_series_REMOTE_133300.cxx delete mode 100644 word_series/word_series_REMOTE_133388.cxx delete mode 100644 word_series/word_series_REMOTE_133937.cxx delete mode 100644 word_series/word_series_REMOTE_134984.cxx diff --git a/word_series/word_series.cxx.orig b/word_series/word_series.cxx.orig deleted file mode 100644 index 71b0d0fe..00000000 --- a/word_series/word_series.cxx.orig +++ /dev/null @@ -1,1040 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - -<<<<<<< HEAD - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); -======= - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); ->>>>>>> origin/main - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BACKUP_133016.cxx b/word_series/word_series_BACKUP_133016.cxx deleted file mode 100644 index 71b0d0fe..00000000 --- a/word_series/word_series_BACKUP_133016.cxx +++ /dev/null @@ -1,1040 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - -<<<<<<< HEAD - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); -======= - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); ->>>>>>> origin/main - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BACKUP_133226.cxx b/word_series/word_series_BACKUP_133226.cxx deleted file mode 100644 index 71b0d0fe..00000000 --- a/word_series/word_series_BACKUP_133226.cxx +++ /dev/null @@ -1,1040 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - -<<<<<<< HEAD - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); -======= - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); ->>>>>>> origin/main - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BACKUP_133300.cxx b/word_series/word_series_BACKUP_133300.cxx deleted file mode 100644 index 71b0d0fe..00000000 --- a/word_series/word_series_BACKUP_133300.cxx +++ /dev/null @@ -1,1040 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - -<<<<<<< HEAD - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); -======= - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); ->>>>>>> origin/main - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BACKUP_133388.cxx b/word_series/word_series_BACKUP_133388.cxx deleted file mode 100644 index 71b0d0fe..00000000 --- a/word_series/word_series_BACKUP_133388.cxx +++ /dev/null @@ -1,1040 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - -<<<<<<< HEAD - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); -======= - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); ->>>>>>> origin/main - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BACKUP_133937.cxx b/word_series/word_series_BACKUP_133937.cxx deleted file mode 100644 index 71b0d0fe..00000000 --- a/word_series/word_series_BACKUP_133937.cxx +++ /dev/null @@ -1,1040 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - -<<<<<<< HEAD - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); -======= - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); ->>>>>>> origin/main - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BACKUP_134984.cxx b/word_series/word_series_BACKUP_134984.cxx deleted file mode 100644 index 71b0d0fe..00000000 --- a/word_series/word_series_BACKUP_134984.cxx +++ /dev/null @@ -1,1040 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - -<<<<<<< HEAD - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); -======= - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); ->>>>>>> origin/main - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BASE_133016.cxx b/word_series/word_series_BASE_133016.cxx deleted file mode 100644 index fd761773..00000000 --- a/word_series/word_series_BASE_133016.cxx +++ /dev/null @@ -1,1031 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64,temp_inputs); - outputs = batchify(64,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BASE_133226.cxx b/word_series/word_series_BASE_133226.cxx deleted file mode 100644 index fd761773..00000000 --- a/word_series/word_series_BASE_133226.cxx +++ /dev/null @@ -1,1031 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64,temp_inputs); - outputs = batchify(64,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BASE_133300.cxx b/word_series/word_series_BASE_133300.cxx deleted file mode 100644 index fd761773..00000000 --- a/word_series/word_series_BASE_133300.cxx +++ /dev/null @@ -1,1031 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64,temp_inputs); - outputs = batchify(64,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BASE_133388.cxx b/word_series/word_series_BASE_133388.cxx deleted file mode 100644 index fd761773..00000000 --- a/word_series/word_series_BASE_133388.cxx +++ /dev/null @@ -1,1031 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64,temp_inputs); - outputs = batchify(64,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BASE_133937.cxx b/word_series/word_series_BASE_133937.cxx deleted file mode 100644 index fd761773..00000000 --- a/word_series/word_series_BASE_133937.cxx +++ /dev/null @@ -1,1031 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64,temp_inputs); - outputs = batchify(64,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_BASE_134984.cxx b/word_series/word_series_BASE_134984.cxx deleted file mode 100644 index fd761773..00000000 --- a/word_series/word_series_BASE_134984.cxx +++ /dev/null @@ -1,1031 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64,temp_inputs); - outputs = batchify(64,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_LOCAL_133016.cxx b/word_series/word_series_LOCAL_133016.cxx deleted file mode 100644 index 5a865c2f..00000000 --- a/word_series/word_series_LOCAL_133016.cxx +++ /dev/null @@ -1,1037 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_LOCAL_133226.cxx b/word_series/word_series_LOCAL_133226.cxx deleted file mode 100644 index 5a865c2f..00000000 --- a/word_series/word_series_LOCAL_133226.cxx +++ /dev/null @@ -1,1037 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_LOCAL_133300.cxx b/word_series/word_series_LOCAL_133300.cxx deleted file mode 100644 index 5a865c2f..00000000 --- a/word_series/word_series_LOCAL_133300.cxx +++ /dev/null @@ -1,1037 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_LOCAL_133388.cxx b/word_series/word_series_LOCAL_133388.cxx deleted file mode 100644 index 5a865c2f..00000000 --- a/word_series/word_series_LOCAL_133388.cxx +++ /dev/null @@ -1,1037 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_LOCAL_133937.cxx b/word_series/word_series_LOCAL_133937.cxx deleted file mode 100644 index 5a865c2f..00000000 --- a/word_series/word_series_LOCAL_133937.cxx +++ /dev/null @@ -1,1037 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_LOCAL_134984.cxx b/word_series/word_series_LOCAL_134984.cxx deleted file mode 100644 index 5a865c2f..00000000 --- a/word_series/word_series_LOCAL_134984.cxx +++ /dev/null @@ -1,1037 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - uint32_t _batch_size; - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - - - get_argument(arguments,"--batch_size", true,_batch_size); - - cs->batch_size =_batch_size; - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) - { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout< >(no_features,vector (data[0][0].size()))); - }else{ - batchData.resize(no_batches,vector >(no_features,vector (batch_size))); - } - - - std::cout< &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_REMOTE_133016.cxx b/word_series/word_series_REMOTE_133016.cxx deleted file mode 100644 index 59bd9cc5..00000000 --- a/word_series/word_series_REMOTE_133016.cxx +++ /dev/null @@ -1,1029 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_REMOTE_133226.cxx b/word_series/word_series_REMOTE_133226.cxx deleted file mode 100644 index 59bd9cc5..00000000 --- a/word_series/word_series_REMOTE_133226.cxx +++ /dev/null @@ -1,1029 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_REMOTE_133300.cxx b/word_series/word_series_REMOTE_133300.cxx deleted file mode 100644 index 59bd9cc5..00000000 --- a/word_series/word_series_REMOTE_133300.cxx +++ /dev/null @@ -1,1029 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_REMOTE_133388.cxx b/word_series/word_series_REMOTE_133388.cxx deleted file mode 100644 index 59bd9cc5..00000000 --- a/word_series/word_series_REMOTE_133388.cxx +++ /dev/null @@ -1,1029 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_REMOTE_133937.cxx b/word_series/word_series_REMOTE_133937.cxx deleted file mode 100644 index 59bd9cc5..00000000 --- a/word_series/word_series_REMOTE_133937.cxx +++ /dev/null @@ -1,1029 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} diff --git a/word_series/word_series_REMOTE_134984.cxx b/word_series/word_series_REMOTE_134984.cxx deleted file mode 100644 index 59bd9cc5..00000000 --- a/word_series/word_series_REMOTE_134984.cxx +++ /dev/null @@ -1,1029 +0,0 @@ -#include - -#include -using std::find; - -#include -using std::ifstream; - -#include -using std::setw; - -#include -using std::endl; -using std::ostream; -using std::cout; - -#include -using std::numeric_limits; - -#include -using std::stringstream; - -#include -using std::invalid_argument; - -#include -using std::string; -using std::getline; - -#include -using std::vector; - -#include -using std::set; - -#include -using std::regex_replace; - -#include "../common/arguments.hxx" -#include "../common/log.hxx" - -#include "word_series.hxx" - -WordSeries::WordSeries(string _name){ - name = _name; - -} - -void WordSeries::add_value(double value){ - values.push_back(value); -} - -double WordSeries::get_value(int i){ - return values[i]; -} - -void WordSeries::calculate_statistics() { - min = numeric_limits::max(); - min_change = numeric_limits::max(); - average = 0.0; - max = -numeric_limits::max(); - max_change = -numeric_limits::max(); - - std_dev = 0.0; - variance = 0.0; - - for (uint32_t i = 0; i < values.size(); i++) { - average += values[i]; - - if (min > values[i]) min = values[i]; - if (max < values[i]) max = values[i]; - - if (i > 0) { - double diff = values[i] - values[i-1]; - - if (diff < min_change) min_change = diff; - if (diff > max_change) max_change = diff; - } - } - - average /= values.size(); - - for (uint32_t i = 0; i < values.size(); i++) { - double diff = values[i] - average; - variance += diff * diff; - } - variance /= values.size() - 1; - - std_dev = sqrt(variance); -} - -void WordSeries::print_statistics() { - Log::info("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); -} - -int WordSeries::get_number_values() const { - return values.size(); -} - -double WordSeries::get_min() const { - return min; -} - -double WordSeries::get_average() const { - return average; -} - -double WordSeries::get_max() const { - return max; -} - -double WordSeries::get_std_dev() const { - return std_dev; -} - -double WordSeries::get_variance() const { - return variance; -} - -double WordSeries::get_min_change() const { - return min_change; -} - -double WordSeries::get_max_change() const { - return max_change; -} - -void WordSeries::normalize_min_max(double min, double max) { - Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); - - for (int i = 0; i < values.size(); i++) { - if (values[i] < min) { - Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); - } - - if (values[i] > max) { - Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); - } - - values[i] = (values[i] - min) / (max - min); - } -} - -//divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { - Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); - - for (int i = 0; i < values.size(); i++) { - values[i] = ((values[i] - avg) / std_dev) / norm_max; - } -} - -void WordSeries::cut(int32_t start, int32_t stop) { - auto first = values.begin() + start; - auto last = values.begin() + stop; - values = vector(first, last); - - //update the statistics after the cut - calculate_statistics(); -} - -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { - double other_average = other->get_average(); - - int32_t length = fmin(values.size(), other->values.size()) - lag; - - double covariance_sum = 0.0; - for (int32_t i = 0; i < length; i++) { - covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); - } - - double other_variance = other->get_variance(); - double correlation; - if (variance < 1e-12 || other_variance < 1e-12) { - correlation = 0.0; - } else { - correlation = (covariance_sum / sqrt(variance * other_variance)) / length; - } - - return correlation; -} - -WordSeries::WordSeries() { -} - -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); - - ws->name = name; - ws->min = min; - ws->average = average; - ws->max = max; - ws->std_dev = std_dev; - ws->variance = variance; - ws->min_change = min_change; - ws->max_change = max_change; - - ws->values = values; - - return ws; -} - -void WordSeries::copy_values(vector &series) { - series = values; -} - - -void string_split_word(const string &s, char delim, vector &result) { - stringstream ss; - ss.str(s); - - string item; - while (getline(ss, item, delim)) { - result.push_back(item); - } -} - -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); - } else { - Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); - } -} - - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); - - vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ - add_word_series(word_index[i]); - } - - for (int i = 0; i < file_words.size(); ++i) - { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ - word_series[word_index[i]]->add_value(0); - } - else{ - word_series[word_index[i]]->add_value(1); - } - } - } - - - - - - number_rows = file_words.size(); - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - series->second->calculate_statistics(); - if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { - Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); - Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); - series->second->print_statistics(); - //word_series.erase(series); - } else { - series->second->print_statistics(); - } - - int series_rows = series->second->get_number_values(); - - if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); - } - - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - -} - -SentenceSeries::~SentenceSeries(){ - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) - { - WordSeries* series = it->second; - word_series.erase (it); - delete series; - } -} - - - - -int SentenceSeries::get_number_rows() const { - return number_rows; -} - -int SentenceSeries::get_number_columns() const { - return word_index.size(); -} - -string SentenceSeries::get_filename() const { - return filename; -} - -vector SentenceSeries::get_word_index() const { - return word_index; -} - -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); -} - -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); -} - -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); -} - -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); -} - -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); -} - -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); -} - -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); -} - -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); -} - -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); -} - -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); -} - -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); - - return first_series->get_correlation(second_series, lag); -} - -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - - cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - - if(word_offset > 0){ - cout<<"testing"<get_value(j); - } - } - } else if (word_offset < 0){ - cout<<"training"<get_value(j); - } - } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { - data[i][j] = word_series[word_index[i]]->get_value(j); - } - } - - } - - -} - -void SentenceSeries::export_word_series(vector< vector > &data ){ - export_word_series(data , 0); -} - -SentenceSeries::SentenceSeries(){ - -} - -SentenceSeries* SentenceSeries::copy(){ - SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; - ss->filename = filename; - ss->word_index = word_index; - ss->vocab= vocab; - - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); - } - - return ss; - -} - -void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { - if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { - Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); - } - } -} - -void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { - vector combined_parameters = input_parameter_names; - combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - - select_parameters(combined_parameters); -} - - - - - - -Corpus::Corpus() : normalize_type("none") { - -} - - -Corpus::~Corpus(){ - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; - } -} - - -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { - all_parameter_names.clear(); - - for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), input_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(input_parameter_names[i]); - } - } - - for (int32_t i = 0; i < (int32_t)output_parameter_names.size(); i++) { - if (find(all_parameter_names.begin(), all_parameter_names.end(), output_parameter_names[i]) == all_parameter_names.end()) { - all_parameter_names.push_back(output_parameter_names[i]); - } - } -} - - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { - vector words; - string filename = filenames[training_indexes[i]]; - ifstream cs_file(filename.c_str()); - - for(string line;getline(cs_file,line);){ - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - - string_split_word(line,' ',words); - words.push_back(""); - } - - set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { - word_index.push_back(*i); - vocab[*i] = vocab.size(); - - } - - } - - - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; - - - for (uint32_t i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - - } -} - - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ - Corpus *cs = new Corpus(); - - cs->filenames.clear(); - cs->word_index.clear(); - cs->vocab.clear(); - - if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { - vector training_filenames; - get_argument_vector(arguments, "--training_filenames", true, training_filenames); - - vector test_filenames; - get_argument_vector(arguments, "--test_filenames", true, test_filenames); - - int current = 0; - for (int i = 0; i < training_filenames.size(); i++) { - cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); - current++; - } - - for (int i = 0; i < test_filenames.size(); i++) { - cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); - current++; - } - - } else { - Log::fatal("Could not find the '--filenames' or the '--training_filenames' and '--test_filenames' command line arguments. Usage instructions:\n"); - //help_message(); - exit(1); - } - - cs->all_parameter_names.clear(); - cs->input_parameter_names.clear(); - cs->output_parameter_names.clear(); - - cs->load_word_library(); - - - return cs; - -} - -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - - cs->load_word_library(); - - return cs; -} - -double Corpus::denormalize(string field_name, double value) { - if (normalize_type.compare("none") == 0) { - return value; - } else if (normalize_type.compare("min_max") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - - value = (value * (max-min)) + min; - - return value; - - } else if (normalize_type.compare("avg_std_dev") == 0) { - double min = normalize_mins[field_name]; - double max = normalize_maxs[field_name]; - double avg = normalize_avgs[field_name]; - double std_dev = normalize_std_devs[field_name]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - value = (value * norm_max * std_dev) + avg; - - return value; - - } else { - Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); - exit(1); - } -} - -void Corpus::normalize_min_max() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - //get the min of all series of the same name - //get the max of all series of the same name - - if (normalize_mins.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - - Log::info("user specified bounds for "); - - } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - Log::info("calculated bounds for "); - } - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs) { - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); - } - } - - normalize_type = "min_max"; -} - -void Corpus::normalize_avg_std_dev() { - Log::info("doing min/max normalization:\n"); - - for (int i = 0; i < all_parameter_names.size(); i++) { - string parameter_name = all_parameter_names[i]; - - double min = numeric_limits::max(); - double max = -numeric_limits::max(); - - double avg = 0.0; - double std_dev = 0.0; - - if (normalize_avgs.count(parameter_name) > 0) { - min = normalize_mins[parameter_name]; - max = normalize_maxs[parameter_name]; - avg = normalize_avgs[parameter_name]; - std_dev = normalize_std_devs[parameter_name]; - - Log::info("user specified avg/std dev for "); - - } else { - double numerator_average = 0.0; - long total_values = 0; - - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; - total_values += n_values; - - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); - - if (current_min < min) min = current_min; - if (current_max > max) max = current_max; - } - - normalize_mins[parameter_name] = min; - normalize_maxs[parameter_name] = max; - - avg = numerator_average / total_values; - - double numerator_std_dev = 0.0; - //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); - } - - std_dev = numerator_std_dev / (total_values - 1); - - normalize_avgs[parameter_name] = avg; - normalize_std_devs[parameter_name] = std_dev; - - - Log::info("calculated bounds for "); - } - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - - //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs) { - normalize_avgs = _normalize_avgs; - normalize_std_devs = _normalize_std_devs; - normalize_mins = _normalize_mins; - normalize_maxs = _normalize_maxs; - - for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { - string field = all_parameter_names[i]; - - if (normalize_avgs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); - Log::fatal("normalize avg fields/values:\n"); - for (auto iterator = normalize_avgs.begin(); iterator != normalize_avgs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_std_devs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); - Log::fatal("normalize std_dev fields/values:\n"); - for (auto iterator = normalize_std_devs.begin(); iterator != normalize_std_devs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_mins.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); - Log::fatal("normalize min fields/values:\n"); - for (auto iterator = normalize_mins.begin(); iterator != normalize_mins.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - if (normalize_maxs.count(field) == 0) { - //field doesn't exist in the normalize values, report an error - Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); - Log::fatal("normalize max fields/values:\n"); - for (auto iterator = normalize_maxs.begin(); iterator != normalize_maxs.end(); iterator++) { - Log::fatal("\t%s: %lf\n", iterator->first.c_str(), iterator->second); - } - exit(1); - } - - double min = normalize_mins[field]; - double max = normalize_maxs[field]; - double avg = normalize_avgs[field]; - double std_dev = normalize_std_devs[field]; - - double norm_min = (min - avg) / std_dev; - double norm_max = (max - avg) / std_dev; - - norm_max = fmax(norm_min, norm_max); - - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); - } - } - - normalize_type = "avg_std_dev"; -} - -vector > > batchify(int batch_size, vector > > data) { - - - vector > > batchData; - - int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; - - - for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; - std:cout << no_batches << " " << std::endl; - } - - if (data[0][0].size() < batch_size) { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - } else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); - } - - - std::cout << batchData.size() << " " << data[0].size() << " " << batchData[0][0].size() << std::endl; - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && (j % batch_size)== 0) batchNo++; - - batchData[batchNo][i][j % batch_size] = data[k][i][j]; - } - } - } - - - return batchData; - -} - - -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - - - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); - } - - inputs = batchify(64, temp_inputs); - outputs = batchify(64, temp_outputs); - -} - -/** - * This exports the time series marked as training series by the training_indexes vector. - */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(training_indexes, word_offset, inputs, outputs); -} - -/** - * This exports the time series marked as test series by the test_indexes vector. - */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); - exit(1); - } - - export_sent_series(test_indexes, word_offset, inputs, outputs); -} - -void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { - exported_series.clear(); - - for (int32_t i = 0; i < sent_series.size(); i++) { - vector current_series; - - sent_series[i]->get_series(field_name, current_series); - exported_series.push_back(current_series); - } -} - - -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - - vector< vector > data; - sent_series[i]->export_word_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - - outfile << data[k][j]; - } - outfile << endl; - } - } -} - -string Corpus::get_normalize_type() const { - return normalize_type; -} - -map Corpus::get_normalize_mins() const { - return normalize_mins; -} - -map Corpus::get_normalize_maxs() const { - return normalize_maxs; -} - -map Corpus::get_normalize_avgs() const { - return normalize_avgs; -} - -map Corpus::get_normalize_std_devs() const { - return normalize_std_devs; -} - - -vector Corpus::get_input_parameter_names() const { - return input_parameter_names; -} - -vector Corpus::get_output_parameter_names() const { - return output_parameter_names; -} - -int Corpus::get_number_series() const { - return sent_series.size(); -} - -int Corpus::get_number_inputs() const { - return input_parameter_names.size(); -} - -int Corpus::get_number_outputs() const { - return output_parameter_names.size(); -} - -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; -} - -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; -} - -SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); -} From 8e87de739f3af55071f79b896c3ac7aa0784f6ef Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Mon, 19 Oct 2020 10:11:30 -0400 Subject: [PATCH 03/25] added comments and documentation --- docs/html/annotated.html | 179 ++ docs/html/arguments_8hxx_source.html | 197 ++ docs/html/bc_s.png | Bin 0 -> 676 bytes docs/html/bdwn.png | Bin 0 -> 147 bytes docs/html/class_c_n_n___edge-members.html | 176 ++ docs/html/class_c_n_n___edge.html | 380 +++ docs/html/class_c_n_n___genome-members.html | 221 ++ 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docs/latex/structsort___r_n_n___recurrent___edges__by__output__depth.tex create mode 100644 docs/latex/structsort__genomes__by__fitness.tex create mode 100644 docs/latex/structsort__genomes__by__predictions.tex create mode 100644 docs/latex/structsort__genomes__by__validation__error.tex create mode 100644 docs/latex/structucvector.tex create mode 100644 docs/latex/structuivector.tex create mode 100644 docs/latex/tabu_doxygen.sty delete mode 100644 temp diff --git a/docs/html/annotated.html b/docs/html/annotated.html new file mode 100644 index 00000000..ce142902 --- /dev/null +++ b/docs/html/annotated.html @@ -0,0 +1,179 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Class List + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + +
+ +
+
+ + +
+ +
+ +
+
+
Class List
+
+
+
Here are the classes, structs, unions and interfaces with brief descriptions:
+
[detail level 12]
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 ▼Nconfig_to_arg
 CConfigToArg
 ▼Nexamm_task
 CExammTask
 ▼Nisland_purging_args
 CIslandPurgingArgs
 ▼Nrec_args
 CRecArgs
 CCNN_Edge
 CCNN_Genome
 CCNN_Node
 CCoin
 CColor
 CColorProfile
 CColorTree
 CConsolidatedStatistics
 CCorpus
 Ccs_less_than_avg
 Ccs_less_than_max
 Ccs_less_than_min
 CDelta_Node
 CENARC_Node
 CENAS_DAG_Node
 CEXACT
 CEXACT_RESULT
 CEXAMM
 CGRU_Node
 CHash
 CHuffmanTree
 CImage
 CImageInterface
 CImages
 CImagesInterface
 CIsland
 CIslandSpeciationStrategy
 CLargeImage
 CLargeImages
 Cless_than_avg
 Cless_than_dfm_avg
 Cless_than_dfm_max
 Cless_than_dfm_min
 Cless_than_max
 Cless_than_min
 CLine
 CLodePNGColorMode
 CLodePNGCompressSettings
 CLodePNGDecoderSettings
 CLodePNGDecompressSettings
 CLodePNGEncoderSettings
 CLodePNGInfo
 CLodePNGState
 CLodePNGTime
 CLog
 CLogFile
 CLSTM_Node
 CMGU_Node
 Cmin_max_heapThis implementation of a min_max_heap was basically ripped from here: https://github.com/itsjohncs/minmaxheap-cpp/blob/master/MinMaxHeap.hpp
 CMosaicImages
 CMultiImagesInterface
 CNeatSpeciationStrategy
 CNormalDistribution
 CParseTurbines
 CPoint
 CRANDOM_DAG_Node
 CRectangle
 CResultSet
 CRNN
 CRNN_Edge
 CRNN_Genome
 CRNN_Node
 CRNN_Node_Interface
 CRNN_Recurrent_Edge
 CRunStatistics
 CSentenceSeries
 Csort_CNN_Edges_by_depth
 Csort_CNN_Edges_by_innovation
 Csort_CNN_Edges_by_output_depth
 Csort_CNN_Nodes_by_depth
 Csort_genomes_by_fitness
 Csort_genomes_by_predictions
 Csort_genomes_by_validation_error
 Csort_RNN_Edges_by_depth
 Csort_RNN_Edges_by_innovation
 Csort_RNN_Edges_by_output_depth
 Csort_RNN_Nodes_by_depth
 Csort_RNN_Nodes_by_innovation
 Csort_RNN_Recurrent_Edges_by_depth
 Csort_RNN_Recurrent_Edges_by_innovation
 Csort_RNN_Recurrent_Edges_by_output_depth
 CSpeciationStrategy
 CSpecies
 CTimeSeries
 CTimeSeriesSet
 CTimeSeriesSets
 CTracker
 Cucvector
 CUGRNN_Node
 Cuivector
 CWordSeries
+
+
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/arguments_8hxx_source.html b/docs/html/arguments_8hxx_source.html new file mode 100644 index 00000000..d78a987a --- /dev/null +++ b/docs/html/arguments_8hxx_source.html @@ -0,0 +1,197 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: common/arguments.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
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+ + +
+ +
+ + +
+
+
+
arguments.hxx
+
+
+
1 #ifndef GIBBS_ARGUMENTS_H
+
2 #define GIBBS_ARGUMENTS_H
+
3 
+
4 #include <iostream>
+
5 #include <vector>
+
6 #include <sstream>
+
7 #include <cstdlib>
+
8 
+
9 using namespace std;
+
10 
+
11 bool argument_exists(vector<string> arguments, string argument);
+
12 
+
13 template <typename T>
+
14 bool get_argument_vector(vector<string> arguments, string argument, bool required, vector<T> &results) {
+
15  bool found = false;
+
16  for (unsigned int i = 0; i < arguments.size(); i++) {
+
17  if (argument.compare(arguments.at(i)) == 0) {
+
18  i++;
+
19  while (i < arguments.size() && arguments.at(i).substr(0,2).compare("--") != 0) {
+
20  T result;
+
21  if ( !(stringstream(arguments.at(i++)) >> result) ) {
+
22  cerr << "ERROR: invalid argument '" << argument << "': " << arguments.at(i) << endl;
+
23  exit(1);
+
24  }
+
25  results.push_back(result);
+
26  }
+
27  found = true;
+
28  break;
+
29  }
+
30  }
+
31 
+
32  if (required && !found) {
+
33  cerr << "ERROR: argument '" << argument << "' required and not found." << endl;
+
34  exit(1);
+
35  }
+
36 
+
37  if (found) {
+
38  /*
+
39  cerr << "parsed argument '" << argument << "' successfully:";
+
40  for (unsigned int i = 0; i < results.size(); i++) {
+
41  cerr << " " << results.at(i);
+
42  }
+
43  cerr << endl;
+
44  */
+
45  }
+
46  return found;
+
47 }
+
48 
+
49 bool get_argument_vector(vector<string> arguments, string argument, bool required, vector<string> &results);
+
50 
+
51 bool get_argument(vector<string> arguments, string argument, bool required, string &result);
+
52 
+
53 template <typename T>
+
54 bool get_argument(vector<string> arguments, string argument, bool required, T &result) {
+
55  bool found = false;
+
56  for (unsigned int i = 0; i < arguments.size(); i++) {
+
57  if (argument.compare(arguments.at(i)) == 0) {
+
58  if ( !(stringstream(arguments.at(++i)) >> result) ) {
+
59  cerr << "ERROR: invalid argument '" << argument << "': " << arguments.at(i) << endl;
+
60  exit(1);
+
61  }
+
62  found = true;
+
63  break;
+
64  }
+
65  }
+
66 
+
67  if (required && !found) {
+
68  cerr << "ERROR: argument '" << argument << "' required and not found." << endl;
+
69  exit(1);
+
70  }
+
71 
+
72  if (found) {
+
73 // cerr << "parsed argument '" << argument << "' successfully: " << result << endl;
+
74  }
+
75  return found;
+
76 }
+
77 
+
78 
+
79 template <typename T1, typename T2>
+
80 bool get_arguments(vector<string> arguments, string argument, bool required, T1 &result1, T2 &result2) {
+
81  bool found = false;
+
82  for (unsigned int i = 0; i < arguments.size(); i++) {
+
83  if (argument.compare(arguments.at(i)) == 0) {
+
84  if ( !(stringstream(arguments.at(++i)) >> result1) ) {
+
85  cerr << "ERROR: invalid argument '" << argument << "': " << arguments.at(i) << endl;
+
86  exit(1);
+
87  }
+
88  if ( !(stringstream(arguments.at(++i)) >> result2) ) {
+
89  cerr << "ERROR: invalid argument '" << argument << "': " << arguments.at(i) << endl;
+
90  exit(1);
+
91  }
+
92  found = true;
+
93  break;
+
94  }
+
95  }
+
96 
+
97  if (required && !found) {
+
98  cerr << "ERROR: argument '" << argument << "' required and not found." << endl;
+
99  exit(1);
+
100  }
+
101 
+
102  if (found) {
+
103 // cerr << "parsed argument '" << argument << "' successfully: " << result1 << " " << result2 << endl;
+
104  }
+
105  return found;
+
106 }
+
107 
+
108 #endif
+
+ +
std::vector::size
T size(T... args)
+ +
std::vector::at
T at(T... args)
+ +
std::string::compare
T compare(T... args)
+
std::endl
T endl(T... args)
+ +
std::exit
T exit(T... args)
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/bc_s.png b/docs/html/bc_s.png new file mode 100644 index 0000000000000000000000000000000000000000..224b29aa9847d5a4b3902efd602b7ddf7d33e6c2 GIT binary patch literal 676 zcmV;V0$crwP)y__>=_9%My z{n931IS})GlGUF8K#6VIbs%684A^L3@%PlP2>_sk`UWPq@f;rU*V%rPy_ekbhXT&s z(GN{DxFv}*vZp`F>S!r||M`I*nOwwKX+BC~3P5N3-)Y{65c;ywYiAh-1*hZcToLHK ztpl1xomJ+Yb}K(cfbJr2=GNOnT!UFA7Vy~fBz8?J>XHsbZoDad^8PxfSa0GDgENZS zuLCEqzb*xWX2CG*b&5IiO#NzrW*;`VC9455M`o1NBh+(k8~`XCEEoC1Ybwf;vr4K3 zg|EB<07?SOqHp9DhLpS&bzgo70I+ghB_#)K7H%AMU3v}xuyQq9&Bm~++VYhF09a+U zl7>n7Jjm$K#b*FONz~fj;I->Bf;ule1prFN9FovcDGBkpg>)O*-}eLnC{6oZHZ$o% zXKW$;0_{8hxHQ>l;_*HATI(`7t#^{$(zLe}h*mqwOc*nRY9=?Sx4OOeVIfI|0V(V2 zBrW#G7Ss9wvzr@>H*`r>zE z+e8bOBgqIgldUJlG(YUDviMB`9+DH8n-s9SXRLyJHO1!=wY^79WYZMTa(wiZ!zP66 zA~!21vmF3H2{ngD;+`6j#~6j;$*f*G_2ZD1E;9(yaw7d-QnSCpK(cR1zU3qU0000< KMNUMnLSTYoA~SLT literal 0 HcmV?d00001 diff --git a/docs/html/bdwn.png b/docs/html/bdwn.png new file mode 100644 index 0000000000000000000000000000000000000000..940a0b950443a0bb1b216ac03c45b8a16c955452 GIT binary patch literal 147 zcmeAS@N?(olHy`uVBq!ia0vp^>_E)H!3HEvS)PKZC{Gv1kP61Pb5HX&C2wk~_T + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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CNN_Edge Member List
+
+
+ +

This is the complete list of members for CNN_Edge, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
accumulate_times(float &total_forward_time, float &total_backward_time, float &total_weight_update_time) (defined in CNN_Edge)CNN_Edge
alter_edge_type() (defined in CNN_Edge)CNN_Edge
batch_size (defined in CNN_Edge)CNN_Edgeprivate
best_scale (defined in CNN_Edge)CNN_Edgeprivate
best_velocity (defined in CNN_Edge)CNN_Edgeprivate
best_velocity_scale (defined in CNN_Edge)CNN_Edgeprivate
best_weights (defined in CNN_Edge)CNN_Edgeprivate
check_output_update(const vector< vector< vector< float > > > &output, const vector< vector< vector< float > > > &input, float value, float weight, float previous_output, int batch_number, int in_y, int in_x, int out_y, int out_x) (defined in CNN_Edge)CNN_Edge
check_weight_update(const vector< vector< vector< float > > > &input, const vector< vector< vector< float > > > &input_deltas, float delta, float previous_delta, float weight_update, float previous_weight_update, int batch_number, int out_y, int out_x, int in_y, int in_x) (defined in CNN_Edge)CNN_Edge
CNN_Edge() (defined in CNN_Edge)CNN_Edge
CNN_Edge(CNN_Node *_input_node, CNN_Node *_output_node, bool _fixed, int _innovation_number, int _type) (defined in CNN_Edge)CNN_Edge
connects(int n1, int n2) const (defined in CNN_Edge)CNN_Edge
copy() const (defined in CNN_Edge)CNN_Edge
disable() (defined in CNN_Edge)CNN_Edge
disabled (defined in CNN_Edge)CNN_Edgeprivate
edge_id (defined in CNN_Edge)CNN_Edgeprivate
enable() (defined in CNN_Edge)CNN_Edge
equals(CNN_Edge *other) const (defined in CNN_Edge)CNN_Edge
exact_id (defined in CNN_Edge)CNN_Edgeprivate
filter_size (defined in CNN_Edge)CNN_Edgeprivate
filter_x (defined in CNN_Edge)CNN_Edgeprivate
filter_y (defined in CNN_Edge)CNN_Edgeprivate
fixed (defined in CNN_Edge)CNN_Edgeprivate
forward_visit() (defined in CNN_Edge)CNN_Edge
forward_visited (defined in CNN_Edge)CNN_Edgeprivate
genome_id (defined in CNN_Edge)CNN_Edgeprivate
get_batch_size() const (defined in CNN_Edge)CNN_Edge
get_filter_size() const (defined in CNN_Edge)CNN_Edge
get_filter_x() const (defined in CNN_Edge)CNN_Edge
get_filter_y() const (defined in CNN_Edge)CNN_Edge
get_innovation_number() const (defined in CNN_Edge)CNN_Edge
get_input_innovation_number() const (defined in CNN_Edge)CNN_Edge
get_input_node() (defined in CNN_Edge)CNN_Edge
get_number_weights() const (defined in CNN_Edge)CNN_Edge
get_output_innovation_number() const (defined in CNN_Edge)CNN_Edge
get_output_node() (defined in CNN_Edge)CNN_Edge
get_scale() const (defined in CNN_Edge)CNN_Edge
get_type() const (defined in CNN_Edge)CNN_Edge
get_weight(int i) const (defined in CNN_Edge)CNN_Edge
get_weight_update(int i) const (defined in CNN_Edge)CNN_Edge
has_nan() const (defined in CNN_Edge)CNN_Edge
has_zero_best_weight() const (defined in CNN_Edge)CNN_Edge
has_zero_weight() const (defined in CNN_Edge)CNN_Edge
initialize_weights(minstd_rand0 &generator, NormalDistribution &normal_distribution) (defined in CNN_Edge)CNN_Edge
innovation_number (defined in CNN_Edge)CNN_Edgeprivate
input_node (defined in CNN_Edge)CNN_Edgeprivate
input_node_innovation_number (defined in CNN_Edge)CNN_Edgeprivate
is_disabled() const (defined in CNN_Edge)CNN_Edge
is_enabled() const (defined in CNN_Edge)CNN_Edge
is_filter_correct() const (defined in CNN_Edge)CNN_Edge
is_forward_visited() const (defined in CNN_Edge)CNN_Edge
is_identical(const CNN_Edge *other, bool testing_checkpoint) (defined in CNN_Edge)CNN_Edge
is_reachable() const (defined in CNN_Edge)CNN_Edge
is_reverse_filter_x() const (defined in CNN_Edge)CNN_Edge
is_reverse_filter_y() const (defined in CNN_Edge)CNN_Edge
is_reverse_visited() const (defined in CNN_Edge)CNN_Edge
needs_init() const (defined in CNN_Edge)CNN_Edge
needs_initialization (defined in CNN_Edge)CNN_Edgeprivate
operator<< (defined in CNN_Edge)CNN_Edgefriend
operator>> (defined in CNN_Edge)CNN_Edgefriend
output_node (defined in CNN_Edge)CNN_Edgeprivate
output_node_innovation_number (defined in CNN_Edge)CNN_Edgeprivate
previous_velocity (defined in CNN_Edge)CNN_Edgeprivate
previous_velocity_scale (defined in CNN_Edge)CNN_Edgeprivate
print(ostream &out) (defined in CNN_Edge)CNN_Edge
print_statistics() (defined in CNN_Edge)CNN_Edge
propagate_backward(bool training, float mu, float learning_rate, float epsilon) (defined in CNN_Edge)CNN_Edge
propagate_backward_time (defined in CNN_Edge)CNN_Edgeprivate
propagate_forward(bool training, bool accumulate_test_statistics, float epsilon, float alpha, bool perform_dropout, float hidden_dropout_probability, minstd_rand0 &generator) (defined in CNN_Edge)CNN_Edge
propagate_forward_time (defined in CNN_Edge)CNN_Edgeprivate
propagate_weight_count() (defined in CNN_Edge)CNN_Edge
reset_times() (defined in CNN_Edge)CNN_Edge
reset_velocities() (defined in CNN_Edge)CNN_Edge
resize() (defined in CNN_Edge)CNN_Edge
reverse_filter_x (defined in CNN_Edge)CNN_Edgeprivate
reverse_filter_y (defined in CNN_Edge)CNN_Edgeprivate
reverse_visit() (defined in CNN_Edge)CNN_Edge
reverse_visited (defined in CNN_Edge)CNN_Edgeprivate
save_best_weights() (defined in CNN_Edge)CNN_Edge
scale (defined in CNN_Edge)CNN_Edgeprivate
set_needs_init() (defined in CNN_Edge)CNN_Edge
set_nodes(const vector< CNN_Node * > nodes) (defined in CNN_Edge)CNN_Edge
set_pools() (defined in CNN_Edge)CNN_Edge
set_unvisited() (defined in CNN_Edge)CNN_Edge
set_weights_to_best() (defined in CNN_Edge)CNN_Edge
type (defined in CNN_Edge)CNN_Edgeprivate
update_batch_size(int new_batch_size) (defined in CNN_Edge)CNN_Edge
update_weight(int i, float diff) (defined in CNN_Edge)CNN_Edge
update_weights(float mu, float learning_rate, float weight_decay) (defined in CNN_Edge)CNN_Edge
weight_update_time (defined in CNN_Edge)CNN_Edgeprivate
weight_updates (defined in CNN_Edge)CNN_Edgeprivate
weights (defined in CNN_Edge)CNN_Edgeprivate
x_pool_offset (defined in CNN_Edge)CNN_Edgeprivate
x_pools (defined in CNN_Edge)CNN_Edgeprivate
y_pool_offset (defined in CNN_Edge)CNN_Edgeprivate
y_pools (defined in CNN_Edge)CNN_Edgeprivate
~CNN_Edge() (defined in CNN_Edge)CNN_Edge
+ +
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+ + diff --git a/docs/html/class_c_n_n___edge.html b/docs/html/class_c_n_n___edge.html new file mode 100644 index 00000000..a665d108 --- /dev/null +++ b/docs/html/class_c_n_n___edge.html @@ -0,0 +1,380 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: CNN_Edge Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +Friends | +List of all members
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CNN_Edge Class Reference
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+Public Member Functions

+ CNN_Edge (CNN_Node *_input_node, CNN_Node *_output_node, bool _fixed, int _innovation_number, int _type)
 
+CNN_Edge * copy () const
 
+bool equals (CNN_Edge *other) const
 
+int get_type () const
 
+bool has_nan () const
 
+void reset_times ()
 
+void accumulate_times (float &total_forward_time, float &total_backward_time, float &total_weight_update_time)
 
+void set_needs_init ()
 
+bool needs_init () const
 
+int get_filter_size () const
 
+int get_filter_x () const
 
+int get_filter_y () const
 
+bool is_reverse_filter_x () const
 
+bool is_reverse_filter_y () const
 
+float get_weight (int i) const
 
+float get_weight_update (int i) const
 
+void update_weight (int i, float diff)
 
+float get_scale () const
 
+void propagate_weight_count ()
 
+void save_best_weights ()
 
+void set_weights_to_best ()
 
+bool set_nodes (const vector< CNN_Node * > nodes)
 
+void set_pools ()
 
+void initialize_weights (minstd_rand0 &generator, NormalDistribution &normal_distribution)
 
+void reset_velocities ()
 
+void resize ()
 
+void alter_edge_type ()
 
+void disable ()
 
+void enable ()
 
+bool is_enabled () const
 
+bool is_disabled () const
 
+void update_batch_size (int new_batch_size)
 
+bool is_reachable () const
 
+bool is_forward_visited () const
 
+bool is_reverse_visited () const
 
+void forward_visit ()
 
+void reverse_visit ()
 
+void set_unvisited ()
 
+bool is_filter_correct () const
 
+int get_number_weights () const
 
+int get_batch_size () const
 
+int get_innovation_number () const
 
+int get_input_innovation_number () const
 
+int get_output_innovation_number () const
 
+bool connects (int n1, int n2) const
 
+bool has_zero_weight () const
 
+bool has_zero_best_weight () const
 
+CNN_Node * get_input_node ()
 
+CNN_Node * get_output_node ()
 
+void print (ostream &out)
 
+void check_output_update (const vector< vector< vector< float > > > &output, const vector< vector< vector< float > > > &input, float value, float weight, float previous_output, int batch_number, int in_y, int in_x, int out_y, int out_x)
 
+void check_weight_update (const vector< vector< vector< float > > > &input, const vector< vector< vector< float > > > &input_deltas, float delta, float previous_delta, float weight_update, float previous_weight_update, int batch_number, int out_y, int out_x, int in_y, int in_x)
 
+void propagate_forward (bool training, bool accumulate_test_statistics, float epsilon, float alpha, bool perform_dropout, float hidden_dropout_probability, minstd_rand0 &generator)
 
+void propagate_backward (bool training, float mu, float learning_rate, float epsilon)
 
+void update_weights (float mu, float learning_rate, float weight_decay)
 
+void print_statistics ()
 
+bool is_identical (const CNN_Edge *other, bool testing_checkpoint)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int edge_id
 
+int exact_id
 
+int genome_id
 
+int type
 
+int innovation_number
 
+int input_node_innovation_number
 
+int output_node_innovation_number
 
+CNN_Node * input_node
 
+CNN_Node * output_node
 
+int batch_size
 
+int filter_x
 
+int filter_y
 
+int filter_size
 
+float * weights
 
+float * weight_updates
 
+float * best_weights
 
+float * previous_velocity
 
+float * best_velocity
 
+float scale
 
+float best_scale
 
+float previous_velocity_scale
 
+float best_velocity_scale
 
+vector< int > y_pools
 
+vector< int > y_pool_offset
 
+vector< int > x_pools
 
+vector< int > x_pool_offset
 
+bool fixed
 
+bool disabled
 
+bool forward_visited
 
+bool reverse_visited
 
+bool reverse_filter_x
 
+bool reverse_filter_y
 
+bool needs_initialization
 
+float propagate_backward_time
 
+float propagate_forward_time
 
+float weight_update_time
 
+ + + + + +

+Friends

+ostream & operator<< (ostream &os, const CNN_Edge *flight)
 
+istream & operator>> (istream &is, CNN_Edge *flight)
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_c_n_n___genome-members.html b/docs/html/class_c_n_n___genome-members.html new file mode 100644 index 00000000..b18b1dcf --- /dev/null +++ b/docs/html/class_c_n_n___genome-members.html @@ -0,0 +1,221 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
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+
CNN_Genome Member List
+
+
+ +

This is the complete list of members for CNN_Genome, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
add_edge(CNN_Edge *edge) (defined in CNN_Genome)CNN_Genome
add_node(CNN_Node *node) (defined in CNN_Genome)CNN_Genome
alpha (defined in CNN_Genome)CNN_Genomeprivate
backprop_order (defined in CNN_Genome)CNN_Genomeprivate
batch_size (defined in CNN_Genome)CNN_Genomeprivate
best_epoch (defined in CNN_Genome)CNN_Genomeprivate
best_validation_error (defined in CNN_Genome)CNN_Genomeprivate
best_validation_predictions (defined in CNN_Genome)CNN_Genomeprivate
check_gradients(const ImagesInterface &images) (defined in CNN_Genome)CNN_Genome
checkpoint_filename (defined in CNN_Genome)CNN_Genomeprivate
CNN_Genome(string filename, bool is_checkpoint)CNN_Genome
CNN_Genome(istream &in, bool is_checkpoint) (defined in CNN_Genome)CNN_Genome
CNN_Genome(int _generation_id, int _padding, int _number_training_images, int _number_validation_images, int _number_test_images, int seed, int _max_epochs, bool _reset_weights, int velocity_reset, float _mu, float _mu_delta, float _learning_rate, float _learning_rate_delta, float _weight_decay, float _weight_decay_delta, int _batch_size, float _epsilon, float _alpha, float _input_dropout_probability, float _hidden_dropout_probability, const vector< CNN_Node * > &_nodes, const vector< CNN_Edge * > &_edges)CNN_Genome
disable_edge(int edge_position) (defined in CNN_Genome)CNN_Genome
edges (defined in CNN_Genome)CNN_Genomeprivate
epoch (defined in CNN_Genome)CNN_Genomeprivate
epsilon (defined in CNN_Genome)CNN_Genomeprivate
equals(CNN_Genome *other) const (defined in CNN_Genome)CNN_Genome
evaluate(const ImagesInterface &images, vector< vector< float > > &predictions) (defined in CNN_Genome)CNN_Genome
evaluate(const ImagesInterface &images, const vector< long > &order, float &total_error, int &correct_predictions, bool perform_backprop, bool accumulate_test_statistics) (defined in CNN_Genome)CNN_Genome
evaluate(string progress_name, const ImagesInterface &images, float &total_error, int &correct_predictions) (defined in CNN_Genome)CNN_Genome
evaluate_images(const ImagesInterface &images, const vector< int > &batch, vector< vector< float > > &predictions, int offset) (defined in CNN_Genome)CNN_Genome
evaluate_images(const ImagesInterface &images, const vector< int > &batch, bool training, float &total_error, int &correct_predictions, bool accumulate_test_statistics) (defined in CNN_Genome)CNN_Genome
evaluate_large_images(const LargeImages &images, string output_directory) (defined in CNN_Genome)CNN_Genome
evaluate_test(const ImagesInterface &test_images) (defined in CNN_Genome)CNN_Genome
exact_id (defined in CNN_Genome)CNN_Genomeprivate
generated_by_map (defined in CNN_Genome)CNN_Genomeprivate
generation_id (defined in CNN_Genome)CNN_Genomeprivate
generator (defined in CNN_Genome)CNN_Genomeprivate
genome_id (defined in CNN_Genome)CNN_Genomeprivate
get_alpha() const (defined in CNN_Genome)CNN_Genome
get_batch_size() const (defined in CNN_Genome)CNN_Genome
get_best_epoch() const (defined in CNN_Genome)CNN_Genome
get_best_validation_error() const (defined in CNN_Genome)CNN_Genome
get_best_validation_predictions() const (defined in CNN_Genome)CNN_Genome
get_best_validation_rate() const (defined in CNN_Genome)CNN_Genome
get_disabled_edges() (defined in CNN_Genome)CNN_Genome
get_disabled_nodes() (defined in CNN_Genome)CNN_Genome
get_edge(int edge_position) (defined in CNN_Genome)CNN_Genome
get_edge_copies(vector< CNN_Edge * > &edge_copies) const (defined in CNN_Genome)CNN_Genome
get_edges() const (defined in CNN_Genome)CNN_Genome
get_epoch() const (defined in CNN_Genome)CNN_Genome
get_exact_id() const (defined in CNN_Genome)CNN_Genome
get_expanded_prediction_matrix(const MultiImagesInterface &images, int image_number, int stride, int prediction_class, vector< vector< float > > &extended_prediction_matrix) (defined in CNN_Genome)CNN_Genome
get_generated_by(string type) (defined in CNN_Genome)CNN_Genome
get_generation_id() const (defined in CNN_Genome)CNN_Genome
get_genome_id() const (defined in CNN_Genome)CNN_Genome
get_hidden_dropout_probability() const (defined in CNN_Genome)CNN_Genome
get_initial_learning_rate() const (defined in CNN_Genome)CNN_Genome
get_initial_mu() const (defined in CNN_Genome)CNN_Genome
get_initial_weight_decay() const (defined in CNN_Genome)CNN_Genome
get_input_dropout_probability() const (defined in CNN_Genome)CNN_Genome
get_learning_rate() const (defined in CNN_Genome)CNN_Genome
get_learning_rate_delta() const (defined in CNN_Genome)CNN_Genome
get_max_epochs() const (defined in CNN_Genome)CNN_Genome
get_mu() const (defined in CNN_Genome)CNN_Genome
get_mu_delta() const (defined in CNN_Genome)CNN_Genome
get_node(int node_position) (defined in CNN_Genome)CNN_Genome
get_node_copies(vector< CNN_Node * > &node_copies) const (defined in CNN_Genome)CNN_Genome
get_nodes() const (defined in CNN_Genome)CNN_Genome
get_number_edges() const (defined in CNN_Genome)CNN_Genome
get_number_enabled_convolutional_edges() const (defined in CNN_Genome)CNN_Genome
get_number_enabled_edges() const (defined in CNN_Genome)CNN_Genome
get_number_enabled_nodes() const (defined in CNN_Genome)CNN_Genome
get_number_enabled_pooling_edges() const (defined in CNN_Genome)CNN_Genome
get_number_input_nodes() const (defined in CNN_Genome)CNN_Genome
get_number_nodes() const (defined in CNN_Genome)CNN_Genome
get_number_softmax_nodes() const (defined in CNN_Genome)CNN_Genome
get_number_test_images() const (defined in CNN_Genome)CNN_Genome
get_number_training_images() const (defined in CNN_Genome)CNN_Genome
get_number_validation_images() const (defined in CNN_Genome)CNN_Genome
get_number_weights() const (defined in CNN_Genome)CNN_Genome
get_operations_estimate() const (defined in CNN_Genome)CNN_Genome
get_padding() const (defined in CNN_Genome)CNN_Genome
get_prediction_matrix(const MultiImagesInterface &images, int image_number, int stride, vector< vector< vector< float > > > &prediction_matrix) (defined in CNN_Genome)CNN_Genome
get_reachable_edges() (defined in CNN_Genome)CNN_Genome
get_reachable_hidden_nodes() (defined in CNN_Genome)CNN_Genome
get_reachable_nodes() (defined in CNN_Genome)CNN_Genome
get_test_error() const (defined in CNN_Genome)CNN_Genome
get_test_predictions() const (defined in CNN_Genome)CNN_Genome
get_test_rate() const (defined in CNN_Genome)CNN_Genome
get_training_error() const (defined in CNN_Genome)CNN_Genome
get_training_predictions() const (defined in CNN_Genome)CNN_Genome
get_training_rate() const (defined in CNN_Genome)CNN_Genome
get_velocity_reset() const (defined in CNN_Genome)CNN_Genome
get_version() const (defined in CNN_Genome)CNN_Genome
get_version_str() const (defined in CNN_Genome)CNN_Genome
get_weight_decay() const (defined in CNN_Genome)CNN_Genome
get_weight_decay_delta() const (defined in CNN_Genome)CNN_Genome
hidden_dropout_probability (defined in CNN_Genome)CNN_Genomeprivate
initial_learning_rate (defined in CNN_Genome)CNN_Genomeprivate
initial_mu (defined in CNN_Genome)CNN_Genomeprivate
initial_weight_decay (defined in CNN_Genome)CNN_Genomeprivate
initialize() (defined in CNN_Genome)CNN_Genome
input_dropout_probability (defined in CNN_Genome)CNN_Genomeprivate
input_nodes (defined in CNN_Genome)CNN_Genomeprivate
is_identical(CNN_Genome *other, bool testing_checkpoint) (defined in CNN_Genome)CNN_Genome
learning_rate (defined in CNN_Genome)CNN_Genomeprivate
learning_rate_delta (defined in CNN_Genome)CNN_Genomeprivate
max_epochs (defined in CNN_Genome)CNN_Genomeprivate
mu (defined in CNN_Genome)CNN_Genomeprivate
mu_delta (defined in CNN_Genome)CNN_Genomeprivate
name (defined in CNN_Genome)CNN_Genomeprivate
nodes (defined in CNN_Genome)CNN_Genomeprivate
normal_distribution (defined in CNN_Genome)CNN_Genomeprivate
number_test_images (defined in CNN_Genome)CNN_Genomeprivate
number_training_images (defined in CNN_Genome)CNN_Genomeprivate
number_validation_images (defined in CNN_Genome)CNN_Genomeprivate
output_filename (defined in CNN_Genome)CNN_Genomeprivate
padding (defined in CNN_Genome)CNN_Genomeprivate
print_graphviz(ostream &out) const (defined in CNN_Genome)CNN_Genome
print_progress(ostream &out, string progress_name, float total_error, int correct_predictions, int number_images) const (defined in CNN_Genome)CNN_Genome
print_results(ostream &out) const (defined in CNN_Genome)CNN_Genome
progress_function (defined in CNN_Genome)CNN_Genomeprivate
read(istream &infile) (defined in CNN_Genome)CNN_Genome
reset(bool _reset_weights) (defined in CNN_Genome)CNN_Genome
reset_weights (defined in CNN_Genome)CNN_Genomeprivate
resize_edges_around_node(int node_position) (defined in CNN_Genome)CNN_Genome
sanity_check(int type) (defined in CNN_Genome)CNN_Genome
save_to_best() (defined in CNN_Genome)CNN_Genome
set_checkpoint_filename(string _checkpoint_filename) (defined in CNN_Genome)CNN_Genome
set_generated_by(string type) (defined in CNN_Genome)CNN_Genome
set_name(string _name) (defined in CNN_Genome)CNN_Genome
set_output_filename(string _output_filename) (defined in CNN_Genome)CNN_Genome
set_progress_function(int(*_progress_function)(float)) (defined in CNN_Genome)CNN_Genome
set_to_best() (defined in CNN_Genome)CNN_Genome
softmax_nodes (defined in CNN_Genome)CNN_Genomeprivate
started_from_checkpoint (defined in CNN_Genome)CNN_Genomeprivate
stochastic_backpropagation(const ImagesInterface &training_images, int training_resize, const ImagesInterface &validation_images) (defined in CNN_Genome)CNN_Genome
stochastic_backpropagation(const ImagesInterface &training_images, const ImagesInterface &validation_images) (defined in CNN_Genome)CNN_Genome
test_error (defined in CNN_Genome)CNN_Genomeprivate
test_predictions (defined in CNN_Genome)CNN_Genomeprivate
training_error (defined in CNN_Genome)CNN_Genomeprivate
training_predictions (defined in CNN_Genome)CNN_Genomeprivate
velocity_reset (defined in CNN_Genome)CNN_Genomeprivate
version_str (defined in CNN_Genome)CNN_Genomeprivate
visit_nodes() (defined in CNN_Genome)CNN_Genome
weight_decay (defined in CNN_Genome)CNN_Genomeprivate
weight_decay_delta (defined in CNN_Genome)CNN_Genomeprivate
write(ostream &outfile) (defined in CNN_Genome)CNN_Genome
write_to_file(string filename) (defined in CNN_Genome)CNN_Genome
~CNN_Genome() (defined in CNN_Genome)CNN_Genome
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_c_n_n___genome.html b/docs/html/class_c_n_n___genome.html new file mode 100644 index 00000000..5a790273 --- /dev/null +++ b/docs/html/class_c_n_n___genome.html @@ -0,0 +1,516 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: CNN_Genome Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +List of all members
+
+
CNN_Genome Class Reference
+
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ CNN_Genome (string filename, bool is_checkpoint)
 Initialize a genome from a file.
 
+ CNN_Genome (istream &in, bool is_checkpoint)
 
+ CNN_Genome (int _generation_id, int _padding, int _number_training_images, int _number_validation_images, int _number_test_images, int seed, int _max_epochs, bool _reset_weights, int velocity_reset, float _mu, float _mu_delta, float _learning_rate, float _learning_rate_delta, float _weight_decay, float _weight_decay_delta, int _batch_size, float _epsilon, float _alpha, float _input_dropout_probability, float _hidden_dropout_probability, const vector< CNN_Node * > &_nodes, const vector< CNN_Edge * > &_edges)
 Iniitalize a genome from a set of nodes and edges.
 
+float get_version () const
 
+string get_version_str () const
 
+int get_genome_id () const
 
+int get_exact_id () const
 
+bool equals (CNN_Genome *other) const
 
+void print_results (ostream &out) const
 
+void print_progress (ostream &out, string progress_name, float total_error, int correct_predictions, int number_images) const
 
+int get_number_training_images () const
 
+int get_number_validation_images () const
 
+int get_number_test_images () const
 
+int get_number_weights () const
 
+int get_padding () const
 
+int get_operations_estimate () const
 
+void set_progress_function (int(*_progress_function)(float))
 
+int get_generation_id () const
 
+float get_best_validation_error () const
 
+float get_best_validation_rate () const
 
+int get_best_validation_predictions () const
 
+float get_training_error () const
 
+float get_training_rate () const
 
+int get_training_predictions () const
 
+float get_test_error () const
 
+float get_test_rate () const
 
+int get_test_predictions () const
 
+int get_best_epoch () const
 
+int get_epoch () const
 
+int get_max_epochs () const
 
+bool sanity_check (int type)
 
+bool visit_nodes ()
 
+void get_node_copies (vector< CNN_Node * > &node_copies) const
 
+void get_edge_copies (vector< CNN_Edge * > &edge_copies) const
 
+const vector< CNN_Node * > get_nodes () const
 
+const vector< CNN_Edge * > get_edges () const
 
+CNN_Node * get_node (int node_position)
 
+CNN_Edge * get_edge (int edge_position)
 
+float get_initial_mu () const
 
+float get_mu () const
 
+float get_mu_delta () const
 
+float get_initial_learning_rate () const
 
+float get_learning_rate () const
 
+float get_learning_rate_delta () const
 
+float get_initial_weight_decay () const
 
+float get_weight_decay () const
 
+float get_weight_decay_delta () const
 
+int get_batch_size () const
 
+float get_alpha () const
 
+int get_velocity_reset () const
 
+float get_input_dropout_probability () const
 
+float get_hidden_dropout_probability () const
 
+int get_number_enabled_pooling_edges () const
 
+int get_number_enabled_convolutional_edges () const
 
+int get_number_enabled_edges () const
 
+int get_number_enabled_nodes () const
 
+int get_number_edges () const
 
+int get_number_nodes () const
 
+int get_number_softmax_nodes () const
 
+int get_number_input_nodes () const
 
+vector< CNN_Node * > get_reachable_nodes ()
 
+vector< CNN_Node * > get_disabled_nodes ()
 
+vector< CNN_Node * > get_reachable_hidden_nodes ()
 
+vector< CNN_Edge * > get_reachable_edges ()
 
+vector< CNN_Edge * > get_disabled_edges ()
 
+void add_node (CNN_Node *node)
 
+void add_edge (CNN_Edge *edge)
 
+bool disable_edge (int edge_position)
 
+void resize_edges_around_node (int node_position)
 
+void evaluate_images (const ImagesInterface &images, const vector< int > &batch, vector< vector< float > > &predictions, int offset)
 
+void evaluate_images (const ImagesInterface &images, const vector< int > &batch, bool training, float &total_error, int &correct_predictions, bool accumulate_test_statistics)
 
+void set_to_best ()
 
+void save_to_best ()
 
+void reset (bool _reset_weights)
 
+void initialize ()
 
+void check_gradients (const ImagesInterface &images)
 
+void evaluate_large_images (const LargeImages &images, string output_directory)
 
+void evaluate (const ImagesInterface &images, vector< vector< float > > &predictions)
 
+void evaluate (const ImagesInterface &images, const vector< long > &order, float &total_error, int &correct_predictions, bool perform_backprop, bool accumulate_test_statistics)
 
+void evaluate (string progress_name, const ImagesInterface &images, float &total_error, int &correct_predictions)
 
+void stochastic_backpropagation (const ImagesInterface &training_images, int training_resize, const ImagesInterface &validation_images)
 
+void stochastic_backpropagation (const ImagesInterface &training_images, const ImagesInterface &validation_images)
 
+void evaluate_test (const ImagesInterface &test_images)
 
+void set_name (string _name)
 
+void set_output_filename (string _output_filename)
 
+void set_checkpoint_filename (string _checkpoint_filename)
 
+void write (ostream &outfile)
 
+void write_to_file (string filename)
 
+void read (istream &infile)
 
+void print_graphviz (ostream &out) const
 
+void set_generated_by (string type)
 
+int get_generated_by (string type)
 
+bool is_identical (CNN_Genome *other, bool testing_checkpoint)
 
+void get_prediction_matrix (const MultiImagesInterface &images, int image_number, int stride, vector< vector< vector< float > > > &prediction_matrix)
 
+void get_expanded_prediction_matrix (const MultiImagesInterface &images, int image_number, int stride, int prediction_class, vector< vector< float > > &extended_prediction_matrix)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+string version_str
 
+int exact_id
 
+int genome_id
 
+vector< CNN_Node * > nodes
 
+vector< CNN_Edge * > edges
 
+vector< CNN_Node * > input_nodes
 
+vector< CNN_Node * > softmax_nodes
 
+NormalDistribution normal_distribution
 
+minstd_rand0 generator
 
+int velocity_reset
 
+int batch_size
 
+float epsilon
 
+float alpha
 
+float input_dropout_probability
 
+float hidden_dropout_probability
 
+float initial_mu
 
+float mu
 
+float mu_delta
 
+float initial_learning_rate
 
+float learning_rate
 
+float learning_rate_delta
 
+float initial_weight_decay
 
+float weight_decay
 
+float weight_decay_delta
 
+int epoch
 
+int max_epochs
 
+bool reset_weights
 
+int padding
 
+int best_epoch
 
+int number_validation_images
 
+float best_validation_error
 
+int best_validation_predictions
 
+int number_training_images
 
+float training_error
 
+int training_predictions
 
+int number_test_images
 
+float test_error
 
+int test_predictions
 
+bool started_from_checkpoint
 
+vector< long > backprop_order
 
+int generation_id
 
+string name
 
+string checkpoint_filename
 
+string output_filename
 
+map< string, int > generated_by_map
 
+int(* progress_function )(float)
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_c_n_n___node-members.html b/docs/html/class_c_n_n___node-members.html new file mode 100644 index 00000000..769e888c --- /dev/null +++ b/docs/html/class_c_n_n___node-members.html @@ -0,0 +1,198 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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CNN_Node Member List
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This is the complete list of members for CNN_Node, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
accumulate_times(float &total_input_time, float &total_output_time) (defined in CNN_Node)CNN_Node
add_input() (defined in CNN_Node)CNN_Node
add_output() (defined in CNN_Node)CNN_Node
add_weight_count(int _weight_count) (defined in CNN_Node)CNN_Node
apply_dropout(float *values, float *gradients, bool perform_dropout, bool accumulate_test_statistics, float dropout_probability, minstd_rand0 &generator) (defined in CNN_Node)CNN_Node
apply_relu(float *values, float *gradients) (defined in CNN_Node)CNN_Node
backpropagate_batch_normalization(bool training, float mu, float learning_rate, float epsilon) (defined in CNN_Node)CNN_Node
backpropagate_relu(float *errors, float *gradients) (defined in CNN_Node)CNN_Node
batch_mean (defined in CNN_Node)CNN_Nodeprivate
batch_normalize(bool training, bool accumulating_test_statistics, float epsilon, float alpha) (defined in CNN_Node)CNN_Node
batch_size (defined in CNN_Node)CNN_Nodeprivate
batch_std_dev (defined in CNN_Node)CNN_Nodeprivate
batch_variance (defined in CNN_Node)CNN_Nodeprivate
best_beta (defined in CNN_Node)CNN_Nodeprivate
best_gamma (defined in CNN_Node)CNN_Nodeprivate
best_running_mean (defined in CNN_Node)CNN_Nodeprivate
best_running_variance (defined in CNN_Node)CNN_Nodeprivate
beta (defined in CNN_Node)CNN_Nodeprivate
CNN_Node() (defined in CNN_Node)CNN_Node
CNN_Node(int _innovation_number, float _depth, int _batch_size, int _input_size_x, int _input_size_y, int type) (defined in CNN_Node)CNN_Node
copy() const (defined in CNN_Node)CNN_Node
depth (defined in CNN_Node)CNN_Nodeprivate
disable() (defined in CNN_Node)CNN_Node
disable_input() (defined in CNN_Node)CNN_Node
disable_output() (defined in CNN_Node)CNN_Node
disabled (defined in CNN_Node)CNN_Nodeprivate
divide_test_statistics(int number_batches) (defined in CNN_Node)CNN_Node
enable() (defined in CNN_Node)CNN_Node
errors_in (defined in CNN_Node)CNN_Nodeprivate
errors_out (defined in CNN_Node)CNN_Nodeprivate
exact_id (defined in CNN_Node)CNN_Nodeprivate
forward_visit() (defined in CNN_Node)CNN_Node
forward_visited (defined in CNN_Node)CNN_Nodeprivate
gamma (defined in CNN_Node)CNN_Nodeprivate
genome_id (defined in CNN_Node)CNN_Nodeprivate
get_batch_size() const (defined in CNN_Node)CNN_Node
get_depth() const (defined in CNN_Node)CNN_Node
get_errors_in() (defined in CNN_Node)CNN_Node
get_errors_out() (defined in CNN_Node)CNN_Node
get_innovation_number() const (defined in CNN_Node)CNN_Node
get_inputs_fired() const (defined in CNN_Node)CNN_Node
get_number_inputs() const (defined in CNN_Node)CNN_Node
get_number_outputs() const (defined in CNN_Node)CNN_Node
get_outputs_fired() const (defined in CNN_Node)CNN_Node
get_pool_gradients() (defined in CNN_Node)CNN_Node
get_relu_gradients() (defined in CNN_Node)CNN_Node
get_size_x() const (defined in CNN_Node)CNN_Node
get_size_y() const (defined in CNN_Node)CNN_Node
get_value_in(int batch_number, int y, int x) (defined in CNN_Node)CNN_Node
get_value_out(int batch_number, int y, int x) (defined in CNN_Node)CNN_Node
get_values_in() (defined in CNN_Node)CNN_Node
get_values_out() (defined in CNN_Node)CNN_Node
get_weight_count() const (defined in CNN_Node)CNN_Node
has_nan() const (defined in CNN_Node)CNN_Node
initialize() (defined in CNN_Node)CNN_Node
innovation_number (defined in CNN_Node)CNN_Nodeprivate
input_fired(bool training, bool accumulate_test_statistics, float epsilon, float alpha, bool perform_dropout, float hidden_dropout_probability, minstd_rand0 &generator) (defined in CNN_Node)CNN_Node
input_fired_time (defined in CNN_Node)CNN_Nodeprivate
inputs_fired (defined in CNN_Node)CNN_Nodeprivate
inverse_variance (defined in CNN_Node)CNN_Nodeprivate
is_disabled() const (defined in CNN_Node)CNN_Node
is_enabled() const (defined in CNN_Node)CNN_Node
is_fixed() const (defined in CNN_Node)CNN_Node
is_forward_visited() const (defined in CNN_Node)CNN_Node
is_hidden() const (defined in CNN_Node)CNN_Node
is_identical(const CNN_Node *other, bool testing_checkpoint) (defined in CNN_Node)CNN_Node
is_input() const (defined in CNN_Node)CNN_Node
is_output() const (defined in CNN_Node)CNN_Node
is_reachable() const (defined in CNN_Node)CNN_Node
is_reverse_visited() const (defined in CNN_Node)CNN_Node
is_softmax() const (defined in CNN_Node)CNN_Node
modify_size_x(int change) (defined in CNN_Node)CNN_Node
modify_size_y(int change) (defined in CNN_Node)CNN_Node
needs_init() const (defined in CNN_Node)CNN_Node
needs_initialization (defined in CNN_Node)CNN_Nodeprivate
node_id (defined in CNN_Node)CNN_Nodeprivate
operator<< (defined in CNN_Node)CNN_Nodefriend
operator>> (defined in CNN_Node)CNN_Nodefriend
output_fired(bool training, float mu, float learning_rate, float epsilon) (defined in CNN_Node)CNN_Node
output_fired_time (defined in CNN_Node)CNN_Nodeprivate
outputs_fired (defined in CNN_Node)CNN_Nodeprivate
pool_gradients (defined in CNN_Node)CNN_Nodeprivate
previous_velocity_beta (defined in CNN_Node)CNN_Nodeprivate
previous_velocity_gamma (defined in CNN_Node)CNN_Nodeprivate
print(ostream &out) (defined in CNN_Node)CNN_Node
print_batch_statistics() (defined in CNN_Node)CNN_Node
print_statistics() (defined in CNN_Node)CNN_Node
print_statistics(const float *values, const float *errors, const float *gradients) (defined in CNN_Node)CNN_Node
relu_gradients (defined in CNN_Node)CNN_Nodeprivate
reset() (defined in CNN_Node)CNN_Node
reset_times() (defined in CNN_Node)CNN_Node
reset_velocities() (defined in CNN_Node)CNN_Node
reset_weight_count() (defined in CNN_Node)CNN_Node
resize_arrays() (defined in CNN_Node)CNN_Node
reverse_visit() (defined in CNN_Node)CNN_Node
reverse_visited (defined in CNN_Node)CNN_Nodeprivate
running_mean (defined in CNN_Node)CNN_Nodeprivate
running_variance (defined in CNN_Node)CNN_Nodeprivate
save_best_weights() (defined in CNN_Node)CNN_Node
set_error_in(int batch_number, int y, int x, float error) (defined in CNN_Node)CNN_Node
set_error_out(int batch_number, int y, int x, float error) (defined in CNN_Node)CNN_Node
set_unvisited() (defined in CNN_Node)CNN_Node
set_value_in(int batch_number, int y, int x, float value) (defined in CNN_Node)CNN_Node
set_value_out(int batch_number, int y, int x, float value) (defined in CNN_Node)CNN_Node
set_values(const ImagesInterface &images, const vector< int > &batch, int channel, bool perform_dropout, bool accumulate_test_statistics, float input_dropout_probability, minstd_rand0 &generator) (defined in CNN_Node)CNN_Node
set_weights_to_best() (defined in CNN_Node)CNN_Node
size_x (defined in CNN_Node)CNN_Nodeprivate
size_y (defined in CNN_Node)CNN_Nodeprivate
total_inputs (defined in CNN_Node)CNN_Nodeprivate
total_outputs (defined in CNN_Node)CNN_Nodeprivate
total_size (defined in CNN_Node)CNN_Nodeprivate
type (defined in CNN_Node)CNN_Nodeprivate
update_batch_size(int new_batch_size) (defined in CNN_Node)CNN_Node
values_in (defined in CNN_Node)CNN_Nodeprivate
values_out (defined in CNN_Node)CNN_Nodeprivate
vectors_correct() const (defined in CNN_Node)CNN_Node
weight_count (defined in CNN_Node)CNN_Nodeprivate
zero_test_statistics() (defined in CNN_Node)CNN_Node
~CNN_Node() (defined in CNN_Node)CNN_Node
+ +
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EXONA: Evolutionary eXploration of Neural Architectures +
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CNN_Node Class Reference
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+Public Member Functions

+ CNN_Node (int _innovation_number, float _depth, int _batch_size, int _input_size_x, int _input_size_y, int type)
 
+CNN_Node * copy () const
 
+bool needs_init () const
 
+int get_batch_size () const
 
+int get_size_x () const
 
+int get_size_y () const
 
+bool vectors_correct () const
 
+void initialize ()
 
+void reset_velocities ()
 
+void reset_weight_count ()
 
+void add_weight_count (int _weight_count)
 
+int get_weight_count () const
 
+int get_innovation_number () const
 
+float get_depth () const
 
+bool is_fixed () const
 
+bool is_hidden () const
 
+bool is_input () const
 
+bool is_output () const
 
+bool is_softmax () const
 
+bool is_reachable () const
 
+bool is_forward_visited () const
 
+bool is_reverse_visited () const
 
+void forward_visit ()
 
+void reverse_visit ()
 
+void set_unvisited ()
 
+bool has_nan () const
 
+void set_values (const ImagesInterface &images, const vector< int > &batch, int channel, bool perform_dropout, bool accumulate_test_statistics, float input_dropout_probability, minstd_rand0 &generator)
 
+float get_value_in (int batch_number, int y, int x)
 
+void set_value_in (int batch_number, int y, int x, float value)
 
+float * get_values_in ()
 
+float get_value_out (int batch_number, int y, int x)
 
+void set_value_out (int batch_number, int y, int x, float value)
 
+float * get_values_out ()
 
+void set_error_in (int batch_number, int y, int x, float error)
 
+float * get_errors_in ()
 
+void set_error_out (int batch_number, int y, int x, float error)
 
+float * get_errors_out ()
 
+float * get_relu_gradients ()
 
+float * get_pool_gradients ()
 
+void print (ostream &out)
 
+void reset_times ()
 
+void accumulate_times (float &total_input_time, float &total_output_time)
 
+void reset ()
 
+void save_best_weights ()
 
+void set_weights_to_best ()
 
+void resize_arrays ()
 
+void update_batch_size (int new_batch_size)
 
+bool modify_size_x (int change)
 
+bool modify_size_y (int change)
 
+bool is_disabled () const
 
+bool is_enabled () const
 
+void disable ()
 
+void enable ()
 
+void add_input ()
 
+void disable_input ()
 
+int get_number_inputs () const
 
+int get_inputs_fired () const
 
+void input_fired (bool training, bool accumulate_test_statistics, float epsilon, float alpha, bool perform_dropout, float hidden_dropout_probability, minstd_rand0 &generator)
 
+void add_output ()
 
+void disable_output ()
 
+int get_number_outputs () const
 
+int get_outputs_fired () const
 
+void output_fired (bool training, float mu, float learning_rate, float epsilon)
 
+void zero_test_statistics ()
 
+void divide_test_statistics (int number_batches)
 
+void print_batch_statistics ()
 
+void batch_normalize (bool training, bool accumulating_test_statistics, float epsilon, float alpha)
 
+void apply_relu (float *values, float *gradients)
 
+void apply_dropout (float *values, float *gradients, bool perform_dropout, bool accumulate_test_statistics, float dropout_probability, minstd_rand0 &generator)
 
+void backpropagate_relu (float *errors, float *gradients)
 
+void backpropagate_batch_normalization (bool training, float mu, float learning_rate, float epsilon)
 
+void print_statistics ()
 
+void print_statistics (const float *values, const float *errors, const float *gradients)
 
+bool is_identical (const CNN_Node *other, bool testing_checkpoint)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int node_id
 
+int exact_id
 
+int genome_id
 
+int innovation_number
 
+float depth
 
+int batch_size
 
+int size_y
 
+int size_x
 
+int total_size
 
+int weight_count
 
+int type
 
+int total_inputs
 
+int inputs_fired
 
+int total_outputs
 
+int outputs_fired
 
+bool forward_visited
 
+bool reverse_visited
 
+bool needs_initialization
 
+bool disabled
 
+float gamma
 
+float best_gamma
 
+float previous_velocity_gamma
 
+float beta
 
+float best_beta
 
+float previous_velocity_beta
 
+float batch_mean
 
+float batch_variance
 
+float batch_std_dev
 
+float inverse_variance
 
+float running_mean
 
+float best_running_mean
 
+float running_variance
 
+float best_running_variance
 
+float * values_out
 
+float * errors_out
 
+float * relu_gradients
 
+float * pool_gradients
 
+float * values_in
 
+float * errors_in
 
+float input_fired_time
 
+float output_fired_time
 
+ + + + + +

+Friends

+ostream & operator<< (ostream &os, const CNN_Node *node)
 
+istream & operator>> (istream &is, CNN_Node *node)
 
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_consolidated_statistics-members.html b/docs/html/class_consolidated_statistics-members.html new file mode 100644 index 00000000..e48a7866 --- /dev/null +++ b/docs/html/class_consolidated_statistics-members.html @@ -0,0 +1,87 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
+
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+
EXONA: Evolutionary eXploration of Neural Architectures +
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+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+
ConsolidatedStatistics Member List
+
+
+ +

This is the complete list of members for ConsolidatedStatistics, including all inherited members.

+ + + + + + + + + +
ConsolidatedStatistics(string _run_type) (defined in ConsolidatedStatistics)ConsolidatedStatistics
dfm_avg (defined in ConsolidatedStatistics)ConsolidatedStatistics
dfm_max (defined in ConsolidatedStatistics)ConsolidatedStatistics
dfm_min (defined in ConsolidatedStatistics)ConsolidatedStatistics
run_type (defined in ConsolidatedStatistics)ConsolidatedStatistics
to_string_avg() (defined in ConsolidatedStatistics)ConsolidatedStatistics
to_string_max() (defined in ConsolidatedStatistics)ConsolidatedStatistics
to_string_min() (defined in ConsolidatedStatistics)ConsolidatedStatistics
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_consolidated_statistics.html b/docs/html/class_consolidated_statistics.html new file mode 100644 index 00000000..610968a7 --- /dev/null +++ b/docs/html/class_consolidated_statistics.html @@ -0,0 +1,115 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: ConsolidatedStatistics Class Reference + + + + + + + + + +
+
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+
EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Public Attributes | +List of all members
+
+
ConsolidatedStatistics Class Reference
+
+
+ + + + + + + + + + +

+Public Member Functions

+ ConsolidatedStatistics (string _run_type)
 
+string to_string_min ()
 
+string to_string_avg ()
 
+string to_string_max ()
 
+ + + + + + + + + +

+Public Attributes

+string run_type
 
+double dfm_min
 
+double dfm_avg
 
+double dfm_max
 
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_corpus-members.html b/docs/html/class_corpus-members.html new file mode 100644 index 00000000..aed6f488 --- /dev/null +++ b/docs/html/class_corpus-members.html @@ -0,0 +1,122 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+
Corpus Member List
+
+
+ +

This is the complete list of members for Corpus, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
all_parameter_namesCorpusprivate
batch_sizeCorpusprivate
Corpus() (defined in Corpus)Corpus
denormalize(string field_name, double value) (defined in Corpus)Corpus
export_sent_series(const vector< int > &series_indexes, int word_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs) (defined in Corpus)Corpus
export_series_by_name(string field_name, vector< vector< double > > &exported_series) (defined in Corpus)Corpus
export_test_series(int word_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)Corpus
export_training_series(int word_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)Corpus
filenamesCorpusprivate
generate_from_arguments(const vector< string > &arguments) (defined in Corpus)Corpusstatic
generate_test(const vector< string > &_test_filenames, const vector< string > &_input_parameter_names, const vector< string > &_output_parameter_names) (defined in Corpus)Corpusstatic
get_input_parameter_names() const (defined in Corpus)Corpus
get_normalize_avgs() const (defined in Corpus)Corpus
get_normalize_maxs() const (defined in Corpus)Corpus
get_normalize_mins() const (defined in Corpus)Corpus
get_normalize_std_devs() const (defined in Corpus)Corpus
get_normalize_type() const (defined in Corpus)Corpus
get_number_inputs() const (defined in Corpus)Corpus
get_number_outputs() const (defined in Corpus)Corpus
get_number_series() const (defined in Corpus)Corpus
get_output_parameter_names() const (defined in Corpus)Corpus
get_set(int32_t i) (defined in Corpus)Corpus
input_parameter_namesCorpusprivate
load_word_library()Corpusprivate
normalize_avg_std_dev() (defined in Corpus)Corpus
normalize_avg_std_dev(const map< string, double > &_normalize_avgs, const map< string, double > &_normalize_std_devs, const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs) (defined in Corpus)Corpus
normalize_avgs (defined in Corpus)Corpusprivate
normalize_maxs (defined in Corpus)Corpusprivate
normalize_min_max() (defined in Corpus)Corpus
normalize_min_max(const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs) (defined in Corpus)Corpus
normalize_mins (defined in Corpus)Corpusprivate
normalize_std_devs (defined in Corpus)Corpusprivate
normalize_type (defined in Corpus)Corpusprivate
output_parameter_namesCorpusprivate
sent_seriesCorpusprivate
set_test_indexes(const vector< int > &_test_indexes) (defined in Corpus)Corpus
set_training_indexes(const vector< int > &_training_indexes) (defined in Corpus)Corpus
test_indexesCorpusprivate
training_indexesCorpusprivate
vocabCorpusprivate
word_indexCorpusprivate
write_sentence_series_sets(string base_filename)Corpus
~Corpus() (defined in Corpus)Corpus
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_corpus.html b/docs/html/class_corpus.html new file mode 100644 index 00000000..4f5c0de9 --- /dev/null +++ b/docs/html/class_corpus.html @@ -0,0 +1,449 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Corpus Class Reference + + + + + + + + + +
+
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+
EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Static Public Member Functions | +Private Member Functions | +Private Attributes | +List of all members
+
+
Corpus Class Reference
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+void normalize_min_max ()
 
+void normalize_min_max (const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs)
 
+void normalize_avg_std_dev ()
 
+void normalize_avg_std_dev (const map< string, double > &_normalize_avgs, const map< string, double > &_normalize_std_devs, const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs)
 
void write_sentence_series_sets (string base_filename)
 Generates the csv data file. More...
 
+void export_sent_series (const vector< int > &series_indexes, int word_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
 
void export_training_series (int word_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
 Exports Corpus Dataset to be used for training in RNN genome. More...
 
void export_test_series (int word_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
 Exports Corpus Dataset to be used for testing in RNN genome. More...
 
+void export_series_by_name (string field_name, vector< vector< double > > &exported_series)
 
+double denormalize (string field_name, double value)
 
+string get_normalize_type () const
 
+map< string, double > get_normalize_mins () const
 
+map< string, double > get_normalize_maxs () const
 
+map< string, double > get_normalize_avgs () const
 
+map< string, double > get_normalize_std_devs () const
 
+vector< string > get_input_parameter_names () const
 
+vector< string > get_output_parameter_names () const
 
+int get_number_series () const
 
+int get_number_inputs () const
 
+int get_number_outputs () const
 
+void set_training_indexes (const vector< int > &_training_indexes)
 
+void set_test_indexes (const vector< int > &_test_indexes)
 
+SentenceSeries * get_set (int32_t i)
 
+ + + + + +

+Static Public Member Functions

+static Corpus * generate_from_arguments (const vector< string > &arguments)
 
+static Corpus * generate_test (const vector< string > &_test_filenames, const vector< string > &_input_parameter_names, const vector< string > &_output_parameter_names)
 
+ + + + +

+Private Member Functions

+void load_word_library ()
 Loads the filenames, word index and the vocabulary of the files.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+uint32_t batch_size
 Specifices the batches in which the dataset is to be divided into chunks.
 
+string normalize_type
 
+vector< string > filenames
 Stores all the filenames including training and testing.
 
+vector< int > training_indexes
 stores the training index of the filenames.
 
+vector< int > test_indexes
 stores the testing index of the filenames.
 
vector< string > input_parameter_names
 stores the input parameter names of the filenames. More...
 
vector< string > output_parameter_names
 stores the input parameter names of the filenames. More...
 
vector< string > all_parameter_names
 stores the input parameter names of the filenames. More...
 
vector< SentenceSeries * > sent_series
 stores the Sentence series used in the filenames. More...
 
+map< string, double > normalize_mins
 
+map< string, double > normalize_maxs
 
+map< string, double > normalize_avgs
 
+map< string, double > normalize_std_devs
 
+vector< string > word_index
 Maps the index to word string of the word series in a particular filename.
 
+map< string, int > vocab
 Maps the word string to the word series in a particular filename.
 
+

Member Function Documentation

+ +

◆ export_test_series()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
void Corpus::export_test_series (int word_offset,
vector< vector< vector< double > > > & inputs,
vector< vector< vector< double > > > & outputs 
)
+
+ +

Exports Corpus Dataset to be used for testing in RNN genome.

+

This exports the time series marked as test series by the test_indexes vector.

+
Parameters
+ + + + + +
series_indexesare the indexes of the input parameters to be used in the dataset
word_offsetis used to predict the after how many words, the predictions to be done in the future.
inputsis the data used for testing in the RNN genome
outpusis the data used for testing in the RNN genome
+
+
+ +
+
+ +

◆ export_training_series()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
void Corpus::export_training_series (int word_offset,
vector< vector< vector< double > > > & inputs,
vector< vector< vector< double > > > & outputs 
)
+
+ +

Exports Corpus Dataset to be used for training in RNN genome.

+

This exports the time series marked as training series by the training_indexes vector.

+
Parameters
+ + + + + +
series_indexesare the indexes of the input parameters to be used in the dataset
word_offsetis used to predict the after how many words, the predictions to be done in the future.
inputsis the data used for training in the RNN genome
outpusis the data used for training in the RNN genome
+
+
+ +
+
+ +

◆ write_sentence_series_sets()

+ +
+
+ + + + + + + + +
void Corpus::write_sentence_series_sets (string base_filename)
+
+ +

Generates the csv data file.

+
Parameters
+ + +
base_filenameis the filename to be used.
+
+
+ +
+
+

Member Data Documentation

+ +

◆ all_parameter_names

+ +
+
+ + + + + +
+ + + + +
vector<string> Corpus::all_parameter_names
+ 		+private
+
+ +

stores the input parameter names of the filenames.

+

stores the unique words used in the filenames.

+ +
+
+ +

◆ input_parameter_names

+ +
+
+ + + + + +
+ + + + +
vector<string> Corpus::input_parameter_names
+ 		+private
+
+ +

stores the input parameter names of the filenames.

+

stores the unique words used in the filenames.

+ +
+
+ +

◆ output_parameter_names

+ +
+
+ + + + + +
+ + + + +
vector<string> Corpus::output_parameter_names
+ 		+private
+
+ +

stores the input parameter names of the filenames.

+

stores the unique words used in the filenames.

+ +
+
+ +

◆ sent_series

+ +
+
+ + + + + +
+ + + + +
vector<SentenceSeries*> Corpus::sent_series
+ 		+private
+
+ +

stores the Sentence series used in the filenames.

+

Each file is divided into sentences and word series is made for each word in the sentence.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_delta___node-members.html b/docs/html/class_delta___node-members.html new file mode 100644 index 00000000..95355b58 --- /dev/null +++ b/docs/html/class_delta___node-members.html @@ -0,0 +1,152 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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Delta_Node Member List
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This is the complete list of members for Delta_Node, including all inherited members.

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alpha (defined in Delta_Node)Delta_Nodeprivate
backward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
beta1 (defined in Delta_Node)Delta_Nodeprivate
beta2 (defined in Delta_Node)Delta_Nodeprivate
copy() const (defined in Delta_Node)Delta_Nodevirtual
d_alpha (defined in Delta_Node)Delta_Nodeprivate
d_beta1 (defined in Delta_Node)Delta_Nodeprivate
d_beta2 (defined in Delta_Node)Delta_Nodeprivate
d_input (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
d_r_bias (defined in Delta_Node)Delta_Nodeprivate
d_v (defined in Delta_Node)Delta_Nodeprivate
d_z_hat_bias (defined in Delta_Node)Delta_Nodeprivate
d_z_prev (defined in Delta_Node)Delta_Nodeprivate
Delta_Node(int _innovation_number, int _type, double _depth) (defined in Delta_Node)Delta_Node
depth (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
enabled (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
equals(RNN_Node_Interface *other) const (defined in RNN_Node_Interface)RNN_Node_Interface
error_fired(int time, double error) (defined in Delta_Node)Delta_Node
error_fired(int32_t time, double error)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
error_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
forward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
get_depth() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_gradient(string gradient_name) (defined in Delta_Node)Delta_Node
get_gradients(vector< double > &gradients) (defined in Delta_Node)Delta_Nodevirtual
get_innovation_number() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_layer_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_node_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_number_weights() const (defined in Delta_Node)Delta_Nodevirtual
get_total_inputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_total_outputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_weights(vector< double > &parameters) const (defined in Delta_Node)Delta_Nodevirtual
get_weights(uint32_t &offset, vector< double > &parameters) const (defined in Delta_Node)Delta_Nodevirtual
initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range) (defined in Delta_Node)Delta_Nodevirtual
initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma) (defined in Delta_Node)Delta_Nodevirtual
initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution< double > &rng) (defined in Delta_Node)Delta_Nodevirtual
initialize_xavier(minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range) (defined in Delta_Node)Delta_Nodevirtual
innovation_number (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
input_fired(int time, double incoming_output) (defined in Delta_Node)Delta_Node
input_fired(int32_t time, double incoming_output)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
input_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
inputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
is_enabled() const (defined in RNN_Node_Interface)RNN_Node_Interface
is_reachable() const (defined in RNN_Node_Interface)RNN_Node_Interface
layer_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
ld_r (defined in Delta_Node)Delta_Nodeprivate
ld_z (defined in Delta_Node)Delta_Nodeprivate
ld_z_cap (defined in Delta_Node)Delta_Nodeprivate
node_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
output_fired(int time, double delta) (defined in Delta_Node)Delta_Node
output_fired(int32_t time, double delta)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
output_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
outputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
parameter_name (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
print_gradient(string gradient_name) (defined in Delta_Node)Delta_Node
r (defined in Delta_Node)Delta_Nodeprivate
r_bias (defined in Delta_Node)Delta_Nodeprivate
reset(int _series_length) (defined in Delta_Node)Delta_Node
reset(int32_t _series_length)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
RNN_Edge (defined in Delta_Node)Delta_Nodefriend
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name) (defined in RNN_Node_Interface)RNN_Node_Interface
series_length (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
set_weights(const vector< double > &parameters) (defined in Delta_Node)Delta_Nodevirtual
set_weights(uint32_t &offset, const vector< double > &parameters) (defined in Delta_Node)Delta_Nodevirtual
total_inputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
total_outputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
try_update_deltas(int time) (defined in Delta_Node)Delta_Node
v (defined in Delta_Node)Delta_Nodeprivate
write_to_stream(ostream &out) (defined in Delta_Node)Delta_Node
z_cap (defined in Delta_Node)Delta_Nodeprivate
z_hat_bias (defined in Delta_Node)Delta_Nodeprivate
~Delta_Node() (defined in Delta_Node)Delta_Node
~RNN_Node_Interface() (defined in RNN_Node_Interface)RNN_Node_Interfacevirtual
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+ + diff --git a/docs/html/class_delta___node.html b/docs/html/class_delta___node.html new file mode 100644 index 00000000..35beacfa --- /dev/null +++ b/docs/html/class_delta___node.html @@ -0,0 +1,325 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Delta_Node Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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Delta_Node Class Reference
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+Inheritance diagram for Delta_Node:
+
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+ + +RNN_Node_Interface + +
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+Public Member Functions

+ Delta_Node (int _innovation_number, int _type, double _depth)
 
+void initialize_lamarckian (minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)
 
+void initialize_xavier (minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range)
 
+void initialize_kaiming (minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)
 
+void initialize_uniform_random (minstd_rand0 &generator, uniform_real_distribution< double > &rng)
 
+double get_gradient (string gradient_name)
 
+void print_gradient (string gradient_name)
 
+void input_fired (int time, double incoming_output)
 
+void try_update_deltas (int time)
 
+void error_fired (int time, double error)
 
+void output_fired (int time, double delta)
 
+uint32_t get_number_weights () const
 
+void get_weights (vector< double > &parameters) const
 
+void set_weights (const vector< double > &parameters)
 
+void get_weights (uint32_t &offset, vector< double > &parameters) const
 
+void set_weights (uint32_t &offset, const vector< double > &parameters)
 
+void get_gradients (vector< double > &gradients)
 
+void reset (int _series_length)
 
+void write_to_stream (ostream &out)
 
+RNN_Node_Interface * copy () const
 
- Public Member Functions inherited from RNN_Node_Interface
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth)
 
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name)
 
+virtual void input_fired (int32_t time, double incoming_output)=0
 
+virtual void output_fired (int32_t time, double delta)=0
 
+virtual void error_fired (int32_t time, double error)=0
 
+virtual void reset (int32_t _series_length)=0
 
+void write_to_stream (ostream &out)
 
+int32_t get_node_type () const
 
+int32_t get_layer_type () const
 
+int32_t get_innovation_number () const
 
+int32_t get_total_inputs () const
 
+int32_t get_total_outputs () const
 
+double get_depth () const
 
+bool equals (RNN_Node_Interface *other) const
 
+bool is_reachable () const
 
+bool is_enabled () const
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+double alpha
 
+double beta1
 
+double beta2
 
+double v
 
+double r_bias
 
+double z_hat_bias
 
+vector< double > d_alpha
 
+vector< double > d_beta1
 
+vector< double > d_beta2
 
+vector< double > d_v
 
+vector< double > d_r_bias
 
+vector< double > d_z_hat_bias
 
+vector< double > d_z_prev
 
+vector< double > r
 
+vector< double > ld_r
 
+vector< double > z_cap
 
+vector< double > ld_z_cap
 
+vector< double > ld_z
 
+ + + +

+Friends

+class RNN_Edge
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from RNN_Node_Interface
+int32_t innovation_number
 
+int32_t layer_type
 
+int32_t node_type
 
+double depth
 
+string parameter_name
 
+bool enabled
 
+bool backward_reachable
 
+bool forward_reachable
 
+int32_t series_length
 
+vector< double > input_values
 
+vector< double > output_values
 
+vector< double > error_values
 
+vector< double > d_input
 
+vector< int32_t > inputs_fired
 
+vector< int32_t > outputs_fired
 
+int32_t total_inputs
 
+int32_t total_outputs
 
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_delta___node.png b/docs/html/class_delta___node.png new file mode 100644 index 0000000000000000000000000000000000000000..c7cc1c299b54ff703fca074c80f60931b9a99ed0 GIT binary patch literal 563 zcmV-30?hr1P)x~0000OP)t-s|Ns90 z008Lh^>vTJkN^MxkN^Mxkifve1&Q1r00008bW%=J0RR90|NsC0)yh;d0005PNklw(LG}K(L3K;JnfZBQFDHVCHoeJqkrqPx8kuD4Ll~7M>>;ZgrP<(n5GvR6 z0Vs%lV&^y>LWsz2O(fd==|~*sggQlCSmwrAP8=S`jhv`o56AC`tLoSR_p&bapoZJ( z5D^)rYlt<0&fa&ookT>s@(-9ph=?(VP!KW3I9LE*Y62LoH$Y6&3n<V0a`tdgbBtq1i+;pRmW*gY$fuuoG3BP)sY^czkYQE z{rBns_^G~ySfg}jw*W`-512y$#vDRHgb>1D{R3v~8?+FB8yo-t002ovPDHLkV1k>k B^6dZs literal 0 HcmV?d00001 diff --git a/docs/html/class_e_n_a_r_c___node-members.html b/docs/html/class_e_n_a_r_c___node-members.html new file mode 100644 index 00000000..f54b12cc --- /dev/null +++ b/docs/html/class_e_n_a_r_c___node-members.html @@ -0,0 +1,173 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
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+
+ + +
+ +
+ +
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+
+
ENARC_Node Member List
+
+
+ +

This is the complete list of members for ENARC_Node, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
backward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
copy() const (defined in ENARC_Node)ENARC_Nodevirtual
d_h_prev (defined in ENARC_Node)ENARC_Nodeprivate
d_input (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
d_rw (defined in ENARC_Node)ENARC_Nodeprivate
d_w1 (defined in ENARC_Node)ENARC_Nodeprivate
d_w2 (defined in ENARC_Node)ENARC_Nodeprivate
d_w3 (defined in ENARC_Node)ENARC_Nodeprivate
d_w4 (defined in ENARC_Node)ENARC_Nodeprivate
d_w5 (defined in ENARC_Node)ENARC_Nodeprivate
d_w6 (defined in ENARC_Node)ENARC_Nodeprivate
d_w7 (defined in ENARC_Node)ENARC_Nodeprivate
d_w8 (defined in ENARC_Node)ENARC_Nodeprivate
d_zw (defined in ENARC_Node)ENARC_Nodeprivate
depth (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
enabled (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
ENARC_Node(int _innovation_number, int _type, double _depth) (defined in ENARC_Node)ENARC_Node
equals(RNN_Node_Interface *other) const (defined in RNN_Node_Interface)RNN_Node_Interface
error_fired(int time, double error) (defined in ENARC_Node)ENARC_Node
error_fired(int32_t time, double error)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
error_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
forward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
get_depth() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_gradient(string gradient_name) (defined in ENARC_Node)ENARC_Node
get_gradients(vector< double > &gradients) (defined in ENARC_Node)ENARC_Nodevirtual
get_innovation_number() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_layer_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_node_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_number_weights() const (defined in ENARC_Node)ENARC_Nodevirtual
get_total_inputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_total_outputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_weights(vector< double > &parameters) const (defined in ENARC_Node)ENARC_Nodevirtual
get_weights(uint32_t &offset, vector< double > &parameters) const (defined in ENARC_Node)ENARC_Nodevirtual
initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range) (defined in ENARC_Node)ENARC_Nodevirtual
initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma) (defined in ENARC_Node)ENARC_Nodevirtual
initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution< double > &rng) (defined in ENARC_Node)ENARC_Nodevirtual
initialize_xavier(minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range) (defined in ENARC_Node)ENARC_Nodevirtual
innovation_number (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
input_fired(int time, double incoming_output) (defined in ENARC_Node)ENARC_Node
input_fired(int32_t time, double incoming_output)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
input_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
inputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
is_enabled() const (defined in RNN_Node_Interface)RNN_Node_Interface
is_reachable() const (defined in RNN_Node_Interface)RNN_Node_Interface
l_d_z (defined in ENARC_Node)ENARC_Nodeprivate
l_w1_z (defined in ENARC_Node)ENARC_Nodeprivate
l_w2_w1 (defined in ENARC_Node)ENARC_Nodeprivate
l_w3_w1 (defined in ENARC_Node)ENARC_Nodeprivate
l_w4_w2 (defined in ENARC_Node)ENARC_Nodeprivate
l_w5_w3 (defined in ENARC_Node)ENARC_Nodeprivate
l_w6_w1 (defined in ENARC_Node)ENARC_Nodeprivate
l_w7_w3 (defined in ENARC_Node)ENARC_Nodeprivate
l_w8_w3 (defined in ENARC_Node)ENARC_Nodeprivate
layer_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
node_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
output_fired(int time, double delta) (defined in ENARC_Node)ENARC_Node
output_fired(int32_t time, double delta)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
output_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
outputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
parameter_name (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
print_gradient(string gradient_name) (defined in ENARC_Node)ENARC_Node
reset(int _series_length) (defined in ENARC_Node)ENARC_Node
reset(int32_t _series_length)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
RNN_Edge (defined in ENARC_Node)ENARC_Nodefriend
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name) (defined in RNN_Node_Interface)RNN_Node_Interface
rw (defined in ENARC_Node)ENARC_Nodeprivate
series_length (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
set_weights(const vector< double > &parameters) (defined in ENARC_Node)ENARC_Nodevirtual
set_weights(uint32_t &offset, const vector< double > &parameters) (defined in ENARC_Node)ENARC_Nodevirtual
total_inputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
total_outputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
try_update_deltas(int time) (defined in ENARC_Node)ENARC_Node
w1 (defined in ENARC_Node)ENARC_Nodeprivate
w1_z (defined in ENARC_Node)ENARC_Nodeprivate
w2 (defined in ENARC_Node)ENARC_Nodeprivate
w2_w1 (defined in ENARC_Node)ENARC_Nodeprivate
w3 (defined in ENARC_Node)ENARC_Nodeprivate
w3_w1 (defined in ENARC_Node)ENARC_Nodeprivate
w4 (defined in ENARC_Node)ENARC_Nodeprivate
w4_w2 (defined in ENARC_Node)ENARC_Nodeprivate
w5 (defined in ENARC_Node)ENARC_Nodeprivate
w5_w3 (defined in ENARC_Node)ENARC_Nodeprivate
w6 (defined in ENARC_Node)ENARC_Nodeprivate
w6_w1 (defined in ENARC_Node)ENARC_Nodeprivate
w7 (defined in ENARC_Node)ENARC_Nodeprivate
w7_w3 (defined in ENARC_Node)ENARC_Nodeprivate
w8 (defined in ENARC_Node)ENARC_Nodeprivate
w8_w3 (defined in ENARC_Node)ENARC_Nodeprivate
write_to_stream(ostream &out) (defined in ENARC_Node)ENARC_Node
z (defined in ENARC_Node)ENARC_Nodeprivate
zw (defined in ENARC_Node)ENARC_Nodeprivate
~ENARC_Node() (defined in ENARC_Node)ENARC_Node
~RNN_Node_Interface() (defined in RNN_Node_Interface)RNN_Node_Interfacevirtual
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+ + diff --git a/docs/html/class_e_n_a_r_c___node.html b/docs/html/class_e_n_a_r_c___node.html new file mode 100644 index 00000000..e424aa9b --- /dev/null +++ b/docs/html/class_e_n_a_r_c___node.html @@ -0,0 +1,388 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: ENARC_Node Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +Friends | +List of all members
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ENARC_Node Class Reference
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+Inheritance diagram for ENARC_Node:
+
+
+ + +RNN_Node_Interface + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ ENARC_Node (int _innovation_number, int _type, double _depth)
 
+void initialize_lamarckian (minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)
 
+void initialize_xavier (minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range)
 
+void initialize_kaiming (minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)
 
+void initialize_uniform_random (minstd_rand0 &generator, uniform_real_distribution< double > &rng)
 
+double get_gradient (string gradient_name)
 
+void print_gradient (string gradient_name)
 
+void input_fired (int time, double incoming_output)
 
+void try_update_deltas (int time)
 
+void error_fired (int time, double error)
 
+void output_fired (int time, double delta)
 
+uint32_t get_number_weights () const
 
+void get_weights (vector< double > &parameters) const
 
+void set_weights (const vector< double > &parameters)
 
+void get_weights (uint32_t &offset, vector< double > &parameters) const
 
+void set_weights (uint32_t &offset, const vector< double > &parameters)
 
+void get_gradients (vector< double > &gradients)
 
+void reset (int _series_length)
 
+void write_to_stream (ostream &out)
 
+RNN_Node_Interface * copy () const
 
- Public Member Functions inherited from RNN_Node_Interface
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth)
 
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name)
 
+virtual void input_fired (int32_t time, double incoming_output)=0
 
+virtual void output_fired (int32_t time, double delta)=0
 
+virtual void error_fired (int32_t time, double error)=0
 
+virtual void reset (int32_t _series_length)=0
 
+void write_to_stream (ostream &out)
 
+int32_t get_node_type () const
 
+int32_t get_layer_type () const
 
+int32_t get_innovation_number () const
 
+int32_t get_total_inputs () const
 
+int32_t get_total_outputs () const
 
+double get_depth () const
 
+bool equals (RNN_Node_Interface *other) const
 
+bool is_reachable () const
 
+bool is_enabled () const
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+double rw
 
+double zw
 
+double w1
 
+double w2
 
+double w3
 
+double w6
 
+double w4
 
+double w5
 
+double w7
 
+double w8
 
+vector< double > d_zw
 
+vector< double > d_rw
 
+vector< double > d_w1
 
+vector< double > d_w2
 
+vector< double > d_w3
 
+vector< double > d_w6
 
+vector< double > d_w4
 
+vector< double > d_w5
 
+vector< double > d_w7
 
+vector< double > d_w8
 
+vector< double > d_h_prev
 
+vector< double > z
 
+vector< double > l_d_z
 
+vector< double > w1_z
 
+vector< double > l_w1_z
 
+vector< double > w2_w1
 
+vector< double > l_w2_w1
 
+vector< double > w3_w1
 
+vector< double > l_w3_w1
 
+vector< double > w6_w1
 
+vector< double > l_w6_w1
 
+vector< double > w4_w2
 
+vector< double > l_w4_w2
 
+vector< double > w5_w3
 
+vector< double > l_w5_w3
 
+vector< double > w7_w3
 
+vector< double > l_w7_w3
 
+vector< double > w8_w3
 
+vector< double > l_w8_w3
 
+ + + +

+Friends

+class RNN_Edge
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from RNN_Node_Interface
+int32_t innovation_number
 
+int32_t layer_type
 
+int32_t node_type
 
+double depth
 
+string parameter_name
 
+bool enabled
 
+bool backward_reachable
 
+bool forward_reachable
 
+int32_t series_length
 
+vector< double > input_values
 
+vector< double > output_values
 
+vector< double > error_values
 
+vector< double > d_input
 
+vector< int32_t > inputs_fired
 
+vector< int32_t > outputs_fired
 
+int32_t total_inputs
 
+int32_t total_outputs
 
+
The documentation for this class was generated from the following files: +
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+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_e_n_a_r_c___node.png b/docs/html/class_e_n_a_r_c___node.png new file mode 100644 index 0000000000000000000000000000000000000000..8fd92794739676d790523644c85339750743a6e4 GIT binary patch literal 567 zcmV-70?7S|P)x~0000OP)t-s|Ns90 z008Lh^>vTJkN^MxkN^Mxkifve1&Q1r00008bW%=J0RR90|NsC0)yh;d0005TNkliv&AZNg%QrD-P`qLC|qVKeXmL;n(EjH^Wy5m_sTa0n4GRv^Ob z_e%GY%B0`1+?G1-s-^MmLFBocj2Y!yhzUXuV$11F877zaEqhU^QW@o0yDcGq_L_Z{ zb4Q(g2T_x4qT~8grE1b{rho3(i-{njO)s(?q=nGFMkd($5JqJPd&sJxBniF;p<+EB zfP$zUJIC=MgospYcA{OM-idKcs8iGz%QVblVt5=UV&eEbjNgfOx1$2qYhCVxs%~$G zh{!6vhu8vW_THU#5)tXj|G*qVM2tCvf`~E3#RB-H9ssL#2Z+bx1{8OIcrvV~xCMmY z4u=4+atMb2j5&lu0LC1`Apm0z;Shi^hj0kM*nfctA%xW;0IZcmI0Rs93XwuLxtQueK%o=)yWS)-bK0I_=>AZ+*o#y*26!9PBZJ_Nv6ryjz1|8_}}kq(1-v002ovPDHLk FV1lKU`fmUL literal 0 HcmV?d00001 diff --git a/docs/html/class_e_n_a_s___d_a_g___node-members.html b/docs/html/class_e_n_a_s___d_a_g___node-members.html new file mode 100644 index 00000000..f3b746d9 --- /dev/null +++ b/docs/html/class_e_n_a_s___d_a_g___node-members.html @@ -0,0 +1,145 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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ENAS_DAG_Node Member List
+
+
+ +

This is the complete list of members for ENAS_DAG_Node, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
activation(double value, int act_operator)ENAS_DAG_Node
activation_derivative(double value, double input, int act_operator)ENAS_DAG_Node
backward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
copy() const (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
d_h_prevENAS_DAG_Nodeprivate
d_input (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
d_rwENAS_DAG_Nodeprivate
d_weightsENAS_DAG_Nodeprivate
d_zwENAS_DAG_Nodeprivate
depth (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
enabled (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
ENAS_DAG_Node(int _innovation_number, int _type, double _depth) (defined in ENAS_DAG_Node)ENAS_DAG_Node
equals(RNN_Node_Interface *other) const (defined in RNN_Node_Interface)RNN_Node_Interface
error_fired(int time, double error) (defined in ENAS_DAG_Node)ENAS_DAG_Node
error_fired(int32_t time, double error)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
error_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
forward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
get_depth() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_gradient(string gradient_name)ENAS_DAG_Node
get_gradients(vector< double > &gradients) (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
get_innovation_number() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_layer_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_node_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_number_weights() const (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
get_total_inputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_total_outputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_weights(vector< double > &parameters) const (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
get_weights(uint32_t &offset, vector< double > &parameters) const (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range) (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma) (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution< double > &rng) (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
initialize_xavier(minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range) (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
innovation_number (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
input_fired(int time, double incoming_output) (defined in ENAS_DAG_Node)ENAS_DAG_Node
input_fired(int32_t time, double incoming_output)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
input_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
inputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
is_enabled() const (defined in RNN_Node_Interface)RNN_Node_Interface
is_reachable() const (defined in RNN_Node_Interface)RNN_Node_Interface
l_NodesENAS_DAG_Nodeprivate
layer_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
node_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
NodesENAS_DAG_Nodeprivate
output_fired(int time, double delta) (defined in ENAS_DAG_Node)ENAS_DAG_Node
output_fired(int32_t time, double delta)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
output_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
outputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
parameter_name (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
print_gradient(string gradient_name)ENAS_DAG_Node
reset(int _series_length) (defined in ENAS_DAG_Node)ENAS_DAG_Node
reset(int32_t _series_length)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
RNN_Edge (defined in ENAS_DAG_Node)ENAS_DAG_Nodefriend
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name) (defined in RNN_Node_Interface)RNN_Node_Interface
rwENAS_DAG_Nodeprivate
series_length (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
set_weights(const vector< double > &parameters) (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
set_weights(uint32_t &offset, const vector< double > &parameters) (defined in ENAS_DAG_Node)ENAS_DAG_Nodevirtual
total_inputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
total_outputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
try_update_deltas(int time) (defined in ENAS_DAG_Node)ENAS_DAG_Node
weightsENAS_DAG_Nodeprivate
write_to_stream(ostream &out) (defined in ENAS_DAG_Node)ENAS_DAG_Node
zwENAS_DAG_Nodeprivate
~ENAS_DAG_Node() (defined in ENAS_DAG_Node)ENAS_DAG_Node
~RNN_Node_Interface() (defined in RNN_Node_Interface)RNN_Node_Interfacevirtual
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_e_n_a_s___d_a_g___node.html b/docs/html/class_e_n_a_s___d_a_g___node.html new file mode 100644 index 00000000..2359098a --- /dev/null +++ b/docs/html/class_e_n_a_s___d_a_g___node.html @@ -0,0 +1,484 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: ENAS_DAG_Node Class Reference + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+Public Member Functions | +Private Attributes | +Friends | +List of all members
+
+
ENAS_DAG_Node Class Reference
+
+
+
+Inheritance diagram for ENAS_DAG_Node:
+
+
+ + +RNN_Node_Interface + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ ENAS_DAG_Node (int _innovation_number, int _type, double _depth)
 
+void initialize_lamarckian (minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)
 
+void initialize_xavier (minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range)
 
+void initialize_kaiming (minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)
 
+void initialize_uniform_random (minstd_rand0 &generator, uniform_real_distribution< double > &rng)
 
double get_gradient (string gradient_name)
 Gives the gradients in essence the deriviative of the weights in the memory cell in the network. More...
 
void print_gradient (string gradient_name)
 Gives the gradients in essence the deriviative of the weights in the memory cell in the network. More...
 
double activation (double value, int act_operator)
 Gives the activations of the node in the memory cell in the network. More...
 
double activation_derivative (double value, double input, int act_operator)
 Gives the activations derviative of the node in the memory cell in the network. More...
 
+void input_fired (int time, double incoming_output)
 
+void try_update_deltas (int time)
 
+void error_fired (int time, double error)
 
+void output_fired (int time, double delta)
 
+uint32_t get_number_weights () const
 
+void get_weights (vector< double > &parameters) const
 
+void set_weights (const vector< double > &parameters)
 
+void get_weights (uint32_t &offset, vector< double > &parameters) const
 
+void set_weights (uint32_t &offset, const vector< double > &parameters)
 
+void get_gradients (vector< double > &gradients)
 
+void reset (int _series_length)
 
+void write_to_stream (ostream &out)
 
+RNN_Node_Interface * copy () const
 
- Public Member Functions inherited from RNN_Node_Interface
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth)
 
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name)
 
+virtual void input_fired (int32_t time, double incoming_output)=0
 
+virtual void output_fired (int32_t time, double delta)=0
 
+virtual void error_fired (int32_t time, double error)=0
 
+virtual void reset (int32_t _series_length)=0
 
+void write_to_stream (ostream &out)
 
+int32_t get_node_type () const
 
+int32_t get_layer_type () const
 
+int32_t get_innovation_number () const
 
+int32_t get_total_inputs () const
 
+int32_t get_total_outputs () const
 
+double get_depth () const
 
+bool equals (RNN_Node_Interface *other) const
 
+bool is_reachable () const
 
+bool is_enabled () const
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+double rw
 Weight of the start node in the memory cell.
 
+double zw
 Weight of the start node in the memory cell.
 
+vector< double > weights
 Weights of the subseqeuent nodes in topological order the memory cell.
 
+vector< double > d_zw
 Gradient of the weight of the start node in the memory cell.
 
+vector< double > d_rw
 Gradient of the weight of the start node in the memory cell.
 
+vector< vector< double > > d_weights
 Gradient of the weights of the other nodes in the memory cell.
 
vector< double > d_h_prev
 Gradient of the previous output i.e. More...
 
+vector< vector< double > > Nodes
 Outputs of the nodes in the memory cell connecting with weight wj from node with weight wi.
 
+vector< vector< double > > l_Nodes
 Derivative of the nodes in the memory cell connecting with weight wj from node with weight wi.
 
+ + + +

+Friends

+class RNN_Edge
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from RNN_Node_Interface
+int32_t innovation_number
 
+int32_t layer_type
 
+int32_t node_type
 
+double depth
 
+string parameter_name
 
+bool enabled
 
+bool backward_reachable
 
+bool forward_reachable
 
+int32_t series_length
 
+vector< double > input_values
 
+vector< double > output_values
 
+vector< double > error_values
 
+vector< double > d_input
 
+vector< int32_t > inputs_fired
 
+vector< int32_t > outputs_fired
 
+int32_t total_inputs
 
+int32_t total_outputs
 
+

Member Function Documentation

+ +

◆ activation()

+ +
+
+ + + + + + + + + + + + + + + + + + +
double ENAS_DAG_Node::activation (double value,
int act_operator 
)
+
+ +

Gives the activations of the node in the memory cell in the network.

+


+

+
Parameters
+ + + +
valueis the output of the node after multiplying weights
act_operatoris the actiation type : sigmoid, tanh, swish, identity
+
+
+
Returns
output of the node after applying activation
+ +
+
+ +

◆ activation_derivative()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
double ENAS_DAG_Node::activation_derivative (double value,
double input,
int act_operator 
)
+
+ +

Gives the activations derviative of the node in the memory cell in the network.

+


+

+
Parameters
+ + + + +
inputis the input of the node before multiplying weights
valueis the output of the node after multiplying weights
act_operatoris the actiation type : sigmoid, tanh, swish, identity
+
+
+
Returns
output derivative of the node after applying activation
+ +
+
+ +

◆ get_gradient()

+ +
+
+ + + + + + + + +
double ENAS_DAG_Node::get_gradient (string gradient_name)
+
+ +

Gives the gradients in essence the deriviative of the weights in the memory cell in the network.

+


+

+
Parameters
+ + +
gradient_namein the weight name.
+
+
+
Returns
gradient.
+ +
+
+ +

◆ print_gradient()

+ +
+
+ + + + + + + + +
void ENAS_DAG_Node::print_gradient (string gradient_name)
+
+ +

Gives the gradients in essence the deriviative of the weights in the memory cell in the network.

+


+

+
Parameters
+ + +
gradient_namein the weight name.
+
+
+

prints the gradient.

+ +
+
+

Member Data Documentation

+ +

◆ d_h_prev

+ +
+
+ + + + + +
+ + + + +
vector<double> ENAS_DAG_Node::d_h_prev
+ 		+private
+
+ +

Gradient of the previous output i.e.

+

t-1 in the memory cell.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_e_n_a_s___d_a_g___node.png b/docs/html/class_e_n_a_s___d_a_g___node.png new file mode 100644 index 0000000000000000000000000000000000000000..699d4d20b81458b9894fb716ac77c32bb635905b GIT binary patch literal 592 zcmV-W0x~0000OP)t-s|Ns90 z008Lh^>vTJkN^MxkN^Mxkifve1&Q1r00008bW%=J0RR90|NsC0)yh;d0005sNkl-Ih>ad(R%_ zI+2U-AX>3~s9%5Sq#-tw`E}wfCz2TpFLHD;La?oH}>*Giq=LGE{A1oO-%ZbD7xRDe6^>BPoT%C>v*p9WI2kIVAhnd+b zy@uEl=nU+N10R_hZWUatpGJOSc|VLinoAmVfc z1OTW8L_h!#35b9IAQBJ(0YD@m0s??YKm-H;k-q|wQc9~82>{k=Km-H;kshLCYw6|p zHq;rqlc|M_sU-lC0mA)xtC+M`GpygC?~z5<{sLmF4G`0H&JD;#2~fEYBwRNk8jM&r z2r-&Lw4!eGu%N@7pb4H8R}j3)-isFZBH>Hnbn9|L4EjWL7afGA?=1*-Jxk2d;06Q$ zsGdRi79K)q0QRtS7Qf7ietie`1RoRmAIi*+P6q(|cl{V*57pVEYYPA(@dFD803rcF e5Gkd!SpNZO8 literal 0 HcmV?d00001 diff --git a/docs/html/class_e_x_a_c_t-members.html b/docs/html/class_e_x_a_c_t-members.html new file mode 100644 index 00000000..73b279a6 --- /dev/null +++ b/docs/html/class_e_x_a_c_t-members.html @@ -0,0 +1,202 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+
EXACT Member List
+
+
+ +

This is the complete list of members for EXACT, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
add_edge(CNN_Genome *child, CNN_Node *node1, CNN_Node *node2, int edge_type) (defined in EXACT)EXACT
alpha_max (defined in EXACT)EXACTprivate
alpha_min (defined in EXACT)EXACTprivate
batch_size_max (defined in EXACT)EXACTprivate
batch_size_min (defined in EXACT)EXACTprivate
best_predictions_genome (defined in EXACT)EXACTprivate
best_predictions_genome_id (defined in EXACT)EXACTprivate
create_child() (defined in EXACT)EXACT
create_mutation() (defined in EXACT)EXACT
crossover_alter_edge_type (defined in EXACT)EXACTprivate
crossover_rate (defined in EXACT)EXACTprivate
edge_add (defined in EXACT)EXACTprivate
edge_alter_type (defined in EXACT)EXACTprivate
edge_disable (defined in EXACT)EXACTprivate
edge_enable (defined in EXACT)EXACTprivate
edge_innovation_count (defined in EXACT)EXACTprivate
edge_split (defined in EXACT)EXACTprivate
epsilon (defined in EXACT)EXACTprivate
EXACT(const ImagesInterface &training_images, const ImagesInterface &validation_images, const ImagesInterface &test_images, int _padding, int _population_size, int _max_epochs, bool _use_sfmp, bool _use_node_operations, int _max_genomes, string _output_directory, string _search_name, bool _reset_weights) (defined in EXACT)EXACT
generate_individual() (defined in EXACT)EXACT
generate_initial_hyperparameters(float &mu, float &mu_delta, float &learning_rate, float &learning_rate_delta, float &weight_decay, float &weight_decay_delta, float &alpha, int &velocity_reset, float &input_dropout_probability, float &hidden_dropout_probability, int &batch_size) (defined in EXACT)EXACT
generate_simplex_hyperparameters(float &mu, float &mu_delta, float &learning_rate, float &learning_rate_delta, float &weight_decay, float &weight_decay_delta, float &alpha, int &velocity_reset, float &input_dropout_probability, float &hidden_dropout_probability, int &batch_size) (defined in EXACT)EXACT
generated_from_map (defined in EXACT)EXACTprivate
generator (defined in EXACT)EXACTprivate
genomes (defined in EXACT)EXACTprivate
genomes_generated (defined in EXACT)EXACTprivate
get_best_genome() (defined in EXACT)EXACT
get_genome(int i) (defined in EXACT)EXACT
get_id() const (defined in EXACT)EXACT
get_inserted_genomes() const (defined in EXACT)EXACT
get_max_genomes() const (defined in EXACT)EXACT
get_number_genomes() const (defined in EXACT)EXACT
get_number_training_images() const (defined in EXACT)EXACT
get_output_directory() const (defined in EXACT)EXACT
get_search_name() const (defined in EXACT)EXACT
get_test_filename() const (defined in EXACT)EXACT
get_training_filename() const (defined in EXACT)EXACT
get_validation_filename() const (defined in EXACT)EXACT
hidden_dropout_probability_max (defined in EXACT)EXACTprivate
hidden_dropout_probability_min (defined in EXACT)EXACTprivate
id (defined in EXACT)EXACTprivate
image_channels (defined in EXACT)EXACTprivate
image_cols (defined in EXACT)EXACTprivate
image_rows (defined in EXACT)EXACTprivate
initial_alpha_max (defined in EXACT)EXACTprivate
initial_alpha_min (defined in EXACT)EXACTprivate
initial_batch_size_max (defined in EXACT)EXACTprivate
initial_batch_size_min (defined in EXACT)EXACTprivate
initial_hidden_dropout_probability_max (defined in EXACT)EXACTprivate
initial_hidden_dropout_probability_min (defined in EXACT)EXACTprivate
initial_input_dropout_probability_max (defined in EXACT)EXACTprivate
initial_input_dropout_probability_min (defined in EXACT)EXACTprivate
initial_learning_rate_delta_max (defined in EXACT)EXACTprivate
initial_learning_rate_delta_min (defined in EXACT)EXACTprivate
initial_learning_rate_max (defined in EXACT)EXACTprivate
initial_learning_rate_min (defined in EXACT)EXACTprivate
initial_mu_delta_max (defined in EXACT)EXACTprivate
initial_mu_delta_min (defined in EXACT)EXACTprivate
initial_mu_max (defined in EXACT)EXACTprivate
initial_mu_min (defined in EXACT)EXACTprivate
initial_velocity_reset_max (defined in EXACT)EXACTprivate
initial_velocity_reset_min (defined in EXACT)EXACTprivate
initial_weight_decay_delta_max (defined in EXACT)EXACTprivate
initial_weight_decay_delta_min (defined in EXACT)EXACTprivate
initial_weight_decay_max (defined in EXACT)EXACTprivate
initial_weight_decay_min (defined in EXACT)EXACTprivate
input_dropout_probability_max (defined in EXACT)EXACTprivate
input_dropout_probability_min (defined in EXACT)EXACTprivate
insert_genome(CNN_Genome *genome) (defined in EXACT)EXACT
inserted_from_map (defined in EXACT)EXACTprivate
inserted_genomes (defined in EXACT)EXACTprivate
is_identical(EXACT *other, bool testing_checkpoint) (defined in EXACT)EXACT
learning_rate_delta_max (defined in EXACT)EXACTprivate
learning_rate_delta_min (defined in EXACT)EXACTprivate
learning_rate_max (defined in EXACT)EXACTprivate
learning_rate_min (defined in EXACT)EXACTprivate
less_fit_parent_crossover (defined in EXACT)EXACTprivate
max_epochs (defined in EXACT)EXACTprivate
max_genomes (defined in EXACT)EXACTprivate
more_fit_parent_crossover (defined in EXACT)EXACTprivate
mu_delta_max (defined in EXACT)EXACTprivate
mu_delta_min (defined in EXACT)EXACTprivate
mu_max (defined in EXACT)EXACTprivate
mu_min (defined in EXACT)EXACTprivate
node_add (defined in EXACT)EXACTprivate
node_change_size (defined in EXACT)EXACTprivate
node_change_size_x (defined in EXACT)EXACTprivate
node_change_size_y (defined in EXACT)EXACTprivate
node_disable (defined in EXACT)EXACTprivate
node_enable (defined in EXACT)EXACTprivate
node_innovation_count (defined in EXACT)EXACTprivate
node_merge (defined in EXACT)EXACTprivate
node_split (defined in EXACT)EXACTprivate
normal_distribution (defined in EXACT)EXACTprivate
number_classes (defined in EXACT)EXACTprivate
number_mutations (defined in EXACT)EXACTprivate
number_test_images (defined in EXACT)EXACTprivate
number_training_images (defined in EXACT)EXACTprivate
number_validation_images (defined in EXACT)EXACTprivate
output_directory (defined in EXACT)EXACTprivate
padding (defined in EXACT)EXACTprivate
population_contains(CNN_Genome *genome) const (defined in EXACT)EXACT
population_size (defined in EXACT)EXACTprivate
reset_weights (defined in EXACT)EXACTprivate
reset_weights_chance (defined in EXACT)EXACTprivate
rng_float (defined in EXACT)EXACTprivate
rng_long (defined in EXACT)EXACTprivate
search_name (defined in EXACT)EXACTprivate
test_filename (defined in EXACT)EXACTprivate
training_filename (defined in EXACT)EXACTprivate
use_node_operations (defined in EXACT)EXACTprivate
use_sfmp (defined in EXACT)EXACTprivate
validation_filename (defined in EXACT)EXACTprivate
velocity_reset_max (defined in EXACT)EXACTprivate
velocity_reset_min (defined in EXACT)EXACTprivate
weight_decay_delta_max (defined in EXACT)EXACTprivate
weight_decay_delta_min (defined in EXACT)EXACTprivate
weight_decay_max (defined in EXACT)EXACTprivate
weight_decay_min (defined in EXACT)EXACTprivate
write_hyperparameters_header() (defined in EXACT)EXACT
write_individual_hyperparameters(CNN_Genome *individual) (defined in EXACT)EXACT
write_statistics(int new_generation_id, float new_fitness) (defined in EXACT)EXACT
write_statistics_header() (defined in EXACT)EXACT
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_e_x_a_c_t.html b/docs/html/class_e_x_a_c_t.html new file mode 100644 index 00000000..df8e18e5 --- /dev/null +++ b/docs/html/class_e_x_a_c_t.html @@ -0,0 +1,460 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: EXACT Class Reference + + + + + + + + + +
+
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+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
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+Public Member Functions | +Private Attributes | +List of all members
+
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EXACT Class Reference
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+Public Member Functions

+ EXACT (const ImagesInterface &training_images, const ImagesInterface &validation_images, const ImagesInterface &test_images, int _padding, int _population_size, int _max_epochs, bool _use_sfmp, bool _use_node_operations, int _max_genomes, string _output_directory, string _search_name, bool _reset_weights)
 
+int32_t population_contains (CNN_Genome *genome) const
 
+CNN_Genome * get_best_genome ()
 
+int get_number_genomes () const
 
+CNN_Genome * get_genome (int i)
 
+void generate_initial_hyperparameters (float &mu, float &mu_delta, float &learning_rate, float &learning_rate_delta, float &weight_decay, float &weight_decay_delta, float &alpha, int &velocity_reset, float &input_dropout_probability, float &hidden_dropout_probability, int &batch_size)
 
+void generate_simplex_hyperparameters (float &mu, float &mu_delta, float &learning_rate, float &learning_rate_delta, float &weight_decay, float &weight_decay_delta, float &alpha, int &velocity_reset, float &input_dropout_probability, float &hidden_dropout_probability, int &batch_size)
 
+bool add_edge (CNN_Genome *child, CNN_Node *node1, CNN_Node *node2, int edge_type)
 
+CNN_Genome * generate_individual ()
 
+CNN_Genome * create_mutation ()
 
+CNN_Genome * create_child ()
 
+bool insert_genome (CNN_Genome *genome)
 
+int get_inserted_genomes () const
 
+int get_max_genomes () const
 
+void write_individual_hyperparameters (CNN_Genome *individual)
 
+void write_statistics (int new_generation_id, float new_fitness)
 
+void write_statistics_header ()
 
+void write_hyperparameters_header ()
 
+int get_id () const
 
+string get_search_name () const
 
+string get_output_directory () const
 
+string get_training_filename () const
 
+string get_validation_filename () const
 
+string get_test_filename () const
 
+int get_number_training_images () const
 
+bool is_identical (EXACT *other, bool testing_checkpoint)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int id
 
+string search_name
 
+string output_directory
 
+string training_filename
 
+string validation_filename
 
+string test_filename
 
+int number_training_images
 
+int number_validation_images
 
+int number_test_images
 
+int padding
 
+int image_channels
 
+int image_rows
 
+int image_cols
 
+int number_classes
 
+int population_size
 
+int node_innovation_count
 
+int edge_innovation_count
 
+minstd_rand0 generator
 
+NormalDistribution normal_distribution
 
+uniform_int_distribution< long > rng_long
 
+uniform_real_distribution< float > rng_float
 
+vector< CNN_Genome * > genomes
 
+int best_predictions_genome_id
 
+CNN_Genome * best_predictions_genome
 
+int genomes_generated
 
+int inserted_genomes
 
+int max_genomes
 
+bool reset_weights
 
+int max_epochs
 
+bool use_sfmp
 
+bool use_node_operations
 
+float initial_batch_size_min
 
+float initial_batch_size_max
 
+float batch_size_min
 
+float batch_size_max
 
+float initial_mu_min
 
+float initial_mu_max
 
+float mu_min
 
+float mu_max
 
+float initial_mu_delta_min
 
+float initial_mu_delta_max
 
+float mu_delta_min
 
+float mu_delta_max
 
+float initial_learning_rate_min
 
+float initial_learning_rate_max
 
+float learning_rate_min
 
+float learning_rate_max
 
+float initial_learning_rate_delta_min
 
+float initial_learning_rate_delta_max
 
+float learning_rate_delta_min
 
+float learning_rate_delta_max
 
+float initial_weight_decay_min
 
+float initial_weight_decay_max
 
+float weight_decay_min
 
+float weight_decay_max
 
+float initial_weight_decay_delta_min
 
+float initial_weight_decay_delta_max
 
+float weight_decay_delta_min
 
+float weight_decay_delta_max
 
+float epsilon
 
+float initial_alpha_min
 
+float initial_alpha_max
 
+float alpha_min
 
+float alpha_max
 
+int initial_velocity_reset_min
 
+int initial_velocity_reset_max
 
+int velocity_reset_min
 
+int velocity_reset_max
 
+float initial_input_dropout_probability_min
 
+float initial_input_dropout_probability_max
 
+float input_dropout_probability_min
 
+float input_dropout_probability_max
 
+float initial_hidden_dropout_probability_min
 
+float initial_hidden_dropout_probability_max
 
+float hidden_dropout_probability_min
 
+float hidden_dropout_probability_max
 
+float reset_weights_chance
 
+float crossover_rate
 
+float more_fit_parent_crossover
 
+float less_fit_parent_crossover
 
+float crossover_alter_edge_type
 
+int number_mutations
 
+float edge_alter_type
 
+float edge_disable
 
+float edge_enable
 
+float edge_split
 
+float edge_add
 
+float node_change_size
 
+float node_change_size_x
 
+float node_change_size_y
 
+float node_add
 
+float node_split
 
+float node_merge
 
+float node_enable
 
+float node_disable
 
+map< string, int > inserted_from_map
 
+map< string, int > generated_from_map
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_e_x_a_m_m-members.html b/docs/html/class_e_x_a_m_m-members.html new file mode 100644 index 00000000..b9075081 --- /dev/null +++ b/docs/html/class_e_x_a_m_m-members.html @@ -0,0 +1,173 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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EXAMM Member List
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This is the complete list of members for EXAMM, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
add_edge_rate (defined in EXAMM)EXAMMprivate
add_node_rate (defined in EXAMM)EXAMMprivate
add_recurrent_edge_rate (defined in EXAMM)EXAMMprivate
attempt_edge_insert(vector< RNN_Edge * > &child_edges, vector< RNN_Node_Interface * > &child_nodes, RNN_Edge *edge, RNN_Edge *second_edge, bool set_enabled) (defined in EXAMM)EXAMM
attempt_node_insert(vector< RNN_Node_Interface * > &child_nodes, const RNN_Node_Interface *node, const vector< double > &new_weights) (defined in EXAMM)EXAMM
attempt_recurrent_edge_insert(vector< RNN_Recurrent_Edge * > &child_recurrent_edges, vector< RNN_Node_Interface * > &child_nodes, RNN_Recurrent_Edge *recurrent_edge, RNN_Recurrent_Edge *second_edge, bool set_enabled) (defined in EXAMM)EXAMM
bp_iterations (defined in EXAMM)EXAMMprivate
check_weight_initialize_validity() (defined in EXAMM)EXAMM
clone_rate (defined in EXAMM)EXAMMprivate
crossover(RNN_Genome *p1, RNN_Genome *p2) (defined in EXAMM)EXAMM
disable_edge_rate (defined in EXAMM)EXAMMprivate
disable_node_rate (defined in EXAMM)EXAMMprivate
dropout_probability (defined in EXAMM)EXAMMprivate
edge_innovation_count (defined in EXAMM)EXAMMprivate
enable_edge_rate (defined in EXAMM)EXAMMprivate
enable_node_rate (defined in EXAMM)EXAMMprivate
epigenetic_weights (defined in EXAMM)EXAMMprivate
EXAMM(int32_t _population_size, int32_t _number_islands, int32_t _max_genomes, int32_t _extinction_event_generation_number, int32_t _islands_to_exterminate, string _island_ranking_method, string _repopulation_method, int32_t _repopulation_mutations, bool _repeat_extinction, string _speciation_method, double _species_threshold, double _fitness_threshold, double _neat_c1, double _neat_c2, double _neat_c3, const vector< string > &_input_parameter_names, const vector< string > &_output_parameter_names, string _normalize_type, const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs, const map< string, double > &_normalize_avgs, const map< string, double > &_normalize_std_devs, WeightType _weight_initialize, WeightType _weight_inheritance, WeightType _mutated_component_weight, int32_t _bp_iterations, double _learning_rate, bool _use_high_threshold, double _high_threshold, bool _use_low_threshold, double _low_threshold, bool _use_dropout, double _dropout_probability, int32_t _min_recurrent_depth, int32_t _max_recurrent_depth, bool _use_regression, string _output_directory, RNN_Genome *seed_genome, bool _start_filled) (defined in EXAMM)EXAMM
extinction_event_generation_number (defined in EXAMM)EXAMMprivate
fitness_threshold (defined in EXAMM)EXAMMprivate
generate_for_transfer_learning(string file_name, int extra_inputs, int extra_outputs) (defined in EXAMM)EXAMM
generate_genome() (defined in EXAMM)EXAMM
generated_counts (defined in EXAMM)EXAMMprivate
generated_from_map (defined in EXAMM)EXAMMprivate
generator (defined in EXAMM)EXAMMprivate
genome_file_name (defined in EXAMM)EXAMMprivate
genomes (defined in EXAMM)EXAMMprivate
get_best_fitness() (defined in EXAMM)EXAMM
get_best_genome() (defined in EXAMM)EXAMM
get_output_directory() const (defined in EXAMM)EXAMM
get_random_node_type() (defined in EXAMM)EXAMM
get_recurrent_depth_dist() (defined in EXAMM)EXAMM
get_worst_fitness() (defined in EXAMM)EXAMM
get_worst_genome() (defined in EXAMM)EXAMM
high_threshold (defined in EXAMM)EXAMMprivate
input_parameter_names (defined in EXAMM)EXAMMprivate
insert_genome(RNN_Genome *genome) (defined in EXAMM)EXAMM
inserted_counts (defined in EXAMM)EXAMMprivate
inserted_from_map (defined in EXAMM)EXAMMprivate
island_ranking_method (defined in EXAMM)EXAMMprivate
learning_rate (defined in EXAMM)EXAMMprivate
less_fit_crossover_rate (defined in EXAMM)EXAMMprivate
log_file (defined in EXAMM)EXAMMprivate
low_threshold (defined in EXAMM)EXAMMprivate
max_genomes (defined in EXAMM)EXAMMprivate
max_recurrent_depth (defined in EXAMM)EXAMMprivate
memory_log (defined in EXAMM)EXAMMprivate
merge_node_rate (defined in EXAMM)EXAMMprivate
min_recurrent_depth (defined in EXAMM)EXAMMprivate
more_fit_crossover_rate (defined in EXAMM)EXAMMprivate
mutate(int32_t max_mutations, RNN_Genome *p1) (defined in EXAMM)EXAMM
mutated_component_weight (defined in EXAMM)EXAMMprivate
neat_c1 (defined in EXAMM)EXAMMprivate
neat_c2 (defined in EXAMM)EXAMMprivate
neat_c3 (defined in EXAMM)EXAMMprivate
node_innovation_count (defined in EXAMM)EXAMMprivate
normalize_avgs (defined in EXAMM)EXAMMprivate
normalize_maxs (defined in EXAMM)EXAMMprivate
normalize_mins (defined in EXAMM)EXAMMprivate
normalize_std_devs (defined in EXAMM)EXAMMprivate
normalize_type (defined in EXAMM)EXAMMprivate
number_inputs (defined in EXAMM)EXAMMprivate
number_islands (defined in EXAMM)EXAMMprivate
number_outputs (defined in EXAMM)EXAMMprivate
op_log_file (defined in EXAMM)EXAMMprivate
op_log_ordering (defined in EXAMM)EXAMMprivate
output_directory (defined in EXAMM)EXAMMprivate
output_parameter_names (defined in EXAMM)EXAMMprivate
population_size (defined in EXAMM)EXAMMprivate
possible_node_types (defined in EXAMM)EXAMMprivate
print() (defined in EXAMM)EXAMM
repeat_extinction (defined in EXAMM)EXAMMprivate
repopulation_method (defined in EXAMM)EXAMMprivate
repopulation_mutations (defined in EXAMM)EXAMMprivate
rng_0_1 (defined in EXAMM)EXAMMprivate
rng_crossover_weight (defined in EXAMM)EXAMMprivate
set_possible_node_types(vector< string > possible_node_type_strings) (defined in EXAMM)EXAMM
speciation_method (defined in EXAMM)EXAMMprivate
speciation_strategy (defined in EXAMM)EXAMMprivate
species_threshold (defined in EXAMM)EXAMMprivate
split_edge_rate (defined in EXAMM)EXAMMprivate
split_node_rate (defined in EXAMM)EXAMMprivate
start_filled (defined in EXAMM)EXAMMprivate
startClock (defined in EXAMM)EXAMMprivate
total_bp_epochs (defined in EXAMM)EXAMMprivate
update_log() (defined in EXAMM)EXAMM
use_dropout (defined in EXAMM)EXAMMprivate
use_high_threshold (defined in EXAMM)EXAMMprivate
use_low_threshold (defined in EXAMM)EXAMMprivate
use_regression (defined in EXAMM)EXAMMprivate
weight_inheritance (defined in EXAMM)EXAMMprivate
weight_initialize (defined in EXAMM)EXAMMprivate
write_memory_log(string filename) (defined in EXAMM)EXAMM
~EXAMM() (defined in EXAMM)EXAMM
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EXONA: Evolutionary eXploration of Neural Architectures +
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EXAMM Class Reference
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+Public Member Functions

+ EXAMM (int32_t _population_size, int32_t _number_islands, int32_t _max_genomes, int32_t _extinction_event_generation_number, int32_t _islands_to_exterminate, string _island_ranking_method, string _repopulation_method, int32_t _repopulation_mutations, bool _repeat_extinction, string _speciation_method, double _species_threshold, double _fitness_threshold, double _neat_c1, double _neat_c2, double _neat_c3, const vector< string > &_input_parameter_names, const vector< string > &_output_parameter_names, string _normalize_type, const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs, const map< string, double > &_normalize_avgs, const map< string, double > &_normalize_std_devs, WeightType _weight_initialize, WeightType _weight_inheritance, WeightType _mutated_component_weight, int32_t _bp_iterations, double _learning_rate, bool _use_high_threshold, double _high_threshold, bool _use_low_threshold, double _low_threshold, bool _use_dropout, double _dropout_probability, int32_t _min_recurrent_depth, int32_t _max_recurrent_depth, bool _use_regression, string _output_directory, RNN_Genome *seed_genome, bool _start_filled)
 
+void print ()
 
+void update_log ()
 
+void write_memory_log (string filename)
 
+void set_possible_node_types (vector< string > possible_node_type_strings)
 
+uniform_int_distribution< int32_t > get_recurrent_depth_dist ()
 
+int get_random_node_type ()
 
+RNN_Genome * generate_genome ()
 
+bool insert_genome (RNN_Genome *genome)
 
+void mutate (int32_t max_mutations, RNN_Genome *p1)
 
+void attempt_node_insert (vector< RNN_Node_Interface * > &child_nodes, const RNN_Node_Interface *node, const vector< double > &new_weights)
 
+void attempt_edge_insert (vector< RNN_Edge * > &child_edges, vector< RNN_Node_Interface * > &child_nodes, RNN_Edge *edge, RNN_Edge *second_edge, bool set_enabled)
 
+void attempt_recurrent_edge_insert (vector< RNN_Recurrent_Edge * > &child_recurrent_edges, vector< RNN_Node_Interface * > &child_nodes, RNN_Recurrent_Edge *recurrent_edge, RNN_Recurrent_Edge *second_edge, bool set_enabled)
 
+RNN_Genome * crossover (RNN_Genome *p1, RNN_Genome *p2)
 
+double get_best_fitness ()
 
+double get_worst_fitness ()
 
+RNN_Genome * get_best_genome ()
 
+RNN_Genome * get_worst_genome ()
 
+string get_output_directory () const
 
+RNN_Genome * generate_for_transfer_learning (string file_name, int extra_inputs, int extra_outputs)
 
+void check_weight_initialize_validity ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int32_t population_size
 
+int32_t number_islands
 
+vector< vector< RNN_Genome * > > genomes
 
+int32_t max_genomes
 
+int32_t total_bp_epochs
 
+int32_t extinction_event_generation_number
 
+string island_ranking_method
 
+string speciation_method
 
+string repopulation_method
 
+int32_t repopulation_mutations
 
+bool repeat_extinction
 
+SpeciationStrategy * speciation_strategy
 
+double species_threshold
 
+double fitness_threshold
 
+double neat_c1
 
+double neat_c2
 
+double neat_c3
 
+int32_t edge_innovation_count
 
+int32_t node_innovation_count
 
+map< string, int32_t > inserted_from_map
 
+map< string, int32_t > generated_from_map
 
+int32_t number_inputs
 
+int32_t number_outputs
 
+int32_t bp_iterations
 
+double learning_rate
 
+bool use_high_threshold
 
+double high_threshold
 
+bool use_low_threshold
 
+double low_threshold
 
+bool use_regression
 
+bool use_dropout
 
+double dropout_probability
 
+minstd_rand0 generator
 
+uniform_real_distribution< double > rng_0_1
 
+uniform_real_distribution< double > rng_crossover_weight
 
+int32_t min_recurrent_depth
 
+int32_t max_recurrent_depth
 
+bool epigenetic_weights
 
+double more_fit_crossover_rate
 
+double less_fit_crossover_rate
 
+double clone_rate
 
+double add_edge_rate
 
+double add_recurrent_edge_rate
 
+double enable_edge_rate
 
+double disable_edge_rate
 
+double split_edge_rate
 
+double add_node_rate
 
+double enable_node_rate
 
+double disable_node_rate
 
+double split_node_rate
 
+double merge_node_rate
 
+vector< int > possible_node_types
 
+vector< string > op_log_ordering
 
+map< string, int32_t > inserted_counts
 
+map< string, int32_t > generated_counts
 
+string output_directory
 
+ofstream * log_file
 
+ofstream * op_log_file
 
+vector< string > input_parameter_names
 
+vector< string > output_parameter_names
 
+string normalize_type
 
+map< string, double > normalize_mins
 
+map< string, double > normalize_maxs
 
+map< string, double > normalize_avgs
 
+map< string, double > normalize_std_devs
 
+WeightType weight_initialize
 
+WeightType weight_inheritance
 
+WeightType mutated_component_weight
 
+ostringstream memory_log
 
+std::chrono::time_point< std::chrono::system_clock > startClock
 
+string genome_file_name
 
+bool start_filled
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_g_r_u___node-members.html b/docs/html/class_g_r_u___node-members.html new file mode 100644 index 00000000..3f957c6c --- /dev/null +++ b/docs/html/class_g_r_u___node-members.html @@ -0,0 +1,159 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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GRU_Node Member List
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This is the complete list of members for GRU_Node, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
backward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
copy() const (defined in GRU_Node)GRU_Nodevirtual
d_h_bias (defined in GRU_Node)GRU_Nodeprivate
d_h_prev (defined in GRU_Node)GRU_Nodeprivate
d_hu (defined in GRU_Node)GRU_Nodeprivate
d_hw (defined in GRU_Node)GRU_Nodeprivate
d_input (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
d_r_bias (defined in GRU_Node)GRU_Nodeprivate
d_ru (defined in GRU_Node)GRU_Nodeprivate
d_rw (defined in GRU_Node)GRU_Nodeprivate
d_z_bias (defined in GRU_Node)GRU_Nodeprivate
d_zu (defined in GRU_Node)GRU_Nodeprivate
d_zw (defined in GRU_Node)GRU_Nodeprivate
depth (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
enabled (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
equals(RNN_Node_Interface *other) const (defined in RNN_Node_Interface)RNN_Node_Interface
error_fired(int time, double error) (defined in GRU_Node)GRU_Node
error_fired(int32_t time, double error)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
error_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
forward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
get_depth() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_gradient(string gradient_name) (defined in GRU_Node)GRU_Node
get_gradients(vector< double > &gradients) (defined in GRU_Node)GRU_Nodevirtual
get_innovation_number() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_layer_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_node_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_number_weights() const (defined in GRU_Node)GRU_Nodevirtual
get_total_inputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_total_outputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_weights(vector< double > &parameters) const (defined in GRU_Node)GRU_Nodevirtual
get_weights(uint32_t &offset, vector< double > &parameters) const (defined in GRU_Node)GRU_Nodevirtual
GRU_Node(int _innovation_number, int _type, double _depth) (defined in GRU_Node)GRU_Node
h_bias (defined in GRU_Node)GRU_Nodeprivate
h_tanh (defined in GRU_Node)GRU_Nodeprivate
hu (defined in GRU_Node)GRU_Nodeprivate
hw (defined in GRU_Node)GRU_Nodeprivate
initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range) (defined in GRU_Node)GRU_Nodevirtual
initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma) (defined in GRU_Node)GRU_Nodevirtual
initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution< double > &rng) (defined in GRU_Node)GRU_Nodevirtual
initialize_xavier(minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range) (defined in GRU_Node)GRU_Nodevirtual
innovation_number (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
input_fired(int time, double incoming_output) (defined in GRU_Node)GRU_Node
input_fired(int32_t time, double incoming_output)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
input_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
inputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
is_enabled() const (defined in RNN_Node_Interface)RNN_Node_Interface
is_reachable() const (defined in RNN_Node_Interface)RNN_Node_Interface
layer_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
ld_h_tanh (defined in GRU_Node)GRU_Nodeprivate
ld_r (defined in GRU_Node)GRU_Nodeprivate
ld_z (defined in GRU_Node)GRU_Nodeprivate
node_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
output_fired(int time, double delta) (defined in GRU_Node)GRU_Node
output_fired(int32_t time, double delta)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
output_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
outputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
parameter_name (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
print_gradient(string gradient_name) (defined in GRU_Node)GRU_Node
r (defined in GRU_Node)GRU_Nodeprivate
r_bias (defined in GRU_Node)GRU_Nodeprivate
reset(int _series_length) (defined in GRU_Node)GRU_Node
reset(int32_t _series_length)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
RNN_Edge (defined in GRU_Node)GRU_Nodefriend
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name) (defined in RNN_Node_Interface)RNN_Node_Interface
ru (defined in GRU_Node)GRU_Nodeprivate
rw (defined in GRU_Node)GRU_Nodeprivate
series_length (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
set_weights(const vector< double > &parameters) (defined in GRU_Node)GRU_Nodevirtual
set_weights(uint32_t &offset, const vector< double > &parameters) (defined in GRU_Node)GRU_Nodevirtual
total_inputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
total_outputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
try_update_deltas(int time) (defined in GRU_Node)GRU_Node
write_to_stream(ostream &out) (defined in GRU_Node)GRU_Node
z (defined in GRU_Node)GRU_Nodeprivate
z_bias (defined in GRU_Node)GRU_Nodeprivate
zu (defined in GRU_Node)GRU_Nodeprivate
zw (defined in GRU_Node)GRU_Nodeprivate
~GRU_Node() (defined in GRU_Node)GRU_Node
~RNN_Node_Interface() (defined in RNN_Node_Interface)RNN_Node_Interfacevirtual
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +Friends | +List of all members
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GRU_Node Class Reference
+
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+Inheritance diagram for GRU_Node:
+
+
+ + +RNN_Node_Interface + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ GRU_Node (int _innovation_number, int _type, double _depth)
 
+void initialize_lamarckian (minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)
 
+void initialize_xavier (minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range)
 
+void initialize_kaiming (minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)
 
+void initialize_uniform_random (minstd_rand0 &generator, uniform_real_distribution< double > &rng)
 
+double get_gradient (string gradient_name)
 
+void print_gradient (string gradient_name)
 
+void input_fired (int time, double incoming_output)
 
+void try_update_deltas (int time)
 
+void error_fired (int time, double error)
 
+void output_fired (int time, double delta)
 
+uint32_t get_number_weights () const
 
+void get_weights (vector< double > &parameters) const
 
+void set_weights (const vector< double > &parameters)
 
+void get_weights (uint32_t &offset, vector< double > &parameters) const
 
+void set_weights (uint32_t &offset, const vector< double > &parameters)
 
+void get_gradients (vector< double > &gradients)
 
+void reset (int _series_length)
 
+void write_to_stream (ostream &out)
 
+RNN_Node_Interface * copy () const
 
- Public Member Functions inherited from RNN_Node_Interface
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth)
 
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name)
 
+virtual void input_fired (int32_t time, double incoming_output)=0
 
+virtual void output_fired (int32_t time, double delta)=0
 
+virtual void error_fired (int32_t time, double error)=0
 
+virtual void reset (int32_t _series_length)=0
 
+void write_to_stream (ostream &out)
 
+int32_t get_node_type () const
 
+int32_t get_layer_type () const
 
+int32_t get_innovation_number () const
 
+int32_t get_total_inputs () const
 
+int32_t get_total_outputs () const
 
+double get_depth () const
 
+bool equals (RNN_Node_Interface *other) const
 
+bool is_reachable () const
 
+bool is_enabled () const
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+double zw
 
+double zu
 
+double z_bias
 
+double rw
 
+double ru
 
+double r_bias
 
+double hw
 
+double hu
 
+double h_bias
 
+vector< double > d_zw
 
+vector< double > d_zu
 
+vector< double > d_z_bias
 
+vector< double > d_rw
 
+vector< double > d_ru
 
+vector< double > d_r_bias
 
+vector< double > d_hw
 
+vector< double > d_hu
 
+vector< double > d_h_bias
 
+vector< double > d_h_prev
 
+vector< double > z
 
+vector< double > ld_z
 
+vector< double > r
 
+vector< double > ld_r
 
+vector< double > h_tanh
 
+vector< double > ld_h_tanh
 
+ + + +

+Friends

+class RNN_Edge
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from RNN_Node_Interface
+int32_t innovation_number
 
+int32_t layer_type
 
+int32_t node_type
 
+double depth
 
+string parameter_name
 
+bool enabled
 
+bool backward_reachable
 
+bool forward_reachable
 
+int32_t series_length
 
+vector< double > input_values
 
+vector< double > output_values
 
+vector< double > error_values
 
+vector< double > d_input
 
+vector< int32_t > inputs_fired
 
+vector< int32_t > outputs_fired
 
+int32_t total_inputs
 
+int32_t total_outputs
 
+
The documentation for this class was generated from the following files: +
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+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_g_r_u___node.png b/docs/html/class_g_r_u___node.png new file mode 100644 index 0000000000000000000000000000000000000000..2910815fbd0149457d865014b5e46941d24972d8 GIT binary patch literal 555 zcmeAS@N?(olHy`uVBq!ia0vp^EkGQ=!3-qdo9eCsQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;=p&|u>=Uzg1>K$UZC|SN)T~hb@5?hepKi0iS-z9!%F%2A_las&>+S7B z)|Z$Y@8fxM)J5RpGVz;MnTjRx%ECFd9E)EalG{`j*v)glIbdTPpM#?ev-RYN6YjTH2_&?@ipsSw=a48*=5a)j2dzT^XwX{OscKroE*gC8aS9Ps#`0%EHg8}5b z12dd0SQ5@C$}kx`6X0W%DCTHq==m$&A>b6D%XA>hvc;L4qsH@@A* zYH&rxW#*Tdup{;jD{9>YR?ge56}(dHz|=a)M;hB+zCOqplOdMf=RLFeDJ z6y4@L^W-JV#EFdSzA(9bxA-N%AY7$neM*yGp+uxcMeSS1g~_*j?e+=FpW$oZe`VVt qQdzv3RpMlQ0@y*Uf=(RWUzx?%NiQp#z^Dt1Sq4v6KbLh*2~7Yl0Ozm( literal 0 HcmV?d00001 diff --git a/docs/html/class_image-members.html b/docs/html/class_image-members.html new file mode 100644 index 00000000..4247df92 --- /dev/null +++ b/docs/html/class_image-members.html @@ -0,0 +1,95 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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Image Member List
+
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This is the complete list of members for Image, including all inherited members.

+ + + + + + + + + + + + + + + + + +
channels (defined in Image)Imageprivate
classification (defined in Image)Imageprivate
get_classification() const (defined in Image)Imagevirtual
get_pixel(int z, int y, int x) const (defined in Image)Image
get_pixel_avg(vector< float > &channel_avgs) const (defined in Image)Image
get_pixel_variance(const vector< float > &channel_avgs, vector< float > &channel_variances) const (defined in Image)Image
height (defined in Image)Imageprivate
Image(ifstream &infile, int _channels, int _width, int _height, int _padding, int _classification, const Images *_images) (defined in Image)Image
images (defined in Image)Imageprivate
Images (defined in Image)Imagefriend
padding (defined in Image)Imageprivate
pixels (defined in Image)Imageprivate
print(ostream &out) (defined in Image)Imagevirtual
scale_0_1() (defined in Image)Image
width (defined in Image)Imageprivate
~ImageInterface()=0 (defined in ImageInterface)ImageInterfacepure virtual
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_image.html b/docs/html/class_image.html new file mode 100644 index 00000000..01c56b10 --- /dev/null +++ b/docs/html/class_image.html @@ -0,0 +1,149 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Image Class Reference + + + + + + + + + +
+
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EXONA: Evolutionary eXploration of Neural Architectures +
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Image Class Reference
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+Inheritance diagram for Image:
+
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+ + +ImageInterface + +
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+Public Member Functions

+ Image (ifstream &infile, int _channels, int _width, int _height, int _padding, int _classification, const Images *_images)
 
+int get_classification () const
 
+void scale_0_1 ()
 
+float get_pixel (int z, int y, int x) const
 
+void get_pixel_avg (vector< float > &channel_avgs) const
 
+void get_pixel_variance (const vector< float > &channel_avgs, vector< float > &channel_variances) const
 
+void print (ostream &out)
 
+ + + + + + + + + + + + + + + +

+Private Attributes

+int padding
 
+int channels
 
+int height
 
+int width
 
+int classification
 
+vector< vector< vector< uint8_t > > > pixels
 
+const Images * images
 
+ + + +

+Friends

+class Images
 
+
The documentation for this class was generated from the following files: +
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+Generated by   +doxygen + 1.8.17 +
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ImageInterface Member List
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This is the complete list of members for ImageInterface, including all inherited members.

+ + + + +
get_classification() const =0 (defined in ImageInterface)ImageInterfacepure virtual
print(ostream &out)=0 (defined in ImageInterface)ImageInterfacepure virtual
~ImageInterface()=0 (defined in ImageInterface)ImageInterfacepure virtual
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_image_interface.html b/docs/html/class_image_interface.html new file mode 100644 index 00000000..be51abdf --- /dev/null +++ b/docs/html/class_image_interface.html @@ -0,0 +1,103 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: ImageInterface Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +List of all members
+
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ImageInterface Class Referenceabstract
+
+
+
+Inheritance diagram for ImageInterface:
+
+
+ + +Image +LargeImage + +
+ + + + + + +

+Public Member Functions

+virtual int get_classification () const =0
 
+virtual void print (ostream &out)=0
 
+
The documentation for this class was generated from the following files: +
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+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_image_interface.png b/docs/html/class_image_interface.png new file mode 100644 index 0000000000000000000000000000000000000000..d5770512c3a3a06fd1160ffea7be08515b7460b2 GIT binary patch literal 670 zcmeAS@N?(olHy`uVBq!ia0vp^CxAGBgBeI>Z$8xoq$C1-LR|m<{|{uoc=NTi|Ih>= z3ycpOIKbL@M;^%KC<*clW&kPzfvcxNj2IZ0bUj@hLn;{G&b@tYwE_=Yd;iv&|L*s; zYFxU;{BXVQ?Pp0_B79_oKZqUh;ZU5zaUk8od}=qtjRL_2=NOg)$ELh8cx?2r^3}a- zU+g>I-1{jyaRT4V+eUj|eVpT#d8pz{dx`2_>-&N8_Nf)sU+`3$`|kW7o2YeD|J&b- zh|OsD9uTH7J>}`fYjsom|5W6wE3J%w_Wa!9O=dgq7a5BwUEUurUYR>(>#Zs8|E0c~ zF8({4J9Jyv`s|c9J(*wa-$ixb-IFc)JErhw_inKRT=lgR^juEqaxQ4K)7n(R;UIi> zi`aqq0zn38J*I*Vpqn`qTZ9>3Ok#Y%72}#Bp_f!7I&t=KQy)F$N4v!w;&*U3_#^Ov zNS?z6HEa#SKX2SF6>Q)F`n9Q>A>@G)gIEm90w!_B4M3*o4vq#^amE!3;wMTZn;0CC zSloDCwQEv|)Y1->xozDZL9BX8o?>+#Djq?`x0Tupb35z3RhAsK;JEzKZjRfeEk`$= zkoh!kaZk2)jC#>-+v7gV4KJBYTH?#+`b;7CfYLhM!c^B!vrq27r}|0Zv&_8-H#CY? zXA0`~s$@=Hb*)P5WF${#u9E(GpGjpa^Vc5lnjyTsYfpXelczhbMdimUCwtB>7x%oC z;-2zuWvb=+H&ItRXYYSAb@!g1r|I|z$)m?`p1Yz@a?kUR{7xKNJkK{hm;_8r44$rjF6*2UngF@q BDL()J literal 0 HcmV?d00001 diff --git a/docs/html/class_images-members.html b/docs/html/class_images-members.html new file mode 100644 index 00000000..075459f9 --- /dev/null +++ b/docs/html/class_images-members.html @@ -0,0 +1,109 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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Images Member List
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+
+ +

This is the complete list of members for Images, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
calculate_avg_std_dev() (defined in Images)Images
channel_avg (defined in Images)Imagesprivate
channel_std_dev (defined in Images)Imagesprivate
channels (defined in Images)Imagesprivate
class_sizes (defined in Images)Imagesprivate
filename (defined in Images)Imagesprivate
get_average() const (defined in Images)Imagesvirtual
get_channel_avg(int channel) const (defined in Images)Imagesvirtual
get_channel_std_dev(int channel) const (defined in Images)Imagesvirtual
get_class_size(int i) const (defined in Images)Imagesvirtual
get_classification(int image) const (defined in Images)Imagesvirtual
get_filename() const (defined in Images)Imagesvirtual
get_image_channels() const (defined in Images)Imagesvirtual
get_image_height() const (defined in Images)Imagesvirtual
get_image_width() const (defined in Images)Imagesvirtual
get_number_classes() const (defined in Images)Imagesvirtual
get_number_images() const (defined in Images)Imagesvirtual
get_pixel(int image, int z, int y, int x) const (defined in Images)Imagesvirtual
get_std_dev() const (defined in Images)Imagesvirtual
had_error (defined in Images)Imagesprivate
height (defined in Images)Imagesprivate
images (defined in Images)Imagesprivate
Images(string binary_filename, int _padding) (defined in Images)Images
Images(string binary_filename, int _padding, const vector< float > &_channeL_avg, const vector< float > &channel_std_dev) (defined in Images)Images
loaded_correctly() const (defined in Images)Images
number_classes (defined in Images)Imagesprivate
number_images (defined in Images)Imagesprivate
padding (defined in Images)Imagesprivate
read_images(string binary_filename) (defined in Images)Images
width (defined in Images)Imagesprivate
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_images.html b/docs/html/class_images.html new file mode 100644 index 00000000..2396477f --- /dev/null +++ b/docs/html/class_images.html @@ -0,0 +1,190 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Images Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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Images Class Reference
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+Inheritance diagram for Images:
+
+
+ + +ImagesInterface + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+int read_images (string binary_filename)
 
+ Images (string binary_filename, int _padding)
 
+ Images (string binary_filename, int _padding, const vector< float > &_channeL_avg, const vector< float > &channel_std_dev)
 
+string get_filename () const
 
+int get_class_size (int i) const
 
+int get_number_classes () const
 
+int get_number_images () const
 
+int get_image_channels () const
 
+int get_image_width () const
 
+int get_image_height () const
 
+int get_classification (int image) const
 
+float get_pixel (int image, int z, int y, int x) const
 
+void calculate_avg_std_dev ()
 
+float get_channel_avg (int channel) const
 
+float get_channel_std_dev (int channel) const
 
+bool loaded_correctly () const
 
+const vector< float > & get_average () const
 
+const vector< float > & get_std_dev () const
 
+ + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+string filename
 
+int number_classes
 
+int number_images
 
+vector< int > class_sizes
 
+int padding
 
+int channels
 
+int width
 
+int height
 
+vector< Image > images
 
+vector< float > channel_avg
 
+vector< float > channel_std_dev
 
+bool had_error
 
+
The documentation for this class was generated from the following files: +
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+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_images.png b/docs/html/class_images.png new file mode 100644 index 0000000000000000000000000000000000000000..65c1be6dd1e4bf51eb9b1051ecac29dc68bdfeb7 GIT binary patch literal 482 zcmeAS@N?(olHy`uVBq!ia0vp^sX!dS!3-qLMZF&bDTx4|5ZC|z{{xvX-h3_XKQsZz z0^LQfaRkP61Pb7PBI6a?62_pjRff8yp( z$`6Dd(L`s zn{cT^Th0{SD7&j)sCw`0#IFTgTK=9CX`is_%&V zuI^KnbMV_Pf4p1u{>h)G_L)dVR=JuO=H5I$YgN>){Qsu@@AuYJCms8~Y3i(73$G_X zWi09s-5aU9|Jw|eQj4FGo|hUXF3pqmteT&wyEMh~7whZu`krETXCCeSUe28Ggt5Rn zSf~Fu&smYv!A6e8$3F$yZqs2It6sxj(N(zy7+KXF+r3?dWMGAq>we)`O_!5%qNp-12EaOLqTcvRN&f UVa{or35*a1Pgg&ebxsLQ0Oc>(DF6Tf literal 0 HcmV?d00001 diff --git a/docs/html/class_images_interface-members.html b/docs/html/class_images_interface-members.html new file mode 100644 index 00000000..f5617cc3 --- /dev/null +++ b/docs/html/class_images_interface-members.html @@ -0,0 +1,92 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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ImagesInterface Member List
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This is the complete list of members for ImagesInterface, including all inherited members.

+ + + + + + + + + + + + + + +
get_average() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_channel_avg(int channel) const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_channel_std_dev(int channel) const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_class_size(int i) const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_classification(int image) const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_filename() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_image_channels() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_image_height() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_image_width() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_number_classes() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_number_images() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_pixel(int image, int z, int y, int x) const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_std_dev() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_images_interface.html b/docs/html/class_images_interface.html new file mode 100644 index 00000000..882730cd --- /dev/null +++ b/docs/html/class_images_interface.html @@ -0,0 +1,137 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: ImagesInterface Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +List of all members
+
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ImagesInterface Class Referenceabstract
+
+
+
+Inheritance diagram for ImagesInterface:
+
+
+ + +Images +MultiImagesInterface +LargeImages +MosaicImages + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+virtual string get_filename () const =0
 
+virtual int get_class_size (int i) const =0
 
+virtual int get_number_classes () const =0
 
+virtual int get_number_images () const =0
 
+virtual int get_image_channels () const =0
 
+virtual int get_image_width () const =0
 
+virtual int get_image_height () const =0
 
+virtual int get_classification (int image) const =0
 
+virtual float get_pixel (int image, int z, int y, int x) const =0
 
+virtual float get_channel_avg (int channel) const =0
 
+virtual float get_channel_std_dev (int channel) const =0
 
+virtual const vector< float > & get_average () const =0
 
+virtual const vector< float > & get_std_dev () const =0
 
+
The documentation for this class was generated from the following file: +
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+Generated by   +doxygen + 1.8.17 +
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+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+
Island Member List
+
+
+ +

This is the complete list of members for Island, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
been_erased()Island
copy_random_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome)Island
copy_two_random_genomes(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome1, RNN_Genome **genome2)Island
do_population_check(int line, int initial_size) (defined in Island)Island
erase_againIslandprivate
erase_island()Island
erasedIslandprivate
erased_generation_idIslandprivate
FILLEDIslandstatic
genomesIslandprivate
get_best_fitness()Island
get_best_genome()Island
get_erase_again_num() (defined in Island)Island
get_erased_generation_id()Island
get_genomes() (defined in Island)Island
get_max_size()Island
get_status()Island
get_worst_fitness()Island
get_worst_genome()Island
idIslandprivate
INITIALIZINGIslandstatic
insert_genome(RNN_Genome *genome)Island
is_full()Island
is_initializing()Island
is_repopulating()Island
Island(int32_t id, int32_t max_size)Island
Island(int32_t id, vector< RNN_Genome * > genomes)Island
latest_generation_idIslandprivate
max_sizeIslandprivate
print(string indent="")Island
REPOPULATINGIslandstatic
set_erase_again_num() (defined in Island)Island
set_latest_generation_id(int32_t _latest_generation_id) (defined in Island)Island
set_status(int32_t status_to_set)Island
size()Island
status (defined in Island)Islandprivate
structure_map (defined in Island)Islandprivate
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_island.html b/docs/html/class_island.html new file mode 100644 index 00000000..a2561e11 --- /dev/null +++ b/docs/html/class_island.html @@ -0,0 +1,666 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Island Class Reference + + + + + + + + + +
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+
EXONA: Evolutionary eXploration of Neural Architectures +
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+ +
+ +
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+Public Member Functions | +Static Public Attributes | +Private Attributes | +List of all members
+
+
Island Class Reference
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 Island (int32_t id, int32_t max_size)
 Initializes an island with a given max size. More...
 
 Island (int32_t id, vector< RNN_Genome * > genomes)
 Initializes an island filled the supplied genomes. More...
 
double get_best_fitness ()
 Returns the fitness of the best genome in the island. More...
 
double get_worst_fitness ()
 Returns the fitness of the worst genome in the island. More...
 
RNN_Genome * get_best_genome ()
 Returns the best genomme in the island. More...
 
RNN_Genome * get_worst_genome ()
 Returns the worst genomme in the island. More...
 
int32_t get_max_size ()
 Returns the maximum number of genomes the island can hold. More...
 
int32_t size ()
 Returns the size of the island. More...
 
bool is_full ()
 Returns true if the island has Island::max_size genomes. More...
 
bool is_initializing ()
 Returns true if the island is initializing, i.e., it's size is <= max_size and it hasn't been cleared out for repopulating. More...
 
bool is_repopulating ()
 Returns true if the island is repopulating, i.e., it's size is <= max_size and it has been full before but cleared out for repopulation. More...
 
void copy_random_genome (uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome)
 Selects a genome from the island at random and returns a copy of it. More...
 
void copy_two_random_genomes (uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome1, RNN_Genome **genome2)
 Selects two different genomes from the island at random and returns copies of them. More...
 
+void do_population_check (int line, int initial_size)
 
int32_t insert_genome (RNN_Genome *genome)
 Inserts a genome into the island. More...
 
void print (string indent="")
 Prints out the state of this island. More...
 
+void erase_island ()
 erases the entire island and set the erased_generation_id.
 
+int32_t get_erased_generation_id ()
 returns the get_erased_generation_id.
 
int32_t get_status ()
 
+void set_status (int32_t status_to_set)
 after erasing the island, sets the island status to repopulating.
 
+bool been_erased ()
 return if this island has been erased before.
 
+vector< RNN_Genome * > get_genomes ()
 
+void set_latest_generation_id (int32_t _latest_generation_id)
 
+int32_t get_erase_again_num ()
 
+void set_erase_again_num ()
 
+ + + + + + + + + + +

+Static Public Attributes

+const static int32_t INITIALIZING = 0
 status flag for if the island is initializing.
 
+const static int32_t FILLED = 1
 status flag for if the island is filled.
 
+const static int32_t REPOPULATING = 2
 status flag for if the island is repopulating.
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int32_t id
 An integer ID for this island.
 
+int32_t max_size
 The maximum number of genomes in the island.
 
+int32_t erased_generation_id = -1
 The latest generation id of an erased island, erased_generation_id = largest_generation_id when this island is erased, to prevent deleted genomes get inserted back.
 
+int32_t latest_generation_id
 The latest generation id of genome being generated, including the ones doing backprop by workers.
 
+vector< RNN_Genome * > genomes
 The genomes on this island, stored in sorted order best (front) to worst (back).
 
+unordered_map< string, vector< RNN_Genome * > > structure_map
 
+int32_t status
 
int32_t erase_again
 
+

The status of this island (either Island:INITIALIZING, Island::FILLED or Island::REPOPULATING

+
+ More...
 
+bool erased
 a flag to track if this islands has been erased
 
+

Constructor & Destructor Documentation

+ +

◆ Island() [1/2]

+ +
+
+ + + + + + + + + + + + + + + + + + +
Island::Island (int32_t id,
int32_t max_size 
)
+
+ +

Initializes an island with a given max size.

+
Parameters
+ + +
max_sizeis the maximum number of genomes in the island.
+
+
+ +
+
+ +

◆ Island() [2/2]

+ +
+
+ + + + + + + + + + + + + + + + + + +
Island::Island (int32_t id,
vector< RNN_Genome * > genomes 
)
+
+ +

Initializes an island filled the supplied genomes.

+

The size of the island will be the size of the supplied genome vector. The island status is set to filled.

+ +
+
+

Member Function Documentation

+ +

◆ copy_random_genome()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
void Island::copy_random_genome (uniform_real_distribution< double > & rng_0_1,
minstd_rand0 & generator,
RNN_Genome ** genome 
)
+
+ +

Selects a genome from the island at random and returns a copy of it.

+
Parameters
+ + + + +
rng_0_1is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive).
generatoris the random number generator
genomewill be the copied genome, an addresss to a pointer needs to be passed.
+
+
+ +
+
+ +

◆ copy_two_random_genomes()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void Island::copy_two_random_genomes (uniform_real_distribution< double > & rng_0_1,
minstd_rand0 & generator,
RNN_Genome ** genome1,
RNN_Genome ** genome2 
)
+
+ +

Selects two different genomes from the island at random and returns copies of them.

+
Parameters
+ + + + + +
rng_0_1is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive).
generatoris the random number generator
genome1will be the first copied genome, an addresss to a pointer needs to be passed.
genome2will be the second copied genome, an addresss to a pointer needs to be passed.
+
+
+ +
+
+ +

◆ get_best_fitness()

+ +
+
+ + + + + + + +
double Island::get_best_fitness ()
+
+ +

Returns the fitness of the best genome in the island.

+
Returns
the best fitness of the island
+ +
+
+ +

◆ get_best_genome()

+ +
+
+ + + + + + + +
RNN_Genome * Island::get_best_genome ()
+
+ +

Returns the best genomme in the island.

+
Returns
the best genome in the island
+ +
+
+ +

◆ get_max_size()

+ +
+
+ + + + + + + +
int32_t Island::get_max_size ()
+
+ +

Returns the maximum number of genomes the island can hold.

+
Returns
the maximum number of genomes this island can have
+ +
+
+ +

◆ get_status()

+ +
+
+ + + + + + + +
int32_t Island::get_status ()
+
+
Returns
the status of the island
+ +
+
+ +

◆ get_worst_fitness()

+ +
+
+ + + + + + + +
double Island::get_worst_fitness ()
+
+ +

Returns the fitness of the worst genome in the island.

+
Returns
the worst fitness of the island
+ +
+
+ +

◆ get_worst_genome()

+ +
+
+ + + + + + + +
RNN_Genome * Island::get_worst_genome ()
+
+ +

Returns the worst genomme in the island.

+
Returns
the worst genome in the island
+ +
+
+ +

◆ insert_genome()

+ +
+
+ + + + + + + + +
int32_t Island::insert_genome (RNN_Genome * genome)
+
+ +

Inserts a genome into the island.

+

Genomes are inserted in best to worst order genomes[0] will have the best fitness and genomes[size - 1] will have the worst.

+
Parameters
+ + +
genomeis the genome to be inserted.
+
+
+
Returns
-1 if not inserted, otherwise the index it was inserted at
+ +
+
+ +

◆ is_full()

+ +
+
+ + + + + + + +
bool Island::is_full ()
+
+ +

Returns true if the island has Island::max_size genomes.

+
Returns
true if the number of genomes in the island is >= size (although it should never be > size).
+ +
+
+ +

◆ is_initializing()

+ +
+
+ + + + + + + +
bool Island::is_initializing ()
+
+ +

Returns true if the island is initializing, i.e., it's size is <= max_size and it hasn't been cleared out for repopulating.

+
Returns
true if island is initializing.
+ +
+
+ +

◆ is_repopulating()

+ +
+
+ + + + + + + +
bool Island::is_repopulating ()
+
+ +

Returns true if the island is repopulating, i.e., it's size is <= max_size and it has been full before but cleared out for repopulation.

+
Returns
true if island is repopulating.
+ +
+
+ +

◆ print()

+ +
+
+ + + + + + + + +
void Island::print (string indent = "")
+
+ +

Prints out the state of this island.

+
Parameters
+ + +
indentis how much to indent what is printed out
+
+
+ +
+
+ +

◆ size()

+ +
+
+ + + + + + + +
int32_t Island::size ()
+
+ +

Returns the size of the island.

+
Returns
the number of genomes in this island.
+ +
+
+

Member Data Documentation

+ +

◆ erase_again

+ +
+
+ + + + + +
+ + + + +
int32_t Island::erase_again
+ 		+private
+
+ +

+

The status of this island (either Island:INITIALIZING, Island::FILLED or Island::REPOPULATING

+
+

+

a flag to track if this islands has been erased

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_island_speciation_strategy-members.html b/docs/html/class_island_speciation_strategy-members.html new file mode 100644 index 00000000..b044e680 --- /dev/null +++ b/docs/html/class_island_speciation_strategy-members.html @@ -0,0 +1,119 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+
IslandSpeciationStrategy Member List
+
+
+ +

This is the complete list of members for IslandSpeciationStrategy, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
extinction_event_generation_numberIslandSpeciationStrategyprivate
fill_island(int32_t best_island)IslandSpeciationStrategy
generate_for_filled_island(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover) (defined in IslandSpeciationStrategy)IslandSpeciationStrategy
generate_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)IslandSpeciationStrategyvirtual
generated_genomesIslandSpeciationStrategyprivate
generation_islandIslandSpeciationStrategyprivate
get_best_fitness()IslandSpeciationStrategyvirtual
get_best_genome()IslandSpeciationStrategyvirtual
get_generated_genomes() constIslandSpeciationStrategyvirtual
get_global_best_genome() (defined in IslandSpeciationStrategy)IslandSpeciationStrategyvirtual
get_inserted_genomes() constIslandSpeciationStrategyvirtual
get_strategy_information_headers() constIslandSpeciationStrategyvirtual
get_strategy_information_values() constIslandSpeciationStrategyvirtual
get_worst_fitness()IslandSpeciationStrategyvirtual
get_worst_genome()IslandSpeciationStrategyvirtual
get_worst_island_by_best_genome()IslandSpeciationStrategy
global_best_genome (defined in IslandSpeciationStrategy)IslandSpeciationStrategyprivate
insert_genome(RNN_Genome *genome)IslandSpeciationStrategyvirtual
inserted_genomesIslandSpeciationStrategyprivate
inter_island_crossover_rateIslandSpeciationStrategyprivate
intra_island_crossover_rateIslandSpeciationStrategyprivate
island_ranking_methodIslandSpeciationStrategyprivate
islandsIslandSpeciationStrategyprivate
islands_full() constIslandSpeciationStrategy
islands_to_exterminateIslandSpeciationStrategyprivate
IslandSpeciationStrategy(int32_t _number_of_islands, int32_t _max_island_size, double _mutation_rate, double _intra_island_crossover_rate, double _inter_island_crossover_rate, RNN_Genome *_seed_genome, string _island_ranking_method, string _repopulation_method, int32_t _extinction_event_generation_number, int32_t _repopulation_mutations, int32_t _islands_to_exterminate, int32_t _max_genomes, bool _repeat_extinction, bool seed_genome_was_minimal)IslandSpeciationStrategy
IslandSpeciationStrategy(int32_t _number_of_islands, int32_t _max_island_size, double _mutation_rate, double _intra_island_crossover_rate, double _inter_island_crossover_rate, RNN_Genome *_seed_genome, string _island_ranking_method, string _repopulation_method, int32_t _extinction_event_generation_number, int32_t _repopulation_mutations, int32_t _islands_to_exterminate, bool seed_genome_was_minimal, function< void(RNN_Genome *)> &modify)IslandSpeciationStrategy
max_genomes (defined in IslandSpeciationStrategy)IslandSpeciationStrategyprivate
max_island_sizeIslandSpeciationStrategyprivate
mutation_rateIslandSpeciationStrategyprivate
number_of_islandsIslandSpeciationStrategyprivate
parents_repopulation(string method, uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)IslandSpeciationStrategy
print(string indent="") constIslandSpeciationStrategyvirtual
rank_islands()IslandSpeciationStrategy
repeat_extinction (defined in IslandSpeciationStrategy)IslandSpeciationStrategyprivate
repopulation_methodIslandSpeciationStrategyprivate
repopulation_mutationsIslandSpeciationStrategyprivate
seed_genomeIslandSpeciationStrategyprivate
seed_genome_was_minimalIslandSpeciationStrategyprivate
set_erased_islands_status() (defined in IslandSpeciationStrategy)IslandSpeciationStrategy
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_island_speciation_strategy.html b/docs/html/class_island_speciation_strategy.html new file mode 100644 index 00000000..0f49ab6e --- /dev/null +++ b/docs/html/class_island_speciation_strategy.html @@ -0,0 +1,943 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: IslandSpeciationStrategy Class Reference + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+Public Member Functions | +Private Attributes | +List of all members
+
+
IslandSpeciationStrategy Class Reference
+
+
+
+Inheritance diagram for IslandSpeciationStrategy:
+
+
+ + +SpeciationStrategy + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 IslandSpeciationStrategy (int32_t _number_of_islands, int32_t _max_island_size, double _mutation_rate, double _intra_island_crossover_rate, double _inter_island_crossover_rate, RNN_Genome *_seed_genome, string _island_ranking_method, string _repopulation_method, int32_t _extinction_event_generation_number, int32_t _repopulation_mutations, int32_t _islands_to_exterminate, int32_t _max_genomes, bool _repeat_extinction, bool seed_genome_was_minimal)
 Creates a new IslandSpeciationStrategy. More...
 
 IslandSpeciationStrategy (int32_t _number_of_islands, int32_t _max_island_size, double _mutation_rate, double _intra_island_crossover_rate, double _inter_island_crossover_rate, RNN_Genome *_seed_genome, string _island_ranking_method, string _repopulation_method, int32_t _extinction_event_generation_number, int32_t _repopulation_mutations, int32_t _islands_to_exterminate, bool seed_genome_was_minimal, function< void(RNN_Genome *)> &modify)
 Transfer learning constructor. More...
 
int32_t get_generated_genomes () const
 
int32_t get_inserted_genomes () const
 
double get_best_fitness ()
 Gets the fitness of the best genome of all the islands. More...
 
double get_worst_fitness ()
 Gets the fitness of the worst genome of all the islands. More...
 
RNN_Genome * get_best_genome ()
 Gets the best genome of all the islands. More...
 
RNN_Genome * get_worst_genome ()
 Gets the the worst genome of all the islands. More...
 
bool islands_full () const
 
int32_t insert_genome (RNN_Genome *genome)
 Inserts a copy of the genome into one of the islands handled by this strategy, determined by the RNN_Genome::get_group_id() method. More...
 
int32_t get_worst_island_by_best_genome ()
 find the worst island in the population, the worst island's best genome is the worst among all the islands More...
 
vector< int32_t > rank_islands ()
 rank the islands by their best fitness, the fitness of ranked islands are in descending order More...
 
RNN_Genome * generate_genome (uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)
 Generates a new genome. More...
 
+RNN_Genome * generate_for_filled_island (uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)
 
void print (string indent="") const
 Prints out all the island's populations. More...
 
+string get_strategy_information_headers () const
 Gets speciation strategy information headers for logs.
 
+string get_strategy_information_values () const
 Gets speciation strategy information values for logs.
 
+RNN_Genome * parents_repopulation (string method, uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)
 Island repopulation through two random parents from two seperate islands, parents can be random genomes or best genome from the island.
 
void fill_island (int32_t best_island)
 fill a island with the best island. More...
 
+RNN_Genome * get_global_best_genome ()
 
+void set_erased_islands_status ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int32_t generation_island
 Used to track which island to generate the next genome from.
 
+int32_t number_of_islands
 the number of islands to have.
 
+int32_t max_island_size
 the maximum number of genomes in an island.
 
double mutation_rate
 How frequently to do mutations. More...
 
double intra_island_crossover_rate
 How frequently to do intra-island crossovers. More...
 
double inter_island_crossover_rate
 How frequently to do inter-island crossovers. More...
 
+int32_t generated_genomes
 How many genomes have been generated by this speciation strategy.
 
+int32_t inserted_genomes
 How many genomes have been inserted into this speciatoin strategy.
 
+RNN_Genome * seed_genome
 keep a reference to the seed genome so we can re-use it across islands and not duplicate innovation numbers.
 
+string island_ranking_method
 The method used to find the worst island in population.
 
+string repopulation_method
 The method used to repopulate the island after being erased.
 
int32_t extinction_event_generation_number
 When EXAMM reaches this generation id, an extinction event will be triggered (i.e. More...
 
+int32_t repopulation_mutations
 When an island is erradicated, it is repopulated with copies of the best genome that have this number of mutations applied to them.
 
+int32_t islands_to_exterminate
 When an extinction event is triggered, this is the number of islands that will be exterminated.
 
+int32_t max_genomes
 
+bool seed_genome_was_minimal
 is true if we passed in a minimal genome (i.e., are not using transfer learning)
 
+bool repeat_extinction
 
+vector< Island * > islands
 All the islands which contain the genomes for this speciation strategy.
 
+RNN_Genome * global_best_genome
 
+

Constructor & Destructor Documentation

+ +

◆ IslandSpeciationStrategy() [1/2]

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
IslandSpeciationStrategy::IslandSpeciationStrategy (int32_t _number_of_islands,
int32_t _max_island_size,
double _mutation_rate,
double _intra_island_crossover_rate,
double _inter_island_crossover_rate,
RNN_Genome * _seed_genome,
string _island_ranking_method,
string _repopulation_method,
int32_t _extinction_event_generation_number,
int32_t _repopulation_mutations,
int32_t _islands_to_exterminate,
int32_t _max_genomes,
bool _repeat_extinction,
bool seed_genome_was_minimal 
)
+
+ +

Creates a new IslandSpeciationStrategy.

+
Parameters
+ + + +
number_of_islandsspecifies how many islands it will us e
max_island_sizespecifies the maximum number of gneomes in an island
+
+
+ +
+
+ +

◆ IslandSpeciationStrategy() [2/2]

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
IslandSpeciationStrategy::IslandSpeciationStrategy (int32_t _number_of_islands,
int32_t _max_island_size,
double _mutation_rate,
double _intra_island_crossover_rate,
double _inter_island_crossover_rate,
RNN_Genome * _seed_genome,
string _island_ranking_method,
string _repopulation_method,
int32_t _extinction_event_generation_number,
int32_t _repopulation_mutations,
int32_t _islands_to_exterminate,
bool seed_genome_was_minimal,
function< void(RNN_Genome *)> & modify 
)
+
+ +

Transfer learning constructor.

+
Parameters
+ + +
Modificationfunction to be applied to every copy of the seed_genome
+
+
+ +
+
+

Member Function Documentation

+ +

◆ fill_island()

+ +
+
+ + + + + + + + +
void IslandSpeciationStrategy::fill_island (int32_t best_island)
+
+ +

fill a island with the best island.

+
Parameters
+ + + +
best_islandis the island id of the best island
fill_islandis the island is of the island to be filled
+
+
+ +
+
+ +

◆ generate_genome()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
RNN_Genome * IslandSpeciationStrategy::generate_genome (uniform_real_distribution< double > & rng_0_1,
minstd_rand0 & generator,
function< void(int32_t, RNN_Genome *)> & mutate,
function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> & crossover 
)
+ 		+virtual
+
+ +

Generates a new genome.

+
Parameters
+ + + + + +
rng_0_1is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive).
generatoris the random number generator
mutateis the a function which performs a mutation on a genome
crossoveris the function which performs crossover between two genomes
+
+
+
Returns
the newly generated genome.
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_best_fitness()

+ +
+
+ + + + + +
+ + + + + + + +
double IslandSpeciationStrategy::get_best_fitness ()
+ 		+virtual
+
+ +

Gets the fitness of the best genome of all the islands.

+
Returns
the best fitness over all islands
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_best_genome()

+ +
+
+ + + + + +
+ + + + + + + +
RNN_Genome * IslandSpeciationStrategy::get_best_genome ()
+ 		+virtual
+
+ +

Gets the best genome of all the islands.

+
Returns
the best genome of all islands or NULL if no genomes have yet been inserted
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_generated_genomes()

+ +
+
+ + + + + +
+ + + + + + + +
int32_t IslandSpeciationStrategy::get_generated_genomes () const
+ 		+virtual
+
+
Returns
the number of generated genomes.
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_inserted_genomes()

+ +
+
+ + + + + +
+ + + + + + + +
int32_t IslandSpeciationStrategy::get_inserted_genomes () const
+ 		+virtual
+
+
Returns
the number of inserted genomes.
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_worst_fitness()

+ +
+
+ + + + + +
+ + + + + + + +
double IslandSpeciationStrategy::get_worst_fitness ()
+ 		+virtual
+
+ +

Gets the fitness of the worst genome of all the islands.

+
Returns
the worst fitness over all islands
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_worst_genome()

+ +
+
+ + + + + +
+ + + + + + + +
RNN_Genome * IslandSpeciationStrategy::get_worst_genome ()
+ 		+virtual
+
+ +

Gets the the worst genome of all the islands.

+
Returns
the worst genome of all islands or NULL if no genomes have yet been inserted
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_worst_island_by_best_genome()

+ +
+
+ + + + + + + +
int32_t IslandSpeciationStrategy::get_worst_island_by_best_genome ()
+
+ +

find the worst island in the population, the worst island's best genome is the worst among all the islands

+
Returns
the worst island id
+ +
+
+ +

◆ insert_genome()

+ +
+
+ + + + + +
+ + + + + + + + +
int32_t IslandSpeciationStrategy::insert_genome (RNN_Genome * genome)
+ 		+virtual
+
+ +

Inserts a copy of the genome into one of the islands handled by this strategy, determined by the RNN_Genome::get_group_id() method.

+

The caller of this method will need to free the memory of the genome passed into this method.

+
Parameters
+ + +
genomeis the genome to insert.
+
+
+
Returns
a value < 0 if the genome was not inserted, 0 if it was a new best genome for all the islands, or > 0 otherwise.
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ islands_full()

+ +
+
+ + + + + + + +
bool IslandSpeciationStrategy::islands_full () const
+
+
Returns
true if all the islands are full
+ +
+
+ +

◆ print()

+ +
+
+ + + + + +
+ + + + + + + + +
void IslandSpeciationStrategy::print (string indent = "") const
+ 		+virtual
+
+ +

Prints out all the island's populations.

+
Parameters
+ + +
indentis how much to indent what is printed out
+
+
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ rank_islands()

+ +
+
+ + + + + + + +
vector< int32_t > IslandSpeciationStrategy::rank_islands ()
+
+ +

rank the islands by their best fitness, the fitness of ranked islands are in descending order

+
Returns
island rank from worst to the best
+ +
+
+

Member Data Documentation

+ +

◆ extinction_event_generation_number

+ +
+
+ + + + + +
+ + + + +
int32_t IslandSpeciationStrategy::extinction_event_generation_number
+ 		+private
+
+ +

When EXAMM reaches this generation id, an extinction event will be triggered (i.e.

+

islands will be killed and repopulated).

+ +
+
+ +

◆ inter_island_crossover_rate

+ +
+
+ + + + + +
+ + + + +
double IslandSpeciationStrategy::inter_island_crossover_rate
+ 		+private
+
+ +

How frequently to do inter-island crossovers.

+

Note that mutation_rate + intra_island_crossover_rate + inter_island_crossover_rate should equal 1, if not they will be scaled down such that they do.

+ +
+
+ +

◆ intra_island_crossover_rate

+ +
+
+ + + + + +
+ + + + +
double IslandSpeciationStrategy::intra_island_crossover_rate
+ 		+private
+
+ +

How frequently to do intra-island crossovers.

+

Note that mutation_rate + intra_island_crossover_rate + inter_island_crossover_rate should equal 1, if not they will be scaled down such that they do.

+ +
+
+ +

◆ mutation_rate

+ +
+
+ + + + + +
+ + + + +
double IslandSpeciationStrategy::mutation_rate
+ 		+private
+
+ +

How frequently to do mutations.

+

Note that mutation_rate + intra_island_crossover_rate + inter_island_crossover_rate should equal 1, if not they will be scaled down such that they do.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_island_speciation_strategy.png b/docs/html/class_island_speciation_strategy.png new file mode 100644 index 0000000000000000000000000000000000000000..b3f561da885ac85d71d8f7bbba94954ba546c551 GIT binary patch literal 676 zcmeAS@N?(olHy`uVBq!ia0vp^(}6gEgBeIFuMu|#QW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;SZyxmj|K#n? z%xq3V+cz)%n6iy3=1<=z$x$eASK*X=#&PgKmLC)X{*tNy8Es?DzW))%(> zwLzhj?cic~|@Q>g=T5yZ7-JB^YkiX#LIe?Un7d zV@VO0zn=1a`EK2_x{_Zrvia*7vL;nNSQWfBJKxH5`jWWoUvDf+s@L^Z)0sNC^;Wd; zR*h#Gp6qwu{qA;Hy)VpxOX#NPVaWSeBn*SlED7TO>2*T6TQAScUi4% zoUH8j=YOM@_o-NlPR!N`^OX8x{c~2`a+RB3-`8$eeffRM4waMZ55(Nwmiv+A!}c!> z94i*Y^t=0PSP5SDZ2}Q05qud=zP z?YRE%5|@n^m#y@B`sAh36xB_ReW5bz5$I{aQ)P%A2}xh2w2pcUSrgxgJ_qd|kPX z_uP(ii?5w2*?n%~ew&!LV)LG_)?T}JUDn1Mut0nFV)JorV4#&Pzq + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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LSTM_Node Member List
+
+
+ +

This is the complete list of members for LSTM_Node, including all inherited members.

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backward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
cell_bias (defined in LSTM_Node)LSTM_Nodeprivate
cell_in_tanh (defined in LSTM_Node)LSTM_Nodeprivate
cell_out_tanh (defined in LSTM_Node)LSTM_Nodeprivate
cell_values (defined in LSTM_Node)LSTM_Nodeprivate
cell_weight (defined in LSTM_Node)LSTM_Nodeprivate
copy() const (defined in LSTM_Node)LSTM_Nodevirtual
d_cell_bias (defined in LSTM_Node)LSTM_Nodeprivate
d_cell_weight (defined in LSTM_Node)LSTM_Nodeprivate
d_forget_gate_bias (defined in LSTM_Node)LSTM_Nodeprivate
d_forget_gate_update_weight (defined in LSTM_Node)LSTM_Nodeprivate
d_forget_gate_weight (defined in LSTM_Node)LSTM_Nodeprivate
d_input (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
d_input_gate_bias (defined in LSTM_Node)LSTM_Nodeprivate
d_input_gate_update_weight (defined in LSTM_Node)LSTM_Nodeprivate
d_input_gate_weight (defined in LSTM_Node)LSTM_Nodeprivate
d_output_gate_bias (defined in LSTM_Node)LSTM_Nodeprivate
d_output_gate_update_weight (defined in LSTM_Node)LSTM_Nodeprivate
d_output_gate_weight (defined in LSTM_Node)LSTM_Nodeprivate
d_prev_cell (defined in LSTM_Node)LSTM_Nodeprivate
depth (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
enabled (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
equals(RNN_Node_Interface *other) const (defined in RNN_Node_Interface)RNN_Node_Interface
error_fired(int time, double error) (defined in LSTM_Node)LSTM_Node
error_fired(int32_t time, double error)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
error_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
forget_gate_bias (defined in LSTM_Node)LSTM_Nodeprivate
forget_gate_update_weight (defined in LSTM_Node)LSTM_Nodeprivate
forget_gate_values (defined in LSTM_Node)LSTM_Nodeprivate
forget_gate_weight (defined in LSTM_Node)LSTM_Nodeprivate
forward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
get_depth() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_gradient(string gradient_name) (defined in LSTM_Node)LSTM_Node
get_gradients(vector< double > &gradients) (defined in LSTM_Node)LSTM_Nodevirtual
get_innovation_number() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_layer_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_node_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_number_weights() const (defined in LSTM_Node)LSTM_Nodevirtual
get_total_inputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_total_outputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_weights(vector< double > &parameters) const (defined in LSTM_Node)LSTM_Nodevirtual
get_weights(uint32_t &offset, vector< double > &parameters) const (defined in LSTM_Node)LSTM_Nodevirtual
initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range) (defined in LSTM_Node)LSTM_Nodevirtual
initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma) (defined in LSTM_Node)LSTM_Nodevirtual
initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution< double > &rng) (defined in LSTM_Node)LSTM_Nodevirtual
initialize_xavier(minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range) (defined in LSTM_Node)LSTM_Nodevirtual
innovation_number (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
input_fired(int time, double incoming_output) (defined in LSTM_Node)LSTM_Node
input_fired(int32_t time, double incoming_output)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
input_gate_bias (defined in LSTM_Node)LSTM_Nodeprivate
input_gate_update_weight (defined in LSTM_Node)LSTM_Nodeprivate
input_gate_values (defined in LSTM_Node)LSTM_Nodeprivate
input_gate_weight (defined in LSTM_Node)LSTM_Nodeprivate
input_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
inputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
is_enabled() const (defined in RNN_Node_Interface)RNN_Node_Interface
is_reachable() const (defined in RNN_Node_Interface)RNN_Node_Interface
layer_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
ld_cell_in (defined in LSTM_Node)LSTM_Nodeprivate
ld_cell_out (defined in LSTM_Node)LSTM_Nodeprivate
ld_forget_gate (defined in LSTM_Node)LSTM_Nodeprivate
ld_input_gate (defined in LSTM_Node)LSTM_Nodeprivate
ld_output_gate (defined in LSTM_Node)LSTM_Nodeprivate
LSTM_Node(int _innovation_number, int _type, double _depth) (defined in LSTM_Node)LSTM_Node
node_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
output_fired(int time, double delta) (defined in LSTM_Node)LSTM_Node
output_fired(int32_t time, double delta)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
output_gate_bias (defined in LSTM_Node)LSTM_Nodeprivate
output_gate_update_weight (defined in LSTM_Node)LSTM_Nodeprivate
output_gate_values (defined in LSTM_Node)LSTM_Nodeprivate
output_gate_weight (defined in LSTM_Node)LSTM_Nodeprivate
output_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
outputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
parameter_name (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
print_gradient(string gradient_name) (defined in LSTM_Node)LSTM_Node
reset(int _series_length) (defined in LSTM_Node)LSTM_Node
reset(int32_t _series_length)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
RNN_Edge (defined in LSTM_Node)LSTM_Nodefriend
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name) (defined in RNN_Node_Interface)RNN_Node_Interface
series_length (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
set_weights(const vector< double > &parameters) (defined in LSTM_Node)LSTM_Nodevirtual
set_weights(uint32_t &offset, const vector< double > &parameters) (defined in LSTM_Node)LSTM_Nodevirtual
total_inputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
total_outputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
try_update_deltas(int time) (defined in LSTM_Node)LSTM_Node
write_to_stream(ostream &out) (defined in LSTM_Node)LSTM_Node
~LSTM_Node() (defined in LSTM_Node)LSTM_Node
~RNN_Node_Interface() (defined in RNN_Node_Interface)RNN_Node_Interfacevirtual
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +Friends | +List of all members
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LSTM_Node Class Reference
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+ + +RNN_Node_Interface + +
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+Public Member Functions

+ LSTM_Node (int _innovation_number, int _type, double _depth)
 
+void initialize_lamarckian (minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)
 
+void initialize_xavier (minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range)
 
+void initialize_kaiming (minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)
 
+void initialize_uniform_random (minstd_rand0 &generator, uniform_real_distribution< double > &rng)
 
+double get_gradient (string gradient_name)
 
+void print_gradient (string gradient_name)
 
+void input_fired (int time, double incoming_output)
 
+void try_update_deltas (int time)
 
+void error_fired (int time, double error)
 
+void output_fired (int time, double delta)
 
+uint32_t get_number_weights () const
 
+void get_weights (vector< double > &parameters) const
 
+void set_weights (const vector< double > &parameters)
 
+void get_weights (uint32_t &offset, vector< double > &parameters) const
 
+void set_weights (uint32_t &offset, const vector< double > &parameters)
 
+void get_gradients (vector< double > &gradients)
 
+void reset (int _series_length)
 
+void write_to_stream (ostream &out)
 
+RNN_Node_Interface * copy () const
 
- Public Member Functions inherited from RNN_Node_Interface
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth)
 
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name)
 
+virtual void input_fired (int32_t time, double incoming_output)=0
 
+virtual void output_fired (int32_t time, double delta)=0
 
+virtual void error_fired (int32_t time, double error)=0
 
+virtual void reset (int32_t _series_length)=0
 
+void write_to_stream (ostream &out)
 
+int32_t get_node_type () const
 
+int32_t get_layer_type () const
 
+int32_t get_innovation_number () const
 
+int32_t get_total_inputs () const
 
+int32_t get_total_outputs () const
 
+double get_depth () const
 
+bool equals (RNN_Node_Interface *other) const
 
+bool is_reachable () const
 
+bool is_enabled () const
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+double output_gate_update_weight
 
+double output_gate_weight
 
+double output_gate_bias
 
+double input_gate_update_weight
 
+double input_gate_weight
 
+double input_gate_bias
 
+double forget_gate_update_weight
 
+double forget_gate_weight
 
+double forget_gate_bias
 
+double cell_weight
 
+double cell_bias
 
+vector< double > output_gate_values
 
+vector< double > input_gate_values
 
+vector< double > forget_gate_values
 
+vector< double > cell_values
 
+vector< double > ld_output_gate
 
+vector< double > ld_input_gate
 
+vector< double > ld_forget_gate
 
+vector< double > cell_in_tanh
 
+vector< double > cell_out_tanh
 
+vector< double > ld_cell_in
 
+vector< double > ld_cell_out
 
+vector< double > d_prev_cell
 
+vector< double > d_output_gate_update_weight
 
+vector< double > d_output_gate_weight
 
+vector< double > d_output_gate_bias
 
+vector< double > d_input_gate_update_weight
 
+vector< double > d_input_gate_weight
 
+vector< double > d_input_gate_bias
 
+vector< double > d_forget_gate_update_weight
 
+vector< double > d_forget_gate_weight
 
+vector< double > d_forget_gate_bias
 
+vector< double > d_cell_weight
 
+vector< double > d_cell_bias
 
+ + + +

+Friends

+class RNN_Edge
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from RNN_Node_Interface
+int32_t innovation_number
 
+int32_t layer_type
 
+int32_t node_type
 
+double depth
 
+string parameter_name
 
+bool enabled
 
+bool backward_reachable
 
+bool forward_reachable
 
+int32_t series_length
 
+vector< double > input_values
 
+vector< double > output_values
 
+vector< double > error_values
 
+vector< double > d_input
 
+vector< int32_t > inputs_fired
 
+vector< int32_t > outputs_fired
 
+int32_t total_inputs
 
+int32_t total_outputs
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_l_s_t_m___node.png b/docs/html/class_l_s_t_m___node.png new file mode 100644 index 0000000000000000000000000000000000000000..c111ee0826f5c010594344368b519bbd0241faae GIT binary patch literal 552 zcmV+@0@wYCP)x~0000OP)t-s|Ns90 z008Lh^>vTJkN^MxkN^Mxkifve1&Q1r00008bW%=J0RR90|NsC0)yh;d0005ENkl8^wz3`P$nnfHI>JA*8(;4)4O=BB2f$bNtp{}N-2vqcmUnJb5I2oW(>A;RbR zq)SeHL~Bhw1o0nN|s$N z9d&XIq9tjf_4;$6s?yC&|I%^f6G23qUSvN=524!{8D|?q7}X){A*+g-WcU$;^7V26 z3Ziu!9LJXsBGRneiGF>0C$@b;ouUpbQ?ch0o5yj&C)U@)_C0YF9SyLBb&UsA+((Cq z$SfU0BnR5N?@l)o5gE#VU=AT7#vDRH#2Djb0sN>Jz-&DM;`Mp}#S + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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LargeImage Member List
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This is the complete list of members for LargeImage, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
alpha (defined in LargeImage)LargeImageprivate
channels (defined in LargeImage)LargeImageprivate
classification (defined in LargeImage)LargeImageprivate
copy() const (defined in LargeImage)LargeImage
draw_png(string filename) const (defined in LargeImage)LargeImage
draw_png_4channel(string filename) const (defined in LargeImage)LargeImage
draw_png_alpha(string filename) const (defined in LargeImage)LargeImage
get_alpha_unnormalized(int y, int x) const (defined in LargeImage)LargeImage
get_channels() const (defined in LargeImage)LargeImage
get_classification() const (defined in LargeImage)LargeImagevirtual
get_height() const (defined in LargeImage)LargeImage
get_number_subimages() const (defined in LargeImage)LargeImage
get_pixel(int z, int y, int x) const (defined in LargeImage)LargeImage
get_pixel_avg(vector< float > &channel_avgs) const (defined in LargeImage)LargeImage
get_pixel_unnormalized(int z, int y, int x) const (defined in LargeImage)LargeImage
get_pixel_variance(const vector< float > &channel_avgs, vector< float > &channel_variances) const (defined in LargeImage)LargeImage
get_width() const (defined in LargeImage)LargeImage
height (defined in LargeImage)LargeImageprivate
images (defined in LargeImage)LargeImageprivate
LargeImage(ifstream &infile, int _number_subimages, int _channels, int _width, int _height, int _padding, int _classification, const LargeImages *_images) (defined in LargeImage)LargeImage
LargeImage(int _number_subimages, int _channels, int _width, int _height, int _padding, int _classification, const vector< vector< vector< uint8_t > > > &_pixels) (defined in LargeImage)LargeImage
LargeImage(int _number_subimages, int _channels, int _width, int _height, int _padding, int _classification, const vector< vector< vector< uint8_t > > > &_pixels, const vector< vector< uint8_t > > &_alpha) (defined in LargeImage)LargeImage
LargeImages (defined in LargeImage)LargeImagefriend
normalize(const vector< float > &channel_avgs, const vector< float > &channel_std_dev) (defined in LargeImage)LargeImage
number_subimages (defined in LargeImage)LargeImageprivate
padding (defined in LargeImage)LargeImageprivate
pixels (defined in LargeImage)LargeImageprivate
print(ostream &out) (defined in LargeImage)LargeImagevirtual
scale_0_1() (defined in LargeImage)LargeImage
set_alpha(const vector< vector< uint8_t > > &_alpha) (defined in LargeImage)LargeImage
set_alpha(const vector< vector< float > > &_alpha) (defined in LargeImage)LargeImage
set_pixel(int z, int y, int x, uint8_t value) (defined in LargeImage)LargeImage
width (defined in LargeImage)LargeImageprivate
~ImageInterface()=0 (defined in ImageInterface)ImageInterfacepure virtual
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+ + diff --git a/docs/html/class_large_image.html b/docs/html/class_large_image.html new file mode 100644 index 00000000..13808546 --- /dev/null +++ b/docs/html/class_large_image.html @@ -0,0 +1,203 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: LargeImage Class Reference + + + + + + + + + +
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LargeImage Class Reference
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+Public Member Functions

+ LargeImage (ifstream &infile, int _number_subimages, int _channels, int _width, int _height, int _padding, int _classification, const LargeImages *_images)
 
+ LargeImage (int _number_subimages, int _channels, int _width, int _height, int _padding, int _classification, const vector< vector< vector< uint8_t > > > &_pixels)
 
+ LargeImage (int _number_subimages, int _channels, int _width, int _height, int _padding, int _classification, const vector< vector< vector< uint8_t > > > &_pixels, const vector< vector< uint8_t > > &_alpha)
 
+int get_classification () const
 
+int get_number_subimages () const
 
+int get_channels () const
 
+int get_height () const
 
+int get_width () const
 
+void scale_0_1 ()
 
+void get_pixel_avg (vector< float > &channel_avgs) const
 
+void get_pixel_variance (const vector< float > &channel_avgs, vector< float > &channel_variances) const
 
+void normalize (const vector< float > &channel_avgs, const vector< float > &channel_std_dev)
 
+void print (ostream &out)
 
+uint8_t get_pixel_unnormalized (int z, int y, int x) const
 
+uint8_t get_alpha_unnormalized (int y, int x) const
 
+void set_pixel (int z, int y, int x, uint8_t value)
 
+uint8_t get_pixel (int z, int y, int x) const
 
+void set_alpha (const vector< vector< uint8_t > > &_alpha)
 
+void set_alpha (const vector< vector< float > > &_alpha)
 
+LargeImage * copy () const
 
+void draw_png (string filename) const
 
+void draw_png_4channel (string filename) const
 
+void draw_png_alpha (string filename) const
 
+ + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int number_subimages
 
+int padding
 
+int channels
 
+int height
 
+int width
 
+int classification
 
+vector< vector< vector< uint8_t > > > pixels
 
+vector< vector< uint8_t > > alpha
 
+const LargeImages * images
 
+ + + +

+Friends

+class LargeImages
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_large_image.png b/docs/html/class_large_image.png new file mode 100644 index 0000000000000000000000000000000000000000..a6ee04f258fb5b7b6a3a8cea5dd6fdd22171d218 GIT binary patch literal 512 zcmV+b0{{JqP)vTJkN^MxkN^Mxkifve1&Q1r00008bW%=J0RR90|NsC0)yh;d0004xNklf$yD8w9N$LVjp_kN=Ak~B?=FG+p9jqfCB zr0j5Yb%V1%z#pEpd>4k}MsYfpVHF(qF%4lSJUvg?>B>LO#~5KcP}f!7v(L+eXIAC? zQvwa|)-Ovc&yM*a=@XY$Hj`AeR8hl^Pka12+g2v_rj={^{nmB3S)V8Czo!>pc-cQW z=-EG}GB{Xa`mUmw8t-urn1ct#t*L`~*z z+uQ(?`QP}d0cP|6eE%#yz(9)+(AVMv^tJc^eJwsfUyBdW*Wv^8_3!x3IX6)_fN5HM zfWF?&e;Dq$`}`VzZ{72G?BcMsuft%xtOJm($B(4_WQ=fb#ds-3 + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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LargeImages Member List
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This is the complete list of members for LargeImages, including all inherited members.

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calculate_avg_std_dev() (defined in LargeImages)LargeImages
channel_avg (defined in LargeImages)LargeImagesprivate
channel_std_dev (defined in LargeImages)LargeImagesprivate
channels (defined in LargeImages)LargeImagesprivate
class_sizes (defined in LargeImages)LargeImagesprivate
copy_image(int i) const (defined in LargeImages)LargeImages
filename (defined in LargeImages)LargeImagesprivate
get_average() const (defined in LargeImages)LargeImagesvirtual
get_channel_avg(int channel) const (defined in LargeImages)LargeImagesvirtual
get_channel_std_dev(int channel) const (defined in LargeImages)LargeImagesvirtual
get_class_size(int i) const (defined in LargeImages)LargeImagesvirtual
get_classification(int subimage) const (defined in LargeImages)LargeImagesvirtual
get_filename() const (defined in LargeImages)LargeImagesvirtual
get_image_channels() const (defined in LargeImages)LargeImagesvirtual
get_image_classification(int image) const (defined in LargeImages)LargeImagesvirtual
get_image_height() const (defined in LargeImages)LargeImagesvirtual
get_image_width() const (defined in LargeImages)LargeImagesvirtual
get_large_image_channels(int image) const (defined in LargeImages)LargeImagesvirtual
get_large_image_height(int image) const (defined in LargeImages)LargeImagesvirtual
get_large_image_width(int image) const (defined in LargeImages)LargeImagesvirtual
get_number_classes() const (defined in LargeImages)LargeImagesvirtual
get_number_images() const (defined in LargeImages)LargeImagesvirtual
get_number_large_images() const (defined in LargeImages)LargeImagesvirtual
get_number_subimages(int i) const (defined in LargeImages)LargeImagesvirtual
get_padding() const (defined in LargeImages)LargeImagesvirtual
get_pixel(int subimage, int z, int y, int x) const (defined in LargeImages)LargeImagesvirtual
get_raw_pixel(int subimage, int z, int y, int x) const (defined in LargeImages)LargeImagesvirtual
get_std_dev() const (defined in LargeImages)LargeImagesvirtual
images (defined in LargeImages)LargeImagesprivate
LargeImages(string binary_filename, int _padding, int _subimage_height, int _subimage_width) (defined in LargeImages)LargeImages
LargeImages(string binary_filename, int _padding, int _subimage_height, int _subimage_width, const vector< float > &_channel_avg, const vector< float > &channel_std_dev) (defined in LargeImages)LargeImages
normalize() (defined in LargeImages)LargeImages
number_classes (defined in LargeImages)LargeImagesprivate
number_images (defined in LargeImages)LargeImagesprivate
padding (defined in LargeImages)LargeImagesprivate
read_images_from_file(string binary_filename) (defined in LargeImages)LargeImages
subimage_height (defined in LargeImages)LargeImagesprivate
subimage_width (defined in LargeImages)LargeImagesprivate
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LargeImages Class Reference
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+Inheritance diagram for LargeImages:
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+Public Member Functions

+int read_images_from_file (string binary_filename)
 
+ LargeImages (string binary_filename, int _padding, int _subimage_height, int _subimage_width)
 
+ LargeImages (string binary_filename, int _padding, int _subimage_height, int _subimage_width, const vector< float > &_channel_avg, const vector< float > &channel_std_dev)
 
+string get_filename () const
 
+int get_class_size (int i) const
 
+int get_number_classes () const
 
+int get_number_images () const
 
+int get_number_large_images () const
 
+int get_number_subimages (int i) const
 
+int get_padding () const
 
+int get_image_channels () const
 
+int get_image_width () const
 
+int get_image_height () const
 
+int get_large_image_channels (int image) const
 
+int get_large_image_width (int image) const
 
+int get_large_image_height (int image) const
 
+int get_image_classification (int image) const
 
+int get_classification (int subimage) const
 
+float get_pixel (int subimage, int z, int y, int x) const
 
+float get_raw_pixel (int subimage, int z, int y, int x) const
 
+void calculate_avg_std_dev ()
 
+float get_channel_avg (int channel) const
 
+float get_channel_std_dev (int channel) const
 
+const vector< float > & get_average () const
 
+const vector< float > & get_std_dev () const
 
+void normalize ()
 
+LargeImage * copy_image (int i) const
 
+ + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+string filename
 
+int number_classes
 
+int number_images
 
+vector< int > class_sizes
 
+int padding
 
+int channels
 
+int subimage_width
 
+int subimage_height
 
+vector< LargeImage > images
 
+vector< float > channel_avg
 
+vector< float > channel_std_dev
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_large_images.png b/docs/html/class_large_images.png new file mode 100644 index 0000000000000000000000000000000000000000..caeeee245a42e6e637f51807803eb0512315d967 GIT binary patch literal 769 zcmeAS@N?(olHy`uVBq!ia0vp^4M5z%!3-oNHVaPyQ2{<7uK)l42Qpv0`C8h4XabN0 z#s>}@VC}pk59D%`1o;Is02P72)l(rx3=B*YJY5_^Dj46+y}PMdfv079>ap+jPlBK7 zb7*8)zkHd}HdTnJMs3NxL)Gt1S^Xzf88I-wV78cK6JB^*zhJU;4OhaiAIuEma<^|E z$&;QISQI_~r=U)vS>>rd742TeZEKZXUTh0K<#Fp&&zDuxzdtS#Xq0I>=XP)(`zjTg zvam@@eHmIhmQ60Ik!~=V+^6;W(E~My_gA&&TSff#=-`-;vRIqpuC;;B!Qbx=NbRoB zt_}^j5hU92O7NL?sKX@LSdIKib1s=2FlbZx>F`-l;^+hw`4juv*go(w+^b%%!BDY; zmBC~w?*^SyEVmfee=g;`!nlTu=Kuo=dLYiQ=X+f62bGoT%mxiS4;CqYT_W78az13O zO5`l-8w(j&)F007uj4A%v5$Sjzgt${rIsqF%(*J|$85XJDepywXV=(k`QN;%$uZl5 z;o`QnuXoN%pSrDJ-H*NR*D;(peIaLxap>ALAx}#fCivU4a0vS9S?=R?nC0m{wY0E6 zc)|Bqp(o#HrHgY4HpWSsah9dr?t6E@fYrEs!7j_E?~_v^;~gk3R^+T}qT?Jm68x z@Nq@%=7tGAf*Q^xvJ44Jeu|`Y>@k#CCAL?Jok1eYj + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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Line Member List
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Line(int _y1, int _x1, int _y2, int _x2) (defined in Line)Line
x1 (defined in Line)Line
x2 (defined in Line)Line
y1 (defined in Line)Line
y2 (defined in Line)Line
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Line Class Reference
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+Public Member Functions

+ Line (int _y1, int _x1, int _y2, int _x2)
 
+ + + + + + + + + +

+Public Attributes

+int y1
 
+int x1
 
+int y2
 
+int x2
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_log-members.html b/docs/html/class_log-members.html new file mode 100644 index 00000000..2e771589 --- /dev/null +++ b/docs/html/class_log-members.html @@ -0,0 +1,120 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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Log Member List
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This is the complete list of members for Log, including all inherited members.

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ALLLogstatic
at_level(int8_t level)Logstatic
clear_rank_restriction()Logstatic
DEBUGLogstatic
debug(const char *format,...)Logstatic
debug_no_header(const char *format,...)Logstatic
ERRORLogstatic
error(const char *format,...)Logstatic
error_no_header(const char *format,...)Logstatic
FATALLogstatic
fatal(const char *format,...)Logstatic
fatal_no_header(const char *format,...)Logstatic
file_message_levelLogprivatestatic
info(const char *format,...)Logstatic
INFOLogstatic
info_no_header(const char *format,...)Logstatic
initialize(const vector< string > &arguments)Logstatic
log_idsLogprivatestatic
log_ids_mutexLogprivatestatic
max_header_lengthLogprivatestatic
max_message_lengthLogprivatestatic
NONELogstatic
output_directoryLogprivatestatic
output_filesLogprivatestatic
parse_level_from_string(string level)Logstatic
process_rankLogprivatestatic
register_command_line_arguments()Logstatic
release_id(string human_readable_id)Logstatic
restrict_to_rank(int32_t _restricted_rank)Logstatic
restricted_rankLogprivatestatic
set_id(string human_readable_id)Logstatic
set_rank(int32_t _process_rank)Logstatic
std_message_levelLogprivatestatic
TRACELogstatic
trace(const char *format,...)Logstatic
trace_no_header(const char *format,...)Logstatic
warning(const char *format,...)Logstatic
WARNINGLogstatic
warning_no_header(const char *format,...)Logstatic
write_message(bool print_header, int8_t message_level, const char *message_type, const char *format, va_list arguments)Logprivatestatic
write_to_fileLogprivatestatic
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Log Class Reference
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+Static Public Member Functions

static void register_command_line_arguments ()
 Registers used command line arguments and instructions with the CommandLine class. More...
 
static int8_t parse_level_from_string (string level)
 
static void initialize (const vector< string > &arguments)
 Initializes the Log given arguments retreived from the CommandLine class. More...
 
static void set_rank (int32_t _process_rank)
 Sets the MPI process rank for this Log. More...
 
static void restrict_to_rank (int32_t _restricted_rank)
 Specifies which MPI process to allow messages from. More...
 
+static void clear_rank_restriction ()
 Clears the MPI process rank restriction from messages allowing any process to write to the log (sets Log::restricted_rank back to -1).
 
static void set_id (string human_readable_id)
 Sets a human readable thread id for this thread. More...
 
static void release_id (string human_readable_id)
 Releases a the human readable thread id previously set by by the provided human readable id;. More...
 
static bool at_level (int8_t level)
 Determines if either output level (the file or standard output) level is above the level passed as a parameter. More...
 
static void fatal (const char *format,...)
 Logs a fatal message. More...
 
static void error (const char *format,...)
 Logs an error message. More...
 
static void warning (const char *format,...)
 Logs a warning message. More...
 
static void info (const char *format,...)
 Logs an info message. More...
 
static void debug (const char *format,...)
 Logs a debug message. More...
 
static void trace (const char *format,...)
 Logs a trace message. More...
 
static void fatal_no_header (const char *format,...)
 Logs a fatal message. More...
 
static void error_no_header (const char *format,...)
 Logs an error message. More...
 
static void warning_no_header (const char *format,...)
 Logs a warning message. More...
 
static void info_no_header (const char *format,...)
 Logs an info message. More...
 
static void debug_no_header (const char *format,...)
 Logs a debug message. More...
 
static void trace_no_header (const char *format,...)
 Logs a trace message. More...
 
+ + + + + + + + + + + + + + + + + + + + + + + + + +

+Static Public Attributes

+static const int8_t NONE = 0
 Specifies no messages will be logged.
 
+static const int8_t FATAL = 1
 Specifies only fatal messages will be logged.
 
+static const int8_t ERROR = 2
 Specifies error and above messages will be logged.
 
+static const int8_t WARNING = 3
 Specifies warning and above messages will be logged.
 
+static const int8_t INFO = 4
 Specifies info and above messages will be logged.
 
+static const int8_t DEBUG = 5
 Specifies debug and above messages will be logged.
 
+static const int8_t TRACE = 6
 Specifies trace and above messages will be logged.
 
+static const int8_t ALL = 7
 Specifies all messages will be logged.
 
+ + + + +

+Static Private Member Functions

static void write_message (bool print_header, int8_t message_level, const char *message_type, const char *format, va_list arguments)
 Potentially writes the message to either standard output or the log file if the message level is high enough. More...
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Static Private Attributes

static int32_t std_message_level = INFO
 Specifies which messages to log. More...
 
static int32_t file_message_level = INFO
 Specifies which messages to log. More...
 
+static bool write_to_file = true
 Specifies if the logs should also be written to a flie.
 
+static int32_t max_header_length = 256
 Specifies the maximum length for the message header.
 
+static int32_t max_message_length = 1024
 Specifies the maximum message buffer length.
 
+static string output_directory = "./logs"
 Specifies which directory to write the log files to (if the logs are being writtten to a file).
 
static map< thread::id, string > log_ids
 A map of C++ thread ids (which are not human readable) to human readable integer ids. More...
 
static int32_t process_rank = -1
 The MPI process rank for this Log instance. More...
 
static int32_t restricted_rank = -1
 Defaults at -1, when set to a process rank (>= 0) the Log will only print messages if its rank is the same as the restricted rank. More...
 
+static map< string, LogFile * > output_files
 A map of human readable ids to output files which the log messages will be written to.
 
static shared_mutex log_ids_mutex
 A std::shared_mutex protecting the Log::thread_ids map. More...
 
+

Member Function Documentation

+ +

◆ at_level()

+ +
+
+ + + + + +
+ + + + + + + + +
bool Log::at_level (int8_t level)
+ 		+static
+
+ +

Determines if either output level (the file or standard output) level is above the level passed as a parameter.

+
Parameters
+ + +
levelis the level to be greater than or equal to
+
+
+
Returns
true if either the file or standard output level is greater than or equal to the passed level
+ +
+
+ +

◆ debug()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::debug (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs a debug message.

+

varargs are the same as in printf.

+ +
+
+ +

◆ debug_no_header()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::debug_no_header (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs a debug message.

+

Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf.

+ +
+
+ +

◆ error()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::error (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs an error message.

+

varargs are the same as in printf.

+ +
+
+ +

◆ error_no_header()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::error_no_header (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs an error message.

+

Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf.

+ +
+
+ +

◆ fatal()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::fatal (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs a fatal message.

+

varargs are the same as in printf.

+ +
+
+ +

◆ fatal_no_header()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::fatal_no_header (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs a fatal message.

+

Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf.

+ +
+
+ +

◆ info()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::info (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs an info message.

+

varargs are the same as in printf.

+ +
+
+ +

◆ info_no_header()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::info_no_header (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs an info message.

+

Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf.

+ +
+
+ +

◆ initialize()

+ +
+
+ + + + + +
+ + + + + + + + +
void Log::initialize (const vector< string > & arguments)
+ 		+static
+
+ +

Initializes the Log given arguments retreived from the CommandLine class.

+
Parameters
+ + +
argumentsthe command line arguments Log::register_command_line_arguments() must be called before Log::initialize()
+
+
+ +
+
+ +

◆ parse_level_from_string()

+ +
+
+ + + + + +
+ + + + + + + + +
int8_t Log::parse_level_from_string (string level)
+ 		+static
+
+
Parameters
+ + +
astring representation of the message level, either as text (e.g., "INFO") or as a number (e.g., 4)
+
+
+
Returns
the message level as an int8_t (i.e., one of the message level constants)
+ +
+
+ +

◆ register_command_line_arguments()

+ +
+
+ + + + + +
+ + + + + + + +
void Log::register_command_line_arguments ()
+ 		+static
+
+ +

Registers used command line arguments and instructions with the CommandLine class.

+

Needs to be called at the beginning of the main method of any programming using the Log class. Will register the following command line arguments and instructions:

    +
  1. log_level : specifies the max message level of messages to be printed
    2. +
  2. log_directory : specifies the output directory for message logs
    3. +
+ +
+
+ +

◆ release_id()

+ +
+
+ + + + + +
+ + + + + + + + +
void Log::release_id (string human_readable_id)
+ 		+static
+
+ +

Releases a the human readable thread id previously set by by the provided human readable id;.

+

This will report an error and exit if this human readable id has not already been set, or if it has been relased by another thread.

+
Parameters
+ + +
human_readable_idis a human readable thread id which has previously been set with Log::set_id(string)
+
+
+ +
+
+ +

◆ restrict_to_rank()

+ +
+
+ + + + + +
+ + + + + + + + +
void Log::restrict_to_rank (int32_t _restricted_rank)
+ 		+static
+
+ +

Specifies which MPI process to allow messages from.

+

A value < 0 will allow messages from any rank.

Parameters
+ + +
_restricted_rankis the MPI rank to only print messages from
+
+
+ +
+
+ +

◆ set_id()

+ +
+
+ + + + + +
+ + + + + + + + +
void Log::set_id (string human_readable_id)
+ 		+static
+
+ +

Sets a human readable thread id for this thread.

+

This will use std::this_thread::get_id() to get c++'s automatically generated thread id, and then put it in the Log::thread_ids map, so it can be used to write cleaner logs.

+

This will report an error and exit if another thread has already reserved this human readable id.

+
Parameters
+ + +
human_readable_ida human readable thread id
+
+
+ +
+
+ +

◆ set_rank()

+ +
+
+ + + + + +
+ + + + + + + + +
void Log::set_rank (int32_t _process_rank)
+ 		+static
+
+ +

Sets the MPI process rank for this Log.

+
Parameters
+ + +
_process_rankis the MPI rank of this process
+
+
+ +
+
+ +

◆ trace()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::trace (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs a trace message.

+

varargs are the same as in printf.

+ +
+
+ +

◆ trace_no_header()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::trace_no_header (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs a trace message.

+

Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf.

+ +
+
+ +

◆ warning()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::warning (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs a warning message.

+

varargs are the same as in printf.

+ +
+
+ +

◆ warning_no_header()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
void Log::warning_no_header (const char * format,
 ... 
)
+ 		+static
+
+ +

Logs a warning message.

+

Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf.

+ +
+
+ +

◆ write_message()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void Log::write_message (bool print_header,
int8_t message_level,
const char * message_type,
const char * format,
va_list arguments 
)
+ 		+staticprivate
+
+ +

Potentially writes the message to either standard output or the log file if the message level is high enough.

+
Parameters
+ + + + + + +
print_headerspecifies if the header to the message should be printed out
message_levelthe level of the message to potentially be printed out
message_typea string representation of this message type
message_typethe format string for this message (as in printf)
argumentsthe arguments for the print statement
+
+
+ +
+
+

Member Data Documentation

+ +

◆ file_message_level

+ +
+
+ + + + + +
+ + + + +
int32_t Log::file_message_level = INFO
+ 		+staticprivate
+
+ +

Specifies which messages to log.

+

Messages will be written to the log file if their type is <= message_level.

+ +
+
+ +

◆ log_ids

+ +
+
+ + + + + +
+ + + + +
map< thread::id, string > Log::log_ids
+ 		+staticprivate
+
+ +

A map of C++ thread ids (which are not human readable) to human readable integer ids.

+

Thread ids are specified with the Log::set_id(string) method.

+ +
+
+ +

◆ log_ids_mutex

+ +
+
+ + + + + +
+ + + + +
shared_mutex Log::log_ids_mutex
+ 		+staticprivate
+
+ +

A std::shared_mutex protecting the Log::thread_ids map.

+

The Log::set_thread_id(int32_t) mehod needs write access so it will use the std::shared_mutex::lock()x and std::shared_mutex()::unlock() mechanism, while the Log::get_thread_id() only needs read access and can used the std::shared_mutex::lock_shared() and std::shared_mutex::unlock_shared() methods.

+ +
+
+ +

◆ process_rank

+ +
+
+ + + + + +
+ + + + +
int32_t Log::process_rank = -1
+ 		+staticprivate
+
+ +

The MPI process rank for this Log instance.

+

Set to -1 if not specified or not using MPI.

+ +
+
+ +

◆ restricted_rank

+ +
+
+ + + + + +
+ + + + +
int32_t Log::restricted_rank = -1
+ 		+staticprivate
+
+ +

Defaults at -1, when set to a process rank (>= 0) the Log will only print messages if its rank is the same as the restricted rank.

+

This is useful, for allowing initialization messages to only be printed out by a single process.

+ +
+
+ +

◆ std_message_level

+ +
+
+ + + + + +
+ + + + +
int32_t Log::std_message_level = INFO
+ 		+staticprivate
+
+ +

Specifies which messages to log.

+

Messages will be written to the standard output log if their type is <= message_level.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_log_file-members.html b/docs/html/class_log_file-members.html new file mode 100644 index 00000000..877d3122 --- /dev/null +++ b/docs/html/class_log_file-members.html @@ -0,0 +1,83 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+ +
+ +
+
+
+
LogFile Member List
+
+
+ +

This is the complete list of members for LogFile, including all inherited members.

+ + + + + +
file (defined in LogFile)LogFileprivate
file_mutex (defined in LogFile)LogFileprivate
Log (defined in LogFile)LogFilefriend
LogFile(FILE *file) (defined in LogFile)LogFile
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_log_file.html b/docs/html/class_log_file.html new file mode 100644 index 00000000..dd3c9bc0 --- /dev/null +++ b/docs/html/class_log_file.html @@ -0,0 +1,107 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: LogFile Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +Friends | +List of all members
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LogFile Class Reference
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+Public Member Functions

+ LogFile (FILE *file)
 
+ + + + + +

+Private Attributes

+FILE * file
 
+mutex file_mutex
 
+ + + +

+Friends

+class Log
 
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_m_g_u___node-members.html b/docs/html/class_m_g_u___node-members.html new file mode 100644 index 00000000..0123207a --- /dev/null +++ b/docs/html/class_m_g_u___node-members.html @@ -0,0 +1,151 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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MGU_Node Member List
+
+
+ +

This is the complete list of members for MGU_Node, including all inherited members.

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backward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
copy() const (defined in MGU_Node)MGU_Nodevirtual
d_f_bias (defined in MGU_Node)MGU_Nodeprivate
d_fu (defined in MGU_Node)MGU_Nodeprivate
d_fw (defined in MGU_Node)MGU_Nodeprivate
d_h_bias (defined in MGU_Node)MGU_Nodeprivate
d_h_prev (defined in MGU_Node)MGU_Nodeprivate
d_hu (defined in MGU_Node)MGU_Nodeprivate
d_hw (defined in MGU_Node)MGU_Nodeprivate
d_input (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
depth (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
enabled (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
equals(RNN_Node_Interface *other) const (defined in RNN_Node_Interface)RNN_Node_Interface
error_fired(int time, double error) (defined in MGU_Node)MGU_Node
error_fired(int32_t time, double error)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
error_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
f (defined in MGU_Node)MGU_Nodeprivate
f_bias (defined in MGU_Node)MGU_Nodeprivate
forward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
fu (defined in MGU_Node)MGU_Nodeprivate
fw (defined in MGU_Node)MGU_Nodeprivate
get_depth() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_gradient(string gradient_name) (defined in MGU_Node)MGU_Node
get_gradients(vector< double > &gradients) (defined in MGU_Node)MGU_Nodevirtual
get_innovation_number() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_layer_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_node_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_number_weights() const (defined in MGU_Node)MGU_Nodevirtual
get_total_inputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_total_outputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_weights(vector< double > &parameters) const (defined in MGU_Node)MGU_Nodevirtual
get_weights(uint32_t &offset, vector< double > &parameters) const (defined in MGU_Node)MGU_Nodevirtual
h_bias (defined in MGU_Node)MGU_Nodeprivate
h_tanh (defined in MGU_Node)MGU_Nodeprivate
hu (defined in MGU_Node)MGU_Nodeprivate
hw (defined in MGU_Node)MGU_Nodeprivate
initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range) (defined in MGU_Node)MGU_Nodevirtual
initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma) (defined in MGU_Node)MGU_Nodevirtual
initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution< double > &rng) (defined in MGU_Node)MGU_Nodevirtual
initialize_xavier(minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range) (defined in MGU_Node)MGU_Nodevirtual
innovation_number (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
input_fired(int time, double incoming_output) (defined in MGU_Node)MGU_Node
input_fired(int32_t time, double incoming_output)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
input_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
inputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
is_enabled() const (defined in RNN_Node_Interface)RNN_Node_Interface
is_reachable() const (defined in RNN_Node_Interface)RNN_Node_Interface
layer_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
ld_f (defined in MGU_Node)MGU_Nodeprivate
ld_h_tanh (defined in MGU_Node)MGU_Nodeprivate
MGU_Node(int _innovation_number, int _layer_type, double _depth) (defined in MGU_Node)MGU_Node
node_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
output_fired(int time, double delta) (defined in MGU_Node)MGU_Node
output_fired(int32_t time, double delta)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
output_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
outputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
parameter_name (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
print_gradient(string gradient_name) (defined in MGU_Node)MGU_Node
reset(int _series_length) (defined in MGU_Node)MGU_Node
reset(int32_t _series_length)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
RNN_Edge (defined in MGU_Node)MGU_Nodefriend
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name) (defined in RNN_Node_Interface)RNN_Node_Interface
series_length (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
set_weights(const vector< double > &parameters) (defined in MGU_Node)MGU_Nodevirtual
set_weights(uint32_t &offset, const vector< double > &parameters) (defined in MGU_Node)MGU_Nodevirtual
total_inputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
total_outputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
try_update_deltas(int time) (defined in MGU_Node)MGU_Node
write_to_stream(ostream &out) (defined in MGU_Node)MGU_Node
~MGU_Node() (defined in MGU_Node)MGU_Node
~RNN_Node_Interface() (defined in RNN_Node_Interface)RNN_Node_Interfacevirtual
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+ + diff --git a/docs/html/class_m_g_u___node.html b/docs/html/class_m_g_u___node.html new file mode 100644 index 00000000..366d22c3 --- /dev/null +++ b/docs/html/class_m_g_u___node.html @@ -0,0 +1,322 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: MGU_Node Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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MGU_Node Class Reference
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+Public Member Functions

+ MGU_Node (int _innovation_number, int _layer_type, double _depth)
 
+void initialize_lamarckian (minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)
 
+void initialize_xavier (minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range)
 
+void initialize_kaiming (minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)
 
+void initialize_uniform_random (minstd_rand0 &generator, uniform_real_distribution< double > &rng)
 
+double get_gradient (string gradient_name)
 
+void print_gradient (string gradient_name)
 
+void input_fired (int time, double incoming_output)
 
+void try_update_deltas (int time)
 
+void error_fired (int time, double error)
 
+void output_fired (int time, double delta)
 
+uint32_t get_number_weights () const
 
+void get_weights (vector< double > &parameters) const
 
+void set_weights (const vector< double > &parameters)
 
+void get_weights (uint32_t &offset, vector< double > &parameters) const
 
+void set_weights (uint32_t &offset, const vector< double > &parameters)
 
+void get_gradients (vector< double > &gradients)
 
+void reset (int _series_length)
 
+void write_to_stream (ostream &out)
 
+RNN_Node_Interface * copy () const
 
- Public Member Functions inherited from RNN_Node_Interface
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth)
 
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name)
 
+virtual void input_fired (int32_t time, double incoming_output)=0
 
+virtual void output_fired (int32_t time, double delta)=0
 
+virtual void error_fired (int32_t time, double error)=0
 
+virtual void reset (int32_t _series_length)=0
 
+void write_to_stream (ostream &out)
 
+int32_t get_node_type () const
 
+int32_t get_layer_type () const
 
+int32_t get_innovation_number () const
 
+int32_t get_total_inputs () const
 
+int32_t get_total_outputs () const
 
+double get_depth () const
 
+bool equals (RNN_Node_Interface *other) const
 
+bool is_reachable () const
 
+bool is_enabled () const
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+double fw
 
+double fu
 
+double f_bias
 
+double hw
 
+double hu
 
+double h_bias
 
+vector< double > d_fw
 
+vector< double > d_fu
 
+vector< double > d_f_bias
 
+vector< double > d_hw
 
+vector< double > d_hu
 
+vector< double > d_h_bias
 
+vector< double > d_h_prev
 
+vector< double > f
 
+vector< double > ld_f
 
+vector< double > h_tanh
 
+vector< double > ld_h_tanh
 
+ + + +

+Friends

+class RNN_Edge
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from RNN_Node_Interface
+int32_t innovation_number
 
+int32_t layer_type
 
+int32_t node_type
 
+double depth
 
+string parameter_name
 
+bool enabled
 
+bool backward_reachable
 
+bool forward_reachable
 
+int32_t series_length
 
+vector< double > input_values
 
+vector< double > output_values
 
+vector< double > error_values
 
+vector< double > d_input
 
+vector< int32_t > inputs_fired
 
+vector< int32_t > outputs_fired
 
+int32_t total_inputs
 
+int32_t total_outputs
 
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_m_g_u___node.png b/docs/html/class_m_g_u___node.png new file mode 100644 index 0000000000000000000000000000000000000000..7cb6a3932901475984c08ebb1f193f9f5e529842 GIT binary patch literal 552 zcmV+@0@wYCP)x~0000OP)t-s|Ns90 z008Lh^>vTJkN^MxkN^Mxkifve1&Q1r00008bW%=J0RR90|NsC0)yh;d0005ENklDtC*)EM5AIUlk6}>c7Mo<7^Q{MCQsN9705l6^O9> zmU3;W%J$p0+g8V2jWoVJh`e@FF|&LMae~l;NHtw4)8zJgWDAvQm08}sX$$#p?O9Jb zKd6h(AZn2o+V6ksR72X$Jf9!z#Y7O%rZ?G6(n4roBNJ?W2%|EEy<{~|iVeO8p<+E> zfP$zGcCO=}5F%2oIf-_E`Xr8HLfxWHEHiKx6NlF^5EJ{?!|{9KDmyA*P3w{ms=Lh& z5s_KChDZT)_P!f-5)tXjKVS|aBE}p-LBts2WC47s7r<;i0pj&~0L2p^-VF089s%Lm z;Sc~;4&e}hF^6ynz?efg1Ypb|90D-r5Dozt`xS@~LYOTAz+5?mLjcA`AhJg)Q!wZH z&c#kOzPllaZ3(^j9%9$N6{0vdCy@!)gIb(b3*ba!*RifJ0#S?1JV5aQMA}^&6UEvB z;7o&onF&e&b^^0f)?y1|fD_#e0dTF+6gBEY=qR`&Ce(EKn`aNYz9;U=4uD(rB}9t6 qq1^%;$v + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+
+
MosaicImages Member List
+
+
+ +

This is the complete list of members for MosaicImages, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
calculate_avg_std_dev() (defined in MosaicImages)MosaicImages
channel_avg (defined in MosaicImages)MosaicImagesprivate
channel_std_dev (defined in MosaicImages)MosaicImagesprivate
channels (defined in MosaicImages)MosaicImagesprivate
class_sizes (defined in MosaicImages)MosaicImagesprivate
copy_image(int i) const (defined in MosaicImages)MosaicImages
filenames (defined in MosaicImages)MosaicImagesprivate
get_average() const (defined in MosaicImages)MosaicImagesvirtual
get_channel_avg(int channel) const (defined in MosaicImages)MosaicImagesvirtual
get_channel_std_dev(int channel) const (defined in MosaicImages)MosaicImagesvirtual
get_class_size(int i) const (defined in MosaicImages)MosaicImagesvirtual
get_classification(int subimage) const (defined in MosaicImages)MosaicImagesvirtual
get_filename() const (defined in MosaicImages)MosaicImagesvirtual
get_image_channels() const (defined in MosaicImages)MosaicImagesvirtual
get_image_classification(int image) const (defined in MosaicImages)MosaicImagesvirtual
get_image_height() const (defined in MosaicImages)MosaicImagesvirtual
get_image_width() const (defined in MosaicImages)MosaicImagesvirtual
get_large_image_channels(int image) const (defined in MosaicImages)MosaicImagesvirtual
get_large_image_height(int image) const (defined in MosaicImages)MosaicImagesvirtual
get_large_image_width(int image) const (defined in MosaicImages)MosaicImagesvirtual
get_mosaic_pixels(string filename, vector< vector< vector< uint8_t > > > &pixels, uint32_t &height, uint32_t &width) (defined in MosaicImages)MosaicImages
get_number_classes() const (defined in MosaicImages)MosaicImagesvirtual
get_number_images() const (defined in MosaicImages)MosaicImagesvirtual
get_number_large_images() const (defined in MosaicImages)MosaicImagesvirtual
get_number_subimages(int i) const (defined in MosaicImages)MosaicImagesvirtual
get_padding() const (defined in MosaicImages)MosaicImagesvirtual
get_pixel(int subimage, int z, int y, int x) const (defined in MosaicImages)MosaicImagesvirtual
get_raw_pixel(int subimage, int z, int y, int x) const (defined in MosaicImages)MosaicImagesvirtual
get_std_dev() const (defined in MosaicImages)MosaicImagesvirtual
images (defined in MosaicImages)MosaicImagesprivate
initialize_counts(const vector< vector< int > > &classes) (defined in MosaicImages)MosaicImages
MosaicImages(vector< string > _filenames, const vector< vector< Point > > &_box_centers, int _box_radius, const vector< vector< int > > &_box_classes, int _padding, int _subimage_height, int _subimage_width) (defined in MosaicImages)MosaicImages
MosaicImages(vector< string > _filenames, const vector< vector< Point > > &_box_centers, int _box_radius, const vector< vector< int > > &_box_classes, int _padding, int _subimage_height, int _subimage_width, const vector< float > &_channel_avg, const vector< float > &_channel_std_dev) (defined in MosaicImages)MosaicImages
MosaicImages(vector< string > _filenames, const vector< vector< Line > > &_lines, int _line_height, const vector< vector< int > > &_line_classes, int _padding, int _subimage_height, int _subimage_width) (defined in MosaicImages)MosaicImages
MosaicImages(vector< string > _filenames, const vector< vector< Line > > &_lines, int _line_height, const vector< vector< int > > &_line_classes, int _padding, int _subimage_height, int _subimage_width, const vector< float > &_channel_avg, const vector< float > &_channel_std_dev) (defined in MosaicImages)MosaicImages
normalize() (defined in MosaicImages)MosaicImages
number_classes (defined in MosaicImages)MosaicImagesprivate
number_images (defined in MosaicImages)MosaicImagesprivate
padding (defined in MosaicImages)MosaicImagesprivate
read_mosaic(string filename, const vector< Point > &box_centers, int box_radius, const vector< int > &box_classes) (defined in MosaicImages)MosaicImages
read_mosaic(string filename, const vector< Line > &lines, int line_height, const vector< int > &line_classes) (defined in MosaicImages)MosaicImages
set_alpha(int i, const vector< vector< float > > &_alpha) (defined in MosaicImages)MosaicImages
set_alpha(int i, const vector< vector< uint8_t > > &_alpha) (defined in MosaicImages)MosaicImages
subimage_height (defined in MosaicImages)MosaicImagesprivate
subimage_width (defined in MosaicImages)MosaicImagesprivate
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+ + diff --git a/docs/html/class_mosaic_images.html b/docs/html/class_mosaic_images.html new file mode 100644 index 00000000..88104fe3 --- /dev/null +++ b/docs/html/class_mosaic_images.html @@ -0,0 +1,236 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: MosaicImages Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +List of all members
+
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MosaicImages Class Reference
+
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+Inheritance diagram for MosaicImages:
+
+
+ + +MultiImagesInterface +ImagesInterface + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+void get_mosaic_pixels (string filename, vector< vector< vector< uint8_t > > > &pixels, uint32_t &height, uint32_t &width)
 
+void read_mosaic (string filename, const vector< Point > &box_centers, int box_radius, const vector< int > &box_classes)
 
+void read_mosaic (string filename, const vector< Line > &lines, int line_height, const vector< int > &line_classes)
 
+void initialize_counts (const vector< vector< int > > &classes)
 
+ MosaicImages (vector< string > _filenames, const vector< vector< Point > > &_box_centers, int _box_radius, const vector< vector< int > > &_box_classes, int _padding, int _subimage_height, int _subimage_width)
 
+ MosaicImages (vector< string > _filenames, const vector< vector< Point > > &_box_centers, int _box_radius, const vector< vector< int > > &_box_classes, int _padding, int _subimage_height, int _subimage_width, const vector< float > &_channel_avg, const vector< float > &_channel_std_dev)
 
+ MosaicImages (vector< string > _filenames, const vector< vector< Line > > &_lines, int _line_height, const vector< vector< int > > &_line_classes, int _padding, int _subimage_height, int _subimage_width)
 
+ MosaicImages (vector< string > _filenames, const vector< vector< Line > > &_lines, int _line_height, const vector< vector< int > > &_line_classes, int _padding, int _subimage_height, int _subimage_width, const vector< float > &_channel_avg, const vector< float > &_channel_std_dev)
 
+string get_filename () const
 
+int get_class_size (int i) const
 
+int get_number_classes () const
 
+int get_number_images () const
 
+int get_number_large_images () const
 
+int get_number_subimages (int i) const
 
+int get_padding () const
 
+int get_image_channels () const
 
+int get_image_width () const
 
+int get_image_height () const
 
+int get_large_image_channels (int image) const
 
+int get_large_image_width (int image) const
 
+int get_large_image_height (int image) const
 
+int get_image_classification (int image) const
 
+int get_classification (int subimage) const
 
+float get_pixel (int subimage, int z, int y, int x) const
 
+float get_raw_pixel (int subimage, int z, int y, int x) const
 
+void calculate_avg_std_dev ()
 
+float get_channel_avg (int channel) const
 
+float get_channel_std_dev (int channel) const
 
+const vector< float > & get_average () const
 
+const vector< float > & get_std_dev () const
 
+void normalize ()
 
+LargeImage * copy_image (int i) const
 
+void set_alpha (int i, const vector< vector< float > > &_alpha)
 
+void set_alpha (int i, const vector< vector< uint8_t > > &_alpha)
 
+ + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+vector< string > filenames
 
+int number_classes
 
+int number_images
 
+vector< int > class_sizes
 
+int padding
 
+int channels
 
+int subimage_width
 
+int subimage_height
 
+vector< LargeImage > images
 
+vector< float > channel_avg
 
+vector< float > channel_std_dev
 
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_mosaic_images.png b/docs/html/class_mosaic_images.png new file mode 100644 index 0000000000000000000000000000000000000000..146d361d88496348ff0aae2c91b4298b17e2834c GIT binary patch literal 782 zcmeAS@N?(olHy`uVBq!ia0vp^4M5z%!3-oNHVaPyQ2{<7uK)l42Qpv0`C8h4XabN0 z#s>}@VC}pk59D%`1o;Is02P72)l(rx3=B+jJzX3_Dj46+&Fp)lz|+$0bn0*apGg~L zmTuZ3x7}^p3V{O$#P#piKivC$R+GA?tz-jZ52K9dzcoqM@(RTLe~27-`dXYp&*t3v z&byX6i&M&MJ}aMTxtgMBJ!y>zyG4JWz(VVMO=ZjQuqB~;Yu`+7W#7cj#@_lX&z0J5}YDwr#adv)2xA}25ubj)WRkU%KH1{0?PjP}&)Bowr?eCMq z`c^I2uvAOns`9e@P)1L_lEr&GKTXnO_Nn!lq+fEE*;szk6YiV)7c$l`H+;XR&BYLx z$;{xJ$(%FWsL|{|-G04BtA@}~to zTpq4^ImGWV=flQ8$uh>Wl>1?p2D}HS%B(P2{zUGj%jv0xE1M2YTjdbaKjYo%29@_O zADw6R)cYdfpl0rAcRiS2gj_#ApL1D6hT#R!&k2kZ#MIbQe_d5y!*Iy!4MW1B`wSMZ zO#NFOXWlE7Qx@YtohQ#w64jnM@ALcQYJ)of@-H^z=nLh80`= z4wY0FR{r`pkLk(L2dxrI+7(|guqkFRq|N`Q!_xdY&C6Gz6r8hk{mZ;2#>CU+N&g05;$`r3^>bP0 Hl+XkK3hqRj literal 0 HcmV?d00001 diff --git a/docs/html/class_multi_images_interface-members.html b/docs/html/class_multi_images_interface-members.html new file mode 100644 index 00000000..e043d5aa --- /dev/null +++ b/docs/html/class_multi_images_interface-members.html @@ -0,0 +1,100 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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MultiImagesInterface Member List
+
+
+ +

This is the complete list of members for MultiImagesInterface, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + +
get_average() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_channel_avg(int channel) const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_channel_std_dev(int channel) const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_class_size(int i) const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_classification(int image) const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_filename() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_image_channels() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_image_classification(int image) const =0 (defined in MultiImagesInterface)MultiImagesInterfacepure virtual
get_image_height() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_image_width() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_large_image_channels(int image) const =0 (defined in MultiImagesInterface)MultiImagesInterfacepure virtual
get_large_image_height(int image) const =0 (defined in MultiImagesInterface)MultiImagesInterfacepure virtual
get_large_image_width(int image) const =0 (defined in MultiImagesInterface)MultiImagesInterfacepure virtual
get_number_classes() const =0 (defined in MultiImagesInterface)MultiImagesInterfacepure virtual
get_number_images() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_number_large_images() const =0 (defined in MultiImagesInterface)MultiImagesInterfacepure virtual
get_number_subimages(int i) const =0 (defined in MultiImagesInterface)MultiImagesInterfacepure virtual
get_padding() const =0 (defined in MultiImagesInterface)MultiImagesInterfacepure virtual
get_pixel(int image, int z, int y, int x) const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
get_raw_pixel(int subimage, int z, int y, int x) const =0 (defined in MultiImagesInterface)MultiImagesInterfacepure virtual
get_std_dev() const =0 (defined in ImagesInterface)ImagesInterfacepure virtual
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_multi_images_interface.html b/docs/html/class_multi_images_interface.html new file mode 100644 index 00000000..f806b9fb --- /dev/null +++ b/docs/html/class_multi_images_interface.html @@ -0,0 +1,161 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: MultiImagesInterface Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +List of all members
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MultiImagesInterface Class Referenceabstract
+
+
+
+Inheritance diagram for MultiImagesInterface:
+
+
+ + +ImagesInterface +LargeImages +MosaicImages + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+virtual int get_number_large_images () const =0
 
+virtual int get_number_subimages (int i) const =0
 
+virtual int get_padding () const =0
 
+virtual int get_large_image_channels (int image) const =0
 
+virtual int get_large_image_width (int image) const =0
 
+virtual int get_large_image_height (int image) const =0
 
+virtual int get_number_classes () const =0
 
+virtual int get_image_classification (int image) const =0
 
+virtual float get_raw_pixel (int subimage, int z, int y, int x) const =0
 
- Public Member Functions inherited from ImagesInterface
+virtual string get_filename () const =0
 
+virtual int get_class_size (int i) const =0
 
+virtual int get_number_images () const =0
 
+virtual int get_image_channels () const =0
 
+virtual int get_image_width () const =0
 
+virtual int get_image_height () const =0
 
+virtual int get_classification (int image) const =0
 
+virtual float get_pixel (int image, int z, int y, int x) const =0
 
+virtual float get_channel_avg (int channel) const =0
 
+virtual float get_channel_std_dev (int channel) const =0
 
+virtual const vector< float > & get_average () const =0
 
+virtual const vector< float > & get_std_dev () const =0
 
+
The documentation for this class was generated from the following file: +
+ +
+Generated by   +doxygen + 1.8.17 +
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EXONA: Evolutionary eXploration of Neural Architectures +
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NeatSpeciationStrategy Member List
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This is the complete list of members for NeatSpeciationStrategy, including all inherited members.

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check_population() (defined in NeatSpeciationStrategy)NeatSpeciationStrategy
check_species() (defined in NeatSpeciationStrategy)NeatSpeciationStrategy
fitness_threshold (defined in NeatSpeciationStrategy)NeatSpeciationStrategyprivate
generate_for_species(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover) (defined in NeatSpeciationStrategy)NeatSpeciationStrategy
generate_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)NeatSpeciationStrategyvirtual
generated_genomesNeatSpeciationStrategyprivate
generation_speciesNeatSpeciationStrategyprivate
generator (defined in NeatSpeciationStrategy)NeatSpeciationStrategyprivate
get_best_fitness()NeatSpeciationStrategyvirtual
get_best_genome()NeatSpeciationStrategyvirtual
get_distance(RNN_Genome *g1, RNN_Genome *g2) (defined in NeatSpeciationStrategy)NeatSpeciationStrategy
get_exceed_number(vector< int32_t > v1, vector< int32_t > v2) (defined in NeatSpeciationStrategy)NeatSpeciationStrategy
get_generated_genomes() constNeatSpeciationStrategyvirtual
get_global_best_genome() (defined in NeatSpeciationStrategy)NeatSpeciationStrategyvirtual
get_inserted_genomes() constNeatSpeciationStrategyvirtual
get_random_species_list() (defined in NeatSpeciationStrategy)NeatSpeciationStrategy
get_strategy_information_headers() constNeatSpeciationStrategyvirtual
get_strategy_information_values() constNeatSpeciationStrategyvirtual
get_worst_fitness()NeatSpeciationStrategyvirtual
get_worst_genome()NeatSpeciationStrategyvirtual
global_best_genome (defined in NeatSpeciationStrategy)NeatSpeciationStrategyprivate
insert_genome(RNN_Genome *genome)NeatSpeciationStrategyvirtual
inserted_genomesNeatSpeciationStrategyprivate
inter_island_crossover_rateNeatSpeciationStrategyprivate
intra_island_crossover_rateNeatSpeciationStrategyprivate
max_genomes (defined in NeatSpeciationStrategy)NeatSpeciationStrategyprivate
minimal_genomeNeatSpeciationStrategyprivate
mutation_rateNeatSpeciationStrategyprivate
neat_c1 (defined in NeatSpeciationStrategy)NeatSpeciationStrategyprivate
neat_c2 (defined in NeatSpeciationStrategy)NeatSpeciationStrategyprivate
neat_c3 (defined in NeatSpeciationStrategy)NeatSpeciationStrategyprivate
Neat_Species (defined in NeatSpeciationStrategy)NeatSpeciationStrategyprivate
NeatSpeciationStrategy(double _mutation_rate, double _intra_island_crossover_rate, double _inter_island_crossover_rate, RNN_Genome *_seed_genome, int32_t _max_genomes, double _species_threshold, double _fitness_threshold, double _neat_c1, double _neat_c2, double _neat_c3, minstd_rand0 &_generator) (defined in NeatSpeciationStrategy)NeatSpeciationStrategy
population_not_improving_countNeatSpeciationStrategyprivate
print(string indent="") constNeatSpeciationStrategyvirtual
rank_species() (defined in NeatSpeciationStrategy)NeatSpeciationStrategy
species_count (defined in NeatSpeciationStrategy)NeatSpeciationStrategyprivate
species_threshold (defined in NeatSpeciationStrategy)NeatSpeciationStrategyprivate
+ +
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+ + diff --git a/docs/html/class_neat_speciation_strategy.html b/docs/html/class_neat_speciation_strategy.html new file mode 100644 index 00000000..41ecb4d5 --- /dev/null +++ b/docs/html/class_neat_speciation_strategy.html @@ -0,0 +1,608 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: NeatSpeciationStrategy Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +List of all members
+
+
NeatSpeciationStrategy Class Reference
+
+
+
+Inheritance diagram for NeatSpeciationStrategy:
+
+
+ + +SpeciationStrategy + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ NeatSpeciationStrategy (double _mutation_rate, double _intra_island_crossover_rate, double _inter_island_crossover_rate, RNN_Genome *_seed_genome, int32_t _max_genomes, double _species_threshold, double _fitness_threshold, double _neat_c1, double _neat_c2, double _neat_c3, minstd_rand0 &_generator)
 
int32_t get_generated_genomes () const
 
int32_t get_inserted_genomes () const
 
double get_best_fitness ()
 Gets the fitness of the best genome of all the islands. More...
 
double get_worst_fitness ()
 Gets the fitness of the worst genome of all the islands. More...
 
RNN_Genome * get_best_genome ()
 Gets the best genome of all the islands. More...
 
RNN_Genome * get_worst_genome ()
 Gets the the worst genome of all the islands. More...
 
int32_t insert_genome (RNN_Genome *genome)
 Inserts a copy of the genome into this speciation strategy. More...
 
RNN_Genome * generate_genome (uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)
 Generates a new genome. More...
 
+RNN_Genome * generate_for_species (uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)
 
void print (string indent="") const
 Prints out all the island's populations. More...
 
+string get_strategy_information_headers () const
 Gets speciation strategy information headers for logs.
 
+string get_strategy_information_values () const
 Gets speciation strategy information values for logs.
 
+RNN_Genome * get_global_best_genome ()
 
+vector< int32_t > get_random_species_list ()
 
+double get_distance (RNN_Genome *g1, RNN_Genome *g2)
 
+int get_exceed_number (vector< int32_t > v1, vector< int32_t > v2)
 
+void rank_species ()
 
+bool check_population ()
 
+void check_species ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int32_t generation_species
 Used to track which island to generate the next genome from.
 
+int32_t species_count
 
+int32_t population_not_improving_count
 < Used to keep track of the number of species generated .
 
+double species_threshold
 
+double fitness_threshold
 
+double neat_c1
 
+double neat_c2
 
+double neat_c3
 
double mutation_rate
 How frequently to do mutations. More...
 
double intra_island_crossover_rate
 How frequently to do intra-island crossovers. More...
 
double inter_island_crossover_rate
 How frequently to do inter-island crossovers. More...
 
+int32_t generated_genomes
 How many genomes have been generated by this speciation strategy.
 
+int32_t inserted_genomes
 How many genomes have been inserted into this speciatoin strategy.
 
+RNN_Genome * minimal_genome
 keep a reference to a minimal genome so we can re-use it across islands and not duplicate innovation numbers.
 
+int32_t max_genomes
 
+minstd_rand0 generator
 
+vector< Species * > Neat_Species
 
+RNN_Genome * global_best_genome
 
+

Member Function Documentation

+ +

◆ generate_genome()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
RNN_Genome * NeatSpeciationStrategy::generate_genome (uniform_real_distribution< double > & rng_0_1,
minstd_rand0 & generator,
function< void(int32_t, RNN_Genome *)> & mutate,
function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> & crossover 
)
+ 		+virtual
+
+ +

Generates a new genome.

+
Parameters
+ + + + + +
rng_0_1is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive).
generatoris the random number generator
mutateis the a function which performs a mutation on a genome
crossoveris the function which performs crossover between two genomes
+
+
+
Returns
the newly generated genome.
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_best_fitness()

+ +
+
+ + + + + +
+ + + + + + + +
double NeatSpeciationStrategy::get_best_fitness ()
+ 		+virtual
+
+ +

Gets the fitness of the best genome of all the islands.

+
Returns
the best fitness over all islands
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_best_genome()

+ +
+
+ + + + + +
+ + + + + + + +
RNN_Genome * NeatSpeciationStrategy::get_best_genome ()
+ 		+virtual
+
+ +

Gets the best genome of all the islands.

+
Returns
the best genome of all islands
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_generated_genomes()

+ +
+
+ + + + + +
+ + + + + + + +
int32_t NeatSpeciationStrategy::get_generated_genomes () const
+ 		+virtual
+
+
Returns
the number of generated genomes.
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_inserted_genomes()

+ +
+
+ + + + + +
+ + + + + + + +
int32_t NeatSpeciationStrategy::get_inserted_genomes () const
+ 		+virtual
+
+
Returns
the number of inserted genomes.
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_worst_fitness()

+ +
+
+ + + + + +
+ + + + + + + +
double NeatSpeciationStrategy::get_worst_fitness ()
+ 		+virtual
+
+ +

Gets the fitness of the worst genome of all the islands.

+
Returns
the worst fitness over all islands
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ get_worst_genome()

+ +
+
+ + + + + +
+ + + + + + + +
RNN_Genome * NeatSpeciationStrategy::get_worst_genome ()
+ 		+virtual
+
+ +

Gets the the worst genome of all the islands.

+
Returns
the worst genome of all islands
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ insert_genome()

+ +
+
+ + + + + +
+ + + + + + + + +
int32_t NeatSpeciationStrategy::insert_genome (RNN_Genome * genome)
+ 		+virtual
+
+ +

Inserts a copy of the genome into this speciation strategy.

+

The caller of this method will need to free the memory of the genome passed into this method.

+
Parameters
+ + +
genomeis the genome to insert.
+
+
+
Returns
a value < 0 if the genome was not inserted, 0 if it was a new best genome or > 0 otherwise.
+ +

Implements SpeciationStrategy.

+ +
+
+ +

◆ print()

+ +
+
+ + + + + +
+ + + + + + + + +
void NeatSpeciationStrategy::print (string indent = "") const
+ 		+virtual
+
+ +

Prints out all the island's populations.

+
Parameters
+ + +
indentis how much to indent what is printed out
+
+
+ +

Implements SpeciationStrategy.

+ +
+
+

Member Data Documentation

+ +

◆ inter_island_crossover_rate

+ +
+
+ + + + + +
+ + + + +
double NeatSpeciationStrategy::inter_island_crossover_rate
+ 		+private
+
+ +

How frequently to do inter-island crossovers.

+

Note that mutation_rate + intra_island_crossover_rate + inter_island_crossover_rate should equal 1, if not they will be scaled down such that they do.

+ +
+
+ +

◆ intra_island_crossover_rate

+ +
+
+ + + + + +
+ + + + +
double NeatSpeciationStrategy::intra_island_crossover_rate
+ 		+private
+
+ +

How frequently to do intra-island crossovers.

+

Note that mutation_rate + intra_island_crossover_rate + inter_island_crossover_rate should equal 1, if not they will be scaled down such that they do.

+ +
+
+ +

◆ mutation_rate

+ +
+
+ + + + + +
+ + + + +
double NeatSpeciationStrategy::mutation_rate
+ 		+private
+
+ +

How frequently to do mutations.

+

Note that mutation_rate + intra_island_crossover_rate + inter_island_crossover_rate should equal 1, if not they will be scaled down such that they do.

+ +
+
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_neat_speciation_strategy.png b/docs/html/class_neat_speciation_strategy.png new file mode 100644 index 0000000000000000000000000000000000000000..0116cc26cc4c449f636bb9f791a9ad5d58bab1e6 GIT binary patch literal 668 zcmeAS@N?(olHy`uVBq!ia0vp^6M#5?gBeI3Ea0&NQW60^A+G=b{|7Q(y!l$%e+Z-k zj1L?*z}k679?0b=3GxeO04f53tEWPY7#NteJzX3_Dj46+ecSg~fyb5KJm~)aiQ9!0 zo@w7p+p^eEJA5Z=((E}BKL3hjj&Gc7p^_=jIzgq9OTqI~3G=PZU1Zgrn>X~Wn8fNY^T=GP3890@%7gi{F-**O~Itl@Yze5)@$TXf0Lzi zW7fKG_N~!Qx;1=Nd?C-~dd&Lb>o9#K@7vhv?ipHr>D70-shc+wpl1UYxUWr z#z|LB-oN6PH6`Wxi`xvo=eN(k&sepo-HvgS?H8UCe8S65`6WlEzRil0z8>>iYqvG$ zpUJ--Uv{1(m!Z92Ba7-s4fKQlYm?^UT?!Z1n2--tpQcatzj;F_j+&fduE-Kw(|UMS__*Go^E8E`<5#sm*K^hPb(JQXF7HAM1E+T z-v6r~%Fpv`-O02neec~gHS0MkSFfh6JRs}Ny(QyR(X)ps@2g4;TB3f)lyG0!U3PL? zO3B$5mJj(B?tOh}@BJ&Lt1Bl>deL|GO>V^AjWtf@CgHP>GUYeiD%`zt?v)7}vcKdu zEPH%DwBiH4?e!V5Av2=>rCHXt| + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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NormalDistribution Member List
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generate (defined in NormalDistribution)NormalDistributionprivate
NormalDistribution() (defined in NormalDistribution)NormalDistribution
operator!=(const NormalDistribution &other) const (defined in NormalDistribution)NormalDistribution
operator<< (defined in NormalDistribution)NormalDistributionfriend
operator==(const NormalDistribution &other) const (defined in NormalDistribution)NormalDistribution
operator>> (defined in NormalDistribution)NormalDistributionfriend
random(minstd_rand0 &generator, float mu, float sigma) (defined in NormalDistribution)NormalDistribution
z0 (defined in NormalDistribution)NormalDistributionprivate
z1 (defined in NormalDistribution)NormalDistributionprivate
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NormalDistribution Class Reference
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+float random (minstd_rand0 &generator, float mu, float sigma)
 
+bool operator== (const NormalDistribution &other) const
 
+bool operator!= (const NormalDistribution &other) const
 
+ + + + + + + +

+Private Attributes

+bool generate
 
+float z0
 
+float z1
 
+ + + + + +

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+ostream & operator<< (ostream &os, const NormalDistribution &normal_distribution)
 
+istream & operator>> (istream &is, NormalDistribution &normal_distribution)
 
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main(String[] arguments) (defined in ParseTurbines)ParseTurbinesinlinestatic
num_column (defined in ParseTurbines)ParseTurbinesstatic
sdf (defined in ParseTurbines)ParseTurbinesstatic
writeHeaders(BufferedWriter bw, String[] columnNames) (defined in ParseTurbines)ParseTurbinesinlinestatic
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class  TurbineLine
 
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+static void writeHeaders (BufferedWriter bw, String[] columnNames) throws IOException
 
+static void main (String[] arguments)
 
+ + + + + +

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+static SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ssXXX")
 
+static int num_column = 122
 
+
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+ Point (int _y, int _x)
 
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+int y
 
+int x
 
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activation(double value, int act_operator) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
activation_derivative(double value, double input, int act_operator) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
backward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
copy() const (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
d_h_prev (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodeprivate
d_input (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
d_rw (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodeprivate
d_weights (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodeprivate
d_zw (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodeprivate
depth (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
enabled (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
equals(RNN_Node_Interface *other) const (defined in RNN_Node_Interface)RNN_Node_Interface
error_fired(int time, double error) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
error_fired(int32_t time, double error)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
error_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
forward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
get_depth() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_gradient(string gradient_name) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
get_gradients(vector< double > &gradients) (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
get_innovation_number() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_layer_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_node_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_number_weights() const (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
get_total_inputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_total_outputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_weights(vector< double > &parameters) const (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
get_weights(uint32_t &offset, vector< double > &parameters) const (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range) (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma) (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution< double > &rng) (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
initialize_xavier(minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range) (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
innovation_number (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
input_fired(int time, double incoming_output) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
input_fired(int32_t time, double incoming_output)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
input_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
inputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
is_enabled() const (defined in RNN_Node_Interface)RNN_Node_Interface
is_reachable() const (defined in RNN_Node_Interface)RNN_Node_Interface
l_Nodes (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodeprivate
layer_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
node_output (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodeprivate
node_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
Nodes (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodeprivate
output_fired(int time, double delta) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
output_fired(int32_t time, double delta)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
output_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
outputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
parameter_name (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
print_gradient(string gradient_name) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
RANDOM_DAG_Node(int _innovation_number, int _type, double _depth) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
reset(int _series_length) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
reset(int32_t _series_length)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
RNN_Edge (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodefriend
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name) (defined in RNN_Node_Interface)RNN_Node_Interface
rw (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodeprivate
series_length (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
set_weights(const vector< double > &parameters) (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
set_weights(uint32_t &offset, const vector< double > &parameters) (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodevirtual
total_inputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
total_outputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
try_update_deltas(int time) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
weights (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodeprivate
write_to_stream(ostream &out) (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
zw (defined in RANDOM_DAG_Node)RANDOM_DAG_Nodeprivate
~RANDOM_DAG_Node() (defined in RANDOM_DAG_Node)RANDOM_DAG_Node
~RNN_Node_Interface() (defined in RNN_Node_Interface)RNN_Node_Interfacevirtual
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EXONA: Evolutionary eXploration of Neural Architectures +
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RANDOM_DAG_Node Class Reference
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+Public Member Functions

+ RANDOM_DAG_Node (int _innovation_number, int _type, double _depth)
 
+void initialize_lamarckian (minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)
 
+void initialize_xavier (minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range)
 
+void initialize_kaiming (minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)
 
+void initialize_uniform_random (minstd_rand0 &generator, uniform_real_distribution< double > &rng)
 
+double get_gradient (string gradient_name)
 
+void print_gradient (string gradient_name)
 
+double activation (double value, int act_operator)
 
+double activation_derivative (double value, double input, int act_operator)
 
+void input_fired (int time, double incoming_output)
 
+void try_update_deltas (int time)
 
+void error_fired (int time, double error)
 
+void output_fired (int time, double delta)
 
+uint32_t get_number_weights () const
 
+void get_weights (vector< double > &parameters) const
 
+void set_weights (const vector< double > &parameters)
 
+void get_weights (uint32_t &offset, vector< double > &parameters) const
 
+void set_weights (uint32_t &offset, const vector< double > &parameters)
 
+void get_gradients (vector< double > &gradients)
 
+void reset (int _series_length)
 
+void write_to_stream (ostream &out)
 
+RNN_Node_Interface * copy () const
 
- Public Member Functions inherited from RNN_Node_Interface
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth)
 
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name)
 
+virtual void input_fired (int32_t time, double incoming_output)=0
 
+virtual void output_fired (int32_t time, double delta)=0
 
+virtual void error_fired (int32_t time, double error)=0
 
+virtual void reset (int32_t _series_length)=0
 
+void write_to_stream (ostream &out)
 
+int32_t get_node_type () const
 
+int32_t get_layer_type () const
 
+int32_t get_innovation_number () const
 
+int32_t get_total_inputs () const
 
+int32_t get_total_outputs () const
 
+double get_depth () const
 
+bool equals (RNN_Node_Interface *other) const
 
+bool is_reachable () const
 
+bool is_enabled () const
 
+ + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+vector< int > node_output
 
+double rw
 
+double zw
 
+vector< double > weights
 
+vector< double > d_zw
 
+vector< double > d_rw
 
+vector< vector< double > > d_weights
 
+vector< double > d_h_prev
 
+vector< vector< double > > Nodes
 
+vector< vector< double > > l_Nodes
 
+ + + +

+Friends

+class RNN_Edge
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from RNN_Node_Interface
+int32_t innovation_number
 
+int32_t layer_type
 
+int32_t node_type
 
+double depth
 
+string parameter_name
 
+bool enabled
 
+bool backward_reachable
 
+bool forward_reachable
 
+int32_t series_length
 
+vector< double > input_values
 
+vector< double > output_values
 
+vector< double > error_values
 
+vector< double > d_input
 
+vector< int32_t > inputs_fired
 
+vector< int32_t > outputs_fired
 
+int32_t total_inputs
 
+int32_t total_outputs
 
+
The documentation for this class was generated from the following files: +
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+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_r_a_n_d_o_m___d_a_g___node.png b/docs/html/class_r_a_n_d_o_m___d_a_g___node.png new file mode 100644 index 0000000000000000000000000000000000000000..8baa48c7b0d582c414fe4e558b23ffcca3764712 GIT binary patch literal 641 zcmeAS@N?(olHy`uVBq!ia0vp^-9Q|`!3-qj?8+2@lth3}i0l9V|AEXGZ@!lHADRGU zf$@O@2Ut7r$OE|?B|(0{3_wL7aP?G(5d#C0fTxRNNCo5DxwqFnR^V|Jx4!iAzyG{# zoGH!DmtR&&m=qo5Gfx&vc%glJTW)uU@1$t~n>>O-fOyL0DbL@}d~d29(!5YETm0EI z;T+Y-R}orH8YSoUrS4RmxnYl2Tn5|LefbZB&t;uGz4+_aNxyDJf3scBZ2MdE^vu1_ zq{|;?Y|%4+ee8Fh@1vz2^J-3B3{NS%?H8-CFZJaWo3D%svbxsS3XUdSUpTL8V+pf* z`j_cClg^2JQ`Nit{i#{po0h+7WwP%drB=DkKYsehnltyO@2)N>n)lvex8*gPz3!8~ z=zkSFt$plW+}ZvfmCRLt@~13Wkunj2*-vlFy)DJ`V{aXU*hMEDd;8Z1bdG#-h-6Ds zowmoFA>2!>VMPjK2O;YJgr!r0!~|IrI90hFLN+nDm`<~u(7)~7Yg_k~pXR?dSiJdr zj?|uSxf74tRfk3IdHzGD_(n)uL~PlWXYZw_)rQ?)kY<@Ed5n45to_gHWEZb3J6ZIK zw>z>&XPx&n21cZ z(*8B6isePzJCgT)^}H7q{Z2k5v2`BJN7FwV#a@3>D;e=?`SN++-iX}4uiQ|-=KiKV y-ww|YZ}9sr_VYf6K{(LWK*wKc2KwaM1^Jn`WY?StzMu+BFAScpelF{r5}E*lH!olS literal 0 HcmV?d00001 diff --git a/docs/html/class_r_n_n-members.html b/docs/html/class_r_n_n-members.html new file mode 100644 index 00000000..e2ac5fd4 --- /dev/null +++ b/docs/html/class_r_n_n-members.html @@ -0,0 +1,115 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+
RNN Member List
+
+
+ +

This is the complete list of members for RNN, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
backward_pass(double error, bool using_dropout, bool training, double dropout_probability) (defined in RNN)RNN
calculate_error_mae(const vector< vector< double > > &expected_outputs) (defined in RNN)RNN
calculate_error_mse(const vector< vector< double > > &expected_outputs) (defined in RNN)RNN
calculate_error_softmax(const vector< vector< double > > &expected_outputs)RNN
edges (defined in RNN)RNNprivate
enable_use_regression(bool _use_regression) (defined in RNN)RNN
fix_parameter_orders(const vector< string > &input_parameter_names, const vector< string > &output_parameter_names) (defined in RNN)RNN
forward_pass(const vector< vector< double > > &series_data, bool using_dropout, bool training, double dropout_probability) (defined in RNN)RNN
get_analytic_gradient(const vector< double > &test_parameters, const vector< vector< double > > &inputs, const vector< vector< double > > &outputs, double &mse, vector< double > &analytic_gradient, bool using_dropout, bool training, double dropout_probability)RNN
get_edge(int i) (defined in RNN)RNN
get_empirical_gradient(const vector< double > &test_parameters, const vector< vector< double > > &inputs, const vector< vector< double > > &outputs, double &mae, vector< double > &empirical_gradient, bool using_dropout, bool training, double dropout_probability) (defined in RNN)RNN
get_mae (defined in RNN)RNNfriend
get_mse (defined in RNN)RNNfriend
get_node(int i) (defined in RNN)RNN
get_number_edges() (defined in RNN)RNN
get_number_nodes() (defined in RNN)RNN
get_number_weights() (defined in RNN)RNN
get_predictions(const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, bool usng_dropout, double dropout_probability) (defined in RNN)RNN
get_weights(vector< double > &parameters) (defined in RNN)RNN
initialize_randomly() (defined in RNN)RNN
input_nodes (defined in RNN)RNNprivate
nodes (defined in RNN)RNNprivate
output_nodes (defined in RNN)RNNprivate
prediction_mae(const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, bool using_dropout, bool training, double dropout_probability) (defined in RNN)RNN
prediction_mse(const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, bool using_dropout, bool training, double dropout_probability) (defined in RNN)RNN
prediction_softmax(const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, bool using_dropout, bool training, double dropout_probability)RNN
recurrent_edges (defined in RNN)RNNprivate
RNN(vector< RNN_Node_Interface * > &_nodes, vector< RNN_Edge * > &_edges, const vector< string > &input_parameter_names, const vector< string > &output_parameter_names) (defined in RNN)RNN
RNN(vector< RNN_Node_Interface * > &_nodes, vector< RNN_Edge * > &_edges, vector< RNN_Recurrent_Edge * > &_recurrent_edges, const vector< string > &input_parameter_names, const vector< string > &output_parameter_names) (defined in RNN)RNN
series_lengthRNNprivate
set_weights(const vector< double > &parameters) (defined in RNN)RNN
use_regressionRNNprivate
validate_parameters(const vector< string > &input_parameter_names, const vector< string > &output_parameter_names) (defined in RNN)RNN
write_predictions(string output_filename, const vector< string > &input_parameter_names, const vector< string > &output_parameter_names, const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, TimeSeriesSets *time_series_sets, bool using_dropout, double dropout_probability) (defined in RNN)RNN
write_predictions(string output_filename, const vector< string > &input_parameter_names, const vector< string > &output_parameter_names, const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, Corpus *word_series_sets, bool using_dropout, double dropout_probability) (defined in RNN)RNN
~RNN() (defined in RNN)RNN
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_r_n_n.html b/docs/html/class_r_n_n.html new file mode 100644 index 00000000..9d5fbb76 --- /dev/null +++ b/docs/html/class_r_n_n.html @@ -0,0 +1,427 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: RNN Class Reference + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
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+ + + + + + + + +
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+ + +
+ +
+ +
+
+
+Public Member Functions | +Private Attributes | +Friends | +List of all members
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+
RNN Class Reference
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ RNN (vector< RNN_Node_Interface * > &_nodes, vector< RNN_Edge * > &_edges, const vector< string > &input_parameter_names, const vector< string > &output_parameter_names)
 
+ RNN (vector< RNN_Node_Interface * > &_nodes, vector< RNN_Edge * > &_edges, vector< RNN_Recurrent_Edge * > &_recurrent_edges, const vector< string > &input_parameter_names, const vector< string > &output_parameter_names)
 
+void fix_parameter_orders (const vector< string > &input_parameter_names, const vector< string > &output_parameter_names)
 
+void validate_parameters (const vector< string > &input_parameter_names, const vector< string > &output_parameter_names)
 
+int get_number_nodes ()
 
+int get_number_edges ()
 
+RNN_Node_Interface * get_node (int i)
 
+RNN_Edge * get_edge (int i)
 
+void forward_pass (const vector< vector< double > > &series_data, bool using_dropout, bool training, double dropout_probability)
 
+void backward_pass (double error, bool using_dropout, bool training, double dropout_probability)
 
double calculate_error_softmax (const vector< vector< double > > &expected_outputs)
 Calculates the sum of cross entropy error by comparing the expected outputs and the predicted outputs. More...
 
+double calculate_error_mse (const vector< vector< double > > &expected_outputs)
 
+double calculate_error_mae (const vector< vector< double > > &expected_outputs)
 
double prediction_softmax (const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, bool using_dropout, bool training, double dropout_probability)
 Calls the calculate_error_softmax after the forward pass across the rnn genome. More...
 
+double prediction_mse (const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, bool using_dropout, bool training, double dropout_probability)
 
+double prediction_mae (const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, bool using_dropout, bool training, double dropout_probability)
 
+vector< double > get_predictions (const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, bool usng_dropout, double dropout_probability)
 
+void write_predictions (string output_filename, const vector< string > &input_parameter_names, const vector< string > &output_parameter_names, const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, TimeSeriesSets *time_series_sets, bool using_dropout, double dropout_probability)
 
+void write_predictions (string output_filename, const vector< string > &input_parameter_names, const vector< string > &output_parameter_names, const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, Corpus *word_series_sets, bool using_dropout, double dropout_probability)
 
+void initialize_randomly ()
 
+void get_weights (vector< double > &parameters)
 
+void set_weights (const vector< double > &parameters)
 
+void enable_use_regression (bool _use_regression)
 
+uint32_t get_number_weights ()
 
void get_analytic_gradient (const vector< double > &test_parameters, const vector< vector< double > > &inputs, const vector< vector< double > > &outputs, double &mse, vector< double > &analytic_gradient, bool using_dropout, bool training, double dropout_probability)
 Updates the analytic gradients after the forward pass across the rnn genome and the backward pass sequentially. More...
 
+void get_empirical_gradient (const vector< double > &test_parameters, const vector< vector< double > > &inputs, const vector< vector< double > > &outputs, double &mae, vector< double > &empirical_gradient, bool using_dropout, bool training, double dropout_probability)
 
+ + + + + + + + + + + + + + + + + +

+Private Attributes

int series_length
 Specifies the length of the dataset. More...
 
bool use_regression
 Specifies whether to use the regression or not. More...
 
+vector< RNN_Node_Interface * > input_nodes
 
+vector< RNN_Node_Interface * > output_nodes
 
+vector< RNN_Node_Interface * > nodes
 
+vector< RNN_Edge * > edges
 
+vector< RNN_Recurrent_Edge * > recurrent_edges
 
+ + + + + +

+Friends

+void get_mse (RNN *genome, const vector< vector< double > > &expected, double &mse, vector< vector< double > > &deltas)
 
+void get_mae (RNN *genome, const vector< vector< double > > &expected, double &mae, vector< vector< double > > &deltas)
 
+

Member Function Documentation

+ +

◆ calculate_error_softmax()

+ +
+
+ + + + + + + + +
double RNN::calculate_error_softmax (const vector< vector< double > > & expected_outputs)
+
+ +

Calculates the sum of cross entropy error by comparing the expected outputs and the predicted outputs.

+


+

+
Parameters
+ + +
expected_outputsis the expected outputs according the dataset.
+
+
+
Returns
sum of the cross entropy error.
+ +
+
+ +

◆ get_analytic_gradient()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void RNN::get_analytic_gradient (const vector< double > & test_parameters,
const vector< vector< double > > & inputs,
const vector< vector< double > > & outputs,
double & mse,
vector< double > & analytic_gradient,
bool using_dropout,
bool training,
double dropout_probability 
)
+
+ +

Updates the analytic gradients after the forward pass across the rnn genome and the backward pass sequentially.

+


+

+
Parameters
+ + + + + + + + +
test_parametersis the dataset parameters used for the forward pass.
inputsare the inputs according to the dataset that is pass to the for forward pass.
outputsare the expected outputs according to the dataset that is pass to the calculate error for backward pass.
analytic_gradientsare the current gradients in the rnn genome that are going to be update after a single pass across the rnn genome.
using_dropoutis the probability for the node being disabled while forward pass training and not during testing.
+
trainingis to specify whether it is training stage or not to use dropout.
dropout_probabilityis the probability by which the the nodes will be disabled. will not work during testing stage and without use_droput = true.
+
+
+
Returns
sum of the cross entropy error over the dataset.
+ +
+
+ +

◆ prediction_softmax()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
double RNN::prediction_softmax (const vector< vector< double > > & series_data,
const vector< vector< double > > & expected_outputs,
bool using_dropout,
bool training,
double dropout_probability 
)
+
+ +

Calls the calculate_error_softmax after the forward pass across the rnn genome.

+


+

+
Parameters
+ + + + + + +
series_datais the dataset used for the forward pass.
expected_outputsis the expected outputs according to the dataset.
using_dropoutis the probability for the node being disabled while forward pass training and not during testing.
+
trainingis to specify whether it is training stage or not to use dropout.
dropout_probabilityis the probability by which the the nodes will be disabled. will not work during testing stage and without use_droput = true.
+
+
+
Returns
sum of the cross entropy error over the dataset.
+ +
+
+

Member Data Documentation

+ +

◆ series_length

+ +
+
+ + + + + +
+ + + + +
int RNN::series_length
+ 		+private
+
+ +

Specifies the length of the dataset.

+


+ It can be the number of the files in case of time series or number of batches in case of word series

+ +
+
+ +

◆ use_regression

+ +
+
+ + + + + +
+ + + + +
bool RNN::use_regression
+ 		+private
+
+ +

Specifies whether to use the regression or not.

+


+ It will use regression in case of time series and will not use regression in case of word series

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_r_n_n___edge-members.html b/docs/html/class_r_n_n___edge-members.html new file mode 100644 index 00000000..8a047cee --- /dev/null +++ b/docs/html/class_r_n_n___edge-members.html @@ -0,0 +1,113 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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RNN_Edge Member List
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This is the complete list of members for RNN_Edge, including all inherited members.

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backward_reachable (defined in RNN_Edge)RNN_Edgeprivate
copy(const vector< RNN_Node_Interface * > new_nodes) (defined in RNN_Edge)RNN_Edge
d_weight (defined in RNN_Edge)RNN_Edgeprivate
deltas (defined in RNN_Edge)RNN_Edgeprivate
dropped_out (defined in RNN_Edge)RNN_Edgeprivate
enabled (defined in RNN_Edge)RNN_Edgeprivate
equals(RNN_Edge *other) const (defined in RNN_Edge)RNN_Edge
EXAMM (defined in RNN_Edge)RNN_Edgefriend
forward_reachable (defined in RNN_Edge)RNN_Edgeprivate
get_gradient() const (defined in RNN_Edge)RNN_Edge
get_innovation_number() const (defined in RNN_Edge)RNN_Edge
get_input_innovation_number() const (defined in RNN_Edge)RNN_Edge
get_input_node() const (defined in RNN_Edge)RNN_Edge
get_output_innovation_number() const (defined in RNN_Edge)RNN_Edge
get_output_node() const (defined in RNN_Edge)RNN_Edge
innovation_number (defined in RNN_Edge)RNN_Edgeprivate
input_innovation_number (defined in RNN_Edge)RNN_Edgeprivate
input_node (defined in RNN_Edge)RNN_Edgeprivate
is_enabled() const (defined in RNN_Edge)RNN_Edge
is_reachable() const (defined in RNN_Edge)RNN_Edge
output_innovation_number (defined in RNN_Edge)RNN_Edgeprivate
output_node (defined in RNN_Edge)RNN_Edgeprivate
outputs (defined in RNN_Edge)RNN_Edgeprivate
propagate_backward(int32_t time) (defined in RNN_Edge)RNN_Edge
propagate_backward(int32_t time, bool training, double dropout_probability) (defined in RNN_Edge)RNN_Edge
propagate_forward(int32_t time) (defined in RNN_Edge)RNN_Edge
propagate_forward(int32_t time, bool training, double dropout_probability) (defined in RNN_Edge)RNN_Edge
reset(int32_t series_length) (defined in RNN_Edge)RNN_Edge
RNN (defined in RNN_Edge)RNN_Edgefriend
RNN_Edge(int32_t _innovation_number, RNN_Node_Interface *_input_node, RNN_Node_Interface *_output_node) (defined in RNN_Edge)RNN_Edge
RNN_Edge(int32_t _innovation_number, int32_t _input_innovation_number, int32_t _output_innovation_number, const vector< RNN_Node_Interface * > &nodes) (defined in RNN_Edge)RNN_Edge
RNN_Genome (defined in RNN_Edge)RNN_Edgefriend
weight (defined in RNN_Edge)RNN_Edgeprivate
write_to_stream(ostream &out) (defined in RNN_Edge)RNN_Edge
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EXONA: Evolutionary eXploration of Neural Architectures +
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RNN_Edge Class Reference
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+Public Member Functions

+ RNN_Edge (int32_t _innovation_number, RNN_Node_Interface *_input_node, RNN_Node_Interface *_output_node)
 
+ RNN_Edge (int32_t _innovation_number, int32_t _input_innovation_number, int32_t _output_innovation_number, const vector< RNN_Node_Interface * > &nodes)
 
+RNN_Edge * copy (const vector< RNN_Node_Interface * > new_nodes)
 
+void reset (int32_t series_length)
 
+void propagate_forward (int32_t time)
 
+void propagate_backward (int32_t time)
 
+void propagate_forward (int32_t time, bool training, double dropout_probability)
 
+void propagate_backward (int32_t time, bool training, double dropout_probability)
 
+double get_gradient () const
 
+int32_t get_innovation_number () const
 
+int32_t get_input_innovation_number () const
 
+int32_t get_output_innovation_number () const
 
+const RNN_Node_Interface * get_input_node () const
 
+const RNN_Node_Interface * get_output_node () const
 
+bool is_enabled () const
 
+bool is_reachable () const
 
+bool equals (RNN_Edge *other) const
 
+void write_to_stream (ostream &out)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int32_t innovation_number
 
+vector< double > outputs
 
+vector< double > deltas
 
+vector< bool > dropped_out
 
+double weight
 
+double d_weight
 
+bool enabled
 
+bool forward_reachable
 
+bool backward_reachable
 
+int32_t input_innovation_number
 
+int32_t output_innovation_number
 
+RNN_Node_Interface * input_node
 
+RNN_Node_Interface * output_node
 
+ + + + + + + +

+Friends

+class RNN_Genome
 
+class RNN
 
+class EXAMM
 
+
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RNN_Genome Member List
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This is the complete list of members for RNN_Genome, including all inherited members.

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adapt_learning_rate (defined in RNN_Genome)RNN_Genomeprivate
add_edge(double mu, double sigma, int32_t &edge_innovation_count) (defined in RNN_Genome)RNN_Genome
add_node(double mu, double sigma, int node_type, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count, int32_t &node_innovation_count) (defined in RNN_Genome)RNN_Genome
add_recurrent_edge(double mu, double sigma, uniform_int_distribution< int32_t > rec_depth_dist, int32_t &edge_innovation_count) (defined in RNN_Genome)RNN_Genome
assign_reachability() (defined in RNN_Genome)RNN_Genome
attempt_edge_insert(RNN_Node_Interface *n1, RNN_Node_Interface *n2, double mu, double sigma, int32_t &edge_innovation_count) (defined in RNN_Genome)RNN_Genome
attempt_recurrent_edge_insert(RNN_Node_Interface *n1, RNN_Node_Interface *n2, double mu, double sigma, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count) (defined in RNN_Genome)RNN_Genome
backpropagate(const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs, const vector< vector< vector< double > > > &validation_inputs, const vector< vector< vector< double > > > &validation_outputs) (defined in RNN_Genome)RNN_Genome
backpropagate_stochastic(const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs, const vector< vector< vector< double > > > &validation_inputs, const vector< vector< vector< double > > > &validation_outputs) (defined in RNN_Genome)RNN_Genome
best_parameters (defined in RNN_Genome)RNN_Genomeprivate
best_validation_mae (defined in RNN_Genome)RNN_Genomeprivate
best_validation_mseRNN_Genomeprivate
bp_iterations (defined in RNN_Genome)RNN_Genomeprivate
clear_generated_by() (defined in RNN_Genome)RNN_Genome
connect_new_input_node(double mu, double sig, RNN_Node_Interface *new_node, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count) (defined in RNN_Genome)RNN_Genome
connect_new_output_node(double mu, double sig, RNN_Node_Interface *new_node, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count) (defined in RNN_Genome)RNN_Genome
connect_node_to_hid_nodes(double mu, double sig, RNN_Node_Interface *new_node, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count, bool from_input) (defined in RNN_Genome)RNN_Genome
copy() (defined in RNN_Genome)RNN_Genome
create_node(double mu, double sigma, int node_type, int32_t &node_innovation_count, double depth) (defined in RNN_Genome)RNN_Genome
disable_dropout() (defined in RNN_Genome)RNN_Genome
disable_edge() (defined in RNN_Genome)RNN_Genome
disable_high_threshold() (defined in RNN_Genome)RNN_Genome
disable_low_threshold() (defined in RNN_Genome)RNN_Genome
disable_node() (defined in RNN_Genome)RNN_Genome
dropout_probability (defined in RNN_Genome)RNN_Genomeprivate
edges (defined in RNN_Genome)RNN_Genomeprivate
enable_dropout(double _dropout_probability) (defined in RNN_Genome)RNN_Genome
enable_edge() (defined in RNN_Genome)RNN_Genome
enable_high_threshold(double _high_threshold) (defined in RNN_Genome)RNN_Genome
enable_low_threshold(double _low_threshold) (defined in RNN_Genome)RNN_Genome
enable_node() (defined in RNN_Genome)RNN_Genome
enable_use_regression(bool _use_regression) (defined in RNN_Genome)RNN_Genome
equals(RNN_Genome *other) (defined in RNN_Genome)RNN_Genome
EXAMM (defined in RNN_Genome)RNN_Genomefriend
generate_recurrent_edges(RNN_Node_Interface *node, double mu, double sigma, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count) (defined in RNN_Genome)RNN_Genome
generated_by_map (defined in RNN_Genome)RNN_Genomeprivate
generated_by_string() (defined in RNN_Genome)RNN_Genome
generation_id (defined in RNN_Genome)RNN_Genomeprivate
generator (defined in RNN_Genome)RNN_Genomeprivate
get_analytic_gradient(vector< RNN * > &rnns, const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs, double &mse, vector< double > &analytic_gradient, bool training) (defined in RNN_Genome)RNN_Genome
get_avg_edge_weight() (defined in RNN_Genome)RNN_Genome
get_avg_recurrent_depth() const (defined in RNN_Genome)RNN_Genome
get_best_parameters() const (defined in RNN_Genome)RNN_Genome
get_best_validation_mae() const (defined in RNN_Genome)RNN_Genome
get_best_validation_mse() const (defined in RNN_Genome)RNN_Genome
get_best_validation_softmax() const (defined in RNN_Genome)RNN_Genome
get_bp_iterations() (defined in RNN_Genome)RNN_Genome
get_color(double weight, bool is_recurrent) (defined in RNN_Genome)RNN_Genome
get_edge_count_str(bool recurrent) (defined in RNN_Genome)RNN_Genome
get_enabled_edge_count() (defined in RNN_Genome)RNN_Genome
get_enabled_node_count(int node_type) (defined in RNN_Genome)RNN_Genome
get_enabled_node_count() (defined in RNN_Genome)RNN_Genome
get_enabled_recurrent_edge_count() (defined in RNN_Genome)RNN_Genome
get_fan_in(int32_t node_innovation) (defined in RNN_Genome)RNN_Genome
get_fan_out(int32_t node_innovation) (defined in RNN_Genome)RNN_Genome
get_fitness() constRNN_Genome
get_generated_by(string type) (defined in RNN_Genome)RNN_Genome
get_generated_by_map() (defined in RNN_Genome)RNN_Genome
get_generation_id() const (defined in RNN_Genome)RNN_Genome
get_group_id() const (defined in RNN_Genome)RNN_Genome
get_innovation_list() (defined in RNN_Genome)RNN_Genome
get_input_edges(int32_t node_innovation, vector< RNN_Edge * > &input_edges, vector< RNN_Recurrent_Edge * > &input_recurrent_edges) (defined in RNN_Genome)RNN_Genome
get_input_parameter_names() const (defined in RNN_Genome)RNN_Genome
get_kaiming_weight(RNN_Node_Interface *output_node) (defined in RNN_Genome)RNN_Genome
get_mae(const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs) (defined in RNN_Genome)RNN_Genome
get_max_edge_innovation_count()RNN_Genome
get_max_node_innovation_count()RNN_Genome
get_mse(const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs) (defined in RNN_Genome)RNN_Genome
get_mu_sigma(const vector< double > &p, double &mu, double &sigma) (defined in RNN_Genome)RNN_Genome
get_node_count(int node_type) (defined in RNN_Genome)RNN_Genome
get_node_count() (defined in RNN_Genome)RNN_Genome
get_node_count_str(int node_type) (defined in RNN_Genome)RNN_Genome
get_normalize_avgs() const (defined in RNN_Genome)RNN_Genome
get_normalize_maxs() const (defined in RNN_Genome)RNN_Genome
get_normalize_mins() const (defined in RNN_Genome)RNN_Genome
get_normalize_std_devs() const (defined in RNN_Genome)RNN_Genome
get_normalize_type() const (defined in RNN_Genome)RNN_Genome
get_number_inputs() (defined in RNN_Genome)RNN_Genome
get_number_outputs() (defined in RNN_Genome)RNN_Genome
get_number_weights() (defined in RNN_Genome)RNN_Genome
get_output_parameter_names() const (defined in RNN_Genome)RNN_Genome
get_predictions(const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs) (defined in RNN_Genome)RNN_Genome
get_random_weight() (defined in RNN_Genome)RNN_Genome
get_rnn() (defined in RNN_Genome)RNN_Genome
get_softmax(const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs)RNN_Genome
get_structural_hash() constRNN_Genome
get_weights(vector< double > &parameters) (defined in RNN_Genome)RNN_Genome
get_xavier_weight(RNN_Node_Interface *output_node) (defined in RNN_Genome)RNN_Genome
group_id (defined in RNN_Genome)RNN_Genomeprivate
has_node_with_innovation(int32_t innovation_number) constRNN_Genome
high_threshold (defined in RNN_Genome)RNN_Genomeprivate
initial_parameters (defined in RNN_Genome)RNN_Genomeprivate
initialize_kaiming(RNN_Node_Interface *n) (defined in RNN_Genome)RNN_Genome
initialize_node_randomly(RNN_Node_Interface *n) (defined in RNN_Genome)RNN_Genome
initialize_randomly() (defined in RNN_Genome)RNN_Genome
initialize_xavier(RNN_Node_Interface *n) (defined in RNN_Genome)RNN_Genome
input_parameter_names (defined in RNN_Genome)RNN_Genomeprivate
IslandSpeciationStrategy (defined in RNN_Genome)RNN_Genomefriend
learning_rate (defined in RNN_Genome)RNN_Genomeprivate
log_filename (defined in RNN_Genome)RNN_Genomeprivate
low_threshold (defined in RNN_Genome)RNN_Genomeprivate
merge_node(double mu, double sigma, int node_type, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count, int32_t &node_innovation_count) (defined in RNN_Genome)RNN_Genome
mutated_component_weight (defined in RNN_Genome)RNN_Genomeprivate
NeatSpeciationStrategy (defined in RNN_Genome)RNN_Genomefriend
nodes (defined in RNN_Genome)RNN_Genomeprivate
normal_distribution (defined in RNN_Genome)RNN_Genomeprivate
normalize_avgs (defined in RNN_Genome)RNN_Genomeprivate
normalize_maxs (defined in RNN_Genome)RNN_Genomeprivate
normalize_mins (defined in RNN_Genome)RNN_Genomeprivate
normalize_std_devs (defined in RNN_Genome)RNN_Genomeprivate
normalize_type (defined in RNN_Genome)RNN_Genomeprivate
output_parameter_names (defined in RNN_Genome)RNN_Genomeprivate
outputs_unreachable() (defined in RNN_Genome)RNN_Genome
print_statistics() (defined in RNN_Genome)RNN_Genome
print_statistics_header() (defined in RNN_Genome)RNN_Genomestatic
read_from_array(char *array, int32_t length) (defined in RNN_Genome)RNN_Genome
read_from_stream(istream &bin_istream) (defined in RNN_Genome)RNN_Genome
RecDepthFrequencyTable (defined in RNN_Genome)RNN_Genomefriend
recurrent_edges (defined in RNN_Genome)RNN_Genomeprivate
rng (defined in RNN_Genome)RNN_Genomeprivate
rng_0_1 (defined in RNN_Genome)RNN_Genomeprivate
rng_1_1 (defined in RNN_Genome)RNN_Genomeprivate
RNN_Genome(vector< RNN_Node_Interface * > &_nodes, vector< RNN_Edge * > &_edges, vector< RNN_Recurrent_Edge * > &_recurrent_edges, WeightType _weight_initialize, WeightType _weight_inheritance, WeightType _mutated_component_weight) (defined in RNN_Genome)RNN_Genome
RNN_Genome(vector< RNN_Node_Interface * > &_nodes, vector< RNN_Edge * > &_edges, vector< RNN_Recurrent_Edge * > &_recurrent_edges, uint16_t seed, WeightType _weight_initialize, WeightType _weight_inheritance, WeightType _mutated_component_weight) (defined in RNN_Genome)RNN_Genome
RNN_Genome(string binary_filename) (defined in RNN_Genome)RNN_Genome
RNN_Genome(char *array, int32_t length) (defined in RNN_Genome)RNN_Genome
RNN_Genome(istream &bin_infile) (defined in RNN_Genome)RNN_Genome
sanity_check() (defined in RNN_Genome)RNN_Genome
set_adapt_learning_rate(bool _adapt_learning_rate) (defined in RNN_Genome)RNN_Genome
set_best_parameters(vector< double > parameters) (defined in RNN_Genome)RNN_Genome
set_bp_iterations(int32_t _bp_iterations) (defined in RNN_Genome)RNN_Genome
set_generated_by(string type) (defined in RNN_Genome)RNN_Genome
set_generation_id(int32_t generation_id) (defined in RNN_Genome)RNN_Genome
set_group_id(int32_t _group_id) (defined in RNN_Genome)RNN_Genome
set_initial_parameters(vector< double > parameters) (defined in RNN_Genome)RNN_Genome
set_learning_rate(double _learning_rate) (defined in RNN_Genome)RNN_Genome
set_log_filename(string _log_filename) (defined in RNN_Genome)RNN_Genome
set_nesterov_momentum(bool _use_nesterov_momentum) (defined in RNN_Genome)RNN_Genome
set_normalize_bounds(string _normalize_type, const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs, const map< string, double > &_normalize_avgs, const map< string, double > &_normalize_std_devs) (defined in RNN_Genome)RNN_Genome
set_parameter_names(const vector< string > &_input_parameter_names, const vector< string > &_output_parameter_names) (defined in RNN_Genome)RNN_Genome
set_reset_weights(bool _use_reset_weights) (defined in RNN_Genome)RNN_Genome
set_weights(const vector< double > &parameters) (defined in RNN_Genome)RNN_Genome
sort_edges_by_depth() (defined in RNN_Genome)RNN_Genome
sort_nodes_by_depth() (defined in RNN_Genome)RNN_Genome
sort_recurrent_edges_by_depth() (defined in RNN_Genome)RNN_Genome
split_edge(double mu, double sigma, int node_type, uniform_int_distribution< int32_t > rec_depth_dist, int32_t &edge_innovation_count, int32_t &node_innovation_count) (defined in RNN_Genome)RNN_Genome
split_node(double mu, double sigma, int node_type, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count, int32_t &node_innovation_count) (defined in RNN_Genome)RNN_Genome
structural_hash (defined in RNN_Genome)RNN_Genomeprivate
transfer_to(const vector< string > &new_input_parameter_names, const vector< string > &new_output_parameter_names, string transfer_learning_version, bool epigenetic_weights, int32_t min_recurrent_depth, int32_t max_recurrent_depth) (defined in RNN_Genome)RNN_Genome
update_generation_map(map< string, int32_t > &generation_map) (defined in RNN_Genome)RNN_Genome
update_innovation_counts(int32_t &node_innovation_count, int32_t &edge_innovation_count) (defined in RNN_Genome)RNN_Genome
use_dropout (defined in RNN_Genome)RNN_Genomeprivate
use_high_norm (defined in RNN_Genome)RNN_Genomeprivate
use_low_norm (defined in RNN_Genome)RNN_Genomeprivate
use_nesterov_momentum (defined in RNN_Genome)RNN_Genomeprivate
use_regressionRNN_Genomeprivate
use_reset_weights (defined in RNN_Genome)RNN_Genomeprivate
weight_inheritance (defined in RNN_Genome)RNN_Genomeprivate
weight_initialize (defined in RNN_Genome)RNN_Genomeprivate
write_graphviz(string filename) (defined in RNN_Genome)RNN_Genome
write_predictions(string output_directory, const vector< string > &input_filenames, const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs, TimeSeriesSets *time_series_sets) (defined in RNN_Genome)RNN_Genome
write_predictions(string output_directory, const vector< string > &input_filenames, const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs, Corpus *word_series_sets)RNN_Genome
write_to_array(char **array, int32_t &length) (defined in RNN_Genome)RNN_Genome
write_to_file(string bin_filename) (defined in RNN_Genome)RNN_Genome
write_to_stream(ostream &bin_stream) (defined in RNN_Genome)RNN_Genome
~RNN_Genome() (defined in RNN_Genome)RNN_Genome
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Static Public Member Functions | +Private Attributes | +Friends | +List of all members
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RNN_Genome Class Reference
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+Public Member Functions

+void sort_nodes_by_depth ()
 
+void sort_edges_by_depth ()
 
+void sort_recurrent_edges_by_depth ()
 
+ RNN_Genome (vector< RNN_Node_Interface * > &_nodes, vector< RNN_Edge * > &_edges, vector< RNN_Recurrent_Edge * > &_recurrent_edges, WeightType _weight_initialize, WeightType _weight_inheritance, WeightType _mutated_component_weight)
 
+ RNN_Genome (vector< RNN_Node_Interface * > &_nodes, vector< RNN_Edge * > &_edges, vector< RNN_Recurrent_Edge * > &_recurrent_edges, uint16_t seed, WeightType _weight_initialize, WeightType _weight_inheritance, WeightType _mutated_component_weight)
 
+RNN_Genome * copy ()
 
+string print_statistics ()
 
+void set_parameter_names (const vector< string > &_input_parameter_names, const vector< string > &_output_parameter_names)
 
+string generated_by_string ()
 
+string get_edge_count_str (bool recurrent)
 
+string get_node_count_str (int node_type)
 
+const map< string, int > * get_generated_by_map ()
 
+double get_avg_recurrent_depth () const
 
+int32_t get_enabled_edge_count ()
 
+int32_t get_enabled_recurrent_edge_count ()
 
+int32_t get_enabled_node_count (int node_type)
 
+int32_t get_node_count (int node_type)
 
+int32_t get_enabled_node_count ()
 
+int32_t get_node_count ()
 
double get_fitness () const
 Calculates the fitness of the rnn genome across the dataset. More...
 
+double get_best_validation_softmax () const
 
+double get_best_validation_mse () const
 
+double get_best_validation_mae () const
 
+void set_normalize_bounds (string _normalize_type, const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs, const map< string, double > &_normalize_avgs, const map< string, double > &_normalize_std_devs)
 
+string get_normalize_type () const
 
+map< string, double > get_normalize_mins () const
 
+map< string, double > get_normalize_maxs () const
 
+map< string, double > get_normalize_avgs () const
 
+map< string, double > get_normalize_std_devs () const
 
+vector< string > get_input_parameter_names () const
 
+vector< string > get_output_parameter_names () const
 
+int32_t get_group_id () const
 
+void set_group_id (int32_t _group_id)
 
+void set_bp_iterations (int32_t _bp_iterations)
 
+int32_t get_bp_iterations ()
 
+void set_learning_rate (double _learning_rate)
 
+void set_adapt_learning_rate (bool _adapt_learning_rate)
 
+void set_nesterov_momentum (bool _use_nesterov_momentum)
 
+void set_reset_weights (bool _use_reset_weights)
 
+void disable_high_threshold ()
 
+void enable_high_threshold (double _high_threshold)
 
+void disable_low_threshold ()
 
+void enable_low_threshold (double _low_threshold)
 
+void disable_dropout ()
 
+void enable_dropout (double _dropout_probability)
 
+void enable_use_regression (bool _use_regression)
 
+void set_log_filename (string _log_filename)
 
+void get_weights (vector< double > &parameters)
 
+void set_weights (const vector< double > &parameters)
 
+uint32_t get_number_weights ()
 
+uint32_t get_number_inputs ()
 
+uint32_t get_number_outputs ()
 
+double get_avg_edge_weight ()
 
+void initialize_randomly ()
 
+void initialize_xavier (RNN_Node_Interface *n)
 
+void initialize_kaiming (RNN_Node_Interface *n)
 
+void initialize_node_randomly (RNN_Node_Interface *n)
 
+double get_xavier_weight (RNN_Node_Interface *output_node)
 
+double get_kaiming_weight (RNN_Node_Interface *output_node)
 
+double get_random_weight ()
 
+void get_input_edges (int32_t node_innovation, vector< RNN_Edge * > &input_edges, vector< RNN_Recurrent_Edge * > &input_recurrent_edges)
 
+int32_t get_fan_in (int32_t node_innovation)
 
+int32_t get_fan_out (int32_t node_innovation)
 
+int32_t get_generation_id () const
 
+void set_generation_id (int32_t generation_id)
 
+void clear_generated_by ()
 
+void update_generation_map (map< string, int32_t > &generation_map)
 
+void set_generated_by (string type)
 
+int32_t get_generated_by (string type)
 
+RNN * get_rnn ()
 
+vector< double > get_best_parameters () const
 
+void set_best_parameters (vector< double > parameters)
 
+void set_initial_parameters (vector< double > parameters)
 
+void get_analytic_gradient (vector< RNN * > &rnns, const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs, double &mse, vector< double > &analytic_gradient, bool training)
 
+void backpropagate (const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs, const vector< vector< vector< double > > > &validation_inputs, const vector< vector< vector< double > > > &validation_outputs)
 
+void backpropagate_stochastic (const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs, const vector< vector< vector< double > > > &validation_inputs, const vector< vector< vector< double > > > &validation_outputs)
 
double get_softmax (const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs)
 Predicts the softmax error after a single pass across the rnn genome. More...
 
+double get_mse (const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs)
 
+double get_mae (const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs)
 
+vector< vector< double > > get_predictions (const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs)
 
+void write_predictions (string output_directory, const vector< string > &input_filenames, const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs, TimeSeriesSets *time_series_sets)
 
void write_predictions (string output_directory, const vector< string > &input_filenames, const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs, Corpus *word_series_sets)
 Predicts the cross entropy loss after a single pass across the rnn genome. More...
 
+void get_mu_sigma (const vector< double > &p, double &mu, double &sigma)
 
+bool sanity_check ()
 
+void assign_reachability ()
 
+bool outputs_unreachable ()
 
+RNN_Node_Interface * create_node (double mu, double sigma, int node_type, int32_t &node_innovation_count, double depth)
 
+bool attempt_edge_insert (RNN_Node_Interface *n1, RNN_Node_Interface *n2, double mu, double sigma, int32_t &edge_innovation_count)
 
+bool attempt_recurrent_edge_insert (RNN_Node_Interface *n1, RNN_Node_Interface *n2, double mu, double sigma, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count)
 
+void generate_recurrent_edges (RNN_Node_Interface *node, double mu, double sigma, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count)
 
+bool add_edge (double mu, double sigma, int32_t &edge_innovation_count)
 
+bool add_recurrent_edge (double mu, double sigma, uniform_int_distribution< int32_t > rec_depth_dist, int32_t &edge_innovation_count)
 
+bool disable_edge ()
 
+bool enable_edge ()
 
+bool split_edge (double mu, double sigma, int node_type, uniform_int_distribution< int32_t > rec_depth_dist, int32_t &edge_innovation_count, int32_t &node_innovation_count)
 
+bool add_node (double mu, double sigma, int node_type, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count, int32_t &node_innovation_count)
 
+bool enable_node ()
 
+bool disable_node ()
 
+bool split_node (double mu, double sigma, int node_type, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count, int32_t &node_innovation_count)
 
+bool merge_node (double mu, double sigma, int node_type, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count, int32_t &node_innovation_count)
 
bool has_node_with_innovation (int32_t innovation_number) const
 Determines if the genome contains a node with the given innovation number. More...
 
+bool equals (RNN_Genome *other)
 
+string get_color (double weight, bool is_recurrent)
 
+void write_graphviz (string filename)
 
+ RNN_Genome (string binary_filename)
 
+ RNN_Genome (char *array, int32_t length)
 
+ RNN_Genome (istream &bin_infile)
 
+void read_from_array (char *array, int32_t length)
 
+void read_from_stream (istream &bin_istream)
 
+void write_to_array (char **array, int32_t &length)
 
+void write_to_file (string bin_filename)
 
+void write_to_stream (ostream &bin_stream)
 
+bool connect_new_input_node (double mu, double sig, RNN_Node_Interface *new_node, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count)
 
+bool connect_new_output_node (double mu, double sig, RNN_Node_Interface *new_node, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count)
 
+bool connect_node_to_hid_nodes (double mu, double sig, RNN_Node_Interface *new_node, uniform_int_distribution< int32_t > dist, int32_t &edge_innovation_count, bool from_input)
 
+void update_innovation_counts (int32_t &node_innovation_count, int32_t &edge_innovation_count)
 
+vector< int32_t > get_innovation_list ()
 
string get_structural_hash () const
 
int get_max_node_innovation_count ()
 
int get_max_edge_innovation_count ()
 
+void transfer_to (const vector< string > &new_input_parameter_names, const vector< string > &new_output_parameter_names, string transfer_learning_version, bool epigenetic_weights, int32_t min_recurrent_depth, int32_t max_recurrent_depth)
 
+ + + +

+Static Public Member Functions

+static string print_statistics_header ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int32_t generation_id
 
+int32_t group_id
 
+int32_t bp_iterations
 
+double learning_rate
 
+bool adapt_learning_rate
 
+bool use_nesterov_momentum
 
+bool use_reset_weights
 
+bool use_high_norm
 
+double high_threshold
 
+bool use_low_norm
 
+double low_threshold
 
bool use_regression
 Specifies whether to use the regression or not. More...
 
+bool use_dropout
 
+double dropout_probability
 
+string structural_hash
 
+string log_filename
 
+WeightType weight_initialize
 
+WeightType weight_inheritance
 
+WeightType mutated_component_weight
 
+map< string, int > generated_by_map
 
+vector< double > initial_parameters
 
double best_validation_mse
 Specifies the validation error across the dataset. More...
 
+double best_validation_mae
 
+vector< double > best_parameters
 
+minstd_rand0 generator
 
+uniform_real_distribution< double > rng
 
+uniform_real_distribution< double > rng_0_1
 
+uniform_real_distribution< double > rng_1_1
 
+NormalDistribution normal_distribution
 
+vector< RNN_Node_Interface * > nodes
 
+vector< RNN_Edge * > edges
 
+vector< RNN_Recurrent_Edge * > recurrent_edges
 
+vector< string > input_parameter_names
 
+vector< string > output_parameter_names
 
+string normalize_type
 
+map< string, double > normalize_mins
 
+map< string, double > normalize_maxs
 
+map< string, double > normalize_avgs
 
+map< string, double > normalize_std_devs
 
+ + + + + + + + + +

+Friends

+class EXAMM
 
+class IslandSpeciationStrategy
 
+class NeatSpeciationStrategy
 
+class RecDepthFrequencyTable
 
+

Member Function Documentation

+ +

◆ get_fitness()

+ +
+
+ + + + + + + +
double RNN_Genome::get_fitness () const
+
+ +

Calculates the fitness of the rnn genome across the dataset.

+


+ This will be the mean squared error in case of regression and cross entropy loss in case of classification.

+
Returns
MSE or SOFTMAX ERROR.
+ +
+
+ +

◆ get_max_edge_innovation_count()

+ +
+
+ + + + + + + +
int RNN_Genome::get_max_edge_innovation_count ()
+
+
Returns
the max innovation number of any edge or recurrent edge in the genome.
+ +
+
+ +

◆ get_max_node_innovation_count()

+ +
+
+ + + + + + + +
int RNN_Genome::get_max_node_innovation_count ()
+
+
Returns
the max innovation number of any node in the genome.
+ +
+
+ +

◆ get_softmax()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
double RNN_Genome::get_softmax (const vector< double > & parameters,
const vector< vector< vector< double > > > & inputs,
const vector< vector< vector< double > > > & outputs 
)
+
+ +

Predicts the softmax error after a single pass across the rnn genome.

+


+

+
Parameters
+ + + + +
parametersis the parameters such as dropout, momentum type used for the forward pass.
inputsare the inputs according to the dataset that is pass to the for forward pass.
outputsare the expected outputs according to the dataset that is pass to the calculate error for backward pass.
+
+
+
Returns
average of the cross entropy error over the dataset.
+ +
+
+ +

◆ get_structural_hash()

+ +
+
+ + + + + + + +
string RNN_Genome::get_structural_hash () const
+
+
Returns
the structural hash (calculated when assign_reachaability is called)
+ +
+
+ +

◆ has_node_with_innovation()

+ +
+
+ + + + + + + + +
bool RNN_Genome::has_node_with_innovation (int32_t innovation_number) const
+
+ +

Determines if the genome contains a node with the given innovation number.

+
Parameters
+ + +
theinnovation number to fine
+
+
+
Returns
true if the genome has a node with the provided innovation number, false otherwise.
+ +
+
+ +

◆ write_predictions()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void RNN_Genome::write_predictions (string output_directory,
const vector< string > & input_filenames,
const vector< double > & parameters,
const vector< vector< vector< double > > > & inputs,
const vector< vector< vector< double > > > & outputs,
Corpus * word_series_sets 
)
+
+ +

Predicts the cross entropy loss after a single pass across the rnn genome.

+


+

+
Parameters
+ + + + + + + +
output_directoryis the file directory that contains all the output files.
inputs_filenamesare the inputs files according to the dataset that is pass to the for forward pass.
inputsare the inputs according to the dataset that is pass to the for forward pass.
+
outputsare the expected outputs according to the dataset that is pass to the calculate error for backward pass.
parametersis the parameters such as dropout, momentum type used for the forward pass.
word_series_setsis the Corpus from the word series dataset.
+
+
+

Calls the write predictions from the rnn class.
+

+ +
+
+

Member Data Documentation

+ +

◆ best_validation_mse

+ +
+
+ + + + + +
+ + + + +
double RNN_Genome::best_validation_mse
+ 		+private
+
+ +

Specifies the validation error across the dataset.

+


+ It will use mean squared error when regression in case of time series and cross entropy loss when regression not used in case of word series

+ +
+
+ +

◆ use_regression

+ +
+
+ + + + + +
+ + + + +
bool RNN_Genome::use_regression
+ 		+private
+
+ +

Specifies whether to use the regression or not.

+


+ It will use regression in case of time series and will not use regression in case of word series

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_r_n_n___node-members.html b/docs/html/class_r_n_n___node-members.html new file mode 100644 index 00000000..2312ae29 --- /dev/null +++ b/docs/html/class_r_n_n___node-members.html @@ -0,0 +1,136 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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RNN_Node Member List
+
+
+ +

This is the complete list of members for RNN_Node, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
backward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
bias (defined in RNN_Node)RNN_Nodeprivate
copy() const (defined in RNN_Node)RNN_Nodevirtual
d_bias (defined in RNN_Node)RNN_Nodeprivate
d_input (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
depth (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
enabled (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
equals(RNN_Node_Interface *other) const (defined in RNN_Node_Interface)RNN_Node_Interface
error_fired(int time, double error) (defined in RNN_Node)RNN_Node
error_fired(int32_t time, double error)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
error_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
forward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
get_depth() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_gradients(vector< double > &gradients) (defined in RNN_Node)RNN_Nodevirtual
get_innovation_number() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_layer_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_node_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_number_weights() const (defined in RNN_Node)RNN_Nodevirtual
get_total_inputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_total_outputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_weights(vector< double > &parameters) const (defined in RNN_Node)RNN_Nodevirtual
get_weights(uint32_t &offset, vector< double > &parameters) const (defined in RNN_Node)RNN_Nodevirtual
initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range) (defined in RNN_Node)RNN_Nodevirtual
initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma) (defined in RNN_Node)RNN_Nodevirtual
initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution< double > &rng) (defined in RNN_Node)RNN_Nodevirtual
initialize_xavier(minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range) (defined in RNN_Node)RNN_Nodevirtual
innovation_number (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
input_fired(int time, double incoming_output) (defined in RNN_Node)RNN_Node
input_fired(int32_t time, double incoming_output)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
input_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
inputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
is_enabled() const (defined in RNN_Node_Interface)RNN_Node_Interface
is_reachable() const (defined in RNN_Node_Interface)RNN_Node_Interface
layer_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
ld_output (defined in RNN_Node)RNN_Nodeprivate
node_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
output_fired(int time, double delta) (defined in RNN_Node)RNN_Node
output_fired(int32_t time, double delta)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
output_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
outputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
parameter_name (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
reset(int _series_length) (defined in RNN_Node)RNN_Node
reset(int32_t _series_length)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
RNN_Edge (defined in RNN_Node)RNN_Nodefriend
RNN_Node(int _innovation_number, int _layer_type, double _depth, int _node_type) (defined in RNN_Node)RNN_Node
RNN_Node(int _innovation_number, int _layer_type, double _depth, int _node_type, string _parameter_name) (defined in RNN_Node)RNN_Node
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name) (defined in RNN_Node_Interface)RNN_Node_Interface
series_length (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
set_weights(const vector< double > &parameters) (defined in RNN_Node)RNN_Nodevirtual
set_weights(uint32_t &offset, const vector< double > &parameters) (defined in RNN_Node)RNN_Nodevirtual
total_inputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
total_outputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
try_update_deltas(int time) (defined in RNN_Node)RNN_Node
write_to_stream(ostream &out) (defined in RNN_Node)RNN_Node
~RNN_Node() (defined in RNN_Node)RNN_Node
~RNN_Node_Interface() (defined in RNN_Node_Interface)RNN_Node_Interfacevirtual
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_r_n_n___node.html b/docs/html/class_r_n_n___node.html new file mode 100644 index 00000000..995ea75e --- /dev/null +++ b/docs/html/class_r_n_n___node.html @@ -0,0 +1,277 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: RNN_Node Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +Friends | +List of all members
+
+
RNN_Node Class Reference
+
+
+
+Inheritance diagram for RNN_Node:
+
+
+ + +RNN_Node_Interface + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ RNN_Node (int _innovation_number, int _layer_type, double _depth, int _node_type)
 
+ RNN_Node (int _innovation_number, int _layer_type, double _depth, int _node_type, string _parameter_name)
 
+void initialize_lamarckian (minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)
 
+void initialize_xavier (minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range)
 
+void initialize_kaiming (minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)
 
+void initialize_uniform_random (minstd_rand0 &generator, uniform_real_distribution< double > &rng)
 
+void input_fired (int time, double incoming_output)
 
+void try_update_deltas (int time)
 
+void output_fired (int time, double delta)
 
+void error_fired (int time, double error)
 
+uint32_t get_number_weights () const
 
+void get_weights (vector< double > &parameters) const
 
+void set_weights (const vector< double > &parameters)
 
+void get_weights (uint32_t &offset, vector< double > &parameters) const
 
+void set_weights (uint32_t &offset, const vector< double > &parameters)
 
+void reset (int _series_length)
 
+void get_gradients (vector< double > &gradients)
 
+RNN_Node_Interface * copy () const
 
+void write_to_stream (ostream &out)
 
- Public Member Functions inherited from RNN_Node_Interface
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth)
 
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name)
 
+virtual void input_fired (int32_t time, double incoming_output)=0
 
+virtual void output_fired (int32_t time, double delta)=0
 
+virtual void error_fired (int32_t time, double error)=0
 
+virtual void reset (int32_t _series_length)=0
 
+void write_to_stream (ostream &out)
 
+int32_t get_node_type () const
 
+int32_t get_layer_type () const
 
+int32_t get_innovation_number () const
 
+int32_t get_total_inputs () const
 
+int32_t get_total_outputs () const
 
+double get_depth () const
 
+bool equals (RNN_Node_Interface *other) const
 
+bool is_reachable () const
 
+bool is_enabled () const
 
+ + + + + + + +

+Private Attributes

+double bias
 
+double d_bias
 
+vector< double > ld_output
 
+ + + +

+Friends

+class RNN_Edge
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from RNN_Node_Interface
+int32_t innovation_number
 
+int32_t layer_type
 
+int32_t node_type
 
+double depth
 
+string parameter_name
 
+bool enabled
 
+bool backward_reachable
 
+bool forward_reachable
 
+int32_t series_length
 
+vector< double > input_values
 
+vector< double > output_values
 
+vector< double > error_values
 
+vector< double > d_input
 
+vector< int32_t > inputs_fired
 
+vector< int32_t > outputs_fired
 
+int32_t total_inputs
 
+int32_t total_outputs
 
+
The documentation for this class was generated from the following files: +
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+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_r_n_n___node.png b/docs/html/class_r_n_n___node.png new file mode 100644 index 0000000000000000000000000000000000000000..3237cd53a63df8119c5db0e7b37a1882b76a0552 GIT binary patch literal 549 zcmeAS@N?(olHy`uVBq!ia0vp^EkGQ=!3-qdo9eCsQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;}+d7n#ik}MbDJmV)a*5cd>O1e$ll<4;?=9H5>gBCI zg;RH8kM(@gJHmK~BkAl*AK7I;GEa+XJ-r$@Z|Q5HQ~p|>yJM_gJ+bYWwnjMj`h4!t zsd;I?ruQwL!J%~Pi_&k$DvrC?PO#jTIJAN zyx41f*TW@6<3DpThoIpz0WPIu66)L!oNXD>Uh@g`ALrG0$M7*_hJPV9$9`Ep#sr|} z7$u51+8KH*Tbvn=$sAH-NalMe(6I48lb{pFW>FJd3w+)1d1?UfJ{jTL&lCU8{c-Ot!;fWl%@NA)?_@E4 lds3eOb{Z?t9h1K@2OO7P+spN82QW? + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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RNN_Node_Interface Member List
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This is the complete list of members for RNN_Node_Interface, including all inherited members.

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backward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
copy() const =0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
d_input (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
depth (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
enabled (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
equals(RNN_Node_Interface *other) const (defined in RNN_Node_Interface)RNN_Node_Interface
error_fired(int32_t time, double error)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
error_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
forward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
get_depth() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_gradients(vector< double > &gradients)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
get_innovation_number() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_layer_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_mae (defined in RNN_Node_Interface)RNN_Node_Interfacefriend
get_mse (defined in RNN_Node_Interface)RNN_Node_Interfacefriend
get_node_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_number_weights() const =0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
get_total_inputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_total_outputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_weights(vector< double > &parameters) const =0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
get_weights(uint32_t &offset, vector< double > &parameters) const =0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution< double > &rng)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
initialize_xavier(minstd_rand0 &generator, uniform_real_distribution< double > &rng_1_1, double range)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
innovation_number (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
input_fired(int32_t time, double incoming_output)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
input_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
inputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
is_enabled() const (defined in RNN_Node_Interface)RNN_Node_Interface
is_reachable() const (defined in RNN_Node_Interface)RNN_Node_Interface
layer_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
node_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
output_fired(int32_t time, double delta)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
output_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
outputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
parameter_name (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
reset(int32_t _series_length)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
RNN (defined in RNN_Node_Interface)RNN_Node_Interfacefriend
RNN_Edge (defined in RNN_Node_Interface)RNN_Node_Interfacefriend
RNN_Genome (defined in RNN_Node_Interface)RNN_Node_Interfacefriend
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Recurrent_Edge (defined in RNN_Node_Interface)RNN_Node_Interfacefriend
series_length (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
set_weights(const vector< double > &parameters)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
set_weights(uint32_t &offset, const vector< double > &parameters)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
total_inputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
total_outputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
write_to_stream(ostream &out) (defined in RNN_Node_Interface)RNN_Node_Interface
~RNN_Node_Interface() (defined in RNN_Node_Interface)RNN_Node_Interfacevirtual
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_r_n_n___node___interface.html b/docs/html/class_r_n_n___node___interface.html new file mode 100644 index 00000000..eb9b698d --- /dev/null +++ b/docs/html/class_r_n_n___node___interface.html @@ -0,0 +1,262 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: RNN_Node_Interface Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Protected Attributes | +Friends | +List of all members
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RNN_Node_Interface Class Referenceabstract
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+Inheritance diagram for RNN_Node_Interface:
+
+
+ + +Delta_Node +ENARC_Node +ENAS_DAG_Node +GRU_Node +LSTM_Node +MGU_Node +RANDOM_DAG_Node +RNN_Node +UGRNN_Node + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth)
 
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name)
 
+virtual void initialize_lamarckian (minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)=0
 
+virtual void initialize_xavier (minstd_rand0 &generator, uniform_real_distribution< double > &rng_1_1, double range)=0
 
+virtual void initialize_kaiming (minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)=0
 
+virtual void initialize_uniform_random (minstd_rand0 &generator, uniform_real_distribution< double > &rng)=0
 
+virtual void input_fired (int32_t time, double incoming_output)=0
 
+virtual void output_fired (int32_t time, double delta)=0
 
+virtual void error_fired (int32_t time, double error)=0
 
+virtual uint32_t get_number_weights () const =0
 
+virtual void get_weights (vector< double > &parameters) const =0
 
+virtual void set_weights (const vector< double > &parameters)=0
 
+virtual void get_weights (uint32_t &offset, vector< double > &parameters) const =0
 
+virtual void set_weights (uint32_t &offset, const vector< double > &parameters)=0
 
+virtual void reset (int32_t _series_length)=0
 
+virtual void get_gradients (vector< double > &gradients)=0
 
+virtual RNN_Node_Interface * copy () const =0
 
+void write_to_stream (ostream &out)
 
+int32_t get_node_type () const
 
+int32_t get_layer_type () const
 
+int32_t get_innovation_number () const
 
+int32_t get_total_inputs () const
 
+int32_t get_total_outputs () const
 
+double get_depth () const
 
+bool equals (RNN_Node_Interface *other) const
 
+bool is_reachable () const
 
+bool is_enabled () const
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Protected Attributes

+int32_t innovation_number
 
+int32_t layer_type
 
+int32_t node_type
 
+double depth
 
+string parameter_name
 
+bool enabled
 
+bool backward_reachable
 
+bool forward_reachable
 
+int32_t series_length
 
+vector< double > input_values
 
+vector< double > output_values
 
+vector< double > error_values
 
+vector< double > d_input
 
+vector< int32_t > inputs_fired
 
+vector< int32_t > outputs_fired
 
+int32_t total_inputs
 
+int32_t total_outputs
 
+ + + + + + + + + + + + + +

+Friends

+class RNN_Edge
 
+class RNN_Recurrent_Edge
 
+class RNN
 
+class RNN_Genome
 
+void get_mse (RNN *genome, const vector< vector< double > > &expected, double &mse, vector< vector< double > > &deltas)
 
+void get_mae (RNN *genome, const vector< vector< double > > &expected, double &mae, vector< vector< double > > &deltas)
 
+
The documentation for this class was generated from the following files: +
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+Generated by   +doxygen + 1.8.17 +
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RNN_Recurrent_Edge Member List
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This is the complete list of members for RNN_Recurrent_Edge, including all inherited members.

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backward_reachable (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
copy(const vector< RNN_Node_Interface * > new_nodes) (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
d_weight (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
deltas (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
enabled (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
equals(RNN_Recurrent_Edge *other) const (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
EXAMM (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgefriend
first_propagate_backward() (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
first_propagate_forward() (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
forward_reachable (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
get_gradient() (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
get_innovation_number() const (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
get_input_innovation_number() const (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
get_input_node() const (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
get_output_innovation_number() const (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
get_output_node() const (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
get_recurrent_depth() const (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
innovation_number (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
input_innovation_number (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
input_node (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
is_enabled() const (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
is_reachable() const (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
output_innovation_number (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
output_node (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
outputs (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
propagate_backward(int32_t time) (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
propagate_forward(int32_t time) (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
RecDepthFrequencyTable (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgefriend
recurrent_depth (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
reset(int32_t _series_length) (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
RNN (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgefriend
RNN_Genome (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgefriend
RNN_Recurrent_Edge(int32_t _innovation_number, int32_t _recurrent_depth, RNN_Node_Interface *_input_node, RNN_Node_Interface *_output_node) (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
RNN_Recurrent_Edge(int32_t _innovation_number, int32_t _recurrent_depth, int32_t _input_innovation_number, int32_t _output_innovation_number, const vector< RNN_Node_Interface * > &nodes) (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
series_length (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
weight (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edgeprivate
write_to_stream(ostream &out) (defined in RNN_Recurrent_Edge)RNN_Recurrent_Edge
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_r_n_n___recurrent___edge.html b/docs/html/class_r_n_n___recurrent___edge.html new file mode 100644 index 00000000..63a41e50 --- /dev/null +++ b/docs/html/class_r_n_n___recurrent___edge.html @@ -0,0 +1,206 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: RNN_Recurrent_Edge Class Reference + + + + + + + + + +
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RNN_Recurrent_Edge Class Reference
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+Public Member Functions

+ RNN_Recurrent_Edge (int32_t _innovation_number, int32_t _recurrent_depth, RNN_Node_Interface *_input_node, RNN_Node_Interface *_output_node)
 
+ RNN_Recurrent_Edge (int32_t _innovation_number, int32_t _recurrent_depth, int32_t _input_innovation_number, int32_t _output_innovation_number, const vector< RNN_Node_Interface * > &nodes)
 
+void reset (int32_t _series_length)
 
+void first_propagate_forward ()
 
+void first_propagate_backward ()
 
+void propagate_forward (int32_t time)
 
+void propagate_backward (int32_t time)
 
+int32_t get_recurrent_depth () const
 
+double get_gradient ()
 
+bool is_enabled () const
 
+bool is_reachable () const
 
+RNN_Recurrent_Edge * copy (const vector< RNN_Node_Interface * > new_nodes)
 
+int32_t get_innovation_number () const
 
+int32_t get_input_innovation_number () const
 
+int32_t get_output_innovation_number () const
 
+const RNN_Node_Interface * get_input_node () const
 
+const RNN_Node_Interface * get_output_node () const
 
+void write_to_stream (ostream &out)
 
+bool equals (RNN_Recurrent_Edge *other) const
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+int32_t innovation_number
 
+int32_t series_length
 
+int32_t recurrent_depth
 
+vector< double > outputs
 
+vector< double > deltas
 
+double weight
 
+double d_weight
 
+bool enabled
 
+bool forward_reachable
 
+bool backward_reachable
 
+int32_t input_innovation_number
 
+int32_t output_innovation_number
 
+RNN_Node_Interface * input_node
 
+RNN_Node_Interface * output_node
 
+ + + + + + + + + +

+Friends

+class RNN_Genome
 
+class RNN
 
+class EXAMM
 
+class RecDepthFrequencyTable
 
+
The documentation for this class was generated from the following files: +
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Rectangle Member List
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Rectangle(int _y1, int _x1, int _y2, int _x2) (defined in Rectangle)Rectangle
x1 (defined in Rectangle)Rectangle
x2 (defined in Rectangle)Rectangle
y1 (defined in Rectangle)Rectangle
y2 (defined in Rectangle)Rectangle
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Rectangle Class Reference
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+Public Member Functions

+ Rectangle (int _y1, int _x1, int _y2, int _x2)
 
+ + + + + + + + + +

+Public Attributes

+int y1
 
+int x1
 
+int y2
 
+int x2
 
+
The documentation for this class was generated from the following files: +
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RunStatistics Member List
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This is the complete list of members for RunStatistics, including all inherited members.

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correlate_header() (defined in RunStatistics)RunStatistics
delta (defined in RunStatistics)RunStatistics
dfm_avg (defined in RunStatistics)RunStatistics
dfm_max (defined in RunStatistics)RunStatistics
dfm_min (defined in RunStatistics)RunStatistics
edge (defined in RunStatistics)RunStatistics
ff (defined in RunStatistics)RunStatistics
gru (defined in RunStatistics)RunStatistics
lstm (defined in RunStatistics)RunStatistics
mae (defined in RunStatistics)RunStatistics
mgu (defined in RunStatistics)RunStatistics
mse (defined in RunStatistics)RunStatistics
node (defined in RunStatistics)RunStatistics
output_name (defined in RunStatistics)RunStatistics
overview_ff_footer(string type) (defined in RunStatistics)RunStatistics
overview_ff_header() (defined in RunStatistics)RunStatistics
overview_footer(string type) (defined in RunStatistics)RunStatistics
overview_header() (defined in RunStatistics)RunStatistics
rec_edge (defined in RunStatistics)RunStatistics
run_type (defined in RunStatistics)RunStatistics
RunStatistics(string _output_name, string _run_type) (defined in RunStatistics)RunStatistics
set_deviation_from_mean_avg(double _dfm_avg) (defined in RunStatistics)RunStatistics
set_deviation_from_mean_max(double _dfm_max) (defined in RunStatistics)RunStatistics
set_deviation_from_mean_min(double _dfm_min) (defined in RunStatistics)RunStatistics
to_overview_ff_string() (defined in RunStatistics)RunStatistics
to_overview_string() (defined in RunStatistics)RunStatistics
to_string_avg() (defined in RunStatistics)RunStatistics
to_string_correlate(string target_name, Tracker &target) (defined in RunStatistics)RunStatistics
to_string_max() (defined in RunStatistics)RunStatistics
to_string_min() (defined in RunStatistics)RunStatistics
to_string_stddev() (defined in RunStatistics)RunStatistics
ugrnn (defined in RunStatistics)RunStatistics
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RunStatistics Class Reference
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+Public Member Functions

+ RunStatistics (string _output_name, string _run_type)
 
+void set_deviation_from_mean_min (double _dfm_min)
 
+void set_deviation_from_mean_avg (double _dfm_avg)
 
+void set_deviation_from_mean_max (double _dfm_max)
 
+string correlate_header ()
 
+string to_string_min ()
 
+string to_string_avg ()
 
+string to_string_max ()
 
+string to_string_stddev ()
 
+string to_string_correlate (string target_name, Tracker &target)
 
+string overview_header ()
 
+string overview_footer (string type)
 
+string to_overview_string ()
 
+string overview_ff_header ()
 
+string overview_ff_footer (string type)
 
+string to_overview_ff_string ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Attributes

+string output_name
 
+string run_type
 
+double dfm_min
 
+double dfm_avg
 
+double dfm_max
 
+Tracker mse
 
+Tracker mae
 
+Tracker edge
 
+Tracker rec_edge
 
+Tracker node
 
+Tracker ff
 
+Tracker lstm
 
+Tracker ugrnn
 
+Tracker delta
 
+Tracker mgu
 
+Tracker gru
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_sentence_series-members.html b/docs/html/class_sentence_series-members.html new file mode 100644 index 00000000..4bf960c5 --- /dev/null +++ b/docs/html/class_sentence_series-members.html @@ -0,0 +1,108 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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SentenceSeries Member List
+
+
+ +

This is the complete list of members for SentenceSeries, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
add_word_series(string name) (defined in SentenceSeries)SentenceSeries
copy() (defined in SentenceSeries)SentenceSeries
export_word_series(vector< vector< double > > &data, int word_offset) (defined in SentenceSeries)SentenceSeries
export_word_series(vector< vector< double > > &data) (defined in SentenceSeries)SentenceSeries
filenameSentenceSeriesprivate
get_average(string word) (defined in SentenceSeries)SentenceSeries
get_correlation(string word1, string word2, int32_t lag) const (defined in SentenceSeries)SentenceSeries
get_filename() const (defined in SentenceSeries)SentenceSeries
get_max(string word) (defined in SentenceSeries)SentenceSeries
get_max_change(string word) (defined in SentenceSeries)SentenceSeries
get_min(string word) (defined in SentenceSeries)SentenceSeries
get_min_change(string word) (defined in SentenceSeries)SentenceSeries
get_number_columns() const (defined in SentenceSeries)SentenceSeries
get_number_rows() const (defined in SentenceSeries)SentenceSeries
get_series(string word_name, vector< double > &series) (defined in SentenceSeries)SentenceSeries
get_std_dev(string word) (defined in SentenceSeries)SentenceSeries
get_variance(string word) (defined in SentenceSeries)SentenceSeries
get_word_index() const (defined in SentenceSeries)SentenceSeries
normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) (defined in SentenceSeries)SentenceSeries
normalize_min_max(string word, double min, double max) (defined in SentenceSeries)SentenceSeries
number_rowsSentenceSeriesprivate
select_parameters(const vector< string > &input_parameter_names, const vector< string > &output_parameter_names) (defined in SentenceSeries)SentenceSeries
select_parameters(const vector< string > &parameter_names) (defined in SentenceSeries)SentenceSeries
SentenceSeries() (defined in SentenceSeries)SentenceSeriesprivate
SentenceSeries(const string _filename, const vector< string > &_word_index, const map< string, int > &_vocab) (defined in SentenceSeries)SentenceSeries
vocabSentenceSeriesprivate
word_indexSentenceSeriesprivate
word_seriesSentenceSeriesprivate
~SentenceSeries() (defined in SentenceSeries)SentenceSeries
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+ + diff --git a/docs/html/class_sentence_series.html b/docs/html/class_sentence_series.html new file mode 100644 index 00000000..9b94e8e4 --- /dev/null +++ b/docs/html/class_sentence_series.html @@ -0,0 +1,202 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: SentenceSeries Class Reference + + + + + + + + + +
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SentenceSeries Class Reference
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+Public Member Functions

+ SentenceSeries (const string _filename, const vector< string > &_word_index, const map< string, int > &_vocab)
 
+void add_word_series (string name)
 
+int get_number_rows () const
 
+int get_number_columns () const
 
+string get_filename () const
 
+vector< string > get_word_index () const
 
+void get_series (string word_name, vector< double > &series)
 
+double get_min (string word)
 
+double get_average (string word)
 
+double get_max (string word)
 
+double get_std_dev (string word)
 
+double get_variance (string word)
 
+double get_min_change (string word)
 
+double get_max_change (string word)
 
+double get_correlation (string word1, string word2, int32_t lag) const
 
+void normalize_min_max (string word, double min, double max)
 
+void normalize_avg_std_dev (string word, double avg, double std_dev, double norm_max)
 
+void export_word_series (vector< vector< double > > &data, int word_offset)
 
+void export_word_series (vector< vector< double > > &data)
 
+SentenceSeries * copy ()
 
+void select_parameters (const vector< string > &input_parameter_names, const vector< string > &output_parameter_names)
 
+void select_parameters (const vector< string > &parameter_names)
 
+ + + + + + + + + + + + + + + + +

+Private Attributes

int number_rows
 Specifies the number of the rows in the dataset. More...
 
+string filename
 Specifices the filename for the training or testing data.
 
+map< string, int > vocab
 Maps the word string to index of the word series in a particular string.
 
+vector< string > word_index
 Maps the index to word string of the word series in a particular string.
 
+map< string, WordSeries * > word_series
 Maps the word string to the word series in a particular string.
 
+

Member Data Documentation

+ +

◆ number_rows

+ +
+
+ + + + + +
+ + + + +
int SentenceSeries::number_rows
+ 		+private
+
+ +

Specifies the number of the rows in the dataset.

+

This tells about the number of the unique words in the dataset.

+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_speciation_strategy-members.html b/docs/html/class_speciation_strategy-members.html new file mode 100644 index 00000000..bf4582f4 --- /dev/null +++ b/docs/html/class_speciation_strategy-members.html @@ -0,0 +1,91 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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SpeciationStrategy Member List
+
+
+ +

This is the complete list of members for SpeciationStrategy, including all inherited members.

+ + + + + + + + + + + + + +
generate_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)=0SpeciationStrategypure virtual
get_best_fitness()=0SpeciationStrategypure virtual
get_best_genome()=0SpeciationStrategypure virtual
get_generated_genomes() const =0SpeciationStrategypure virtual
get_global_best_genome()=0 (defined in SpeciationStrategy)SpeciationStrategypure virtual
get_inserted_genomes() const =0SpeciationStrategypure virtual
get_strategy_information_headers() const =0SpeciationStrategypure virtual
get_strategy_information_values() const =0SpeciationStrategypure virtual
get_worst_fitness()=0SpeciationStrategypure virtual
get_worst_genome()=0SpeciationStrategypure virtual
insert_genome(RNN_Genome *genome)=0SpeciationStrategypure virtual
print(string indent="") const =0SpeciationStrategypure virtual
+ +
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+ + diff --git a/docs/html/class_speciation_strategy.html b/docs/html/class_speciation_strategy.html new file mode 100644 index 00000000..be062467 --- /dev/null +++ b/docs/html/class_speciation_strategy.html @@ -0,0 +1,445 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: SpeciationStrategy Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +List of all members
+
+
SpeciationStrategy Class Referenceabstract
+
+
+
+Inheritance diagram for SpeciationStrategy:
+
+
+ + +IslandSpeciationStrategy +NeatSpeciationStrategy + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

virtual int32_t get_generated_genomes () const =0
 
virtual int32_t get_inserted_genomes () const =0
 
virtual double get_best_fitness ()=0
 Gets the fitness of the best genome of all the islands. More...
 
virtual double get_worst_fitness ()=0
 Gets the fitness of the worst genome of all the islands. More...
 
virtual RNN_Genome * get_best_genome ()=0
 Gets the best genome of all the islands. More...
 
virtual RNN_Genome * get_worst_genome ()=0
 Gets the the worst genome of all the islands. More...
 
virtual int32_t insert_genome (RNN_Genome *genome)=0
 Inserts a copy of the genome into this speciation strategy. More...
 
virtual RNN_Genome * generate_genome (uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)=0
 Generates a new genome. More...
 
virtual void print (string indent="") const =0
 Prints out all the island's populations. More...
 
+virtual string get_strategy_information_headers () const =0
 Gets speciation strategy information headers for logs.
 
+virtual string get_strategy_information_values () const =0
 Gets speciation strategy information values for logs.
 
+virtual RNN_Genome * get_global_best_genome ()=0
 
+

Member Function Documentation

+ +

◆ generate_genome()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
virtual RNN_Genome* SpeciationStrategy::generate_genome (uniform_real_distribution< double > & rng_0_1,
minstd_rand0 & generator,
function< void(int32_t, RNN_Genome *)> & mutate,
function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> & crossover 
)
+ 		+pure virtual
+
+ +

Generates a new genome.

+
Parameters
+ + + + + +
rng_0_1is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive).
generatoris the random number generator
mutateis the a function which performs a mutation on a genome
crossoveris the function which performs crossover between two genomes
+
+
+
Returns
the newly generated genome.
+ +

Implemented in IslandSpeciationStrategy, and NeatSpeciationStrategy.

+ +
+
+ +

◆ get_best_fitness()

+ +
+
+ + + + + +
+ + + + + + + +
virtual double SpeciationStrategy::get_best_fitness ()
+ 		+pure virtual
+
+ +

Gets the fitness of the best genome of all the islands.

+
Returns
the best fitness over all islands
+ +

Implemented in IslandSpeciationStrategy, and NeatSpeciationStrategy.

+ +
+
+ +

◆ get_best_genome()

+ +
+
+ + + + + +
+ + + + + + + +
virtual RNN_Genome* SpeciationStrategy::get_best_genome ()
+ 		+pure virtual
+
+ +

Gets the best genome of all the islands.

+
Returns
the best genome of all islands
+ +

Implemented in IslandSpeciationStrategy, and NeatSpeciationStrategy.

+ +
+
+ +

◆ get_generated_genomes()

+ +
+
+ + + + + +
+ + + + + + + +
virtual int32_t SpeciationStrategy::get_generated_genomes () const
+ 		+pure virtual
+
+
Returns
the number of generated genomes.
+ +

Implemented in IslandSpeciationStrategy, and NeatSpeciationStrategy.

+ +
+
+ +

◆ get_inserted_genomes()

+ +
+
+ + + + + +
+ + + + + + + +
virtual int32_t SpeciationStrategy::get_inserted_genomes () const
+ 		+pure virtual
+
+
Returns
the number of inserted genomes.
+ +

Implemented in IslandSpeciationStrategy, and NeatSpeciationStrategy.

+ +
+
+ +

◆ get_worst_fitness()

+ +
+
+ + + + + +
+ + + + + + + +
virtual double SpeciationStrategy::get_worst_fitness ()
+ 		+pure virtual
+
+ +

Gets the fitness of the worst genome of all the islands.

+
Returns
the worst fitness over all islands
+ +

Implemented in IslandSpeciationStrategy, and NeatSpeciationStrategy.

+ +
+
+ +

◆ get_worst_genome()

+ +
+
+ + + + + +
+ + + + + + + +
virtual RNN_Genome* SpeciationStrategy::get_worst_genome ()
+ 		+pure virtual
+
+ +

Gets the the worst genome of all the islands.

+
Returns
the worst genome of all islands
+ +

Implemented in IslandSpeciationStrategy, and NeatSpeciationStrategy.

+ +
+
+ +

◆ insert_genome()

+ +
+
+ + + + + +
+ + + + + + + + +
virtual int32_t SpeciationStrategy::insert_genome (RNN_Genome * genome)
+ 		+pure virtual
+
+ +

Inserts a copy of the genome into this speciation strategy.

+

The caller of this method will need to free the memory of the genome passed into this method.

+
Parameters
+ + +
genomeis the genome to insert.
+
+
+
Returns
a value < 0 if the genome was not inserted, 0 if it was a new best genome or > 0 otherwise.
+ +

Implemented in IslandSpeciationStrategy, and NeatSpeciationStrategy.

+ +
+
+ +

◆ print()

+ +
+
+ + + + + +
+ + + + + + + + +
virtual void SpeciationStrategy::print (string indent = "") const
+ 		+pure virtual
+
+ +

Prints out all the island's populations.

+
Parameters
+ + +
indentis how much to indent what is printed out
+
+
+ +

Implemented in IslandSpeciationStrategy, and NeatSpeciationStrategy.

+ +
+
+
The documentation for this class was generated from the following file: +
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+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_speciation_strategy.png b/docs/html/class_speciation_strategy.png new file mode 100644 index 0000000000000000000000000000000000000000..173163e9210ea22d6d7510afe1574d66c51e601b GIT binary patch literal 914 zcmeAS@N?(olHy`uVBq!ia0y~yV6*_T12~w0x&khR)W<9g^q87;J5qn_`LniT#;^9Hy4Rl{ioPBn=Y9I_`Tbwij;&jHzTs#B z&)Ll`8+X5!P00DSJ!{t!{h9h#V{3%-uV0ySPrY{ij<3JVk9E!Qi>V4(c5UJ8hYY6j z_d-t}-STcvlJ3)^I}#Vw%y#~)V=1`c@#oox4+bSG?Z0)yDdxE*f8FLC+Ua++AAOSi zKKIeJzomCVr?;nu>y{U0F<5SSx2pZQ_JJF7ru>M$f8Z!%%#z%l47_@L3zXmIH0avJ z%N(!=ib@m|GO)$SG%$*@Cp-{7;(OVom;J!}M79Ekbq^mgtTR5J$M?WUkMDsMe?E|< z1@wMPHcDtTpau?z`yu_v+0QopyayV1_B%n%Mzab(_rY4$Hi1L4XE<{_^zDgHn6Tub z#!=5p1v@M}jUGHwIoZ%XS%q28ZxWA6VX%y+`P2L7AAI>Sd5P|)=Hs#QTbG&fm!;np z{_gYQQl9?1Svyky$@mZ_ahC=3Zm}Y2Tx$!l$n-ouxdYPG9^LboZ+95>RZ#3T`jlxp@a~;Zgky zbv0X+R(sDbnHJTa*YNhEFk=OJk<{z6Z zJguJ1dvr|jdfC>6N4_5A-SX(uGs%LZrAKcb@x1gtSw!9QJ4z6O!t{fHxD&_Yzz%N3 cmXAO9dk#2gEzopr03?I1g8%>k literal 0 HcmV?d00001 diff --git a/docs/html/class_species-members.html b/docs/html/class_species-members.html new file mode 100644 index 00000000..fdcc8666 --- /dev/null +++ b/docs/html/class_species-members.html @@ -0,0 +1,101 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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Species Member List
+
+
+ +

This is the complete list of members for Species, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + +
contains(RNN_Genome *genome)Species
copy_random_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome)Species
copy_two_random_genomes(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome1, RNN_Genome **genome2)Species
erase_species() (defined in Species)Species
fitness_sharing_remove(double fitness_threshold, function< double(RNN_Genome *, RNN_Genome *)> &get_distance) (defined in Species)Species
genomesSpeciesprivate
get_best_fitness()Species
get_best_genome()Species
get_genomes() (defined in Species)Species
get_latested_genome() (defined in Species)Species
get_random_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator)Species
get_species_not_improving_count() (defined in Species)Species
get_worst_fitness()Species
get_worst_genome()Species
idSpeciesprivate
insert_genome(RNN_Genome *genome)Species
inserted_genome_id (defined in Species)Speciesprivate
print(string indent="")Species
set_species_not_improving_count(int32_t count) (defined in Species)Species
size()Species
Species(int32_t id)Species
species_not_improving_count (defined in Species)Speciesprivate
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+ + diff --git a/docs/html/class_species.html b/docs/html/class_species.html new file mode 100644 index 00000000..55e9cc61 --- /dev/null +++ b/docs/html/class_species.html @@ -0,0 +1,462 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Species Class Reference + + + + + + + + + +
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+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
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+ + + + + + + + +
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+ + +
+ +
+ +
+
+
+Public Member Functions | +Private Attributes | +List of all members
+
+
Species Class Reference
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ Species (int32_t id)
 Initializes a species.
 
double get_best_fitness ()
 Returns the fitness of the best genome in the island. More...
 
double get_worst_fitness ()
 Returns the fitness of the worst genome in the island. More...
 
RNN_Genome * get_best_genome ()
 Returns the best genomme in the island. More...
 
RNN_Genome * get_worst_genome ()
 Returns the worst genomme in the island. More...
 
RNN_Genome * get_random_genome (uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator)
 Returns the best genomme in the island. More...
 
int32_t size ()
 Returns the size of the island. More...
 
int32_t contains (RNN_Genome *genome)
 Checks to see if a genome already exists in the island, using the RNN_Genome::equals method (which checks to see if all edges and nodes are the same, but not necessarily weights). More...
 
void copy_random_genome (uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome)
 Selects a genome from the island at random and returns a copy of it. More...
 
void copy_two_random_genomes (uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome1, RNN_Genome **genome2)
 Selects two different genomes from the island at random and returns copies of them. More...
 
int32_t insert_genome (RNN_Genome *genome)
 Inserts a genome into the island. More...
 
void print (string indent="")
 Prints out the state of this island. More...
 
+vector< RNN_Genome * > get_genomes ()
 
+RNN_Genome * get_latested_genome ()
 
+void fitness_sharing_remove (double fitness_threshold, function< double(RNN_Genome *, RNN_Genome *)> &get_distance)
 
+void erase_species ()
 
+int32_t get_species_not_improving_count ()
 
+void set_species_not_improving_count (int32_t count)
 
+ + + + + + + + + + + +

+Private Attributes

+int32_t id
 An integer ID for this species.
 
+vector< int32_t > inserted_genome_id
 
+vector< RNN_Genome * > genomes
 The genomes on this species, stored in sorted order best (front) to worst (back).
 
+int32_t species_not_improving_count
 
+

Member Function Documentation

+ +

◆ contains()

+ +
+
+ + + + + + + + +
int32_t Species::contains (RNN_Genome * genome)
+
+ +

Checks to see if a genome already exists in the island, using the RNN_Genome::equals method (which checks to see if all edges and nodes are the same, but not necessarily weights).

+
Returns
the index in the island of the duplicate genome, -1 otherwise.
+ +
+
+ +

◆ copy_random_genome()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + +
void Species::copy_random_genome (uniform_real_distribution< double > & rng_0_1,
minstd_rand0 & generator,
RNN_Genome ** genome 
)
+
+ +

Selects a genome from the island at random and returns a copy of it.

+
Parameters
+ + + + +
rng_0_1is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive).
generatoris the random number generator
genomewill be the copied genome, an addresss to a pointer needs to be passed.
+
+
+ +
+
+ +

◆ copy_two_random_genomes()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void Species::copy_two_random_genomes (uniform_real_distribution< double > & rng_0_1,
minstd_rand0 & generator,
RNN_Genome ** genome1,
RNN_Genome ** genome2 
)
+
+ +

Selects two different genomes from the island at random and returns copies of them.

+
Parameters
+ + + + + +
rng_0_1is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive).
generatoris the random number generator
genome1will be the first copied genome, an addresss to a pointer needs to be passed.
genome2will be the second copied genome, an addresss to a pointer needs to be passed.
+
+
+ +
+
+ +

◆ get_best_fitness()

+ +
+
+ + + + + + + +
double Species::get_best_fitness ()
+
+ +

Returns the fitness of the best genome in the island.

+
Returns
the best fitness of the island
+ +
+
+ +

◆ get_best_genome()

+ +
+
+ + + + + + + +
RNN_Genome * Species::get_best_genome ()
+
+ +

Returns the best genomme in the island.

+
Returns
the best genome in the island
+ +
+
+ +

◆ get_random_genome()

+ +
+
+ + + + + + + + + + + + + + + + + + +
RNN_Genome * Species::get_random_genome (uniform_real_distribution< double > & rng_0_1,
minstd_rand0 & generator 
)
+
+ +

Returns the best genomme in the island.

+
Returns
the best genome in the island
+ +
+
+ +

◆ get_worst_fitness()

+ +
+
+ + + + + + + +
double Species::get_worst_fitness ()
+
+ +

Returns the fitness of the worst genome in the island.

+
Returns
the worst fitness of the island
+ +
+
+ +

◆ get_worst_genome()

+ +
+
+ + + + + + + +
RNN_Genome * Species::get_worst_genome ()
+
+ +

Returns the worst genomme in the island.

+
Returns
the worst genome in the island
+ +
+
+ +

◆ insert_genome()

+ +
+
+ + + + + + + + +
int32_t Species::insert_genome (RNN_Genome * genome)
+
+ +

Inserts a genome into the island.

+

Genomes are inserted in best to worst order genomes[0] will have the best fitness and genomes[size - 1] will have the worst.

+
Parameters
+ + +
genomeis the genome to be inserted.
+
+
+
Returns
-1 if not inserted, otherwise the index it was inserted at
+ +
+
+ +

◆ print()

+ +
+
+ + + + + + + + +
void Species::print (string indent = "")
+
+ +

Prints out the state of this island.

+
Parameters
+ + +
indentis how much to indent what is printed out
+
+
+ +
+
+ +

◆ size()

+ +
+
+ + + + + + + +
int32_t Species::size ()
+
+ +

Returns the size of the island.

+
Returns
the number of genomes in this island.
+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_time_series-members.html b/docs/html/class_time_series-members.html new file mode 100644 index 00000000..6cbc40e2 --- /dev/null +++ b/docs/html/class_time_series-members.html @@ -0,0 +1,108 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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+
+
TimeSeries Member List
+
+
+ +

This is the complete list of members for TimeSeries, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
add_value(double value) (defined in TimeSeries)TimeSeries
average (defined in TimeSeries)TimeSeriesprivate
calculate_statistics() (defined in TimeSeries)TimeSeries
copy() (defined in TimeSeries)TimeSeries
copy_values(vector< double > &series) (defined in TimeSeries)TimeSeries
cut(int32_t start, int32_t stop) (defined in TimeSeries)TimeSeries
get_average() const (defined in TimeSeries)TimeSeries
get_correlation(const TimeSeries *other, int32_t lag) const (defined in TimeSeries)TimeSeries
get_max() const (defined in TimeSeries)TimeSeries
get_max_change() const (defined in TimeSeries)TimeSeries
get_min() const (defined in TimeSeries)TimeSeries
get_min_change() const (defined in TimeSeries)TimeSeries
get_number_values() const (defined in TimeSeries)TimeSeries
get_std_dev() const (defined in TimeSeries)TimeSeries
get_value(int i) (defined in TimeSeries)TimeSeries
get_variance() const (defined in TimeSeries)TimeSeries
max (defined in TimeSeries)TimeSeriesprivate
max_change (defined in TimeSeries)TimeSeriesprivate
min (defined in TimeSeries)TimeSeriesprivate
min_change (defined in TimeSeries)TimeSeriesprivate
name (defined in TimeSeries)TimeSeriesprivate
normalize_avg_std_dev(double avg, double std_dev, double norm_max) (defined in TimeSeries)TimeSeries
normalize_min_max(double min, double max) (defined in TimeSeries)TimeSeries
print_statistics() (defined in TimeSeries)TimeSeries
std_dev (defined in TimeSeries)TimeSeriesprivate
TimeSeries() (defined in TimeSeries)TimeSeriesprivate
TimeSeries(string _name) (defined in TimeSeries)TimeSeries
values (defined in TimeSeries)TimeSeriesprivate
variance (defined in TimeSeries)TimeSeriesprivate
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EXONA: Evolutionary eXploration of Neural Architectures +
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TimeSeries Class Reference
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+Public Member Functions

+ TimeSeries (string _name)
 
+void add_value (double value)
 
+double get_value (int i)
 
+void calculate_statistics ()
 
+void print_statistics ()
 
+int get_number_values () const
 
+double get_min () const
 
+double get_average () const
 
+double get_max () const
 
+double get_std_dev () const
 
+double get_variance () const
 
+double get_min_change () const
 
+double get_max_change () const
 
+void normalize_min_max (double min, double max)
 
+void normalize_avg_std_dev (double avg, double std_dev, double norm_max)
 
+void cut (int32_t start, int32_t stop)
 
+double get_correlation (const TimeSeries *other, int32_t lag) const
 
+TimeSeries * copy ()
 
+void copy_values (vector< double > &series)
 
+ + + + + + + + + + + + + + + + + + + +

+Private Attributes

+string name
 
+double min
 
+double average
 
+double max
 
+double std_dev
 
+double variance
 
+double min_change
 
+double max_change
 
+vector< double > values
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_time_series_set-members.html b/docs/html/class_time_series_set-members.html new file mode 100644 index 00000000..68d2431e --- /dev/null +++ b/docs/html/class_time_series_set-members.html @@ -0,0 +1,110 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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TimeSeriesSet Member List
+
+
+ +

This is the complete list of members for TimeSeriesSet, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
add_time_series(string name) (defined in TimeSeriesSet)TimeSeriesSet
copy() (defined in TimeSeriesSet)TimeSeriesSet
cut(int32_t start, int32_t stop) (defined in TimeSeriesSet)TimeSeriesSet
export_time_series(vector< vector< double > > &data) (defined in TimeSeriesSet)TimeSeriesSet
export_time_series(vector< vector< double > > &data, const vector< string > &requested_fields) (defined in TimeSeriesSet)TimeSeriesSet
export_time_series(vector< vector< double > > &data, const vector< string > &requested_fields, const vector< string > &shift_fields, int32_t time_offset)TimeSeriesSet
fields (defined in TimeSeriesSet)TimeSeriesSetprivate
filename (defined in TimeSeriesSet)TimeSeriesSetprivate
get_average(string field) (defined in TimeSeriesSet)TimeSeriesSet
get_correlation(string field1, string field2, int32_t lag) const (defined in TimeSeriesSet)TimeSeriesSet
get_fields() const (defined in TimeSeriesSet)TimeSeriesSet
get_filename() const (defined in TimeSeriesSet)TimeSeriesSet
get_max(string field) (defined in TimeSeriesSet)TimeSeriesSet
get_max_change(string field) (defined in TimeSeriesSet)TimeSeriesSet
get_min(string field) (defined in TimeSeriesSet)TimeSeriesSet
get_min_change(string field) (defined in TimeSeriesSet)TimeSeriesSet
get_number_columns() const (defined in TimeSeriesSet)TimeSeriesSet
get_number_rows() const (defined in TimeSeriesSet)TimeSeriesSet
get_series(string field_name, vector< double > &series) (defined in TimeSeriesSet)TimeSeriesSet
get_std_dev(string field) (defined in TimeSeriesSet)TimeSeriesSet
get_variance(string field) (defined in TimeSeriesSet)TimeSeriesSet
normalize_avg_std_dev(string field, double avg, double std_dev, double norm_max) (defined in TimeSeriesSet)TimeSeriesSet
normalize_min_max(string field, double min, double max) (defined in TimeSeriesSet)TimeSeriesSet
number_rows (defined in TimeSeriesSet)TimeSeriesSetprivate
select_parameters(const vector< string > &parameter_names) (defined in TimeSeriesSet)TimeSeriesSet
select_parameters(const vector< string > &input_parameter_names, const vector< string > &output_parameter_names) (defined in TimeSeriesSet)TimeSeriesSet
split(int slices, vector< TimeSeriesSet * > &sub_series) (defined in TimeSeriesSet)TimeSeriesSet
time_series (defined in TimeSeriesSet)TimeSeriesSetprivate
TimeSeriesSet() (defined in TimeSeriesSet)TimeSeriesSetprivate
TimeSeriesSet(string _filename, const vector< string > &_fields) (defined in TimeSeriesSet)TimeSeriesSet
~TimeSeriesSet() (defined in TimeSeriesSet)TimeSeriesSet
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TimeSeriesSet Class Reference
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+Public Member Functions

+ TimeSeriesSet (string _filename, const vector< string > &_fields)
 
+void add_time_series (string name)
 
+int get_number_rows () const
 
+int get_number_columns () const
 
+string get_filename () const
 
+vector< string > get_fields () const
 
+void get_series (string field_name, vector< double > &series)
 
+double get_min (string field)
 
+double get_average (string field)
 
+double get_max (string field)
 
+double get_std_dev (string field)
 
+double get_variance (string field)
 
+double get_min_change (string field)
 
+double get_max_change (string field)
 
+double get_correlation (string field1, string field2, int32_t lag) const
 
+void normalize_min_max (string field, double min, double max)
 
+void normalize_avg_std_dev (string field, double avg, double std_dev, double norm_max)
 
+void export_time_series (vector< vector< double > > &data)
 
+void export_time_series (vector< vector< double > > &data, const vector< string > &requested_fields)
 
void export_time_series (vector< vector< double > > &data, const vector< string > &requested_fields, const vector< string > &shift_fields, int32_t time_offset)
 Time offset < 0 generates input data. More...
 
+TimeSeriesSet * copy ()
 
+void cut (int32_t start, int32_t stop)
 
+void split (int slices, vector< TimeSeriesSet * > &sub_series)
 
+void select_parameters (const vector< string > &parameter_names)
 
+void select_parameters (const vector< string > &input_parameter_names, const vector< string > &output_parameter_names)
 
+ + + + + + + + + +

+Private Attributes

+int number_rows
 
+string filename
 
+vector< string > fields
 
+map< string, TimeSeries * > time_series
 
+

Member Function Documentation

+ +

◆ export_time_series()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void TimeSeriesSet::export_time_series (vector< vector< double > > & data,
const vector< string > & requested_fields,
const vector< string > & shift_fields,
int32_t time_offset 
)
+
+ +

Time offset < 0 generates input data.

+

Do not use the last <time_offset> values Time offset > 0 generates output data. Do not use the first <time_offset> values

+ +
+
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_time_series_sets-members.html b/docs/html/class_time_series_sets-members.html new file mode 100644 index 00000000..5a5948a8 --- /dev/null +++ b/docs/html/class_time_series_sets-members.html @@ -0,0 +1,124 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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TimeSeriesSets Member List
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This is the complete list of members for TimeSeriesSets, including all inherited members.

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all_parameter_names (defined in TimeSeriesSets)TimeSeriesSetsprivate
denormalize(string field_name, double value) (defined in TimeSeriesSets)TimeSeriesSets
export_series_by_name(string field_name, vector< vector< double > > &exported_series)TimeSeriesSets
export_test_series(int time_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)TimeSeriesSets
export_time_series(const vector< int > &series_indexes, int time_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)TimeSeriesSets
export_training_series(int time_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)TimeSeriesSets
filenames (defined in TimeSeriesSets)TimeSeriesSetsprivate
generate_from_arguments(const vector< string > &arguments) (defined in TimeSeriesSets)TimeSeriesSetsstatic
generate_test(const vector< string > &_test_filenames, const vector< string > &_input_parameter_names, const vector< string > &_output_parameter_names) (defined in TimeSeriesSets)TimeSeriesSetsstatic
get_input_parameter_names() const (defined in TimeSeriesSets)TimeSeriesSets
get_normalize_avgs() const (defined in TimeSeriesSets)TimeSeriesSets
get_normalize_maxs() const (defined in TimeSeriesSets)TimeSeriesSets
get_normalize_mins() const (defined in TimeSeriesSets)TimeSeriesSets
get_normalize_std_devs() const (defined in TimeSeriesSets)TimeSeriesSets
get_normalize_type() const (defined in TimeSeriesSets)TimeSeriesSets
get_number_inputs() const (defined in TimeSeriesSets)TimeSeriesSets
get_number_outputs() const (defined in TimeSeriesSets)TimeSeriesSets
get_number_series() const (defined in TimeSeriesSets)TimeSeriesSets
get_output_parameter_names() const (defined in TimeSeriesSets)TimeSeriesSets
get_set(int32_t i) (defined in TimeSeriesSets)TimeSeriesSets
help_message() (defined in TimeSeriesSets)TimeSeriesSetsstatic
input_parameter_names (defined in TimeSeriesSets)TimeSeriesSetsprivate
load_time_series() (defined in TimeSeriesSets)TimeSeriesSetsprivate
normalize_avg_std_dev() (defined in TimeSeriesSets)TimeSeriesSets
normalize_avg_std_dev(const map< string, double > &_normalize_avgs, const map< string, double > &_normalize_std_devs, const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs) (defined in TimeSeriesSets)TimeSeriesSets
normalize_avgs (defined in TimeSeriesSets)TimeSeriesSetsprivate
normalize_maxs (defined in TimeSeriesSets)TimeSeriesSetsprivate
normalize_min_max() (defined in TimeSeriesSets)TimeSeriesSets
normalize_min_max(const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs) (defined in TimeSeriesSets)TimeSeriesSets
normalize_mins (defined in TimeSeriesSets)TimeSeriesSetsprivate
normalize_std_devs (defined in TimeSeriesSets)TimeSeriesSetsprivate
normalize_type (defined in TimeSeriesSets)TimeSeriesSetsprivate
output_parameter_names (defined in TimeSeriesSets)TimeSeriesSetsprivate
parse_parameters_string(const vector< string > &p) (defined in TimeSeriesSets)TimeSeriesSetsprivate
set_test_indexes(const vector< int > &_test_indexes) (defined in TimeSeriesSets)TimeSeriesSets
set_training_indexes(const vector< int > &_training_indexes) (defined in TimeSeriesSets)TimeSeriesSets
shift_parameter_names (defined in TimeSeriesSets)TimeSeriesSetsprivate
split_all(int number_slices) (defined in TimeSeriesSets)TimeSeriesSets
split_series(int series, int number_slices) (defined in TimeSeriesSets)TimeSeriesSets
test_indexes (defined in TimeSeriesSets)TimeSeriesSetsprivate
time_series (defined in TimeSeriesSets)TimeSeriesSetsprivate
TimeSeriesSets() (defined in TimeSeriesSets)TimeSeriesSets
training_indexes (defined in TimeSeriesSets)TimeSeriesSetsprivate
write_time_series_sets(string base_filename) (defined in TimeSeriesSets)TimeSeriesSets
~TimeSeriesSets() (defined in TimeSeriesSets)TimeSeriesSets
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+Public Member Functions

+void normalize_min_max ()
 
+void normalize_min_max (const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs)
 
+void normalize_avg_std_dev ()
 
+void normalize_avg_std_dev (const map< string, double > &_normalize_avgs, const map< string, double > &_normalize_std_devs, const map< string, double > &_normalize_mins, const map< string, double > &_normalize_maxs)
 
+void split_series (int series, int number_slices)
 
+void split_all (int number_slices)
 
+void write_time_series_sets (string base_filename)
 
+void export_time_series (const vector< int > &series_indexes, int time_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
 the series argument is a vector of indexes of the time series that was initially loaded that are to be exported
 
+void export_training_series (int time_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
 This exports the time series marked as training series by the training_indexes vector.
 
+void export_test_series (int time_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
 This exports the time series marked as test series by the test_indexes vector.
 
+void export_series_by_name (string field_name, vector< vector< double > > &exported_series)
 This exports from all the loaded time series a particular column.
 
+double denormalize (string field_name, double value)
 
+string get_normalize_type () const
 
+map< string, double > get_normalize_mins () const
 
+map< string, double > get_normalize_maxs () const
 
+map< string, double > get_normalize_avgs () const
 
+map< string, double > get_normalize_std_devs () const
 
+vector< string > get_input_parameter_names () const
 
+vector< string > get_output_parameter_names () const
 
+int get_number_series () const
 
+int get_number_inputs () const
 
+int get_number_outputs () const
 
+void set_training_indexes (const vector< int > &_training_indexes)
 
+void set_test_indexes (const vector< int > &_test_indexes)
 
+TimeSeriesSet * get_set (int32_t i)
 
+ + + + + + + +

+Static Public Member Functions

+static void help_message ()
 
+static TimeSeriesSets * generate_from_arguments (const vector< string > &arguments)
 
+static TimeSeriesSets * generate_test (const vector< string > &_test_filenames, const vector< string > &_input_parameter_names, const vector< string > &_output_parameter_names)
 
+ + + + + +

+Private Member Functions

+void parse_parameters_string (const vector< string > &p)
 
+void load_time_series ()
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+string normalize_type
 
+vector< string > filenames
 
+vector< int > training_indexes
 
+vector< int > test_indexes
 
+vector< string > input_parameter_names
 
+vector< string > output_parameter_names
 
+vector< string > shift_parameter_names
 
+vector< string > all_parameter_names
 
+vector< TimeSeriesSet * > time_series
 
+map< string, double > normalize_mins
 
+map< string, double > normalize_maxs
 
+map< string, double > normalize_avgs
 
+map< string, double > normalize_std_devs
 
+
The documentation for this class was generated from the following files: +
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+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_tracker-members.html b/docs/html/class_tracker-members.html new file mode 100644 index 00000000..d974764b --- /dev/null +++ b/docs/html/class_tracker-members.html @@ -0,0 +1,91 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+
Tracker Member List
+
+
+ +

This is the complete list of members for Tracker, including all inherited members.

+ + + + + + + + + + + + + +
_max (defined in Tracker)Trackerprivate
_min (defined in Tracker)Trackerprivate
avg() const (defined in Tracker)Tracker
correlate(Tracker &other) (defined in Tracker)Tracker
count (defined in Tracker)Trackerprivate
max() const (defined in Tracker)Tracker
min() const (defined in Tracker)Tracker
stddev() (defined in Tracker)Tracker
sum (defined in Tracker)Trackerprivate
track(double value) (defined in Tracker)Tracker
Tracker() (defined in Tracker)Tracker
values (defined in Tracker)Trackerprivate
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_tracker.html b/docs/html/class_tracker.html new file mode 100644 index 00000000..fb869e87 --- /dev/null +++ b/docs/html/class_tracker.html @@ -0,0 +1,124 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Tracker Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +List of all members
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Tracker Class Reference
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+ + + + + + + + + + + + + + +

+Public Member Functions

+void track (double value)
 
+double min () const
 
+double max () const
 
+double avg () const
 
+double stddev ()
 
+double correlate (Tracker &other)
 
+ + + + + + + + + + + +

+Private Attributes

+int32_t count
 
+double _min
 
+double sum
 
+double _max
 
+vector< double > values
 
+
The documentation for this class was generated from the following files: +
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+ + diff --git a/docs/html/class_u_g_r_n_n___node-members.html b/docs/html/class_u_g_r_n_n___node-members.html new file mode 100644 index 00000000..8e61affb --- /dev/null +++ b/docs/html/class_u_g_r_n_n___node-members.html @@ -0,0 +1,151 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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UGRNN_Node Member List
+
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+ +

This is the complete list of members for UGRNN_Node, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
backward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
c (defined in UGRNN_Node)UGRNN_Nodeprivate
c_bias (defined in UGRNN_Node)UGRNN_Nodeprivate
ch (defined in UGRNN_Node)UGRNN_Nodeprivate
copy() const (defined in UGRNN_Node)UGRNN_Nodevirtual
cw (defined in UGRNN_Node)UGRNN_Nodeprivate
d_c_bias (defined in UGRNN_Node)UGRNN_Nodeprivate
d_ch (defined in UGRNN_Node)UGRNN_Nodeprivate
d_cw (defined in UGRNN_Node)UGRNN_Nodeprivate
d_g_bias (defined in UGRNN_Node)UGRNN_Nodeprivate
d_gh (defined in UGRNN_Node)UGRNN_Nodeprivate
d_gw (defined in UGRNN_Node)UGRNN_Nodeprivate
d_h_prev (defined in UGRNN_Node)UGRNN_Nodeprivate
d_input (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
depth (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
enabled (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
equals(RNN_Node_Interface *other) const (defined in RNN_Node_Interface)RNN_Node_Interface
error_fired(int time, double error) (defined in UGRNN_Node)UGRNN_Node
error_fired(int32_t time, double error)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
error_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
forward_reachable (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
g (defined in UGRNN_Node)UGRNN_Nodeprivate
g_bias (defined in UGRNN_Node)UGRNN_Nodeprivate
get_depth() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_gradient(string gradient_name) (defined in UGRNN_Node)UGRNN_Node
get_gradients(vector< double > &gradients) (defined in UGRNN_Node)UGRNN_Nodevirtual
get_innovation_number() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_layer_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_node_type() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_number_weights() const (defined in UGRNN_Node)UGRNN_Nodevirtual
get_total_inputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_total_outputs() const (defined in RNN_Node_Interface)RNN_Node_Interface
get_weights(vector< double > &parameters) const (defined in UGRNN_Node)UGRNN_Nodevirtual
get_weights(uint32_t &offset, vector< double > &parameters) const (defined in UGRNN_Node)UGRNN_Nodevirtual
gh (defined in UGRNN_Node)UGRNN_Nodeprivate
gw (defined in UGRNN_Node)UGRNN_Nodeprivate
initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range) (defined in UGRNN_Node)UGRNN_Nodevirtual
initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma) (defined in UGRNN_Node)UGRNN_Nodevirtual
initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution< double > &rng) (defined in UGRNN_Node)UGRNN_Nodevirtual
initialize_xavier(minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range) (defined in UGRNN_Node)UGRNN_Nodevirtual
innovation_number (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
input_fired(int time, double incoming_output) (defined in UGRNN_Node)UGRNN_Node
input_fired(int32_t time, double incoming_output)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
input_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
inputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
is_enabled() const (defined in RNN_Node_Interface)RNN_Node_Interface
is_reachable() const (defined in RNN_Node_Interface)RNN_Node_Interface
layer_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
ld_c (defined in UGRNN_Node)UGRNN_Nodeprivate
ld_g (defined in UGRNN_Node)UGRNN_Nodeprivate
node_type (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
output_fired(int time, double delta) (defined in UGRNN_Node)UGRNN_Node
output_fired(int32_t time, double delta)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
output_values (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
outputs_fired (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
parameter_name (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
print_gradient(string gradient_name) (defined in UGRNN_Node)UGRNN_Node
reset(int _series_length) (defined in UGRNN_Node)UGRNN_Node
reset(int32_t _series_length)=0 (defined in RNN_Node_Interface)RNN_Node_Interfacepure virtual
RNN_Edge (defined in UGRNN_Node)UGRNN_Nodefriend
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth) (defined in RNN_Node_Interface)RNN_Node_Interface
RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name) (defined in RNN_Node_Interface)RNN_Node_Interface
series_length (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
set_weights(const vector< double > &parameters) (defined in UGRNN_Node)UGRNN_Nodevirtual
set_weights(uint32_t &offset, const vector< double > &parameters) (defined in UGRNN_Node)UGRNN_Nodevirtual
total_inputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
total_outputs (defined in RNN_Node_Interface)RNN_Node_Interfaceprotected
try_update_deltas(int time) (defined in UGRNN_Node)UGRNN_Node
UGRNN_Node(int _innovation_number, int _type, double _depth) (defined in UGRNN_Node)UGRNN_Node
write_to_stream(ostream &out) (defined in UGRNN_Node)UGRNN_Node
~RNN_Node_Interface() (defined in RNN_Node_Interface)RNN_Node_Interfacevirtual
~UGRNN_Node() (defined in UGRNN_Node)UGRNN_Node
+ +
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+ + diff --git a/docs/html/class_u_g_r_n_n___node.html b/docs/html/class_u_g_r_n_n___node.html new file mode 100644 index 00000000..3dda2b90 --- /dev/null +++ b/docs/html/class_u_g_r_n_n___node.html @@ -0,0 +1,322 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: UGRNN_Node Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +Friends | +List of all members
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UGRNN_Node Class Reference
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+Inheritance diagram for UGRNN_Node:
+
+
+ + +RNN_Node_Interface + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ UGRNN_Node (int _innovation_number, int _type, double _depth)
 
+void initialize_lamarckian (minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma)
 
+void initialize_xavier (minstd_rand0 &generator, uniform_real_distribution< double > &rng1_1, double range)
 
+void initialize_kaiming (minstd_rand0 &generator, NormalDistribution &normal_distribution, double range)
 
+void initialize_uniform_random (minstd_rand0 &generator, uniform_real_distribution< double > &rng)
 
+double get_gradient (string gradient_name)
 
+void print_gradient (string gradient_name)
 
+void input_fired (int time, double incoming_output)
 
+void try_update_deltas (int time)
 
+void error_fired (int time, double error)
 
+void output_fired (int time, double delta)
 
+uint32_t get_number_weights () const
 
+void get_weights (vector< double > &parameters) const
 
+void set_weights (const vector< double > &parameters)
 
+void get_weights (uint32_t &offset, vector< double > &parameters) const
 
+void set_weights (uint32_t &offset, const vector< double > &parameters)
 
+void get_gradients (vector< double > &gradients)
 
+void reset (int _series_length)
 
+void write_to_stream (ostream &out)
 
+RNN_Node_Interface * copy () const
 
- Public Member Functions inherited from RNN_Node_Interface
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth)
 
+ RNN_Node_Interface (int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name)
 
+virtual void input_fired (int32_t time, double incoming_output)=0
 
+virtual void output_fired (int32_t time, double delta)=0
 
+virtual void error_fired (int32_t time, double error)=0
 
+virtual void reset (int32_t _series_length)=0
 
+void write_to_stream (ostream &out)
 
+int32_t get_node_type () const
 
+int32_t get_layer_type () const
 
+int32_t get_innovation_number () const
 
+int32_t get_total_inputs () const
 
+int32_t get_total_outputs () const
 
+double get_depth () const
 
+bool equals (RNN_Node_Interface *other) const
 
+bool is_reachable () const
 
+bool is_enabled () const
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

+double cw
 
+double ch
 
+double c_bias
 
+double gw
 
+double gh
 
+double g_bias
 
+vector< double > d_cw
 
+vector< double > d_ch
 
+vector< double > d_c_bias
 
+vector< double > d_gw
 
+vector< double > d_gh
 
+vector< double > d_g_bias
 
+vector< double > d_h_prev
 
+vector< double > c
 
+vector< double > ld_c
 
+vector< double > g
 
+vector< double > ld_g
 
+ + + +

+Friends

+class RNN_Edge
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from RNN_Node_Interface
+int32_t innovation_number
 
+int32_t layer_type
 
+int32_t node_type
 
+double depth
 
+string parameter_name
 
+bool enabled
 
+bool backward_reachable
 
+bool forward_reachable
 
+int32_t series_length
 
+vector< double > input_values
 
+vector< double > output_values
 
+vector< double > error_values
 
+vector< double > d_input
 
+vector< int32_t > inputs_fired
 
+vector< int32_t > outputs_fired
 
+int32_t total_inputs
 
+int32_t total_outputs
 
+
The documentation for this class was generated from the following files: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_u_g_r_n_n___node.png b/docs/html/class_u_g_r_n_n___node.png new file mode 100644 index 0000000000000000000000000000000000000000..97b0318165a522f42b920d19c16293624e49a3df GIT binary patch literal 569 zcmeAS@N?(olHy`uVBq!ia0vp^EkGQ=!3-qdo9eCsQW60^A+G=b{|7Q(y!l$%e`o@b z1;z&s9ANFdBM;u58v}41+YerY6Os@Z36)(*bZhQBJ zpi5=>^BJM{S}hzU9CPNCy)s%luP%NM zt4oK^{4pvN5q5N$eJ^Fd2miw3QZo~zoh7cSzENG>7nSluu%+v35hK?kUzwQW-2zIN z)>wDe)&ElIOLn@u$WP~Tp_p*{^+H9pZ}Zz9zdy41O-6(6+HAWO&+M#ST+&`z9uad$ zzrXu*vx`gHCwqf-0l{Rxg$hYMg_{`aRn-I$2YHVj&gQe-504RAGIvVw4rey>&33GU&{mT9Vn zWn>#i1&1^Ad^zN~;^?HTW?rAGf!#d(1{YonJi8mN$k116YklgjnZ(-}lJdQspDH~B zH?~{+_p8ydX87kM-*iLieaUtPcaX0@j%?ykY&rRhvFxPujnJb(MZkDw@O1TaS?83{ F1OV*0_EP`= literal 0 HcmV?d00001 diff --git a/docs/html/class_word_series-members.html b/docs/html/class_word_series-members.html new file mode 100644 index 00000000..2c59a88f --- /dev/null +++ b/docs/html/class_word_series-members.html @@ -0,0 +1,109 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+
+
WordSeries Member List
+
+
+ +

This is the complete list of members for WordSeries, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
add_value(double value) (defined in WordSeries)WordSeries
averageWordSeriesprivate
calculate_statistics() (defined in WordSeries)WordSeries
copy() (defined in WordSeries)WordSeries
copy_values(vector< double > &series) (defined in WordSeries)WordSeries
cut(int32_t start, int32_t stop) (defined in WordSeries)WordSeries
get_average() const (defined in WordSeries)WordSeries
get_correlation(const WordSeries *other, int32_t lag) const (defined in WordSeries)WordSeries
get_max() const (defined in WordSeries)WordSeries
get_max_change() const (defined in WordSeries)WordSeries
get_min() const (defined in WordSeries)WordSeries
get_min_change() const (defined in WordSeries)WordSeries
get_number_values() const (defined in WordSeries)WordSeries
get_std_dev() const (defined in WordSeries)WordSeries
get_value(int i) (defined in WordSeries)WordSeries
get_variance() const (defined in WordSeries)WordSeries
maxWordSeriesprivate
max_change (defined in WordSeries)WordSeriesprivate
minWordSeriesprivate
min_change (defined in WordSeries)WordSeriesprivate
nameWordSeriesprivate
normalize_avg_std_dev(double avg, double std_dev, double norm_max) (defined in WordSeries)WordSeries
normalize_min_max(double min, double max) (defined in WordSeries)WordSeries
print_statistics() (defined in WordSeries)WordSeries
std_devWordSeriesprivate
valuesWordSeriesprivate
varianceWordSeriesprivate
vocab_sizeWordSeriesprivate
WordSeries() (defined in WordSeries)WordSeriesprivate
WordSeries(string _name) (defined in WordSeries)WordSeries
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/class_word_series.html b/docs/html/class_word_series.html new file mode 100644 index 00000000..5f2e1a06 --- /dev/null +++ b/docs/html/class_word_series.html @@ -0,0 +1,331 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: WordSeries Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Private Attributes | +List of all members
+
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WordSeries Class Reference
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+ WordSeries (string _name)
 
+void add_value (double value)
 
+double get_value (int i)
 
+void calculate_statistics ()
 
+void print_statistics ()
 
+int get_number_values () const
 
+double get_min () const
 
+double get_average () const
 
+double get_max () const
 
+double get_std_dev () const
 
+double get_variance () const
 
+double get_min_change () const
 
+double get_max_change () const
 
+void normalize_min_max (double min, double max)
 
+void normalize_avg_std_dev (double avg, double std_dev, double norm_max)
 
+void cut (int32_t start, int32_t stop)
 
+double get_correlation (const WordSeries *other, int32_t lag) const
 
+WordSeries * copy ()
 
+void copy_values (vector< double > &series)
 
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Private Attributes

string name
 Specifies the string name in the data. More...
 
+int vocab_size
 Specifies the number of the unique strings in the dataset.
 
double min
 Specifies the minimum of the word series in a particular string. More...
 
double average
 Specifies the average of the word series in a particular string. More...
 
double max
 Specifies the maximum of the word series in a particular string. More...
 
double std_dev
 Specifies the std. More...
 
double variance
 Specifies the variance of the word series in a particular string. More...
 
+double min_change
 
+double max_change
 
+vector< double > values
 Storers the values in the word series respective of the time in a particular string.
 
+

Member Data Documentation

+ +

◆ average

+ +
+
+ + + + + +
+ + + + +
double WordSeries::average
+ 		+private
+
+ +

Specifies the average of the word series in a particular string.

+

This tells about the words which are used the most.

+ +
+
+ +

◆ max

+ +
+
+ + + + + +
+ + + + +
double WordSeries::max
+ 		+private
+
+ +

Specifies the maximum of the word series in a particular string.

+

This could be 0 or 1

+ +
+
+ +

◆ min

+ +
+
+ + + + + +
+ + + + +
double WordSeries::min
+ 		+private
+
+ +

Specifies the minimum of the word series in a particular string.

+

This could be 0 or 1

+ +
+
+ +

◆ name

+ +
+
+ + + + + +
+ + + + +
string WordSeries::name
+ 		+private
+
+ +

Specifies the string name in the data.

+

All the unique strings in the datasets will have its each word series.

+ +
+
+ +

◆ std_dev

+ +
+
+ + + + + +
+ + + + +
double WordSeries::std_dev
+ 		+private
+
+ +

Specifies the std.

+

deviation of the word series in a particular string.

+ +
+
+ +

◆ variance

+ +
+
+ + + + + +
+ + + + +
double WordSeries::variance
+ 		+private
+
+ +

Specifies the variance of the word series in a particular string.

+

This tells about the closeness of a word in the dataset.

+ +
+
+
The documentation for this class was generated from the following files: +
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config_to_arg.ConfigToArg Member List
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This is the complete list of members for config_to_arg.ConfigToArg, including all inherited members.

+ + + + + + + +
__init__(self, config, name, filename, config_options={}, defaults={}, types={}, constraints={})config_to_arg.ConfigToArg
config_options (defined in config_to_arg.ConfigToArg)config_to_arg.ConfigToArg
constraints (defined in config_to_arg.ConfigToArg)config_to_arg.ConfigToArg
optional_config_option_defaults (defined in config_to_arg.ConfigToArg)config_to_arg.ConfigToArg
to_args(self) (defined in config_to_arg.ConfigToArg)config_to_arg.ConfigToArg
types (defined in config_to_arg.ConfigToArg)config_to_arg.ConfigToArg
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config_to_arg.ConfigToArg Class Reference
+
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+Inheritance diagram for config_to_arg.ConfigToArg:
+
+
+ + +examm_task.ExammTask + +
+ + + + + + +

+Public Member Functions

def __init__ (self, config, name, filename, config_options={}, defaults={}, types={}, constraints={})
 
+def to_args (self)
 
+ + + + + + + + + +

+Public Attributes

+ config_options
 
+ optional_config_option_defaults
 
+ types
 
+ constraints
 
+

Detailed Description

+
A easily configurable class meant to be used for different configuration sections.
+

Constructor & Destructor Documentation

+ +

◆ __init__()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
def config_to_arg.ConfigToArg.__init__ ( self,
 config,
 name,
 filename,
 config_options = {},
 defaults = {},
 types = {},
 constraints = {} 
)
+
+
:param config a dictionary from the parsed config file
+:param name the name of this configuration section (e.g. examm.<section_name>)
+:param filename name / path of the configuration file
+
+:param config_options a map from config option name to a lambda used to turn
+the parameter into a list of command line arguments. The lambda takes self and the
+value of the parameter as arguments.
+
+:param defaults a map from config option name to a lambda that
+returns the default value of a parameter. If a config option does not appear
+in this map then it is not optional. If the value of an option is None,
+it won't be added to the argument list / it will remain unset.
+
+:param types maps config option name to a set of the valid types for that config option.
+
+:param constraints maps config option name to a tuple, the first element is a lambda that
+takes the config option value as an argument and returns True if it meets all constraints.
+The second value of the tuple is a short description of the constraints.
+
+
+
+
The documentation for this class was generated from the following file:
    +
  • cli/config_to_arg.py
    • +
+
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+Generated by   +doxygen + 1.8.17 +
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Class Index
+
+
+
c | d | e | g | h | i | l | m | n | p | r | s | t | u | w
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
  c  
+		ExammTask (examm_task)   less_than_max   Point   sort_RNN_Edges_by_innovation   
  g  
+		less_than_min   
  r  
+		sort_RNN_Edges_by_output_depth   
CNN_Edge   Line   sort_RNN_Nodes_by_depth   
CNN_Genome   GRU_Node   LodePNGColorMode   RANDOM_DAG_Node   sort_RNN_Nodes_by_innovation   
CNN_Node   
  h  
+		LodePNGCompressSettings   RecArgs (rec_args)   sort_RNN_Recurrent_Edges_by_depth   
Coin   LodePNGDecoderSettings   Rectangle   sort_RNN_Recurrent_Edges_by_innovation   
Color   Hash   LodePNGDecompressSettings   ResultSet   sort_RNN_Recurrent_Edges_by_output_depth   
ColorProfile   HuffmanTree   LodePNGEncoderSettings   RNN   SpeciationStrategy   
ColorTree   
  i  
+		LodePNGInfo   RNN_Edge   Species   
ConfigToArg (config_to_arg)   LodePNGState   RNN_Genome   
  t  
+
ConsolidatedStatistics   Image   LodePNGTime   RNN_Node   
Corpus   ImageInterface   Log   RNN_Node_Interface   TimeSeries   
cs_less_than_avg   Images   LogFile   RNN_Recurrent_Edge   TimeSeriesSet   
cs_less_than_max   ImagesInterface   LSTM_Node   RunStatistics   TimeSeriesSets   
cs_less_than_min   Island   
  m  
+
  s  
+		Tracker   
  d  
+		IslandPurgingArgs (island_purging_args)   
  u  
+
IslandSpeciationStrategy   MGU_Node   SentenceSeries   
Delta_Node   
  l  
+		min_max_heap   sort_CNN_Edges_by_depth   ucvector   
  e  
+		MosaicImages   sort_CNN_Edges_by_innovation   UGRNN_Node   
LargeImage   MultiImagesInterface   sort_CNN_Edges_by_output_depth   uivector   
ENARC_Node   LargeImages   
  n  
+		sort_CNN_Nodes_by_depth   
  w  
+
ENAS_DAG_Node   less_than_avg   sort_genomes_by_fitness   
EXACT   less_than_dfm_avg   NeatSpeciationStrategy   sort_genomes_by_predictions   WordSeries   
EXACT_RESULT   less_than_dfm_max   NormalDistribution   sort_genomes_by_validation_error   
EXAMM   less_than_dfm_min   
  p  
+		sort_RNN_Edges_by_depth   
ParseTurbines   
+
c | d | e | g | h | i | l | m | n | p | r | s | t | u | w
+
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examm_task.ExammTask Member List
+
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+ +

This is the complete list of members for examm_task.ExammTask, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + +
__init__(self, toml_object, file_path, run_number) (defined in examm_task.ExammTask)examm_task.ExammTask
config_to_arg::ConfigToArg.__init__(self, config, name, filename, config_options={}, defaults={}, types={}, constraints={})config_to_arg.ConfigToArg
ALL_NODE_TYPES (defined in examm_task.ExammTask)examm_task.ExammTaskstatic
all_strings(self, some_list) (defined in examm_task.ExammTask)examm_task.ExammTask
CONFIG_OPTIONS (defined in examm_task.ExammTask)examm_task.ExammTaskstatic
config_options (defined in config_to_arg.ConfigToArg)config_to_arg.ConfigToArg
constraints (defined in config_to_arg.ConfigToArg)config_to_arg.ConfigToArg
CONSTRAINTS (defined in examm_task.ExammTask)examm_task.ExammTaskstatic
DEFAULTS (defined in examm_task.ExammTask)examm_task.ExammTaskstatic
examm_config (defined in examm_task.ExammTask)examm_task.ExammTask
filename (defined in examm_task.ExammTask)examm_task.ExammTask
glob_to_all(paths) (defined in examm_task.ExammTask)examm_task.ExammTaskstatic
optional_config_option_defaults (defined in config_to_arg.ConfigToArg)config_to_arg.ConfigToArg
output_directory (defined in examm_task.ExammTask)examm_task.ExammTask
parallelism (defined in examm_task.ExammTask)examm_task.ExammTask
to_args(self) (defined in config_to_arg.ConfigToArg)config_to_arg.ConfigToArg
to_command(self) (defined in examm_task.ExammTask)examm_task.ExammTask
TYPES (defined in examm_task.ExammTask)examm_task.ExammTaskstatic
types (defined in config_to_arg.ConfigToArg)config_to_arg.ConfigToArg
version (defined in examm_task.ExammTask)examm_task.ExammTask
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/classexamm__task_1_1_examm_task.html b/docs/html/classexamm__task_1_1_examm_task.html new file mode 100644 index 00000000..8f89f56b --- /dev/null +++ b/docs/html/classexamm__task_1_1_examm_task.html @@ -0,0 +1,317 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: examm_task.ExammTask Class Reference + + + + + + + + + +
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examm_task.ExammTask Class Reference
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+Inheritance diagram for examm_task.ExammTask:
+
+
+ + +config_to_arg.ConfigToArg + +
+ + + + + + + + + + + + + +

+Public Member Functions

+def __init__ (self, toml_object, file_path, run_number)
 
+def to_command (self)
 
+def all_strings (self, some_list)
 
- Public Member Functions inherited from config_to_arg.ConfigToArg
def __init__ (self, config, name, filename, config_options={}, defaults={}, types={}, constraints={})
 
+def to_args (self)
 
+ + + +

+Static Public Member Functions

+def glob_to_all (paths)
 
+ + + + + + + + + + + + + + + + + + + + +

+Public Attributes

+ examm_config
 
+ filename
 
+ version
 
+ parallelism
 
+ output_directory
 
- Public Attributes inherited from config_to_arg.ConfigToArg
+ config_options
 
+ optional_config_option_defaults
 
+ types
 
+ constraints
 
+ + + + + + + + + + + +

+Static Public Attributes

+list ALL_NODE_TYPES = [ 'simple', 'UGRNN', 'MGU', 'GRU', 'delta', 'LSTM' , 'ENARC' ]
 
dictionary CONFIG_OPTIONS
 
dictionary DEFAULTS
 
dictionary TYPES
 
dictionary CONSTRAINTS
 
+

Member Data Documentation

+ +

◆ CONFIG_OPTIONS

+ +
+
+ + + + + +
+ + + + +
dictionary examm_task.ExammTask.CONFIG_OPTIONS
+ 		+static
+
+Initial value:
= {
+
"training_files": lambda self, x: ['--training_filenames'] + ExammTask.glob_to_all(x),
+
"test_files": lambda self, x: ['--test_filenames'] + ExammTask.glob_to_all(x),
+
"time_offset": lambda self, x: ['--time_offset', str(x)],
+
"input_parameters": lambda self, x: ['--input_parameter_names'] + list(map(str, x)),
+
"output_parameters": lambda self, x: ['--output_parameter_names'] + list(map(str, x)),
+
"n_islands": lambda self, x: ['--number_islands', str(x)],
+
"population_size": lambda self, x: ['--population_size', str(x)],
+
"max_genomes": lambda self, x: ['--max_genomes', str(x)],
+
"bp_iterations": lambda self, x: ['--bp_iterations', str(x)],
+
"output_directory": lambda self, x: ['--output_directory', str(x)],
+
"node_types": lambda self, x: ['--possible_node_types'] + list(map(str, x)),
+
"rec": lambda self, x: RecArgs(x, self.filename).to_args(),
+
"island_purging": lambda self, x: IslandPurgingArgs(x, self.filename).to_args()
+
}
+
+
+
+ +

◆ CONSTRAINTS

+ +
+
+ + + + + +
+ + + + +
dictionary examm_task.ExammTask.CONSTRAINTS
+ 		+static
+
+Initial value:
= {
+
"training_files": (lambda self: self.all_strings(self.training_files),
+
"must be a list of strings"),
+
"test_files": (lambda self: self.all_strings(self.test_files),
+
"must be a list of strings"),
+
"time_offset": (lambda self: self.time_offset > 0, "must a positive integer"),
+
"input_parameters": (lambda self: self.all_strings(self.input_parameters),
+
"must be a list of strings"),
+
"output_parameters": (lambda self: self.all_strings(self.output_parameters),
+
"must be a list of strings"),
+
"n_islands": (lambda self: self.n_islands > 0, "must be a positive integer"),
+
"population_size": (lambda self: self.population_size > 0, "must be a positive integer"),
+
"max_genomes": (lambda self: self.max_genomes > 0, "must be a positive integer"),
+
"bp_iterations": (lambda self: self.bp_iterations >= 0, "must be a non-negative integer"),
+
"output_directory": (lambda self: True, "must be a valid path"),
+
"node_types": (lambda self: self.all_strings(self.node_types) \
+
and set(self.node_types).issubset(set(ExammTask.ALL_NODE_TYPES)),
+
"must be a subset of " + str(ALL_NODE_TYPES)),
+
# Subsections should be of type dict
+
"rec": (lambda self: True, ""),
+
"island_purging": (lambda self: True, "")
+
+
}
+
+
+
+ +

◆ DEFAULTS

+ +
+
+ + + + + +
+ + + + +
dictionary examm_task.ExammTask.DEFAULTS
+ 		+static
+
+Initial value:
= {
+
"node_types": lambda: ExammTask.ALL_NODE_TYPES,
+
"rec": lambda: dict(),
+
"island_purging": lambda: dict()
+
}
+
+
+
+ +

◆ TYPES

+ +
+
+ + + + + +
+ + + + +
dictionary examm_task.ExammTask.TYPES
+ 		+static
+
+Initial value:
= {
+
"training_files": {list},
+
"test_files": {list},
+
"time_offset": {int},
+
"input_parameters": {list},
+
"output_parameters": {list},
+
"n_islands": {int},
+
"population_size": {int},
+
"max_genomes": {int},
+
"bp_iterations": {int},
+
"output_directory": {str},
+
"node_types": {list},
+
# Subsections should be of type dict
+
"rec": {dict},
+
"island_purging": {dict}
+
+
}
+
+
+
+
The documentation for this class was generated from the following file:
    +
  • cli/examm_task.py
    • +
+
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/classexamm__task_1_1_examm_task.png b/docs/html/classexamm__task_1_1_examm_task.png new file mode 100644 index 0000000000000000000000000000000000000000..a2c28de2985a220a900b755427c5f22e69b933df GIT binary patch literal 696 zcmeAS@N?(olHy`uVBq!ia0vp^%YZn5gBeIJnfuQXNJ#|vgt-3y{~ySF@#br3|Dg#$ z78oBmaDcV*jy#adQ4-`A%m7pb0#{Fk7%?y~xqG@ehEy=VoqKcLVgmt}es0bG|4&Y5 zh`VKRtbCcVvxvJ$^r@pCFMT+*N$2$6qe`CT*N;q5(f9T7{B(&mje~2_nX+Aqp1!SL z)jh*fpIuJ$efz~OZSz@!XK8!hEZv*O(|y#&YO8$M_o^G`wtHJMoQyH8owdo-de_X| zx@^a;g@zrv(|jq>{;WConwtF*{f`eWH7#E=duG|vS31p=%W54{^G)VoK9G~X@4%Kw zy&3MJ6$!_hs+iLzguFg)@Oamo=- zrOZDvlb9-q#V@-JojD%@$0lpE(6^VC+217iSIS7 uv*P(1%wJmXd&%(M3FZOqG!Df(arQgHmA=LQZ2JOCW(=OLelF{r5}E*mkUwSs literal 0 HcmV?d00001 diff --git a/docs/html/classisland__purging__args_1_1_island_purging_args-members.html b/docs/html/classisland__purging__args_1_1_island_purging_args-members.html new file mode 100644 index 00000000..f36c5fbb --- /dev/null +++ b/docs/html/classisland__purging__args_1_1_island_purging_args-members.html @@ -0,0 +1,91 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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island_purging_args.IslandPurgingArgs Member List
+
+
+ +

This is the complete list of members for island_purging_args.IslandPurgingArgs, including all inherited members.

+ + + + + + + + + +
__init__(self, rec, filename) (defined in island_purging_args.IslandPurgingArgs)island_purging_args.IslandPurgingArgs
c2a (defined in island_purging_args.IslandPurgingArgs)island_purging_args.IslandPurgingArgs
CONFIG_OPTIONS (defined in island_purging_args.IslandPurgingArgs)island_purging_args.IslandPurgingArgsstatic
CONSTRAINTS (defined in island_purging_args.IslandPurgingArgs)island_purging_args.IslandPurgingArgsstatic
DEFAULTS (defined in island_purging_args.IslandPurgingArgs)island_purging_args.IslandPurgingArgsstatic
SELECTION_METHOD_MAP (defined in island_purging_args.IslandPurgingArgs)island_purging_args.IslandPurgingArgsstatic
to_args(self) (defined in island_purging_args.IslandPurgingArgs)island_purging_args.IslandPurgingArgs
TYPES (defined in island_purging_args.IslandPurgingArgs)island_purging_args.IslandPurgingArgsstatic
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/classisland__purging__args_1_1_island_purging_args.html b/docs/html/classisland__purging__args_1_1_island_purging_args.html new file mode 100644 index 00000000..0799a42b --- /dev/null +++ b/docs/html/classisland__purging__args_1_1_island_purging_args.html @@ -0,0 +1,249 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: island_purging_args.IslandPurgingArgs Class Reference + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+Public Member Functions | +Public Attributes | +Static Public Attributes | +List of all members
+
+
island_purging_args.IslandPurgingArgs Class Reference
+
+
+ + + + + + +

+Public Member Functions

+def __init__ (self, rec, filename)
 
+def to_args (self)
 
+ + + +

+Public Attributes

+ c2a
 
+ + + + + + + + + + + +

+Static Public Attributes

dictionary SELECTION_METHOD_MAP
 
dictionary CONFIG_OPTIONS
 
dictionary DEFAULTS
 
dictionary TYPES
 
dictionary CONSTRAINTS
 
+

Member Data Documentation

+ +

◆ CONFIG_OPTIONS

+ +
+
+ + + + + +
+ + + + +
dictionary island_purging_args.IslandPurgingArgs.CONFIG_OPTIONS
+ 		+static
+
+Initial value:
= {
+
'selection_method': lambda self, x: ['--check_on_island_method', IslandPurgingArgs.SELECTION_METHOD_MAP[x]],
+
'period': lambda self, x: ['--num_genomes_check_worst_fit', str(x)],
+
}
+
+
+
+ +

◆ CONSTRAINTS

+ +
+
+ + + + + +
+ + + + +
dictionary island_purging_args.IslandPurgingArgs.CONSTRAINTS
+ 		+static
+
+Initial value:
= {
+
'period': (lambda self: self.period > 0,
+
"must be a positive integer"),
+
'selection_method': (lambda self: self.selection_method in {'worst_best'},
+
"must be unset or 'worst_best'")
+
}
+
+
+
+ +

◆ DEFAULTS

+ +
+
+ + + + + +
+ + + + +
dictionary island_purging_args.IslandPurgingArgs.DEFAULTS
+ 		+static
+
+Initial value:
= {
+
'selection_method': lambda: None,
+
'period': lambda: None,
+
}
+
+
+
+ +

◆ SELECTION_METHOD_MAP

+ +
+
+ + + + + +
+ + + + +
dictionary island_purging_args.IslandPurgingArgs.SELECTION_METHOD_MAP
+ 		+static
+
+Initial value:
= {
+
'worst_best': 'clear_island_with_worst_best_genome'
+
}
+
+
+
+ +

◆ TYPES

+ +
+
+ + + + + +
+ + + + +
dictionary island_purging_args.IslandPurgingArgs.TYPES
+ 		+static
+
+Initial value:
= {
+
'period': {int},
+
'selection_method': {str}
+
}
+
+
+
+
The documentation for this class was generated from the following file:
    +
  • cli/island_purging_args.py
    • +
+
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/classmin__max__heap-members.html b/docs/html/classmin__max__heap-members.html new file mode 100644 index 00000000..71228586 --- /dev/null +++ b/docs/html/classmin__max__heap-members.html @@ -0,0 +1,109 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+
min_max_heap< T > Member List
+
+
+ +

This is the complete list of members for min_max_heap< T >, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
cbegin()min_max_heap< T >inline
cend()min_max_heap< T >inline
clear()min_max_heap< T >inline
const_iterator typedef (defined in min_max_heap< T >)min_max_heap< T >private
delete_element(uint32_t z) (defined in min_max_heap< T >)min_max_heap< T >inlineprivate
empty() const (defined in min_max_heap< T >)min_max_heap< T >inline
enqueue(const T &e)min_max_heap< T >inline
erase(size_t index)min_max_heap< T >inline
find_max() const (defined in min_max_heap< T >)min_max_heap< T >inline
find_min() const (defined in min_max_heap< T >)min_max_heap< T >inline
find_min_index() const (defined in min_max_heap< T >)min_max_heap< T >inlineprivate
heap (defined in min_max_heap< T >)min_max_heap< T >private
is_on_max_level(uint32_t z) (defined in min_max_heap< T >)min_max_heap< T >inlineprivatestatic
is_on_min_level(uint32_t z) (defined in min_max_heap< T >)min_max_heap< T >inlineprivatestatic
left_child(uint32_t z) (defined in min_max_heap< T >)min_max_heap< T >inlineprivatestatic
less_than (defined in min_max_heap< T >)min_max_heap< T >private
min_max_heap(std::function< bool(const T &, const T &)> _less_than, uint32_t size_hint=-1)min_max_heap< T >inline
operator[](size_t index)min_max_heap< T >inline
parent(uint32_t z) (defined in min_max_heap< T >)min_max_heap< T >inlineprivatestatic
pop() (defined in min_max_heap< T >)min_max_heap< T >inline
pop_max()min_max_heap< T >inline
pop_min()min_max_heap< T >inline
reserve(size_t n)min_max_heap< T >inline
right_child(uint32_t z) (defined in min_max_heap< T >)min_max_heap< T >inlineprivatestatic
size() const (defined in min_max_heap< T >)min_max_heap< T >inline
trickle_down(uint32_t z) (defined in min_max_heap< T >)min_max_heap< T >inlineprivate
trickle_down_inner(const uint32_t z) (defined in min_max_heap< T >)min_max_heap< T >inlineprivate
trickle_up(uint32_t z) (defined in min_max_heap< T >)min_max_heap< T >inlineprivate
trickle_up_inner(uint32_t z) (defined in min_max_heap< T >)min_max_heap< T >inlineprivate
~min_max_heap() (defined in min_max_heap< T >)min_max_heap< T >inline
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/classmin__max__heap.html b/docs/html/classmin__max__heap.html new file mode 100644 index 00000000..20041143 --- /dev/null +++ b/docs/html/classmin__max__heap.html @@ -0,0 +1,371 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: min_max_heap< T > Class Template Reference + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+Public Member Functions | +Private Types | +Private Member Functions | +Static Private Member Functions | +Private Attributes | +List of all members
+
+
min_max_heap< T > Class Template Reference
+
+
+ +

This implementation of a min_max_heap was basically ripped from here: https://github.com/itsjohncs/minmaxheap-cpp/blob/master/MinMaxHeap.hpp. + More...

+ +

#include <min_max_heap.hxx>

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

 min_max_heap (std::function< bool(const T &, const T &)> _less_than, uint32_t size_hint=-1)
 Creates a new min_max_heap. More...
 
+bool empty () const
 
+uint32_t size () const
 
+void enqueue (const T &e)
 Adds the element e to the heap (in the correct order of course).
 
+const T & find_max () const
 
+const T & find_min () const
 
T pop_max ()
 Returns and removes the maximum item in this min-max heap. More...
 
+T pop ()
 
T pop_min ()
 Returns and removes the minimum item in this min-max heap. More...
 
const_iterator cbegin ()
 returns the const_iterator to the beginning of the underlying vector. More...
 
const_iterator cend ()
 returns the const_iterator pointing past the end of the underlying vector (i.e. More...
 
+T & operator[] (size_t index)
 Implementation of the indexing operator to allow direct access.
 
+T erase (size_t index)
 Allows for deletion of an element at a specified index in O(log(n)) time.
 
+void reserve (size_t n)
 Ensures that the underlying vector can hold at least n elements.
 
+void clear ()
 Deletes every element in this heap.
 
+ + + +

+Private Types

+typedef std::vector< T >::const_iterator const_iterator
 
+ + + + + + + + + + + + + + + +

+Private Member Functions

+template<bool max_level>
void trickle_up_inner (uint32_t z)
 
+void trickle_up (uint32_t z)
 
+template<bool max_level>
void trickle_down_inner (const uint32_t z)
 
+void trickle_down (uint32_t z)
 
+uint32_t find_min_index () const
 
+T delete_element (uint32_t z)
 
+ + + + + + + + + + + +

+Static Private Member Functions

+static uint32_t parent (uint32_t z)
 
+static uint32_t left_child (uint32_t z)
 
+static uint32_t right_child (uint32_t z)
 
+static bool is_on_min_level (uint32_t z)
 
+static bool is_on_max_level (uint32_t z)
 
+ + + + + +

+Private Attributes

+vector< T > heap
 
+function< bool(const T &, const T &)> less_than
 
+

Detailed Description

+

template<class T>
+class min_max_heap< T >

+ +

This implementation of a min_max_heap was basically ripped from here: https://github.com/itsjohncs/minmaxheap-cpp/blob/master/MinMaxHeap.hpp.

+

Along with this for reference https://en.wikipedia.org/wiki/Min-max_heap

+

The first layer (e.g. the layer which only contains the root node) contains the maximum element.

+

Constructor & Destructor Documentation

+ +

◆ min_max_heap()

+ +
+
+
+template<class T >
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
min_max_heap< T >::min_max_heap (std::function< bool(const T &, const T &)> _less_than,
uint32_t size_hint = -1 
)
+ 		+inline
+
+ +

Creates a new min_max_heap.

+

The only parameter is a function that will compare two elements, and determine if the first is less than the second. This could probably be done in a better way with generics but I'm not confident in doing so.

+ +
+
+

Member Function Documentation

+ +

◆ cbegin()

+ +
+
+
+template<class T >
+ + + + + +
+ + + + + + + +
const_iterator min_max_heap< T >::cbegin ()
+ 		+inline
+
+ +

returns the const_iterator to the beginning of the underlying vector.

+

There is no non-const iterator because the ordering of the data structure could potentially be changed.

+ +
+
+ +

◆ cend()

+ +
+
+
+template<class T >
+ + + + + +
+ + + + + + + +
const_iterator min_max_heap< T >::cend ()
+ 		+inline
+
+ +

returns the const_iterator pointing past the end of the underlying vector (i.e.

+

this is an invalid reference). There is no non-const end iterator because the ordering of the data structure must be preserved.

+ +
+
+ +

◆ pop_max()

+ +
+
+
+template<class T >
+ + + + + +
+ + + + + + + +
T min_max_heap< T >::pop_max ()
+ 		+inline
+
+ +

Returns and removes the maximum item in this min-max heap.

+

If the heap is emoty it will throw an underflow_error

+ +
+
+ +

◆ pop_min()

+ +
+
+
+template<class T >
+ + + + + +
+ + + + + + + +
T min_max_heap< T >::pop_min ()
+ 		+inline
+
+ +

Returns and removes the minimum item in this min-max heap.

+

If the heap is empty, it will throw an underflow_error

+ +
+
+
The documentation for this class was generated from the following file: +
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/classrec__args_1_1_rec_args-members.html b/docs/html/classrec__args_1_1_rec_args-members.html new file mode 100644 index 00000000..bbbabb40 --- /dev/null +++ b/docs/html/classrec__args_1_1_rec_args-members.html @@ -0,0 +1,90 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
rec_args.RecArgs Member List
+
+
+ +

This is the complete list of members for rec_args.RecArgs, including all inherited members.

+ + + + + + + + +
__init__(self, rec, filename) (defined in rec_args.RecArgs)rec_args.RecArgs
c2a (defined in rec_args.RecArgs)rec_args.RecArgs
CONFIG_OPTIONS (defined in rec_args.RecArgs)rec_args.RecArgsstatic
CONSTRAINTS (defined in rec_args.RecArgs)rec_args.RecArgsstatic
DEFAULTS (defined in rec_args.RecArgs)rec_args.RecArgsstatic
to_args(self) (defined in rec_args.RecArgs)rec_args.RecArgs
TYPES (defined in rec_args.RecArgs)rec_args.RecArgsstatic
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/classrec__args_1_1_rec_args.html b/docs/html/classrec__args_1_1_rec_args.html new file mode 100644 index 00000000..77018727 --- /dev/null +++ b/docs/html/classrec__args_1_1_rec_args.html @@ -0,0 +1,232 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rec_args.RecArgs Class Reference + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
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+Public Member Functions | +Public Attributes | +Static Public Attributes | +List of all members
+
+
rec_args.RecArgs Class Reference
+
+
+ + + + + + +

+Public Member Functions

+def __init__ (self, rec, filename)
 
+def to_args (self)
 
+ + + +

+Public Attributes

+ c2a
 
+ + + + + + + + + +

+Static Public Attributes

dictionary CONFIG_OPTIONS
 
dictionary DEFAULTS
 
dictionary TYPES
 
dictionary CONSTRAINTS
 
+

Member Data Documentation

+ +

◆ CONFIG_OPTIONS

+ +
+
+ + + + + +
+ + + + +
dictionary rec_args.RecArgs.CONFIG_OPTIONS
+ 		+static
+
+Initial value:
= {
+
'min': lambda self, x: ['--rec_delay_min', str(x)],
+
'max': lambda self, x: ['--rec_delay_max', str(x)],
+
'dist': lambda self, x: ['--rec_sampling_distribution', str(x)],
+
'population': lambda self, x: ['--rec_sampling_population', str(x)],
+
}
+
+
+
+ +

◆ CONSTRAINTS

+ +
+
+ + + + + +
+ + + + +
dictionary rec_args.RecArgs.CONSTRAINTS
+ 		+static
+
+Initial value:
= {
+
'min': (lambda self: self.min < self.max and self.min >= 1,
+
"must be less than examm.rec.max and at least 1"),
+
'max': (lambda self: self.max > self.min and self.max >= 2,
+
"must be greater than examm.rec.min and at least 2"),
+
'dist': (lambda self: self.dist in {'uniform', 'histogram', 'normal'},
+
"must be one of 'uniform', 'histogram', or 'normal'"),
+
'population': (lambda self: self.population in {'global', 'island'},
+
"must be either 'global' or 'island'")
+
}
+
+
+
+ +

◆ DEFAULTS

+ +
+
+ + + + + +
+ + + + +
dictionary rec_args.RecArgs.DEFAULTS
+ 		+static
+
+Initial value:
= {
+
'min': lambda: 1,
+
'max': lambda: 50,
+
'dist': lambda: 'uniform',
+
'population': lambda: 'global',
+
}
+
+
+
+ +

◆ TYPES

+ +
+
+ + + + + +
+ + + + +
dictionary rec_args.RecArgs.TYPES
+ 		+static
+
+Initial value:
= {
+
'min': {int},
+
'max': {int},
+
'population': {str},
+
'dist': {str},
+
}
+
+
+
+
The documentation for this class was generated from the following file:
    +
  • cli/rec_args.py
    • +
+
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/closed.png b/docs/html/closed.png new file mode 100644 index 0000000000000000000000000000000000000000..98cc2c909da37a6df914fbf67780eebd99c597f5 GIT binary patch literal 132 zcmeAS@N?(olHy`uVBq!ia0vp^oFL4>1|%O$WD@{V-kvUwAr*{o@8{^CZMh(5KoB^r_<4^zF@3)Cp&&t3hdujKf f*?bjBoY!V+E))@{xMcbjXe@)LtDnm{r-UW|*e5JT literal 0 HcmV?d00001 diff --git a/docs/html/cnn__edge_8hxx_source.html b/docs/html/cnn__edge_8hxx_source.html new file mode 100644 index 00000000..4cb5c210 --- /dev/null +++ b/docs/html/cnn__edge_8hxx_source.html @@ -0,0 +1,336 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: cnn/cnn_edge.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
cnn_edge.hxx
+
+
+
1 #ifndef CNN_EDGE_H
+
2 #define CNN_EDGE_H
+
3 
+
4 #include <fstream>
+
5 using std::ofstream;
+
6 using std::ifstream;
+
7 
+
8 #include <iostream>
+
9 using std::cout;
+
10 using std::cerr;
+
11 using std::endl;
+
12 using std::ostream;
+
13 using std::istream;
+
14 
+
15 #include <random>
+
16 using std::minstd_rand0;
+
17 using std::normal_distribution;
+
18 
+
19 #include <string>
+
20 using std::string;
+
21 
+
22 #include <vector>
+
23 using std::vector;
+
24 
+
25 #include "cnn_node.hxx"
+
26 #include "image_tools/image_set.hxx"
+
27 #include "common/random.hxx"
+
28 
+
29 #define CONVOLUTIONAL 0
+
30 #define POOLING 1
+
31 
+
32 class CNN_Edge {
+
33  private:
+
34  int edge_id;
+
35  int exact_id;
+
36  int genome_id;
+
37 
+
38  int type;
+
39  int innovation_number;
+
40 
+
41  int input_node_innovation_number;
+
42  int output_node_innovation_number;
+
43 
+
44  CNN_Node *input_node;
+
45  CNN_Node *output_node;
+
46 
+
47  int batch_size;
+
48  int filter_x, filter_y;
+
49  int filter_size;
+
50  float *weights;
+
51  float *weight_updates;
+
52  float *best_weights;
+
53 
+
54  float *previous_velocity;
+
55  float *best_velocity;
+
56 
+
57  float scale;
+
58  float best_scale;
+
59  float previous_velocity_scale;
+
60  float best_velocity_scale;
+
61 
+
62  vector<int> y_pools;
+
63  vector<int> y_pool_offset;
+
64  vector<int> x_pools;
+
65  vector<int> x_pool_offset;
+
66 
+
67  bool fixed;
+
68  bool disabled;
+
69  bool forward_visited;
+
70  bool reverse_visited;
+
71 
+
72  bool reverse_filter_x;
+
73  bool reverse_filter_y;
+
74  bool needs_initialization;
+
75 
+
76  float propagate_backward_time;
+
77  float propagate_forward_time;
+
78  float weight_update_time;
+
79 
+
80  public:
+
81  CNN_Edge();
+
82 
+
83  CNN_Edge(CNN_Node *_input_node, CNN_Node *_output_node, bool _fixed, int _innovation_number, int _type);
+
84 
+
85 #ifdef _MYSQL_
+
86  CNN_Edge(int edge_id);
+
87  void export_to_database(int exact_id, int genome_id);
+
88 
+
89  int get_edge_id() const;
+
90 #endif
+
91 
+
92 
+
93  CNN_Edge* copy() const;
+
94 
+
95  ~CNN_Edge();
+
96 
+
97  bool equals(CNN_Edge *other) const;
+
98 
+
99  int get_type() const;
+
100 
+
101  bool has_nan() const;
+
102 
+
103  void reset_times();
+
104  void accumulate_times(float &total_forward_time, float &total_backward_time, float &total_weight_update_time);
+
105 
+
106  void set_needs_init();
+
107  bool needs_init() const;
+
108  int get_filter_size() const;
+
109  int get_filter_x() const;
+
110  int get_filter_y() const;
+
111 
+
112  bool is_reverse_filter_x() const;
+
113  bool is_reverse_filter_y() const;
+
114 
+
115  float get_weight(int i) const;
+
116  float get_weight_update(int i) const;
+
117  void update_weight(int i, float diff);
+
118 
+
119  float get_scale() const;
+
120 
+
121  void propagate_weight_count();
+
122 
+
123  void save_best_weights();
+
124  void set_weights_to_best();
+
125 
+
126  bool set_nodes(const vector<CNN_Node*> nodes);
+
127  void set_pools();
+
128 
+
129  void initialize_weights(minstd_rand0 &generator, NormalDistribution &normal_distribution);
+
130  void reset_velocities();
+
131  void resize();
+
132 
+
133  void alter_edge_type();
+
134  void disable();
+
135  void enable();
+
136  bool is_enabled() const;
+
137  bool is_disabled() const;
+
138  void update_batch_size(int new_batch_size);
+
139 
+
140  bool is_reachable() const;
+
141  bool is_forward_visited() const;
+
142  bool is_reverse_visited() const;
+
143  void forward_visit();
+
144  void reverse_visit();
+
145  void set_unvisited();
+
146 
+
147  bool is_filter_correct() const;
+
148 
+
149  int get_number_weights() const;
+
150  int get_batch_size() const;
+
151 
+
152  int get_innovation_number() const;
+
153  int get_input_innovation_number() const;
+
154  int get_output_innovation_number() const;
+
155 
+
156  bool connects(int n1, int n2) const;
+
157 
+
158  bool has_zero_weight() const;
+
159  bool has_zero_best_weight() const;
+
160 
+
161  CNN_Node* get_input_node();
+
162 
+
163  CNN_Node* get_output_node();
+
164 
+
165  void print(ostream &out);
+
166 
+
167  void check_output_update(const vector< vector< vector<float> > > &output, const vector< vector< vector<float> > > &input, float value, float weight, float previous_output, int batch_number, int in_y, int in_x, int out_y, int out_x);
+
168 
+
169  void check_weight_update(const vector< vector< vector<float> > > &input, const vector< vector< vector<float> > > &input_deltas, float delta, float previous_delta, float weight_update, float previous_weight_update, int batch_number, int out_y, int out_x, int in_y, int in_x);
+
170 
+
171  void propagate_forward(bool training, bool accumulate_test_statistics, float epsilon, float alpha, bool perform_dropout, float hidden_dropout_probability, minstd_rand0 &generator);
+
172 
+
173  void propagate_backward(bool training, float mu, float learning_rate, float epsilon);
+
174  void update_weights(float mu, float learning_rate, float weight_decay);
+
175 
+
176  void print_statistics();
+
177 
+
178  bool is_identical(const CNN_Edge *other, bool testing_checkpoint);
+
179 
+
180  friend ostream &operator<<(ostream &os, const CNN_Edge* flight);
+
181  friend istream &operator>>(istream &is, CNN_Edge* flight);
+
182 };
+
183 
+
184 int random_edge_type(float random_value);
+
185 
+
186 void parse_float_2d(float **output, istringstream &iss, int size_x, int size_y);
+
187 void parse_vector_2d(vector<vector<float>> &output, istringstream &iss, int size_x, int size_y);
+
188 
+
189 struct sort_CNN_Edges_by_depth {
+
190  bool operator()(CNN_Edge *n1, CNN_Edge *n2) {
+
191  if (n1->get_input_node()->get_depth() < n2->get_input_node()->get_depth()) {
+
192  return true;
+
193 
+
194  } else if (n1->get_input_node()->get_depth() == n2->get_input_node()->get_depth()) {
+
195  //make sure the order of the edges is *always* the same
+
196  //going through the edges in different orders may effect the output
+
197  //of backpropagation
+
198  if (n1->get_innovation_number() < n2->get_innovation_number()) {
+
199  return true;
+
200  } else {
+
201  return false;
+
202  }
+
203 
+
204  } else {
+
205  return false;
+
206  }
+
207  }
+
208 };
+
209 
+
210 struct sort_CNN_Edges_by_output_depth {
+
211  bool operator()(CNN_Edge *n1, CNN_Edge *n2) {
+
212  if (n1->get_output_node()->get_depth() < n2->get_output_node()->get_depth()) {
+
213  return true;
+
214 
+
215  } else if (n1->get_output_node()->get_depth() == n2->get_output_node()->get_depth()) {
+
216  //make sure the order of the edges is *always* the same
+
217  //going through the edges in different orders may effect the output
+
218  //of backpropagation
+
219  if (n1->get_innovation_number() < n2->get_innovation_number()) {
+
220  return true;
+
221  } else {
+
222  return false;
+
223  }
+
224 
+
225  } else {
+
226  return false;
+
227  }
+
228  }
+
229 };
+
230 
+
231 
+
232 struct sort_CNN_Edges_by_innovation {
+
233  bool operator()(CNN_Edge *n1, CNN_Edge *n2) {
+
234  return n1->get_innovation_number() < n2->get_innovation_number();
+
235  }
+
236 };
+
237 
+
238 
+
239 
+
240 #endif
+
+
sort_CNN_Edges_by_depth
Definition: cnn_edge.hxx:189
+ + + +
sort_CNN_Edges_by_output_depth
Definition: cnn_edge.hxx:210
+
CNN_Edge
Definition: cnn_edge.hxx:32
+ +
NormalDistribution
Definition: random.hxx:19
+
CNN_Node
Definition: cnn_node.hxx:49
+
sort_CNN_Edges_by_innovation
Definition: cnn_edge.hxx:232
+ + + +
std::endl
T endl(T... args)
+ + + +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/cnn__genome_8hxx_source.html b/docs/html/cnn__genome_8hxx_source.html new file mode 100644 index 00000000..507f725d --- /dev/null +++ b/docs/html/cnn__genome_8hxx_source.html @@ -0,0 +1,356 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: cnn/cnn_genome.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
cnn_genome.hxx
+
+
+
1 #ifndef CNN_GENOME_H
+
2 #define CNN_GENOME_H
+
3 
+
4 #include <map>
+
5 using std::map;
+
6 
+
7 #include <random>
+
8 using std::minstd_rand0;
+
9 
+
10 #include <vector>
+
11 using std::vector;
+
12 
+
13 #include "image_tools/image_set.hxx"
+
14 #include "image_tools/large_image_set.hxx"
+
15 #include "cnn_node.hxx"
+
16 #include "cnn_edge.hxx"
+
17 #include "common/random.hxx"
+
18 
+
19 #define SANITY_CHECK_BEFORE_INSERT 0
+
20 #define SANITY_CHECK_AFTER_GENERATION 1
+
21 
+
22 //mysql can't handl the max float value for some reason
+
23 #define EXACT_MAX_FLOAT 10000000
+
24 
+
25 class CNN_Genome {
+
26  private:
+
27  string version_str;
+
28  int exact_id;
+
29  int genome_id;
+
30 
+
31  vector<CNN_Node*> nodes;
+
32  vector<CNN_Edge*> edges;
+
33 
+
34  vector<CNN_Node*> input_nodes;
+
35  vector<CNN_Node*> softmax_nodes;
+
36 
+
37  NormalDistribution normal_distribution;
+
38  minstd_rand0 generator;
+
39 
+
40  int velocity_reset;
+
41  int batch_size;
+
42 
+
43  float epsilon;
+
44  float alpha;
+
45 
+
46  float input_dropout_probability;
+
47  float hidden_dropout_probability;
+
48 
+
49  float initial_mu;
+
50  float mu;
+
51  float mu_delta;
+
52 
+
53  float initial_learning_rate;
+
54  float learning_rate;
+
55  float learning_rate_delta;
+
56 
+
57  float initial_weight_decay;
+
58  float weight_decay;
+
59  float weight_decay_delta;
+
60 
+
61  int epoch;
+
62  int max_epochs;
+
63  bool reset_weights;
+
64 
+
65  int padding;
+
66 
+
67  int best_epoch;
+
68  int number_validation_images;
+
69  float best_validation_error;
+
70  int best_validation_predictions;
+
71 
+
72  int number_training_images;
+
73  float training_error;
+
74  int training_predictions;
+
75 
+
76  int number_test_images;
+
77  float test_error;
+
78  int test_predictions;
+
79 
+
80  bool started_from_checkpoint;
+
81  vector<long> backprop_order;
+
82 
+
83  int generation_id;
+
84  string name;
+
85  string checkpoint_filename;
+
86  string output_filename;
+
87 
+
88  map<string, int> generated_by_map;
+
89 
+
90  int (*progress_function)(float);
+
91 
+
92  public:
+
96  CNN_Genome(string filename, bool is_checkpoint);
+
97  CNN_Genome(istream &in, bool is_checkpoint);
+
98 
+
102  CNN_Genome(int _generation_id, int _padding, int _number_training_images, int _number_validation_images, int _number_test_images, int seed, int _max_epochs, bool _reset_weights, int velocity_reset, float _mu, float _mu_delta, float _learning_rate, float _learning_rate_delta, float _weight_decay, float _weight_decay_delta, int _batch_size, float _epsilon, float _alpha, float _input_dropout_probability, float _hidden_dropout_probability, const vector<CNN_Node*> &_nodes, const vector<CNN_Edge*> &_edges);
+
103 
+
104  ~CNN_Genome();
+
105 
+
106 #ifdef _MYSQL_
+
107  CNN_Genome(int genome_id);
+
108  void export_to_database(int exact_id);
+
109 #endif
+
110 
+
111  float get_version() const;
+
112  string get_version_str() const;
+
113 
+
114 
+
115  int get_genome_id() const;
+
116  int get_exact_id() const;
+
117 
+
118  bool equals(CNN_Genome *other) const;
+
119 
+
120  void print_results(ostream &out) const;
+
121 
+
122  void print_progress(ostream &out, string progress_name, float total_error, int correct_predictions, int number_images) const;
+
123 
+
124  int get_number_training_images() const;
+
125  int get_number_validation_images() const;
+
126  int get_number_test_images() const;
+
127 
+
128  int get_number_weights() const;
+
129 
+
130  int get_padding() const;
+
131 
+
132  int get_operations_estimate() const;
+
133 
+
134  void set_progress_function(int (*_progress_function)(float));
+
135 
+
136  int get_generation_id() const;
+
137 
+
138  float get_best_validation_error() const;
+
139  float get_best_validation_rate() const;
+
140  int get_best_validation_predictions() const;
+
141 
+
142  float get_training_error() const;
+
143  float get_training_rate() const;
+
144  int get_training_predictions() const;
+
145 
+
146  float get_test_error() const;
+
147  float get_test_rate() const;
+
148  int get_test_predictions() const;
+
149 
+
150  int get_best_epoch() const;
+
151 
+
152  int get_epoch() const;
+
153  int get_max_epochs() const;
+
154 
+
155  bool sanity_check(int type);
+
156  bool visit_nodes();
+
157 
+
158  void get_node_copies(vector<CNN_Node*> &node_copies) const;
+
159  void get_edge_copies(vector<CNN_Edge*> &edge_copies) const;
+
160 
+
161  const vector<CNN_Node*> get_nodes() const;
+
162  const vector<CNN_Edge*> get_edges() const;
+
163 
+
164  CNN_Node* get_node(int node_position);
+
165  CNN_Edge* get_edge(int edge_position);
+
166 
+
167  float get_initial_mu() const;
+
168  float get_mu() const;
+
169  float get_mu_delta() const;
+
170  float get_initial_learning_rate() const;
+
171  float get_learning_rate() const;
+
172  float get_learning_rate_delta() const;
+
173  float get_initial_weight_decay() const;
+
174  float get_weight_decay() const;
+
175  float get_weight_decay_delta() const;
+
176  int get_batch_size() const;
+
177 
+
178  float get_alpha() const;
+
179  int get_velocity_reset() const;
+
180 
+
181  float get_input_dropout_probability() const;
+
182  float get_hidden_dropout_probability() const;
+
183 
+
184  int get_number_enabled_pooling_edges() const;
+
185  int get_number_enabled_convolutional_edges() const;
+
186  int get_number_enabled_edges() const;
+
187  int get_number_enabled_nodes() const;
+
188 
+
189  int get_number_edges() const;
+
190  int get_number_nodes() const;
+
191  int get_number_softmax_nodes() const;
+
192  int get_number_input_nodes() const;
+
193 
+
194  vector<CNN_Node*> get_reachable_nodes();
+
195  vector<CNN_Node*> get_disabled_nodes();
+
196  vector<CNN_Node*> get_reachable_hidden_nodes();
+
197  vector<CNN_Edge*> get_reachable_edges();
+
198  vector<CNN_Edge*> get_disabled_edges();
+
199 
+
200  void add_node(CNN_Node* node);
+
201  void add_edge(CNN_Edge* edge);
+
202  bool disable_edge(int edge_position);
+
203 
+
204  void resize_edges_around_node(int node_position);
+
205 
+
206  void evaluate_images(const ImagesInterface &images, const vector<int> &batch, vector< vector<float> > &predictions, int offset);
+
207  void evaluate_images(const ImagesInterface &images, const vector<int> &batch, bool training, float &total_error, int &correct_predictions, bool accumulate_test_statistics);
+
208 
+
209  void set_to_best();
+
210  void save_to_best();
+
211 
+
212  void reset(bool _reset_weights);
+
213  void initialize();
+
214 
+
215  void check_gradients(const ImagesInterface &images);
+
216 
+
217  void evaluate_large_images(const LargeImages &images, string output_directory);
+
218 
+
219  void evaluate(const ImagesInterface &images, vector< vector<float> > &predictions);
+
220  void evaluate(const ImagesInterface &images, const vector<long> &order, float &total_error, int &correct_predictions, bool perform_backprop, bool accumulate_test_statistics);
+
221  void evaluate(string progress_name, const ImagesInterface &images, float &total_error, int &correct_predictions);
+
222 
+
223  void stochastic_backpropagation(const ImagesInterface &training_images, int training_resize, const ImagesInterface &validation_images);
+
224  void stochastic_backpropagation(const ImagesInterface &training_images, const ImagesInterface &validation_images);
+
225 
+
226  void evaluate_test(const ImagesInterface &test_images);
+
227 
+
228  void set_name(string _name);
+
229  void set_output_filename(string _output_filename);
+
230  void set_checkpoint_filename(string _checkpoint_filename);
+
231 
+
232  void write(ostream &outfile);
+
233  void write_to_file(string filename);
+
234 
+
235  void read(istream &infile);
+
236 
+
237  void print_graphviz(ostream &out) const;
+
238 
+
239  void set_generated_by(string type);
+
240  int get_generated_by(string type);
+
241 
+
242  bool is_identical(CNN_Genome *other, bool testing_checkpoint);
+
243 
+
244  void get_prediction_matrix(const MultiImagesInterface &images, int image_number, int stride, vector< vector< vector<float> > > &prediction_matrix);
+
245  void get_expanded_prediction_matrix(const MultiImagesInterface &images, int image_number, int stride, int prediction_class, vector< vector<float> > &extended_prediction_matrix);
+
246 
+
247 };
+
248 
+
249 void write_map(ostream &out, map<string, int> &m);
+
250 void read_map(istream &in, map<string, int> &m);
+
251 
+
252 struct sort_genomes_by_validation_error {
+
253  bool operator()(CNN_Genome *g1, CNN_Genome *g2) {
+
254  return g1->get_best_validation_error() < g2->get_best_validation_error();
+
255  }
+
256 };
+
257 
+
258 struct sort_genomes_by_predictions {
+
259  bool operator()(CNN_Genome *g1, CNN_Genome *g2) {
+
260  return g1->get_best_validation_predictions() > g2->get_best_validation_predictions();
+
261  }
+
262 };
+
263 
+
264 
+
265 
+
266 #endif
+
+
sort_genomes_by_predictions
Definition: cnn_genome.hxx:258
+
MultiImagesInterface
Definition: image_set_interface.hxx:52
+ +
LargeImages
Definition: large_image_set.hxx:77
+
CNN_Genome::CNN_Genome
CNN_Genome(string filename, bool is_checkpoint)
Initialize a genome from a file.
Definition: cnn_genome.cxx:98
+
CNN_Edge
Definition: cnn_edge.hxx:32
+ +
NormalDistribution
Definition: random.hxx:19
+
CNN_Genome
Definition: cnn_genome.hxx:25
+
CNN_Node
Definition: cnn_node.hxx:49
+ + +
ImagesInterface
Definition: image_set_interface.hxx:28
+
sort_genomes_by_validation_error
Definition: cnn_genome.hxx:252
+ + + +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/cnn__node_8hxx_source.html b/docs/html/cnn__node_8hxx_source.html new file mode 100644 index 00000000..f0eb19cc --- /dev/null +++ b/docs/html/cnn__node_8hxx_source.html @@ -0,0 +1,345 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: cnn/cnn_node.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
cnn_node.hxx
+
+
+
1 #ifndef CNN_NEAT_NODE_H
+
2 #define CNN_NEAT_NODE_H
+
3 
+
4 #include <fstream>
+
5 using std::ofstream;
+
6 using std::ifstream;
+
7 
+
8 #include <iostream>
+
9 using std::cout;
+
10 using std::cerr;
+
11 using std::endl;
+
12 using std::ostream;
+
13 using std::istream;
+
14 
+
15 #include <random>
+
16 using std::minstd_rand0;
+
17 using std::uniform_real_distribution;
+
18 
+
19 #include <sstream>
+
20 using std::istringstream;
+
21 
+
22 #include <string>
+
23 using std::string;
+
24 
+
25 #include <vector>
+
26 using std::vector;
+
27 
+
28 
+
29 #include "image_tools/image_set.hxx"
+
30 
+
31 #ifdef _MYSQL_
+
32 #include "common/db_conn.hxx"
+
33 #endif
+
34 
+
35 #include "common/random.hxx"
+
36 
+
37 #define RELU_MIN 0
+
38 #define RELU_MIN_LEAK 0.005
+
39 
+
40 #define RELU_MAX 5.5
+
41 #define RELU_MAX_LEAK 0.00
+
42 
+
43 #define INPUT_NODE 0
+
44 #define HIDDEN_NODE 1
+
45 #define OUTPUT_NODE 2
+
46 #define SOFTMAX_NODE 3
+
47 
+
48 
+
49 class CNN_Node {
+
50  private:
+
51  int node_id;
+
52  int exact_id;
+
53  int genome_id;
+
54 
+
55  int innovation_number;
+
56  float depth;
+
57 
+
58  int batch_size, size_y, size_x;
+
59  int total_size;
+
60 
+
61  int weight_count;
+
62 
+
63  int type;
+
64 
+
65  int total_inputs;
+
66  int inputs_fired;
+
67 
+
68  int total_outputs;
+
69  int outputs_fired;
+
70 
+
71  bool forward_visited;
+
72  bool reverse_visited;
+
73  bool needs_initialization;
+
74 
+
75  bool disabled;
+
76 
+
77  float gamma;
+
78  float best_gamma;
+
79  float previous_velocity_gamma;
+
80 
+
81  float beta;
+
82  float best_beta;
+
83  float previous_velocity_beta;
+
84 
+
85  float batch_mean;
+
86  float batch_variance; //sigma squared
+
87  float batch_std_dev; //sqrt(batch_variance + epsilon);
+
88  float inverse_variance;
+
89 
+
90  float running_mean;
+
91  float best_running_mean;
+
92  float running_variance;
+
93  float best_running_variance;
+
94 
+
95  //batch number x size_y x size_x
+
96  float *values_out;
+
97  float *errors_out;
+
98  float *relu_gradients;
+
99  float *pool_gradients;
+
100 
+
101  float *values_in;
+
102  float *errors_in;
+
103 
+
104  float input_fired_time;
+
105  float output_fired_time;
+
106 
+
107 
+
108  public:
+
109  CNN_Node();
+
110  ~CNN_Node();
+
111 
+
112  CNN_Node(int _innovation_number, float _depth, int _batch_size, int _input_size_x, int _input_size_y, int type);
+
113 
+
114  CNN_Node* copy() const;
+
115 
+
116 #ifdef _MYSQL_
+
117  CNN_Node(int node_id);
+
118  void export_to_database(int exact_id, int genome_id);
+
119 
+
120  int get_node_id() const;
+
121 #endif
+
122 
+
123  bool needs_init() const;
+
124  int get_batch_size() const;
+
125  int get_size_x() const;
+
126  int get_size_y() const;
+
127  bool vectors_correct() const;
+
128 
+
129  void initialize();
+
130 
+
131  void reset_velocities();
+
132 
+
133  void reset_weight_count();
+
134  void add_weight_count(int _weight_count);
+
135  int get_weight_count() const;
+
136 
+
137  int get_innovation_number() const;
+
138 
+
139  float get_depth() const;
+
140 
+
141  bool is_fixed() const;
+
142  bool is_hidden() const;
+
143  bool is_input() const;
+
144  bool is_output() const;
+
145  bool is_softmax() const;
+
146 
+
147  bool is_reachable() const;
+
148  bool is_forward_visited() const;
+
149  bool is_reverse_visited() const;
+
150  void forward_visit();
+
151  void reverse_visit();
+
152  void set_unvisited();
+
153 
+
154  bool has_nan() const;
+
155 
+
156  void set_values(const ImagesInterface &images, const vector<int> &batch, int channel, bool perform_dropout, bool accumulate_test_statistics, float input_dropout_probability, minstd_rand0 &generator);
+
157 
+
158  float get_value_in(int batch_number, int y, int x);
+
159  void set_value_in(int batch_number, int y, int x, float value);
+
160  float* get_values_in();
+
161 
+
162  float get_value_out(int batch_number, int y, int x);
+
163  void set_value_out(int batch_number, int y, int x, float value);
+
164  float* get_values_out();
+
165 
+
166 
+
167  void set_error_in(int batch_number, int y, int x, float error);
+
168  float* get_errors_in();
+
169 
+
170  void set_error_out(int batch_number, int y, int x, float error);
+
171  float* get_errors_out();
+
172 
+
173 
+
174  float* get_relu_gradients();
+
175  float* get_pool_gradients();
+
176 
+
177  void print(ostream &out);
+
178 
+
179  void reset_times();
+
180  void accumulate_times(float &total_input_time, float &total_output_time);
+
181 
+
182  void reset();
+
183  void save_best_weights();
+
184  void set_weights_to_best();
+
185 
+
186  void resize_arrays();
+
187  void update_batch_size(int new_batch_size);
+
188  bool modify_size_x(int change);
+
189  bool modify_size_y(int change);
+
190 
+
191  bool is_disabled() const;
+
192  bool is_enabled() const;
+
193  void disable();
+
194  void enable();
+
195 
+
196  void add_input();
+
197  void disable_input();
+
198  int get_number_inputs() const;
+
199  int get_inputs_fired() const;
+
200  void input_fired(bool training, bool accumulate_test_statistics, float epsilon, float alpha, bool perform_dropout, float hidden_dropout_probability, minstd_rand0 &generator);
+
201 
+
202  void add_output();
+
203  void disable_output();
+
204  int get_number_outputs() const;
+
205  int get_outputs_fired() const;
+
206  void output_fired(bool training, float mu, float learning_rate, float epsilon);
+
207 
+
208  void zero_test_statistics();
+
209  void divide_test_statistics(int number_batches);
+
210 
+
211  void print_batch_statistics();
+
212 
+
213  void batch_normalize(bool training, bool accumulating_test_statistics, float epsilon, float alpha);
+
214  void apply_relu(float* values, float* gradients);
+
215  void apply_dropout(float* values, float* gradients, bool perform_dropout, bool accumulate_test_statistics, float dropout_probability, minstd_rand0 &generator);
+
216 
+
217  //void backpropagate_dropout();
+
218  void backpropagate_relu(float* errors, float* gradients);
+
219  void backpropagate_batch_normalization(bool training, float mu, float learning_rate, float epsilon);
+
220 
+
221  void print_statistics();
+
222  void print_statistics(const float* values, const float* errors, const float* gradients);
+
223 
+
224  bool is_identical(const CNN_Node *other, bool testing_checkpoint);
+
225 
+
226  friend ostream &operator<<(ostream &os, const CNN_Node* node);
+
227  friend istream &operator>>(istream &is, CNN_Node* node);
+
228 };
+
229 
+
230 float read_hexfloat(istream &infile);
+
231 void write_hexfloat(ostream &outfile, float value);
+
232 
+
233 struct sort_CNN_Nodes_by_depth {
+
234  bool operator()(const CNN_Node *n1, const CNN_Node *n2) {
+
235  if (n1->get_depth() < n2->get_depth()) {
+
236  return true;
+
237  } else if (n1->get_depth() == n2->get_depth()) {
+
238  //make sure the order of the nodes is *always* the same
+
239  //going through the nodes in different orders may change
+
240  //the output of backpropagation
+
241  if (n1->get_innovation_number() < n2->get_innovation_number()) {
+
242  return true;
+
243  } else {
+
244  return false;
+
245  }
+
246  } else {
+
247  return false;
+
248  }
+
249  }
+
250 };
+
251 
+
252 #endif
+
+ + + + + +
sort_CNN_Nodes_by_depth
Definition: cnn_node.hxx:233
+
CNN_Node
Definition: cnn_node.hxx:49
+ + +
ImagesInterface
Definition: image_set_interface.hxx:28
+
std::endl
T endl(T... args)
+ + + +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/color__table_8hxx_source.html b/docs/html/color__table_8hxx_source.html new file mode 100644 index 00000000..f41812cc --- /dev/null +++ b/docs/html/color__table_8hxx_source.html @@ -0,0 +1,92 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: common/color_table.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
color_table.hxx
+
+
+
1 #pragma once
+
2 
+
3 #include <cstdint>
+
4 
+
5 struct Color {
+
6  int16_t red;
+
7  int16_t green;
+
8  int16_t blue;
+
9 };
+
10 
+
11 Color get_colormap(double value);
+
+
Color
Definition: color_table.hxx:5
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/comparison_8hxx_source.html b/docs/html/comparison_8hxx_source.html new file mode 100644 index 00000000..022eca85 --- /dev/null +++ b/docs/html/comparison_8hxx_source.html @@ -0,0 +1,141 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: cnn/comparison.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
comparison.hxx
+
+
+
1 #ifndef EXACT_COMPARISON_HXX
+
2 #define EXACT_COMPARISON_HXX
+
3 
+
4 #include <iostream>
+
5 using std::cerr;
+
6 using std::endl;
+
7 
+
8 #include <map>
+
9 using std::map;
+
10 
+
11 #include <random>
+
12 using std::minstd_rand0;
+
13 using std::uniform_int_distribution;
+
14 using std::uniform_real_distribution;
+
15 
+
16 #include <string>
+
17 using std::string;
+
18 
+
19 #include <vector>
+
20 using std::vector;
+
21 
+
22 #include "common/random.hxx"
+
23 
+
24 bool are_different(string variable_name, const int &v1, const int &v2);
+
25 bool are_different(string variable_name, const float &v1, const float &v2);
+
26 bool are_different(string variable_name, const string &v1, const string &v2);
+
27 bool are_different(string variable_name, const minstd_rand0 &v1, const minstd_rand0 &v2);
+
28 bool are_different(string variable_name, const NormalDistribution &v1, const NormalDistribution &v2);
+
29 bool are_different(string variable_name, const uniform_int_distribution<long> &v1, const uniform_int_distribution<long> &v2);
+
30 bool are_different(string variable_name, const uniform_real_distribution<float> &v1, const uniform_real_distribution<float> &v2);
+
31 
+
32 bool are_different(string variable_name, const vector<int> &v1, const vector<int> &v2);
+
33 bool are_different(string variable_name, const vector<long> &v1, const vector<long> &v2);
+
34 
+
35 bool are_different(string variable_name, const map<string, int> &v1, const map<string, int> &v2);
+
36 
+
37 bool are_different(string variable_name, int size, const float* v1, const float* v2);
+
38 
+
39 
+
40 /*
+
41 
+
42 template <>
+
43 bool are_different<string>(string variable_name, const string &v1, const string &v2);
+
44 
+
45 template <>
+
46 bool are_different<vector<long>>(string variable_name, const vector<long> &v1, const vector<long> &v2);
+
47 
+
48 template <>
+
49 bool are_different<map<string, int>>(string variable_name, const map<string, int> &v1, const map<string, int> &v2);
+
50 */
+
51 
+
52 #endif
+
+ + + + +
NormalDistribution
Definition: random.hxx:19
+ + + +
std::endl
T endl(T... args)
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/db__conn_8hxx_source.html b/docs/html/db__conn_8hxx_source.html new file mode 100644 index 00000000..b75ace2e --- /dev/null +++ b/docs/html/db__conn_8hxx_source.html @@ -0,0 +1,98 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: common/db_conn.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
db_conn.hxx
+
+
+
1 #ifndef EXACT_DB_CONN_HXX
+
2 #define EXACT_DB_CONN_HXX
+
3 
+
4 #include "mysql.h"
+
5 
+
6 #define mysql_exact_query(query) __mysql_check(query, __FILE__, __LINE__)
+
7 
+
8 extern MYSQL *exact_db_conn;
+
9 
+
10 void set_db_info_filename(string _filename);
+
11 
+
12 void __mysql_check(string query, const char *file, const int line);
+
13 
+
14 void initialize_exact_database();
+
15 
+
16 int mysql_exact_last_insert_id();
+
17 
+
18 #endif
+
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/delta__node_8hxx_source.html b/docs/html/delta__node_8hxx_source.html new file mode 100644 index 00000000..b3bda7de --- /dev/null +++ b/docs/html/delta__node_8hxx_source.html @@ -0,0 +1,169 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/delta_node.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
delta_node.hxx
+
+
+
1 #ifndef EXAMM_DELTA_NODE_HXX
+
2 #define EXAMM_DELTA_NODE_HXX
+
3 
+
4 #include <string>
+
5 using std::string;
+
6 
+
7 #include <random>
+
8 using std::minstd_rand0;
+
9 using std::uniform_real_distribution;
+
10 
+
11 #include <vector>
+
12 using std::vector;
+
13 
+
14 #include "common/random.hxx"
+
15 
+
16 #include "rnn_node_interface.hxx"
+
17 
+
18 class Delta_Node : public RNN_Node_Interface {
+
19  private:
+
20  double alpha;
+
21  double beta1;
+
22  double beta2;
+
23  double v;
+
24 
+
25  double r_bias;
+
26  double z_hat_bias;
+
27 
+
28  vector<double> d_alpha;
+
29  vector<double> d_beta1;
+
30  vector<double> d_beta2;
+
31  vector<double> d_v;
+
32  vector<double> d_r_bias;
+
33  vector<double> d_z_hat_bias;
+
34  vector<double> d_z_prev;
+
35 
+
36  vector<double> r;
+
37  vector<double> ld_r;
+
38  vector<double> z_cap;
+
39  vector<double> ld_z_cap;
+
40  vector<double> ld_z;
+
41 
+
42  public:
+
43 
+
44  Delta_Node(int _innovation_number, int _type, double _depth);
+
45  ~Delta_Node();
+
46 
+
47  void initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma);
+
48  void initialize_xavier(minstd_rand0 &generator, uniform_real_distribution<double> &rng1_1, double range);
+
49  void initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range);
+
50  void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution<double> &rng);
+
51 
+
52  double get_gradient(string gradient_name);
+
53  void print_gradient(string gradient_name);
+
54 
+
55  void input_fired(int time, double incoming_output);
+
56 
+
57  void try_update_deltas(int time);
+
58  void error_fired(int time, double error);
+
59  void output_fired(int time, double delta);
+
60 
+
61  uint32_t get_number_weights() const;
+
62 
+
63  void get_weights(vector<double> &parameters) const;
+
64  void set_weights(const vector<double> &parameters);
+
65 
+
66  void get_weights(uint32_t &offset, vector<double> &parameters) const;
+
67  void set_weights(uint32_t &offset, const vector<double> &parameters);
+
68 
+
69  void get_gradients(vector<double> &gradients);
+
70 
+
71  void reset(int _series_length);
+
72 
+
73  void write_to_stream(ostream &out);
+
74 
+
75  RNN_Node_Interface* copy() const;
+
76 
+
77  friend class RNN_Edge;
+
78 
+
79 };
+
80 #endif
+
+
RNN_Edge
Definition: rnn_edge.hxx:6
+ + + +
NormalDistribution
Definition: random.hxx:19
+ + +
Delta_Node
Definition: delta_node.hxx:18
+
RNN_Node_Interface
Definition: rnn_node_interface.hxx:133
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/dir_13620b7f1330796542fcb7e43593aaf8.html b/docs/html/dir_13620b7f1330796542fcb7e43593aaf8.html new file mode 100644 index 00000000..fe3652da --- /dev/null +++ b/docs/html/dir_13620b7f1330796542fcb7e43593aaf8.html @@ -0,0 +1,80 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: mpi Directory Reference + + + + + + + + + +
+
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EXONA: Evolutionary eXploration of Neural Architectures +
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+
mpi Directory Reference
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+
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+Directories

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EXONA: Evolutionary eXploration of Neural Architectures +
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+Directories

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+ border-top: 1px solid #A8B8D9; + border-left: 1px solid #A8B8D9; + border-right: 1px solid #A8B8D9; + border-top-right-radius: 4px; + border-top-left-radius: 4px; + margin-bottom: -1px; + background-image: url('nav_f.png'); + background-repeat: repeat-x; + background-color: #E2E8F2; + line-height: 1.25; + font-weight: 300; + float:left; +} + +.permalink +{ + font-size: 65%; + display: inline-block; + vertical-align: middle; +} + +.memtemplate { + font-size: 80%; + color: #4665A2; + font-weight: normal; + margin-left: 9px; +} + +.memnav { + background-color: #EBEFF6; + border: 1px solid #A3B4D7; + text-align: center; + margin: 2px; + margin-right: 15px; + padding: 2px; +} + +.mempage { + width: 100%; +} + +.memitem { + padding: 0; + margin-bottom: 10px; + margin-right: 5px; + -webkit-transition: box-shadow 0.5s linear; + -moz-transition: box-shadow 0.5s linear; + -ms-transition: box-shadow 0.5s linear; + -o-transition: box-shadow 0.5s linear; + transition: box-shadow 0.5s linear; + display: table !important; + width: 100%; +} + +.memitem.glow { + box-shadow: 0 0 15px cyan; +} + +.memname { + font-weight: 400; + margin-left: 6px; +} + +.memname td { + vertical-align: bottom; +} + +.memproto, dl.reflist dt { + border-top: 1px solid #A8B8D9; + border-left: 1px solid #A8B8D9; + border-right: 1px solid #A8B8D9; + padding: 6px 0px 6px 0px; + color: #253555; + font-weight: bold; + text-shadow: 0px 1px 1px rgba(255, 255, 255, 0.9); + background-color: #DFE5F1; + /* opera specific markup */ + box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + border-top-right-radius: 4px; + /* firefox specific markup */ + -moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px; + -moz-border-radius-topright: 4px; + /* webkit specific markup */ + -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + -webkit-border-top-right-radius: 4px; + +} + +.overload { + font-family: "courier new",courier,monospace; + font-size: 65%; +} + +.memdoc, dl.reflist dd { + border-bottom: 1px solid #A8B8D9; + border-left: 1px solid #A8B8D9; + border-right: 1px solid #A8B8D9; + padding: 6px 10px 2px 10px; + background-color: #FBFCFD; + border-top-width: 0; + background-image:url('nav_g.png'); + background-repeat:repeat-x; + background-color: #FFFFFF; + /* opera specific markup */ + border-bottom-left-radius: 4px; + border-bottom-right-radius: 4px; + box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + /* firefox specific markup */ + -moz-border-radius-bottomleft: 4px; + -moz-border-radius-bottomright: 4px; + -moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px; + /* webkit specific markup */ + -webkit-border-bottom-left-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); +} + +dl.reflist dt { + padding: 5px; +} + +dl.reflist dd { + margin: 0px 0px 10px 0px; + padding: 5px; +} + +.paramkey { + text-align: right; +} + +.paramtype { + white-space: nowrap; +} + +.paramname { + color: #602020; + white-space: nowrap; +} +.paramname em { + font-style: normal; +} +.paramname code { + line-height: 14px; +} + +.params, .retval, .exception, .tparams { + margin-left: 0px; + padding-left: 0px; +} + +.params .paramname, .retval .paramname, .tparams .paramname, .exception .paramname { + font-weight: bold; + vertical-align: top; +} + +.params .paramtype, .tparams .paramtype { + font-style: italic; + vertical-align: top; +} + +.params .paramdir, .tparams .paramdir { + font-family: "courier new",courier,monospace; + vertical-align: top; +} + +table.mlabels { + border-spacing: 0px; +} + +td.mlabels-left { + width: 100%; + padding: 0px; +} + +td.mlabels-right { + vertical-align: bottom; + padding: 0px; + white-space: nowrap; +} + +span.mlabels { + margin-left: 8px; +} + +span.mlabel { + background-color: #728DC1; + border-top:1px solid #5373B4; + border-left:1px solid #5373B4; + border-right:1px solid #C4CFE5; + border-bottom:1px solid #C4CFE5; + text-shadow: none; + color: white; + margin-right: 4px; + padding: 2px 3px; + border-radius: 3px; + font-size: 7pt; + white-space: nowrap; + vertical-align: middle; +} + + + +/* @end */ + +/* these are for tree view inside a (index) page */ + +div.directory { + margin: 10px 0px; + border-top: 1px solid #9CAFD4; + border-bottom: 1px solid #9CAFD4; + width: 100%; +} + +.directory table { + border-collapse:collapse; +} + +.directory td { + margin: 0px; + padding: 0px; + vertical-align: top; +} + +.directory td.entry { + white-space: nowrap; + padding-right: 6px; + padding-top: 3px; +} + +.directory td.entry a { + outline:none; +} + +.directory td.entry a img { + border: none; +} + +.directory td.desc { + width: 100%; + padding-left: 6px; + padding-right: 6px; + padding-top: 3px; + border-left: 1px solid rgba(0,0,0,0.05); +} + +.directory tr.even { + padding-left: 6px; + background-color: #F7F8FB; +} + +.directory img { + vertical-align: -30%; +} + +.directory .levels { + white-space: nowrap; + width: 100%; + text-align: right; + font-size: 9pt; +} + +.directory .levels span { + cursor: pointer; + padding-left: 2px; + padding-right: 2px; + color: #3D578C; +} + +.arrow { + color: #9CAFD4; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + cursor: pointer; + font-size: 80%; + display: inline-block; + width: 16px; + height: 22px; +} + +.icon { + font-family: Arial, Helvetica; + font-weight: bold; + font-size: 12px; + height: 14px; + width: 16px; + display: inline-block; + background-color: #728DC1; + color: white; + text-align: center; + border-radius: 4px; + margin-left: 2px; + margin-right: 2px; +} + +.icona { + width: 24px; + height: 22px; + display: inline-block; +} + +.iconfopen { + width: 24px; + height: 18px; + margin-bottom: 4px; + background-image:url('folderopen.png'); + background-position: 0px -4px; + background-repeat: repeat-y; + vertical-align:top; + display: inline-block; +} + +.iconfclosed { + width: 24px; + height: 18px; + margin-bottom: 4px; + background-image:url('folderclosed.png'); + background-position: 0px -4px; + background-repeat: repeat-y; + vertical-align:top; + display: inline-block; +} + +.icondoc { + width: 24px; + height: 18px; + margin-bottom: 4px; + background-image:url('doc.png'); + background-position: 0px -4px; + background-repeat: repeat-y; + vertical-align:top; + display: inline-block; +} + +table.directory { + font: 400 14px Roboto,sans-serif; +} + +/* @end */ + +div.dynheader { + margin-top: 8px; + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +address { + font-style: normal; + color: #2A3D61; +} + +table.doxtable caption { + caption-side: top; +} + +table.doxtable { + border-collapse:collapse; + margin-top: 4px; + margin-bottom: 4px; +} + +table.doxtable td, table.doxtable th { + border: 1px solid #2D4068; + padding: 3px 7px 2px; +} + +table.doxtable th { + background-color: #374F7F; + color: #FFFFFF; + font-size: 110%; + padding-bottom: 4px; + padding-top: 5px; +} + +table.fieldtable { + /*width: 100%;*/ + margin-bottom: 10px; + border: 1px solid #A8B8D9; + border-spacing: 0px; + -moz-border-radius: 4px; + -webkit-border-radius: 4px; + border-radius: 4px; + -moz-box-shadow: rgba(0, 0, 0, 0.15) 2px 2px 2px; + -webkit-box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15); + box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15); +} + +.fieldtable td, .fieldtable th { + padding: 3px 7px 2px; +} + +.fieldtable td.fieldtype, .fieldtable td.fieldname { + white-space: nowrap; + border-right: 1px solid #A8B8D9; + border-bottom: 1px solid #A8B8D9; + vertical-align: top; +} + +.fieldtable td.fieldname { + padding-top: 3px; +} + +.fieldtable td.fielddoc { + border-bottom: 1px solid #A8B8D9; + /*width: 100%;*/ +} + +.fieldtable td.fielddoc p:first-child { + margin-top: 0px; +} + +.fieldtable td.fielddoc p:last-child { + margin-bottom: 2px; +} + +.fieldtable tr:last-child td { + border-bottom: none; +} + +.fieldtable th { + background-image:url('nav_f.png'); + background-repeat:repeat-x; + background-color: #E2E8F2; + font-size: 90%; + color: #253555; + padding-bottom: 4px; + padding-top: 5px; + text-align:left; + font-weight: 400; + -moz-border-radius-topleft: 4px; + -moz-border-radius-topright: 4px; + -webkit-border-top-left-radius: 4px; + -webkit-border-top-right-radius: 4px; + border-top-left-radius: 4px; + border-top-right-radius: 4px; + border-bottom: 1px solid #A8B8D9; +} + + +.tabsearch { + top: 0px; + left: 10px; + height: 36px; + background-image: url('tab_b.png'); + z-index: 101; + overflow: hidden; + font-size: 13px; +} + +.navpath ul +{ + font-size: 11px; + background-image:url('tab_b.png'); + background-repeat:repeat-x; + background-position: 0 -5px; + height:30px; + line-height:30px; + color:#8AA0CC; + border:solid 1px #C2CDE4; + overflow:hidden; + margin:0px; + padding:0px; +} + +.navpath li +{ + list-style-type:none; + float:left; + padding-left:10px; + padding-right:15px; + background-image:url('bc_s.png'); + background-repeat:no-repeat; + background-position:right; + color:#364D7C; +} + +.navpath li.navelem a +{ + height:32px; + display:block; + text-decoration: none; + outline: none; + color: #283A5D; + font-family: 'Lucida Grande',Geneva,Helvetica,Arial,sans-serif; + text-shadow: 0px 1px 1px rgba(255, 255, 255, 0.9); + text-decoration: none; +} + +.navpath li.navelem a:hover +{ + color:#6884BD; +} + +.navpath li.footer +{ + list-style-type:none; + float:right; + padding-left:10px; + padding-right:15px; + background-image:none; + background-repeat:no-repeat; + background-position:right; + color:#364D7C; + font-size: 8pt; +} + + +div.summary +{ + float: right; + font-size: 8pt; + padding-right: 5px; + width: 50%; + text-align: right; +} + +div.summary a +{ + white-space: nowrap; +} + +table.classindex +{ + margin: 10px; + white-space: nowrap; + margin-left: 3%; + margin-right: 3%; + width: 94%; + border: 0; + border-spacing: 0; + padding: 0; +} + +div.ingroups +{ + font-size: 8pt; + width: 50%; + text-align: left; +} + +div.ingroups a +{ + white-space: nowrap; +} + +div.header +{ + background-image:url('nav_h.png'); + background-repeat:repeat-x; + background-color: #F9FAFC; + margin: 0px; + border-bottom: 1px solid #C4CFE5; +} + +div.headertitle +{ + padding: 5px 5px 5px 10px; +} + +.PageDocRTL-title div.headertitle { + text-align: right; + direction: rtl; +} + +dl { + padding: 0 0 0 0; +} + +/* dl.note, dl.warning, dl.attention, dl.pre, dl.post, dl.invariant, dl.deprecated, dl.todo, dl.test, dl.bug, dl.examples */ +dl.section { + margin-left: 0px; + padding-left: 0px; +} + +dl.section.DocNodeRTL { + margin-right: 0px; + padding-right: 0px; +} + +dl.note { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #D0C000; +} + +dl.note.DocNodeRTL { + margin-left: 0; + padding-left: 0; + border-left: 0; + margin-right: -7px; + padding-right: 3px; + border-right: 4px solid; + border-color: #D0C000; +} + +dl.warning, dl.attention { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #FF0000; +} + +dl.warning.DocNodeRTL, dl.attention.DocNodeRTL { + margin-left: 0; + padding-left: 0; + border-left: 0; + margin-right: -7px; + padding-right: 3px; + border-right: 4px solid; + border-color: #FF0000; +} + +dl.pre, dl.post, dl.invariant { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #00D000; +} + +dl.pre.DocNodeRTL, dl.post.DocNodeRTL, dl.invariant.DocNodeRTL { + margin-left: 0; + padding-left: 0; + border-left: 0; + margin-right: -7px; + padding-right: 3px; + border-right: 4px solid; + border-color: #00D000; +} + +dl.deprecated { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #505050; +} + +dl.deprecated.DocNodeRTL { + margin-left: 0; + padding-left: 0; + border-left: 0; + margin-right: -7px; + padding-right: 3px; + border-right: 4px solid; + border-color: #505050; +} + +dl.todo { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #00C0E0; +} + +dl.todo.DocNodeRTL { + margin-left: 0; + padding-left: 0; + border-left: 0; + margin-right: -7px; + padding-right: 3px; + border-right: 4px solid; + border-color: #00C0E0; +} + +dl.test { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #3030E0; +} + +dl.test.DocNodeRTL { + margin-left: 0; + padding-left: 0; + border-left: 0; + margin-right: -7px; + padding-right: 3px; + border-right: 4px solid; + border-color: #3030E0; +} + +dl.bug { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #C08050; +} + +dl.bug.DocNodeRTL { + margin-left: 0; + padding-left: 0; + border-left: 0; + margin-right: -7px; + padding-right: 3px; + border-right: 4px solid; + border-color: #C08050; +} + +dl.section dd { + margin-bottom: 6px; +} + + +#projectlogo +{ + text-align: center; + vertical-align: bottom; + border-collapse: separate; +} + +#projectlogo img +{ + border: 0px none; +} + +#projectalign +{ + vertical-align: middle; +} + +#projectname +{ + font: 300% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 2px 0px; +} + +#projectbrief +{ + font: 120% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 0px; +} + +#projectnumber +{ + font: 50% Tahoma, Arial,sans-serif; + margin: 0px; + padding: 0px; +} + +#titlearea +{ + padding: 0px; + margin: 0px; + width: 100%; + border-bottom: 1px solid #5373B4; +} + +.image +{ + text-align: center; +} + +.dotgraph +{ + text-align: center; +} + +.mscgraph +{ + text-align: center; +} + +.plantumlgraph +{ + text-align: center; +} + +.diagraph +{ + text-align: center; +} + +.caption +{ + font-weight: bold; +} + +div.zoom +{ + border: 1px solid #90A5CE; +} + +dl.citelist { + margin-bottom:50px; +} + +dl.citelist dt { + color:#334975; + float:left; + font-weight:bold; + margin-right:10px; + padding:5px; +} + +dl.citelist dd { + margin:2px 0; + padding:5px 0; +} + +div.toc { + padding: 14px 25px; + background-color: #F4F6FA; + border: 1px solid #D8DFEE; + border-radius: 7px 7px 7px 7px; + float: right; + height: auto; + margin: 0 8px 10px 10px; + width: 200px; +} + +.PageDocRTL-title div.toc { + float: left !important; + text-align: right; +} + +div.toc li { + background: url("bdwn.png") no-repeat scroll 0 5px transparent; + font: 10px/1.2 Verdana,DejaVu Sans,Geneva,sans-serif; + margin-top: 5px; + padding-left: 10px; + padding-top: 2px; +} + +.PageDocRTL-title div.toc li { + background-position-x: right !important; + padding-left: 0 !important; + padding-right: 10px; +} + +div.toc h3 { + font: bold 12px/1.2 Arial,FreeSans,sans-serif; + color: #4665A2; + border-bottom: 0 none; + margin: 0; +} + +div.toc ul { + list-style: none outside none; + border: medium none; + padding: 0px; +} + +div.toc li.level1 { + margin-left: 0px; +} + +div.toc li.level2 { + margin-left: 15px; +} + +div.toc li.level3 { + margin-left: 30px; +} + +div.toc li.level4 { + margin-left: 45px; +} + +.PageDocRTL-title div.toc li.level1 { + margin-left: 0 !important; + margin-right: 0; +} + +.PageDocRTL-title div.toc li.level2 { + margin-left: 0 !important; + margin-right: 15px; +} + +.PageDocRTL-title div.toc li.level3 { + margin-left: 0 !important; + margin-right: 30px; +} + +.PageDocRTL-title div.toc li.level4 { + margin-left: 0 !important; + margin-right: 45px; +} + +.inherit_header { + font-weight: bold; + color: gray; + cursor: pointer; + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.inherit_header td { + padding: 6px 0px 2px 5px; +} + +.inherit { + display: none; +} + +tr.heading h2 { + margin-top: 12px; + margin-bottom: 4px; +} + +/* tooltip related style info */ + +.ttc { + position: absolute; + display: none; +} + +#powerTip { + cursor: default; + white-space: nowrap; + background-color: white; + border: 1px solid gray; + border-radius: 4px 4px 4px 4px; + box-shadow: 1px 1px 7px gray; + display: none; + font-size: smaller; + max-width: 80%; + opacity: 0.9; + padding: 1ex 1em 1em; + position: absolute; + z-index: 2147483647; +} + +#powerTip div.ttdoc { + color: grey; + font-style: italic; +} + +#powerTip div.ttname a { + font-weight: bold; +} + +#powerTip div.ttname { + font-weight: bold; +} + +#powerTip div.ttdeci { + color: #006318; +} + +#powerTip div { + margin: 0px; + padding: 0px; + font: 12px/16px Roboto,sans-serif; +} + +#powerTip:before, #powerTip:after { + content: ""; + position: absolute; + margin: 0px; +} + +#powerTip.n:after, #powerTip.n:before, +#powerTip.s:after, #powerTip.s:before, +#powerTip.w:after, #powerTip.w:before, +#powerTip.e:after, #powerTip.e:before, +#powerTip.ne:after, #powerTip.ne:before, +#powerTip.se:after, #powerTip.se:before, +#powerTip.nw:after, #powerTip.nw:before, +#powerTip.sw:after, #powerTip.sw:before { + border: solid transparent; + content: " "; + height: 0; + width: 0; + position: absolute; +} + +#powerTip.n:after, #powerTip.s:after, +#powerTip.w:after, #powerTip.e:after, +#powerTip.nw:after, #powerTip.ne:after, +#powerTip.sw:after, #powerTip.se:after { + border-color: rgba(255, 255, 255, 0); +} + +#powerTip.n:before, #powerTip.s:before, +#powerTip.w:before, #powerTip.e:before, +#powerTip.nw:before, #powerTip.ne:before, +#powerTip.sw:before, #powerTip.se:before { + border-color: rgba(128, 128, 128, 0); +} + +#powerTip.n:after, #powerTip.n:before, +#powerTip.ne:after, #powerTip.ne:before, +#powerTip.nw:after, #powerTip.nw:before { + top: 100%; +} + +#powerTip.n:after, #powerTip.ne:after, #powerTip.nw:after { + border-top-color: #FFFFFF; + border-width: 10px; + margin: 0px -10px; +} +#powerTip.n:before { + border-top-color: #808080; + border-width: 11px; + margin: 0px -11px; +} +#powerTip.n:after, #powerTip.n:before { + left: 50%; +} + +#powerTip.nw:after, #powerTip.nw:before { + right: 14px; +} + +#powerTip.ne:after, #powerTip.ne:before { + left: 14px; +} + +#powerTip.s:after, #powerTip.s:before, +#powerTip.se:after, #powerTip.se:before, +#powerTip.sw:after, #powerTip.sw:before { + bottom: 100%; +} + +#powerTip.s:after, #powerTip.se:after, #powerTip.sw:after { + border-bottom-color: #FFFFFF; + border-width: 10px; + margin: 0px -10px; +} + +#powerTip.s:before, #powerTip.se:before, #powerTip.sw:before { + border-bottom-color: #808080; + border-width: 11px; + margin: 0px -11px; +} + +#powerTip.s:after, #powerTip.s:before { + left: 50%; +} + +#powerTip.sw:after, #powerTip.sw:before { + right: 14px; +} + +#powerTip.se:after, #powerTip.se:before { + left: 14px; +} + +#powerTip.e:after, #powerTip.e:before { + left: 100%; +} +#powerTip.e:after { + border-left-color: #FFFFFF; + border-width: 10px; + top: 50%; + margin-top: -10px; +} +#powerTip.e:before { + border-left-color: #808080; + border-width: 11px; + top: 50%; + margin-top: -11px; +} + +#powerTip.w:after, #powerTip.w:before { + right: 100%; +} +#powerTip.w:after { + border-right-color: #FFFFFF; + border-width: 10px; + top: 50%; + margin-top: -10px; +} +#powerTip.w:before { + border-right-color: #808080; + border-width: 11px; + top: 50%; + margin-top: -11px; +} + +@media print +{ + #top { display: none; } + #side-nav { display: none; } + #nav-path { display: none; } + body { overflow:visible; } + h1, h2, h3, h4, h5, h6 { page-break-after: avoid; } + .summary { display: none; } + .memitem { page-break-inside: avoid; } + #doc-content + { + margin-left:0 !important; + height:auto !important; + width:auto !important; + overflow:inherit; + display:inline; + } +} + +/* @group Markdown */ + +/* +table.markdownTable { + border-collapse:collapse; + margin-top: 4px; + margin-bottom: 4px; +} + +table.markdownTable td, table.markdownTable th { + border: 1px solid #2D4068; + padding: 3px 7px 2px; +} + +table.markdownTableHead tr { +} + +table.markdownTableBodyLeft td, table.markdownTable th { + border: 1px solid #2D4068; + padding: 3px 7px 2px; +} + +th.markdownTableHeadLeft th.markdownTableHeadRight th.markdownTableHeadCenter th.markdownTableHeadNone { + background-color: #374F7F; + color: #FFFFFF; + font-size: 110%; + padding-bottom: 4px; + padding-top: 5px; +} + +th.markdownTableHeadLeft { + text-align: left +} + +th.markdownTableHeadRight { + text-align: right +} + +th.markdownTableHeadCenter { + text-align: center +} +*/ + +table.markdownTable { + border-collapse:collapse; + margin-top: 4px; + margin-bottom: 4px; +} + +table.markdownTable td, table.markdownTable th { + border: 1px solid #2D4068; 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you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation; either version 2 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License along + with this program; if not, write to the Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. + + @licend The above is the entire license notice + for the JavaScript code in this file + */ +function toggleVisibility(linkObj) +{ + var base = $(linkObj).attr('id'); + var summary = $('#'+base+'-summary'); + var content = $('#'+base+'-content'); + var trigger = $('#'+base+'-trigger'); + var src=$(trigger).attr('src'); + if (content.is(':visible')===true) { + content.hide(); + summary.show(); + $(linkObj).addClass('closed').removeClass('opened'); + $(trigger).attr('src',src.substring(0,src.length-8)+'closed.png'); + } else { + content.show(); + summary.hide(); + $(linkObj).removeClass('closed').addClass('opened'); + $(trigger).attr('src',src.substring(0,src.length-10)+'open.png'); + } + return false; +} + +function updateStripes() +{ + $('table.directory tr'). + removeClass('even').filter(':visible:even').addClass('even'); +} + +function toggleLevel(level) +{ + $('table.directory tr').each(function() { + var l = this.id.split('_').length-1; + var i = $('#img'+this.id.substring(3)); + var a = $('#arr'+this.id.substring(3)); + if (l + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/enarc_node.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
enarc_node.hxx
+
+
+
1 #ifndef EXAMM_ENARC_NODE_HXX
+
2 #define EXAMM_ENARC_NODE_HXX
+
3 
+
4 #include <string>
+
5 using std::string;
+
6 
+
7 #include <random>
+
8 using std::minstd_rand0;
+
9 using std::uniform_real_distribution;
+
10 
+
11 #include <vector>
+
12 using std::vector;
+
13 
+
14 #include "common/random.hxx"
+
15 
+
16 #include "rnn_node_interface.hxx"
+
17 
+
18 class ENARC_Node : public RNN_Node_Interface{
+
19  private:
+
20 
+
21  double rw;
+
22  double zw;
+
23 
+
24  double w1;
+
25  double w2;
+
26  double w3;
+
27  double w6;
+
28  double w4;
+
29  double w5;
+
30  double w7;
+
31  double w8;
+
32 
+
33  vector<double> d_zw;
+
34  vector<double> d_rw;
+
35 
+
36  vector<double> d_w1;
+
37 
+
38  vector<double> d_w2;
+
39  vector<double> d_w3;
+
40  vector<double> d_w6;
+
41 
+
42  vector<double> d_w4;
+
43  vector<double> d_w5;
+
44  vector<double> d_w7;
+
45  vector<double> d_w8;
+
46 
+
47  vector<double> d_h_prev;
+
48 
+
49  vector<double> z;
+
50  vector<double> l_d_z;
+
51 
+
52  vector<double> w1_z;
+
53  vector<double> l_w1_z;
+
54 
+
55  vector<double> w2_w1;
+
56  vector<double> l_w2_w1;
+
57 
+
58  vector<double> w3_w1;
+
59  vector<double> l_w3_w1;
+
60 
+
61  vector<double> w6_w1;
+
62  vector<double> l_w6_w1;
+
63 
+
64  vector<double> w4_w2;
+
65  vector<double> l_w4_w2;
+
66 
+
67  vector<double> w5_w3;
+
68  vector<double> l_w5_w3;
+
69 
+
70  vector<double> w7_w3;
+
71  vector<double> l_w7_w3;
+
72 
+
73  vector<double> w8_w3;
+
74  vector<double> l_w8_w3;
+
75 
+
76 
+
77  public:
+
78  ENARC_Node(int _innovation_number, int _type, double _depth);
+
79  ~ENARC_Node();
+
80 
+
81  void initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma);
+
82  void initialize_xavier(minstd_rand0 &generator, uniform_real_distribution<double> &rng1_1, double range);
+
83  void initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range);
+
84  void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution<double> &rng);
+
85 
+
86 
+
87  double get_gradient(string gradient_name);
+
88  void print_gradient(string gradient_name);
+
89 
+
90  void input_fired(int time, double incoming_output);
+
91 
+
92  void try_update_deltas(int time);
+
93  void error_fired(int time, double error);
+
94  void output_fired(int time, double delta);
+
95 
+
96  uint32_t get_number_weights() const;
+
97 
+
98  void get_weights(vector<double> &parameters) const;
+
99  void set_weights(const vector<double> &parameters);
+
100 
+
101  void get_weights(uint32_t &offset, vector<double> &parameters) const;
+
102  void set_weights(uint32_t &offset, const vector<double> &parameters);
+
103 
+
104  void get_gradients(vector<double> &gradients);
+
105 
+
106  void reset(int _series_length);
+
107 
+
108  void write_to_stream(ostream &out);
+
109 
+
110  RNN_Node_Interface* copy() const;
+
111 
+
112  friend class RNN_Edge;
+
113 
+
114 };
+
115 
+
116 
+
117 
+
118 #endif
+
+
RNN_Edge
Definition: rnn_edge.hxx:6
+ + + +
NormalDistribution
Definition: random.hxx:19
+ + +
ENARC_Node
Definition: enarc_node.hxx:18
+
RNN_Node_Interface
Definition: rnn_node_interface.hxx:133
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/enas__dag__node_8hxx_source.html b/docs/html/enas__dag__node_8hxx_source.html new file mode 100644 index 00000000..1dc27100 --- /dev/null +++ b/docs/html/enas__dag__node_8hxx_source.html @@ -0,0 +1,198 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/enas_dag_node.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
enas_dag_node.hxx
+
+
+
1 #ifndef EXAMM_ENAS_DAG_NODE_HXX
+
2 #define EXAMM_ENAS_DAG_NODE_HXX
+
3 
+
4 #include <string>
+
5 using std::string;
+
6 
+
7 #include <random>
+
8 using std::minstd_rand0;
+
9 using std::uniform_real_distribution;
+
10 
+
11 #include <vector>
+
12 using std::vector;
+
13 
+
14 #include <utility>
+
15 using std::pair;
+
16 using std::make_pair;
+
17 
+
18 
+
19 #include "common/random.hxx"
+
20 
+
21 #include "rnn_node_interface.hxx"
+
22 
+
23 class ENAS_DAG_Node : public RNN_Node_Interface{
+
24  private:
+
25 
+
29  double rw;
+
33  double zw;
+
34 
+
35 
+
39  vector<double> weights;
+
40 
+
41 
+
45  vector<double> d_zw;
+
49  vector<double> d_rw;
+
50 
+
54  vector<vector<double>> d_weights;
+
55 
+
59  vector<double> d_h_prev;
+
60 
+
64  vector<vector<double>> Nodes;
+
65 
+
69  vector<vector<double>> l_Nodes;
+
70 
+
71 
+
72  public:
+
73 
+
74  ENAS_DAG_Node(int _innovation_number, int _type, double _depth);
+
75  ~ENAS_DAG_Node();
+
76 
+
77 
+
78  void initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma);
+
79  void initialize_xavier(minstd_rand0 &generator, uniform_real_distribution<double> &rng1_1, double range);
+
80  void initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range);
+
81  void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution<double> &rng);
+
82 
+
83 
+
91  double get_gradient(string gradient_name);
+
92 
+
100  void print_gradient(string gradient_name);
+
101 
+
102 
+
111  double activation(double value, int act_operator);
+
112 
+
122  double activation_derivative(double value, double input, int act_operator);
+
123 
+
124  void input_fired(int time, double incoming_output);
+
125 
+
126  void try_update_deltas(int time);
+
127  void error_fired(int time, double error);
+
128  void output_fired(int time, double delta);
+
129 
+
130  uint32_t get_number_weights() const;
+
131 
+
132  void get_weights(vector<double> &parameters) const;
+
133  void set_weights(const vector<double> &parameters);
+
134 
+
135  void get_weights(uint32_t &offset, vector<double> &parameters) const;
+
136  void set_weights(uint32_t &offset, const vector<double> &parameters);
+
137 
+
138  void get_gradients(vector<double> &gradients);
+
139 
+
140  void reset(int _series_length);
+
141 
+
142  void write_to_stream(ostream &out);
+
143 
+
144  RNN_Node_Interface* copy() const;
+
145 
+
146  friend class RNN_Edge;
+
147 
+
148 };
+
149 
+
150 
+
151 
+
152 #endif
+
+
RNN_Edge
Definition: rnn_edge.hxx:6
+ +
ENAS_DAG_Node::print_gradient
void print_gradient(string gradient_name)
Gives the gradients in essence the deriviative of the weights in the memory cell in the network.
Definition: enas_dag_node.cxx:128
+
ENAS_DAG_Node::get_gradient
double get_gradient(string gradient_name)
Gives the gradients in essence the deriviative of the weights in the memory cell in the network.
Definition: enas_dag_node.cxx:96
+ + +
ENAS_DAG_Node::d_rw
vector< double > d_rw
Gradient of the weight of the start node in the memory cell.
Definition: enas_dag_node.hxx:49
+
ENAS_DAG_Node::activation
double activation(double value, int act_operator)
Gives the activations of the node in the memory cell in the network.
Definition: enas_dag_node.cxx:132
+
ENAS_DAG_Node::activation_derivative
double activation_derivative(double value, double input, int act_operator)
Gives the activations derviative of the node in the memory cell in the network.
Definition: enas_dag_node.cxx:141
+ +
NormalDistribution
Definition: random.hxx:19
+ +
ENAS_DAG_Node::weights
vector< double > weights
Weights of the subseqeuent nodes in topological order the memory cell.
Definition: enas_dag_node.hxx:39
+ +
ENAS_DAG_Node::zw
double zw
Weight of the start node in the memory cell.
Definition: enas_dag_node.hxx:33
+
ENAS_DAG_Node::Nodes
vector< vector< double > > Nodes
Outputs of the nodes in the memory cell connecting with weight wj from node with weight wi.
Definition: enas_dag_node.hxx:64
+
ENAS_DAG_Node
Definition: enas_dag_node.hxx:23
+
ENAS_DAG_Node::l_Nodes
vector< vector< double > > l_Nodes
Derivative of the nodes in the memory cell connecting with weight wj from node with weight wi.
Definition: enas_dag_node.hxx:69
+
ENAS_DAG_Node::d_h_prev
vector< double > d_h_prev
Gradient of the previous output i.e.
Definition: enas_dag_node.hxx:59
+
ENAS_DAG_Node::rw
double rw
Weight of the start node in the memory cell.
Definition: enas_dag_node.hxx:29
+
std::make_pair
T make_pair(T... args)
+
ENAS_DAG_Node::d_weights
vector< vector< double > > d_weights
Gradient of the weights of the other nodes in the memory cell.
Definition: enas_dag_node.hxx:54
+
RNN_Node_Interface
Definition: rnn_node_interface.hxx:133
+
ENAS_DAG_Node::d_zw
vector< double > d_zw
Gradient of the weight of the start node in the memory cell.
Definition: enas_dag_node.hxx:45
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/exact_8hxx_source.html b/docs/html/exact_8hxx_source.html new file mode 100644 index 00000000..faf3464f --- /dev/null +++ b/docs/html/exact_8hxx_source.html @@ -0,0 +1,303 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: cnn/exact.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
exact.hxx
+
+
+
1 #ifndef EXACT_H
+
2 #define EXACT_H
+
3 
+
4 #include <algorithm>
+
5 using std::sort;
+
6 
+
7 #include <iostream>
+
8 using std::ostream;
+
9 using std::istream;
+
10 
+
11 #include <limits>
+
12 using std::numeric_limits;
+
13 
+
14 #include <random>
+
15 using std::minstd_rand0;
+
16 using std::normal_distribution;
+
17 using std::uniform_int_distribution;
+
18 using std::uniform_real_distribution;
+
19 
+
20 #include <string>
+
21 using std::to_string;
+
22 
+
23 #include <vector>
+
24 using std::vector;
+
25 
+
26 #include "image_tools/image_set.hxx"
+
27 #include "cnn_node.hxx"
+
28 #include "cnn_edge.hxx"
+
29 #include "cnn_genome.hxx"
+
30 
+
31 
+
32 class EXACT {
+
33  private:
+
34  int id;
+
35 
+
36  string search_name;
+
37  string output_directory;
+
38  string training_filename;
+
39  string validation_filename;
+
40  string test_filename;
+
41 
+
42  int number_training_images;
+
43  int number_validation_images;
+
44  int number_test_images;
+
45 
+
46  int padding;
+
47 
+
48  int image_channels;
+
49  int image_rows;
+
50  int image_cols;
+
51  int number_classes;
+
52 
+
53  int population_size;
+
54  int node_innovation_count;
+
55  int edge_innovation_count;
+
56 
+
57  minstd_rand0 generator;
+
58  NormalDistribution normal_distribution;
+
59  uniform_int_distribution<long> rng_long;
+
60  uniform_real_distribution<float> rng_float;
+
61 
+
62  vector< CNN_Genome* > genomes;
+
63 
+
64  int best_predictions_genome_id;
+
65  CNN_Genome *best_predictions_genome;
+
66 
+
67  int genomes_generated;
+
68  int inserted_genomes;
+
69  int max_genomes;
+
70 
+
71  bool reset_weights;
+
72  int max_epochs;
+
73  bool use_sfmp;
+
74  bool use_node_operations;
+
75 
+
76  float initial_batch_size_min;
+
77  float initial_batch_size_max;
+
78  float batch_size_min;
+
79  float batch_size_max;
+
80 
+
81  float initial_mu_min;
+
82  float initial_mu_max;
+
83  float mu_min;
+
84  float mu_max;
+
85 
+
86  float initial_mu_delta_min;
+
87  float initial_mu_delta_max;
+
88  float mu_delta_min;
+
89  float mu_delta_max;
+
90 
+
91  float initial_learning_rate_min;
+
92  float initial_learning_rate_max;
+
93  float learning_rate_min;
+
94  float learning_rate_max;
+
95 
+
96  float initial_learning_rate_delta_min;
+
97  float initial_learning_rate_delta_max;
+
98  float learning_rate_delta_min;
+
99  float learning_rate_delta_max;
+
100 
+
101  float initial_weight_decay_min;
+
102  float initial_weight_decay_max;
+
103  float weight_decay_min;
+
104  float weight_decay_max;
+
105 
+
106  float initial_weight_decay_delta_min;
+
107  float initial_weight_decay_delta_max;
+
108  float weight_decay_delta_min;
+
109  float weight_decay_delta_max;
+
110 
+
111  float epsilon;
+
112 
+
113  float initial_alpha_min;
+
114  float initial_alpha_max;
+
115  float alpha_min;
+
116  float alpha_max;
+
117 
+
118  int initial_velocity_reset_min;
+
119  int initial_velocity_reset_max;
+
120  int velocity_reset_min;
+
121  int velocity_reset_max;
+
122 
+
123  float initial_input_dropout_probability_min;
+
124  float initial_input_dropout_probability_max;
+
125  float input_dropout_probability_min;
+
126  float input_dropout_probability_max;
+
127 
+
128  float initial_hidden_dropout_probability_min;
+
129  float initial_hidden_dropout_probability_max;
+
130  float hidden_dropout_probability_min;
+
131  float hidden_dropout_probability_max;
+
132 
+
133  float reset_weights_chance;
+
134 
+
135  float crossover_rate;
+
136  float more_fit_parent_crossover;
+
137  float less_fit_parent_crossover;
+
138  float crossover_alter_edge_type;
+
139 
+
140  int number_mutations;
+
141  float edge_alter_type;
+
142  float edge_disable;
+
143  float edge_enable;
+
144  float edge_split;
+
145  float edge_add;
+
146  float node_change_size;
+
147  float node_change_size_x;
+
148  float node_change_size_y;
+
149  float node_add;
+
150  float node_split;
+
151  float node_merge;
+
152  float node_enable;
+
153  float node_disable;
+
154 
+
155  map<string, int> inserted_from_map;
+
156  map<string, int> generated_from_map;
+
157 
+
158  public:
+
159 #ifdef _MYSQL_
+
160  static bool exists_in_database(int exact_id);
+
161  EXACT(int exact_id);
+
162 
+
163  void export_to_database();
+
164  void update_database();
+
165 #endif
+
166 
+
167  EXACT(const ImagesInterface &training_images, const ImagesInterface &validation_images, const ImagesInterface &test_images, int _padding, int _population_size, int _max_epochs, bool _use_sfmp, bool _use_node_operations, int _max_genomes, string _output_directory, string _search_name, bool _reset_weights);
+
168 
+
169  int32_t population_contains(CNN_Genome *genome) const;
+
170  CNN_Genome* get_best_genome();
+
171 
+
172  int get_number_genomes() const;
+
173  CNN_Genome* get_genome(int i);
+
174 
+
175  void generate_initial_hyperparameters(float &mu, float &mu_delta, float &learning_rate, float &learning_rate_delta, float &weight_decay, float &weight_decay_delta, float &alpha, int &velocity_reset, float &input_dropout_probability, float &hidden_dropout_probability, int &batch_size);
+
176 
+
177  void generate_simplex_hyperparameters(float &mu, float &mu_delta, float &learning_rate, float &learning_rate_delta, float &weight_decay, float &weight_decay_delta, float &alpha, int &velocity_reset, float &input_dropout_probability, float &hidden_dropout_probability, int &batch_size);
+
178 
+
179 
+
180  bool add_edge(CNN_Genome *child, CNN_Node *node1, CNN_Node *node2, int edge_type);
+
181 
+
182  CNN_Genome* generate_individual();
+
183  CNN_Genome* create_mutation();
+
184  CNN_Genome* create_child();
+
185 
+
186  bool insert_genome(CNN_Genome* genome);
+
187 
+
188  int get_inserted_genomes() const;
+
189  int get_max_genomes() const;
+
190 
+
191  void write_individual_hyperparameters(CNN_Genome *individual);
+
192  void write_statistics(int new_generation_id, float new_fitness);
+
193  void write_statistics_header();
+
194  void write_hyperparameters_header();
+
195 
+
196  int get_id() const;
+
197  string get_search_name() const;
+
198  string get_output_directory() const;
+
199  string get_training_filename() const;
+
200  string get_validation_filename() const;
+
201  string get_test_filename() const;
+
202  int get_number_training_images() const;
+
203 
+
204  bool is_identical(EXACT *other, bool testing_checkpoint);
+
205 };
+
206 
+
207 #endif
+
+ + + +
NormalDistribution
Definition: random.hxx:19
+
CNN_Genome
Definition: cnn_genome.hxx:25
+
std::sort
T sort(T... args)
+ +
CNN_Node
Definition: cnn_node.hxx:49
+ + +
ImagesInterface
Definition: image_set_interface.hxx:28
+
std::to_string
T to_string(T... args)
+
EXACT
Definition: exact.hxx:32
+ + + + +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/examm_8hxx_source.html b/docs/html/examm_8hxx_source.html new file mode 100644 index 00000000..6a2d65c2 --- /dev/null +++ b/docs/html/examm_8hxx_source.html @@ -0,0 +1,300 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/examm.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
examm.hxx
+
+
+
1 #ifndef EXAMM_HXX
+
2 #define EXAMM_HXX
+
3 
+
4 #include <fstream>
+
5 using std::ofstream;
+
6 
+
7 #include <map>
+
8 using std::map;
+
9 
+
10 #include <sstream>
+
11 using std::ostringstream;
+
12 
+
13 #include <string>
+
14 using std::string;
+
15 using std::to_string;
+
16 
+
17 #include <vector>
+
18 using std::vector;
+
19 
+
20 #include "rnn_genome.hxx"
+
21 #include "speciation_strategy.hxx"
+
22 #include "common/weight_initialize.hxx"
+
23 
+
24 class EXAMM {
+
25  private:
+
26  int32_t population_size;
+
27  int32_t number_islands;
+
28 
+
29  vector< vector<RNN_Genome*> > genomes;
+
30 
+
31  int32_t max_genomes;
+
32  int32_t total_bp_epochs;
+
33 
+
34  int32_t extinction_event_generation_number;
+
35  string island_ranking_method;
+
36 
+
37  string speciation_method;
+
38  string repopulation_method;
+
39  int32_t repopulation_mutations;
+
40  bool repeat_extinction;
+
41 
+
42  SpeciationStrategy *speciation_strategy;
+
43 
+
44  double species_threshold;
+
45  double fitness_threshold;
+
46  double neat_c1;
+
47  double neat_c2;
+
48  double neat_c3;
+
49 
+
50  int32_t edge_innovation_count;
+
51  int32_t node_innovation_count;
+
52 
+
53  map<string, int32_t> inserted_from_map;
+
54  map<string, int32_t> generated_from_map;
+
55 
+
56  int32_t number_inputs;
+
57  int32_t number_outputs;
+
58  int32_t bp_iterations;
+
59  double learning_rate;
+
60 
+
61  bool use_high_threshold;
+
62  double high_threshold;
+
63 
+
64  bool use_low_threshold;
+
65  double low_threshold;
+
66 
+
67  bool use_regression;
+
68  bool use_dropout;
+
69  double dropout_probability;
+
70 
+
71  minstd_rand0 generator;
+
72  uniform_real_distribution<double> rng_0_1;
+
73  uniform_real_distribution<double> rng_crossover_weight;
+
74 
+
75  int32_t min_recurrent_depth;
+
76  int32_t max_recurrent_depth;
+
77 
+
78 
+
79  bool epigenetic_weights;
+
80 
+
81  double more_fit_crossover_rate;
+
82  double less_fit_crossover_rate;
+
83 
+
84  double clone_rate;
+
85 
+
86  double add_edge_rate;
+
87  double add_recurrent_edge_rate;
+
88  double enable_edge_rate;
+
89  double disable_edge_rate;
+
90  double split_edge_rate;
+
91 
+
92  double add_node_rate;
+
93  double enable_node_rate;
+
94  double disable_node_rate;
+
95  double split_node_rate;
+
96  double merge_node_rate;
+
97 
+
98  vector<int> possible_node_types;
+
99 
+
100  vector<string> op_log_ordering;
+
101  map<string, int32_t> inserted_counts;
+
102  map<string, int32_t> generated_counts;
+
103 
+
104  string output_directory;
+
105  ofstream *log_file;
+
106  ofstream *op_log_file;
+
107 
+
108  vector<string> input_parameter_names;
+
109  vector<string> output_parameter_names;
+
110 
+
111  string normalize_type;
+
112  map<string,double> normalize_mins;
+
113  map<string,double> normalize_maxs;
+
114  map<string,double> normalize_avgs;
+
115  map<string,double> normalize_std_devs;
+
116 
+
117  WeightType weight_initialize;
+
118  WeightType weight_inheritance;
+
119  WeightType mutated_component_weight;
+
120 
+
121  ostringstream memory_log;
+
122 
+
123  std::chrono::time_point<std::chrono::system_clock> startClock;
+
124 
+
125  string genome_file_name;
+
126 
+
127  bool start_filled;
+
128 
+
129  public:
+
130  EXAMM( int32_t _population_size,
+
131  int32_t _number_islands,
+
132  int32_t _max_genomes,
+
133  int32_t _extinction_event_generation_number,
+
134  int32_t _islands_to_exterminate,
+
135  string _island_ranking_method,
+
136  string _repopulation_method,
+
137  int32_t _repopulation_mutations,
+
138  bool _repeat_extinction,
+
139  string _speciation_method,
+
140  double _species_threshold,
+
141  double _fitness_threshold,
+
142  double _neat_c1,
+
143  double _neat_c2,
+
144  double _neat_c3,
+
145  const vector<string> &_input_parameter_names,
+
146  const vector<string> &_output_parameter_names,
+
147  string _normalize_type,
+
148  const map<string,double> &_normalize_mins,
+
149  const map<string,double> &_normalize_maxs,
+
150  const map<string,double> &_normalize_avgs,
+
151  const map<string,double> &_normalize_std_devs,
+
152  WeightType _weight_initialize,
+
153  WeightType _weight_inheritance,
+
154  WeightType _mutated_component_weight,
+
155  int32_t _bp_iterations,
+
156  double _learning_rate,
+
157  bool _use_high_threshold,
+
158  double _high_threshold,
+
159  bool _use_low_threshold,
+
160  double _low_threshold,
+
161  bool _use_dropout,
+
162  double _dropout_probability,
+
163  int32_t _min_recurrent_depth,
+
164  int32_t _max_recurrent_depth,
+
165  bool _use_regression,
+
166  string _output_directory,
+
167  RNN_Genome *seed_genome,
+
168  bool _start_filled);
+
169 
+
170 
+
171  ~EXAMM();
+
172 
+
173  void print();
+
174  void update_log();
+
175  void write_memory_log(string filename);
+
176 
+
177  void set_possible_node_types(vector<string> possible_node_type_strings);
+
178 
+
179  uniform_int_distribution<int32_t> get_recurrent_depth_dist();
+
180 
+
181  int get_random_node_type();
+
182 
+
183  RNN_Genome* generate_genome();
+
184  bool insert_genome(RNN_Genome* genome);
+
185 
+
186  void mutate(int32_t max_mutations, RNN_Genome *p1);
+
187 
+
188  void attempt_node_insert(vector<RNN_Node_Interface*> &child_nodes, const RNN_Node_Interface *node, const vector<double> &new_weights);
+
189  void attempt_edge_insert(vector<RNN_Edge*> &child_edges, vector<RNN_Node_Interface*> &child_nodes, RNN_Edge *edge, RNN_Edge *second_edge, bool set_enabled);
+
190  void attempt_recurrent_edge_insert(vector<RNN_Recurrent_Edge*> &child_recurrent_edges, vector<RNN_Node_Interface*> &child_nodes, RNN_Recurrent_Edge *recurrent_edge, RNN_Recurrent_Edge *second_edge, bool set_enabled);
+
191  RNN_Genome* crossover(RNN_Genome *p1, RNN_Genome *p2);
+
192 
+
193  double get_best_fitness();
+
194  double get_worst_fitness();
+
195  RNN_Genome* get_best_genome();
+
196  RNN_Genome* get_worst_genome();
+
197 
+
198  string get_output_directory() const;
+
199  RNN_Genome* generate_for_transfer_learning(string file_name, int extra_inputs, int extra_outputs) ;
+
200 
+
201  void check_weight_initialize_validity();
+
202 };
+
203 
+
204 #endif
+
+
RNN_Edge
Definition: rnn_edge.hxx:6
+ + + +
RNN_Recurrent_Edge
Definition: rnn_recurrent_edge.hxx:8
+ +
SpeciationStrategy
Definition: speciation_strategy.hxx:13
+ + +
std::to_string
T to_string(T... args)
+ + +
EXAMM
Definition: examm.hxx:24
+ +
RNN_Genome
Definition: rnn_genome.hxx:37
+
RNN_Node_Interface
Definition: rnn_node_interface.hxx:133
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/exp_8hxx_source.html b/docs/html/exp_8hxx_source.html new file mode 100644 index 00000000..3f3e7072 --- /dev/null +++ b/docs/html/exp_8hxx_source.html @@ -0,0 +1,88 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: common/exp.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
exp.hxx
+
+
+
1 #ifndef EXACT_EXP_HXX
+
2 #define EXACT_EXP_HXX
+
3 
+
4 float exact_exp(float);
+
5 
+
6 float exact_sqrt(float);
+
7 
+
8 #endif
+
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/files.html b/docs/html/files.html new file mode 100644 index 00000000..e6873183 --- /dev/null +++ b/docs/html/files.html @@ -0,0 +1,141 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: File List + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + +
+ +
+
+ + +
+ +
+ +
+
+
File List
+
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Here is a list of all documented files with brief descriptions:
+
[detail level 12]
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 ▼ cnn
 cnn_edge.hxx
 cnn_genome.hxx
 cnn_node.hxx
 comparison.hxx
 exact.hxx
 pooling.hxx
 propagation.hxx
 ▼ common
 arguments.hxx
 color_table.hxx
 db_conn.hxx
 exp.hxx
 files.hxx
 log.hxx
 random.hxx
 version.hxx
 weight_initialize.hxx
 ▼ image_tools
 image_set.hxx
 image_set_interface.hxx
 large_image_set.hxx
 lodepng.h
 mosaic_image_set.hxx
 ▼ mpi
 run_statistics.hxx
 tracker.hxx
 ▼ opencl
 opencl_utils.hxx
 ▼ rnn
 delta_node.hxx
 enarc_node.hxx
 enas_dag_node.hxx
 examm.hxx
 generate_nn.hxx
 gru_node.hxx
 island.hxx
 island_speciation_strategy.hxx
 lstm_node.hxx
 mgu_node.hxx
 min_max_heap.hxx
 mse.hxx
 neat_speciation_strategy.hxx
 random_dag_node.hxx
 rnn.hxx
 rnn_edge.hxx
 rnn_genome.hxx
 rnn_node.hxx
 rnn_node_interface.hxx
 rnn_recurrent_edge.hxx
 speciation_strategy.hxx
 species.hxx
 ugrnn_node.hxx
 ▼ rnn_tests
 gradient_test.hxx
 ▼ server
 make_jobs.hxx
 ▼ time_series
 time_series.hxx
 ▼ word_series
 word_series.hxx
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+ + diff --git a/docs/html/files_8hxx_source.html b/docs/html/files_8hxx_source.html new file mode 100644 index 00000000..69980ba3 --- /dev/null +++ b/docs/html/files_8hxx_source.html @@ -0,0 +1,97 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: common/files.hxx Source File + + + + + + + + + +
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EXONA: Evolutionary eXploration of Neural Architectures +
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files.hxx
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1 #ifndef EXACT_BOINC_COMMON_HXX
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2 #define EXACT_BOINC_COMMON_HXX
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3 
+
4 #include <stdexcept>
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5 using std::runtime_error;
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6 
+
7 #include <string>
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8 using std::string;
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9 
+
10 
+
11 string get_file_as_string(string file_path) noexcept(false);
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12 
+
13 int mkpath(const char *path, mode_t mode);
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14 
+
15 #endif
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+ + + +
+Generated by   +doxygen + 1.8.17 +
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EXONA: Evolutionary eXploration of Neural Architectures +
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+ +
+ + +
+
+
+
generate_nn.hxx
+
+
+
1 #ifndef RNN_GENERATE_NN_HXX
+
2 #define RNN_GENERATE_NN_HXX
+
3 
+
4 #include <string>
+
5 using std::string;
+
6 
+
7 #include <vector>
+
8 using std::vector;
+
9 
+
10 #include "rnn/rnn_genome.hxx"
+
11 #include "common/weight_initialize.hxx"
+
12 
+
13 RNN_Genome* create_ff(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize, WeightType weight_inheritance, WeightType mutated_component_weight);
+
14 
+
15 RNN_Genome* create_jordan(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize);
+
16 
+
17 RNN_Genome* create_elman(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize);
+
18 
+
19 RNN_Genome* create_lstm(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize);
+
20 
+
21 RNN_Genome* create_delta(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize);
+
22 
+
23 RNN_Genome* create_gru(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize);
+
24 
+
25 RNN_Genome* create_enarc(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize);
+
26 
+
27 RNN_Genome* create_enas_dag(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize);
+
28 
+
29 RNN_Genome* create_random_dag(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize);
+
30 
+
31 RNN_Genome* create_mgu(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize);
+
32 
+
33 RNN_Genome* create_ugrnn(const vector<string> &input_parameter_names, int number_hidden_layers, int number_hidden_nodes, const vector<string> &output_parameter_names, int max_recurrent_depth, WeightType weight_initialize);
+
34 
+
35 #endif
+
+ + +
RNN_Genome
Definition: rnn_genome.hxx:37
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/gradient__test_8hxx_source.html b/docs/html/gradient__test_8hxx_source.html new file mode 100644 index 00000000..64d58c1b --- /dev/null +++ b/docs/html/gradient__test_8hxx_source.html @@ -0,0 +1,124 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn_tests/gradient_test.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
gradient_test.hxx
+
+
+
1 #ifndef EXAMM_GRADIENT_TEST
+
2 #define EXAMM_GRADIENT_TEST
+
3 
+
4 #include <chrono>
+
5 
+
6 #include <fstream>
+
7 using std::getline;
+
8 using std::ifstream;
+
9 
+
10 #include <random>
+
11 using std::minstd_rand0;
+
12 using std::uniform_real_distribution;
+
13 
+
14 #include <string>
+
15 using std::string;
+
16 
+
17 #include <vector>
+
18 using std::vector;
+
19 
+
20 #include "common/arguments.hxx"
+
21 
+
22 #include "rnn/lstm_node.hxx"
+
23 #include "rnn/rnn_edge.hxx"
+
24 #include "rnn/rnn_genome.hxx"
+
25 #include "rnn/rnn_node.hxx"
+
26 #include "rnn/rnn_node_interface.hxx"
+
27 
+
28 #include "rnn/generate_nn.hxx"
+
29 
+
30 #include "time_series/time_series.hxx"
+
31 
+
32 void initialize_generator();
+
33 void generate_random_vector(int number_parameters, vector<double> &v);
+
34 
+
35 void gradient_test(string name, RNN_Genome *genome, const vector< vector<double> > &inputs, const vector< vector<double> > &outputs);
+
36 
+
37 #endif
+
+ + + + +
RNN_Genome
Definition: rnn_genome.hxx:37
+
std::getline
T getline(T... args)
+ + +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/gru__node_8hxx_source.html b/docs/html/gru__node_8hxx_source.html new file mode 100644 index 00000000..df8c6777 --- /dev/null +++ b/docs/html/gru__node_8hxx_source.html @@ -0,0 +1,178 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/gru_node.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
gru_node.hxx
+
+
+
1 #ifndef EXAMM_GRU_NODE_HXX
+
2 #define EXAMM_GRU_NODE_HXX
+
3 
+
4 #include <string>
+
5 using std::string;
+
6 
+
7 #include <random>
+
8 using std::minstd_rand0;
+
9 using std::uniform_real_distribution;
+
10 
+
11 #include <vector>
+
12 using std::vector;
+
13 
+
14 #include "common/random.hxx"
+
15 
+
16 #include "rnn_node_interface.hxx"
+
17 
+
18 class GRU_Node : public RNN_Node_Interface {
+
19  private:
+
20  double zw;
+
21  double zu;
+
22  double z_bias;
+
23 
+
24  double rw;
+
25  double ru;
+
26  double r_bias;
+
27 
+
28  double hw;
+
29  double hu;
+
30  double h_bias;
+
31 
+
32  vector<double> d_zw;
+
33  vector<double> d_zu;
+
34  vector<double> d_z_bias;
+
35  vector<double> d_rw;
+
36  vector<double> d_ru;
+
37  vector<double> d_r_bias;
+
38  vector<double> d_hw;
+
39  vector<double> d_hu;
+
40  vector<double> d_h_bias;
+
41 
+
42  vector<double> d_h_prev;
+
43 
+
44  vector<double> z;
+
45  vector<double> ld_z;
+
46  vector<double> r;
+
47  vector<double> ld_r;
+
48  vector<double> h_tanh;
+
49  vector<double> ld_h_tanh;
+
50 
+
51  public:
+
52 
+
53  GRU_Node(int _innovation_number, int _type, double _depth);
+
54  ~GRU_Node();
+
55 
+
56  void initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma);
+
57  void initialize_xavier(minstd_rand0 &generator, uniform_real_distribution<double> &rng1_1, double range);
+
58  void initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range);
+
59  void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution<double> &rng);
+
60 
+
61  double get_gradient(string gradient_name);
+
62  void print_gradient(string gradient_name);
+
63 
+
64  void input_fired(int time, double incoming_output);
+
65 
+
66  void try_update_deltas(int time);
+
67  void error_fired(int time, double error);
+
68  void output_fired(int time, double delta);
+
69 
+
70  uint32_t get_number_weights() const;
+
71 
+
72  void get_weights(vector<double> &parameters) const;
+
73  void set_weights(const vector<double> &parameters);
+
74 
+
75  void get_weights(uint32_t &offset, vector<double> &parameters) const;
+
76  void set_weights(uint32_t &offset, const vector<double> &parameters);
+
77 
+
78  void get_gradients(vector<double> &gradients);
+
79 
+
80  void reset(int _series_length);
+
81 
+
82  void write_to_stream(ostream &out);
+
83 
+
84  RNN_Node_Interface* copy() const;
+
85 
+
86  friend class RNN_Edge;
+
87 
+
88 };
+
89 #endif
+
+
RNN_Edge
Definition: rnn_edge.hxx:6
+ + +
GRU_Node
Definition: gru_node.hxx:18
+ +
NormalDistribution
Definition: random.hxx:19
+ + +
RNN_Node_Interface
Definition: rnn_node_interface.hxx:133
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/hierarchy.html b/docs/html/hierarchy.html new file mode 100644 index 00000000..13358b6f --- /dev/null +++ b/docs/html/hierarchy.html @@ -0,0 +1,175 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Class Hierarchy + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + +
+ +
+
+ + +
+ +
+ +
+
+
Class Hierarchy
+
+
+
This inheritance list is sorted roughly, but not completely, alphabetically:
+
[detail level 123]
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 CCNN_Edge
 CCNN_Genome
 CCNN_Node
 CCoin
 CColor
 CColorProfile
 CColorTree
 ▼Cconfig_to_arg.ConfigToArg
 Cexamm_task.ExammTask
 CConsolidatedStatistics
 CCorpus
 Ccs_less_than_avg
 Ccs_less_than_max
 Ccs_less_than_min
 CEXACT
 CEXACT_RESULT
 CEXAMM
 CHash
 CHuffmanTree
 ▼CImageInterface
 CImage
 CLargeImage
 ▼CImagesInterface
 CImages
 ▼CMultiImagesInterface
 CLargeImages
 CMosaicImages
 CIsland
 Cisland_purging_args.IslandPurgingArgs
 Cless_than_avg
 Cless_than_dfm_avg
 Cless_than_dfm_max
 Cless_than_dfm_min
 Cless_than_max
 Cless_than_min
 CLine
 CLodePNGColorMode
 CLodePNGCompressSettings
 CLodePNGDecoderSettings
 CLodePNGDecompressSettings
 CLodePNGEncoderSettings
 CLodePNGInfo
 CLodePNGState
 CLodePNGTime
 CLog
 CLogFile
 Cmin_max_heap< T >This implementation of a min_max_heap was basically ripped from here: https://github.com/itsjohncs/minmaxheap-cpp/blob/master/MinMaxHeap.hpp
 CNormalDistribution
 CParseTurbines
 CPoint
 Crec_args.RecArgs
 CRectangle
 CResultSet
 CRNN
 CRNN_Edge
 CRNN_Genome
 ▼CRNN_Node_Interface
 CDelta_Node
 CENARC_Node
 CENAS_DAG_Node
 CGRU_Node
 CLSTM_Node
 CMGU_Node
 CRANDOM_DAG_Node
 CRNN_Node
 CUGRNN_Node
 CRNN_Recurrent_Edge
 CRunStatistics
 CSentenceSeries
 Csort_CNN_Edges_by_depth
 Csort_CNN_Edges_by_innovation
 Csort_CNN_Edges_by_output_depth
 Csort_CNN_Nodes_by_depth
 Csort_genomes_by_fitness
 Csort_genomes_by_predictions
 Csort_genomes_by_validation_error
 Csort_RNN_Edges_by_depth
 Csort_RNN_Edges_by_innovation
 Csort_RNN_Edges_by_output_depth
 Csort_RNN_Nodes_by_depth
 Csort_RNN_Nodes_by_innovation
 Csort_RNN_Recurrent_Edges_by_depth
 Csort_RNN_Recurrent_Edges_by_innovation
 Csort_RNN_Recurrent_Edges_by_output_depth
 ▼CSpeciationStrategy
 CIslandSpeciationStrategy
 CNeatSpeciationStrategy
 CSpecies
 CTimeSeries
 CTimeSeriesSet
 CTimeSeriesSets
 CTracker
 Cucvector
 Cuivector
 CWordSeries
+
+
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/image__set_8hxx_source.html b/docs/html/image__set_8hxx_source.html new file mode 100644 index 00000000..6f5476d9 --- /dev/null +++ b/docs/html/image__set_8hxx_source.html @@ -0,0 +1,194 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: image_tools/image_set.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
image_set.hxx
+
+
+
1 #ifndef IMAGE_SET_HXX
+
2 #define IMAGE_SET_HXX
+
3 
+
4 #include <fstream>
+
5 using std::ifstream;
+
6 
+
7 #include <iostream>
+
8 using std::ostream;
+
9 
+
10 #include <string>
+
11 using std::string;
+
12 
+
13 #include <vector>
+
14 using std::vector;
+
15 
+
16 #include "image_set_interface.hxx"
+
17 
+
18 typedef class Images Images;
+
19 
+
20 class Image : public ImageInterface {
+
21  friend class Images;
+
22 
+
23  private:
+
24  int padding;
+
25  int channels;
+
26  int height;
+
27  int width;
+
28  int classification;
+
29  vector< vector< vector<uint8_t> > > pixels;
+
30 
+
31  //reference to images to get channel avgs and std_Devs
+
32  const Images *images;
+
33 
+
34  public:
+
35 
+
36  Image(ifstream &infile, int _channels, int _width, int _height, int _padding, int _classification, const Images *_images);
+
37 
+
38  int get_classification() const;
+
39 
+
40  void scale_0_1();
+
41 
+
42  float get_pixel(int z, int y, int x) const;
+
43 
+
44  void get_pixel_avg(vector<float> &channel_avgs) const;
+
45  void get_pixel_variance(const vector<float> &channel_avgs, vector<float> &channel_variances) const;
+
46  //void normalize(const vector<float> &channel_avgs, const vector<float> &channel_std_dev);
+
47 
+
48  void print(ostream &out);
+
49 };
+
50 
+
51 class Images : public ImagesInterface {
+
52  private:
+
53  string filename;
+
54 
+
55  int number_classes;
+
56  int number_images;
+
57 
+
58  vector<int> class_sizes;
+
59 
+
60  int padding;
+
61  int channels, width, height;
+
62 
+
63  vector<Image> images;
+
64 
+
65  vector<float> channel_avg;
+
66  vector<float> channel_std_dev;
+
67 
+
68  bool had_error;
+
69  public:
+
70  int read_images(string binary_filename);
+
71 
+
72  Images(string binary_filename, int _padding);
+
73  Images(string binary_filename, int _padding, const vector<float> &_channeL_avg, const vector<float> &channel_std_dev);
+
74 
+
75  string get_filename() const;
+
76 
+
77  int get_class_size(int i) const;
+
78 
+
79  int get_number_classes() const;
+
80 
+
81  int get_number_images() const;
+
82 
+
83  int get_image_channels() const;
+
84  int get_image_width() const;
+
85  int get_image_height() const;
+
86 
+
87  int get_classification(int image) const;
+
88  float get_pixel(int image, int z, int y, int x) const;
+
89 
+
90  void calculate_avg_std_dev();
+
91 
+
92  float get_channel_avg(int channel) const;
+
93 
+
94  float get_channel_std_dev(int channel) const;
+
95 
+
96  bool loaded_correctly() const;
+
97 
+
98 
+
99  const vector<float>& get_average() const;
+
100  const vector<float>& get_std_dev() const;
+
101 
+
102  //void normalize();
+
103 };
+
104 
+
105 #endif
+
106 
+
+ + +
Image
Definition: image_set.hxx:20
+ +
ImagesInterface
Definition: image_set_interface.hxx:28
+
Images
Definition: image_set.hxx:51
+
ImageInterface
Definition: image_set_interface.hxx:18
+ + +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/image__set__interface_8hxx_source.html b/docs/html/image__set__interface_8hxx_source.html new file mode 100644 index 00000000..0e07e3d6 --- /dev/null +++ b/docs/html/image__set__interface_8hxx_source.html @@ -0,0 +1,159 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: image_tools/image_set_interface.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
image_set_interface.hxx
+
+
+
1 #ifndef IMAGE_SET_INTERFACE_HXX
+
2 #define IMAGE_SET_INTERFACE_HXX
+
3 
+
4 #include <fstream>
+
5 using std::ifstream;
+
6 
+
7 #include <iostream>
+
8 using std::ostream;
+
9 
+
10 #include <string>
+
11 using std::string;
+
12 
+
13 #include <vector>
+
14 using std::vector;
+
15 
+
16 typedef class Image Image;
+
17 
+
18 class ImageInterface {
+
19  public:
+
20 
+
21  virtual int get_classification() const = 0;
+
22 
+
23  virtual void print(ostream &out) = 0;
+
24 
+
25  virtual ~ImageInterface() = 0;
+
26 };
+
27 
+
28 class ImagesInterface {
+
29  public:
+
30  virtual string get_filename() const = 0;
+
31 
+
32  virtual int get_class_size(int i) const = 0;
+
33 
+
34  virtual int get_number_classes() const = 0;
+
35 
+
36  virtual int get_number_images() const = 0;
+
37 
+
38  virtual int get_image_channels() const = 0;
+
39  virtual int get_image_width() const = 0;
+
40  virtual int get_image_height() const = 0;
+
41 
+
42  virtual int get_classification(int image) const = 0;
+
43  virtual float get_pixel(int image, int z, int y, int x) const = 0;
+
44 
+
45  virtual float get_channel_avg(int channel) const = 0;
+
46  virtual float get_channel_std_dev(int channel) const = 0;
+
47 
+
48  virtual const vector<float>& get_average() const = 0;
+
49  virtual const vector<float>& get_std_dev() const = 0;
+
50 };
+
51 
+
52 class MultiImagesInterface : public ImagesInterface {
+
53  public:
+
54  virtual int get_number_large_images() const = 0;
+
55  virtual int get_number_subimages(int i) const = 0;
+
56 
+
57  virtual int get_padding() const = 0;
+
58 
+
59  virtual int get_large_image_channels(int image) const = 0;
+
60  virtual int get_large_image_width(int image) const = 0;
+
61  virtual int get_large_image_height(int image) const = 0;
+
62 
+
63  virtual int get_number_classes() const = 0;
+
64 
+
65  virtual int get_image_classification(int image) const = 0;
+
66 
+
67  virtual float get_raw_pixel(int subimage, int z, int y, int x) const = 0;
+
68 };
+
69 
+
70 #endif
+
71 
+
+ +
MultiImagesInterface
Definition: image_set_interface.hxx:52
+ +
Image
Definition: image_set.hxx:20
+ +
ImagesInterface
Definition: image_set_interface.hxx:28
+
ImageInterface
Definition: image_set_interface.hxx:18
+ + +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/index.html b/docs/html/index.html new file mode 100644 index 00000000..e5c89cfa --- /dev/null +++ b/docs/html/index.html @@ -0,0 +1,76 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Main Page + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + +
+ +
+
+ + +
+ +
+ +
+
+
EXONA: Evolutionary eXploration of Neural Architectures Documentation
+
+
+
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/island_8hxx_source.html b/docs/html/island_8hxx_source.html new file mode 100644 index 00000000..3e74aa27 --- /dev/null +++ b/docs/html/island_8hxx_source.html @@ -0,0 +1,209 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/island.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
island.hxx
+
+
+
1 #ifndef EXAMM_ISLAND_STRATEGY_HXX
+
2 #define EXAMM_ISLAND_STRATEGY_HXX
+
3 
+
4 #include <algorithm>
+
5 using std::sort;
+
6 using std::upper_bound;
+
7 
+
8 #include <random>
+
9 using std::minstd_rand0;
+
10 using std::uniform_real_distribution;
+
11 
+
12 #include <string>
+
13 using std::string;
+
14 
+
15 #include <unordered_map>
+
16 using std::unordered_map;
+
17 
+
18 
+
19 #include "rnn_genome.hxx"
+
20 
+
21 
+
22 class Island {
+
23  private:
+
24  int32_t id;
+
25  int32_t max_size;
+
26  int32_t erased_generation_id = -1;
+
28  int32_t latest_generation_id;
+
33  vector<RNN_Genome*> genomes;
+
34 
+
35  unordered_map<string, vector<RNN_Genome*>> structure_map;
+
36  int32_t status;
+
38  int32_t erase_again;
+
39  bool erased;
+
41  public:
+
42  const static int32_t INITIALIZING = 0;
+
43  const static int32_t FILLED = 1;
+
44  const static int32_t REPOPULATING = 2;
+
51  Island(int32_t id, int32_t max_size);
+
52 
+
57  Island(int32_t id, vector<RNN_Genome*> genomes);
+
58 
+
64  double get_best_fitness();
+
65 
+
71  double get_worst_fitness();
+
72 
+
78  RNN_Genome *get_best_genome();
+
79 
+
85  RNN_Genome *get_worst_genome();
+
86 
+
92  int32_t get_max_size();
+
93 
+
94 
+
100  int32_t size();
+
101 
+
108  bool is_full();
+
109 
+
116  bool is_initializing();
+
117 
+
124  bool is_repopulating();
+
125 
+
133  void copy_random_genome(uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome);
+
134 
+
143  void copy_two_random_genomes(uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome1, RNN_Genome **genome2);
+
144 
+
145  void do_population_check(int line, int initial_size);
+
146 
+
156  int32_t insert_genome(RNN_Genome* genome);
+
157 
+
163  void print(string indent = "");
+
164 
+
168  void erase_island();
+
169 
+
173  int32_t get_erased_generation_id();
+
174 
+
178  int32_t get_status();
+
179 
+
180 
+
184  void set_status(int32_t status_to_set);
+
185 
+
189  bool been_erased();
+
190 
+
191  vector<RNN_Genome *> get_genomes();
+
192 
+
193  void set_latest_generation_id(int32_t _latest_generation_id);
+
194 
+
195  int32_t get_erase_again_num();
+
196 
+
197  void set_erase_again_num();
+
198 };
+
199 
+
200 #endif
+
+
Island::get_worst_fitness
double get_worst_fitness()
Returns the fitness of the worst genome in the island.
Definition: island.cxx:47
+ +
Island::get_best_fitness
double get_best_fitness()
Returns the fitness of the best genome in the island.
Definition: island.cxx:41
+
Island::been_erased
bool been_erased()
return if this island has been erased before.
Definition: island.cxx:361
+
Island
Definition: island.hxx:22
+ +
Island::insert_genome
int32_t insert_genome(RNN_Genome *genome)
Inserts a genome into the island.
Definition: island.cxx:107
+
Island::get_status
int32_t get_status()
Definition: island.cxx:346
+
Island::copy_random_genome
void copy_random_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome)
Selects a genome from the island at random and returns a copy of it.
Definition: island.cxx:75
+
Island::REPOPULATING
const static int32_t REPOPULATING
status flag for if the island is repopulating.
Definition: island.hxx:44
+
Island::Island
Island(int32_t id, int32_t max_size)
Initializes an island with a given max size.
Definition: island.cxx:25
+
Island::set_status
void set_status(int32_t status_to_set)
after erasing the island, sets the island status to repopulating.
Definition: island.cxx:350
+
Island::genomes
vector< RNN_Genome * > genomes
The genomes on this island, stored in sorted order best (front) to worst (back).
Definition: island.hxx:33
+
Island::erase_again
int32_t erase_again
The status of this island (either Island:INITIALIZING, Island::FILLED or Island::REPOPULATING
Definition: island.hxx:38
+ +
Island::erased
bool erased
a flag to track if this islands has been erased
Definition: island.hxx:39
+
std::sort
T sort(T... args)
+
Island::erased_generation_id
int32_t erased_generation_id
The latest generation id of an erased island, erased_generation_id = largest_generation_id when this ...
Definition: island.hxx:26
+
Island::id
int32_t id
An integer ID for this island.
Definition: island.hxx:24
+ +
Island::is_repopulating
bool is_repopulating()
Returns true if the island is repopulating, i.e., it's size is <= max_size and it has been full befor...
Definition: island.cxx:69
+
Island::FILLED
const static int32_t FILLED
status flag for if the island is filled.
Definition: island.hxx:43
+
Island::print
void print(string indent="")
Prints out the state of this island.
Definition: island.cxx:314
+
Island::max_size
int32_t max_size
The maximum number of genomes in the island.
Definition: island.hxx:25
+
Island::latest_generation_id
int32_t latest_generation_id
The latest generation id of genome being generated, including the ones doing backprop by workers.
Definition: island.hxx:28
+
Island::is_full
bool is_full()
Returns true if the island has Island::max_size genomes.
Definition: island.cxx:61
+
Island::get_worst_genome
RNN_Genome * get_worst_genome()
Returns the worst genomme in the island.
Definition: island.cxx:36
+
Island::INITIALIZING
const static int32_t INITIALIZING
status flag for if the island is initializing.
Definition: island.hxx:42
+
Island::get_erased_generation_id
int32_t get_erased_generation_id()
returns the get_erased_generation_id.
Definition: island.cxx:342
+
std::upper_bound
T upper_bound(T... args)
+
Island::erase_island
void erase_island()
erases the entire island and set the erased_generation_id.
Definition: island.cxx:325
+
RNN_Genome
Definition: rnn_genome.hxx:37
+
Island::is_initializing
bool is_initializing()
Returns true if the island is initializing, i.e., it's size is <= max_size and it hasn't been cleared...
Definition: island.cxx:65
+
Island::copy_two_random_genomes
void copy_two_random_genomes(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome1, RNN_Genome **genome2)
Selects two different genomes from the island at random and returns copies of them.
Definition: island.cxx:80
+
Island::size
int32_t size()
Returns the size of the island.
Definition: island.cxx:57
+
Island::get_max_size
int32_t get_max_size()
Returns the maximum number of genomes the island can hold.
Definition: island.cxx:53
+
Island::get_best_genome
RNN_Genome * get_best_genome()
Returns the best genomme in the island.
Definition: island.cxx:31
+ + +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/island__speciation__strategy_8hxx_source.html b/docs/html/island__speciation__strategy_8hxx_source.html new file mode 100644 index 00000000..de311aee --- /dev/null +++ b/docs/html/island__speciation__strategy_8hxx_source.html @@ -0,0 +1,223 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/island_speciation_strategy.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
island_speciation_strategy.hxx
+
+
+
1 #ifndef EXAMM_ISLAND_SPECIATION_STRATEGY_HXX
+
2 #define EXAMM_ISLAND_SPECIATION_STRATEGY_HXX
+
3 
+
4 #include <functional>
+
5 using std::function;
+
6 
+
7 #include <random>
+
8 using std::minstd_rand0;
+
9 using std::uniform_real_distribution;
+
10 
+
11 
+
12 #include <string>
+
13 using std::string;
+
14 
+
15 #include "island.hxx"
+
16 #include "rnn_genome.hxx"
+
17 #include "speciation_strategy.hxx"
+
18 
+
19 class IslandSpeciationStrategy : public SpeciationStrategy {
+
20  private:
+
21  int32_t generation_island;
+
23  int32_t number_of_islands;
+
25  int32_t max_island_size;
+
27  double mutation_rate;
+
28  double intra_island_crossover_rate;
+
29  double inter_island_crossover_rate;
+
31  int32_t generated_genomes;
+
32  int32_t inserted_genomes;
+
34  RNN_Genome *seed_genome;
+
36  string island_ranking_method;
+
38  string repopulation_method;
+
40  int32_t extinction_event_generation_number;
+
41  int32_t repopulation_mutations;
+
42  int32_t islands_to_exterminate;
+
43  int32_t max_genomes;
+
44 
+
45  bool seed_genome_was_minimal;
+
47  bool repeat_extinction;
+
48  // int32_t worst_island;
+
52  vector<Island*> islands;
+
53  RNN_Genome* global_best_genome;
+
54 
+
55  public:
+
56  //static void register_command_line_arguments();
+
57  //static IslandSpeciationStrategy* generate_from_command_line();
+
58 
+
65  IslandSpeciationStrategy(int32_t _number_of_islands, int32_t _max_island_size,
+
66  double _mutation_rate, double _intra_island_crossover_rate,
+
67  double _inter_island_crossover_rate, RNN_Genome *_seed_genome,
+
68  string _island_ranking_method, string _repopulation_method,
+
69  int32_t _extinction_event_generation_number, int32_t _repopulation_mutations,
+
70  int32_t _islands_to_exterminate, int32_t _max_genomes,
+
71  bool _repeat_extinction, bool seed_genome_was_minimal);
+
72 
+
77  IslandSpeciationStrategy(int32_t _number_of_islands, int32_t _max_island_size,
+
78  double _mutation_rate, double _intra_island_crossover_rate,
+
79  double _inter_island_crossover_rate, RNN_Genome *_seed_genome,
+
80  string _island_ranking_method, string _repopulation_method,
+
81  int32_t _extinction_event_generation_number, int32_t _repopulation_mutations,
+
82  int32_t _islands_to_exterminate, bool seed_genome_was_minimal, function<void (RNN_Genome*)> &modify);
+
83 
+
87  int32_t get_generated_genomes() const;
+
88 
+
92  int32_t get_inserted_genomes() const;
+
93 
+
98  double get_best_fitness();
+
99 
+
104  double get_worst_fitness();
+
105 
+
110  RNN_Genome* get_best_genome();
+
111 
+
116  RNN_Genome* get_worst_genome();
+
117 
+
121  bool islands_full() const;
+
122 
+
134  int32_t insert_genome(RNN_Genome* genome);
+
135 
+
141  int32_t get_worst_island_by_best_genome();
+
142 
+
148  vector<int32_t> rank_islands();
+
149 
+
160  RNN_Genome* generate_genome(uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator, function<void (int32_t, RNN_Genome*)> &mutate, function<RNN_Genome* (RNN_Genome*, RNN_Genome *)> &crossover);
+
161 
+
162  RNN_Genome* generate_for_filled_island(uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator, function<void (int32_t, RNN_Genome*)> &mutate, function<RNN_Genome* (RNN_Genome*, RNN_Genome *)> &crossover);
+
168  void print(string indent = "") const;
+
169 
+
173  string get_strategy_information_headers() const;
+
174 
+
178  string get_strategy_information_values() const;
+
179 
+
184  RNN_Genome* parents_repopulation(string method,uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator, function<void (int32_t, RNN_Genome*)> &mutate, function<RNN_Genome* (RNN_Genome*, RNN_Genome *)> &crossover);
+
185 
+
191  void fill_island(int32_t best_island);
+
192 
+
193  RNN_Genome* get_global_best_genome();
+
194 
+
195  void set_erased_islands_status();
+
196 
+
197 };
+
198 
+
199 
+
200 #endif
+
+
IslandSpeciationStrategy::inter_island_crossover_rate
double inter_island_crossover_rate
How frequently to do inter-island crossovers.
Definition: island_speciation_strategy.hxx:29
+
IslandSpeciationStrategy::max_island_size
int32_t max_island_size
the maximum number of genomes in an island.
Definition: island_speciation_strategy.hxx:25
+ +
IslandSpeciationStrategy::get_generated_genomes
int32_t get_generated_genomes() const
Definition: island_speciation_strategy.cxx:132
+
IslandSpeciationStrategy::mutation_rate
double mutation_rate
How frequently to do mutations.
Definition: island_speciation_strategy.hxx:27
+
IslandSpeciationStrategy::get_strategy_information_values
string get_strategy_information_values() const
Gets speciation strategy information values for logs.
Definition: island_speciation_strategy.cxx:560
+ +
IslandSpeciationStrategy::generation_island
int32_t generation_island
Used to track which island to generate the next genome from.
Definition: island_speciation_strategy.hxx:21
+
IslandSpeciationStrategy::repopulation_mutations
int32_t repopulation_mutations
When an island is erradicated, it is repopulated with copies of the best genome that have this number...
Definition: island_speciation_strategy.hxx:41
+
IslandSpeciationStrategy::islands
vector< Island * > islands
All the islands which contain the genomes for this speciation strategy.
Definition: island_speciation_strategy.hxx:52
+ + +
SpeciationStrategy
Definition: speciation_strategy.hxx:13
+
IslandSpeciationStrategy::extinction_event_generation_number
int32_t extinction_event_generation_number
When EXAMM reaches this generation id, an extinction event will be triggered (i.e.
Definition: island_speciation_strategy.hxx:40
+
IslandSpeciationStrategy
Definition: island_speciation_strategy.hxx:19
+ +
IslandSpeciationStrategy::repopulation_method
string repopulation_method
The method used to repopulate the island after being erased.
Definition: island_speciation_strategy.hxx:38
+
IslandSpeciationStrategy::seed_genome
RNN_Genome * seed_genome
keep a reference to the seed genome so we can re-use it across islands and not duplicate innovation n...
Definition: island_speciation_strategy.hxx:34
+
IslandSpeciationStrategy::inserted_genomes
int32_t inserted_genomes
How many genomes have been inserted into this speciatoin strategy.
Definition: island_speciation_strategy.hxx:32
+
IslandSpeciationStrategy::islands_full
bool islands_full() const
Definition: island_speciation_strategy.cxx:179
+
IslandSpeciationStrategy::insert_genome
int32_t insert_genome(RNN_Genome *genome)
Inserts a copy of the genome into one of the islands handled by this strategy, determined by the RNN_...
Definition: island_speciation_strategy.cxx:190
+
IslandSpeciationStrategy::get_best_genome
RNN_Genome * get_best_genome()
Gets the best genome of all the islands.
Definition: island_speciation_strategy.cxx:140
+
IslandSpeciationStrategy::rank_islands
vector< int32_t > rank_islands()
rank the islands by their best fitness, the fitness of ranked islands are in descending order
Definition: island_speciation_strategy.cxx:258
+
IslandSpeciationStrategy::islands_to_exterminate
int32_t islands_to_exterminate
When an extinction event is triggered, this is the number of islands that will be exterminated.
Definition: island_speciation_strategy.hxx:42
+
IslandSpeciationStrategy::generate_genome
RNN_Genome * generate_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)
Generates a new genome.
Definition: island_speciation_strategy.cxx:299
+
RNN_Genome
Definition: rnn_genome.hxx:37
+
IslandSpeciationStrategy::IslandSpeciationStrategy
IslandSpeciationStrategy(int32_t _number_of_islands, int32_t _max_island_size, double _mutation_rate, double _intra_island_crossover_rate, double _inter_island_crossover_rate, RNN_Genome *_seed_genome, string _island_ranking_method, string _repopulation_method, int32_t _extinction_event_generation_number, int32_t _repopulation_mutations, int32_t _islands_to_exterminate, int32_t _max_genomes, bool _repeat_extinction, bool seed_genome_was_minimal)
Creates a new IslandSpeciationStrategy.
Definition: island_speciation_strategy.cxx:25
+
IslandSpeciationStrategy::get_strategy_information_headers
string get_strategy_information_headers() const
Gets speciation strategy information headers for logs.
Definition: island_speciation_strategy.cxx:542
+
IslandSpeciationStrategy::get_best_fitness
double get_best_fitness()
Gets the fitness of the best genome of all the islands.
Definition: island_speciation_strategy.cxx:167
+
IslandSpeciationStrategy::number_of_islands
int32_t number_of_islands
the number of islands to have.
Definition: island_speciation_strategy.hxx:23
+
IslandSpeciationStrategy::get_inserted_genomes
int32_t get_inserted_genomes() const
Definition: island_speciation_strategy.cxx:136
+
IslandSpeciationStrategy::get_worst_genome
RNN_Genome * get_worst_genome()
Gets the the worst genome of all the islands.
Definition: island_speciation_strategy.cxx:145
+
IslandSpeciationStrategy::fill_island
void fill_island(int32_t best_island)
fill a island with the best island.
Definition: island_speciation_strategy.cxx:615
+
IslandSpeciationStrategy::intra_island_crossover_rate
double intra_island_crossover_rate
How frequently to do intra-island crossovers.
Definition: island_speciation_strategy.hxx:28
+
IslandSpeciationStrategy::get_worst_island_by_best_genome
int32_t get_worst_island_by_best_genome()
find the worst island in the population, the worst island's best genome is the worst among all the is...
Definition: island_speciation_strategy.cxx:242
+
IslandSpeciationStrategy::generated_genomes
int32_t generated_genomes
How many genomes have been generated by this speciation strategy.
Definition: island_speciation_strategy.hxx:31
+
IslandSpeciationStrategy::print
void print(string indent="") const
Prints out all the island's populations.
Definition: island_speciation_strategy.cxx:531
+
IslandSpeciationStrategy::parents_repopulation
RNN_Genome * parents_repopulation(string method, uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)
Island repopulation through two random parents from two seperate islands, parents can be random genom...
Definition: island_speciation_strategy.cxx:573
+
IslandSpeciationStrategy::seed_genome_was_minimal
bool seed_genome_was_minimal
is true if we passed in a minimal genome (i.e., are not using transfer learning)
Definition: island_speciation_strategy.hxx:45
+
IslandSpeciationStrategy::get_worst_fitness
double get_worst_fitness()
Gets the fitness of the worst genome of all the islands.
Definition: island_speciation_strategy.cxx:173
+
IslandSpeciationStrategy::island_ranking_method
string island_ranking_method
The method used to find the worst island in population.
Definition: island_speciation_strategy.hxx:36
+ +
+Generated by   +doxygen + 1.8.17 +
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orientationchange":$.proxy(this.winResize,this)},i)),this.opts.subIndicators&&(this.$subArrow=$("").addClass("sub-arrow"),this.opts.subIndicatorsText&&this.$subArrow.html(this.opts.subIndicatorsText)),initMouseDetection()}if(this.$firstSub=this.$root.find("ul").each(function(){e.menuInit($(this))}).eq(0),this.$firstLink=this.$root.find("a").eq(0),this.opts.markCurrentItem){var s=/(index|default)\.[^#\?\/]*/i,o=/#.*/,a=window.location.href.replace(s,""),n=a.replace(o,"");this.$root.find("a").each(function(){var t=this.href.replace(s,""),i=$(this);(t==a||t==n)&&(i.addClass("current"),e.opts.markCurrentTree&&i.parentsUntil("[data-smartmenus-id]","ul").each(function(){$(this).dataSM("parent-a").addClass("current")}))})}this.wasCollapsible=this.isCollapsible()},destroy:function(t){if(!t){var e=".smartmenus";this.$root.removeData("smartmenus").removeAttr("data-smartmenus-id").removeDataSM("level").off(e),e+=this.rootId,$(document).off(e),$(window).off(e),this.opts.subIndicators&&(this.$subArrow=null)}this.menuHideAll();var i=this;this.$root.find("ul").each(function(){var t=$(this);t.dataSM("scroll-arrows")&&t.dataSM("scroll-arrows").remove(),t.dataSM("shown-before")&&((i.opts.subMenusMinWidth||i.opts.subMenusMaxWidth)&&t.css({width:"",minWidth:"",maxWidth:""}).removeClass("sm-nowrap"),t.dataSM("scroll-arrows")&&t.dataSM("scroll-arrows").remove(),t.css({zIndex:"",top:"",left:"",marginLeft:"",marginTop:"",display:""})),0==(t.attr("id")||"").indexOf(i.accessIdPrefix)&&t.removeAttr("id")}).removeDataSM("in-mega").removeDataSM("shown-before").removeDataSM("scroll-arrows").removeDataSM("parent-a").removeDataSM("level").removeDataSM("beforefirstshowfired").removeAttr("role").removeAttr("aria-hidden").removeAttr("aria-labelledby").removeAttr("aria-expanded"),this.$root.find("a.has-submenu").each(function(){var t=$(this);0==t.attr("id").indexOf(i.accessIdPrefix)&&t.removeAttr("id")}).removeClass("has-submenu").removeDataSM("sub").removeAttr("aria-haspopup").removeAttr("aria-controls").removeAttr("aria-expanded").closest("li").removeDataSM("sub"),this.opts.subIndicators&&this.$root.find("span.sub-arrow").remove(),this.opts.markCurrentItem&&this.$root.find("a.current").removeClass("current"),t||(this.$root=null,this.$firstLink=null,this.$firstSub=null,this.$disableOverlay&&(this.$disableOverlay.remove(),this.$disableOverlay=null),menuTrees.splice($.inArray(this,menuTrees),1))},disable:function(t){if(!this.disabled){if(this.menuHideAll(),!t&&!this.opts.isPopup&&this.$root.is(":visible")){var e=this.$root.offset();this.$disableOverlay=$('
').css({position:"absolute",top:e.top,left:e.left,width:this.$root.outerWidth(),height:this.$root.outerHeight(),zIndex:this.getStartZIndex(!0),opacity:0}).appendTo(document.body)}this.disabled=!0}},docClick:function(t){return this.$touchScrollingSub?(this.$touchScrollingSub=null,void 0):((this.visibleSubMenus.length&&!$.contains(this.$root[0],t.target)||$(t.target).closest("a").length)&&this.menuHideAll(),void 0)},docTouchEnd:function(){if(this.lastTouch){if(!(!this.visibleSubMenus.length||void 0!==this.lastTouch.x2&&this.lastTouch.x1!=this.lastTouch.x2||void 0!==this.lastTouch.y2&&this.lastTouch.y1!=this.lastTouch.y2||this.lastTouch.target&&$.contains(this.$root[0],this.lastTouch.target))){this.hideTimeout&&(clearTimeout(this.hideTimeout),this.hideTimeout=0);var t=this;this.hideTimeout=setTimeout(function(){t.menuHideAll()},350)}this.lastTouch=null}},docTouchMove:function(t){if(this.lastTouch){var e=t.originalEvent.touches[0];this.lastTouch.x2=e.pageX,this.lastTouch.y2=e.pageY}},docTouchStart:function(t){var e=t.originalEvent.touches[0];this.lastTouch={x1:e.pageX,y1:e.pageY,target:e.target}},enable:function(){this.disabled&&(this.$disableOverlay&&(this.$disableOverlay.remove(),this.$disableOverlay=null),this.disabled=!1)},getClosestMenu:function(t){for(var e=$(t).closest("ul");e.dataSM("in-mega");)e=e.parent().closest("ul");return e[0]||null},getHeight:function(t){return this.getOffset(t,!0)},getOffset:function(t,e){var i;"none"==t.css("display")&&(i={position:t[0].style.position,visibility:t[0].style.visibility},t.css({position:"absolute",visibility:"hidden"}).show());var s=t[0].getBoundingClientRect&&t[0].getBoundingClientRect(),o=s&&(e?s.height||s.bottom-s.top:s.width||s.right-s.left);return o||0===o||(o=e?t[0].offsetHeight:t[0].offsetWidth),i&&t.hide().css(i),o},getStartZIndex:function(t){var e=parseInt(this[t?"$root":"$firstSub"].css("z-index"));return!t&&isNaN(e)&&(e=parseInt(this.$root.css("z-index"))),isNaN(e)?1:e},getTouchPoint:function(t){return t.touches&&t.touches[0]||t.changedTouches&&t.changedTouches[0]||t},getViewport:function(t){var e=t?"Height":"Width",i=document.documentElement["client"+e],s=window["inner"+e];return s&&(i=Math.min(i,s)),i},getViewportHeight:function(){return this.getViewport(!0)},getViewportWidth:function(){return this.getViewport()},getWidth:function(t){return this.getOffset(t)},handleEvents:function(){return!this.disabled&&this.isCSSOn()},handleItemEvents:function(t){return this.handleEvents()&&!this.isLinkInMegaMenu(t)},isCollapsible:function(){return"static"==this.$firstSub.css("position")},isCSSOn:function(){return"inline"!=this.$firstLink.css("display")},isFixed:function(){var t="fixed"==this.$root.css("position");return t||this.$root.parentsUntil("body").each(function(){return"fixed"==$(this).css("position")?(t=!0,!1):void 0}),t},isLinkInMegaMenu:function(t){return $(this.getClosestMenu(t[0])).hasClass("mega-menu")},isTouchMode:function(){return!mouse||this.opts.noMouseOver||this.isCollapsible()},itemActivate:function(t,e){var i=t.closest("ul"),s=i.dataSM("level");if(s>1&&(!this.activatedItems[s-2]||this.activatedItems[s-2][0]!=i.dataSM("parent-a")[0])){var o=this;$(i.parentsUntil("[data-smartmenus-id]","ul").get().reverse()).add(i).each(function(){o.itemActivate($(this).dataSM("parent-a"))})}if((!this.isCollapsible()||e)&&this.menuHideSubMenus(this.activatedItems[s-1]&&this.activatedItems[s-1][0]==t[0]?s:s-1),this.activatedItems[s-1]=t,this.$root.triggerHandler("activate.smapi",t[0])!==!1){var a=t.dataSM("sub");a&&(this.isTouchMode()||!this.opts.showOnClick||this.clickActivated)&&this.menuShow(a)}},itemBlur:function(t){var e=$(t.currentTarget);this.handleItemEvents(e)&&this.$root.triggerHandler("blur.smapi",e[0])},itemClick:function(t){var e=$(t.currentTarget);if(this.handleItemEvents(e)){if(this.$touchScrollingSub&&this.$touchScrollingSub[0]==e.closest("ul")[0])return this.$touchScrollingSub=null,t.stopPropagation(),!1;if(this.$root.triggerHandler("click.smapi",e[0])===!1)return!1;var i=$(t.target).is(".sub-arrow"),s=e.dataSM("sub"),o=s?2==s.dataSM("level"):!1,a=this.isCollapsible(),n=/toggle$/.test(this.opts.collapsibleBehavior),r=/link$/.test(this.opts.collapsibleBehavior),h=/^accordion/.test(this.opts.collapsibleBehavior);if(s&&!s.is(":visible")){if((!r||!a||i)&&(this.opts.showOnClick&&o&&(this.clickActivated=!0),this.itemActivate(e,h),s.is(":visible")))return this.focusActivated=!0,!1}else if(a&&(n||i))return this.itemActivate(e,h),this.menuHide(s),n&&(this.focusActivated=!1),!1;return this.opts.showOnClick&&o||e.hasClass("disabled")||this.$root.triggerHandler("select.smapi",e[0])===!1?!1:void 0}},itemDown:function(t){var e=$(t.currentTarget);this.handleItemEvents(e)&&e.dataSM("mousedown",!0)},itemEnter:function(t){var e=$(t.currentTarget);if(this.handleItemEvents(e)){if(!this.isTouchMode()){this.showTimeout&&(clearTimeout(this.showTimeout),this.showTimeout=0);var i=this;this.showTimeout=setTimeout(function(){i.itemActivate(e)},this.opts.showOnClick&&1==e.closest("ul").dataSM("level")?1:this.opts.showTimeout)}this.$root.triggerHandler("mouseenter.smapi",e[0])}},itemFocus:function(t){var e=$(t.currentTarget);this.handleItemEvents(e)&&(!this.focusActivated||this.isTouchMode()&&e.dataSM("mousedown")||this.activatedItems.length&&this.activatedItems[this.activatedItems.length-1][0]==e[0]||this.itemActivate(e,!0),this.$root.triggerHandler("focus.smapi",e[0]))},itemLeave:function(t){var e=$(t.currentTarget);this.handleItemEvents(e)&&(this.isTouchMode()||(e[0].blur(),this.showTimeout&&(clearTimeout(this.showTimeout),this.showTimeout=0)),e.removeDataSM("mousedown"),this.$root.triggerHandler("mouseleave.smapi",e[0]))},menuHide:function(t){if(this.$root.triggerHandler("beforehide.smapi",t[0])!==!1&&(canAnimate&&t.stop(!0,!0),"none"!=t.css("display"))){var e=function(){t.css("z-index","")};this.isCollapsible()?canAnimate&&this.opts.collapsibleHideFunction?this.opts.collapsibleHideFunction.call(this,t,e):t.hide(this.opts.collapsibleHideDuration,e):canAnimate&&this.opts.hideFunction?this.opts.hideFunction.call(this,t,e):t.hide(this.opts.hideDuration,e),t.dataSM("scroll")&&(this.menuScrollStop(t),t.css({"touch-action":"","-ms-touch-action":"","-webkit-transform":"",transform:""}).off(".smartmenus_scroll").removeDataSM("scroll").dataSM("scroll-arrows").hide()),t.dataSM("parent-a").removeClass("highlighted").attr("aria-expanded","false"),t.attr({"aria-expanded":"false","aria-hidden":"true"});var i=t.dataSM("level");this.activatedItems.splice(i-1,1),this.visibleSubMenus.splice($.inArray(t,this.visibleSubMenus),1),this.$root.triggerHandler("hide.smapi",t[0])}},menuHideAll:function(){this.showTimeout&&(clearTimeout(this.showTimeout),this.showTimeout=0);for(var t=this.opts.isPopup?1:0,e=this.visibleSubMenus.length-1;e>=t;e--)this.menuHide(this.visibleSubMenus[e]);this.opts.isPopup&&(canAnimate&&this.$root.stop(!0,!0),this.$root.is(":visible")&&(canAnimate&&this.opts.hideFunction?this.opts.hideFunction.call(this,this.$root):this.$root.hide(this.opts.hideDuration))),this.activatedItems=[],this.visibleSubMenus=[],this.clickActivated=!1,this.focusActivated=!1,this.zIndexInc=0,this.$root.triggerHandler("hideAll.smapi")},menuHideSubMenus:function(t){for(var e=this.activatedItems.length-1;e>=t;e--){var i=this.activatedItems[e].dataSM("sub");i&&this.menuHide(i)}},menuInit:function(t){if(!t.dataSM("in-mega")){t.hasClass("mega-menu")&&t.find("ul").dataSM("in-mega",!0);for(var e=2,i=t[0];(i=i.parentNode.parentNode)!=this.$root[0];)e++;var s=t.prevAll("a").eq(-1);s.length||(s=t.prevAll().find("a").eq(-1)),s.addClass("has-submenu").dataSM("sub",t),t.dataSM("parent-a",s).dataSM("level",e).parent().dataSM("sub",t);var o=s.attr("id")||this.accessIdPrefix+ ++this.idInc,a=t.attr("id")||this.accessIdPrefix+ ++this.idInc;s.attr({id:o,"aria-haspopup":"true","aria-controls":a,"aria-expanded":"false"}),t.attr({id:a,role:"group","aria-hidden":"true","aria-labelledby":o,"aria-expanded":"false"}),this.opts.subIndicators&&s[this.opts.subIndicatorsPos](this.$subArrow.clone())}},menuPosition:function(t){var e,i,s=t.dataSM("parent-a"),o=s.closest("li"),a=o.parent(),n=t.dataSM("level"),r=this.getWidth(t),h=this.getHeight(t),u=s.offset(),l=u.left,c=u.top,d=this.getWidth(s),m=this.getHeight(s),p=$(window),f=p.scrollLeft(),v=p.scrollTop(),b=this.getViewportWidth(),S=this.getViewportHeight(),g=a.parent().is("[data-sm-horizontal-sub]")||2==n&&!a.hasClass("sm-vertical"),M=this.opts.rightToLeftSubMenus&&!o.is("[data-sm-reverse]")||!this.opts.rightToLeftSubMenus&&o.is("[data-sm-reverse]"),w=2==n?this.opts.mainMenuSubOffsetX:this.opts.subMenusSubOffsetX,T=2==n?this.opts.mainMenuSubOffsetY:this.opts.subMenusSubOffsetY;if(g?(e=M?d-r-w:w,i=this.opts.bottomToTopSubMenus?-h-T:m+T):(e=M?w-r:d-w,i=this.opts.bottomToTopSubMenus?m-T-h:T),this.opts.keepInViewport){var y=l+e,I=c+i;if(M&&f>y?e=g?f-y+e:d-w:!M&&y+r>f+b&&(e=g?f+b-r-y+e:w-r),g||(S>h&&I+h>v+S?i+=v+S-h-I:(h>=S||v>I)&&(i+=v-I)),g&&(I+h>v+S+.49||v>I)||!g&&h>S+.49){var x=this;t.dataSM("scroll-arrows")||t.dataSM("scroll-arrows",$([$('')[0],$('')[0]]).on({mouseenter:function(){t.dataSM("scroll").up=$(this).hasClass("scroll-up"),x.menuScroll(t)},mouseleave:function(e){x.menuScrollStop(t),x.menuScrollOut(t,e)},"mousewheel DOMMouseScroll":function(t){t.preventDefault()}}).insertAfter(t));var A=".smartmenus_scroll";if(t.dataSM("scroll",{y:this.cssTransforms3d?0:i-m,step:1,itemH:m,subH:h,arrowDownH:this.getHeight(t.dataSM("scroll-arrows").eq(1))}).on(getEventsNS({mouseover:function(e){x.menuScrollOver(t,e)},mouseout:function(e){x.menuScrollOut(t,e)},"mousewheel DOMMouseScroll":function(e){x.menuScrollMousewheel(t,e)}},A)).dataSM("scroll-arrows").css({top:"auto",left:"0",marginLeft:e+(parseInt(t.css("border-left-width"))||0),width:r-(parseInt(t.css("border-left-width"))||0)-(parseInt(t.css("border-right-width"))||0),zIndex:t.css("z-index")}).eq(g&&this.opts.bottomToTopSubMenus?0:1).show(),this.isFixed()){var C={};C[touchEvents?"touchstart touchmove touchend":"pointerdown pointermove pointerup MSPointerDown MSPointerMove MSPointerUp"]=function(e){x.menuScrollTouch(t,e)},t.css({"touch-action":"none","-ms-touch-action":"none"}).on(getEventsNS(C,A))}}}t.css({top:"auto",left:"0",marginLeft:e,marginTop:i-m})},menuScroll:function(t,e,i){var s,o=t.dataSM("scroll"),a=t.dataSM("scroll-arrows"),n=o.up?o.upEnd:o.downEnd;if(!e&&o.momentum){if(o.momentum*=.92,s=o.momentum,.5>s)return this.menuScrollStop(t),void 0}else s=i||(e||!this.opts.scrollAccelerate?this.opts.scrollStep:Math.floor(o.step));var r=t.dataSM("level");if(this.activatedItems[r-1]&&this.activatedItems[r-1].dataSM("sub")&&this.activatedItems[r-1].dataSM("sub").is(":visible")&&this.menuHideSubMenus(r-1),o.y=o.up&&o.y>=n||!o.up&&n>=o.y?o.y:Math.abs(n-o.y)>s?o.y+(o.up?s:-s):n,t.css(this.cssTransforms3d?{"-webkit-transform":"translate3d(0, "+o.y+"px, 0)",transform:"translate3d(0, "+o.y+"px, 0)"}:{marginTop:o.y}),mouse&&(o.up&&o.y>o.downEnd||!o.up&&o.y0;t.dataSM("scroll-arrows").eq(i?0:1).is(":visible")&&(t.dataSM("scroll").up=i,this.menuScroll(t,!0))}e.preventDefault()},menuScrollOut:function(t,e){mouse&&(/^scroll-(up|down)/.test((e.relatedTarget||"").className)||(t[0]==e.relatedTarget||$.contains(t[0],e.relatedTarget))&&this.getClosestMenu(e.relatedTarget)==t[0]||t.dataSM("scroll-arrows").css("visibility","hidden"))},menuScrollOver:function(t,e){if(mouse&&!/^scroll-(up|down)/.test(e.target.className)&&this.getClosestMenu(e.target)==t[0]){this.menuScrollRefreshData(t);var i=t.dataSM("scroll"),s=$(window).scrollTop()-t.dataSM("parent-a").offset().top-i.itemH;t.dataSM("scroll-arrows").eq(0).css("margin-top",s).end().eq(1).css("margin-top",s+this.getViewportHeight()-i.arrowDownH).end().css("visibility","visible")}},menuScrollRefreshData:function(t){var e=t.dataSM("scroll"),i=$(window).scrollTop()-t.dataSM("parent-a").offset().top-e.itemH;this.cssTransforms3d&&(i=-(parseFloat(t.css("margin-top"))-i)),$.extend(e,{upEnd:i,downEnd:i+this.getViewportHeight()-e.subH})},menuScrollStop:function(t){return this.scrollTimeout?(cancelAnimationFrame(this.scrollTimeout),this.scrollTimeout=0,t.dataSM("scroll").step=1,!0):void 0},menuScrollTouch:function(t,e){if(e=e.originalEvent,isTouchEvent(e)){var i=this.getTouchPoint(e);if(this.getClosestMenu(i.target)==t[0]){var s=t.dataSM("scroll");if(/(start|down)$/i.test(e.type))this.menuScrollStop(t)?(e.preventDefault(),this.$touchScrollingSub=t):this.$touchScrollingSub=null,this.menuScrollRefreshData(t),$.extend(s,{touchStartY:i.pageY,touchStartTime:e.timeStamp});else if(/move$/i.test(e.type)){var o=void 0!==s.touchY?s.touchY:s.touchStartY;if(void 0!==o&&o!=i.pageY){this.$touchScrollingSub=t;var a=i.pageY>o;void 0!==s.up&&s.up!=a&&$.extend(s,{touchStartY:i.pageY,touchStartTime:e.timeStamp}),$.extend(s,{up:a,touchY:i.pageY}),this.menuScroll(t,!0,Math.abs(i.pageY-o))}e.preventDefault()}else void 0!==s.touchY&&((s.momentum=15*Math.pow(Math.abs(i.pageY-s.touchStartY)/(e.timeStamp-s.touchStartTime),2))&&(this.menuScrollStop(t),this.menuScroll(t),e.preventDefault()),delete s.touchY)}}},menuShow:function(t){if((t.dataSM("beforefirstshowfired")||(t.dataSM("beforefirstshowfired",!0),this.$root.triggerHandler("beforefirstshow.smapi",t[0])!==!1))&&this.$root.triggerHandler("beforeshow.smapi",t[0])!==!1&&(t.dataSM("shown-before",!0),canAnimate&&t.stop(!0,!0),!t.is(":visible"))){var e=t.dataSM("parent-a"),i=this.isCollapsible();if((this.opts.keepHighlighted||i)&&e.addClass("highlighted"),i)t.removeClass("sm-nowrap").css({zIndex:"",width:"auto",minWidth:"",maxWidth:"",top:"",left:"",marginLeft:"",marginTop:""});else{if(t.css("z-index",this.zIndexInc=(this.zIndexInc||this.getStartZIndex())+1),(this.opts.subMenusMinWidth||this.opts.subMenusMaxWidth)&&(t.css({width:"auto",minWidth:"",maxWidth:""}).addClass("sm-nowrap"),this.opts.subMenusMinWidth&&t.css("min-width",this.opts.subMenusMinWidth),this.opts.subMenusMaxWidth)){var s=this.getWidth(t);t.css("max-width",this.opts.subMenusMaxWidth),s>this.getWidth(t)&&t.removeClass("sm-nowrap").css("width",this.opts.subMenusMaxWidth)}this.menuPosition(t)}var o=function(){t.css("overflow","")};i?canAnimate&&this.opts.collapsibleShowFunction?this.opts.collapsibleShowFunction.call(this,t,o):t.show(this.opts.collapsibleShowDuration,o):canAnimate&&this.opts.showFunction?this.opts.showFunction.call(this,t,o):t.show(this.opts.showDuration,o),e.attr("aria-expanded","true"),t.attr({"aria-expanded":"true","aria-hidden":"false"}),this.visibleSubMenus.push(t),this.$root.triggerHandler("show.smapi",t[0])}},popupHide:function(t){this.hideTimeout&&(clearTimeout(this.hideTimeout),this.hideTimeout=0);var e=this;this.hideTimeout=setTimeout(function(){e.menuHideAll()},t?1:this.opts.hideTimeout)},popupShow:function(t,e){if(!this.opts.isPopup)return alert('SmartMenus jQuery Error:\n\nIf you want to show this menu via the "popupShow" method, set the isPopup:true option.'),void 0;if(this.hideTimeout&&(clearTimeout(this.hideTimeout),this.hideTimeout=0),this.$root.dataSM("shown-before",!0),canAnimate&&this.$root.stop(!0,!0),!this.$root.is(":visible")){this.$root.css({left:t,top:e});var i=this,s=function(){i.$root.css("overflow","")};canAnimate&&this.opts.showFunction?this.opts.showFunction.call(this,this.$root,s):this.$root.show(this.opts.showDuration,s),this.visibleSubMenus[0]=this.$root}},refresh:function(){this.destroy(!0),this.init(!0)},rootKeyDown:function(t){if(this.handleEvents())switch(t.keyCode){case 27:var e=this.activatedItems[0];if(e){this.menuHideAll(),e[0].focus();var i=e.dataSM("sub");i&&this.menuHide(i)}break;case 32:var s=$(t.target);if(s.is("a")&&this.handleItemEvents(s)){var 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00000000..da22bb25 --- /dev/null +++ b/docs/html/large__image__set_8hxx_source.html @@ -0,0 +1,236 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: image_tools/large_image_set.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
large_image_set.hxx
+
+
+
1 #ifndef LARGE_IMAGE_SET_HXX
+
2 #define LARGE_IMAGE_SET_HXX
+
3 
+
4 #include <fstream>
+
5 using std::ifstream;
+
6 
+
7 #include <iostream>
+
8 using std::ostream;
+
9 
+
10 #include <string>
+
11 using std::string;
+
12 
+
13 #include <vector>
+
14 using std::vector;
+
15 
+
16 #include "image_set_interface.hxx"
+
17 
+
18 typedef class LargeImages LargeImages;
+
19 
+
20 class LargeImage : public ImageInterface {
+
21  private:
+
22  friend class LargeImages;
+
23 
+
24  int number_subimages;
+
25  int padding;
+
26  int channels;
+
27  int height;
+
28  int width;
+
29  int classification;
+
30  vector< vector< vector<uint8_t> > > pixels;
+
31  vector< vector<uint8_t> > alpha;
+
32 
+
33  //reference to images to get channel avgs and std_Devs
+
34  const LargeImages *images;
+
35 
+
36  public:
+
37 
+
38  LargeImage(ifstream &infile, int _number_subimages, int _channels, int _width, int _height, int _padding, int _classification, const LargeImages *_images);
+
39  LargeImage(int _number_subimages, int _channels, int _width, int _height, int _padding, int _classification, const vector< vector< vector<uint8_t> > > &_pixels);
+
40  LargeImage(int _number_subimages, int _channels, int _width, int _height, int _padding, int _classification, const vector< vector< vector<uint8_t> > > &_pixels, const vector< vector<uint8_t> > &_alpha);
+
41 
+
42  int get_classification() const;
+
43 
+
44  int get_number_subimages() const;
+
45  int get_channels() const;
+
46  int get_height() const;
+
47  int get_width() const;
+
48 
+
49  void scale_0_1();
+
50  void get_pixel_avg(vector<float> &channel_avgs) const;
+
51  void get_pixel_variance(const vector<float> &channel_avgs, vector<float> &channel_variances) const;
+
52  void normalize(const vector<float> &channel_avgs, const vector<float> &channel_std_dev);
+
53 
+
54  void print(ostream &out);
+
55 
+
56  uint8_t get_pixel_unnormalized(int z, int y, int x) const;
+
57  uint8_t get_alpha_unnormalized(int y, int x) const;
+
58  void set_pixel(int z, int y, int x, uint8_t value);
+
59  uint8_t get_pixel(int z, int y, int x) const;
+
60 
+
61  void set_alpha(const vector< vector<uint8_t> > &_alpha);
+
62  void set_alpha(const vector< vector<float> > &_alpha);
+
63 
+
64  LargeImage* copy() const;
+
65 
+
66  void draw_png(string filename) const;
+
67  void draw_png_4channel(string filename) const;
+
68  void draw_png_alpha(string filename) const;
+
69 
+
70 #ifdef _HAS_TIFF_
+
71  void draw_tiff(string filename) const;
+
72  void draw_tiff_4channel(string filename) const;
+
73  void draw_tiff_alpha(string filename) const;
+
74 #endif
+
75 };
+
76 
+
77 class LargeImages : public MultiImagesInterface {
+
78  private:
+
79  string filename;
+
80 
+
81  int number_classes;
+
82  int number_images;
+
83 
+
84  vector<int> class_sizes;
+
85 
+
86  int padding;
+
87  int channels;
+
88  int subimage_width, subimage_height;
+
89 
+
90  vector<LargeImage> images;
+
91 
+
92  vector<float> channel_avg;
+
93  vector<float> channel_std_dev;
+
94 
+
95  public:
+
96  int read_images_from_file(string binary_filename);
+
97 
+
98  LargeImages(string binary_filename, int _padding, int _subimage_height, int _subimage_width);
+
99  LargeImages(string binary_filename, int _padding, int _subimage_height, int _subimage_width, const vector<float> &_channel_avg, const vector<float> &channel_std_dev);
+
100 
+
101  string get_filename() const;
+
102 
+
103  int get_class_size(int i) const;
+
104 
+
105  int get_number_classes() const;
+
106 
+
107  int get_number_images() const;
+
108  int get_number_large_images() const;
+
109  int get_number_subimages(int i) const;
+
110 
+
111  int get_padding() const;
+
112 
+
113  int get_image_channels() const;
+
114  int get_image_width() const;
+
115  int get_image_height() const;
+
116 
+
117  int get_large_image_channels(int image) const;
+
118  int get_large_image_width(int image) const;
+
119  int get_large_image_height(int image) const;
+
120 
+
121 
+
122  int get_image_classification(int image) const;
+
123  int get_classification(int subimage) const;
+
124  float get_pixel(int subimage, int z, int y, int x) const;
+
125  float get_raw_pixel(int subimage, int z, int y, int x) const;
+
126 
+
127  void calculate_avg_std_dev();
+
128 
+
129  float get_channel_avg(int channel) const;
+
130 
+
131  float get_channel_std_dev(int channel) const;
+
132 
+
133 
+
134  const vector<float>& get_average() const;
+
135  const vector<float>& get_std_dev() const;
+
136 
+
137  void normalize();
+
138 
+
139  LargeImage* copy_image(int i) const;
+
140 
+
141 #ifdef _HAS_TIFF_
+
142  int read_images_from_directory(string directory);
+
143  int read_images_from_directories(string directory);
+
144 #endif
+
145 };
+
146 
+
147 #endif
+
148 
+
+ +
MultiImagesInterface
Definition: image_set_interface.hxx:52
+ +
LargeImages
Definition: large_image_set.hxx:77
+
LargeImage
Definition: large_image_set.hxx:20
+ +
ImageInterface
Definition: image_set_interface.hxx:18
+ + +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/lodepng_8h_source.html b/docs/html/lodepng_8h_source.html new file mode 100644 index 00000000..a40618ae --- /dev/null +++ b/docs/html/lodepng_8h_source.html @@ -0,0 +1,1806 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: image_tools/lodepng.h Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
lodepng.h
+
+
+
1 /*
+
2 LodePNG version 20131222
+
3 
+
4 Copyright (c) 2005-2013 Lode Vandevenne
+
5 
+
6 This software is provided 'as-is', without any express or implied
+
7 warranty. In no event will the authors be held liable for any damages
+
8 arising from the use of this software.
+
9 
+
10 Permission is granted to anyone to use this software for any purpose,
+
11 including commercial applications, and to alter it and redistribute it
+
12 freely, subject to the following restrictions:
+
13 
+
14  1. The origin of this software must not be misrepresented; you must not
+
15  claim that you wrote the original software. If you use this software
+
16  in a product, an acknowledgment in the product documentation would be
+
17  appreciated but is not required.
+
18 
+
19  2. Altered source versions must be plainly marked as such, and must not be
+
20  misrepresented as being the original software.
+
21 
+
22  3. This notice may not be removed or altered from any source
+
23  distribution.
+
24 */
+
25 
+
26 #ifndef LODEPNG_H
+
27 #define LODEPNG_H
+
28 
+
29 #include <string.h> /*for size_t*/
+
30 
+
31 #ifdef __cplusplus
+
32 #include <vector>
+
33 #include <string>
+
34 #endif /*__cplusplus*/
+
35 
+
36 /*
+
37 The following #defines are used to create code sections. They can be disabled
+
38 to disable code sections, which can give faster compile time and smaller binary.
+
39 The "NO_COMPILE" defines are designed to be used to pass as defines to the
+
40 compiler command to disable them without modifying this header, e.g.
+
41 -DLODEPNG_NO_COMPILE_ZLIB for gcc.
+
42 */
+
43 /*deflate & zlib. If disabled, you must specify alternative zlib functions in
+
44 the custom_zlib field of the compress and decompress settings*/
+
45 #ifndef LODEPNG_NO_COMPILE_ZLIB
+
46 #define LODEPNG_COMPILE_ZLIB
+
47 #endif
+
48 /*png encoder and png decoder*/
+
49 #ifndef LODEPNG_NO_COMPILE_PNG
+
50 #define LODEPNG_COMPILE_PNG
+
51 #endif
+
52 /*deflate&zlib decoder and png decoder*/
+
53 #ifndef LODEPNG_NO_COMPILE_DECODER
+
54 #define LODEPNG_COMPILE_DECODER
+
55 #endif
+
56 /*deflate&zlib encoder and png encoder*/
+
57 #ifndef LODEPNG_NO_COMPILE_ENCODER
+
58 #define LODEPNG_COMPILE_ENCODER
+
59 #endif
+
60 /*the optional built in harddisk file loading and saving functions*/
+
61 #ifndef LODEPNG_NO_COMPILE_DISK
+
62 #define LODEPNG_COMPILE_DISK
+
63 #endif
+
64 /*support for chunks other than IHDR, IDAT, PLTE, tRNS, IEND: ancillary and unknown chunks*/
+
65 #ifndef LODEPNG_NO_COMPILE_ANCILLARY_CHUNKS
+
66 #define LODEPNG_COMPILE_ANCILLARY_CHUNKS
+
67 #endif
+
68 /*ability to convert error numerical codes to English text string*/
+
69 #ifndef LODEPNG_NO_COMPILE_ERROR_TEXT
+
70 #define LODEPNG_COMPILE_ERROR_TEXT
+
71 #endif
+
72 /*Compile the default allocators (C's free, malloc and realloc). If you disable this,
+
73 you can define the functions lodepng_free, lodepng_malloc and lodepng_realloc in your
+
74 source files with custom allocators.*/
+
75 #ifndef LODEPNG_NO_COMPILE_ALLOCATORS
+
76 #define LODEPNG_COMPILE_ALLOCATORS
+
77 #endif
+
78 /*compile the C++ version (you can disable the C++ wrapper here even when compiling for C++)*/
+
79 #ifdef __cplusplus
+
80 #ifndef LODEPNG_NO_COMPILE_CPP
+
81 #define LODEPNG_COMPILE_CPP
+
82 #endif
+
83 #endif
+
84 
+
85 #ifdef LODEPNG_COMPILE_PNG
+
86 /*The PNG color types (also used for raw).*/
+
87 typedef enum LodePNGColorType
+
88 {
+
89  LCT_GREY = 0, /*greyscale: 1,2,4,8,16 bit*/
+
90  LCT_RGB = 2, /*RGB: 8,16 bit*/
+
91  LCT_PALETTE = 3, /*palette: 1,2,4,8 bit*/
+
92  LCT_GREY_ALPHA = 4, /*greyscale with alpha: 8,16 bit*/
+
93  LCT_RGBA = 6 /*RGB with alpha: 8,16 bit*/
+
94 } LodePNGColorType;
+
95 
+
96 #ifdef LODEPNG_COMPILE_DECODER
+
97 /*
+
98 Converts PNG data in memory to raw pixel data.
+
99 out: Output parameter. Pointer to buffer that will contain the raw pixel data.
+
100  After decoding, its size is w * h * (bytes per pixel) bytes larger than
+
101  initially. Bytes per pixel depends on colortype and bitdepth.
+
102  Must be freed after usage with free(*out).
+
103  Note: for 16-bit per channel colors, uses big endian format like PNG does.
+
104 w: Output parameter. Pointer to width of pixel data.
+
105 h: Output parameter. Pointer to height of pixel data.
+
106 in: Memory buffer with the PNG file.
+
107 insize: size of the in buffer.
+
108 colortype: the desired color type for the raw output image. See explanation on PNG color types.
+
109 bitdepth: the desired bit depth for the raw output image. See explanation on PNG color types.
+
110 Return value: LodePNG error code (0 means no error).
+
111 */
+
112 unsigned lodepng_decode_memory(unsigned char** out, unsigned* w, unsigned* h,
+
113  const unsigned char* in, size_t insize,
+
114  LodePNGColorType colortype, unsigned bitdepth);
+
115 
+
116 /*Same as lodepng_decode_memory, but always decodes to 32-bit RGBA raw image*/
+
117 unsigned lodepng_decode32(unsigned char** out, unsigned* w, unsigned* h,
+
118  const unsigned char* in, size_t insize);
+
119 
+
120 /*Same as lodepng_decode_memory, but always decodes to 24-bit RGB raw image*/
+
121 unsigned lodepng_decode24(unsigned char** out, unsigned* w, unsigned* h,
+
122  const unsigned char* in, size_t insize);
+
123 
+
124 #ifdef LODEPNG_COMPILE_DISK
+
125 /*
+
126 Load PNG from disk, from file with given name.
+
127 Same as the other decode functions, but instead takes a filename as input.
+
128 */
+
129 unsigned lodepng_decode_file(unsigned char** out, unsigned* w, unsigned* h,
+
130  const char* filename,
+
131  LodePNGColorType colortype, unsigned bitdepth);
+
132 
+
133 /*Same as lodepng_decode_file, but always decodes to 32-bit RGBA raw image.*/
+
134 unsigned lodepng_decode32_file(unsigned char** out, unsigned* w, unsigned* h,
+
135  const char* filename);
+
136 
+
137 /*Same as lodepng_decode_file, but always decodes to 24-bit RGB raw image.*/
+
138 unsigned lodepng_decode24_file(unsigned char** out, unsigned* w, unsigned* h,
+
139  const char* filename);
+
140 #endif /*LODEPNG_COMPILE_DISK*/
+
141 #endif /*LODEPNG_COMPILE_DECODER*/
+
142 
+
143 
+
144 #ifdef LODEPNG_COMPILE_ENCODER
+
145 /*
+
146 Converts raw pixel data into a PNG image in memory. The colortype and bitdepth
+
147  of the output PNG image cannot be chosen, they are automatically determined
+
148  by the colortype, bitdepth and content of the input pixel data.
+
149  Note: for 16-bit per channel colors, needs big endian format like PNG does.
+
150 out: Output parameter. Pointer to buffer that will contain the PNG image data.
+
151  Must be freed after usage with free(*out).
+
152 outsize: Output parameter. Pointer to the size in bytes of the out buffer.
+
153 image: The raw pixel data to encode. The size of this buffer should be
+
154  w * h * (bytes per pixel), bytes per pixel depends on colortype and bitdepth.
+
155 w: width of the raw pixel data in pixels.
+
156 h: height of the raw pixel data in pixels.
+
157 colortype: the color type of the raw input image. See explanation on PNG color types.
+
158 bitdepth: the bit depth of the raw input image. See explanation on PNG color types.
+
159 Return value: LodePNG error code (0 means no error).
+
160 */
+
161 unsigned lodepng_encode_memory(unsigned char** out, size_t* outsize,
+
162  const unsigned char* image, unsigned w, unsigned h,
+
163  LodePNGColorType colortype, unsigned bitdepth);
+
164 
+
165 /*Same as lodepng_encode_memory, but always encodes from 32-bit RGBA raw image.*/
+
166 unsigned lodepng_encode32(unsigned char** out, size_t* outsize,
+
167  const unsigned char* image, unsigned w, unsigned h);
+
168 
+
169 /*Same as lodepng_encode_memory, but always encodes from 24-bit RGB raw image.*/
+
170 unsigned lodepng_encode24(unsigned char** out, size_t* outsize,
+
171  const unsigned char* image, unsigned w, unsigned h);
+
172 
+
173 #ifdef LODEPNG_COMPILE_DISK
+
174 /*
+
175 Converts raw pixel data into a PNG file on disk.
+
176 Same as the other encode functions, but instead takes a filename as output.
+
177 NOTE: This overwrites existing files without warning!
+
178 */
+
179 unsigned lodepng_encode_file(const char* filename,
+
180  const unsigned char* image, unsigned w, unsigned h,
+
181  LodePNGColorType colortype, unsigned bitdepth);
+
182 
+
183 /*Same as lodepng_encode_file, but always encodes from 32-bit RGBA raw image.*/
+
184 unsigned lodepng_encode32_file(const char* filename,
+
185  const unsigned char* image, unsigned w, unsigned h);
+
186 
+
187 /*Same as lodepng_encode_file, but always encodes from 24-bit RGB raw image.*/
+
188 unsigned lodepng_encode24_file(const char* filename,
+
189  const unsigned char* image, unsigned w, unsigned h);
+
190 #endif /*LODEPNG_COMPILE_DISK*/
+
191 #endif /*LODEPNG_COMPILE_ENCODER*/
+
192 
+
193 
+
194 #ifdef LODEPNG_COMPILE_CPP
+
195 namespace lodepng
+
196 {
+
197 #ifdef LODEPNG_COMPILE_DECODER
+
198 /*Same as lodepng_decode_memory, but decodes to an std::vector.*/
+
199 unsigned decode(std::vector<unsigned char>& out, unsigned& w, unsigned& h,
+
200  const unsigned char* in, size_t insize,
+
201  LodePNGColorType colortype = LCT_RGBA, unsigned bitdepth = 8);
+
202 unsigned decode(std::vector<unsigned char>& out, unsigned& w, unsigned& h,
+
203  const std::vector<unsigned char>& in,
+
204  LodePNGColorType colortype = LCT_RGBA, unsigned bitdepth = 8);
+
205 #ifdef LODEPNG_COMPILE_DISK
+
206 /*
+
207 Converts PNG file from disk to raw pixel data in memory.
+
208 Same as the other decode functions, but instead takes a filename as input.
+
209 */
+
210 unsigned decode(std::vector<unsigned char>& out, unsigned& w, unsigned& h,
+
211  const std::string& filename,
+
212  LodePNGColorType colortype = LCT_RGBA, unsigned bitdepth = 8);
+
213 #endif //LODEPNG_COMPILE_DISK
+
214 #endif //LODEPNG_COMPILE_DECODER
+
215 
+
216 #ifdef LODEPNG_COMPILE_ENCODER
+
217 /*Same as lodepng_encode_memory, but encodes to an std::vector.*/
+
218 unsigned encode(std::vector<unsigned char>& out,
+
219  const unsigned char* in, unsigned w, unsigned h,
+
220  LodePNGColorType colortype = LCT_RGBA, unsigned bitdepth = 8);
+
221 unsigned encode(std::vector<unsigned char>& out,
+
222  const std::vector<unsigned char>& in, unsigned w, unsigned h,
+
223  LodePNGColorType colortype = LCT_RGBA, unsigned bitdepth = 8);
+
224 #ifdef LODEPNG_COMPILE_DISK
+
225 /*
+
226 Converts 32-bit RGBA raw pixel data into a PNG file on disk.
+
227 Same as the other encode functions, but instead takes a filename as output.
+
228 NOTE: This overwrites existing files without warning!
+
229 */
+
230 unsigned encode(const std::string& filename,
+
231  const unsigned char* in, unsigned w, unsigned h,
+
232  LodePNGColorType colortype = LCT_RGBA, unsigned bitdepth = 8);
+
233 unsigned encode(const std::string& filename,
+
234  const std::vector<unsigned char>& in, unsigned w, unsigned h,
+
235  LodePNGColorType colortype = LCT_RGBA, unsigned bitdepth = 8);
+
236 #endif //LODEPNG_COMPILE_DISK
+
237 #endif //LODEPNG_COMPILE_ENCODER
+
238 } //namespace lodepng
+
239 #endif /*LODEPNG_COMPILE_CPP*/
+
240 #endif /*LODEPNG_COMPILE_PNG*/
+
241 
+
242 #ifdef LODEPNG_COMPILE_ERROR_TEXT
+
243 /*Returns an English description of the numerical error code.*/
+
244 const char* lodepng_error_text(unsigned code);
+
245 #endif /*LODEPNG_COMPILE_ERROR_TEXT*/
+
246 
+
247 #ifdef LODEPNG_COMPILE_DECODER
+
248 /*Settings for zlib decompression*/
+
249 typedef struct LodePNGDecompressSettings LodePNGDecompressSettings;
+
250 struct LodePNGDecompressSettings
+
251 {
+
252  unsigned ignore_adler32; /*if 1, continue and don't give an error message if the Adler32 checksum is corrupted*/
+
253 
+
254  /*use custom zlib decoder instead of built in one (default: null)*/
+
255  unsigned (*custom_zlib)(unsigned char**, size_t*,
+
256  const unsigned char*, size_t,
+
257  const LodePNGDecompressSettings*);
+
258  /*use custom deflate decoder instead of built in one (default: null)
+
259  if custom_zlib is used, custom_deflate is ignored since only the built in
+
260  zlib function will call custom_deflate*/
+
261  unsigned (*custom_inflate)(unsigned char**, size_t*,
+
262  const unsigned char*, size_t,
+
263  const LodePNGDecompressSettings*);
+
264 
+
265  const void* custom_context; /*optional custom settings for custom functions*/
+
266 };
+
267 
+
268 extern const LodePNGDecompressSettings lodepng_default_decompress_settings;
+
269 void lodepng_decompress_settings_init(LodePNGDecompressSettings* settings);
+
270 #endif /*LODEPNG_COMPILE_DECODER*/
+
271 
+
272 #ifdef LODEPNG_COMPILE_ENCODER
+
273 /*
+
274 Settings for zlib compression. Tweaking these settings tweaks the balance
+
275 between speed and compression ratio.
+
276 */
+
277 typedef struct LodePNGCompressSettings LodePNGCompressSettings;
+
278 struct LodePNGCompressSettings /*deflate = compress*/
+
279 {
+
280  /*LZ77 related settings*/
+
281  unsigned btype; /*the block type for LZ (0, 1, 2 or 3, see zlib standard). Should be 2 for proper compression.*/
+
282  unsigned use_lz77; /*whether or not to use LZ77. Should be 1 for proper compression.*/
+
283  unsigned windowsize; /*must be a power of two <= 32768. higher compresses more but is slower. Typical value: 2048.*/
+
284  unsigned minmatch; /*mininum lz77 length. 3 is normally best, 6 can be better for some PNGs. Default: 0*/
+
285  unsigned nicematch; /*stop searching if >= this length found. Set to 258 for best compression. Default: 128*/
+
286  unsigned lazymatching; /*use lazy matching: better compression but a bit slower. Default: true*/
+
287 
+
288  /*use custom zlib encoder instead of built in one (default: null)*/
+
289  unsigned (*custom_zlib)(unsigned char**, size_t*,
+
290  const unsigned char*, size_t,
+
291  const LodePNGCompressSettings*);
+
292  /*use custom deflate encoder instead of built in one (default: null)
+
293  if custom_zlib is used, custom_deflate is ignored since only the built in
+
294  zlib function will call custom_deflate*/
+
295  unsigned (*custom_deflate)(unsigned char**, size_t*,
+
296  const unsigned char*, size_t,
+
297  const LodePNGCompressSettings*);
+
298 
+
299  const void* custom_context; /*optional custom settings for custom functions*/
+
300 };
+
301 
+
302 extern const LodePNGCompressSettings lodepng_default_compress_settings;
+
303 void lodepng_compress_settings_init(LodePNGCompressSettings* settings);
+
304 #endif /*LODEPNG_COMPILE_ENCODER*/
+
305 
+
306 #ifdef LODEPNG_COMPILE_PNG
+
307 /*
+
308 Color mode of an image. Contains all information required to decode the pixel
+
309 bits to RGBA colors. This information is the same as used in the PNG file
+
310 format, and is used both for PNG and raw image data in LodePNG.
+
311 */
+
312 typedef struct LodePNGColorMode
+
313 {
+
314  /*header (IHDR)*/
+
315  LodePNGColorType colortype; /*color type, see PNG standard or documentation further in this header file*/
+
316  unsigned bitdepth; /*bits per sample, see PNG standard or documentation further in this header file*/
+
317 
+
318  /*
+
319  palette (PLTE and tRNS)
+
320 
+
321  Dynamically allocated with the colors of the palette, including alpha.
+
322  When encoding a PNG, to store your colors in the palette of the LodePNGColorMode, first use
+
323  lodepng_palette_clear, then for each color use lodepng_palette_add.
+
324  If you encode an image without alpha with palette, don't forget to put value 255 in each A byte of the palette.
+
325 
+
326  When decoding, by default you can ignore this palette, since LodePNG already
+
327  fills the palette colors in the pixels of the raw RGBA output.
+
328 
+
329  The palette is only supported for color type 3.
+
330  */
+
331  unsigned char* palette; /*palette in RGBARGBA... order. When allocated, must be either 0, or have size 1024*/
+
332  size_t palettesize; /*palette size in number of colors (amount of bytes is 4 * palettesize)*/
+
333 
+
334  /*
+
335  transparent color key (tRNS)
+
336 
+
337  This color uses the same bit depth as the bitdepth value in this struct, which can be 1-bit to 16-bit.
+
338  For greyscale PNGs, r, g and b will all 3 be set to the same.
+
339 
+
340  When decoding, by default you can ignore this information, since LodePNG sets
+
341  pixels with this key to transparent already in the raw RGBA output.
+
342 
+
343  The color key is only supported for color types 0 and 2.
+
344  */
+
345  unsigned key_defined; /*is a transparent color key given? 0 = false, 1 = true*/
+
346  unsigned key_r; /*red/greyscale component of color key*/
+
347  unsigned key_g; /*green component of color key*/
+
348  unsigned key_b; /*blue component of color key*/
+
349 } LodePNGColorMode;
+
350 
+
351 /*init, cleanup and copy functions to use with this struct*/
+
352 void lodepng_color_mode_init(LodePNGColorMode* info);
+
353 void lodepng_color_mode_cleanup(LodePNGColorMode* info);
+
354 /*return value is error code (0 means no error)*/
+
355 unsigned lodepng_color_mode_copy(LodePNGColorMode* dest, const LodePNGColorMode* source);
+
356 
+
357 void lodepng_palette_clear(LodePNGColorMode* info);
+
358 /*add 1 color to the palette*/
+
359 unsigned lodepng_palette_add(LodePNGColorMode* info,
+
360  unsigned char r, unsigned char g, unsigned char b, unsigned char a);
+
361 
+
362 /*get the total amount of bits per pixel, based on colortype and bitdepth in the struct*/
+
363 unsigned lodepng_get_bpp(const LodePNGColorMode* info);
+
364 /*get the amount of color channels used, based on colortype in the struct.
+
365 If a palette is used, it counts as 1 channel.*/
+
366 unsigned lodepng_get_channels(const LodePNGColorMode* info);
+
367 /*is it a greyscale type? (only colortype 0 or 4)*/
+
368 unsigned lodepng_is_greyscale_type(const LodePNGColorMode* info);
+
369 /*has it got an alpha channel? (only colortype 2 or 6)*/
+
370 unsigned lodepng_is_alpha_type(const LodePNGColorMode* info);
+
371 /*has it got a palette? (only colortype 3)*/
+
372 unsigned lodepng_is_palette_type(const LodePNGColorMode* info);
+
373 /*only returns true if there is a palette and there is a value in the palette with alpha < 255.
+
374 Loops through the palette to check this.*/
+
375 unsigned lodepng_has_palette_alpha(const LodePNGColorMode* info);
+
376 /*
+
377 Check if the given color info indicates the possibility of having non-opaque pixels in the PNG image.
+
378 Returns true if the image can have translucent or invisible pixels (it still be opaque if it doesn't use such pixels).
+
379 Returns false if the image can only have opaque pixels.
+
380 In detail, it returns true only if it's a color type with alpha, or has a palette with non-opaque values,
+
381 or if "key_defined" is true.
+
382 */
+
383 unsigned lodepng_can_have_alpha(const LodePNGColorMode* info);
+
384 /*Returns the byte size of a raw image buffer with given width, height and color mode*/
+
385 size_t lodepng_get_raw_size(unsigned w, unsigned h, const LodePNGColorMode* color);
+
386 
+
387 #ifdef LODEPNG_COMPILE_ANCILLARY_CHUNKS
+
388 /*The information of a Time chunk in PNG.*/
+
389 typedef struct LodePNGTime
+
390 {
+
391  unsigned year; /*2 bytes used (0-65535)*/
+
392  unsigned month; /*1-12*/
+
393  unsigned day; /*1-31*/
+
394  unsigned hour; /*0-23*/
+
395  unsigned minute; /*0-59*/
+
396  unsigned second; /*0-60 (to allow for leap seconds)*/
+
397 } LodePNGTime;
+
398 #endif /*LODEPNG_COMPILE_ANCILLARY_CHUNKS*/
+
399 
+
400 /*Information about the PNG image, except pixels, width and height.*/
+
401 typedef struct LodePNGInfo
+
402 {
+
403  /*header (IHDR), palette (PLTE) and transparency (tRNS) chunks*/
+
404  unsigned compression_method;/*compression method of the original file. Always 0.*/
+
405  unsigned filter_method; /*filter method of the original file*/
+
406  unsigned interlace_method; /*interlace method of the original file*/
+
407  LodePNGColorMode color; /*color type and bits, palette and transparency of the PNG file*/
+
408 
+
409 #ifdef LODEPNG_COMPILE_ANCILLARY_CHUNKS
+
410  /*
+
411  suggested background color chunk (bKGD)
+
412  This color uses the same color mode as the PNG (except alpha channel), which can be 1-bit to 16-bit.
+
413 
+
414  For greyscale PNGs, r, g and b will all 3 be set to the same. When encoding
+
415  the encoder writes the red one. For palette PNGs: When decoding, the RGB value
+
416  will be stored, not a palette index. But when encoding, specify the index of
+
417  the palette in background_r, the other two are then ignored.
+
418 
+
419  The decoder does not use this background color to edit the color of pixels.
+
420  */
+
421  unsigned background_defined; /*is a suggested background color given?*/
+
422  unsigned background_r; /*red component of suggested background color*/
+
423  unsigned background_g; /*green component of suggested background color*/
+
424  unsigned background_b; /*blue component of suggested background color*/
+
425 
+
426  /*
+
427  non-international text chunks (tEXt and zTXt)
+
428 
+
429  The char** arrays each contain num strings. The actual messages are in
+
430  text_strings, while text_keys are keywords that give a short description what
+
431  the actual text represents, e.g. Title, Author, Description, or anything else.
+
432 
+
433  A keyword is minimum 1 character and maximum 79 characters long. It's
+
434  discouraged to use a single line length longer than 79 characters for texts.
+
435 
+
436  Don't allocate these text buffers yourself. Use the init/cleanup functions
+
437  correctly and use lodepng_add_text and lodepng_clear_text.
+
438  */
+
439  size_t text_num; /*the amount of texts in these char** buffers (there may be more texts in itext)*/
+
440  char** text_keys; /*the keyword of a text chunk (e.g. "Comment")*/
+
441  char** text_strings; /*the actual text*/
+
442 
+
443  /*
+
444  international text chunks (iTXt)
+
445  Similar to the non-international text chunks, but with additional strings
+
446  "langtags" and "transkeys".
+
447  */
+
448  size_t itext_num; /*the amount of international texts in this PNG*/
+
449  char** itext_keys; /*the English keyword of the text chunk (e.g. "Comment")*/
+
450  char** itext_langtags; /*language tag for this text's language, ISO/IEC 646 string, e.g. ISO 639 language tag*/
+
451  char** itext_transkeys; /*keyword translated to the international language - UTF-8 string*/
+
452  char** itext_strings; /*the actual international text - UTF-8 string*/
+
453 
+
454  /*time chunk (tIME)*/
+
455  unsigned time_defined; /*set to 1 to make the encoder generate a tIME chunk*/
+
456  LodePNGTime time;
+
457 
+
458  /*phys chunk (pHYs)*/
+
459  unsigned phys_defined; /*if 0, there is no pHYs chunk and the values below are undefined, if 1 else there is one*/
+
460  unsigned phys_x; /*pixels per unit in x direction*/
+
461  unsigned phys_y; /*pixels per unit in y direction*/
+
462  unsigned phys_unit; /*may be 0 (unknown unit) or 1 (metre)*/
+
463 
+
464  /*
+
465  unknown chunks
+
466  There are 3 buffers, one for each position in the PNG where unknown chunks can appear
+
467  each buffer contains all unknown chunks for that position consecutively
+
468  The 3 buffers are the unknown chunks between certain critical chunks:
+
469  0: IHDR-PLTE, 1: PLTE-IDAT, 2: IDAT-IEND
+
470  Do not allocate or traverse this data yourself. Use the chunk traversing functions declared
+
471  later, such as lodepng_chunk_next and lodepng_chunk_append, to read/write this struct.
+
472  */
+
473  unsigned char* unknown_chunks_data[3];
+
474  size_t unknown_chunks_size[3]; /*size in bytes of the unknown chunks, given for protection*/
+
475 #endif /*LODEPNG_COMPILE_ANCILLARY_CHUNKS*/
+
476 } LodePNGInfo;
+
477 
+
478 /*init, cleanup and copy functions to use with this struct*/
+
479 void lodepng_info_init(LodePNGInfo* info);
+
480 void lodepng_info_cleanup(LodePNGInfo* info);
+
481 /*return value is error code (0 means no error)*/
+
482 unsigned lodepng_info_copy(LodePNGInfo* dest, const LodePNGInfo* source);
+
483 
+
484 #ifdef LODEPNG_COMPILE_ANCILLARY_CHUNKS
+
485 void lodepng_clear_text(LodePNGInfo* info); /*use this to clear the texts again after you filled them in*/
+
486 unsigned lodepng_add_text(LodePNGInfo* info, const char* key, const char* str); /*push back both texts at once*/
+
487 
+
488 void lodepng_clear_itext(LodePNGInfo* info); /*use this to clear the itexts again after you filled them in*/
+
489 unsigned lodepng_add_itext(LodePNGInfo* info, const char* key, const char* langtag,
+
490  const char* transkey, const char* str); /*push back the 4 texts of 1 chunk at once*/
+
491 #endif /*LODEPNG_COMPILE_ANCILLARY_CHUNKS*/
+
492 
+
493 /*
+
494 Converts raw buffer from one color type to another color type, based on
+
495 LodePNGColorMode structs to describe the input and output color type.
+
496 See the reference manual at the end of this header file to see which color conversions are supported.
+
497 return value = LodePNG error code (0 if all went ok, an error if the conversion isn't supported)
+
498 The out buffer must have size (w * h * bpp + 7) / 8, where bpp is the bits per pixel
+
499 of the output color type (lodepng_get_bpp)
+
500 The fix_png value works as described in struct LodePNGDecoderSettings.
+
501 Note: for 16-bit per channel colors, uses big endian format like PNG does.
+
502 */
+
503 unsigned lodepng_convert(unsigned char* out, const unsigned char* in,
+
504  LodePNGColorMode* mode_out, const LodePNGColorMode* mode_in,
+
505  unsigned w, unsigned h, unsigned fix_png);
+
506 
+
507 #ifdef LODEPNG_COMPILE_DECODER
+
508 /*
+
509 Settings for the decoder. This contains settings for the PNG and the Zlib
+
510 decoder, but not the Info settings from the Info structs.
+
511 */
+
512 typedef struct LodePNGDecoderSettings
+
513 {
+
514  LodePNGDecompressSettings zlibsettings; /*in here is the setting to ignore Adler32 checksums*/
+
515 
+
516  unsigned ignore_crc; /*ignore CRC checksums*/
+
517  /*
+
518  The fix_png setting, if 1, makes the decoder tolerant towards some PNG images
+
519  that do not correctly follow the PNG specification. This only supports errors
+
520  that are fixable, were found in images that are actually used on the web, and
+
521  are silently tolerated by other decoders as well. Currently only one such fix
+
522  is implemented: if a palette index is out of bounds given the palette size,
+
523  interpret it as opaque black.
+
524  By default this value is 0, which makes it stop with an error on such images.
+
525  */
+
526  unsigned fix_png;
+
527  unsigned color_convert; /*whether to convert the PNG to the color type you want. Default: yes*/
+
528 
+
529 #ifdef LODEPNG_COMPILE_ANCILLARY_CHUNKS
+
530  unsigned read_text_chunks; /*if false but remember_unknown_chunks is true, they're stored in the unknown chunks*/
+
531  /*store all bytes from unknown chunks in the LodePNGInfo (off by default, useful for a png editor)*/
+
532  unsigned remember_unknown_chunks;
+
533 #endif /*LODEPNG_COMPILE_ANCILLARY_CHUNKS*/
+
534 } LodePNGDecoderSettings;
+
535 
+
536 void lodepng_decoder_settings_init(LodePNGDecoderSettings* settings);
+
537 #endif /*LODEPNG_COMPILE_DECODER*/
+
538 
+
539 #ifdef LODEPNG_COMPILE_ENCODER
+
540 /*automatically use color type with less bits per pixel if losslessly possible. Default: AUTO*/
+
541 typedef enum LodePNGFilterStrategy
+
542 {
+
543  /*every filter at zero*/
+
544  LFS_ZERO,
+
545  /*Use filter that gives minumum sum, as described in the official PNG filter heuristic.*/
+
546  LFS_MINSUM,
+
547  /*Use the filter type that gives smallest Shannon entropy for this scanline. Depending
+
548  on the image, this is better or worse than minsum.*/
+
549  LFS_ENTROPY,
+
550  /*
+
551  Brute-force-search PNG filters by compressing each filter for each scanline.
+
552  Experimental, very slow, and only rarely gives better compression than MINSUM.
+
553  */
+
554  LFS_BRUTE_FORCE,
+
555  /*use predefined_filters buffer: you specify the filter type for each scanline*/
+
556  LFS_PREDEFINED
+
557 } LodePNGFilterStrategy;
+
558 
+
559 /*automatically use color type with less bits per pixel if losslessly possible. Default: LAC_AUTO*/
+
560 typedef enum LodePNGAutoConvert
+
561 {
+
562  LAC_NO, /*use color type user requested*/
+
563  LAC_ALPHA, /*use color type user requested, but if only opaque pixels and RGBA or grey+alpha, use RGB or grey*/
+
564  LAC_AUTO, /*use PNG color type that can losslessly represent the uncompressed image the smallest possible*/
+
565  /*
+
566  like AUTO, but do not choose 1, 2 or 4 bit per pixel types.
+
567  sometimes a PNG image compresses worse if less than 8 bits per pixels.
+
568  */
+
569  LAC_AUTO_NO_NIBBLES,
+
570  /*
+
571  like AUTO, but never choose palette color type. For small images, encoding
+
572  the palette may take more bytes than what is gained. Note that AUTO also
+
573  already prevents encoding the palette for extremely small images, but that may
+
574  not be sufficient because due to the compression it cannot predict when to
+
575  switch.
+
576  */
+
577  LAC_AUTO_NO_PALETTE,
+
578  LAC_AUTO_NO_NIBBLES_NO_PALETTE
+
579 } LodePNGAutoConvert;
+
580 
+
581 
+
582 /*
+
583 Automatically chooses color type that gives smallest amount of bits in the
+
584 output image, e.g. grey if there are only greyscale pixels, palette if there
+
585 are less than 256 colors, ...
+
586 The auto_convert parameter allows limiting it to not use palette, ...
+
587 */
+
588 unsigned lodepng_auto_choose_color(LodePNGColorMode* mode_out,
+
589  const unsigned char* image, unsigned w, unsigned h,
+
590  const LodePNGColorMode* mode_in,
+
591  LodePNGAutoConvert auto_convert);
+
592 
+
593 /*Settings for the encoder.*/
+
594 typedef struct LodePNGEncoderSettings
+
595 {
+
596  LodePNGCompressSettings zlibsettings; /*settings for the zlib encoder, such as window size, ...*/
+
597 
+
598  LodePNGAutoConvert auto_convert; /*how to automatically choose output PNG color type, if at all*/
+
599 
+
600  /*If true, follows the official PNG heuristic: if the PNG uses a palette or lower than
+
601  8 bit depth, set all filters to zero. Otherwise use the filter_strategy. Note that to
+
602  completely follow the official PNG heuristic, filter_palette_zero must be true and
+
603  filter_strategy must be LFS_MINSUM*/
+
604  unsigned filter_palette_zero;
+
605  /*Which filter strategy to use when not using zeroes due to filter_palette_zero.
+
606  Set filter_palette_zero to 0 to ensure always using your chosen strategy. Default: LFS_MINSUM*/
+
607  LodePNGFilterStrategy filter_strategy;
+
608  /*used if filter_strategy is LFS_PREDEFINED. In that case, this must point to a buffer with
+
609  the same length as the amount of scanlines in the image, and each value must <= 5. You
+
610  have to cleanup this buffer, LodePNG will never free it. Don't forget that filter_palette_zero
+
611  must be set to 0 to ensure this is also used on palette or low bitdepth images.*/
+
612  const unsigned char* predefined_filters;
+
613 
+
614  /*force creating a PLTE chunk if colortype is 2 or 6 (= a suggested palette).
+
615  If colortype is 3, PLTE is _always_ created.*/
+
616  unsigned force_palette;
+
617 #ifdef LODEPNG_COMPILE_ANCILLARY_CHUNKS
+
618  /*add LodePNG identifier and version as a text chunk, for debugging*/
+
619  unsigned add_id;
+
620  /*encode text chunks as zTXt chunks instead of tEXt chunks, and use compression in iTXt chunks*/
+
621  unsigned text_compression;
+
622 #endif /*LODEPNG_COMPILE_ANCILLARY_CHUNKS*/
+
623 } LodePNGEncoderSettings;
+
624 
+
625 void lodepng_encoder_settings_init(LodePNGEncoderSettings* settings);
+
626 #endif /*LODEPNG_COMPILE_ENCODER*/
+
627 
+
628 
+
629 #if defined(LODEPNG_COMPILE_DECODER) || defined(LODEPNG_COMPILE_ENCODER)
+
630 /*The settings, state and information for extended encoding and decoding.*/
+
631 typedef struct LodePNGState
+
632 {
+
633 #ifdef LODEPNG_COMPILE_DECODER
+
634  LodePNGDecoderSettings decoder; /*the decoding settings*/
+
635 #endif /*LODEPNG_COMPILE_DECODER*/
+
636 #ifdef LODEPNG_COMPILE_ENCODER
+
637  LodePNGEncoderSettings encoder; /*the encoding settings*/
+
638 #endif /*LODEPNG_COMPILE_ENCODER*/
+
639  LodePNGColorMode info_raw; /*specifies the format in which you would like to get the raw pixel buffer*/
+
640  LodePNGInfo info_png; /*info of the PNG image obtained after decoding*/
+
641  unsigned error;
+
642 #ifdef LODEPNG_COMPILE_CPP
+
643  //For the lodepng::State subclass.
+
644  virtual ~LodePNGState(){}
+
645 #endif
+
646 } LodePNGState;
+
647 
+
648 /*init, cleanup and copy functions to use with this struct*/
+
649 void lodepng_state_init(LodePNGState* state);
+
650 void lodepng_state_cleanup(LodePNGState* state);
+
651 void lodepng_state_copy(LodePNGState* dest, const LodePNGState* source);
+
652 #endif /* defined(LODEPNG_COMPILE_DECODER) || defined(LODEPNG_COMPILE_ENCODER) */
+
653 
+
654 #ifdef LODEPNG_COMPILE_DECODER
+
655 /*
+
656 Same as lodepng_decode_memory, but uses a LodePNGState to allow custom settings and
+
657 getting much more information about the PNG image and color mode.
+
658 */
+
659 unsigned lodepng_decode(unsigned char** out, unsigned* w, unsigned* h,
+
660  LodePNGState* state,
+
661  const unsigned char* in, size_t insize);
+
662 
+
663 /*
+
664 Read the PNG header, but not the actual data. This returns only the information
+
665 that is in the header chunk of the PNG, such as width, height and color type. The
+
666 information is placed in the info_png field of the LodePNGState.
+
667 */
+
668 unsigned lodepng_inspect(unsigned* w, unsigned* h,
+
669  LodePNGState* state,
+
670  const unsigned char* in, size_t insize);
+
671 #endif /*LODEPNG_COMPILE_DECODER*/
+
672 
+
673 
+
674 #ifdef LODEPNG_COMPILE_ENCODER
+
675 /*This function allocates the out buffer with standard malloc and stores the size in *outsize.*/
+
676 unsigned lodepng_encode(unsigned char** out, size_t* outsize,
+
677  const unsigned char* image, unsigned w, unsigned h,
+
678  LodePNGState* state);
+
679 #endif /*LODEPNG_COMPILE_ENCODER*/
+
680 
+
681 /*
+
682 The lodepng_chunk functions are normally not needed, except to traverse the
+
683 unknown chunks stored in the LodePNGInfo struct, or add new ones to it.
+
684 It also allows traversing the chunks of an encoded PNG file yourself.
+
685 
+
686 PNG standard chunk naming conventions:
+
687 First byte: uppercase = critical, lowercase = ancillary
+
688 Second byte: uppercase = public, lowercase = private
+
689 Third byte: must be uppercase
+
690 Fourth byte: uppercase = unsafe to copy, lowercase = safe to copy
+
691 */
+
692 
+
693 /*get the length of the data of the chunk. Total chunk length has 12 bytes more.*/
+
694 unsigned lodepng_chunk_length(const unsigned char* chunk);
+
695 
+
696 /*puts the 4-byte type in null terminated string*/
+
697 void lodepng_chunk_type(char type[5], const unsigned char* chunk);
+
698 
+
699 /*check if the type is the given type*/
+
700 unsigned char lodepng_chunk_type_equals(const unsigned char* chunk, const char* type);
+
701 
+
702 /*0: it's one of the critical chunk types, 1: it's an ancillary chunk (see PNG standard)*/
+
703 unsigned char lodepng_chunk_ancillary(const unsigned char* chunk);
+
704 
+
705 /*0: public, 1: private (see PNG standard)*/
+
706 unsigned char lodepng_chunk_private(const unsigned char* chunk);
+
707 
+
708 /*0: the chunk is unsafe to copy, 1: the chunk is safe to copy (see PNG standard)*/
+
709 unsigned char lodepng_chunk_safetocopy(const unsigned char* chunk);
+
710 
+
711 /*get pointer to the data of the chunk, where the input points to the header of the chunk*/
+
712 unsigned char* lodepng_chunk_data(unsigned char* chunk);
+
713 const unsigned char* lodepng_chunk_data_const(const unsigned char* chunk);
+
714 
+
715 /*returns 0 if the crc is correct, 1 if it's incorrect (0 for OK as usual!)*/
+
716 unsigned lodepng_chunk_check_crc(const unsigned char* chunk);
+
717 
+
718 /*generates the correct CRC from the data and puts it in the last 4 bytes of the chunk*/
+
719 void lodepng_chunk_generate_crc(unsigned char* chunk);
+
720 
+
721 /*iterate to next chunks. don't use on IEND chunk, as there is no next chunk then*/
+
722 unsigned char* lodepng_chunk_next(unsigned char* chunk);
+
723 const unsigned char* lodepng_chunk_next_const(const unsigned char* chunk);
+
724 
+
725 /*
+
726 Appends chunk to the data in out. The given chunk should already have its chunk header.
+
727 The out variable and outlength are updated to reflect the new reallocated buffer.
+
728 Returns error code (0 if it went ok)
+
729 */
+
730 unsigned lodepng_chunk_append(unsigned char** out, size_t* outlength, const unsigned char* chunk);
+
731 
+
732 /*
+
733 Appends new chunk to out. The chunk to append is given by giving its length, type
+
734 and data separately. The type is a 4-letter string.
+
735 The out variable and outlength are updated to reflect the new reallocated buffer.
+
736 Returne error code (0 if it went ok)
+
737 */
+
738 unsigned lodepng_chunk_create(unsigned char** out, size_t* outlength, unsigned length,
+
739  const char* type, const unsigned char* data);
+
740 
+
741 
+
742 /*Calculate CRC32 of buffer*/
+
743 unsigned lodepng_crc32(const unsigned char* buf, size_t len);
+
744 #endif /*LODEPNG_COMPILE_PNG*/
+
745 
+
746 
+
747 #ifdef LODEPNG_COMPILE_ZLIB
+
748 /*
+
749 This zlib part can be used independently to zlib compress and decompress a
+
750 buffer. It cannot be used to create gzip files however, and it only supports the
+
751 part of zlib that is required for PNG, it does not support dictionaries.
+
752 */
+
753 
+
754 #ifdef LODEPNG_COMPILE_DECODER
+
755 /*Inflate a buffer. Inflate is the decompression step of deflate. Out buffer must be freed after use.*/
+
756 unsigned lodepng_inflate(unsigned char** out, size_t* outsize,
+
757  const unsigned char* in, size_t insize,
+
758  const LodePNGDecompressSettings* settings);
+
759 
+
760 /*
+
761 Decompresses Zlib data. Reallocates the out buffer and appends the data. The
+
762 data must be according to the zlib specification.
+
763 Either, *out must be NULL and *outsize must be 0, or, *out must be a valid
+
764 buffer and *outsize its size in bytes. out must be freed by user after usage.
+
765 */
+
766 unsigned lodepng_zlib_decompress(unsigned char** out, size_t* outsize,
+
767  const unsigned char* in, size_t insize,
+
768  const LodePNGDecompressSettings* settings);
+
769 #endif /*LODEPNG_COMPILE_DECODER*/
+
770 
+
771 #ifdef LODEPNG_COMPILE_ENCODER
+
772 /*
+
773 Compresses data with Zlib. Reallocates the out buffer and appends the data.
+
774 Zlib adds a small header and trailer around the deflate data.
+
775 The data is output in the format of the zlib specification.
+
776 Either, *out must be NULL and *outsize must be 0, or, *out must be a valid
+
777 buffer and *outsize its size in bytes. out must be freed by user after usage.
+
778 */
+
779 unsigned lodepng_zlib_compress(unsigned char** out, size_t* outsize,
+
780  const unsigned char* in, size_t insize,
+
781  const LodePNGCompressSettings* settings);
+
782 
+
783 /*
+
784 Find length-limited Huffman code for given frequencies. This function is in the
+
785 public interface only for tests, it's used internally by lodepng_deflate.
+
786 */
+
787 unsigned lodepng_huffman_code_lengths(unsigned* lengths, const unsigned* frequencies,
+
788  size_t numcodes, unsigned maxbitlen);
+
789 
+
790 /*Compress a buffer with deflate. See RFC 1951. Out buffer must be freed after use.*/
+
791 unsigned lodepng_deflate(unsigned char** out, size_t* outsize,
+
792  const unsigned char* in, size_t insize,
+
793  const LodePNGCompressSettings* settings);
+
794 
+
795 #endif /*LODEPNG_COMPILE_ENCODER*/
+
796 #endif /*LODEPNG_COMPILE_ZLIB*/
+
797 
+
798 #ifdef LODEPNG_COMPILE_DISK
+
799 /*
+
800 Load a file from disk into buffer. The function allocates the out buffer, and
+
801 after usage you should free it.
+
802 out: output parameter, contains pointer to loaded buffer.
+
803 outsize: output parameter, size of the allocated out buffer
+
804 filename: the path to the file to load
+
805 return value: error code (0 means ok)
+
806 */
+
807 unsigned lodepng_load_file(unsigned char** out, size_t* outsize, const char* filename);
+
808 
+
809 /*
+
810 Save a file from buffer to disk. Warning, if it exists, this function overwrites
+
811 the file without warning!
+
812 buffer: the buffer to write
+
813 buffersize: size of the buffer to write
+
814 filename: the path to the file to save to
+
815 return value: error code (0 means ok)
+
816 */
+
817 unsigned lodepng_save_file(const unsigned char* buffer, size_t buffersize, const char* filename);
+
818 #endif /*LODEPNG_COMPILE_DISK*/
+
819 
+
820 #ifdef LODEPNG_COMPILE_CPP
+
821 //The LodePNG C++ wrapper uses std::vectors instead of manually allocated memory buffers.
+
822 namespace lodepng
+
823 {
+
824 #ifdef LODEPNG_COMPILE_PNG
+
825 class State : public LodePNGState
+
826 {
+
827  public:
+
828  State();
+
829  State(const State& other);
+
830  virtual ~State();
+
831  State& operator=(const State& other);
+
832 };
+
833 
+
834 #ifdef LODEPNG_COMPILE_DECODER
+
835 //Same as other lodepng::decode, but using a State for more settings and information.
+
836 unsigned decode(std::vector<unsigned char>& out, unsigned& w, unsigned& h,
+
837  State& state,
+
838  const unsigned char* in, size_t insize);
+
839 unsigned decode(std::vector<unsigned char>& out, unsigned& w, unsigned& h,
+
840  State& state,
+
841  const std::vector<unsigned char>& in);
+
842 #endif /*LODEPNG_COMPILE_DECODER*/
+
843 
+
844 #ifdef LODEPNG_COMPILE_ENCODER
+
845 //Same as other lodepng::encode, but using a State for more settings and information.
+
846 unsigned encode(std::vector<unsigned char>& out,
+
847  const unsigned char* in, unsigned w, unsigned h,
+
848  State& state);
+
849 unsigned encode(std::vector<unsigned char>& out,
+
850  const std::vector<unsigned char>& in, unsigned w, unsigned h,
+
851  State& state);
+
852 #endif /*LODEPNG_COMPILE_ENCODER*/
+
853 
+
854 #ifdef LODEPNG_COMPILE_DISK
+
855 /*
+
856 Load a file from disk into an std::vector. If the vector is empty, then either
+
857 the file doesn't exist or is an empty file.
+
858 */
+
859 void load_file(std::vector<unsigned char>& buffer, const std::string& filename);
+
860 
+
861 /*
+
862 Save the binary data in an std::vector to a file on disk. The file is overwritten
+
863 without warning.
+
864 */
+
865 void save_file(const std::vector<unsigned char>& buffer, const std::string& filename);
+
866 #endif //LODEPNG_COMPILE_DISK
+
867 #endif //LODEPNG_COMPILE_PNG
+
868 
+
869 #ifdef LODEPNG_COMPILE_ZLIB
+
870 #ifdef LODEPNG_COMPILE_DECODER
+
871 //Zlib-decompress an unsigned char buffer
+
872 unsigned decompress(std::vector<unsigned char>& out, const unsigned char* in, size_t insize,
+
873  const LodePNGDecompressSettings& settings = lodepng_default_decompress_settings);
+
874 
+
875 //Zlib-decompress an std::vector
+
876 unsigned decompress(std::vector<unsigned char>& out, const std::vector<unsigned char>& in,
+
877  const LodePNGDecompressSettings& settings = lodepng_default_decompress_settings);
+
878 #endif //LODEPNG_COMPILE_DECODER
+
879 
+
880 #ifdef LODEPNG_COMPILE_ENCODER
+
881 //Zlib-compress an unsigned char buffer
+
882 unsigned compress(std::vector<unsigned char>& out, const unsigned char* in, size_t insize,
+
883  const LodePNGCompressSettings& settings = lodepng_default_compress_settings);
+
884 
+
885 //Zlib-compress an std::vector
+
886 unsigned compress(std::vector<unsigned char>& out, const std::vector<unsigned char>& in,
+
887  const LodePNGCompressSettings& settings = lodepng_default_compress_settings);
+
888 #endif //LODEPNG_COMPILE_ENCODER
+
889 #endif //LODEPNG_COMPILE_ZLIB
+
890 } //namespace lodepng
+
891 #endif /*LODEPNG_COMPILE_CPP*/
+
892 
+
893 /*
+
894 TODO:
+
895 [.] test if there are no memory leaks or security exploits - done a lot but needs to be checked often
+
896 [.] check compatibility with vareous compilers - done but needs to be redone for every newer version
+
897 [X] converting color to 16-bit per channel types
+
898 [ ] read all public PNG chunk types (but never let the color profile and gamma ones touch RGB values)
+
899 [ ] make sure encoder generates no chunks with size > (2^31)-1
+
900 [ ] partial decoding (stream processing)
+
901 [X] let the "isFullyOpaque" function check color keys and transparent palettes too
+
902 [X] better name for the variables "codes", "codesD", "codelengthcodes", "clcl" and "lldl"
+
903 [ ] don't stop decoding on errors like 69, 57, 58 (make warnings)
+
904 [ ] make option to choose if the raw image with non multiple of 8 bits per scanline should have padding bits or not
+
905 [ ] let the C++ wrapper catch exceptions coming from the standard library and return LodePNG error codes
+
906 */
+
907 
+
908 #endif /*LODEPNG_H inclusion guard*/
+
909 
+
910 /*
+
911 LodePNG Documentation
+
912 ---------------------
+
913 
+
914 0. table of contents
+
915 --------------------
+
916 
+
917  1. about
+
918  1.1. supported features
+
919  1.2. features not supported
+
920  2. C and C++ version
+
921  3. security
+
922  4. decoding
+
923  5. encoding
+
924  6. color conversions
+
925  6.1. PNG color types
+
926  6.2. color conversions
+
927  6.3. padding bits
+
928  6.4. A note about 16-bits per channel and endianness
+
929  7. error values
+
930  8. chunks and PNG editing
+
931  9. compiler support
+
932  10. examples
+
933  10.1. decoder C++ example
+
934  10.2. decoder C example
+
935  11. changes
+
936  12. contact information
+
937 
+
938 
+
939 1. about
+
940 --------
+
941 
+
942 PNG is a file format to store raster images losslessly with good compression,
+
943 supporting different color types and alpha channel.
+
944 
+
945 LodePNG is a PNG codec according to the Portable Network Graphics (PNG)
+
946 Specification (Second Edition) - W3C Recommendation 10 November 2003.
+
947 
+
948 The specifications used are:
+
949 
+
950 *) Portable Network Graphics (PNG) Specification (Second Edition):
+
951  http://www.w3.org/TR/2003/REC-PNG-20031110
+
952 *) RFC 1950 ZLIB Compressed Data Format version 3.3:
+
953  http://www.gzip.org/zlib/rfc-zlib.html
+
954 *) RFC 1951 DEFLATE Compressed Data Format Specification ver 1.3:
+
955  http://www.gzip.org/zlib/rfc-deflate.html
+
956 
+
957 The most recent version of LodePNG can currently be found at
+
958 http://lodev.org/lodepng/
+
959 
+
960 LodePNG works both in C (ISO C90) and C++, with a C++ wrapper that adds
+
961 extra functionality.
+
962 
+
963 LodePNG exists out of two files:
+
964 -lodepng.h: the header file for both C and C++
+
965 -lodepng.c(pp): give it the name lodepng.c or lodepng.cpp (or .cc) depending on your usage
+
966 
+
967 If you want to start using LodePNG right away without reading this doc, get the
+
968 examples from the LodePNG website to see how to use it in code, or check the
+
969 smaller examples in chapter 13 here.
+
970 
+
971 LodePNG is simple but only supports the basic requirements. To achieve
+
972 simplicity, the following design choices were made: There are no dependencies
+
973 on any external library. There are functions to decode and encode a PNG with
+
974 a single function call, and extended versions of these functions taking a
+
975 LodePNGState struct allowing to specify or get more information. By default
+
976 the colors of the raw image are always RGB or RGBA, no matter what color type
+
977 the PNG file uses. To read and write files, there are simple functions to
+
978 convert the files to/from buffers in memory.
+
979 
+
980 This all makes LodePNG suitable for loading textures in games, demos and small
+
981 programs, ... It's less suitable for full fledged image editors, loading PNGs
+
982 over network (it requires all the image data to be available before decoding can
+
983 begin), life-critical systems, ...
+
984 
+
985 1.1. supported features
+
986 -----------------------
+
987 
+
988 The following features are supported by the decoder:
+
989 
+
990 *) decoding of PNGs with any color type, bit depth and interlace mode, to a 24- or 32-bit color raw image,
+
991  or the same color type as the PNG
+
992 *) encoding of PNGs, from any raw image to 24- or 32-bit color, or the same color type as the raw image
+
993 *) Adam7 interlace and deinterlace for any color type
+
994 *) loading the image from harddisk or decoding it from a buffer from other sources than harddisk
+
995 *) support for alpha channels, including RGBA color model, translucent palettes and color keying
+
996 *) zlib decompression (inflate)
+
997 *) zlib compression (deflate)
+
998 *) CRC32 and ADLER32 checksums
+
999 *) handling of unknown chunks, allowing making a PNG editor that stores custom and unknown chunks.
+
1000 *) the following chunks are supported (generated/interpreted) by both encoder and decoder:
+
1001  IHDR: header information
+
1002  PLTE: color palette
+
1003  IDAT: pixel data
+
1004  IEND: the final chunk
+
1005  tRNS: transparency for palettized images
+
1006  tEXt: textual information
+
1007  zTXt: compressed textual information
+
1008  iTXt: international textual information
+
1009  bKGD: suggested background color
+
1010  pHYs: physical dimensions
+
1011  tIME: modification time
+
1012 
+
1013 1.2. features not supported
+
1014 ---------------------------
+
1015 
+
1016 The following features are _not_ supported:
+
1017 
+
1018 *) some features needed to make a conformant PNG-Editor might be still missing.
+
1019 *) partial loading/stream processing. All data must be available and is processed in one call.
+
1020 *) The following public chunks are not supported but treated as unknown chunks by LodePNG
+
1021  cHRM, gAMA, iCCP, sRGB, sBIT, hIST, sPLT
+
1022  Some of these are not supported on purpose: LodePNG wants to provide the RGB values
+
1023  stored in the pixels, not values modified by system dependent gamma or color models.
+
1024 
+
1025 
+
1026 2. C and C++ version
+
1027 --------------------
+
1028 
+
1029 The C version uses buffers allocated with alloc that you need to free()
+
1030 yourself. You need to use init and cleanup functions for each struct whenever
+
1031 using a struct from the C version to avoid exploits and memory leaks.
+
1032 
+
1033 The C++ version has extra functions with std::vectors in the interface and the
+
1034 lodepng::State class which is a LodePNGState with constructor and destructor.
+
1035 
+
1036 These files work without modification for both C and C++ compilers because all
+
1037 the additional C++ code is in "#ifdef __cplusplus" blocks that make C-compilers
+
1038 ignore it, and the C code is made to compile both with strict ISO C90 and C++.
+
1039 
+
1040 To use the C++ version, you need to rename the source file to lodepng.cpp
+
1041 (instead of lodepng.c), and compile it with a C++ compiler.
+
1042 
+
1043 To use the C version, you need to rename the source file to lodepng.c (instead
+
1044 of lodepng.cpp), and compile it with a C compiler.
+
1045 
+
1046 
+
1047 3. Security
+
1048 -----------
+
1049 
+
1050 Even if carefully designed, it's always possible that LodePNG contains possible
+
1051 exploits. If you discover one, please let me know, and it will be fixed.
+
1052 
+
1053 When using LodePNG, care has to be taken with the C version of LodePNG, as well
+
1054 as the C-style structs when working with C++. The following conventions are used
+
1055 for all C-style structs:
+
1056 
+
1057 -if a struct has a corresponding init function, always call the init function when making a new one
+
1058 -if a struct has a corresponding cleanup function, call it before the struct disappears to avoid memory leaks
+
1059 -if a struct has a corresponding copy function, use the copy function instead of "=".
+
1060  The destination must also be inited already.
+
1061 
+
1062 
+
1063 4. Decoding
+
1064 -----------
+
1065 
+
1066 Decoding converts a PNG compressed image to a raw pixel buffer.
+
1067 
+
1068 Most documentation on using the decoder is at its declarations in the header
+
1069 above. For C, simple decoding can be done with functions such as
+
1070 lodepng_decode32, and more advanced decoding can be done with the struct
+
1071 LodePNGState and lodepng_decode. For C++, all decoding can be done with the
+
1072 various lodepng::decode functions, and lodepng::State can be used for advanced
+
1073 features.
+
1074 
+
1075 When using the LodePNGState, it uses the following fields for decoding:
+
1076 *) LodePNGInfo info_png: it stores extra information about the PNG (the input) in here
+
1077 *) LodePNGColorMode info_raw: here you can say what color mode of the raw image (the output) you want to get
+
1078 *) LodePNGDecoderSettings decoder: you can specify a few extra settings for the decoder to use
+
1079 
+
1080 LodePNGInfo info_png
+
1081 --------------------
+
1082 
+
1083 After decoding, this contains extra information of the PNG image, except the actual
+
1084 pixels, width and height because these are already gotten directly from the decoder
+
1085 functions.
+
1086 
+
1087 It contains for example the original color type of the PNG image, text comments,
+
1088 suggested background color, etc... More details about the LodePNGInfo struct are
+
1089 at its declaration documentation.
+
1090 
+
1091 LodePNGColorMode info_raw
+
1092 -------------------------
+
1093 
+
1094 When decoding, here you can specify which color type you want
+
1095 the resulting raw image to be. If this is different from the colortype of the
+
1096 PNG, then the decoder will automatically convert the result. This conversion
+
1097 always works, except if you want it to convert a color PNG to greyscale or to
+
1098 a palette with missing colors.
+
1099 
+
1100 By default, 32-bit color is used for the result.
+
1101 
+
1102 LodePNGDecoderSettings decoder
+
1103 ------------------------------
+
1104 
+
1105 The settings can be used to ignore the errors created by invalid CRC and Adler32
+
1106 chunks, and to disable the decoding of tEXt chunks.
+
1107 
+
1108 There's also a setting color_convert, true by default. If false, no conversion
+
1109 is done, the resulting data will be as it was in the PNG (after decompression)
+
1110 and you'll have to puzzle the colors of the pixels together yourself using the
+
1111 color type information in the LodePNGInfo.
+
1112 
+
1113 
+
1114 5. Encoding
+
1115 -----------
+
1116 
+
1117 Encoding converts a raw pixel buffer to a PNG compressed image.
+
1118 
+
1119 Most documentation on using the encoder is at its declarations in the header
+
1120 above. For C, simple encoding can be done with functions such as
+
1121 lodepng_encode32, and more advanced decoding can be done with the struct
+
1122 LodePNGState and lodepng_encode. For C++, all encoding can be done with the
+
1123 various lodepng::encode functions, and lodepng::State can be used for advanced
+
1124 features.
+
1125 
+
1126 Like the decoder, the encoder can also give errors. However it gives less errors
+
1127 since the encoder input is trusted, the decoder input (a PNG image that could
+
1128 be forged by anyone) is not trusted.
+
1129 
+
1130 When using the LodePNGState, it uses the following fields for encoding:
+
1131 *) LodePNGInfo info_png: here you specify how you want the PNG (the output) to be.
+
1132 *) LodePNGColorMode info_raw: here you say what color type of the raw image (the input) has
+
1133 *) LodePNGEncoderSettings encoder: you can specify a few settings for the encoder to use
+
1134 
+
1135 LodePNGInfo info_png
+
1136 --------------------
+
1137 
+
1138 When encoding, you use this the opposite way as when decoding: for encoding,
+
1139 you fill in the values you want the PNG to have before encoding. By default it's
+
1140 not needed to specify a color type for the PNG since it's automatically chosen,
+
1141 but it's possible to choose it yourself given the right settings.
+
1142 
+
1143 The encoder will not always exactly match the LodePNGInfo struct you give,
+
1144 it tries as close as possible. Some things are ignored by the encoder. The
+
1145 encoder uses, for example, the following settings from it when applicable:
+
1146 colortype and bitdepth, text chunks, time chunk, the color key, the palette, the
+
1147 background color, the interlace method, unknown chunks, ...
+
1148 
+
1149 When encoding to a PNG with colortype 3, the encoder will generate a PLTE chunk.
+
1150 If the palette contains any colors for which the alpha channel is not 255 (so
+
1151 there are translucent colors in the palette), it'll add a tRNS chunk.
+
1152 
+
1153 LodePNGColorMode info_raw
+
1154 -------------------------
+
1155 
+
1156 You specify the color type of the raw image that you give to the input here,
+
1157 including a possible transparent color key and palette you happen to be using in
+
1158 your raw image data.
+
1159 
+
1160 By default, 32-bit color is assumed, meaning your input has to be in RGBA
+
1161 format with 4 bytes (unsigned chars) per pixel.
+
1162 
+
1163 LodePNGEncoderSettings encoder
+
1164 ------------------------------
+
1165 
+
1166 The following settings are supported (some are in sub-structs):
+
1167 *) auto_convert: when this option is enabled, the encoder will
+
1168 automatically choose the smallest possible color mode (including color key) that
+
1169 can encode the colors of all pixels without information loss.
+
1170 *) btype: the block type for LZ77. 0 = uncompressed, 1 = fixed huffman tree,
+
1171  2 = dynamic huffman tree (best compression). Should be 2 for proper
+
1172  compression.
+
1173 *) use_lz77: whether or not to use LZ77 for compressed block types. Should be
+
1174  true for proper compression.
+
1175 *) windowsize: the window size used by the LZ77 encoder (1 - 32768). Has value
+
1176  2048 by default, but can be set to 32768 for better, but slow, compression.
+
1177 *) force_palette: if colortype is 2 or 6, you can make the encoder write a PLTE
+
1178  chunk if force_palette is true. This can used as suggested palette to convert
+
1179  to by viewers that don't support more than 256 colors (if those still exist)
+
1180 *) add_id: add text chunk "Encoder: LodePNG <version>" to the image.
+
1181 *) text_compression: default 1. If 1, it'll store texts as zTXt instead of tEXt chunks.
+
1182  zTXt chunks use zlib compression on the text. This gives a smaller result on
+
1183  large texts but a larger result on small texts (such as a single program name).
+
1184  It's all tEXt or all zTXt though, there's no separate setting per text yet.
+
1185 
+
1186 
+
1187 6. color conversions
+
1188 --------------------
+
1189 
+
1190 An important thing to note about LodePNG, is that the color type of the PNG, and
+
1191 the color type of the raw image, are completely independent. By default, when
+
1192 you decode a PNG, you get the result as a raw image in the color type you want,
+
1193 no matter whether the PNG was encoded with a palette, greyscale or RGBA color.
+
1194 And if you encode an image, by default LodePNG will automatically choose the PNG
+
1195 color type that gives good compression based on the values of colors and amount
+
1196 of colors in the image. It can be configured to let you control it instead as
+
1197 well, though.
+
1198 
+
1199 To be able to do this, LodePNG does conversions from one color mode to another.
+
1200 It can convert from almost any color type to any other color type, except the
+
1201 following conversions: RGB to greyscale is not supported, and converting to a
+
1202 palette when the palette doesn't have a required color is not supported. This is
+
1203 not supported on purpose: this is information loss which requires a color
+
1204 reduction algorithm that is beyong the scope of a PNG encoder (yes, RGB to grey
+
1205 is easy, but there are multiple ways if you want to give some channels more
+
1206 weight).
+
1207 
+
1208 By default, when decoding, you get the raw image in 32-bit RGBA or 24-bit RGB
+
1209 color, no matter what color type the PNG has. And by default when encoding,
+
1210 LodePNG automatically picks the best color model for the output PNG, and expects
+
1211 the input image to be 32-bit RGBA or 24-bit RGB. So, unless you want to control
+
1212 the color format of the images yourself, you can skip this chapter.
+
1213 
+
1214 6.1. PNG color types
+
1215 --------------------
+
1216 
+
1217 A PNG image can have many color types, ranging from 1-bit color to 64-bit color,
+
1218 as well as palettized color modes. After the zlib decompression and unfiltering
+
1219 in the PNG image is done, the raw pixel data will have that color type and thus
+
1220 a certain amount of bits per pixel. If you want the output raw image after
+
1221 decoding to have another color type, a conversion is done by LodePNG.
+
1222 
+
1223 The PNG specification gives the following color types:
+
1224 
+
1225 0: greyscale, bit depths 1, 2, 4, 8, 16
+
1226 2: RGB, bit depths 8 and 16
+
1227 3: palette, bit depths 1, 2, 4 and 8
+
1228 4: greyscale with alpha, bit depths 8 and 16
+
1229 6: RGBA, bit depths 8 and 16
+
1230 
+
1231 Bit depth is the amount of bits per pixel per color channel. So the total amount
+
1232 of bits per pixel is: amount of channels * bitdepth.
+
1233 
+
1234 6.2. color conversions
+
1235 ----------------------
+
1236 
+
1237 As explained in the sections about the encoder and decoder, you can specify
+
1238 color types and bit depths in info_png and info_raw to change the default
+
1239 behaviour.
+
1240 
+
1241 If, when decoding, you want the raw image to be something else than the default,
+
1242 you need to set the color type and bit depth you want in the LodePNGColorMode,
+
1243 or the parameters of the simple function of LodePNG you're using.
+
1244 
+
1245 If, when encoding, you use another color type than the default in the input
+
1246 image, you need to specify its color type and bit depth in the LodePNGColorMode
+
1247 of the raw image, or use the parameters of the simplefunction of LodePNG you're
+
1248 using.
+
1249 
+
1250 If, when encoding, you don't want LodePNG to choose the output PNG color type
+
1251 but control it yourself, you need to set auto_convert in the encoder settings
+
1252 to LAC_NONE, and specify the color type you want in the LodePNGInfo of the
+
1253 encoder.
+
1254 
+
1255 If you do any of the above, LodePNG may need to do a color conversion, which
+
1256 follows the rules below, and may sometimes not be allowed.
+
1257 
+
1258 To avoid some confusion:
+
1259 -the decoder converts from PNG to raw image
+
1260 -the encoder converts from raw image to PNG
+
1261 -the colortype and bitdepth in LodePNGColorMode info_raw, are those of the raw image
+
1262 -the colortype and bitdepth in the color field of LodePNGInfo info_png, are those of the PNG
+
1263 -when encoding, the color type in LodePNGInfo is ignored if auto_convert
+
1264  is enabled, it is automatically generated instead
+
1265 -when decoding, the color type in LodePNGInfo is set by the decoder to that of the original
+
1266  PNG image, but it can be ignored since the raw image has the color type you requested instead
+
1267 -if the color type of the LodePNGColorMode and PNG image aren't the same, a conversion
+
1268  between the color types is done if the color types are supported. If it is not
+
1269  supported, an error is returned. If the types are the same, no conversion is done.
+
1270 -even though some conversions aren't supported, LodePNG supports loading PNGs from any
+
1271  colortype and saving PNGs to any colortype, sometimes it just requires preparing
+
1272  the raw image correctly before encoding.
+
1273 -both encoder and decoder use the same color converter.
+
1274 
+
1275 Non supported color conversions:
+
1276 -color to greyscale: no error is thrown, but the result will look ugly because
+
1277 only the red channel is taken
+
1278 -anything, to palette when that palette does not have that color in it: in this
+
1279 case an error is thrown
+
1280 
+
1281 Supported color conversions:
+
1282 -anything to 8-bit RGB, 8-bit RGBA, 16-bit RGB, 16-bit RGBA
+
1283 -any grey or grey+alpha, to grey or grey+alpha
+
1284 -anything to a palette, as long as the palette has the requested colors in it
+
1285 -removing alpha channel
+
1286 -higher to smaller bitdepth, and vice versa
+
1287 
+
1288 If you want no color conversion to be done:
+
1289 -In the encoder, you can make it save a PNG with any color type by giving the
+
1290 raw color mode and LodePNGInfo the same color mode, and setting auto_convert to
+
1291 LAC_NO.
+
1292 -In the decoder, you can make it store the pixel data in the same color type
+
1293 as the PNG has, by setting the color_convert setting to false. Settings in
+
1294 info_raw are then ignored.
+
1295 
+
1296 The function lodepng_convert does the color conversion. It is available in the
+
1297 interface but normally isn't needed since the encoder and decoder already call
+
1298 it.
+
1299 
+
1300 6.3. padding bits
+
1301 -----------------
+
1302 
+
1303 In the PNG file format, if a less than 8-bit per pixel color type is used and the scanlines
+
1304 have a bit amount that isn't a multiple of 8, then padding bits are used so that each
+
1305 scanline starts at a fresh byte. But that is NOT true for the LodePNG raw input and output.
+
1306 The raw input image you give to the encoder, and the raw output image you get from the decoder
+
1307 will NOT have these padding bits, e.g. in the case of a 1-bit image with a width
+
1308 of 7 pixels, the first pixel of the second scanline will the the 8th bit of the first byte,
+
1309 not the first bit of a new byte.
+
1310 
+
1311 6.4. A note about 16-bits per channel and endianness
+
1312 ----------------------------------------------------
+
1313 
+
1314 LodePNG uses unsigned char arrays for 16-bit per channel colors too, just like
+
1315 for any other color format. The 16-bit values are stored in big endian (most
+
1316 significant byte first) in these arrays. This is the opposite order of the
+
1317 little endian used by x86 CPU's.
+
1318 
+
1319 LodePNG always uses big endian because the PNG file format does so internally.
+
1320 Conversions to other formats than PNG uses internally are not supported by
+
1321 LodePNG on purpose, there are myriads of formats, including endianness of 16-bit
+
1322 colors, the order in which you store R, G, B and A, and so on. Supporting and
+
1323 converting to/from all that is outside the scope of LodePNG.
+
1324 
+
1325 This may mean that, depending on your use case, you may want to convert the big
+
1326 endian output of LodePNG to little endian with a for loop. This is certainly not
+
1327 always needed, many applications and libraries support big endian 16-bit colors
+
1328 anyway, but it means you cannot simply cast the unsigned char* buffer to an
+
1329 unsigned short* buffer on x86 CPUs.
+
1330 
+
1331 
+
1332 7. error values
+
1333 ---------------
+
1334 
+
1335 All functions in LodePNG that return an error code, return 0 if everything went
+
1336 OK, or a non-zero code if there was an error.
+
1337 
+
1338 The meaning of the LodePNG error values can be retrieved with the function
+
1339 lodepng_error_text: given the numerical error code, it returns a description
+
1340 of the error in English as a string.
+
1341 
+
1342 Check the implementation of lodepng_error_text to see the meaning of each code.
+
1343 
+
1344 
+
1345 8. chunks and PNG editing
+
1346 -------------------------
+
1347 
+
1348 If you want to add extra chunks to a PNG you encode, or use LodePNG for a PNG
+
1349 editor that should follow the rules about handling of unknown chunks, or if your
+
1350 program is able to read other types of chunks than the ones handled by LodePNG,
+
1351 then that's possible with the chunk functions of LodePNG.
+
1352 
+
1353 A PNG chunk has the following layout:
+
1354 
+
1355 4 bytes length
+
1356 4 bytes type name
+
1357 length bytes data
+
1358 4 bytes CRC
+
1359 
+
1360 8.1. iterating through chunks
+
1361 -----------------------------
+
1362 
+
1363 If you have a buffer containing the PNG image data, then the first chunk (the
+
1364 IHDR chunk) starts at byte number 8 of that buffer. The first 8 bytes are the
+
1365 signature of the PNG and are not part of a chunk. But if you start at byte 8
+
1366 then you have a chunk, and can check the following things of it.
+
1367 
+
1368 NOTE: none of these functions check for memory buffer boundaries. To avoid
+
1369 exploits, always make sure the buffer contains all the data of the chunks.
+
1370 When using lodepng_chunk_next, make sure the returned value is within the
+
1371 allocated memory.
+
1372 
+
1373 unsigned lodepng_chunk_length(const unsigned char* chunk):
+
1374 
+
1375 Get the length of the chunk's data. The total chunk length is this length + 12.
+
1376 
+
1377 void lodepng_chunk_type(char type[5], const unsigned char* chunk):
+
1378 unsigned char lodepng_chunk_type_equals(const unsigned char* chunk, const char* type):
+
1379 
+
1380 Get the type of the chunk or compare if it's a certain type
+
1381 
+
1382 unsigned char lodepng_chunk_critical(const unsigned char* chunk):
+
1383 unsigned char lodepng_chunk_private(const unsigned char* chunk):
+
1384 unsigned char lodepng_chunk_safetocopy(const unsigned char* chunk):
+
1385 
+
1386 Check if the chunk is critical in the PNG standard (only IHDR, PLTE, IDAT and IEND are).
+
1387 Check if the chunk is private (public chunks are part of the standard, private ones not).
+
1388 Check if the chunk is safe to copy. If it's not, then, when modifying data in a critical
+
1389 chunk, unsafe to copy chunks of the old image may NOT be saved in the new one if your
+
1390 program doesn't handle that type of unknown chunk.
+
1391 
+
1392 unsigned char* lodepng_chunk_data(unsigned char* chunk):
+
1393 const unsigned char* lodepng_chunk_data_const(const unsigned char* chunk):
+
1394 
+
1395 Get a pointer to the start of the data of the chunk.
+
1396 
+
1397 unsigned lodepng_chunk_check_crc(const unsigned char* chunk):
+
1398 void lodepng_chunk_generate_crc(unsigned char* chunk):
+
1399 
+
1400 Check if the crc is correct or generate a correct one.
+
1401 
+
1402 unsigned char* lodepng_chunk_next(unsigned char* chunk):
+
1403 const unsigned char* lodepng_chunk_next_const(const unsigned char* chunk):
+
1404 
+
1405 Iterate to the next chunk. This works if you have a buffer with consecutive chunks. Note that these
+
1406 functions do no boundary checking of the allocated data whatsoever, so make sure there is enough
+
1407 data available in the buffer to be able to go to the next chunk.
+
1408 
+
1409 unsigned lodepng_chunk_append(unsigned char** out, size_t* outlength, const unsigned char* chunk):
+
1410 unsigned lodepng_chunk_create(unsigned char** out, size_t* outlength, unsigned length,
+
1411  const char* type, const unsigned char* data):
+
1412 
+
1413 These functions are used to create new chunks that are appended to the data in *out that has
+
1414 length *outlength. The append function appends an existing chunk to the new data. The create
+
1415 function creates a new chunk with the given parameters and appends it. Type is the 4-letter
+
1416 name of the chunk.
+
1417 
+
1418 8.2. chunks in info_png
+
1419 -----------------------
+
1420 
+
1421 The LodePNGInfo struct contains fields with the unknown chunk in it. It has 3
+
1422 buffers (each with size) to contain 3 types of unknown chunks:
+
1423 the ones that come before the PLTE chunk, the ones that come between the PLTE
+
1424 and the IDAT chunks, and the ones that come after the IDAT chunks.
+
1425 It's necessary to make the distionction between these 3 cases because the PNG
+
1426 standard forces to keep the ordering of unknown chunks compared to the critical
+
1427 chunks, but does not force any other ordering rules.
+
1428 
+
1429 info_png.unknown_chunks_data[0] is the chunks before PLTE
+
1430 info_png.unknown_chunks_data[1] is the chunks after PLTE, before IDAT
+
1431 info_png.unknown_chunks_data[2] is the chunks after IDAT
+
1432 
+
1433 The chunks in these 3 buffers can be iterated through and read by using the same
+
1434 way described in the previous subchapter.
+
1435 
+
1436 When using the decoder to decode a PNG, you can make it store all unknown chunks
+
1437 if you set the option settings.remember_unknown_chunks to 1. By default, this
+
1438 option is off (0).
+
1439 
+
1440 The encoder will always encode unknown chunks that are stored in the info_png.
+
1441 If you need it to add a particular chunk that isn't known by LodePNG, you can
+
1442 use lodepng_chunk_append or lodepng_chunk_create to the chunk data in
+
1443 info_png.unknown_chunks_data[x].
+
1444 
+
1445 Chunks that are known by LodePNG should not be added in that way. E.g. to make
+
1446 LodePNG add a bKGD chunk, set background_defined to true and add the correct
+
1447 parameters there instead.
+
1448 
+
1449 
+
1450 9. compiler support
+
1451 -------------------
+
1452 
+
1453 No libraries other than the current standard C library are needed to compile
+
1454 LodePNG. For the C++ version, only the standard C++ library is needed on top.
+
1455 Add the files lodepng.c(pp) and lodepng.h to your project, include
+
1456 lodepng.h where needed, and your program can read/write PNG files.
+
1457 
+
1458 If performance is important, use optimization when compiling! For both the
+
1459 encoder and decoder, this makes a large difference.
+
1460 
+
1461 Make sure that LodePNG is compiled with the same compiler of the same version
+
1462 and with the same settings as the rest of the program, or the interfaces with
+
1463 std::vectors and std::strings in C++ can be incompatible.
+
1464 
+
1465 CHAR_BITS must be 8 or higher, because LodePNG uses unsigned chars for octets.
+
1466 
+
1467 *) gcc and g++
+
1468 
+
1469 LodePNG is developed in gcc so this compiler is natively supported. It gives no
+
1470 warnings with compiler options "-Wall -Wextra -pedantic -ansi", with gcc and g++
+
1471 version 4.7.1 on Linux, 32-bit and 64-bit.
+
1472 
+
1473 *) Mingw
+
1474 
+
1475 The Mingw compiler (a port of gcc) for Windows is fully supported by LodePNG.
+
1476 
+
1477 *) Visual Studio 2005 and up, Visual C++ Express Edition 2005 and up
+
1478 
+
1479 Visual Studio may give warnings about 'fopen' being deprecated. A multiplatform library
+
1480 can't support the proposed Visual Studio alternative however, so LodePNG keeps using
+
1481 fopen. If you don't want to see the deprecated warnings, put this on top of lodepng.h
+
1482 before the inclusions:
+
1483 #define _CRT_SECURE_NO_DEPRECATE
+
1484 
+
1485 Other than the above warnings, LodePNG should be warning-free with warning
+
1486 level 3 (W3). Warning level 4 (W4) will give warnings about integer conversions.
+
1487 I'm not planning to resolve these warnings. To get rid of them, let Visual
+
1488 Studio use warning level W3 for lodepng.cpp only: right click lodepng.cpp,
+
1489 Properties, C/C++, General, Warning Level: Level 3 (/W3).
+
1490 
+
1491 Visual Studio may want "stdafx.h" files to be included in each source file and
+
1492 give an error "unexpected end of file while looking for precompiled header".
+
1493 That is not standard C++ and will not be added to the stock LodePNG. You can
+
1494 disable it for lodepng.cpp only by right clicking it, Properties, C/C++,
+
1495 Precompiled Headers, and set it to Not Using Precompiled Headers there.
+
1496 
+
1497 *) Visual Studio 6.0
+
1498 
+
1499 LodePNG support for Visual Studio 6.0 is not guaranteed because VS6 doesn't
+
1500 follow the C++ standard correctly.
+
1501 
+
1502 *) Comeau C/C++
+
1503 
+
1504 Vesion 20070107 compiles without problems on the Comeau C/C++ Online Test Drive
+
1505 at http://www.comeaucomputing.com/tryitout in both C90 and C++ mode.
+
1506 
+
1507 *) Compilers on Macintosh
+
1508 
+
1509 LodePNG has been reported to work both with the gcc and LLVM for Macintosh, both
+
1510 for C and C++.
+
1511 
+
1512 *) Other Compilers
+
1513 
+
1514 If you encounter problems on other compilers, feel free to let me know and I may
+
1515 try to fix it if the compiler is modern standards complient.
+
1516 
+
1517 
+
1518 10. examples
+
1519 ------------
+
1520 
+
1521 This decoder example shows the most basic usage of LodePNG. More complex
+
1522 examples can be found on the LodePNG website.
+
1523 
+
1524 10.1. decoder C++ example
+
1525 -------------------------
+
1526 
+
1527 #include "lodepng.h"
+
1528 #include <iostream>
+
1529 
+
1530 int main(int argc, char *argv[])
+
1531 {
+
1532  const char* filename = argc > 1 ? argv[1] : "test.png";
+
1533 
+
1534  //load and decode
+
1535  std::vector<unsigned char> image;
+
1536  unsigned width, height;
+
1537  unsigned error = lodepng::decode(image, width, height, filename);
+
1538 
+
1539  //if there's an error, display it
+
1540  if(error) std::cout << "decoder error " << error << ": " << lodepng_error_text(error) << std::endl;
+
1541 
+
1542  //the pixels are now in the vector "image", 4 bytes per pixel, ordered RGBARGBA..., use it as texture, draw it, ...
+
1543 }
+
1544 
+
1545 10.2. decoder C example
+
1546 -----------------------
+
1547 
+
1548 #include "lodepng.h"
+
1549 
+
1550 int main(int argc, char *argv[])
+
1551 {
+
1552  unsigned error;
+
1553  unsigned char* image;
+
1554  size_t width, height;
+
1555  const char* filename = argc > 1 ? argv[1] : "test.png";
+
1556 
+
1557  error = lodepng_decode32_file(&image, &width, &height, filename);
+
1558 
+
1559  if(error) printf("decoder error %u: %s\n", error, lodepng_error_text(error));
+
1560 
+
1561  / * use image here * /
+
1562 
+
1563  free(image);
+
1564  return 0;
+
1565 }
+
1566 
+
1567 
+
1568 11. changes
+
1569 -----------
+
1570 
+
1571 The version number of LodePNG is the date of the change given in the format
+
1572 yyyymmdd.
+
1573 
+
1574 Some changes aren't backwards compatible. Those are indicated with a (!)
+
1575 symbol.
+
1576 
+
1577 *) 22 dec 2013: Power of two windowsize required for optimization.
+
1578 *) 15 apr 2013: Fixed bug with LAC_ALPHA and color key.
+
1579 *) 25 mar 2013: Added an optional feature to ignore some PNG errors (fix_png).
+
1580 *) 11 mar 2013 (!): Bugfix with custom free. Changed from "my" to "lodepng_"
+
1581  prefix for the custom allocators and made it possible with a new #define to
+
1582  use custom ones in your project without needing to change lodepng's code.
+
1583 *) 28 jan 2013: Bugfix with color key.
+
1584 *) 27 okt 2012: Tweaks in text chunk keyword length error handling.
+
1585 *) 8 okt 2012 (!): Added new filter strategy (entropy) and new auto color mode.
+
1586  (no palette). Better deflate tree encoding. New compression tweak settings.
+
1587  Faster color conversions while decoding. Some internal cleanups.
+
1588 *) 23 sep 2012: Reduced warnings in Visual Studio a little bit.
+
1589 *) 1 sep 2012 (!): Removed #define's for giving custom (de)compression functions
+
1590  and made it work with function pointers instead.
+
1591 *) 23 jun 2012: Added more filter strategies. Made it easier to use custom alloc
+
1592  and free functions and toggle #defines from compiler flags. Small fixes.
+
1593 *) 6 may 2012 (!): Made plugging in custom zlib/deflate functions more flexible.
+
1594 *) 22 apr 2012 (!): Made interface more consistent, renaming a lot. Removed
+
1595  redundant C++ codec classes. Reduced amount of structs. Everything changed,
+
1596  but it is cleaner now imho and functionality remains the same. Also fixed
+
1597  several bugs and shrinked the implementation code. Made new samples.
+
1598 *) 6 nov 2011 (!): By default, the encoder now automatically chooses the best
+
1599  PNG color model and bit depth, based on the amount and type of colors of the
+
1600  raw image. For this, autoLeaveOutAlphaChannel replaced by auto_choose_color.
+
1601 *) 9 okt 2011: simpler hash chain implementation for the encoder.
+
1602 *) 8 sep 2011: lz77 encoder lazy matching instead of greedy matching.
+
1603 *) 23 aug 2011: tweaked the zlib compression parameters after benchmarking.
+
1604  A bug with the PNG filtertype heuristic was fixed, so that it chooses much
+
1605  better ones (it's quite significant). A setting to do an experimental, slow,
+
1606  brute force search for PNG filter types is added.
+
1607 *) 17 aug 2011 (!): changed some C zlib related function names.
+
1608 *) 16 aug 2011: made the code less wide (max 120 characters per line).
+
1609 *) 17 apr 2011: code cleanup. Bugfixes. Convert low to 16-bit per sample colors.
+
1610 *) 21 feb 2011: fixed compiling for C90. Fixed compiling with sections disabled.
+
1611 *) 11 dec 2010: encoding is made faster, based on suggestion by Peter Eastman
+
1612  to optimize long sequences of zeros.
+
1613 *) 13 nov 2010: added LodePNG_InfoColor_hasPaletteAlpha and
+
1614  LodePNG_InfoColor_canHaveAlpha functions for convenience.
+
1615 *) 7 nov 2010: added LodePNG_error_text function to get error code description.
+
1616 *) 30 okt 2010: made decoding slightly faster
+
1617 *) 26 okt 2010: (!) changed some C function and struct names (more consistent).
+
1618  Reorganized the documentation and the declaration order in the header.
+
1619 *) 08 aug 2010: only changed some comments and external samples.
+
1620 *) 05 jul 2010: fixed bug thanks to warnings in the new gcc version.
+
1621 *) 14 mar 2010: fixed bug where too much memory was allocated for char buffers.
+
1622 *) 02 sep 2008: fixed bug where it could create empty tree that linux apps could
+
1623  read by ignoring the problem but windows apps couldn't.
+
1624 *) 06 jun 2008: added more error checks for out of memory cases.
+
1625 *) 26 apr 2008: added a few more checks here and there to ensure more safety.
+
1626 *) 06 mar 2008: crash with encoding of strings fixed
+
1627 *) 02 feb 2008: support for international text chunks added (iTXt)
+
1628 *) 23 jan 2008: small cleanups, and #defines to divide code in sections
+
1629 *) 20 jan 2008: support for unknown chunks allowing using LodePNG for an editor.
+
1630 *) 18 jan 2008: support for tIME and pHYs chunks added to encoder and decoder.
+
1631 *) 17 jan 2008: ability to encode and decode compressed zTXt chunks added
+
1632  Also vareous fixes, such as in the deflate and the padding bits code.
+
1633 *) 13 jan 2008: Added ability to encode Adam7-interlaced images. Improved
+
1634  filtering code of encoder.
+
1635 *) 07 jan 2008: (!) changed LodePNG to use ISO C90 instead of C++. A
+
1636  C++ wrapper around this provides an interface almost identical to before.
+
1637  Having LodePNG be pure ISO C90 makes it more portable. The C and C++ code
+
1638  are together in these files but it works both for C and C++ compilers.
+
1639 *) 29 dec 2007: (!) changed most integer types to unsigned int + other tweaks
+
1640 *) 30 aug 2007: bug fixed which makes this Borland C++ compatible
+
1641 *) 09 aug 2007: some VS2005 warnings removed again
+
1642 *) 21 jul 2007: deflate code placed in new namespace separate from zlib code
+
1643 *) 08 jun 2007: fixed bug with 2- and 4-bit color, and small interlaced images
+
1644 *) 04 jun 2007: improved support for Visual Studio 2005: crash with accessing
+
1645  invalid std::vector element [0] fixed, and level 3 and 4 warnings removed
+
1646 *) 02 jun 2007: made the encoder add a tag with version by default
+
1647 *) 27 may 2007: zlib and png code separated (but still in the same file),
+
1648  simple encoder/decoder functions added for more simple usage cases
+
1649 *) 19 may 2007: minor fixes, some code cleaning, new error added (error 69),
+
1650  moved some examples from here to lodepng_examples.cpp
+
1651 *) 12 may 2007: palette decoding bug fixed
+
1652 *) 24 apr 2007: changed the license from BSD to the zlib license
+
1653 *) 11 mar 2007: very simple addition: ability to encode bKGD chunks.
+
1654 *) 04 mar 2007: (!) tEXt chunk related fixes, and support for encoding
+
1655  palettized PNG images. Plus little interface change with palette and texts.
+
1656 *) 03 mar 2007: Made it encode dynamic Huffman shorter with repeat codes.
+
1657  Fixed a bug where the end code of a block had length 0 in the Huffman tree.
+
1658 *) 26 feb 2007: Huffman compression with dynamic trees (BTYPE 2) now implemented
+
1659  and supported by the encoder, resulting in smaller PNGs at the output.
+
1660 *) 27 jan 2007: Made the Adler-32 test faster so that a timewaste is gone.
+
1661 *) 24 jan 2007: gave encoder an error interface. Added color conversion from any
+
1662  greyscale type to 8-bit greyscale with or without alpha.
+
1663 *) 21 jan 2007: (!) Totally changed the interface. It allows more color types
+
1664  to convert to and is more uniform. See the manual for how it works now.
+
1665 *) 07 jan 2007: Some cleanup & fixes, and a few changes over the last days:
+
1666  encode/decode custom tEXt chunks, separate classes for zlib & deflate, and
+
1667  at last made the decoder give errors for incorrect Adler32 or Crc.
+
1668 *) 01 jan 2007: Fixed bug with encoding PNGs with less than 8 bits per channel.
+
1669 *) 29 dec 2006: Added support for encoding images without alpha channel, and
+
1670  cleaned out code as well as making certain parts faster.
+
1671 *) 28 dec 2006: Added "Settings" to the encoder.
+
1672 *) 26 dec 2006: The encoder now does LZ77 encoding and produces much smaller files now.
+
1673  Removed some code duplication in the decoder. Fixed little bug in an example.
+
1674 *) 09 dec 2006: (!) Placed output parameters of public functions as first parameter.
+
1675  Fixed a bug of the decoder with 16-bit per color.
+
1676 *) 15 okt 2006: Changed documentation structure
+
1677 *) 09 okt 2006: Encoder class added. It encodes a valid PNG image from the
+
1678  given image buffer, however for now it's not compressed.
+
1679 *) 08 sep 2006: (!) Changed to interface with a Decoder class
+
1680 *) 30 jul 2006: (!) LodePNG_InfoPng , width and height are now retrieved in different
+
1681  way. Renamed decodePNG to decodePNGGeneric.
+
1682 *) 29 jul 2006: (!) Changed the interface: image info is now returned as a
+
1683  struct of type LodePNG::LodePNG_Info, instead of a vector, which was a bit clumsy.
+
1684 *) 28 jul 2006: Cleaned the code and added new error checks.
+
1685  Corrected terminology "deflate" into "inflate".
+
1686 *) 23 jun 2006: Added SDL example in the documentation in the header, this
+
1687  example allows easy debugging by displaying the PNG and its transparency.
+
1688 *) 22 jun 2006: (!) Changed way to obtain error value. Added
+
1689  loadFile function for convenience. Made decodePNG32 faster.
+
1690 *) 21 jun 2006: (!) Changed type of info vector to unsigned.
+
1691  Changed position of palette in info vector. Fixed an important bug that
+
1692  happened on PNGs with an uncompressed block.
+
1693 *) 16 jun 2006: Internally changed unsigned into unsigned where
+
1694  needed, and performed some optimizations.
+
1695 *) 07 jun 2006: (!) Renamed functions to decodePNG and placed them
+
1696  in LodePNG namespace. Changed the order of the parameters. Rewrote the
+
1697  documentation in the header. Renamed files to lodepng.cpp and lodepng.h
+
1698 *) 22 apr 2006: Optimized and improved some code
+
1699 *) 07 sep 2005: (!) Changed to std::vector interface
+
1700 *) 12 aug 2005: Initial release (C++, decoder only)
+
1701 
+
1702 
+
1703 12. contact information
+
1704 -----------------------
+
1705 
+
1706 Feel free to contact me with suggestions, problems, comments, ... concerning
+
1707 LodePNG. If you encounter a PNG image that doesn't work properly with this
+
1708 decoder, feel free to send it and I'll use it to find and fix the problem.
+
1709 
+
1710 My email address is (puzzle the account and domain together with an @ symbol):
+
1711 Domain: gmail dot com.
+
1712 Account: lode dot vandevenne.
+
1713 
+
1714 
+
1715 Copyright (c) 2005-2013 Lode Vandevenne
+
1716 */
+
+ +
LodePNGInfo
Definition: lodepng.h:401
+ +
LodePNGCompressSettings
Definition: lodepng.h:278
+
LodePNGEncoderSettings
Definition: lodepng.h:594
+
LodePNGDecoderSettings
Definition: lodepng.h:512
+
LodePNGState
Definition: lodepng.h:631
+
LodePNGDecompressSettings
Definition: lodepng.h:250
+
LodePNGTime
Definition: lodepng.h:389
+
LodePNGColorMode
Definition: lodepng.h:312
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/log_8hxx_source.html b/docs/html/log_8hxx_source.html new file mode 100644 index 00000000..141b045d --- /dev/null +++ b/docs/html/log_8hxx_source.html @@ -0,0 +1,234 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: common/log.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
log.hxx
+
+
+
1 #ifndef EXONA_LOG
+
2 #define EXONA_LOG
+
3 
+
4 #include <cstdio>
+
5 
+
6 #include <iostream>
+
7 using std::ofstream;
+
8 
+
9 #include <map>
+
10 using std::map;
+
11 
+
12 #include <mutex>
+
13 using std::mutex;
+
14 
+
15 #include <shared_mutex>
+
16 using std::shared_mutex;
+
17 
+
18 #include <string>
+
19 using std::string;
+
20 
+
21 #include <thread>
+
22 using std::thread;
+
23 
+
24 class LogFile {
+
25  private:
+
26  FILE* file;
+
27  mutex file_mutex;
+
28 
+
29  public:
+
30  LogFile(FILE* file);
+
31 
+
32  friend class Log;
+
33 };
+
34 
+
35 class Log {
+
36  private:
+
41  static int32_t std_message_level;
+
42 
+
48  static int32_t file_message_level;
+
49 
+
53  static bool write_to_file;
+
54 
+
58  static int32_t max_header_length;
+
59 
+
63  static int32_t max_message_length;
+
64 
+
69  static string output_directory;
+
70 
+
77  static map<thread::id, string> log_ids;
+
78 
+
82  static int32_t process_rank;
+
83 
+
87  static int32_t restricted_rank;
+
88 
+
93  static map<string, LogFile*> output_files;
+
94 
+
99  static shared_mutex log_ids_mutex;
+
100 
+
101 
+
111  static void write_message(bool print_header, int8_t message_level, const char *message_type, const char *format, va_list arguments);
+
112 
+
113  public:
+
114  static const int8_t NONE = 0;
+
115  static const int8_t FATAL = 1;
+
116  static const int8_t ERROR = 2;
+
117  static const int8_t WARNING = 3;
+
118  static const int8_t INFO = 4;
+
119  static const int8_t DEBUG = 5;
+
120  static const int8_t TRACE = 6;
+
121  static const int8_t ALL = 7;
+
129  static void register_command_line_arguments();
+
130 
+
136  static int8_t parse_level_from_string(string level);
+
137 
+
143  static void initialize(const vector<string> &arguments);
+
144 
+
150  static void set_rank(int32_t _process_rank);
+
151 
+
156  static void restrict_to_rank(int32_t _restricted_rank);
+
157 
+
161  static void clear_rank_restriction();
+
162 
+
175  static void set_id(string human_readable_id);
+
176 
+
187  static void release_id(string human_readable_id);
+
188 
+
189 
+
198  static bool at_level(int8_t level);
+
199 
+
200  static void fatal(const char* format, ...);
+
201  static void error(const char* format, ...);
+
202  static void warning(const char* format, ...);
+
203  static void info(const char* format, ...);
+
204  static void debug(const char* format, ...);
+
205  static void trace(const char* format, ...);
+
207  static void fatal_no_header(const char* format, ...);
+
208  static void error_no_header(const char* format, ...);
+
209  static void warning_no_header(const char* format, ...);
+
210  static void info_no_header(const char* format, ...);
+
211  static void debug_no_header(const char* format, ...);
+
212  static void trace_no_header(const char* format, ...);
+
214 };
+
215 
+
216 
+
217 #endif
+
+
Log::fatal
static void fatal(const char *format,...)
Logs a fatal message.
Definition: log.cxx:216
+
Log::debug
static void debug(const char *format,...)
Logs a debug message.
Definition: log.cxx:264
+
Log::restrict_to_rank
static void restrict_to_rank(int32_t _restricted_rank)
Specifies which MPI process to allow messages from.
Definition: log.cxx:105
+
Log::warning_no_header
static void warning_no_header(const char *format,...)
Logs a warning message.
Definition: log.cxx:312
+
Log::trace_no_header
static void trace_no_header(const char *format,...)
Logs a trace message.
Definition: log.cxx:348
+ +
Log::file_message_level
static int32_t file_message_level
Specifies which messages to log.
Definition: log.hxx:48
+
Log::warning
static void warning(const char *format,...)
Logs a warning message.
Definition: log.cxx:240
+
Log::std_message_level
static int32_t std_message_level
Specifies which messages to log.
Definition: log.hxx:41
+
Log::set_rank
static void set_rank(int32_t _process_rank)
Sets the MPI process rank for this Log.
Definition: log.cxx:101
+
Log::DEBUG
static const int8_t DEBUG
Specifies debug and above messages will be logged.
Definition: log.hxx:119
+
Log::output_files
static map< string, LogFile * > output_files
A map of human readable ids to output files which the log messages will be written to.
Definition: log.hxx:93
+
Log
Definition: log.hxx:35
+
LogFile
Definition: log.hxx:24
+
Log::info
static void info(const char *format,...)
Logs an info message.
Definition: log.cxx:252
+
Log::write_message
static void write_message(bool print_header, int8_t message_level, const char *message_type, const char *format, va_list arguments)
Potentially writes the message to either standard output or the log file if the message level is high...
Definition: log.cxx:150
+
Log::ALL
static const int8_t ALL
Specifies all messages will be logged.
Definition: log.hxx:121
+
Log::error
static void error(const char *format,...)
Logs an error message.
Definition: log.cxx:228
+
Log::max_header_length
static int32_t max_header_length
Specifies the maximum length for the message header.
Definition: log.hxx:58
+ +
Log::error_no_header
static void error_no_header(const char *format,...)
Logs an error message.
Definition: log.cxx:300
+
Log::restricted_rank
static int32_t restricted_rank
Defaults at -1, when set to a process rank (>= 0) the Log will only print messages if its rank is the...
Definition: log.hxx:87
+
Log::info_no_header
static void info_no_header(const char *format,...)
Logs an info message.
Definition: log.cxx:324
+
Log::WARNING
static const int8_t WARNING
Specifies warning and above messages will be logged.
Definition: log.hxx:117
+
Log::INFO
static const int8_t INFO
Specifies info and above messages will be logged.
Definition: log.hxx:118
+ +
Log::TRACE
static const int8_t TRACE
Specifies trace and above messages will be logged.
Definition: log.hxx:120
+
Log::debug_no_header
static void debug_no_header(const char *format,...)
Logs a debug message.
Definition: log.cxx:336
+
Log::NONE
static const int8_t NONE
Specifies no messages will be logged.
Definition: log.hxx:114
+
Log::parse_level_from_string
static int8_t parse_level_from_string(string level)
Definition: log.cxx:48
+
Log::clear_rank_restriction
static void clear_rank_restriction()
Clears the MPI process rank restriction from messages allowing any process to write to the log (sets ...
Definition: log.cxx:109
+
Log::at_level
static bool at_level(int8_t level)
Determines if either output level (the file or standard output) level is above the level passed as a ...
Definition: log.cxx:212
+ +
Log::initialize
static void initialize(const vector< string > &arguments)
Initializes the Log given arguments retreived from the CommandLine class.
Definition: log.cxx:80
+
Log::set_id
static void set_id(string human_readable_id)
Sets a human readable thread id for this thread.
Definition: log.cxx:113
+
Log::fatal_no_header
static void fatal_no_header(const char *format,...)
Logs a fatal message.
Definition: log.cxx:288
+
Log::write_to_file
static bool write_to_file
Specifies if the logs should also be written to a flie.
Definition: log.hxx:53
+
Log::log_ids
static map< thread::id, string > log_ids
A map of C++ thread ids (which are not human readable) to human readable integer ids.
Definition: log.hxx:77
+
Log::trace
static void trace(const char *format,...)
Logs a trace message.
Definition: log.cxx:276
+
Log::process_rank
static int32_t process_rank
The MPI process rank for this Log instance.
Definition: log.hxx:82
+ +
Log::release_id
static void release_id(string human_readable_id)
Releases a the human readable thread id previously set by by the provided human readable id;.
Definition: log.cxx:125
+ +
Log::max_message_length
static int32_t max_message_length
Specifies the maximum message buffer length.
Definition: log.hxx:63
+
Log::output_directory
static string output_directory
Specifies which directory to write the log files to (if the logs are being writtten to a file).
Definition: log.hxx:69
+
Log::register_command_line_arguments
static void register_command_line_arguments()
Registers used command line arguments and instructions with the CommandLine class.
Definition: log.cxx:43
+
Log::FATAL
static const int8_t FATAL
Specifies only fatal messages will be logged.
Definition: log.hxx:115
+
Log::log_ids_mutex
static shared_mutex log_ids_mutex
A std::shared_mutex protecting the Log::thread_ids map.
Definition: log.hxx:99
+
Log::ERROR
static const int8_t ERROR
Specifies error and above messages will be logged.
Definition: log.hxx:116
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/lstm__node_8hxx_source.html b/docs/html/lstm__node_8hxx_source.html new file mode 100644 index 00000000..8fda0920 --- /dev/null +++ b/docs/html/lstm__node_8hxx_source.html @@ -0,0 +1,192 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/lstm_node.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
lstm_node.hxx
+
+
+
1 #ifndef EXAMM_LSTM_NODE_HXX
+
2 #define EXAMM_LSTM_NODE_HXX
+
3 
+
4 #include <string>
+
5 using std::string;
+
6 
+
7 #include <random>
+
8 using std::minstd_rand0;
+
9 using std::uniform_real_distribution;
+
10 
+
11 #include <vector>
+
12 using std::vector;
+
13 
+
14 #include "common/random.hxx"
+
15 
+
16 #include "rnn_node_interface.hxx"
+
17 
+
18 class LSTM_Node : public RNN_Node_Interface {
+
19  private:
+
20  double output_gate_update_weight;
+
21  double output_gate_weight;
+
22  double output_gate_bias;
+
23 
+
24  double input_gate_update_weight;
+
25  double input_gate_weight;
+
26  double input_gate_bias;
+
27 
+
28  double forget_gate_update_weight;
+
29  double forget_gate_weight;
+
30  double forget_gate_bias;
+
31 
+
32  double cell_weight;
+
33  double cell_bias;
+
34 
+
35  vector<double> output_gate_values;
+
36  vector<double> input_gate_values;
+
37  vector<double> forget_gate_values;
+
38  vector<double> cell_values;
+
39 
+
40  vector<double> ld_output_gate;
+
41  vector<double> ld_input_gate;
+
42  vector<double> ld_forget_gate;
+
43 
+
44  vector<double> cell_in_tanh;
+
45  vector<double> cell_out_tanh;
+
46  vector<double> ld_cell_in;
+
47  vector<double> ld_cell_out;
+
48 
+
49  vector<double> d_prev_cell;
+
50 
+
51  vector<double> d_output_gate_update_weight;
+
52  vector<double> d_output_gate_weight;
+
53  vector<double> d_output_gate_bias;
+
54 
+
55  vector<double> d_input_gate_update_weight;
+
56  vector<double> d_input_gate_weight;
+
57  vector<double> d_input_gate_bias;
+
58 
+
59  vector<double> d_forget_gate_update_weight;
+
60  vector<double> d_forget_gate_weight;
+
61  vector<double> d_forget_gate_bias;
+
62 
+
63  vector<double> d_cell_weight;
+
64  vector<double> d_cell_bias;
+
65 
+
66  public:
+
67 
+
68  LSTM_Node(int _innovation_number, int _type, double _depth);
+
69  ~LSTM_Node();
+
70 
+
71  void initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma);
+
72  void initialize_xavier(minstd_rand0 &generator, uniform_real_distribution<double> &rng1_1, double range);
+
73  void initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range);
+
74  void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution<double> &rng);
+
75 
+
76  double get_gradient(string gradient_name);
+
77  void print_gradient(string gradient_name);
+
78 
+
79  void input_fired(int time, double incoming_output);
+
80 
+
81  void try_update_deltas(int time);
+
82  void error_fired(int time, double error);
+
83  void output_fired(int time, double delta);
+
84 
+
85  uint32_t get_number_weights() const;
+
86 
+
87  void get_weights(vector<double> &parameters) const;
+
88  void set_weights(const vector<double> &parameters);
+
89 
+
90  void get_weights(uint32_t &offset, vector<double> &parameters) const;
+
91  void set_weights(uint32_t &offset, const vector<double> &parameters);
+
92 
+
93  void get_gradients(vector<double> &gradients);
+
94 
+
95  void reset(int _series_length);
+
96 
+
97  void write_to_stream(ostream &out);
+
98 
+
99  RNN_Node_Interface* copy() const;
+
100 
+
101  friend class RNN_Edge;
+
102 };
+
103 #endif
+
+
RNN_Edge
Definition: rnn_edge.hxx:6
+ + +
LSTM_Node
Definition: lstm_node.hxx:18
+ +
NormalDistribution
Definition: random.hxx:19
+ + +
RNN_Node_Interface
Definition: rnn_node_interface.hxx:133
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/make__jobs_8hxx_source.html b/docs/html/make__jobs_8hxx_source.html new file mode 100644 index 00000000..73e87c64 --- /dev/null +++ b/docs/html/make__jobs_8hxx_source.html @@ -0,0 +1,96 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: server/make_jobs.hxx Source File + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
make_jobs.hxx
+
+
+
1 #ifndef EXACT_MAKE_JOBS_HXX
+
2 #define EXACT_MAKE_JOBS_HXX
+
3 
+
4 #include "cnn/exact.hxx"
+
5 
+
6 #define CUSHION 100
+
7 #define WORKUNITS_TO_GENERATE 100
+
8 #define REPLICATION_FACTOR 1
+
9 #define SLEEP_TIME 10
+
10 
+
11 bool low_on_workunits();
+
12 void make_jobs(EXACT *exact, int workunits_to_generate);
+
13 void init_work_generation(string app_name);
+
14 
+
15 #endif
+
+
EXACT
Definition: exact.hxx:32
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/md__r_e_a_d_m_e.html b/docs/html/md__r_e_a_d_m_e.html new file mode 100644 index 00000000..d6970b04 --- /dev/null +++ b/docs/html/md__r_e_a_d_m_e.html @@ -0,0 +1,155 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Getting Started and Prerequisites + + + + + + + + + +
+
+ + + + + + +
+
EXONA: Evolutionary eXploration of Neural Architectures +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ +
+
+
+
Getting Started and Prerequisites
+
+
+

EXONA has been developed to compile using CMake, which should be installed before attempting to compile. To use the MPI version, a version of MPI (such as OpenMPI) should be installed. EXACT currently requires libtiff and libpng The EXACT algorithm can also checkpoint to a database, however this is not required. To enable this I recommend installing libmysql-dev via apt-get on Linux systems, or mysql via homebrew on OSX. Other than that, EXACT/EXALT/EXAMM has no prerequesites other than c++11 compatible compiler.

+

If you are using OSX, to set up the environment:

+
$ brew install cmake
+
$ brew install mysql
+
$ xcode-select --install
+

To build:

+
~/exact $ mkdir build
+
~/exact $ cd build
+
~/exact/build $ cmake ..
+
~/exact/build $ make
+

You may also want to have graphviz installed so you can generate images of the evolved neural networks. EXACT/EXALT/EXAMM will write out evolved genomes in a .gv (graphviz) format for this. For example, can generate a pdf from a gv file (assuming graphviz is installed with):

+
$ dot -Tpdf genome.gv -o genome.pdf
+

+EXAMM: Evolutionary eXploration of Augmenting Memory Models and EXALT: Evolutionary eXploration of Augmenting LSTM Topologies

+

Source code for EXALT/EXAMM can be found in the rnn subdirectory. EXALT has been enhanced with the ability to utilize more recurrent memory cells and has been renamed EXAMM. The memory cells currently implemented are Delta-RNN, GRU, LSTM, MGU, and UGRNNs. Some example time series data has been provided as part of two publications on EXALT and EXAMM, which also provide implementation details:

+
    +
  1. Alex Ororbia, AbdElRahman ElSaid, and Travis Desell. Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution. The Genetic and Evolutionary Computation Conference (GECCO 2019). Prague, Czech Republic. July 8-12, 2019.
    2. +
  2. AbdElRahman ElSaid, Steven Benson, Shuchita Patwardhan, David Stadem and Travis Desell. Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters. The 22nd International Conference on the Applications of Evolutionary Computation (EvoStar: EvoApps 2019). Leipzig, Germany. April 24-26, 2019.
    3. +
+

These datasets can be found in the datasets directory, and provide example CSV files which you can use with EXAMM. EXAMM can be run in two different ways, a multithreaded version:

+
./multithreaded/examm_mt --number_threads 9 --training_filenames ../datasets/2018_coal/burner_[0-9].csv --test_filenames ../datasets/2018_coal/burner_1[0-1].csv --time_offset 1 --input_parameter_names Conditioner_Inlet_Temp Conditioner_Outlet_Temp Coal_Feeder_Rate Primary_Air_Flow Primary_Air_Split System_Secondary_Air_Flow_Total Secondary_Air_Flow Secondary_Air_Split Tertiary_Air_Split Total_Comb_Air_Flow Supp_Fuel_Flow Main_Flm_Int --output_parameter_names Main_Flm_Int --number_islands 10 --population_size 10 --max_genomes 2000 --bp_iterations 10 --output_directory "./test_output" --possible_node_types simple UGRNN MGU GRU delta LSTM --std_message_level INFO --file_message_level INFO
+

And a parallel version using MPI:

+
~/exact/build/ $ mpirun -np 9 ./mpi/examm_mpi --training_filenames ../datasets/2018_coal/burner_[0-9].csv --test_filenames ../datasets/2018_coal/burner_1[0-1].csv --time_offset 1 --input_parameter_names Conditioner_Inlet_Temp Conditioner_Outlet_Temp Coal_Feeder_Rate Primary_Air_Flow Primary_Air_Split System_Secondary_Air_Flow_Total Secondary_Air_Flow Secondary_Air_Split Tertiary_Air_Split Total_Comb_Air_Flow Supp_Fuel_Flow Main_Flm_Int --output_parameter_names Main_Flm_Int --number_islands 10 --population_size 10 --max_genomes 2000 --bp_iterations 10 --output_directory "./test_output" --possible_node_types simple UGRNN MGU GRU delta LSTM --std_message_level INFO --file_message_level INFO
+

Which will run EXAMM with 9 threads or 9 processes, respectively. Note that EXAMM uses one thread/process as the master and this typically just waits on the results of backprop so you if you have 8 processors/cores available you can usually run EXAMM with 9 processes/threads for better performance. A performance log of RNN fitnesses will be exported into fitness_log.csv, as well as the best found RNNs into the specified output directory, in this case *./test_output*. You can control the level of message logging for standard output with –std_message_level (options are NONE, FATAL, ERROR, WARNING, INFO, DEBUG, TRACE and ALL) and message logging to files (which will be placed in the output directory) with –file_message_level. Separate logging files will be made for each thread/process.

+

The aviation data can be run similarly, however it the data should be normalized first (which can be done with the –normalize command line parameter), e.g.:

+
./multithreaded/examm_mt --number_threads 9 --training_filenames ../datasets/2018_ngafid/flight_[0-7].csv --test_filenames ../datasets/2018_ngafid/flight_[8-9].csv --time_offset 1 --input_parameter_names "AltAGL" "E1 CHT1" "E1 CHT2" "E1 CHT3" "E1 CHT4" "E1 EGT1" "E1 EGT2" "E1 EGT3" "E1 EGT4" "E1 OilP" "E1 OilT" "E1 RPM" "FQtyL" "FQtyR" "GndSpd" "IAS" "LatAc" "NormAc" "OAT" "Pitch" "Roll" "TAS" "volt1" "volt2" "VSpd" "VSpdG" --output_parameter_names Pitch --number_islands 10 --population_size 10 --max_genomes 2000 --bp_iterations 10 --output_directory "./test_output" --possible_node_types simple UGRNN MGU GRU delta LSTM --normalize --std_message_level INFO --file_message_level INFO
+

The –time_offset parameter specifies how many time steps in the future EXAMM should predict for the output parameter(s). The –number_islands is the number of islands of populations that EXAMM will use, and the –population_size parameter specifies how many individuals/genomes are in each island. The –bp_iterations specifies how many epochs/iterations backpropagation should be run for each generated RNN genome.

+

The

+

+EXACT: Evolutionary Exploration of Augmenting Convolutional Topologies

+

This repository provides source code for the Evolutionary Exploration of Augmenting Convolutional Topologies algorithm. This algorithm progressively evolves convolutional neural networks for image classification problems. The algorithm is asychronous, which allows for easy multithreading and parallelization. Code is provided for running EXACT as a BOINC volunteer computing project, on a cluster or supercomputer using MPI or on a desktop or laptop using threads.

+

We've built and run EXACT on both an Ubuntu Linux high performance computing cluser and OSX laptops and desktops. We have not tried it on Windows. If there are issues with the CMake scripts please let us know and we'll update them.

+

If you want to set this up on your own BOINC volunteer computing project, we recommend sending us an email as this is a rather complicated process.

+

For more information on EXACT please see our following publications:

+
    +
  1. Travis Desell. Accelerating the Evolution of Convolutional Neural Networks with Node-Level Mutations and Epigenetic Weight Initialization. arXiv: Neural and Evolutionary Computing (cs.NE). November, 2018.
    2. +
  2. Travis Desell. Developing a Volunteer Computing Project to Evolve Convolutional Neural Networks and Their Hyperparameters. The 13th IEEE International Conference on eScience (eScience 2017). Auckland, New Zealand. October 24-27 2017.
    3. +
+

+Setting up Training and Testing Data

+

This version EXACT is set up to run using the MNIST Handwritten Digits Dataset. However it expects a different data format where the images and labels are combined. You can download the data and convert it as follows:

+
~/exact $ mkdir datasets
+
~/exact $ cd datasets
+
+
~/exact/datasets $ wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
+
~/exact/datasets $ wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
+
~/exact/datasets $ wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
+
~/exact/datasets $ wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
+
+
~/exact/datasets $ unzip train-images-idx3-ubyte.gz
+
~/exact/datasets $ unzip train-labels-idx1-ubyte.gz
+
~/exact/datasets $ unzip t10k-images-idx3-ubyte.gz
+
~/exact/datasets $ unzip t10k-labels-idx1-ubyte.gz
+
+
~/exact/datasets $ cd ../build
+
~/exact/build $ ./image_tools/convert_mnist_data ../datasets/train-images-idx3-ubyte ../datasets/train-labels-idx1-ubyte ../datasets/mnist_training_data.bin 60000
+
~/exact/build $ ./image_tools/convert_mnist_data ../datasets/t10k-images-idx3-ubyte ../datasets/t10k-labels-idx1-ubyte ../datasets/mnist_testing_data.bin 10000
+

You can also split up either the training or test data to add a third dataset of validation images for more robust analysis, e.g., so you can train on one set, use the validation set to determine the best CNNs for EXACT to keep, and then after the evolution process is completed you can test the best found genome(s) on the test set which has not been seen before. You can split up the MNIST training data into a 50k training set and 10k validation set as follows:

+
~/exact/build/ $ ./image_tools/split_mnist_data ../datasets/train-images-idx3-ubyte ../datasets/train-labels-idx1-ubyte ../datasets/mnist_train_50k.bin ../datasets/mnist_validation_10k.bin 60000 1000
+

Where the usage is:

+
./image_tools/split_mnist_data <mnist image file> <mnist label file> <output training file> <output validation file> <expected number of images> <validation images per label>
+

So this will take 1000 images of each type from the training data and put them into the specified validation file (mnist_validation_10k.bin) and the rest of the images will be in the training set (mnist_train_50k.bin).

+

+Running EXACT

+

You can then run EXACT in a similar manner to EXAMM, either using threads or MPI. For the threaded version:

+
~/exact/build/ $ ./multithreaded/exact_mt --number_threads 9 --padding 2 --training_file ../datasets/mnist_train_50k.bin --validation_file ../datasets/mnist_validation_10k.bin --testing_file ../datasets/mnist_testing_data.bin --population_size 30 --max_epochs 5 --use_sfmp 1 --use_node_operations 1 --max_genomes 1000 --output_directory ./test_exact --search_name "test" --reset_edges 0 --images_resize 50000
+

And for the MPI version:

+
~/exact/build/ $ mpirun -np 9 ./mpi/exact_mpi --padding 2 --training_file ../datasets/mnist_train_50k.bin --validation_file ../datasets/mnist_validation_10k.bin --testing_file ../datasets/mnist_testing_data.bin --population_size 30 --max_epochs 5 --use_sfmp 1 --use_node_operations 1 --max_genomes 1000 --output_directory ./test_exact --search_name "test" --reset_edges 0 --images_resize 50000
+

Which will run EXACT with 9 threads or processes, respectively. The –use_sfmp argument turns on or off scaled fractional max pooling (which allows for pooling operations between feature maps of any size), the –use_node_operations argument turns on or off node level mutations (see the EXACT and EXAMM papers), the –reset_edges parameter turns on or off Lamarckian weight evolution (turning it on will evolve and train networks faster) and the –images_resize parameter allows EXACT to train CNNs on a subset of the training data to speed the evolution process (e.g., –images_resize 5000 would train each CNN on a different subset of 5k images from the training data, as opposed to the full 50k).

+

+Example Genomes from GECCO 2017

+

Our submission to GECCO describes a set of best found genomes for the MNIST handwritten digits dataset. These can be found in the genomes subdirectory of the project. Please checkout the tag for the GECCO paper to use the version of EXACT these CNN genome files were generated with:

+
git checkout -b exact_gecco gecco_2017
+

After compiling this version and setting up the MNIST training and testing data as described in the previous section, these genomes can be run over the training and testing data for validation as follows:

+
~/exact/build/ $ ./tests/evaluate_cnn --training_data ../datasets/mnist_training_data.bin --testing_data ../datasets/mnist_testing_data.bin --genome_file ../genomes/genome_46823
+
~/exact/build/ $ ./tests/evaluate_cnn --training_data ../datasets/mnist_training_data.bin --testing_data ../datasets/mnist_testing_data.bin --genome_file ../genomes/genome_57302
+
~/exact/build/ $ ./tests/evaluate_cnn --training_data ../datasets/mnist_training_data.bin --testing_data ../datasets/mnist_testing_data.bin --genome_file ../genomes/genome_59455
+
~/exact/build/ $ ./tests/evaluate_cnn --training_data ../datasets/mnist_training_data.bin --testing_data ../datasets/mnist_testing_data.bin --genome_file ../genomes/genome_59920
+
+
+ +
+Generated by   +doxygen + 1.8.17 +
+ + diff --git a/docs/html/menu.js b/docs/html/menu.js new file mode 100644 index 00000000..433c15b8 --- /dev/null +++ b/docs/html/menu.js @@ -0,0 +1,50 @@ +/* + @licstart The following is the entire license notice for the + JavaScript code in this file. + + Copyright (C) 1997-2017 by Dimitri van Heesch + + This program is free software; you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation; either version 2 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License along + with this program; if not, write to the Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. + + @licend The above is the entire license notice + for the JavaScript code in this file + */ +function initMenu(relPath,searchEnabled,serverSide,searchPage,search) { + function makeTree(data,relPath) { + var result=''; + if ('children' in data) { + result+=''; + } + return result; + } + + $('#main-nav').append(makeTree(menudata,relPath)); + $('#main-nav').children(':first').addClass('sm sm-dox').attr('id','main-menu'); + if (searchEnabled) { + if (serverSide) { + $('#main-menu').append('
    • '); + } else { + $('#main-menu').append('
    • '); + } + } + $('#main-menu').smartmenus(); +} +/* @license-end */ diff --git a/docs/html/menudata.js b/docs/html/menudata.js new file mode 100644 index 00000000..3b9a87e3 --- /dev/null +++ b/docs/html/menudata.js @@ -0,0 +1,96 @@ +/* +@licstart The following is the entire license notice for the +JavaScript code in this file. + +Copyright (C) 1997-2019 by Dimitri van Heesch + +This program is free software; you can redistribute it and/or modify +it under the terms of version 2 of the GNU General Public License as published by +the Free Software Foundation + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. + +@licend The above is the entire license notice +for the JavaScript code in this file +*/ +var menudata={children:[ +{text:"Main Page",url:"index.html"}, +{text:"Related Pages",url:"pages.html"}, +{text:"Namespaces",url:"namespaces.html",children:[ +{text:"Namespace List",url:"namespaces.html"}]}, +{text:"Classes",url:"annotated.html",children:[ +{text:"Class List",url:"annotated.html"}, +{text:"Class Index",url:"classes.html"}, +{text:"Class Hierarchy",url:"hierarchy.html"}, +{text:"Class Members",url:"functions.html",children:[ +{text:"All",url:"functions.html",children:[ +{text:"_",url:"functions.html#index__5F"}, +{text:"a",url:"functions_a.html#index_a"}, +{text:"b",url:"functions_b.html#index_b"}, +{text:"c",url:"functions_c.html#index_c"}, +{text:"d",url:"functions_d.html#index_d"}, +{text:"e",url:"functions_e.html#index_e"}, +{text:"f",url:"functions_f.html#index_f"}, +{text:"g",url:"functions_g.html#index_g"}, +{text:"h",url:"functions_h.html#index_h"}, +{text:"i",url:"functions_i.html#index_i"}, +{text:"l",url:"functions_l.html#index_l"}, +{text:"m",url:"functions_m.html#index_m"}, +{text:"n",url:"functions_n.html#index_n"}, +{text:"o",url:"functions_o.html#index_o"}, +{text:"p",url:"functions_p.html#index_p"}, +{text:"r",url:"functions_r.html#index_r"}, +{text:"s",url:"functions_s.html#index_s"}, +{text:"t",url:"functions_t.html#index_t"}, +{text:"u",url:"functions_u.html#index_u"}, +{text:"v",url:"functions_v.html#index_v"}, +{text:"w",url:"functions_w.html#index_w"}, +{text:"z",url:"functions_z.html#index_z"}]}, +{text:"Functions",url:"functions_func.html",children:[ +{text:"_",url:"functions_func.html#index__5F"}, +{text:"a",url:"functions_func.html#index_a"}, +{text:"b",url:"functions_func.html#index_b"}, +{text:"c",url:"functions_func.html#index_c"}, +{text:"d",url:"functions_func.html#index_d"}, +{text:"e",url:"functions_func.html#index_e"}, +{text:"f",url:"functions_func.html#index_f"}, +{text:"g",url:"functions_func.html#index_g"}, +{text:"h",url:"functions_func.html#index_h"}, +{text:"i",url:"functions_func.html#index_i"}, +{text:"l",url:"functions_func.html#index_l"}, +{text:"m",url:"functions_func.html#index_m"}, +{text:"o",url:"functions_func.html#index_o"}, +{text:"p",url:"functions_func.html#index_p"}, +{text:"r",url:"functions_func.html#index_r"}, +{text:"s",url:"functions_func.html#index_s"}, +{text:"t",url:"functions_func.html#index_t"}, +{text:"w",url:"functions_func.html#index_w"}]}, +{text:"Variables",url:"functions_vars.html",children:[ +{text:"a",url:"functions_vars.html#index_a"}, +{text:"b",url:"functions_vars.html#index_b"}, +{text:"d",url:"functions_vars.html#index_d"}, +{text:"e",url:"functions_vars.html#index_e"}, +{text:"f",url:"functions_vars.html#index_f"}, +{text:"g",url:"functions_vars.html#index_g"}, +{text:"i",url:"functions_vars.html#index_i"}, +{text:"l",url:"functions_vars.html#index_l"}, +{text:"m",url:"functions_vars.html#index_m"}, +{text:"n",url:"functions_vars.html#index_n"}, +{text:"o",url:"functions_vars.html#index_o"}, +{text:"p",url:"functions_vars.html#index_p"}, +{text:"r",url:"functions_vars.html#index_r"}, +{text:"s",url:"functions_vars.html#index_s"}, +{text:"t",url:"functions_vars.html#index_t"}, +{text:"u",url:"functions_vars.html#index_u"}, +{text:"v",url:"functions_vars.html#index_v"}, +{text:"w",url:"functions_vars.html#index_w"}, +{text:"z",url:"functions_vars.html#index_z"}]}]}]}, +{text:"Files",url:"files.html",children:[ +{text:"File List",url:"files.html"}]}]} diff --git a/docs/html/mgu__node_8hxx_source.html b/docs/html/mgu__node_8hxx_source.html new file mode 100644 index 00000000..aa2a4bf8 --- /dev/null +++ b/docs/html/mgu__node_8hxx_source.html @@ -0,0 +1,169 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/mgu_node.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    mgu_node.hxx
    +
    +
    +
    1 #ifndef EXAMM_MGU_NODE_HXX
    +
    2 #define EXAMM_MGU_NODE_HXX
    +
    3 
    +
    4 #include <string>
    +
    5 using std::string;
    +
    6 
    +
    7 #include <random>
    +
    8 using std::minstd_rand0;
    +
    9 using std::uniform_real_distribution;
    +
    10 
    +
    11 #include <vector>
    +
    12 using std::vector;
    +
    13 
    +
    14 #include "common/random.hxx"
    +
    15 
    +
    16 #include "rnn_node_interface.hxx"
    +
    17 
    +
    18 class MGU_Node : public RNN_Node_Interface {
    +
    19  private:
    +
    20  double fw;
    +
    21  double fu;
    +
    22  double f_bias;
    +
    23 
    +
    24  double hw;
    +
    25  double hu;
    +
    26  double h_bias;
    +
    27 
    +
    28  vector<double> d_fw;
    +
    29  vector<double> d_fu;
    +
    30  vector<double> d_f_bias;
    +
    31  vector<double> d_hw;
    +
    32  vector<double> d_hu;
    +
    33  vector<double> d_h_bias;
    +
    34 
    +
    35  vector<double> d_h_prev;
    +
    36 
    +
    37  vector<double> f;
    +
    38  vector<double> ld_f;
    +
    39  vector<double> h_tanh;
    +
    40  vector<double> ld_h_tanh;
    +
    41 
    +
    42  public:
    +
    43 
    +
    44  MGU_Node(int _innovation_number, int _layer_type, double _depth);
    +
    45  ~MGU_Node();
    +
    46 
    +
    47  void initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma);
    +
    48  void initialize_xavier(minstd_rand0 &generator, uniform_real_distribution<double> &rng1_1, double range);
    +
    49  void initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range);
    +
    50  void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution<double> &rng);
    +
    51 
    +
    52  double get_gradient(string gradient_name);
    +
    53  void print_gradient(string gradient_name);
    +
    54 
    +
    55  void input_fired(int time, double incoming_output);
    +
    56 
    +
    57  void try_update_deltas(int time);
    +
    58  void error_fired(int time, double error);
    +
    59  void output_fired(int time, double delta);
    +
    60 
    +
    61  uint32_t get_number_weights() const;
    +
    62 
    +
    63  void get_weights(vector<double> &parameters) const;
    +
    64  void set_weights(const vector<double> &parameters);
    +
    65 
    +
    66  void get_weights(uint32_t &offset, vector<double> &parameters) const;
    +
    67  void set_weights(uint32_t &offset, const vector<double> &parameters);
    +
    68 
    +
    69  void get_gradients(vector<double> &gradients);
    +
    70 
    +
    71  void reset(int _series_length);
    +
    72 
    +
    73  void write_to_stream(ostream &out);
    +
    74 
    +
    75  RNN_Node_Interface* copy() const;
    +
    76 
    +
    77  friend class RNN_Edge;
    +
    78 
    +
    79 };
    +
    80 #endif
    +
    +
    RNN_Edge
    Definition: rnn_edge.hxx:6
    + +
    MGU_Node
    Definition: mgu_node.hxx:18
    + + +
    NormalDistribution
    Definition: random.hxx:19
    + + +
    RNN_Node_Interface
    Definition: rnn_node_interface.hxx:133
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/min__max__heap_8hxx_source.html b/docs/html/min__max__heap_8hxx_source.html new file mode 100644 index 00000000..ca4bb909 --- /dev/null +++ b/docs/html/min__max__heap_8hxx_source.html @@ -0,0 +1,309 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/min_max_heap.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    min_max_heap.hxx
    +
    +
    +
    1 inline uint32_t log2(uint32_t x) {
    +
    2  // This won't work on non x86 platforms
    +
    3  // https://stackoverflow.com/questions/994593/how-to-do-an-integer-log2-in-c
    +
    4 #if defined(__x86_64__) || defined(__i386__)
    +
    5  uint32_t y;
    +
    6  asm ( "\tbsr %1, %0\n"
    +
    7  : "=r"(y)
    +
    8  : "r" (x)
    +
    9  );
    +
    10  return y;
    +
    11 #else
    +
    12  if (x == 1) return 0;
    +
    13  uint32_t ret = 0;
    +
    14  while (x > 1) {
    +
    15  x >>= 1;
    +
    16  ret++;
    +
    17  }
    +
    18  return ret;
    +
    19 #endif
    +
    20 }
    +
    21 
    +
    31 template<class T>
    +
    32 class min_max_heap {
    +
    33 
    +
    34  typedef typename std::vector<T>::const_iterator const_iterator;
    +
    35 
    +
    36  vector<T> heap;
    +
    37  function<bool(const T&, const T&)> less_than;
    +
    38 
    +
    39  static inline uint32_t parent(uint32_t z) { return (z - 1) / 2; }
    +
    40 
    +
    41  static inline uint32_t left_child(uint32_t z) { return z + z + 1; }
    +
    42 
    +
    43  static inline uint32_t right_child(uint32_t z) { return z + z + 2; }
    +
    44 
    +
    45  static inline bool is_on_min_level(uint32_t z) { return log2(z + 1) % 2 == 1; }
    +
    46 
    +
    47  static inline bool is_on_max_level(uint32_t z) { return log2(z + 1) % 2 == 0; }
    +
    48 
    +
    49  template<bool max_level> void trickle_up_inner(uint32_t z) {
    +
    50  if (z == 0) return;
    +
    51 
    +
    52  uint32_t z_grandparent = parent(z);
    +
    53  if (z_grandparent == 0) return;
    +
    54 
    +
    55  z_grandparent = parent(z_grandparent);
    +
    56 
    +
    57  if (less_than(heap[z], heap[z_grandparent]) ^ max_level) {
    +
    58  std::swap(heap[z], heap[z_grandparent]);
    +
    59  trickle_up_inner<max_level>(z_grandparent);
    +
    60  }
    +
    61  }
    +
    62 
    +
    63  void trickle_up(uint32_t z) {
    +
    64  if (z == 0) return;
    +
    65 
    +
    66  uint32_t z_parent = parent(z);
    +
    67 
    +
    68  if (is_on_min_level(z)) {
    +
    69  if (less_than(heap[z_parent], heap[z])) {
    +
    70  std::swap(heap[z_parent], heap[z]);
    +
    71  trickle_up_inner<true>(z_parent);
    +
    72  } else {
    +
    73  trickle_up_inner<false>(z);
    +
    74  }
    +
    75  } else {
    +
    76  if (less_than(heap[z], heap[z_parent])) {
    +
    77  std::swap(heap[z], heap[z_parent]);
    +
    78  trickle_up_inner<false>(z_parent);
    +
    79  } else {
    +
    80  trickle_up_inner<true>(z);
    +
    81  }
    +
    82  }
    +
    83  }
    +
    84 
    +
    85  template<bool max_level> void trickle_down_inner(const uint32_t z) {
    +
    86  if (z >= heap.size())
    +
    87  throw std::invalid_argument("Element specified by z does not exist");
    +
    88 
    +
    89  uint32_t smallest_node = z;
    +
    90  uint32_t left = left_child(z);
    +
    91  uint32_t right = left + 1;
    +
    92 
    +
    93  if (left < heap.size() && (less_than(heap[left], heap[smallest_node]) ^ max_level))
    +
    94  smallest_node = left;
    +
    95  if (right < heap.size() && (less_than(heap[right], heap[smallest_node]) ^ max_level))
    +
    96  smallest_node = right;
    +
    97 
    +
    98  uint32_t left_grandchild = left_child(left);
    +
    99  for (uint32_t i = 0; i < 4 && left_grandchild + i < heap.size(); i++)
    +
    100  if (less_than(heap[left_grandchild + i], heap[smallest_node]) ^ max_level)
    +
    101  smallest_node = left_grandchild + i;
    +
    102 
    +
    103  if (z == smallest_node) return;
    +
    104 
    +
    105  std::swap(heap[z], heap[smallest_node]);
    +
    106  if (smallest_node - left > 1) { // smallest node was a grandchild
    +
    107  if (less_than(heap[parent(smallest_node)], heap[smallest_node]) ^ max_level)
    +
    108  std::swap(heap[parent(smallest_node)], heap[smallest_node]);
    +
    109 
    +
    110  trickle_down_inner<max_level>(smallest_node);
    +
    111  }
    +
    112  }
    +
    113 
    +
    114  void trickle_down(uint32_t z) {
    +
    115  if (is_on_min_level(z))
    +
    116  trickle_down_inner<false>(z);
    +
    117  else
    +
    118  trickle_down_inner<true>(z);
    +
    119  }
    +
    120 
    +
    121  uint32_t find_min_index() const {
    +
    122  switch (heap.size()) {
    +
    123  case 0:
    +
    124  throw std::underflow_error("There is no minimum element because the heap is empty");
    +
    125  case 1:
    +
    126  return 0;
    +
    127  case 2:
    +
    128  return 1;
    +
    129  default:
    +
    130  return less_than(heap[1], heap[2]) ? 1 : 2;
    +
    131  }
    +
    132  }
    +
    133 
    +
    134  T delete_element(uint32_t z) {
    +
    135  if (z >= (uint32_t) heap.size())
    +
    136  throw std::underflow_error("Cannot delete element from the heap because it does not exist");
    +
    137 
    +
    138  if (z == heap.size() - 1) {
    +
    139  T e = heap.back();
    +
    140  heap.pop_back();
    +
    141  return e;
    +
    142  }
    +
    143 
    +
    144  std::swap(heap[z], heap[heap.size() - 1]);
    +
    145 
    +
    146  T e = heap.back();
    +
    147  heap.pop_back();
    +
    148 
    +
    149  trickle_down(z);
    +
    150 
    +
    151  return e;
    +
    152  }
    +
    153 
    +
    154 public:
    +
    155 
    +
    161  min_max_heap(std::function<bool(const T&, const T&)> _less_than, uint32_t size_hint=-1)
    +
    162  : less_than(_less_than) {
    +
    163  if (size_hint >= 0)
    +
    164  heap.reserve(size_hint);
    +
    165  }
    +
    166  ~min_max_heap() { }
    +
    167 
    +
    168  bool empty() const { return heap.size() == 0; }
    +
    169 
    +
    170  uint32_t size() const { return (uint32_t) heap.size(); }
    +
    171 
    +
    175  void enqueue(const T& e) {
    +
    176  heap.push_back(e);
    +
    177  trickle_up(heap.size() - 1);
    +
    178  }
    +
    179 
    +
    180  const T& find_max() const {
    +
    181  if (empty())
    +
    182  throw std::underflow_error("There is no max element because the heap is empty");
    +
    183 
    +
    184  return heap[0];
    +
    185  }
    +
    186 
    +
    187  const T& find_min() const {
    +
    188  return heap[find_min_index()];
    +
    189  }
    +
    190 
    +
    195  T pop_max() {
    +
    196  if (heap.size() == 0)
    +
    197  throw std::underflow_error("No max element exists because the heap is empty");
    +
    198 
    +
    199  return delete_element(0);
    +
    200  }
    +
    201 
    +
    202  T pop() { return pop_max(); }
    +
    203 
    +
    208  T pop_min() {
    +
    209  if (heap.size() == 0)
    +
    210  throw std::underflow_error("No minimum element exists because the heap is empty");
    +
    211 
    +
    212  return delete_element(find_min_index());
    +
    213  }
    +
    214 
    +
    219  const_iterator cbegin() {
    +
    220  return heap.begin();
    +
    221  }
    +
    222 
    +
    228  const_iterator cend() {
    +
    229  return heap.end();
    +
    230  }
    +
    231 
    +
    235  T& operator[](size_t index) {
    +
    236  return heap[index];
    +
    237  }
    +
    238 
    +
    242  T erase(size_t index) {
    +
    243  return delete_element((uint32_t) index);
    +
    244  }
    +
    245 
    +
    249  void reserve(size_t n) {
    +
    250  heap.reserve(n);
    +
    251  }
    +
    252 
    +
    256  void clear() {
    +
    257  heap.clear();
    +
    258  }
    +
    259 };
    +
    + +
    min_max_heap::pop_min
    T pop_min()
    Returns and removes the minimum item in this min-max heap.
    Definition: min_max_heap.hxx:208
    + + +
    min_max_heap::cend
    const_iterator cend()
    returns the const_iterator pointing past the end of the underlying vector (i.e.
    Definition: min_max_heap.hxx:228
    +
    min_max_heap
    This implementation of a min_max_heap was basically ripped from here: https://github....
    Definition: min_max_heap.hxx:32
    +
    min_max_heap::enqueue
    void enqueue(const T &e)
    Adds the element e to the heap (in the correct order of course).
    Definition: min_max_heap.hxx:175
    +
    min_max_heap::cbegin
    const_iterator cbegin()
    returns the const_iterator to the beginning of the underlying vector.
    Definition: min_max_heap.hxx:219
    + +
    min_max_heap::operator[]
    T & operator[](size_t index)
    Implementation of the indexing operator to allow direct access.
    Definition: min_max_heap.hxx:235
    +
    min_max_heap::min_max_heap
    min_max_heap(std::function< bool(const T &, const T &)> _less_than, uint32_t size_hint=-1)
    Creates a new min_max_heap.
    Definition: min_max_heap.hxx:161
    +
    std::swap
    T swap(T... args)
    +
    min_max_heap::reserve
    void reserve(size_t n)
    Ensures that the underlying vector can hold at least n elements.
    Definition: min_max_heap.hxx:249
    +
    min_max_heap::pop_max
    T pop_max()
    Returns and removes the maximum item in this min-max heap.
    Definition: min_max_heap.hxx:195
    +
    min_max_heap::clear
    void clear()
    Deletes every element in this heap.
    Definition: min_max_heap.hxx:256
    +
    min_max_heap::erase
    T erase(size_t index)
    Allows for deletion of an element at a specified index in O(log(n)) time.
    Definition: min_max_heap.hxx:242
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/mosaic__image__set_8hxx_source.html b/docs/html/mosaic__image__set_8hxx_source.html new file mode 100644 index 00000000..d1d83ebe --- /dev/null +++ b/docs/html/mosaic__image__set_8hxx_source.html @@ -0,0 +1,221 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: image_tools/mosaic_image_set.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    mosaic_image_set.hxx
    +
    +
    +
    1 #ifndef MOSAIC_IMAGE_SET_HXX
    +
    2 #define MOSAIC_IMAGE_SET_HXX
    +
    3 
    +
    4 #include <fstream>
    +
    5 using std::ifstream;
    +
    6 
    +
    7 #include <iostream>
    +
    8 using std::ostream;
    +
    9 
    +
    10 #include <string>
    +
    11 using std::string;
    +
    12 
    +
    13 #include <vector>
    +
    14 using std::vector;
    +
    15 
    +
    16 #include "image_set_interface.hxx"
    +
    17 #include "large_image_set.hxx"
    +
    18 
    +
    19 typedef class MosaicImages MosaicImages;
    +
    20 
    +
    21 class Point {
    +
    22  public:
    +
    23  int y;
    +
    24  int x;
    +
    25 
    +
    26  Point(int _y, int _x);
    +
    27 };
    +
    28 
    +
    29 class Line {
    +
    30  public:
    +
    31  int y1;
    +
    32  int x1;
    +
    33  int y2;
    +
    34  int x2;
    +
    35 
    +
    36  Line(int _y1, int _x1, int _y2, int _x2);
    +
    37 };
    +
    38 
    +
    39 class Rectangle {
    +
    40  public:
    +
    41  int y1;
    +
    42  int x1;
    +
    43  int y2;
    +
    44  int x2;
    +
    45 
    +
    46  Rectangle(int _y1, int _x1, int _y2, int _x2);
    +
    47 };
    +
    48 
    +
    49 
    +
    50 
    +
    51 class MosaicImages : public MultiImagesInterface {
    +
    52  private:
    +
    53  vector<string> filenames;
    +
    54 
    +
    55  int number_classes;
    +
    56  int number_images;
    +
    57 
    +
    58  vector<int> class_sizes;
    +
    59 
    +
    60  int padding;
    +
    61  int channels;
    +
    62  int subimage_width, subimage_height;
    +
    63 
    +
    64  vector<LargeImage> images;
    +
    65 
    +
    66  vector<float> channel_avg;
    +
    67  vector<float> channel_std_dev;
    +
    68 
    +
    69  public:
    +
    70  void get_mosaic_pixels(string filename, vector< vector< vector<uint8_t> > > &pixels, uint32_t &height, uint32_t &width);
    +
    71 
    +
    72  void read_mosaic(string filename, const vector<Point> &box_centers, int box_radius, const vector<int> &box_classes);
    +
    73  void read_mosaic(string filename, const vector<Line> &lines, int line_height, const vector<int> &line_classes);
    +
    74 
    +
    75  void initialize_counts(const vector< vector<int> > &classes);
    +
    76 
    +
    77  MosaicImages(vector<string> _filenames, const vector< vector<Point> > &_box_centers, int _box_radius, const vector< vector<int> > &_box_classes, int _padding, int _subimage_height, int _subimage_width);
    +
    78 
    +
    79  MosaicImages(vector<string> _filenames, const vector< vector<Point> > &_box_centers, int _box_radius, const vector< vector<int> > &_box_classes, int _padding, int _subimage_height, int _subimage_width, const vector<float> &_channel_avg, const vector<float> &_channel_std_dev);
    +
    80 
    +
    81  MosaicImages(vector<string> _filenames, const vector< vector<Line> > &_lines, int _line_height, const vector< vector<int> > &_line_classes, int _padding, int _subimage_height, int _subimage_width);
    +
    82 
    +
    83  MosaicImages(vector<string> _filenames, const vector< vector<Line> > &_lines, int _line_height, const vector< vector<int> > &_line_classes, int _padding, int _subimage_height, int _subimage_width, const vector<float> &_channel_avg, const vector<float> &_channel_std_dev);
    +
    84 
    +
    85 
    +
    86  string get_filename() const;
    +
    87 
    +
    88  int get_class_size(int i) const;
    +
    89 
    +
    90  int get_number_classes() const;
    +
    91 
    +
    92  int get_number_images() const;
    +
    93  int get_number_large_images() const;
    +
    94  int get_number_subimages(int i) const;
    +
    95 
    +
    96  int get_padding() const;
    +
    97 
    +
    98  int get_image_channels() const;
    +
    99  int get_image_width() const;
    +
    100  int get_image_height() const;
    +
    101 
    +
    102  int get_large_image_channels(int image) const;
    +
    103  int get_large_image_width(int image) const;
    +
    104  int get_large_image_height(int image) const;
    +
    105 
    +
    106 
    +
    107  int get_image_classification(int image) const;
    +
    108  int get_classification(int subimage) const;
    +
    109  float get_pixel(int subimage, int z, int y, int x) const;
    +
    110  float get_raw_pixel(int subimage, int z, int y, int x) const;
    +
    111 
    +
    112  void calculate_avg_std_dev();
    +
    113 
    +
    114  float get_channel_avg(int channel) const;
    +
    115 
    +
    116  float get_channel_std_dev(int channel) const;
    +
    117 
    +
    118 
    +
    119  const vector<float>& get_average() const;
    +
    120  const vector<float>& get_std_dev() const;
    +
    121 
    +
    122  void normalize();
    +
    123 
    +
    124  LargeImage* copy_image(int i) const;
    +
    125 
    +
    126  void set_alpha(int i, const vector< vector<float> > &_alpha);
    +
    127  void set_alpha(int i, const vector< vector<uint8_t> > &_alpha);
    +
    128 };
    +
    129 
    +
    130 #endif
    +
    131 
    +
    + +
    Point
    Definition: mosaic_image_set.hxx:21
    +
    MultiImagesInterface
    Definition: image_set_interface.hxx:52
    + +
    LargeImage
    Definition: large_image_set.hxx:20
    + +
    MosaicImages
    Definition: mosaic_image_set.hxx:51
    +
    Rectangle
    Definition: mosaic_image_set.hxx:39
    +
    Line
    Definition: mosaic_image_set.hxx:29
    + + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/mse_8hxx_source.html b/docs/html/mse_8hxx_source.html new file mode 100644 index 00000000..2ce056c5 --- /dev/null +++ b/docs/html/mse_8hxx_source.html @@ -0,0 +1,98 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/mse.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    mse.hxx
    +
    +
    +
    1 #ifndef EXAMM_MSE_HXX
    +
    2 #define EXAMM_MSE_HXX
    +
    3 
    +
    4 #include <vector>
    +
    5 using std::vector;
    +
    6 
    +
    7 #include "rnn.hxx"
    +
    8 
    +
    9 void get_mse(const vector<double> &output_values, const vector<double> &expected, double &mse, vector<double> &deltas);
    +
    10 void get_mse(RNN* genome, const vector< vector<double> > &expected, double &mse, vector< vector<double> > &deltas);
    +
    11 
    +
    12 void get_mae(const vector<double> &output_values, const vector<double> &expected, double &mae, vector<double> &deltas);
    +
    13 void get_mae(RNN* genome, const vector< vector<double> > &expected, double &mae, vector< vector<double> > &deltas);
    +
    14 
    +
    15 
    +
    16 #endif
    +
    + +
    RNN
    Definition: rnn.hxx:18
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/namespaceplot__multi__time__series.html b/docs/html/namespaceplot__multi__time__series.html new file mode 100644 index 00000000..d74a0681 --- /dev/null +++ b/docs/html/namespaceplot__multi__time__series.html @@ -0,0 +1,127 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: plot_multi_time_series Namespace Reference + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + +
    +
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    +Variables
    +
    +
    plot_multi_time_series Namespace Reference
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    +Variables

    + csv_filename = sys.argv[1]
     
    + parameter_name = sys.argv[2]
     
    + xlabel_name = sys.argv[3]
     
    + output_filename = sys.argv[4]
     
    + offsets = sys.argv[5:]
     
    + values = genfromtxt(csv_filename, delimiter=",")
     
    + rows = len(values)
     
    + cols = len(values[0])
     
    + figsize
     
    + label
     
    + linewidth
     
    + loc
     
    + bbox_inches
     
    +

    Detailed Description

    +
    usage examples:
+    python plot_multi_time_series.py ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/net_plant_heat_rate_predictions.csv "Net Plant Heat Rate" "Hours" ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/net_plant_heat_rate_multi.png 1 2 4 8
+    python plot_multi_time_series.py ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/nose_gas_temperature_predictons.csv "Nose Gas Temperature" "Hours" ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/nose_gas_temperature_multi.png 1 2 4 8
+    python plot_multi_time_series.py ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_extra_3.csv "Flame Intensity (Plant ~ Fuel)" "Minutes" ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_extra_3.png 1 15 30 60 120 240 480
+    python plot_multi_time_series.py ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_plant_fuel_3.csv "Flame Intensity (Plant + Fuel)" "Minutes" ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_plant_fuel_3.png 1 15 30 60 120 240 480
+    python plot_multi_time_series.py ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_plant_3.csv "Flame Intensity (Plant Only)" "Minutes" ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_plant_3.png 1 15 30 60 120 240 480
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    EXONA: Evolutionary eXploration of Neural Architectures +
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    Namespace List
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    Here is a list of all documented namespaces with brief descriptions:
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     Nplot_multi_time_series
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    + + diff --git a/docs/html/nav_f.png b/docs/html/nav_f.png new file mode 100644 index 0000000000000000000000000000000000000000..72a58a529ed3a9ed6aa0c51a79cf207e026deee2 GIT binary patch literal 153 zcmeAS@N?(olHy`uVBq!ia0vp^j6iI`!2~2XGqLUlQVE_ejv*C{Z|{2ZH7M}7UYxc) zn!W8uqtnIQ>_z8U literal 0 HcmV?d00001 diff --git a/docs/html/nav_g.png b/docs/html/nav_g.png new file mode 100644 index 0000000000000000000000000000000000000000..2093a237a94f6c83e19ec6e5fd42f7ddabdafa81 GIT binary patch literal 95 zcmeAS@N?(olHy`uVBq!ia0vp^j6lrB!3HFm1ilyoDK$?Q$B+ufw|5PB85lU25BhtE tr?otc=hd~V+ws&_A@j8Fiv!KF$B+ufw|5=67#uj90@pIL wZ=Q8~_Ju`#59=RjDrmm`tMD@M=!-l18IR?&vFVdQ&MBb@0HFXL + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/neat_speciation_strategy.hxx Source File + + + + + + + + + +
    +
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    +
    EXONA: Evolutionary eXploration of Neural Architectures +
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    +
    neat_speciation_strategy.hxx
    +
    +
    +
    1 #ifndef EXAMM_NEAT_SPECIATION_STRATEGY_HXX
    +
    2 #define EXAMM_NEAT_SPECIATION_STRATEGY_HXX
    +
    3 
    +
    4 #include <functional>
    +
    5 using std::function;
    +
    6 
    +
    7 #include <string>
    +
    8 using std::string;
    +
    9 
    +
    10 #include <random>
    +
    11 using std::minstd_rand0;
    +
    12 using std::uniform_real_distribution;
    +
    13 
    +
    14 #include "species.hxx"
    +
    15 #include "rnn_genome.hxx"
    +
    16 #include "speciation_strategy.hxx"
    +
    17 
    +
    18 class NeatSpeciationStrategy : public SpeciationStrategy {
    +
    19  private:
    +
    20  int32_t generation_species;
    +
    21  int32_t species_count;
    +
    22  int32_t population_not_improving_count;
    +
    23  double species_threshold;
    +
    24  double fitness_threshold;
    +
    25  double neat_c1;
    +
    26  double neat_c2;
    +
    27  double neat_c3;
    +
    28  double mutation_rate;
    +
    29  double intra_island_crossover_rate;
    +
    30  double inter_island_crossover_rate;
    +
    32  int32_t generated_genomes;
    +
    33  int32_t inserted_genomes;
    +
    35  RNN_Genome *minimal_genome;
    +
    37  int32_t max_genomes;
    +
    38 
    +
    39  minstd_rand0 generator;
    +
    40 
    +
    41  vector<Species*> Neat_Species;
    +
    42  RNN_Genome* global_best_genome;
    +
    43  public:
    +
    44 
    +
    45  NeatSpeciationStrategy( double _mutation_rate, double _intra_island_crossover_rate,
    +
    46  double _inter_island_crossover_rate, RNN_Genome *_seed_genome,
    +
    47  int32_t _max_genomes, double _species_threshold, double _fitness_threshold,
    +
    48  double _neat_c1, double _neat_c2, double _neat_c3, minstd_rand0 &_generator);
    +
    52  int32_t get_generated_genomes() const;
    +
    53 
    +
    57  int32_t get_inserted_genomes() const;
    +
    58 
    +
    63  double get_best_fitness();
    +
    64 
    +
    69  double get_worst_fitness();
    +
    70 
    +
    75  RNN_Genome* get_best_genome();
    +
    76 
    +
    81  RNN_Genome* get_worst_genome();
    +
    82 
    +
    93  int32_t insert_genome(RNN_Genome* genome);
    +
    94 
    +
    105  RNN_Genome* generate_genome(uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator, function<void (int32_t, RNN_Genome*)> &mutate, function<RNN_Genome* (RNN_Genome*, RNN_Genome *)> &crossover);
    +
    106 
    +
    107  RNN_Genome* generate_for_species(uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator, function<void (int32_t, RNN_Genome*)> &mutate, function<RNN_Genome* (RNN_Genome*, RNN_Genome *)> &crossover);
    +
    108 
    +
    114  void print(string indent = "") const;
    +
    115 
    +
    119  string get_strategy_information_headers() const;
    +
    120 
    +
    124  string get_strategy_information_values() const;
    +
    125 
    +
    126  RNN_Genome* get_global_best_genome();
    +
    127 
    +
    128  vector<int32_t> get_random_species_list();
    +
    129 
    +
    130  double get_distance(RNN_Genome* g1, RNN_Genome* g2);
    +
    131 
    +
    132  int get_exceed_number(vector<int32_t> v1, vector<int32_t> v2);
    +
    133 
    +
    134  void rank_species();
    +
    135 
    +
    136  bool check_population();
    +
    137 
    +
    138  void check_species();
    +
    139 
    +
    140 };
    +
    141 
    +
    142 #endif
    +
    143 
    +
    +
    NeatSpeciationStrategy::get_best_genome
    RNN_Genome * get_best_genome()
    Gets the best genome of all the islands.
    Definition: neat_speciation_strategy.cxx:83
    + +
    NeatSpeciationStrategy::generated_genomes
    int32_t generated_genomes
    How many genomes have been generated by this speciation strategy.
    Definition: neat_speciation_strategy.hxx:32
    +
    NeatSpeciationStrategy::get_strategy_information_values
    string get_strategy_information_values() const
    Gets speciation strategy information values for logs.
    Definition: neat_speciation_strategy.cxx:389
    + +
    NeatSpeciationStrategy::mutation_rate
    double mutation_rate
    How frequently to do mutations.
    Definition: neat_speciation_strategy.hxx:28
    +
    NeatSpeciationStrategy::get_worst_fitness
    double get_worst_fitness()
    Gets the fitness of the worst genome of all the islands.
    Definition: neat_speciation_strategy.cxx:131
    + +
    NeatSpeciationStrategy::population_not_improving_count
    int32_t population_not_improving_count
    < Used to keep track of the number of species generated .
    Definition: neat_speciation_strategy.hxx:22
    + +
    SpeciationStrategy
    Definition: speciation_strategy.hxx:13
    +
    NeatSpeciationStrategy::generation_species
    int32_t generation_species
    Used to track which island to generate the next genome from.
    Definition: neat_speciation_strategy.hxx:20
    +
    NeatSpeciationStrategy::minimal_genome
    RNN_Genome * minimal_genome
    keep a reference to a minimal genome so we can re-use it across islands and not duplicate innovation ...
    Definition: neat_speciation_strategy.hxx:35
    +
    NeatSpeciationStrategy::inter_island_crossover_rate
    double inter_island_crossover_rate
    How frequently to do inter-island crossovers.
    Definition: neat_speciation_strategy.hxx:30
    + +
    NeatSpeciationStrategy::get_strategy_information_headers
    string get_strategy_information_headers() const
    Gets speciation strategy information headers for logs.
    Definition: neat_speciation_strategy.cxx:371
    +
    NeatSpeciationStrategy
    Definition: neat_speciation_strategy.hxx:18
    +
    NeatSpeciationStrategy::get_generated_genomes
    int32_t get_generated_genomes() const
    Definition: neat_speciation_strategy.cxx:75
    +
    NeatSpeciationStrategy::inserted_genomes
    int32_t inserted_genomes
    How many genomes have been inserted into this speciatoin strategy.
    Definition: neat_speciation_strategy.hxx:33
    +
    NeatSpeciationStrategy::generate_genome
    RNN_Genome * generate_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)
    Generates a new genome.
    Definition: neat_speciation_strategy.cxx:229
    +
    NeatSpeciationStrategy::print
    void print(string indent="") const
    Prints out all the island's populations.
    Definition: neat_speciation_strategy.cxx:360
    +
    NeatSpeciationStrategy::get_best_fitness
    double get_best_fitness()
    Gets the fitness of the best genome of all the islands.
    Definition: neat_speciation_strategy.cxx:125
    +
    RNN_Genome
    Definition: rnn_genome.hxx:37
    +
    NeatSpeciationStrategy::get_worst_genome
    RNN_Genome * get_worst_genome()
    Gets the the worst genome of all the islands.
    Definition: neat_speciation_strategy.cxx:103
    +
    NeatSpeciationStrategy::insert_genome
    int32_t insert_genome(RNN_Genome *genome)
    Inserts a copy of the genome into this speciation strategy.
    Definition: neat_speciation_strategy.cxx:140
    +
    NeatSpeciationStrategy::get_inserted_genomes
    int32_t get_inserted_genomes() const
    Definition: neat_speciation_strategy.cxx:79
    +
    NeatSpeciationStrategy::intra_island_crossover_rate
    double intra_island_crossover_rate
    How frequently to do intra-island crossovers.
    Definition: neat_speciation_strategy.hxx:29
    + +
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    + + diff --git a/docs/html/open.png b/docs/html/open.png new file mode 100644 index 0000000000000000000000000000000000000000..30f75c7efe2dd0c9e956e35b69777a02751f048b GIT binary patch literal 123 zcmeAS@N?(olHy`uVBq!ia0vp^oFL4>1|%O$WD@{VPM$7~Ar*{o?;hlAFyLXmaDC0y znK1_#cQqJWPES%4Uujug^TE?jMft$}Eq^WaR~)%f)vSNs&gek&x%A9X9sM + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: opencl/opencl_utils.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
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    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
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    + + +
    + +
    + + +
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    +
    +
    opencl_utils.hxx
    +
    +
    +
    1 #ifdef __APPLE__
    +
    2 #include <OpenCL/cl.h>
    +
    3 #else
    +
    4 #include <CL/cl.h>
    +
    5 #endif
    +
    6 
    +
    7 
    +
    8 /* check for an error and print an error message if found */
    +
    9 void check_error(cl_int err, const char* fmt, ...);
    +
    10 
    +
    11 /* Find a GPU or CPU associated with the first available platform */
    +
    12 cl_device_id create_device();
    +
    13 
    +
    14 /* Create program from a file and compile it */
    +
    15 cl_program build_program(cl_context ctx, cl_device_id dev, const char* filename);
    +
    16 
    +
    17 
    +
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    Here is a list of all related documentation pages:
    + + +
     Getting Started and Prerequisites
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    + + diff --git a/docs/html/pooling_8hxx_source.html b/docs/html/pooling_8hxx_source.html new file mode 100644 index 00000000..b5f5bf8f --- /dev/null +++ b/docs/html/pooling_8hxx_source.html @@ -0,0 +1,109 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: cnn/pooling.hxx Source File + + + + + + + + + +
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    EXONA: Evolutionary eXploration of Neural Architectures +
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    + + +
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    +
    +
    pooling.hxx
    +
    +
    +
    1 #ifndef EXACT_POOLING_HXX
    +
    2 #define EXACT_POOLING_HXX
    +
    3 
    +
    4 #include <vector>
    +
    5 using std::vector;
    +
    6 
    +
    7 void update_offset(vector<int> &pools, vector<int> &offset);
    +
    8 void initialize_pools(vector<int> &pools, vector<int> &offset, int input_size, int output_size);
    +
    9 
    +
    10 
    +
    11 void pool_forward(const float* input, float scale, float *pool_gradients, float* output, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t output_size_y, int32_t output_size_x, vector<int> &y_pools, vector<int> &x_pools, vector<int> &y_pool_offset, vector<int> &x_pool_offset, minstd_rand0 &generator, bool training, bool max_pooling);
    +
    12 
    +
    13 void pool_forward_ry(const float* input, float scale, float *pool_gradients, float* output, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t output_size_y, int32_t output_size_x, vector<int> &y_pools, vector<int> &x_pools, vector<int> &y_pool_offset, vector<int> &x_pool_offset, minstd_rand0 &generator, bool training, bool max_pooling);
    +
    14 
    +
    15 void pool_forward_rx(const float* input, float scale, float *pool_gradients, float* output, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t output_size_y, int32_t output_size_x, vector<int> &y_pools, vector<int> &x_pools, vector<int> &y_pool_offset, vector<int> &x_pool_offset, minstd_rand0 &generator, bool training, bool max_pooling);
    +
    16 
    +
    17 void pool_forward_ry_rx(const float* input, float scale, float *pool_gradients, float* output, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t output_size_y, int32_t output_size_x, vector<int> &y_pools, vector<int> &x_pools, vector<int> &y_pool_offset, vector<int> &x_pool_offset, minstd_rand0 &generator, bool training, bool max_pooling);
    +
    18 
    +
    19 
    +
    20 void pool_backward(float* input_errors, float &scale_update, const float* inputs, const float *pool_gradients, const float* output_errors, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t output_size_y, int32_t output_size_x, vector<int> &y_pools, vector<int> &x_pools, vector<int> &y_pool_offset, vector<int> &x_pool_offset);
    +
    21 
    +
    22 void pool_backward_ry(float* input_errors, float &scale_update, const float* inputs, const float *pool_gradients, const float* output_errors, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t output_size_y, int32_t output_size_x, vector<int> &y_pools, vector<int> &x_pools, vector<int> &y_pool_offset, vector<int> &x_pool_offset);
    +
    23 
    +
    24 void pool_backward_rx(float* input_errors, float &scale_update, const float* inputs, const float *pool_gradients, const float* output_errors, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t output_size_y, int32_t output_size_x, vector<int> &y_pools, vector<int> &x_pools, vector<int> &y_pool_offset, vector<int> &x_pool_offset);
    +
    25 
    +
    26 void pool_backward_ry_rx(float* input_errors, float &scale_update, const float* inputs, const float *pool_gradients, const float* output_errors, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t output_size_y, int32_t output_size_x, vector<int> &y_pools, vector<int> &x_pools, vector<int> &y_pool_offset, vector<int> &x_pool_offset);
    +
    27 
    +
    28 #endif
    +
    + + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/propagation_8hxx_source.html b/docs/html/propagation_8hxx_source.html new file mode 100644 index 00000000..516c699a --- /dev/null +++ b/docs/html/propagation_8hxx_source.html @@ -0,0 +1,107 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: cnn/propagation.hxx Source File + + + + + + + + + +
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    EXONA: Evolutionary eXploration of Neural Architectures +
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    propagation.hxx
    +
    +
    +
    1 #ifndef CNN_PROPAGATION_H
    +
    2 #define CNN_PROPAGATION_H
    +
    3 
    +
    4 #include "stdint.h"
    +
    5 
    +
    6 #include <vector>
    +
    7 using std::vector;
    +
    8 
    +
    9 void prop_forward(const float* input, const float* weights, float* output, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t filter_y, int32_t filter_x, int32_t output_size_y, int32_t output_size_x);
    +
    10 
    +
    11 void prop_forward_ry(const float* input, const float* weights, float* output, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t filter_y, int32_t filter_x, int32_t output_size_y, int32_t output_size_x);
    +
    12 
    +
    13 void prop_forward_rx(const float* input, const float* weights, float* output, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t filter_y, int32_t filter_x, int32_t output_size_y, int32_t output_size_x);
    +
    14 
    +
    15 void prop_forward_ry_rx(const float* input, const float* weights, float* output, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t filter_y, int32_t filter_x, int32_t output_size_y, int32_t output_size_x);
    +
    16 
    +
    17 
    +
    18 void prop_backward(float* output_errors, float* input, float* input_errors, float* weight_updates, float* weights, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t filter_y, int32_t filter_x, int32_t output_size_y, int32_t output_size_x);
    +
    19 
    +
    20 void prop_backward_ry(float* output_errors, float* input, float* input_errors, float* weight_updates, float* weights, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t filter_y, int32_t filter_x, int32_t output_size_y, int32_t output_size_x);
    +
    21 
    +
    22 void prop_backward_rx(float* output_errors, float* input, float* input_errors, float* weight_updates, float* weights, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t filter_y, int32_t filter_x, int32_t output_size_y, int32_t output_size_x);
    +
    23 
    +
    24 void prop_backward_ry_rx(float* output_errors, float* input, float* input_errors, float* weight_updates, float* weights, int32_t batch_size, int32_t input_size_y, int32_t input_size_x, int32_t filter_y, int32_t filter_x, int32_t output_size_y, int32_t output_size_x);
    +
    25 
    +
    26 #endif
    +
    + + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/random_8hxx_source.html b/docs/html/random_8hxx_source.html new file mode 100644 index 00000000..d4ced033 --- /dev/null +++ b/docs/html/random_8hxx_source.html @@ -0,0 +1,123 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: common/random.hxx Source File + + + + + + + + + +
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    + + + + + + +
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    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
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    + + +
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    +
    random.hxx
    +
    +
    +
    1 #ifndef EXACT_RANDOM_HXX
    +
    2 #define EXACT_RANDOM_HXX
    +
    3 
    +
    4 #include <iostream>
    +
    5 using std::ostream;
    +
    6 using std::istream;
    +
    7 
    +
    8 #include <random>
    +
    9 using std::minstd_rand0;
    +
    10 
    +
    11 #include <vector>
    +
    12 using std::vector;
    +
    13 
    +
    14 void fisher_yates_shuffle(minstd_rand0 &generator, vector<int> &v);
    +
    15 void fisher_yates_shuffle(minstd_rand0 &generator, vector<long> &v);
    +
    16 
    +
    17 float random_0_1(minstd_rand0 &generator);
    +
    18 
    +
    19 class NormalDistribution {
    +
    20  private:
    +
    21  bool generate;
    +
    22  float z0;
    +
    23  float z1;
    +
    24 
    +
    25  public:
    +
    26 
    +
    27  NormalDistribution();
    +
    28 
    +
    29  float random(minstd_rand0 &generator, float mu, float sigma);
    +
    30 
    +
    31  friend ostream &operator<<(ostream &os, const NormalDistribution &normal_distribution);
    +
    32  friend istream &operator>>(istream &is, NormalDistribution &normal_distribution);
    +
    33 
    +
    34  bool operator==(const NormalDistribution &other) const;
    +
    35  bool operator!=(const NormalDistribution &other) const;
    +
    36 };
    +
    37 
    +
    38 #endif
    +
    + + +
    NormalDistribution
    Definition: random.hxx:19
    + + + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/random__dag__node_8hxx_source.html b/docs/html/random__dag__node_8hxx_source.html new file mode 100644 index 00000000..4fd8c2e0 --- /dev/null +++ b/docs/html/random__dag__node_8hxx_source.html @@ -0,0 +1,188 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/random_dag_node.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    random_dag_node.hxx
    +
    +
    +
    1 #ifndef EXAMM_RANDOM_DAG_NODE_HXX
    +
    2 #define EXAMM_RANDOM_DAG_NODE_HXX
    +
    3 
    +
    4 #include <string>
    +
    5 using std::string;
    +
    6 
    +
    7 #include <random>
    +
    8 using std::minstd_rand0;
    +
    9 using std::uniform_real_distribution;
    +
    10 
    +
    11 #include <vector>
    +
    12 using std::vector;
    +
    13 
    +
    14 #include <utility>
    +
    15 using std::pair;
    +
    16 using std::make_pair;
    +
    17 
    +
    18 
    +
    19 #include "common/random.hxx"
    +
    20 
    +
    21 #include "rnn_node_interface.hxx"
    +
    22 
    +
    23 class RANDOM_DAG_Node : public RNN_Node_Interface{
    +
    24  private:
    +
    25 
    +
    26  vector<int> node_output;
    +
    27  // starting node 0
    +
    28  double rw;
    +
    29  double zw;
    +
    30 
    +
    31  // weights for other nodes
    +
    32  vector<double> weights;
    +
    33 
    +
    34 
    +
    35  // gradients of starting node 0
    +
    36  vector<double> d_zw;
    +
    37  vector<double> d_rw;
    +
    38 
    +
    39  // gradients of other nodes
    +
    40  vector<vector<double>> d_weights;
    +
    41 
    +
    42  // gradient of prev output
    +
    43  vector<double> d_h_prev;
    +
    44 
    +
    45  // output of edge between node with weight wj from node with weight wi
    +
    46  vector<vector<double>> Nodes;
    +
    47  // derivative of edge between node with weight wj from node with weight wi
    +
    48  vector<vector<double>> l_Nodes;
    +
    49 
    +
    50 
    +
    51  public:
    +
    52 
    +
    53  RANDOM_DAG_Node(int _innovation_number, int _type, double _depth);
    +
    54  ~RANDOM_DAG_Node();
    +
    55 
    +
    56 
    +
    57  void initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma);
    +
    58  void initialize_xavier(minstd_rand0 &generator, uniform_real_distribution<double> &rng1_1, double range);
    +
    59  void initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range);
    +
    60  void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution<double> &rng);
    +
    61 
    +
    62 
    +
    63  double get_gradient(string gradient_name);
    +
    64  void print_gradient(string gradient_name);
    +
    65 
    +
    66  double activation(double value, int act_operator);
    +
    67  double activation_derivative(double value, double input, int act_operator);
    +
    68 
    +
    69  void input_fired(int time, double incoming_output);
    +
    70 
    +
    71  void try_update_deltas(int time);
    +
    72  void error_fired(int time, double error);
    +
    73  void output_fired(int time, double delta);
    +
    74 
    +
    75  uint32_t get_number_weights() const;
    +
    76 
    +
    77  void get_weights(vector<double> &parameters) const;
    +
    78  void set_weights(const vector<double> &parameters);
    +
    79 
    +
    80  void get_weights(uint32_t &offset, vector<double> &parameters) const;
    +
    81  void set_weights(uint32_t &offset, const vector<double> &parameters);
    +
    82 
    +
    83  void get_gradients(vector<double> &gradients);
    +
    84 
    +
    85  void reset(int _series_length);
    +
    86 
    +
    87  void write_to_stream(ostream &out);
    +
    88 
    +
    89  RNN_Node_Interface* copy() const;
    +
    90 
    +
    91  friend class RNN_Edge;
    +
    92 
    +
    93 };
    +
    94 
    +
    95 
    +
    96 
    +
    97 #endif
    +
    +
    RANDOM_DAG_Node
    Definition: random_dag_node.hxx:23
    +
    RNN_Edge
    Definition: rnn_edge.hxx:6
    + + + + +
    NormalDistribution
    Definition: random.hxx:19
    + + +
    std::make_pair
    T make_pair(T... args)
    +
    RNN_Node_Interface
    Definition: rnn_node_interface.hxx:133
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/rnn_8hxx_source.html b/docs/html/rnn_8hxx_source.html new file mode 100644 index 00000000..89926e81 --- /dev/null +++ b/docs/html/rnn_8hxx_source.html @@ -0,0 +1,175 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/rnn.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    rnn.hxx
    +
    +
    +
    1 #ifndef EXAMM_RNN_GENOME_HXX
    +
    2 #define EXAMM_RNN_GENOME_HXX
    +
    3 
    +
    4 #include <string>
    +
    5 using std::string;
    +
    6 
    +
    7 #include <vector>
    +
    8 using std::vector;
    +
    9 
    +
    10 
    +
    11 #include "rnn_node_interface.hxx"
    +
    12 #include "rnn_edge.hxx"
    +
    13 #include "rnn_recurrent_edge.hxx"
    +
    14 
    +
    15 #include "time_series/time_series.hxx"
    +
    16 #include "word_series/word_series.hxx"
    +
    17 
    +
    18 class RNN {
    +
    19  private:
    +
    20 
    +
    25  int series_length;
    +
    26 
    +
    32  bool use_regression;
    +
    33 
    +
    34  vector<RNN_Node_Interface*> input_nodes;
    +
    35  vector<RNN_Node_Interface*> output_nodes;
    +
    36 
    +
    37  vector<RNN_Node_Interface*> nodes;
    +
    38  vector<RNN_Edge*> edges;
    +
    39  vector<RNN_Recurrent_Edge*> recurrent_edges;
    +
    40 
    +
    41  public:
    +
    42  RNN(vector<RNN_Node_Interface*> &_nodes, vector<RNN_Edge*> &_edges, const vector<string> &input_parameter_names, const vector<string> &output_parameter_names);
    +
    43  RNN(vector<RNN_Node_Interface*> &_nodes, vector<RNN_Edge*> &_edges, vector<RNN_Recurrent_Edge*> &_recurrent_edges, const vector<string> &input_parameter_names, const vector<string> &output_parameter_names);
    +
    44  ~RNN();
    +
    45 
    +
    46  void fix_parameter_orders(const vector<string> &input_parameter_names, const vector<string> &output_parameter_names);
    +
    47  void validate_parameters(const vector<string> &input_parameter_names, const vector<string> &output_parameter_names);
    +
    48 
    +
    49  int get_number_nodes();
    +
    50  int get_number_edges();
    +
    51 
    +
    52  RNN_Node_Interface* get_node(int i);
    +
    53  RNN_Edge* get_edge(int i);
    +
    54 
    +
    55  void forward_pass(const vector< vector<double> > &series_data, bool using_dropout, bool training, double dropout_probability);
    +
    56  void backward_pass(double error, bool using_dropout, bool training, double dropout_probability);
    +
    57 
    +
    58 
    +
    66  double calculate_error_softmax(const vector< vector<double> > &expected_outputs);
    +
    67  double calculate_error_mse(const vector< vector<double> > &expected_outputs);
    +
    68  double calculate_error_mae(const vector< vector<double> > &expected_outputs);
    +
    69 
    +
    70 
    +
    71 
    +
    83  double prediction_softmax(const vector< vector<double> > &series_data, const vector< vector<double> > &expected_outputs, bool using_dropout, bool training, double dropout_probability);
    +
    84  double prediction_mse(const vector< vector<double> > &series_data, const vector< vector<double> > &expected_outputs, bool using_dropout, bool training, double dropout_probability);
    +
    85  double prediction_mae(const vector< vector<double> > &series_data, const vector< vector<double> > &expected_outputs, bool using_dropout, bool training, double dropout_probability);
    +
    86 
    +
    87 
    +
    88  vector<double> get_predictions(const vector< vector<double> > &series_data, const vector< vector<double> > &expected_outputs, bool usng_dropout, double dropout_probability);
    +
    89 
    +
    90  void write_predictions(string output_filename, const vector<string> &input_parameter_names, const vector<string> &output_parameter_names, const vector< vector<double> > &series_data, const vector< vector<double> > &expected_outputs, TimeSeriesSets *time_series_sets, bool using_dropout, double dropout_probability);
    +
    91  void write_predictions(string output_filename, const vector<string> &input_parameter_names, const vector<string> &output_parameter_names, const vector< vector<double> > &series_data, const vector< vector<double> > &expected_outputs, Corpus *word_series_sets, bool using_dropout, double dropout_probability);
    +
    92 
    +
    93  void initialize_randomly();
    +
    94  void get_weights(vector<double> &parameters);
    +
    95  void set_weights(const vector<double> &parameters);
    +
    96  void enable_use_regression(bool _use_regression);
    +
    97 
    +
    98  uint32_t get_number_weights();
    +
    99 
    +
    100 
    +
    114  void get_analytic_gradient(const vector<double> &test_parameters, const vector< vector<double> > &inputs, const vector< vector<double> > &outputs, double &mse, vector<double> &analytic_gradient, bool using_dropout, bool training, double dropout_probability);
    +
    115  void get_empirical_gradient(const vector<double> &test_parameters, const vector< vector<double> > &inputs, const vector< vector<double> > &outputs, double &mae, vector<double> &empirical_gradient, bool using_dropout, bool training, double dropout_probability);
    +
    116 
    +
    117  //RNN* copy();
    +
    118 
    +
    119  friend void get_mse(RNN* genome, const vector< vector<double> > &expected, double &mse, vector< vector<double> > &deltas);
    +
    120  friend void get_mae(RNN* genome, const vector< vector<double> > &expected, double &mae, vector< vector<double> > &deltas);
    +
    121 };
    +
    122 
    +
    123 #endif
    +
    +
    RNN::use_regression
    bool use_regression
    Specifies whether to use the regression or not.
    Definition: rnn.hxx:32
    +
    RNN_Edge
    Definition: rnn_edge.hxx:6
    + +
    RNN::prediction_softmax
    double prediction_softmax(const vector< vector< double > > &series_data, const vector< vector< double > > &expected_outputs, bool using_dropout, bool training, double dropout_probability)
    Calls the calculate_error_softmax after the forward pass across the rnn genome.
    Definition: rnn.cxx:491
    + +
    RNN::get_analytic_gradient
    void get_analytic_gradient(const vector< double > &test_parameters, const vector< vector< double > > &inputs, const vector< vector< double > > &outputs, double &mse, vector< double > &analytic_gradient, bool using_dropout, bool training, double dropout_probability)
    Updates the analytic gradients after the forward pass across the rnn genome and the backward pass seq...
    Definition: rnn.cxx:633
    +
    RNN::calculate_error_softmax
    double calculate_error_softmax(const vector< vector< double > > &expected_outputs)
    Calculates the sum of cross entropy error by comparing the expected outputs and the predicted outputs...
    Definition: rnn.cxx:409
    +
    TimeSeriesSets
    Definition: time_series.hxx:114
    +
    Corpus
    Definition: word_series.hxx:163
    +
    RNN
    Definition: rnn.hxx:18
    +
    RNN_Node_Interface
    Definition: rnn_node_interface.hxx:133
    +
    RNN::series_length
    int series_length
    Specifies the length of the dataset.
    Definition: rnn.hxx:25
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/rnn__edge_8hxx_source.html b/docs/html/rnn__edge_8hxx_source.html new file mode 100644 index 00000000..a5e0e526 --- /dev/null +++ b/docs/html/rnn__edge_8hxx_source.html @@ -0,0 +1,206 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/rnn_edge.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    rnn_edge.hxx
    +
    +
    +
    1 #ifndef EXAMM_RNN_EDGE_HXX
    +
    2 #define EXAMM_RNN_EDGE_HXX
    +
    3 
    +
    4 #include "rnn_node_interface.hxx"
    +
    5 
    +
    6 class RNN_Edge {
    +
    7  private:
    +
    8  int32_t innovation_number;
    +
    9 
    +
    10  vector<double> outputs;
    +
    11  vector<double> deltas;
    +
    12  vector<bool> dropped_out;
    +
    13 
    +
    14  double weight;
    +
    15  double d_weight;
    +
    16 
    +
    17  bool enabled;
    +
    18  bool forward_reachable;
    +
    19  bool backward_reachable;
    +
    20 
    +
    21  int32_t input_innovation_number;
    +
    22  int32_t output_innovation_number;
    +
    23 
    +
    24  RNN_Node_Interface *input_node;
    +
    25  RNN_Node_Interface *output_node;
    +
    26 
    +
    27  public:
    +
    28  RNN_Edge(int32_t _innovation_number, RNN_Node_Interface *_input_node, RNN_Node_Interface *_output_node);
    +
    29 
    +
    30  RNN_Edge(int32_t _innovation_number, int32_t _input_innovation_number, int32_t _output_innovation_number, const vector<RNN_Node_Interface*> &nodes);
    +
    31 
    +
    32  RNN_Edge* copy(const vector<RNN_Node_Interface*> new_nodes);
    +
    33 
    +
    34  void reset(int32_t series_length);
    +
    35 
    +
    36  void propagate_forward(int32_t time);
    +
    37  void propagate_backward(int32_t time);
    +
    38 
    +
    39  void propagate_forward(int32_t time, bool training, double dropout_probability);
    +
    40  void propagate_backward(int32_t time, bool training, double dropout_probability);
    +
    41 
    +
    42  double get_gradient() const;
    +
    43  int32_t get_innovation_number() const;
    +
    44  int32_t get_input_innovation_number() const;
    +
    45  int32_t get_output_innovation_number() const;
    +
    46 
    +
    47  const RNN_Node_Interface* get_input_node() const;
    +
    48  const RNN_Node_Interface* get_output_node() const;
    +
    49 
    +
    50  bool is_enabled() const;
    +
    51  bool is_reachable() const;
    +
    52 
    +
    53  bool equals (RNN_Edge *other) const;
    +
    54 
    +
    55  void write_to_stream(ostream &out);
    +
    56 
    +
    57  friend class RNN_Genome;
    +
    58  friend class RNN;
    +
    59  friend class EXAMM;
    +
    60 };
    +
    61 
    +
    62 
    +
    63 struct sort_RNN_Edges_by_depth {
    +
    64  bool operator()(RNN_Edge *n1, RNN_Edge *n2) {
    +
    65  if (n1->get_input_node()->get_depth() < n2->get_input_node()->get_depth()) {
    +
    66  return true;
    +
    67 
    +
    68  } else if (n1->get_input_node()->get_depth() == n2->get_input_node()->get_depth()) {
    +
    69  //make sure the order of the edges is *always* the same
    +
    70  //going through the edges in different orders may effect the output
    +
    71  //of backpropagation
    +
    72  if (n1->get_innovation_number() < n2->get_innovation_number()) {
    +
    73  return true;
    +
    74  } else {
    +
    75  return false;
    +
    76  }
    +
    77 
    +
    78  } else {
    +
    79  return false;
    +
    80  }
    +
    81  }
    +
    82 };
    +
    83 
    +
    84 struct sort_RNN_Edges_by_output_depth {
    +
    85  bool operator()(RNN_Edge *n1, RNN_Edge *n2) {
    +
    86  if (n1->get_output_node()->get_depth() < n2->get_output_node()->get_depth()) {
    +
    87  return true;
    +
    88 
    +
    89  } else if (n1->get_output_node()->get_depth() == n2->get_output_node()->get_depth()) {
    +
    90  //make sure the order of the edges is *always* the same
    +
    91  //going through the edges in different orders may effect the output
    +
    92  //of backpropagation
    +
    93  if (n1->get_innovation_number() < n2->get_innovation_number()) {
    +
    94  return true;
    +
    95  } else {
    +
    96  return false;
    +
    97  }
    +
    98 
    +
    99  } else {
    +
    100  return false;
    +
    101  }
    +
    102  }
    +
    103 };
    +
    104 
    +
    105 
    +
    106 struct sort_RNN_Edges_by_innovation {
    +
    107  bool operator()(RNN_Edge *n1, RNN_Edge *n2) {
    +
    108  return n1->get_innovation_number() < n2->get_innovation_number();
    +
    109  }
    +
    110 };
    +
    111 
    +
    112 
    +
    113 
    +
    114 
    +
    115 
    +
    116 #endif
    +
    +
    RNN_Edge
    Definition: rnn_edge.hxx:6
    + +
    sort_RNN_Edges_by_depth
    Definition: rnn_edge.hxx:63
    +
    sort_RNN_Edges_by_innovation
    Definition: rnn_edge.hxx:106
    + +
    EXAMM
    Definition: examm.hxx:24
    +
    RNN_Genome
    Definition: rnn_genome.hxx:37
    +
    sort_RNN_Edges_by_output_depth
    Definition: rnn_edge.hxx:84
    +
    RNN
    Definition: rnn.hxx:18
    +
    RNN_Node_Interface
    Definition: rnn_node_interface.hxx:133
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/rnn__genome_8hxx_source.html b/docs/html/rnn__genome_8hxx_source.html new file mode 100644 index 00000000..20876820 --- /dev/null +++ b/docs/html/rnn__genome_8hxx_source.html @@ -0,0 +1,407 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/rnn_genome.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    rnn_genome.hxx
    +
    +
    +
    1 #ifndef RNN_BPTT_HXX
    +
    2 #define RNN_BPTT_HXX
    +
    3 
    +
    4 #include <fstream>
    +
    5 using std::istream;
    +
    6 using std::ifstream;
    +
    7 using std::ostream;
    +
    8 using std::ofstream;
    +
    9 
    +
    10 #include <map>
    +
    11 using std::map;
    +
    12 
    +
    13 #include <random>
    +
    14 using std::minstd_rand0;
    +
    15 using std::uniform_real_distribution;
    +
    16 using std::uniform_int_distribution;
    +
    17 using std::mt19937;
    +
    18 
    +
    19 #include <vector>
    +
    20 using std::vector;
    +
    21 
    +
    22 #include "rnn.hxx"
    +
    23 #include "rnn_node_interface.hxx"
    +
    24 #include "rnn_edge.hxx"
    +
    25 #include "rnn_recurrent_edge.hxx"
    +
    26 
    +
    27 #include "common/random.hxx"
    +
    28 #include "common/weight_initialize.hxx"
    +
    29 #include "time_series/time_series.hxx"
    +
    30 #include "word_series/word_series.hxx"
    +
    31 
    +
    32 //mysql can't handle the max float value for some reason
    +
    33 #define EXAMM_MAX_DOUBLE 10000000
    +
    34 
    +
    35 string parse_fitness(double fitness);
    +
    36 
    +
    37 class RNN_Genome {
    +
    38  private:
    +
    39  int32_t generation_id;
    +
    40  int32_t group_id;
    +
    41 
    +
    42  int32_t bp_iterations;
    +
    43  double learning_rate;
    +
    44  bool adapt_learning_rate;
    +
    45  bool use_nesterov_momentum;
    +
    46  bool use_reset_weights;
    +
    47 
    +
    48  bool use_high_norm;
    +
    49  double high_threshold;
    +
    50  bool use_low_norm;
    +
    51  double low_threshold;
    +
    52 
    +
    58  bool use_regression;
    +
    59 
    +
    60  bool use_dropout;
    +
    61  double dropout_probability;
    +
    62 
    +
    63  string structural_hash;
    +
    64 
    +
    65  string log_filename;
    +
    66 
    +
    67  WeightType weight_initialize;
    +
    68  WeightType weight_inheritance;
    +
    69  WeightType mutated_component_weight;
    +
    70 
    +
    71  map<string, int> generated_by_map;
    +
    72 
    +
    73  vector<double> initial_parameters;
    +
    74 
    +
    80  double best_validation_mse;
    +
    81  double best_validation_mae;
    +
    82  vector<double> best_parameters;
    +
    83 
    +
    84  minstd_rand0 generator;
    +
    85 
    +
    86  uniform_real_distribution<double> rng;
    +
    87  uniform_real_distribution<double> rng_0_1;
    +
    88  uniform_real_distribution<double> rng_1_1;
    +
    89  NormalDistribution normal_distribution;
    +
    90 
    +
    91  vector<RNN_Node_Interface*> nodes;
    +
    92  vector<RNN_Edge*> edges;
    +
    93  vector<RNN_Recurrent_Edge*> recurrent_edges;
    +
    94 
    +
    95  vector<string> input_parameter_names;
    +
    96  vector<string> output_parameter_names;
    +
    97 
    +
    98  string normalize_type;
    +
    99  map<string,double> normalize_mins;
    +
    100  map<string,double> normalize_maxs;
    +
    101  map<string,double> normalize_avgs;
    +
    102  map<string,double> normalize_std_devs;
    +
    103 
    +
    104  // vector<int32_t> innovation_list;
    +
    105 
    +
    106  public:
    +
    107  void sort_nodes_by_depth();
    +
    108  void sort_edges_by_depth();
    +
    109  void sort_recurrent_edges_by_depth();
    +
    110 
    +
    111  RNN_Genome(vector<RNN_Node_Interface*> &_nodes, vector<RNN_Edge*> &_edges, vector<RNN_Recurrent_Edge*> &_recurrent_edges, WeightType _weight_initialize, WeightType _weight_inheritance, WeightType _mutated_component_weight);
    +
    112  RNN_Genome(vector<RNN_Node_Interface*> &_nodes, vector<RNN_Edge*> &_edges, vector<RNN_Recurrent_Edge*> &_recurrent_edges, uint16_t seed, WeightType _weight_initialize, WeightType _weight_inheritance, WeightType _mutated_component_weight);
    +
    113 
    +
    114  RNN_Genome* copy();
    +
    115 
    +
    116  ~RNN_Genome();
    +
    117 
    +
    118  static string print_statistics_header();
    +
    119  string print_statistics();
    +
    120 
    +
    121  void set_parameter_names(const vector<string> &_input_parameter_names, const vector<string> &_output_parameter_names);
    +
    122 
    +
    123  string generated_by_string();
    +
    124 
    +
    125  string get_edge_count_str(bool recurrent);
    +
    126  string get_node_count_str(int node_type);
    +
    127 
    +
    128  const map<string, int> *get_generated_by_map();
    +
    129 
    +
    130  double get_avg_recurrent_depth() const;
    +
    131 
    +
    132  int32_t get_enabled_edge_count();
    +
    133  int32_t get_enabled_recurrent_edge_count();
    +
    134  int32_t get_enabled_node_count(int node_type);
    +
    135  int32_t get_node_count(int node_type);
    +
    136  int32_t get_enabled_node_count();
    +
    137  int32_t get_node_count();
    +
    138 
    +
    139 
    +
    146  double get_fitness() const;
    +
    147 
    +
    148 
    +
    149  double get_best_validation_softmax() const;
    +
    150  double get_best_validation_mse() const;
    +
    151  double get_best_validation_mae() const;
    +
    152 
    +
    153 
    +
    154  void set_normalize_bounds(string _normalize_type, const map<string,double> &_normalize_mins, const map<string,double> &_normalize_maxs, const map<string,double> &_normalize_avgs, const map<string,double> &_normalize_std_devs);
    +
    155 
    +
    156  string get_normalize_type() const;
    +
    157  map<string,double> get_normalize_mins() const;
    +
    158  map<string,double> get_normalize_maxs() const;
    +
    159  map<string,double> get_normalize_avgs() const;
    +
    160  map<string,double> get_normalize_std_devs() const;
    +
    161 
    +
    162  vector<string> get_input_parameter_names() const;
    +
    163  vector<string> get_output_parameter_names() const;
    +
    164 
    +
    165  int32_t get_group_id() const;
    +
    166  void set_group_id(int32_t _group_id);
    +
    167 
    +
    168 
    +
    169  void set_bp_iterations(int32_t _bp_iterations);
    +
    170  int32_t get_bp_iterations();
    +
    171 
    +
    172  void set_learning_rate(double _learning_rate);
    +
    173  void set_adapt_learning_rate(bool _adapt_learning_rate);
    +
    174  void set_nesterov_momentum(bool _use_nesterov_momentum);
    +
    175  void set_reset_weights(bool _use_reset_weights);
    +
    176  void disable_high_threshold();
    +
    177  void enable_high_threshold(double _high_threshold);
    +
    178  void disable_low_threshold();
    +
    179  void enable_low_threshold(double _low_threshold);
    +
    180  void disable_dropout();
    +
    181  void enable_dropout(double _dropout_probability);
    +
    182  void enable_use_regression(bool _use_regression);
    +
    183  void set_log_filename(string _log_filename);
    +
    184 
    +
    185  void get_weights(vector<double> &parameters);
    +
    186  void set_weights(const vector<double> &parameters);
    +
    187 
    +
    188  uint32_t get_number_weights();
    +
    189  uint32_t get_number_inputs();
    +
    190  uint32_t get_number_outputs();
    +
    191 
    +
    192  double get_avg_edge_weight();
    +
    193  void initialize_randomly();
    +
    194  void initialize_xavier(RNN_Node_Interface* n);
    +
    195  void initialize_kaiming(RNN_Node_Interface* n);
    +
    196  void initialize_node_randomly(RNN_Node_Interface* n);
    +
    197  double get_xavier_weight(RNN_Node_Interface* output_node);
    +
    198  double get_kaiming_weight(RNN_Node_Interface* output_node);
    +
    199  double get_random_weight();
    +
    200 
    +
    201 
    +
    202  void get_input_edges(int32_t node_innovation, vector< RNN_Edge*> &input_edges, vector< RNN_Recurrent_Edge*> &input_recurrent_edges);
    +
    203  int32_t get_fan_in(int32_t node_innovation);
    +
    204  int32_t get_fan_out(int32_t node_innovation);
    +
    205 
    +
    206  int32_t get_generation_id() const;
    +
    207  void set_generation_id(int32_t generation_id);
    +
    208 
    +
    209  void clear_generated_by();
    +
    210  void update_generation_map(map<string, int32_t> &generation_map);
    +
    211  void set_generated_by(string type);
    +
    212  int32_t get_generated_by(string type);
    +
    213 
    +
    214 
    +
    215  RNN* get_rnn();
    +
    216  vector<double> get_best_parameters() const;
    +
    217 
    +
    218  void set_best_parameters( vector<double> parameters); //INFO: ADDED BY ABDELRAHMAN TO USE FOR TRANSFER LEARNING
    +
    219  void set_initial_parameters( vector<double> parameters); //INFO: ADDED BY ABDELRAHMAN TO USE FOR TRANSFER LEARNING
    +
    220 
    +
    221  void get_analytic_gradient(vector<RNN*> &rnns, const vector<double> &parameters, const vector< vector< vector<double> > > &inputs, const vector< vector< vector<double> > > &outputs, double &mse, vector<double> &analytic_gradient, bool training);
    +
    222 
    +
    223  void backpropagate(const vector< vector< vector<double> > > &inputs, const vector< vector< vector<double> > > &outputs, const vector< vector< vector<double> > > &validation_inputs, const vector< vector< vector<double> > > &validation_outputs);
    +
    224 
    +
    225  void backpropagate_stochastic(const vector< vector< vector<double> > > &inputs, const vector< vector< vector<double> > > &outputs, const vector< vector< vector<double> > > &validation_inputs, const vector< vector< vector<double> > > &validation_outputs);
    +
    226 
    +
    227 
    +
    237  double get_softmax(const vector<double> &parameters, const vector< vector< vector<double> > > &inputs, const vector< vector< vector<double> > > &outputs);
    +
    238  double get_mse(const vector<double> &parameters, const vector< vector< vector<double> > > &inputs, const vector< vector< vector<double> > > &outputs);
    +
    239  double get_mae(const vector<double> &parameters, const vector< vector< vector<double> > > &inputs, const vector< vector< vector<double> > > &outputs);
    +
    240 
    +
    241 
    +
    242  vector< vector<double> > get_predictions(const vector<double> &parameters, const vector< vector< vector<double> > > &inputs, const vector< vector< vector<double> > > &outputs);
    +
    243  void write_predictions(string output_directory, const vector<string> &input_filenames, const vector<double> &parameters, const vector< vector< vector<double> > > &inputs, const vector< vector< vector<double> > > &outputs, TimeSeriesSets *time_series_sets);
    +
    244 
    +
    257  void write_predictions(string output_directory, const vector<string> &input_filenames, const vector<double> &parameters, const vector< vector< vector<double> > > &inputs, const vector< vector< vector<double> > > &outputs, Corpus * word_series_sets);
    +
    258 
    +
    259  void get_mu_sigma(const vector<double> &p, double &mu, double &sigma);
    +
    260 
    +
    261  bool sanity_check();
    +
    262  void assign_reachability();
    +
    263  bool outputs_unreachable();
    +
    264 
    +
    265  RNN_Node_Interface* create_node(double mu, double sigma, int node_type, int32_t &node_innovation_count, double depth);
    +
    266 
    +
    267  bool attempt_edge_insert(RNN_Node_Interface *n1, RNN_Node_Interface *n2, double mu, double sigma, int32_t &edge_innovation_count);
    +
    268  bool attempt_recurrent_edge_insert(RNN_Node_Interface *n1, RNN_Node_Interface *n2, double mu, double sigma, uniform_int_distribution<int32_t> dist, int32_t &edge_innovation_count);
    +
    269 
    +
    270  //after adding an Elman or Jordan node, generate the circular RNN edge for Elman and the
    +
    271  //edges from output to this node for Jordan.
    +
    272  void generate_recurrent_edges(RNN_Node_Interface *node, double mu, double sigma, uniform_int_distribution<int32_t> dist, int32_t &edge_innovation_count);
    +
    273 
    +
    274  bool add_edge(double mu, double sigma, int32_t &edge_innovation_count);
    +
    275  bool add_recurrent_edge(double mu, double sigma, uniform_int_distribution<int32_t> rec_depth_dist, int32_t &edge_innovation_count);
    +
    276  bool disable_edge();
    +
    277  bool enable_edge();
    +
    278  bool split_edge(double mu, double sigma, int node_type, uniform_int_distribution<int32_t> rec_depth_dist, int32_t &edge_innovation_count, int32_t &node_innovation_count);
    +
    279 
    +
    280 
    +
    281  bool add_node(double mu, double sigma, int node_type, uniform_int_distribution<int32_t> dist, int32_t &edge_innovation_count, int32_t &node_innovation_count);
    +
    282 
    +
    283  bool enable_node();
    +
    284  bool disable_node();
    +
    285  bool split_node(double mu, double sigma, int node_type, uniform_int_distribution<int32_t> dist, int32_t &edge_innovation_count, int32_t &node_innovation_count);
    +
    286  bool merge_node(double mu, double sigma, int node_type, uniform_int_distribution<int32_t> dist, int32_t &edge_innovation_count, int32_t &node_innovation_count);
    +
    287 
    +
    295  bool has_node_with_innovation(int32_t innovation_number) const;
    +
    296 
    +
    297  bool equals(RNN_Genome *other);
    +
    298 
    +
    299  string get_color(double weight, bool is_recurrent);
    +
    300  void write_graphviz(string filename);
    +
    301 
    +
    302  RNN_Genome(string binary_filename);
    +
    303  RNN_Genome(char* array, int32_t length);
    +
    304  RNN_Genome(istream &bin_infile);
    +
    305 
    +
    306  void read_from_array(char *array, int32_t length);
    +
    307  void read_from_stream(istream &bin_istream);
    +
    308 
    +
    309  void write_to_array(char **array, int32_t &length);
    +
    310  void write_to_file(string bin_filename);
    +
    311  void write_to_stream(ostream &bin_stream);
    +
    312 
    +
    313  bool connect_new_input_node( double mu, double sig, RNN_Node_Interface *new_node, uniform_int_distribution<int32_t> dist, int32_t &edge_innovation_count );
    +
    314  bool connect_new_output_node( double mu, double sig, RNN_Node_Interface *new_node, uniform_int_distribution<int32_t> dist, int32_t &edge_innovation_count );
    +
    315  bool connect_node_to_hid_nodes( double mu, double sig, RNN_Node_Interface *new_node, uniform_int_distribution<int32_t> dist, int32_t &edge_innovation_count, bool from_input );
    +
    316 
    +
    317  void update_innovation_counts(int32_t &node_innovation_count, int32_t &edge_innovation_count);
    +
    318 
    +
    319  vector<int32_t> get_innovation_list();
    +
    323  string get_structural_hash() const;
    +
    324 
    +
    328  int get_max_node_innovation_count();
    +
    329 
    +
    333  int get_max_edge_innovation_count();
    +
    334 
    +
    335  void transfer_to(const vector<string> &new_input_parameter_names, const vector<string> &new_output_parameter_names, string transfer_learning_version, bool epigenetic_weights, int32_t min_recurrent_depth, int32_t max_recurrent_depth);
    +
    336 
    +
    337  friend class EXAMM;
    +
    338  friend class IslandSpeciationStrategy;
    +
    339  friend class NeatSpeciationStrategy;
    +
    340  friend class RecDepthFrequencyTable;
    +
    341 };
    +
    342 
    +
    343 struct sort_genomes_by_fitness {
    +
    344  bool operator()(RNN_Genome *g1, RNN_Genome *g2) {
    +
    345  return g1->get_fitness() < g2->get_fitness();
    +
    346  }
    +
    347 };
    +
    348 
    +
    349 void write_binary_string(ostream &out, string s, string name);
    +
    350 void read_binary_string(istream &in, string &s, string name);
    +
    351 
    +
    352 #endif
    +
    + + +
    RNN_Genome::get_structural_hash
    string get_structural_hash() const
    Definition: rnn_genome.cxx:3666
    + +
    NormalDistribution
    Definition: random.hxx:19
    +
    RNN_Genome::get_fitness
    double get_fitness() const
    Calculates the fitness of the rnn genome across the dataset.
    Definition: rnn_genome.cxx:850
    +
    RNN_Genome::get_max_edge_innovation_count
    int get_max_edge_innovation_count()
    Definition: rnn_genome.cxx:3680
    +
    IslandSpeciationStrategy
    Definition: island_speciation_strategy.hxx:19
    + + +
    RNN_Genome::use_regression
    bool use_regression
    Specifies whether to use the regression or not.
    Definition: rnn_genome.hxx:58
    +
    NeatSpeciationStrategy
    Definition: neat_speciation_strategy.hxx:18
    + + +
    RNN_Genome::has_node_with_innovation
    bool has_node_with_innovation(int32_t innovation_number) const
    Determines if the genome contains a node with the given innovation number.
    Definition: rnn_genome.cxx:1605
    + +
    TimeSeriesSets
    Definition: time_series.hxx:114
    +
    EXAMM
    Definition: examm.hxx:24
    +
    Corpus
    Definition: word_series.hxx:163
    +
    RNN_Genome
    Definition: rnn_genome.hxx:37
    +
    RNN_Genome::best_validation_mse
    double best_validation_mse
    Specifies the validation error across the dataset.
    Definition: rnn_genome.hxx:80
    +
    sort_genomes_by_fitness
    Definition: rnn_genome.hxx:343
    +
    RNN
    Definition: rnn.hxx:18
    + +
    RNN_Genome::get_max_node_innovation_count
    int get_max_node_innovation_count()
    Definition: rnn_genome.cxx:3670
    +
    RNN_Node_Interface
    Definition: rnn_node_interface.hxx:133
    +
    RNN_Genome::get_softmax
    double get_softmax(const vector< double > &parameters, const vector< vector< vector< double > > > &inputs, const vector< vector< vector< double > > > &outputs)
    Predicts the softmax error after a single pass across the rnn genome.
    Definition: rnn_genome.cxx:1467
    + + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/rnn__node_8hxx_source.html b/docs/html/rnn__node_8hxx_source.html new file mode 100644 index 00000000..54238afe --- /dev/null +++ b/docs/html/rnn__node_8hxx_source.html @@ -0,0 +1,141 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/rnn_node.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
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    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    rnn_node.hxx
    +
    +
    +
    1 #ifndef EXAMM_RNN_NODE_HXX
    +
    2 #define EXAMM_RNN_NODE_HXX
    +
    3 
    +
    4 #include <vector>
    +
    5 using std::vector;
    +
    6 
    +
    7 #include "rnn_node_interface.hxx"
    +
    8 
    +
    9 class RNN_Node : public RNN_Node_Interface {
    +
    10  private:
    +
    11  double bias;
    +
    12  double d_bias;
    +
    13 
    +
    14  vector<double> ld_output;
    +
    15 
    +
    16  public:
    +
    17 
    +
    18  //constructor for hidden nodes
    +
    19  RNN_Node(int _innovation_number, int _layer_type, double _depth, int _node_type);
    +
    20 
    +
    21  //constructor for input and output nodes
    +
    22  RNN_Node(int _innovation_number, int _layer_type, double _depth, int _node_type, string _parameter_name);
    +
    23  ~RNN_Node();
    +
    24 
    +
    25  void initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma);
    +
    26  void initialize_xavier(minstd_rand0 &generator, uniform_real_distribution<double> &rng1_1, double range);
    +
    27  void initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range);
    +
    28  void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution<double> &rng);
    +
    29 
    +
    30  void input_fired(int time, double incoming_output);
    +
    31 
    +
    32  void try_update_deltas(int time);
    +
    33  void output_fired(int time, double delta);
    +
    34  void error_fired(int time, double error);
    +
    35 
    +
    36  uint32_t get_number_weights() const ;
    +
    37  void get_weights(vector<double> &parameters) const;
    +
    38  void set_weights(const vector<double> &parameters);
    +
    39  void get_weights(uint32_t &offset, vector<double> &parameters) const;
    +
    40  void set_weights(uint32_t &offset, const vector<double> &parameters);
    +
    41 
    +
    42  void reset(int _series_length);
    +
    43 
    +
    44  void get_gradients(vector<double> &gradients);
    +
    45 
    +
    46  RNN_Node_Interface* copy() const;
    +
    47 
    +
    48  void write_to_stream(ostream &out);
    +
    49 
    +
    50  friend class RNN_Edge;
    +
    51 };
    +
    52 
    +
    53 #endif
    +
    +
    RNN_Edge
    Definition: rnn_edge.hxx:6
    +
    RNN_Node
    Definition: rnn_node.hxx:9
    + + +
    NormalDistribution
    Definition: random.hxx:19
    + + +
    RNN_Node_Interface
    Definition: rnn_node_interface.hxx:133
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/rnn__node__interface_8hxx_source.html b/docs/html/rnn__node__interface_8hxx_source.html new file mode 100644 index 00000000..6eaa6b7d --- /dev/null +++ b/docs/html/rnn__node__interface_8hxx_source.html @@ -0,0 +1,248 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/rnn_node_interface.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
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    + + +
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    +
    rnn_node_interface.hxx
    +
    +
    +
    1 #ifndef EXAMM_RNN_NODE_INTERFACE_HXX
    +
    2 #define EXAMM_RNN_NODE_INTERFACE_HXX
    +
    3 
    +
    4 #include <cstdint>
    +
    5 
    +
    6 #include <fstream>
    +
    7 using std::ostream;
    +
    8 
    +
    9 #include <random>
    +
    10 using std::minstd_rand0;
    +
    11 using std::uniform_real_distribution;
    +
    12 
    +
    13 #include <string>
    +
    14 using std::string;
    +
    15 
    +
    16 #include <vector>
    +
    17 using std::vector;
    +
    18 
    +
    19 #include "common/random.hxx"
    +
    20 
    +
    21 class RNN;
    +
    22 
    +
    23 #define INPUT_LAYER 0
    +
    24 #define HIDDEN_LAYER 1
    +
    25 #define OUTPUT_LAYER 2
    +
    27 extern const int32_t NUMBER_NODE_TYPES;
    +
    28 extern const string NODE_TYPES[];
    +
    29 
    +
    30 #define SIMPLE_NODE 0
    +
    31 #define JORDAN_NODE 1
    +
    32 #define ELMAN_NODE 2
    +
    33 #define UGRNN_NODE 3
    +
    34 #define MGU_NODE 4
    +
    35 #define GRU_NODE 5
    +
    36 #define DELTA_NODE 6
    +
    37 #define LSTM_NODE 7
    +
    38 #define ENARC_NODE 8
    +
    39 #define ENAS_DAG_NODE 9
    +
    40 #define RANDOM_DAG_NODE 10
    +
    51 double sigmoid(double value);
    +
    52 
    +
    61 double swish(double value);
    +
    62 
    +
    71 double leakyReLU(double value);
    +
    72 
    +
    81 double identity(double value);
    +
    82 
    +
    83 
    +
    92 double sigmoid_derivative(double value);
    +
    93 
    +
    102 double tanh_derivative(double value);
    +
    103 
    +
    112 double swish_derivative(double value, double input);
    +
    113 
    +
    121 double leakyReLU_derivative(double input);
    +
    122 
    +
    128 double identity_derivative();
    +
    129 
    +
    130 
    +
    131 double bound(double value);
    +
    132 
    +
    133 class RNN_Node_Interface {
    +
    134  protected:
    +
    135  int32_t innovation_number;
    +
    136  int32_t layer_type;
    +
    137  int32_t node_type;
    +
    138 
    +
    139  double depth;
    +
    140 
    +
    141  //this is only used for input and output nodes to track
    +
    142  //which parameter they are assigned to
    +
    143  string parameter_name;
    +
    144 
    +
    145  bool enabled;
    +
    146  bool backward_reachable;
    +
    147  bool forward_reachable;
    +
    148 
    +
    149  int32_t series_length;
    +
    150 
    +
    151  vector<double> input_values;
    +
    152  vector<double> output_values;
    +
    153  vector<double> error_values;
    +
    154  vector<double> d_input;
    +
    155 
    +
    156  vector<int32_t> inputs_fired;
    +
    157  vector<int32_t> outputs_fired;
    +
    158  int32_t total_inputs;
    +
    159  int32_t total_outputs;
    +
    160  public:
    +
    161  //this constructor is for hidden nodes
    +
    162  RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth);
    +
    163 
    +
    164  //this constructor is for input and output nodes (as they have an associated parameter name
    +
    165  RNN_Node_Interface(int32_t _innovation_number, int32_t _layer_type, double _depth, string _parameter_name);
    +
    166  virtual ~RNN_Node_Interface();
    +
    167 
    +
    168  virtual void initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma) = 0;
    +
    169  virtual void initialize_xavier(minstd_rand0 &generator, uniform_real_distribution<double> &rng_1_1, double range) = 0;
    +
    170  virtual void initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range) = 0;
    +
    171  virtual void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution<double> &rng) = 0;
    +
    172 
    +
    173  virtual void input_fired(int32_t time, double incoming_output) = 0;
    +
    174  virtual void output_fired(int32_t time, double delta) = 0;
    +
    175  virtual void error_fired(int32_t time, double error) = 0;
    +
    176 
    +
    177  virtual uint32_t get_number_weights() const = 0;
    +
    178 
    +
    179  virtual void get_weights(vector<double> &parameters) const = 0;
    +
    180  virtual void set_weights(const vector<double> &parameters) = 0;
    +
    181  virtual void get_weights(uint32_t &offset, vector<double> &parameters) const = 0;
    +
    182  virtual void set_weights(uint32_t &offset, const vector<double> &parameters) = 0;
    +
    183  virtual void reset(int32_t _series_length) = 0;
    +
    184 
    +
    185  virtual void get_gradients(vector<double> &gradients) = 0;
    +
    186 
    +
    187  virtual RNN_Node_Interface* copy() const = 0;
    +
    188 
    +
    189  void write_to_stream(ostream &out);
    +
    190 
    +
    191  int32_t get_node_type() const;
    +
    192  int32_t get_layer_type() const;
    +
    193  int32_t get_innovation_number() const;
    +
    194  int32_t get_total_inputs() const;
    +
    195  int32_t get_total_outputs() const;
    +
    196 
    +
    197  double get_depth() const;
    +
    198  bool equals(RNN_Node_Interface *other) const;
    +
    199 
    +
    200  bool is_reachable() const;
    +
    201  bool is_enabled() const;
    +
    202 
    +
    203  friend class RNN_Edge;
    +
    204  friend class RNN_Recurrent_Edge;
    +
    205  friend class RNN;
    +
    206  friend class RNN_Genome;
    +
    207 
    +
    208  friend void get_mse(RNN* genome, const vector< vector<double> > &expected, double &mse, vector< vector<double> > &deltas);
    +
    209  friend void get_mae(RNN* genome, const vector< vector<double> > &expected, double &mae, vector< vector<double> > &deltas);
    +
    210 };
    +
    211 
    +
    212 
    +
    213 struct sort_RNN_Nodes_by_innovation {
    +
    214  bool operator()(RNN_Node_Interface *n1, RNN_Node_Interface *n2) {
    +
    215  return n1->get_innovation_number() < n2->get_innovation_number();
    +
    216  }
    +
    217 };
    +
    218 
    +
    219 
    +
    220 struct sort_RNN_Nodes_by_depth {
    +
    221  bool operator()(RNN_Node_Interface *n1, RNN_Node_Interface *n2) {
    +
    222  return n1->get_depth() < n2->get_depth();
    +
    223  }
    +
    224 };
    +
    225 
    +
    226 #endif
    +
    +
    RNN_Edge
    Definition: rnn_edge.hxx:6
    + + +
    RNN_Recurrent_Edge
    Definition: rnn_recurrent_edge.hxx:8
    + +
    NormalDistribution
    Definition: random.hxx:19
    + + +
    sort_RNN_Nodes_by_depth
    Definition: rnn_node_interface.hxx:220
    +
    RNN_Genome
    Definition: rnn_genome.hxx:37
    +
    sort_RNN_Nodes_by_innovation
    Definition: rnn_node_interface.hxx:213
    +
    RNN
    Definition: rnn.hxx:18
    +
    RNN_Node_Interface
    Definition: rnn_node_interface.hxx:133
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/rnn__recurrent__edge_8hxx_source.html b/docs/html/rnn__recurrent__edge_8hxx_source.html new file mode 100644 index 00000000..8efbdf0c --- /dev/null +++ b/docs/html/rnn__recurrent__edge_8hxx_source.html @@ -0,0 +1,209 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/rnn_recurrent_edge.hxx Source File + + + + + + + + + +
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    + + + + + + +
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    EXONA: Evolutionary eXploration of Neural Architectures +
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    +
    + + + + + + + + +
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    + + +
    + +
    + + +
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    +
    +
    rnn_recurrent_edge.hxx
    +
    +
    +
    1 #ifndef EXAMM_RNN_RECURRENT_EDGE_HXX
    +
    2 #define EXAMM_RNN_RECURRENT_EDGE_HXX
    +
    3 
    +
    4 class RNN;
    +
    5 
    +
    6 #include "rnn_node_interface.hxx"
    +
    7 
    +
    8 class RNN_Recurrent_Edge {
    +
    9  private:
    +
    10  int32_t innovation_number;
    +
    11  int32_t series_length;
    +
    12 
    +
    13  //how far in the past to get the value
    +
    14  int32_t recurrent_depth;
    +
    15 
    +
    16  vector<double> outputs;
    +
    17  vector<double> deltas;
    +
    18 
    +
    19  double weight;
    +
    20  double d_weight;
    +
    21 
    +
    22  bool enabled;
    +
    23  bool forward_reachable;
    +
    24  bool backward_reachable;
    +
    25 
    +
    26  int32_t input_innovation_number;
    +
    27  int32_t output_innovation_number;
    +
    28 
    +
    29  RNN_Node_Interface *input_node;
    +
    30  RNN_Node_Interface *output_node;
    +
    31 
    +
    32  public:
    +
    33  RNN_Recurrent_Edge(int32_t _innovation_number, int32_t _recurrent_depth, RNN_Node_Interface *_input_node, RNN_Node_Interface *_output_node);
    +
    34 
    +
    35  RNN_Recurrent_Edge(int32_t _innovation_number, int32_t _recurrent_depth, int32_t _input_innovation_number, int32_t _output_innovation_number, const vector<RNN_Node_Interface*> &nodes);
    +
    36 
    +
    37  void reset(int32_t _series_length);
    +
    38 
    +
    39  void first_propagate_forward();
    +
    40  void first_propagate_backward();
    +
    41  void propagate_forward(int32_t time);
    +
    42  void propagate_backward(int32_t time);
    +
    43 
    +
    44  int32_t get_recurrent_depth() const;
    +
    45  double get_gradient();
    +
    46  bool is_enabled() const;
    +
    47  bool is_reachable() const;
    +
    48 
    +
    49  RNN_Recurrent_Edge* copy(const vector<RNN_Node_Interface*> new_nodes);
    +
    50 
    +
    51  int32_t get_innovation_number() const;
    +
    52  int32_t get_input_innovation_number() const;
    +
    53  int32_t get_output_innovation_number() const;
    +
    54 
    +
    55  const RNN_Node_Interface* get_input_node() const;
    +
    56  const RNN_Node_Interface* get_output_node() const;
    +
    57 
    +
    58  void write_to_stream(ostream &out);
    +
    59 
    +
    60  bool equals(RNN_Recurrent_Edge *other) const;
    +
    61 
    +
    62  friend class RNN_Genome;
    +
    63  friend class RNN;
    +
    64  friend class EXAMM;
    +
    65  friend class RecDepthFrequencyTable;
    +
    66 };
    +
    67 
    +
    68 
    +
    69 struct sort_RNN_Recurrent_Edges_by_depth {
    +
    70  bool operator()(RNN_Recurrent_Edge *n1, RNN_Recurrent_Edge *n2) {
    +
    71  if (n1->get_input_node()->get_depth() < n2->get_input_node()->get_depth()) {
    +
    72  return true;
    +
    73 
    +
    74  } else if (n1->get_input_node()->get_depth() == n2->get_input_node()->get_depth()) {
    +
    75  //make sure the order of the edges is *always* the same
    +
    76  //going through the edges in different orders may effect the output
    +
    77  //of backpropagation
    +
    78  if (n1->get_innovation_number() < n2->get_innovation_number()) {
    +
    79  return true;
    +
    80  } else {
    +
    81  return false;
    +
    82  }
    +
    83 
    +
    84  } else {
    +
    85  return false;
    +
    86  }
    +
    87  }
    +
    88 };
    +
    89 
    +
    90 struct sort_RNN_Recurrent_Edges_by_output_depth {
    +
    91  bool operator()(RNN_Recurrent_Edge *n1, RNN_Recurrent_Edge *n2) {
    +
    92  if (n1->get_output_node()->get_depth() < n2->get_output_node()->get_depth()) {
    +
    93  return true;
    +
    94 
    +
    95  } else if (n1->get_output_node()->get_depth() == n2->get_output_node()->get_depth()) {
    +
    96  //make sure the order of the edges is *always* the same
    +
    97  //going through the edges in different orders may effect the output
    +
    98  //of backpropagation
    +
    99  if (n1->get_innovation_number() < n2->get_innovation_number()) {
    +
    100  return true;
    +
    101  } else {
    +
    102  return false;
    +
    103  }
    +
    104 
    +
    105  } else {
    +
    106  return false;
    +
    107  }
    +
    108  }
    +
    109 };
    +
    110 
    +
    111 
    +
    112 struct sort_RNN_Recurrent_Edges_by_innovation {
    +
    113  bool operator()(RNN_Recurrent_Edge *n1, RNN_Recurrent_Edge *n2) {
    +
    114  return n1->get_innovation_number() < n2->get_innovation_number();
    +
    115  }
    +
    116 };
    +
    117 
    +
    118 
    +
    119 #endif
    +
    + +
    RNN_Recurrent_Edge
    Definition: rnn_recurrent_edge.hxx:8
    +
    sort_RNN_Recurrent_Edges_by_depth
    Definition: rnn_recurrent_edge.hxx:69
    + +
    sort_RNN_Recurrent_Edges_by_innovation
    Definition: rnn_recurrent_edge.hxx:112
    +
    EXAMM
    Definition: examm.hxx:24
    +
    RNN_Genome
    Definition: rnn_genome.hxx:37
    +
    sort_RNN_Recurrent_Edges_by_output_depth
    Definition: rnn_recurrent_edge.hxx:90
    +
    RNN
    Definition: rnn.hxx:18
    +
    RNN_Node_Interface
    Definition: rnn_node_interface.hxx:133
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/run__statistics_8hxx_source.html b/docs/html/run__statistics_8hxx_source.html new file mode 100644 index 00000000..51601c81 --- /dev/null +++ b/docs/html/run__statistics_8hxx_source.html @@ -0,0 +1,231 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: mpi/run_statistics.hxx Source File + + + + + + + + + +
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    EXONA: Evolutionary eXploration of Neural Architectures +
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    + + +
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    +
    run_statistics.hxx
    +
    +
    +
    1 #ifndef EXACT_RUN_STATISTICS
    +
    2 #define EXACT_RUN_STATISTICS
    +
    3 
    +
    4 #include <string>
    +
    5 using std::string;
    +
    6 
    +
    7 #include "tracker.hxx"
    +
    8 
    +
    9 string fix_run_type(string run_type);
    +
    10 
    +
    11 class ConsolidatedStatistics {
    +
    12  public:
    +
    13  string run_type;
    +
    14 
    +
    15  double dfm_min;
    +
    16  double dfm_avg;
    +
    17  double dfm_max;
    +
    18 
    +
    19  ConsolidatedStatistics(string _run_type);
    +
    20 
    +
    21  string to_string_min();
    +
    22  string to_string_avg();
    +
    23  string to_string_max();
    +
    24 };
    +
    25 
    +
    26 struct cs_less_than_min {
    +
    27  inline bool operator() (const ConsolidatedStatistics *s1, const ConsolidatedStatistics *s2)
    +
    28  {
    +
    29  return (s1->dfm_min < s2->dfm_min);
    +
    30  }
    +
    31 };
    +
    32 
    +
    33 struct cs_less_than_avg {
    +
    34  inline bool operator() (const ConsolidatedStatistics *s1, const ConsolidatedStatistics *s2)
    +
    35  {
    +
    36  return (s1->dfm_avg < s2->dfm_avg);
    +
    37  }
    +
    38 };
    +
    39 
    +
    40 struct cs_less_than_max {
    +
    41  inline bool operator() (const ConsolidatedStatistics *s1, const ConsolidatedStatistics *s2)
    +
    42  {
    +
    43  return (s1->dfm_max < s2->dfm_max);
    +
    44  }
    +
    45 };
    +
    46 
    +
    47 
    +
    48 class RunStatistics {
    +
    49  public:
    +
    50  string output_name;
    +
    51  string run_type;
    +
    52 
    +
    53  double dfm_min;
    +
    54  double dfm_avg;
    +
    55  double dfm_max;
    +
    56 
    +
    57  Tracker mse;
    +
    58  Tracker mae;
    +
    59  Tracker edge;
    +
    60  Tracker rec_edge;
    +
    61  Tracker node;
    +
    62  Tracker ff;
    +
    63  Tracker lstm;
    +
    64  Tracker ugrnn;
    +
    65  Tracker delta;
    +
    66  Tracker mgu;
    +
    67  Tracker gru;
    +
    68 
    +
    69  RunStatistics(string _output_name, string _run_type);
    +
    70 
    +
    71  void set_deviation_from_mean_min(double _dfm_min);
    +
    72  void set_deviation_from_mean_avg(double _dfm_avg);
    +
    73  void set_deviation_from_mean_max(double _dfm_max);
    +
    74 
    +
    75  string correlate_header();
    +
    76 
    +
    77  string to_string_min();
    +
    78  string to_string_avg();
    +
    79  string to_string_max();
    +
    80  string to_string_stddev();
    +
    81  string to_string_correlate(string target_name, Tracker &target);
    +
    82 
    +
    83  string overview_header();
    +
    84  string overview_footer(string type);
    +
    85  string to_overview_string();
    +
    86 
    +
    87  string overview_ff_header();
    +
    88  string overview_ff_footer(string type);
    +
    89  string to_overview_ff_string();
    +
    90 };
    +
    91 
    +
    92 struct less_than_min {
    +
    93  inline bool operator() (const RunStatistics *s1, const RunStatistics *s2)
    +
    94  {
    +
    95  return (s1->mae.min() < s2->mae.min());
    +
    96  }
    +
    97 };
    +
    98 
    +
    99 struct less_than_avg {
    +
    100  inline bool operator() (const RunStatistics *s1, const RunStatistics *s2)
    +
    101  {
    +
    102  return (s1->mae.avg() < s2->mae.avg());
    +
    103  }
    +
    104 };
    +
    105 
    +
    106 struct less_than_max {
    +
    107  inline bool operator() (const RunStatistics *s1, const RunStatistics *s2)
    +
    108  {
    +
    109  return (s1->mae.max() < s2->mae.max());
    +
    110  }
    +
    111 };
    +
    112 
    +
    113 struct less_than_dfm_min {
    +
    114  inline bool operator() (const RunStatistics *s1, const RunStatistics *s2)
    +
    115  {
    +
    116  return (s1->dfm_min < s2->dfm_min);
    +
    117  }
    +
    118 };
    +
    119 
    +
    120 struct less_than_dfm_avg {
    +
    121  inline bool operator() (const RunStatistics *s1, const RunStatistics *s2)
    +
    122  {
    +
    123  return (s1->dfm_avg < s2->dfm_avg);
    +
    124  }
    +
    125 };
    +
    126 
    +
    127 struct less_than_dfm_max {
    +
    128  inline bool operator() (const RunStatistics *s1, const RunStatistics *s2)
    +
    129  {
    +
    130  return (s1->dfm_max < s2->dfm_max);
    +
    131  }
    +
    132 };
    +
    133 
    +
    134 
    +
    135 
    +
    136 
    +
    137 
    +
    138 #endif
    +
    + +
    RunStatistics
    Definition: run_statistics.hxx:48
    +
    less_than_min
    Definition: run_statistics.hxx:92
    +
    less_than_dfm_min
    Definition: run_statistics.hxx:113
    +
    Tracker
    Definition: tracker.hxx:9
    +
    cs_less_than_min
    Definition: run_statistics.hxx:26
    +
    less_than_avg
    Definition: run_statistics.hxx:99
    +
    ConsolidatedStatistics
    Definition: run_statistics.hxx:11
    +
    less_than_max
    Definition: run_statistics.hxx:106
    +
    less_than_dfm_max
    Definition: run_statistics.hxx:127
    +
    cs_less_than_avg
    Definition: run_statistics.hxx:33
    +
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+} + +.right #MSearchSelect { + right:5px; +} + +#MSearchField { + display:block; + position:absolute; + height:19px; + background:url('search_m.png') repeat-x; + border:none; + width:115px; + margin-left:20px; + padding-left:4px; + color: #909090; + outline: none; + font: 9pt Arial, Verdana, sans-serif; + -webkit-border-radius: 0px; +} + +#FSearchBox #MSearchField { + margin-left:15px; +} + +#MSearchBox .right { + display:block; + position:absolute; + right:10px; + top:8px; + width:20px; + height:19px; + background:url('search_r.png') no-repeat; + background-position:left; +} + +#MSearchClose { + display: none; + position: absolute; + top: 4px; + background : none; + border: none; + margin: 0px 4px 0px 0px; + padding: 0px 0px; + outline: none; +} + +.left #MSearchClose { + left: 6px; +} + +.right #MSearchClose { + right: 2px; +} + +.MSearchBoxActive #MSearchField { + color: #000000; +} + +/*---------------- Search filter selection */ + +#MSearchSelectWindow { + display: none; + position: absolute; + left: 0; top: 0; + border: 1px solid #90A5CE; + background-color: #F9FAFC; + z-index: 10001; + padding-top: 4px; + padding-bottom: 4px; + -moz-border-radius: 4px; + -webkit-border-top-left-radius: 4px; + -webkit-border-top-right-radius: 4px; + -webkit-border-bottom-left-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); +} + +.SelectItem { + font: 8pt Arial, Verdana, sans-serif; + padding-left: 2px; + padding-right: 12px; + border: 0px; +} + +span.SelectionMark { + margin-right: 4px; + font-family: monospace; + outline-style: none; + text-decoration: none; +} + +a.SelectItem { + display: block; + outline-style: none; + color: #000000; + text-decoration: none; + padding-left: 6px; + padding-right: 12px; +} + +a.SelectItem:focus, +a.SelectItem:active { + color: #000000; + outline-style: none; + text-decoration: none; +} + +a.SelectItem:hover { + color: #FFFFFF; + background-color: #3D578C; + outline-style: none; + text-decoration: none; + cursor: pointer; + display: block; +} + +/*---------------- Search results window */ + +iframe#MSearchResults { + width: 60ex; + height: 15em; +} + +#MSearchResultsWindow { + display: none; + position: absolute; + left: 0; top: 0; + border: 1px solid #000; + background-color: #EEF1F7; + z-index:10000; +} + +/* ----------------------------------- */ + + +#SRIndex { + clear:both; + padding-bottom: 15px; +} + +.SREntry { + font-size: 10pt; + padding-left: 1ex; +} + +.SRPage .SREntry { + font-size: 8pt; + padding: 1px 5px; +} + +body.SRPage { + margin: 5px 2px; +} + +.SRChildren { + padding-left: 3ex; padding-bottom: .5em +} + +.SRPage .SRChildren { + display: none; +} + +.SRSymbol { + font-weight: bold; + color: #425E97; + font-family: Arial, Verdana, sans-serif; + text-decoration: none; + outline: none; +} + +a.SRScope { + display: block; + color: #425E97; + font-family: Arial, Verdana, sans-serif; + text-decoration: none; + outline: none; +} + +a.SRSymbol:focus, a.SRSymbol:active, +a.SRScope:focus, a.SRScope:active { + text-decoration: underline; +} + +span.SRScope { + padding-left: 4px; +} + +.SRPage .SRStatus { + padding: 2px 5px; + font-size: 8pt; + font-style: italic; +} + +.SRResult { + display: none; +} + +DIV.searchresults { + margin-left: 10px; + margin-right: 10px; +} + +/*---------------- External search page results */ + +.searchresult { + background-color: #F0F3F8; +} + +.pages b { + color: white; + padding: 5px 5px 3px 5px; + background-image: url("../tab_a.png"); + background-repeat: repeat-x; + text-shadow: 0 1px 1px #000000; +} + +.pages { + line-height: 17px; + margin-left: 4px; + text-decoration: none; +} + +.hl { + font-weight: bold; +} + +#searchresults { + margin-bottom: 20px; +} + +.searchpages { + margin-top: 10px; +} + diff --git a/docs/html/search/search.js b/docs/html/search/search.js new file mode 100644 index 00000000..a554ab9c --- /dev/null +++ b/docs/html/search/search.js @@ -0,0 +1,814 @@ +/* + @licstart The following is the entire license notice for the + JavaScript code in this file. + + Copyright (C) 1997-2017 by Dimitri van Heesch + + This program is free software; you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation; either version 2 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License along + with this program; if not, write to the Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. + + @licend The above is the entire license notice + for the JavaScript code in this file + */ +function convertToId(search) +{ + var result = ''; + for (i=0;i do a search + { + this.Search(); + } + } + + this.OnSearchSelectKey = function(evt) + { + var e = (evt) ? evt : window.event; // for IE + if (e.keyCode==40 && this.searchIndex0) // Up + { + this.searchIndex--; + this.OnSelectItem(this.searchIndex); + } + else if (e.keyCode==13 || e.keyCode==27) + { + this.OnSelectItem(this.searchIndex); + this.CloseSelectionWindow(); + this.DOMSearchField().focus(); + } + return false; + } + + // --------- Actions + + // Closes the results window. + this.CloseResultsWindow = function() + { + this.DOMPopupSearchResultsWindow().style.display = 'none'; + this.DOMSearchClose().style.display = 'none'; + this.Activate(false); + } + + this.CloseSelectionWindow = function() + { + this.DOMSearchSelectWindow().style.display = 'none'; + } + + // Performs a search. + this.Search = function() + { + this.keyTimeout = 0; + + // strip leading whitespace + var searchValue = this.DOMSearchField().value.replace(/^ +/, ""); + + var code = searchValue.toLowerCase().charCodeAt(0); + var idxChar = searchValue.substr(0, 1).toLowerCase(); + if ( 0xD800 <= code && code <= 0xDBFF && searchValue > 1) // surrogate pair + { + idxChar = searchValue.substr(0, 2); + } + + var resultsPage; + var resultsPageWithSearch; + var hasResultsPage; + + var idx = indexSectionsWithContent[this.searchIndex].indexOf(idxChar); + if (idx!=-1) + { + var hexCode=idx.toString(16); + resultsPage = this.resultsPath + '/' + indexSectionNames[this.searchIndex] + '_' + hexCode + '.html'; + resultsPageWithSearch = resultsPage+'?'+escape(searchValue); + hasResultsPage = true; + } + else // nothing available for this search term + { + resultsPage = this.resultsPath + '/nomatches.html'; + resultsPageWithSearch = resultsPage; + hasResultsPage = false; + } + + window.frames.MSearchResults.location = resultsPageWithSearch; + var domPopupSearchResultsWindow = this.DOMPopupSearchResultsWindow(); + + if (domPopupSearchResultsWindow.style.display!='block') + { + var domSearchBox = this.DOMSearchBox(); + this.DOMSearchClose().style.display = 'inline'; + if (this.insideFrame) + { + var domPopupSearchResults = this.DOMPopupSearchResults(); + domPopupSearchResultsWindow.style.position = 'relative'; + domPopupSearchResultsWindow.style.display = 'block'; + var width = document.body.clientWidth - 8; // the -8 is for IE :-( + domPopupSearchResultsWindow.style.width = width + 'px'; + domPopupSearchResults.style.width = width + 'px'; + } + else + { + var domPopupSearchResults = this.DOMPopupSearchResults(); + var left = getXPos(domSearchBox) + 150; // domSearchBox.offsetWidth; + var top = getYPos(domSearchBox) + 20; // domSearchBox.offsetHeight + 1; + domPopupSearchResultsWindow.style.display = 'block'; + left -= domPopupSearchResults.offsetWidth; + domPopupSearchResultsWindow.style.top = top + 'px'; + domPopupSearchResultsWindow.style.left = left + 'px'; + } + } + + this.lastSearchValue = searchValue; + this.lastResultsPage = resultsPage; + } + + // -------- Activation Functions + + // Activates or deactivates the search panel, resetting things to + // their default values if necessary. + this.Activate = function(isActive) + { + if (isActive || // open it + this.DOMPopupSearchResultsWindow().style.display == 'block' + ) + { + this.DOMSearchBox().className = 'MSearchBoxActive'; + + var searchField = this.DOMSearchField(); + + if (searchField.value == this.searchLabel) // clear "Search" term upon entry + { + searchField.value = ''; + this.searchActive = true; + } + } + else if (!isActive) // directly remove the panel + { + this.DOMSearchBox().className = 'MSearchBoxInactive'; + this.DOMSearchField().value = this.searchLabel; + this.searchActive = false; + this.lastSearchValue = '' + this.lastResultsPage = ''; + } + } +} + +// ----------------------------------------------------------------------- + +// The class that handles everything on the search results page. +function SearchResults(name) +{ + // The number of matches from the last run of . + this.lastMatchCount = 0; + this.lastKey = 0; + this.repeatOn = false; + + // Toggles the visibility of the passed element ID. + this.FindChildElement = function(id) + { + var parentElement = document.getElementById(id); + var element = parentElement.firstChild; + + while (element && element!=parentElement) + { + if (element.nodeName == 'DIV' && element.className == 'SRChildren') + { + return element; + } + + if (element.nodeName == 'DIV' && element.hasChildNodes()) + { + element = element.firstChild; + } + else if (element.nextSibling) + { + element = element.nextSibling; + } + else + { + do + { + element = element.parentNode; + } + while (element && element!=parentElement && !element.nextSibling); + + if (element && element!=parentElement) + { + element = element.nextSibling; + } + } + } + } + + this.Toggle = function(id) + { + var element = this.FindChildElement(id); + if (element) + { + if (element.style.display == 'block') + { + element.style.display = 'none'; + } + else + { + element.style.display = 'block'; + } + } + } + + // Searches for the passed string. If there is no parameter, + // it takes it from the URL query. + // + // Always returns true, since other documents may try to call it + // and that may or may not be possible. + this.Search = function(search) + { + if (!search) // get search word from URL + { + search = window.location.search; + search = search.substring(1); // Remove the leading '?' + search = unescape(search); + } + + search = search.replace(/^ +/, ""); // strip leading spaces + search = search.replace(/ +$/, ""); // strip trailing spaces + search = search.toLowerCase(); + search = convertToId(search); + + var resultRows = document.getElementsByTagName("div"); + var matches = 0; + + var i = 0; + while (i < resultRows.length) + { + var row = resultRows.item(i); + if (row.className == "SRResult") + { + var rowMatchName = row.id.toLowerCase(); + rowMatchName = rowMatchName.replace(/^sr\d*_/, ''); // strip 'sr123_' + + if (search.length<=rowMatchName.length && + rowMatchName.substr(0, search.length)==search) + { + row.style.display = 'block'; + matches++; + } + else + { + row.style.display = 'none'; + } + } + i++; + } + document.getElementById("Searching").style.display='none'; + if (matches == 0) // no results + { + document.getElementById("NoMatches").style.display='block'; + } + else // at least one result + { + document.getElementById("NoMatches").style.display='none'; + } + this.lastMatchCount = matches; + return true; + } + + // return the first item with index index or higher that is visible + this.NavNext = function(index) + { + var focusItem; + while (1) + { + var focusName = 'Item'+index; + focusItem = document.getElementById(focusName); + if (focusItem && focusItem.parentNode.parentNode.style.display=='block') + { + break; + } + else if (!focusItem) // last element + { + break; + } + focusItem=null; + index++; + } + return focusItem; + } + + this.NavPrev = function(index) + { + var focusItem; + while (1) + { + var focusName = 'Item'+index; + focusItem = document.getElementById(focusName); + if (focusItem && focusItem.parentNode.parentNode.style.display=='block') + { + break; + } + else if (!focusItem) // last element + { + break; + } + focusItem=null; + index--; + } + return focusItem; + } + + this.ProcessKeys = function(e) + { + if (e.type == "keydown") + { + this.repeatOn = false; + this.lastKey = e.keyCode; + } + else if (e.type == "keypress") + { + if (!this.repeatOn) + { + if (this.lastKey) this.repeatOn = true; + return false; // ignore first keypress after keydown + } + } + else if (e.type == "keyup") + { + this.lastKey = 0; + this.repeatOn = false; + } + return this.lastKey!=0; + } + + this.Nav = function(evt,itemIndex) + { + var e = (evt) ? evt : window.event; // for IE + if (e.keyCode==13) return true; + if (!this.ProcessKeys(e)) return false; + + if (this.lastKey==38) // Up + { + var newIndex = itemIndex-1; + var focusItem = this.NavPrev(newIndex); + if (focusItem) + { + var child = this.FindChildElement(focusItem.parentNode.parentNode.id); + if (child && child.style.display == 'block') // children visible + { + var n=0; + var tmpElem; + while (1) // search for last child + { + tmpElem = document.getElementById('Item'+newIndex+'_c'+n); + if (tmpElem) + { + focusItem = tmpElem; + } + else // found it! + { + break; + } + n++; + } + } + } + if (focusItem) + { + focusItem.focus(); + } + else // return focus to search field + { + parent.document.getElementById("MSearchField").focus(); + } + } + else if (this.lastKey==40) // Down + { + var newIndex = itemIndex+1; + var focusItem; + var item = document.getElementById('Item'+itemIndex); + var elem = this.FindChildElement(item.parentNode.parentNode.id); + if (elem && elem.style.display == 'block') // children visible + { + focusItem = document.getElementById('Item'+itemIndex+'_c0'); + } + if (!focusItem) focusItem = this.NavNext(newIndex); + if (focusItem) focusItem.focus(); + } + else if (this.lastKey==39) // Right + { + var item = document.getElementById('Item'+itemIndex); + var elem = this.FindChildElement(item.parentNode.parentNode.id); + if (elem) elem.style.display = 'block'; + } + else if (this.lastKey==37) // Left + { + var item = document.getElementById('Item'+itemIndex); + var elem = this.FindChildElement(item.parentNode.parentNode.id); + if (elem) elem.style.display = 'none'; + } + else if (this.lastKey==27) // Escape + { + parent.searchBox.CloseResultsWindow(); + parent.document.getElementById("MSearchField").focus(); + } + else if (this.lastKey==13) // Enter + { + return true; + } + return false; + } + + this.NavChild = function(evt,itemIndex,childIndex) + { + var e = (evt) ? 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    speciation_strategy.hxx
    +
    +
    +
    1 #ifndef EXAMM_SPECIATION_STRATEGY_HXX
    +
    2 #define EXAMM_SPECIATION_STRATEGY_HXX
    +
    3 
    +
    4 #include <functional>
    +
    5 using std::function;
    +
    6 #include <string>
    +
    7 using std::string;
    +
    8 #include <random>
    +
    9 using std::minstd_rand0;
    +
    10 using std::uniform_real_distribution;
    +
    11 
    +
    12 
    +
    13 class SpeciationStrategy {
    +
    14 
    +
    15  public:
    +
    19  virtual int32_t get_generated_genomes() const = 0;
    +
    20 
    +
    24  virtual int32_t get_inserted_genomes() const = 0;
    +
    25 
    +
    30  virtual double get_best_fitness() = 0;
    +
    31 
    +
    36  virtual double get_worst_fitness() = 0;
    +
    37 
    +
    42  virtual RNN_Genome* get_best_genome() = 0;
    +
    43 
    +
    48  virtual RNN_Genome* get_worst_genome() = 0;
    +
    49 
    +
    60  virtual int32_t insert_genome(RNN_Genome* genome) = 0;
    +
    61 
    +
    72  virtual RNN_Genome* generate_genome(uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator, function<void (int32_t, RNN_Genome*)> &mutate, function<RNN_Genome* (RNN_Genome*, RNN_Genome *)> &crossover) = 0;
    +
    73 
    +
    74 
    +
    80  virtual void print(string indent = "") const = 0;
    +
    81 
    +
    85  virtual string get_strategy_information_headers() const = 0;
    +
    86 
    +
    90  virtual string get_strategy_information_values() const = 0;
    +
    91 
    +
    92  virtual RNN_Genome* get_global_best_genome() = 0;
    +
    93 };
    +
    94 
    +
    95 #endif
    +
    96 
    +
    +
    SpeciationStrategy::get_worst_genome
    virtual RNN_Genome * get_worst_genome()=0
    Gets the the worst genome of all the islands.
    + +
    SpeciationStrategy::generate_genome
    virtual RNN_Genome * generate_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, function< void(int32_t, RNN_Genome *)> &mutate, function< RNN_Genome *(RNN_Genome *, RNN_Genome *)> &crossover)=0
    Generates a new genome.
    + +
    SpeciationStrategy::get_best_genome
    virtual RNN_Genome * get_best_genome()=0
    Gets the best genome of all the islands.
    + +
    SpeciationStrategy
    Definition: speciation_strategy.hxx:13
    +
    SpeciationStrategy::get_strategy_information_headers
    virtual string get_strategy_information_headers() const =0
    Gets speciation strategy information headers for logs.
    + +
    SpeciationStrategy::insert_genome
    virtual int32_t insert_genome(RNN_Genome *genome)=0
    Inserts a copy of the genome into this speciation strategy.
    +
    SpeciationStrategy::get_inserted_genomes
    virtual int32_t get_inserted_genomes() const =0
    +
    SpeciationStrategy::get_generated_genomes
    virtual int32_t get_generated_genomes() const =0
    +
    SpeciationStrategy::get_best_fitness
    virtual double get_best_fitness()=0
    Gets the fitness of the best genome of all the islands.
    +
    SpeciationStrategy::get_strategy_information_values
    virtual string get_strategy_information_values() const =0
    Gets speciation strategy information values for logs.
    +
    RNN_Genome
    Definition: rnn_genome.hxx:37
    +
    SpeciationStrategy::get_worst_fitness
    virtual double get_worst_fitness()=0
    Gets the fitness of the worst genome of all the islands.
    +
    SpeciationStrategy::print
    virtual void print(string indent="") const =0
    Prints out all the island's populations.
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/species_8hxx_source.html b/docs/html/species_8hxx_source.html new file mode 100644 index 00000000..a1919223 --- /dev/null +++ b/docs/html/species_8hxx_source.html @@ -0,0 +1,175 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/species.hxx Source File + + + + + + + + + +
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    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
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    + + + + + + + + +
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    + + +
    + +
    + + +
    +
    +
    +
    species.hxx
    +
    +
    +
    1 #ifndef EXAMM_NEAT_STRATEGY_HXX
    +
    2 #define EXAMM_NEAT_STRATEGY_HXX
    +
    3 
    +
    4 #include <functional>
    +
    5 using std::function;
    +
    6 
    +
    7 #include <algorithm>
    +
    8 using std::sort;
    +
    9 using std::upper_bound;
    +
    10 
    +
    11 #include <random>
    +
    12 using std::minstd_rand0;
    +
    13 using std::uniform_real_distribution;
    +
    14 
    +
    15 #include <string>
    +
    16 using std::string;
    +
    17 
    +
    18 #include <vector>
    +
    19 
    +
    20 #include "rnn_genome.hxx"
    +
    21 
    +
    22 
    +
    23 class Species {
    +
    24  private:
    +
    25  int32_t id;
    +
    27  // int32_t latest_inserted_generation_id; /**< The latest generation id of genome being generated, including the ones doing backprop by workers */
    +
    28 
    +
    29  vector<int32_t> inserted_genome_id;
    +
    33  vector<RNN_Genome *> genomes;
    +
    34 
    +
    35  int32_t species_not_improving_count;
    +
    36 
    +
    37  public:
    +
    41  Species(int32_t id);
    +
    42 
    +
    43 
    +
    49  double get_best_fitness();
    +
    50 
    +
    56  double get_worst_fitness();
    +
    57 
    +
    63  RNN_Genome *get_best_genome();
    +
    64 
    +
    70  RNN_Genome *get_worst_genome();
    +
    71 
    +
    77  RNN_Genome *get_random_genome(uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator);
    +
    78 
    +
    84  int32_t size();
    +
    85 
    +
    93  int32_t contains(RNN_Genome* genome);
    +
    94 
    +
    102  void copy_random_genome(uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome);
    +
    103 
    +
    112  void copy_two_random_genomes(uniform_real_distribution<double> &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome1, RNN_Genome **genome2);
    +
    113 
    +
    123  int32_t insert_genome(RNN_Genome* genome);
    +
    124 
    +
    130  void print(string indent = "");
    +
    131 
    +
    132  vector<RNN_Genome *> get_genomes();
    +
    133 
    +
    134  RNN_Genome* get_latested_genome();
    +
    135 
    +
    136  void fitness_sharing_remove(double fitness_threshold, function<double (RNN_Genome*, RNN_Genome*)> &get_distance);
    +
    137 
    +
    138  void erase_species();
    +
    139 
    +
    140  int32_t get_species_not_improving_count();
    +
    141 
    +
    142  void set_species_not_improving_count(int32_t count);
    +
    143 };
    +
    144 
    +
    145 #endif
    +
    +
    Species::insert_genome
    int32_t insert_genome(RNN_Genome *genome)
    Inserts a genome into the island.
    Definition: species.cxx:94
    +
    Species::get_best_fitness
    double get_best_fitness()
    Returns the fitness of the best genome in the island.
    Definition: species.cxx:42
    + + + + +
    std::sort
    T sort(T... args)
    + +
    Species::copy_random_genome
    void copy_random_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome)
    Selects a genome from the island at random and returns a copy of it.
    Definition: species.cxx:69
    +
    Species::get_worst_fitness
    double get_worst_fitness()
    Returns the fitness of the worst genome in the island.
    Definition: species.cxx:48
    +
    Species::print
    void print(string indent="")
    Prints out the state of this island.
    Definition: species.cxx:158
    +
    Species::get_worst_genome
    RNN_Genome * get_worst_genome()
    Returns the worst genomme in the island.
    Definition: species.cxx:29
    +
    Species::copy_two_random_genomes
    void copy_two_random_genomes(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator, RNN_Genome **genome1, RNN_Genome **genome2)
    Selects two different genomes from the island at random and returns copies of them.
    Definition: species.cxx:74
    +
    Species::contains
    int32_t contains(RNN_Genome *genome)
    Checks to see if a genome already exists in the island, using the RNN_Genome::equals method (which ch...
    Definition: species.cxx:58
    +
    std::upper_bound
    T upper_bound(T... args)
    +
    Species::get_best_genome
    RNN_Genome * get_best_genome()
    Returns the best genomme in the island.
    Definition: species.cxx:24
    +
    RNN_Genome
    Definition: rnn_genome.hxx:37
    +
    Species::size
    int32_t size()
    Returns the size of the island.
    Definition: species.cxx:54
    +
    Species::id
    int32_t id
    An integer ID for this species.
    Definition: species.hxx:25
    +
    Species
    Definition: species.hxx:23
    +
    Species::Species
    Species(int32_t id)
    Initializes a species.
    Definition: species.cxx:21
    +
    Species::get_random_genome
    RNN_Genome * get_random_genome(uniform_real_distribution< double > &rng_0_1, minstd_rand0 &generator)
    Returns the best genomme in the island.
    Definition: species.cxx:34
    +
    Species::genomes
    vector< RNN_Genome * > genomes
    The genomes on this species, stored in sorted order best (front) to worst (back).
    Definition: species.hxx:33
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/splitbar.png b/docs/html/splitbar.png new file mode 100644 index 0000000000000000000000000000000000000000..fe895f2c58179b471a22d8320b39a4bd7312ec8e GIT binary patch literal 314 zcmeAS@N?(olHy`uVBq!ia0vp^Yzz!63>-{AmhX=Jf(#6djGiuzAr*{o?=JLmPLyc> z_*`QK&+BH@jWrYJ7>r6%keRM@)Qyv8R=enp0jiI>aWlGyB58O zFVR20d+y`K7vDw(hJF3;>dD*3-?v=<8M)@x|EEGLnJsniYK!2U1 Y!`|5biEc?d1`HDhPgg&ebxsLQ02F6;9RL6T literal 0 HcmV?d00001 diff --git a/docs/html/struct_coin-members.html b/docs/html/struct_coin-members.html new file mode 100644 index 00000000..8d0be68f --- /dev/null +++ b/docs/html/struct_coin-members.html @@ -0,0 +1,81 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    +Public Attributes | +List of all members
    +
    +
    Coin Struct Reference
    +
    +
    + + + + + + +

    +Public Attributes

    +uivector symbols
     
    +float weight
     
    +
    The documentation for this struct was generated from the following file:
      +
    • image_tools/lodepng.cpp
      • +
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    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_color-members.html b/docs/html/struct_color-members.html new file mode 100644 index 00000000..1c19a641 --- /dev/null +++ b/docs/html/struct_color-members.html @@ -0,0 +1,82 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    Color Member List
    +
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    This is the complete list of members for Color, including all inherited members.

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    blue (defined in Color)Color
    green (defined in Color)Color
    red (defined in Color)Color
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_color.html b/docs/html/struct_color.html new file mode 100644 index 00000000..a0ee3419 --- /dev/null +++ b/docs/html/struct_color.html @@ -0,0 +1,95 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Color Struct Reference + + + + + + + + + +
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    Color Struct Reference
    +
    +
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    +Public Attributes

    +int16_t red
     
    +int16_t green
     
    +int16_t blue
     
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_color_profile-members.html b/docs/html/struct_color_profile-members.html new file mode 100644 index 00000000..135927ab --- /dev/null +++ b/docs/html/struct_color_profile-members.html @@ -0,0 +1,96 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    ColorProfile Member List
    +
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    This is the complete list of members for ColorProfile, including all inherited members.

    + + + + + + + + + + + + + + + + + + +
    alpha (defined in ColorProfile)ColorProfile
    alpha_done (defined in ColorProfile)ColorProfile
    colored (defined in ColorProfile)ColorProfile
    colored_done (defined in ColorProfile)ColorProfile
    greybits (defined in ColorProfile)ColorProfile
    greybits_done (defined in ColorProfile)ColorProfile
    key (defined in ColorProfile)ColorProfile
    key_b (defined in ColorProfile)ColorProfile
    key_g (defined in ColorProfile)ColorProfile
    key_r (defined in ColorProfile)ColorProfile
    maxnumcolors (defined in ColorProfile)ColorProfile
    numcolors (defined in ColorProfile)ColorProfile
    numcolors_done (defined in ColorProfile)ColorProfile
    palette (defined in ColorProfile)ColorProfile
    sixteenbit (defined in ColorProfile)ColorProfile
    sixteenbit_done (defined in ColorProfile)ColorProfile
    tree (defined in ColorProfile)ColorProfile
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    + + diff --git a/docs/html/struct_color_profile.html b/docs/html/struct_color_profile.html new file mode 100644 index 00000000..e32a2fab --- /dev/null +++ b/docs/html/struct_color_profile.html @@ -0,0 +1,137 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: ColorProfile Struct Reference + + + + + + + + + +
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    ColorProfile Struct Reference
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    +unsigned char sixteenbit
     
    +unsigned char sixteenbit_done
     
    +unsigned char colored
     
    +unsigned char colored_done
     
    +unsigned char key
     
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    +unsigned short key_g
     
    +unsigned short key_b
     
    +unsigned char alpha
     
    +unsigned char alpha_done
     
    +unsigned numcolors
     
    +ColorTree tree
     
    +unsigned char * palette
     
    +unsigned maxnumcolors
     
    +unsigned char numcolors_done
     
    +unsigned greybits
     
    +unsigned char greybits_done
     
    +
    The documentation for this struct was generated from the following file:
      +
    • image_tools/lodepng.cpp
      • +
    +
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    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_color_tree-members.html b/docs/html/struct_color_tree-members.html new file mode 100644 index 00000000..871f0724 --- /dev/null +++ b/docs/html/struct_color_tree-members.html @@ -0,0 +1,81 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    ColorTree Member List
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    children (defined in ColorTree)ColorTree
    index (defined in ColorTree)ColorTree
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_color_tree.html b/docs/html/struct_color_tree.html new file mode 100644 index 00000000..c4b274ce --- /dev/null +++ b/docs/html/struct_color_tree.html @@ -0,0 +1,92 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: ColorTree Struct Reference + + + + + + + + + +
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    ColorTree Struct Reference
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    +Public Attributes

    +ColorTree * children [16]
     
    +int index
     
    +
    The documentation for this struct was generated from the following file:
      +
    • image_tools/lodepng.cpp
      • +
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    +Generated by   +doxygen + 1.8.17 +
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    EXACT_RESULT Member List
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    file_contents (defined in EXACT_RESULT)EXACT_RESULT
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    +Generated by   +doxygen + 1.8.17 +
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    EXACT_RESULT Struct Reference
    +
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    +Public Attributes

    +string file_contents
     
    +
    The documentation for this struct was generated from the following file:
      +
    • server/exact_validation_policy.cxx
      • +
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    Hash Member List
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    This is the complete list of members for Hash, including all inherited members.

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    chain (defined in Hash)Hash
    head (defined in Hash)Hash
    val (defined in Hash)Hash
    zeros (defined in Hash)Hash
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    +Generated by   +doxygen + 1.8.17 +
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    Hash Struct Reference
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    +int * head
     
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    +
    The documentation for this struct was generated from the following file:
      +
    • image_tools/lodepng.cpp
      • +
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    + + diff --git a/docs/html/struct_huffman_tree-members.html b/docs/html/struct_huffman_tree-members.html new file mode 100644 index 00000000..adec6600 --- /dev/null +++ b/docs/html/struct_huffman_tree-members.html @@ -0,0 +1,84 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    HuffmanTree Member List
    +
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    This is the complete list of members for HuffmanTree, including all inherited members.

    + + + + + + +
    lengths (defined in HuffmanTree)HuffmanTree
    maxbitlen (defined in HuffmanTree)HuffmanTree
    numcodes (defined in HuffmanTree)HuffmanTree
    tree1d (defined in HuffmanTree)HuffmanTree
    tree2d (defined in HuffmanTree)HuffmanTree
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    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_huffman_tree.html b/docs/html/struct_huffman_tree.html new file mode 100644 index 00000000..42f1c78b --- /dev/null +++ b/docs/html/struct_huffman_tree.html @@ -0,0 +1,101 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: HuffmanTree Struct Reference + + + + + + + + + +
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    +Public Attributes | +List of all members
    +
    +
    HuffmanTree Struct Reference
    +
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    +Public Attributes

    +unsigned * tree2d
     
    +unsigned * tree1d
     
    +unsigned * lengths
     
    +unsigned maxbitlen
     
    +unsigned numcodes
     
    +
    The documentation for this struct was generated from the following file:
      +
    • image_tools/lodepng.cpp
      • +
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    + + diff --git a/docs/html/struct_lode_p_n_g_color_mode-members.html b/docs/html/struct_lode_p_n_g_color_mode-members.html new file mode 100644 index 00000000..8638cb58 --- /dev/null +++ b/docs/html/struct_lode_p_n_g_color_mode-members.html @@ -0,0 +1,87 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    LodePNGColorMode Member List
    +
    +
    + +

    This is the complete list of members for LodePNGColorMode, including all inherited members.

    + + + + + + + + + +
    bitdepth (defined in LodePNGColorMode)LodePNGColorMode
    colortype (defined in LodePNGColorMode)LodePNGColorMode
    key_b (defined in LodePNGColorMode)LodePNGColorMode
    key_defined (defined in LodePNGColorMode)LodePNGColorMode
    key_g (defined in LodePNGColorMode)LodePNGColorMode
    key_r (defined in LodePNGColorMode)LodePNGColorMode
    palette (defined in LodePNGColorMode)LodePNGColorMode
    palettesize (defined in LodePNGColorMode)LodePNGColorMode
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_lode_p_n_g_color_mode.html b/docs/html/struct_lode_p_n_g_color_mode.html new file mode 100644 index 00000000..0c4266bc --- /dev/null +++ b/docs/html/struct_lode_p_n_g_color_mode.html @@ -0,0 +1,110 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: LodePNGColorMode Struct Reference + + + + + + + + + +
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    +Public Attributes | +List of all members
    +
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    LodePNGColorMode Struct Reference
    +
    +
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    +Public Attributes

    +LodePNGColorType colortype
     
    +unsigned bitdepth
     
    +unsigned char * palette
     
    +size_t palettesize
     
    +unsigned key_defined
     
    +unsigned key_r
     
    +unsigned key_g
     
    +unsigned key_b
     
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_lode_p_n_g_compress_settings-members.html b/docs/html/struct_lode_p_n_g_compress_settings-members.html new file mode 100644 index 00000000..dfd37500 --- /dev/null +++ b/docs/html/struct_lode_p_n_g_compress_settings-members.html @@ -0,0 +1,88 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    LodePNGCompressSettings Member List
    +
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    + +

    This is the complete list of members for LodePNGCompressSettings, including all inherited members.

    + + + + + + + + + + +
    btype (defined in LodePNGCompressSettings)LodePNGCompressSettings
    custom_context (defined in LodePNGCompressSettings)LodePNGCompressSettings
    custom_deflate (defined in LodePNGCompressSettings)LodePNGCompressSettings
    custom_zlib (defined in LodePNGCompressSettings)LodePNGCompressSettings
    lazymatching (defined in LodePNGCompressSettings)LodePNGCompressSettings
    minmatch (defined in LodePNGCompressSettings)LodePNGCompressSettings
    nicematch (defined in LodePNGCompressSettings)LodePNGCompressSettings
    use_lz77 (defined in LodePNGCompressSettings)LodePNGCompressSettings
    windowsize (defined in LodePNGCompressSettings)LodePNGCompressSettings
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_lode_p_n_g_compress_settings.html b/docs/html/struct_lode_p_n_g_compress_settings.html new file mode 100644 index 00000000..b57ed94a --- /dev/null +++ b/docs/html/struct_lode_p_n_g_compress_settings.html @@ -0,0 +1,113 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: LodePNGCompressSettings Struct Reference + + + + + + + + + +
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    LodePNGCompressSettings Struct Reference
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    +unsigned btype
     
    +unsigned use_lz77
     
    +unsigned windowsize
     
    +unsigned minmatch
     
    +unsigned nicematch
     
    +unsigned lazymatching
     
    +unsigned(* custom_zlib )(unsigned char **, size_t *, const unsigned char *, size_t, const LodePNGCompressSettings *)
     
    +unsigned(* custom_deflate )(unsigned char **, size_t *, const unsigned char *, size_t, const LodePNGCompressSettings *)
     
    +const void * custom_context
     
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_lode_p_n_g_decoder_settings-members.html b/docs/html/struct_lode_p_n_g_decoder_settings-members.html new file mode 100644 index 00000000..e6ef4d6e --- /dev/null +++ b/docs/html/struct_lode_p_n_g_decoder_settings-members.html @@ -0,0 +1,85 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    LodePNGDecoderSettings Member List
    +
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    This is the complete list of members for LodePNGDecoderSettings, including all inherited members.

    + + + + + + + +
    color_convert (defined in LodePNGDecoderSettings)LodePNGDecoderSettings
    fix_png (defined in LodePNGDecoderSettings)LodePNGDecoderSettings
    ignore_crc (defined in LodePNGDecoderSettings)LodePNGDecoderSettings
    read_text_chunks (defined in LodePNGDecoderSettings)LodePNGDecoderSettings
    remember_unknown_chunks (defined in LodePNGDecoderSettings)LodePNGDecoderSettings
    zlibsettings (defined in LodePNGDecoderSettings)LodePNGDecoderSettings
    + +
    +Generated by   +doxygen + 1.8.17 +
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    LodePNGDecoderSettings Struct Reference
    +
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    +Public Attributes

    +LodePNGDecompressSettings zlibsettings
     
    +unsigned ignore_crc
     
    +unsigned fix_png
     
    +unsigned color_convert
     
    +unsigned read_text_chunks
     
    +unsigned remember_unknown_chunks
     
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_lode_p_n_g_decompress_settings-members.html b/docs/html/struct_lode_p_n_g_decompress_settings-members.html new file mode 100644 index 00000000..add5cc47 --- /dev/null +++ b/docs/html/struct_lode_p_n_g_decompress_settings-members.html @@ -0,0 +1,83 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    LodePNGDecompressSettings Member List
    +
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    This is the complete list of members for LodePNGDecompressSettings, including all inherited members.

    + + + + + +
    custom_context (defined in LodePNGDecompressSettings)LodePNGDecompressSettings
    custom_inflate (defined in LodePNGDecompressSettings)LodePNGDecompressSettings
    custom_zlib (defined in LodePNGDecompressSettings)LodePNGDecompressSettings
    ignore_adler32 (defined in LodePNGDecompressSettings)LodePNGDecompressSettings
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/struct_lode_p_n_g_decompress_settings.html b/docs/html/struct_lode_p_n_g_decompress_settings.html new file mode 100644 index 00000000..426403dd --- /dev/null +++ b/docs/html/struct_lode_p_n_g_decompress_settings.html @@ -0,0 +1,98 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: LodePNGDecompressSettings Struct Reference + + + + + + + + + +
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    EXONA: Evolutionary eXploration of Neural Architectures +
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    +Public Attributes | +List of all members
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    LodePNGDecompressSettings Struct Reference
    +
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    +Public Attributes

    +unsigned ignore_adler32
     
    +unsigned(* custom_zlib )(unsigned char **, size_t *, const unsigned char *, size_t, const LodePNGDecompressSettings *)
     
    +unsigned(* custom_inflate )(unsigned char **, size_t *, const unsigned char *, size_t, const LodePNGDecompressSettings *)
     
    +const void * custom_context
     
    +
    The documentation for this struct was generated from the following file: +
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    + + diff --git a/docs/html/struct_lode_p_n_g_encoder_settings-members.html b/docs/html/struct_lode_p_n_g_encoder_settings-members.html new file mode 100644 index 00000000..cf465a23 --- /dev/null +++ b/docs/html/struct_lode_p_n_g_encoder_settings-members.html @@ -0,0 +1,87 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    LodePNGEncoderSettings Member List
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    add_id (defined in LodePNGEncoderSettings)LodePNGEncoderSettings
    auto_convert (defined in LodePNGEncoderSettings)LodePNGEncoderSettings
    filter_palette_zero (defined in LodePNGEncoderSettings)LodePNGEncoderSettings
    filter_strategy (defined in LodePNGEncoderSettings)LodePNGEncoderSettings
    force_palette (defined in LodePNGEncoderSettings)LodePNGEncoderSettings
    predefined_filters (defined in LodePNGEncoderSettings)LodePNGEncoderSettings
    text_compression (defined in LodePNGEncoderSettings)LodePNGEncoderSettings
    zlibsettings (defined in LodePNGEncoderSettings)LodePNGEncoderSettings
    + +
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    +Public Attributes

    +LodePNGCompressSettings zlibsettings
     
    +LodePNGAutoConvert auto_convert
     
    +unsigned filter_palette_zero
     
    +LodePNGFilterStrategy filter_strategy
     
    +const unsigned char * predefined_filters
     
    +unsigned force_palette
     
    +unsigned add_id
     
    +unsigned text_compression
     
    +
    The documentation for this struct was generated from the following file: +
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    background_b (defined in LodePNGInfo)LodePNGInfo
    background_defined (defined in LodePNGInfo)LodePNGInfo
    background_g (defined in LodePNGInfo)LodePNGInfo
    background_r (defined in LodePNGInfo)LodePNGInfo
    color (defined in LodePNGInfo)LodePNGInfo
    compression_method (defined in LodePNGInfo)LodePNGInfo
    filter_method (defined in LodePNGInfo)LodePNGInfo
    interlace_method (defined in LodePNGInfo)LodePNGInfo
    itext_keys (defined in LodePNGInfo)LodePNGInfo
    itext_langtags (defined in LodePNGInfo)LodePNGInfo
    itext_num (defined in LodePNGInfo)LodePNGInfo
    itext_strings (defined in LodePNGInfo)LodePNGInfo
    itext_transkeys (defined in LodePNGInfo)LodePNGInfo
    phys_defined (defined in LodePNGInfo)LodePNGInfo
    phys_unit (defined in LodePNGInfo)LodePNGInfo
    phys_x (defined in LodePNGInfo)LodePNGInfo
    phys_y (defined in LodePNGInfo)LodePNGInfo
    text_keys (defined in LodePNGInfo)LodePNGInfo
    text_num (defined in LodePNGInfo)LodePNGInfo
    text_strings (defined in LodePNGInfo)LodePNGInfo
    time (defined in LodePNGInfo)LodePNGInfo
    time_defined (defined in LodePNGInfo)LodePNGInfo
    unknown_chunks_data (defined in LodePNGInfo)LodePNGInfo
    unknown_chunks_size (defined in LodePNGInfo)LodePNGInfo
    + +
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    +Public Attributes

    +unsigned compression_method
     
    +unsigned filter_method
     
    +unsigned interlace_method
     
    +LodePNGColorMode color
     
    +unsigned background_defined
     
    +unsigned background_r
     
    +unsigned background_g
     
    +unsigned background_b
     
    +size_t text_num
     
    +char ** text_keys
     
    +char ** text_strings
     
    +size_t itext_num
     
    +char ** itext_keys
     
    +char ** itext_langtags
     
    +char ** itext_transkeys
     
    +char ** itext_strings
     
    +unsigned time_defined
     
    +LodePNGTime time
     
    +unsigned phys_defined
     
    +unsigned phys_x
     
    +unsigned phys_y
     
    +unsigned phys_unit
     
    +unsigned char * unknown_chunks_data [3]
     
    +size_t unknown_chunks_size [3]
     
    +
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    decoder (defined in LodePNGState)LodePNGState
    encoder (defined in LodePNGState)LodePNGState
    error (defined in LodePNGState)LodePNGState
    info_png (defined in LodePNGState)LodePNGState
    info_raw (defined in LodePNGState)LodePNGState
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    +LodePNGDecoderSettings decoder
     
    +LodePNGEncoderSettings encoder
     
    +LodePNGColorMode info_raw
     
    +LodePNGInfo info_png
     
    +unsigned error
     
    +
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    day (defined in LodePNGTime)LodePNGTime
    hour (defined in LodePNGTime)LodePNGTime
    minute (defined in LodePNGTime)LodePNGTime
    month (defined in LodePNGTime)LodePNGTime
    second (defined in LodePNGTime)LodePNGTime
    year (defined in LodePNGTime)LodePNGTime
    + +
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    +unsigned year
     
    +unsigned month
     
    +unsigned day
     
    +unsigned hour
     
    +unsigned minute
     
    +unsigned second
     
    +
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    job (defined in ResultSet)ResultSet
    milliseconds (defined in ResultSet)ResultSet
    test_mae (defined in ResultSet)ResultSet
    test_mse (defined in ResultSet)ResultSet
    training_mae (defined in ResultSet)ResultSet
    training_mse (defined in ResultSet)ResultSet
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    +int job
     
    +double training_mae
     
    +double training_mse
     
    +double test_mae
     
    +double test_mse
     
    +long milliseconds
     
    +
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      +
    • mpi/rnn_kfold_sweep.cxx
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    operator()(const ConsolidatedStatistics *s1, const ConsolidatedStatistics *s2) (defined in cs_less_than_avg)cs_less_than_avginline
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    +bool operator() (const ConsolidatedStatistics *s1, const ConsolidatedStatistics *s2)
     
    +
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    operator()(const ConsolidatedStatistics *s1, const ConsolidatedStatistics *s2) (defined in cs_less_than_max)cs_less_than_maxinline
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    +bool operator() (const ConsolidatedStatistics *s1, const ConsolidatedStatistics *s2)
     
    +
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    operator()(const ConsolidatedStatistics *s1, const ConsolidatedStatistics *s2) (defined in cs_less_than_min)cs_less_than_mininline
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    +bool operator() (const ConsolidatedStatistics *s1, const ConsolidatedStatistics *s2)
     
    +
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    operator()(const RunStatistics *s1, const RunStatistics *s2) (defined in less_than_avg)less_than_avginline
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    operator()(const RunStatistics *s1, const RunStatistics *s2) (defined in less_than_dfm_avg)less_than_dfm_avginline
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    +bool operator() (const RunStatistics *s1, const RunStatistics *s2)
     
    +
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    operator()(const RunStatistics *s1, const RunStatistics *s2) (defined in less_than_dfm_max)less_than_dfm_maxinline
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    +bool operator() (const RunStatistics *s1, const RunStatistics *s2)
     
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    The documentation for this struct was generated from the following file: +
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    +Generated by   +doxygen + 1.8.17 +
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    operator()(const RunStatistics *s1, const RunStatistics *s2) (defined in less_than_dfm_min)less_than_dfm_mininline
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    +Generated by   +doxygen + 1.8.17 +
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    +bool operator() (const RunStatistics *s1, const RunStatistics *s2)
     
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    The documentation for this struct was generated from the following file: +
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    +Generated by   +doxygen + 1.8.17 +
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    operator()(const RunStatistics *s1, const RunStatistics *s2) (defined in less_than_max)less_than_maxinline
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    +Generated by   +doxygen + 1.8.17 +
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    +Public Member Functions

    +bool operator() (const RunStatistics *s1, const RunStatistics *s2)
     
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    The documentation for this struct was generated from the following file: +
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    +Generated by   +doxygen + 1.8.17 +
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    operator()(const RunStatistics *s1, const RunStatistics *s2) (defined in less_than_min)less_than_mininline
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    +Generated by   +doxygen + 1.8.17 +
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    +Public Member Functions

    +bool operator() (const RunStatistics *s1, const RunStatistics *s2)
     
    +
    The documentation for this struct was generated from the following file: +
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    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/structsort___c_n_n___edges__by__depth-members.html b/docs/html/structsort___c_n_n___edges__by__depth-members.html new file mode 100644 index 00000000..1e67c175 --- /dev/null +++ b/docs/html/structsort___c_n_n___edges__by__depth-members.html @@ -0,0 +1,80 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    sort_CNN_Edges_by_depth Member List
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    operator()(CNN_Edge *n1, CNN_Edge *n2) (defined in sort_CNN_Edges_by_depth)sort_CNN_Edges_by_depthinline
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    +Generated by   +doxygen + 1.8.17 +
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    sort_CNN_Edges_by_depth Struct Reference
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    +Public Member Functions

    +bool operator() (CNN_Edge *n1, CNN_Edge *n2)
     
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    The documentation for this struct was generated from the following file: +
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    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/structsort___c_n_n___edges__by__innovation-members.html b/docs/html/structsort___c_n_n___edges__by__innovation-members.html new file mode 100644 index 00000000..ee9b1296 --- /dev/null +++ b/docs/html/structsort___c_n_n___edges__by__innovation-members.html @@ -0,0 +1,80 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    operator()(CNN_Edge *n1, CNN_Edge *n2) (defined in sort_CNN_Edges_by_innovation)sort_CNN_Edges_by_innovationinline
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    +Generated by   +doxygen + 1.8.17 +
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    sort_CNN_Edges_by_innovation Struct Reference
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    +Public Member Functions

    +bool operator() (CNN_Edge *n1, CNN_Edge *n2)
     
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    The documentation for this struct was generated from the following file: +
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    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/structsort___c_n_n___edges__by__output__depth-members.html b/docs/html/structsort___c_n_n___edges__by__output__depth-members.html new file mode 100644 index 00000000..201554d6 --- /dev/null +++ b/docs/html/structsort___c_n_n___edges__by__output__depth-members.html @@ -0,0 +1,80 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    operator()(CNN_Edge *n1, CNN_Edge *n2) (defined in sort_CNN_Edges_by_output_depth)sort_CNN_Edges_by_output_depthinline
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    +Generated by   +doxygen + 1.8.17 +
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    +Public Member Functions

    +bool operator() (CNN_Edge *n1, CNN_Edge *n2)
     
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/structsort___c_n_n___nodes__by__depth-members.html b/docs/html/structsort___c_n_n___nodes__by__depth-members.html new file mode 100644 index 00000000..bb9b4899 --- /dev/null +++ b/docs/html/structsort___c_n_n___nodes__by__depth-members.html @@ -0,0 +1,80 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    operator()(const CNN_Node *n1, const CNN_Node *n2) (defined in sort_CNN_Nodes_by_depth)sort_CNN_Nodes_by_depthinline
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    +Generated by   +doxygen + 1.8.17 +
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    +bool operator() (const CNN_Node *n1, const CNN_Node *n2)
     
    +
    The documentation for this struct was generated from the following file: +
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    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/structsort___r_n_n___edges__by__depth-members.html b/docs/html/structsort___r_n_n___edges__by__depth-members.html new file mode 100644 index 00000000..2a9e582d --- /dev/null +++ b/docs/html/structsort___r_n_n___edges__by__depth-members.html @@ -0,0 +1,80 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    operator()(RNN_Edge *n1, RNN_Edge *n2) (defined in sort_RNN_Edges_by_depth)sort_RNN_Edges_by_depthinline
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    +Generated by   +doxygen + 1.8.17 +
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    +bool operator() (RNN_Edge *n1, RNN_Edge *n2)
     
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    The documentation for this struct was generated from the following file: +
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    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/structsort___r_n_n___edges__by__innovation-members.html b/docs/html/structsort___r_n_n___edges__by__innovation-members.html new file mode 100644 index 00000000..0361f2a3 --- /dev/null +++ b/docs/html/structsort___r_n_n___edges__by__innovation-members.html @@ -0,0 +1,80 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: Member List + + + + + + + + + +
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    operator()(RNN_Edge *n1, RNN_Edge *n2) (defined in sort_RNN_Edges_by_innovation)sort_RNN_Edges_by_innovationinline
    + +
    +Generated by   +doxygen + 1.8.17 +
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    +bool operator() (RNN_Edge *n1, RNN_Edge *n2)
     
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated by   +doxygen + 1.8.17 +
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    operator()(RNN_Edge *n1, RNN_Edge *n2) (defined in sort_RNN_Edges_by_output_depth)sort_RNN_Edges_by_output_depthinline
    + +
    +Generated by   +doxygen + 1.8.17 +
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    +bool operator() (RNN_Edge *n1, RNN_Edge *n2)
     
    +
    The documentation for this struct was generated from the following file: +
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    +Generated by   +doxygen + 1.8.17 +
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    operator()(RNN_Node_Interface *n1, RNN_Node_Interface *n2) (defined in sort_RNN_Nodes_by_depth)sort_RNN_Nodes_by_depthinline
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    operator()(RNN_Node_Interface *n1, RNN_Node_Interface *n2) (defined in sort_RNN_Nodes_by_innovation)sort_RNN_Nodes_by_innovationinline
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    sort_RNN_Nodes_by_innovation Struct Reference
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    operator()(RNN_Recurrent_Edge *n1, RNN_Recurrent_Edge *n2) (defined in sort_RNN_Recurrent_Edges_by_depth)sort_RNN_Recurrent_Edges_by_depthinline
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    operator()(RNN_Recurrent_Edge *n1, RNN_Recurrent_Edge *n2) (defined in sort_RNN_Recurrent_Edges_by_innovation)sort_RNN_Recurrent_Edges_by_innovationinline
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    +bool operator() (RNN_Recurrent_Edge *n1, RNN_Recurrent_Edge *n2)
     
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    operator()(RNN_Recurrent_Edge *n1, RNN_Recurrent_Edge *n2) (defined in sort_RNN_Recurrent_Edges_by_output_depth)sort_RNN_Recurrent_Edges_by_output_depthinline
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    +bool operator() (RNN_Recurrent_Edge *n1, RNN_Recurrent_Edge *n2)
     
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    operator()(RNN_Genome *g1, RNN_Genome *g2) (defined in sort_genomes_by_fitness)sort_genomes_by_fitnessinline
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    +bool operator() (RNN_Genome *g1, RNN_Genome *g2)
     
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    operator()(CNN_Genome *g1, CNN_Genome *g2) (defined in sort_genomes_by_predictions)sort_genomes_by_predictionsinline
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    +bool operator() (CNN_Genome *g1, CNN_Genome *g2)
     
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    operator()(CNN_Genome *g1, CNN_Genome *g2) (defined in sort_genomes_by_validation_error)sort_genomes_by_validation_errorinline
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00000000..2a7b475f --- /dev/null +++ b/docs/html/time__series_8hxx_source.html @@ -0,0 +1,279 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: time_series/time_series.hxx Source File + + + + + + + + + +
    +
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    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
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    + + +
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    +
    +
    time_series.hxx
    +
    +
    +
    1 #ifndef EXAMM_TIME_SERIES_HXX
    +
    2 #define EXAMM_TIME_SERIES_HXX
    +
    3 
    +
    4 #include <iostream>
    +
    5 using std::ostream;
    +
    6 
    +
    7 #include <string>
    +
    8 using std::string;
    +
    9 
    +
    10 #include <map>
    +
    11 using std::map;
    +
    12 
    +
    13 #include <vector>
    +
    14 using std::vector;
    +
    15 
    +
    16 class TimeSeries {
    +
    17  private:
    +
    18  string name;
    +
    19 
    +
    20  double min;
    +
    21  double average;
    +
    22  double max;
    +
    23  double std_dev;
    +
    24  double variance;
    +
    25  double min_change;
    +
    26  double max_change;
    +
    27 
    +
    28  vector<double> values;
    +
    29 
    +
    30  TimeSeries();
    +
    31  public:
    +
    32  TimeSeries(string _name);
    +
    33 
    +
    34  void add_value(double value);
    +
    35  double get_value(int i);
    +
    36 
    +
    37  void calculate_statistics();
    +
    38  void print_statistics();
    +
    39 
    +
    40  int get_number_values() const;
    +
    41 
    +
    42  double get_min() const;
    +
    43  double get_average() const;
    +
    44  double get_max() const;
    +
    45  double get_std_dev() const;
    +
    46  double get_variance() const;
    +
    47  double get_min_change() const;
    +
    48  double get_max_change() const;
    +
    49 
    +
    50 
    +
    51  void normalize_min_max(double min, double max);
    +
    52  void normalize_avg_std_dev(double avg, double std_dev, double norm_max);
    +
    53 
    +
    54  void cut(int32_t start, int32_t stop);
    +
    55 
    +
    56  double get_correlation(const TimeSeries *other, int32_t lag) const;
    +
    57 
    +
    58  TimeSeries* copy();
    +
    59 
    +
    60  void copy_values(vector<double> &series);
    +
    61 };
    +
    62 
    +
    63 class TimeSeriesSet {
    +
    64  private:
    +
    65  int number_rows;
    +
    66  string filename;
    +
    67 
    +
    68  vector<string> fields;
    +
    69 
    +
    70  map<string, TimeSeries*> time_series;
    +
    71 
    +
    72  TimeSeriesSet();
    +
    73  public:
    +
    74 
    +
    75 
    +
    76  TimeSeriesSet(string _filename, const vector<string> &_fields);
    +
    77  ~TimeSeriesSet();
    +
    78  void add_time_series(string name);
    +
    79 
    +
    80  int get_number_rows() const;
    +
    81  int get_number_columns() const;
    +
    82  string get_filename() const;
    +
    83 
    +
    84  vector<string> get_fields() const;
    +
    85 
    +
    86  void get_series(string field_name, vector<double> &series);
    +
    87 
    +
    88  double get_min(string field);
    +
    89  double get_average(string field);
    +
    90  double get_max(string field);
    +
    91  double get_std_dev(string field);
    +
    92  double get_variance(string field);
    +
    93  double get_min_change(string field);
    +
    94  double get_max_change(string field);
    +
    95 
    +
    96  double get_correlation(string field1, string field2, int32_t lag) const;
    +
    97 
    +
    98  void normalize_min_max(string field, double min, double max);
    +
    99  void normalize_avg_std_dev(string field, double avg, double std_dev, double norm_max);
    +
    100 
    +
    101  void export_time_series(vector< vector<double> > &data);
    +
    102  void export_time_series(vector< vector<double> > &data, const vector<string> &requested_fields);
    +
    103  void export_time_series(vector< vector<double> > &data, const vector<string> &requested_fields, const vector<string> &shift_fields, int32_t time_offset);
    +
    104 
    +
    105  TimeSeriesSet* copy();
    +
    106 
    +
    107  void cut(int32_t start, int32_t stop);
    +
    108  void split(int slices, vector<TimeSeriesSet*> &sub_series);
    +
    109 
    +
    110  void select_parameters(const vector<string> &parameter_names);
    +
    111  void select_parameters(const vector<string> &input_parameter_names, const vector<string> &output_parameter_names);
    +
    112 };
    +
    113 
    +
    114 class TimeSeriesSets {
    +
    115  private:
    +
    116  string normalize_type;
    +
    117 
    +
    118  vector<string> filenames;
    +
    119 
    +
    120  vector<int> training_indexes;
    +
    121  vector<int> test_indexes;
    +
    122 
    +
    123  vector<string> input_parameter_names;
    +
    124  vector<string> output_parameter_names;
    +
    125  vector<string> shift_parameter_names;
    +
    126  vector<string> all_parameter_names;
    +
    127 
    +
    128  vector<TimeSeriesSet*> time_series;
    +
    129 
    +
    130  map<string,double> normalize_mins;
    +
    131  map<string,double> normalize_maxs;
    +
    132 
    +
    133  map<string,double> normalize_avgs;
    +
    134  map<string,double> normalize_std_devs;
    +
    135 
    +
    136  void parse_parameters_string(const vector<string> &p);
    +
    137  void load_time_series();
    +
    138 
    +
    139  public:
    +
    140  static void help_message();
    +
    141 
    +
    142  TimeSeriesSets();
    +
    143  ~TimeSeriesSets();
    +
    144  static TimeSeriesSets* generate_from_arguments(const vector<string> &arguments);
    +
    145  static TimeSeriesSets* generate_test(const vector<string> &_test_filenames, const vector<string> &_input_parameter_names, const vector<string> &_output_parameter_names);
    +
    146 
    +
    147  void normalize_min_max();
    +
    148  void normalize_min_max(const map<string,double> &_normalize_mins, const map<string,double> &_normalize_maxs);
    +
    149 
    +
    150  void normalize_avg_std_dev();
    +
    151  void normalize_avg_std_dev(const map<string,double> &_normalize_avgs, const map<string,double> &_normalize_std_devs, const map<string,double> &_normalize_mins, const map<string,double> &_normalize_maxs);
    +
    152 
    +
    153  void split_series(int series, int number_slices);
    +
    154  void split_all(int number_slices);
    +
    155 
    +
    156  void write_time_series_sets(string base_filename);
    +
    157 
    +
    158  void export_time_series(const vector<int> &series_indexes, int time_offset, vector< vector< vector<double> > > &inputs, vector< vector< vector<double> > > &outputs);
    +
    159 
    +
    160  void export_training_series(int time_offset, vector< vector< vector<double> > > &inputs, vector< vector< vector<double> > > &outputs);
    +
    161 
    +
    162  void export_test_series(int time_offset, vector< vector< vector<double> > > &inputs, vector< vector< vector<double> > > &outputs);
    +
    163 
    +
    164  void export_series_by_name(string field_name, vector< vector<double> > &exported_series);
    +
    165 
    +
    166  double denormalize(string field_name, double value);
    +
    167 
    +
    168  string get_normalize_type() const;
    +
    169  map<string,double> get_normalize_mins() const;
    +
    170  map<string,double> get_normalize_maxs() const;
    +
    171  map<string,double> get_normalize_avgs() const;
    +
    172  map<string,double> get_normalize_std_devs() const;
    +
    173 
    +
    174  vector<string> get_input_parameter_names() const;
    +
    175  vector<string> get_output_parameter_names() const;
    +
    176 
    +
    177  int get_number_series() const;
    +
    178 
    +
    179  int get_number_inputs() const;
    +
    180  int get_number_outputs() const;
    +
    181 
    +
    182  void set_training_indexes(const vector<int> &_training_indexes);
    +
    183  void set_test_indexes(const vector<int> &_test_indexes);
    +
    184 
    +
    185  TimeSeriesSet *get_set(int32_t i);
    +
    186 };
    +
    187 
    +
    188 #endif
    +
    + + +
    TimeSeriesSets::export_test_series
    void export_test_series(int time_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
    This exports the time series marked as test series by the test_indexes vector.
    Definition: time_series.cxx:1092
    +
    TimeSeriesSet
    Definition: time_series.hxx:63
    +
    TimeSeriesSets::export_series_by_name
    void export_series_by_name(string field_name, vector< vector< double > > &exported_series)
    This exports from all the loaded time series a particular column.
    Definition: time_series.cxx:1105
    +
    TimeSeries
    Definition: time_series.hxx:16
    + +
    TimeSeriesSets::export_time_series
    void export_time_series(const vector< int > &series_indexes, int time_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
    the series argument is a vector of indexes of the time series that was initially loaded that are to b...
    Definition: time_series.cxx:1065
    + +
    TimeSeriesSets
    Definition: time_series.hxx:114
    +
    TimeSeriesSets::export_training_series
    void export_training_series(int time_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
    This exports the time series marked as training series by the training_indexes vector.
    Definition: time_series.cxx:1080
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/tracker_8hxx_source.html b/docs/html/tracker_8hxx_source.html new file mode 100644 index 00000000..9a2b6142 --- /dev/null +++ b/docs/html/tracker_8hxx_source.html @@ -0,0 +1,113 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: mpi/tracker.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    tracker.hxx
    +
    +
    +
    1 #ifndef EXAMM_TRACKER_HXX
    +
    2 #define EXAMM_TRACKER_HXX
    +
    3 
    +
    4 #include <vector>
    +
    5 using std::vector;
    +
    6 
    +
    7 #include "stdint.h"
    +
    8 
    +
    9 class Tracker {
    +
    10  private:
    +
    11  int32_t count;
    +
    12  double _min;
    +
    13  double sum;
    +
    14  double _max;
    +
    15 
    +
    16  vector<double> values;
    +
    17 
    +
    18  public:
    +
    19  Tracker();
    +
    20 
    +
    21  void track(double value);
    +
    22 
    +
    23  double min() const;
    +
    24  double max() const;
    +
    25  double avg() const;
    +
    26  double stddev();
    +
    27 
    +
    28  double correlate(Tracker &other);
    +
    29 };
    +
    30 
    +
    31 #endif
    +
    + +
    Tracker
    Definition: tracker.hxx:9
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/ugrnn__node_8hxx_source.html b/docs/html/ugrnn__node_8hxx_source.html new file mode 100644 index 00000000..f562d0b1 --- /dev/null +++ b/docs/html/ugrnn__node_8hxx_source.html @@ -0,0 +1,169 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: rnn/ugrnn_node.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    ugrnn_node.hxx
    +
    +
    +
    1 #ifndef EXAMM_UGRNN_NODE_HXX
    +
    2 #define EXAMM_UGRNN_NODE_HXX
    +
    3 
    +
    4 #include <string>
    +
    5 using std::string;
    +
    6 
    +
    7 #include <random>
    +
    8 using std::minstd_rand0;
    +
    9 using std::uniform_real_distribution;
    +
    10 
    +
    11 #include <vector>
    +
    12 using std::vector;
    +
    13 
    +
    14 #include "common/random.hxx"
    +
    15 
    +
    16 #include "rnn_node_interface.hxx"
    +
    17 
    +
    18 class UGRNN_Node : public RNN_Node_Interface {
    +
    19  private:
    +
    20  double cw;
    +
    21  double ch;
    +
    22  double c_bias;
    +
    23 
    +
    24  double gw;
    +
    25  double gh;
    +
    26  double g_bias;
    +
    27 
    +
    28  vector<double> d_cw;
    +
    29  vector<double> d_ch;
    +
    30  vector<double> d_c_bias;
    +
    31  vector<double> d_gw;
    +
    32  vector<double> d_gh;
    +
    33  vector<double> d_g_bias;
    +
    34 
    +
    35  vector<double> d_h_prev;
    +
    36 
    +
    37  vector<double> c;
    +
    38  vector<double> ld_c;
    +
    39  vector<double> g;
    +
    40  vector<double> ld_g;
    +
    41 
    +
    42  public:
    +
    43 
    +
    44  UGRNN_Node(int _innovation_number, int _type, double _depth);
    +
    45  ~UGRNN_Node();
    +
    46 
    +
    47  void initialize_lamarckian(minstd_rand0 &generator, NormalDistribution &normal_distribution, double mu, double sigma);
    +
    48  void initialize_xavier(minstd_rand0 &generator, uniform_real_distribution<double> &rng1_1, double range);
    +
    49  void initialize_kaiming(minstd_rand0 &generator, NormalDistribution &normal_distribution, double range);
    +
    50  void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution<double> &rng);
    +
    51 
    +
    52  double get_gradient(string gradient_name);
    +
    53  void print_gradient(string gradient_name);
    +
    54 
    +
    55  void input_fired(int time, double incoming_output);
    +
    56 
    +
    57  void try_update_deltas(int time);
    +
    58  void error_fired(int time, double error);
    +
    59  void output_fired(int time, double delta);
    +
    60 
    +
    61  uint32_t get_number_weights() const;
    +
    62 
    +
    63  void get_weights(vector<double> &parameters) const;
    +
    64  void set_weights(const vector<double> &parameters);
    +
    65 
    +
    66  void get_weights(uint32_t &offset, vector<double> &parameters) const;
    +
    67  void set_weights(uint32_t &offset, const vector<double> &parameters);
    +
    68 
    +
    69  void get_gradients(vector<double> &gradients);
    +
    70 
    +
    71  void reset(int _series_length);
    +
    72 
    +
    73  void write_to_stream(ostream &out);
    +
    74 
    +
    75  RNN_Node_Interface* copy() const;
    +
    76 
    +
    77  friend class RNN_Edge;
    +
    78 
    +
    79 };
    +
    80 #endif
    +
    +
    RNN_Edge
    Definition: rnn_edge.hxx:6
    + +
    UGRNN_Node
    Definition: ugrnn_node.hxx:18
    + + +
    NormalDistribution
    Definition: random.hxx:19
    + + +
    RNN_Node_Interface
    Definition: rnn_node_interface.hxx:133
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/version_8hxx_source.html b/docs/html/version_8hxx_source.html new file mode 100644 index 00000000..e3b79101 --- /dev/null +++ b/docs/html/version_8hxx_source.html @@ -0,0 +1,86 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: common/version.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    version.hxx
    +
    +
    +
    1 #ifndef EXACT_VERSION_HXX
    +
    2 #define EXACT_VERSION_HXX
    +
    3 
    +
    4 #define EXACT_VERSION_STR "v0.33"
    +
    5 
    +
    6 #endif
    +
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/weight__initialize_8hxx_source.html b/docs/html/weight__initialize_8hxx_source.html new file mode 100644 index 00000000..02b4dac2 --- /dev/null +++ b/docs/html/weight__initialize_8hxx_source.html @@ -0,0 +1,120 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: common/weight_initialize.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
    +
    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    weight_initialize.hxx
    +
    +
    +
    1 #ifndef WEIGHT_INITIALIZE_HXX
    +
    2 #define WEIGHT_INITIALIZE_HXX
    +
    3 
    +
    4 
    +
    5 enum WeightType {
    +
    6  RANDOM = 0,
    +
    7  XAVIER = 1,
    +
    8  KAIMING = 2,
    +
    9  LAMARCKIAN = 3,
    +
    10  NONE = -1
    +
    11 };
    +
    12 
    +
    13 static string WEIGHT_TYPES_STRING[] = {"random", "xavier", "kaiming", "lamarckian"};
    +
    14 static int32_t NUM_WEIGHT_TYPES = 4;
    +
    15 
    +
    16 
    +
    17 inline WeightType get_enum_from_string(string input_string) {
    +
    18  WeightType weight_type;
    +
    19  for (int i = 0; i < NUM_WEIGHT_TYPES; i++) {
    +
    20  if (input_string.compare(WEIGHT_TYPES_STRING[i]) == 0) {
    +
    21  weight_type = static_cast<WeightType>(i);
    +
    22  }
    +
    23  }
    +
    24  return weight_type;
    +
    25 }
    +
    26 
    +
    27 template <typename Weight_Type>
    +
    28 int32_t enum_to_integer(Weight_Type weight) {
    +
    29  return static_cast<typename std::underlying_type<Weight_Type>::type>(weight);
    +
    30 }
    +
    31 
    +
    32 inline WeightType integer_to_enum (int32_t input_int) {
    +
    33  WeightType weight_type = static_cast<WeightType>(input_int);
    +
    34  return weight_type;
    +
    35 }
    +
    36 
    +
    37 
    +
    38 
    +
    39 #endif
    +
    + + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/html/word__series_8hxx_source.html b/docs/html/word__series_8hxx_source.html new file mode 100644 index 00000000..449b1935 --- /dev/null +++ b/docs/html/word__series_8hxx_source.html @@ -0,0 +1,312 @@ + + + + + + + +EXONA: Evolutionary eXploration of Neural Architectures: word_series/word_series.hxx Source File + + + + + + + + + +
    +
    + + + + + + +
    +
    EXONA: Evolutionary eXploration of Neural Architectures +
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    +
    + + + + + + + + +
    +
    + + +
    + +
    + + +
    +
    +
    +
    word_series.hxx
    +
    +
    +
    1 #ifndef EXAMM_WORD_SERIES_HXX
    +
    2 #define EXAMM_WORD_SERIES_HXX
    +
    3 
    +
    4 #include <iostream>
    +
    5 using std::ostream;
    +
    6 
    +
    7 #include <string>
    +
    8 using std::string;
    +
    9 
    +
    10 #include <map>
    +
    11 using std::map;
    +
    12 
    +
    13 #include <vector>
    +
    14 using std::vector;
    +
    15 
    +
    16 
    +
    17  class WordSeries{
    +
    18  private:
    +
    19 
    +
    24  string name;
    +
    28  int vocab_size;
    +
    29 
    +
    34  double min;
    +
    39  double average;
    +
    44  double max;
    +
    48  double std_dev;
    +
    53  double variance;
    +
    54  double min_change;
    +
    55  double max_change;
    +
    56 
    +
    60  vector<double> values;
    +
    61  WordSeries();
    +
    62 
    +
    63  public:
    +
    64  WordSeries(string _name);
    +
    65 
    +
    66  void add_value(double value);
    +
    67  double get_value(int i);
    +
    68 
    +
    69  void calculate_statistics();
    +
    70  void print_statistics();
    +
    71 
    +
    72  int get_number_values() const;
    +
    73 
    +
    74  double get_min() const;
    +
    75  double get_average() const;
    +
    76  double get_max() const;
    +
    77  double get_std_dev() const;
    +
    78  double get_variance() const;
    +
    79  double get_min_change() const;
    +
    80  double get_max_change() const;
    +
    81 
    +
    82 
    +
    83  void normalize_min_max(double min, double max);
    +
    84  void normalize_avg_std_dev(double avg, double std_dev, double norm_max);
    +
    85 
    +
    86  void cut(int32_t start, int32_t stop);
    +
    87 
    +
    88  double get_correlation(const WordSeries *other, int32_t lag) const;
    +
    89 
    +
    90  WordSeries* copy();
    +
    91 
    +
    92  void copy_values(vector<double> &series);
    +
    93 
    +
    94  };
    +
    95 
    +
    96  class SentenceSeries {
    +
    97  private:
    +
    98 
    +
    103  int number_rows;
    +
    107  string filename;
    +
    111  map<string,int> vocab;
    +
    115  vector<string> word_index;
    +
    119  map <string,WordSeries*> word_series;
    +
    120 
    +
    121 
    +
    122  SentenceSeries();
    +
    123  public:
    +
    124 
    +
    125 
    +
    126 SentenceSeries(const string _filename,const vector<string> & _word_index , const map<string,int> &_vocab); ~SentenceSeries();
    +
    127  void add_word_series(string name);
    +
    128 
    +
    129  int get_number_rows() const;
    +
    130  int get_number_columns() const;
    +
    131 
    +
    132  string get_filename() const;
    +
    133 
    +
    134  vector<string> get_word_index() const;
    +
    135 
    +
    136 
    +
    137  void get_series(string word_name, vector<double> &series);
    +
    138 
    +
    139  double get_min(string word);
    +
    140  double get_average(string word);
    +
    141  double get_max(string word);
    +
    142  double get_std_dev(string word);
    +
    143  double get_variance(string word);
    +
    144  double get_min_change(string word);
    +
    145  double get_max_change(string word);
    +
    146 
    +
    147  double get_correlation(string word1, string word2, int32_t lag) const;
    +
    148 
    +
    149  void normalize_min_max(string word, double min, double max);
    +
    150  void normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max);
    +
    151 
    +
    152  void export_word_series(vector< vector<double> > &data , int word_offset);
    +
    153  void export_word_series(vector< vector<double> > &data );
    +
    154 
    +
    155  SentenceSeries* copy();
    +
    156 
    +
    157  void select_parameters(const vector<string> &input_parameter_names, const vector<string> &output_parameter_names);
    +
    158  void select_parameters(const vector<string> &parameter_names);
    +
    159 
    +
    160 };
    +
    161 
    +
    162 
    +
    163 class Corpus {
    +
    164 
    +
    165  private:
    +
    166 
    +
    170  uint32_t batch_size;
    +
    171  string normalize_type;
    +
    172 
    +
    176  vector<string> filenames;
    +
    177 
    +
    181  vector<int> training_indexes;
    +
    182 
    +
    186  vector<int> test_indexes;
    +
    187 
    +
    192  vector<string> input_parameter_names;
    +
    197  vector<string> output_parameter_names;
    +
    202  vector<string> all_parameter_names;
    +
    203 
    +
    208  vector<SentenceSeries*> sent_series;
    +
    209 
    +
    210  map<string,double> normalize_mins;
    +
    211  map<string,double> normalize_maxs;
    +
    212 
    +
    213  map<string,double> normalize_avgs;
    +
    214  map<string,double> normalize_std_devs;
    +
    215 
    +
    216 
    +
    220  vector<string> word_index;
    +
    221 
    +
    225  map<string,int> vocab;
    +
    226 
    +
    230  void load_word_library();
    +
    231 
    +
    232 
    +
    233  public:
    +
    234 
    +
    235  Corpus();
    +
    236  ~Corpus();
    +
    237 
    +
    238  static Corpus* generate_from_arguments(const vector<string> &arguments);
    +
    239  static Corpus* generate_test(const vector<string> &_test_filenames, const vector<string> &_input_parameter_names, const vector<string> &_output_parameter_names);
    +
    240 
    +
    241  void normalize_min_max();
    +
    242  void normalize_min_max(const map<string,double> &_normalize_mins, const map<string,double> &_normalize_maxs);
    +
    243 
    +
    244  void normalize_avg_std_dev();
    +
    245  void normalize_avg_std_dev(const map<string,double> &_normalize_avgs, const map<string,double> &_normalize_std_devs, const map<string,double> &_normalize_mins, const map<string,double> &_normalize_maxs);
    +
    246 
    +
    247 
    +
    253  void write_sentence_series_sets(string base_filename);
    +
    254 
    +
    255  void export_sent_series(const vector<int> &series_indexes, int word_offset, vector< vector< vector<double> > > &inputs, vector< vector< vector<double> > > &outputs);
    +
    256 
    +
    266  void export_training_series(int word_offset, vector< vector< vector<double> > > &inputs, vector< vector< vector<double> > > &outputs);
    +
    267 
    +
    277  void export_test_series(int word_offset, vector< vector< vector<double> > > &inputs, vector< vector< vector<double> > > &outputs);
    +
    278 
    +
    279  void export_series_by_name(string field_name, vector< vector<double> > &exported_series);
    +
    280 
    +
    281  double denormalize(string field_name, double value);
    +
    282 
    +
    283  string get_normalize_type() const;
    +
    284  map<string,double> get_normalize_mins() const;
    +
    285  map<string,double> get_normalize_maxs() const;
    +
    286  map<string,double> get_normalize_avgs() const;
    +
    287  map<string,double> get_normalize_std_devs() const;
    +
    288 
    +
    289  vector<string> get_input_parameter_names() const;
    +
    290  vector<string> get_output_parameter_names() const;
    +
    291 
    +
    292  int get_number_series() const;
    +
    293 
    +
    294  int get_number_inputs() const;
    +
    295  int get_number_outputs() const;
    +
    296 
    +
    297  void set_training_indexes(const vector<int> &_training_indexes);
    +
    298  void set_test_indexes(const vector<int> &_test_indexes);
    +
    299 
    +
    300  SentenceSeries *get_set(int32_t i);
    +
    301 };
    +
    302 
    +
    303 #endif
    +
    + +
    Corpus::export_training_series
    void export_training_series(int word_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
    Exports Corpus Dataset to be used for training in RNN genome.
    Definition: word_series.cxx:920
    +
    WordSeries::vocab_size
    int vocab_size
    Specifies the number of the unique strings in the dataset.
    Definition: word_series.hxx:28
    +
    Corpus::write_sentence_series_sets
    void write_sentence_series_sets(string base_filename)
    Generates the csv data file.
    Definition: word_series.cxx:953
    + +
    Corpus::training_indexes
    vector< int > training_indexes
    stores the training index of the filenames.
    Definition: word_series.hxx:181
    +
    SentenceSeries::vocab
    map< string, int > vocab
    Maps the word string to index of the word series in a particular string.
    Definition: word_series.hxx:111
    +
    Corpus::output_parameter_names
    vector< string > output_parameter_names
    stores the input parameter names of the filenames.
    Definition: word_series.hxx:197
    +
    Corpus::export_test_series
    void export_test_series(int word_offset, vector< vector< vector< double > > > &inputs, vector< vector< vector< double > > > &outputs)
    Exports Corpus Dataset to be used for testing in RNN genome.
    Definition: word_series.cxx:932
    +
    Corpus::filenames
    vector< string > filenames
    Stores all the filenames including training and testing.
    Definition: word_series.hxx:176
    +
    WordSeries::average
    double average
    Specifies the average of the word series in a particular string.
    Definition: word_series.hxx:39
    +
    SentenceSeries::word_series
    map< string, WordSeries * > word_series
    Maps the word string to the word series in a particular string.
    Definition: word_series.hxx:119
    +
    Corpus::word_index
    vector< string > word_index
    Maps the index to word string of the word series in a particular filename.
    Definition: word_series.hxx:220
    + +
    WordSeries::name
    string name
    Specifies the string name in the data.
    Definition: word_series.hxx:24
    +
    SentenceSeries::filename
    string filename
    Specifices the filename for the training or testing data.
    Definition: word_series.hxx:107
    +
    Corpus::batch_size
    uint32_t batch_size
    Specifices the batches in which the dataset is to be divided into chunks.
    Definition: word_series.hxx:170
    +
    SentenceSeries
    Definition: word_series.hxx:96
    +
    WordSeries::values
    vector< double > values
    Storers the values in the word series respective of the time in a particular string.
    Definition: word_series.hxx:60
    + +
    WordSeries::std_dev
    double std_dev
    Specifies the std.
    Definition: word_series.hxx:48
    +
    Corpus
    Definition: word_series.hxx:163
    +
    Corpus::sent_series
    vector< SentenceSeries * > sent_series
    stores the Sentence series used in the filenames.
    Definition: word_series.hxx:208
    +
    Corpus::input_parameter_names
    vector< string > input_parameter_names
    stores the input parameter names of the filenames.
    Definition: word_series.hxx:192
    +
    WordSeries::variance
    double variance
    Specifies the variance of the word series in a particular string.
    Definition: word_series.hxx:53
    +
    WordSeries::max
    double max
    Specifies the maximum of the word series in a particular string.
    Definition: word_series.hxx:44
    +
    Corpus::all_parameter_names
    vector< string > all_parameter_names
    stores the input parameter names of the filenames.
    Definition: word_series.hxx:202
    +
    WordSeries
    Definition: word_series.hxx:17
    +
    SentenceSeries::number_rows
    int number_rows
    Specifies the number of the rows in the dataset.
    Definition: word_series.hxx:103
    +
    WordSeries::min
    double min
    Specifies the minimum of the word series in a particular string.
    Definition: word_series.hxx:34
    +
    Corpus::test_indexes
    vector< int > test_indexes
    stores the testing index of the filenames.
    Definition: word_series.hxx:186
    +
    Corpus::vocab
    map< string, int > vocab
    Maps the word string to the word series in a particular filename.
    Definition: word_series.hxx:225
    +
    SentenceSeries::word_index
    vector< string > word_index
    Maps the index to word string of the word series in a particular string.
    Definition: word_series.hxx:115
    +
    Corpus::load_word_library
    void load_word_library()
    Loads the filenames, word index and the vocabulary of the files.
    Definition: word_series.cxx:479
    + +
    +Generated by   +doxygen + 1.8.17 +
    + + diff --git a/docs/latex/Makefile b/docs/latex/Makefile new file mode 100644 index 00000000..877c9ccc --- /dev/null +++ b/docs/latex/Makefile @@ -0,0 +1,23 @@ +LATEX_CMD=pdflatex + +all: refman.pdf + +pdf: refman.pdf + +refman.pdf: clean refman.tex + $(LATEX_CMD) refman + makeindex refman.idx + $(LATEX_CMD) refman + latex_count=8 ; \ + while egrep -s 'Rerun (LaTeX|to get cross-references right)' refman.log && [ $$latex_count -gt 0 ] ;\ + do \ + echo "Rerunning latex...." ;\ + $(LATEX_CMD) refman ;\ + latex_count=`expr $$latex_count - 1` ;\ + done + makeindex refman.idx + $(LATEX_CMD) refman + + +clean: + rm -f *.ps *.dvi *.aux *.toc *.idx *.ind *.ilg *.log *.out *.brf *.blg *.bbl refman.pdf diff --git a/docs/latex/annotated.tex b/docs/latex/annotated.tex new file mode 100644 index 00000000..3f191e7b --- /dev/null +++ b/docs/latex/annotated.tex @@ -0,0 +1,98 @@ +\doxysection{Class List} +Here are the classes, structs, unions and interfaces with brief descriptions\+:\begin{DoxyCompactList} +\item\contentsline{section}{\mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} }{\pageref{class_c_n_n___edge}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} }{\pageref{class_c_n_n___genome}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} }{\pageref{class_c_n_n___node}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_coin}{Coin}} }{\pageref{struct_coin}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_color}{Color}} }{\pageref{struct_color}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_color_profile}{Color\+Profile}} }{\pageref{struct_color_profile}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_color_tree}{Color\+Tree}} }{\pageref{struct_color_tree}}{} +\item\contentsline{section}{\mbox{\hyperlink{classconfig__to__arg_1_1_config_to_arg}{config\+\_\+to\+\_\+arg.\+Config\+To\+Arg}} }{\pageref{classconfig__to__arg_1_1_config_to_arg}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_consolidated_statistics}{Consolidated\+Statistics}} }{\pageref{class_consolidated_statistics}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_corpus}{Corpus}} }{\pageref{class_corpus}}{} +\item\contentsline{section}{\mbox{\hyperlink{structcs__less__than__avg}{cs\+\_\+less\+\_\+than\+\_\+avg}} }{\pageref{structcs__less__than__avg}}{} +\item\contentsline{section}{\mbox{\hyperlink{structcs__less__than__max}{cs\+\_\+less\+\_\+than\+\_\+max}} }{\pageref{structcs__less__than__max}}{} +\item\contentsline{section}{\mbox{\hyperlink{structcs__less__than__min}{cs\+\_\+less\+\_\+than\+\_\+min}} }{\pageref{structcs__less__than__min}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_delta___node}{Delta\+\_\+\+Node}} }{\pageref{class_delta___node}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_e_n_a_r_c___node}{E\+N\+A\+R\+C\+\_\+\+Node}} }{\pageref{class_e_n_a_r_c___node}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_e_n_a_s___d_a_g___node}{E\+N\+A\+S\+\_\+\+D\+A\+G\+\_\+\+Node}} }{\pageref{class_e_n_a_s___d_a_g___node}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} }{\pageref{class_e_x_a_c_t}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_e_x_a_c_t___r_e_s_u_l_t}{E\+X\+A\+C\+T\+\_\+\+R\+E\+S\+U\+LT}} }{\pageref{struct_e_x_a_c_t___r_e_s_u_l_t}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}} }{\pageref{class_e_x_a_m_m}}{} +\item\contentsline{section}{\mbox{\hyperlink{classexamm__task_1_1_examm_task}{examm\+\_\+task.\+Examm\+Task}} }{\pageref{classexamm__task_1_1_examm_task}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_g_r_u___node}{G\+R\+U\+\_\+\+Node}} }{\pageref{class_g_r_u___node}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_hash}{Hash}} }{\pageref{struct_hash}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_huffman_tree}{Huffman\+Tree}} }{\pageref{struct_huffman_tree}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_image}{Image}} }{\pageref{class_image}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_image_interface}{Image\+Interface}} }{\pageref{class_image_interface}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_images}{Images}} }{\pageref{class_images}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_images_interface}{Images\+Interface}} }{\pageref{class_images_interface}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_island}{Island}} }{\pageref{class_island}}{} +\item\contentsline{section}{\mbox{\hyperlink{classisland__purging__args_1_1_island_purging_args}{island\+\_\+purging\+\_\+args.\+Island\+Purging\+Args}} }{\pageref{classisland__purging__args_1_1_island_purging_args}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_island_speciation_strategy}{Island\+Speciation\+Strategy}} }{\pageref{class_island_speciation_strategy}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_large_image}{Large\+Image}} }{\pageref{class_large_image}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_large_images}{Large\+Images}} }{\pageref{class_large_images}}{} +\item\contentsline{section}{\mbox{\hyperlink{structless__than__avg}{less\+\_\+than\+\_\+avg}} }{\pageref{structless__than__avg}}{} +\item\contentsline{section}{\mbox{\hyperlink{structless__than__dfm__avg}{less\+\_\+than\+\_\+dfm\+\_\+avg}} }{\pageref{structless__than__dfm__avg}}{} +\item\contentsline{section}{\mbox{\hyperlink{structless__than__dfm__max}{less\+\_\+than\+\_\+dfm\+\_\+max}} }{\pageref{structless__than__dfm__max}}{} +\item\contentsline{section}{\mbox{\hyperlink{structless__than__dfm__min}{less\+\_\+than\+\_\+dfm\+\_\+min}} }{\pageref{structless__than__dfm__min}}{} +\item\contentsline{section}{\mbox{\hyperlink{structless__than__max}{less\+\_\+than\+\_\+max}} }{\pageref{structless__than__max}}{} +\item\contentsline{section}{\mbox{\hyperlink{structless__than__min}{less\+\_\+than\+\_\+min}} }{\pageref{structless__than__min}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_line}{Line}} }{\pageref{class_line}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_lode_p_n_g_color_mode}{Lode\+P\+N\+G\+Color\+Mode}} }{\pageref{struct_lode_p_n_g_color_mode}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_lode_p_n_g_compress_settings}{Lode\+P\+N\+G\+Compress\+Settings}} }{\pageref{struct_lode_p_n_g_compress_settings}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_lode_p_n_g_decoder_settings}{Lode\+P\+N\+G\+Decoder\+Settings}} }{\pageref{struct_lode_p_n_g_decoder_settings}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_lode_p_n_g_decompress_settings}{Lode\+P\+N\+G\+Decompress\+Settings}} }{\pageref{struct_lode_p_n_g_decompress_settings}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_lode_p_n_g_encoder_settings}{Lode\+P\+N\+G\+Encoder\+Settings}} }{\pageref{struct_lode_p_n_g_encoder_settings}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_lode_p_n_g_info}{Lode\+P\+N\+G\+Info}} }{\pageref{struct_lode_p_n_g_info}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_lode_p_n_g_state}{Lode\+P\+N\+G\+State}} }{\pageref{struct_lode_p_n_g_state}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_lode_p_n_g_time}{Lode\+P\+N\+G\+Time}} }{\pageref{struct_lode_p_n_g_time}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_log}{Log}} }{\pageref{class_log}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_log_file}{Log\+File}} }{\pageref{class_log_file}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_l_s_t_m___node}{L\+S\+T\+M\+\_\+\+Node}} }{\pageref{class_l_s_t_m___node}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_m_g_u___node}{M\+G\+U\+\_\+\+Node}} }{\pageref{class_m_g_u___node}}{} +\item\contentsline{section}{\mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap$<$ T $>$}} \\*This implementation of a \mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}} was basically ripped from here\+: \href{https://github.com/itsjohncs/minmaxheap-cpp/blob/master/MinMaxHeap.hpp}{\texttt{ https\+://github.\+com/itsjohncs/minmaxheap-\/cpp/blob/master/\+Min\+Max\+Heap.\+hpp}} }{\pageref{classmin__max__heap}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_mosaic_images}{Mosaic\+Images}} }{\pageref{class_mosaic_images}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_multi_images_interface}{Multi\+Images\+Interface}} }{\pageref{class_multi_images_interface}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_neat_speciation_strategy}{Neat\+Speciation\+Strategy}} }{\pageref{class_neat_speciation_strategy}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} }{\pageref{class_normal_distribution}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_parse_turbines}{Parse\+Turbines}} }{\pageref{class_parse_turbines}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_point}{Point}} }{\pageref{class_point}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_r_a_n_d_o_m___d_a_g___node}{R\+A\+N\+D\+O\+M\+\_\+\+D\+A\+G\+\_\+\+Node}} }{\pageref{class_r_a_n_d_o_m___d_a_g___node}}{} +\item\contentsline{section}{\mbox{\hyperlink{classrec__args_1_1_rec_args}{rec\+\_\+args.\+Rec\+Args}} }{\pageref{classrec__args_1_1_rec_args}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_rectangle}{Rectangle}} }{\pageref{class_rectangle}}{} +\item\contentsline{section}{\mbox{\hyperlink{struct_result_set}{Result\+Set}} }{\pageref{struct_result_set}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_r_n_n}{R\+NN}} }{\pageref{class_r_n_n}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} }{\pageref{class_r_n_n___edge}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} }{\pageref{class_r_n_n___genome}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_r_n_n___node}{R\+N\+N\+\_\+\+Node}} }{\pageref{class_r_n_n___node}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} }{\pageref{class_r_n_n___node___interface}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} }{\pageref{class_r_n_n___recurrent___edge}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} }{\pageref{class_run_statistics}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_sentence_series}{Sentence\+Series}} }{\pageref{class_sentence_series}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___c_n_n___edges__by__depth}{sort\+\_\+\+C\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+depth}} }{\pageref{structsort___c_n_n___edges__by__depth}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___c_n_n___edges__by__innovation}{sort\+\_\+\+C\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+innovation}} }{\pageref{structsort___c_n_n___edges__by__innovation}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___c_n_n___edges__by__output__depth}{sort\+\_\+\+C\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+output\+\_\+depth}} }{\pageref{structsort___c_n_n___edges__by__output__depth}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___c_n_n___nodes__by__depth}{sort\+\_\+\+C\+N\+N\+\_\+\+Nodes\+\_\+by\+\_\+depth}} }{\pageref{structsort___c_n_n___nodes__by__depth}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort__genomes__by__fitness}{sort\+\_\+genomes\+\_\+by\+\_\+fitness}} }{\pageref{structsort__genomes__by__fitness}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort__genomes__by__predictions}{sort\+\_\+genomes\+\_\+by\+\_\+predictions}} }{\pageref{structsort__genomes__by__predictions}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort__genomes__by__validation__error}{sort\+\_\+genomes\+\_\+by\+\_\+validation\+\_\+error}} }{\pageref{structsort__genomes__by__validation__error}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___r_n_n___edges__by__depth}{sort\+\_\+\+R\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+depth}} }{\pageref{structsort___r_n_n___edges__by__depth}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___r_n_n___edges__by__innovation}{sort\+\_\+\+R\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+innovation}} }{\pageref{structsort___r_n_n___edges__by__innovation}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___r_n_n___edges__by__output__depth}{sort\+\_\+\+R\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+output\+\_\+depth}} }{\pageref{structsort___r_n_n___edges__by__output__depth}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___r_n_n___nodes__by__depth}{sort\+\_\+\+R\+N\+N\+\_\+\+Nodes\+\_\+by\+\_\+depth}} }{\pageref{structsort___r_n_n___nodes__by__depth}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___r_n_n___nodes__by__innovation}{sort\+\_\+\+R\+N\+N\+\_\+\+Nodes\+\_\+by\+\_\+innovation}} }{\pageref{structsort___r_n_n___nodes__by__innovation}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___r_n_n___recurrent___edges__by__depth}{sort\+\_\+\+R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edges\+\_\+by\+\_\+depth}} }{\pageref{structsort___r_n_n___recurrent___edges__by__depth}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___r_n_n___recurrent___edges__by__innovation}{sort\+\_\+\+R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edges\+\_\+by\+\_\+innovation}} }{\pageref{structsort___r_n_n___recurrent___edges__by__innovation}}{} +\item\contentsline{section}{\mbox{\hyperlink{structsort___r_n_n___recurrent___edges__by__output__depth}{sort\+\_\+\+R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edges\+\_\+by\+\_\+output\+\_\+depth}} }{\pageref{structsort___r_n_n___recurrent___edges__by__output__depth}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_speciation_strategy}{Speciation\+Strategy}} }{\pageref{class_speciation_strategy}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_species}{Species}} }{\pageref{class_species}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_time_series}{Time\+Series}} }{\pageref{class_time_series}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_time_series_set}{Time\+Series\+Set}} }{\pageref{class_time_series_set}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_time_series_sets}{Time\+Series\+Sets}} }{\pageref{class_time_series_sets}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_tracker}{Tracker}} }{\pageref{class_tracker}}{} +\item\contentsline{section}{\mbox{\hyperlink{structucvector}{ucvector}} }{\pageref{structucvector}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_u_g_r_n_n___node}{U\+G\+R\+N\+N\+\_\+\+Node}} }{\pageref{class_u_g_r_n_n___node}}{} +\item\contentsline{section}{\mbox{\hyperlink{structuivector}{uivector}} }{\pageref{structuivector}}{} +\item\contentsline{section}{\mbox{\hyperlink{class_word_series}{Word\+Series}} }{\pageref{class_word_series}}{} +\end{DoxyCompactList} diff --git a/docs/latex/class_c_n_n___edge.tex b/docs/latex/class_c_n_n___edge.tex new file mode 100644 index 00000000..f79adac2 --- /dev/null +++ b/docs/latex/class_c_n_n___edge.tex @@ -0,0 +1,302 @@ +\hypertarget{class_c_n_n___edge}{}\doxysection{C\+N\+N\+\_\+\+Edge Class Reference} +\label{class_c_n_n___edge}\index{CNN\_Edge@{CNN\_Edge}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a393992439d3fa9f5b8303bdddd37a905}\label{class_c_n_n___edge_a393992439d3fa9f5b8303bdddd37a905}} +{\bfseries C\+N\+N\+\_\+\+Edge} (\mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$\+\_\+input\+\_\+node, \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$\+\_\+output\+\_\+node, bool \+\_\+fixed, int \+\_\+innovation\+\_\+number, int \+\_\+type) +\item +\mbox{\Hypertarget{class_c_n_n___edge_a0ca269427c5dcf8a18176f5f84e46de0}\label{class_c_n_n___edge_a0ca269427c5dcf8a18176f5f84e46de0}} +\mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$ {\bfseries copy} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a5890de3d1e265af4652f0c06960606d8}\label{class_c_n_n___edge_a5890de3d1e265af4652f0c06960606d8}} +bool {\bfseries equals} (\mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$other) const +\item +\mbox{\Hypertarget{class_c_n_n___edge_adc7eb1c61f7dca12b641d6bd3c074764}\label{class_c_n_n___edge_adc7eb1c61f7dca12b641d6bd3c074764}} +int {\bfseries get\+\_\+type} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a235115dbbc633422419eab97c0aaa485}\label{class_c_n_n___edge_a235115dbbc633422419eab97c0aaa485}} +bool {\bfseries has\+\_\+nan} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a1e106cf86f525d34bde7e46284a92f05}\label{class_c_n_n___edge_a1e106cf86f525d34bde7e46284a92f05}} +void {\bfseries reset\+\_\+times} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a62b7b235e66053c76a2336d0e6910f23}\label{class_c_n_n___edge_a62b7b235e66053c76a2336d0e6910f23}} +void {\bfseries accumulate\+\_\+times} (float \&total\+\_\+forward\+\_\+time, float \&total\+\_\+backward\+\_\+time, float \&total\+\_\+weight\+\_\+update\+\_\+time) +\item +\mbox{\Hypertarget{class_c_n_n___edge_a3a99e57646dea4a3494a11bf40d0ffe7}\label{class_c_n_n___edge_a3a99e57646dea4a3494a11bf40d0ffe7}} +void {\bfseries set\+\_\+needs\+\_\+init} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a84a82475d253dab87a1d3d22a40d8e05}\label{class_c_n_n___edge_a84a82475d253dab87a1d3d22a40d8e05}} +bool {\bfseries needs\+\_\+init} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a449f2a47fc8f9010597eceee8937b892}\label{class_c_n_n___edge_a449f2a47fc8f9010597eceee8937b892}} +int {\bfseries get\+\_\+filter\+\_\+size} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a89ac5e2224c8c6f9813f95a5c09427a7}\label{class_c_n_n___edge_a89ac5e2224c8c6f9813f95a5c09427a7}} +int {\bfseries get\+\_\+filter\+\_\+x} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a71beb960e9d7a47ef1e6d18eb2e443d0}\label{class_c_n_n___edge_a71beb960e9d7a47ef1e6d18eb2e443d0}} +int {\bfseries get\+\_\+filter\+\_\+y} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a4093bbf8a52471ee6ec38867b3d664bf}\label{class_c_n_n___edge_a4093bbf8a52471ee6ec38867b3d664bf}} +bool {\bfseries is\+\_\+reverse\+\_\+filter\+\_\+x} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_aa0e32f49037c9e2b18dbe0dc6f1b8113}\label{class_c_n_n___edge_aa0e32f49037c9e2b18dbe0dc6f1b8113}} +bool {\bfseries is\+\_\+reverse\+\_\+filter\+\_\+y} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a5f2bf2c707f6ac938bd12cabd324a53e}\label{class_c_n_n___edge_a5f2bf2c707f6ac938bd12cabd324a53e}} +float {\bfseries get\+\_\+weight} (int i) const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a8484cfa7937acc2954b4b8f1fabba0bb}\label{class_c_n_n___edge_a8484cfa7937acc2954b4b8f1fabba0bb}} +float {\bfseries get\+\_\+weight\+\_\+update} (int i) const +\item +\mbox{\Hypertarget{class_c_n_n___edge_adecd1554fc8c3f3293f87e7a25bd50a1}\label{class_c_n_n___edge_adecd1554fc8c3f3293f87e7a25bd50a1}} +void {\bfseries update\+\_\+weight} (int i, float diff) +\item +\mbox{\Hypertarget{class_c_n_n___edge_ab1498bfe9487f2ed7882407684fd56e0}\label{class_c_n_n___edge_ab1498bfe9487f2ed7882407684fd56e0}} +float {\bfseries get\+\_\+scale} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_ad5c41eb1d293ad8202c42a19d2a8f059}\label{class_c_n_n___edge_ad5c41eb1d293ad8202c42a19d2a8f059}} +void {\bfseries propagate\+\_\+weight\+\_\+count} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_ae7a32253d2af6e39275dbe72d39adc84}\label{class_c_n_n___edge_ae7a32253d2af6e39275dbe72d39adc84}} +void {\bfseries save\+\_\+best\+\_\+weights} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a57c8f1cd4205ebe4d694f65af9f01b9f}\label{class_c_n_n___edge_a57c8f1cd4205ebe4d694f65af9f01b9f}} +void {\bfseries set\+\_\+weights\+\_\+to\+\_\+best} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a90d71a1f82e18c45d73265c39194262f}\label{class_c_n_n___edge_a90d71a1f82e18c45d73265c39194262f}} +bool {\bfseries set\+\_\+nodes} (const \textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ $>$ nodes) +\item +\mbox{\Hypertarget{class_c_n_n___edge_a0c3e9187be900987d5d7bd443dfbde85}\label{class_c_n_n___edge_a0c3e9187be900987d5d7bd443dfbde85}} +void {\bfseries set\+\_\+pools} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a9094afaa98fc598b0b6760b2c364f6e3}\label{class_c_n_n___edge_a9094afaa98fc598b0b6760b2c364f6e3}} +void {\bfseries initialize\+\_\+weights} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&\textbf{ normal\+\_\+distribution}) +\item +\mbox{\Hypertarget{class_c_n_n___edge_a879047fcaa69d34a75aa414954ad1fea}\label{class_c_n_n___edge_a879047fcaa69d34a75aa414954ad1fea}} +void {\bfseries reset\+\_\+velocities} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a2c42f6f5424d6c982b97f19452870b05}\label{class_c_n_n___edge_a2c42f6f5424d6c982b97f19452870b05}} +void {\bfseries resize} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a6e7547eb09765a8dd73893286e147d6a}\label{class_c_n_n___edge_a6e7547eb09765a8dd73893286e147d6a}} +void {\bfseries alter\+\_\+edge\+\_\+type} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a97caaebdefd16f5097fb62262b02a90a}\label{class_c_n_n___edge_a97caaebdefd16f5097fb62262b02a90a}} +void {\bfseries disable} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_ab141cc75a239789eb7321787698c72f2}\label{class_c_n_n___edge_ab141cc75a239789eb7321787698c72f2}} +void {\bfseries enable} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_aece1d64d23c03ab3dd1f9ab2db11d0fb}\label{class_c_n_n___edge_aece1d64d23c03ab3dd1f9ab2db11d0fb}} +bool {\bfseries is\+\_\+enabled} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a34119f0fce37aada658664c6948ff207}\label{class_c_n_n___edge_a34119f0fce37aada658664c6948ff207}} +bool {\bfseries is\+\_\+disabled} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a3e8a478c19c65fa028d28809a5a80d4a}\label{class_c_n_n___edge_a3e8a478c19c65fa028d28809a5a80d4a}} +void {\bfseries update\+\_\+batch\+\_\+size} (int new\+\_\+batch\+\_\+size) +\item +\mbox{\Hypertarget{class_c_n_n___edge_a4cdf5858bd94ee593db3a6052664a845}\label{class_c_n_n___edge_a4cdf5858bd94ee593db3a6052664a845}} +bool {\bfseries is\+\_\+reachable} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a46ffbd31b402c1c5434e6551e8375cbc}\label{class_c_n_n___edge_a46ffbd31b402c1c5434e6551e8375cbc}} +bool {\bfseries is\+\_\+forward\+\_\+visited} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a3d0bb8e668901098c485b0cb4a61df68}\label{class_c_n_n___edge_a3d0bb8e668901098c485b0cb4a61df68}} +bool {\bfseries is\+\_\+reverse\+\_\+visited} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a1413564a9b66050e78e3a34e55b735f1}\label{class_c_n_n___edge_a1413564a9b66050e78e3a34e55b735f1}} +void {\bfseries forward\+\_\+visit} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a9275d5e3f00e01685b99f57071d81eaf}\label{class_c_n_n___edge_a9275d5e3f00e01685b99f57071d81eaf}} +void {\bfseries reverse\+\_\+visit} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a830864feac5da7d0aa9e2a0c3cc17591}\label{class_c_n_n___edge_a830864feac5da7d0aa9e2a0c3cc17591}} +void {\bfseries set\+\_\+unvisited} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a5d16bae951861b2ff41875b8fcd1b3e4}\label{class_c_n_n___edge_a5d16bae951861b2ff41875b8fcd1b3e4}} +bool {\bfseries is\+\_\+filter\+\_\+correct} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a0fb1635e09c843946ad0c2fddbccabb0}\label{class_c_n_n___edge_a0fb1635e09c843946ad0c2fddbccabb0}} +int {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a562291e832437c984e5b9410e237d67a}\label{class_c_n_n___edge_a562291e832437c984e5b9410e237d67a}} +int {\bfseries get\+\_\+batch\+\_\+size} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a5f8bb38e098ce2b23149a9728d394e4e}\label{class_c_n_n___edge_a5f8bb38e098ce2b23149a9728d394e4e}} +int {\bfseries get\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_ad68d37a115713873530c80e1af401ed3}\label{class_c_n_n___edge_ad68d37a115713873530c80e1af401ed3}} +int {\bfseries get\+\_\+input\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a0b0f8ad1f6fc153880560d100f853472}\label{class_c_n_n___edge_a0b0f8ad1f6fc153880560d100f853472}} +int {\bfseries get\+\_\+output\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_ab60be7bea68b136a0cead7d41b53d6d9}\label{class_c_n_n___edge_ab60be7bea68b136a0cead7d41b53d6d9}} +bool {\bfseries connects} (int n1, int n2) const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a107d216821713cc2f949966681a47456}\label{class_c_n_n___edge_a107d216821713cc2f949966681a47456}} +bool {\bfseries has\+\_\+zero\+\_\+weight} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a1b6e79e46537e87c394c59cbb0086f55}\label{class_c_n_n___edge_a1b6e79e46537e87c394c59cbb0086f55}} +bool {\bfseries has\+\_\+zero\+\_\+best\+\_\+weight} () const +\item +\mbox{\Hypertarget{class_c_n_n___edge_a4952f6c8bc8ac66031c41eda0ccc8307}\label{class_c_n_n___edge_a4952f6c8bc8ac66031c41eda0ccc8307}} +\mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ {\bfseries get\+\_\+input\+\_\+node} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a8dd11313b721e4d2aba7e0cf97a2f965}\label{class_c_n_n___edge_a8dd11313b721e4d2aba7e0cf97a2f965}} +\mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ {\bfseries get\+\_\+output\+\_\+node} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_afe494e7869d0f2a6333404fd69658366}\label{class_c_n_n___edge_afe494e7869d0f2a6333404fd69658366}} +void {\bfseries print} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_c_n_n___edge_af569644fcce0ec92dc06a6a6ef19a230}\label{class_c_n_n___edge_af569644fcce0ec92dc06a6a6ef19a230}} +void {\bfseries check\+\_\+output\+\_\+update} (const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ float $>$ $>$ $>$ \&output, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ float $>$ $>$ $>$ \&input, float value, float weight, float previous\+\_\+output, int batch\+\_\+number, int in\+\_\+y, int in\+\_\+x, int out\+\_\+y, int out\+\_\+x) +\item +\mbox{\Hypertarget{class_c_n_n___edge_a8299ff4d423ffa723361b9a43bd16a69}\label{class_c_n_n___edge_a8299ff4d423ffa723361b9a43bd16a69}} +void {\bfseries check\+\_\+weight\+\_\+update} (const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ float $>$ $>$ $>$ \&input, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ float $>$ $>$ $>$ \&input\+\_\+deltas, float delta, float previous\+\_\+delta, float weight\+\_\+update, float previous\+\_\+weight\+\_\+update, int batch\+\_\+number, int out\+\_\+y, int out\+\_\+x, int in\+\_\+y, int in\+\_\+x) +\item +\mbox{\Hypertarget{class_c_n_n___edge_a1d8a8d6b8c20df05c9b720289ca318f8}\label{class_c_n_n___edge_a1d8a8d6b8c20df05c9b720289ca318f8}} +void {\bfseries propagate\+\_\+forward} (bool training, bool accumulate\+\_\+test\+\_\+statistics, float epsilon, float alpha, bool perform\+\_\+dropout, float hidden\+\_\+dropout\+\_\+probability, \textbf{ minstd\+\_\+rand0} \&generator) +\item +\mbox{\Hypertarget{class_c_n_n___edge_a94c37324e2d6d606103de3fb62db7118}\label{class_c_n_n___edge_a94c37324e2d6d606103de3fb62db7118}} +void {\bfseries propagate\+\_\+backward} (bool training, float mu, float learning\+\_\+rate, float epsilon) +\item +\mbox{\Hypertarget{class_c_n_n___edge_ac3e956a85a405b2bcf8fe8ab6d8e1e58}\label{class_c_n_n___edge_ac3e956a85a405b2bcf8fe8ab6d8e1e58}} +void {\bfseries update\+\_\+weights} (float mu, float learning\+\_\+rate, float weight\+\_\+decay) +\item +\mbox{\Hypertarget{class_c_n_n___edge_ad170b84e87da90339233112f86e1613e}\label{class_c_n_n___edge_ad170b84e87da90339233112f86e1613e}} +void {\bfseries print\+\_\+statistics} () +\item +\mbox{\Hypertarget{class_c_n_n___edge_a29904381ca17b002223b2361a6b53f04}\label{class_c_n_n___edge_a29904381ca17b002223b2361a6b53f04}} +bool {\bfseries is\+\_\+identical} (const \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$other, bool testing\+\_\+checkpoint) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_c_n_n___edge_af93eb3353bc0d6e81b4cf8bfb6847aa9}\label{class_c_n_n___edge_af93eb3353bc0d6e81b4cf8bfb6847aa9}} +int {\bfseries edge\+\_\+id} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a394b667358dd4a8110005032d9836319}\label{class_c_n_n___edge_a394b667358dd4a8110005032d9836319}} +int {\bfseries exact\+\_\+id} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a83c2fb1f602d7e5d7129532eaab412b8}\label{class_c_n_n___edge_a83c2fb1f602d7e5d7129532eaab412b8}} +int {\bfseries genome\+\_\+id} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a327dca105036a7d4a8e90cfb11e9658e}\label{class_c_n_n___edge_a327dca105036a7d4a8e90cfb11e9658e}} +int {\bfseries type} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a91dcb21d5dceff5dba93e8d77ff79773}\label{class_c_n_n___edge_a91dcb21d5dceff5dba93e8d77ff79773}} +int {\bfseries innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a29d56aa8136b0c4beacd200de4e4b4a3}\label{class_c_n_n___edge_a29d56aa8136b0c4beacd200de4e4b4a3}} +int {\bfseries input\+\_\+node\+\_\+innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_c_n_n___edge_af7696207cb7be7ca7457090fd50e723e}\label{class_c_n_n___edge_af7696207cb7be7ca7457090fd50e723e}} +int {\bfseries output\+\_\+node\+\_\+innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a04c1a41e0a6887d9baa01f07b038b0d9}\label{class_c_n_n___edge_a04c1a41e0a6887d9baa01f07b038b0d9}} +\mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ {\bfseries input\+\_\+node} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a73943cbef6220099d2907acdd5c25de0}\label{class_c_n_n___edge_a73943cbef6220099d2907acdd5c25de0}} +\mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ {\bfseries output\+\_\+node} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a6ef99178874979dc7d8ea5427f4a4d4d}\label{class_c_n_n___edge_a6ef99178874979dc7d8ea5427f4a4d4d}} +int {\bfseries batch\+\_\+size} +\item +\mbox{\Hypertarget{class_c_n_n___edge_aff731cfe8b0554458edb8351987f006f}\label{class_c_n_n___edge_aff731cfe8b0554458edb8351987f006f}} +int {\bfseries filter\+\_\+x} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a26f3c199f299af851dd4605abf1807b4}\label{class_c_n_n___edge_a26f3c199f299af851dd4605abf1807b4}} +int {\bfseries filter\+\_\+y} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a002f9a9abd7bd9432cf7131d0dcc2c78}\label{class_c_n_n___edge_a002f9a9abd7bd9432cf7131d0dcc2c78}} +int {\bfseries filter\+\_\+size} +\item +\mbox{\Hypertarget{class_c_n_n___edge_aeef2e1f9e311fdd9294dc62ade5be8c6}\label{class_c_n_n___edge_aeef2e1f9e311fdd9294dc62ade5be8c6}} +float $\ast$ {\bfseries weights} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a9092c98c254c9d4daa72dc01936726d4}\label{class_c_n_n___edge_a9092c98c254c9d4daa72dc01936726d4}} +float $\ast$ {\bfseries weight\+\_\+updates} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a61bc33918727171b3f63ef4732e044b9}\label{class_c_n_n___edge_a61bc33918727171b3f63ef4732e044b9}} +float $\ast$ {\bfseries best\+\_\+weights} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a4cf16caf63f4f39b33a308d3a28ead36}\label{class_c_n_n___edge_a4cf16caf63f4f39b33a308d3a28ead36}} +float $\ast$ {\bfseries previous\+\_\+velocity} +\item +\mbox{\Hypertarget{class_c_n_n___edge_adfec5ccecca015c5a6c35cf74a806d54}\label{class_c_n_n___edge_adfec5ccecca015c5a6c35cf74a806d54}} +float $\ast$ {\bfseries best\+\_\+velocity} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a53599d438d226caf6e8eff16bada29b1}\label{class_c_n_n___edge_a53599d438d226caf6e8eff16bada29b1}} +float {\bfseries scale} +\item +\mbox{\Hypertarget{class_c_n_n___edge_ad854815b7cc6e806a7d4a047402eacd7}\label{class_c_n_n___edge_ad854815b7cc6e806a7d4a047402eacd7}} +float {\bfseries best\+\_\+scale} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a14d1270e6660cd0a7d583f5abd52cb14}\label{class_c_n_n___edge_a14d1270e6660cd0a7d583f5abd52cb14}} +float {\bfseries previous\+\_\+velocity\+\_\+scale} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a5714ae5816bb0c630c7bbcb8b976d226}\label{class_c_n_n___edge_a5714ae5816bb0c630c7bbcb8b976d226}} +float {\bfseries best\+\_\+velocity\+\_\+scale} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a285c0bd37f85765f8ccbe2e9fa822fd9}\label{class_c_n_n___edge_a285c0bd37f85765f8ccbe2e9fa822fd9}} +\textbf{ vector}$<$ int $>$ {\bfseries y\+\_\+pools} +\item +\mbox{\Hypertarget{class_c_n_n___edge_ac1a332de0880bc8559af455194590f3a}\label{class_c_n_n___edge_ac1a332de0880bc8559af455194590f3a}} +\textbf{ vector}$<$ int $>$ {\bfseries y\+\_\+pool\+\_\+offset} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a2c0ddda5ad30f47c7c17cb6ff71884f4}\label{class_c_n_n___edge_a2c0ddda5ad30f47c7c17cb6ff71884f4}} +\textbf{ vector}$<$ int $>$ {\bfseries x\+\_\+pools} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a9cd2c032fa44f087d3cdb2ddacdc8b5e}\label{class_c_n_n___edge_a9cd2c032fa44f087d3cdb2ddacdc8b5e}} +\textbf{ vector}$<$ int $>$ {\bfseries x\+\_\+pool\+\_\+offset} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a8e6e94d3fc108fd597ab4c276aac7de7}\label{class_c_n_n___edge_a8e6e94d3fc108fd597ab4c276aac7de7}} +bool {\bfseries fixed} +\item +\mbox{\Hypertarget{class_c_n_n___edge_afc168fec81b44a696a87605f5173f71b}\label{class_c_n_n___edge_afc168fec81b44a696a87605f5173f71b}} +bool {\bfseries disabled} +\item +\mbox{\Hypertarget{class_c_n_n___edge_acf502d059b4a7e7adce337ecd32b7d52}\label{class_c_n_n___edge_acf502d059b4a7e7adce337ecd32b7d52}} +bool {\bfseries forward\+\_\+visited} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a3de0d853b4c8fb94ce0ad7e919fc1ad9}\label{class_c_n_n___edge_a3de0d853b4c8fb94ce0ad7e919fc1ad9}} +bool {\bfseries reverse\+\_\+visited} +\item +\mbox{\Hypertarget{class_c_n_n___edge_aeb0b4263d8de812eeb744bcda1450392}\label{class_c_n_n___edge_aeb0b4263d8de812eeb744bcda1450392}} +bool {\bfseries reverse\+\_\+filter\+\_\+x} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a239c6883dc77a2b2595409256ec0d5fd}\label{class_c_n_n___edge_a239c6883dc77a2b2595409256ec0d5fd}} +bool {\bfseries reverse\+\_\+filter\+\_\+y} +\item +\mbox{\Hypertarget{class_c_n_n___edge_af3316b68e4c8dbfbb74b2a41c45685e5}\label{class_c_n_n___edge_af3316b68e4c8dbfbb74b2a41c45685e5}} +bool {\bfseries needs\+\_\+initialization} +\item +\mbox{\Hypertarget{class_c_n_n___edge_adc3f7d1a84234d4cf8fe2b5e15079435}\label{class_c_n_n___edge_adc3f7d1a84234d4cf8fe2b5e15079435}} +float {\bfseries propagate\+\_\+backward\+\_\+time} +\item +\mbox{\Hypertarget{class_c_n_n___edge_a577df1b2b7d652fb0b51d995fe9e4188}\label{class_c_n_n___edge_a577df1b2b7d652fb0b51d995fe9e4188}} +float {\bfseries propagate\+\_\+forward\+\_\+time} +\item +\mbox{\Hypertarget{class_c_n_n___edge_ac099a1ba29c489842c9de557d3f330cb}\label{class_c_n_n___edge_ac099a1ba29c489842c9de557d3f330cb}} +float {\bfseries weight\+\_\+update\+\_\+time} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_c_n_n___edge_ad5af187ffab233b052f2d1cf35ebcb06}\label{class_c_n_n___edge_ad5af187ffab233b052f2d1cf35ebcb06}} +\textbf{ ostream} \& {\bfseries operator$<$$<$} (\textbf{ ostream} \&os, const \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$flight) +\item +\mbox{\Hypertarget{class_c_n_n___edge_ac97834d05fe9cba14891bae85445e571}\label{class_c_n_n___edge_ac97834d05fe9cba14891bae85445e571}} +\textbf{ istream} \& {\bfseries operator$>$$>$} (\textbf{ istream} \&is, \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$flight) +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +cnn/cnn\+\_\+edge.\+hxx\item +cnn/cnn\+\_\+edge.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_c_n_n___genome.tex b/docs/latex/class_c_n_n___genome.tex new file mode 100644 index 00000000..74f4c20c --- /dev/null +++ b/docs/latex/class_c_n_n___genome.tex @@ -0,0 +1,437 @@ +\hypertarget{class_c_n_n___genome}{}\doxysection{C\+N\+N\+\_\+\+Genome Class Reference} +\label{class_c_n_n___genome}\index{CNN\_Genome@{CNN\_Genome}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_c_n_n___genome_ae18524655bb0300fe3668d865d5c0df6}\label{class_c_n_n___genome_ae18524655bb0300fe3668d865d5c0df6}} +\mbox{\hyperlink{class_c_n_n___genome_ae18524655bb0300fe3668d865d5c0df6}{C\+N\+N\+\_\+\+Genome}} (\textbf{ string} filename, bool is\+\_\+checkpoint) +\begin{DoxyCompactList}\small\item\em Initialize a genome from a file. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_c_n_n___genome_ab21934379e8bda34b63578bc21048b2a}\label{class_c_n_n___genome_ab21934379e8bda34b63578bc21048b2a}} +{\bfseries C\+N\+N\+\_\+\+Genome} (\textbf{ istream} \&in, bool is\+\_\+checkpoint) +\item +\mbox{\Hypertarget{class_c_n_n___genome_ae4b107989d96112f5a911706d1e907d4}\label{class_c_n_n___genome_ae4b107989d96112f5a911706d1e907d4}} +\mbox{\hyperlink{class_c_n_n___genome_ae4b107989d96112f5a911706d1e907d4}{C\+N\+N\+\_\+\+Genome}} (int \+\_\+generation\+\_\+id, int \+\_\+padding, int \+\_\+number\+\_\+training\+\_\+images, int \+\_\+number\+\_\+validation\+\_\+images, int \+\_\+number\+\_\+test\+\_\+images, int seed, int \+\_\+max\+\_\+epochs, bool \+\_\+reset\+\_\+weights, int velocity\+\_\+reset, float \+\_\+mu, float \+\_\+mu\+\_\+delta, float \+\_\+learning\+\_\+rate, float \+\_\+learning\+\_\+rate\+\_\+delta, float \+\_\+weight\+\_\+decay, float \+\_\+weight\+\_\+decay\+\_\+delta, int \+\_\+batch\+\_\+size, float \+\_\+epsilon, float \+\_\+alpha, float \+\_\+input\+\_\+dropout\+\_\+probability, float \+\_\+hidden\+\_\+dropout\+\_\+probability, const \textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ $>$ \&\+\_\+nodes, const \textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$ $>$ \&\+\_\+edges) +\begin{DoxyCompactList}\small\item\em Iniitalize a genome from a set of nodes and edges. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_c_n_n___genome_af0bb41aec1d8b31142f04fb7daaf26e1}\label{class_c_n_n___genome_af0bb41aec1d8b31142f04fb7daaf26e1}} +float {\bfseries get\+\_\+version} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1416c5453d71d7a114fd91ba7263d990}\label{class_c_n_n___genome_a1416c5453d71d7a114fd91ba7263d990}} +\textbf{ string} {\bfseries get\+\_\+version\+\_\+str} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_ae55578d56f9d2d3db133ee6d6aca943e}\label{class_c_n_n___genome_ae55578d56f9d2d3db133ee6d6aca943e}} +int {\bfseries get\+\_\+genome\+\_\+id} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1186779df9e221abe5103fa00bcee784}\label{class_c_n_n___genome_a1186779df9e221abe5103fa00bcee784}} +int {\bfseries get\+\_\+exact\+\_\+id} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a8c8a383caa0e74941e16769580a73bb9}\label{class_c_n_n___genome_a8c8a383caa0e74941e16769580a73bb9}} +bool {\bfseries equals} (\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$other) const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a4195a122389cbeadf5ee98e244f1d797}\label{class_c_n_n___genome_a4195a122389cbeadf5ee98e244f1d797}} +void {\bfseries print\+\_\+results} (\textbf{ ostream} \&out) const +\item +\mbox{\Hypertarget{class_c_n_n___genome_ac6fc3a34ca5fb67b2817144790a24abf}\label{class_c_n_n___genome_ac6fc3a34ca5fb67b2817144790a24abf}} +void {\bfseries print\+\_\+progress} (\textbf{ ostream} \&out, \textbf{ string} progress\+\_\+name, float total\+\_\+error, int correct\+\_\+predictions, int number\+\_\+images) const +\item +\mbox{\Hypertarget{class_c_n_n___genome_af19d64181ce63fd22ca0e3847d7b4138}\label{class_c_n_n___genome_af19d64181ce63fd22ca0e3847d7b4138}} +int {\bfseries get\+\_\+number\+\_\+training\+\_\+images} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a9cf04a3f8504f9eba4596626fed25b3a}\label{class_c_n_n___genome_a9cf04a3f8504f9eba4596626fed25b3a}} +int {\bfseries get\+\_\+number\+\_\+validation\+\_\+images} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a5ac5e4639b6732076a6630204c9b52c1}\label{class_c_n_n___genome_a5ac5e4639b6732076a6630204c9b52c1}} +int {\bfseries get\+\_\+number\+\_\+test\+\_\+images} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_af7069613effa874c2ae4fc1bc81c7252}\label{class_c_n_n___genome_af7069613effa874c2ae4fc1bc81c7252}} +int {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_ab86b3c90222f0c62f3de8c08daa8213b}\label{class_c_n_n___genome_ab86b3c90222f0c62f3de8c08daa8213b}} +int {\bfseries get\+\_\+padding} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a58e7ac382ba0d6c50437607e27cec493}\label{class_c_n_n___genome_a58e7ac382ba0d6c50437607e27cec493}} +int {\bfseries get\+\_\+operations\+\_\+estimate} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_ae59e3d08935cfdf70d6fa52165d1baae}\label{class_c_n_n___genome_ae59e3d08935cfdf70d6fa52165d1baae}} +void {\bfseries set\+\_\+progress\+\_\+function} (int($\ast$\+\_\+progress\+\_\+function)(float)) +\item +\mbox{\Hypertarget{class_c_n_n___genome_aee4a6c206052e77fc1f22cf9f60b32a2}\label{class_c_n_n___genome_aee4a6c206052e77fc1f22cf9f60b32a2}} +int {\bfseries get\+\_\+generation\+\_\+id} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_acc1d545155b43baf4b50cfe1f6ebff5d}\label{class_c_n_n___genome_acc1d545155b43baf4b50cfe1f6ebff5d}} +float {\bfseries get\+\_\+best\+\_\+validation\+\_\+error} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a213b77af2b5e494ce27a819cfdd1dca7}\label{class_c_n_n___genome_a213b77af2b5e494ce27a819cfdd1dca7}} +float {\bfseries get\+\_\+best\+\_\+validation\+\_\+rate} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a40423dc3b69a304a44fc6c8eee93018e}\label{class_c_n_n___genome_a40423dc3b69a304a44fc6c8eee93018e}} +int {\bfseries get\+\_\+best\+\_\+validation\+\_\+predictions} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_ab23554e9eecbeb27850d0d7363b29b2c}\label{class_c_n_n___genome_ab23554e9eecbeb27850d0d7363b29b2c}} +float {\bfseries get\+\_\+training\+\_\+error} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a96cafb18d364c500af0c6888ca8f97be}\label{class_c_n_n___genome_a96cafb18d364c500af0c6888ca8f97be}} +float {\bfseries get\+\_\+training\+\_\+rate} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a189a3d7f3247bda91fbda8a27a4c79b4}\label{class_c_n_n___genome_a189a3d7f3247bda91fbda8a27a4c79b4}} +int {\bfseries get\+\_\+training\+\_\+predictions} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a7f947e20da9ff95bad666ff945be6884}\label{class_c_n_n___genome_a7f947e20da9ff95bad666ff945be6884}} +float {\bfseries get\+\_\+test\+\_\+error} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_afdbd689521112afc854918fa89688c50}\label{class_c_n_n___genome_afdbd689521112afc854918fa89688c50}} +float {\bfseries get\+\_\+test\+\_\+rate} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1480570ea904cf79b10ed1cb2f450f25}\label{class_c_n_n___genome_a1480570ea904cf79b10ed1cb2f450f25}} +int {\bfseries get\+\_\+test\+\_\+predictions} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_ac8e94c459a0e700fbf22589ed76e5e01}\label{class_c_n_n___genome_ac8e94c459a0e700fbf22589ed76e5e01}} +int {\bfseries get\+\_\+best\+\_\+epoch} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1a014a77880328435a83c02696c7c839}\label{class_c_n_n___genome_a1a014a77880328435a83c02696c7c839}} +int {\bfseries get\+\_\+epoch} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_aef9229d56cccbef1b27b80e913e32cf1}\label{class_c_n_n___genome_aef9229d56cccbef1b27b80e913e32cf1}} +int {\bfseries get\+\_\+max\+\_\+epochs} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a57d0bcd832ac1ba4a4935802d522eeae}\label{class_c_n_n___genome_a57d0bcd832ac1ba4a4935802d522eeae}} +bool {\bfseries sanity\+\_\+check} (int type) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1f7e009a473c0c976ae38f8dbdbba651}\label{class_c_n_n___genome_a1f7e009a473c0c976ae38f8dbdbba651}} +bool {\bfseries visit\+\_\+nodes} () +\item +\mbox{\Hypertarget{class_c_n_n___genome_a77d372ff192b058f1f2c402ebfcc5d88}\label{class_c_n_n___genome_a77d372ff192b058f1f2c402ebfcc5d88}} +void {\bfseries get\+\_\+node\+\_\+copies} (\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ $>$ \&node\+\_\+copies) const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a3a2ca7ebf67582adaa974ce34719ba86}\label{class_c_n_n___genome_a3a2ca7ebf67582adaa974ce34719ba86}} +void {\bfseries get\+\_\+edge\+\_\+copies} (\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$ $>$ \&edge\+\_\+copies) const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1b25477dcf14069f6fbd22519bfe045a}\label{class_c_n_n___genome_a1b25477dcf14069f6fbd22519bfe045a}} +const \textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ $>$ {\bfseries get\+\_\+nodes} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a812e09d1d3a7b7b692dc90e3eadcade2}\label{class_c_n_n___genome_a812e09d1d3a7b7b692dc90e3eadcade2}} +const \textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$ $>$ {\bfseries get\+\_\+edges} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_af01fbb1efcd85f8b3b4193054b23b026}\label{class_c_n_n___genome_af01fbb1efcd85f8b3b4193054b23b026}} +\mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ {\bfseries get\+\_\+node} (int node\+\_\+position) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a6e149da8cfd3fdba2d9fa4328793c56c}\label{class_c_n_n___genome_a6e149da8cfd3fdba2d9fa4328793c56c}} +\mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$ {\bfseries get\+\_\+edge} (int edge\+\_\+position) +\item +\mbox{\Hypertarget{class_c_n_n___genome_abc2744e4baa6babaef51a9d9a88c3ceb}\label{class_c_n_n___genome_abc2744e4baa6babaef51a9d9a88c3ceb}} +float {\bfseries get\+\_\+initial\+\_\+mu} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a9458d8f45ffa3f99e72d54cdc26ed996}\label{class_c_n_n___genome_a9458d8f45ffa3f99e72d54cdc26ed996}} +float {\bfseries get\+\_\+mu} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_ab0db2dbb95cc5994d2238a2175416a7e}\label{class_c_n_n___genome_ab0db2dbb95cc5994d2238a2175416a7e}} +float {\bfseries get\+\_\+mu\+\_\+delta} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a8d4b52580fb57549d1f02abb017122b7}\label{class_c_n_n___genome_a8d4b52580fb57549d1f02abb017122b7}} +float {\bfseries get\+\_\+initial\+\_\+learning\+\_\+rate} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a77e5ff16a16fd684d9560e7f6cb4d1ce}\label{class_c_n_n___genome_a77e5ff16a16fd684d9560e7f6cb4d1ce}} +float {\bfseries get\+\_\+learning\+\_\+rate} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a337a3dc3e61ed54cf933d440e3273f1d}\label{class_c_n_n___genome_a337a3dc3e61ed54cf933d440e3273f1d}} +float {\bfseries get\+\_\+learning\+\_\+rate\+\_\+delta} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a54be8a8283134d3604229f8db36c4925}\label{class_c_n_n___genome_a54be8a8283134d3604229f8db36c4925}} +float {\bfseries get\+\_\+initial\+\_\+weight\+\_\+decay} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_ac681a2bdfd1c62dd094852202f7586a1}\label{class_c_n_n___genome_ac681a2bdfd1c62dd094852202f7586a1}} +float {\bfseries get\+\_\+weight\+\_\+decay} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_af917480e3106d18bfaf6efe506b5bdbd}\label{class_c_n_n___genome_af917480e3106d18bfaf6efe506b5bdbd}} +float {\bfseries get\+\_\+weight\+\_\+decay\+\_\+delta} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1da04fd2ccb5f6713f1b6dab16475e7e}\label{class_c_n_n___genome_a1da04fd2ccb5f6713f1b6dab16475e7e}} +int {\bfseries get\+\_\+batch\+\_\+size} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a86be8901d0ad32425e9c5bc061f9cc16}\label{class_c_n_n___genome_a86be8901d0ad32425e9c5bc061f9cc16}} +float {\bfseries get\+\_\+alpha} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a560aa11ea77f947fc7b34317061ed136}\label{class_c_n_n___genome_a560aa11ea77f947fc7b34317061ed136}} +int {\bfseries get\+\_\+velocity\+\_\+reset} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_af8addfaca375ff55d55659618e58d539}\label{class_c_n_n___genome_af8addfaca375ff55d55659618e58d539}} +float {\bfseries get\+\_\+input\+\_\+dropout\+\_\+probability} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a2f9b39d44e214a47574a86756c6e3518}\label{class_c_n_n___genome_a2f9b39d44e214a47574a86756c6e3518}} +float {\bfseries get\+\_\+hidden\+\_\+dropout\+\_\+probability} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_abf55862d06230d2288952ca375a48f13}\label{class_c_n_n___genome_abf55862d06230d2288952ca375a48f13}} +int {\bfseries get\+\_\+number\+\_\+enabled\+\_\+pooling\+\_\+edges} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a8b6e1c4ff9ef182fdf5ae6670cd7b21a}\label{class_c_n_n___genome_a8b6e1c4ff9ef182fdf5ae6670cd7b21a}} +int {\bfseries get\+\_\+number\+\_\+enabled\+\_\+convolutional\+\_\+edges} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a021173acc8f2cb715edc0c53c191ca53}\label{class_c_n_n___genome_a021173acc8f2cb715edc0c53c191ca53}} +int {\bfseries get\+\_\+number\+\_\+enabled\+\_\+edges} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a5f782c1644fd12ca91266cf542e065dd}\label{class_c_n_n___genome_a5f782c1644fd12ca91266cf542e065dd}} +int {\bfseries get\+\_\+number\+\_\+enabled\+\_\+nodes} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a11b24b766bdddbb28c8fdf9dca8d574a}\label{class_c_n_n___genome_a11b24b766bdddbb28c8fdf9dca8d574a}} +int {\bfseries get\+\_\+number\+\_\+edges} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a48fc01c03e5a9d56a86ed1f50b94feb2}\label{class_c_n_n___genome_a48fc01c03e5a9d56a86ed1f50b94feb2}} +int {\bfseries get\+\_\+number\+\_\+nodes} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_aae660fe85174a2885cf6b1c71fb7b841}\label{class_c_n_n___genome_aae660fe85174a2885cf6b1c71fb7b841}} +int {\bfseries get\+\_\+number\+\_\+softmax\+\_\+nodes} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1d00576381fe9efc259b8155118d0335}\label{class_c_n_n___genome_a1d00576381fe9efc259b8155118d0335}} +int {\bfseries get\+\_\+number\+\_\+input\+\_\+nodes} () const +\item +\mbox{\Hypertarget{class_c_n_n___genome_afca459f19b3eea0d83526babb32136e7}\label{class_c_n_n___genome_afca459f19b3eea0d83526babb32136e7}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ $>$ {\bfseries get\+\_\+reachable\+\_\+nodes} () +\item +\mbox{\Hypertarget{class_c_n_n___genome_adc666fe37f2e415ed0f532b1c618bf3c}\label{class_c_n_n___genome_adc666fe37f2e415ed0f532b1c618bf3c}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ $>$ {\bfseries get\+\_\+disabled\+\_\+nodes} () +\item +\mbox{\Hypertarget{class_c_n_n___genome_ac826dff27daecd13c162a9eee491e418}\label{class_c_n_n___genome_ac826dff27daecd13c162a9eee491e418}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ $>$ {\bfseries get\+\_\+reachable\+\_\+hidden\+\_\+nodes} () +\item +\mbox{\Hypertarget{class_c_n_n___genome_afb475602c0a6d4967cdad87322b134c7}\label{class_c_n_n___genome_afb475602c0a6d4967cdad87322b134c7}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$ $>$ {\bfseries get\+\_\+reachable\+\_\+edges} () +\item +\mbox{\Hypertarget{class_c_n_n___genome_a9656139298afb536faae6c25fe5264ae}\label{class_c_n_n___genome_a9656139298afb536faae6c25fe5264ae}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$ $>$ {\bfseries get\+\_\+disabled\+\_\+edges} () +\item +\mbox{\Hypertarget{class_c_n_n___genome_aa597a2315550e2354ce1abf19353113c}\label{class_c_n_n___genome_aa597a2315550e2354ce1abf19353113c}} +void {\bfseries add\+\_\+node} (\mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$node) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a74cdf069363531153ead12755fe0818d}\label{class_c_n_n___genome_a74cdf069363531153ead12755fe0818d}} +void {\bfseries add\+\_\+edge} (\mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$edge) +\item +\mbox{\Hypertarget{class_c_n_n___genome_ad7dab31282a3201c351083d777b0b4e1}\label{class_c_n_n___genome_ad7dab31282a3201c351083d777b0b4e1}} +bool {\bfseries disable\+\_\+edge} (int edge\+\_\+position) +\item +\mbox{\Hypertarget{class_c_n_n___genome_aca2d62978470998f101b47b1c68790f9}\label{class_c_n_n___genome_aca2d62978470998f101b47b1c68790f9}} +void {\bfseries resize\+\_\+edges\+\_\+around\+\_\+node} (int node\+\_\+position) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a60170454cfdcd4aa76a7e28d7b2b6116}\label{class_c_n_n___genome_a60170454cfdcd4aa76a7e28d7b2b6116}} +void {\bfseries evaluate\+\_\+images} (const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&images, const \textbf{ vector}$<$ int $>$ \&batch, \textbf{ vector}$<$ \textbf{ vector}$<$ float $>$ $>$ \&predictions, int offset) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a77b133e9ce83f80d95c4d6ae6b9fed24}\label{class_c_n_n___genome_a77b133e9ce83f80d95c4d6ae6b9fed24}} +void {\bfseries evaluate\+\_\+images} (const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&images, const \textbf{ vector}$<$ int $>$ \&batch, bool training, float \&total\+\_\+error, int \&correct\+\_\+predictions, bool accumulate\+\_\+test\+\_\+statistics) +\item +\mbox{\Hypertarget{class_c_n_n___genome_ae39e5304bfd879928cda14d21882a92f}\label{class_c_n_n___genome_ae39e5304bfd879928cda14d21882a92f}} +void {\bfseries set\+\_\+to\+\_\+best} () +\item +\mbox{\Hypertarget{class_c_n_n___genome_adb365cb89711f1f9eb440c2dbe5bc852}\label{class_c_n_n___genome_adb365cb89711f1f9eb440c2dbe5bc852}} +void {\bfseries save\+\_\+to\+\_\+best} () +\item +\mbox{\Hypertarget{class_c_n_n___genome_aa312282aefdf1b6e9095d644240f8074}\label{class_c_n_n___genome_aa312282aefdf1b6e9095d644240f8074}} +void {\bfseries reset} (bool \+\_\+reset\+\_\+weights) +\item +\mbox{\Hypertarget{class_c_n_n___genome_aeeabfaa18ba2b5035f61f45a5cbb50ea}\label{class_c_n_n___genome_aeeabfaa18ba2b5035f61f45a5cbb50ea}} +void {\bfseries initialize} () +\item +\mbox{\Hypertarget{class_c_n_n___genome_a6db242fa1537dd87ead1c22f6b26a71f}\label{class_c_n_n___genome_a6db242fa1537dd87ead1c22f6b26a71f}} +void {\bfseries check\+\_\+gradients} (const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&images) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1ac051de433d1d6df4d69fc3b3ae6e46}\label{class_c_n_n___genome_a1ac051de433d1d6df4d69fc3b3ae6e46}} +void {\bfseries evaluate\+\_\+large\+\_\+images} (const \mbox{\hyperlink{class_large_images}{Large\+Images}} \&images, \textbf{ string} output\+\_\+directory) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1c7499369dab9cffa6e8d4c8aa386377}\label{class_c_n_n___genome_a1c7499369dab9cffa6e8d4c8aa386377}} +void {\bfseries evaluate} (const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&images, \textbf{ vector}$<$ \textbf{ vector}$<$ float $>$ $>$ \&predictions) +\item +\mbox{\Hypertarget{class_c_n_n___genome_aa8a981822b6d88f2cf58dc5f0ec5585d}\label{class_c_n_n___genome_aa8a981822b6d88f2cf58dc5f0ec5585d}} +void {\bfseries evaluate} (const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&images, const \textbf{ vector}$<$ long $>$ \&order, float \&total\+\_\+error, int \&correct\+\_\+predictions, bool perform\+\_\+backprop, bool accumulate\+\_\+test\+\_\+statistics) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a5e880e13a74c12700dc52028b457499f}\label{class_c_n_n___genome_a5e880e13a74c12700dc52028b457499f}} +void {\bfseries evaluate} (\textbf{ string} progress\+\_\+name, const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&images, float \&total\+\_\+error, int \&correct\+\_\+predictions) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a7eeba6b8378a89f5491f6aa59097f019}\label{class_c_n_n___genome_a7eeba6b8378a89f5491f6aa59097f019}} +void {\bfseries stochastic\+\_\+backpropagation} (const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&training\+\_\+images, int training\+\_\+resize, const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&validation\+\_\+images) +\item +\mbox{\Hypertarget{class_c_n_n___genome_af4889b04c3b364682458a96968158266}\label{class_c_n_n___genome_af4889b04c3b364682458a96968158266}} +void {\bfseries stochastic\+\_\+backpropagation} (const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&training\+\_\+images, const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&validation\+\_\+images) +\item +\mbox{\Hypertarget{class_c_n_n___genome_ae20429bd9698fac4b55cfa37ad7c9f71}\label{class_c_n_n___genome_ae20429bd9698fac4b55cfa37ad7c9f71}} +void {\bfseries evaluate\+\_\+test} (const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&test\+\_\+images) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a85ae5260e1c2ebe171b546662c111835}\label{class_c_n_n___genome_a85ae5260e1c2ebe171b546662c111835}} +void {\bfseries set\+\_\+name} (\textbf{ string} \+\_\+name) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a4175bd5c6ff25f8ff21f34f4658f0c23}\label{class_c_n_n___genome_a4175bd5c6ff25f8ff21f34f4658f0c23}} +void {\bfseries set\+\_\+output\+\_\+filename} (\textbf{ string} \+\_\+output\+\_\+filename) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a5531e673b134739e49639bdda5e4686b}\label{class_c_n_n___genome_a5531e673b134739e49639bdda5e4686b}} +void {\bfseries set\+\_\+checkpoint\+\_\+filename} (\textbf{ string} \+\_\+checkpoint\+\_\+filename) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a3fef9794a3d7807b91fbcc3c9705b13b}\label{class_c_n_n___genome_a3fef9794a3d7807b91fbcc3c9705b13b}} +void {\bfseries write} (\textbf{ ostream} \&outfile) +\item +\mbox{\Hypertarget{class_c_n_n___genome_afa2cc2a83a85aa19f9f8e1cf07cbbb97}\label{class_c_n_n___genome_afa2cc2a83a85aa19f9f8e1cf07cbbb97}} +void {\bfseries write\+\_\+to\+\_\+file} (\textbf{ string} filename) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a2b5abc704f704970794545413ff9ceb7}\label{class_c_n_n___genome_a2b5abc704f704970794545413ff9ceb7}} +void {\bfseries read} (\textbf{ istream} \&infile) +\item +\mbox{\Hypertarget{class_c_n_n___genome_af7828e7478f94c5dd84e4ceb70d7a1e7}\label{class_c_n_n___genome_af7828e7478f94c5dd84e4ceb70d7a1e7}} +void {\bfseries print\+\_\+graphviz} (\textbf{ ostream} \&out) const +\item +\mbox{\Hypertarget{class_c_n_n___genome_aa9b653c4b4a3f966a94673f4fad6d140}\label{class_c_n_n___genome_aa9b653c4b4a3f966a94673f4fad6d140}} +void {\bfseries set\+\_\+generated\+\_\+by} (\textbf{ string} type) +\item +\mbox{\Hypertarget{class_c_n_n___genome_ab824e4f90319ae5645c961ed7a76337d}\label{class_c_n_n___genome_ab824e4f90319ae5645c961ed7a76337d}} +int {\bfseries get\+\_\+generated\+\_\+by} (\textbf{ string} type) +\item +\mbox{\Hypertarget{class_c_n_n___genome_ac8dcb67ff2c52197ed45cb02ec50bde0}\label{class_c_n_n___genome_ac8dcb67ff2c52197ed45cb02ec50bde0}} +bool {\bfseries is\+\_\+identical} (\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$other, bool testing\+\_\+checkpoint) +\item +\mbox{\Hypertarget{class_c_n_n___genome_a0ca4c4d9016b9ba05e09ebc4cbc5027c}\label{class_c_n_n___genome_a0ca4c4d9016b9ba05e09ebc4cbc5027c}} +void {\bfseries get\+\_\+prediction\+\_\+matrix} (const \mbox{\hyperlink{class_multi_images_interface}{Multi\+Images\+Interface}} \&images, int image\+\_\+number, int stride, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ float $>$ $>$ $>$ \&prediction\+\_\+matrix) +\item +\mbox{\Hypertarget{class_c_n_n___genome_aaffe635334abb8652512a3bb62c7306d}\label{class_c_n_n___genome_aaffe635334abb8652512a3bb62c7306d}} +void {\bfseries get\+\_\+expanded\+\_\+prediction\+\_\+matrix} (const \mbox{\hyperlink{class_multi_images_interface}{Multi\+Images\+Interface}} \&images, int image\+\_\+number, int stride, int prediction\+\_\+class, \textbf{ vector}$<$ \textbf{ vector}$<$ float $>$ $>$ \&extended\+\_\+prediction\+\_\+matrix) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a8ec27ef16f563520749095b1cb31a00e}\label{class_c_n_n___genome_a8ec27ef16f563520749095b1cb31a00e}} +\textbf{ string} {\bfseries version\+\_\+str} +\item +\mbox{\Hypertarget{class_c_n_n___genome_ac36f72edddbedbc5b1d21127fba3f198}\label{class_c_n_n___genome_ac36f72edddbedbc5b1d21127fba3f198}} +int {\bfseries exact\+\_\+id} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a27f577f2161178a174d80f06c04961df}\label{class_c_n_n___genome_a27f577f2161178a174d80f06c04961df}} +int {\bfseries genome\+\_\+id} +\item +\mbox{\Hypertarget{class_c_n_n___genome_ada5b45d8d524ab587c1d0e54a7b3fc7a}\label{class_c_n_n___genome_ada5b45d8d524ab587c1d0e54a7b3fc7a}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ $>$ {\bfseries nodes} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a858ccc25b70c274ef12825c0bf8c97ed}\label{class_c_n_n___genome_a858ccc25b70c274ef12825c0bf8c97ed}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$ $>$ {\bfseries edges} +\item +\mbox{\Hypertarget{class_c_n_n___genome_aedb7d6ec908e1c3c3b026c086e1bf290}\label{class_c_n_n___genome_aedb7d6ec908e1c3c3b026c086e1bf290}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ $>$ {\bfseries input\+\_\+nodes} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a0457c013332baf3b29ad27310ce28525}\label{class_c_n_n___genome_a0457c013332baf3b29ad27310ce28525}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ $>$ {\bfseries softmax\+\_\+nodes} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a30cbdec8592617d40fe88e60b3585370}\label{class_c_n_n___genome_a30cbdec8592617d40fe88e60b3585370}} +\mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} {\bfseries normal\+\_\+distribution} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a3dff866f114f70a0880bf96670a7955a}\label{class_c_n_n___genome_a3dff866f114f70a0880bf96670a7955a}} +\textbf{ minstd\+\_\+rand0} {\bfseries generator} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a71018bde5fd2254c289e62e3965bb5e0}\label{class_c_n_n___genome_a71018bde5fd2254c289e62e3965bb5e0}} +int {\bfseries velocity\+\_\+reset} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a74125b4d86b279816108708d9d085c98}\label{class_c_n_n___genome_a74125b4d86b279816108708d9d085c98}} +int {\bfseries batch\+\_\+size} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a8f752eafed625bf9624293262628237c}\label{class_c_n_n___genome_a8f752eafed625bf9624293262628237c}} +float {\bfseries epsilon} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a5e53cc85b3525115a46ac286d8294264}\label{class_c_n_n___genome_a5e53cc85b3525115a46ac286d8294264}} +float {\bfseries alpha} +\item +\mbox{\Hypertarget{class_c_n_n___genome_af326f0f80e61dfa272eebae6e7d206f1}\label{class_c_n_n___genome_af326f0f80e61dfa272eebae6e7d206f1}} +float {\bfseries input\+\_\+dropout\+\_\+probability} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a89a70d4c6e9e15d2f759104b4570f5ee}\label{class_c_n_n___genome_a89a70d4c6e9e15d2f759104b4570f5ee}} +float {\bfseries hidden\+\_\+dropout\+\_\+probability} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a6273045a2a43be195624434d4711f968}\label{class_c_n_n___genome_a6273045a2a43be195624434d4711f968}} +float {\bfseries initial\+\_\+mu} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a09d0057d079d3a813861ee0cd2545f99}\label{class_c_n_n___genome_a09d0057d079d3a813861ee0cd2545f99}} +float {\bfseries mu} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a28ef1f9d9c2e42606dee81157810feca}\label{class_c_n_n___genome_a28ef1f9d9c2e42606dee81157810feca}} +float {\bfseries mu\+\_\+delta} +\item +\mbox{\Hypertarget{class_c_n_n___genome_ac318f81565dcd73fb11c43253aa76405}\label{class_c_n_n___genome_ac318f81565dcd73fb11c43253aa76405}} +float {\bfseries initial\+\_\+learning\+\_\+rate} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a6ec506b690d93b4ede8fd50443f9c72b}\label{class_c_n_n___genome_a6ec506b690d93b4ede8fd50443f9c72b}} +float {\bfseries learning\+\_\+rate} +\item +\mbox{\Hypertarget{class_c_n_n___genome_aa75e9627afcfc4b57c401c2bdb1fa630}\label{class_c_n_n___genome_aa75e9627afcfc4b57c401c2bdb1fa630}} +float {\bfseries learning\+\_\+rate\+\_\+delta} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a2e4aae2e8d305db02e73fb4b43e6183f}\label{class_c_n_n___genome_a2e4aae2e8d305db02e73fb4b43e6183f}} +float {\bfseries initial\+\_\+weight\+\_\+decay} +\item +\mbox{\Hypertarget{class_c_n_n___genome_ac5da4d6ba98e776e19ab662bdc891d83}\label{class_c_n_n___genome_ac5da4d6ba98e776e19ab662bdc891d83}} +float {\bfseries weight\+\_\+decay} +\item +\mbox{\Hypertarget{class_c_n_n___genome_aa260f75e031ee5fa8bc84bef197dcae8}\label{class_c_n_n___genome_aa260f75e031ee5fa8bc84bef197dcae8}} +float {\bfseries weight\+\_\+decay\+\_\+delta} +\item +\mbox{\Hypertarget{class_c_n_n___genome_af309bfba7b9c32d871fae60b710211a6}\label{class_c_n_n___genome_af309bfba7b9c32d871fae60b710211a6}} +int {\bfseries epoch} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a29820b7906c794e0911069de6615f8ee}\label{class_c_n_n___genome_a29820b7906c794e0911069de6615f8ee}} +int {\bfseries max\+\_\+epochs} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a72b47518f4df7e9543134eefd8d7752b}\label{class_c_n_n___genome_a72b47518f4df7e9543134eefd8d7752b}} +bool {\bfseries reset\+\_\+weights} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1ae4b704e9aee01dd99158debbe14dbd}\label{class_c_n_n___genome_a1ae4b704e9aee01dd99158debbe14dbd}} +int {\bfseries padding} +\item +\mbox{\Hypertarget{class_c_n_n___genome_ae7681541ee912f598a92f07564f98aba}\label{class_c_n_n___genome_ae7681541ee912f598a92f07564f98aba}} +int {\bfseries best\+\_\+epoch} +\item +\mbox{\Hypertarget{class_c_n_n___genome_abc7ebbed239b6ec0958451b7f2e80c27}\label{class_c_n_n___genome_abc7ebbed239b6ec0958451b7f2e80c27}} +int {\bfseries number\+\_\+validation\+\_\+images} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a437d8900924b0fc10becf61c6ce69ba0}\label{class_c_n_n___genome_a437d8900924b0fc10becf61c6ce69ba0}} +float {\bfseries best\+\_\+validation\+\_\+error} +\item +\mbox{\Hypertarget{class_c_n_n___genome_ae25e7459934838e21b8d82bcc1945b09}\label{class_c_n_n___genome_ae25e7459934838e21b8d82bcc1945b09}} +int {\bfseries best\+\_\+validation\+\_\+predictions} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a7161c7c21005c3f26a6c5595719e1d3b}\label{class_c_n_n___genome_a7161c7c21005c3f26a6c5595719e1d3b}} +int {\bfseries number\+\_\+training\+\_\+images} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a0e29c833d8769002b536389a4071b57f}\label{class_c_n_n___genome_a0e29c833d8769002b536389a4071b57f}} +float {\bfseries training\+\_\+error} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a447a92e0e3d05fbdbd90f75812a9be29}\label{class_c_n_n___genome_a447a92e0e3d05fbdbd90f75812a9be29}} +int {\bfseries training\+\_\+predictions} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a561d2b8ab2680f31678f3de8a8af7c44}\label{class_c_n_n___genome_a561d2b8ab2680f31678f3de8a8af7c44}} +int {\bfseries number\+\_\+test\+\_\+images} +\item +\mbox{\Hypertarget{class_c_n_n___genome_ac6186df452259dbc064d9c57382be559}\label{class_c_n_n___genome_ac6186df452259dbc064d9c57382be559}} +float {\bfseries test\+\_\+error} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a7d7dd209af6e2a62a1fa657be347f22c}\label{class_c_n_n___genome_a7d7dd209af6e2a62a1fa657be347f22c}} +int {\bfseries test\+\_\+predictions} +\item +\mbox{\Hypertarget{class_c_n_n___genome_aa4ff950358e4a22dea3e98cbd91ce9ba}\label{class_c_n_n___genome_aa4ff950358e4a22dea3e98cbd91ce9ba}} +bool {\bfseries started\+\_\+from\+\_\+checkpoint} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a1a2fa204e78bd5f8f6e50cac857b4d39}\label{class_c_n_n___genome_a1a2fa204e78bd5f8f6e50cac857b4d39}} +\textbf{ vector}$<$ long $>$ {\bfseries backprop\+\_\+order} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a90c910d9a927cd22478c1c8085940479}\label{class_c_n_n___genome_a90c910d9a927cd22478c1c8085940479}} +int {\bfseries generation\+\_\+id} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a275979e3dd5f97a9850572e27cfa78f9}\label{class_c_n_n___genome_a275979e3dd5f97a9850572e27cfa78f9}} +\textbf{ string} {\bfseries name} +\item +\mbox{\Hypertarget{class_c_n_n___genome_add78222f1f20b75bac8588902c541da6}\label{class_c_n_n___genome_add78222f1f20b75bac8588902c541da6}} +\textbf{ string} {\bfseries checkpoint\+\_\+filename} +\item +\mbox{\Hypertarget{class_c_n_n___genome_a9f3c6108b842c628a11ab083df584815}\label{class_c_n_n___genome_a9f3c6108b842c628a11ab083df584815}} +\textbf{ string} {\bfseries output\+\_\+filename} +\item +\mbox{\Hypertarget{class_c_n_n___genome_adf26166816b56a45408a6a271ca686d2}\label{class_c_n_n___genome_adf26166816b56a45408a6a271ca686d2}} +\textbf{ map}$<$ \textbf{ string}, int $>$ {\bfseries generated\+\_\+by\+\_\+map} +\item +\mbox{\Hypertarget{class_c_n_n___genome_af767929c34570ff9528f1a12a792fad7}\label{class_c_n_n___genome_af767929c34570ff9528f1a12a792fad7}} +int($\ast$ {\bfseries progress\+\_\+function} )(float) +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +cnn/cnn\+\_\+genome.\+hxx\item +cnn/cnn\+\_\+genome.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_c_n_n___node.tex b/docs/latex/class_c_n_n___node.tex new file mode 100644 index 00000000..6e8be1d2 --- /dev/null +++ b/docs/latex/class_c_n_n___node.tex @@ -0,0 +1,368 @@ +\hypertarget{class_c_n_n___node}{}\doxysection{C\+N\+N\+\_\+\+Node Class Reference} +\label{class_c_n_n___node}\index{CNN\_Node@{CNN\_Node}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_c_n_n___node_a7fac811a3a94c72d882a6d32fbcb1331}\label{class_c_n_n___node_a7fac811a3a94c72d882a6d32fbcb1331}} +{\bfseries C\+N\+N\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, float \+\_\+depth, int \+\_\+batch\+\_\+size, int \+\_\+input\+\_\+size\+\_\+x, int \+\_\+input\+\_\+size\+\_\+y, int type) +\item +\mbox{\Hypertarget{class_c_n_n___node_a3ddd61a216467d8291b295df007bed14}\label{class_c_n_n___node_a3ddd61a216467d8291b295df007bed14}} +\mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$ {\bfseries copy} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a10ccc5ceee1c8ca77c3f239614f37b89}\label{class_c_n_n___node_a10ccc5ceee1c8ca77c3f239614f37b89}} +bool {\bfseries needs\+\_\+init} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a0c7ee6105f2fc7d6772f51af08c3f7f2}\label{class_c_n_n___node_a0c7ee6105f2fc7d6772f51af08c3f7f2}} +int {\bfseries get\+\_\+batch\+\_\+size} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_ab133cb2d976c9ac2f834b15109e17c76}\label{class_c_n_n___node_ab133cb2d976c9ac2f834b15109e17c76}} +int {\bfseries get\+\_\+size\+\_\+x} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a9631d419115cce60208a7f5da8f26458}\label{class_c_n_n___node_a9631d419115cce60208a7f5da8f26458}} +int {\bfseries get\+\_\+size\+\_\+y} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a1aa0396fa302785e75e77cd0feb048c1}\label{class_c_n_n___node_a1aa0396fa302785e75e77cd0feb048c1}} +bool {\bfseries vectors\+\_\+correct} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a93f322e7b1614a5cb9143c2ba4f23468}\label{class_c_n_n___node_a93f322e7b1614a5cb9143c2ba4f23468}} +void {\bfseries initialize} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a7c0d3c9ce3aca0d696d44b6e06e90f78}\label{class_c_n_n___node_a7c0d3c9ce3aca0d696d44b6e06e90f78}} +void {\bfseries reset\+\_\+velocities} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a9016ca96dcc2ccaed39cd2c7486e0b5e}\label{class_c_n_n___node_a9016ca96dcc2ccaed39cd2c7486e0b5e}} +void {\bfseries reset\+\_\+weight\+\_\+count} () +\item +\mbox{\Hypertarget{class_c_n_n___node_afdb33fe03f23336cb00b32d8c177e947}\label{class_c_n_n___node_afdb33fe03f23336cb00b32d8c177e947}} +void {\bfseries add\+\_\+weight\+\_\+count} (int \+\_\+weight\+\_\+count) +\item +\mbox{\Hypertarget{class_c_n_n___node_ac349298a69de8dd6e1f09f87844c20c7}\label{class_c_n_n___node_ac349298a69de8dd6e1f09f87844c20c7}} +int {\bfseries get\+\_\+weight\+\_\+count} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a82c31aa7775b78f3b496144cc6dd014d}\label{class_c_n_n___node_a82c31aa7775b78f3b496144cc6dd014d}} +int {\bfseries get\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_ab0d3c239cf7da939115b5ad0f3ca932f}\label{class_c_n_n___node_ab0d3c239cf7da939115b5ad0f3ca932f}} +float {\bfseries get\+\_\+depth} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a4bee46b025cb107e543d54cf2ad92be2}\label{class_c_n_n___node_a4bee46b025cb107e543d54cf2ad92be2}} +bool {\bfseries is\+\_\+fixed} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a50032c817d28ed666d934f252ced849f}\label{class_c_n_n___node_a50032c817d28ed666d934f252ced849f}} +bool {\bfseries is\+\_\+hidden} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a26bb78e53b932cdd9a4905a027e278a3}\label{class_c_n_n___node_a26bb78e53b932cdd9a4905a027e278a3}} +bool {\bfseries is\+\_\+input} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_aa6ffb6ef2242ea80a274bf7f8ab5aa9b}\label{class_c_n_n___node_aa6ffb6ef2242ea80a274bf7f8ab5aa9b}} +bool {\bfseries is\+\_\+output} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_ac50b24ab52445fecefd3f9323d708714}\label{class_c_n_n___node_ac50b24ab52445fecefd3f9323d708714}} +bool {\bfseries is\+\_\+softmax} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_aa14b29627546c76ebd175ff8a2ab5cdc}\label{class_c_n_n___node_aa14b29627546c76ebd175ff8a2ab5cdc}} +bool {\bfseries is\+\_\+reachable} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a44b7064159403af223b83edb821bba45}\label{class_c_n_n___node_a44b7064159403af223b83edb821bba45}} +bool {\bfseries is\+\_\+forward\+\_\+visited} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a4b6df2f1c1198f2e279028ad5b98d502}\label{class_c_n_n___node_a4b6df2f1c1198f2e279028ad5b98d502}} +bool {\bfseries is\+\_\+reverse\+\_\+visited} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a0e943c8a1fa356c99b5e62912ca14ffd}\label{class_c_n_n___node_a0e943c8a1fa356c99b5e62912ca14ffd}} +void {\bfseries forward\+\_\+visit} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a896c1950897a167716129d6a7d11f20d}\label{class_c_n_n___node_a896c1950897a167716129d6a7d11f20d}} +void {\bfseries reverse\+\_\+visit} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a0eb70dfab0003755fc8122c1eb6fca64}\label{class_c_n_n___node_a0eb70dfab0003755fc8122c1eb6fca64}} +void {\bfseries set\+\_\+unvisited} () +\item +\mbox{\Hypertarget{class_c_n_n___node_ab7f02bda228d0b621033d3416ce9b37f}\label{class_c_n_n___node_ab7f02bda228d0b621033d3416ce9b37f}} +bool {\bfseries has\+\_\+nan} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_addacf3e2055ec68a569fdbcaf2cb9803}\label{class_c_n_n___node_addacf3e2055ec68a569fdbcaf2cb9803}} +void {\bfseries set\+\_\+values} (const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&images, const \textbf{ vector}$<$ int $>$ \&batch, int channel, bool perform\+\_\+dropout, bool accumulate\+\_\+test\+\_\+statistics, float input\+\_\+dropout\+\_\+probability, \textbf{ minstd\+\_\+rand0} \&generator) +\item +\mbox{\Hypertarget{class_c_n_n___node_adcf8d2efd9f7beb871bd32aed3c20c7f}\label{class_c_n_n___node_adcf8d2efd9f7beb871bd32aed3c20c7f}} +float {\bfseries get\+\_\+value\+\_\+in} (int batch\+\_\+number, int y, int x) +\item +\mbox{\Hypertarget{class_c_n_n___node_a78f492da74637456031434b81f4763c9}\label{class_c_n_n___node_a78f492da74637456031434b81f4763c9}} +void {\bfseries set\+\_\+value\+\_\+in} (int batch\+\_\+number, int y, int x, float value) +\item +\mbox{\Hypertarget{class_c_n_n___node_aaaa5d54832589fa38dba19785949698e}\label{class_c_n_n___node_aaaa5d54832589fa38dba19785949698e}} +float $\ast$ {\bfseries get\+\_\+values\+\_\+in} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a7e55f5caafbf85b61416c538ecd28e16}\label{class_c_n_n___node_a7e55f5caafbf85b61416c538ecd28e16}} +float {\bfseries get\+\_\+value\+\_\+out} (int batch\+\_\+number, int y, int x) +\item +\mbox{\Hypertarget{class_c_n_n___node_a5e9494c537fe15d6f24b8f14fef41caf}\label{class_c_n_n___node_a5e9494c537fe15d6f24b8f14fef41caf}} +void {\bfseries set\+\_\+value\+\_\+out} (int batch\+\_\+number, int y, int x, float value) +\item +\mbox{\Hypertarget{class_c_n_n___node_a7c1dae2f0efc24198d13e5615b4f17d5}\label{class_c_n_n___node_a7c1dae2f0efc24198d13e5615b4f17d5}} +float $\ast$ {\bfseries get\+\_\+values\+\_\+out} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a9f8098a04cc00329861336f523164e5c}\label{class_c_n_n___node_a9f8098a04cc00329861336f523164e5c}} +void {\bfseries set\+\_\+error\+\_\+in} (int batch\+\_\+number, int y, int x, float error) +\item +\mbox{\Hypertarget{class_c_n_n___node_aa89763d32219a2ab847401f6d9459c12}\label{class_c_n_n___node_aa89763d32219a2ab847401f6d9459c12}} +float $\ast$ {\bfseries get\+\_\+errors\+\_\+in} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a9e1cc30fe980bf7d8680634e12527ba2}\label{class_c_n_n___node_a9e1cc30fe980bf7d8680634e12527ba2}} +void {\bfseries set\+\_\+error\+\_\+out} (int batch\+\_\+number, int y, int x, float error) +\item +\mbox{\Hypertarget{class_c_n_n___node_a5e304d0072441b5e064d352844b589d5}\label{class_c_n_n___node_a5e304d0072441b5e064d352844b589d5}} +float $\ast$ {\bfseries get\+\_\+errors\+\_\+out} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a94552e9df3b03247a1ac4acc1f53104b}\label{class_c_n_n___node_a94552e9df3b03247a1ac4acc1f53104b}} +float $\ast$ {\bfseries get\+\_\+relu\+\_\+gradients} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a9c148a8bf4c8308159463097dacb546a}\label{class_c_n_n___node_a9c148a8bf4c8308159463097dacb546a}} +float $\ast$ {\bfseries get\+\_\+pool\+\_\+gradients} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a5545a477e5cf1c47f918287c0ee8991e}\label{class_c_n_n___node_a5545a477e5cf1c47f918287c0ee8991e}} +void {\bfseries print} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_c_n_n___node_af3ff47519ff3ca15ec77f54fc7bff575}\label{class_c_n_n___node_af3ff47519ff3ca15ec77f54fc7bff575}} +void {\bfseries reset\+\_\+times} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a2fc877dfc8daf04f99974a6c0e1681c8}\label{class_c_n_n___node_a2fc877dfc8daf04f99974a6c0e1681c8}} +void {\bfseries accumulate\+\_\+times} (float \&total\+\_\+input\+\_\+time, float \&total\+\_\+output\+\_\+time) +\item +\mbox{\Hypertarget{class_c_n_n___node_a45914da7893271c41437339c44260457}\label{class_c_n_n___node_a45914da7893271c41437339c44260457}} +void {\bfseries reset} () +\item +\mbox{\Hypertarget{class_c_n_n___node_ae1a095bae6efd0662fb636bd7af2580d}\label{class_c_n_n___node_ae1a095bae6efd0662fb636bd7af2580d}} +void {\bfseries save\+\_\+best\+\_\+weights} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a5df980bded9a293c72d02e51ffd424c3}\label{class_c_n_n___node_a5df980bded9a293c72d02e51ffd424c3}} +void {\bfseries set\+\_\+weights\+\_\+to\+\_\+best} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a77a9fe238761e081479de9dcda5bec19}\label{class_c_n_n___node_a77a9fe238761e081479de9dcda5bec19}} +void {\bfseries resize\+\_\+arrays} () +\item +\mbox{\Hypertarget{class_c_n_n___node_acf89a51d610460a9d12fe775f3dadc41}\label{class_c_n_n___node_acf89a51d610460a9d12fe775f3dadc41}} +void {\bfseries update\+\_\+batch\+\_\+size} (int new\+\_\+batch\+\_\+size) +\item +\mbox{\Hypertarget{class_c_n_n___node_a7308e8d3046605cb3f1bbd7fd1e7935f}\label{class_c_n_n___node_a7308e8d3046605cb3f1bbd7fd1e7935f}} +bool {\bfseries modify\+\_\+size\+\_\+x} (int change) +\item +\mbox{\Hypertarget{class_c_n_n___node_ad6541f7c55b7159b1eee403ab4172063}\label{class_c_n_n___node_ad6541f7c55b7159b1eee403ab4172063}} +bool {\bfseries modify\+\_\+size\+\_\+y} (int change) +\item +\mbox{\Hypertarget{class_c_n_n___node_a6bdf7998cffe6ad25b50b6accc14ac92}\label{class_c_n_n___node_a6bdf7998cffe6ad25b50b6accc14ac92}} +bool {\bfseries is\+\_\+disabled} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_ad9cefd57c67147c84a5a9fbb39b958fd}\label{class_c_n_n___node_ad9cefd57c67147c84a5a9fbb39b958fd}} +bool {\bfseries is\+\_\+enabled} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a2026883925174924c1f6ad861e628a48}\label{class_c_n_n___node_a2026883925174924c1f6ad861e628a48}} +void {\bfseries disable} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a35a124b721b0f688acfbf9c898796412}\label{class_c_n_n___node_a35a124b721b0f688acfbf9c898796412}} +void {\bfseries enable} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a8a502ddc219b178c89979a4380031c4e}\label{class_c_n_n___node_a8a502ddc219b178c89979a4380031c4e}} +void {\bfseries add\+\_\+input} () +\item +\mbox{\Hypertarget{class_c_n_n___node_aaa18eee36c34b5876fd0be8eb73c1695}\label{class_c_n_n___node_aaa18eee36c34b5876fd0be8eb73c1695}} +void {\bfseries disable\+\_\+input} () +\item +\mbox{\Hypertarget{class_c_n_n___node_aa74369ca703333e20f3749c1a7c7d942}\label{class_c_n_n___node_aa74369ca703333e20f3749c1a7c7d942}} +int {\bfseries get\+\_\+number\+\_\+inputs} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a34a6a332fbe96ff58a21234686165ce2}\label{class_c_n_n___node_a34a6a332fbe96ff58a21234686165ce2}} +int {\bfseries get\+\_\+inputs\+\_\+fired} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a95f9e09d3b30e92cfb484761834007fe}\label{class_c_n_n___node_a95f9e09d3b30e92cfb484761834007fe}} +void {\bfseries input\+\_\+fired} (bool training, bool accumulate\+\_\+test\+\_\+statistics, float epsilon, float alpha, bool perform\+\_\+dropout, float hidden\+\_\+dropout\+\_\+probability, \textbf{ minstd\+\_\+rand0} \&generator) +\item +\mbox{\Hypertarget{class_c_n_n___node_a82c8a76bcdccdeb744d0d98ac5fcc8f7}\label{class_c_n_n___node_a82c8a76bcdccdeb744d0d98ac5fcc8f7}} +void {\bfseries add\+\_\+output} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a874a74001c153eabc0903b1e2cfbc816}\label{class_c_n_n___node_a874a74001c153eabc0903b1e2cfbc816}} +void {\bfseries disable\+\_\+output} () +\item +\mbox{\Hypertarget{class_c_n_n___node_ac90c64c548b3fcaaa635e7e70dd28a75}\label{class_c_n_n___node_ac90c64c548b3fcaaa635e7e70dd28a75}} +int {\bfseries get\+\_\+number\+\_\+outputs} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a8b3bcb6a8bec755478d124d8eb4d7bcb}\label{class_c_n_n___node_a8b3bcb6a8bec755478d124d8eb4d7bcb}} +int {\bfseries get\+\_\+outputs\+\_\+fired} () const +\item +\mbox{\Hypertarget{class_c_n_n___node_a73e631513cac0f8386e653d772124ec8}\label{class_c_n_n___node_a73e631513cac0f8386e653d772124ec8}} +void {\bfseries output\+\_\+fired} (bool training, float mu, float learning\+\_\+rate, float epsilon) +\item +\mbox{\Hypertarget{class_c_n_n___node_a416215a5a047d685d20994e6d0514faf}\label{class_c_n_n___node_a416215a5a047d685d20994e6d0514faf}} +void {\bfseries zero\+\_\+test\+\_\+statistics} () +\item +\mbox{\Hypertarget{class_c_n_n___node_af733af754d399ed4d96f7ada035b1fa0}\label{class_c_n_n___node_af733af754d399ed4d96f7ada035b1fa0}} +void {\bfseries divide\+\_\+test\+\_\+statistics} (int number\+\_\+batches) +\item +\mbox{\Hypertarget{class_c_n_n___node_ad51e4f8982a2be914a9621cec7140f17}\label{class_c_n_n___node_ad51e4f8982a2be914a9621cec7140f17}} +void {\bfseries print\+\_\+batch\+\_\+statistics} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a7b56c38a2b36b767e360cc06201111e0}\label{class_c_n_n___node_a7b56c38a2b36b767e360cc06201111e0}} +void {\bfseries batch\+\_\+normalize} (bool training, bool accumulating\+\_\+test\+\_\+statistics, float epsilon, float alpha) +\item +\mbox{\Hypertarget{class_c_n_n___node_ada7ad72a17075fb7480896459ea2dec0}\label{class_c_n_n___node_ada7ad72a17075fb7480896459ea2dec0}} +void {\bfseries apply\+\_\+relu} (float $\ast$values, float $\ast$gradients) +\item +\mbox{\Hypertarget{class_c_n_n___node_ac124771e160359ed84d5da7de57e90ed}\label{class_c_n_n___node_ac124771e160359ed84d5da7de57e90ed}} +void {\bfseries apply\+\_\+dropout} (float $\ast$values, float $\ast$gradients, bool perform\+\_\+dropout, bool accumulate\+\_\+test\+\_\+statistics, float dropout\+\_\+probability, \textbf{ minstd\+\_\+rand0} \&generator) +\item +\mbox{\Hypertarget{class_c_n_n___node_aea17b6b82653892fbe35c5f23856c920}\label{class_c_n_n___node_aea17b6b82653892fbe35c5f23856c920}} +void {\bfseries backpropagate\+\_\+relu} (float $\ast$errors, float $\ast$gradients) +\item +\mbox{\Hypertarget{class_c_n_n___node_a6899f13b532e45adf9ed220a29c8e31c}\label{class_c_n_n___node_a6899f13b532e45adf9ed220a29c8e31c}} +void {\bfseries backpropagate\+\_\+batch\+\_\+normalization} (bool training, float mu, float learning\+\_\+rate, float epsilon) +\item +\mbox{\Hypertarget{class_c_n_n___node_a524e825e83aba98b41ae3a74dce5fd35}\label{class_c_n_n___node_a524e825e83aba98b41ae3a74dce5fd35}} +void {\bfseries print\+\_\+statistics} () +\item +\mbox{\Hypertarget{class_c_n_n___node_a019d38e2c4af8891732fef5404cba275}\label{class_c_n_n___node_a019d38e2c4af8891732fef5404cba275}} +void {\bfseries print\+\_\+statistics} (const float $\ast$values, const float $\ast$errors, const float $\ast$gradients) +\item +\mbox{\Hypertarget{class_c_n_n___node_ad41d106565981c6b0a71532785cd1cbe}\label{class_c_n_n___node_ad41d106565981c6b0a71532785cd1cbe}} +bool {\bfseries is\+\_\+identical} (const \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$other, bool testing\+\_\+checkpoint) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_c_n_n___node_a03966e763ef4accb0e28df17abb892b1}\label{class_c_n_n___node_a03966e763ef4accb0e28df17abb892b1}} +int {\bfseries node\+\_\+id} +\item +\mbox{\Hypertarget{class_c_n_n___node_ada66344b8f4741a540e2454a827943e2}\label{class_c_n_n___node_ada66344b8f4741a540e2454a827943e2}} +int {\bfseries exact\+\_\+id} +\item +\mbox{\Hypertarget{class_c_n_n___node_ae663ed7191d7a87159cec7cdc9f5d09f}\label{class_c_n_n___node_ae663ed7191d7a87159cec7cdc9f5d09f}} +int {\bfseries genome\+\_\+id} +\item +\mbox{\Hypertarget{class_c_n_n___node_acd634a60f4d480e3a8304dfe4ec1bb3c}\label{class_c_n_n___node_acd634a60f4d480e3a8304dfe4ec1bb3c}} +int {\bfseries innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_c_n_n___node_aabba69c0213b99b444f66176b7696053}\label{class_c_n_n___node_aabba69c0213b99b444f66176b7696053}} +float {\bfseries depth} +\item +\mbox{\Hypertarget{class_c_n_n___node_a5cb0c9502b1201c50848aff3f2833bf5}\label{class_c_n_n___node_a5cb0c9502b1201c50848aff3f2833bf5}} +int {\bfseries batch\+\_\+size} +\item +\mbox{\Hypertarget{class_c_n_n___node_addf9f0af701825e8e9f1c680e34dddcc}\label{class_c_n_n___node_addf9f0af701825e8e9f1c680e34dddcc}} +int {\bfseries size\+\_\+y} +\item +\mbox{\Hypertarget{class_c_n_n___node_afcdc28ad54efad4bf65d9b371de48ae8}\label{class_c_n_n___node_afcdc28ad54efad4bf65d9b371de48ae8}} +int {\bfseries size\+\_\+x} +\item +\mbox{\Hypertarget{class_c_n_n___node_a14b71944753522d0b129f9f4414b3ef6}\label{class_c_n_n___node_a14b71944753522d0b129f9f4414b3ef6}} +int {\bfseries total\+\_\+size} +\item +\mbox{\Hypertarget{class_c_n_n___node_a7006c7aff1fd01323a29f050fe8e3f55}\label{class_c_n_n___node_a7006c7aff1fd01323a29f050fe8e3f55}} +int {\bfseries weight\+\_\+count} +\item +\mbox{\Hypertarget{class_c_n_n___node_af12bb6a121f0187dad4bba3eee98b4b1}\label{class_c_n_n___node_af12bb6a121f0187dad4bba3eee98b4b1}} +int {\bfseries type} +\item +\mbox{\Hypertarget{class_c_n_n___node_ab4988c3c511d321cfc62f2df0db4e558}\label{class_c_n_n___node_ab4988c3c511d321cfc62f2df0db4e558}} +int {\bfseries total\+\_\+inputs} +\item +\mbox{\Hypertarget{class_c_n_n___node_af164ba232f24240d857ce6adc73fa9ac}\label{class_c_n_n___node_af164ba232f24240d857ce6adc73fa9ac}} +int {\bfseries inputs\+\_\+fired} +\item +\mbox{\Hypertarget{class_c_n_n___node_a920df63ffc1999c9b5656118f1ce1d4f}\label{class_c_n_n___node_a920df63ffc1999c9b5656118f1ce1d4f}} +int {\bfseries total\+\_\+outputs} +\item +\mbox{\Hypertarget{class_c_n_n___node_a62be452864b38e8add79da1a6855c6b5}\label{class_c_n_n___node_a62be452864b38e8add79da1a6855c6b5}} +int {\bfseries outputs\+\_\+fired} +\item +\mbox{\Hypertarget{class_c_n_n___node_ae4f2f7364c1299faad66979a708803f7}\label{class_c_n_n___node_ae4f2f7364c1299faad66979a708803f7}} +bool {\bfseries forward\+\_\+visited} +\item +\mbox{\Hypertarget{class_c_n_n___node_ae43eb8c82a5aa1efbcf493b56e782ca3}\label{class_c_n_n___node_ae43eb8c82a5aa1efbcf493b56e782ca3}} +bool {\bfseries reverse\+\_\+visited} +\item +\mbox{\Hypertarget{class_c_n_n___node_a93e5c12387161ad0ae20f9132dc59f70}\label{class_c_n_n___node_a93e5c12387161ad0ae20f9132dc59f70}} +bool {\bfseries needs\+\_\+initialization} +\item +\mbox{\Hypertarget{class_c_n_n___node_a3d1ce55fe8704f7c48befecdf77595d2}\label{class_c_n_n___node_a3d1ce55fe8704f7c48befecdf77595d2}} +bool {\bfseries disabled} +\item +\mbox{\Hypertarget{class_c_n_n___node_a551836af838cafd882e6ebc48d745476}\label{class_c_n_n___node_a551836af838cafd882e6ebc48d745476}} +float {\bfseries gamma} +\item +\mbox{\Hypertarget{class_c_n_n___node_a340aac04a2e9d6e0f3d4298ba361a3e4}\label{class_c_n_n___node_a340aac04a2e9d6e0f3d4298ba361a3e4}} +float {\bfseries best\+\_\+gamma} +\item +\mbox{\Hypertarget{class_c_n_n___node_a0b1c0b1b1ee2747d88a94b55b96ca3d5}\label{class_c_n_n___node_a0b1c0b1b1ee2747d88a94b55b96ca3d5}} +float {\bfseries previous\+\_\+velocity\+\_\+gamma} +\item +\mbox{\Hypertarget{class_c_n_n___node_add699ffb2c54aca9d5f0864f82a21d67}\label{class_c_n_n___node_add699ffb2c54aca9d5f0864f82a21d67}} +float {\bfseries beta} +\item +\mbox{\Hypertarget{class_c_n_n___node_a08c236f1208b0daadef49828863dd0b4}\label{class_c_n_n___node_a08c236f1208b0daadef49828863dd0b4}} +float {\bfseries best\+\_\+beta} +\item +\mbox{\Hypertarget{class_c_n_n___node_a9aaa89b5aa3cf824d6315d6979706c50}\label{class_c_n_n___node_a9aaa89b5aa3cf824d6315d6979706c50}} +float {\bfseries previous\+\_\+velocity\+\_\+beta} +\item +\mbox{\Hypertarget{class_c_n_n___node_ae0dfab82a1999d1175d0317bea4b85fa}\label{class_c_n_n___node_ae0dfab82a1999d1175d0317bea4b85fa}} +float {\bfseries batch\+\_\+mean} +\item +\mbox{\Hypertarget{class_c_n_n___node_ac512a27a5d17c6466a64deba39609635}\label{class_c_n_n___node_ac512a27a5d17c6466a64deba39609635}} +float {\bfseries batch\+\_\+variance} +\item +\mbox{\Hypertarget{class_c_n_n___node_a53015a2df4ccafa3e9de321bc370f54b}\label{class_c_n_n___node_a53015a2df4ccafa3e9de321bc370f54b}} +float {\bfseries batch\+\_\+std\+\_\+dev} +\item +\mbox{\Hypertarget{class_c_n_n___node_a5918a89bfe2d064fa2bf53c726ff08e4}\label{class_c_n_n___node_a5918a89bfe2d064fa2bf53c726ff08e4}} +float {\bfseries inverse\+\_\+variance} +\item +\mbox{\Hypertarget{class_c_n_n___node_aac811c7ca46293b25bea7b3c200c10b5}\label{class_c_n_n___node_aac811c7ca46293b25bea7b3c200c10b5}} +float {\bfseries running\+\_\+mean} +\item +\mbox{\Hypertarget{class_c_n_n___node_aacf47230fe9a7ec45ce80d69c2bae043}\label{class_c_n_n___node_aacf47230fe9a7ec45ce80d69c2bae043}} +float {\bfseries best\+\_\+running\+\_\+mean} +\item +\mbox{\Hypertarget{class_c_n_n___node_ae833075e2cb7ffb50a3c3b31b3f4481f}\label{class_c_n_n___node_ae833075e2cb7ffb50a3c3b31b3f4481f}} +float {\bfseries running\+\_\+variance} +\item +\mbox{\Hypertarget{class_c_n_n___node_af7d13376400403b35d47e64feea3c728}\label{class_c_n_n___node_af7d13376400403b35d47e64feea3c728}} +float {\bfseries best\+\_\+running\+\_\+variance} +\item +\mbox{\Hypertarget{class_c_n_n___node_ac3141d4b38090d5af50cfe9390f555a0}\label{class_c_n_n___node_ac3141d4b38090d5af50cfe9390f555a0}} +float $\ast$ {\bfseries values\+\_\+out} +\item +\mbox{\Hypertarget{class_c_n_n___node_a82183f346839f30b7fc125fd30435987}\label{class_c_n_n___node_a82183f346839f30b7fc125fd30435987}} +float $\ast$ {\bfseries errors\+\_\+out} +\item +\mbox{\Hypertarget{class_c_n_n___node_a379c92d540105cb445eac690a8203c89}\label{class_c_n_n___node_a379c92d540105cb445eac690a8203c89}} +float $\ast$ {\bfseries relu\+\_\+gradients} +\item +\mbox{\Hypertarget{class_c_n_n___node_ac2b84c2825549f9b2a0ccbe608c7b300}\label{class_c_n_n___node_ac2b84c2825549f9b2a0ccbe608c7b300}} +float $\ast$ {\bfseries pool\+\_\+gradients} +\item +\mbox{\Hypertarget{class_c_n_n___node_acfb7cb01d52549ab277412991dcd862b}\label{class_c_n_n___node_acfb7cb01d52549ab277412991dcd862b}} +float $\ast$ {\bfseries values\+\_\+in} +\item +\mbox{\Hypertarget{class_c_n_n___node_a47b823b6dc8e0ca2cbc5378d12c7bcba}\label{class_c_n_n___node_a47b823b6dc8e0ca2cbc5378d12c7bcba}} +float $\ast$ {\bfseries errors\+\_\+in} +\item +\mbox{\Hypertarget{class_c_n_n___node_a782b427e7bd9ec698412d25380edd59b}\label{class_c_n_n___node_a782b427e7bd9ec698412d25380edd59b}} +float {\bfseries input\+\_\+fired\+\_\+time} +\item +\mbox{\Hypertarget{class_c_n_n___node_a83fc5bf2ac3d28ad3fe624cd7e2760b2}\label{class_c_n_n___node_a83fc5bf2ac3d28ad3fe624cd7e2760b2}} +float {\bfseries output\+\_\+fired\+\_\+time} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_c_n_n___node_aff36aced5b29a9f389582b74da662d49}\label{class_c_n_n___node_aff36aced5b29a9f389582b74da662d49}} +\textbf{ ostream} \& {\bfseries operator$<$$<$} (\textbf{ ostream} \&os, const \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$node) +\item +\mbox{\Hypertarget{class_c_n_n___node_a3da48b7b2c40c40a7c9fa64b5b0e630e}\label{class_c_n_n___node_a3da48b7b2c40c40a7c9fa64b5b0e630e}} +\textbf{ istream} \& {\bfseries operator$>$$>$} (\textbf{ istream} \&is, \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$node) +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +cnn/cnn\+\_\+node.\+hxx\item +cnn/cnn\+\_\+node.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_consolidated_statistics.tex b/docs/latex/class_consolidated_statistics.tex new file mode 100644 index 00000000..6c6d4b4f --- /dev/null +++ b/docs/latex/class_consolidated_statistics.tex @@ -0,0 +1,38 @@ +\hypertarget{class_consolidated_statistics}{}\doxysection{Consolidated\+Statistics Class Reference} +\label{class_consolidated_statistics}\index{ConsolidatedStatistics@{ConsolidatedStatistics}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_consolidated_statistics_a1dedb8fe0ebec15343eb8cc43dd13919}\label{class_consolidated_statistics_a1dedb8fe0ebec15343eb8cc43dd13919}} +{\bfseries Consolidated\+Statistics} (\textbf{ string} \+\_\+run\+\_\+type) +\item +\mbox{\Hypertarget{class_consolidated_statistics_a5f7c3c4a15770ba410ed702502a69e14}\label{class_consolidated_statistics_a5f7c3c4a15770ba410ed702502a69e14}} +\textbf{ string} {\bfseries to\+\_\+string\+\_\+min} () +\item +\mbox{\Hypertarget{class_consolidated_statistics_a516c9b01f648ebc06ec77e77dbbc92ba}\label{class_consolidated_statistics_a516c9b01f648ebc06ec77e77dbbc92ba}} +\textbf{ string} {\bfseries to\+\_\+string\+\_\+avg} () +\item +\mbox{\Hypertarget{class_consolidated_statistics_a712f35c52e6099b119aac3c9be392792}\label{class_consolidated_statistics_a712f35c52e6099b119aac3c9be392792}} +\textbf{ string} {\bfseries to\+\_\+string\+\_\+max} () +\end{DoxyCompactItemize} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_consolidated_statistics_a440db85e7e7056589c3b337d1a480e75}\label{class_consolidated_statistics_a440db85e7e7056589c3b337d1a480e75}} +\textbf{ string} {\bfseries run\+\_\+type} +\item +\mbox{\Hypertarget{class_consolidated_statistics_a4e279cbf435a754e54ca6a670f5050f3}\label{class_consolidated_statistics_a4e279cbf435a754e54ca6a670f5050f3}} +double {\bfseries dfm\+\_\+min} +\item +\mbox{\Hypertarget{class_consolidated_statistics_a389b132b9af4877b5895befaecc0808a}\label{class_consolidated_statistics_a389b132b9af4877b5895befaecc0808a}} +double {\bfseries dfm\+\_\+avg} +\item +\mbox{\Hypertarget{class_consolidated_statistics_a602d1d945c8ddf957c7b58146a39bb4b}\label{class_consolidated_statistics_a602d1d945c8ddf957c7b58146a39bb4b}} +double {\bfseries dfm\+\_\+max} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\item +mpi/run\+\_\+statistics.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_corpus.tex b/docs/latex/class_corpus.tex new file mode 100644 index 00000000..785887f8 --- /dev/null +++ b/docs/latex/class_corpus.tex @@ -0,0 +1,243 @@ +\hypertarget{class_corpus}{}\doxysection{Corpus Class Reference} +\label{class_corpus}\index{Corpus@{Corpus}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_corpus_a23d832cf3a12a9ee603a1f53bb038154}\label{class_corpus_a23d832cf3a12a9ee603a1f53bb038154}} +void {\bfseries normalize\+\_\+min\+\_\+max} () +\item +\mbox{\Hypertarget{class_corpus_a8a64fb7be666c2b8405042d8969da4ad}\label{class_corpus_a8a64fb7be666c2b8405042d8969da4ad}} +void {\bfseries normalize\+\_\+min\+\_\+max} (const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+mins, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+maxs) +\item +\mbox{\Hypertarget{class_corpus_a26fbbb6a85a20a132739fc4ac716e918}\label{class_corpus_a26fbbb6a85a20a132739fc4ac716e918}} +void {\bfseries normalize\+\_\+avg\+\_\+std\+\_\+dev} () +\item +\mbox{\Hypertarget{class_corpus_a0e0f6f33a28ee2aa0df884dfa449694b}\label{class_corpus_a0e0f6f33a28ee2aa0df884dfa449694b}} +void {\bfseries normalize\+\_\+avg\+\_\+std\+\_\+dev} (const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+avgs, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+std\+\_\+devs, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+mins, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+maxs) +\item +void \mbox{\hyperlink{class_corpus_a45ee2e625bab4930a20b8448d0e168b0}{write\+\_\+sentence\+\_\+series\+\_\+sets}} (\textbf{ string} base\+\_\+filename) +\begin{DoxyCompactList}\small\item\em Generates the csv data file. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_corpus_ab48e664f5d9a26ccfaa4c7cdae4bd6c5}\label{class_corpus_ab48e664f5d9a26ccfaa4c7cdae4bd6c5}} +void {\bfseries export\+\_\+sent\+\_\+series} (const \textbf{ vector}$<$ int $>$ \&series\+\_\+indexes, int word\+\_\+offset, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs) +\item +void \mbox{\hyperlink{class_corpus_a241439045ad1a83d6104b4350961e1b3}{export\+\_\+training\+\_\+series}} (int word\+\_\+offset, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs) +\begin{DoxyCompactList}\small\item\em Exports \mbox{\hyperlink{class_corpus}{Corpus}} Dataset to be used for training in \mbox{\hyperlink{class_r_n_n}{R\+NN}} genome. \end{DoxyCompactList}\item +void \mbox{\hyperlink{class_corpus_a1559311071b5de22e15561a39225ec12}{export\+\_\+test\+\_\+series}} (int word\+\_\+offset, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs) +\begin{DoxyCompactList}\small\item\em Exports \mbox{\hyperlink{class_corpus}{Corpus}} Dataset to be used for testing in \mbox{\hyperlink{class_r_n_n}{R\+NN}} genome. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_corpus_a7ef4daa0e73036729c3a9663d1bc519e}\label{class_corpus_a7ef4daa0e73036729c3a9663d1bc519e}} +void {\bfseries export\+\_\+series\+\_\+by\+\_\+name} (\textbf{ string} field\+\_\+name, \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&exported\+\_\+series) +\item +\mbox{\Hypertarget{class_corpus_a7fed4b5ae39cc83fea053d77e90ea277}\label{class_corpus_a7fed4b5ae39cc83fea053d77e90ea277}} +double {\bfseries denormalize} (\textbf{ string} field\+\_\+name, double value) +\item +\mbox{\Hypertarget{class_corpus_a8e15b1120aee21462550b7d20c90d9d3}\label{class_corpus_a8e15b1120aee21462550b7d20c90d9d3}} +\textbf{ string} {\bfseries get\+\_\+normalize\+\_\+type} () const +\item +\mbox{\Hypertarget{class_corpus_a50481b3ece46954b7f82ee2ec8332c8d}\label{class_corpus_a50481b3ece46954b7f82ee2ec8332c8d}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+mins} () const +\item +\mbox{\Hypertarget{class_corpus_a00d42e7ff99d2ae4f5bd20efb9050dc6}\label{class_corpus_a00d42e7ff99d2ae4f5bd20efb9050dc6}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+maxs} () const +\item +\mbox{\Hypertarget{class_corpus_a6db7e7bf9035bf0a37663a67b09576b8}\label{class_corpus_a6db7e7bf9035bf0a37663a67b09576b8}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+avgs} () const +\item +\mbox{\Hypertarget{class_corpus_a3c45272a3f18d0eae2de38cd856d7bf1}\label{class_corpus_a3c45272a3f18d0eae2de38cd856d7bf1}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+std\+\_\+devs} () const +\item +\mbox{\Hypertarget{class_corpus_a409ed06f61136525286e544102041ed9}\label{class_corpus_a409ed06f61136525286e544102041ed9}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries get\+\_\+input\+\_\+parameter\+\_\+names} () const +\item +\mbox{\Hypertarget{class_corpus_a16bb6db54fb5a33906c93390fa225355}\label{class_corpus_a16bb6db54fb5a33906c93390fa225355}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries get\+\_\+output\+\_\+parameter\+\_\+names} () const +\item +\mbox{\Hypertarget{class_corpus_addfbd62c925c543856502c9e026f33f2}\label{class_corpus_addfbd62c925c543856502c9e026f33f2}} +int {\bfseries get\+\_\+number\+\_\+series} () const +\item +\mbox{\Hypertarget{class_corpus_a000a739a06e012f98d8ac7128c75f2eb}\label{class_corpus_a000a739a06e012f98d8ac7128c75f2eb}} +int {\bfseries get\+\_\+number\+\_\+inputs} () const +\item +\mbox{\Hypertarget{class_corpus_a8a81ce0bea175ce0dea77e85eafeb12b}\label{class_corpus_a8a81ce0bea175ce0dea77e85eafeb12b}} +int {\bfseries get\+\_\+number\+\_\+outputs} () const +\item +\mbox{\Hypertarget{class_corpus_a8fa96a8aa8dee4d4c5de9e2d67130e23}\label{class_corpus_a8fa96a8aa8dee4d4c5de9e2d67130e23}} +void {\bfseries set\+\_\+training\+\_\+indexes} (const \textbf{ vector}$<$ int $>$ \&\+\_\+training\+\_\+indexes) +\item +\mbox{\Hypertarget{class_corpus_a8f69c56c30fbc39495911946c6323c7e}\label{class_corpus_a8f69c56c30fbc39495911946c6323c7e}} +void {\bfseries set\+\_\+test\+\_\+indexes} (const \textbf{ vector}$<$ int $>$ \&\+\_\+test\+\_\+indexes) +\item +\mbox{\Hypertarget{class_corpus_a9f476c6ad48e92bb80531d314115377f}\label{class_corpus_a9f476c6ad48e92bb80531d314115377f}} +\mbox{\hyperlink{class_sentence_series}{Sentence\+Series}} $\ast$ {\bfseries get\+\_\+set} (\textbf{ int32\+\_\+t} i) +\end{DoxyCompactItemize} +\doxysubsection*{Static Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_corpus_aacb92512dd5c353c1085f9dcd303d440}\label{class_corpus_aacb92512dd5c353c1085f9dcd303d440}} +static \mbox{\hyperlink{class_corpus}{Corpus}} $\ast$ {\bfseries generate\+\_\+from\+\_\+arguments} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&arguments) +\item +\mbox{\Hypertarget{class_corpus_a184bd2800418e018f0650a9f59fbc4cb}\label{class_corpus_a184bd2800418e018f0650a9f59fbc4cb}} +static \mbox{\hyperlink{class_corpus}{Corpus}} $\ast$ {\bfseries generate\+\_\+test} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+test\+\_\+filenames, const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+output\+\_\+parameter\+\_\+names) +\end{DoxyCompactItemize} +\doxysubsection*{Private Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_corpus_aef1a06e5a764a0ffa2ecc8b4df042692}\label{class_corpus_aef1a06e5a764a0ffa2ecc8b4df042692}} +void \mbox{\hyperlink{class_corpus_aef1a06e5a764a0ffa2ecc8b4df042692}{load\+\_\+word\+\_\+library}} () +\begin{DoxyCompactList}\small\item\em Loads the filenames, word index and the vocabulary of the files. \end{DoxyCompactList}\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_corpus_aaf5714da66d6475437fdae9fe262bb6a}\label{class_corpus_aaf5714da66d6475437fdae9fe262bb6a}} +uint32\+\_\+t \mbox{\hyperlink{class_corpus_aaf5714da66d6475437fdae9fe262bb6a}{batch\+\_\+size}} +\begin{DoxyCompactList}\small\item\em Specifices the batches in which the dataset is to be divided into chunks. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_corpus_a8f5019313c7f0e7067755f79df4dcbe5}\label{class_corpus_a8f5019313c7f0e7067755f79df4dcbe5}} +\textbf{ string} {\bfseries normalize\+\_\+type} +\item +\mbox{\Hypertarget{class_corpus_acdc26dc71f4664b914bc6f4ddd08f5c7}\label{class_corpus_acdc26dc71f4664b914bc6f4ddd08f5c7}} +\textbf{ vector}$<$ \textbf{ string} $>$ \mbox{\hyperlink{class_corpus_acdc26dc71f4664b914bc6f4ddd08f5c7}{filenames}} +\begin{DoxyCompactList}\small\item\em Stores all the filenames including training and testing. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_corpus_acc4af3ddc788396aede04a140e15f8b6}\label{class_corpus_acc4af3ddc788396aede04a140e15f8b6}} +\textbf{ vector}$<$ int $>$ \mbox{\hyperlink{class_corpus_acc4af3ddc788396aede04a140e15f8b6}{training\+\_\+indexes}} +\begin{DoxyCompactList}\small\item\em stores the training index of the filenames. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_corpus_a5bfd2fe992d2f34b735b21196b9bd18b}\label{class_corpus_a5bfd2fe992d2f34b735b21196b9bd18b}} +\textbf{ vector}$<$ int $>$ \mbox{\hyperlink{class_corpus_a5bfd2fe992d2f34b735b21196b9bd18b}{test\+\_\+indexes}} +\begin{DoxyCompactList}\small\item\em stores the testing index of the filenames. \end{DoxyCompactList}\item +\textbf{ vector}$<$ \textbf{ string} $>$ \mbox{\hyperlink{class_corpus_a21a1934fd5493a82fc572226639525c6}{input\+\_\+parameter\+\_\+names}} +\begin{DoxyCompactList}\small\item\em stores the input parameter names of the filenames. \end{DoxyCompactList}\item +\textbf{ vector}$<$ \textbf{ string} $>$ \mbox{\hyperlink{class_corpus_ac7b85b5b0a376016c63b58200aa03e19}{output\+\_\+parameter\+\_\+names}} +\begin{DoxyCompactList}\small\item\em stores the input parameter names of the filenames. \end{DoxyCompactList}\item +\textbf{ vector}$<$ \textbf{ string} $>$ \mbox{\hyperlink{class_corpus_a084591c7f27b8ad55bc68266fe461161}{all\+\_\+parameter\+\_\+names}} +\begin{DoxyCompactList}\small\item\em stores the input parameter names of the filenames. \end{DoxyCompactList}\item +\textbf{ vector}$<$ \mbox{\hyperlink{class_sentence_series}{Sentence\+Series}} $\ast$ $>$ \mbox{\hyperlink{class_corpus_a96d8a8ed499eb739f08d8d7536008688}{sent\+\_\+series}} +\begin{DoxyCompactList}\small\item\em stores the Sentence series used in the filenames. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_corpus_a0a851ff0d48771c3b6f2573d7476966a}\label{class_corpus_a0a851ff0d48771c3b6f2573d7476966a}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+mins} +\item +\mbox{\Hypertarget{class_corpus_a22f825861bbb3139717013a6301e3add}\label{class_corpus_a22f825861bbb3139717013a6301e3add}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+maxs} +\item +\mbox{\Hypertarget{class_corpus_a2344bdd0b80ef4fdb30c2f67ef1a1ea0}\label{class_corpus_a2344bdd0b80ef4fdb30c2f67ef1a1ea0}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+avgs} +\item +\mbox{\Hypertarget{class_corpus_aaaee4f6927310905cf812db4fc2e268a}\label{class_corpus_aaaee4f6927310905cf812db4fc2e268a}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+std\+\_\+devs} +\item +\mbox{\Hypertarget{class_corpus_ad420d9f3cecbc7ea4af0ff68a37e386d}\label{class_corpus_ad420d9f3cecbc7ea4af0ff68a37e386d}} +\textbf{ vector}$<$ \textbf{ string} $>$ \mbox{\hyperlink{class_corpus_ad420d9f3cecbc7ea4af0ff68a37e386d}{word\+\_\+index}} +\begin{DoxyCompactList}\small\item\em Maps the index to word string of the word series in a particular filename. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_corpus_a7c44a0b8ab422b3fbe30e165e6f1bd34}\label{class_corpus_a7c44a0b8ab422b3fbe30e165e6f1bd34}} +\textbf{ map}$<$ \textbf{ string}, int $>$ \mbox{\hyperlink{class_corpus_a7c44a0b8ab422b3fbe30e165e6f1bd34}{vocab}} +\begin{DoxyCompactList}\small\item\em Maps the word string to the word series in a particular filename. \end{DoxyCompactList}\end{DoxyCompactItemize} + + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_corpus_a1559311071b5de22e15561a39225ec12}\label{class_corpus_a1559311071b5de22e15561a39225ec12}} +\index{Corpus@{Corpus}!export\_test\_series@{export\_test\_series}} +\index{export\_test\_series@{export\_test\_series}!Corpus@{Corpus}} +\doxysubsubsection{\texorpdfstring{export\_test\_series()}{export\_test\_series()}} +{\footnotesize\ttfamily void Corpus\+::export\+\_\+test\+\_\+series (\begin{DoxyParamCaption}\item[{int}]{word\+\_\+offset, }\item[{\textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&}]{inputs, }\item[{\textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&}]{outputs }\end{DoxyParamCaption})} + + + +Exports \mbox{\hyperlink{class_corpus}{Corpus}} Dataset to be used for testing in \mbox{\hyperlink{class_r_n_n}{R\+NN}} genome. + +This exports the time series marked as test series by the test\+\_\+indexes vector. + + +\begin{DoxyParams}{Parameters} +{\em series\+\_\+indexes} & are the indexes of the input parameters to be used in the dataset \\ +\hline +{\em word\+\_\+offset} & is used to predict the after how many words, the predictions to be done in the future. \\ +\hline +{\em inputs} & is the data used for testing in the \mbox{\hyperlink{class_r_n_n}{R\+NN}} genome \\ +\hline +{\em outpus} & is the data used for testing in the \mbox{\hyperlink{class_r_n_n}{R\+NN}} genome \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_corpus_a241439045ad1a83d6104b4350961e1b3}\label{class_corpus_a241439045ad1a83d6104b4350961e1b3}} +\index{Corpus@{Corpus}!export\_training\_series@{export\_training\_series}} +\index{export\_training\_series@{export\_training\_series}!Corpus@{Corpus}} +\doxysubsubsection{\texorpdfstring{export\_training\_series()}{export\_training\_series()}} +{\footnotesize\ttfamily void Corpus\+::export\+\_\+training\+\_\+series (\begin{DoxyParamCaption}\item[{int}]{word\+\_\+offset, }\item[{\textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&}]{inputs, }\item[{\textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&}]{outputs }\end{DoxyParamCaption})} + + + +Exports \mbox{\hyperlink{class_corpus}{Corpus}} Dataset to be used for training in \mbox{\hyperlink{class_r_n_n}{R\+NN}} genome. + +This exports the time series marked as training series by the training\+\_\+indexes vector. + + +\begin{DoxyParams}{Parameters} +{\em series\+\_\+indexes} & are the indexes of the input parameters to be used in the dataset \\ +\hline +{\em word\+\_\+offset} & is used to predict the after how many words, the predictions to be done in the future. \\ +\hline +{\em inputs} & is the data used for training in the \mbox{\hyperlink{class_r_n_n}{R\+NN}} genome \\ +\hline +{\em outpus} & is the data used for training in the \mbox{\hyperlink{class_r_n_n}{R\+NN}} genome \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_corpus_a45ee2e625bab4930a20b8448d0e168b0}\label{class_corpus_a45ee2e625bab4930a20b8448d0e168b0}} +\index{Corpus@{Corpus}!write\_sentence\_series\_sets@{write\_sentence\_series\_sets}} +\index{write\_sentence\_series\_sets@{write\_sentence\_series\_sets}!Corpus@{Corpus}} +\doxysubsubsection{\texorpdfstring{write\_sentence\_series\_sets()}{write\_sentence\_series\_sets()}} +{\footnotesize\ttfamily void Corpus\+::write\+\_\+sentence\+\_\+series\+\_\+sets (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{base\+\_\+filename }\end{DoxyParamCaption})} + + + +Generates the csv data file. + + +\begin{DoxyParams}{Parameters} +{\em base\+\_\+filename} & is the filename to be used. \\ +\hline +\end{DoxyParams} + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{class_corpus_a084591c7f27b8ad55bc68266fe461161}\label{class_corpus_a084591c7f27b8ad55bc68266fe461161}} +\index{Corpus@{Corpus}!all\_parameter\_names@{all\_parameter\_names}} +\index{all\_parameter\_names@{all\_parameter\_names}!Corpus@{Corpus}} +\doxysubsubsection{\texorpdfstring{all\_parameter\_names}{all\_parameter\_names}} +{\footnotesize\ttfamily \textbf{ vector}$<$\textbf{ string}$>$ Corpus\+::all\+\_\+parameter\+\_\+names\hspace{0.3cm}{\ttfamily [private]}} + + + +stores the input parameter names of the filenames. + +stores the unique words used in the filenames. \mbox{\Hypertarget{class_corpus_a21a1934fd5493a82fc572226639525c6}\label{class_corpus_a21a1934fd5493a82fc572226639525c6}} +\index{Corpus@{Corpus}!input\_parameter\_names@{input\_parameter\_names}} +\index{input\_parameter\_names@{input\_parameter\_names}!Corpus@{Corpus}} +\doxysubsubsection{\texorpdfstring{input\_parameter\_names}{input\_parameter\_names}} +{\footnotesize\ttfamily \textbf{ vector}$<$\textbf{ string}$>$ Corpus\+::input\+\_\+parameter\+\_\+names\hspace{0.3cm}{\ttfamily [private]}} + + + +stores the input parameter names of the filenames. + +stores the unique words used in the filenames. \mbox{\Hypertarget{class_corpus_ac7b85b5b0a376016c63b58200aa03e19}\label{class_corpus_ac7b85b5b0a376016c63b58200aa03e19}} +\index{Corpus@{Corpus}!output\_parameter\_names@{output\_parameter\_names}} +\index{output\_parameter\_names@{output\_parameter\_names}!Corpus@{Corpus}} +\doxysubsubsection{\texorpdfstring{output\_parameter\_names}{output\_parameter\_names}} +{\footnotesize\ttfamily \textbf{ vector}$<$\textbf{ string}$>$ Corpus\+::output\+\_\+parameter\+\_\+names\hspace{0.3cm}{\ttfamily [private]}} + + + +stores the input parameter names of the filenames. + +stores the unique words used in the filenames. \mbox{\Hypertarget{class_corpus_a96d8a8ed499eb739f08d8d7536008688}\label{class_corpus_a96d8a8ed499eb739f08d8d7536008688}} +\index{Corpus@{Corpus}!sent\_series@{sent\_series}} +\index{sent\_series@{sent\_series}!Corpus@{Corpus}} +\doxysubsubsection{\texorpdfstring{sent\_series}{sent\_series}} +{\footnotesize\ttfamily \textbf{ vector}$<$\mbox{\hyperlink{class_sentence_series}{Sentence\+Series}}$\ast$$>$ Corpus\+::sent\+\_\+series\hspace{0.3cm}{\ttfamily [private]}} + + + +stores the Sentence series used in the filenames. + +Each file is divided into sentences and word series is made for each word in the sentence. + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +word\+\_\+series/word\+\_\+series.\+hxx\item +word\+\_\+series/word\+\_\+series.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_delta___node.eps b/docs/latex/class_delta___node.eps new file mode 100644 index 00000000..a04cbe59 --- /dev/null +++ b/docs/latex/class_delta___node.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 285.714286 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.750000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(Delta_Node) cw +(RNN_Node_Interface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (Delta_Node) 0.000000 0.000000 box + (RNN_Node_Interface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_delta___node.tex b/docs/latex/class_delta___node.tex new file mode 100644 index 00000000..86d6ad1c --- /dev/null +++ b/docs/latex/class_delta___node.tex @@ -0,0 +1,141 @@ +\hypertarget{class_delta___node}{}\doxysection{Delta\+\_\+\+Node Class Reference} +\label{class_delta___node}\index{Delta\_Node@{Delta\_Node}} +Inheritance diagram for Delta\+\_\+\+Node\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_delta___node} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_delta___node_aa0fcdcc9a14cb0421adbee632e4dec25}\label{class_delta___node_aa0fcdcc9a14cb0421adbee632e4dec25}} +{\bfseries Delta\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, int \+\_\+type, double \+\_\+depth) +\item +\mbox{\Hypertarget{class_delta___node_a0d934b77b614f4dfbfe12b63ec5a5067}\label{class_delta___node_a0d934b77b614f4dfbfe12b63ec5a5067}} +void {\bfseries initialize\+\_\+lamarckian} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double mu, double sigma) +\item +\mbox{\Hypertarget{class_delta___node_a3b3f80bf04c45ef87dcf132373ba43e1}\label{class_delta___node_a3b3f80bf04c45ef87dcf132373ba43e1}} +void {\bfseries initialize\+\_\+xavier} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng1\+\_\+1, double range) +\item +\mbox{\Hypertarget{class_delta___node_aee39237727e94cafd8493ba22177b8ce}\label{class_delta___node_aee39237727e94cafd8493ba22177b8ce}} +void {\bfseries initialize\+\_\+kaiming} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double range) +\item +\mbox{\Hypertarget{class_delta___node_a44468c970dcf768d94edbb4a3cf733c7}\label{class_delta___node_a44468c970dcf768d94edbb4a3cf733c7}} +void {\bfseries initialize\+\_\+uniform\+\_\+random} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng) +\item +\mbox{\Hypertarget{class_delta___node_a1c773f7244ab4caef2a90f5d7eb25693}\label{class_delta___node_a1c773f7244ab4caef2a90f5d7eb25693}} +double {\bfseries get\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_delta___node_a5c16896fd4d85f4dacef7ea683122e13}\label{class_delta___node_a5c16896fd4d85f4dacef7ea683122e13}} +void {\bfseries print\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_delta___node_aa93bdebc5a026df31e2bdb998e54bf24}\label{class_delta___node_aa93bdebc5a026df31e2bdb998e54bf24}} +void {\bfseries input\+\_\+fired} (int time, double incoming\+\_\+output) +\item +\mbox{\Hypertarget{class_delta___node_a56c5c2819b4ec112ff72ab513708f3d1}\label{class_delta___node_a56c5c2819b4ec112ff72ab513708f3d1}} +void {\bfseries try\+\_\+update\+\_\+deltas} (int time) +\item +\mbox{\Hypertarget{class_delta___node_a8341222206c2f7afb9f6ecbdcaabfd56}\label{class_delta___node_a8341222206c2f7afb9f6ecbdcaabfd56}} +void {\bfseries error\+\_\+fired} (int time, double error) +\item +\mbox{\Hypertarget{class_delta___node_a5c657c18dc893f128a0d0973f3581a9d}\label{class_delta___node_a5c657c18dc893f128a0d0973f3581a9d}} +void {\bfseries output\+\_\+fired} (int time, double delta) +\item +\mbox{\Hypertarget{class_delta___node_a9dc4bfdc50658ded929366dd72b4b4f2}\label{class_delta___node_a9dc4bfdc50658ded929366dd72b4b4f2}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_delta___node_a0e0f1e30946b944c0b4da7a49b2cf455}\label{class_delta___node_a0e0f1e30946b944c0b4da7a49b2cf455}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_delta___node_a0b0c2519dbf1925df8ef239a6b230657}\label{class_delta___node_a0b0c2519dbf1925df8ef239a6b230657}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_delta___node_a509ced64e050351dfdb37aa057d1e309}\label{class_delta___node_a509ced64e050351dfdb37aa057d1e309}} +void {\bfseries get\+\_\+weights} (uint32\+\_\+t \&offset, \textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_delta___node_a6388a73c384be47213f94d46c11ad892}\label{class_delta___node_a6388a73c384be47213f94d46c11ad892}} +void {\bfseries set\+\_\+weights} (uint32\+\_\+t \&offset, const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_delta___node_a5f93718f152572499cafcf8043f88c29}\label{class_delta___node_a5f93718f152572499cafcf8043f88c29}} +void {\bfseries get\+\_\+gradients} (\textbf{ vector}$<$ double $>$ \&gradients) +\item +\mbox{\Hypertarget{class_delta___node_a228974d714f74f6a12cdebed9fd96402}\label{class_delta___node_a228974d714f74f6a12cdebed9fd96402}} +void {\bfseries reset} (int \+\_\+series\+\_\+length) +\item +\mbox{\Hypertarget{class_delta___node_aa838c07af65ed777140b8007ed3bd990}\label{class_delta___node_aa838c07af65ed777140b8007ed3bd990}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_delta___node_a754bbc40dbf0ed2d0a52b93462c0c0db}\label{class_delta___node_a754bbc40dbf0ed2d0a52b93462c0c0db}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries copy} () const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_delta___node_a5842bb9b8001147b9d36784e807c1d92}\label{class_delta___node_a5842bb9b8001147b9d36784e807c1d92}} +double {\bfseries alpha} +\item +\mbox{\Hypertarget{class_delta___node_a1405949e85ccf1b26e04499f6530ce55}\label{class_delta___node_a1405949e85ccf1b26e04499f6530ce55}} +double {\bfseries beta1} +\item +\mbox{\Hypertarget{class_delta___node_a410a1dd48fd0ec5089a1039e13c7094c}\label{class_delta___node_a410a1dd48fd0ec5089a1039e13c7094c}} +double {\bfseries beta2} +\item +\mbox{\Hypertarget{class_delta___node_a318f793688014482aa71ee2ff68cf065}\label{class_delta___node_a318f793688014482aa71ee2ff68cf065}} +double {\bfseries v} +\item +\mbox{\Hypertarget{class_delta___node_a4049be4eabb520530bad332836de56a6}\label{class_delta___node_a4049be4eabb520530bad332836de56a6}} +double {\bfseries r\+\_\+bias} +\item +\mbox{\Hypertarget{class_delta___node_aa4e6f60695de0b62e6362c7d75ceb9a1}\label{class_delta___node_aa4e6f60695de0b62e6362c7d75ceb9a1}} +double {\bfseries z\+\_\+hat\+\_\+bias} +\item +\mbox{\Hypertarget{class_delta___node_aaf72317f7dfca900c43fb70d4d575db5}\label{class_delta___node_aaf72317f7dfca900c43fb70d4d575db5}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+alpha} +\item +\mbox{\Hypertarget{class_delta___node_a5583496ebae8e9f5b0e2bc3cffb8ea42}\label{class_delta___node_a5583496ebae8e9f5b0e2bc3cffb8ea42}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+beta1} +\item +\mbox{\Hypertarget{class_delta___node_a163a331bc078e46c4310e36955089c06}\label{class_delta___node_a163a331bc078e46c4310e36955089c06}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+beta2} +\item +\mbox{\Hypertarget{class_delta___node_a48faf376a9867e62578cf1ebed71d8f3}\label{class_delta___node_a48faf376a9867e62578cf1ebed71d8f3}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+v} +\item +\mbox{\Hypertarget{class_delta___node_a5eddbf64caf53ee8fd088dfba312da03}\label{class_delta___node_a5eddbf64caf53ee8fd088dfba312da03}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+r\+\_\+bias} +\item +\mbox{\Hypertarget{class_delta___node_a1b885a11d3f0368426c9fb00671501c7}\label{class_delta___node_a1b885a11d3f0368426c9fb00671501c7}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+z\+\_\+hat\+\_\+bias} +\item +\mbox{\Hypertarget{class_delta___node_a1ea15f5c27f1091abac67e8be3920682}\label{class_delta___node_a1ea15f5c27f1091abac67e8be3920682}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+z\+\_\+prev} +\item +\mbox{\Hypertarget{class_delta___node_a1d0143851eb38c33a0a9299d31afa68d}\label{class_delta___node_a1d0143851eb38c33a0a9299d31afa68d}} +\textbf{ vector}$<$ double $>$ {\bfseries r} +\item +\mbox{\Hypertarget{class_delta___node_aca83b5fd3da670c25633948c266a3605}\label{class_delta___node_aca83b5fd3da670c25633948c266a3605}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+r} +\item +\mbox{\Hypertarget{class_delta___node_a7a17d2655139a227504a9c86d565a98b}\label{class_delta___node_a7a17d2655139a227504a9c86d565a98b}} +\textbf{ vector}$<$ double $>$ {\bfseries z\+\_\+cap} +\item +\mbox{\Hypertarget{class_delta___node_a6ba1981953ad1474be72ab354deb5521}\label{class_delta___node_a6ba1981953ad1474be72ab354deb5521}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+z\+\_\+cap} +\item +\mbox{\Hypertarget{class_delta___node_a3892cbf24ceb16b9acd21128d242c7f6}\label{class_delta___node_a3892cbf24ceb16b9acd21128d242c7f6}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+z} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_delta___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}\label{class_delta___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}} +class {\bfseries R\+N\+N\+\_\+\+Edge} +\end{DoxyCompactItemize} +\doxysubsection*{Additional Inherited Members} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/delta\+\_\+node.\+hxx\item +rnn/delta\+\_\+node.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_e_n_a_r_c___node.eps b/docs/latex/class_e_n_a_r_c___node.eps new file mode 100644 index 00000000..c40505a1 --- /dev/null +++ b/docs/latex/class_e_n_a_r_c___node.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 285.714286 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.750000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(ENARC_Node) cw +(RNN_Node_Interface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (ENARC_Node) 0.000000 0.000000 box + (RNN_Node_Interface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_e_n_a_r_c___node.tex b/docs/latex/class_e_n_a_r_c___node.tex new file mode 100644 index 00000000..c58eb740 --- /dev/null +++ b/docs/latex/class_e_n_a_r_c___node.tex @@ -0,0 +1,204 @@ +\hypertarget{class_e_n_a_r_c___node}{}\doxysection{E\+N\+A\+R\+C\+\_\+\+Node Class Reference} +\label{class_e_n_a_r_c___node}\index{ENARC\_Node@{ENARC\_Node}} +Inheritance diagram for E\+N\+A\+R\+C\+\_\+\+Node\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_e_n_a_r_c___node} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_aec22bfe3168aff3c1dda2597cca59e40}\label{class_e_n_a_r_c___node_aec22bfe3168aff3c1dda2597cca59e40}} +{\bfseries E\+N\+A\+R\+C\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, int \+\_\+type, double \+\_\+depth) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_ad187be3f03e0bb09649160eccf529911}\label{class_e_n_a_r_c___node_ad187be3f03e0bb09649160eccf529911}} +void {\bfseries initialize\+\_\+lamarckian} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double mu, double sigma) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_ac184b11d1a33eb8eb4fb1509260c39cc}\label{class_e_n_a_r_c___node_ac184b11d1a33eb8eb4fb1509260c39cc}} +void {\bfseries initialize\+\_\+xavier} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng1\+\_\+1, double range) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_aa786c9606508e6278997e843696b9159}\label{class_e_n_a_r_c___node_aa786c9606508e6278997e843696b9159}} +void {\bfseries initialize\+\_\+kaiming} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double range) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a93180f0441e68cc6f4f2f48e1b691c82}\label{class_e_n_a_r_c___node_a93180f0441e68cc6f4f2f48e1b691c82}} +void {\bfseries initialize\+\_\+uniform\+\_\+random} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a88426c2f2e350b6e736d6a7fb14dc0fe}\label{class_e_n_a_r_c___node_a88426c2f2e350b6e736d6a7fb14dc0fe}} +double {\bfseries get\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_af969a489f00c9f3e1e6acf9b34b96ed8}\label{class_e_n_a_r_c___node_af969a489f00c9f3e1e6acf9b34b96ed8}} +void {\bfseries print\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_ae446a816d8b870b6f6abfc9f39804f7b}\label{class_e_n_a_r_c___node_ae446a816d8b870b6f6abfc9f39804f7b}} +void {\bfseries input\+\_\+fired} (int time, double incoming\+\_\+output) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a37280a7486e83ec111d8aee180aa4660}\label{class_e_n_a_r_c___node_a37280a7486e83ec111d8aee180aa4660}} +void {\bfseries try\+\_\+update\+\_\+deltas} (int time) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a87ced04e586ab905e7fab66a43d8c8b0}\label{class_e_n_a_r_c___node_a87ced04e586ab905e7fab66a43d8c8b0}} +void {\bfseries error\+\_\+fired} (int time, double error) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_ac4c21f1a1c6a72d1d277662a57f6eca4}\label{class_e_n_a_r_c___node_ac4c21f1a1c6a72d1d277662a57f6eca4}} +void {\bfseries output\+\_\+fired} (int time, double delta) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_afcaf0598d88dbf90a95da40c8dd1acbc}\label{class_e_n_a_r_c___node_afcaf0598d88dbf90a95da40c8dd1acbc}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_acdd0f964f5bafc809f46a3cbc6da7ad5}\label{class_e_n_a_r_c___node_acdd0f964f5bafc809f46a3cbc6da7ad5}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a524aa8da5b4417a7b63a804b1d9c250b}\label{class_e_n_a_r_c___node_a524aa8da5b4417a7b63a804b1d9c250b}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a4cfa1214bb9f07e9046a7cc2addc8dd0}\label{class_e_n_a_r_c___node_a4cfa1214bb9f07e9046a7cc2addc8dd0}} +void {\bfseries get\+\_\+weights} (uint32\+\_\+t \&offset, \textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a5f1d8a54d46d0073d4813cba9d6bfb92}\label{class_e_n_a_r_c___node_a5f1d8a54d46d0073d4813cba9d6bfb92}} +void {\bfseries set\+\_\+weights} (uint32\+\_\+t \&offset, const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_acc90208a1d3168512586cdffaf2f7037}\label{class_e_n_a_r_c___node_acc90208a1d3168512586cdffaf2f7037}} +void {\bfseries get\+\_\+gradients} (\textbf{ vector}$<$ double $>$ \&gradients) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_ae63df61935afbe27e7c948c144ead7aa}\label{class_e_n_a_r_c___node_ae63df61935afbe27e7c948c144ead7aa}} +void {\bfseries reset} (int \+\_\+series\+\_\+length) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a8744c1e1af8e3680931c4a83a896281d}\label{class_e_n_a_r_c___node_a8744c1e1af8e3680931c4a83a896281d}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a57b9c7e028398ff1cc59877eae60764d}\label{class_e_n_a_r_c___node_a57b9c7e028398ff1cc59877eae60764d}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries copy} () const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a4e2ad9ab106978d25ea2037c03827c57}\label{class_e_n_a_r_c___node_a4e2ad9ab106978d25ea2037c03827c57}} +double {\bfseries rw} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a10cd986e7fc71d48c3b025842f27f7a5}\label{class_e_n_a_r_c___node_a10cd986e7fc71d48c3b025842f27f7a5}} +double {\bfseries zw} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a96d6daeda5737c974c8ce7f542c9c4ea}\label{class_e_n_a_r_c___node_a96d6daeda5737c974c8ce7f542c9c4ea}} +double {\bfseries w1} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_abe2a0fb799e0d5aec3f21ca9933152a8}\label{class_e_n_a_r_c___node_abe2a0fb799e0d5aec3f21ca9933152a8}} +double {\bfseries w2} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a4e88b388b3d54ae9eda4360b8f0bba27}\label{class_e_n_a_r_c___node_a4e88b388b3d54ae9eda4360b8f0bba27}} +double {\bfseries w3} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a8ff50975ba5c5b4c9c96accab8798ec7}\label{class_e_n_a_r_c___node_a8ff50975ba5c5b4c9c96accab8798ec7}} +double {\bfseries w6} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a259708047b1b8f89c0a6556a9c58a36f}\label{class_e_n_a_r_c___node_a259708047b1b8f89c0a6556a9c58a36f}} +double {\bfseries w4} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a331a5c5409c297fba27969352dbeae94}\label{class_e_n_a_r_c___node_a331a5c5409c297fba27969352dbeae94}} +double {\bfseries w5} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a05ea1be265c112d3ccec8d41bfd38424}\label{class_e_n_a_r_c___node_a05ea1be265c112d3ccec8d41bfd38424}} +double {\bfseries w7} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_af6a1d3e04469983a5b3527ddc6b14c77}\label{class_e_n_a_r_c___node_af6a1d3e04469983a5b3527ddc6b14c77}} +double {\bfseries w8} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_ad7841c8eea3712a0b68568f2f6b243e4}\label{class_e_n_a_r_c___node_ad7841c8eea3712a0b68568f2f6b243e4}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+zw} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_ab388f1892dd2a27afffa7de5b5454916}\label{class_e_n_a_r_c___node_ab388f1892dd2a27afffa7de5b5454916}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+rw} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a1c1233d50d7f76a4989156e2166be216}\label{class_e_n_a_r_c___node_a1c1233d50d7f76a4989156e2166be216}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+w1} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a5420bb64b3dcedc8f39c6543ed6bec7b}\label{class_e_n_a_r_c___node_a5420bb64b3dcedc8f39c6543ed6bec7b}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+w2} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a8f8f20a85814624540d4ca2e9cc0cdbd}\label{class_e_n_a_r_c___node_a8f8f20a85814624540d4ca2e9cc0cdbd}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+w3} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_ad8f6c6a8041658d8b6455868141d31b2}\label{class_e_n_a_r_c___node_ad8f6c6a8041658d8b6455868141d31b2}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+w6} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a0efdb4db4a0d943745f6903617644786}\label{class_e_n_a_r_c___node_a0efdb4db4a0d943745f6903617644786}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+w4} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_aad83f857a0d19784b920824bf2ad563a}\label{class_e_n_a_r_c___node_aad83f857a0d19784b920824bf2ad563a}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+w5} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a493a42e6415bc016dded89d721a066a5}\label{class_e_n_a_r_c___node_a493a42e6415bc016dded89d721a066a5}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+w7} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a8ffd978b09add0c642467d085ab9ce7c}\label{class_e_n_a_r_c___node_a8ffd978b09add0c642467d085ab9ce7c}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+w8} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a4abb08de2c50185a9a72436a308cb5bd}\label{class_e_n_a_r_c___node_a4abb08de2c50185a9a72436a308cb5bd}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+h\+\_\+prev} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a4ba4d16da41119a44e71f944bfa48f04}\label{class_e_n_a_r_c___node_a4ba4d16da41119a44e71f944bfa48f04}} +\textbf{ vector}$<$ double $>$ {\bfseries z} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_af2d437e85901ccfb67f30023ee295ab1}\label{class_e_n_a_r_c___node_af2d437e85901ccfb67f30023ee295ab1}} +\textbf{ vector}$<$ double $>$ {\bfseries l\+\_\+d\+\_\+z} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a9f23cfa4f2c1df4f577836e130810124}\label{class_e_n_a_r_c___node_a9f23cfa4f2c1df4f577836e130810124}} +\textbf{ vector}$<$ double $>$ {\bfseries w1\+\_\+z} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_afb5f1edb3b2f5910a8eaeccb5f6ac95e}\label{class_e_n_a_r_c___node_afb5f1edb3b2f5910a8eaeccb5f6ac95e}} +\textbf{ vector}$<$ double $>$ {\bfseries l\+\_\+w1\+\_\+z} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_af4bd0efc36a2b7e66c82a5937ad18351}\label{class_e_n_a_r_c___node_af4bd0efc36a2b7e66c82a5937ad18351}} +\textbf{ vector}$<$ double $>$ {\bfseries w2\+\_\+w1} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a6c3548e92f4e29d121804361a8d8f4fd}\label{class_e_n_a_r_c___node_a6c3548e92f4e29d121804361a8d8f4fd}} +\textbf{ vector}$<$ double $>$ {\bfseries l\+\_\+w2\+\_\+w1} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a070be73b34e7532d785c099765d8aa33}\label{class_e_n_a_r_c___node_a070be73b34e7532d785c099765d8aa33}} +\textbf{ vector}$<$ double $>$ {\bfseries w3\+\_\+w1} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a4532d97550344dd255750b4d7d0aad12}\label{class_e_n_a_r_c___node_a4532d97550344dd255750b4d7d0aad12}} +\textbf{ vector}$<$ double $>$ {\bfseries l\+\_\+w3\+\_\+w1} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a2f526d35d6c834e2d1923a67f5c25328}\label{class_e_n_a_r_c___node_a2f526d35d6c834e2d1923a67f5c25328}} +\textbf{ vector}$<$ double $>$ {\bfseries w6\+\_\+w1} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a540ee7e272e9249a9ce9c6f507cf4f42}\label{class_e_n_a_r_c___node_a540ee7e272e9249a9ce9c6f507cf4f42}} +\textbf{ vector}$<$ double $>$ {\bfseries l\+\_\+w6\+\_\+w1} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a9a7ee2eced187bd9188db34405d25da3}\label{class_e_n_a_r_c___node_a9a7ee2eced187bd9188db34405d25da3}} +\textbf{ vector}$<$ double $>$ {\bfseries w4\+\_\+w2} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a41df03b0d58b33c6cb0de62a35d37df3}\label{class_e_n_a_r_c___node_a41df03b0d58b33c6cb0de62a35d37df3}} +\textbf{ vector}$<$ double $>$ {\bfseries l\+\_\+w4\+\_\+w2} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a020ef4fd283a913406ac468d39a67e7e}\label{class_e_n_a_r_c___node_a020ef4fd283a913406ac468d39a67e7e}} +\textbf{ vector}$<$ double $>$ {\bfseries w5\+\_\+w3} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_abbf3b0f335fe95413b8f933ab8286251}\label{class_e_n_a_r_c___node_abbf3b0f335fe95413b8f933ab8286251}} +\textbf{ vector}$<$ double $>$ {\bfseries l\+\_\+w5\+\_\+w3} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_ab4843c59781ce2c18c57cfda7b345c4d}\label{class_e_n_a_r_c___node_ab4843c59781ce2c18c57cfda7b345c4d}} +\textbf{ vector}$<$ double $>$ {\bfseries w7\+\_\+w3} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a4668072e54af5828c66630180cc099af}\label{class_e_n_a_r_c___node_a4668072e54af5828c66630180cc099af}} +\textbf{ vector}$<$ double $>$ {\bfseries l\+\_\+w7\+\_\+w3} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a0c2e0d77a4a4878ad54929827829cfa7}\label{class_e_n_a_r_c___node_a0c2e0d77a4a4878ad54929827829cfa7}} +\textbf{ vector}$<$ double $>$ {\bfseries w8\+\_\+w3} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a8216b7b36cc86f2f453b4727022d757e}\label{class_e_n_a_r_c___node_a8216b7b36cc86f2f453b4727022d757e}} +\textbf{ vector}$<$ double $>$ {\bfseries l\+\_\+w8\+\_\+w3} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_e_n_a_r_c___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}\label{class_e_n_a_r_c___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}} +class {\bfseries R\+N\+N\+\_\+\+Edge} +\end{DoxyCompactItemize} +\doxysubsection*{Additional Inherited Members} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/enarc\+\_\+node.\+hxx\item +rnn/enarc\+\_\+node.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_e_n_a_s___d_a_g___node.eps b/docs/latex/class_e_n_a_s___d_a_g___node.eps new file mode 100644 index 00000000..088b5b3b --- /dev/null +++ b/docs/latex/class_e_n_a_s___d_a_g___node.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 285.714286 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.750000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(ENAS_DAG_Node) cw +(RNN_Node_Interface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (ENAS_DAG_Node) 0.000000 0.000000 box + (RNN_Node_Interface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_e_n_a_s___d_a_g___node.tex b/docs/latex/class_e_n_a_s___d_a_g___node.tex new file mode 100644 index 00000000..d81f645a --- /dev/null +++ b/docs/latex/class_e_n_a_s___d_a_g___node.tex @@ -0,0 +1,218 @@ +\hypertarget{class_e_n_a_s___d_a_g___node}{}\doxysection{E\+N\+A\+S\+\_\+\+D\+A\+G\+\_\+\+Node Class Reference} +\label{class_e_n_a_s___d_a_g___node}\index{ENAS\_DAG\_Node@{ENAS\_DAG\_Node}} +Inheritance diagram for E\+N\+A\+S\+\_\+\+D\+A\+G\+\_\+\+Node\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_e_n_a_s___d_a_g___node} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_ad2e4167a9eef0cebb3204926e8f6631d}\label{class_e_n_a_s___d_a_g___node_ad2e4167a9eef0cebb3204926e8f6631d}} +{\bfseries E\+N\+A\+S\+\_\+\+D\+A\+G\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, int \+\_\+type, double \+\_\+depth) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a88d5ba425f633f19849d395e636d2d9d}\label{class_e_n_a_s___d_a_g___node_a88d5ba425f633f19849d395e636d2d9d}} +void {\bfseries initialize\+\_\+lamarckian} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double mu, double sigma) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_aa5441589f008b822d9a67b9bda3f77d6}\label{class_e_n_a_s___d_a_g___node_aa5441589f008b822d9a67b9bda3f77d6}} +void {\bfseries initialize\+\_\+xavier} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng1\+\_\+1, double range) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_ac1807ef7a5ec4801f352564e675468d0}\label{class_e_n_a_s___d_a_g___node_ac1807ef7a5ec4801f352564e675468d0}} +void {\bfseries initialize\+\_\+kaiming} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double range) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_ab3f1568013cb9e1e4f7543c7e8e82fd7}\label{class_e_n_a_s___d_a_g___node_ab3f1568013cb9e1e4f7543c7e8e82fd7}} +void {\bfseries initialize\+\_\+uniform\+\_\+random} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng) +\item +double \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_afac6a8241afd3cae5c8d7955e8518c59}{get\+\_\+gradient}} (\textbf{ string} gradient\+\_\+name) +\begin{DoxyCompactList}\small\item\em Gives the gradients in essence the deriviative of the weights in the memory cell in the network. \end{DoxyCompactList}\item +void \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_a0676683aa9a220611f4e99a8a44e6e96}{print\+\_\+gradient}} (\textbf{ string} gradient\+\_\+name) +\begin{DoxyCompactList}\small\item\em Gives the gradients in essence the deriviative of the weights in the memory cell in the network. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_a58e17b40715e32a28a57c2ec34cd1997}{activation}} (double value, int act\+\_\+operator) +\begin{DoxyCompactList}\small\item\em Gives the activations of the node in the memory cell in the network. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_ae73d6fdfcaa9f0ae60628e9529c4373d}{activation\+\_\+derivative}} (double value, double input, int act\+\_\+operator) +\begin{DoxyCompactList}\small\item\em Gives the activations derviative of the node in the memory cell in the network. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a4b43e0a43e9a6cfc5602571747bd2ee9}\label{class_e_n_a_s___d_a_g___node_a4b43e0a43e9a6cfc5602571747bd2ee9}} +void {\bfseries input\+\_\+fired} (int time, double incoming\+\_\+output) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_aaa7340e05d8634d6a51e4a8f7b76179a}\label{class_e_n_a_s___d_a_g___node_aaa7340e05d8634d6a51e4a8f7b76179a}} +void {\bfseries try\+\_\+update\+\_\+deltas} (int time) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a575a87ce22d5db310f2d41022a0cdfb2}\label{class_e_n_a_s___d_a_g___node_a575a87ce22d5db310f2d41022a0cdfb2}} +void {\bfseries error\+\_\+fired} (int time, double error) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a0383c06026dff9751fd5449a845508bf}\label{class_e_n_a_s___d_a_g___node_a0383c06026dff9751fd5449a845508bf}} +void {\bfseries output\+\_\+fired} (int time, double delta) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a155c08c18ff6af0b5a828459b5e91534}\label{class_e_n_a_s___d_a_g___node_a155c08c18ff6af0b5a828459b5e91534}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a0cbec5c4e3155dcd89a46d780afe6e71}\label{class_e_n_a_s___d_a_g___node_a0cbec5c4e3155dcd89a46d780afe6e71}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a4616ae3f60ecb167ea02ed781d32dc09}\label{class_e_n_a_s___d_a_g___node_a4616ae3f60ecb167ea02ed781d32dc09}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a2765dfaa7273f9da0fccdda0d9358311}\label{class_e_n_a_s___d_a_g___node_a2765dfaa7273f9da0fccdda0d9358311}} +void {\bfseries get\+\_\+weights} (uint32\+\_\+t \&offset, \textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_acc6c518fc61f9c8b9e35972b88022176}\label{class_e_n_a_s___d_a_g___node_acc6c518fc61f9c8b9e35972b88022176}} +void {\bfseries set\+\_\+weights} (uint32\+\_\+t \&offset, const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a7569014230b09b47e3307ad371e78a10}\label{class_e_n_a_s___d_a_g___node_a7569014230b09b47e3307ad371e78a10}} +void {\bfseries get\+\_\+gradients} (\textbf{ vector}$<$ double $>$ \&gradients) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a0e3e26638335941ec7b174990218330e}\label{class_e_n_a_s___d_a_g___node_a0e3e26638335941ec7b174990218330e}} +void {\bfseries reset} (int \+\_\+series\+\_\+length) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_ab84b22eaca81b3f627c7022aac03576f}\label{class_e_n_a_s___d_a_g___node_ab84b22eaca81b3f627c7022aac03576f}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_aea98ee85dae02da8fa3ecb5bc2b4f895}\label{class_e_n_a_s___d_a_g___node_aea98ee85dae02da8fa3ecb5bc2b4f895}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries copy} () const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a96691f6c8700d1443045691b435db9b4}\label{class_e_n_a_s___d_a_g___node_a96691f6c8700d1443045691b435db9b4}} +double \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_a96691f6c8700d1443045691b435db9b4}{rw}} +\begin{DoxyCompactList}\small\item\em Weight of the start node in the memory cell. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a543c626068b87030fb140a4ebeb38f30}\label{class_e_n_a_s___d_a_g___node_a543c626068b87030fb140a4ebeb38f30}} +double \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_a543c626068b87030fb140a4ebeb38f30}{zw}} +\begin{DoxyCompactList}\small\item\em Weight of the start node in the memory cell. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a169c584563029fdec7d51390c6bef838}\label{class_e_n_a_s___d_a_g___node_a169c584563029fdec7d51390c6bef838}} +\textbf{ vector}$<$ double $>$ \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_a169c584563029fdec7d51390c6bef838}{weights}} +\begin{DoxyCompactList}\small\item\em Weights of the subseqeuent nodes in topological order the memory cell. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a6df39cdee9de29977d989383c312afeb}\label{class_e_n_a_s___d_a_g___node_a6df39cdee9de29977d989383c312afeb}} +\textbf{ vector}$<$ double $>$ \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_a6df39cdee9de29977d989383c312afeb}{d\+\_\+zw}} +\begin{DoxyCompactList}\small\item\em Gradient of the weight of the start node in the memory cell. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a4c2c49ca7aed21901ad5d79d3f921459}\label{class_e_n_a_s___d_a_g___node_a4c2c49ca7aed21901ad5d79d3f921459}} +\textbf{ vector}$<$ double $>$ \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_a4c2c49ca7aed21901ad5d79d3f921459}{d\+\_\+rw}} +\begin{DoxyCompactList}\small\item\em Gradient of the weight of the start node in the memory cell. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a127cf43bd1e8014d9fb951cf26c9b737}\label{class_e_n_a_s___d_a_g___node_a127cf43bd1e8014d9fb951cf26c9b737}} +\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_a127cf43bd1e8014d9fb951cf26c9b737}{d\+\_\+weights}} +\begin{DoxyCompactList}\small\item\em Gradient of the weights of the other nodes in the memory cell. \end{DoxyCompactList}\item +\textbf{ vector}$<$ double $>$ \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_a366df37adbb4bc2824846ef60b6707e3}{d\+\_\+h\+\_\+prev}} +\begin{DoxyCompactList}\small\item\em Gradient of the previous output i.\+e. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_ae96b8e1d3d379d097aff2d879ca25fdb}\label{class_e_n_a_s___d_a_g___node_ae96b8e1d3d379d097aff2d879ca25fdb}} +\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_ae96b8e1d3d379d097aff2d879ca25fdb}{Nodes}} +\begin{DoxyCompactList}\small\item\em Outputs of the nodes in the memory cell connecting with weight wj from node with weight wi. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a0b7939ba90a6db0ba2dda9731eefc38a}\label{class_e_n_a_s___d_a_g___node_a0b7939ba90a6db0ba2dda9731eefc38a}} +\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \mbox{\hyperlink{class_e_n_a_s___d_a_g___node_a0b7939ba90a6db0ba2dda9731eefc38a}{l\+\_\+\+Nodes}} +\begin{DoxyCompactList}\small\item\em Derivative of the nodes in the memory cell connecting with weight wj from node with weight wi. \end{DoxyCompactList}\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}\label{class_e_n_a_s___d_a_g___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}} +class {\bfseries R\+N\+N\+\_\+\+Edge} +\end{DoxyCompactItemize} +\doxysubsection*{Additional Inherited Members} + + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a58e17b40715e32a28a57c2ec34cd1997}\label{class_e_n_a_s___d_a_g___node_a58e17b40715e32a28a57c2ec34cd1997}} +\index{ENAS\_DAG\_Node@{ENAS\_DAG\_Node}!activation@{activation}} +\index{activation@{activation}!ENAS\_DAG\_Node@{ENAS\_DAG\_Node}} +\doxysubsubsection{\texorpdfstring{activation()}{activation()}} +{\footnotesize\ttfamily double E\+N\+A\+S\+\_\+\+D\+A\+G\+\_\+\+Node\+::activation (\begin{DoxyParamCaption}\item[{double}]{value, }\item[{int}]{act\+\_\+operator }\end{DoxyParamCaption})} + + + +Gives the activations of the node in the memory cell in the network. + +~\newline + + + +\begin{DoxyParams}{Parameters} +{\em value} & is the output of the node after multiplying weights \\ +\hline +{\em act\+\_\+operator} & is the actiation type \+: sigmoid, tanh, swish, identity\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +output of the node after applying activation +\end{DoxyReturn} +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_ae73d6fdfcaa9f0ae60628e9529c4373d}\label{class_e_n_a_s___d_a_g___node_ae73d6fdfcaa9f0ae60628e9529c4373d}} +\index{ENAS\_DAG\_Node@{ENAS\_DAG\_Node}!activation\_derivative@{activation\_derivative}} +\index{activation\_derivative@{activation\_derivative}!ENAS\_DAG\_Node@{ENAS\_DAG\_Node}} +\doxysubsubsection{\texorpdfstring{activation\_derivative()}{activation\_derivative()}} +{\footnotesize\ttfamily double E\+N\+A\+S\+\_\+\+D\+A\+G\+\_\+\+Node\+::activation\+\_\+derivative (\begin{DoxyParamCaption}\item[{double}]{value, }\item[{double}]{input, }\item[{int}]{act\+\_\+operator }\end{DoxyParamCaption})} + + + +Gives the activations derviative of the node in the memory cell in the network. + +~\newline + + + +\begin{DoxyParams}{Parameters} +{\em input} & is the input of the node before multiplying weights \\ +\hline +{\em value} & is the output of the node after multiplying weights \\ +\hline +{\em act\+\_\+operator} & is the actiation type \+: sigmoid, tanh, swish, identity\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +output derivative of the node after applying activation +\end{DoxyReturn} +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_afac6a8241afd3cae5c8d7955e8518c59}\label{class_e_n_a_s___d_a_g___node_afac6a8241afd3cae5c8d7955e8518c59}} +\index{ENAS\_DAG\_Node@{ENAS\_DAG\_Node}!get\_gradient@{get\_gradient}} +\index{get\_gradient@{get\_gradient}!ENAS\_DAG\_Node@{ENAS\_DAG\_Node}} +\doxysubsubsection{\texorpdfstring{get\_gradient()}{get\_gradient()}} +{\footnotesize\ttfamily double E\+N\+A\+S\+\_\+\+D\+A\+G\+\_\+\+Node\+::get\+\_\+gradient (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{gradient\+\_\+name }\end{DoxyParamCaption})} + + + +Gives the gradients in essence the deriviative of the weights in the memory cell in the network. + +~\newline + + + +\begin{DoxyParams}{Parameters} +{\em gradient\+\_\+name} & in the weight name.\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +gradient. +\end{DoxyReturn} +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a0676683aa9a220611f4e99a8a44e6e96}\label{class_e_n_a_s___d_a_g___node_a0676683aa9a220611f4e99a8a44e6e96}} +\index{ENAS\_DAG\_Node@{ENAS\_DAG\_Node}!print\_gradient@{print\_gradient}} +\index{print\_gradient@{print\_gradient}!ENAS\_DAG\_Node@{ENAS\_DAG\_Node}} +\doxysubsubsection{\texorpdfstring{print\_gradient()}{print\_gradient()}} +{\footnotesize\ttfamily void E\+N\+A\+S\+\_\+\+D\+A\+G\+\_\+\+Node\+::print\+\_\+gradient (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{gradient\+\_\+name }\end{DoxyParamCaption})} + + + +Gives the gradients in essence the deriviative of the weights in the memory cell in the network. + +~\newline + + + +\begin{DoxyParams}{Parameters} +{\em gradient\+\_\+name} & in the weight name.\\ +\hline +\end{DoxyParams} +prints the gradient. + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{class_e_n_a_s___d_a_g___node_a366df37adbb4bc2824846ef60b6707e3}\label{class_e_n_a_s___d_a_g___node_a366df37adbb4bc2824846ef60b6707e3}} +\index{ENAS\_DAG\_Node@{ENAS\_DAG\_Node}!d\_h\_prev@{d\_h\_prev}} +\index{d\_h\_prev@{d\_h\_prev}!ENAS\_DAG\_Node@{ENAS\_DAG\_Node}} +\doxysubsubsection{\texorpdfstring{d\_h\_prev}{d\_h\_prev}} +{\footnotesize\ttfamily \textbf{ vector}$<$double$>$ E\+N\+A\+S\+\_\+\+D\+A\+G\+\_\+\+Node\+::d\+\_\+h\+\_\+prev\hspace{0.3cm}{\ttfamily [private]}} + + + +Gradient of the previous output i.\+e. + +t-\/1 in the memory cell. + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/enas\+\_\+dag\+\_\+node.\+hxx\item +rnn/enas\+\_\+dag\+\_\+node.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_e_x_a_c_t.tex b/docs/latex/class_e_x_a_c_t.tex new file mode 100644 index 00000000..fdbafd93 --- /dev/null +++ b/docs/latex/class_e_x_a_c_t.tex @@ -0,0 +1,383 @@ +\hypertarget{class_e_x_a_c_t}{}\doxysection{E\+X\+A\+CT Class Reference} +\label{class_e_x_a_c_t}\index{EXACT@{EXACT}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aa07172a12a4b5681bc94143292eb480e}\label{class_e_x_a_c_t_aa07172a12a4b5681bc94143292eb480e}} +{\bfseries E\+X\+A\+CT} (const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&training\+\_\+images, const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&validation\+\_\+images, const \mbox{\hyperlink{class_images_interface}{Images\+Interface}} \&test\+\_\+images, int \+\_\+padding, int \+\_\+population\+\_\+size, int \+\_\+max\+\_\+epochs, bool \+\_\+use\+\_\+sfmp, bool \+\_\+use\+\_\+node\+\_\+operations, int \+\_\+max\+\_\+genomes, \textbf{ string} \+\_\+output\+\_\+directory, \textbf{ string} \+\_\+search\+\_\+name, bool \+\_\+reset\+\_\+weights) +\item +\mbox{\Hypertarget{class_e_x_a_c_t_af7e6a8aaba03bbea142eff270b591891}\label{class_e_x_a_c_t_af7e6a8aaba03bbea142eff270b591891}} +int32\+\_\+t {\bfseries population\+\_\+contains} (\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$genome) const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aa753110ab31a4388bdbc871cebbd7640}\label{class_e_x_a_c_t_aa753110ab31a4388bdbc871cebbd7640}} +\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries get\+\_\+best\+\_\+genome} () +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aa59949f19aaa70d7d8e02883de8289ae}\label{class_e_x_a_c_t_aa59949f19aaa70d7d8e02883de8289ae}} +int {\bfseries get\+\_\+number\+\_\+genomes} () const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a054bca8b67f47478ba5ad4ea0fa3309a}\label{class_e_x_a_c_t_a054bca8b67f47478ba5ad4ea0fa3309a}} +\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries get\+\_\+genome} (int i) +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a4898413f61036a8fe5a1238480eee9cf}\label{class_e_x_a_c_t_a4898413f61036a8fe5a1238480eee9cf}} +void {\bfseries generate\+\_\+initial\+\_\+hyperparameters} (float \&mu, float \&mu\+\_\+delta, float \&learning\+\_\+rate, float \&learning\+\_\+rate\+\_\+delta, float \&weight\+\_\+decay, float \&weight\+\_\+decay\+\_\+delta, float \&alpha, int \&velocity\+\_\+reset, float \&input\+\_\+dropout\+\_\+probability, float \&hidden\+\_\+dropout\+\_\+probability, int \&batch\+\_\+size) +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a5c47f423ef8c16231ef44a2b44edee34}\label{class_e_x_a_c_t_a5c47f423ef8c16231ef44a2b44edee34}} +void {\bfseries generate\+\_\+simplex\+\_\+hyperparameters} (float \&mu, float \&mu\+\_\+delta, float \&learning\+\_\+rate, float \&learning\+\_\+rate\+\_\+delta, float \&weight\+\_\+decay, float \&weight\+\_\+decay\+\_\+delta, float \&alpha, int \&velocity\+\_\+reset, float \&input\+\_\+dropout\+\_\+probability, float \&hidden\+\_\+dropout\+\_\+probability, int \&batch\+\_\+size) +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a2a0590a7d4a48a08b92d4ef318231eff}\label{class_e_x_a_c_t_a2a0590a7d4a48a08b92d4ef318231eff}} +bool {\bfseries add\+\_\+edge} (\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$child, \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$node1, \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$node2, int edge\+\_\+type) +\item +\mbox{\Hypertarget{class_e_x_a_c_t_af5df7c2b42c9bc317d97f9d5e55678aa}\label{class_e_x_a_c_t_af5df7c2b42c9bc317d97f9d5e55678aa}} +\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries generate\+\_\+individual} () +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a41fd91c869d2267495b5fde95e675646}\label{class_e_x_a_c_t_a41fd91c869d2267495b5fde95e675646}} +\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries create\+\_\+mutation} () +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a7a240b0ecead548bd7229ac8048172f2}\label{class_e_x_a_c_t_a7a240b0ecead548bd7229ac8048172f2}} +\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries create\+\_\+child} () +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a420b2f6845d6c983428e62a55c35e41f}\label{class_e_x_a_c_t_a420b2f6845d6c983428e62a55c35e41f}} +bool {\bfseries insert\+\_\+genome} (\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$genome) +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a10649d1ee68d808d79b212d672adea69}\label{class_e_x_a_c_t_a10649d1ee68d808d79b212d672adea69}} +int {\bfseries get\+\_\+inserted\+\_\+genomes} () const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a9bf0ff23c967cedfcbb27bf381a078cc}\label{class_e_x_a_c_t_a9bf0ff23c967cedfcbb27bf381a078cc}} +int {\bfseries get\+\_\+max\+\_\+genomes} () const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_acbbaa9e959a10c3876c36984efe375c5}\label{class_e_x_a_c_t_acbbaa9e959a10c3876c36984efe375c5}} +void {\bfseries write\+\_\+individual\+\_\+hyperparameters} (\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$individual) +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a22f6484a62bf7b7869be4319434810d6}\label{class_e_x_a_c_t_a22f6484a62bf7b7869be4319434810d6}} +void {\bfseries write\+\_\+statistics} (int new\+\_\+generation\+\_\+id, float new\+\_\+fitness) +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a4e27fa2931089664ac96c249d510daab}\label{class_e_x_a_c_t_a4e27fa2931089664ac96c249d510daab}} +void {\bfseries write\+\_\+statistics\+\_\+header} () +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a8f3b7ec35707d6099537c379e409503a}\label{class_e_x_a_c_t_a8f3b7ec35707d6099537c379e409503a}} +void {\bfseries write\+\_\+hyperparameters\+\_\+header} () +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a11d273bfda915a4c9bdf6e259f4f965c}\label{class_e_x_a_c_t_a11d273bfda915a4c9bdf6e259f4f965c}} +int {\bfseries get\+\_\+id} () const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ada53369a2343d171a5700033b4db9103}\label{class_e_x_a_c_t_ada53369a2343d171a5700033b4db9103}} +\textbf{ string} {\bfseries get\+\_\+search\+\_\+name} () const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a1685cdd24397d3050308cceeea137637}\label{class_e_x_a_c_t_a1685cdd24397d3050308cceeea137637}} +\textbf{ string} {\bfseries get\+\_\+output\+\_\+directory} () const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a5dad4819829401f274f5fa1eb5db27c0}\label{class_e_x_a_c_t_a5dad4819829401f274f5fa1eb5db27c0}} +\textbf{ string} {\bfseries get\+\_\+training\+\_\+filename} () const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aec2a96922bf9a907eb298e881d577528}\label{class_e_x_a_c_t_aec2a96922bf9a907eb298e881d577528}} +\textbf{ string} {\bfseries get\+\_\+validation\+\_\+filename} () const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a1d210ba2fcba345c5d0c99a689fc26b1}\label{class_e_x_a_c_t_a1d210ba2fcba345c5d0c99a689fc26b1}} +\textbf{ string} {\bfseries get\+\_\+test\+\_\+filename} () const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_af29d9ebd9827e46e4986f8efa22537cf}\label{class_e_x_a_c_t_af29d9ebd9827e46e4986f8efa22537cf}} +int {\bfseries get\+\_\+number\+\_\+training\+\_\+images} () const +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a735a669490c74d148ecaf38a080b1649}\label{class_e_x_a_c_t_a735a669490c74d148ecaf38a080b1649}} +bool {\bfseries is\+\_\+identical} (\mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} $\ast$other, bool testing\+\_\+checkpoint) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a4ff6a3e7aa2147d89f3070abca292219}\label{class_e_x_a_c_t_a4ff6a3e7aa2147d89f3070abca292219}} +int {\bfseries id} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a7dea673ae78b80d8d3b1b36765e23da6}\label{class_e_x_a_c_t_a7dea673ae78b80d8d3b1b36765e23da6}} +\textbf{ string} {\bfseries search\+\_\+name} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a4d4d54112b92f273117d592e64d4aa53}\label{class_e_x_a_c_t_a4d4d54112b92f273117d592e64d4aa53}} +\textbf{ string} {\bfseries output\+\_\+directory} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a52273b3e82104a5ab2636a691538f262}\label{class_e_x_a_c_t_a52273b3e82104a5ab2636a691538f262}} +\textbf{ string} {\bfseries training\+\_\+filename} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a2da36490884fa2088902c7827f9ee9ad}\label{class_e_x_a_c_t_a2da36490884fa2088902c7827f9ee9ad}} +\textbf{ string} {\bfseries validation\+\_\+filename} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ab16d32b0276bd633448df98bd2d0c26f}\label{class_e_x_a_c_t_ab16d32b0276bd633448df98bd2d0c26f}} +\textbf{ string} {\bfseries test\+\_\+filename} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a1193dab8b80999507953105260b89c90}\label{class_e_x_a_c_t_a1193dab8b80999507953105260b89c90}} +int {\bfseries number\+\_\+training\+\_\+images} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a770120c255c746cf78f1ef01a77013a2}\label{class_e_x_a_c_t_a770120c255c746cf78f1ef01a77013a2}} +int {\bfseries number\+\_\+validation\+\_\+images} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a2d9fd0187461822f9d9cad8460a8160d}\label{class_e_x_a_c_t_a2d9fd0187461822f9d9cad8460a8160d}} +int {\bfseries number\+\_\+test\+\_\+images} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ac0ec229aa866a59ea8f0edb027d52e1f}\label{class_e_x_a_c_t_ac0ec229aa866a59ea8f0edb027d52e1f}} +int {\bfseries padding} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a0c3b512c72cead879e599e280d84d1f7}\label{class_e_x_a_c_t_a0c3b512c72cead879e599e280d84d1f7}} +int {\bfseries image\+\_\+channels} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a594da931a28af75820a291d6148218a8}\label{class_e_x_a_c_t_a594da931a28af75820a291d6148218a8}} +int {\bfseries image\+\_\+rows} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ae6a04f163763949b3b57e3742980b61b}\label{class_e_x_a_c_t_ae6a04f163763949b3b57e3742980b61b}} +int {\bfseries image\+\_\+cols} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a48b74e4466fd787d8feced4fed0a3dec}\label{class_e_x_a_c_t_a48b74e4466fd787d8feced4fed0a3dec}} +int {\bfseries number\+\_\+classes} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aff849680b6129075ccae0c9ab8e1d8d6}\label{class_e_x_a_c_t_aff849680b6129075ccae0c9ab8e1d8d6}} +int {\bfseries population\+\_\+size} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a0370980df6acd821683d88e9bdd33962}\label{class_e_x_a_c_t_a0370980df6acd821683d88e9bdd33962}} +int {\bfseries node\+\_\+innovation\+\_\+count} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a57b13360bf5056eab42b2d7763c487dc}\label{class_e_x_a_c_t_a57b13360bf5056eab42b2d7763c487dc}} +int {\bfseries edge\+\_\+innovation\+\_\+count} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a4b92bc0f8023082bcd01b4d152d2a1fe}\label{class_e_x_a_c_t_a4b92bc0f8023082bcd01b4d152d2a1fe}} +\textbf{ minstd\+\_\+rand0} {\bfseries generator} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a6813eddd66480ac274f06baf7f0dc41c}\label{class_e_x_a_c_t_a6813eddd66480ac274f06baf7f0dc41c}} +\mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} {\bfseries normal\+\_\+distribution} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aa142f7cc9aa4e16100cd2f40a9e9eee5}\label{class_e_x_a_c_t_aa142f7cc9aa4e16100cd2f40a9e9eee5}} +\textbf{ uniform\+\_\+int\+\_\+distribution}$<$ long $>$ {\bfseries rng\+\_\+long} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a1f556f776d8bb3840f602f5697e91187}\label{class_e_x_a_c_t_a1f556f776d8bb3840f602f5697e91187}} +\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ float $>$ {\bfseries rng\+\_\+float} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a8dd2e61aa97ce250f94b363dc083cb40}\label{class_e_x_a_c_t_a8dd2e61aa97ce250f94b363dc083cb40}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$ $>$ {\bfseries genomes} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ad34ad2c58c04929977541e699b946a59}\label{class_e_x_a_c_t_ad34ad2c58c04929977541e699b946a59}} +int {\bfseries best\+\_\+predictions\+\_\+genome\+\_\+id} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_afb769815695dd899af1bd37ebb3a1371}\label{class_e_x_a_c_t_afb769815695dd899af1bd37ebb3a1371}} +\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries best\+\_\+predictions\+\_\+genome} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_adc11a9c24ed821a389d48ea2def3831e}\label{class_e_x_a_c_t_adc11a9c24ed821a389d48ea2def3831e}} +int {\bfseries genomes\+\_\+generated} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aa45127bc39970a958a3b9270d22fb408}\label{class_e_x_a_c_t_aa45127bc39970a958a3b9270d22fb408}} +int {\bfseries inserted\+\_\+genomes} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aad0ec22f335640892800c001c974aed3}\label{class_e_x_a_c_t_aad0ec22f335640892800c001c974aed3}} +int {\bfseries max\+\_\+genomes} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ac7902f4f729f7255ea276185013376a0}\label{class_e_x_a_c_t_ac7902f4f729f7255ea276185013376a0}} +bool {\bfseries reset\+\_\+weights} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a55615bb47f3710112a8396fd0ba5f49c}\label{class_e_x_a_c_t_a55615bb47f3710112a8396fd0ba5f49c}} +int {\bfseries max\+\_\+epochs} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a14e69858b11edeeadd7672dea53e2dcf}\label{class_e_x_a_c_t_a14e69858b11edeeadd7672dea53e2dcf}} +bool {\bfseries use\+\_\+sfmp} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a037fa89581a059e40ee74ba06505cae1}\label{class_e_x_a_c_t_a037fa89581a059e40ee74ba06505cae1}} +bool {\bfseries use\+\_\+node\+\_\+operations} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_af36089a98a0f3157804245844c95afcd}\label{class_e_x_a_c_t_af36089a98a0f3157804245844c95afcd}} +float {\bfseries initial\+\_\+batch\+\_\+size\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a2e78240010f9585e5274f5cdaae4f37f}\label{class_e_x_a_c_t_a2e78240010f9585e5274f5cdaae4f37f}} +float {\bfseries initial\+\_\+batch\+\_\+size\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a171f31801dbd5fdf3c0fe5f942494673}\label{class_e_x_a_c_t_a171f31801dbd5fdf3c0fe5f942494673}} +float {\bfseries batch\+\_\+size\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a189ae715563346daf3468fe991ecf7d7}\label{class_e_x_a_c_t_a189ae715563346daf3468fe991ecf7d7}} +float {\bfseries batch\+\_\+size\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a71af8f5f60b9905da3e38536a253f520}\label{class_e_x_a_c_t_a71af8f5f60b9905da3e38536a253f520}} +float {\bfseries initial\+\_\+mu\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_af336cdca4cb632381be7afacfd6fd649}\label{class_e_x_a_c_t_af336cdca4cb632381be7afacfd6fd649}} +float {\bfseries initial\+\_\+mu\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ae2c963f8d839d8794d22431ad3294026}\label{class_e_x_a_c_t_ae2c963f8d839d8794d22431ad3294026}} +float {\bfseries mu\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a06a68b3fd84b5464f0bd9bb6456153d6}\label{class_e_x_a_c_t_a06a68b3fd84b5464f0bd9bb6456153d6}} +float {\bfseries mu\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a901dabdead107ad59ec450fceb9910be}\label{class_e_x_a_c_t_a901dabdead107ad59ec450fceb9910be}} +float {\bfseries initial\+\_\+mu\+\_\+delta\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a06ccd9e8330002332a81f42617b72a19}\label{class_e_x_a_c_t_a06ccd9e8330002332a81f42617b72a19}} +float {\bfseries initial\+\_\+mu\+\_\+delta\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a53cc7850b2aacf1db32c3bc8ce875fef}\label{class_e_x_a_c_t_a53cc7850b2aacf1db32c3bc8ce875fef}} +float {\bfseries mu\+\_\+delta\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a62e5f568b3879740cc93dacb8d39ef51}\label{class_e_x_a_c_t_a62e5f568b3879740cc93dacb8d39ef51}} +float {\bfseries mu\+\_\+delta\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a3e68a1aa008edbe22e77f2794a2ea782}\label{class_e_x_a_c_t_a3e68a1aa008edbe22e77f2794a2ea782}} +float {\bfseries initial\+\_\+learning\+\_\+rate\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_abc7124cc432b7633de44ad8b9c95bf78}\label{class_e_x_a_c_t_abc7124cc432b7633de44ad8b9c95bf78}} +float {\bfseries initial\+\_\+learning\+\_\+rate\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a3a559680cdac64c9f8c2e3510d902965}\label{class_e_x_a_c_t_a3a559680cdac64c9f8c2e3510d902965}} +float {\bfseries learning\+\_\+rate\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a44a2e19e4d277a75192c5f2a6e01369c}\label{class_e_x_a_c_t_a44a2e19e4d277a75192c5f2a6e01369c}} +float {\bfseries learning\+\_\+rate\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a0ddf4a909928e70016fd84d29a00a0ba}\label{class_e_x_a_c_t_a0ddf4a909928e70016fd84d29a00a0ba}} +float {\bfseries initial\+\_\+learning\+\_\+rate\+\_\+delta\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a0f71b07d6d7e6a3e4c860c78026f0469}\label{class_e_x_a_c_t_a0f71b07d6d7e6a3e4c860c78026f0469}} +float {\bfseries initial\+\_\+learning\+\_\+rate\+\_\+delta\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aa4551e9b6d8a08a2bb29e7ad903bf658}\label{class_e_x_a_c_t_aa4551e9b6d8a08a2bb29e7ad903bf658}} +float {\bfseries learning\+\_\+rate\+\_\+delta\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a0ff91f386b64a8a664566bd1dd06ff97}\label{class_e_x_a_c_t_a0ff91f386b64a8a664566bd1dd06ff97}} +float {\bfseries learning\+\_\+rate\+\_\+delta\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ae1c3ef27ec684bf73dff51ef0fda94d6}\label{class_e_x_a_c_t_ae1c3ef27ec684bf73dff51ef0fda94d6}} +float {\bfseries initial\+\_\+weight\+\_\+decay\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a8854feec34a613e96611dd2ac1ee6d4c}\label{class_e_x_a_c_t_a8854feec34a613e96611dd2ac1ee6d4c}} +float {\bfseries initial\+\_\+weight\+\_\+decay\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a4ef2a871380f76b554cbc5e30aa37822}\label{class_e_x_a_c_t_a4ef2a871380f76b554cbc5e30aa37822}} +float {\bfseries weight\+\_\+decay\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a2e791d47208a0266dd4764e4f0639192}\label{class_e_x_a_c_t_a2e791d47208a0266dd4764e4f0639192}} +float {\bfseries weight\+\_\+decay\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a28169e82f7b2190953c395f424a9b8c9}\label{class_e_x_a_c_t_a28169e82f7b2190953c395f424a9b8c9}} +float {\bfseries initial\+\_\+weight\+\_\+decay\+\_\+delta\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a0d89b3d47602abd40241159372c28a54}\label{class_e_x_a_c_t_a0d89b3d47602abd40241159372c28a54}} +float {\bfseries initial\+\_\+weight\+\_\+decay\+\_\+delta\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a114f920b4ba5fa5b430c6599314231ee}\label{class_e_x_a_c_t_a114f920b4ba5fa5b430c6599314231ee}} +float {\bfseries weight\+\_\+decay\+\_\+delta\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a0ee47bce29272ea9cf82df4b37c22464}\label{class_e_x_a_c_t_a0ee47bce29272ea9cf82df4b37c22464}} +float {\bfseries weight\+\_\+decay\+\_\+delta\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a75e1c0fbf65d2d87f475271e5ecf59f5}\label{class_e_x_a_c_t_a75e1c0fbf65d2d87f475271e5ecf59f5}} +float {\bfseries epsilon} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a8b044ed2291164ac57c78f438cea57e8}\label{class_e_x_a_c_t_a8b044ed2291164ac57c78f438cea57e8}} +float {\bfseries initial\+\_\+alpha\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a16d3a39a229b96d44eacf496f128bf3b}\label{class_e_x_a_c_t_a16d3a39a229b96d44eacf496f128bf3b}} +float {\bfseries initial\+\_\+alpha\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a4df0d4b449d591ea00aa9bb71d8230db}\label{class_e_x_a_c_t_a4df0d4b449d591ea00aa9bb71d8230db}} +float {\bfseries alpha\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a7b4a1892d88f47d660815129866e6952}\label{class_e_x_a_c_t_a7b4a1892d88f47d660815129866e6952}} +float {\bfseries alpha\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a603facb7d274f3ab6b1b476a01a0abb0}\label{class_e_x_a_c_t_a603facb7d274f3ab6b1b476a01a0abb0}} +int {\bfseries initial\+\_\+velocity\+\_\+reset\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ae0e98a582cd54fcec52eb3caa81905cf}\label{class_e_x_a_c_t_ae0e98a582cd54fcec52eb3caa81905cf}} +int {\bfseries initial\+\_\+velocity\+\_\+reset\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ab3c0f20b8a0577d0ef34600e6eb90e53}\label{class_e_x_a_c_t_ab3c0f20b8a0577d0ef34600e6eb90e53}} +int {\bfseries velocity\+\_\+reset\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aa659ec75a996c034748318c7e4953f55}\label{class_e_x_a_c_t_aa659ec75a996c034748318c7e4953f55}} +int {\bfseries velocity\+\_\+reset\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a0a3449dbe5c9212a9fc56125125bd617}\label{class_e_x_a_c_t_a0a3449dbe5c9212a9fc56125125bd617}} +float {\bfseries initial\+\_\+input\+\_\+dropout\+\_\+probability\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ae89ac4a3cf182906e1c99afcacad6ced}\label{class_e_x_a_c_t_ae89ac4a3cf182906e1c99afcacad6ced}} +float {\bfseries initial\+\_\+input\+\_\+dropout\+\_\+probability\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ad52f47b38df3bac7c6c763676a294644}\label{class_e_x_a_c_t_ad52f47b38df3bac7c6c763676a294644}} +float {\bfseries input\+\_\+dropout\+\_\+probability\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_afc6663ee25e8c3db6ad07e935b02708d}\label{class_e_x_a_c_t_afc6663ee25e8c3db6ad07e935b02708d}} +float {\bfseries input\+\_\+dropout\+\_\+probability\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a39a3de3ed0d016a42e95416775100b0c}\label{class_e_x_a_c_t_a39a3de3ed0d016a42e95416775100b0c}} +float {\bfseries initial\+\_\+hidden\+\_\+dropout\+\_\+probability\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a24d3eb1fa4140650a0f1514ddefe99a1}\label{class_e_x_a_c_t_a24d3eb1fa4140650a0f1514ddefe99a1}} +float {\bfseries initial\+\_\+hidden\+\_\+dropout\+\_\+probability\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a92918e0dd76fbf90000a4a7f30484a46}\label{class_e_x_a_c_t_a92918e0dd76fbf90000a4a7f30484a46}} +float {\bfseries hidden\+\_\+dropout\+\_\+probability\+\_\+min} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a0e39ecc89d8ae9db58cba9fe89d47d5e}\label{class_e_x_a_c_t_a0e39ecc89d8ae9db58cba9fe89d47d5e}} +float {\bfseries hidden\+\_\+dropout\+\_\+probability\+\_\+max} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a41b5a93e9bd66bf6478f86c6d039a810}\label{class_e_x_a_c_t_a41b5a93e9bd66bf6478f86c6d039a810}} +float {\bfseries reset\+\_\+weights\+\_\+chance} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a95f5a06c7658d3162e2971ba72eca92b}\label{class_e_x_a_c_t_a95f5a06c7658d3162e2971ba72eca92b}} +float {\bfseries crossover\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_af7f60e5cef67d16eec1f227b470394c8}\label{class_e_x_a_c_t_af7f60e5cef67d16eec1f227b470394c8}} +float {\bfseries more\+\_\+fit\+\_\+parent\+\_\+crossover} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a3aa717e8c14243ed74eba9a6fc5db8bd}\label{class_e_x_a_c_t_a3aa717e8c14243ed74eba9a6fc5db8bd}} +float {\bfseries less\+\_\+fit\+\_\+parent\+\_\+crossover} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a55be467a8ebb54294b3afc5033f1a865}\label{class_e_x_a_c_t_a55be467a8ebb54294b3afc5033f1a865}} +float {\bfseries crossover\+\_\+alter\+\_\+edge\+\_\+type} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_aeb0da7991485c61f8642ff86670cb9b9}\label{class_e_x_a_c_t_aeb0da7991485c61f8642ff86670cb9b9}} +int {\bfseries number\+\_\+mutations} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a8e1ce20cf29cdec578f092e40bb4f2f4}\label{class_e_x_a_c_t_a8e1ce20cf29cdec578f092e40bb4f2f4}} +float {\bfseries edge\+\_\+alter\+\_\+type} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a7339211897c27d3e315b8e64e875d8bf}\label{class_e_x_a_c_t_a7339211897c27d3e315b8e64e875d8bf}} +float {\bfseries edge\+\_\+disable} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_ab10171a7d43b44c94cf677d2ae0cd1c0}\label{class_e_x_a_c_t_ab10171a7d43b44c94cf677d2ae0cd1c0}} +float {\bfseries edge\+\_\+enable} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a6d762a47caa4db8375396a668d241956}\label{class_e_x_a_c_t_a6d762a47caa4db8375396a668d241956}} +float {\bfseries edge\+\_\+split} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a917d5a47f3cbb3ed0a4d4b579ed31167}\label{class_e_x_a_c_t_a917d5a47f3cbb3ed0a4d4b579ed31167}} +float {\bfseries edge\+\_\+add} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a091e33bc8d581d40457b9e11ed25f634}\label{class_e_x_a_c_t_a091e33bc8d581d40457b9e11ed25f634}} +float {\bfseries node\+\_\+change\+\_\+size} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_af5e4848023660565d37a7b473cb0b601}\label{class_e_x_a_c_t_af5e4848023660565d37a7b473cb0b601}} +float {\bfseries node\+\_\+change\+\_\+size\+\_\+x} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_abeac3de237097a910c03710936e7f399}\label{class_e_x_a_c_t_abeac3de237097a910c03710936e7f399}} +float {\bfseries node\+\_\+change\+\_\+size\+\_\+y} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_af92bdf48f36df6b4f24b201e99e678be}\label{class_e_x_a_c_t_af92bdf48f36df6b4f24b201e99e678be}} +float {\bfseries node\+\_\+add} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_adeee07b000e7c8c6894e048c7854b7c2}\label{class_e_x_a_c_t_adeee07b000e7c8c6894e048c7854b7c2}} +float {\bfseries node\+\_\+split} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a20f46775a93711394c9135d2feeb0394}\label{class_e_x_a_c_t_a20f46775a93711394c9135d2feeb0394}} +float {\bfseries node\+\_\+merge} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_af43fc01bd9ab3fdf43b4f54566a6bdf6}\label{class_e_x_a_c_t_af43fc01bd9ab3fdf43b4f54566a6bdf6}} +float {\bfseries node\+\_\+enable} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a73ad115e727984f98a9384dbe578a365}\label{class_e_x_a_c_t_a73ad115e727984f98a9384dbe578a365}} +float {\bfseries node\+\_\+disable} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a25cf5132e753b0cd6e93c6b9ee04ee91}\label{class_e_x_a_c_t_a25cf5132e753b0cd6e93c6b9ee04ee91}} +\textbf{ map}$<$ \textbf{ string}, int $>$ {\bfseries inserted\+\_\+from\+\_\+map} +\item +\mbox{\Hypertarget{class_e_x_a_c_t_a3c3cb7bde73a2608c162f2edf002d0ea}\label{class_e_x_a_c_t_a3c3cb7bde73a2608c162f2edf002d0ea}} +\textbf{ map}$<$ \textbf{ string}, int $>$ {\bfseries generated\+\_\+from\+\_\+map} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +cnn/exact.\+hxx\item +cnn/exact.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_e_x_a_m_m.tex b/docs/latex/class_e_x_a_m_m.tex new file mode 100644 index 00000000..264269b6 --- /dev/null +++ b/docs/latex/class_e_x_a_m_m.tex @@ -0,0 +1,293 @@ +\hypertarget{class_e_x_a_m_m}{}\doxysection{E\+X\+A\+MM Class Reference} +\label{class_e_x_a_m_m}\index{EXAMM@{EXAMM}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a6a5ab3247fb1b1dc0804095181a690e8}\label{class_e_x_a_m_m_a6a5ab3247fb1b1dc0804095181a690e8}} +{\bfseries E\+X\+A\+MM} (int32\+\_\+t \+\_\+population\+\_\+size, int32\+\_\+t \+\_\+number\+\_\+islands, int32\+\_\+t \+\_\+max\+\_\+genomes, int32\+\_\+t \+\_\+extinction\+\_\+event\+\_\+generation\+\_\+number, int32\+\_\+t \+\_\+islands\+\_\+to\+\_\+exterminate, \textbf{ string} \+\_\+island\+\_\+ranking\+\_\+method, \textbf{ string} \+\_\+repopulation\+\_\+method, int32\+\_\+t \+\_\+repopulation\+\_\+mutations, bool \+\_\+repeat\+\_\+extinction, \textbf{ string} \+\_\+speciation\+\_\+method, double \+\_\+species\+\_\+threshold, double \+\_\+fitness\+\_\+threshold, double \+\_\+neat\+\_\+c1, double \+\_\+neat\+\_\+c2, double \+\_\+neat\+\_\+c3, const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+output\+\_\+parameter\+\_\+names, \textbf{ string} \+\_\+normalize\+\_\+type, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+mins, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+maxs, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+avgs, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+std\+\_\+devs, Weight\+Type \+\_\+weight\+\_\+initialize, Weight\+Type \+\_\+weight\+\_\+inheritance, Weight\+Type \+\_\+mutated\+\_\+component\+\_\+weight, int32\+\_\+t \+\_\+bp\+\_\+iterations, double \+\_\+learning\+\_\+rate, bool \+\_\+use\+\_\+high\+\_\+threshold, double \+\_\+high\+\_\+threshold, bool \+\_\+use\+\_\+low\+\_\+threshold, double \+\_\+low\+\_\+threshold, bool \+\_\+use\+\_\+dropout, double \+\_\+dropout\+\_\+probability, int32\+\_\+t \+\_\+min\+\_\+recurrent\+\_\+depth, int32\+\_\+t \+\_\+max\+\_\+recurrent\+\_\+depth, bool \+\_\+use\+\_\+regression, \textbf{ string} \+\_\+output\+\_\+directory, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$seed\+\_\+genome, bool \+\_\+start\+\_\+filled) +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a513a581a6f62588c171a020769a00721}\label{class_e_x_a_m_m_a513a581a6f62588c171a020769a00721}} +void {\bfseries print} () +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a3f1d179ed9868a6ea31f540a1f2dd01d}\label{class_e_x_a_m_m_a3f1d179ed9868a6ea31f540a1f2dd01d}} +void {\bfseries update\+\_\+log} () +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ac515c3c0da368cfead977258dc4c5fcf}\label{class_e_x_a_m_m_ac515c3c0da368cfead977258dc4c5fcf}} +void {\bfseries write\+\_\+memory\+\_\+log} (\textbf{ string} filename) +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a18b82d31a77efaf4ab29c3f1b35c9d05}\label{class_e_x_a_m_m_a18b82d31a77efaf4ab29c3f1b35c9d05}} +void {\bfseries set\+\_\+possible\+\_\+node\+\_\+types} (\textbf{ vector}$<$ \textbf{ string} $>$ possible\+\_\+node\+\_\+type\+\_\+strings) +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af933b3e3977086cc79576228228d204e}\label{class_e_x_a_m_m_af933b3e3977086cc79576228228d204e}} +\textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ {\bfseries get\+\_\+recurrent\+\_\+depth\+\_\+dist} () +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a610c44be6274f9210ed97c4ade988c5c}\label{class_e_x_a_m_m_a610c44be6274f9210ed97c4ade988c5c}} +int {\bfseries get\+\_\+random\+\_\+node\+\_\+type} () +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a39367bdce7e9eae18d62da785875a106}\label{class_e_x_a_m_m_a39367bdce7e9eae18d62da785875a106}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries generate\+\_\+genome} () +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a1ce57d9a370a0a62185b883378d01fac}\label{class_e_x_a_m_m_a1ce57d9a370a0a62185b883378d01fac}} +bool {\bfseries insert\+\_\+genome} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$genome) +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ae3a5a9f5fbaf9d8a7ac0ce7e5d649afa}\label{class_e_x_a_m_m_ae3a5a9f5fbaf9d8a7ac0ce7e5d649afa}} +void {\bfseries mutate} (int32\+\_\+t max\+\_\+mutations, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$p1) +\item +\mbox{\Hypertarget{class_e_x_a_m_m_aa35e9d1a7c791b18e887dbc50fc9c31e}\label{class_e_x_a_m_m_aa35e9d1a7c791b18e887dbc50fc9c31e}} +void {\bfseries attempt\+\_\+node\+\_\+insert} (\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ \&child\+\_\+nodes, const \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$node, const \textbf{ vector}$<$ double $>$ \&new\+\_\+weights) +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a4a4ebe08b32e094f864ac195c52bec0c}\label{class_e_x_a_m_m_a4a4ebe08b32e094f864ac195c52bec0c}} +void {\bfseries attempt\+\_\+edge\+\_\+insert} (\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$ $>$ \&child\+\_\+edges, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ \&child\+\_\+nodes, \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$edge, \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$second\+\_\+edge, bool set\+\_\+enabled) +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a742756b154742bcb13b2a0c39213434e}\label{class_e_x_a_m_m_a742756b154742bcb13b2a0c39213434e}} +void {\bfseries attempt\+\_\+recurrent\+\_\+edge\+\_\+insert} (\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$ $>$ \&child\+\_\+recurrent\+\_\+edges, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ \&child\+\_\+nodes, \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$recurrent\+\_\+edge, \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$second\+\_\+edge, bool set\+\_\+enabled) +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a00fc892dfaa53823f794546763d7ea01}\label{class_e_x_a_m_m_a00fc892dfaa53823f794546763d7ea01}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries crossover} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$p1, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$p2) +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ad4ccccc7c2e33b3648f00a4d7ea51e04}\label{class_e_x_a_m_m_ad4ccccc7c2e33b3648f00a4d7ea51e04}} +double {\bfseries get\+\_\+best\+\_\+fitness} () +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a22845d05f489e0b6f4fe40161648e9d2}\label{class_e_x_a_m_m_a22845d05f489e0b6f4fe40161648e9d2}} +double {\bfseries get\+\_\+worst\+\_\+fitness} () +\item +\mbox{\Hypertarget{class_e_x_a_m_m_adde23dd0f662da31b946331c94263ad1}\label{class_e_x_a_m_m_adde23dd0f662da31b946331c94263ad1}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries get\+\_\+best\+\_\+genome} () +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ad71eb3566c412c44ab4807274c12ed6e}\label{class_e_x_a_m_m_ad71eb3566c412c44ab4807274c12ed6e}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries get\+\_\+worst\+\_\+genome} () +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a5ac84a240c9e9bd191106099da210ae8}\label{class_e_x_a_m_m_a5ac84a240c9e9bd191106099da210ae8}} +\textbf{ string} {\bfseries get\+\_\+output\+\_\+directory} () const +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a7bbe9fdf1c7385c055b89fcf91be344a}\label{class_e_x_a_m_m_a7bbe9fdf1c7385c055b89fcf91be344a}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries generate\+\_\+for\+\_\+transfer\+\_\+learning} (\textbf{ string} file\+\_\+name, int extra\+\_\+inputs, int extra\+\_\+outputs) +\item +\mbox{\Hypertarget{class_e_x_a_m_m_aa079110bb4851d500353e50603098ff8}\label{class_e_x_a_m_m_aa079110bb4851d500353e50603098ff8}} +void {\bfseries check\+\_\+weight\+\_\+initialize\+\_\+validity} () +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a484f9db7741fbeb220562e547eb52295}\label{class_e_x_a_m_m_a484f9db7741fbeb220562e547eb52295}} +int32\+\_\+t {\bfseries population\+\_\+size} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a0d131ef4be4ce8eb6cf26928a50b339d}\label{class_e_x_a_m_m_a0d131ef4be4ce8eb6cf26928a50b339d}} +int32\+\_\+t {\bfseries number\+\_\+islands} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a333cfbb8e95c9f8f8e04132469809670}\label{class_e_x_a_m_m_a333cfbb8e95c9f8f8e04132469809670}} +\textbf{ vector}$<$ \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ $>$ $>$ {\bfseries genomes} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_aeba31aef7fc9e9cd0e0352f817564fea}\label{class_e_x_a_m_m_aeba31aef7fc9e9cd0e0352f817564fea}} +int32\+\_\+t {\bfseries max\+\_\+genomes} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a3a6adb05377e0575d2ddc5b306310bf0}\label{class_e_x_a_m_m_a3a6adb05377e0575d2ddc5b306310bf0}} +int32\+\_\+t {\bfseries total\+\_\+bp\+\_\+epochs} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ab0a89db7d6ec3cfcff466495b09473d9}\label{class_e_x_a_m_m_ab0a89db7d6ec3cfcff466495b09473d9}} +int32\+\_\+t {\bfseries extinction\+\_\+event\+\_\+generation\+\_\+number} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a514b2a26b5de5a5b65c1e52e91b5204e}\label{class_e_x_a_m_m_a514b2a26b5de5a5b65c1e52e91b5204e}} +\textbf{ string} {\bfseries island\+\_\+ranking\+\_\+method} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a800b29f78aa4f81fd4b7208b0f91d144}\label{class_e_x_a_m_m_a800b29f78aa4f81fd4b7208b0f91d144}} +\textbf{ string} {\bfseries speciation\+\_\+method} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_aa7da68d2505e066e5cf0d5cc86278984}\label{class_e_x_a_m_m_aa7da68d2505e066e5cf0d5cc86278984}} +\textbf{ string} {\bfseries repopulation\+\_\+method} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a14a1db987175b6c9ae5a9ccabdb3de09}\label{class_e_x_a_m_m_a14a1db987175b6c9ae5a9ccabdb3de09}} +int32\+\_\+t {\bfseries repopulation\+\_\+mutations} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a9191e3d50d547a989cfa991bf5642dbb}\label{class_e_x_a_m_m_a9191e3d50d547a989cfa991bf5642dbb}} +bool {\bfseries repeat\+\_\+extinction} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_adad5ab744a0cd4ef16932e97c9f2c736}\label{class_e_x_a_m_m_adad5ab744a0cd4ef16932e97c9f2c736}} +\mbox{\hyperlink{class_speciation_strategy}{Speciation\+Strategy}} $\ast$ {\bfseries speciation\+\_\+strategy} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a686d89db5d70648447f124aefaaa05eb}\label{class_e_x_a_m_m_a686d89db5d70648447f124aefaaa05eb}} +double {\bfseries species\+\_\+threshold} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a29eb789be88cc49b7355378ec23ba2e1}\label{class_e_x_a_m_m_a29eb789be88cc49b7355378ec23ba2e1}} +double {\bfseries fitness\+\_\+threshold} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af9de69d98c5eedd108c8e98f42a80801}\label{class_e_x_a_m_m_af9de69d98c5eedd108c8e98f42a80801}} +double {\bfseries neat\+\_\+c1} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ade22ef3e79195e273731bdaa469d172d}\label{class_e_x_a_m_m_ade22ef3e79195e273731bdaa469d172d}} +double {\bfseries neat\+\_\+c2} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ab8f06eee79de903077e623c036a618b0}\label{class_e_x_a_m_m_ab8f06eee79de903077e623c036a618b0}} +double {\bfseries neat\+\_\+c3} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af65c12c7da090b34a9ee1fb2820c881c}\label{class_e_x_a_m_m_af65c12c7da090b34a9ee1fb2820c881c}} +int32\+\_\+t {\bfseries edge\+\_\+innovation\+\_\+count} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a73b2ab576001cd4dbc68a7f85e5d010b}\label{class_e_x_a_m_m_a73b2ab576001cd4dbc68a7f85e5d010b}} +int32\+\_\+t {\bfseries node\+\_\+innovation\+\_\+count} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ae87d5ff7f1c0741b5b63c8f3e13cb6c1}\label{class_e_x_a_m_m_ae87d5ff7f1c0741b5b63c8f3e13cb6c1}} +\textbf{ map}$<$ \textbf{ string}, int32\+\_\+t $>$ {\bfseries inserted\+\_\+from\+\_\+map} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a038e0d921ba95959bc1ba3e2597d2278}\label{class_e_x_a_m_m_a038e0d921ba95959bc1ba3e2597d2278}} +\textbf{ map}$<$ \textbf{ string}, int32\+\_\+t $>$ {\bfseries generated\+\_\+from\+\_\+map} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a82e7a9bb676cd8e50c674ea327ae0105}\label{class_e_x_a_m_m_a82e7a9bb676cd8e50c674ea327ae0105}} +int32\+\_\+t {\bfseries number\+\_\+inputs} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a976f696e48c644ac8ca4db0564c68fae}\label{class_e_x_a_m_m_a976f696e48c644ac8ca4db0564c68fae}} +int32\+\_\+t {\bfseries number\+\_\+outputs} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af728315e072eff293edf414463cfa8ee}\label{class_e_x_a_m_m_af728315e072eff293edf414463cfa8ee}} +int32\+\_\+t {\bfseries bp\+\_\+iterations} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a8f4526ee7f5025ddfd3c3e8508d35a31}\label{class_e_x_a_m_m_a8f4526ee7f5025ddfd3c3e8508d35a31}} +double {\bfseries learning\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a523b65221a3c6a12109566d546ca8eb4}\label{class_e_x_a_m_m_a523b65221a3c6a12109566d546ca8eb4}} +bool {\bfseries use\+\_\+high\+\_\+threshold} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ad5f96a3f30dacb0a07bc9a36b4d14587}\label{class_e_x_a_m_m_ad5f96a3f30dacb0a07bc9a36b4d14587}} +double {\bfseries high\+\_\+threshold} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ae14437846948541c0fb7858b28902b2d}\label{class_e_x_a_m_m_ae14437846948541c0fb7858b28902b2d}} +bool {\bfseries use\+\_\+low\+\_\+threshold} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a695ffdd5e759e6c9d9871e4ef062a71b}\label{class_e_x_a_m_m_a695ffdd5e759e6c9d9871e4ef062a71b}} +double {\bfseries low\+\_\+threshold} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a86f61e92b5f305b6fd5d0a97525c65c3}\label{class_e_x_a_m_m_a86f61e92b5f305b6fd5d0a97525c65c3}} +bool {\bfseries use\+\_\+regression} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_afd4fa3e5efd0e9cb766c9231d8171346}\label{class_e_x_a_m_m_afd4fa3e5efd0e9cb766c9231d8171346}} +bool {\bfseries use\+\_\+dropout} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af6f7f042753a4558c725cdd9f908387d}\label{class_e_x_a_m_m_af6f7f042753a4558c725cdd9f908387d}} +double {\bfseries dropout\+\_\+probability} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a52f1620736849808b77c6cdfbbbc1eb2}\label{class_e_x_a_m_m_a52f1620736849808b77c6cdfbbbc1eb2}} +\textbf{ minstd\+\_\+rand0} {\bfseries generator} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a736bad0d6f9fa49ab69cf04f7a22e719}\label{class_e_x_a_m_m_a736bad0d6f9fa49ab69cf04f7a22e719}} +\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ {\bfseries rng\+\_\+0\+\_\+1} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_adc21cb4fdd3a8b8e6c563cfe51680809}\label{class_e_x_a_m_m_adc21cb4fdd3a8b8e6c563cfe51680809}} +\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ {\bfseries rng\+\_\+crossover\+\_\+weight} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a8286ca108523787eb1600809c5e83a44}\label{class_e_x_a_m_m_a8286ca108523787eb1600809c5e83a44}} +int32\+\_\+t {\bfseries min\+\_\+recurrent\+\_\+depth} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_abada195a47a6b78f2854937503dec80a}\label{class_e_x_a_m_m_abada195a47a6b78f2854937503dec80a}} +int32\+\_\+t {\bfseries max\+\_\+recurrent\+\_\+depth} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a8f098192b53805162bb0d1bdf9bbf35d}\label{class_e_x_a_m_m_a8f098192b53805162bb0d1bdf9bbf35d}} +bool {\bfseries epigenetic\+\_\+weights} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a84cb33ac5b43b633888ef5f75a47b3e3}\label{class_e_x_a_m_m_a84cb33ac5b43b633888ef5f75a47b3e3}} +double {\bfseries more\+\_\+fit\+\_\+crossover\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_aaa95489eda646a34c816ab9f406e621c}\label{class_e_x_a_m_m_aaa95489eda646a34c816ab9f406e621c}} +double {\bfseries less\+\_\+fit\+\_\+crossover\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a827faaa986b2d591d34fd0f424770baa}\label{class_e_x_a_m_m_a827faaa986b2d591d34fd0f424770baa}} +double {\bfseries clone\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af811390efbd47f3e351d6ff5ba80359b}\label{class_e_x_a_m_m_af811390efbd47f3e351d6ff5ba80359b}} +double {\bfseries add\+\_\+edge\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a1ae178e015d9f5bedf3bf3fa0612cc46}\label{class_e_x_a_m_m_a1ae178e015d9f5bedf3bf3fa0612cc46}} +double {\bfseries add\+\_\+recurrent\+\_\+edge\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_abd9f2255296d2b27da1daa630ae03468}\label{class_e_x_a_m_m_abd9f2255296d2b27da1daa630ae03468}} +double {\bfseries enable\+\_\+edge\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a65f2a5d6b82b598400b45fd7d36ec5ae}\label{class_e_x_a_m_m_a65f2a5d6b82b598400b45fd7d36ec5ae}} +double {\bfseries disable\+\_\+edge\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ac1e7ce6ba10c3e7e55e033aea5ee8c8f}\label{class_e_x_a_m_m_ac1e7ce6ba10c3e7e55e033aea5ee8c8f}} +double {\bfseries split\+\_\+edge\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a14dda5bb350c0b577fe5eaccdcf0c27d}\label{class_e_x_a_m_m_a14dda5bb350c0b577fe5eaccdcf0c27d}} +double {\bfseries add\+\_\+node\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ad0ca4fa9b18313a353f79037a61abd25}\label{class_e_x_a_m_m_ad0ca4fa9b18313a353f79037a61abd25}} +double {\bfseries enable\+\_\+node\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a401b1c9cd83e67aa903444fac445aa73}\label{class_e_x_a_m_m_a401b1c9cd83e67aa903444fac445aa73}} +double {\bfseries disable\+\_\+node\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af6796f889761d669e40e4ca0594bc35e}\label{class_e_x_a_m_m_af6796f889761d669e40e4ca0594bc35e}} +double {\bfseries split\+\_\+node\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a16705344fd24daac4e32c71378732e43}\label{class_e_x_a_m_m_a16705344fd24daac4e32c71378732e43}} +double {\bfseries merge\+\_\+node\+\_\+rate} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a7168959e8a00a01a0685efdbdf12b69f}\label{class_e_x_a_m_m_a7168959e8a00a01a0685efdbdf12b69f}} +\textbf{ vector}$<$ int $>$ {\bfseries possible\+\_\+node\+\_\+types} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_aaa892b6fb59c969da5b2692a71f1c0be}\label{class_e_x_a_m_m_aaa892b6fb59c969da5b2692a71f1c0be}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries op\+\_\+log\+\_\+ordering} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a3c123b0e57634c8fd5c9579c646ff246}\label{class_e_x_a_m_m_a3c123b0e57634c8fd5c9579c646ff246}} +\textbf{ map}$<$ \textbf{ string}, int32\+\_\+t $>$ {\bfseries inserted\+\_\+counts} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_aa80c7e4ac359e29d3598de579d2e3955}\label{class_e_x_a_m_m_aa80c7e4ac359e29d3598de579d2e3955}} +\textbf{ map}$<$ \textbf{ string}, int32\+\_\+t $>$ {\bfseries generated\+\_\+counts} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ae90d743c11e1caf3bcec50a0f047500f}\label{class_e_x_a_m_m_ae90d743c11e1caf3bcec50a0f047500f}} +\textbf{ string} {\bfseries output\+\_\+directory} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a96cad2561f2de297758077cb2073eb01}\label{class_e_x_a_m_m_a96cad2561f2de297758077cb2073eb01}} +\textbf{ ofstream} $\ast$ {\bfseries log\+\_\+file} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a013871c8b00cc0b0c2bafc2a9e3430bf}\label{class_e_x_a_m_m_a013871c8b00cc0b0c2bafc2a9e3430bf}} +\textbf{ ofstream} $\ast$ {\bfseries op\+\_\+log\+\_\+file} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_aa27aa494864eb7d644d1a9a40e46b296}\label{class_e_x_a_m_m_aa27aa494864eb7d644d1a9a40e46b296}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries input\+\_\+parameter\+\_\+names} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a1e8eda421e0f5b6657fc1de1f3aab061}\label{class_e_x_a_m_m_a1e8eda421e0f5b6657fc1de1f3aab061}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries output\+\_\+parameter\+\_\+names} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_aac15f2d814d064d532654163bdfe59b7}\label{class_e_x_a_m_m_aac15f2d814d064d532654163bdfe59b7}} +\textbf{ string} {\bfseries normalize\+\_\+type} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af6c66967a76ab7fec38d51f58ee16c23}\label{class_e_x_a_m_m_af6c66967a76ab7fec38d51f58ee16c23}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+mins} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_adca5b3f12b8a76084bf33ff1d1f6bb86}\label{class_e_x_a_m_m_adca5b3f12b8a76084bf33ff1d1f6bb86}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+maxs} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ab79d11a8a1de85eda433c2f2b113d76f}\label{class_e_x_a_m_m_ab79d11a8a1de85eda433c2f2b113d76f}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+avgs} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a5d5c51686b6a4c29d7dde79cc1f672e3}\label{class_e_x_a_m_m_a5d5c51686b6a4c29d7dde79cc1f672e3}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+std\+\_\+devs} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_ac3c271a84c7011a8f497d0aafc99abe3}\label{class_e_x_a_m_m_ac3c271a84c7011a8f497d0aafc99abe3}} +Weight\+Type {\bfseries weight\+\_\+initialize} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a6bbec074d37309ed972e79e533881495}\label{class_e_x_a_m_m_a6bbec074d37309ed972e79e533881495}} +Weight\+Type {\bfseries weight\+\_\+inheritance} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a7910e24ff2f54bc8dc8473e5008df2a7}\label{class_e_x_a_m_m_a7910e24ff2f54bc8dc8473e5008df2a7}} +Weight\+Type {\bfseries mutated\+\_\+component\+\_\+weight} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af5137255c6f1d41f20d85a81f00e17e0}\label{class_e_x_a_m_m_af5137255c6f1d41f20d85a81f00e17e0}} +\textbf{ ostringstream} {\bfseries memory\+\_\+log} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_a4372a02836f005b24e05d2625cdb9a94}\label{class_e_x_a_m_m_a4372a02836f005b24e05d2625cdb9a94}} +\textbf{ std\+::chrono\+::time\+\_\+point}$<$ \textbf{ std\+::chrono\+::system\+\_\+clock} $>$ {\bfseries start\+Clock} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af3f2803027dace8f58cc1de4108a5969}\label{class_e_x_a_m_m_af3f2803027dace8f58cc1de4108a5969}} +\textbf{ string} {\bfseries genome\+\_\+file\+\_\+name} +\item +\mbox{\Hypertarget{class_e_x_a_m_m_af769d270e5f7e8f1e7318b085a2ccef6}\label{class_e_x_a_m_m_af769d270e5f7e8f1e7318b085a2ccef6}} +bool {\bfseries start\+\_\+filled} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/examm.\+hxx\item +rnn/examm.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_g_r_u___node.eps b/docs/latex/class_g_r_u___node.eps new file mode 100644 index 00000000..56861fd8 --- /dev/null +++ b/docs/latex/class_g_r_u___node.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 285.714286 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.750000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(GRU_Node) cw +(RNN_Node_Interface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (GRU_Node) 0.000000 0.000000 box + (RNN_Node_Interface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_g_r_u___node.tex b/docs/latex/class_g_r_u___node.tex new file mode 100644 index 00000000..2b8f3c6e --- /dev/null +++ b/docs/latex/class_g_r_u___node.tex @@ -0,0 +1,162 @@ +\hypertarget{class_g_r_u___node}{}\doxysection{G\+R\+U\+\_\+\+Node Class Reference} +\label{class_g_r_u___node}\index{GRU\_Node@{GRU\_Node}} +Inheritance diagram for G\+R\+U\+\_\+\+Node\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_g_r_u___node} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_g_r_u___node_a9290627eb7f65f0a817166fc9a83deaf}\label{class_g_r_u___node_a9290627eb7f65f0a817166fc9a83deaf}} +{\bfseries G\+R\+U\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, int \+\_\+type, double \+\_\+depth) +\item +\mbox{\Hypertarget{class_g_r_u___node_a6276428b8f55291b39827fad7c13c568}\label{class_g_r_u___node_a6276428b8f55291b39827fad7c13c568}} +void {\bfseries initialize\+\_\+lamarckian} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double mu, double sigma) +\item +\mbox{\Hypertarget{class_g_r_u___node_a59a18ab71467975aad4f6b50fc8c033c}\label{class_g_r_u___node_a59a18ab71467975aad4f6b50fc8c033c}} +void {\bfseries initialize\+\_\+xavier} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng1\+\_\+1, double range) +\item +\mbox{\Hypertarget{class_g_r_u___node_a4df65c67bf73824f7c0b4a5f141c2925}\label{class_g_r_u___node_a4df65c67bf73824f7c0b4a5f141c2925}} +void {\bfseries initialize\+\_\+kaiming} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double range) +\item +\mbox{\Hypertarget{class_g_r_u___node_a8acb6dac67ef6265dce246020c505092}\label{class_g_r_u___node_a8acb6dac67ef6265dce246020c505092}} +void {\bfseries initialize\+\_\+uniform\+\_\+random} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng) +\item +\mbox{\Hypertarget{class_g_r_u___node_a7d2c49b8a815898f193ed62d24cc059a}\label{class_g_r_u___node_a7d2c49b8a815898f193ed62d24cc059a}} +double {\bfseries get\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_g_r_u___node_a750f2894c1e9cde9c2e4cf1799950dbd}\label{class_g_r_u___node_a750f2894c1e9cde9c2e4cf1799950dbd}} +void {\bfseries print\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_g_r_u___node_a2626d54382771db603ee92bdb0edb623}\label{class_g_r_u___node_a2626d54382771db603ee92bdb0edb623}} +void {\bfseries input\+\_\+fired} (int time, double incoming\+\_\+output) +\item +\mbox{\Hypertarget{class_g_r_u___node_a4741a9a2b0524e5eeb4da5588bfa4878}\label{class_g_r_u___node_a4741a9a2b0524e5eeb4da5588bfa4878}} +void {\bfseries try\+\_\+update\+\_\+deltas} (int time) +\item +\mbox{\Hypertarget{class_g_r_u___node_a4c088bf430f95e1e1a309ad49e4d0e88}\label{class_g_r_u___node_a4c088bf430f95e1e1a309ad49e4d0e88}} +void {\bfseries error\+\_\+fired} (int time, double error) +\item +\mbox{\Hypertarget{class_g_r_u___node_af8a4d1b2158cf5abd5ff0fd19cdb8f52}\label{class_g_r_u___node_af8a4d1b2158cf5abd5ff0fd19cdb8f52}} +void {\bfseries output\+\_\+fired} (int time, double delta) +\item +\mbox{\Hypertarget{class_g_r_u___node_a8c618720c3ebd3220698495867c7918e}\label{class_g_r_u___node_a8c618720c3ebd3220698495867c7918e}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_g_r_u___node_acff5bfeb130f64e47ac92f1f6f35ea68}\label{class_g_r_u___node_acff5bfeb130f64e47ac92f1f6f35ea68}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_g_r_u___node_a7a98506b58b829ef58531fd042ffecde}\label{class_g_r_u___node_a7a98506b58b829ef58531fd042ffecde}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_g_r_u___node_ad80f886343ea7ce8474b07f82a4c90b7}\label{class_g_r_u___node_ad80f886343ea7ce8474b07f82a4c90b7}} +void {\bfseries get\+\_\+weights} (uint32\+\_\+t \&offset, \textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_g_r_u___node_a2ed0b01e09a7f4b0fe40375b3a18e57b}\label{class_g_r_u___node_a2ed0b01e09a7f4b0fe40375b3a18e57b}} +void {\bfseries set\+\_\+weights} (uint32\+\_\+t \&offset, const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_g_r_u___node_a875ecae1dfd8ca7a73260db90981f59f}\label{class_g_r_u___node_a875ecae1dfd8ca7a73260db90981f59f}} +void {\bfseries get\+\_\+gradients} (\textbf{ vector}$<$ double $>$ \&gradients) +\item +\mbox{\Hypertarget{class_g_r_u___node_a4c99a90aafead390cfa5f28b49d103f5}\label{class_g_r_u___node_a4c99a90aafead390cfa5f28b49d103f5}} +void {\bfseries reset} (int \+\_\+series\+\_\+length) +\item +\mbox{\Hypertarget{class_g_r_u___node_a17025c5da3d8f8a4b3852ebf51c1ec56}\label{class_g_r_u___node_a17025c5da3d8f8a4b3852ebf51c1ec56}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_g_r_u___node_a8e6614d26cafdb1b54809194cf99039d}\label{class_g_r_u___node_a8e6614d26cafdb1b54809194cf99039d}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries copy} () const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_g_r_u___node_adfda8cf730509abc7be08aaa5eece7c8}\label{class_g_r_u___node_adfda8cf730509abc7be08aaa5eece7c8}} +double {\bfseries zw} +\item +\mbox{\Hypertarget{class_g_r_u___node_a0681570d4c2eb97b148fc09652351c3b}\label{class_g_r_u___node_a0681570d4c2eb97b148fc09652351c3b}} +double {\bfseries zu} +\item +\mbox{\Hypertarget{class_g_r_u___node_ae40e512f1fa08d0ac1d786ba0b9c4e1d}\label{class_g_r_u___node_ae40e512f1fa08d0ac1d786ba0b9c4e1d}} +double {\bfseries z\+\_\+bias} +\item +\mbox{\Hypertarget{class_g_r_u___node_a7f83e2f0370545f9fa9fdeed2641663e}\label{class_g_r_u___node_a7f83e2f0370545f9fa9fdeed2641663e}} +double {\bfseries rw} +\item +\mbox{\Hypertarget{class_g_r_u___node_a21d746b030090ad6cc58990af3f5c84b}\label{class_g_r_u___node_a21d746b030090ad6cc58990af3f5c84b}} +double {\bfseries ru} +\item +\mbox{\Hypertarget{class_g_r_u___node_a88f13121e1bd3f6405dd1e351e74df32}\label{class_g_r_u___node_a88f13121e1bd3f6405dd1e351e74df32}} +double {\bfseries r\+\_\+bias} +\item +\mbox{\Hypertarget{class_g_r_u___node_a92916dfb0c9a09c1d9b489a4b6d502f1}\label{class_g_r_u___node_a92916dfb0c9a09c1d9b489a4b6d502f1}} +double {\bfseries hw} +\item +\mbox{\Hypertarget{class_g_r_u___node_a62fd634e0fdad504d25d79ddbf1e8c19}\label{class_g_r_u___node_a62fd634e0fdad504d25d79ddbf1e8c19}} +double {\bfseries hu} +\item +\mbox{\Hypertarget{class_g_r_u___node_a213aa84800bae687954e45cb2d4ee740}\label{class_g_r_u___node_a213aa84800bae687954e45cb2d4ee740}} +double {\bfseries h\+\_\+bias} +\item +\mbox{\Hypertarget{class_g_r_u___node_ae9c001b84a7e2bdb50be16b3a8d9b5cd}\label{class_g_r_u___node_ae9c001b84a7e2bdb50be16b3a8d9b5cd}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+zw} +\item +\mbox{\Hypertarget{class_g_r_u___node_a959cb5095ecfe19bbecf337bb2f6067c}\label{class_g_r_u___node_a959cb5095ecfe19bbecf337bb2f6067c}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+zu} +\item +\mbox{\Hypertarget{class_g_r_u___node_a3d5b01282977ff8c7d829d493746a018}\label{class_g_r_u___node_a3d5b01282977ff8c7d829d493746a018}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+z\+\_\+bias} +\item +\mbox{\Hypertarget{class_g_r_u___node_ac0a8249393c540d37e2a688e90297f93}\label{class_g_r_u___node_ac0a8249393c540d37e2a688e90297f93}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+rw} +\item +\mbox{\Hypertarget{class_g_r_u___node_a23ea806db494beac6d543b9c663ef983}\label{class_g_r_u___node_a23ea806db494beac6d543b9c663ef983}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+ru} +\item +\mbox{\Hypertarget{class_g_r_u___node_aa72325a1ebfe041b9c59f5d82b012d1c}\label{class_g_r_u___node_aa72325a1ebfe041b9c59f5d82b012d1c}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+r\+\_\+bias} +\item +\mbox{\Hypertarget{class_g_r_u___node_a0be3f045d49a8e5d0e49fa82aca34c27}\label{class_g_r_u___node_a0be3f045d49a8e5d0e49fa82aca34c27}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+hw} +\item +\mbox{\Hypertarget{class_g_r_u___node_a7120f96783cc01048a3ced5ed14cb328}\label{class_g_r_u___node_a7120f96783cc01048a3ced5ed14cb328}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+hu} +\item +\mbox{\Hypertarget{class_g_r_u___node_a80a58648994dcbfb96b0b08eb59af26c}\label{class_g_r_u___node_a80a58648994dcbfb96b0b08eb59af26c}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+h\+\_\+bias} +\item +\mbox{\Hypertarget{class_g_r_u___node_aee14707bf605402f11aa4c5d70f26064}\label{class_g_r_u___node_aee14707bf605402f11aa4c5d70f26064}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+h\+\_\+prev} +\item +\mbox{\Hypertarget{class_g_r_u___node_a1a07fb4eeb68aa877191a7fa84719a8c}\label{class_g_r_u___node_a1a07fb4eeb68aa877191a7fa84719a8c}} +\textbf{ vector}$<$ double $>$ {\bfseries z} +\item +\mbox{\Hypertarget{class_g_r_u___node_a18dec99edc33a59b20bcfdb56f3f3b61}\label{class_g_r_u___node_a18dec99edc33a59b20bcfdb56f3f3b61}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+z} +\item +\mbox{\Hypertarget{class_g_r_u___node_a7ce42080b2c04b8ba839c2b9247be546}\label{class_g_r_u___node_a7ce42080b2c04b8ba839c2b9247be546}} +\textbf{ vector}$<$ double $>$ {\bfseries r} +\item +\mbox{\Hypertarget{class_g_r_u___node_aa91cab5d6d2d591df54de73fe828f61d}\label{class_g_r_u___node_aa91cab5d6d2d591df54de73fe828f61d}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+r} +\item +\mbox{\Hypertarget{class_g_r_u___node_ab382c6061700a43df3d6a1ae1ef476b0}\label{class_g_r_u___node_ab382c6061700a43df3d6a1ae1ef476b0}} +\textbf{ vector}$<$ double $>$ {\bfseries h\+\_\+tanh} +\item +\mbox{\Hypertarget{class_g_r_u___node_a2a820bf16d1e4f0853e56274eeec028f}\label{class_g_r_u___node_a2a820bf16d1e4f0853e56274eeec028f}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+h\+\_\+tanh} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_g_r_u___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}\label{class_g_r_u___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}} +class {\bfseries R\+N\+N\+\_\+\+Edge} +\end{DoxyCompactItemize} +\doxysubsection*{Additional Inherited Members} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/gru\+\_\+node.\+hxx\item +rnn/gru\+\_\+node.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_image.eps b/docs/latex/class_image.eps new file mode 100644 index 00000000..67439f9f --- /dev/null +++ b/docs/latex/class_image.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 388.349515 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.287500 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(Image) cw +(ImageInterface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (Image) 0.000000 0.000000 box + (ImageInterface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_image.tex b/docs/latex/class_image.tex new file mode 100644 index 00000000..a7b5137c --- /dev/null +++ b/docs/latex/class_image.tex @@ -0,0 +1,68 @@ +\hypertarget{class_image}{}\doxysection{Image Class Reference} +\label{class_image}\index{Image@{Image}} +Inheritance diagram for Image\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_image} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_image_a00263b59b18cd105c6ec282f76fd499a}\label{class_image_a00263b59b18cd105c6ec282f76fd499a}} +{\bfseries Image} (\textbf{ ifstream} \&infile, int \+\_\+channels, int \+\_\+width, int \+\_\+height, int \+\_\+padding, int \+\_\+classification, const \mbox{\hyperlink{class_images}{Images}} $\ast$\+\_\+images) +\item +\mbox{\Hypertarget{class_image_ae35418c50cee043b8f24d75d0d5c125d}\label{class_image_ae35418c50cee043b8f24d75d0d5c125d}} +int {\bfseries get\+\_\+classification} () const +\item +\mbox{\Hypertarget{class_image_a48a37444c7bf67df9b0345ce5643f7e7}\label{class_image_a48a37444c7bf67df9b0345ce5643f7e7}} +void {\bfseries scale\+\_\+0\+\_\+1} () +\item +\mbox{\Hypertarget{class_image_a4075190e36763e3bd418de444692f413}\label{class_image_a4075190e36763e3bd418de444692f413}} +float {\bfseries get\+\_\+pixel} (int z, int y, int x) const +\item +\mbox{\Hypertarget{class_image_a20dc44298e59932e7eb7815d689ddf41}\label{class_image_a20dc44298e59932e7eb7815d689ddf41}} +void {\bfseries get\+\_\+pixel\+\_\+avg} (\textbf{ vector}$<$ float $>$ \&channel\+\_\+avgs) const +\item +\mbox{\Hypertarget{class_image_a77d042408cb32c534d9d290861269574}\label{class_image_a77d042408cb32c534d9d290861269574}} +void {\bfseries get\+\_\+pixel\+\_\+variance} (const \textbf{ vector}$<$ float $>$ \&channel\+\_\+avgs, \textbf{ vector}$<$ float $>$ \&channel\+\_\+variances) const +\item +\mbox{\Hypertarget{class_image_adb6a48efc25c7c12df5984c578e131d7}\label{class_image_adb6a48efc25c7c12df5984c578e131d7}} +void {\bfseries print} (\textbf{ ostream} \&out) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_image_a3378bec385533755ab2877c8e552e36e}\label{class_image_a3378bec385533755ab2877c8e552e36e}} +int {\bfseries padding} +\item +\mbox{\Hypertarget{class_image_a73572ed4b7f15054c5a598c6aabffd86}\label{class_image_a73572ed4b7f15054c5a598c6aabffd86}} +int {\bfseries channels} +\item +\mbox{\Hypertarget{class_image_a51df43db420c9c0b57536cb2dd36de5c}\label{class_image_a51df43db420c9c0b57536cb2dd36de5c}} +int {\bfseries height} +\item +\mbox{\Hypertarget{class_image_ab8d12f635013c04159cd4d3d972bac88}\label{class_image_ab8d12f635013c04159cd4d3d972bac88}} +int {\bfseries width} +\item +\mbox{\Hypertarget{class_image_afffa25f14f8c9aca3e6014c71fb33937}\label{class_image_afffa25f14f8c9aca3e6014c71fb33937}} +int {\bfseries classification} +\item +\mbox{\Hypertarget{class_image_a42870ee0359c95f19672220f40c02ade}\label{class_image_a42870ee0359c95f19672220f40c02ade}} +\textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ uint8\+\_\+t $>$ $>$ $>$ {\bfseries pixels} +\item +\mbox{\Hypertarget{class_image_af8e17e1591540036cbd27ee309df7cff}\label{class_image_af8e17e1591540036cbd27ee309df7cff}} +const \mbox{\hyperlink{class_images}{Images}} $\ast$ {\bfseries images} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_image_a29a8da4b8508c35f965394bec8b4af16}\label{class_image_a29a8da4b8508c35f965394bec8b4af16}} +class {\bfseries Images} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/image\+\_\+set.\+hxx\item +image\+\_\+tools/image\+\_\+set.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_image_interface.eps b/docs/latex/class_image_interface.eps new file mode 100644 index 00000000..1bfddb17 --- /dev/null +++ b/docs/latex/class_image_interface.eps @@ -0,0 +1,203 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 194.174757 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 2.575000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 2 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(ImageInterface) cw +(Image) cw +(LargeImage) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (ImageInterface) 0.500000 1.000000 box + (Image) 0.000000 0.000000 box + (LargeImage) 1.000000 0.000000 box + +% ----- relations ----- + +solid +1 0.500000 0.250000 out +solid +0.000000 1.000000 1.000000 conn +solid +0 0.000000 0.750000 in +solid +0 1.000000 0.750000 in diff --git a/docs/latex/class_image_interface.tex b/docs/latex/class_image_interface.tex new file mode 100644 index 00000000..6a55e10a --- /dev/null +++ b/docs/latex/class_image_interface.tex @@ -0,0 +1,23 @@ +\hypertarget{class_image_interface}{}\doxysection{Image\+Interface Class Reference} +\label{class_image_interface}\index{ImageInterface@{ImageInterface}} +Inheritance diagram for Image\+Interface\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_image_interface} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_image_interface_a8185d7f7ef306f65f833b1045fc6f17e}\label{class_image_interface_a8185d7f7ef306f65f833b1045fc6f17e}} +virtual int {\bfseries get\+\_\+classification} () const =0 +\item +\mbox{\Hypertarget{class_image_interface_a9eec1a62dae9f3aad32046fbf47ad601}\label{class_image_interface_a9eec1a62dae9f3aad32046fbf47ad601}} +virtual void {\bfseries print} (\textbf{ ostream} \&out)=0 +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/image\+\_\+set\+\_\+interface.\+hxx\item +image\+\_\+tools/large\+\_\+image\+\_\+set.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_images.eps b/docs/latex/class_images.eps new file mode 100644 index 00000000..76d4416a --- /dev/null +++ b/docs/latex/class_images.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 366.972477 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.362500 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(Images) cw +(ImagesInterface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (Images) 0.000000 0.000000 box + (ImagesInterface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_images.tex b/docs/latex/class_images.tex new file mode 100644 index 00000000..00cbe7d4 --- /dev/null +++ b/docs/latex/class_images.tex @@ -0,0 +1,110 @@ +\hypertarget{class_images}{}\doxysection{Images Class Reference} +\label{class_images}\index{Images@{Images}} +Inheritance diagram for Images\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_images} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_images_a9d399faae60bbf9104dfdf64e2acfef1}\label{class_images_a9d399faae60bbf9104dfdf64e2acfef1}} +int {\bfseries read\+\_\+images} (\textbf{ string} binary\+\_\+filename) +\item +\mbox{\Hypertarget{class_images_a63e020cd282310d87bc7b299b14dfc50}\label{class_images_a63e020cd282310d87bc7b299b14dfc50}} +{\bfseries Images} (\textbf{ string} binary\+\_\+filename, int \+\_\+padding) +\item +\mbox{\Hypertarget{class_images_aa47b6fec71913825b91b591c4084de82}\label{class_images_aa47b6fec71913825b91b591c4084de82}} +{\bfseries Images} (\textbf{ string} binary\+\_\+filename, int \+\_\+padding, const \textbf{ vector}$<$ float $>$ \&\+\_\+channe\+L\+\_\+avg, const \textbf{ vector}$<$ float $>$ \&channel\+\_\+std\+\_\+dev) +\item +\mbox{\Hypertarget{class_images_a909d6de83dc353e9b9cd652396ff654b}\label{class_images_a909d6de83dc353e9b9cd652396ff654b}} +\textbf{ string} {\bfseries get\+\_\+filename} () const +\item +\mbox{\Hypertarget{class_images_a72094ddc5c4bc6d130e96a44e698ae1d}\label{class_images_a72094ddc5c4bc6d130e96a44e698ae1d}} +int {\bfseries get\+\_\+class\+\_\+size} (int i) const +\item +\mbox{\Hypertarget{class_images_a194730c90f956efc9fa10d51a76a2c96}\label{class_images_a194730c90f956efc9fa10d51a76a2c96}} +int {\bfseries get\+\_\+number\+\_\+classes} () const +\item +\mbox{\Hypertarget{class_images_a7274ea0aa64b25606927e292e1a7feec}\label{class_images_a7274ea0aa64b25606927e292e1a7feec}} +int {\bfseries get\+\_\+number\+\_\+images} () const +\item +\mbox{\Hypertarget{class_images_a3f7c87e587cc3cd7226858255c91b7ca}\label{class_images_a3f7c87e587cc3cd7226858255c91b7ca}} +int {\bfseries get\+\_\+image\+\_\+channels} () const +\item +\mbox{\Hypertarget{class_images_a152be30298d1509d10f6ee51be3fee65}\label{class_images_a152be30298d1509d10f6ee51be3fee65}} +int {\bfseries get\+\_\+image\+\_\+width} () const +\item +\mbox{\Hypertarget{class_images_aa6ad2d0286b2140348e247ce35f6ec45}\label{class_images_aa6ad2d0286b2140348e247ce35f6ec45}} +int {\bfseries get\+\_\+image\+\_\+height} () const +\item +\mbox{\Hypertarget{class_images_a8563bde202920005b9c28aeab015ebef}\label{class_images_a8563bde202920005b9c28aeab015ebef}} +int {\bfseries get\+\_\+classification} (int image) const +\item +\mbox{\Hypertarget{class_images_adec8e3768a8e9cfc42cd966a04a343e4}\label{class_images_adec8e3768a8e9cfc42cd966a04a343e4}} +float {\bfseries get\+\_\+pixel} (int image, int z, int y, int x) const +\item +\mbox{\Hypertarget{class_images_a7a849e6fc03f758f8c7f27c7efc89e53}\label{class_images_a7a849e6fc03f758f8c7f27c7efc89e53}} +void {\bfseries calculate\+\_\+avg\+\_\+std\+\_\+dev} () +\item +\mbox{\Hypertarget{class_images_a775524150347fc76ed292285d6f6089e}\label{class_images_a775524150347fc76ed292285d6f6089e}} +float {\bfseries get\+\_\+channel\+\_\+avg} (int channel) const +\item +\mbox{\Hypertarget{class_images_ad3069b2400be30a99774cacbc3bc53fc}\label{class_images_ad3069b2400be30a99774cacbc3bc53fc}} +float {\bfseries get\+\_\+channel\+\_\+std\+\_\+dev} (int channel) const +\item +\mbox{\Hypertarget{class_images_a5ca7a107de1b4465ee2a8a69fc46975c}\label{class_images_a5ca7a107de1b4465ee2a8a69fc46975c}} +bool {\bfseries loaded\+\_\+correctly} () const +\item +\mbox{\Hypertarget{class_images_aea5a7b61eb7ceaa3e7151b474371af5e}\label{class_images_aea5a7b61eb7ceaa3e7151b474371af5e}} +const \textbf{ vector}$<$ float $>$ \& {\bfseries get\+\_\+average} () const +\item +\mbox{\Hypertarget{class_images_a1e1cc16fbc25f065dcd9d0019cbe996b}\label{class_images_a1e1cc16fbc25f065dcd9d0019cbe996b}} +const \textbf{ vector}$<$ float $>$ \& {\bfseries get\+\_\+std\+\_\+dev} () const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_images_aeadfc5719508f55f8c59ca0e14405e18}\label{class_images_aeadfc5719508f55f8c59ca0e14405e18}} +\textbf{ string} {\bfseries filename} +\item +\mbox{\Hypertarget{class_images_a296b6e987241f10969b59f55f11d6b40}\label{class_images_a296b6e987241f10969b59f55f11d6b40}} +int {\bfseries number\+\_\+classes} +\item +\mbox{\Hypertarget{class_images_a354e40904956ddc5034d5471714fec06}\label{class_images_a354e40904956ddc5034d5471714fec06}} +int {\bfseries number\+\_\+images} +\item +\mbox{\Hypertarget{class_images_ad2dc57d3d9071b3b71f5263eff9b2340}\label{class_images_ad2dc57d3d9071b3b71f5263eff9b2340}} +\textbf{ vector}$<$ int $>$ {\bfseries class\+\_\+sizes} +\item +\mbox{\Hypertarget{class_images_a1bdc541858b398b563120ad63f668dee}\label{class_images_a1bdc541858b398b563120ad63f668dee}} +int {\bfseries padding} +\item +\mbox{\Hypertarget{class_images_a8b73593cbd007c65351f007181c9a305}\label{class_images_a8b73593cbd007c65351f007181c9a305}} +int {\bfseries channels} +\item +\mbox{\Hypertarget{class_images_a8b5be76a044a2a5fea1defde5f890cc7}\label{class_images_a8b5be76a044a2a5fea1defde5f890cc7}} +int {\bfseries width} +\item +\mbox{\Hypertarget{class_images_a744614362ca31b78143cea5914c6bfc3}\label{class_images_a744614362ca31b78143cea5914c6bfc3}} +int {\bfseries height} +\item +\mbox{\Hypertarget{class_images_aaccaeb7f13a167d16ecb31806fcb1d59}\label{class_images_aaccaeb7f13a167d16ecb31806fcb1d59}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_image}{Image}} $>$ {\bfseries images} +\item +\mbox{\Hypertarget{class_images_a3ffa1cfdb4d88855ae6c37c3bf19f39c}\label{class_images_a3ffa1cfdb4d88855ae6c37c3bf19f39c}} +\textbf{ vector}$<$ float $>$ {\bfseries channel\+\_\+avg} +\item +\mbox{\Hypertarget{class_images_a98034433657ad03b7e6eabaa0c8576c3}\label{class_images_a98034433657ad03b7e6eabaa0c8576c3}} +\textbf{ vector}$<$ float $>$ {\bfseries channel\+\_\+std\+\_\+dev} +\item +\mbox{\Hypertarget{class_images_aeadb139b6ea3fbaf594ad9f8e4146e83}\label{class_images_aeadb139b6ea3fbaf594ad9f8e4146e83}} +bool {\bfseries had\+\_\+error} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/image\+\_\+set.\+hxx\item +image\+\_\+tools/image\+\_\+set.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_images_interface.eps b/docs/latex/class_images_interface.eps new file mode 100644 index 00000000..3d8f83f6 --- /dev/null +++ b/docs/latex/class_images_interface.eps @@ -0,0 +1,215 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 147.058824 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 3.400000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 3 def +/cols 3 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(ImagesInterface) cw +(Images) cw +(MultiImagesInterface) cw +(LargeImages) cw +(MosaicImages) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (ImagesInterface) 0.500000 2.000000 box + (Images) 0.000000 1.000000 box + (MultiImagesInterface) 1.000000 1.000000 box + (LargeImages) 0.500000 0.000000 box + (MosaicImages) 1.500000 0.000000 box + +% ----- relations ----- + +solid +1 0.500000 1.250000 out +solid +0.000000 1.000000 2.000000 conn +solid +0 0.000000 1.750000 in +solid +0 1.000000 1.750000 in +solid +1 1.000000 0.250000 out +solid +0.500000 1.500000 1.000000 conn +solid +0 0.500000 0.750000 in +solid +0 1.500000 0.750000 in diff --git a/docs/latex/class_images_interface.tex b/docs/latex/class_images_interface.tex new file mode 100644 index 00000000..74e31677 --- /dev/null +++ b/docs/latex/class_images_interface.tex @@ -0,0 +1,55 @@ +\hypertarget{class_images_interface}{}\doxysection{Images\+Interface Class Reference} +\label{class_images_interface}\index{ImagesInterface@{ImagesInterface}} +Inheritance diagram for Images\+Interface\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=3.000000cm]{class_images_interface} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_images_interface_a0e825abe3f7a6c73588e9f78eb734147}\label{class_images_interface_a0e825abe3f7a6c73588e9f78eb734147}} +virtual \textbf{ string} {\bfseries get\+\_\+filename} () const =0 +\item +\mbox{\Hypertarget{class_images_interface_a74a24a60adadacccd94d9e59f0253a36}\label{class_images_interface_a74a24a60adadacccd94d9e59f0253a36}} +virtual int {\bfseries get\+\_\+class\+\_\+size} (int i) const =0 +\item +\mbox{\Hypertarget{class_images_interface_a111e4cb4e084287a7320c865090284c6}\label{class_images_interface_a111e4cb4e084287a7320c865090284c6}} +virtual int {\bfseries get\+\_\+number\+\_\+classes} () const =0 +\item +\mbox{\Hypertarget{class_images_interface_ac70474e1bee638335ca8f2d4783f824f}\label{class_images_interface_ac70474e1bee638335ca8f2d4783f824f}} +virtual int {\bfseries get\+\_\+number\+\_\+images} () const =0 +\item +\mbox{\Hypertarget{class_images_interface_ab2ca5cd20034b462b6cc4ed9203ac654}\label{class_images_interface_ab2ca5cd20034b462b6cc4ed9203ac654}} +virtual int {\bfseries get\+\_\+image\+\_\+channels} () const =0 +\item +\mbox{\Hypertarget{class_images_interface_adf3286c20f638a907cc6b8d73ab23935}\label{class_images_interface_adf3286c20f638a907cc6b8d73ab23935}} +virtual int {\bfseries get\+\_\+image\+\_\+width} () const =0 +\item +\mbox{\Hypertarget{class_images_interface_a912157ee3c4e5bd7ca10788598cd595b}\label{class_images_interface_a912157ee3c4e5bd7ca10788598cd595b}} +virtual int {\bfseries get\+\_\+image\+\_\+height} () const =0 +\item +\mbox{\Hypertarget{class_images_interface_a3665871dc1f3bc6dd03d80307364d7e8}\label{class_images_interface_a3665871dc1f3bc6dd03d80307364d7e8}} +virtual int {\bfseries get\+\_\+classification} (int image) const =0 +\item +\mbox{\Hypertarget{class_images_interface_aeae66532b878577d4cd0bf7b4aa10638}\label{class_images_interface_aeae66532b878577d4cd0bf7b4aa10638}} +virtual float {\bfseries get\+\_\+pixel} (int image, int z, int y, int x) const =0 +\item +\mbox{\Hypertarget{class_images_interface_a97907b0c401fea4c219162e0ad892fd8}\label{class_images_interface_a97907b0c401fea4c219162e0ad892fd8}} +virtual float {\bfseries get\+\_\+channel\+\_\+avg} (int channel) const =0 +\item +\mbox{\Hypertarget{class_images_interface_a3ecfce3ab205f37273c304be8ea642fb}\label{class_images_interface_a3ecfce3ab205f37273c304be8ea642fb}} +virtual float {\bfseries get\+\_\+channel\+\_\+std\+\_\+dev} (int channel) const =0 +\item +\mbox{\Hypertarget{class_images_interface_aa449f74e5381d3701697d3f3e92ef164}\label{class_images_interface_aa449f74e5381d3701697d3f3e92ef164}} +virtual const \textbf{ vector}$<$ float $>$ \& {\bfseries get\+\_\+average} () const =0 +\item +\mbox{\Hypertarget{class_images_interface_a3a26b620cf600922d3572c41d7d43c38}\label{class_images_interface_a3a26b620cf600922d3572c41d7d43c38}} +virtual const \textbf{ vector}$<$ float $>$ \& {\bfseries get\+\_\+std\+\_\+dev} () const =0 +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/image\+\_\+set\+\_\+interface.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_island.tex b/docs/latex/class_island.tex new file mode 100644 index 00000000..2560f951 --- /dev/null +++ b/docs/latex/class_island.tex @@ -0,0 +1,360 @@ +\hypertarget{class_island}{}\doxysection{Island Class Reference} +\label{class_island}\index{Island@{Island}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\hyperlink{class_island_a32f068675c8a7f999cb18464d00e8119}{Island}} (int32\+\_\+t \mbox{\hyperlink{class_island_a859a134cc183618af109dd69b85f2955}{id}}, int32\+\_\+t \mbox{\hyperlink{class_island_a5973473a3aad42bec285f02522cacce8}{max\+\_\+size}}) +\begin{DoxyCompactList}\small\item\em Initializes an island with a given max size. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_island_a02fc26cf7fb95d750c501494b855707d}{Island}} (int32\+\_\+t \mbox{\hyperlink{class_island_a859a134cc183618af109dd69b85f2955}{id}}, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ $>$ \mbox{\hyperlink{class_island_a41c1201d69104c49438c97dbc8c93db2}{genomes}}) +\begin{DoxyCompactList}\small\item\em Initializes an island filled the supplied genomes. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_island_aea94baab5d4faa09454131236baf187c}{get\+\_\+best\+\_\+fitness}} () +\begin{DoxyCompactList}\small\item\em Returns the fitness of the best genome in the island. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_island_ac135f9b252512f04afcd7f8c2f3f621f}{get\+\_\+worst\+\_\+fitness}} () +\begin{DoxyCompactList}\small\item\em Returns the fitness of the worst genome in the island. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_island_abf327f29018863338f4eb351c1f4a2c8}{get\+\_\+best\+\_\+genome}} () +\begin{DoxyCompactList}\small\item\em Returns the best genomme in the island. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_island_a2f5d1d504a6bc11d0d63cf9d4bea5a63}{get\+\_\+worst\+\_\+genome}} () +\begin{DoxyCompactList}\small\item\em Returns the worst genomme in the island. \end{DoxyCompactList}\item +int32\+\_\+t \mbox{\hyperlink{class_island_ac5cae801aac1403f3523a7949e415417}{get\+\_\+max\+\_\+size}} () +\begin{DoxyCompactList}\small\item\em Returns the maximum number of genomes the island can hold. \end{DoxyCompactList}\item +int32\+\_\+t \mbox{\hyperlink{class_island_a41f743dc56fa3c26f1e709222416cadf}{size}} () +\begin{DoxyCompactList}\small\item\em Returns the size of the island. \end{DoxyCompactList}\item +bool \mbox{\hyperlink{class_island_a42a519a1626c5db6004173529a5e2627}{is\+\_\+full}} () +\begin{DoxyCompactList}\small\item\em Returns true if the island has \mbox{\hyperlink{class_island_a5973473a3aad42bec285f02522cacce8}{Island\+::max\+\_\+size}} genomes. \end{DoxyCompactList}\item +bool \mbox{\hyperlink{class_island_a4d7e730b2c3e9cc53ae3205b1fbe2930}{is\+\_\+initializing}} () +\begin{DoxyCompactList}\small\item\em Returns true if the island is initializing, i.\+e., it\textquotesingle{}s size is $<$= max\+\_\+size and it hasn\textquotesingle{}t been cleared out for repopulating. \end{DoxyCompactList}\item +bool \mbox{\hyperlink{class_island_a79525b52b34ef3674b837072b5d34d19}{is\+\_\+repopulating}} () +\begin{DoxyCompactList}\small\item\em Returns true if the island is repopulating, i.\+e., it\textquotesingle{}s size is $<$= max\+\_\+size and it has been full before but cleared out for repopulation. \end{DoxyCompactList}\item +void \mbox{\hyperlink{class_island_a7c3b5331a7ee1dff6206a855e6b5c8b8}{copy\+\_\+random\+\_\+genome}} (\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$genome) +\begin{DoxyCompactList}\small\item\em Selects a genome from the island at random and returns a copy of it. \end{DoxyCompactList}\item +void \mbox{\hyperlink{class_island_ac49713b90f9110f52c5083540448df8d}{copy\+\_\+two\+\_\+random\+\_\+genomes}} (\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$genome1, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$genome2) +\begin{DoxyCompactList}\small\item\em Selects two different genomes from the island at random and returns copies of them. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_af4b7a1caadd05d62d95ae4f6b2e2d378}\label{class_island_af4b7a1caadd05d62d95ae4f6b2e2d378}} +void {\bfseries do\+\_\+population\+\_\+check} (int line, int initial\+\_\+size) +\item +int32\+\_\+t \mbox{\hyperlink{class_island_a105174354a7313b3c6520892a3de653e}{insert\+\_\+genome}} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$genome) +\begin{DoxyCompactList}\small\item\em Inserts a genome into the island. \end{DoxyCompactList}\item +void \mbox{\hyperlink{class_island_a9d092c81f7d97c72972dfbdf6fb7cb14}{print}} (\textbf{ string} indent=\char`\"{}\char`\"{}) +\begin{DoxyCompactList}\small\item\em Prints out the state of this island. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_a733a2accb72bce21194747fd3c1ee819}\label{class_island_a733a2accb72bce21194747fd3c1ee819}} +void \mbox{\hyperlink{class_island_a733a2accb72bce21194747fd3c1ee819}{erase\+\_\+island}} () +\begin{DoxyCompactList}\small\item\em erases the entire island and set the erased\+\_\+generation\+\_\+id. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_acb96603328b5dff91b41470424e7494e}\label{class_island_acb96603328b5dff91b41470424e7494e}} +int32\+\_\+t \mbox{\hyperlink{class_island_acb96603328b5dff91b41470424e7494e}{get\+\_\+erased\+\_\+generation\+\_\+id}} () +\begin{DoxyCompactList}\small\item\em returns the get\+\_\+erased\+\_\+generation\+\_\+id. \end{DoxyCompactList}\item +int32\+\_\+t \mbox{\hyperlink{class_island_a8c941beca36c75f2c4fc2fd0d2dfc2c5}{get\+\_\+status}} () +\item +\mbox{\Hypertarget{class_island_abee9ebff7f0da46fa3dde1718f675689}\label{class_island_abee9ebff7f0da46fa3dde1718f675689}} +void \mbox{\hyperlink{class_island_abee9ebff7f0da46fa3dde1718f675689}{set\+\_\+status}} (int32\+\_\+t status\+\_\+to\+\_\+set) +\begin{DoxyCompactList}\small\item\em after erasing the island, sets the island status to repopulating. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_abb653cd2fc60819a0ab2c204930a7e8a}\label{class_island_abb653cd2fc60819a0ab2c204930a7e8a}} +bool \mbox{\hyperlink{class_island_abb653cd2fc60819a0ab2c204930a7e8a}{been\+\_\+erased}} () +\begin{DoxyCompactList}\small\item\em return if this island has been erased before. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_a8a6f5c217a58f9a2b58c00372bea85f2}\label{class_island_a8a6f5c217a58f9a2b58c00372bea85f2}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ $>$ {\bfseries get\+\_\+genomes} () +\item +\mbox{\Hypertarget{class_island_acc95dd5cc774123a9b0a24416d090c95}\label{class_island_acc95dd5cc774123a9b0a24416d090c95}} +void {\bfseries set\+\_\+latest\+\_\+generation\+\_\+id} (int32\+\_\+t \+\_\+latest\+\_\+generation\+\_\+id) +\item +\mbox{\Hypertarget{class_island_ab821afe58054f2a256d3bbd5c08fbee0}\label{class_island_ab821afe58054f2a256d3bbd5c08fbee0}} +int32\+\_\+t {\bfseries get\+\_\+erase\+\_\+again\+\_\+num} () +\item +\mbox{\Hypertarget{class_island_ad0d7c02825f98385aa9ff00ef66e20b5}\label{class_island_ad0d7c02825f98385aa9ff00ef66e20b5}} +void {\bfseries set\+\_\+erase\+\_\+again\+\_\+num} () +\end{DoxyCompactItemize} +\doxysubsection*{Static Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_island_acb98955eb9f69a25c7e3af81f4d5cc5c}\label{class_island_acb98955eb9f69a25c7e3af81f4d5cc5c}} +const static int32\+\_\+t \mbox{\hyperlink{class_island_acb98955eb9f69a25c7e3af81f4d5cc5c}{I\+N\+I\+T\+I\+A\+L\+I\+Z\+I\+NG}} = 0 +\begin{DoxyCompactList}\small\item\em status flag for if the island is initializing. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_a857212729b8405e77721f499d0b85513}\label{class_island_a857212729b8405e77721f499d0b85513}} +const static int32\+\_\+t \mbox{\hyperlink{class_island_a857212729b8405e77721f499d0b85513}{F\+I\+L\+L\+ED}} = 1 +\begin{DoxyCompactList}\small\item\em status flag for if the island is filled. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_a26a2558e99586f017335aa0ef4612758}\label{class_island_a26a2558e99586f017335aa0ef4612758}} +const static int32\+\_\+t \mbox{\hyperlink{class_island_a26a2558e99586f017335aa0ef4612758}{R\+E\+P\+O\+P\+U\+L\+A\+T\+I\+NG}} = 2 +\begin{DoxyCompactList}\small\item\em status flag for if the island is repopulating. \end{DoxyCompactList}\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_island_a859a134cc183618af109dd69b85f2955}\label{class_island_a859a134cc183618af109dd69b85f2955}} +int32\+\_\+t \mbox{\hyperlink{class_island_a859a134cc183618af109dd69b85f2955}{id}} +\begin{DoxyCompactList}\small\item\em An integer ID for this island. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_a5973473a3aad42bec285f02522cacce8}\label{class_island_a5973473a3aad42bec285f02522cacce8}} +int32\+\_\+t \mbox{\hyperlink{class_island_a5973473a3aad42bec285f02522cacce8}{max\+\_\+size}} +\begin{DoxyCompactList}\small\item\em The maximum number of genomes in the island. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_a054b583b4364d07d8c7819daf9bcdb47}\label{class_island_a054b583b4364d07d8c7819daf9bcdb47}} +int32\+\_\+t \mbox{\hyperlink{class_island_a054b583b4364d07d8c7819daf9bcdb47}{erased\+\_\+generation\+\_\+id}} = -\/1 +\begin{DoxyCompactList}\small\item\em The latest generation id of an erased island, erased\+\_\+generation\+\_\+id = largest\+\_\+generation\+\_\+id when this island is erased, to prevent deleted genomes get inserted back. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_a24a6f4f8d54cb448ccd3be82385c2e02}\label{class_island_a24a6f4f8d54cb448ccd3be82385c2e02}} +int32\+\_\+t \mbox{\hyperlink{class_island_a24a6f4f8d54cb448ccd3be82385c2e02}{latest\+\_\+generation\+\_\+id}} +\begin{DoxyCompactList}\small\item\em The latest generation id of genome being generated, including the ones doing backprop by workers. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_a41c1201d69104c49438c97dbc8c93db2}\label{class_island_a41c1201d69104c49438c97dbc8c93db2}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ $>$ \mbox{\hyperlink{class_island_a41c1201d69104c49438c97dbc8c93db2}{genomes}} +\begin{DoxyCompactList}\small\item\em The genomes on this island, stored in sorted order best (front) to worst (back). \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_acec5d3319641d6b494157fdb737e0622}\label{class_island_acec5d3319641d6b494157fdb737e0622}} +\textbf{ unordered\+\_\+map}$<$ \textbf{ string}, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ $>$ $>$ {\bfseries structure\+\_\+map} +\item +\mbox{\Hypertarget{class_island_a6cd2fa92f5e9a23dbabc47f050c1837d}\label{class_island_a6cd2fa92f5e9a23dbabc47f050c1837d}} +int32\+\_\+t {\bfseries status} +\item +int32\+\_\+t \mbox{\hyperlink{class_island_ae46bdcec5ad489d18a4bbd9f22d9cabc}{erase\+\_\+again}} +\begin{DoxyCompactList}\small\item\em \begin{quote} +The status of this island (either \mbox{\hyperlink{class_island}{Island}}\+:I\+N\+I\+T\+I\+A\+L\+I\+Z\+I\+NG, \mbox{\hyperlink{class_island_a857212729b8405e77721f499d0b85513}{Island\+::\+F\+I\+L\+L\+ED}} or \mbox{\hyperlink{class_island_a26a2558e99586f017335aa0ef4612758}{Island\+::\+R\+E\+P\+O\+P\+U\+L\+A\+T\+I\+NG}} \end{quote} +\end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_aa676d36c51b69868c42d08fbcf6733c8}\label{class_island_aa676d36c51b69868c42d08fbcf6733c8}} +bool \mbox{\hyperlink{class_island_aa676d36c51b69868c42d08fbcf6733c8}{erased}} +\begin{DoxyCompactList}\small\item\em a flag to track if this islands has been erased \end{DoxyCompactList}\end{DoxyCompactItemize} + + +\doxysubsection{Constructor \& Destructor Documentation} +\mbox{\Hypertarget{class_island_a32f068675c8a7f999cb18464d00e8119}\label{class_island_a32f068675c8a7f999cb18464d00e8119}} +\index{Island@{Island}!Island@{Island}} +\index{Island@{Island}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{Island()}{Island()}\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} +{\footnotesize\ttfamily Island\+::\+Island (\begin{DoxyParamCaption}\item[{int32\+\_\+t}]{id, }\item[{int32\+\_\+t}]{max\+\_\+size }\end{DoxyParamCaption})} + + + +Initializes an island with a given max size. + + +\begin{DoxyParams}{Parameters} +{\em max\+\_\+size} & is the maximum number of genomes in the island. \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_island_a02fc26cf7fb95d750c501494b855707d}\label{class_island_a02fc26cf7fb95d750c501494b855707d}} +\index{Island@{Island}!Island@{Island}} +\index{Island@{Island}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{Island()}{Island()}\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} +{\footnotesize\ttfamily Island\+::\+Island (\begin{DoxyParamCaption}\item[{int32\+\_\+t}]{id, }\item[{\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ $>$}]{genomes }\end{DoxyParamCaption})} + + + +Initializes an island filled the supplied genomes. + +The size of the island will be the size of the supplied genome vector. The island status is set to filled. + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_island_a7c3b5331a7ee1dff6206a855e6b5c8b8}\label{class_island_a7c3b5331a7ee1dff6206a855e6b5c8b8}} +\index{Island@{Island}!copy\_random\_genome@{copy\_random\_genome}} +\index{copy\_random\_genome@{copy\_random\_genome}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{copy\_random\_genome()}{copy\_random\_genome()}} +{\footnotesize\ttfamily void Island\+::copy\+\_\+random\+\_\+genome (\begin{DoxyParamCaption}\item[{\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&}]{rng\+\_\+0\+\_\+1, }\item[{\textbf{ minstd\+\_\+rand0} \&}]{generator, }\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$}]{genome }\end{DoxyParamCaption})} + + + +Selects a genome from the island at random and returns a copy of it. + + +\begin{DoxyParams}{Parameters} +{\em rng\+\_\+0\+\_\+1} & is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive). \\ +\hline +{\em generator} & is the random number generator \\ +\hline +{\em genome} & will be the copied genome, an addresss to a pointer needs to be passed. \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_island_ac49713b90f9110f52c5083540448df8d}\label{class_island_ac49713b90f9110f52c5083540448df8d}} +\index{Island@{Island}!copy\_two\_random\_genomes@{copy\_two\_random\_genomes}} +\index{copy\_two\_random\_genomes@{copy\_two\_random\_genomes}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{copy\_two\_random\_genomes()}{copy\_two\_random\_genomes()}} +{\footnotesize\ttfamily void Island\+::copy\+\_\+two\+\_\+random\+\_\+genomes (\begin{DoxyParamCaption}\item[{\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&}]{rng\+\_\+0\+\_\+1, }\item[{\textbf{ minstd\+\_\+rand0} \&}]{generator, }\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$}]{genome1, }\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$}]{genome2 }\end{DoxyParamCaption})} + + + +Selects two different genomes from the island at random and returns copies of them. + + +\begin{DoxyParams}{Parameters} +{\em rng\+\_\+0\+\_\+1} & is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive). \\ +\hline +{\em generator} & is the random number generator \\ +\hline +{\em genome1} & will be the first copied genome, an addresss to a pointer needs to be passed. \\ +\hline +{\em genome2} & will be the second copied genome, an addresss to a pointer needs to be passed. \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_island_aea94baab5d4faa09454131236baf187c}\label{class_island_aea94baab5d4faa09454131236baf187c}} +\index{Island@{Island}!get\_best\_fitness@{get\_best\_fitness}} +\index{get\_best\_fitness@{get\_best\_fitness}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{get\_best\_fitness()}{get\_best\_fitness()}} +{\footnotesize\ttfamily double Island\+::get\+\_\+best\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the fitness of the best genome in the island. + +\begin{DoxyReturn}{Returns} +the best fitness of the island +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_abf327f29018863338f4eb351c1f4a2c8}\label{class_island_abf327f29018863338f4eb351c1f4a2c8}} +\index{Island@{Island}!get\_best\_genome@{get\_best\_genome}} +\index{get\_best\_genome@{get\_best\_genome}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{get\_best\_genome()}{get\_best\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Island\+::get\+\_\+best\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the best genomme in the island. + +\begin{DoxyReturn}{Returns} +the best genome in the island +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_ac5cae801aac1403f3523a7949e415417}\label{class_island_ac5cae801aac1403f3523a7949e415417}} +\index{Island@{Island}!get\_max\_size@{get\_max\_size}} +\index{get\_max\_size@{get\_max\_size}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{get\_max\_size()}{get\_max\_size()}} +{\footnotesize\ttfamily int32\+\_\+t Island\+::get\+\_\+max\+\_\+size (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the maximum number of genomes the island can hold. + +\begin{DoxyReturn}{Returns} +the maximum number of genomes this island can have +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_a8c941beca36c75f2c4fc2fd0d2dfc2c5}\label{class_island_a8c941beca36c75f2c4fc2fd0d2dfc2c5}} +\index{Island@{Island}!get\_status@{get\_status}} +\index{get\_status@{get\_status}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{get\_status()}{get\_status()}} +{\footnotesize\ttfamily int32\+\_\+t Island\+::get\+\_\+status (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + +\begin{DoxyReturn}{Returns} +the status of the island +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_ac135f9b252512f04afcd7f8c2f3f621f}\label{class_island_ac135f9b252512f04afcd7f8c2f3f621f}} +\index{Island@{Island}!get\_worst\_fitness@{get\_worst\_fitness}} +\index{get\_worst\_fitness@{get\_worst\_fitness}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{get\_worst\_fitness()}{get\_worst\_fitness()}} +{\footnotesize\ttfamily double Island\+::get\+\_\+worst\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the fitness of the worst genome in the island. + +\begin{DoxyReturn}{Returns} +the worst fitness of the island +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_a2f5d1d504a6bc11d0d63cf9d4bea5a63}\label{class_island_a2f5d1d504a6bc11d0d63cf9d4bea5a63}} +\index{Island@{Island}!get\_worst\_genome@{get\_worst\_genome}} +\index{get\_worst\_genome@{get\_worst\_genome}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{get\_worst\_genome()}{get\_worst\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Island\+::get\+\_\+worst\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the worst genomme in the island. + +\begin{DoxyReturn}{Returns} +the worst genome in the island +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_a105174354a7313b3c6520892a3de653e}\label{class_island_a105174354a7313b3c6520892a3de653e}} +\index{Island@{Island}!insert\_genome@{insert\_genome}} +\index{insert\_genome@{insert\_genome}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{insert\_genome()}{insert\_genome()}} +{\footnotesize\ttfamily int32\+\_\+t Island\+::insert\+\_\+genome (\begin{DoxyParamCaption}\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$}]{genome }\end{DoxyParamCaption})} + + + +Inserts a genome into the island. + +Genomes are inserted in best to worst order genomes\mbox{[}0\mbox{]} will have the best fitness and genomes\mbox{[}size -\/ 1\mbox{]} will have the worst. + + +\begin{DoxyParams}{Parameters} +{\em genome} & is the genome to be inserted. \\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +-\/1 if not inserted, otherwise the index it was inserted at +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_a42a519a1626c5db6004173529a5e2627}\label{class_island_a42a519a1626c5db6004173529a5e2627}} +\index{Island@{Island}!is\_full@{is\_full}} +\index{is\_full@{is\_full}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{is\_full()}{is\_full()}} +{\footnotesize\ttfamily bool Island\+::is\+\_\+full (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns true if the island has \mbox{\hyperlink{class_island_a5973473a3aad42bec285f02522cacce8}{Island\+::max\+\_\+size}} genomes. + +\begin{DoxyReturn}{Returns} +true if the number of genomes in the island is $>$= size (although it should never be $>$ size). +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_a4d7e730b2c3e9cc53ae3205b1fbe2930}\label{class_island_a4d7e730b2c3e9cc53ae3205b1fbe2930}} +\index{Island@{Island}!is\_initializing@{is\_initializing}} +\index{is\_initializing@{is\_initializing}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{is\_initializing()}{is\_initializing()}} +{\footnotesize\ttfamily bool Island\+::is\+\_\+initializing (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns true if the island is initializing, i.\+e., it\textquotesingle{}s size is $<$= max\+\_\+size and it hasn\textquotesingle{}t been cleared out for repopulating. + +\begin{DoxyReturn}{Returns} +true if island is initializing. +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_a79525b52b34ef3674b837072b5d34d19}\label{class_island_a79525b52b34ef3674b837072b5d34d19}} +\index{Island@{Island}!is\_repopulating@{is\_repopulating}} +\index{is\_repopulating@{is\_repopulating}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{is\_repopulating()}{is\_repopulating()}} +{\footnotesize\ttfamily bool Island\+::is\+\_\+repopulating (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns true if the island is repopulating, i.\+e., it\textquotesingle{}s size is $<$= max\+\_\+size and it has been full before but cleared out for repopulation. + +\begin{DoxyReturn}{Returns} +true if island is repopulating. +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_a9d092c81f7d97c72972dfbdf6fb7cb14}\label{class_island_a9d092c81f7d97c72972dfbdf6fb7cb14}} +\index{Island@{Island}!print@{print}} +\index{print@{print}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{print()}{print()}} +{\footnotesize\ttfamily void Island\+::print (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{indent = {\ttfamily \char`\"{}\char`\"{}} }\end{DoxyParamCaption})} + + + +Prints out the state of this island. + + +\begin{DoxyParams}{Parameters} +{\em indent} & is how much to indent what is printed out \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_island_a41f743dc56fa3c26f1e709222416cadf}\label{class_island_a41f743dc56fa3c26f1e709222416cadf}} +\index{Island@{Island}!size@{size}} +\index{size@{size}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{size()}{size()}} +{\footnotesize\ttfamily int32\+\_\+t Island\+::size (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the size of the island. + +\begin{DoxyReturn}{Returns} +the number of genomes in this island. +\end{DoxyReturn} + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{class_island_ae46bdcec5ad489d18a4bbd9f22d9cabc}\label{class_island_ae46bdcec5ad489d18a4bbd9f22d9cabc}} +\index{Island@{Island}!erase\_again@{erase\_again}} +\index{erase\_again@{erase\_again}!Island@{Island}} +\doxysubsubsection{\texorpdfstring{erase\_again}{erase\_again}} +{\footnotesize\ttfamily int32\+\_\+t Island\+::erase\+\_\+again\hspace{0.3cm}{\ttfamily [private]}} + + + +\begin{quote} +The status of this island (either \mbox{\hyperlink{class_island}{Island}}\+:I\+N\+I\+T\+I\+A\+L\+I\+Z\+I\+NG, \mbox{\hyperlink{class_island_a857212729b8405e77721f499d0b85513}{Island\+::\+F\+I\+L\+L\+ED}} or \mbox{\hyperlink{class_island_a26a2558e99586f017335aa0ef4612758}{Island\+::\+R\+E\+P\+O\+P\+U\+L\+A\+T\+I\+NG}} \end{quote} + + +a flag to track if this islands has been erased + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/island.\+hxx\item +rnn/island.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_island_speciation_strategy.eps b/docs/latex/class_island_speciation_strategy.eps new file mode 100644 index 00000000..57b7865c --- /dev/null +++ b/docs/latex/class_island_speciation_strategy.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 251.572327 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.987500 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(IslandSpeciationStrategy) cw +(SpeciationStrategy) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (IslandSpeciationStrategy) 0.000000 0.000000 box + (SpeciationStrategy) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_island_speciation_strategy.tex b/docs/latex/class_island_speciation_strategy.tex new file mode 100644 index 00000000..cad293db --- /dev/null +++ b/docs/latex/class_island_speciation_strategy.tex @@ -0,0 +1,419 @@ +\hypertarget{class_island_speciation_strategy}{}\doxysection{Island\+Speciation\+Strategy Class Reference} +\label{class_island_speciation_strategy}\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}} +Inheritance diagram for Island\+Speciation\+Strategy\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_island_speciation_strategy} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\hyperlink{class_island_speciation_strategy_a62a9572d5d379edd35bda87dc81b335d}{Island\+Speciation\+Strategy}} (int32\+\_\+t \+\_\+number\+\_\+of\+\_\+islands, int32\+\_\+t \+\_\+max\+\_\+island\+\_\+size, double \+\_\+mutation\+\_\+rate, double \+\_\+intra\+\_\+island\+\_\+crossover\+\_\+rate, double \+\_\+inter\+\_\+island\+\_\+crossover\+\_\+rate, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$\+\_\+seed\+\_\+genome, \textbf{ string} \+\_\+island\+\_\+ranking\+\_\+method, \textbf{ string} \+\_\+repopulation\+\_\+method, int32\+\_\+t \+\_\+extinction\+\_\+event\+\_\+generation\+\_\+number, int32\+\_\+t \+\_\+repopulation\+\_\+mutations, int32\+\_\+t \+\_\+islands\+\_\+to\+\_\+exterminate, int32\+\_\+t \+\_\+max\+\_\+genomes, bool \+\_\+repeat\+\_\+extinction, bool \mbox{\hyperlink{class_island_speciation_strategy_a2001746533019215214d281f5a62fee2}{seed\+\_\+genome\+\_\+was\+\_\+minimal}}) +\begin{DoxyCompactList}\small\item\em Creates a new \mbox{\hyperlink{class_island_speciation_strategy}{Island\+Speciation\+Strategy}}. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_island_speciation_strategy_a3731543755dde917289df1b0630543b7}{Island\+Speciation\+Strategy}} (int32\+\_\+t \+\_\+number\+\_\+of\+\_\+islands, int32\+\_\+t \+\_\+max\+\_\+island\+\_\+size, double \+\_\+mutation\+\_\+rate, double \+\_\+intra\+\_\+island\+\_\+crossover\+\_\+rate, double \+\_\+inter\+\_\+island\+\_\+crossover\+\_\+rate, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$\+\_\+seed\+\_\+genome, \textbf{ string} \+\_\+island\+\_\+ranking\+\_\+method, \textbf{ string} \+\_\+repopulation\+\_\+method, int32\+\_\+t \+\_\+extinction\+\_\+event\+\_\+generation\+\_\+number, int32\+\_\+t \+\_\+repopulation\+\_\+mutations, int32\+\_\+t \+\_\+islands\+\_\+to\+\_\+exterminate, bool \mbox{\hyperlink{class_island_speciation_strategy_a2001746533019215214d281f5a62fee2}{seed\+\_\+genome\+\_\+was\+\_\+minimal}}, \textbf{ function}$<$ void(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&modify) +\begin{DoxyCompactList}\small\item\em Transfer learning constructor. \end{DoxyCompactList}\item +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_a2b75aca60616268e4a6aee93ee4e5aee}{get\+\_\+generated\+\_\+genomes}} () const +\item +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_ab44e2abd372cd97932dd87ed1b9c62c2}{get\+\_\+inserted\+\_\+genomes}} () const +\item +double \mbox{\hyperlink{class_island_speciation_strategy_a4db5c946c86373cf256177b35553b808}{get\+\_\+best\+\_\+fitness}} () +\begin{DoxyCompactList}\small\item\em Gets the fitness of the best genome of all the islands. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_island_speciation_strategy_a516627db390391867d6dd02f78e27caf}{get\+\_\+worst\+\_\+fitness}} () +\begin{DoxyCompactList}\small\item\em Gets the fitness of the worst genome of all the islands. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_island_speciation_strategy_a1a8a770c165b22a5070e11a3f579375e}{get\+\_\+best\+\_\+genome}} () +\begin{DoxyCompactList}\small\item\em Gets the best genome of all the islands. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_island_speciation_strategy_a563860351e5f176d539bc297949d490f}{get\+\_\+worst\+\_\+genome}} () +\begin{DoxyCompactList}\small\item\em Gets the the worst genome of all the islands. \end{DoxyCompactList}\item +bool \mbox{\hyperlink{class_island_speciation_strategy_a57d264f45976d729bfb0a59129e1fd5a}{islands\+\_\+full}} () const +\item +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_a6ae98982d01c68214e4bd703fe18146f}{insert\+\_\+genome}} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$genome) +\begin{DoxyCompactList}\small\item\em Inserts a {\bfseries{copy}} of the genome into one of the islands handled by this strategy, determined by the R\+N\+N\+\_\+\+Genome\+::get\+\_\+group\+\_\+id() method. \end{DoxyCompactList}\item +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_a4103cc2b64eb18788c8c32118094645a}{get\+\_\+worst\+\_\+island\+\_\+by\+\_\+best\+\_\+genome}} () +\begin{DoxyCompactList}\small\item\em find the worst island in the population, the worst island\textquotesingle{}s best genome is the worst among all the islands \end{DoxyCompactList}\item +\textbf{ vector}$<$ int32\+\_\+t $>$ \mbox{\hyperlink{class_island_speciation_strategy_a4dc7a8138b13b6ca22acbe9038e90ae4}{rank\+\_\+islands}} () +\begin{DoxyCompactList}\small\item\em rank the islands by their best fitness, the fitness of ranked islands are in descending order \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_island_speciation_strategy_a1d209dc38c374ffcb8ac7c34f72b2ce9}{generate\+\_\+genome}} (\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator, \textbf{ function}$<$ void(int32\+\_\+t, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&mutate, \textbf{ function}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&crossover) +\begin{DoxyCompactList}\small\item\em Generates a new genome. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_a8763001966861643be095b5c051fa1f9}\label{class_island_speciation_strategy_a8763001966861643be095b5c051fa1f9}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries generate\+\_\+for\+\_\+filled\+\_\+island} (\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator, \textbf{ function}$<$ void(int32\+\_\+t, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&mutate, \textbf{ function}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&crossover) +\item +void \mbox{\hyperlink{class_island_speciation_strategy_a4ba6785df117932f3c465cbc31a5ca3f}{print}} (\textbf{ string} indent=\char`\"{}\char`\"{}) const +\begin{DoxyCompactList}\small\item\em Prints out all the island\textquotesingle{}s populations. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_ae578a7475f4825a7fa7fbf70738070c5}\label{class_island_speciation_strategy_ae578a7475f4825a7fa7fbf70738070c5}} +\textbf{ string} \mbox{\hyperlink{class_island_speciation_strategy_ae578a7475f4825a7fa7fbf70738070c5}{get\+\_\+strategy\+\_\+information\+\_\+headers}} () const +\begin{DoxyCompactList}\small\item\em Gets speciation strategy information headers for logs. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_a2b29208908a8a27a5693e27401790a97}\label{class_island_speciation_strategy_a2b29208908a8a27a5693e27401790a97}} +\textbf{ string} \mbox{\hyperlink{class_island_speciation_strategy_a2b29208908a8a27a5693e27401790a97}{get\+\_\+strategy\+\_\+information\+\_\+values}} () const +\begin{DoxyCompactList}\small\item\em Gets speciation strategy information values for logs. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_aa26da7c800d7ab2b1299554a490deb4c}\label{class_island_speciation_strategy_aa26da7c800d7ab2b1299554a490deb4c}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_island_speciation_strategy_aa26da7c800d7ab2b1299554a490deb4c}{parents\+\_\+repopulation}} (\textbf{ string} method, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator, \textbf{ function}$<$ void(int32\+\_\+t, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&mutate, \textbf{ function}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&crossover) +\begin{DoxyCompactList}\small\item\em \mbox{\hyperlink{class_island}{Island}} repopulation through two random parents from two seperate islands, parents can be random genomes or best genome from the island. \end{DoxyCompactList}\item +void \mbox{\hyperlink{class_island_speciation_strategy_aea495f09600ae8ab003e67957d174814}{fill\+\_\+island}} (int32\+\_\+t best\+\_\+island) +\begin{DoxyCompactList}\small\item\em fill a island with the best island. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_af104950bd763f5166da9e574ff660be7}\label{class_island_speciation_strategy_af104950bd763f5166da9e574ff660be7}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries get\+\_\+global\+\_\+best\+\_\+genome} () +\item +\mbox{\Hypertarget{class_island_speciation_strategy_a2a467e8e681d28de3c05fe4f357c75ab}\label{class_island_speciation_strategy_a2a467e8e681d28de3c05fe4f357c75ab}} +void {\bfseries set\+\_\+erased\+\_\+islands\+\_\+status} () +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_island_speciation_strategy_aab18708f4ad07f725782234e31ee011f}\label{class_island_speciation_strategy_aab18708f4ad07f725782234e31ee011f}} +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_aab18708f4ad07f725782234e31ee011f}{generation\+\_\+island}} +\begin{DoxyCompactList}\small\item\em Used to track which island to generate the next genome from. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_adf6fad09de8e0b8ccc927580e4bd68f4}\label{class_island_speciation_strategy_adf6fad09de8e0b8ccc927580e4bd68f4}} +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_adf6fad09de8e0b8ccc927580e4bd68f4}{number\+\_\+of\+\_\+islands}} +\begin{DoxyCompactList}\small\item\em the number of islands to have. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_ac78b4a8411e69dfea9b0a7b5aa3e3d99}\label{class_island_speciation_strategy_ac78b4a8411e69dfea9b0a7b5aa3e3d99}} +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_ac78b4a8411e69dfea9b0a7b5aa3e3d99}{max\+\_\+island\+\_\+size}} +\begin{DoxyCompactList}\small\item\em the maximum number of genomes in an island. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_island_speciation_strategy_aa5969191a3d5f23deea86d85c88292f0}{mutation\+\_\+rate}} +\begin{DoxyCompactList}\small\item\em How frequently to do mutations. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_island_speciation_strategy_a60bc752d6262d42b1dd294d8c4da563d}{intra\+\_\+island\+\_\+crossover\+\_\+rate}} +\begin{DoxyCompactList}\small\item\em How frequently to do intra-\/island crossovers. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_island_speciation_strategy_af4c685f5be866da1bb4395506aec112e}{inter\+\_\+island\+\_\+crossover\+\_\+rate}} +\begin{DoxyCompactList}\small\item\em How frequently to do inter-\/island crossovers. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_a0878978f59055a4a5a7aa54dc4b905a3}\label{class_island_speciation_strategy_a0878978f59055a4a5a7aa54dc4b905a3}} +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_a0878978f59055a4a5a7aa54dc4b905a3}{generated\+\_\+genomes}} +\begin{DoxyCompactList}\small\item\em How many genomes have been generated by this speciation strategy. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_af5d118e77d6c24c328f8d477f776f15e}\label{class_island_speciation_strategy_af5d118e77d6c24c328f8d477f776f15e}} +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_af5d118e77d6c24c328f8d477f776f15e}{inserted\+\_\+genomes}} +\begin{DoxyCompactList}\small\item\em How many genomes have been inserted into this speciatoin strategy. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_a7b3dd21a20e4a14aa2a0657c73c3d608}\label{class_island_speciation_strategy_a7b3dd21a20e4a14aa2a0657c73c3d608}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_island_speciation_strategy_a7b3dd21a20e4a14aa2a0657c73c3d608}{seed\+\_\+genome}} +\begin{DoxyCompactList}\small\item\em keep a reference to the seed genome so we can re-\/use it across islands and not duplicate innovation numbers. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_a2e62f197c3bcc0d40369ac7a7ea9ed31}\label{class_island_speciation_strategy_a2e62f197c3bcc0d40369ac7a7ea9ed31}} +\textbf{ string} \mbox{\hyperlink{class_island_speciation_strategy_a2e62f197c3bcc0d40369ac7a7ea9ed31}{island\+\_\+ranking\+\_\+method}} +\begin{DoxyCompactList}\small\item\em The method used to find the worst island in population. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_adcdbda3a002564d49f286fb2699f37f5}\label{class_island_speciation_strategy_adcdbda3a002564d49f286fb2699f37f5}} +\textbf{ string} \mbox{\hyperlink{class_island_speciation_strategy_adcdbda3a002564d49f286fb2699f37f5}{repopulation\+\_\+method}} +\begin{DoxyCompactList}\small\item\em The method used to repopulate the island after being erased. \end{DoxyCompactList}\item +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_acf9f4b165c831eada8b46224eab05443}{extinction\+\_\+event\+\_\+generation\+\_\+number}} +\begin{DoxyCompactList}\small\item\em When \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}} reaches this generation id, an extinction event will be triggered (i.\+e. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_af8729a80fa4e31059e5ae020476e5cd3}\label{class_island_speciation_strategy_af8729a80fa4e31059e5ae020476e5cd3}} +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_af8729a80fa4e31059e5ae020476e5cd3}{repopulation\+\_\+mutations}} +\begin{DoxyCompactList}\small\item\em When an island is erradicated, it is repopulated with copies of the best genome that have this number of mutations applied to them. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_a51a285855bc78f42965b8d68a6e36257}\label{class_island_speciation_strategy_a51a285855bc78f42965b8d68a6e36257}} +int32\+\_\+t \mbox{\hyperlink{class_island_speciation_strategy_a51a285855bc78f42965b8d68a6e36257}{islands\+\_\+to\+\_\+exterminate}} +\begin{DoxyCompactList}\small\item\em When an extinction event is triggered, this is the number of islands that will be exterminated. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_af762308abf1f29ef20450b6d953a485f}\label{class_island_speciation_strategy_af762308abf1f29ef20450b6d953a485f}} +int32\+\_\+t {\bfseries max\+\_\+genomes} +\item +\mbox{\Hypertarget{class_island_speciation_strategy_a2001746533019215214d281f5a62fee2}\label{class_island_speciation_strategy_a2001746533019215214d281f5a62fee2}} +bool \mbox{\hyperlink{class_island_speciation_strategy_a2001746533019215214d281f5a62fee2}{seed\+\_\+genome\+\_\+was\+\_\+minimal}} +\begin{DoxyCompactList}\small\item\em is true if we passed in a minimal genome (i.\+e., are not using transfer learning) \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_a225690c3cb06534ac83236ede2abab5d}\label{class_island_speciation_strategy_a225690c3cb06534ac83236ede2abab5d}} +bool {\bfseries repeat\+\_\+extinction} +\item +\mbox{\Hypertarget{class_island_speciation_strategy_a51947f81a70302a029b332b6d097beda}\label{class_island_speciation_strategy_a51947f81a70302a029b332b6d097beda}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_island}{Island}} $\ast$ $>$ \mbox{\hyperlink{class_island_speciation_strategy_a51947f81a70302a029b332b6d097beda}{islands}} +\begin{DoxyCompactList}\small\item\em All the islands which contain the genomes for this speciation strategy. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_island_speciation_strategy_ac5e0e57d0758c12a13f4c867cf1d9e7a}\label{class_island_speciation_strategy_ac5e0e57d0758c12a13f4c867cf1d9e7a}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries global\+\_\+best\+\_\+genome} +\end{DoxyCompactItemize} + + +\doxysubsection{Constructor \& Destructor Documentation} +\mbox{\Hypertarget{class_island_speciation_strategy_a62a9572d5d379edd35bda87dc81b335d}\label{class_island_speciation_strategy_a62a9572d5d379edd35bda87dc81b335d}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{IslandSpeciationStrategy()}{IslandSpeciationStrategy()}\hspace{0.1cm}{\footnotesize\ttfamily [1/2]}} +{\footnotesize\ttfamily Island\+Speciation\+Strategy\+::\+Island\+Speciation\+Strategy (\begin{DoxyParamCaption}\item[{int32\+\_\+t}]{\+\_\+number\+\_\+of\+\_\+islands, }\item[{int32\+\_\+t}]{\+\_\+max\+\_\+island\+\_\+size, }\item[{double}]{\+\_\+mutation\+\_\+rate, }\item[{double}]{\+\_\+intra\+\_\+island\+\_\+crossover\+\_\+rate, }\item[{double}]{\+\_\+inter\+\_\+island\+\_\+crossover\+\_\+rate, }\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$}]{\+\_\+seed\+\_\+genome, }\item[{\textbf{ string}}]{\+\_\+island\+\_\+ranking\+\_\+method, }\item[{\textbf{ string}}]{\+\_\+repopulation\+\_\+method, }\item[{int32\+\_\+t}]{\+\_\+extinction\+\_\+event\+\_\+generation\+\_\+number, }\item[{int32\+\_\+t}]{\+\_\+repopulation\+\_\+mutations, }\item[{int32\+\_\+t}]{\+\_\+islands\+\_\+to\+\_\+exterminate, }\item[{int32\+\_\+t}]{\+\_\+max\+\_\+genomes, }\item[{bool}]{\+\_\+repeat\+\_\+extinction, }\item[{bool}]{seed\+\_\+genome\+\_\+was\+\_\+minimal }\end{DoxyParamCaption})} + + + +Creates a new \mbox{\hyperlink{class_island_speciation_strategy}{Island\+Speciation\+Strategy}}. + + +\begin{DoxyParams}{Parameters} +{\em number\+\_\+of\+\_\+islands} & specifies how many islands it will us e \\ +\hline +{\em max\+\_\+island\+\_\+size} & specifies the maximum number of gneomes in an island \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_island_speciation_strategy_a3731543755dde917289df1b0630543b7}\label{class_island_speciation_strategy_a3731543755dde917289df1b0630543b7}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{IslandSpeciationStrategy()}{IslandSpeciationStrategy()}\hspace{0.1cm}{\footnotesize\ttfamily [2/2]}} +{\footnotesize\ttfamily Island\+Speciation\+Strategy\+::\+Island\+Speciation\+Strategy (\begin{DoxyParamCaption}\item[{int32\+\_\+t}]{\+\_\+number\+\_\+of\+\_\+islands, }\item[{int32\+\_\+t}]{\+\_\+max\+\_\+island\+\_\+size, }\item[{double}]{\+\_\+mutation\+\_\+rate, }\item[{double}]{\+\_\+intra\+\_\+island\+\_\+crossover\+\_\+rate, }\item[{double}]{\+\_\+inter\+\_\+island\+\_\+crossover\+\_\+rate, }\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$}]{\+\_\+seed\+\_\+genome, }\item[{\textbf{ string}}]{\+\_\+island\+\_\+ranking\+\_\+method, }\item[{\textbf{ string}}]{\+\_\+repopulation\+\_\+method, }\item[{int32\+\_\+t}]{\+\_\+extinction\+\_\+event\+\_\+generation\+\_\+number, }\item[{int32\+\_\+t}]{\+\_\+repopulation\+\_\+mutations, }\item[{int32\+\_\+t}]{\+\_\+islands\+\_\+to\+\_\+exterminate, }\item[{bool}]{seed\+\_\+genome\+\_\+was\+\_\+minimal, }\item[{\textbf{ function}$<$ void(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&}]{modify }\end{DoxyParamCaption})} + + + +Transfer learning constructor. + + +\begin{DoxyParams}{Parameters} +{\em Modification} & function to be applied to every copy of the seed\+\_\+genome \\ +\hline +\end{DoxyParams} + + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_island_speciation_strategy_aea495f09600ae8ab003e67957d174814}\label{class_island_speciation_strategy_aea495f09600ae8ab003e67957d174814}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!fill\_island@{fill\_island}} +\index{fill\_island@{fill\_island}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{fill\_island()}{fill\_island()}} +{\footnotesize\ttfamily void Island\+Speciation\+Strategy\+::fill\+\_\+island (\begin{DoxyParamCaption}\item[{int32\+\_\+t}]{best\+\_\+island }\end{DoxyParamCaption})} + + + +fill a island with the best island. + + +\begin{DoxyParams}{Parameters} +{\em best\+\_\+island} & is the island id of the best island \\ +\hline +{\em fill\+\_\+island} & is the island is of the island to be filled \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_island_speciation_strategy_a1d209dc38c374ffcb8ac7c34f72b2ce9}\label{class_island_speciation_strategy_a1d209dc38c374ffcb8ac7c34f72b2ce9}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!generate\_genome@{generate\_genome}} +\index{generate\_genome@{generate\_genome}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{generate\_genome()}{generate\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Island\+Speciation\+Strategy\+::generate\+\_\+genome (\begin{DoxyParamCaption}\item[{\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&}]{rng\+\_\+0\+\_\+1, }\item[{\textbf{ minstd\+\_\+rand0} \&}]{generator, }\item[{\textbf{ function}$<$ void(int32\+\_\+t, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&}]{mutate, }\item[{\textbf{ function}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&}]{crossover }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Generates a new genome. + + +\begin{DoxyParams}{Parameters} +{\em rng\+\_\+0\+\_\+1} & is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive). \\ +\hline +{\em generator} & is the random number generator \\ +\hline +{\em mutate} & is the a function which performs a mutation on a genome \\ +\hline +{\em crossover} & is the function which performs crossover between two genomes\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +the newly generated genome. +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a76f36f0dd7c47727aef912d48f0fa26b}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_island_speciation_strategy_a4db5c946c86373cf256177b35553b808}\label{class_island_speciation_strategy_a4db5c946c86373cf256177b35553b808}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!get\_best\_fitness@{get\_best\_fitness}} +\index{get\_best\_fitness@{get\_best\_fitness}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_best\_fitness()}{get\_best\_fitness()}} +{\footnotesize\ttfamily double Island\+Speciation\+Strategy\+::get\+\_\+best\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Gets the fitness of the best genome of all the islands. + +\begin{DoxyReturn}{Returns} +the best fitness over all islands +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_afa3f2d35da2172a32ed06588ef09d4d0}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_island_speciation_strategy_a1a8a770c165b22a5070e11a3f579375e}\label{class_island_speciation_strategy_a1a8a770c165b22a5070e11a3f579375e}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!get\_best\_genome@{get\_best\_genome}} +\index{get\_best\_genome@{get\_best\_genome}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_best\_genome()}{get\_best\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Island\+Speciation\+Strategy\+::get\+\_\+best\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Gets the best genome of all the islands. + +\begin{DoxyReturn}{Returns} +the best genome of all islands or N\+U\+LL if no genomes have yet been inserted +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_ad0796098d09c25bfee14d9f264bcba2d}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_island_speciation_strategy_a2b75aca60616268e4a6aee93ee4e5aee}\label{class_island_speciation_strategy_a2b75aca60616268e4a6aee93ee4e5aee}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!get\_generated\_genomes@{get\_generated\_genomes}} +\index{get\_generated\_genomes@{get\_generated\_genomes}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_generated\_genomes()}{get\_generated\_genomes()}} +{\footnotesize\ttfamily int32\+\_\+t Island\+Speciation\+Strategy\+::get\+\_\+generated\+\_\+genomes (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [virtual]}} + +\begin{DoxyReturn}{Returns} +the number of generated genomes. +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a7699a7050ed5a66693f19e834e9ba082}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_island_speciation_strategy_ab44e2abd372cd97932dd87ed1b9c62c2}\label{class_island_speciation_strategy_ab44e2abd372cd97932dd87ed1b9c62c2}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!get\_inserted\_genomes@{get\_inserted\_genomes}} +\index{get\_inserted\_genomes@{get\_inserted\_genomes}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_inserted\_genomes()}{get\_inserted\_genomes()}} +{\footnotesize\ttfamily int32\+\_\+t Island\+Speciation\+Strategy\+::get\+\_\+inserted\+\_\+genomes (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [virtual]}} + +\begin{DoxyReturn}{Returns} +the number of inserted genomes. +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a1f07309130568d8cc79da44c8da6ca69}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_island_speciation_strategy_a516627db390391867d6dd02f78e27caf}\label{class_island_speciation_strategy_a516627db390391867d6dd02f78e27caf}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!get\_worst\_fitness@{get\_worst\_fitness}} +\index{get\_worst\_fitness@{get\_worst\_fitness}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_worst\_fitness()}{get\_worst\_fitness()}} +{\footnotesize\ttfamily double Island\+Speciation\+Strategy\+::get\+\_\+worst\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Gets the fitness of the worst genome of all the islands. + +\begin{DoxyReturn}{Returns} +the worst fitness over all islands +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a6bdef578fe33c628a81db35d75eaa473}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_island_speciation_strategy_a563860351e5f176d539bc297949d490f}\label{class_island_speciation_strategy_a563860351e5f176d539bc297949d490f}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!get\_worst\_genome@{get\_worst\_genome}} +\index{get\_worst\_genome@{get\_worst\_genome}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_worst\_genome()}{get\_worst\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Island\+Speciation\+Strategy\+::get\+\_\+worst\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Gets the the worst genome of all the islands. + +\begin{DoxyReturn}{Returns} +the worst genome of all islands or N\+U\+LL if no genomes have yet been inserted +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_ae84e9c1e0eaf02886b41b1d9adf488f4}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_island_speciation_strategy_a4103cc2b64eb18788c8c32118094645a}\label{class_island_speciation_strategy_a4103cc2b64eb18788c8c32118094645a}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!get\_worst\_island\_by\_best\_genome@{get\_worst\_island\_by\_best\_genome}} +\index{get\_worst\_island\_by\_best\_genome@{get\_worst\_island\_by\_best\_genome}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_worst\_island\_by\_best\_genome()}{get\_worst\_island\_by\_best\_genome()}} +{\footnotesize\ttfamily int32\+\_\+t Island\+Speciation\+Strategy\+::get\+\_\+worst\+\_\+island\+\_\+by\+\_\+best\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +find the worst island in the population, the worst island\textquotesingle{}s best genome is the worst among all the islands + +\begin{DoxyReturn}{Returns} +the worst island id +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_speciation_strategy_a6ae98982d01c68214e4bd703fe18146f}\label{class_island_speciation_strategy_a6ae98982d01c68214e4bd703fe18146f}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!insert\_genome@{insert\_genome}} +\index{insert\_genome@{insert\_genome}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{insert\_genome()}{insert\_genome()}} +{\footnotesize\ttfamily int32\+\_\+t Island\+Speciation\+Strategy\+::insert\+\_\+genome (\begin{DoxyParamCaption}\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$}]{genome }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Inserts a {\bfseries{copy}} of the genome into one of the islands handled by this strategy, determined by the R\+N\+N\+\_\+\+Genome\+::get\+\_\+group\+\_\+id() method. + +The caller of this method will need to free the memory of the genome passed into this method. + + +\begin{DoxyParams}{Parameters} +{\em genome} & is the genome to insert. \\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +a value $<$ 0 if the genome was not inserted, 0 if it was a new best genome for all the islands, or $>$ 0 otherwise. +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a1fd87178f38052aa934312e49819e509}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_island_speciation_strategy_a57d264f45976d729bfb0a59129e1fd5a}\label{class_island_speciation_strategy_a57d264f45976d729bfb0a59129e1fd5a}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!islands\_full@{islands\_full}} +\index{islands\_full@{islands\_full}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{islands\_full()}{islands\_full()}} +{\footnotesize\ttfamily bool Island\+Speciation\+Strategy\+::islands\+\_\+full (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const} + +\begin{DoxyReturn}{Returns} +true if all the islands are full +\end{DoxyReturn} +\mbox{\Hypertarget{class_island_speciation_strategy_a4ba6785df117932f3c465cbc31a5ca3f}\label{class_island_speciation_strategy_a4ba6785df117932f3c465cbc31a5ca3f}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!print@{print}} +\index{print@{print}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{print()}{print()}} +{\footnotesize\ttfamily void Island\+Speciation\+Strategy\+::print (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{indent = {\ttfamily \char`\"{}\char`\"{}} }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Prints out all the island\textquotesingle{}s populations. + + +\begin{DoxyParams}{Parameters} +{\em indent} & is how much to indent what is printed out \\ +\hline +\end{DoxyParams} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a1d44f4c87ab00182e61f0fb364f274a9}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_island_speciation_strategy_a4dc7a8138b13b6ca22acbe9038e90ae4}\label{class_island_speciation_strategy_a4dc7a8138b13b6ca22acbe9038e90ae4}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!rank\_islands@{rank\_islands}} +\index{rank\_islands@{rank\_islands}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{rank\_islands()}{rank\_islands()}} +{\footnotesize\ttfamily \textbf{ vector}$<$ int32\+\_\+t $>$ Island\+Speciation\+Strategy\+::rank\+\_\+islands (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +rank the islands by their best fitness, the fitness of ranked islands are in descending order + +\begin{DoxyReturn}{Returns} +island rank from worst to the best +\end{DoxyReturn} + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{class_island_speciation_strategy_acf9f4b165c831eada8b46224eab05443}\label{class_island_speciation_strategy_acf9f4b165c831eada8b46224eab05443}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!extinction\_event\_generation\_number@{extinction\_event\_generation\_number}} +\index{extinction\_event\_generation\_number@{extinction\_event\_generation\_number}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{extinction\_event\_generation\_number}{extinction\_event\_generation\_number}} +{\footnotesize\ttfamily int32\+\_\+t Island\+Speciation\+Strategy\+::extinction\+\_\+event\+\_\+generation\+\_\+number\hspace{0.3cm}{\ttfamily [private]}} + + + +When \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}} reaches this generation id, an extinction event will be triggered (i.\+e. + +islands will be killed and repopulated). \mbox{\Hypertarget{class_island_speciation_strategy_af4c685f5be866da1bb4395506aec112e}\label{class_island_speciation_strategy_af4c685f5be866da1bb4395506aec112e}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!inter\_island\_crossover\_rate@{inter\_island\_crossover\_rate}} +\index{inter\_island\_crossover\_rate@{inter\_island\_crossover\_rate}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{inter\_island\_crossover\_rate}{inter\_island\_crossover\_rate}} +{\footnotesize\ttfamily double Island\+Speciation\+Strategy\+::inter\+\_\+island\+\_\+crossover\+\_\+rate\hspace{0.3cm}{\ttfamily [private]}} + + + +How frequently to do inter-\/island crossovers. + +Note that mutation\+\_\+rate + intra\+\_\+island\+\_\+crossover\+\_\+rate + inter\+\_\+island\+\_\+crossover\+\_\+rate should equal 1, if not they will be scaled down such that they do. \mbox{\Hypertarget{class_island_speciation_strategy_a60bc752d6262d42b1dd294d8c4da563d}\label{class_island_speciation_strategy_a60bc752d6262d42b1dd294d8c4da563d}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!intra\_island\_crossover\_rate@{intra\_island\_crossover\_rate}} +\index{intra\_island\_crossover\_rate@{intra\_island\_crossover\_rate}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{intra\_island\_crossover\_rate}{intra\_island\_crossover\_rate}} +{\footnotesize\ttfamily double Island\+Speciation\+Strategy\+::intra\+\_\+island\+\_\+crossover\+\_\+rate\hspace{0.3cm}{\ttfamily [private]}} + + + +How frequently to do intra-\/island crossovers. + +Note that mutation\+\_\+rate + intra\+\_\+island\+\_\+crossover\+\_\+rate + inter\+\_\+island\+\_\+crossover\+\_\+rate should equal 1, if not they will be scaled down such that they do. \mbox{\Hypertarget{class_island_speciation_strategy_aa5969191a3d5f23deea86d85c88292f0}\label{class_island_speciation_strategy_aa5969191a3d5f23deea86d85c88292f0}} +\index{IslandSpeciationStrategy@{IslandSpeciationStrategy}!mutation\_rate@{mutation\_rate}} +\index{mutation\_rate@{mutation\_rate}!IslandSpeciationStrategy@{IslandSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{mutation\_rate}{mutation\_rate}} +{\footnotesize\ttfamily double Island\+Speciation\+Strategy\+::mutation\+\_\+rate\hspace{0.3cm}{\ttfamily [private]}} + + + +How frequently to do mutations. + +Note that mutation\+\_\+rate + intra\+\_\+island\+\_\+crossover\+\_\+rate + inter\+\_\+island\+\_\+crossover\+\_\+rate should equal 1, if not they will be scaled down such that they do. + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/island\+\_\+speciation\+\_\+strategy.\+hxx\item +rnn/island\+\_\+speciation\+\_\+strategy.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_l_s_t_m___node.eps b/docs/latex/class_l_s_t_m___node.eps new file mode 100644 index 00000000..bfb6364e --- /dev/null +++ b/docs/latex/class_l_s_t_m___node.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 285.714286 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.750000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(LSTM_Node) cw +(RNN_Node_Interface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (LSTM_Node) 0.000000 0.000000 box + (RNN_Node_Interface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_l_s_t_m___node.tex b/docs/latex/class_l_s_t_m___node.tex new file mode 100644 index 00000000..76e24c38 --- /dev/null +++ b/docs/latex/class_l_s_t_m___node.tex @@ -0,0 +1,189 @@ +\hypertarget{class_l_s_t_m___node}{}\doxysection{L\+S\+T\+M\+\_\+\+Node Class Reference} +\label{class_l_s_t_m___node}\index{LSTM\_Node@{LSTM\_Node}} +Inheritance diagram for L\+S\+T\+M\+\_\+\+Node\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_l_s_t_m___node} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_aede0ded08385b7de9cd3f7be255c62c6}\label{class_l_s_t_m___node_aede0ded08385b7de9cd3f7be255c62c6}} +{\bfseries L\+S\+T\+M\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, int \+\_\+type, double \+\_\+depth) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a32cc552ae0bbdc9363b5a02266eb6094}\label{class_l_s_t_m___node_a32cc552ae0bbdc9363b5a02266eb6094}} +void {\bfseries initialize\+\_\+lamarckian} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double mu, double sigma) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_af334b263e401ddcf653439a4ca930f40}\label{class_l_s_t_m___node_af334b263e401ddcf653439a4ca930f40}} +void {\bfseries initialize\+\_\+xavier} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng1\+\_\+1, double range) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a5509f1d068fd6c70c84425d782a2416a}\label{class_l_s_t_m___node_a5509f1d068fd6c70c84425d782a2416a}} +void {\bfseries initialize\+\_\+kaiming} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double range) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a41c8c24423dee73f7b54fb2bd545c44b}\label{class_l_s_t_m___node_a41c8c24423dee73f7b54fb2bd545c44b}} +void {\bfseries initialize\+\_\+uniform\+\_\+random} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ae57827aeae92a3dd7f9b483d9007194d}\label{class_l_s_t_m___node_ae57827aeae92a3dd7f9b483d9007194d}} +double {\bfseries get\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a66fd6d160869bb0b61019375a74ff950}\label{class_l_s_t_m___node_a66fd6d160869bb0b61019375a74ff950}} +void {\bfseries print\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a74414bdd00d5a21f85d1eab3c24f5f5b}\label{class_l_s_t_m___node_a74414bdd00d5a21f85d1eab3c24f5f5b}} +void {\bfseries input\+\_\+fired} (int time, double incoming\+\_\+output) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ab92c179e7b6cb7f6e300e7220b0a5252}\label{class_l_s_t_m___node_ab92c179e7b6cb7f6e300e7220b0a5252}} +void {\bfseries try\+\_\+update\+\_\+deltas} (int time) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a808eec94e42e90324bf6cba6252aa7b5}\label{class_l_s_t_m___node_a808eec94e42e90324bf6cba6252aa7b5}} +void {\bfseries error\+\_\+fired} (int time, double error) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_aa49da071b12d581368555ef6e3eb835c}\label{class_l_s_t_m___node_aa49da071b12d581368555ef6e3eb835c}} +void {\bfseries output\+\_\+fired} (int time, double delta) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_afcfe9e362541835c8aff72a0d476792f}\label{class_l_s_t_m___node_afcfe9e362541835c8aff72a0d476792f}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_l_s_t_m___node_aca551a5602ae11f34ea0cfa6eb09691a}\label{class_l_s_t_m___node_aca551a5602ae11f34ea0cfa6eb09691a}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a8cdeac6ef873c5a2309e72130747cd5f}\label{class_l_s_t_m___node_a8cdeac6ef873c5a2309e72130747cd5f}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_abd1e2bc87e77ce1267d88acb9828f2be}\label{class_l_s_t_m___node_abd1e2bc87e77ce1267d88acb9828f2be}} +void {\bfseries get\+\_\+weights} (uint32\+\_\+t \&offset, \textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ac3ded3843152582159a225424b4c50f9}\label{class_l_s_t_m___node_ac3ded3843152582159a225424b4c50f9}} +void {\bfseries set\+\_\+weights} (uint32\+\_\+t \&offset, const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_af0e1d6c248dafce960d35f592d4b7bdc}\label{class_l_s_t_m___node_af0e1d6c248dafce960d35f592d4b7bdc}} +void {\bfseries get\+\_\+gradients} (\textbf{ vector}$<$ double $>$ \&gradients) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ae4bb4af65e14318814141a4665de48fa}\label{class_l_s_t_m___node_ae4bb4af65e14318814141a4665de48fa}} +void {\bfseries reset} (int \+\_\+series\+\_\+length) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a3d9171e8745d4ab00becd86753f11911}\label{class_l_s_t_m___node_a3d9171e8745d4ab00becd86753f11911}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ac2db752af703b1a3a184499bf6fa8fc8}\label{class_l_s_t_m___node_ac2db752af703b1a3a184499bf6fa8fc8}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries copy} () const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a0ee8e33b0259807850d054abb243e7be}\label{class_l_s_t_m___node_a0ee8e33b0259807850d054abb243e7be}} +double {\bfseries output\+\_\+gate\+\_\+update\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_aa533f17d8ec43d66c6c88d92aa084769}\label{class_l_s_t_m___node_aa533f17d8ec43d66c6c88d92aa084769}} +double {\bfseries output\+\_\+gate\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a05f10b65b2219956ee48a6fc8e874d36}\label{class_l_s_t_m___node_a05f10b65b2219956ee48a6fc8e874d36}} +double {\bfseries output\+\_\+gate\+\_\+bias} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a8f88d74ac21b891f39a6cc65f0d6a81b}\label{class_l_s_t_m___node_a8f88d74ac21b891f39a6cc65f0d6a81b}} +double {\bfseries input\+\_\+gate\+\_\+update\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_aef26f0572b7a7d44d8af55ffa133319b}\label{class_l_s_t_m___node_aef26f0572b7a7d44d8af55ffa133319b}} +double {\bfseries input\+\_\+gate\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ae5753340e5e520cd1971c3bbddc4cbe3}\label{class_l_s_t_m___node_ae5753340e5e520cd1971c3bbddc4cbe3}} +double {\bfseries input\+\_\+gate\+\_\+bias} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a307883e974538d4d052657cd14a0bdb3}\label{class_l_s_t_m___node_a307883e974538d4d052657cd14a0bdb3}} +double {\bfseries forget\+\_\+gate\+\_\+update\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a10353c2533f369c5dcaee1424ab50c04}\label{class_l_s_t_m___node_a10353c2533f369c5dcaee1424ab50c04}} +double {\bfseries forget\+\_\+gate\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a087f9f583012fecda4b6f76944dae8bf}\label{class_l_s_t_m___node_a087f9f583012fecda4b6f76944dae8bf}} +double {\bfseries forget\+\_\+gate\+\_\+bias} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a2c59e3299528daf0183436dd3d9a6e96}\label{class_l_s_t_m___node_a2c59e3299528daf0183436dd3d9a6e96}} +double {\bfseries cell\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a575b460baecb27f1767af35501432df9}\label{class_l_s_t_m___node_a575b460baecb27f1767af35501432df9}} +double {\bfseries cell\+\_\+bias} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_af02f45197f76e91e54c3fd994e4ed543}\label{class_l_s_t_m___node_af02f45197f76e91e54c3fd994e4ed543}} +\textbf{ vector}$<$ double $>$ {\bfseries output\+\_\+gate\+\_\+values} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ae1f98fdddfa0603a4a16f671f3787ee3}\label{class_l_s_t_m___node_ae1f98fdddfa0603a4a16f671f3787ee3}} +\textbf{ vector}$<$ double $>$ {\bfseries input\+\_\+gate\+\_\+values} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ae2ec5871a40cb5c1b6d175058e92b6e9}\label{class_l_s_t_m___node_ae2ec5871a40cb5c1b6d175058e92b6e9}} +\textbf{ vector}$<$ double $>$ {\bfseries forget\+\_\+gate\+\_\+values} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a2eb15e5c51ecb1a34d7a516aca40e8b4}\label{class_l_s_t_m___node_a2eb15e5c51ecb1a34d7a516aca40e8b4}} +\textbf{ vector}$<$ double $>$ {\bfseries cell\+\_\+values} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a37ac337386988e8e8d7b02a3b35a295b}\label{class_l_s_t_m___node_a37ac337386988e8e8d7b02a3b35a295b}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+output\+\_\+gate} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a62deef47dedd6e588abca9f3ca1a872e}\label{class_l_s_t_m___node_a62deef47dedd6e588abca9f3ca1a872e}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+input\+\_\+gate} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a64907518e1f7ef82c378c414eef3d781}\label{class_l_s_t_m___node_a64907518e1f7ef82c378c414eef3d781}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+forget\+\_\+gate} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a04b0c5d9ca313d9ed9c2a20186764e4c}\label{class_l_s_t_m___node_a04b0c5d9ca313d9ed9c2a20186764e4c}} +\textbf{ vector}$<$ double $>$ {\bfseries cell\+\_\+in\+\_\+tanh} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ae077cebf907c23dc9c1ae367307cc016}\label{class_l_s_t_m___node_ae077cebf907c23dc9c1ae367307cc016}} +\textbf{ vector}$<$ double $>$ {\bfseries cell\+\_\+out\+\_\+tanh} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_af940e2f222935ea7c8558e45c58078ed}\label{class_l_s_t_m___node_af940e2f222935ea7c8558e45c58078ed}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+cell\+\_\+in} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ae6bebf6c0d8927d824584b26b47e7edd}\label{class_l_s_t_m___node_ae6bebf6c0d8927d824584b26b47e7edd}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+cell\+\_\+out} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a68956f741805cac46a7f9e87589a7aa9}\label{class_l_s_t_m___node_a68956f741805cac46a7f9e87589a7aa9}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+prev\+\_\+cell} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a0c8a1839e23d87173fae7c7b91750cd8}\label{class_l_s_t_m___node_a0c8a1839e23d87173fae7c7b91750cd8}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+output\+\_\+gate\+\_\+update\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_accba317d9c292c7f1c872387de3473b1}\label{class_l_s_t_m___node_accba317d9c292c7f1c872387de3473b1}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+output\+\_\+gate\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a1c669909ae6998acf1cc23b211208e6c}\label{class_l_s_t_m___node_a1c669909ae6998acf1cc23b211208e6c}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+output\+\_\+gate\+\_\+bias} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a45ab782fe8f43b2a87a3a7ed09dc32c8}\label{class_l_s_t_m___node_a45ab782fe8f43b2a87a3a7ed09dc32c8}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+input\+\_\+gate\+\_\+update\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a3177d6ec68b192e8f6ddeafefbf4a241}\label{class_l_s_t_m___node_a3177d6ec68b192e8f6ddeafefbf4a241}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+input\+\_\+gate\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a4e73a481f84892fa0635b8aa82a480df}\label{class_l_s_t_m___node_a4e73a481f84892fa0635b8aa82a480df}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+input\+\_\+gate\+\_\+bias} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a9b8655deb08ca139ecb2fb367bf87bf8}\label{class_l_s_t_m___node_a9b8655deb08ca139ecb2fb367bf87bf8}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+forget\+\_\+gate\+\_\+update\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a7c0cdb3462828384fb13d3814f4fe08d}\label{class_l_s_t_m___node_a7c0cdb3462828384fb13d3814f4fe08d}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+forget\+\_\+gate\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_ad99e90d6fb339a17fbe437cf8723c40e}\label{class_l_s_t_m___node_ad99e90d6fb339a17fbe437cf8723c40e}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+forget\+\_\+gate\+\_\+bias} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a44a1d1fb77379e1e13c6a79bce066e9c}\label{class_l_s_t_m___node_a44a1d1fb77379e1e13c6a79bce066e9c}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+cell\+\_\+weight} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_aef7404e8bf566f1b453da5b144c8198d}\label{class_l_s_t_m___node_aef7404e8bf566f1b453da5b144c8198d}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+cell\+\_\+bias} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_l_s_t_m___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}\label{class_l_s_t_m___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}} +class {\bfseries R\+N\+N\+\_\+\+Edge} +\end{DoxyCompactItemize} +\doxysubsection*{Additional Inherited Members} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/lstm\+\_\+node.\+hxx\item +rnn/lstm\+\_\+node.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_large_image.eps b/docs/latex/class_large_image.eps new file mode 100644 index 00000000..3d0ce173 --- /dev/null +++ b/docs/latex/class_large_image.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 388.349515 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.287500 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(LargeImage) cw +(ImageInterface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (LargeImage) 0.000000 0.000000 box + (ImageInterface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_large_image.tex b/docs/latex/class_large_image.tex new file mode 100644 index 00000000..83d8cb36 --- /dev/null +++ b/docs/latex/class_large_image.tex @@ -0,0 +1,122 @@ +\hypertarget{class_large_image}{}\doxysection{Large\+Image Class Reference} +\label{class_large_image}\index{LargeImage@{LargeImage}} +Inheritance diagram for Large\+Image\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_large_image} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_large_image_a8678301873cbe109b20376af2f8072e2}\label{class_large_image_a8678301873cbe109b20376af2f8072e2}} +{\bfseries Large\+Image} (\textbf{ ifstream} \&infile, int \+\_\+number\+\_\+subimages, int \+\_\+channels, int \+\_\+width, int \+\_\+height, int \+\_\+padding, int \+\_\+classification, const \mbox{\hyperlink{class_large_images}{Large\+Images}} $\ast$\+\_\+images) +\item +\mbox{\Hypertarget{class_large_image_acf887fb82a57366d4d868881198bfc3a}\label{class_large_image_acf887fb82a57366d4d868881198bfc3a}} +{\bfseries Large\+Image} (int \+\_\+number\+\_\+subimages, int \+\_\+channels, int \+\_\+width, int \+\_\+height, int \+\_\+padding, int \+\_\+classification, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ uint8\+\_\+t $>$ $>$ $>$ \&\+\_\+pixels) +\item +\mbox{\Hypertarget{class_large_image_a3ac029a1cb3504320368b4f73541eeab}\label{class_large_image_a3ac029a1cb3504320368b4f73541eeab}} +{\bfseries Large\+Image} (int \+\_\+number\+\_\+subimages, int \+\_\+channels, int \+\_\+width, int \+\_\+height, int \+\_\+padding, int \+\_\+classification, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ uint8\+\_\+t $>$ $>$ $>$ \&\+\_\+pixels, const \textbf{ vector}$<$ \textbf{ vector}$<$ uint8\+\_\+t $>$ $>$ \&\+\_\+alpha) +\item +\mbox{\Hypertarget{class_large_image_a4fc7ac1ffabc00af459a884c8c9d34b7}\label{class_large_image_a4fc7ac1ffabc00af459a884c8c9d34b7}} +int {\bfseries get\+\_\+classification} () const +\item +\mbox{\Hypertarget{class_large_image_aef358a8476e1a4de74490ecdeba2312d}\label{class_large_image_aef358a8476e1a4de74490ecdeba2312d}} +int {\bfseries get\+\_\+number\+\_\+subimages} () const +\item +\mbox{\Hypertarget{class_large_image_a02c99add76f7c666f16ba10e3ad5cfef}\label{class_large_image_a02c99add76f7c666f16ba10e3ad5cfef}} +int {\bfseries get\+\_\+channels} () const +\item +\mbox{\Hypertarget{class_large_image_a31ef96819089a47675fd3693b04c139d}\label{class_large_image_a31ef96819089a47675fd3693b04c139d}} +int {\bfseries get\+\_\+height} () const +\item +\mbox{\Hypertarget{class_large_image_a10f59a23c9098f07ac3df958ce58ad86}\label{class_large_image_a10f59a23c9098f07ac3df958ce58ad86}} +int {\bfseries get\+\_\+width} () const +\item +\mbox{\Hypertarget{class_large_image_a40ee039fc8f05aa763287cee3bd678a8}\label{class_large_image_a40ee039fc8f05aa763287cee3bd678a8}} +void {\bfseries scale\+\_\+0\+\_\+1} () +\item +\mbox{\Hypertarget{class_large_image_ae279bb3875461ae63e22cdee43834178}\label{class_large_image_ae279bb3875461ae63e22cdee43834178}} +void {\bfseries get\+\_\+pixel\+\_\+avg} (\textbf{ vector}$<$ float $>$ \&channel\+\_\+avgs) const +\item +\mbox{\Hypertarget{class_large_image_a98d1b69620d083165532943ba5ac8524}\label{class_large_image_a98d1b69620d083165532943ba5ac8524}} +void {\bfseries get\+\_\+pixel\+\_\+variance} (const \textbf{ vector}$<$ float $>$ \&channel\+\_\+avgs, \textbf{ vector}$<$ float $>$ \&channel\+\_\+variances) const +\item +\mbox{\Hypertarget{class_large_image_a0527c2170e359b9686aa852bf405da18}\label{class_large_image_a0527c2170e359b9686aa852bf405da18}} +void {\bfseries normalize} (const \textbf{ vector}$<$ float $>$ \&channel\+\_\+avgs, const \textbf{ vector}$<$ float $>$ \&channel\+\_\+std\+\_\+dev) +\item +\mbox{\Hypertarget{class_large_image_a159b2becc98cdfc4fdf026eae66dafe6}\label{class_large_image_a159b2becc98cdfc4fdf026eae66dafe6}} +void {\bfseries print} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_large_image_a1da4a439bb3907b43e49dc41e713cff5}\label{class_large_image_a1da4a439bb3907b43e49dc41e713cff5}} +uint8\+\_\+t {\bfseries get\+\_\+pixel\+\_\+unnormalized} (int z, int y, int x) const +\item +\mbox{\Hypertarget{class_large_image_aff8fe834610a75b57ebbf65827788bf9}\label{class_large_image_aff8fe834610a75b57ebbf65827788bf9}} +uint8\+\_\+t {\bfseries get\+\_\+alpha\+\_\+unnormalized} (int y, int x) const +\item +\mbox{\Hypertarget{class_large_image_afc055d776b19b7c787cfc81ba3aba749}\label{class_large_image_afc055d776b19b7c787cfc81ba3aba749}} +void {\bfseries set\+\_\+pixel} (int z, int y, int x, uint8\+\_\+t value) +\item +\mbox{\Hypertarget{class_large_image_a4360cdcdb238b119173059749edb821c}\label{class_large_image_a4360cdcdb238b119173059749edb821c}} +uint8\+\_\+t {\bfseries get\+\_\+pixel} (int z, int y, int x) const +\item +\mbox{\Hypertarget{class_large_image_a2dfa7e90751535d5524eb2e73c71160a}\label{class_large_image_a2dfa7e90751535d5524eb2e73c71160a}} +void {\bfseries set\+\_\+alpha} (const \textbf{ vector}$<$ \textbf{ vector}$<$ uint8\+\_\+t $>$ $>$ \&\+\_\+alpha) +\item +\mbox{\Hypertarget{class_large_image_a7a8dc92927e6f7f6e00f5ac03cec46f6}\label{class_large_image_a7a8dc92927e6f7f6e00f5ac03cec46f6}} +void {\bfseries set\+\_\+alpha} (const \textbf{ vector}$<$ \textbf{ vector}$<$ float $>$ $>$ \&\+\_\+alpha) +\item +\mbox{\Hypertarget{class_large_image_aa4f6dd3a4823c4d26dad07fe1d9be5c1}\label{class_large_image_aa4f6dd3a4823c4d26dad07fe1d9be5c1}} +\mbox{\hyperlink{class_large_image}{Large\+Image}} $\ast$ {\bfseries copy} () const +\item +\mbox{\Hypertarget{class_large_image_a6500eafbabf95ef66261443b94f04481}\label{class_large_image_a6500eafbabf95ef66261443b94f04481}} +void {\bfseries draw\+\_\+png} (\textbf{ string} filename) const +\item +\mbox{\Hypertarget{class_large_image_a9559ca003f4dda91ee43cce77bfda5ca}\label{class_large_image_a9559ca003f4dda91ee43cce77bfda5ca}} +void {\bfseries draw\+\_\+png\+\_\+4channel} (\textbf{ string} filename) const +\item +\mbox{\Hypertarget{class_large_image_a04bf20b00c76dacc5d12a7796b86b6a8}\label{class_large_image_a04bf20b00c76dacc5d12a7796b86b6a8}} +void {\bfseries draw\+\_\+png\+\_\+alpha} (\textbf{ string} filename) const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_large_image_a58eef48962c16c7ca837894cf4f77535}\label{class_large_image_a58eef48962c16c7ca837894cf4f77535}} +int {\bfseries number\+\_\+subimages} +\item +\mbox{\Hypertarget{class_large_image_a0f0a4e62717b17314dd7b08d6f8cceb8}\label{class_large_image_a0f0a4e62717b17314dd7b08d6f8cceb8}} +int {\bfseries padding} +\item +\mbox{\Hypertarget{class_large_image_a87f7467f6da134b04186643fb492098f}\label{class_large_image_a87f7467f6da134b04186643fb492098f}} +int {\bfseries channels} +\item +\mbox{\Hypertarget{class_large_image_ad5b592d320336f9cfa242325290fbf5b}\label{class_large_image_ad5b592d320336f9cfa242325290fbf5b}} +int {\bfseries height} +\item +\mbox{\Hypertarget{class_large_image_ac6fcdfd1d1ef8ba42501fb78d3d6ae6d}\label{class_large_image_ac6fcdfd1d1ef8ba42501fb78d3d6ae6d}} +int {\bfseries width} +\item +\mbox{\Hypertarget{class_large_image_a0b0e6df79566c32d4f65a2fc936c172b}\label{class_large_image_a0b0e6df79566c32d4f65a2fc936c172b}} +int {\bfseries classification} +\item +\mbox{\Hypertarget{class_large_image_a6a80cd73f3e1564cfef64022420a77cd}\label{class_large_image_a6a80cd73f3e1564cfef64022420a77cd}} +\textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ uint8\+\_\+t $>$ $>$ $>$ {\bfseries pixels} +\item +\mbox{\Hypertarget{class_large_image_aca0fcf094ed3c4c27cff032398afef30}\label{class_large_image_aca0fcf094ed3c4c27cff032398afef30}} +\textbf{ vector}$<$ \textbf{ vector}$<$ uint8\+\_\+t $>$ $>$ {\bfseries alpha} +\item +\mbox{\Hypertarget{class_large_image_aa1eb933aebd546a8e3ed87a2f13d0114}\label{class_large_image_aa1eb933aebd546a8e3ed87a2f13d0114}} +const \mbox{\hyperlink{class_large_images}{Large\+Images}} $\ast$ {\bfseries images} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_large_image_a5368e97073c7df237833cef145d563a0}\label{class_large_image_a5368e97073c7df237833cef145d563a0}} +class {\bfseries Large\+Images} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/large\+\_\+image\+\_\+set.\+hxx\item +image\+\_\+tools/large\+\_\+image\+\_\+set.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_large_images.eps b/docs/latex/class_large_images.eps new file mode 100644 index 00000000..841f123a --- /dev/null +++ b/docs/latex/class_large_images.eps @@ -0,0 +1,203 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 441.176471 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.133333 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 3 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(LargeImages) cw +(MultiImagesInterface) cw +(ImagesInterface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (LargeImages) 0.000000 0.000000 box + (MultiImagesInterface) 0.000000 1.000000 box + (ImagesInterface) 0.000000 2.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in +solid +0 0.000000 1.000000 out +solid +1 0.000000 2.000000 in diff --git a/docs/latex/class_large_images.tex b/docs/latex/class_large_images.tex new file mode 100644 index 00000000..ca132556 --- /dev/null +++ b/docs/latex/class_large_images.tex @@ -0,0 +1,134 @@ +\hypertarget{class_large_images}{}\doxysection{Large\+Images Class Reference} +\label{class_large_images}\index{LargeImages@{LargeImages}} +Inheritance diagram for Large\+Images\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=3.000000cm]{class_large_images} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_large_images_abae9d7a5e94f6956a235cbc8f6162b58}\label{class_large_images_abae9d7a5e94f6956a235cbc8f6162b58}} +int {\bfseries read\+\_\+images\+\_\+from\+\_\+file} (\textbf{ string} binary\+\_\+filename) +\item +\mbox{\Hypertarget{class_large_images_a3b988b76312653dc9bcee497646a79f5}\label{class_large_images_a3b988b76312653dc9bcee497646a79f5}} +{\bfseries Large\+Images} (\textbf{ string} binary\+\_\+filename, int \+\_\+padding, int \+\_\+subimage\+\_\+height, int \+\_\+subimage\+\_\+width) +\item +\mbox{\Hypertarget{class_large_images_ae532aed17a1a0c402c051a729ef1d031}\label{class_large_images_ae532aed17a1a0c402c051a729ef1d031}} +{\bfseries Large\+Images} (\textbf{ string} binary\+\_\+filename, int \+\_\+padding, int \+\_\+subimage\+\_\+height, int \+\_\+subimage\+\_\+width, const \textbf{ vector}$<$ float $>$ \&\+\_\+channel\+\_\+avg, const \textbf{ vector}$<$ float $>$ \&channel\+\_\+std\+\_\+dev) +\item +\mbox{\Hypertarget{class_large_images_a861f894a38271f805403af8636eb812b}\label{class_large_images_a861f894a38271f805403af8636eb812b}} +\textbf{ string} {\bfseries get\+\_\+filename} () const +\item +\mbox{\Hypertarget{class_large_images_a775cf9aa65329ae44e6bdb4ec85eef8b}\label{class_large_images_a775cf9aa65329ae44e6bdb4ec85eef8b}} +int {\bfseries get\+\_\+class\+\_\+size} (int i) const +\item +\mbox{\Hypertarget{class_large_images_ac718faad0873e9486ccd39b63197f525}\label{class_large_images_ac718faad0873e9486ccd39b63197f525}} +int {\bfseries get\+\_\+number\+\_\+classes} () const +\item +\mbox{\Hypertarget{class_large_images_a9cac1e44ff386b147896cf28a8624d7f}\label{class_large_images_a9cac1e44ff386b147896cf28a8624d7f}} +int {\bfseries get\+\_\+number\+\_\+images} () const +\item +\mbox{\Hypertarget{class_large_images_a862c96a0510e96541b806c0d20a89701}\label{class_large_images_a862c96a0510e96541b806c0d20a89701}} +int {\bfseries get\+\_\+number\+\_\+large\+\_\+images} () const +\item +\mbox{\Hypertarget{class_large_images_aadf7bebac3373bd03fcbbf4e7ab910da}\label{class_large_images_aadf7bebac3373bd03fcbbf4e7ab910da}} +int {\bfseries get\+\_\+number\+\_\+subimages} (int i) const +\item +\mbox{\Hypertarget{class_large_images_a9d8ada5c6eb90371b96689f85c319365}\label{class_large_images_a9d8ada5c6eb90371b96689f85c319365}} +int {\bfseries get\+\_\+padding} () const +\item +\mbox{\Hypertarget{class_large_images_a99649e9453ee5b4876ee5a457db3e97c}\label{class_large_images_a99649e9453ee5b4876ee5a457db3e97c}} +int {\bfseries get\+\_\+image\+\_\+channels} () const +\item +\mbox{\Hypertarget{class_large_images_ae921fcdf3aa98b5828da0c12ee2a6d01}\label{class_large_images_ae921fcdf3aa98b5828da0c12ee2a6d01}} +int {\bfseries get\+\_\+image\+\_\+width} () const +\item +\mbox{\Hypertarget{class_large_images_af7ee7177aed41a82533d7dedd62fc727}\label{class_large_images_af7ee7177aed41a82533d7dedd62fc727}} +int {\bfseries get\+\_\+image\+\_\+height} () const +\item +\mbox{\Hypertarget{class_large_images_a077fef6341653d7075a38b95ef9e11ee}\label{class_large_images_a077fef6341653d7075a38b95ef9e11ee}} +int {\bfseries get\+\_\+large\+\_\+image\+\_\+channels} (int image) const +\item +\mbox{\Hypertarget{class_large_images_a00264c01a319140eecbc8ee05b40cf3e}\label{class_large_images_a00264c01a319140eecbc8ee05b40cf3e}} +int {\bfseries get\+\_\+large\+\_\+image\+\_\+width} (int image) const +\item +\mbox{\Hypertarget{class_large_images_aae1ce6b40bfc9f5fd3e81d7afc1a2baf}\label{class_large_images_aae1ce6b40bfc9f5fd3e81d7afc1a2baf}} +int {\bfseries get\+\_\+large\+\_\+image\+\_\+height} (int image) const +\item +\mbox{\Hypertarget{class_large_images_aa7c303b7d91848d8ed55fde1d4e08f22}\label{class_large_images_aa7c303b7d91848d8ed55fde1d4e08f22}} +int {\bfseries get\+\_\+image\+\_\+classification} (int image) const +\item +\mbox{\Hypertarget{class_large_images_a0df9f112a2a1674a78914eede69feb48}\label{class_large_images_a0df9f112a2a1674a78914eede69feb48}} +int {\bfseries get\+\_\+classification} (int subimage) const +\item +\mbox{\Hypertarget{class_large_images_ab5aa13812806d6308695e0b3f13b9cd2}\label{class_large_images_ab5aa13812806d6308695e0b3f13b9cd2}} +float {\bfseries get\+\_\+pixel} (int subimage, int z, int y, int x) const +\item +\mbox{\Hypertarget{class_large_images_a041172239fd1685606a891bc2787a9e2}\label{class_large_images_a041172239fd1685606a891bc2787a9e2}} +float {\bfseries get\+\_\+raw\+\_\+pixel} (int subimage, int z, int y, int x) const +\item +\mbox{\Hypertarget{class_large_images_ab7609a02aa77b62f0cacd463469a9660}\label{class_large_images_ab7609a02aa77b62f0cacd463469a9660}} +void {\bfseries calculate\+\_\+avg\+\_\+std\+\_\+dev} () +\item +\mbox{\Hypertarget{class_large_images_ada525689c70283770c1f25cee6925872}\label{class_large_images_ada525689c70283770c1f25cee6925872}} +float {\bfseries get\+\_\+channel\+\_\+avg} (int channel) const +\item +\mbox{\Hypertarget{class_large_images_a8e8ac5408d261787e60da968d488743c}\label{class_large_images_a8e8ac5408d261787e60da968d488743c}} +float {\bfseries get\+\_\+channel\+\_\+std\+\_\+dev} (int channel) const +\item +\mbox{\Hypertarget{class_large_images_abbc43c063eb314581c35e15b9a0b5023}\label{class_large_images_abbc43c063eb314581c35e15b9a0b5023}} +const \textbf{ vector}$<$ float $>$ \& {\bfseries get\+\_\+average} () const +\item +\mbox{\Hypertarget{class_large_images_aae227d9f39bdbdf6cc43fe41ee69797b}\label{class_large_images_aae227d9f39bdbdf6cc43fe41ee69797b}} +const \textbf{ vector}$<$ float $>$ \& {\bfseries get\+\_\+std\+\_\+dev} () const +\item +\mbox{\Hypertarget{class_large_images_a573af65747c205865387aa19b7dc1a1e}\label{class_large_images_a573af65747c205865387aa19b7dc1a1e}} +void {\bfseries normalize} () +\item +\mbox{\Hypertarget{class_large_images_ab33ecc4e2e4cd1a8b9d1a32274f8cbfd}\label{class_large_images_ab33ecc4e2e4cd1a8b9d1a32274f8cbfd}} +\mbox{\hyperlink{class_large_image}{Large\+Image}} $\ast$ {\bfseries copy\+\_\+image} (int i) const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_large_images_a0ea05822e1f124cb50230a027ba76103}\label{class_large_images_a0ea05822e1f124cb50230a027ba76103}} +\textbf{ string} {\bfseries filename} +\item +\mbox{\Hypertarget{class_large_images_aaf3761bc4b42a96ea1552b03804a39eb}\label{class_large_images_aaf3761bc4b42a96ea1552b03804a39eb}} +int {\bfseries number\+\_\+classes} +\item +\mbox{\Hypertarget{class_large_images_a782d49aa1b2a6a3b6b2420a89c2c59f5}\label{class_large_images_a782d49aa1b2a6a3b6b2420a89c2c59f5}} +int {\bfseries number\+\_\+images} +\item +\mbox{\Hypertarget{class_large_images_aa12320b3af74e854688c9b54b441d55f}\label{class_large_images_aa12320b3af74e854688c9b54b441d55f}} +\textbf{ vector}$<$ int $>$ {\bfseries class\+\_\+sizes} +\item +\mbox{\Hypertarget{class_large_images_aaab29ddb98cee4392a61d6611c7661fa}\label{class_large_images_aaab29ddb98cee4392a61d6611c7661fa}} +int {\bfseries padding} +\item +\mbox{\Hypertarget{class_large_images_a6fa158fb9a68a1b05c1d90a8fba093e7}\label{class_large_images_a6fa158fb9a68a1b05c1d90a8fba093e7}} +int {\bfseries channels} +\item +\mbox{\Hypertarget{class_large_images_a6a2819069b91ce022bd494cc5e4a91e3}\label{class_large_images_a6a2819069b91ce022bd494cc5e4a91e3}} +int {\bfseries subimage\+\_\+width} +\item +\mbox{\Hypertarget{class_large_images_a7a653c34470307851becf592e774d877}\label{class_large_images_a7a653c34470307851becf592e774d877}} +int {\bfseries subimage\+\_\+height} +\item +\mbox{\Hypertarget{class_large_images_a831f085192a46be28a0c75934591ad3d}\label{class_large_images_a831f085192a46be28a0c75934591ad3d}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_large_image}{Large\+Image}} $>$ {\bfseries images} +\item +\mbox{\Hypertarget{class_large_images_a5edb43a808cacb2bc21ce46d123dc05f}\label{class_large_images_a5edb43a808cacb2bc21ce46d123dc05f}} +\textbf{ vector}$<$ float $>$ {\bfseries channel\+\_\+avg} +\item +\mbox{\Hypertarget{class_large_images_ac45b1f977e41d511db018dd9bcae003a}\label{class_large_images_ac45b1f977e41d511db018dd9bcae003a}} +\textbf{ vector}$<$ float $>$ {\bfseries channel\+\_\+std\+\_\+dev} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/large\+\_\+image\+\_\+set.\+hxx\item +image\+\_\+tools/large\+\_\+image\+\_\+set.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_line.tex b/docs/latex/class_line.tex new file mode 100644 index 00000000..39cb7860 --- /dev/null +++ b/docs/latex/class_line.tex @@ -0,0 +1,29 @@ +\hypertarget{class_line}{}\doxysection{Line Class Reference} +\label{class_line}\index{Line@{Line}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_line_a495909e935abfa7fe77a47be7a733a8d}\label{class_line_a495909e935abfa7fe77a47be7a733a8d}} +{\bfseries Line} (int \+\_\+y1, int \+\_\+x1, int \+\_\+y2, int \+\_\+x2) +\end{DoxyCompactItemize} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_line_a850c96af61bd595a68b5e867540204f1}\label{class_line_a850c96af61bd595a68b5e867540204f1}} +int {\bfseries y1} +\item +\mbox{\Hypertarget{class_line_a1c37aeef714f6c96454c2a9a2dadb69a}\label{class_line_a1c37aeef714f6c96454c2a9a2dadb69a}} +int {\bfseries x1} +\item +\mbox{\Hypertarget{class_line_ae31c92ab0df730a2fdd34201ce6feb9d}\label{class_line_ae31c92ab0df730a2fdd34201ce6feb9d}} +int {\bfseries y2} +\item +\mbox{\Hypertarget{class_line_a4f08d1038eba337d6f0fb32713e528f1}\label{class_line_a4f08d1038eba337d6f0fb32713e528f1}} +int {\bfseries x2} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/mosaic\+\_\+image\+\_\+set.\+hxx\item +image\+\_\+tools/mosaic\+\_\+image\+\_\+set.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_log.tex b/docs/latex/class_log.tex new file mode 100644 index 00000000..5534442e --- /dev/null +++ b/docs/latex/class_log.tex @@ -0,0 +1,452 @@ +\hypertarget{class_log}{}\doxysection{Log Class Reference} +\label{class_log}\index{Log@{Log}} +\doxysubsection*{Static Public Member Functions} +\begin{DoxyCompactItemize} +\item +static void \mbox{\hyperlink{class_log_a1e95d22090ba14b096609c67487f9761}{register\+\_\+command\+\_\+line\+\_\+arguments}} () +\begin{DoxyCompactList}\small\item\em Registers used command line arguments and instructions with the Command\+Line class. \end{DoxyCompactList}\item +static \textbf{ int8\+\_\+t} \mbox{\hyperlink{class_log_a92a5eb55bc8e480aa984d6d592f07d69}{parse\+\_\+level\+\_\+from\+\_\+string}} (\textbf{ string} level) +\item +static void \mbox{\hyperlink{class_log_afc35697517d849ab8cf4a774d2b32ba8}{initialize}} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&arguments) +\begin{DoxyCompactList}\small\item\em Initializes the \mbox{\hyperlink{class_log}{Log}} given arguments retreived from the Command\+Line class. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_a6183417026fe34b0d1494f1504b52b8f}{set\+\_\+rank}} (\textbf{ int32\+\_\+t} \+\_\+process\+\_\+rank) +\begin{DoxyCompactList}\small\item\em Sets the M\+PI process rank for this \mbox{\hyperlink{class_log}{Log}}. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_a4718a27c58db20d66ac3c511931d475b}{restrict\+\_\+to\+\_\+rank}} (\textbf{ int32\+\_\+t} \+\_\+restricted\+\_\+rank) +\begin{DoxyCompactList}\small\item\em Specifies which M\+PI process to allow messages from. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_a1cecca268a9c418480a0ac325303f7a0}\label{class_log_a1cecca268a9c418480a0ac325303f7a0}} +static void \mbox{\hyperlink{class_log_a1cecca268a9c418480a0ac325303f7a0}{clear\+\_\+rank\+\_\+restriction}} () +\begin{DoxyCompactList}\small\item\em Clears the M\+PI process rank restriction from messages allowing any process to write to the log (sets \mbox{\hyperlink{class_log_a47ec200a53bfc6b8b6a39bc91d5dbc86}{Log\+::restricted\+\_\+rank}} back to -\/1). \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_ac02fea24138d69a35521d82ee64bb90d}{set\+\_\+id}} (\textbf{ string} human\+\_\+readable\+\_\+id) +\begin{DoxyCompactList}\small\item\em Sets a human readable thread id for this thread. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_aecae136e87aadf153c98f4f1f92ac44f}{release\+\_\+id}} (\textbf{ string} human\+\_\+readable\+\_\+id) +\begin{DoxyCompactList}\small\item\em Releases a the human readable thread id previously set by by the provided human readable id;. \end{DoxyCompactList}\item +static bool \mbox{\hyperlink{class_log_afdfbb3c30e059607ccf10a6ef5e0de9a}{at\+\_\+level}} (\textbf{ int8\+\_\+t} level) +\begin{DoxyCompactList}\small\item\em Determines if either output level (the file or standard output) level is above the level passed as a parameter. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_a805517b44b61d2b5151a2f4794596a7e}{fatal}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs a fatal message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_a89f6eebb445913e66423614839225db2}{error}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs an error message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_a477d1ab8cce3a2ef12cb8006fa3c6bf5}{warning}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs a warning message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_adb225c396180e0d8f73580e71eae6554}{info}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs an info message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_a044ee1254485cb519d0576dc71103f0a}{debug}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs a debug message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_ad0d504bc7f5957a814e9de5ea08f982a}{trace}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs a trace message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_ad2850dbb9d26c84c3fc2bcecc93493d6}{fatal\+\_\+no\+\_\+header}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs a fatal message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_ac3e898dc093679bb7d1e3a3f98872158}{error\+\_\+no\+\_\+header}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs an error message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_a9bbfa4b8f4fb2262d914f00ba04dd6c6}{warning\+\_\+no\+\_\+header}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs a warning message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_a4b4380a986ebf18e448221d78a236bc3}{info\+\_\+no\+\_\+header}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs an info message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_a6c4b9de38585df6c03defcbe0e420268}{debug\+\_\+no\+\_\+header}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs a debug message. \end{DoxyCompactList}\item +static void \mbox{\hyperlink{class_log_a5eca5217a6fc23a54cff35ff12ad04f6}{trace\+\_\+no\+\_\+header}} (const char $\ast$format,...) +\begin{DoxyCompactList}\small\item\em Logs a trace message. \end{DoxyCompactList}\end{DoxyCompactItemize} +\doxysubsection*{Static Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_log_a44e61f1a4560b557bd8b3374cd87e0e3}\label{class_log_a44e61f1a4560b557bd8b3374cd87e0e3}} +static const int8\+\_\+t \mbox{\hyperlink{class_log_a44e61f1a4560b557bd8b3374cd87e0e3}{N\+O\+NE}} = 0 +\begin{DoxyCompactList}\small\item\em Specifies no messages will be logged. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_a829c89b207740611e743a6e2f07e477f}\label{class_log_a829c89b207740611e743a6e2f07e477f}} +static const int8\+\_\+t \mbox{\hyperlink{class_log_a829c89b207740611e743a6e2f07e477f}{F\+A\+T\+AL}} = 1 +\begin{DoxyCompactList}\small\item\em Specifies only fatal messages will be logged. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_af3c3afdba50a451fd24b6014a9f6e427}\label{class_log_af3c3afdba50a451fd24b6014a9f6e427}} +static const int8\+\_\+t \mbox{\hyperlink{class_log_af3c3afdba50a451fd24b6014a9f6e427}{E\+R\+R\+OR}} = 2 +\begin{DoxyCompactList}\small\item\em Specifies error and above messages will be logged. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_a02c6c399678d6687f38c6600751aa2e7}\label{class_log_a02c6c399678d6687f38c6600751aa2e7}} +static const int8\+\_\+t \mbox{\hyperlink{class_log_a02c6c399678d6687f38c6600751aa2e7}{W\+A\+R\+N\+I\+NG}} = 3 +\begin{DoxyCompactList}\small\item\em Specifies warning and above messages will be logged. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_a70efafdaaf797f644bb24a8e1c7f5643}\label{class_log_a70efafdaaf797f644bb24a8e1c7f5643}} +static const int8\+\_\+t \mbox{\hyperlink{class_log_a70efafdaaf797f644bb24a8e1c7f5643}{I\+N\+FO}} = 4 +\begin{DoxyCompactList}\small\item\em Specifies info and above messages will be logged. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_addf4eb67a84c9ee39ed3028005f25e40}\label{class_log_addf4eb67a84c9ee39ed3028005f25e40}} +static const int8\+\_\+t \mbox{\hyperlink{class_log_addf4eb67a84c9ee39ed3028005f25e40}{D\+E\+B\+UG}} = 5 +\begin{DoxyCompactList}\small\item\em Specifies debug and above messages will be logged. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_a696a49a4d58146f0ee69be1562991e12}\label{class_log_a696a49a4d58146f0ee69be1562991e12}} +static const int8\+\_\+t \mbox{\hyperlink{class_log_a696a49a4d58146f0ee69be1562991e12}{T\+R\+A\+CE}} = 6 +\begin{DoxyCompactList}\small\item\em Specifies trace and above messages will be logged. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_a4249272125f8149b2d042101bcc6b49f}\label{class_log_a4249272125f8149b2d042101bcc6b49f}} +static const int8\+\_\+t \mbox{\hyperlink{class_log_a4249272125f8149b2d042101bcc6b49f}{A\+LL}} = 7 +\begin{DoxyCompactList}\small\item\em Specifies all messages will be logged. \end{DoxyCompactList}\end{DoxyCompactItemize} +\doxysubsection*{Static Private Member Functions} +\begin{DoxyCompactItemize} +\item +static void \mbox{\hyperlink{class_log_aff6a4531e693fd53ad051c7fef28c6aa}{write\+\_\+message}} (bool print\+\_\+header, \textbf{ int8\+\_\+t} message\+\_\+level, const char $\ast$message\+\_\+type, const char $\ast$format, \textbf{ va\+\_\+list} arguments) +\begin{DoxyCompactList}\small\item\em Potentially writes the message to either standard output or the log file if the message level is high enough. \end{DoxyCompactList}\end{DoxyCompactItemize} +\doxysubsection*{Static Private Attributes} +\begin{DoxyCompactItemize} +\item +static int32\+\_\+t \mbox{\hyperlink{class_log_a633c6eafa442ecfb3b3322f89496e52e}{std\+\_\+message\+\_\+level}} = \mbox{\hyperlink{class_log_a70efafdaaf797f644bb24a8e1c7f5643}{I\+N\+FO}} +\begin{DoxyCompactList}\small\item\em Specifies which messages to log. \end{DoxyCompactList}\item +static int32\+\_\+t \mbox{\hyperlink{class_log_a08b261dd43ff89110e602a3a61f6ec61}{file\+\_\+message\+\_\+level}} = \mbox{\hyperlink{class_log_a70efafdaaf797f644bb24a8e1c7f5643}{I\+N\+FO}} +\begin{DoxyCompactList}\small\item\em Specifies which messages to log. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_a20bd437aa24965f6e9650fd5e2d48923}\label{class_log_a20bd437aa24965f6e9650fd5e2d48923}} +static bool \mbox{\hyperlink{class_log_a20bd437aa24965f6e9650fd5e2d48923}{write\+\_\+to\+\_\+file}} = true +\begin{DoxyCompactList}\small\item\em Specifies if the logs should also be written to a flie. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_aec94f82d0ed91efcb398ce15f2f14a34}\label{class_log_aec94f82d0ed91efcb398ce15f2f14a34}} +static int32\+\_\+t \mbox{\hyperlink{class_log_aec94f82d0ed91efcb398ce15f2f14a34}{max\+\_\+header\+\_\+length}} = 256 +\begin{DoxyCompactList}\small\item\em Specifies the maximum length for the message header. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_a5503fcf17f82f95b1090a797af98343c}\label{class_log_a5503fcf17f82f95b1090a797af98343c}} +static int32\+\_\+t \mbox{\hyperlink{class_log_a5503fcf17f82f95b1090a797af98343c}{max\+\_\+message\+\_\+length}} = 1024 +\begin{DoxyCompactList}\small\item\em Specifies the maximum message buffer length. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_a18dc29ac4d511d8eab1bb9bc5771e7bf}\label{class_log_a18dc29ac4d511d8eab1bb9bc5771e7bf}} +static \textbf{ string} \mbox{\hyperlink{class_log_a18dc29ac4d511d8eab1bb9bc5771e7bf}{output\+\_\+directory}} = \char`\"{}./logs\char`\"{} +\begin{DoxyCompactList}\small\item\em Specifies which directory to write the log files to (if the logs are being writtten to a file). \end{DoxyCompactList}\item +static \textbf{ map}$<$ thread\+::id, \textbf{ string} $>$ \mbox{\hyperlink{class_log_a814d0d060c29987d5d43d55b3d8d542c}{log\+\_\+ids}} +\begin{DoxyCompactList}\small\item\em A map of C++ thread ids (which are not human readable) to human readable integer ids. \end{DoxyCompactList}\item +static int32\+\_\+t \mbox{\hyperlink{class_log_ad6bd8ce041323bb69e69aeb9edbe12ee}{process\+\_\+rank}} = -\/1 +\begin{DoxyCompactList}\small\item\em The M\+PI process rank for this \mbox{\hyperlink{class_log}{Log}} instance. \end{DoxyCompactList}\item +static int32\+\_\+t \mbox{\hyperlink{class_log_a47ec200a53bfc6b8b6a39bc91d5dbc86}{restricted\+\_\+rank}} = -\/1 +\begin{DoxyCompactList}\small\item\em Defaults at -\/1, when set to a process rank ($>$= 0) the \mbox{\hyperlink{class_log}{Log}} will only print messages if its rank is the same as the restricted rank. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_log_afe8e50d6e52f39d4ed931f13eb49af5f}\label{class_log_afe8e50d6e52f39d4ed931f13eb49af5f}} +static \textbf{ map}$<$ \textbf{ string}, \mbox{\hyperlink{class_log_file}{Log\+File}} $\ast$ $>$ \mbox{\hyperlink{class_log_afe8e50d6e52f39d4ed931f13eb49af5f}{output\+\_\+files}} +\begin{DoxyCompactList}\small\item\em A map of human readable ids to output files which the log messages will be written to. \end{DoxyCompactList}\item +static shared\+\_\+mutex \mbox{\hyperlink{class_log_aa8bf3e23cf88dc4e48aafd791fe93cd7}{log\+\_\+ids\+\_\+mutex}} +\begin{DoxyCompactList}\small\item\em A std\+::shared\+\_\+mutex protecting the Log\+::thread\+\_\+ids map. \end{DoxyCompactList}\end{DoxyCompactItemize} + + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_log_afdfbb3c30e059607ccf10a6ef5e0de9a}\label{class_log_afdfbb3c30e059607ccf10a6ef5e0de9a}} +\index{Log@{Log}!at\_level@{at\_level}} +\index{at\_level@{at\_level}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{at\_level()}{at\_level()}} +{\footnotesize\ttfamily bool Log\+::at\+\_\+level (\begin{DoxyParamCaption}\item[{\textbf{ int8\+\_\+t}}]{level }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Determines if either output level (the file or standard output) level is above the level passed as a parameter. + + +\begin{DoxyParams}{Parameters} +{\em level} & is the level to be greater than or equal to\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +true if either the file or standard output level is greater than or equal to the passed level +\end{DoxyReturn} +\mbox{\Hypertarget{class_log_a044ee1254485cb519d0576dc71103f0a}\label{class_log_a044ee1254485cb519d0576dc71103f0a}} +\index{Log@{Log}!debug@{debug}} +\index{debug@{debug}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{debug()}{debug()}} +{\footnotesize\ttfamily void Log\+::debug (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs a debug message. + +varargs are the same as in printf. \mbox{\Hypertarget{class_log_a6c4b9de38585df6c03defcbe0e420268}\label{class_log_a6c4b9de38585df6c03defcbe0e420268}} +\index{Log@{Log}!debug\_no\_header@{debug\_no\_header}} +\index{debug\_no\_header@{debug\_no\_header}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{debug\_no\_header()}{debug\_no\_header()}} +{\footnotesize\ttfamily void Log\+::debug\+\_\+no\+\_\+header (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs a debug message. + +Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf. \mbox{\Hypertarget{class_log_a89f6eebb445913e66423614839225db2}\label{class_log_a89f6eebb445913e66423614839225db2}} +\index{Log@{Log}!error@{error}} +\index{error@{error}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{error()}{error()}} +{\footnotesize\ttfamily void Log\+::error (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs an error message. + +varargs are the same as in printf. \mbox{\Hypertarget{class_log_ac3e898dc093679bb7d1e3a3f98872158}\label{class_log_ac3e898dc093679bb7d1e3a3f98872158}} +\index{Log@{Log}!error\_no\_header@{error\_no\_header}} +\index{error\_no\_header@{error\_no\_header}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{error\_no\_header()}{error\_no\_header()}} +{\footnotesize\ttfamily void Log\+::error\+\_\+no\+\_\+header (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs an error message. + +Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf. \mbox{\Hypertarget{class_log_a805517b44b61d2b5151a2f4794596a7e}\label{class_log_a805517b44b61d2b5151a2f4794596a7e}} +\index{Log@{Log}!fatal@{fatal}} +\index{fatal@{fatal}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{fatal()}{fatal()}} +{\footnotesize\ttfamily void Log\+::fatal (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs a fatal message. + +varargs are the same as in printf. \mbox{\Hypertarget{class_log_ad2850dbb9d26c84c3fc2bcecc93493d6}\label{class_log_ad2850dbb9d26c84c3fc2bcecc93493d6}} +\index{Log@{Log}!fatal\_no\_header@{fatal\_no\_header}} +\index{fatal\_no\_header@{fatal\_no\_header}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{fatal\_no\_header()}{fatal\_no\_header()}} +{\footnotesize\ttfamily void Log\+::fatal\+\_\+no\+\_\+header (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs a fatal message. + +Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf. \mbox{\Hypertarget{class_log_adb225c396180e0d8f73580e71eae6554}\label{class_log_adb225c396180e0d8f73580e71eae6554}} +\index{Log@{Log}!info@{info}} +\index{info@{info}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{info()}{info()}} +{\footnotesize\ttfamily void Log\+::info (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs an info message. + +varargs are the same as in printf. \mbox{\Hypertarget{class_log_a4b4380a986ebf18e448221d78a236bc3}\label{class_log_a4b4380a986ebf18e448221d78a236bc3}} +\index{Log@{Log}!info\_no\_header@{info\_no\_header}} +\index{info\_no\_header@{info\_no\_header}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{info\_no\_header()}{info\_no\_header()}} +{\footnotesize\ttfamily void Log\+::info\+\_\+no\+\_\+header (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs an info message. + +Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf. \mbox{\Hypertarget{class_log_afc35697517d849ab8cf4a774d2b32ba8}\label{class_log_afc35697517d849ab8cf4a774d2b32ba8}} +\index{Log@{Log}!initialize@{initialize}} +\index{initialize@{initialize}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{initialize()}{initialize()}} +{\footnotesize\ttfamily void Log\+::initialize (\begin{DoxyParamCaption}\item[{const \textbf{ vector}$<$ \textbf{ string} $>$ \&}]{arguments }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Initializes the \mbox{\hyperlink{class_log}{Log}} given arguments retreived from the Command\+Line class. + + +\begin{DoxyParams}{Parameters} +{\em arguments} & the command line arguments \mbox{\hyperlink{class_log_a1e95d22090ba14b096609c67487f9761}{Log\+::register\+\_\+command\+\_\+line\+\_\+arguments()}} must be called before \mbox{\hyperlink{class_log_afc35697517d849ab8cf4a774d2b32ba8}{Log\+::initialize()}} \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_log_a92a5eb55bc8e480aa984d6d592f07d69}\label{class_log_a92a5eb55bc8e480aa984d6d592f07d69}} +\index{Log@{Log}!parse\_level\_from\_string@{parse\_level\_from\_string}} +\index{parse\_level\_from\_string@{parse\_level\_from\_string}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{parse\_level\_from\_string()}{parse\_level\_from\_string()}} +{\footnotesize\ttfamily \textbf{ int8\+\_\+t} Log\+::parse\+\_\+level\+\_\+from\+\_\+string (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{level }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + +\begin{DoxyParams}{Parameters} +{\em a} & string representation of the message level, either as text (e.\+g., \char`\"{}\+I\+N\+F\+O\char`\"{}) or as a number (e.\+g., 4)\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +the message level as an int8\+\_\+t (i.\+e., one of the message level constants) +\end{DoxyReturn} +\mbox{\Hypertarget{class_log_a1e95d22090ba14b096609c67487f9761}\label{class_log_a1e95d22090ba14b096609c67487f9761}} +\index{Log@{Log}!register\_command\_line\_arguments@{register\_command\_line\_arguments}} +\index{register\_command\_line\_arguments@{register\_command\_line\_arguments}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{register\_command\_line\_arguments()}{register\_command\_line\_arguments()}} +{\footnotesize\ttfamily void Log\+::register\+\_\+command\+\_\+line\+\_\+arguments (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Registers used command line arguments and instructions with the Command\+Line class. + +Needs to be called at the beginning of the main method of any programming using the \mbox{\hyperlink{class_log}{Log}} class. Will register the following command line arguments and instructions\+: +\begin{DoxyEnumerate} +\item log\+\_\+level \+: specifies the max message level of messages to be printed +\item log\+\_\+directory \+: specifies the output directory for message logs +\end{DoxyEnumerate}\mbox{\Hypertarget{class_log_aecae136e87aadf153c98f4f1f92ac44f}\label{class_log_aecae136e87aadf153c98f4f1f92ac44f}} +\index{Log@{Log}!release\_id@{release\_id}} +\index{release\_id@{release\_id}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{release\_id()}{release\_id()}} +{\footnotesize\ttfamily void Log\+::release\+\_\+id (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{human\+\_\+readable\+\_\+id }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Releases a the human readable thread id previously set by by the provided human readable id;. + +This will report an error and exit if this human readable id has not already been set, or if it has been relased by another thread. + + +\begin{DoxyParams}{Parameters} +{\em human\+\_\+readable\+\_\+id} & is a human readable thread id which has previously been set with \mbox{\hyperlink{class_log_ac02fea24138d69a35521d82ee64bb90d}{Log\+::set\+\_\+id(string)}} \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_log_a4718a27c58db20d66ac3c511931d475b}\label{class_log_a4718a27c58db20d66ac3c511931d475b}} +\index{Log@{Log}!restrict\_to\_rank@{restrict\_to\_rank}} +\index{restrict\_to\_rank@{restrict\_to\_rank}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{restrict\_to\_rank()}{restrict\_to\_rank()}} +{\footnotesize\ttfamily void Log\+::restrict\+\_\+to\+\_\+rank (\begin{DoxyParamCaption}\item[{\textbf{ int32\+\_\+t}}]{\+\_\+restricted\+\_\+rank }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Specifies which M\+PI process to allow messages from. + +A value $<$ 0 will allow messages from any rank. +\begin{DoxyParams}{Parameters} +{\em \+\_\+restricted\+\_\+rank} & is the M\+PI rank to only print messages from \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_log_ac02fea24138d69a35521d82ee64bb90d}\label{class_log_ac02fea24138d69a35521d82ee64bb90d}} +\index{Log@{Log}!set\_id@{set\_id}} +\index{set\_id@{set\_id}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{set\_id()}{set\_id()}} +{\footnotesize\ttfamily void Log\+::set\+\_\+id (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{human\+\_\+readable\+\_\+id }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Sets a human readable thread id for this thread. + +This will use \textbf{ std\+::this\+\_\+thread\+::get\+\_\+id()} to get c++\textquotesingle{}s automatically generated thread id, and then put it in the Log\+::thread\+\_\+ids map, so it can be used to write cleaner logs. + +This will report an error and exit if another thread has already reserved this human readable id. + + +\begin{DoxyParams}{Parameters} +{\em human\+\_\+readable\+\_\+id} & a human readable thread id \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_log_a6183417026fe34b0d1494f1504b52b8f}\label{class_log_a6183417026fe34b0d1494f1504b52b8f}} +\index{Log@{Log}!set\_rank@{set\_rank}} +\index{set\_rank@{set\_rank}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{set\_rank()}{set\_rank()}} +{\footnotesize\ttfamily void Log\+::set\+\_\+rank (\begin{DoxyParamCaption}\item[{\textbf{ int32\+\_\+t}}]{\+\_\+process\+\_\+rank }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Sets the M\+PI process rank for this \mbox{\hyperlink{class_log}{Log}}. + + +\begin{DoxyParams}{Parameters} +{\em \+\_\+process\+\_\+rank} & is the M\+PI rank of this process \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_log_ad0d504bc7f5957a814e9de5ea08f982a}\label{class_log_ad0d504bc7f5957a814e9de5ea08f982a}} +\index{Log@{Log}!trace@{trace}} +\index{trace@{trace}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{trace()}{trace()}} +{\footnotesize\ttfamily void Log\+::trace (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs a trace message. + +varargs are the same as in printf. \mbox{\Hypertarget{class_log_a5eca5217a6fc23a54cff35ff12ad04f6}\label{class_log_a5eca5217a6fc23a54cff35ff12ad04f6}} +\index{Log@{Log}!trace\_no\_header@{trace\_no\_header}} +\index{trace\_no\_header@{trace\_no\_header}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{trace\_no\_header()}{trace\_no\_header()}} +{\footnotesize\ttfamily void Log\+::trace\+\_\+no\+\_\+header (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs a trace message. + +Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf. \mbox{\Hypertarget{class_log_a477d1ab8cce3a2ef12cb8006fa3c6bf5}\label{class_log_a477d1ab8cce3a2ef12cb8006fa3c6bf5}} +\index{Log@{Log}!warning@{warning}} +\index{warning@{warning}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{warning()}{warning()}} +{\footnotesize\ttfamily void Log\+::warning (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs a warning message. + +varargs are the same as in printf. \mbox{\Hypertarget{class_log_a9bbfa4b8f4fb2262d914f00ba04dd6c6}\label{class_log_a9bbfa4b8f4fb2262d914f00ba04dd6c6}} +\index{Log@{Log}!warning\_no\_header@{warning\_no\_header}} +\index{warning\_no\_header@{warning\_no\_header}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{warning\_no\_header()}{warning\_no\_header()}} +{\footnotesize\ttfamily void Log\+::warning\+\_\+no\+\_\+header (\begin{DoxyParamCaption}\item[{const char $\ast$}]{format, }\item[{}]{... }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}} + + + +Logs a warning message. + +Does not print the message header (useful if doing multiple log prints to the same line). varargs are the same as in printf. \mbox{\Hypertarget{class_log_aff6a4531e693fd53ad051c7fef28c6aa}\label{class_log_aff6a4531e693fd53ad051c7fef28c6aa}} +\index{Log@{Log}!write\_message@{write\_message}} +\index{write\_message@{write\_message}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{write\_message()}{write\_message()}} +{\footnotesize\ttfamily void Log\+::write\+\_\+message (\begin{DoxyParamCaption}\item[{bool}]{print\+\_\+header, }\item[{\textbf{ int8\+\_\+t}}]{message\+\_\+level, }\item[{const char $\ast$}]{message\+\_\+type, }\item[{const char $\ast$}]{format, }\item[{\textbf{ va\+\_\+list}}]{arguments }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [static]}, {\ttfamily [private]}} + + + +Potentially writes the message to either standard output or the log file if the message level is high enough. + + +\begin{DoxyParams}{Parameters} +{\em print\+\_\+header} & specifies if the header to the message should be printed out \\ +\hline +{\em message\+\_\+level} & the level of the message to potentially be printed out \\ +\hline +{\em message\+\_\+type} & a string representation of this message type \\ +\hline +{\em message\+\_\+type} & the format string for this message (as in printf) \\ +\hline +{\em arguments} & the arguments for the print statement \\ +\hline +\end{DoxyParams} + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{class_log_a08b261dd43ff89110e602a3a61f6ec61}\label{class_log_a08b261dd43ff89110e602a3a61f6ec61}} +\index{Log@{Log}!file\_message\_level@{file\_message\_level}} +\index{file\_message\_level@{file\_message\_level}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{file\_message\_level}{file\_message\_level}} +{\footnotesize\ttfamily int32\+\_\+t Log\+::file\+\_\+message\+\_\+level = \mbox{\hyperlink{class_log_a70efafdaaf797f644bb24a8e1c7f5643}{I\+N\+FO}}\hspace{0.3cm}{\ttfamily [static]}, {\ttfamily [private]}} + + + +Specifies which messages to log. + +Messages will be written to the log file if their type is $<$= message\+\_\+level. \mbox{\Hypertarget{class_log_a814d0d060c29987d5d43d55b3d8d542c}\label{class_log_a814d0d060c29987d5d43d55b3d8d542c}} +\index{Log@{Log}!log\_ids@{log\_ids}} +\index{log\_ids@{log\_ids}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{log\_ids}{log\_ids}} +{\footnotesize\ttfamily \textbf{ map}$<$ thread\+::id, \textbf{ string} $>$ Log\+::log\+\_\+ids\hspace{0.3cm}{\ttfamily [static]}, {\ttfamily [private]}} + + + +A map of C++ thread ids (which are not human readable) to human readable integer ids. + +Thread ids are specified with the \mbox{\hyperlink{class_log_ac02fea24138d69a35521d82ee64bb90d}{Log\+::set\+\_\+id(string)}} method. \mbox{\Hypertarget{class_log_aa8bf3e23cf88dc4e48aafd791fe93cd7}\label{class_log_aa8bf3e23cf88dc4e48aafd791fe93cd7}} +\index{Log@{Log}!log\_ids\_mutex@{log\_ids\_mutex}} +\index{log\_ids\_mutex@{log\_ids\_mutex}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{log\_ids\_mutex}{log\_ids\_mutex}} +{\footnotesize\ttfamily shared\+\_\+mutex Log\+::log\+\_\+ids\+\_\+mutex\hspace{0.3cm}{\ttfamily [static]}, {\ttfamily [private]}} + + + +A std\+::shared\+\_\+mutex protecting the Log\+::thread\+\_\+ids map. + +The Log\+::set\+\_\+thread\+\_\+id(int32\+\_\+t) mehod needs write access so it will use the std\+::shared\+\_\+mutex\+::lock()x and std\+::shared\+\_\+mutex()\+::unlock() mechanism, while the Log\+::get\+\_\+thread\+\_\+id() only needs read access and can used the std\+::shared\+\_\+mutex\+::lock\+\_\+shared() and std\+::shared\+\_\+mutex\+::unlock\+\_\+shared() methods. \mbox{\Hypertarget{class_log_ad6bd8ce041323bb69e69aeb9edbe12ee}\label{class_log_ad6bd8ce041323bb69e69aeb9edbe12ee}} +\index{Log@{Log}!process\_rank@{process\_rank}} +\index{process\_rank@{process\_rank}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{process\_rank}{process\_rank}} +{\footnotesize\ttfamily int32\+\_\+t Log\+::process\+\_\+rank = -\/1\hspace{0.3cm}{\ttfamily [static]}, {\ttfamily [private]}} + + + +The M\+PI process rank for this \mbox{\hyperlink{class_log}{Log}} instance. + +Set to -\/1 if not specified or not using M\+PI. \mbox{\Hypertarget{class_log_a47ec200a53bfc6b8b6a39bc91d5dbc86}\label{class_log_a47ec200a53bfc6b8b6a39bc91d5dbc86}} +\index{Log@{Log}!restricted\_rank@{restricted\_rank}} +\index{restricted\_rank@{restricted\_rank}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{restricted\_rank}{restricted\_rank}} +{\footnotesize\ttfamily int32\+\_\+t Log\+::restricted\+\_\+rank = -\/1\hspace{0.3cm}{\ttfamily [static]}, {\ttfamily [private]}} + + + +Defaults at -\/1, when set to a process rank ($>$= 0) the \mbox{\hyperlink{class_log}{Log}} will only print messages if its rank is the same as the restricted rank. + +This is useful, for allowing initialization messages to only be printed out by a single process. \mbox{\Hypertarget{class_log_a633c6eafa442ecfb3b3322f89496e52e}\label{class_log_a633c6eafa442ecfb3b3322f89496e52e}} +\index{Log@{Log}!std\_message\_level@{std\_message\_level}} +\index{std\_message\_level@{std\_message\_level}!Log@{Log}} +\doxysubsubsection{\texorpdfstring{std\_message\_level}{std\_message\_level}} +{\footnotesize\ttfamily int32\+\_\+t Log\+::std\+\_\+message\+\_\+level = \mbox{\hyperlink{class_log_a70efafdaaf797f644bb24a8e1c7f5643}{I\+N\+FO}}\hspace{0.3cm}{\ttfamily [static]}, {\ttfamily [private]}} + + + +Specifies which messages to log. + +Messages will be written to the standard output log if their type is $<$= message\+\_\+level. + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +common/log.\+hxx\item +common/log.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_log_file.tex b/docs/latex/class_log_file.tex new file mode 100644 index 00000000..916f84a4 --- /dev/null +++ b/docs/latex/class_log_file.tex @@ -0,0 +1,29 @@ +\hypertarget{class_log_file}{}\doxysection{Log\+File Class Reference} +\label{class_log_file}\index{LogFile@{LogFile}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_log_file_aa12e8d10af350349c814ccf6b8db0492}\label{class_log_file_aa12e8d10af350349c814ccf6b8db0492}} +{\bfseries Log\+File} (\textbf{ F\+I\+LE} $\ast$file) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_log_file_a816eeda13280b7a06ced4b9695e0b126}\label{class_log_file_a816eeda13280b7a06ced4b9695e0b126}} +F\+I\+LE $\ast$ {\bfseries file} +\item +\mbox{\Hypertarget{class_log_file_a1f6b5b1f5363cf079b7f46f7b6fa75f7}\label{class_log_file_a1f6b5b1f5363cf079b7f46f7b6fa75f7}} +\textbf{ mutex} {\bfseries file\+\_\+mutex} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_log_file_acd1b2a0733103b7bbeb76b467ea85446}\label{class_log_file_acd1b2a0733103b7bbeb76b467ea85446}} +class {\bfseries Log} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +common/log.\+hxx\item +common/log.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_m_g_u___node.eps b/docs/latex/class_m_g_u___node.eps new file mode 100644 index 00000000..e5f7533f --- /dev/null +++ b/docs/latex/class_m_g_u___node.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 285.714286 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.750000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(MGU_Node) cw +(RNN_Node_Interface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (MGU_Node) 0.000000 0.000000 box + (RNN_Node_Interface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_m_g_u___node.tex b/docs/latex/class_m_g_u___node.tex new file mode 100644 index 00000000..3db09df6 --- /dev/null +++ b/docs/latex/class_m_g_u___node.tex @@ -0,0 +1,138 @@ +\hypertarget{class_m_g_u___node}{}\doxysection{M\+G\+U\+\_\+\+Node Class Reference} +\label{class_m_g_u___node}\index{MGU\_Node@{MGU\_Node}} +Inheritance diagram for M\+G\+U\+\_\+\+Node\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_m_g_u___node} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_m_g_u___node_adbfa27b40e21775d40f408f4a13cc961}\label{class_m_g_u___node_adbfa27b40e21775d40f408f4a13cc961}} +{\bfseries M\+G\+U\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, int \+\_\+layer\+\_\+type, double \+\_\+depth) +\item +\mbox{\Hypertarget{class_m_g_u___node_af97bed43890644f139a45e2dd38417ee}\label{class_m_g_u___node_af97bed43890644f139a45e2dd38417ee}} +void {\bfseries initialize\+\_\+lamarckian} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double mu, double sigma) +\item +\mbox{\Hypertarget{class_m_g_u___node_ae01aaf9fde23c982d87fe4745bf4f99e}\label{class_m_g_u___node_ae01aaf9fde23c982d87fe4745bf4f99e}} +void {\bfseries initialize\+\_\+xavier} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng1\+\_\+1, double range) +\item +\mbox{\Hypertarget{class_m_g_u___node_a84e89c422e46633eb6e9a4141a128c05}\label{class_m_g_u___node_a84e89c422e46633eb6e9a4141a128c05}} +void {\bfseries initialize\+\_\+kaiming} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double range) +\item +\mbox{\Hypertarget{class_m_g_u___node_a21f1d0735e0f136a225eaca91ffedda1}\label{class_m_g_u___node_a21f1d0735e0f136a225eaca91ffedda1}} +void {\bfseries initialize\+\_\+uniform\+\_\+random} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng) +\item +\mbox{\Hypertarget{class_m_g_u___node_a79db451930e095a48b421ce73a94351a}\label{class_m_g_u___node_a79db451930e095a48b421ce73a94351a}} +double {\bfseries get\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_m_g_u___node_abff88d124338c417b5353951077b781f}\label{class_m_g_u___node_abff88d124338c417b5353951077b781f}} +void {\bfseries print\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_m_g_u___node_a7342ba8dae3a741759af7b3028a7acbd}\label{class_m_g_u___node_a7342ba8dae3a741759af7b3028a7acbd}} +void {\bfseries input\+\_\+fired} (int time, double incoming\+\_\+output) +\item +\mbox{\Hypertarget{class_m_g_u___node_a245f2d12613e4b369c3d6fba651668eb}\label{class_m_g_u___node_a245f2d12613e4b369c3d6fba651668eb}} +void {\bfseries try\+\_\+update\+\_\+deltas} (int time) +\item +\mbox{\Hypertarget{class_m_g_u___node_a57ae756cc7a540865bff3377d618d074}\label{class_m_g_u___node_a57ae756cc7a540865bff3377d618d074}} +void {\bfseries error\+\_\+fired} (int time, double error) +\item +\mbox{\Hypertarget{class_m_g_u___node_a2316a14d38af846810aac93c177570ca}\label{class_m_g_u___node_a2316a14d38af846810aac93c177570ca}} +void {\bfseries output\+\_\+fired} (int time, double delta) +\item +\mbox{\Hypertarget{class_m_g_u___node_a68f0c9f331857de597f7b8ecd6c081ec}\label{class_m_g_u___node_a68f0c9f331857de597f7b8ecd6c081ec}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_m_g_u___node_adb9cd6b69a7eb7ac15f74a7f70a4c1d9}\label{class_m_g_u___node_adb9cd6b69a7eb7ac15f74a7f70a4c1d9}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_m_g_u___node_aec03379e016362c70a848243d79f0675}\label{class_m_g_u___node_aec03379e016362c70a848243d79f0675}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_m_g_u___node_a663ea070f5e5697a7ec51dd33efcb114}\label{class_m_g_u___node_a663ea070f5e5697a7ec51dd33efcb114}} +void {\bfseries get\+\_\+weights} (uint32\+\_\+t \&offset, \textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_m_g_u___node_a695f79eda388f0bd81a51f28e38378ba}\label{class_m_g_u___node_a695f79eda388f0bd81a51f28e38378ba}} +void {\bfseries set\+\_\+weights} (uint32\+\_\+t \&offset, const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_m_g_u___node_ae7e441a318345695c7857567234db2d1}\label{class_m_g_u___node_ae7e441a318345695c7857567234db2d1}} +void {\bfseries get\+\_\+gradients} (\textbf{ vector}$<$ double $>$ \&gradients) +\item +\mbox{\Hypertarget{class_m_g_u___node_a9b97f62ce3f3950860b687b35310b455}\label{class_m_g_u___node_a9b97f62ce3f3950860b687b35310b455}} +void {\bfseries reset} (int \+\_\+series\+\_\+length) +\item +\mbox{\Hypertarget{class_m_g_u___node_ae9f6eed9009ad8206f6c4b536fbcad71}\label{class_m_g_u___node_ae9f6eed9009ad8206f6c4b536fbcad71}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_m_g_u___node_aa12c75aa79e859cf45311f01215e79da}\label{class_m_g_u___node_aa12c75aa79e859cf45311f01215e79da}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries copy} () const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_m_g_u___node_a57c309efb724c8a1c1dafc2322a0e9fb}\label{class_m_g_u___node_a57c309efb724c8a1c1dafc2322a0e9fb}} +double {\bfseries fw} +\item +\mbox{\Hypertarget{class_m_g_u___node_a75d820e377fca910165903c69ad3f1e6}\label{class_m_g_u___node_a75d820e377fca910165903c69ad3f1e6}} +double {\bfseries fu} +\item +\mbox{\Hypertarget{class_m_g_u___node_a3ce215d1f541001a88a0ede4f5d38d37}\label{class_m_g_u___node_a3ce215d1f541001a88a0ede4f5d38d37}} +double {\bfseries f\+\_\+bias} +\item +\mbox{\Hypertarget{class_m_g_u___node_a524291c13724123aa95bfae97bc1a266}\label{class_m_g_u___node_a524291c13724123aa95bfae97bc1a266}} +double {\bfseries hw} +\item +\mbox{\Hypertarget{class_m_g_u___node_a94fab7c0b111fb0f633eb03b888cd595}\label{class_m_g_u___node_a94fab7c0b111fb0f633eb03b888cd595}} +double {\bfseries hu} +\item +\mbox{\Hypertarget{class_m_g_u___node_a6641db2b11d665b8823b3c74425de019}\label{class_m_g_u___node_a6641db2b11d665b8823b3c74425de019}} +double {\bfseries h\+\_\+bias} +\item +\mbox{\Hypertarget{class_m_g_u___node_a120a37c95edc4706edaa437842ffd0f4}\label{class_m_g_u___node_a120a37c95edc4706edaa437842ffd0f4}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+fw} +\item +\mbox{\Hypertarget{class_m_g_u___node_ac5959cad9ada6cdf72eaacd2e9a3de5e}\label{class_m_g_u___node_ac5959cad9ada6cdf72eaacd2e9a3de5e}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+fu} +\item +\mbox{\Hypertarget{class_m_g_u___node_a9c2a603ee5044bea97bc0b6472bf251a}\label{class_m_g_u___node_a9c2a603ee5044bea97bc0b6472bf251a}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+f\+\_\+bias} +\item +\mbox{\Hypertarget{class_m_g_u___node_a25479dc6e0bc5e94bb3563f762600948}\label{class_m_g_u___node_a25479dc6e0bc5e94bb3563f762600948}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+hw} +\item +\mbox{\Hypertarget{class_m_g_u___node_afa9c2d60f79ea5e585c8ef34fb6a3bc1}\label{class_m_g_u___node_afa9c2d60f79ea5e585c8ef34fb6a3bc1}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+hu} +\item +\mbox{\Hypertarget{class_m_g_u___node_ae69487b9daf3224dfbcb49ce682c438f}\label{class_m_g_u___node_ae69487b9daf3224dfbcb49ce682c438f}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+h\+\_\+bias} +\item +\mbox{\Hypertarget{class_m_g_u___node_a1c6877225f71712c0d63b23c44210116}\label{class_m_g_u___node_a1c6877225f71712c0d63b23c44210116}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+h\+\_\+prev} +\item +\mbox{\Hypertarget{class_m_g_u___node_a392b657d7eaf2f8aa279736c98944026}\label{class_m_g_u___node_a392b657d7eaf2f8aa279736c98944026}} +\textbf{ vector}$<$ double $>$ {\bfseries f} +\item +\mbox{\Hypertarget{class_m_g_u___node_ae1da6cc00de07c2d6a15bea098b76f9d}\label{class_m_g_u___node_ae1da6cc00de07c2d6a15bea098b76f9d}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+f} +\item +\mbox{\Hypertarget{class_m_g_u___node_aa11d31445bfdfccea366191e1c906db5}\label{class_m_g_u___node_aa11d31445bfdfccea366191e1c906db5}} +\textbf{ vector}$<$ double $>$ {\bfseries h\+\_\+tanh} +\item +\mbox{\Hypertarget{class_m_g_u___node_ad0740ae0b13db6322fdf97ce7ed73f5e}\label{class_m_g_u___node_ad0740ae0b13db6322fdf97ce7ed73f5e}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+h\+\_\+tanh} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_m_g_u___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}\label{class_m_g_u___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}} +class {\bfseries R\+N\+N\+\_\+\+Edge} +\end{DoxyCompactItemize} +\doxysubsection*{Additional Inherited Members} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/mgu\+\_\+node.\+hxx\item +rnn/mgu\+\_\+node.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_mosaic_images.eps b/docs/latex/class_mosaic_images.eps new file mode 100644 index 00000000..00f0bd70 --- /dev/null +++ b/docs/latex/class_mosaic_images.eps @@ -0,0 +1,203 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 441.176471 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.133333 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 3 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(MosaicImages) cw +(MultiImagesInterface) cw +(ImagesInterface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (MosaicImages) 0.000000 0.000000 box + (MultiImagesInterface) 0.000000 1.000000 box + (ImagesInterface) 0.000000 2.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in +solid +0 0.000000 1.000000 out +solid +1 0.000000 2.000000 in diff --git a/docs/latex/class_mosaic_images.tex b/docs/latex/class_mosaic_images.tex new file mode 100644 index 00000000..cd3f7669 --- /dev/null +++ b/docs/latex/class_mosaic_images.tex @@ -0,0 +1,155 @@ +\hypertarget{class_mosaic_images}{}\doxysection{Mosaic\+Images Class Reference} +\label{class_mosaic_images}\index{MosaicImages@{MosaicImages}} +Inheritance diagram for Mosaic\+Images\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=3.000000cm]{class_mosaic_images} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_mosaic_images_aac889fdea29db07b12f263cc9a825d6b}\label{class_mosaic_images_aac889fdea29db07b12f263cc9a825d6b}} +void {\bfseries get\+\_\+mosaic\+\_\+pixels} (\textbf{ string} filename, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ uint8\+\_\+t $>$ $>$ $>$ \&pixels, uint32\+\_\+t \&height, uint32\+\_\+t \&width) +\item +\mbox{\Hypertarget{class_mosaic_images_adc8995b0e2b7e50c5fb9e4bcff1fa1c0}\label{class_mosaic_images_adc8995b0e2b7e50c5fb9e4bcff1fa1c0}} +void {\bfseries read\+\_\+mosaic} (\textbf{ string} filename, const \textbf{ vector}$<$ \mbox{\hyperlink{class_point}{Point}} $>$ \&box\+\_\+centers, int box\+\_\+radius, const \textbf{ vector}$<$ int $>$ \&box\+\_\+classes) +\item +\mbox{\Hypertarget{class_mosaic_images_a6f523557b38a1d27c2dc0ddbe1521025}\label{class_mosaic_images_a6f523557b38a1d27c2dc0ddbe1521025}} +void {\bfseries read\+\_\+mosaic} (\textbf{ string} filename, const \textbf{ vector}$<$ \mbox{\hyperlink{class_line}{Line}} $>$ \&lines, int line\+\_\+height, const \textbf{ vector}$<$ int $>$ \&line\+\_\+classes) +\item +\mbox{\Hypertarget{class_mosaic_images_ad0b8a3629672e88622581dba57b90128}\label{class_mosaic_images_ad0b8a3629672e88622581dba57b90128}} +void {\bfseries initialize\+\_\+counts} (const \textbf{ vector}$<$ \textbf{ vector}$<$ int $>$ $>$ \&classes) +\item +\mbox{\Hypertarget{class_mosaic_images_a007e0e06db88264a28c46a62b92f04c6}\label{class_mosaic_images_a007e0e06db88264a28c46a62b92f04c6}} +{\bfseries Mosaic\+Images} (\textbf{ vector}$<$ \textbf{ string} $>$ \+\_\+filenames, const \textbf{ vector}$<$ \textbf{ vector}$<$ \mbox{\hyperlink{class_point}{Point}} $>$ $>$ \&\+\_\+box\+\_\+centers, int \+\_\+box\+\_\+radius, const \textbf{ vector}$<$ \textbf{ vector}$<$ int $>$ $>$ \&\+\_\+box\+\_\+classes, int \+\_\+padding, int \+\_\+subimage\+\_\+height, int \+\_\+subimage\+\_\+width) +\item +\mbox{\Hypertarget{class_mosaic_images_a510a0ce5a0a2c67edd7c8366edd5eaaf}\label{class_mosaic_images_a510a0ce5a0a2c67edd7c8366edd5eaaf}} +{\bfseries Mosaic\+Images} (\textbf{ vector}$<$ \textbf{ string} $>$ \+\_\+filenames, const \textbf{ vector}$<$ \textbf{ vector}$<$ \mbox{\hyperlink{class_point}{Point}} $>$ $>$ \&\+\_\+box\+\_\+centers, int \+\_\+box\+\_\+radius, const \textbf{ vector}$<$ \textbf{ vector}$<$ int $>$ $>$ \&\+\_\+box\+\_\+classes, int \+\_\+padding, int \+\_\+subimage\+\_\+height, int \+\_\+subimage\+\_\+width, const \textbf{ vector}$<$ float $>$ \&\+\_\+channel\+\_\+avg, const \textbf{ vector}$<$ float $>$ \&\+\_\+channel\+\_\+std\+\_\+dev) +\item +\mbox{\Hypertarget{class_mosaic_images_af882240a89407c0d6bde0d61f6feeb85}\label{class_mosaic_images_af882240a89407c0d6bde0d61f6feeb85}} +{\bfseries Mosaic\+Images} (\textbf{ vector}$<$ \textbf{ string} $>$ \+\_\+filenames, const \textbf{ vector}$<$ \textbf{ vector}$<$ \mbox{\hyperlink{class_line}{Line}} $>$ $>$ \&\+\_\+lines, int \+\_\+line\+\_\+height, const \textbf{ vector}$<$ \textbf{ vector}$<$ int $>$ $>$ \&\+\_\+line\+\_\+classes, int \+\_\+padding, int \+\_\+subimage\+\_\+height, int \+\_\+subimage\+\_\+width) +\item +\mbox{\Hypertarget{class_mosaic_images_acc91ef7b0131e03ec4a1a20f3ac0880d}\label{class_mosaic_images_acc91ef7b0131e03ec4a1a20f3ac0880d}} +{\bfseries Mosaic\+Images} (\textbf{ vector}$<$ \textbf{ string} $>$ \+\_\+filenames, const \textbf{ vector}$<$ \textbf{ vector}$<$ \mbox{\hyperlink{class_line}{Line}} $>$ $>$ \&\+\_\+lines, int \+\_\+line\+\_\+height, const \textbf{ vector}$<$ \textbf{ vector}$<$ int $>$ $>$ \&\+\_\+line\+\_\+classes, int \+\_\+padding, int \+\_\+subimage\+\_\+height, int \+\_\+subimage\+\_\+width, const \textbf{ vector}$<$ float $>$ \&\+\_\+channel\+\_\+avg, const \textbf{ vector}$<$ float $>$ \&\+\_\+channel\+\_\+std\+\_\+dev) +\item +\mbox{\Hypertarget{class_mosaic_images_ad325fceab4348f86cd8be5b38e937b64}\label{class_mosaic_images_ad325fceab4348f86cd8be5b38e937b64}} +\textbf{ string} {\bfseries get\+\_\+filename} () const +\item +\mbox{\Hypertarget{class_mosaic_images_a1de525feb692019108e19c1e6d1db3df}\label{class_mosaic_images_a1de525feb692019108e19c1e6d1db3df}} +int {\bfseries get\+\_\+class\+\_\+size} (int i) const +\item +\mbox{\Hypertarget{class_mosaic_images_abe86913cd32ce1c6180fe407cec91c0c}\label{class_mosaic_images_abe86913cd32ce1c6180fe407cec91c0c}} +int {\bfseries get\+\_\+number\+\_\+classes} () const +\item +\mbox{\Hypertarget{class_mosaic_images_a8af1006d26fd60bf2ae38b4a823be4e3}\label{class_mosaic_images_a8af1006d26fd60bf2ae38b4a823be4e3}} +int {\bfseries get\+\_\+number\+\_\+images} () const +\item +\mbox{\Hypertarget{class_mosaic_images_a37fe41bb67e68a3807f41e3058b25046}\label{class_mosaic_images_a37fe41bb67e68a3807f41e3058b25046}} +int {\bfseries get\+\_\+number\+\_\+large\+\_\+images} () const +\item +\mbox{\Hypertarget{class_mosaic_images_aec5dfeb2f1962ac5050b0e632fba6389}\label{class_mosaic_images_aec5dfeb2f1962ac5050b0e632fba6389}} +int {\bfseries get\+\_\+number\+\_\+subimages} (int i) const +\item +\mbox{\Hypertarget{class_mosaic_images_aab4cddd13b516d914939895447a62020}\label{class_mosaic_images_aab4cddd13b516d914939895447a62020}} +int {\bfseries get\+\_\+padding} () const +\item +\mbox{\Hypertarget{class_mosaic_images_abccc75a668b25f1049d8320dd8ace724}\label{class_mosaic_images_abccc75a668b25f1049d8320dd8ace724}} +int {\bfseries get\+\_\+image\+\_\+channels} () const +\item +\mbox{\Hypertarget{class_mosaic_images_a5cdccfed470bc8afc913694749a74e17}\label{class_mosaic_images_a5cdccfed470bc8afc913694749a74e17}} +int {\bfseries get\+\_\+image\+\_\+width} () const +\item +\mbox{\Hypertarget{class_mosaic_images_acf2376a5ceeeaa7bfb20ed982b0fd092}\label{class_mosaic_images_acf2376a5ceeeaa7bfb20ed982b0fd092}} +int {\bfseries get\+\_\+image\+\_\+height} () const +\item +\mbox{\Hypertarget{class_mosaic_images_ac65a01146d8b06d51466d8e90abd5d8f}\label{class_mosaic_images_ac65a01146d8b06d51466d8e90abd5d8f}} +int {\bfseries get\+\_\+large\+\_\+image\+\_\+channels} (int image) const +\item +\mbox{\Hypertarget{class_mosaic_images_ac006d53107ac14108f51d5098b8fb4a1}\label{class_mosaic_images_ac006d53107ac14108f51d5098b8fb4a1}} +int {\bfseries get\+\_\+large\+\_\+image\+\_\+width} (int image) const +\item +\mbox{\Hypertarget{class_mosaic_images_a6bf73d95f96cf4fd2285f758381e40f2}\label{class_mosaic_images_a6bf73d95f96cf4fd2285f758381e40f2}} +int {\bfseries get\+\_\+large\+\_\+image\+\_\+height} (int image) const +\item +\mbox{\Hypertarget{class_mosaic_images_a4cf8078f212d403536c419d0e96c764f}\label{class_mosaic_images_a4cf8078f212d403536c419d0e96c764f}} +int {\bfseries get\+\_\+image\+\_\+classification} (int image) const +\item +\mbox{\Hypertarget{class_mosaic_images_afeefc659cd491256bbc2e15f68eec4d2}\label{class_mosaic_images_afeefc659cd491256bbc2e15f68eec4d2}} +int {\bfseries get\+\_\+classification} (int subimage) const +\item +\mbox{\Hypertarget{class_mosaic_images_a92fe7f2591f600788c4059760291ef06}\label{class_mosaic_images_a92fe7f2591f600788c4059760291ef06}} +float {\bfseries get\+\_\+pixel} (int subimage, int z, int y, int x) const +\item +\mbox{\Hypertarget{class_mosaic_images_ac5276c1194c374a9f70f2b87cbeca2e3}\label{class_mosaic_images_ac5276c1194c374a9f70f2b87cbeca2e3}} +float {\bfseries get\+\_\+raw\+\_\+pixel} (int subimage, int z, int y, int x) const +\item +\mbox{\Hypertarget{class_mosaic_images_a20866c4ca79cddad3f077b49ed4be769}\label{class_mosaic_images_a20866c4ca79cddad3f077b49ed4be769}} +void {\bfseries calculate\+\_\+avg\+\_\+std\+\_\+dev} () +\item +\mbox{\Hypertarget{class_mosaic_images_a02c1a6124ef802c69eee55b48423b196}\label{class_mosaic_images_a02c1a6124ef802c69eee55b48423b196}} +float {\bfseries get\+\_\+channel\+\_\+avg} (int channel) const +\item +\mbox{\Hypertarget{class_mosaic_images_afb638a6ac9a51208ba1bdfa0b935c723}\label{class_mosaic_images_afb638a6ac9a51208ba1bdfa0b935c723}} +float {\bfseries get\+\_\+channel\+\_\+std\+\_\+dev} (int channel) const +\item +\mbox{\Hypertarget{class_mosaic_images_ac46141d659f627ee6e12b6a685edb45c}\label{class_mosaic_images_ac46141d659f627ee6e12b6a685edb45c}} +const \textbf{ vector}$<$ float $>$ \& {\bfseries get\+\_\+average} () const +\item +\mbox{\Hypertarget{class_mosaic_images_a1114957ecf2c38040141bd285b9e9e18}\label{class_mosaic_images_a1114957ecf2c38040141bd285b9e9e18}} +const \textbf{ vector}$<$ float $>$ \& {\bfseries get\+\_\+std\+\_\+dev} () const +\item +\mbox{\Hypertarget{class_mosaic_images_a04f7e20bb3beb0d21a8d65c51502f858}\label{class_mosaic_images_a04f7e20bb3beb0d21a8d65c51502f858}} +void {\bfseries normalize} () +\item +\mbox{\Hypertarget{class_mosaic_images_a0d5ff3a4cb10356d89b695ab1999abde}\label{class_mosaic_images_a0d5ff3a4cb10356d89b695ab1999abde}} +\mbox{\hyperlink{class_large_image}{Large\+Image}} $\ast$ {\bfseries copy\+\_\+image} (int i) const +\item +\mbox{\Hypertarget{class_mosaic_images_a546fc5bb15eec99eeb740a8874f91f51}\label{class_mosaic_images_a546fc5bb15eec99eeb740a8874f91f51}} +void {\bfseries set\+\_\+alpha} (int i, const \textbf{ vector}$<$ \textbf{ vector}$<$ float $>$ $>$ \&\+\_\+alpha) +\item +\mbox{\Hypertarget{class_mosaic_images_a8c27e81fdf358f3715e012a35241e731}\label{class_mosaic_images_a8c27e81fdf358f3715e012a35241e731}} +void {\bfseries set\+\_\+alpha} (int i, const \textbf{ vector}$<$ \textbf{ vector}$<$ uint8\+\_\+t $>$ $>$ \&\+\_\+alpha) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_mosaic_images_a42e432357c571bb533bccd097c392c6c}\label{class_mosaic_images_a42e432357c571bb533bccd097c392c6c}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries filenames} +\item +\mbox{\Hypertarget{class_mosaic_images_ae8179fe59198f2d18d51c3b351c67b82}\label{class_mosaic_images_ae8179fe59198f2d18d51c3b351c67b82}} +int {\bfseries number\+\_\+classes} +\item +\mbox{\Hypertarget{class_mosaic_images_af592a7b3101ce41d452a839226ddcc2f}\label{class_mosaic_images_af592a7b3101ce41d452a839226ddcc2f}} +int {\bfseries number\+\_\+images} +\item +\mbox{\Hypertarget{class_mosaic_images_a59e127dbdda91e7471b2db6bdcae3d22}\label{class_mosaic_images_a59e127dbdda91e7471b2db6bdcae3d22}} +\textbf{ vector}$<$ int $>$ {\bfseries class\+\_\+sizes} +\item +\mbox{\Hypertarget{class_mosaic_images_a2e6260212e557a17be7c6f3032e5ca97}\label{class_mosaic_images_a2e6260212e557a17be7c6f3032e5ca97}} +int {\bfseries padding} +\item +\mbox{\Hypertarget{class_mosaic_images_a62edb4acb537c33da7d41d17e7057b6b}\label{class_mosaic_images_a62edb4acb537c33da7d41d17e7057b6b}} +int {\bfseries channels} +\item +\mbox{\Hypertarget{class_mosaic_images_a15a3ff69b78833031233d2aa12242984}\label{class_mosaic_images_a15a3ff69b78833031233d2aa12242984}} +int {\bfseries subimage\+\_\+width} +\item +\mbox{\Hypertarget{class_mosaic_images_a6012320a1b04d0e567db136a1366c886}\label{class_mosaic_images_a6012320a1b04d0e567db136a1366c886}} +int {\bfseries subimage\+\_\+height} +\item +\mbox{\Hypertarget{class_mosaic_images_a039ae8bf2073e0582416497c6c76b2fd}\label{class_mosaic_images_a039ae8bf2073e0582416497c6c76b2fd}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_large_image}{Large\+Image}} $>$ {\bfseries images} +\item +\mbox{\Hypertarget{class_mosaic_images_ae4292e50a95fa6c690978b3ebd8bb2e7}\label{class_mosaic_images_ae4292e50a95fa6c690978b3ebd8bb2e7}} +\textbf{ vector}$<$ float $>$ {\bfseries channel\+\_\+avg} +\item +\mbox{\Hypertarget{class_mosaic_images_a2aac949f567bbf8c2805c4131b76dfe4}\label{class_mosaic_images_a2aac949f567bbf8c2805c4131b76dfe4}} +\textbf{ vector}$<$ float $>$ {\bfseries channel\+\_\+std\+\_\+dev} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/mosaic\+\_\+image\+\_\+set.\+hxx\item +image\+\_\+tools/mosaic\+\_\+image\+\_\+set.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_multi_images_interface.eps b/docs/latex/class_multi_images_interface.eps new file mode 100644 index 00000000..de78507b --- /dev/null +++ b/docs/latex/class_multi_images_interface.eps @@ -0,0 +1,209 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 220.588235 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 2.266667 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 3 def +/cols 2 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(MultiImagesInterface) cw +(ImagesInterface) cw +(LargeImages) cw +(MosaicImages) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (MultiImagesInterface) 0.500000 1.000000 box + (ImagesInterface) 0.500000 2.000000 box + (LargeImages) 0.000000 0.000000 box + (MosaicImages) 1.000000 0.000000 box + +% ----- relations ----- + +solid +0 0.500000 1.000000 out +solid +1 0.500000 2.000000 in +solid +1 0.500000 0.250000 out +solid +0.000000 1.000000 1.000000 conn +solid +0 0.000000 0.750000 in +solid +0 1.000000 0.750000 in diff --git a/docs/latex/class_multi_images_interface.tex b/docs/latex/class_multi_images_interface.tex new file mode 100644 index 00000000..6d0bac3e --- /dev/null +++ b/docs/latex/class_multi_images_interface.tex @@ -0,0 +1,43 @@ +\hypertarget{class_multi_images_interface}{}\doxysection{Multi\+Images\+Interface Class Reference} +\label{class_multi_images_interface}\index{MultiImagesInterface@{MultiImagesInterface}} +Inheritance diagram for Multi\+Images\+Interface\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=3.000000cm]{class_multi_images_interface} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_multi_images_interface_af4d508b4438c0d2489676c4efdc14ad3}\label{class_multi_images_interface_af4d508b4438c0d2489676c4efdc14ad3}} +virtual int {\bfseries get\+\_\+number\+\_\+large\+\_\+images} () const =0 +\item +\mbox{\Hypertarget{class_multi_images_interface_a85fb694852852b193ded189061df511f}\label{class_multi_images_interface_a85fb694852852b193ded189061df511f}} +virtual int {\bfseries get\+\_\+number\+\_\+subimages} (int i) const =0 +\item +\mbox{\Hypertarget{class_multi_images_interface_a2b759ca3bb6cf2e2f74bc1c095a265e5}\label{class_multi_images_interface_a2b759ca3bb6cf2e2f74bc1c095a265e5}} +virtual int {\bfseries get\+\_\+padding} () const =0 +\item +\mbox{\Hypertarget{class_multi_images_interface_aae945c1557a1a1aaaa729cdbb3593773}\label{class_multi_images_interface_aae945c1557a1a1aaaa729cdbb3593773}} +virtual int {\bfseries get\+\_\+large\+\_\+image\+\_\+channels} (int image) const =0 +\item +\mbox{\Hypertarget{class_multi_images_interface_a35162419cfe2d837001c8cf26600e08f}\label{class_multi_images_interface_a35162419cfe2d837001c8cf26600e08f}} +virtual int {\bfseries get\+\_\+large\+\_\+image\+\_\+width} (int image) const =0 +\item +\mbox{\Hypertarget{class_multi_images_interface_a6cac6e285fc451ab65b6f6967e9f5029}\label{class_multi_images_interface_a6cac6e285fc451ab65b6f6967e9f5029}} +virtual int {\bfseries get\+\_\+large\+\_\+image\+\_\+height} (int image) const =0 +\item +\mbox{\Hypertarget{class_multi_images_interface_adf4b52ae1874c6ab3c3904cde3095bc8}\label{class_multi_images_interface_adf4b52ae1874c6ab3c3904cde3095bc8}} +virtual int {\bfseries get\+\_\+number\+\_\+classes} () const =0 +\item +\mbox{\Hypertarget{class_multi_images_interface_a3616d358bee4792ee6b22af75bddfcb6}\label{class_multi_images_interface_a3616d358bee4792ee6b22af75bddfcb6}} +virtual int {\bfseries get\+\_\+image\+\_\+classification} (int image) const =0 +\item +\mbox{\Hypertarget{class_multi_images_interface_ac7d52913a277f190a3731a3873119a65}\label{class_multi_images_interface_ac7d52913a277f190a3731a3873119a65}} +virtual float {\bfseries get\+\_\+raw\+\_\+pixel} (int subimage, int z, int y, int x) const =0 +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/image\+\_\+set\+\_\+interface.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_neat_speciation_strategy.eps b/docs/latex/class_neat_speciation_strategy.eps new file mode 100644 index 00000000..35558810 --- /dev/null +++ b/docs/latex/class_neat_speciation_strategy.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 263.157895 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.900000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(NeatSpeciationStrategy) cw +(SpeciationStrategy) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (NeatSpeciationStrategy) 0.000000 0.000000 box + (SpeciationStrategy) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_neat_speciation_strategy.tex b/docs/latex/class_neat_speciation_strategy.tex new file mode 100644 index 00000000..e07a8432 --- /dev/null +++ b/docs/latex/class_neat_speciation_strategy.tex @@ -0,0 +1,323 @@ +\hypertarget{class_neat_speciation_strategy}{}\doxysection{Neat\+Speciation\+Strategy Class Reference} +\label{class_neat_speciation_strategy}\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}} +Inheritance diagram for Neat\+Speciation\+Strategy\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_neat_speciation_strategy} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a903c5e6d68d601d30ae70c5235895a13}\label{class_neat_speciation_strategy_a903c5e6d68d601d30ae70c5235895a13}} +{\bfseries Neat\+Speciation\+Strategy} (double \+\_\+mutation\+\_\+rate, double \+\_\+intra\+\_\+island\+\_\+crossover\+\_\+rate, double \+\_\+inter\+\_\+island\+\_\+crossover\+\_\+rate, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$\+\_\+seed\+\_\+genome, int32\+\_\+t \+\_\+max\+\_\+genomes, double \+\_\+species\+\_\+threshold, double \+\_\+fitness\+\_\+threshold, double \+\_\+neat\+\_\+c1, double \+\_\+neat\+\_\+c2, double \+\_\+neat\+\_\+c3, \textbf{ minstd\+\_\+rand0} \&\+\_\+generator) +\item +int32\+\_\+t \mbox{\hyperlink{class_neat_speciation_strategy_af7e48fd1afb5a9c1b422ad251366263e}{get\+\_\+generated\+\_\+genomes}} () const +\item +int32\+\_\+t \mbox{\hyperlink{class_neat_speciation_strategy_aba55a9f0006af882fd613bd0676555eb}{get\+\_\+inserted\+\_\+genomes}} () const +\item +double \mbox{\hyperlink{class_neat_speciation_strategy_a4aab3d6d1abf3b1f2fd7cf7b8a4a1d99}{get\+\_\+best\+\_\+fitness}} () +\begin{DoxyCompactList}\small\item\em Gets the fitness of the best genome of all the islands. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_neat_speciation_strategy_acbd3eaa05db725de60074276cd0bb449}{get\+\_\+worst\+\_\+fitness}} () +\begin{DoxyCompactList}\small\item\em Gets the fitness of the worst genome of all the islands. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_neat_speciation_strategy_aecf62aeba94c1f8d786215c10ea4774b}{get\+\_\+best\+\_\+genome}} () +\begin{DoxyCompactList}\small\item\em Gets the best genome of all the islands. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_neat_speciation_strategy_a8bc6ad6a0740e2f6072aef3ad14dd3f7}{get\+\_\+worst\+\_\+genome}} () +\begin{DoxyCompactList}\small\item\em Gets the the worst genome of all the islands. \end{DoxyCompactList}\item +int32\+\_\+t \mbox{\hyperlink{class_neat_speciation_strategy_aa2b2521a400435cbd1856aadda2051e0}{insert\+\_\+genome}} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$genome) +\begin{DoxyCompactList}\small\item\em Inserts a {\bfseries{copy}} of the genome into this speciation strategy. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_neat_speciation_strategy_ae86f8696ddab1b6cc595b6e2ef0371ae}{generate\+\_\+genome}} (\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator, \textbf{ function}$<$ void(int32\+\_\+t, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&mutate, \textbf{ function}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&crossover) +\begin{DoxyCompactList}\small\item\em Generates a new genome. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a0c58ebbcfbc29127d02378a46ecfcb19}\label{class_neat_speciation_strategy_a0c58ebbcfbc29127d02378a46ecfcb19}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries generate\+\_\+for\+\_\+species} (\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator, \textbf{ function}$<$ void(int32\+\_\+t, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&mutate, \textbf{ function}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&crossover) +\item +void \mbox{\hyperlink{class_neat_speciation_strategy_abef17e0293c2e0a64b3b4b3b9b7775d2}{print}} (\textbf{ string} indent=\char`\"{}\char`\"{}) const +\begin{DoxyCompactList}\small\item\em Prints out all the island\textquotesingle{}s populations. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a0117c886c0aedbb22e8f4ee6803ab669}\label{class_neat_speciation_strategy_a0117c886c0aedbb22e8f4ee6803ab669}} +\textbf{ string} \mbox{\hyperlink{class_neat_speciation_strategy_a0117c886c0aedbb22e8f4ee6803ab669}{get\+\_\+strategy\+\_\+information\+\_\+headers}} () const +\begin{DoxyCompactList}\small\item\em Gets speciation strategy information headers for logs. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a3f5175b016921853c2e9461475bff164}\label{class_neat_speciation_strategy_a3f5175b016921853c2e9461475bff164}} +\textbf{ string} \mbox{\hyperlink{class_neat_speciation_strategy_a3f5175b016921853c2e9461475bff164}{get\+\_\+strategy\+\_\+information\+\_\+values}} () const +\begin{DoxyCompactList}\small\item\em Gets speciation strategy information values for logs. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a2047928c8e521b5f039ab374628b6c8f}\label{class_neat_speciation_strategy_a2047928c8e521b5f039ab374628b6c8f}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries get\+\_\+global\+\_\+best\+\_\+genome} () +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_aa4fe2ee6a14cf1dcf0174c79033f0399}\label{class_neat_speciation_strategy_aa4fe2ee6a14cf1dcf0174c79033f0399}} +\textbf{ vector}$<$ int32\+\_\+t $>$ {\bfseries get\+\_\+random\+\_\+species\+\_\+list} () +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_acd0cc1b9a7b4a72dbac191d9cb384912}\label{class_neat_speciation_strategy_acd0cc1b9a7b4a72dbac191d9cb384912}} +double {\bfseries get\+\_\+distance} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$g1, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$g2) +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a8753b08324b5bc36b53229710ab8370b}\label{class_neat_speciation_strategy_a8753b08324b5bc36b53229710ab8370b}} +int {\bfseries get\+\_\+exceed\+\_\+number} (\textbf{ vector}$<$ int32\+\_\+t $>$ v1, \textbf{ vector}$<$ int32\+\_\+t $>$ v2) +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a5b617be236a503807bf3617bf4a30bf6}\label{class_neat_speciation_strategy_a5b617be236a503807bf3617bf4a30bf6}} +void {\bfseries rank\+\_\+species} () +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_adc2f2a0cacd0bb4efcd2d2dda51dedce}\label{class_neat_speciation_strategy_adc2f2a0cacd0bb4efcd2d2dda51dedce}} +bool {\bfseries check\+\_\+population} () +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_aff8357ced2aa6777899749f62dc22a5d}\label{class_neat_speciation_strategy_aff8357ced2aa6777899749f62dc22a5d}} +void {\bfseries check\+\_\+species} () +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a8c33c9c76990e80839167c2791773bfd}\label{class_neat_speciation_strategy_a8c33c9c76990e80839167c2791773bfd}} +int32\+\_\+t \mbox{\hyperlink{class_neat_speciation_strategy_a8c33c9c76990e80839167c2791773bfd}{generation\+\_\+species}} +\begin{DoxyCompactList}\small\item\em Used to track which island to generate the next genome from. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a65363f273bd436b14d5b31e01bfd4778}\label{class_neat_speciation_strategy_a65363f273bd436b14d5b31e01bfd4778}} +int32\+\_\+t {\bfseries species\+\_\+count} +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_aa9cf3794cccbfabb1d4e32897bf3a965}\label{class_neat_speciation_strategy_aa9cf3794cccbfabb1d4e32897bf3a965}} +int32\+\_\+t \mbox{\hyperlink{class_neat_speciation_strategy_aa9cf3794cccbfabb1d4e32897bf3a965}{population\+\_\+not\+\_\+improving\+\_\+count}} +\begin{DoxyCompactList}\small\item\em $<$ Used to keep track of the number of species generated . \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a63e36d8b6a91d1f2ce76dd84606f5865}\label{class_neat_speciation_strategy_a63e36d8b6a91d1f2ce76dd84606f5865}} +double {\bfseries species\+\_\+threshold} +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a24ce4ebee07701d240dbcc6a4e9561d2}\label{class_neat_speciation_strategy_a24ce4ebee07701d240dbcc6a4e9561d2}} +double {\bfseries fitness\+\_\+threshold} +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a0eb7a7bb185e655c8cdd928321aaeee1}\label{class_neat_speciation_strategy_a0eb7a7bb185e655c8cdd928321aaeee1}} +double {\bfseries neat\+\_\+c1} +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a0554532c31c545b4c8d5d6a073699145}\label{class_neat_speciation_strategy_a0554532c31c545b4c8d5d6a073699145}} +double {\bfseries neat\+\_\+c2} +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a2bf558da2a6715ee6d8ae1e17ee7c6aa}\label{class_neat_speciation_strategy_a2bf558da2a6715ee6d8ae1e17ee7c6aa}} +double {\bfseries neat\+\_\+c3} +\item +double \mbox{\hyperlink{class_neat_speciation_strategy_aacd4a3966b8d8578b3cc1f67c03485b2}{mutation\+\_\+rate}} +\begin{DoxyCompactList}\small\item\em How frequently to do mutations. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_neat_speciation_strategy_ae212edf21f96938c934ad7f824f5d4d7}{intra\+\_\+island\+\_\+crossover\+\_\+rate}} +\begin{DoxyCompactList}\small\item\em How frequently to do intra-\/island crossovers. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_neat_speciation_strategy_ae407ee52a926c60746041987e60f1ecd}{inter\+\_\+island\+\_\+crossover\+\_\+rate}} +\begin{DoxyCompactList}\small\item\em How frequently to do inter-\/island crossovers. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_neat_speciation_strategy_aa2ecd78ba30f3cc72d2bf1367d5899bc}\label{class_neat_speciation_strategy_aa2ecd78ba30f3cc72d2bf1367d5899bc}} +int32\+\_\+t \mbox{\hyperlink{class_neat_speciation_strategy_aa2ecd78ba30f3cc72d2bf1367d5899bc}{generated\+\_\+genomes}} +\begin{DoxyCompactList}\small\item\em How many genomes have been generated by this speciation strategy. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a863ed8485d76c0a036f7a533148888e8}\label{class_neat_speciation_strategy_a863ed8485d76c0a036f7a533148888e8}} +int32\+\_\+t \mbox{\hyperlink{class_neat_speciation_strategy_a863ed8485d76c0a036f7a533148888e8}{inserted\+\_\+genomes}} +\begin{DoxyCompactList}\small\item\em How many genomes have been inserted into this speciatoin strategy. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_neat_speciation_strategy_ab991acafcd0032a3b7b54feae384414d}\label{class_neat_speciation_strategy_ab991acafcd0032a3b7b54feae384414d}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_neat_speciation_strategy_ab991acafcd0032a3b7b54feae384414d}{minimal\+\_\+genome}} +\begin{DoxyCompactList}\small\item\em keep a reference to a minimal genome so we can re-\/use it across islands and not duplicate innovation numbers. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_neat_speciation_strategy_ab49abdc7dbe12df0c445d84258fa3cd5}\label{class_neat_speciation_strategy_ab49abdc7dbe12df0c445d84258fa3cd5}} +int32\+\_\+t {\bfseries max\+\_\+genomes} +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_afdfc19b3f2f3f7f59baf7f86844af1f3}\label{class_neat_speciation_strategy_afdfc19b3f2f3f7f59baf7f86844af1f3}} +\textbf{ minstd\+\_\+rand0} {\bfseries generator} +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_a3b5fce274787384916bc8d6c564fde3c}\label{class_neat_speciation_strategy_a3b5fce274787384916bc8d6c564fde3c}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_species}{Species}} $\ast$ $>$ {\bfseries Neat\+\_\+\+Species} +\item +\mbox{\Hypertarget{class_neat_speciation_strategy_ab6026e12cef3b892c672d3ab3297d392}\label{class_neat_speciation_strategy_ab6026e12cef3b892c672d3ab3297d392}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries global\+\_\+best\+\_\+genome} +\end{DoxyCompactItemize} + + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_neat_speciation_strategy_ae86f8696ddab1b6cc595b6e2ef0371ae}\label{class_neat_speciation_strategy_ae86f8696ddab1b6cc595b6e2ef0371ae}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!generate\_genome@{generate\_genome}} +\index{generate\_genome@{generate\_genome}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{generate\_genome()}{generate\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Neat\+Speciation\+Strategy\+::generate\+\_\+genome (\begin{DoxyParamCaption}\item[{\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&}]{rng\+\_\+0\+\_\+1, }\item[{\textbf{ minstd\+\_\+rand0} \&}]{generator, }\item[{\textbf{ function}$<$ void(int32\+\_\+t, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&}]{mutate, }\item[{\textbf{ function}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&}]{crossover }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Generates a new genome. + + +\begin{DoxyParams}{Parameters} +{\em rng\+\_\+0\+\_\+1} & is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive). \\ +\hline +{\em generator} & is the random number generator \\ +\hline +{\em mutate} & is the a function which performs a mutation on a genome \\ +\hline +{\em crossover} & is the function which performs crossover between two genomes\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +the newly generated genome. +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a76f36f0dd7c47727aef912d48f0fa26b}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_neat_speciation_strategy_a4aab3d6d1abf3b1f2fd7cf7b8a4a1d99}\label{class_neat_speciation_strategy_a4aab3d6d1abf3b1f2fd7cf7b8a4a1d99}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!get\_best\_fitness@{get\_best\_fitness}} +\index{get\_best\_fitness@{get\_best\_fitness}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_best\_fitness()}{get\_best\_fitness()}} +{\footnotesize\ttfamily double Neat\+Speciation\+Strategy\+::get\+\_\+best\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Gets the fitness of the best genome of all the islands. + +\begin{DoxyReturn}{Returns} +the best fitness over all islands +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_afa3f2d35da2172a32ed06588ef09d4d0}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_neat_speciation_strategy_aecf62aeba94c1f8d786215c10ea4774b}\label{class_neat_speciation_strategy_aecf62aeba94c1f8d786215c10ea4774b}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!get\_best\_genome@{get\_best\_genome}} +\index{get\_best\_genome@{get\_best\_genome}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_best\_genome()}{get\_best\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Neat\+Speciation\+Strategy\+::get\+\_\+best\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Gets the best genome of all the islands. + +\begin{DoxyReturn}{Returns} +the best genome of all islands +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_ad0796098d09c25bfee14d9f264bcba2d}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_neat_speciation_strategy_af7e48fd1afb5a9c1b422ad251366263e}\label{class_neat_speciation_strategy_af7e48fd1afb5a9c1b422ad251366263e}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!get\_generated\_genomes@{get\_generated\_genomes}} +\index{get\_generated\_genomes@{get\_generated\_genomes}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_generated\_genomes()}{get\_generated\_genomes()}} +{\footnotesize\ttfamily int32\+\_\+t Neat\+Speciation\+Strategy\+::get\+\_\+generated\+\_\+genomes (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [virtual]}} + +\begin{DoxyReturn}{Returns} +the number of generated genomes. +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a7699a7050ed5a66693f19e834e9ba082}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_neat_speciation_strategy_aba55a9f0006af882fd613bd0676555eb}\label{class_neat_speciation_strategy_aba55a9f0006af882fd613bd0676555eb}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!get\_inserted\_genomes@{get\_inserted\_genomes}} +\index{get\_inserted\_genomes@{get\_inserted\_genomes}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_inserted\_genomes()}{get\_inserted\_genomes()}} +{\footnotesize\ttfamily int32\+\_\+t Neat\+Speciation\+Strategy\+::get\+\_\+inserted\+\_\+genomes (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [virtual]}} + +\begin{DoxyReturn}{Returns} +the number of inserted genomes. +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a1f07309130568d8cc79da44c8da6ca69}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_neat_speciation_strategy_acbd3eaa05db725de60074276cd0bb449}\label{class_neat_speciation_strategy_acbd3eaa05db725de60074276cd0bb449}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!get\_worst\_fitness@{get\_worst\_fitness}} +\index{get\_worst\_fitness@{get\_worst\_fitness}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_worst\_fitness()}{get\_worst\_fitness()}} +{\footnotesize\ttfamily double Neat\+Speciation\+Strategy\+::get\+\_\+worst\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Gets the fitness of the worst genome of all the islands. + +\begin{DoxyReturn}{Returns} +the worst fitness over all islands +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a6bdef578fe33c628a81db35d75eaa473}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_neat_speciation_strategy_a8bc6ad6a0740e2f6072aef3ad14dd3f7}\label{class_neat_speciation_strategy_a8bc6ad6a0740e2f6072aef3ad14dd3f7}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!get\_worst\_genome@{get\_worst\_genome}} +\index{get\_worst\_genome@{get\_worst\_genome}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_worst\_genome()}{get\_worst\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Neat\+Speciation\+Strategy\+::get\+\_\+worst\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Gets the the worst genome of all the islands. + +\begin{DoxyReturn}{Returns} +the worst genome of all islands +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_ae84e9c1e0eaf02886b41b1d9adf488f4}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_neat_speciation_strategy_aa2b2521a400435cbd1856aadda2051e0}\label{class_neat_speciation_strategy_aa2b2521a400435cbd1856aadda2051e0}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!insert\_genome@{insert\_genome}} +\index{insert\_genome@{insert\_genome}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{insert\_genome()}{insert\_genome()}} +{\footnotesize\ttfamily int32\+\_\+t Neat\+Speciation\+Strategy\+::insert\+\_\+genome (\begin{DoxyParamCaption}\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$}]{genome }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Inserts a {\bfseries{copy}} of the genome into this speciation strategy. + +The caller of this method will need to free the memory of the genome passed into this method. + + +\begin{DoxyParams}{Parameters} +{\em genome} & is the genome to insert. \\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +a value $<$ 0 if the genome was not inserted, 0 if it was a new best genome or $>$ 0 otherwise. +\end{DoxyReturn} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a1fd87178f38052aa934312e49819e509}{Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_neat_speciation_strategy_abef17e0293c2e0a64b3b4b3b9b7775d2}\label{class_neat_speciation_strategy_abef17e0293c2e0a64b3b4b3b9b7775d2}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!print@{print}} +\index{print@{print}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{print()}{print()}} +{\footnotesize\ttfamily void Neat\+Speciation\+Strategy\+::print (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{indent = {\ttfamily \char`\"{}\char`\"{}} }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [virtual]}} + + + +Prints out all the island\textquotesingle{}s populations. + + +\begin{DoxyParams}{Parameters} +{\em indent} & is how much to indent what is printed out \\ +\hline +\end{DoxyParams} + + +Implements \mbox{\hyperlink{class_speciation_strategy_a1d44f4c87ab00182e61f0fb364f274a9}{Speciation\+Strategy}}. + + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{class_neat_speciation_strategy_ae407ee52a926c60746041987e60f1ecd}\label{class_neat_speciation_strategy_ae407ee52a926c60746041987e60f1ecd}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!inter\_island\_crossover\_rate@{inter\_island\_crossover\_rate}} +\index{inter\_island\_crossover\_rate@{inter\_island\_crossover\_rate}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{inter\_island\_crossover\_rate}{inter\_island\_crossover\_rate}} +{\footnotesize\ttfamily double Neat\+Speciation\+Strategy\+::inter\+\_\+island\+\_\+crossover\+\_\+rate\hspace{0.3cm}{\ttfamily [private]}} + + + +How frequently to do inter-\/island crossovers. + +Note that mutation\+\_\+rate + intra\+\_\+island\+\_\+crossover\+\_\+rate + inter\+\_\+island\+\_\+crossover\+\_\+rate should equal 1, if not they will be scaled down such that they do. \mbox{\Hypertarget{class_neat_speciation_strategy_ae212edf21f96938c934ad7f824f5d4d7}\label{class_neat_speciation_strategy_ae212edf21f96938c934ad7f824f5d4d7}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!intra\_island\_crossover\_rate@{intra\_island\_crossover\_rate}} +\index{intra\_island\_crossover\_rate@{intra\_island\_crossover\_rate}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{intra\_island\_crossover\_rate}{intra\_island\_crossover\_rate}} +{\footnotesize\ttfamily double Neat\+Speciation\+Strategy\+::intra\+\_\+island\+\_\+crossover\+\_\+rate\hspace{0.3cm}{\ttfamily [private]}} + + + +How frequently to do intra-\/island crossovers. + +Note that mutation\+\_\+rate + intra\+\_\+island\+\_\+crossover\+\_\+rate + inter\+\_\+island\+\_\+crossover\+\_\+rate should equal 1, if not they will be scaled down such that they do. \mbox{\Hypertarget{class_neat_speciation_strategy_aacd4a3966b8d8578b3cc1f67c03485b2}\label{class_neat_speciation_strategy_aacd4a3966b8d8578b3cc1f67c03485b2}} +\index{NeatSpeciationStrategy@{NeatSpeciationStrategy}!mutation\_rate@{mutation\_rate}} +\index{mutation\_rate@{mutation\_rate}!NeatSpeciationStrategy@{NeatSpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{mutation\_rate}{mutation\_rate}} +{\footnotesize\ttfamily double Neat\+Speciation\+Strategy\+::mutation\+\_\+rate\hspace{0.3cm}{\ttfamily [private]}} + + + +How frequently to do mutations. + +Note that mutation\+\_\+rate + intra\+\_\+island\+\_\+crossover\+\_\+rate + inter\+\_\+island\+\_\+crossover\+\_\+rate should equal 1, if not they will be scaled down such that they do. + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/neat\+\_\+speciation\+\_\+strategy.\+hxx\item +rnn/neat\+\_\+speciation\+\_\+strategy.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_normal_distribution.tex b/docs/latex/class_normal_distribution.tex new file mode 100644 index 00000000..cc607e11 --- /dev/null +++ b/docs/latex/class_normal_distribution.tex @@ -0,0 +1,41 @@ +\hypertarget{class_normal_distribution}{}\doxysection{Normal\+Distribution Class Reference} +\label{class_normal_distribution}\index{NormalDistribution@{NormalDistribution}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_normal_distribution_ab2dab37dbdf9a427dabb479f95db595d}\label{class_normal_distribution_ab2dab37dbdf9a427dabb479f95db595d}} +float {\bfseries random} (\textbf{ minstd\+\_\+rand0} \&generator, float mu, float sigma) +\item +\mbox{\Hypertarget{class_normal_distribution_a6887413d2b61c9b50cacc957dffb9858}\label{class_normal_distribution_a6887413d2b61c9b50cacc957dffb9858}} +bool {\bfseries operator==} (const \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&other) const +\item +\mbox{\Hypertarget{class_normal_distribution_af0e334ba55eedd5b5ca33230245768f7}\label{class_normal_distribution_af0e334ba55eedd5b5ca33230245768f7}} +bool {\bfseries operator!=} (const \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&other) const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_normal_distribution_acb87f6e2f9181d9884ea7b6f4af6aecc}\label{class_normal_distribution_acb87f6e2f9181d9884ea7b6f4af6aecc}} +bool {\bfseries generate} +\item +\mbox{\Hypertarget{class_normal_distribution_aef551958a237bce2c6e5ea2f10742bd1}\label{class_normal_distribution_aef551958a237bce2c6e5ea2f10742bd1}} +float {\bfseries z0} +\item +\mbox{\Hypertarget{class_normal_distribution_a5366b865121db215e337e2238a988351}\label{class_normal_distribution_a5366b865121db215e337e2238a988351}} +float {\bfseries z1} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_normal_distribution_af476ede4785cf59ad4ef894baa3d1388}\label{class_normal_distribution_af476ede4785cf59ad4ef894baa3d1388}} +\textbf{ ostream} \& {\bfseries operator$<$$<$} (\textbf{ ostream} \&os, const \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution) +\item +\mbox{\Hypertarget{class_normal_distribution_ae816f2a96eda8e6de20e204179f6cef3}\label{class_normal_distribution_ae816f2a96eda8e6de20e204179f6cef3}} +\textbf{ istream} \& {\bfseries operator$>$$>$} (\textbf{ istream} \&is, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution) +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +common/random.\+hxx\item +common/random.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_parse_turbines.tex b/docs/latex/class_parse_turbines.tex new file mode 100644 index 00000000..12662312 --- /dev/null +++ b/docs/latex/class_parse_turbines.tex @@ -0,0 +1,30 @@ +\hypertarget{class_parse_turbines}{}\doxysection{Parse\+Turbines Class Reference} +\label{class_parse_turbines}\index{ParseTurbines@{ParseTurbines}} +\doxysubsection*{Classes} +\begin{DoxyCompactItemize} +\item +class {\bfseries Turbine\+Line} +\end{DoxyCompactItemize} +\doxysubsection*{Static Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_parse_turbines_a17d1c386e532e47531c7138def35a1a5}\label{class_parse_turbines_a17d1c386e532e47531c7138def35a1a5}} +static void {\bfseries write\+Headers} (Buffered\+Writer bw, String\mbox{[}$\,$\mbox{]} column\+Names) throws I\+O\+Exception +\item +\mbox{\Hypertarget{class_parse_turbines_a995484f869c1a8fa5a90028572236308}\label{class_parse_turbines_a995484f869c1a8fa5a90028572236308}} +static void {\bfseries main} (String\mbox{[}$\,$\mbox{]} arguments) +\end{DoxyCompactItemize} +\doxysubsection*{Static Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_parse_turbines_a3ad4e1811d19cec2e64d939e2a1d8f81}\label{class_parse_turbines_a3ad4e1811d19cec2e64d939e2a1d8f81}} +static Simple\+Date\+Format {\bfseries sdf} = new Simple\+Date\+Format(\char`\"{}yyyy-\/MM-\/dd\textquotesingle{}T\textquotesingle{}H\+H\+:mm\+:ss\+X\+XX\char`\"{}) +\item +\mbox{\Hypertarget{class_parse_turbines_a60c1f15988dc560f5c5a28c51d02a26f}\label{class_parse_turbines_a60c1f15988dc560f5c5a28c51d02a26f}} +static int {\bfseries num\+\_\+column} = 122 +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} +\item +datasets/2020\+\_\+engie\+\_\+wind/Parse\+Turbines.\+java\end{DoxyCompactItemize} diff --git a/docs/latex/class_point.tex b/docs/latex/class_point.tex new file mode 100644 index 00000000..e887aa00 --- /dev/null +++ b/docs/latex/class_point.tex @@ -0,0 +1,23 @@ +\hypertarget{class_point}{}\doxysection{Point Class Reference} +\label{class_point}\index{Point@{Point}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_point_ae5af86d9f01d1707873318989f7c4088}\label{class_point_ae5af86d9f01d1707873318989f7c4088}} +{\bfseries Point} (int \+\_\+y, int \+\_\+x) +\end{DoxyCompactItemize} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_point_a2e1b5fb2b2a83571f5c0bc0f66a73cf7}\label{class_point_a2e1b5fb2b2a83571f5c0bc0f66a73cf7}} +int {\bfseries y} +\item +\mbox{\Hypertarget{class_point_a8c779e11e694b20e0946105a9f5de842}\label{class_point_a8c779e11e694b20e0946105a9f5de842}} +int {\bfseries x} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/mosaic\+\_\+image\+\_\+set.\+hxx\item +image\+\_\+tools/mosaic\+\_\+image\+\_\+set.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_r_a_n_d_o_m___d_a_g___node.eps b/docs/latex/class_r_a_n_d_o_m___d_a_g___node.eps new file mode 100644 index 00000000..1f915b25 --- /dev/null +++ b/docs/latex/class_r_a_n_d_o_m___d_a_g___node.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 272.108844 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.837500 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(RANDOM_DAG_Node) cw +(RNN_Node_Interface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (RANDOM_DAG_Node) 0.000000 0.000000 box + (RNN_Node_Interface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_r_a_n_d_o_m___d_a_g___node.tex b/docs/latex/class_r_a_n_d_o_m___d_a_g___node.tex new file mode 100644 index 00000000..a3d94b5a --- /dev/null +++ b/docs/latex/class_r_a_n_d_o_m___d_a_g___node.tex @@ -0,0 +1,123 @@ +\hypertarget{class_r_a_n_d_o_m___d_a_g___node}{}\doxysection{R\+A\+N\+D\+O\+M\+\_\+\+D\+A\+G\+\_\+\+Node Class Reference} +\label{class_r_a_n_d_o_m___d_a_g___node}\index{RANDOM\_DAG\_Node@{RANDOM\_DAG\_Node}} +Inheritance diagram for R\+A\+N\+D\+O\+M\+\_\+\+D\+A\+G\+\_\+\+Node\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_r_a_n_d_o_m___d_a_g___node} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_adec1b9be9f1931ba4cf7aa01741ffed3}\label{class_r_a_n_d_o_m___d_a_g___node_adec1b9be9f1931ba4cf7aa01741ffed3}} +{\bfseries R\+A\+N\+D\+O\+M\+\_\+\+D\+A\+G\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, int \+\_\+type, double \+\_\+depth) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a2e4c0aec006309beaf41684fd7f2d283}\label{class_r_a_n_d_o_m___d_a_g___node_a2e4c0aec006309beaf41684fd7f2d283}} +void {\bfseries initialize\+\_\+lamarckian} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double mu, double sigma) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a58bd17b4e9c7209fccf861f8fab687aa}\label{class_r_a_n_d_o_m___d_a_g___node_a58bd17b4e9c7209fccf861f8fab687aa}} +void {\bfseries initialize\+\_\+xavier} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng1\+\_\+1, double range) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_af66b9326a1c62354bf529f4d7ab1b6ad}\label{class_r_a_n_d_o_m___d_a_g___node_af66b9326a1c62354bf529f4d7ab1b6ad}} +void {\bfseries initialize\+\_\+kaiming} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double range) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a740ccf88947098a40be51070483508e5}\label{class_r_a_n_d_o_m___d_a_g___node_a740ccf88947098a40be51070483508e5}} +void {\bfseries initialize\+\_\+uniform\+\_\+random} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a67dd4045bf2333a399caee534469b5f7}\label{class_r_a_n_d_o_m___d_a_g___node_a67dd4045bf2333a399caee534469b5f7}} +double {\bfseries get\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a7cc10fd4d34b8b0e36cd91304041fdc1}\label{class_r_a_n_d_o_m___d_a_g___node_a7cc10fd4d34b8b0e36cd91304041fdc1}} +void {\bfseries print\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a4495aaab90263b73dfdc7cb1abfa235d}\label{class_r_a_n_d_o_m___d_a_g___node_a4495aaab90263b73dfdc7cb1abfa235d}} +double {\bfseries activation} (double value, int act\+\_\+operator) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a5d6bb3b52bb80226009f89a4ec84de28}\label{class_r_a_n_d_o_m___d_a_g___node_a5d6bb3b52bb80226009f89a4ec84de28}} +double {\bfseries activation\+\_\+derivative} (double value, double input, int act\+\_\+operator) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a1d9556dd97cc2d1b784f172666fe7015}\label{class_r_a_n_d_o_m___d_a_g___node_a1d9556dd97cc2d1b784f172666fe7015}} +void {\bfseries input\+\_\+fired} (int time, double incoming\+\_\+output) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a6314cb9edd1d044c3092891976cb5239}\label{class_r_a_n_d_o_m___d_a_g___node_a6314cb9edd1d044c3092891976cb5239}} +void {\bfseries try\+\_\+update\+\_\+deltas} (int time) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a04b077e1127db3212b5453457d32960c}\label{class_r_a_n_d_o_m___d_a_g___node_a04b077e1127db3212b5453457d32960c}} +void {\bfseries error\+\_\+fired} (int time, double error) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a516182d6897e7ae6ce88cbe1978a9bb2}\label{class_r_a_n_d_o_m___d_a_g___node_a516182d6897e7ae6ce88cbe1978a9bb2}} +void {\bfseries output\+\_\+fired} (int time, double delta) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_ab30c087e233c86c0d07722ae1f755075}\label{class_r_a_n_d_o_m___d_a_g___node_ab30c087e233c86c0d07722ae1f755075}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a13a43059ceda27b97579e6437c48a3d1}\label{class_r_a_n_d_o_m___d_a_g___node_a13a43059ceda27b97579e6437c48a3d1}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_adc220aff7ecee8b15ae0919fe8ca1071}\label{class_r_a_n_d_o_m___d_a_g___node_adc220aff7ecee8b15ae0919fe8ca1071}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a36381c7fea6088573b91811d9e02ff42}\label{class_r_a_n_d_o_m___d_a_g___node_a36381c7fea6088573b91811d9e02ff42}} +void {\bfseries get\+\_\+weights} (uint32\+\_\+t \&offset, \textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a8e05477f90a398d0ae066b17ad192583}\label{class_r_a_n_d_o_m___d_a_g___node_a8e05477f90a398d0ae066b17ad192583}} +void {\bfseries set\+\_\+weights} (uint32\+\_\+t \&offset, const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a8407b237aa07eeac13d6729e7e414130}\label{class_r_a_n_d_o_m___d_a_g___node_a8407b237aa07eeac13d6729e7e414130}} +void {\bfseries get\+\_\+gradients} (\textbf{ vector}$<$ double $>$ \&gradients) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a4896518988d24bce6d87abef2a8ff618}\label{class_r_a_n_d_o_m___d_a_g___node_a4896518988d24bce6d87abef2a8ff618}} +void {\bfseries reset} (int \+\_\+series\+\_\+length) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a16a7e4e713cc5cfb0981b89306ae4d40}\label{class_r_a_n_d_o_m___d_a_g___node_a16a7e4e713cc5cfb0981b89306ae4d40}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a095f8dce9c8d61f98f305ed1b4699afa}\label{class_r_a_n_d_o_m___d_a_g___node_a095f8dce9c8d61f98f305ed1b4699afa}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries copy} () const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_af0f334e481df8d85e25782ae57d18ab4}\label{class_r_a_n_d_o_m___d_a_g___node_af0f334e481df8d85e25782ae57d18ab4}} +\textbf{ vector}$<$ int $>$ {\bfseries node\+\_\+output} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a6d00d0b268d442a0b37ceb3856f85be9}\label{class_r_a_n_d_o_m___d_a_g___node_a6d00d0b268d442a0b37ceb3856f85be9}} +double {\bfseries rw} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a2e51ac16c91ff6bf22aa217a26c7d956}\label{class_r_a_n_d_o_m___d_a_g___node_a2e51ac16c91ff6bf22aa217a26c7d956}} +double {\bfseries zw} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a6639205d23348856f0c8591121131830}\label{class_r_a_n_d_o_m___d_a_g___node_a6639205d23348856f0c8591121131830}} +\textbf{ vector}$<$ double $>$ {\bfseries weights} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a48afb2bea453a271dce3e005b9bc8510}\label{class_r_a_n_d_o_m___d_a_g___node_a48afb2bea453a271dce3e005b9bc8510}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+zw} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_ab67321581146fae8017c8b19a7f83383}\label{class_r_a_n_d_o_m___d_a_g___node_ab67321581146fae8017c8b19a7f83383}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+rw} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a273ce64dcabb4bf4c4722285c62924be}\label{class_r_a_n_d_o_m___d_a_g___node_a273ce64dcabb4bf4c4722285c62924be}} +\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ {\bfseries d\+\_\+weights} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a4884b63d89fd68219f26dde4cbd4915f}\label{class_r_a_n_d_o_m___d_a_g___node_a4884b63d89fd68219f26dde4cbd4915f}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+h\+\_\+prev} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_aec949ee089f5998c899c9f0c69759378}\label{class_r_a_n_d_o_m___d_a_g___node_aec949ee089f5998c899c9f0c69759378}} +\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ {\bfseries Nodes} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_abbd47151e3b1d958829706925f5f3eff}\label{class_r_a_n_d_o_m___d_a_g___node_abbd47151e3b1d958829706925f5f3eff}} +\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ {\bfseries l\+\_\+\+Nodes} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_a_n_d_o_m___d_a_g___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}\label{class_r_a_n_d_o_m___d_a_g___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}} +class {\bfseries R\+N\+N\+\_\+\+Edge} +\end{DoxyCompactItemize} +\doxysubsection*{Additional Inherited Members} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/random\+\_\+dag\+\_\+node.\+hxx\item +rnn/random\+\_\+dag\+\_\+node.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_r_n_n.tex b/docs/latex/class_r_n_n.tex new file mode 100644 index 00000000..b981a84d --- /dev/null +++ b/docs/latex/class_r_n_n.tex @@ -0,0 +1,230 @@ +\hypertarget{class_r_n_n}{}\doxysection{R\+NN Class Reference} +\label{class_r_n_n}\index{RNN@{RNN}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n_a21abeabbb35cbd741947ad14dd2b34ee}\label{class_r_n_n_a21abeabbb35cbd741947ad14dd2b34ee}} +{\bfseries R\+NN} (\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ \&\+\_\+nodes, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$ $>$ \&\+\_\+edges, const \textbf{ vector}$<$ \textbf{ string} $>$ \&input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&output\+\_\+parameter\+\_\+names) +\item +\mbox{\Hypertarget{class_r_n_n_a64568874f663d44ee0fef78b439e36b1}\label{class_r_n_n_a64568874f663d44ee0fef78b439e36b1}} +{\bfseries R\+NN} (\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ \&\+\_\+nodes, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$ $>$ \&\+\_\+edges, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$ $>$ \&\+\_\+recurrent\+\_\+edges, const \textbf{ vector}$<$ \textbf{ string} $>$ \&input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&output\+\_\+parameter\+\_\+names) +\item +\mbox{\Hypertarget{class_r_n_n_a800ff6a57eda063798e081d56dad4659}\label{class_r_n_n_a800ff6a57eda063798e081d56dad4659}} +void {\bfseries fix\+\_\+parameter\+\_\+orders} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&output\+\_\+parameter\+\_\+names) +\item +\mbox{\Hypertarget{class_r_n_n_a77c4b96d7c7f5ae4a0424b89299c3596}\label{class_r_n_n_a77c4b96d7c7f5ae4a0424b89299c3596}} +void {\bfseries validate\+\_\+parameters} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&output\+\_\+parameter\+\_\+names) +\item +\mbox{\Hypertarget{class_r_n_n_a6b6ed3e806e12ab441a05983ede06338}\label{class_r_n_n_a6b6ed3e806e12ab441a05983ede06338}} +int {\bfseries get\+\_\+number\+\_\+nodes} () +\item +\mbox{\Hypertarget{class_r_n_n_a88e40fc2dee042a8812aefce69c0c6b7}\label{class_r_n_n_a88e40fc2dee042a8812aefce69c0c6b7}} +int {\bfseries get\+\_\+number\+\_\+edges} () +\item +\mbox{\Hypertarget{class_r_n_n_afd9b76cd6e8b6b7412fd7604e414a8e2}\label{class_r_n_n_afd9b76cd6e8b6b7412fd7604e414a8e2}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries get\+\_\+node} (int i) +\item +\mbox{\Hypertarget{class_r_n_n_a6c481f94d0d061a102a0d4d6586452ac}\label{class_r_n_n_a6c481f94d0d061a102a0d4d6586452ac}} +\mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$ {\bfseries get\+\_\+edge} (int i) +\item +\mbox{\Hypertarget{class_r_n_n_a09548131fe6647737628f8803f7555b9}\label{class_r_n_n_a09548131fe6647737628f8803f7555b9}} +void {\bfseries forward\+\_\+pass} (const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&series\+\_\+data, bool using\+\_\+dropout, bool training, double dropout\+\_\+probability) +\item +\mbox{\Hypertarget{class_r_n_n_a36a017fe90949a25e8368a03482712b0}\label{class_r_n_n_a36a017fe90949a25e8368a03482712b0}} +void {\bfseries backward\+\_\+pass} (double error, bool using\+\_\+dropout, bool training, double dropout\+\_\+probability) +\item +double \mbox{\hyperlink{class_r_n_n_aa5fb8d612f560a05bfa188453578c1a2}{calculate\+\_\+error\+\_\+softmax}} (const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected\+\_\+outputs) +\begin{DoxyCompactList}\small\item\em Calculates the sum of cross entropy error by comparing the expected outputs and the predicted outputs. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_r_n_n_a99cb157b0238b827561808c92719b24f}\label{class_r_n_n_a99cb157b0238b827561808c92719b24f}} +double {\bfseries calculate\+\_\+error\+\_\+mse} (const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected\+\_\+outputs) +\item +\mbox{\Hypertarget{class_r_n_n_a4b33d44e30062abd3caf1582aeadadd6}\label{class_r_n_n_a4b33d44e30062abd3caf1582aeadadd6}} +double {\bfseries calculate\+\_\+error\+\_\+mae} (const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected\+\_\+outputs) +\item +double \mbox{\hyperlink{class_r_n_n_a7245cfe333987f29d59dd1c1442391ec}{prediction\+\_\+softmax}} (const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&series\+\_\+data, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected\+\_\+outputs, bool using\+\_\+dropout, bool training, double dropout\+\_\+probability) +\begin{DoxyCompactList}\small\item\em Calls the calculate\+\_\+error\+\_\+softmax after the forward pass across the rnn genome. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_r_n_n_a70c2fd7634e15fcceba72dcc350f3cd9}\label{class_r_n_n_a70c2fd7634e15fcceba72dcc350f3cd9}} +double {\bfseries prediction\+\_\+mse} (const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&series\+\_\+data, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected\+\_\+outputs, bool using\+\_\+dropout, bool training, double dropout\+\_\+probability) +\item +\mbox{\Hypertarget{class_r_n_n_abc61ce5eb7ccb4b027aab28f01dfb578}\label{class_r_n_n_abc61ce5eb7ccb4b027aab28f01dfb578}} +double {\bfseries prediction\+\_\+mae} (const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&series\+\_\+data, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected\+\_\+outputs, bool using\+\_\+dropout, bool training, double dropout\+\_\+probability) +\item +\mbox{\Hypertarget{class_r_n_n_a5ccda82155037f616c6db9bb73c52b3b}\label{class_r_n_n_a5ccda82155037f616c6db9bb73c52b3b}} +\textbf{ vector}$<$ double $>$ {\bfseries get\+\_\+predictions} (const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&series\+\_\+data, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected\+\_\+outputs, bool usng\+\_\+dropout, double dropout\+\_\+probability) +\item +\mbox{\Hypertarget{class_r_n_n_a6b951718d90f8a3398a500816aa0b25a}\label{class_r_n_n_a6b951718d90f8a3398a500816aa0b25a}} +void {\bfseries write\+\_\+predictions} (\textbf{ string} output\+\_\+filename, const \textbf{ vector}$<$ \textbf{ string} $>$ \&input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&output\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&series\+\_\+data, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected\+\_\+outputs, \mbox{\hyperlink{class_time_series_sets}{Time\+Series\+Sets}} $\ast$time\+\_\+series\+\_\+sets, bool using\+\_\+dropout, double dropout\+\_\+probability) +\item +\mbox{\Hypertarget{class_r_n_n_a45ff518bedc99a07d557b9bdaacd3bcc}\label{class_r_n_n_a45ff518bedc99a07d557b9bdaacd3bcc}} +void {\bfseries write\+\_\+predictions} (\textbf{ string} output\+\_\+filename, const \textbf{ vector}$<$ \textbf{ string} $>$ \&input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&output\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&series\+\_\+data, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected\+\_\+outputs, \mbox{\hyperlink{class_corpus}{Corpus}} $\ast$word\+\_\+series\+\_\+sets, bool using\+\_\+dropout, double dropout\+\_\+probability) +\item +\mbox{\Hypertarget{class_r_n_n_a32e059e267405b96be71b7476d96e3d4}\label{class_r_n_n_a32e059e267405b96be71b7476d96e3d4}} +void {\bfseries initialize\+\_\+randomly} () +\item +\mbox{\Hypertarget{class_r_n_n_a69e4bba4cb37eae14acbb56027c3ac69}\label{class_r_n_n_a69e4bba4cb37eae14acbb56027c3ac69}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_r_n_n_a5ab51c54eafb4349472563cd75c8da6e}\label{class_r_n_n_a5ab51c54eafb4349472563cd75c8da6e}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_r_n_n_a31b31a223ac1d528361e537765942784}\label{class_r_n_n_a31b31a223ac1d528361e537765942784}} +void {\bfseries enable\+\_\+use\+\_\+regression} (bool \+\_\+use\+\_\+regression) +\item +\mbox{\Hypertarget{class_r_n_n_a5078184d1b06414742a10cdf413283c2}\label{class_r_n_n_a5078184d1b06414742a10cdf413283c2}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () +\item +void \mbox{\hyperlink{class_r_n_n_aad5f51508b5cb978a6343e3234bb8892}{get\+\_\+analytic\+\_\+gradient}} (const \textbf{ vector}$<$ double $>$ \&test\+\_\+parameters, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&outputs, double \&mse, \textbf{ vector}$<$ double $>$ \&analytic\+\_\+gradient, bool using\+\_\+dropout, bool training, double dropout\+\_\+probability) +\begin{DoxyCompactList}\small\item\em Updates the analytic gradients after the forward pass across the rnn genome and the backward pass sequentially. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_r_n_n_a7a81b82f7e69907f7d5f2ce37048db68}\label{class_r_n_n_a7a81b82f7e69907f7d5f2ce37048db68}} +void {\bfseries get\+\_\+empirical\+\_\+gradient} (const \textbf{ vector}$<$ double $>$ \&test\+\_\+parameters, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&outputs, double \&mae, \textbf{ vector}$<$ double $>$ \&empirical\+\_\+gradient, bool using\+\_\+dropout, bool training, double dropout\+\_\+probability) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +int \mbox{\hyperlink{class_r_n_n_ad70d91c21e6cd8890467d3f64eac0b9a}{series\+\_\+length}} +\begin{DoxyCompactList}\small\item\em Specifies the length of the dataset. \end{DoxyCompactList}\item +bool \mbox{\hyperlink{class_r_n_n_a17b9ce91b0ad9f0848f296021e5acd5f}{use\+\_\+regression}} +\begin{DoxyCompactList}\small\item\em Specifies whether to use the regression or not. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_r_n_n_a1737ef6591c418cd8accf3c55c1909c1}\label{class_r_n_n_a1737ef6591c418cd8accf3c55c1909c1}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ {\bfseries input\+\_\+nodes} +\item +\mbox{\Hypertarget{class_r_n_n_a40e160514ff6c67b7c13d9ac98cd4027}\label{class_r_n_n_a40e160514ff6c67b7c13d9ac98cd4027}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ {\bfseries output\+\_\+nodes} +\item +\mbox{\Hypertarget{class_r_n_n_afe1275047d2603912692c9ee4ce371b5}\label{class_r_n_n_afe1275047d2603912692c9ee4ce371b5}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ {\bfseries nodes} +\item +\mbox{\Hypertarget{class_r_n_n_a6db496ced949fb7a2374b436d516cfa9}\label{class_r_n_n_a6db496ced949fb7a2374b436d516cfa9}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$ $>$ {\bfseries edges} +\item +\mbox{\Hypertarget{class_r_n_n_a8538540654aacffe64fda78d83f14c69}\label{class_r_n_n_a8538540654aacffe64fda78d83f14c69}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$ $>$ {\bfseries recurrent\+\_\+edges} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n_a3c6fd1b1841f1808870f8f228c5d021e}\label{class_r_n_n_a3c6fd1b1841f1808870f8f228c5d021e}} +void {\bfseries get\+\_\+mse} (\mbox{\hyperlink{class_r_n_n}{R\+NN}} $\ast$genome, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected, double \&mse, \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&deltas) +\item +\mbox{\Hypertarget{class_r_n_n_a744ea213e57442af35ac90ea397ce97c}\label{class_r_n_n_a744ea213e57442af35ac90ea397ce97c}} +void {\bfseries get\+\_\+mae} (\mbox{\hyperlink{class_r_n_n}{R\+NN}} $\ast$genome, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected, double \&mae, \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&deltas) +\end{DoxyCompactItemize} + + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_r_n_n_aa5fb8d612f560a05bfa188453578c1a2}\label{class_r_n_n_aa5fb8d612f560a05bfa188453578c1a2}} +\index{RNN@{RNN}!calculate\_error\_softmax@{calculate\_error\_softmax}} +\index{calculate\_error\_softmax@{calculate\_error\_softmax}!RNN@{RNN}} +\doxysubsubsection{\texorpdfstring{calculate\_error\_softmax()}{calculate\_error\_softmax()}} +{\footnotesize\ttfamily double R\+N\+N\+::calculate\+\_\+error\+\_\+softmax (\begin{DoxyParamCaption}\item[{const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&}]{expected\+\_\+outputs }\end{DoxyParamCaption})} + + + +Calculates the sum of cross entropy error by comparing the expected outputs and the predicted outputs. + +~\newline + + + +\begin{DoxyParams}{Parameters} +{\em expected\+\_\+outputs} & is the expected outputs according the dataset.\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +sum of the cross entropy error. +\end{DoxyReturn} +\mbox{\Hypertarget{class_r_n_n_aad5f51508b5cb978a6343e3234bb8892}\label{class_r_n_n_aad5f51508b5cb978a6343e3234bb8892}} +\index{RNN@{RNN}!get\_analytic\_gradient@{get\_analytic\_gradient}} +\index{get\_analytic\_gradient@{get\_analytic\_gradient}!RNN@{RNN}} +\doxysubsubsection{\texorpdfstring{get\_analytic\_gradient()}{get\_analytic\_gradient()}} +{\footnotesize\ttfamily void R\+N\+N\+::get\+\_\+analytic\+\_\+gradient (\begin{DoxyParamCaption}\item[{const \textbf{ vector}$<$ double $>$ \&}]{test\+\_\+parameters, }\item[{const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&}]{inputs, }\item[{const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&}]{outputs, }\item[{double \&}]{mse, }\item[{\textbf{ vector}$<$ double $>$ \&}]{analytic\+\_\+gradient, }\item[{bool}]{using\+\_\+dropout, }\item[{bool}]{training, }\item[{double}]{dropout\+\_\+probability }\end{DoxyParamCaption})} + + + +Updates the analytic gradients after the forward pass across the rnn genome and the backward pass sequentially. + +~\newline + + + +\begin{DoxyParams}{Parameters} +{\em test\+\_\+parameters} & is the dataset parameters used for the forward pass. \\ +\hline +{\em inputs} & are the inputs according to the dataset that is pass to the for forward pass. \\ +\hline +{\em outputs} & are the expected outputs according to the dataset that is pass to the calculate error for backward pass. \\ +\hline +{\em analytic\+\_\+gradients} & are the current gradients in the rnn genome that are going to be update after a single pass across the rnn genome. \\ +\hline +{\em using\+\_\+dropout} & is the probability for the node being disabled while forward pass training and not during testing. ~\newline + \\ +\hline +{\em training} & is to specify whether it is training stage or not to use dropout. \\ +\hline +{\em dropout\+\_\+probability} & is the probability by which the the nodes will be disabled. will not work during testing stage and without use\+\_\+droput = true.\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +sum of the cross entropy error over the dataset. +\end{DoxyReturn} +\mbox{\Hypertarget{class_r_n_n_a7245cfe333987f29d59dd1c1442391ec}\label{class_r_n_n_a7245cfe333987f29d59dd1c1442391ec}} +\index{RNN@{RNN}!prediction\_softmax@{prediction\_softmax}} +\index{prediction\_softmax@{prediction\_softmax}!RNN@{RNN}} +\doxysubsubsection{\texorpdfstring{prediction\_softmax()}{prediction\_softmax()}} +{\footnotesize\ttfamily double R\+N\+N\+::prediction\+\_\+softmax (\begin{DoxyParamCaption}\item[{const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&}]{series\+\_\+data, }\item[{const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&}]{expected\+\_\+outputs, }\item[{bool}]{using\+\_\+dropout, }\item[{bool}]{training, }\item[{double}]{dropout\+\_\+probability }\end{DoxyParamCaption})} + + + +Calls the calculate\+\_\+error\+\_\+softmax after the forward pass across the rnn genome. + +~\newline + + + +\begin{DoxyParams}{Parameters} +{\em series\+\_\+data} & is the dataset used for the forward pass. \\ +\hline +{\em expected\+\_\+outputs} & is the expected outputs according to the dataset. \\ +\hline +{\em using\+\_\+dropout} & is the probability for the node being disabled while forward pass training and not during testing. ~\newline + \\ +\hline +{\em training} & is to specify whether it is training stage or not to use dropout. \\ +\hline +{\em dropout\+\_\+probability} & is the probability by which the the nodes will be disabled. will not work during testing stage and without use\+\_\+droput = true.\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +sum of the cross entropy error over the dataset. +\end{DoxyReturn} + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{class_r_n_n_ad70d91c21e6cd8890467d3f64eac0b9a}\label{class_r_n_n_ad70d91c21e6cd8890467d3f64eac0b9a}} +\index{RNN@{RNN}!series\_length@{series\_length}} +\index{series\_length@{series\_length}!RNN@{RNN}} +\doxysubsubsection{\texorpdfstring{series\_length}{series\_length}} +{\footnotesize\ttfamily int R\+N\+N\+::series\+\_\+length\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies the length of the dataset. + +~\newline + It can be the number of the files in case of time series or number of batches in case of word series \mbox{\Hypertarget{class_r_n_n_a17b9ce91b0ad9f0848f296021e5acd5f}\label{class_r_n_n_a17b9ce91b0ad9f0848f296021e5acd5f}} +\index{RNN@{RNN}!use\_regression@{use\_regression}} +\index{use\_regression@{use\_regression}!RNN@{RNN}} +\doxysubsubsection{\texorpdfstring{use\_regression}{use\_regression}} +{\footnotesize\ttfamily bool R\+N\+N\+::use\+\_\+regression\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies whether to use the regression or not. + +~\newline + It will use regression in case of time series and will not use regression in case of word series + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/rnn.\+hxx\item +rnn/rnn.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_r_n_n___edge.tex b/docs/latex/class_r_n_n___edge.tex new file mode 100644 index 00000000..b8b52c1d --- /dev/null +++ b/docs/latex/class_r_n_n___edge.tex @@ -0,0 +1,119 @@ +\hypertarget{class_r_n_n___edge}{}\doxysection{R\+N\+N\+\_\+\+Edge Class Reference} +\label{class_r_n_n___edge}\index{RNN\_Edge@{RNN\_Edge}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a5ac3e7d542f6c73630fa2fa6180e6a6e}\label{class_r_n_n___edge_a5ac3e7d542f6c73630fa2fa6180e6a6e}} +{\bfseries R\+N\+N\+\_\+\+Edge} (int32\+\_\+t \+\_\+innovation\+\_\+number, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$\+\_\+input\+\_\+node, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$\+\_\+output\+\_\+node) +\item +\mbox{\Hypertarget{class_r_n_n___edge_a83cead431f54004364db7a35a1b28b33}\label{class_r_n_n___edge_a83cead431f54004364db7a35a1b28b33}} +{\bfseries R\+N\+N\+\_\+\+Edge} (int32\+\_\+t \+\_\+innovation\+\_\+number, int32\+\_\+t \+\_\+input\+\_\+innovation\+\_\+number, int32\+\_\+t \+\_\+output\+\_\+innovation\+\_\+number, const \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ \&nodes) +\item +\mbox{\Hypertarget{class_r_n_n___edge_affee08331aa22823e144e44575a624c8}\label{class_r_n_n___edge_affee08331aa22823e144e44575a624c8}} +\mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$ {\bfseries copy} (const \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ new\+\_\+nodes) +\item +\mbox{\Hypertarget{class_r_n_n___edge_af2dabd1c3c19630cd73091220369db9a}\label{class_r_n_n___edge_af2dabd1c3c19630cd73091220369db9a}} +void {\bfseries reset} (int32\+\_\+t series\+\_\+length) +\item +\mbox{\Hypertarget{class_r_n_n___edge_abf10ac845aca38a8a4348c9f9882a3f8}\label{class_r_n_n___edge_abf10ac845aca38a8a4348c9f9882a3f8}} +void {\bfseries propagate\+\_\+forward} (int32\+\_\+t time) +\item +\mbox{\Hypertarget{class_r_n_n___edge_ac30711ae1b1d5cd4de60c8d36b4f2402}\label{class_r_n_n___edge_ac30711ae1b1d5cd4de60c8d36b4f2402}} +void {\bfseries propagate\+\_\+backward} (int32\+\_\+t time) +\item +\mbox{\Hypertarget{class_r_n_n___edge_a2ce7cd502eb446048a50c73dbbee3fa8}\label{class_r_n_n___edge_a2ce7cd502eb446048a50c73dbbee3fa8}} +void {\bfseries propagate\+\_\+forward} (int32\+\_\+t time, bool training, double dropout\+\_\+probability) +\item +\mbox{\Hypertarget{class_r_n_n___edge_a8dfd3965968c1f388c167740d5d467b1}\label{class_r_n_n___edge_a8dfd3965968c1f388c167740d5d467b1}} +void {\bfseries propagate\+\_\+backward} (int32\+\_\+t time, bool training, double dropout\+\_\+probability) +\item +\mbox{\Hypertarget{class_r_n_n___edge_a9da85112ebe0c15a130561512416ac50}\label{class_r_n_n___edge_a9da85112ebe0c15a130561512416ac50}} +double {\bfseries get\+\_\+gradient} () const +\item +\mbox{\Hypertarget{class_r_n_n___edge_a6e46cad9dc07f64dd53bc69c4cc24cbf}\label{class_r_n_n___edge_a6e46cad9dc07f64dd53bc69c4cc24cbf}} +int32\+\_\+t {\bfseries get\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_r_n_n___edge_a08c8bcf4490c93eacb8b56f138a5cdc9}\label{class_r_n_n___edge_a08c8bcf4490c93eacb8b56f138a5cdc9}} +int32\+\_\+t {\bfseries get\+\_\+input\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_r_n_n___edge_a19acff4994685e5511fe68c7e023ee1f}\label{class_r_n_n___edge_a19acff4994685e5511fe68c7e023ee1f}} +int32\+\_\+t {\bfseries get\+\_\+output\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_r_n_n___edge_ad23ce64daa1ca24e3b44ba7921178e05}\label{class_r_n_n___edge_ad23ce64daa1ca24e3b44ba7921178e05}} +const \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries get\+\_\+input\+\_\+node} () const +\item +\mbox{\Hypertarget{class_r_n_n___edge_a41e97bea2f6bd77f7d4a37537e5fcccd}\label{class_r_n_n___edge_a41e97bea2f6bd77f7d4a37537e5fcccd}} +const \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries get\+\_\+output\+\_\+node} () const +\item +\mbox{\Hypertarget{class_r_n_n___edge_a93635d648cef357f25b4be265f0d35b4}\label{class_r_n_n___edge_a93635d648cef357f25b4be265f0d35b4}} +bool {\bfseries is\+\_\+enabled} () const +\item +\mbox{\Hypertarget{class_r_n_n___edge_a82f0ba53ee029112f11764d600253c0b}\label{class_r_n_n___edge_a82f0ba53ee029112f11764d600253c0b}} +bool {\bfseries is\+\_\+reachable} () const +\item +\mbox{\Hypertarget{class_r_n_n___edge_a37e3db9194813ac62f97e12c787a3ab8}\label{class_r_n_n___edge_a37e3db9194813ac62f97e12c787a3ab8}} +bool {\bfseries equals} (\mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$other) const +\item +\mbox{\Hypertarget{class_r_n_n___edge_adfd1832a680684a52bd5cb2a01516bab}\label{class_r_n_n___edge_adfd1832a680684a52bd5cb2a01516bab}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a0b5b4a80326fce5684fc3ce8f6ead337}\label{class_r_n_n___edge_a0b5b4a80326fce5684fc3ce8f6ead337}} +int32\+\_\+t {\bfseries innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a95ca42172215ae81b0746a72ed626379}\label{class_r_n_n___edge_a95ca42172215ae81b0746a72ed626379}} +\textbf{ vector}$<$ double $>$ {\bfseries outputs} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a6d46039bb8ac92466a2abbd7cf926aab}\label{class_r_n_n___edge_a6d46039bb8ac92466a2abbd7cf926aab}} +\textbf{ vector}$<$ double $>$ {\bfseries deltas} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a12b8c7737fc0cd6d625de8ed52615f0d}\label{class_r_n_n___edge_a12b8c7737fc0cd6d625de8ed52615f0d}} +\textbf{ vector}$<$ bool $>$ {\bfseries dropped\+\_\+out} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a62d076001bdfe871de0fb94f19caab3a}\label{class_r_n_n___edge_a62d076001bdfe871de0fb94f19caab3a}} +double {\bfseries weight} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a6ccfe9204c48430f1c6291098827ea6e}\label{class_r_n_n___edge_a6ccfe9204c48430f1c6291098827ea6e}} +double {\bfseries d\+\_\+weight} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a35d14d51918ce7ac7ce0ac36ed24914b}\label{class_r_n_n___edge_a35d14d51918ce7ac7ce0ac36ed24914b}} +bool {\bfseries enabled} +\item +\mbox{\Hypertarget{class_r_n_n___edge_ada1c0cfe252b6ce5e09ff3661e4e1836}\label{class_r_n_n___edge_ada1c0cfe252b6ce5e09ff3661e4e1836}} +bool {\bfseries forward\+\_\+reachable} +\item +\mbox{\Hypertarget{class_r_n_n___edge_aea5850e5b85d87c34ea6e3311a432c37}\label{class_r_n_n___edge_aea5850e5b85d87c34ea6e3311a432c37}} +bool {\bfseries backward\+\_\+reachable} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a25d80a7b19c875dc36d7060439388f0d}\label{class_r_n_n___edge_a25d80a7b19c875dc36d7060439388f0d}} +int32\+\_\+t {\bfseries input\+\_\+innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a6748311dc4fbac3f4c7b9653a7966667}\label{class_r_n_n___edge_a6748311dc4fbac3f4c7b9653a7966667}} +int32\+\_\+t {\bfseries output\+\_\+innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_r_n_n___edge_ad7ca422248fcb876d3f1f2656099f32c}\label{class_r_n_n___edge_ad7ca422248fcb876d3f1f2656099f32c}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries input\+\_\+node} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a8f9625396d3501fd1158f726b632df89}\label{class_r_n_n___edge_a8f9625396d3501fd1158f726b632df89}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries output\+\_\+node} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___edge_ad7617134ea6662be9dc1ad79223de4d1}\label{class_r_n_n___edge_ad7617134ea6662be9dc1ad79223de4d1}} +class {\bfseries R\+N\+N\+\_\+\+Genome} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a67a4ae33f841a6e1d186c84e9490a802}\label{class_r_n_n___edge_a67a4ae33f841a6e1d186c84e9490a802}} +class {\bfseries R\+NN} +\item +\mbox{\Hypertarget{class_r_n_n___edge_a7850c8d4e61c10934acf1a9a98347cf3}\label{class_r_n_n___edge_a7850c8d4e61c10934acf1a9a98347cf3}} +class {\bfseries E\+X\+A\+MM} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+edge.\+hxx\item +rnn/rnn\+\_\+edge.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_r_n_n___genome.tex b/docs/latex/class_r_n_n___genome.tex new file mode 100644 index 00000000..652527d1 --- /dev/null +++ b/docs/latex/class_r_n_n___genome.tex @@ -0,0 +1,650 @@ +\hypertarget{class_r_n_n___genome}{}\doxysection{R\+N\+N\+\_\+\+Genome Class Reference} +\label{class_r_n_n___genome}\index{RNN\_Genome@{RNN\_Genome}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a66960c0b91b46a46c6860d1174e42368}\label{class_r_n_n___genome_a66960c0b91b46a46c6860d1174e42368}} +void {\bfseries sort\+\_\+nodes\+\_\+by\+\_\+depth} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_ab3743c3086ae83fd2475d5c064e6d0fe}\label{class_r_n_n___genome_ab3743c3086ae83fd2475d5c064e6d0fe}} +void {\bfseries sort\+\_\+edges\+\_\+by\+\_\+depth} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a72dc9b6a29ed0e8f64cc92b3de1b4d02}\label{class_r_n_n___genome_a72dc9b6a29ed0e8f64cc92b3de1b4d02}} +void {\bfseries sort\+\_\+recurrent\+\_\+edges\+\_\+by\+\_\+depth} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a0f5155af2c2d67c15e85510a94621bb8}\label{class_r_n_n___genome_a0f5155af2c2d67c15e85510a94621bb8}} +{\bfseries R\+N\+N\+\_\+\+Genome} (\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ \&\+\_\+nodes, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$ $>$ \&\+\_\+edges, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$ $>$ \&\+\_\+recurrent\+\_\+edges, Weight\+Type \+\_\+weight\+\_\+initialize, Weight\+Type \+\_\+weight\+\_\+inheritance, Weight\+Type \+\_\+mutated\+\_\+component\+\_\+weight) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a091df20267aa5fc843bdaa84ac077efc}\label{class_r_n_n___genome_a091df20267aa5fc843bdaa84ac077efc}} +{\bfseries R\+N\+N\+\_\+\+Genome} (\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ \&\+\_\+nodes, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$ $>$ \&\+\_\+edges, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$ $>$ \&\+\_\+recurrent\+\_\+edges, uint16\+\_\+t seed, Weight\+Type \+\_\+weight\+\_\+initialize, Weight\+Type \+\_\+weight\+\_\+inheritance, Weight\+Type \+\_\+mutated\+\_\+component\+\_\+weight) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a1782576a10611f574b3865ddafd7eb91}\label{class_r_n_n___genome_a1782576a10611f574b3865ddafd7eb91}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries copy} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a83f67d89955699b42fc1136eba088807}\label{class_r_n_n___genome_a83f67d89955699b42fc1136eba088807}} +\textbf{ string} {\bfseries print\+\_\+statistics} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_aed26c23d65f7bb7ee67e2d79e87c2c72}\label{class_r_n_n___genome_aed26c23d65f7bb7ee67e2d79e87c2c72}} +void {\bfseries set\+\_\+parameter\+\_\+names} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+output\+\_\+parameter\+\_\+names) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a8a01d8879b7edd2969f4c972b3150bb6}\label{class_r_n_n___genome_a8a01d8879b7edd2969f4c972b3150bb6}} +\textbf{ string} {\bfseries generated\+\_\+by\+\_\+string} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a212131c42eca158e330d8f6a1092e171}\label{class_r_n_n___genome_a212131c42eca158e330d8f6a1092e171}} +\textbf{ string} {\bfseries get\+\_\+edge\+\_\+count\+\_\+str} (bool recurrent) +\item +\mbox{\Hypertarget{class_r_n_n___genome_adcb3339cd91e233d0a559ac989a02471}\label{class_r_n_n___genome_adcb3339cd91e233d0a559ac989a02471}} +\textbf{ string} {\bfseries get\+\_\+node\+\_\+count\+\_\+str} (int node\+\_\+type) +\item +\mbox{\Hypertarget{class_r_n_n___genome_ad88ccbef55ee10ee240a65354653598c}\label{class_r_n_n___genome_ad88ccbef55ee10ee240a65354653598c}} +const \textbf{ map}$<$ \textbf{ string}, int $>$ $\ast$ {\bfseries get\+\_\+generated\+\_\+by\+\_\+map} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_af11137101439173d6ba118353e88df24}\label{class_r_n_n___genome_af11137101439173d6ba118353e88df24}} +double {\bfseries get\+\_\+avg\+\_\+recurrent\+\_\+depth} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_a9d0e0f59fd2b53d3743b1fe9be4c3fc9}\label{class_r_n_n___genome_a9d0e0f59fd2b53d3743b1fe9be4c3fc9}} +int32\+\_\+t {\bfseries get\+\_\+enabled\+\_\+edge\+\_\+count} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a5c14ba528c5f901b7b803087897520c2}\label{class_r_n_n___genome_a5c14ba528c5f901b7b803087897520c2}} +int32\+\_\+t {\bfseries get\+\_\+enabled\+\_\+recurrent\+\_\+edge\+\_\+count} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_acbd85bfd135b341d3d9a70d0ad822cbb}\label{class_r_n_n___genome_acbd85bfd135b341d3d9a70d0ad822cbb}} +int32\+\_\+t {\bfseries get\+\_\+enabled\+\_\+node\+\_\+count} (int node\+\_\+type) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a9bedc6a19b9e1e12935a3699e969fb71}\label{class_r_n_n___genome_a9bedc6a19b9e1e12935a3699e969fb71}} +int32\+\_\+t {\bfseries get\+\_\+node\+\_\+count} (int node\+\_\+type) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a06ac8fb6c99c6d38d0f4cb3f027e5801}\label{class_r_n_n___genome_a06ac8fb6c99c6d38d0f4cb3f027e5801}} +int32\+\_\+t {\bfseries get\+\_\+enabled\+\_\+node\+\_\+count} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a20ad53cfc8710b8fd430949468bbeb3b}\label{class_r_n_n___genome_a20ad53cfc8710b8fd430949468bbeb3b}} +int32\+\_\+t {\bfseries get\+\_\+node\+\_\+count} () +\item +double \mbox{\hyperlink{class_r_n_n___genome_a38239d73966179c24fc12254810b31ad}{get\+\_\+fitness}} () const +\begin{DoxyCompactList}\small\item\em Calculates the fitness of the rnn genome across the dataset. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_r_n_n___genome_abfcd45e830ba1314bba71a78714f8a2b}\label{class_r_n_n___genome_abfcd45e830ba1314bba71a78714f8a2b}} +double {\bfseries get\+\_\+best\+\_\+validation\+\_\+softmax} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_aaf123cc513e0470e01b750d1abbf1157}\label{class_r_n_n___genome_aaf123cc513e0470e01b750d1abbf1157}} +double {\bfseries get\+\_\+best\+\_\+validation\+\_\+mse} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_a97da8014031f402075d429084f0c80f6}\label{class_r_n_n___genome_a97da8014031f402075d429084f0c80f6}} +double {\bfseries get\+\_\+best\+\_\+validation\+\_\+mae} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_a356fdef84cfe515b92361da844a08b92}\label{class_r_n_n___genome_a356fdef84cfe515b92361da844a08b92}} +void {\bfseries set\+\_\+normalize\+\_\+bounds} (\textbf{ string} \+\_\+normalize\+\_\+type, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+mins, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+maxs, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+avgs, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+std\+\_\+devs) +\item +\mbox{\Hypertarget{class_r_n_n___genome_aa1879125c00a2f10a0340c6710b67bc1}\label{class_r_n_n___genome_aa1879125c00a2f10a0340c6710b67bc1}} +\textbf{ string} {\bfseries get\+\_\+normalize\+\_\+type} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_ae4c30737fb55176f366d8d610b78a55e}\label{class_r_n_n___genome_ae4c30737fb55176f366d8d610b78a55e}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+mins} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_a40f8257d8b1ef0f0a0b182ba8b66d9b9}\label{class_r_n_n___genome_a40f8257d8b1ef0f0a0b182ba8b66d9b9}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+maxs} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_ab6a390ad1c87a4878f2950142e62f3bc}\label{class_r_n_n___genome_ab6a390ad1c87a4878f2950142e62f3bc}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+avgs} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_ad0db6c6233a6daa6f438cf2d5db0c9f2}\label{class_r_n_n___genome_ad0db6c6233a6daa6f438cf2d5db0c9f2}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+std\+\_\+devs} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_a72cc75d302a64e3079e187cb8d64d61c}\label{class_r_n_n___genome_a72cc75d302a64e3079e187cb8d64d61c}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries get\+\_\+input\+\_\+parameter\+\_\+names} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_ac452120765bcfbd9d37af84647162943}\label{class_r_n_n___genome_ac452120765bcfbd9d37af84647162943}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries get\+\_\+output\+\_\+parameter\+\_\+names} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_a8534de9ea6357046eec7d2b6aff53422}\label{class_r_n_n___genome_a8534de9ea6357046eec7d2b6aff53422}} +int32\+\_\+t {\bfseries get\+\_\+group\+\_\+id} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_a450b17a3e9914f8329078e4e82b85bf9}\label{class_r_n_n___genome_a450b17a3e9914f8329078e4e82b85bf9}} +void {\bfseries set\+\_\+group\+\_\+id} (int32\+\_\+t \+\_\+group\+\_\+id) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a007bc7f7ec3ab8c6f147c7960bf6ca69}\label{class_r_n_n___genome_a007bc7f7ec3ab8c6f147c7960bf6ca69}} +void {\bfseries set\+\_\+bp\+\_\+iterations} (int32\+\_\+t \+\_\+bp\+\_\+iterations) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a9cb77797f280bc0f6a98b627870478bf}\label{class_r_n_n___genome_a9cb77797f280bc0f6a98b627870478bf}} +int32\+\_\+t {\bfseries get\+\_\+bp\+\_\+iterations} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a249776f9a530e45b5e02f887d4fbc42d}\label{class_r_n_n___genome_a249776f9a530e45b5e02f887d4fbc42d}} +void {\bfseries set\+\_\+learning\+\_\+rate} (double \+\_\+learning\+\_\+rate) +\item +\mbox{\Hypertarget{class_r_n_n___genome_ae0e1fb220409cb71afd0426fc87b69c5}\label{class_r_n_n___genome_ae0e1fb220409cb71afd0426fc87b69c5}} +void {\bfseries set\+\_\+adapt\+\_\+learning\+\_\+rate} (bool \+\_\+adapt\+\_\+learning\+\_\+rate) +\item +\mbox{\Hypertarget{class_r_n_n___genome_afcc133d26cd3b515eecb493b5d26e5e5}\label{class_r_n_n___genome_afcc133d26cd3b515eecb493b5d26e5e5}} +void {\bfseries set\+\_\+nesterov\+\_\+momentum} (bool \+\_\+use\+\_\+nesterov\+\_\+momentum) +\item +\mbox{\Hypertarget{class_r_n_n___genome_ac51b8b5e4a0c52d9c74d8642eb3908c3}\label{class_r_n_n___genome_ac51b8b5e4a0c52d9c74d8642eb3908c3}} +void {\bfseries set\+\_\+reset\+\_\+weights} (bool \+\_\+use\+\_\+reset\+\_\+weights) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a23e757715bbd365f37df9034cb5e20db}\label{class_r_n_n___genome_a23e757715bbd365f37df9034cb5e20db}} +void {\bfseries disable\+\_\+high\+\_\+threshold} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_af892ac7b3d0c85f604b0988835c7760d}\label{class_r_n_n___genome_af892ac7b3d0c85f604b0988835c7760d}} +void {\bfseries enable\+\_\+high\+\_\+threshold} (double \+\_\+high\+\_\+threshold) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a8f90bc578f49916e33702d388efea6c5}\label{class_r_n_n___genome_a8f90bc578f49916e33702d388efea6c5}} +void {\bfseries disable\+\_\+low\+\_\+threshold} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a9027d063eb6f547ad263d2524be060d1}\label{class_r_n_n___genome_a9027d063eb6f547ad263d2524be060d1}} +void {\bfseries enable\+\_\+low\+\_\+threshold} (double \+\_\+low\+\_\+threshold) +\item +\mbox{\Hypertarget{class_r_n_n___genome_abf866fb4c1137fde15ebdb1bcaba0c24}\label{class_r_n_n___genome_abf866fb4c1137fde15ebdb1bcaba0c24}} +void {\bfseries disable\+\_\+dropout} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a0b1e01c4938a89bfd6079e5eae0d3d64}\label{class_r_n_n___genome_a0b1e01c4938a89bfd6079e5eae0d3d64}} +void {\bfseries enable\+\_\+dropout} (double \+\_\+dropout\+\_\+probability) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a1ca1e60c581d2442d26a26f9dbc2ebc5}\label{class_r_n_n___genome_a1ca1e60c581d2442d26a26f9dbc2ebc5}} +void {\bfseries enable\+\_\+use\+\_\+regression} (bool \+\_\+use\+\_\+regression) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a87192a16e373ce2eaa81201b9fffeb42}\label{class_r_n_n___genome_a87192a16e373ce2eaa81201b9fffeb42}} +void {\bfseries set\+\_\+log\+\_\+filename} (\textbf{ string} \+\_\+log\+\_\+filename) +\item +\mbox{\Hypertarget{class_r_n_n___genome_ade2a50c4611c1ebab333b53006741291}\label{class_r_n_n___genome_ade2a50c4611c1ebab333b53006741291}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_r_n_n___genome_af2bc003f2b4fb4ea301a1727b434e5ca}\label{class_r_n_n___genome_af2bc003f2b4fb4ea301a1727b434e5ca}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a264428965475586ea07857ec4ab682e2}\label{class_r_n_n___genome_a264428965475586ea07857ec4ab682e2}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_aa7855f35dc3459050d9c81a777996c9e}\label{class_r_n_n___genome_aa7855f35dc3459050d9c81a777996c9e}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+inputs} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a0ffec6a3fec90bc8e1db97948d2cd03c}\label{class_r_n_n___genome_a0ffec6a3fec90bc8e1db97948d2cd03c}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+outputs} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a1d46e77d530732d6a8f98d5b285f8811}\label{class_r_n_n___genome_a1d46e77d530732d6a8f98d5b285f8811}} +double {\bfseries get\+\_\+avg\+\_\+edge\+\_\+weight} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_aca040bbc38324fd184f0724a4b3329ad}\label{class_r_n_n___genome_aca040bbc38324fd184f0724a4b3329ad}} +void {\bfseries initialize\+\_\+randomly} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a2285acbea30208b94f25ddf875d7d33d}\label{class_r_n_n___genome_a2285acbea30208b94f25ddf875d7d33d}} +void {\bfseries initialize\+\_\+xavier} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a5d58f36743bcab8e9725b885b8624d48}\label{class_r_n_n___genome_a5d58f36743bcab8e9725b885b8624d48}} +void {\bfseries initialize\+\_\+kaiming} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a1c2040aefaa09768a16f251204b1a6df}\label{class_r_n_n___genome_a1c2040aefaa09768a16f251204b1a6df}} +void {\bfseries initialize\+\_\+node\+\_\+randomly} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a7f6cad5cec08a70b5b390fecf21a5709}\label{class_r_n_n___genome_a7f6cad5cec08a70b5b390fecf21a5709}} +double {\bfseries get\+\_\+xavier\+\_\+weight} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$output\+\_\+node) +\item +\mbox{\Hypertarget{class_r_n_n___genome_abdcc66772dcfa732d3d41768e35a3577}\label{class_r_n_n___genome_abdcc66772dcfa732d3d41768e35a3577}} +double {\bfseries get\+\_\+kaiming\+\_\+weight} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$output\+\_\+node) +\item +\mbox{\Hypertarget{class_r_n_n___genome_ab1c4a73021355e3708bf47737c19d9fb}\label{class_r_n_n___genome_ab1c4a73021355e3708bf47737c19d9fb}} +double {\bfseries get\+\_\+random\+\_\+weight} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a96f02ce73d4cd74032bfec4a4d72d934}\label{class_r_n_n___genome_a96f02ce73d4cd74032bfec4a4d72d934}} +void {\bfseries get\+\_\+input\+\_\+edges} (int32\+\_\+t node\+\_\+innovation, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$ $>$ \&input\+\_\+edges, \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$ $>$ \&input\+\_\+recurrent\+\_\+edges) +\item +\mbox{\Hypertarget{class_r_n_n___genome_aa80dc56239fcd560a6ddff4eec4508a3}\label{class_r_n_n___genome_aa80dc56239fcd560a6ddff4eec4508a3}} +int32\+\_\+t {\bfseries get\+\_\+fan\+\_\+in} (int32\+\_\+t node\+\_\+innovation) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a2b0ad83b8a7f9a8528cec1ec7f48a8d5}\label{class_r_n_n___genome_a2b0ad83b8a7f9a8528cec1ec7f48a8d5}} +int32\+\_\+t {\bfseries get\+\_\+fan\+\_\+out} (int32\+\_\+t node\+\_\+innovation) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a6919035ccf8aaf070cfb996ac990fc59}\label{class_r_n_n___genome_a6919035ccf8aaf070cfb996ac990fc59}} +int32\+\_\+t {\bfseries get\+\_\+generation\+\_\+id} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_a5c9ba154bc5cfa107c9a2c3f0799cf2f}\label{class_r_n_n___genome_a5c9ba154bc5cfa107c9a2c3f0799cf2f}} +void {\bfseries set\+\_\+generation\+\_\+id} (int32\+\_\+t generation\+\_\+id) +\item +\mbox{\Hypertarget{class_r_n_n___genome_adb150f05ee96d379d628fa46d44fd0af}\label{class_r_n_n___genome_adb150f05ee96d379d628fa46d44fd0af}} +void {\bfseries clear\+\_\+generated\+\_\+by} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_abaceca4d87f395f4fe59d82fe3511620}\label{class_r_n_n___genome_abaceca4d87f395f4fe59d82fe3511620}} +void {\bfseries update\+\_\+generation\+\_\+map} (\textbf{ map}$<$ \textbf{ string}, int32\+\_\+t $>$ \&generation\+\_\+map) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a985a920952e94a3181acfb2fb45d6a97}\label{class_r_n_n___genome_a985a920952e94a3181acfb2fb45d6a97}} +void {\bfseries set\+\_\+generated\+\_\+by} (\textbf{ string} type) +\item +\mbox{\Hypertarget{class_r_n_n___genome_abf78eb6d242054e5feb470a1a7a90e32}\label{class_r_n_n___genome_abf78eb6d242054e5feb470a1a7a90e32}} +int32\+\_\+t {\bfseries get\+\_\+generated\+\_\+by} (\textbf{ string} type) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a3400cc5dc992928c82c26822165cb819}\label{class_r_n_n___genome_a3400cc5dc992928c82c26822165cb819}} +\mbox{\hyperlink{class_r_n_n}{R\+NN}} $\ast$ {\bfseries get\+\_\+rnn} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a1789575964a2fa2d4264a50932c5c025}\label{class_r_n_n___genome_a1789575964a2fa2d4264a50932c5c025}} +\textbf{ vector}$<$ double $>$ {\bfseries get\+\_\+best\+\_\+parameters} () const +\item +\mbox{\Hypertarget{class_r_n_n___genome_aaaea6e2b199d43a19cb21e83858dde19}\label{class_r_n_n___genome_aaaea6e2b199d43a19cb21e83858dde19}} +void {\bfseries set\+\_\+best\+\_\+parameters} (\textbf{ vector}$<$ double $>$ parameters) +\item +\mbox{\Hypertarget{class_r_n_n___genome_aa7f1bef460f6fde273acf43f125280aa}\label{class_r_n_n___genome_aa7f1bef460f6fde273acf43f125280aa}} +void {\bfseries set\+\_\+initial\+\_\+parameters} (\textbf{ vector}$<$ double $>$ parameters) +\item +\mbox{\Hypertarget{class_r_n_n___genome_aed3f4217cedde954a2adca8eb4d96d33}\label{class_r_n_n___genome_aed3f4217cedde954a2adca8eb4d96d33}} +void {\bfseries get\+\_\+analytic\+\_\+gradient} (\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n}{R\+NN}} $\ast$ $>$ \&rnns, const \textbf{ vector}$<$ double $>$ \¶meters, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs, double \&mse, \textbf{ vector}$<$ double $>$ \&analytic\+\_\+gradient, bool training) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a1bc7f521895ce6f4435409711d60cffa}\label{class_r_n_n___genome_a1bc7f521895ce6f4435409711d60cffa}} +void {\bfseries backpropagate} (const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&validation\+\_\+inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&validation\+\_\+outputs) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a64bf3531936724db69102d78aa72abda}\label{class_r_n_n___genome_a64bf3531936724db69102d78aa72abda}} +void {\bfseries backpropagate\+\_\+stochastic} (const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&validation\+\_\+inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&validation\+\_\+outputs) +\item +double \mbox{\hyperlink{class_r_n_n___genome_a8f267b82d174d4a10bd48049e025a99b}{get\+\_\+softmax}} (const \textbf{ vector}$<$ double $>$ \¶meters, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs) +\begin{DoxyCompactList}\small\item\em Predicts the softmax error after a single pass across the rnn genome. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_r_n_n___genome_ad03562bf5b84189749b619ade77423b2}\label{class_r_n_n___genome_ad03562bf5b84189749b619ade77423b2}} +double {\bfseries get\+\_\+mse} (const \textbf{ vector}$<$ double $>$ \¶meters, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a2aea775ce9deb36424b5fa11e6f028f6}\label{class_r_n_n___genome_a2aea775ce9deb36424b5fa11e6f028f6}} +double {\bfseries get\+\_\+mae} (const \textbf{ vector}$<$ double $>$ \¶meters, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs) +\item +\mbox{\Hypertarget{class_r_n_n___genome_ab9fa1bd698b3849785ecb75735f76700}\label{class_r_n_n___genome_ab9fa1bd698b3849785ecb75735f76700}} +\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ {\bfseries get\+\_\+predictions} (const \textbf{ vector}$<$ double $>$ \¶meters, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a0456c6b90b2ea7d56727f9319880b751}\label{class_r_n_n___genome_a0456c6b90b2ea7d56727f9319880b751}} +void {\bfseries write\+\_\+predictions} (\textbf{ string} output\+\_\+directory, const \textbf{ vector}$<$ \textbf{ string} $>$ \&input\+\_\+filenames, const \textbf{ vector}$<$ double $>$ \¶meters, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs, \mbox{\hyperlink{class_time_series_sets}{Time\+Series\+Sets}} $\ast$time\+\_\+series\+\_\+sets) +\item +void \mbox{\hyperlink{class_r_n_n___genome_a99769540d03c292a1e63bd753ae58ceb}{write\+\_\+predictions}} (\textbf{ string} output\+\_\+directory, const \textbf{ vector}$<$ \textbf{ string} $>$ \&input\+\_\+filenames, const \textbf{ vector}$<$ double $>$ \¶meters, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs, \mbox{\hyperlink{class_corpus}{Corpus}} $\ast$word\+\_\+series\+\_\+sets) +\begin{DoxyCompactList}\small\item\em Predicts the cross entropy loss after a single pass across the rnn genome. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_r_n_n___genome_a4529fe0bbd50b47ff71cd0ac92e6724f}\label{class_r_n_n___genome_a4529fe0bbd50b47ff71cd0ac92e6724f}} +void {\bfseries get\+\_\+mu\+\_\+sigma} (const \textbf{ vector}$<$ double $>$ \&p, double \&mu, double \&sigma) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a42f17b1f58cfdcee8ca24211edc7b1d3}\label{class_r_n_n___genome_a42f17b1f58cfdcee8ca24211edc7b1d3}} +bool {\bfseries sanity\+\_\+check} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a7c75aa2e12aba2b589aa5a6eb18f306f}\label{class_r_n_n___genome_a7c75aa2e12aba2b589aa5a6eb18f306f}} +void {\bfseries assign\+\_\+reachability} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a6ee3f18c2865772de6c825db034b999d}\label{class_r_n_n___genome_a6ee3f18c2865772de6c825db034b999d}} +bool {\bfseries outputs\+\_\+unreachable} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_af0e46a5c28b25f7584b6aa2c9728837b}\label{class_r_n_n___genome_af0e46a5c28b25f7584b6aa2c9728837b}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries create\+\_\+node} (double mu, double sigma, int node\+\_\+type, int32\+\_\+t \&node\+\_\+innovation\+\_\+count, double depth) +\item +\mbox{\Hypertarget{class_r_n_n___genome_af2630750a56148d4fa66029a6f2fc897}\label{class_r_n_n___genome_af2630750a56148d4fa66029a6f2fc897}} +bool {\bfseries attempt\+\_\+edge\+\_\+insert} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n1, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n2, double mu, double sigma, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_aa8813813b650d6ef902cc5424173fff3}\label{class_r_n_n___genome_aa8813813b650d6ef902cc5424173fff3}} +bool {\bfseries attempt\+\_\+recurrent\+\_\+edge\+\_\+insert} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n1, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n2, double mu, double sigma, \textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ dist, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a0f6d4c8731ad48d7567f96ffb7d2daff}\label{class_r_n_n___genome_a0f6d4c8731ad48d7567f96ffb7d2daff}} +void {\bfseries generate\+\_\+recurrent\+\_\+edges} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$node, double mu, double sigma, \textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ dist, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a3633b14fd8c998addc4fadc024b49a4a}\label{class_r_n_n___genome_a3633b14fd8c998addc4fadc024b49a4a}} +bool {\bfseries add\+\_\+edge} (double mu, double sigma, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_adef568d6f4a9a7664da6e9a69e6815d5}\label{class_r_n_n___genome_adef568d6f4a9a7664da6e9a69e6815d5}} +bool {\bfseries add\+\_\+recurrent\+\_\+edge} (double mu, double sigma, \textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ rec\+\_\+depth\+\_\+dist, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a929f90bb0a514ce39df0df2b49b9ecb3}\label{class_r_n_n___genome_a929f90bb0a514ce39df0df2b49b9ecb3}} +bool {\bfseries disable\+\_\+edge} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a8750e34101cb7b11adc5e46fabbb1520}\label{class_r_n_n___genome_a8750e34101cb7b11adc5e46fabbb1520}} +bool {\bfseries enable\+\_\+edge} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_afb77ce59b60841c105d590cece8fd0a0}\label{class_r_n_n___genome_afb77ce59b60841c105d590cece8fd0a0}} +bool {\bfseries split\+\_\+edge} (double mu, double sigma, int node\+\_\+type, \textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ rec\+\_\+depth\+\_\+dist, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count, int32\+\_\+t \&node\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a1bc4c4a51f7363ba84263ff8b7d7e60c}\label{class_r_n_n___genome_a1bc4c4a51f7363ba84263ff8b7d7e60c}} +bool {\bfseries add\+\_\+node} (double mu, double sigma, int node\+\_\+type, \textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ dist, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count, int32\+\_\+t \&node\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a124000a36ef379d275973d1455bebd47}\label{class_r_n_n___genome_a124000a36ef379d275973d1455bebd47}} +bool {\bfseries enable\+\_\+node} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_af6f3e5fd54576d21bd39694c92df5a51}\label{class_r_n_n___genome_af6f3e5fd54576d21bd39694c92df5a51}} +bool {\bfseries disable\+\_\+node} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_afe24d50cba6c6f0e97ff3a219acdbe60}\label{class_r_n_n___genome_afe24d50cba6c6f0e97ff3a219acdbe60}} +bool {\bfseries split\+\_\+node} (double mu, double sigma, int node\+\_\+type, \textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ dist, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count, int32\+\_\+t \&node\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a1cfdbee9da6d350572e6115ede269e1e}\label{class_r_n_n___genome_a1cfdbee9da6d350572e6115ede269e1e}} +bool {\bfseries merge\+\_\+node} (double mu, double sigma, int node\+\_\+type, \textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ dist, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count, int32\+\_\+t \&node\+\_\+innovation\+\_\+count) +\item +bool \mbox{\hyperlink{class_r_n_n___genome_a8d67b0d3f328bfc4a8dd459a61fb1cf3}{has\+\_\+node\+\_\+with\+\_\+innovation}} (int32\+\_\+t innovation\+\_\+number) const +\begin{DoxyCompactList}\small\item\em Determines if the genome contains a node with the given innovation number. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_r_n_n___genome_a21e4ae94483c013b626183c424541a34}\label{class_r_n_n___genome_a21e4ae94483c013b626183c424541a34}} +bool {\bfseries equals} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$other) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a04c8b0b81e476408f02a946aac21da0e}\label{class_r_n_n___genome_a04c8b0b81e476408f02a946aac21da0e}} +\textbf{ string} {\bfseries get\+\_\+color} (double weight, bool is\+\_\+recurrent) +\item +\mbox{\Hypertarget{class_r_n_n___genome_af3bd77a6fa56241a0fdedf97b17be99f}\label{class_r_n_n___genome_af3bd77a6fa56241a0fdedf97b17be99f}} +void {\bfseries write\+\_\+graphviz} (\textbf{ string} filename) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a8871a0dfe94d75aa4648b820ff54692c}\label{class_r_n_n___genome_a8871a0dfe94d75aa4648b820ff54692c}} +{\bfseries R\+N\+N\+\_\+\+Genome} (\textbf{ string} binary\+\_\+filename) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a258321d0827f4352de07e335b7f9de09}\label{class_r_n_n___genome_a258321d0827f4352de07e335b7f9de09}} +{\bfseries R\+N\+N\+\_\+\+Genome} (char $\ast$array, int32\+\_\+t length) +\item +\mbox{\Hypertarget{class_r_n_n___genome_ad57685db52aae8760788c963d2e78c2f}\label{class_r_n_n___genome_ad57685db52aae8760788c963d2e78c2f}} +{\bfseries R\+N\+N\+\_\+\+Genome} (\textbf{ istream} \&bin\+\_\+infile) +\item +\mbox{\Hypertarget{class_r_n_n___genome_ad81f903f12f95c48b9bf26497d5e69fa}\label{class_r_n_n___genome_ad81f903f12f95c48b9bf26497d5e69fa}} +void {\bfseries read\+\_\+from\+\_\+array} (char $\ast$array, int32\+\_\+t length) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a7711076fea5f12a33ce6acbbb34efdf1}\label{class_r_n_n___genome_a7711076fea5f12a33ce6acbbb34efdf1}} +void {\bfseries read\+\_\+from\+\_\+stream} (\textbf{ istream} \&bin\+\_\+istream) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a5c2f9a4d8a0fda974b0016290cd8de64}\label{class_r_n_n___genome_a5c2f9a4d8a0fda974b0016290cd8de64}} +void {\bfseries write\+\_\+to\+\_\+array} (char $\ast$$\ast$array, int32\+\_\+t \&length) +\item +\mbox{\Hypertarget{class_r_n_n___genome_aaef90bb253e56822e7d04835b1b5e504}\label{class_r_n_n___genome_aaef90bb253e56822e7d04835b1b5e504}} +void {\bfseries write\+\_\+to\+\_\+file} (\textbf{ string} bin\+\_\+filename) +\item +\mbox{\Hypertarget{class_r_n_n___genome_aa6fe50829e2dddded98d19bb3e5a309f}\label{class_r_n_n___genome_aa6fe50829e2dddded98d19bb3e5a309f}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&bin\+\_\+stream) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a6ec88463918000224f75af7e5052bf75}\label{class_r_n_n___genome_a6ec88463918000224f75af7e5052bf75}} +bool {\bfseries connect\+\_\+new\+\_\+input\+\_\+node} (double mu, double sig, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$new\+\_\+node, \textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ dist, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_ac299924fe6cd871caeb39425690d94de}\label{class_r_n_n___genome_ac299924fe6cd871caeb39425690d94de}} +bool {\bfseries connect\+\_\+new\+\_\+output\+\_\+node} (double mu, double sig, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$new\+\_\+node, \textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ dist, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_af367aa062f8c1cabcae2610cb9b729c4}\label{class_r_n_n___genome_af367aa062f8c1cabcae2610cb9b729c4}} +bool {\bfseries connect\+\_\+node\+\_\+to\+\_\+hid\+\_\+nodes} (double mu, double sig, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$new\+\_\+node, \textbf{ uniform\+\_\+int\+\_\+distribution}$<$ int32\+\_\+t $>$ dist, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count, bool from\+\_\+input) +\item +\mbox{\Hypertarget{class_r_n_n___genome_ab3ee5993d83d8332395a833868193626}\label{class_r_n_n___genome_ab3ee5993d83d8332395a833868193626}} +void {\bfseries update\+\_\+innovation\+\_\+counts} (int32\+\_\+t \&node\+\_\+innovation\+\_\+count, int32\+\_\+t \&edge\+\_\+innovation\+\_\+count) +\item +\mbox{\Hypertarget{class_r_n_n___genome_a42e6139dcd340607495e71392659c26b}\label{class_r_n_n___genome_a42e6139dcd340607495e71392659c26b}} +\textbf{ vector}$<$ int32\+\_\+t $>$ {\bfseries get\+\_\+innovation\+\_\+list} () +\item +\textbf{ string} \mbox{\hyperlink{class_r_n_n___genome_a280b5a80c457fd341fe82ec9cc930d01}{get\+\_\+structural\+\_\+hash}} () const +\item +int \mbox{\hyperlink{class_r_n_n___genome_ae504f790ddce6366e49a4892b86e12cb}{get\+\_\+max\+\_\+node\+\_\+innovation\+\_\+count}} () +\item +int \mbox{\hyperlink{class_r_n_n___genome_aaffc30ae458b6dcf16a0e7484ca7bcfc}{get\+\_\+max\+\_\+edge\+\_\+innovation\+\_\+count}} () +\item +\mbox{\Hypertarget{class_r_n_n___genome_a0501aac0dd58912ff2ec0b52f0da01c4}\label{class_r_n_n___genome_a0501aac0dd58912ff2ec0b52f0da01c4}} +void {\bfseries transfer\+\_\+to} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&new\+\_\+input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&new\+\_\+output\+\_\+parameter\+\_\+names, \textbf{ string} transfer\+\_\+learning\+\_\+version, bool epigenetic\+\_\+weights, int32\+\_\+t min\+\_\+recurrent\+\_\+depth, int32\+\_\+t max\+\_\+recurrent\+\_\+depth) +\end{DoxyCompactItemize} +\doxysubsection*{Static Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a6c4a5f09e4512e3baa0980e9163e91b7}\label{class_r_n_n___genome_a6c4a5f09e4512e3baa0980e9163e91b7}} +static \textbf{ string} {\bfseries print\+\_\+statistics\+\_\+header} () +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___genome_ac0f39f060efc10708b62daf97cff19eb}\label{class_r_n_n___genome_ac0f39f060efc10708b62daf97cff19eb}} +int32\+\_\+t {\bfseries generation\+\_\+id} +\item +\mbox{\Hypertarget{class_r_n_n___genome_ac5cf0908399122afd146d2af5bd97523}\label{class_r_n_n___genome_ac5cf0908399122afd146d2af5bd97523}} +int32\+\_\+t {\bfseries group\+\_\+id} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a549d5a47dd9707afecb51d108bc46890}\label{class_r_n_n___genome_a549d5a47dd9707afecb51d108bc46890}} +int32\+\_\+t {\bfseries bp\+\_\+iterations} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a28ca3b990df56545d00e9d6f206ecf25}\label{class_r_n_n___genome_a28ca3b990df56545d00e9d6f206ecf25}} +double {\bfseries learning\+\_\+rate} +\item +\mbox{\Hypertarget{class_r_n_n___genome_ab132530c91cb6b47327aab23d6d3a623}\label{class_r_n_n___genome_ab132530c91cb6b47327aab23d6d3a623}} +bool {\bfseries adapt\+\_\+learning\+\_\+rate} +\item +\mbox{\Hypertarget{class_r_n_n___genome_af71ce9896e3ec8eb6810be6b130aa587}\label{class_r_n_n___genome_af71ce9896e3ec8eb6810be6b130aa587}} +bool {\bfseries use\+\_\+nesterov\+\_\+momentum} +\item +\mbox{\Hypertarget{class_r_n_n___genome_aed3fd4319fe285048b03c5030ac876b8}\label{class_r_n_n___genome_aed3fd4319fe285048b03c5030ac876b8}} +bool {\bfseries use\+\_\+reset\+\_\+weights} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a87efb2d019061b1f19bd9041eb58a0e7}\label{class_r_n_n___genome_a87efb2d019061b1f19bd9041eb58a0e7}} +bool {\bfseries use\+\_\+high\+\_\+norm} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a9991701d9cf37d0aee5bd7afdc89d79d}\label{class_r_n_n___genome_a9991701d9cf37d0aee5bd7afdc89d79d}} +double {\bfseries high\+\_\+threshold} +\item +\mbox{\Hypertarget{class_r_n_n___genome_aa9d5bc09716c8cb2dc514ef33b67280e}\label{class_r_n_n___genome_aa9d5bc09716c8cb2dc514ef33b67280e}} +bool {\bfseries use\+\_\+low\+\_\+norm} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a6e0428ab9fb0a3f797ce0de996bbb554}\label{class_r_n_n___genome_a6e0428ab9fb0a3f797ce0de996bbb554}} +double {\bfseries low\+\_\+threshold} +\item +bool \mbox{\hyperlink{class_r_n_n___genome_a32113888aee78f2b95490a5289f47a44}{use\+\_\+regression}} +\begin{DoxyCompactList}\small\item\em Specifies whether to use the regression or not. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_r_n_n___genome_a982bd358ebf49ec05960b1839f8d2095}\label{class_r_n_n___genome_a982bd358ebf49ec05960b1839f8d2095}} +bool {\bfseries use\+\_\+dropout} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a23ef058698f4d91d0d4372ddd2f3156c}\label{class_r_n_n___genome_a23ef058698f4d91d0d4372ddd2f3156c}} +double {\bfseries dropout\+\_\+probability} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a4ead06dcfb48733f762b66a107ddf745}\label{class_r_n_n___genome_a4ead06dcfb48733f762b66a107ddf745}} +\textbf{ string} {\bfseries structural\+\_\+hash} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a10aa7b56e9bd83a6b31576693b53d484}\label{class_r_n_n___genome_a10aa7b56e9bd83a6b31576693b53d484}} +\textbf{ string} {\bfseries log\+\_\+filename} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a2bc298962160c8ddb3595b53bc087d27}\label{class_r_n_n___genome_a2bc298962160c8ddb3595b53bc087d27}} +Weight\+Type {\bfseries weight\+\_\+initialize} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a6b864f03e265778d56e759db893254dc}\label{class_r_n_n___genome_a6b864f03e265778d56e759db893254dc}} +Weight\+Type {\bfseries weight\+\_\+inheritance} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a84c977813cf1a038ad8c76ae05d39621}\label{class_r_n_n___genome_a84c977813cf1a038ad8c76ae05d39621}} +Weight\+Type {\bfseries mutated\+\_\+component\+\_\+weight} +\item +\mbox{\Hypertarget{class_r_n_n___genome_ab2f1e6748763704716c1aaa3856d5147}\label{class_r_n_n___genome_ab2f1e6748763704716c1aaa3856d5147}} +\textbf{ map}$<$ \textbf{ string}, int $>$ {\bfseries generated\+\_\+by\+\_\+map} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a2b2695cd1b529cbd42eda2cc9a89bec9}\label{class_r_n_n___genome_a2b2695cd1b529cbd42eda2cc9a89bec9}} +\textbf{ vector}$<$ double $>$ {\bfseries initial\+\_\+parameters} +\item +double \mbox{\hyperlink{class_r_n_n___genome_a41ba624f5a9311ee8ef0803ae6063156}{best\+\_\+validation\+\_\+mse}} +\begin{DoxyCompactList}\small\item\em Specifies the validation error across the dataset. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_r_n_n___genome_aa0ebdcc59036fb56c0b0e38df798a8ef}\label{class_r_n_n___genome_aa0ebdcc59036fb56c0b0e38df798a8ef}} +double {\bfseries best\+\_\+validation\+\_\+mae} +\item +\mbox{\Hypertarget{class_r_n_n___genome_ab95f6fdfca08416ae9d71e9a52e0fa3d}\label{class_r_n_n___genome_ab95f6fdfca08416ae9d71e9a52e0fa3d}} +\textbf{ vector}$<$ double $>$ {\bfseries best\+\_\+parameters} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a4a5dca851f01b965be380ffbcdc33b57}\label{class_r_n_n___genome_a4a5dca851f01b965be380ffbcdc33b57}} +\textbf{ minstd\+\_\+rand0} {\bfseries generator} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a0ab32e35e17e2e6660d8444053ec9f77}\label{class_r_n_n___genome_a0ab32e35e17e2e6660d8444053ec9f77}} +\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ {\bfseries rng} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a9c65ce48c124c3be69034ce65f3a80a0}\label{class_r_n_n___genome_a9c65ce48c124c3be69034ce65f3a80a0}} +\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ {\bfseries rng\+\_\+0\+\_\+1} +\item +\mbox{\Hypertarget{class_r_n_n___genome_add22d35c49c6c3809fa0f57571d93f00}\label{class_r_n_n___genome_add22d35c49c6c3809fa0f57571d93f00}} +\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ {\bfseries rng\+\_\+1\+\_\+1} +\item +\mbox{\Hypertarget{class_r_n_n___genome_aefae807922ef9ca63672881492a0332d}\label{class_r_n_n___genome_aefae807922ef9ca63672881492a0332d}} +\mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} {\bfseries normal\+\_\+distribution} +\item +\mbox{\Hypertarget{class_r_n_n___genome_aab8bf323d2d3f9505381fcfe5b34dc91}\label{class_r_n_n___genome_aab8bf323d2d3f9505381fcfe5b34dc91}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ {\bfseries nodes} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a053474b0df28940abe6d23933e48520e}\label{class_r_n_n___genome_a053474b0df28940abe6d23933e48520e}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$ $>$ {\bfseries edges} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a114b967983487743d10288134cc22ae8}\label{class_r_n_n___genome_a114b967983487743d10288134cc22ae8}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$ $>$ {\bfseries recurrent\+\_\+edges} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a0aa86f627d6ada55fc7585c906d70bb1}\label{class_r_n_n___genome_a0aa86f627d6ada55fc7585c906d70bb1}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries input\+\_\+parameter\+\_\+names} +\item +\mbox{\Hypertarget{class_r_n_n___genome_af8e2a4c13f37159471e6742bbd090bc6}\label{class_r_n_n___genome_af8e2a4c13f37159471e6742bbd090bc6}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries output\+\_\+parameter\+\_\+names} +\item +\mbox{\Hypertarget{class_r_n_n___genome_afb2145baf4921ad48c5e3cc983eff260}\label{class_r_n_n___genome_afb2145baf4921ad48c5e3cc983eff260}} +\textbf{ string} {\bfseries normalize\+\_\+type} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a95c8f89b1073ebf4112f90c1bb5ee946}\label{class_r_n_n___genome_a95c8f89b1073ebf4112f90c1bb5ee946}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+mins} +\item +\mbox{\Hypertarget{class_r_n_n___genome_aa0977c75168c3d09504ed333cc8784e7}\label{class_r_n_n___genome_aa0977c75168c3d09504ed333cc8784e7}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+maxs} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a408454997cceaff6039b08a069d9b42e}\label{class_r_n_n___genome_a408454997cceaff6039b08a069d9b42e}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+avgs} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a2e24e77171f6853be24f2e488fefc04b}\label{class_r_n_n___genome_a2e24e77171f6853be24f2e488fefc04b}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+std\+\_\+devs} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___genome_a7850c8d4e61c10934acf1a9a98347cf3}\label{class_r_n_n___genome_a7850c8d4e61c10934acf1a9a98347cf3}} +class {\bfseries E\+X\+A\+MM} +\item +\mbox{\Hypertarget{class_r_n_n___genome_aec7752aa97db7b461eca0d28865aff20}\label{class_r_n_n___genome_aec7752aa97db7b461eca0d28865aff20}} +class {\bfseries Island\+Speciation\+Strategy} +\item +\mbox{\Hypertarget{class_r_n_n___genome_afde0239d25f6ec4aac551d7b6eaf5bc3}\label{class_r_n_n___genome_afde0239d25f6ec4aac551d7b6eaf5bc3}} +class {\bfseries Neat\+Speciation\+Strategy} +\item +\mbox{\Hypertarget{class_r_n_n___genome_aa1d85c88a38f21ffb0ccedd474289b0e}\label{class_r_n_n___genome_aa1d85c88a38f21ffb0ccedd474289b0e}} +class {\bfseries Rec\+Depth\+Frequency\+Table} +\end{DoxyCompactItemize} + + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_r_n_n___genome_a38239d73966179c24fc12254810b31ad}\label{class_r_n_n___genome_a38239d73966179c24fc12254810b31ad}} +\index{RNN\_Genome@{RNN\_Genome}!get\_fitness@{get\_fitness}} +\index{get\_fitness@{get\_fitness}!RNN\_Genome@{RNN\_Genome}} +\doxysubsubsection{\texorpdfstring{get\_fitness()}{get\_fitness()}} +{\footnotesize\ttfamily double R\+N\+N\+\_\+\+Genome\+::get\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const} + + + +Calculates the fitness of the rnn genome across the dataset. + +~\newline + This will be the mean squared error in case of regression and cross entropy loss in case of classification. + +\begin{DoxyReturn}{Returns} +M\+SE or S\+O\+F\+T\+M\+AX E\+R\+R\+OR. +\end{DoxyReturn} +\mbox{\Hypertarget{class_r_n_n___genome_aaffc30ae458b6dcf16a0e7484ca7bcfc}\label{class_r_n_n___genome_aaffc30ae458b6dcf16a0e7484ca7bcfc}} +\index{RNN\_Genome@{RNN\_Genome}!get\_max\_edge\_innovation\_count@{get\_max\_edge\_innovation\_count}} +\index{get\_max\_edge\_innovation\_count@{get\_max\_edge\_innovation\_count}!RNN\_Genome@{RNN\_Genome}} +\doxysubsubsection{\texorpdfstring{get\_max\_edge\_innovation\_count()}{get\_max\_edge\_innovation\_count()}} +{\footnotesize\ttfamily int R\+N\+N\+\_\+\+Genome\+::get\+\_\+max\+\_\+edge\+\_\+innovation\+\_\+count (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + +\begin{DoxyReturn}{Returns} +the max innovation number of any edge or recurrent edge in the genome. +\end{DoxyReturn} +\mbox{\Hypertarget{class_r_n_n___genome_ae504f790ddce6366e49a4892b86e12cb}\label{class_r_n_n___genome_ae504f790ddce6366e49a4892b86e12cb}} +\index{RNN\_Genome@{RNN\_Genome}!get\_max\_node\_innovation\_count@{get\_max\_node\_innovation\_count}} +\index{get\_max\_node\_innovation\_count@{get\_max\_node\_innovation\_count}!RNN\_Genome@{RNN\_Genome}} +\doxysubsubsection{\texorpdfstring{get\_max\_node\_innovation\_count()}{get\_max\_node\_innovation\_count()}} +{\footnotesize\ttfamily int R\+N\+N\+\_\+\+Genome\+::get\+\_\+max\+\_\+node\+\_\+innovation\+\_\+count (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + +\begin{DoxyReturn}{Returns} +the max innovation number of any node in the genome. +\end{DoxyReturn} +\mbox{\Hypertarget{class_r_n_n___genome_a8f267b82d174d4a10bd48049e025a99b}\label{class_r_n_n___genome_a8f267b82d174d4a10bd48049e025a99b}} +\index{RNN\_Genome@{RNN\_Genome}!get\_softmax@{get\_softmax}} +\index{get\_softmax@{get\_softmax}!RNN\_Genome@{RNN\_Genome}} +\doxysubsubsection{\texorpdfstring{get\_softmax()}{get\_softmax()}} +{\footnotesize\ttfamily double R\+N\+N\+\_\+\+Genome\+::get\+\_\+softmax (\begin{DoxyParamCaption}\item[{const \textbf{ vector}$<$ double $>$ \&}]{parameters, }\item[{const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&}]{inputs, }\item[{const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&}]{outputs }\end{DoxyParamCaption})} + + + +Predicts the softmax error after a single pass across the rnn genome. + +~\newline + + + +\begin{DoxyParams}{Parameters} +{\em parameters} & is the parameters such as dropout, momentum type used for the forward pass. \\ +\hline +{\em inputs} & are the inputs according to the dataset that is pass to the for forward pass. \\ +\hline +{\em outputs} & are the expected outputs according to the dataset that is pass to the calculate error for backward pass.\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +average of the cross entropy error over the dataset. +\end{DoxyReturn} +\mbox{\Hypertarget{class_r_n_n___genome_a280b5a80c457fd341fe82ec9cc930d01}\label{class_r_n_n___genome_a280b5a80c457fd341fe82ec9cc930d01}} +\index{RNN\_Genome@{RNN\_Genome}!get\_structural\_hash@{get\_structural\_hash}} +\index{get\_structural\_hash@{get\_structural\_hash}!RNN\_Genome@{RNN\_Genome}} +\doxysubsubsection{\texorpdfstring{get\_structural\_hash()}{get\_structural\_hash()}} +{\footnotesize\ttfamily \textbf{ string} R\+N\+N\+\_\+\+Genome\+::get\+\_\+structural\+\_\+hash (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const} + +\begin{DoxyReturn}{Returns} +the structural hash (calculated when assign\+\_\+reachaability is called) +\end{DoxyReturn} +\mbox{\Hypertarget{class_r_n_n___genome_a8d67b0d3f328bfc4a8dd459a61fb1cf3}\label{class_r_n_n___genome_a8d67b0d3f328bfc4a8dd459a61fb1cf3}} +\index{RNN\_Genome@{RNN\_Genome}!has\_node\_with\_innovation@{has\_node\_with\_innovation}} +\index{has\_node\_with\_innovation@{has\_node\_with\_innovation}!RNN\_Genome@{RNN\_Genome}} +\doxysubsubsection{\texorpdfstring{has\_node\_with\_innovation()}{has\_node\_with\_innovation()}} +{\footnotesize\ttfamily bool R\+N\+N\+\_\+\+Genome\+::has\+\_\+node\+\_\+with\+\_\+innovation (\begin{DoxyParamCaption}\item[{int32\+\_\+t}]{innovation\+\_\+number }\end{DoxyParamCaption}) const} + + + +Determines if the genome contains a node with the given innovation number. + + +\begin{DoxyParams}{Parameters} +{\em the} & innovation number to fine\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +true if the genome has a node with the provided innovation number, false otherwise. +\end{DoxyReturn} +\mbox{\Hypertarget{class_r_n_n___genome_a99769540d03c292a1e63bd753ae58ceb}\label{class_r_n_n___genome_a99769540d03c292a1e63bd753ae58ceb}} +\index{RNN\_Genome@{RNN\_Genome}!write\_predictions@{write\_predictions}} +\index{write\_predictions@{write\_predictions}!RNN\_Genome@{RNN\_Genome}} +\doxysubsubsection{\texorpdfstring{write\_predictions()}{write\_predictions()}} +{\footnotesize\ttfamily void R\+N\+N\+\_\+\+Genome\+::write\+\_\+predictions (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{output\+\_\+directory, }\item[{const \textbf{ vector}$<$ \textbf{ string} $>$ \&}]{input\+\_\+filenames, }\item[{const \textbf{ vector}$<$ double $>$ \&}]{parameters, }\item[{const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&}]{inputs, }\item[{const \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&}]{outputs, }\item[{\mbox{\hyperlink{class_corpus}{Corpus}} $\ast$}]{word\+\_\+series\+\_\+sets }\end{DoxyParamCaption})} + + + +Predicts the cross entropy loss after a single pass across the rnn genome. + +~\newline + + + +\begin{DoxyParams}{Parameters} +{\em output\+\_\+directory} & is the file directory that contains all the output files. \\ +\hline +{\em inputs\+\_\+filenames} & are the inputs files according to the dataset that is pass to the for forward pass. \\ +\hline +{\em inputs} & are the inputs according to the dataset that is pass to the for forward pass. ~\newline + \\ +\hline +{\em outputs} & are the expected outputs according to the dataset that is pass to the calculate error for backward pass. \\ +\hline +{\em parameters} & is the parameters such as dropout, momentum type used for the forward pass. \\ +\hline +{\em word\+\_\+series\+\_\+sets} & is the \mbox{\hyperlink{class_corpus}{Corpus}} from the word series dataset.\\ +\hline +\end{DoxyParams} +Calls the write predictions from the rnn class. ~\newline + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{class_r_n_n___genome_a41ba624f5a9311ee8ef0803ae6063156}\label{class_r_n_n___genome_a41ba624f5a9311ee8ef0803ae6063156}} +\index{RNN\_Genome@{RNN\_Genome}!best\_validation\_mse@{best\_validation\_mse}} +\index{best\_validation\_mse@{best\_validation\_mse}!RNN\_Genome@{RNN\_Genome}} +\doxysubsubsection{\texorpdfstring{best\_validation\_mse}{best\_validation\_mse}} +{\footnotesize\ttfamily double R\+N\+N\+\_\+\+Genome\+::best\+\_\+validation\+\_\+mse\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies the validation error across the dataset. + +~\newline + It will use mean squared error when regression in case of time series and cross entropy loss when regression not used in case of word series \mbox{\Hypertarget{class_r_n_n___genome_a32113888aee78f2b95490a5289f47a44}\label{class_r_n_n___genome_a32113888aee78f2b95490a5289f47a44}} +\index{RNN\_Genome@{RNN\_Genome}!use\_regression@{use\_regression}} +\index{use\_regression@{use\_regression}!RNN\_Genome@{RNN\_Genome}} +\doxysubsubsection{\texorpdfstring{use\_regression}{use\_regression}} +{\footnotesize\ttfamily bool R\+N\+N\+\_\+\+Genome\+::use\+\_\+regression\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies whether to use the regression or not. + +~\newline + It will use regression in case of time series and will not use regression in case of word series + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+genome.\+hxx\item +rnn/rnn\+\_\+genome.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_r_n_n___node.eps b/docs/latex/class_r_n_n___node.eps new file mode 100644 index 00000000..e012d429 --- /dev/null +++ b/docs/latex/class_r_n_n___node.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 285.714286 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.750000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(RNN_Node) cw +(RNN_Node_Interface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (RNN_Node) 0.000000 0.000000 box + (RNN_Node_Interface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_r_n_n___node.tex b/docs/latex/class_r_n_n___node.tex new file mode 100644 index 00000000..fd928eee --- /dev/null +++ b/docs/latex/class_r_n_n___node.tex @@ -0,0 +1,93 @@ +\hypertarget{class_r_n_n___node}{}\doxysection{R\+N\+N\+\_\+\+Node Class Reference} +\label{class_r_n_n___node}\index{RNN\_Node@{RNN\_Node}} +Inheritance diagram for R\+N\+N\+\_\+\+Node\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_r_n_n___node} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___node_a02f17cf2588bda29b9dc48bc963a82f8}\label{class_r_n_n___node_a02f17cf2588bda29b9dc48bc963a82f8}} +{\bfseries R\+N\+N\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, int \+\_\+layer\+\_\+type, double \+\_\+depth, int \+\_\+node\+\_\+type) +\item +\mbox{\Hypertarget{class_r_n_n___node_a3b9ceb06fe24c27175e51eb844981aeb}\label{class_r_n_n___node_a3b9ceb06fe24c27175e51eb844981aeb}} +{\bfseries R\+N\+N\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, int \+\_\+layer\+\_\+type, double \+\_\+depth, int \+\_\+node\+\_\+type, \textbf{ string} \+\_\+parameter\+\_\+name) +\item +\mbox{\Hypertarget{class_r_n_n___node_a1f51482502ea3a9d60398e9118664cc7}\label{class_r_n_n___node_a1f51482502ea3a9d60398e9118664cc7}} +void {\bfseries initialize\+\_\+lamarckian} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double mu, double sigma) +\item +\mbox{\Hypertarget{class_r_n_n___node_aeb4837a7203200646cca4292bf162651}\label{class_r_n_n___node_aeb4837a7203200646cca4292bf162651}} +void {\bfseries initialize\+\_\+xavier} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng1\+\_\+1, double range) +\item +\mbox{\Hypertarget{class_r_n_n___node_af9918b6e717a2fa6123a94a3af730ea3}\label{class_r_n_n___node_af9918b6e717a2fa6123a94a3af730ea3}} +void {\bfseries initialize\+\_\+kaiming} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double range) +\item +\mbox{\Hypertarget{class_r_n_n___node_aadea7251fb3737601dd54fced64c4493}\label{class_r_n_n___node_aadea7251fb3737601dd54fced64c4493}} +void {\bfseries initialize\+\_\+uniform\+\_\+random} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng) +\item +\mbox{\Hypertarget{class_r_n_n___node_a86910e07f7ea5b5f4e2623ef3c3c98c3}\label{class_r_n_n___node_a86910e07f7ea5b5f4e2623ef3c3c98c3}} +void {\bfseries input\+\_\+fired} (int time, double incoming\+\_\+output) +\item +\mbox{\Hypertarget{class_r_n_n___node_a1c98a7abaf87ab32c938a1e24f2b01ca}\label{class_r_n_n___node_a1c98a7abaf87ab32c938a1e24f2b01ca}} +void {\bfseries try\+\_\+update\+\_\+deltas} (int time) +\item +\mbox{\Hypertarget{class_r_n_n___node_a29c50ad9a3f633b06428d9db93b64ae8}\label{class_r_n_n___node_a29c50ad9a3f633b06428d9db93b64ae8}} +void {\bfseries output\+\_\+fired} (int time, double delta) +\item +\mbox{\Hypertarget{class_r_n_n___node_a03edb0e838cde2e01b73d845d9d6ca62}\label{class_r_n_n___node_a03edb0e838cde2e01b73d845d9d6ca62}} +void {\bfseries error\+\_\+fired} (int time, double error) +\item +\mbox{\Hypertarget{class_r_n_n___node_ac1a3bf5e7c4fe93b67e927899d4a0263}\label{class_r_n_n___node_ac1a3bf5e7c4fe93b67e927899d4a0263}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_r_n_n___node_a7749f740fb57f65d8b0778929d8f8668}\label{class_r_n_n___node_a7749f740fb57f65d8b0778929d8f8668}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_r_n_n___node_a13192a2c1d1e587827be5d8e003cbe05}\label{class_r_n_n___node_a13192a2c1d1e587827be5d8e003cbe05}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_r_n_n___node_a8c02e26e838585a48aa8639950f5cd9c}\label{class_r_n_n___node_a8c02e26e838585a48aa8639950f5cd9c}} +void {\bfseries get\+\_\+weights} (uint32\+\_\+t \&offset, \textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_r_n_n___node_aae3fe04e2c6eb18466c6e6fcb19bc97b}\label{class_r_n_n___node_aae3fe04e2c6eb18466c6e6fcb19bc97b}} +void {\bfseries set\+\_\+weights} (uint32\+\_\+t \&offset, const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_r_n_n___node_a209e68001af6e3eac8fd2903cd1fc967}\label{class_r_n_n___node_a209e68001af6e3eac8fd2903cd1fc967}} +void {\bfseries reset} (int \+\_\+series\+\_\+length) +\item +\mbox{\Hypertarget{class_r_n_n___node_abf695f8d9775e998eef3a47875608081}\label{class_r_n_n___node_abf695f8d9775e998eef3a47875608081}} +void {\bfseries get\+\_\+gradients} (\textbf{ vector}$<$ double $>$ \&gradients) +\item +\mbox{\Hypertarget{class_r_n_n___node_a4774edbef71ad13050408a285e7a9c4f}\label{class_r_n_n___node_a4774edbef71ad13050408a285e7a9c4f}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries copy} () const +\item +\mbox{\Hypertarget{class_r_n_n___node_a8028785fee24985de849c1d30d602fbe}\label{class_r_n_n___node_a8028785fee24985de849c1d30d602fbe}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___node_ab99c963decabc8c825187d04c99ef882}\label{class_r_n_n___node_ab99c963decabc8c825187d04c99ef882}} +double {\bfseries bias} +\item +\mbox{\Hypertarget{class_r_n_n___node_a429f8cfe7d6ffc993ea52d6165ee895e}\label{class_r_n_n___node_a429f8cfe7d6ffc993ea52d6165ee895e}} +double {\bfseries d\+\_\+bias} +\item +\mbox{\Hypertarget{class_r_n_n___node_aa531ec01369636171d6551fbf9c347c0}\label{class_r_n_n___node_aa531ec01369636171d6551fbf9c347c0}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+output} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}\label{class_r_n_n___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}} +class {\bfseries R\+N\+N\+\_\+\+Edge} +\end{DoxyCompactItemize} +\doxysubsection*{Additional Inherited Members} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+node.\+hxx\item +rnn/rnn\+\_\+node.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_r_n_n___node___interface.eps b/docs/latex/class_r_n_n___node___interface.eps new file mode 100644 index 00000000..60465271 --- /dev/null +++ b/docs/latex/class_r_n_n___node___interface.eps @@ -0,0 +1,231 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 680.272109 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 0.735000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 10 def +/cols 2 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(RNN_Node_Interface) cw +(Delta_Node) cw +(ENARC_Node) cw +(ENAS_DAG_Node) cw +(GRU_Node) cw +(LSTM_Node) cw +(MGU_Node) cw +(RANDOM_DAG_Node) cw +(RNN_Node) cw +(UGRNN_Node) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (RNN_Node_Interface) 0.000000 9.000000 box + (Delta_Node) 1.000000 8.000000 box + (ENARC_Node) 1.000000 7.000000 box + (ENAS_DAG_Node) 1.000000 6.000000 box + (GRU_Node) 1.000000 5.000000 box + (LSTM_Node) 1.000000 4.000000 box + (MGU_Node) 1.000000 3.000000 box + (RANDOM_DAG_Node) 1.000000 2.000000 box + (RNN_Node) 1.000000 1.000000 box + (UGRNN_Node) 1.000000 0.000000 box + +% ----- relations ----- + +solid +1 0.000000 8.250000 out +solid +0 0.000000 8.500000 hedge +solid +0 0.000000 7.500000 hedge +solid +0 0.000000 6.500000 hedge +solid +0 0.000000 5.500000 hedge +solid +0 0.000000 4.500000 hedge +solid +0 0.000000 3.500000 hedge +solid +0 0.000000 2.500000 hedge +solid +0 0.000000 1.500000 hedge +solid +0 0.000000 0.500000 hedge +solid +0.000000 9.000000 0.500000 vedge diff --git a/docs/latex/class_r_n_n___node___interface.tex b/docs/latex/class_r_n_n___node___interface.tex new file mode 100644 index 00000000..07a6663c --- /dev/null +++ b/docs/latex/class_r_n_n___node___interface.tex @@ -0,0 +1,173 @@ +\hypertarget{class_r_n_n___node___interface}{}\doxysection{R\+N\+N\+\_\+\+Node\+\_\+\+Interface Class Reference} +\label{class_r_n_n___node___interface}\index{RNN\_Node\_Interface@{RNN\_Node\_Interface}} +Inheritance diagram for R\+N\+N\+\_\+\+Node\+\_\+\+Interface\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=10.000000cm]{class_r_n_n___node___interface} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a1ed251d94070783d6b2b0c1cf86d242c}\label{class_r_n_n___node___interface_a1ed251d94070783d6b2b0c1cf86d242c}} +{\bfseries R\+N\+N\+\_\+\+Node\+\_\+\+Interface} (int32\+\_\+t \+\_\+innovation\+\_\+number, int32\+\_\+t \+\_\+layer\+\_\+type, double \+\_\+depth) +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a12039a9af458fd5891dab3a8ea50ffb1}\label{class_r_n_n___node___interface_a12039a9af458fd5891dab3a8ea50ffb1}} +{\bfseries R\+N\+N\+\_\+\+Node\+\_\+\+Interface} (int32\+\_\+t \+\_\+innovation\+\_\+number, int32\+\_\+t \+\_\+layer\+\_\+type, double \+\_\+depth, \textbf{ string} \+\_\+parameter\+\_\+name) +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_aa7c16f34b699b23e2d30dbd6fe871913}\label{class_r_n_n___node___interface_aa7c16f34b699b23e2d30dbd6fe871913}} +virtual void {\bfseries initialize\+\_\+lamarckian} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double mu, double sigma)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a82a6cd2740ad9ec251ce70b9ccf7d996}\label{class_r_n_n___node___interface_a82a6cd2740ad9ec251ce70b9ccf7d996}} +virtual void {\bfseries initialize\+\_\+xavier} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+1\+\_\+1, double range)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a151b062476ee718b6fa2c3dad7e1321e}\label{class_r_n_n___node___interface_a151b062476ee718b6fa2c3dad7e1321e}} +virtual void {\bfseries initialize\+\_\+kaiming} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double range)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a98663b8e304619c8d8605f5a56432ea5}\label{class_r_n_n___node___interface_a98663b8e304619c8d8605f5a56432ea5}} +virtual void {\bfseries initialize\+\_\+uniform\+\_\+random} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_abb54d5403840c537bc19682349d3e762}\label{class_r_n_n___node___interface_abb54d5403840c537bc19682349d3e762}} +virtual void {\bfseries input\+\_\+fired} (int32\+\_\+t time, double incoming\+\_\+output)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a7494e26e1a8f314c8b7d9b74317d3d50}\label{class_r_n_n___node___interface_a7494e26e1a8f314c8b7d9b74317d3d50}} +virtual void {\bfseries output\+\_\+fired} (int32\+\_\+t time, double delta)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_ab633cfb84590079eb3ebea113d61ba4f}\label{class_r_n_n___node___interface_ab633cfb84590079eb3ebea113d61ba4f}} +virtual void {\bfseries error\+\_\+fired} (int32\+\_\+t time, double error)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a9dae249230f9d8e11110194a640e80c6}\label{class_r_n_n___node___interface_a9dae249230f9d8e11110194a640e80c6}} +virtual uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () const =0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_abba707a02d11beb0fe4c2e03cc0dc42a}\label{class_r_n_n___node___interface_abba707a02d11beb0fe4c2e03cc0dc42a}} +virtual void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) const =0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_af688d6a3b57727e8911231e1d5eebd33}\label{class_r_n_n___node___interface_af688d6a3b57727e8911231e1d5eebd33}} +virtual void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_aedcd5447e23943fe1da641ada677d534}\label{class_r_n_n___node___interface_aedcd5447e23943fe1da641ada677d534}} +virtual void {\bfseries get\+\_\+weights} (uint32\+\_\+t \&offset, \textbf{ vector}$<$ double $>$ \¶meters) const =0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a649275fa498dc678b3ca338d1cb9d783}\label{class_r_n_n___node___interface_a649275fa498dc678b3ca338d1cb9d783}} +virtual void {\bfseries set\+\_\+weights} (uint32\+\_\+t \&offset, const \textbf{ vector}$<$ double $>$ \¶meters)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_afc25f125bd5cb202da61775a129aa8e7}\label{class_r_n_n___node___interface_afc25f125bd5cb202da61775a129aa8e7}} +virtual void {\bfseries reset} (int32\+\_\+t \+\_\+series\+\_\+length)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_acd325630d2e23b6474cac709a6fe8aa9}\label{class_r_n_n___node___interface_acd325630d2e23b6474cac709a6fe8aa9}} +virtual void {\bfseries get\+\_\+gradients} (\textbf{ vector}$<$ double $>$ \&gradients)=0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a39bcbf8e1dfda95c76c4b4dde3756aab}\label{class_r_n_n___node___interface_a39bcbf8e1dfda95c76c4b4dde3756aab}} +virtual \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries copy} () const =0 +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a4d8c4486d28d4868ec1278412af9e475}\label{class_r_n_n___node___interface_a4d8c4486d28d4868ec1278412af9e475}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a79fbf0876637a93db67d589c94c60eeb}\label{class_r_n_n___node___interface_a79fbf0876637a93db67d589c94c60eeb}} +int32\+\_\+t {\bfseries get\+\_\+node\+\_\+type} () const +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a07da15405db940a34d98ca1d2558fc3d}\label{class_r_n_n___node___interface_a07da15405db940a34d98ca1d2558fc3d}} +int32\+\_\+t {\bfseries get\+\_\+layer\+\_\+type} () const +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a2889720fbe6f13b3429e425054adee10}\label{class_r_n_n___node___interface_a2889720fbe6f13b3429e425054adee10}} +int32\+\_\+t {\bfseries get\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a50ae2eb211394265e4e3a1f852fc11f0}\label{class_r_n_n___node___interface_a50ae2eb211394265e4e3a1f852fc11f0}} +int32\+\_\+t {\bfseries get\+\_\+total\+\_\+inputs} () const +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_ac8140b8f6edb1bc09684506d5c73aed4}\label{class_r_n_n___node___interface_ac8140b8f6edb1bc09684506d5c73aed4}} +int32\+\_\+t {\bfseries get\+\_\+total\+\_\+outputs} () const +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a9b0894ad2ea635baeff5412cb28ad9c8}\label{class_r_n_n___node___interface_a9b0894ad2ea635baeff5412cb28ad9c8}} +double {\bfseries get\+\_\+depth} () const +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a6ed255dc833d76ee085c28c386de98a5}\label{class_r_n_n___node___interface_a6ed255dc833d76ee085c28c386de98a5}} +bool {\bfseries equals} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$other) const +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_aa257c9ad4810286a299bd2dbf4bb60c5}\label{class_r_n_n___node___interface_aa257c9ad4810286a299bd2dbf4bb60c5}} +bool {\bfseries is\+\_\+reachable} () const +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a06c31a95dd9778bb6514ddcd52686787}\label{class_r_n_n___node___interface_a06c31a95dd9778bb6514ddcd52686787}} +bool {\bfseries is\+\_\+enabled} () const +\end{DoxyCompactItemize} +\doxysubsection*{Protected Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_ab3d935f5a1caa3900c4505da98e915f1}\label{class_r_n_n___node___interface_ab3d935f5a1caa3900c4505da98e915f1}} +int32\+\_\+t {\bfseries innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_aa54842fb73233d8f91a159d87aeb381e}\label{class_r_n_n___node___interface_aa54842fb73233d8f91a159d87aeb381e}} +int32\+\_\+t {\bfseries layer\+\_\+type} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a91dcdce3c6c1e68304d51f44fa73d576}\label{class_r_n_n___node___interface_a91dcdce3c6c1e68304d51f44fa73d576}} +int32\+\_\+t {\bfseries node\+\_\+type} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a2aecf23b8204552a75b1f3e94ce3459c}\label{class_r_n_n___node___interface_a2aecf23b8204552a75b1f3e94ce3459c}} +double {\bfseries depth} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_aa58f88dcf01abb3960b55e8ed30a3255}\label{class_r_n_n___node___interface_aa58f88dcf01abb3960b55e8ed30a3255}} +\textbf{ string} {\bfseries parameter\+\_\+name} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a3a3a3a7db0f530439a25e83d79cb87d4}\label{class_r_n_n___node___interface_a3a3a3a7db0f530439a25e83d79cb87d4}} +bool {\bfseries enabled} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a0b6f3cfc74d94eee742bb4e397018d3f}\label{class_r_n_n___node___interface_a0b6f3cfc74d94eee742bb4e397018d3f}} +bool {\bfseries backward\+\_\+reachable} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a191a9b1cc5fa8f7dd97fbf6028abfe40}\label{class_r_n_n___node___interface_a191a9b1cc5fa8f7dd97fbf6028abfe40}} +bool {\bfseries forward\+\_\+reachable} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_afb2167b706fbbc8caf8b364d7934c3d9}\label{class_r_n_n___node___interface_afb2167b706fbbc8caf8b364d7934c3d9}} +int32\+\_\+t {\bfseries series\+\_\+length} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a9395b960e01323ccd24183a1f45d5938}\label{class_r_n_n___node___interface_a9395b960e01323ccd24183a1f45d5938}} +\textbf{ vector}$<$ double $>$ {\bfseries input\+\_\+values} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_ad7ed0327697e9492b383b8779ea52a29}\label{class_r_n_n___node___interface_ad7ed0327697e9492b383b8779ea52a29}} +\textbf{ vector}$<$ double $>$ {\bfseries output\+\_\+values} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a511a9004d71ce185b061ce8d5022ca64}\label{class_r_n_n___node___interface_a511a9004d71ce185b061ce8d5022ca64}} +\textbf{ vector}$<$ double $>$ {\bfseries error\+\_\+values} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a7c6204d57bb37a40c7b243110c0f7a34}\label{class_r_n_n___node___interface_a7c6204d57bb37a40c7b243110c0f7a34}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+input} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a396bea6cb6d6dd595e53e252356f09c6}\label{class_r_n_n___node___interface_a396bea6cb6d6dd595e53e252356f09c6}} +\textbf{ vector}$<$ int32\+\_\+t $>$ {\bfseries inputs\+\_\+fired} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_af1cbfce95ce6034addf1696a045d8d12}\label{class_r_n_n___node___interface_af1cbfce95ce6034addf1696a045d8d12}} +\textbf{ vector}$<$ int32\+\_\+t $>$ {\bfseries outputs\+\_\+fired} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a772814201f16a86ee6ea1f8700e7826b}\label{class_r_n_n___node___interface_a772814201f16a86ee6ea1f8700e7826b}} +int32\+\_\+t {\bfseries total\+\_\+inputs} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a0148cba9d2537a52d5402e246e14209c}\label{class_r_n_n___node___interface_a0148cba9d2537a52d5402e246e14209c}} +int32\+\_\+t {\bfseries total\+\_\+outputs} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a6ed93c885e63e64a8aa0ab2b7f637f0e}\label{class_r_n_n___node___interface_a6ed93c885e63e64a8aa0ab2b7f637f0e}} +class {\bfseries R\+N\+N\+\_\+\+Edge} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a4a4558cee1b41082e2c3123452a35b99}\label{class_r_n_n___node___interface_a4a4558cee1b41082e2c3123452a35b99}} +class {\bfseries R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a67a4ae33f841a6e1d186c84e9490a802}\label{class_r_n_n___node___interface_a67a4ae33f841a6e1d186c84e9490a802}} +class {\bfseries R\+NN} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_ad7617134ea6662be9dc1ad79223de4d1}\label{class_r_n_n___node___interface_ad7617134ea6662be9dc1ad79223de4d1}} +class {\bfseries R\+N\+N\+\_\+\+Genome} +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a3c6fd1b1841f1808870f8f228c5d021e}\label{class_r_n_n___node___interface_a3c6fd1b1841f1808870f8f228c5d021e}} +void {\bfseries get\+\_\+mse} (\mbox{\hyperlink{class_r_n_n}{R\+NN}} $\ast$genome, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected, double \&mse, \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&deltas) +\item +\mbox{\Hypertarget{class_r_n_n___node___interface_a744ea213e57442af35ac90ea397ce97c}\label{class_r_n_n___node___interface_a744ea213e57442af35ac90ea397ce97c}} +void {\bfseries get\+\_\+mae} (\mbox{\hyperlink{class_r_n_n}{R\+NN}} $\ast$genome, const \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&expected, double \&mae, \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&deltas) +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+node\+\_\+interface.\+hxx\item +rnn/rnn\+\_\+node\+\_\+interface.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_r_n_n___recurrent___edge.tex b/docs/latex/class_r_n_n___recurrent___edge.tex new file mode 100644 index 00000000..480dc0c7 --- /dev/null +++ b/docs/latex/class_r_n_n___recurrent___edge.tex @@ -0,0 +1,128 @@ +\hypertarget{class_r_n_n___recurrent___edge}{}\doxysection{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge Class Reference} +\label{class_r_n_n___recurrent___edge}\index{RNN\_Recurrent\_Edge@{RNN\_Recurrent\_Edge}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a72713f1cbaad9fbeb86eb1ffef0c2efd}\label{class_r_n_n___recurrent___edge_a72713f1cbaad9fbeb86eb1ffef0c2efd}} +{\bfseries R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge} (int32\+\_\+t \+\_\+innovation\+\_\+number, int32\+\_\+t \+\_\+recurrent\+\_\+depth, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$\+\_\+input\+\_\+node, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$\+\_\+output\+\_\+node) +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a044d926f5d94ce66dee9f9614d959153}\label{class_r_n_n___recurrent___edge_a044d926f5d94ce66dee9f9614d959153}} +{\bfseries R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge} (int32\+\_\+t \+\_\+innovation\+\_\+number, int32\+\_\+t \+\_\+recurrent\+\_\+depth, int32\+\_\+t \+\_\+input\+\_\+innovation\+\_\+number, int32\+\_\+t \+\_\+output\+\_\+innovation\+\_\+number, const \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ \&nodes) +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_acc912e2029fdfdb75b5c702568f4a390}\label{class_r_n_n___recurrent___edge_acc912e2029fdfdb75b5c702568f4a390}} +void {\bfseries reset} (int32\+\_\+t \+\_\+series\+\_\+length) +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a88e73a1cce0055a9d9109bca3a3ba7d0}\label{class_r_n_n___recurrent___edge_a88e73a1cce0055a9d9109bca3a3ba7d0}} +void {\bfseries first\+\_\+propagate\+\_\+forward} () +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a75a5e034d3e6e41114e5594e205bafe0}\label{class_r_n_n___recurrent___edge_a75a5e034d3e6e41114e5594e205bafe0}} +void {\bfseries first\+\_\+propagate\+\_\+backward} () +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a2b09fcc3a29bfa00f5b16e5cc4d4af76}\label{class_r_n_n___recurrent___edge_a2b09fcc3a29bfa00f5b16e5cc4d4af76}} +void {\bfseries propagate\+\_\+forward} (int32\+\_\+t time) +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a810844d50e03886c1559cf9a7e59bec5}\label{class_r_n_n___recurrent___edge_a810844d50e03886c1559cf9a7e59bec5}} +void {\bfseries propagate\+\_\+backward} (int32\+\_\+t time) +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a16621650b521d5ebf2ebd7b08a2534bb}\label{class_r_n_n___recurrent___edge_a16621650b521d5ebf2ebd7b08a2534bb}} +int32\+\_\+t {\bfseries get\+\_\+recurrent\+\_\+depth} () const +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a5540cafacf5e66f78b7e819a51d054fc}\label{class_r_n_n___recurrent___edge_a5540cafacf5e66f78b7e819a51d054fc}} +double {\bfseries get\+\_\+gradient} () +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a21df6982830969182d0b55ae8595e197}\label{class_r_n_n___recurrent___edge_a21df6982830969182d0b55ae8595e197}} +bool {\bfseries is\+\_\+enabled} () const +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a12988eeeb3de196e475ea52599ad4b9a}\label{class_r_n_n___recurrent___edge_a12988eeeb3de196e475ea52599ad4b9a}} +bool {\bfseries is\+\_\+reachable} () const +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_accc2d4b08cd9c10b0f35cf518671a25a}\label{class_r_n_n___recurrent___edge_accc2d4b08cd9c10b0f35cf518671a25a}} +\mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$ {\bfseries copy} (const \textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ $>$ new\+\_\+nodes) +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_abdf545b03b660ce08b5eb97e79429e7b}\label{class_r_n_n___recurrent___edge_abdf545b03b660ce08b5eb97e79429e7b}} +int32\+\_\+t {\bfseries get\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a372a3fad06ee08fc7686111e6be81004}\label{class_r_n_n___recurrent___edge_a372a3fad06ee08fc7686111e6be81004}} +int32\+\_\+t {\bfseries get\+\_\+input\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_afeb227fa13fe2946edd506e6016c48b3}\label{class_r_n_n___recurrent___edge_afeb227fa13fe2946edd506e6016c48b3}} +int32\+\_\+t {\bfseries get\+\_\+output\+\_\+innovation\+\_\+number} () const +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a0b66bd9658e2049245c7bc2c14707c17}\label{class_r_n_n___recurrent___edge_a0b66bd9658e2049245c7bc2c14707c17}} +const \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries get\+\_\+input\+\_\+node} () const +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_aa61575182a11fdedf9d149e705558512}\label{class_r_n_n___recurrent___edge_aa61575182a11fdedf9d149e705558512}} +const \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries get\+\_\+output\+\_\+node} () const +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_aa67b33ae980f8208d4b504614069a658}\label{class_r_n_n___recurrent___edge_aa67b33ae980f8208d4b504614069a658}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a28305f514020e5833190881b9b16114b}\label{class_r_n_n___recurrent___edge_a28305f514020e5833190881b9b16114b}} +bool {\bfseries equals} (\mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$other) const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a5ce53524137cada52785a207f105a9b9}\label{class_r_n_n___recurrent___edge_a5ce53524137cada52785a207f105a9b9}} +int32\+\_\+t {\bfseries innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a0b892f520f86e3007e7db209eb60e084}\label{class_r_n_n___recurrent___edge_a0b892f520f86e3007e7db209eb60e084}} +int32\+\_\+t {\bfseries series\+\_\+length} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a1136d65abbe681dacd19146ace2cc887}\label{class_r_n_n___recurrent___edge_a1136d65abbe681dacd19146ace2cc887}} +int32\+\_\+t {\bfseries recurrent\+\_\+depth} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_aadc45131f473bf541125dd20d0ed6c2b}\label{class_r_n_n___recurrent___edge_aadc45131f473bf541125dd20d0ed6c2b}} +\textbf{ vector}$<$ double $>$ {\bfseries outputs} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a57e7ef7d0001405829d3e1d6da66a9e8}\label{class_r_n_n___recurrent___edge_a57e7ef7d0001405829d3e1d6da66a9e8}} +\textbf{ vector}$<$ double $>$ {\bfseries deltas} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a3dad7b68a85e16389ce990dff0aac740}\label{class_r_n_n___recurrent___edge_a3dad7b68a85e16389ce990dff0aac740}} +double {\bfseries weight} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_abd495ce6865389118f07b99907f2a370}\label{class_r_n_n___recurrent___edge_abd495ce6865389118f07b99907f2a370}} +double {\bfseries d\+\_\+weight} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_af18d16ba4f12d42d08d021d08fb39f3a}\label{class_r_n_n___recurrent___edge_af18d16ba4f12d42d08d021d08fb39f3a}} +bool {\bfseries enabled} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a1b411f2aee9fd86a788c4ea5a02e596b}\label{class_r_n_n___recurrent___edge_a1b411f2aee9fd86a788c4ea5a02e596b}} +bool {\bfseries forward\+\_\+reachable} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a89c11eddab2fbb0161cb539ec8ea568d}\label{class_r_n_n___recurrent___edge_a89c11eddab2fbb0161cb539ec8ea568d}} +bool {\bfseries backward\+\_\+reachable} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_aaa0ba8b635b327b14c98191a3aea5d5c}\label{class_r_n_n___recurrent___edge_aaa0ba8b635b327b14c98191a3aea5d5c}} +int32\+\_\+t {\bfseries input\+\_\+innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_aa7eb8f59343e7bde97be982800b4e9b8}\label{class_r_n_n___recurrent___edge_aa7eb8f59343e7bde97be982800b4e9b8}} +int32\+\_\+t {\bfseries output\+\_\+innovation\+\_\+number} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_adf689004642ab9b6aeced74710d0bf81}\label{class_r_n_n___recurrent___edge_adf689004642ab9b6aeced74710d0bf81}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries input\+\_\+node} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_adc1cdb8da7effc4a9bf72162171351f4}\label{class_r_n_n___recurrent___edge_adc1cdb8da7effc4a9bf72162171351f4}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries output\+\_\+node} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_ad7617134ea6662be9dc1ad79223de4d1}\label{class_r_n_n___recurrent___edge_ad7617134ea6662be9dc1ad79223de4d1}} +class {\bfseries R\+N\+N\+\_\+\+Genome} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a67a4ae33f841a6e1d186c84e9490a802}\label{class_r_n_n___recurrent___edge_a67a4ae33f841a6e1d186c84e9490a802}} +class {\bfseries R\+NN} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_a7850c8d4e61c10934acf1a9a98347cf3}\label{class_r_n_n___recurrent___edge_a7850c8d4e61c10934acf1a9a98347cf3}} +class {\bfseries E\+X\+A\+MM} +\item +\mbox{\Hypertarget{class_r_n_n___recurrent___edge_aa1d85c88a38f21ffb0ccedd474289b0e}\label{class_r_n_n___recurrent___edge_aa1d85c88a38f21ffb0ccedd474289b0e}} +class {\bfseries Rec\+Depth\+Frequency\+Table} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+recurrent\+\_\+edge.\+hxx\item +rnn/rnn\+\_\+recurrent\+\_\+edge.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_rectangle.tex b/docs/latex/class_rectangle.tex new file mode 100644 index 00000000..10ad35ce --- /dev/null +++ b/docs/latex/class_rectangle.tex @@ -0,0 +1,29 @@ +\hypertarget{class_rectangle}{}\doxysection{Rectangle Class Reference} +\label{class_rectangle}\index{Rectangle@{Rectangle}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_rectangle_aa9fae674a9a6b318018142b5f42f3768}\label{class_rectangle_aa9fae674a9a6b318018142b5f42f3768}} +{\bfseries Rectangle} (int \+\_\+y1, int \+\_\+x1, int \+\_\+y2, int \+\_\+x2) +\end{DoxyCompactItemize} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_rectangle_ac2b150995b6a32b57eb714cdef42e605}\label{class_rectangle_ac2b150995b6a32b57eb714cdef42e605}} +int {\bfseries y1} +\item +\mbox{\Hypertarget{class_rectangle_a064169457a135887c8bf000ce818ce53}\label{class_rectangle_a064169457a135887c8bf000ce818ce53}} +int {\bfseries x1} +\item +\mbox{\Hypertarget{class_rectangle_abeb95aefc414dc72b87788e3b59cbce4}\label{class_rectangle_abeb95aefc414dc72b87788e3b59cbce4}} +int {\bfseries y2} +\item +\mbox{\Hypertarget{class_rectangle_a6be48348b223ae31303071f26713da5e}\label{class_rectangle_a6be48348b223ae31303071f26713da5e}} +int {\bfseries x2} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/mosaic\+\_\+image\+\_\+set.\+hxx\item +image\+\_\+tools/mosaic\+\_\+image\+\_\+set.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_run_statistics.tex b/docs/latex/class_run_statistics.tex new file mode 100644 index 00000000..af84fb12 --- /dev/null +++ b/docs/latex/class_run_statistics.tex @@ -0,0 +1,110 @@ +\hypertarget{class_run_statistics}{}\doxysection{Run\+Statistics Class Reference} +\label{class_run_statistics}\index{RunStatistics@{RunStatistics}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_run_statistics_aada18d05928d15f0937b647e0e0ddb36}\label{class_run_statistics_aada18d05928d15f0937b647e0e0ddb36}} +{\bfseries Run\+Statistics} (\textbf{ string} \+\_\+output\+\_\+name, \textbf{ string} \+\_\+run\+\_\+type) +\item +\mbox{\Hypertarget{class_run_statistics_adb42eaf8aaeacb5350d0a285bfcabb2e}\label{class_run_statistics_adb42eaf8aaeacb5350d0a285bfcabb2e}} +void {\bfseries set\+\_\+deviation\+\_\+from\+\_\+mean\+\_\+min} (double \+\_\+dfm\+\_\+min) +\item +\mbox{\Hypertarget{class_run_statistics_a17cd01d16423bb7aed2d95cb502e7417}\label{class_run_statistics_a17cd01d16423bb7aed2d95cb502e7417}} +void {\bfseries set\+\_\+deviation\+\_\+from\+\_\+mean\+\_\+avg} (double \+\_\+dfm\+\_\+avg) +\item +\mbox{\Hypertarget{class_run_statistics_a46f148f5743fabb9e87262a73a6eaf5f}\label{class_run_statistics_a46f148f5743fabb9e87262a73a6eaf5f}} +void {\bfseries set\+\_\+deviation\+\_\+from\+\_\+mean\+\_\+max} (double \+\_\+dfm\+\_\+max) +\item +\mbox{\Hypertarget{class_run_statistics_aeae58ae7ee17f92de0ab01edcb3b1241}\label{class_run_statistics_aeae58ae7ee17f92de0ab01edcb3b1241}} +\textbf{ string} {\bfseries correlate\+\_\+header} () +\item +\mbox{\Hypertarget{class_run_statistics_aa1a246e13f1a1d1646042d9334c5637c}\label{class_run_statistics_aa1a246e13f1a1d1646042d9334c5637c}} +\textbf{ string} {\bfseries to\+\_\+string\+\_\+min} () +\item +\mbox{\Hypertarget{class_run_statistics_a2264c70c93551e52ef650f738a8838cd}\label{class_run_statistics_a2264c70c93551e52ef650f738a8838cd}} +\textbf{ string} {\bfseries to\+\_\+string\+\_\+avg} () +\item +\mbox{\Hypertarget{class_run_statistics_a0672f3422bd8d4da2c9bad4d99f86f88}\label{class_run_statistics_a0672f3422bd8d4da2c9bad4d99f86f88}} +\textbf{ string} {\bfseries to\+\_\+string\+\_\+max} () +\item +\mbox{\Hypertarget{class_run_statistics_ae3b3969f67faa836c080a5cf89b13a9b}\label{class_run_statistics_ae3b3969f67faa836c080a5cf89b13a9b}} +\textbf{ string} {\bfseries to\+\_\+string\+\_\+stddev} () +\item +\mbox{\Hypertarget{class_run_statistics_afaee4dedd2d97487a214b5bfffe99c4f}\label{class_run_statistics_afaee4dedd2d97487a214b5bfffe99c4f}} +\textbf{ string} {\bfseries to\+\_\+string\+\_\+correlate} (\textbf{ string} target\+\_\+name, \mbox{\hyperlink{class_tracker}{Tracker}} \&target) +\item +\mbox{\Hypertarget{class_run_statistics_a3ec72004f8e035b187bab2be5d1e0edb}\label{class_run_statistics_a3ec72004f8e035b187bab2be5d1e0edb}} +\textbf{ string} {\bfseries overview\+\_\+header} () +\item +\mbox{\Hypertarget{class_run_statistics_a9c22f12ae25f1391823c1aa9a8ec496f}\label{class_run_statistics_a9c22f12ae25f1391823c1aa9a8ec496f}} +\textbf{ string} {\bfseries overview\+\_\+footer} (\textbf{ string} type) +\item +\mbox{\Hypertarget{class_run_statistics_a6965719818ac052e7ce7056c925b131c}\label{class_run_statistics_a6965719818ac052e7ce7056c925b131c}} +\textbf{ string} {\bfseries to\+\_\+overview\+\_\+string} () +\item +\mbox{\Hypertarget{class_run_statistics_ade365d166a83c798a832c82c8ec2f446}\label{class_run_statistics_ade365d166a83c798a832c82c8ec2f446}} +\textbf{ string} {\bfseries overview\+\_\+ff\+\_\+header} () +\item +\mbox{\Hypertarget{class_run_statistics_a26b47c6c07e076a5436859c6a589a284}\label{class_run_statistics_a26b47c6c07e076a5436859c6a589a284}} +\textbf{ string} {\bfseries overview\+\_\+ff\+\_\+footer} (\textbf{ string} type) +\item +\mbox{\Hypertarget{class_run_statistics_a7ba9240ab76af54b36b3ad061f9159e6}\label{class_run_statistics_a7ba9240ab76af54b36b3ad061f9159e6}} +\textbf{ string} {\bfseries to\+\_\+overview\+\_\+ff\+\_\+string} () +\end{DoxyCompactItemize} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_run_statistics_a85ca360217e0b1442b99aa1a63db39d8}\label{class_run_statistics_a85ca360217e0b1442b99aa1a63db39d8}} +\textbf{ string} {\bfseries output\+\_\+name} +\item +\mbox{\Hypertarget{class_run_statistics_a698543923a23620e0da79ade9f5544a9}\label{class_run_statistics_a698543923a23620e0da79ade9f5544a9}} +\textbf{ string} {\bfseries run\+\_\+type} +\item +\mbox{\Hypertarget{class_run_statistics_a180c209c150f21e35f1812e52c951020}\label{class_run_statistics_a180c209c150f21e35f1812e52c951020}} +double {\bfseries dfm\+\_\+min} +\item +\mbox{\Hypertarget{class_run_statistics_a73924a4ce6b3a6899792a64b14d7c302}\label{class_run_statistics_a73924a4ce6b3a6899792a64b14d7c302}} +double {\bfseries dfm\+\_\+avg} +\item +\mbox{\Hypertarget{class_run_statistics_a7c72d9798c3df4a0e37a76d9748bacab}\label{class_run_statistics_a7c72d9798c3df4a0e37a76d9748bacab}} +double {\bfseries dfm\+\_\+max} +\item +\mbox{\Hypertarget{class_run_statistics_a6454692ca4f352c67b1a1f515399dc24}\label{class_run_statistics_a6454692ca4f352c67b1a1f515399dc24}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries mse} +\item +\mbox{\Hypertarget{class_run_statistics_ad5cb8f22785dab8f211f3c5ea60da6cf}\label{class_run_statistics_ad5cb8f22785dab8f211f3c5ea60da6cf}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries mae} +\item +\mbox{\Hypertarget{class_run_statistics_a6d03169747c83ee1270155f6f86db7d9}\label{class_run_statistics_a6d03169747c83ee1270155f6f86db7d9}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries edge} +\item +\mbox{\Hypertarget{class_run_statistics_a4b6270cd47016d6cb056b0026ead6f0d}\label{class_run_statistics_a4b6270cd47016d6cb056b0026ead6f0d}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries rec\+\_\+edge} +\item +\mbox{\Hypertarget{class_run_statistics_a86b66d07c34cb344b8606e87df6402de}\label{class_run_statistics_a86b66d07c34cb344b8606e87df6402de}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries node} +\item +\mbox{\Hypertarget{class_run_statistics_a96f173dd4491983a29289b7bc4c1b684}\label{class_run_statistics_a96f173dd4491983a29289b7bc4c1b684}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries ff} +\item +\mbox{\Hypertarget{class_run_statistics_a621cc928e8474ee8b3e83b597095bd5b}\label{class_run_statistics_a621cc928e8474ee8b3e83b597095bd5b}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries lstm} +\item +\mbox{\Hypertarget{class_run_statistics_a443cf2682a9c5916f6e33d26867ec46d}\label{class_run_statistics_a443cf2682a9c5916f6e33d26867ec46d}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries ugrnn} +\item +\mbox{\Hypertarget{class_run_statistics_ad6a81270497f657aa4b78b22245a5e81}\label{class_run_statistics_ad6a81270497f657aa4b78b22245a5e81}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries delta} +\item +\mbox{\Hypertarget{class_run_statistics_a56d3a974f7ff761fd8b4b1de384a81fb}\label{class_run_statistics_a56d3a974f7ff761fd8b4b1de384a81fb}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries mgu} +\item +\mbox{\Hypertarget{class_run_statistics_a896d634bc643651ac0dfb033460d8ca9}\label{class_run_statistics_a896d634bc643651ac0dfb033460d8ca9}} +\mbox{\hyperlink{class_tracker}{Tracker}} {\bfseries gru} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\item +mpi/run\+\_\+statistics.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_sentence_series.tex b/docs/latex/class_sentence_series.tex new file mode 100644 index 00000000..40f38b6a --- /dev/null +++ b/docs/latex/class_sentence_series.tex @@ -0,0 +1,107 @@ +\hypertarget{class_sentence_series}{}\doxysection{Sentence\+Series Class Reference} +\label{class_sentence_series}\index{SentenceSeries@{SentenceSeries}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_sentence_series_a9dd5e8b2b6405969ec0b6f1639a0cd49}\label{class_sentence_series_a9dd5e8b2b6405969ec0b6f1639a0cd49}} +{\bfseries Sentence\+Series} (const \textbf{ string} \+\_\+filename, const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+word\+\_\+index, const \textbf{ map}$<$ \textbf{ string}, int $>$ \&\+\_\+vocab) +\item +\mbox{\Hypertarget{class_sentence_series_a86183fa3e7691b3f4956e33eb5ed5036}\label{class_sentence_series_a86183fa3e7691b3f4956e33eb5ed5036}} +void {\bfseries add\+\_\+word\+\_\+series} (\textbf{ string} name) +\item +\mbox{\Hypertarget{class_sentence_series_a256748d3020dfb23320fec748285bfcd}\label{class_sentence_series_a256748d3020dfb23320fec748285bfcd}} +int {\bfseries get\+\_\+number\+\_\+rows} () const +\item +\mbox{\Hypertarget{class_sentence_series_aa9d03674846a3ca929bf4c19f6d159a2}\label{class_sentence_series_aa9d03674846a3ca929bf4c19f6d159a2}} +int {\bfseries get\+\_\+number\+\_\+columns} () const +\item +\mbox{\Hypertarget{class_sentence_series_aa98fbace558de995c1d09fe9320786a0}\label{class_sentence_series_aa98fbace558de995c1d09fe9320786a0}} +\textbf{ string} {\bfseries get\+\_\+filename} () const +\item +\mbox{\Hypertarget{class_sentence_series_ae508f8a74847a39cd05f43933b652380}\label{class_sentence_series_ae508f8a74847a39cd05f43933b652380}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries get\+\_\+word\+\_\+index} () const +\item +\mbox{\Hypertarget{class_sentence_series_a912fbfb10140e366162396c82a260a8c}\label{class_sentence_series_a912fbfb10140e366162396c82a260a8c}} +void {\bfseries get\+\_\+series} (\textbf{ string} word\+\_\+name, \textbf{ vector}$<$ double $>$ \&series) +\item +\mbox{\Hypertarget{class_sentence_series_a192d7b7c36b2974d1a757bca19fd7116}\label{class_sentence_series_a192d7b7c36b2974d1a757bca19fd7116}} +double {\bfseries get\+\_\+min} (\textbf{ string} word) +\item +\mbox{\Hypertarget{class_sentence_series_ada1c70ab14a3af0961fa5f9536bc7a73}\label{class_sentence_series_ada1c70ab14a3af0961fa5f9536bc7a73}} +double {\bfseries get\+\_\+average} (\textbf{ string} word) +\item +\mbox{\Hypertarget{class_sentence_series_a15e2b176f35810f7b2732aad331e71df}\label{class_sentence_series_a15e2b176f35810f7b2732aad331e71df}} +double {\bfseries get\+\_\+max} (\textbf{ string} word) +\item +\mbox{\Hypertarget{class_sentence_series_a5257b87e1506b11490c25a2189d18fec}\label{class_sentence_series_a5257b87e1506b11490c25a2189d18fec}} +double {\bfseries get\+\_\+std\+\_\+dev} (\textbf{ string} word) +\item +\mbox{\Hypertarget{class_sentence_series_a229548660c2d523582d9cefada876a83}\label{class_sentence_series_a229548660c2d523582d9cefada876a83}} +double {\bfseries get\+\_\+variance} (\textbf{ string} word) +\item +\mbox{\Hypertarget{class_sentence_series_a9a769265817ce471dd8c5d12c8e95bd2}\label{class_sentence_series_a9a769265817ce471dd8c5d12c8e95bd2}} +double {\bfseries get\+\_\+min\+\_\+change} (\textbf{ string} word) +\item +\mbox{\Hypertarget{class_sentence_series_a92b13d7397a094e5f377f2fbc4215c19}\label{class_sentence_series_a92b13d7397a094e5f377f2fbc4215c19}} +double {\bfseries get\+\_\+max\+\_\+change} (\textbf{ string} word) +\item +\mbox{\Hypertarget{class_sentence_series_a69379261de439200738732a3af56b2c4}\label{class_sentence_series_a69379261de439200738732a3af56b2c4}} +double {\bfseries get\+\_\+correlation} (\textbf{ string} word1, \textbf{ string} word2, \textbf{ int32\+\_\+t} lag) const +\item +\mbox{\Hypertarget{class_sentence_series_ab4b3eae0c436701a5350037ac0119c10}\label{class_sentence_series_ab4b3eae0c436701a5350037ac0119c10}} +void {\bfseries normalize\+\_\+min\+\_\+max} (\textbf{ string} word, double min, double max) +\item +\mbox{\Hypertarget{class_sentence_series_a6ab9942ca671b0b6b1f445ef992db753}\label{class_sentence_series_a6ab9942ca671b0b6b1f445ef992db753}} +void {\bfseries normalize\+\_\+avg\+\_\+std\+\_\+dev} (\textbf{ string} word, double avg, double std\+\_\+dev, double norm\+\_\+max) +\item +\mbox{\Hypertarget{class_sentence_series_a68a6931ce95999cb1d23ef6a39d2c65d}\label{class_sentence_series_a68a6931ce95999cb1d23ef6a39d2c65d}} +void {\bfseries export\+\_\+word\+\_\+series} (\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&data, int word\+\_\+offset) +\item +\mbox{\Hypertarget{class_sentence_series_ad6acf989bd60775b629ed3c854c024a0}\label{class_sentence_series_ad6acf989bd60775b629ed3c854c024a0}} +void {\bfseries export\+\_\+word\+\_\+series} (\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&data) +\item +\mbox{\Hypertarget{class_sentence_series_a836512c4aa3a805780bbc67483a78cd5}\label{class_sentence_series_a836512c4aa3a805780bbc67483a78cd5}} +\mbox{\hyperlink{class_sentence_series}{Sentence\+Series}} $\ast$ {\bfseries copy} () +\item +\mbox{\Hypertarget{class_sentence_series_ac28370290290d06f8207884a4abe9465}\label{class_sentence_series_ac28370290290d06f8207884a4abe9465}} +void {\bfseries select\+\_\+parameters} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&output\+\_\+parameter\+\_\+names) +\item +\mbox{\Hypertarget{class_sentence_series_ac179ff27ac1d0d2373cf40ddddcf7189}\label{class_sentence_series_ac179ff27ac1d0d2373cf40ddddcf7189}} +void {\bfseries select\+\_\+parameters} (const \textbf{ vector}$<$ \textbf{ string} $>$ \¶meter\+\_\+names) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +int \mbox{\hyperlink{class_sentence_series_a8051bf7722f693797d589fbb6835afc2}{number\+\_\+rows}} +\begin{DoxyCompactList}\small\item\em Specifies the number of the rows in the dataset. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_sentence_series_a71ecf2220dbde9a683361a418fd79caa}\label{class_sentence_series_a71ecf2220dbde9a683361a418fd79caa}} +\textbf{ string} \mbox{\hyperlink{class_sentence_series_a71ecf2220dbde9a683361a418fd79caa}{filename}} +\begin{DoxyCompactList}\small\item\em Specifices the filename for the training or testing data. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_sentence_series_a12648e58e8101346fb1d0b9008f89084}\label{class_sentence_series_a12648e58e8101346fb1d0b9008f89084}} +\textbf{ map}$<$ \textbf{ string}, int $>$ \mbox{\hyperlink{class_sentence_series_a12648e58e8101346fb1d0b9008f89084}{vocab}} +\begin{DoxyCompactList}\small\item\em Maps the word string to index of the word series in a particular string. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_sentence_series_ad0c1da927f87e6778900f1e93faf8a8e}\label{class_sentence_series_ad0c1da927f87e6778900f1e93faf8a8e}} +\textbf{ vector}$<$ \textbf{ string} $>$ \mbox{\hyperlink{class_sentence_series_ad0c1da927f87e6778900f1e93faf8a8e}{word\+\_\+index}} +\begin{DoxyCompactList}\small\item\em Maps the index to word string of the word series in a particular string. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_sentence_series_a78609974545b5e015f3a5f477d876c4b}\label{class_sentence_series_a78609974545b5e015f3a5f477d876c4b}} +\textbf{ map}$<$ \textbf{ string}, \mbox{\hyperlink{class_word_series}{Word\+Series}} $\ast$ $>$ \mbox{\hyperlink{class_sentence_series_a78609974545b5e015f3a5f477d876c4b}{word\+\_\+series}} +\begin{DoxyCompactList}\small\item\em Maps the word string to the word series in a particular string. \end{DoxyCompactList}\end{DoxyCompactItemize} + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{class_sentence_series_a8051bf7722f693797d589fbb6835afc2}\label{class_sentence_series_a8051bf7722f693797d589fbb6835afc2}} +\index{SentenceSeries@{SentenceSeries}!number\_rows@{number\_rows}} +\index{number\_rows@{number\_rows}!SentenceSeries@{SentenceSeries}} +\doxysubsubsection{\texorpdfstring{number\_rows}{number\_rows}} +{\footnotesize\ttfamily int Sentence\+Series\+::number\+\_\+rows\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies the number of the rows in the dataset. + +This tells about the number of the unique words in the dataset. + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +word\+\_\+series/word\+\_\+series.\+hxx\item +word\+\_\+series/word\+\_\+series.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_speciation_strategy.eps b/docs/latex/class_speciation_strategy.eps new file mode 100644 index 00000000..e6260bd3 --- /dev/null +++ b/docs/latex/class_speciation_strategy.eps @@ -0,0 +1,203 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 125.786164 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 3.975000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 2 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(SpeciationStrategy) cw +(IslandSpeciationStrategy) cw +(NeatSpeciationStrategy) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (SpeciationStrategy) 0.500000 1.000000 box + (IslandSpeciationStrategy) 0.000000 0.000000 box + (NeatSpeciationStrategy) 1.000000 0.000000 box + +% ----- relations ----- + +solid +1 0.500000 0.250000 out +solid +0.000000 1.000000 1.000000 conn +solid +0 0.000000 0.750000 in +solid +0 1.000000 0.750000 in diff --git a/docs/latex/class_speciation_strategy.tex b/docs/latex/class_speciation_strategy.tex new file mode 100644 index 00000000..924c78c5 --- /dev/null +++ b/docs/latex/class_speciation_strategy.tex @@ -0,0 +1,211 @@ +\hypertarget{class_speciation_strategy}{}\doxysection{Speciation\+Strategy Class Reference} +\label{class_speciation_strategy}\index{SpeciationStrategy@{SpeciationStrategy}} +Inheritance diagram for Speciation\+Strategy\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_speciation_strategy} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +virtual int32\+\_\+t \mbox{\hyperlink{class_speciation_strategy_a7699a7050ed5a66693f19e834e9ba082}{get\+\_\+generated\+\_\+genomes}} () const =0 +\item +virtual int32\+\_\+t \mbox{\hyperlink{class_speciation_strategy_a1f07309130568d8cc79da44c8da6ca69}{get\+\_\+inserted\+\_\+genomes}} () const =0 +\item +virtual double \mbox{\hyperlink{class_speciation_strategy_afa3f2d35da2172a32ed06588ef09d4d0}{get\+\_\+best\+\_\+fitness}} ()=0 +\begin{DoxyCompactList}\small\item\em Gets the fitness of the best genome of all the islands. \end{DoxyCompactList}\item +virtual double \mbox{\hyperlink{class_speciation_strategy_a6bdef578fe33c628a81db35d75eaa473}{get\+\_\+worst\+\_\+fitness}} ()=0 +\begin{DoxyCompactList}\small\item\em Gets the fitness of the worst genome of all the islands. \end{DoxyCompactList}\item +virtual \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_speciation_strategy_ad0796098d09c25bfee14d9f264bcba2d}{get\+\_\+best\+\_\+genome}} ()=0 +\begin{DoxyCompactList}\small\item\em Gets the best genome of all the islands. \end{DoxyCompactList}\item +virtual \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_speciation_strategy_ae84e9c1e0eaf02886b41b1d9adf488f4}{get\+\_\+worst\+\_\+genome}} ()=0 +\begin{DoxyCompactList}\small\item\em Gets the the worst genome of all the islands. \end{DoxyCompactList}\item +virtual int32\+\_\+t \mbox{\hyperlink{class_speciation_strategy_a1fd87178f38052aa934312e49819e509}{insert\+\_\+genome}} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$genome)=0 +\begin{DoxyCompactList}\small\item\em Inserts a {\bfseries{copy}} of the genome into this speciation strategy. \end{DoxyCompactList}\item +virtual \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_speciation_strategy_a76f36f0dd7c47727aef912d48f0fa26b}{generate\+\_\+genome}} (\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator, \textbf{ function}$<$ void(int32\+\_\+t, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&mutate, \textbf{ function}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&crossover)=0 +\begin{DoxyCompactList}\small\item\em Generates a new genome. \end{DoxyCompactList}\item +virtual void \mbox{\hyperlink{class_speciation_strategy_a1d44f4c87ab00182e61f0fb364f274a9}{print}} (\textbf{ string} indent=\char`\"{}\char`\"{}) const =0 +\begin{DoxyCompactList}\small\item\em Prints out all the island\textquotesingle{}s populations. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_speciation_strategy_a163f199bc1dbf37157942cd3778cca20}\label{class_speciation_strategy_a163f199bc1dbf37157942cd3778cca20}} +virtual \textbf{ string} \mbox{\hyperlink{class_speciation_strategy_a163f199bc1dbf37157942cd3778cca20}{get\+\_\+strategy\+\_\+information\+\_\+headers}} () const =0 +\begin{DoxyCompactList}\small\item\em Gets speciation strategy information headers for logs. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_speciation_strategy_a5c6bbff0c19e3d347cef936d7b357fc0}\label{class_speciation_strategy_a5c6bbff0c19e3d347cef936d7b357fc0}} +virtual \textbf{ string} \mbox{\hyperlink{class_speciation_strategy_a5c6bbff0c19e3d347cef936d7b357fc0}{get\+\_\+strategy\+\_\+information\+\_\+values}} () const =0 +\begin{DoxyCompactList}\small\item\em Gets speciation strategy information values for logs. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_speciation_strategy_a2f6f73672fca1cc43a761c9bd15a2888}\label{class_speciation_strategy_a2f6f73672fca1cc43a761c9bd15a2888}} +virtual \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries get\+\_\+global\+\_\+best\+\_\+genome} ()=0 +\end{DoxyCompactItemize} + + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_speciation_strategy_a76f36f0dd7c47727aef912d48f0fa26b}\label{class_speciation_strategy_a76f36f0dd7c47727aef912d48f0fa26b}} +\index{SpeciationStrategy@{SpeciationStrategy}!generate\_genome@{generate\_genome}} +\index{generate\_genome@{generate\_genome}!SpeciationStrategy@{SpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{generate\_genome()}{generate\_genome()}} +{\footnotesize\ttfamily virtual \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}}$\ast$ Speciation\+Strategy\+::generate\+\_\+genome (\begin{DoxyParamCaption}\item[{\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&}]{rng\+\_\+0\+\_\+1, }\item[{\textbf{ minstd\+\_\+rand0} \&}]{generator, }\item[{\textbf{ function}$<$ void(int32\+\_\+t, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&}]{mutate, }\item[{\textbf{ function}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&}]{crossover }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [pure virtual]}} + + + +Generates a new genome. + + +\begin{DoxyParams}{Parameters} +{\em rng\+\_\+0\+\_\+1} & is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive). \\ +\hline +{\em generator} & is the random number generator \\ +\hline +{\em mutate} & is the a function which performs a mutation on a genome \\ +\hline +{\em crossover} & is the function which performs crossover between two genomes\\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +the newly generated genome. +\end{DoxyReturn} + + +Implemented in \mbox{\hyperlink{class_island_speciation_strategy_a1d209dc38c374ffcb8ac7c34f72b2ce9}{Island\+Speciation\+Strategy}}, and \mbox{\hyperlink{class_neat_speciation_strategy_ae86f8696ddab1b6cc595b6e2ef0371ae}{Neat\+Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_speciation_strategy_afa3f2d35da2172a32ed06588ef09d4d0}\label{class_speciation_strategy_afa3f2d35da2172a32ed06588ef09d4d0}} +\index{SpeciationStrategy@{SpeciationStrategy}!get\_best\_fitness@{get\_best\_fitness}} +\index{get\_best\_fitness@{get\_best\_fitness}!SpeciationStrategy@{SpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_best\_fitness()}{get\_best\_fitness()}} +{\footnotesize\ttfamily virtual double Speciation\+Strategy\+::get\+\_\+best\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [pure virtual]}} + + + +Gets the fitness of the best genome of all the islands. + +\begin{DoxyReturn}{Returns} +the best fitness over all islands +\end{DoxyReturn} + + +Implemented in \mbox{\hyperlink{class_island_speciation_strategy_a4db5c946c86373cf256177b35553b808}{Island\+Speciation\+Strategy}}, and \mbox{\hyperlink{class_neat_speciation_strategy_a4aab3d6d1abf3b1f2fd7cf7b8a4a1d99}{Neat\+Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_speciation_strategy_ad0796098d09c25bfee14d9f264bcba2d}\label{class_speciation_strategy_ad0796098d09c25bfee14d9f264bcba2d}} +\index{SpeciationStrategy@{SpeciationStrategy}!get\_best\_genome@{get\_best\_genome}} +\index{get\_best\_genome@{get\_best\_genome}!SpeciationStrategy@{SpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_best\_genome()}{get\_best\_genome()}} +{\footnotesize\ttfamily virtual \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}}$\ast$ Speciation\+Strategy\+::get\+\_\+best\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [pure virtual]}} + + + +Gets the best genome of all the islands. + +\begin{DoxyReturn}{Returns} +the best genome of all islands +\end{DoxyReturn} + + +Implemented in \mbox{\hyperlink{class_island_speciation_strategy_a1a8a770c165b22a5070e11a3f579375e}{Island\+Speciation\+Strategy}}, and \mbox{\hyperlink{class_neat_speciation_strategy_aecf62aeba94c1f8d786215c10ea4774b}{Neat\+Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_speciation_strategy_a7699a7050ed5a66693f19e834e9ba082}\label{class_speciation_strategy_a7699a7050ed5a66693f19e834e9ba082}} +\index{SpeciationStrategy@{SpeciationStrategy}!get\_generated\_genomes@{get\_generated\_genomes}} +\index{get\_generated\_genomes@{get\_generated\_genomes}!SpeciationStrategy@{SpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_generated\_genomes()}{get\_generated\_genomes()}} +{\footnotesize\ttfamily virtual int32\+\_\+t Speciation\+Strategy\+::get\+\_\+generated\+\_\+genomes (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [pure virtual]}} + +\begin{DoxyReturn}{Returns} +the number of generated genomes. +\end{DoxyReturn} + + +Implemented in \mbox{\hyperlink{class_island_speciation_strategy_a2b75aca60616268e4a6aee93ee4e5aee}{Island\+Speciation\+Strategy}}, and \mbox{\hyperlink{class_neat_speciation_strategy_af7e48fd1afb5a9c1b422ad251366263e}{Neat\+Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_speciation_strategy_a1f07309130568d8cc79da44c8da6ca69}\label{class_speciation_strategy_a1f07309130568d8cc79da44c8da6ca69}} +\index{SpeciationStrategy@{SpeciationStrategy}!get\_inserted\_genomes@{get\_inserted\_genomes}} +\index{get\_inserted\_genomes@{get\_inserted\_genomes}!SpeciationStrategy@{SpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_inserted\_genomes()}{get\_inserted\_genomes()}} +{\footnotesize\ttfamily virtual int32\+\_\+t Speciation\+Strategy\+::get\+\_\+inserted\+\_\+genomes (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [pure virtual]}} + +\begin{DoxyReturn}{Returns} +the number of inserted genomes. +\end{DoxyReturn} + + +Implemented in \mbox{\hyperlink{class_island_speciation_strategy_ab44e2abd372cd97932dd87ed1b9c62c2}{Island\+Speciation\+Strategy}}, and \mbox{\hyperlink{class_neat_speciation_strategy_aba55a9f0006af882fd613bd0676555eb}{Neat\+Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_speciation_strategy_a6bdef578fe33c628a81db35d75eaa473}\label{class_speciation_strategy_a6bdef578fe33c628a81db35d75eaa473}} +\index{SpeciationStrategy@{SpeciationStrategy}!get\_worst\_fitness@{get\_worst\_fitness}} +\index{get\_worst\_fitness@{get\_worst\_fitness}!SpeciationStrategy@{SpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_worst\_fitness()}{get\_worst\_fitness()}} +{\footnotesize\ttfamily virtual double Speciation\+Strategy\+::get\+\_\+worst\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [pure virtual]}} + + + +Gets the fitness of the worst genome of all the islands. + +\begin{DoxyReturn}{Returns} +the worst fitness over all islands +\end{DoxyReturn} + + +Implemented in \mbox{\hyperlink{class_island_speciation_strategy_a516627db390391867d6dd02f78e27caf}{Island\+Speciation\+Strategy}}, and \mbox{\hyperlink{class_neat_speciation_strategy_acbd3eaa05db725de60074276cd0bb449}{Neat\+Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_speciation_strategy_ae84e9c1e0eaf02886b41b1d9adf488f4}\label{class_speciation_strategy_ae84e9c1e0eaf02886b41b1d9adf488f4}} +\index{SpeciationStrategy@{SpeciationStrategy}!get\_worst\_genome@{get\_worst\_genome}} +\index{get\_worst\_genome@{get\_worst\_genome}!SpeciationStrategy@{SpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{get\_worst\_genome()}{get\_worst\_genome()}} +{\footnotesize\ttfamily virtual \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}}$\ast$ Speciation\+Strategy\+::get\+\_\+worst\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [pure virtual]}} + + + +Gets the the worst genome of all the islands. + +\begin{DoxyReturn}{Returns} +the worst genome of all islands +\end{DoxyReturn} + + +Implemented in \mbox{\hyperlink{class_island_speciation_strategy_a563860351e5f176d539bc297949d490f}{Island\+Speciation\+Strategy}}, and \mbox{\hyperlink{class_neat_speciation_strategy_a8bc6ad6a0740e2f6072aef3ad14dd3f7}{Neat\+Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_speciation_strategy_a1fd87178f38052aa934312e49819e509}\label{class_speciation_strategy_a1fd87178f38052aa934312e49819e509}} +\index{SpeciationStrategy@{SpeciationStrategy}!insert\_genome@{insert\_genome}} +\index{insert\_genome@{insert\_genome}!SpeciationStrategy@{SpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{insert\_genome()}{insert\_genome()}} +{\footnotesize\ttfamily virtual int32\+\_\+t Speciation\+Strategy\+::insert\+\_\+genome (\begin{DoxyParamCaption}\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$}]{genome }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [pure virtual]}} + + + +Inserts a {\bfseries{copy}} of the genome into this speciation strategy. + +The caller of this method will need to free the memory of the genome passed into this method. + + +\begin{DoxyParams}{Parameters} +{\em genome} & is the genome to insert. \\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +a value $<$ 0 if the genome was not inserted, 0 if it was a new best genome or $>$ 0 otherwise. +\end{DoxyReturn} + + +Implemented in \mbox{\hyperlink{class_island_speciation_strategy_a6ae98982d01c68214e4bd703fe18146f}{Island\+Speciation\+Strategy}}, and \mbox{\hyperlink{class_neat_speciation_strategy_aa2b2521a400435cbd1856aadda2051e0}{Neat\+Speciation\+Strategy}}. + +\mbox{\Hypertarget{class_speciation_strategy_a1d44f4c87ab00182e61f0fb364f274a9}\label{class_speciation_strategy_a1d44f4c87ab00182e61f0fb364f274a9}} +\index{SpeciationStrategy@{SpeciationStrategy}!print@{print}} +\index{print@{print}!SpeciationStrategy@{SpeciationStrategy}} +\doxysubsubsection{\texorpdfstring{print()}{print()}} +{\footnotesize\ttfamily virtual void Speciation\+Strategy\+::print (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{indent = {\ttfamily \char`\"{}\char`\"{}} }\end{DoxyParamCaption}) const\hspace{0.3cm}{\ttfamily [pure virtual]}} + + + +Prints out all the island\textquotesingle{}s populations. + + +\begin{DoxyParams}{Parameters} +{\em indent} & is how much to indent what is printed out \\ +\hline +\end{DoxyParams} + + +Implemented in \mbox{\hyperlink{class_island_speciation_strategy_a4ba6785df117932f3c465cbc31a5ca3f}{Island\+Speciation\+Strategy}}, and \mbox{\hyperlink{class_neat_speciation_strategy_abef17e0293c2e0a64b3b4b3b9b7775d2}{Neat\+Speciation\+Strategy}}. + + + +The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/speciation\+\_\+strategy.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_species.tex b/docs/latex/class_species.tex new file mode 100644 index 00000000..9ecce1ae --- /dev/null +++ b/docs/latex/class_species.tex @@ -0,0 +1,238 @@ +\hypertarget{class_species}{}\doxysection{Species Class Reference} +\label{class_species}\index{Species@{Species}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_species_a58b75aebb5ad400b89cb41e5d7700092}\label{class_species_a58b75aebb5ad400b89cb41e5d7700092}} +\mbox{\hyperlink{class_species_a58b75aebb5ad400b89cb41e5d7700092}{Species}} (int32\+\_\+t \mbox{\hyperlink{class_species_a2e22624d59864dbecd5ee16103b458bd}{id}}) +\begin{DoxyCompactList}\small\item\em Initializes a species. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_species_a2e3034b48d9ed1f263128ffc8ca3a295}{get\+\_\+best\+\_\+fitness}} () +\begin{DoxyCompactList}\small\item\em Returns the fitness of the best genome in the island. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_species_a10669846e7646b47696d90838e379a94}{get\+\_\+worst\+\_\+fitness}} () +\begin{DoxyCompactList}\small\item\em Returns the fitness of the worst genome in the island. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_species_a01dcea3ec17810b46167e751b2e6f4cf}{get\+\_\+best\+\_\+genome}} () +\begin{DoxyCompactList}\small\item\em Returns the best genomme in the island. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_species_a0a224a20baa5eb5055dada490c738d30}{get\+\_\+worst\+\_\+genome}} () +\begin{DoxyCompactList}\small\item\em Returns the worst genomme in the island. \end{DoxyCompactList}\item +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ \mbox{\hyperlink{class_species_afceb50adb6350b2576e897c30380fee5}{get\+\_\+random\+\_\+genome}} (\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator) +\begin{DoxyCompactList}\small\item\em Returns the best genomme in the island. \end{DoxyCompactList}\item +int32\+\_\+t \mbox{\hyperlink{class_species_af9978e8876f3d43c5238225ad7b8de34}{size}} () +\begin{DoxyCompactList}\small\item\em Returns the size of the island. \end{DoxyCompactList}\item +int32\+\_\+t \mbox{\hyperlink{class_species_aac66b182441d124023b4df1167c7bc35}{contains}} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$genome) +\begin{DoxyCompactList}\small\item\em Checks to see if a genome already exists in the island, using the R\+N\+N\+\_\+\+Genome\+::equals method (which checks to see if all edges and nodes are the same, but not necessarily weights). \end{DoxyCompactList}\item +void \mbox{\hyperlink{class_species_add70418d8b64a815998e66c89824d646}{copy\+\_\+random\+\_\+genome}} (\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$genome) +\begin{DoxyCompactList}\small\item\em Selects a genome from the island at random and returns a copy of it. \end{DoxyCompactList}\item +void \mbox{\hyperlink{class_species_a1f3a1b23040cc266a8c9e49436f0c8a7}{copy\+\_\+two\+\_\+random\+\_\+genomes}} (\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng\+\_\+0\+\_\+1, \textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$genome1, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$genome2) +\begin{DoxyCompactList}\small\item\em Selects two different genomes from the island at random and returns copies of them. \end{DoxyCompactList}\item +int32\+\_\+t \mbox{\hyperlink{class_species_a459a9676c5c86837233c6cc4c3843475}{insert\+\_\+genome}} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$genome) +\begin{DoxyCompactList}\small\item\em Inserts a genome into the island. \end{DoxyCompactList}\item +void \mbox{\hyperlink{class_species_a2e9e0780d547c253c8532b5866b7bd4d}{print}} (\textbf{ string} indent=\char`\"{}\char`\"{}) +\begin{DoxyCompactList}\small\item\em Prints out the state of this island. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_species_aa17a7a4dbde760d366939d913b2274e0}\label{class_species_aa17a7a4dbde760d366939d913b2274e0}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ $>$ {\bfseries get\+\_\+genomes} () +\item +\mbox{\Hypertarget{class_species_aae7bdabaf8e88c01b611543baa5f3e29}\label{class_species_aae7bdabaf8e88c01b611543baa5f3e29}} +\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ {\bfseries get\+\_\+latested\+\_\+genome} () +\item +\mbox{\Hypertarget{class_species_ac49adf285a7511ee9d0eef9abb84cb66}\label{class_species_ac49adf285a7511ee9d0eef9abb84cb66}} +void {\bfseries fitness\+\_\+sharing\+\_\+remove} (double fitness\+\_\+threshold, \textbf{ function}$<$ double(\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$)$>$ \&get\+\_\+distance) +\item +\mbox{\Hypertarget{class_species_a0121e0eb365c4f4b599fe8f352c4e90d}\label{class_species_a0121e0eb365c4f4b599fe8f352c4e90d}} +void {\bfseries erase\+\_\+species} () +\item +\mbox{\Hypertarget{class_species_a83f64a0c9132b5c21e625500211ba130}\label{class_species_a83f64a0c9132b5c21e625500211ba130}} +int32\+\_\+t {\bfseries get\+\_\+species\+\_\+not\+\_\+improving\+\_\+count} () +\item +\mbox{\Hypertarget{class_species_aa2ee080743b2d5ee89f156482736d704}\label{class_species_aa2ee080743b2d5ee89f156482736d704}} +void {\bfseries set\+\_\+species\+\_\+not\+\_\+improving\+\_\+count} (int32\+\_\+t count) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_species_a2e22624d59864dbecd5ee16103b458bd}\label{class_species_a2e22624d59864dbecd5ee16103b458bd}} +int32\+\_\+t \mbox{\hyperlink{class_species_a2e22624d59864dbecd5ee16103b458bd}{id}} +\begin{DoxyCompactList}\small\item\em An integer ID for this species. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_species_a995da794f2f4a8b8b314d3d402875d5b}\label{class_species_a995da794f2f4a8b8b314d3d402875d5b}} +\textbf{ vector}$<$ int32\+\_\+t $>$ {\bfseries inserted\+\_\+genome\+\_\+id} +\item +\mbox{\Hypertarget{class_species_a6b156c1f48620bc39bc9efe6c9324e81}\label{class_species_a6b156c1f48620bc39bc9efe6c9324e81}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ $>$ \mbox{\hyperlink{class_species_a6b156c1f48620bc39bc9efe6c9324e81}{genomes}} +\begin{DoxyCompactList}\small\item\em The genomes on this species, stored in sorted order best (front) to worst (back). \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_species_a5b23e228631a99dadd9602b46324a20e}\label{class_species_a5b23e228631a99dadd9602b46324a20e}} +int32\+\_\+t {\bfseries species\+\_\+not\+\_\+improving\+\_\+count} +\end{DoxyCompactItemize} + + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_species_aac66b182441d124023b4df1167c7bc35}\label{class_species_aac66b182441d124023b4df1167c7bc35}} +\index{Species@{Species}!contains@{contains}} +\index{contains@{contains}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{contains()}{contains()}} +{\footnotesize\ttfamily int32\+\_\+t Species\+::contains (\begin{DoxyParamCaption}\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$}]{genome }\end{DoxyParamCaption})} + + + +Checks to see if a genome already exists in the island, using the R\+N\+N\+\_\+\+Genome\+::equals method (which checks to see if all edges and nodes are the same, but not necessarily weights). + +\begin{DoxyReturn}{Returns} +the index in the island of the duplicate genome, -\/1 otherwise. +\end{DoxyReturn} +\mbox{\Hypertarget{class_species_add70418d8b64a815998e66c89824d646}\label{class_species_add70418d8b64a815998e66c89824d646}} +\index{Species@{Species}!copy\_random\_genome@{copy\_random\_genome}} +\index{copy\_random\_genome@{copy\_random\_genome}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{copy\_random\_genome()}{copy\_random\_genome()}} +{\footnotesize\ttfamily void Species\+::copy\+\_\+random\+\_\+genome (\begin{DoxyParamCaption}\item[{\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&}]{rng\+\_\+0\+\_\+1, }\item[{\textbf{ minstd\+\_\+rand0} \&}]{generator, }\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$}]{genome }\end{DoxyParamCaption})} + + + +Selects a genome from the island at random and returns a copy of it. + + +\begin{DoxyParams}{Parameters} +{\em rng\+\_\+0\+\_\+1} & is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive). \\ +\hline +{\em generator} & is the random number generator \\ +\hline +{\em genome} & will be the copied genome, an addresss to a pointer needs to be passed. \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_species_a1f3a1b23040cc266a8c9e49436f0c8a7}\label{class_species_a1f3a1b23040cc266a8c9e49436f0c8a7}} +\index{Species@{Species}!copy\_two\_random\_genomes@{copy\_two\_random\_genomes}} +\index{copy\_two\_random\_genomes@{copy\_two\_random\_genomes}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{copy\_two\_random\_genomes()}{copy\_two\_random\_genomes()}} +{\footnotesize\ttfamily void Species\+::copy\+\_\+two\+\_\+random\+\_\+genomes (\begin{DoxyParamCaption}\item[{\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&}]{rng\+\_\+0\+\_\+1, }\item[{\textbf{ minstd\+\_\+rand0} \&}]{generator, }\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$}]{genome1, }\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$$\ast$}]{genome2 }\end{DoxyParamCaption})} + + + +Selects two different genomes from the island at random and returns copies of them. + + +\begin{DoxyParams}{Parameters} +{\em rng\+\_\+0\+\_\+1} & is the random number distribution that generates random numbers between 0 (inclusive) and 1 (non=inclusive). \\ +\hline +{\em generator} & is the random number generator \\ +\hline +{\em genome1} & will be the first copied genome, an addresss to a pointer needs to be passed. \\ +\hline +{\em genome2} & will be the second copied genome, an addresss to a pointer needs to be passed. \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_species_a2e3034b48d9ed1f263128ffc8ca3a295}\label{class_species_a2e3034b48d9ed1f263128ffc8ca3a295}} +\index{Species@{Species}!get\_best\_fitness@{get\_best\_fitness}} +\index{get\_best\_fitness@{get\_best\_fitness}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{get\_best\_fitness()}{get\_best\_fitness()}} +{\footnotesize\ttfamily double Species\+::get\+\_\+best\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the fitness of the best genome in the island. + +\begin{DoxyReturn}{Returns} +the best fitness of the island +\end{DoxyReturn} +\mbox{\Hypertarget{class_species_a01dcea3ec17810b46167e751b2e6f4cf}\label{class_species_a01dcea3ec17810b46167e751b2e6f4cf}} +\index{Species@{Species}!get\_best\_genome@{get\_best\_genome}} +\index{get\_best\_genome@{get\_best\_genome}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{get\_best\_genome()}{get\_best\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Species\+::get\+\_\+best\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the best genomme in the island. + +\begin{DoxyReturn}{Returns} +the best genome in the island +\end{DoxyReturn} +\mbox{\Hypertarget{class_species_afceb50adb6350b2576e897c30380fee5}\label{class_species_afceb50adb6350b2576e897c30380fee5}} +\index{Species@{Species}!get\_random\_genome@{get\_random\_genome}} +\index{get\_random\_genome@{get\_random\_genome}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{get\_random\_genome()}{get\_random\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Species\+::get\+\_\+random\+\_\+genome (\begin{DoxyParamCaption}\item[{\textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&}]{rng\+\_\+0\+\_\+1, }\item[{\textbf{ minstd\+\_\+rand0} \&}]{generator }\end{DoxyParamCaption})} + + + +Returns the best genomme in the island. + +\begin{DoxyReturn}{Returns} +the best genome in the island +\end{DoxyReturn} +\mbox{\Hypertarget{class_species_a10669846e7646b47696d90838e379a94}\label{class_species_a10669846e7646b47696d90838e379a94}} +\index{Species@{Species}!get\_worst\_fitness@{get\_worst\_fitness}} +\index{get\_worst\_fitness@{get\_worst\_fitness}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{get\_worst\_fitness()}{get\_worst\_fitness()}} +{\footnotesize\ttfamily double Species\+::get\+\_\+worst\+\_\+fitness (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the fitness of the worst genome in the island. + +\begin{DoxyReturn}{Returns} +the worst fitness of the island +\end{DoxyReturn} +\mbox{\Hypertarget{class_species_a0a224a20baa5eb5055dada490c738d30}\label{class_species_a0a224a20baa5eb5055dada490c738d30}} +\index{Species@{Species}!get\_worst\_genome@{get\_worst\_genome}} +\index{get\_worst\_genome@{get\_worst\_genome}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{get\_worst\_genome()}{get\_worst\_genome()}} +{\footnotesize\ttfamily \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$ Species\+::get\+\_\+worst\+\_\+genome (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the worst genomme in the island. + +\begin{DoxyReturn}{Returns} +the worst genome in the island +\end{DoxyReturn} +\mbox{\Hypertarget{class_species_a459a9676c5c86837233c6cc4c3843475}\label{class_species_a459a9676c5c86837233c6cc4c3843475}} +\index{Species@{Species}!insert\_genome@{insert\_genome}} +\index{insert\_genome@{insert\_genome}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{insert\_genome()}{insert\_genome()}} +{\footnotesize\ttfamily int32\+\_\+t Species\+::insert\+\_\+genome (\begin{DoxyParamCaption}\item[{\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$}]{genome }\end{DoxyParamCaption})} + + + +Inserts a genome into the island. + +Genomes are inserted in best to worst order genomes\mbox{[}0\mbox{]} will have the best fitness and genomes\mbox{[}size -\/ 1\mbox{]} will have the worst. + + +\begin{DoxyParams}{Parameters} +{\em genome} & is the genome to be inserted. \\ +\hline +\end{DoxyParams} +\begin{DoxyReturn}{Returns} +-\/1 if not inserted, otherwise the index it was inserted at +\end{DoxyReturn} +\mbox{\Hypertarget{class_species_a2e9e0780d547c253c8532b5866b7bd4d}\label{class_species_a2e9e0780d547c253c8532b5866b7bd4d}} +\index{Species@{Species}!print@{print}} +\index{print@{print}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{print()}{print()}} +{\footnotesize\ttfamily void Species\+::print (\begin{DoxyParamCaption}\item[{\textbf{ string}}]{indent = {\ttfamily \char`\"{}\char`\"{}} }\end{DoxyParamCaption})} + + + +Prints out the state of this island. + + +\begin{DoxyParams}{Parameters} +{\em indent} & is how much to indent what is printed out \\ +\hline +\end{DoxyParams} +\mbox{\Hypertarget{class_species_af9978e8876f3d43c5238225ad7b8de34}\label{class_species_af9978e8876f3d43c5238225ad7b8de34}} +\index{Species@{Species}!size@{size}} +\index{size@{size}!Species@{Species}} +\doxysubsubsection{\texorpdfstring{size()}{size()}} +{\footnotesize\ttfamily int32\+\_\+t Species\+::size (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})} + + + +Returns the size of the island. + +\begin{DoxyReturn}{Returns} +the number of genomes in this island. +\end{DoxyReturn} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/species.\+hxx\item +rnn/species.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_time_series.tex b/docs/latex/class_time_series.tex new file mode 100644 index 00000000..2d7e6e3a --- /dev/null +++ b/docs/latex/class_time_series.tex @@ -0,0 +1,98 @@ +\hypertarget{class_time_series}{}\doxysection{Time\+Series Class Reference} +\label{class_time_series}\index{TimeSeries@{TimeSeries}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_time_series_a94dec87ce25a87b4efbbfc49234d34e1}\label{class_time_series_a94dec87ce25a87b4efbbfc49234d34e1}} +{\bfseries Time\+Series} (\textbf{ string} \+\_\+name) +\item +\mbox{\Hypertarget{class_time_series_af5210b5c89d3753c6ee188195c0bcd44}\label{class_time_series_af5210b5c89d3753c6ee188195c0bcd44}} +void {\bfseries add\+\_\+value} (double value) +\item +\mbox{\Hypertarget{class_time_series_a60c68c99c8dc1e093c48d8ff87878a89}\label{class_time_series_a60c68c99c8dc1e093c48d8ff87878a89}} +double {\bfseries get\+\_\+value} (int i) +\item +\mbox{\Hypertarget{class_time_series_aff81a5561bc2d42a2a4b47d81548a5e6}\label{class_time_series_aff81a5561bc2d42a2a4b47d81548a5e6}} +void {\bfseries calculate\+\_\+statistics} () +\item +\mbox{\Hypertarget{class_time_series_a67adbe58dcc6dbf93fd6b8663ea008af}\label{class_time_series_a67adbe58dcc6dbf93fd6b8663ea008af}} +void {\bfseries print\+\_\+statistics} () +\item +\mbox{\Hypertarget{class_time_series_aebd5cdea1096573ecb1c06cc7eda66a5}\label{class_time_series_aebd5cdea1096573ecb1c06cc7eda66a5}} +int {\bfseries get\+\_\+number\+\_\+values} () const +\item +\mbox{\Hypertarget{class_time_series_a913b6c3364c5505da43489514b4f2819}\label{class_time_series_a913b6c3364c5505da43489514b4f2819}} +double {\bfseries get\+\_\+min} () const +\item +\mbox{\Hypertarget{class_time_series_af1489ec51ebbf6871add6a5c6220d9d5}\label{class_time_series_af1489ec51ebbf6871add6a5c6220d9d5}} +double {\bfseries get\+\_\+average} () const +\item +\mbox{\Hypertarget{class_time_series_a214c4e79f8850a38f5a70826fdf0201e}\label{class_time_series_a214c4e79f8850a38f5a70826fdf0201e}} +double {\bfseries get\+\_\+max} () const +\item +\mbox{\Hypertarget{class_time_series_a674dc685e1792210bf9a47adadf6425d}\label{class_time_series_a674dc685e1792210bf9a47adadf6425d}} +double {\bfseries get\+\_\+std\+\_\+dev} () const +\item +\mbox{\Hypertarget{class_time_series_a1aaa0b6a8ddf7ce5120580005fcf39a7}\label{class_time_series_a1aaa0b6a8ddf7ce5120580005fcf39a7}} +double {\bfseries get\+\_\+variance} () const +\item +\mbox{\Hypertarget{class_time_series_ada09d4dbd4bbe50b8fca5295e66797a4}\label{class_time_series_ada09d4dbd4bbe50b8fca5295e66797a4}} +double {\bfseries get\+\_\+min\+\_\+change} () const +\item +\mbox{\Hypertarget{class_time_series_aead2c24ed81ffe155f8216c3f7b9095e}\label{class_time_series_aead2c24ed81ffe155f8216c3f7b9095e}} +double {\bfseries get\+\_\+max\+\_\+change} () const +\item +\mbox{\Hypertarget{class_time_series_a9f747fa2694e96804f20a0626f7cb46c}\label{class_time_series_a9f747fa2694e96804f20a0626f7cb46c}} +void {\bfseries normalize\+\_\+min\+\_\+max} (double min, double max) +\item +\mbox{\Hypertarget{class_time_series_a5a54bcfbadba29460621c974e2a94f44}\label{class_time_series_a5a54bcfbadba29460621c974e2a94f44}} +void {\bfseries normalize\+\_\+avg\+\_\+std\+\_\+dev} (double avg, double std\+\_\+dev, double norm\+\_\+max) +\item +\mbox{\Hypertarget{class_time_series_a1c87cf135c425de70550710f9c5f1d0f}\label{class_time_series_a1c87cf135c425de70550710f9c5f1d0f}} +void {\bfseries cut} (\textbf{ int32\+\_\+t} start, \textbf{ int32\+\_\+t} stop) +\item +\mbox{\Hypertarget{class_time_series_ac3868470a5ed960f9a737e42b49baa4e}\label{class_time_series_ac3868470a5ed960f9a737e42b49baa4e}} +double {\bfseries get\+\_\+correlation} (const \mbox{\hyperlink{class_time_series}{Time\+Series}} $\ast$other, \textbf{ int32\+\_\+t} lag) const +\item +\mbox{\Hypertarget{class_time_series_aff3fd5901ab67b755fec5a7d767c35a2}\label{class_time_series_aff3fd5901ab67b755fec5a7d767c35a2}} +\mbox{\hyperlink{class_time_series}{Time\+Series}} $\ast$ {\bfseries copy} () +\item +\mbox{\Hypertarget{class_time_series_a309faa5de03f6011ed67c83a6976d16a}\label{class_time_series_a309faa5de03f6011ed67c83a6976d16a}} +void {\bfseries copy\+\_\+values} (\textbf{ vector}$<$ double $>$ \&series) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_time_series_aa9fdde695032e2f5e0c5e3f48924bd49}\label{class_time_series_aa9fdde695032e2f5e0c5e3f48924bd49}} +\textbf{ string} {\bfseries name} +\item +\mbox{\Hypertarget{class_time_series_a5dabaf7e9ae4c56b97a377ce8bd24558}\label{class_time_series_a5dabaf7e9ae4c56b97a377ce8bd24558}} +double {\bfseries min} +\item +\mbox{\Hypertarget{class_time_series_afc947d70351cc4ef3b10ed6e6d77c7ce}\label{class_time_series_afc947d70351cc4ef3b10ed6e6d77c7ce}} +double {\bfseries average} +\item +\mbox{\Hypertarget{class_time_series_ae34f322d950a627c902923ff82bcae19}\label{class_time_series_ae34f322d950a627c902923ff82bcae19}} +double {\bfseries max} +\item +\mbox{\Hypertarget{class_time_series_a15bfcb1deb136cd05624a6da9c4be8cc}\label{class_time_series_a15bfcb1deb136cd05624a6da9c4be8cc}} +double {\bfseries std\+\_\+dev} +\item +\mbox{\Hypertarget{class_time_series_ab6f7818746f7178a4be203fd8add0daa}\label{class_time_series_ab6f7818746f7178a4be203fd8add0daa}} +double {\bfseries variance} +\item +\mbox{\Hypertarget{class_time_series_a3aadd7d227502c90f655004014c35879}\label{class_time_series_a3aadd7d227502c90f655004014c35879}} +double {\bfseries min\+\_\+change} +\item +\mbox{\Hypertarget{class_time_series_a44bebfe451c623358a16af4d4e2d4a38}\label{class_time_series_a44bebfe451c623358a16af4d4e2d4a38}} +double {\bfseries max\+\_\+change} +\item +\mbox{\Hypertarget{class_time_series_a18f879a5a1d1b4efd3ed3063b1d6bf17}\label{class_time_series_a18f879a5a1d1b4efd3ed3063b1d6bf17}} +\textbf{ vector}$<$ double $>$ {\bfseries values} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +time\+\_\+series/time\+\_\+series.\+hxx\item +time\+\_\+series/time\+\_\+series.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_time_series_set.tex b/docs/latex/class_time_series_set.tex new file mode 100644 index 00000000..d2f8a5e7 --- /dev/null +++ b/docs/latex/class_time_series_set.tex @@ -0,0 +1,113 @@ +\hypertarget{class_time_series_set}{}\doxysection{Time\+Series\+Set Class Reference} +\label{class_time_series_set}\index{TimeSeriesSet@{TimeSeriesSet}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_time_series_set_a755abcf37b7074e042e88f1110911bde}\label{class_time_series_set_a755abcf37b7074e042e88f1110911bde}} +{\bfseries Time\+Series\+Set} (\textbf{ string} \+\_\+filename, const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+fields) +\item +\mbox{\Hypertarget{class_time_series_set_aacbb4f17ae19d87e33d14da1eadd9e1c}\label{class_time_series_set_aacbb4f17ae19d87e33d14da1eadd9e1c}} +void {\bfseries add\+\_\+time\+\_\+series} (\textbf{ string} name) +\item +\mbox{\Hypertarget{class_time_series_set_a8cb306ad530c7fd6d5ac2a46e0a2e380}\label{class_time_series_set_a8cb306ad530c7fd6d5ac2a46e0a2e380}} +int {\bfseries get\+\_\+number\+\_\+rows} () const +\item +\mbox{\Hypertarget{class_time_series_set_ac1c657e69b5b9cfe2d556e57eaf0a97c}\label{class_time_series_set_ac1c657e69b5b9cfe2d556e57eaf0a97c}} +int {\bfseries get\+\_\+number\+\_\+columns} () const +\item +\mbox{\Hypertarget{class_time_series_set_a09e2668a0f2e47d0542ad0d0600050fc}\label{class_time_series_set_a09e2668a0f2e47d0542ad0d0600050fc}} +\textbf{ string} {\bfseries get\+\_\+filename} () const +\item +\mbox{\Hypertarget{class_time_series_set_a3e3ef7b133e911ae4fc9bf9f273f7b37}\label{class_time_series_set_a3e3ef7b133e911ae4fc9bf9f273f7b37}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries get\+\_\+fields} () const +\item +\mbox{\Hypertarget{class_time_series_set_a389df753e39d4061306f6e194139537f}\label{class_time_series_set_a389df753e39d4061306f6e194139537f}} +void {\bfseries get\+\_\+series} (\textbf{ string} field\+\_\+name, \textbf{ vector}$<$ double $>$ \&series) +\item +\mbox{\Hypertarget{class_time_series_set_a01c4671a033ea5796718106a76f9f532}\label{class_time_series_set_a01c4671a033ea5796718106a76f9f532}} +double {\bfseries get\+\_\+min} (\textbf{ string} field) +\item +\mbox{\Hypertarget{class_time_series_set_a457d0766952cd6403105fbfb468dd8b4}\label{class_time_series_set_a457d0766952cd6403105fbfb468dd8b4}} +double {\bfseries get\+\_\+average} (\textbf{ string} field) +\item +\mbox{\Hypertarget{class_time_series_set_a25ffdd437ba30df4953206f0554cb5a9}\label{class_time_series_set_a25ffdd437ba30df4953206f0554cb5a9}} +double {\bfseries get\+\_\+max} (\textbf{ string} field) +\item +\mbox{\Hypertarget{class_time_series_set_a8d55aa333f30f362a2f4074f48d2690a}\label{class_time_series_set_a8d55aa333f30f362a2f4074f48d2690a}} +double {\bfseries get\+\_\+std\+\_\+dev} (\textbf{ string} field) +\item +\mbox{\Hypertarget{class_time_series_set_a6185f4f4193eb125c9814608aab8f743}\label{class_time_series_set_a6185f4f4193eb125c9814608aab8f743}} +double {\bfseries get\+\_\+variance} (\textbf{ string} field) +\item +\mbox{\Hypertarget{class_time_series_set_a0b39178fddc2bf0e2ba66b2e2025161e}\label{class_time_series_set_a0b39178fddc2bf0e2ba66b2e2025161e}} +double {\bfseries get\+\_\+min\+\_\+change} (\textbf{ string} field) +\item +\mbox{\Hypertarget{class_time_series_set_a4db801d1a487c0f6f025b1523a42b9a4}\label{class_time_series_set_a4db801d1a487c0f6f025b1523a42b9a4}} +double {\bfseries get\+\_\+max\+\_\+change} (\textbf{ string} field) +\item +\mbox{\Hypertarget{class_time_series_set_a47eecbd2fcfc2733ea6e8c6b236fbb03}\label{class_time_series_set_a47eecbd2fcfc2733ea6e8c6b236fbb03}} +double {\bfseries get\+\_\+correlation} (\textbf{ string} field1, \textbf{ string} field2, \textbf{ int32\+\_\+t} lag) const +\item +\mbox{\Hypertarget{class_time_series_set_a6d800188060b9d30a1c6dd2100a05423}\label{class_time_series_set_a6d800188060b9d30a1c6dd2100a05423}} +void {\bfseries normalize\+\_\+min\+\_\+max} (\textbf{ string} field, double min, double max) +\item +\mbox{\Hypertarget{class_time_series_set_a183ed4556eea1c313c4ab50d9dde251c}\label{class_time_series_set_a183ed4556eea1c313c4ab50d9dde251c}} +void {\bfseries normalize\+\_\+avg\+\_\+std\+\_\+dev} (\textbf{ string} field, double avg, double std\+\_\+dev, double norm\+\_\+max) +\item +\mbox{\Hypertarget{class_time_series_set_aa55e6945dfc550f36fab50178dc06ffe}\label{class_time_series_set_aa55e6945dfc550f36fab50178dc06ffe}} +void {\bfseries export\+\_\+time\+\_\+series} (\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&data) +\item +\mbox{\Hypertarget{class_time_series_set_af91af3295680f6596fc68eb10c2a7845}\label{class_time_series_set_af91af3295680f6596fc68eb10c2a7845}} +void {\bfseries export\+\_\+time\+\_\+series} (\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&data, const \textbf{ vector}$<$ \textbf{ string} $>$ \&requested\+\_\+fields) +\item +void \mbox{\hyperlink{class_time_series_set_ad85e413579132f448ec10545f733d452}{export\+\_\+time\+\_\+series}} (\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&data, const \textbf{ vector}$<$ \textbf{ string} $>$ \&requested\+\_\+fields, const \textbf{ vector}$<$ \textbf{ string} $>$ \&shift\+\_\+fields, \textbf{ int32\+\_\+t} time\+\_\+offset) +\begin{DoxyCompactList}\small\item\em Time offset $<$ 0 generates input data. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_time_series_set_aec44a37363eaecc446c81b58cb44781b}\label{class_time_series_set_aec44a37363eaecc446c81b58cb44781b}} +\mbox{\hyperlink{class_time_series_set}{Time\+Series\+Set}} $\ast$ {\bfseries copy} () +\item +\mbox{\Hypertarget{class_time_series_set_a04988df43c793bdf97f9d2d94ca40b74}\label{class_time_series_set_a04988df43c793bdf97f9d2d94ca40b74}} +void {\bfseries cut} (\textbf{ int32\+\_\+t} start, \textbf{ int32\+\_\+t} stop) +\item +\mbox{\Hypertarget{class_time_series_set_a9e5eff858b9fb54cff400103e47932af}\label{class_time_series_set_a9e5eff858b9fb54cff400103e47932af}} +void {\bfseries split} (int slices, \textbf{ vector}$<$ \mbox{\hyperlink{class_time_series_set}{Time\+Series\+Set}} $\ast$ $>$ \&sub\+\_\+series) +\item +\mbox{\Hypertarget{class_time_series_set_a4c775dbf6a7d679b812f74582db413c4}\label{class_time_series_set_a4c775dbf6a7d679b812f74582db413c4}} +void {\bfseries select\+\_\+parameters} (const \textbf{ vector}$<$ \textbf{ string} $>$ \¶meter\+\_\+names) +\item +\mbox{\Hypertarget{class_time_series_set_aa52318fd9b6ae2c91e4bbd0c73901b92}\label{class_time_series_set_aa52318fd9b6ae2c91e4bbd0c73901b92}} +void {\bfseries select\+\_\+parameters} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&output\+\_\+parameter\+\_\+names) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_time_series_set_a03997d74a405f641741509f14d3adfa5}\label{class_time_series_set_a03997d74a405f641741509f14d3adfa5}} +int {\bfseries number\+\_\+rows} +\item +\mbox{\Hypertarget{class_time_series_set_a92f2df07d70032d4bf466011583f8fbb}\label{class_time_series_set_a92f2df07d70032d4bf466011583f8fbb}} +\textbf{ string} {\bfseries filename} +\item +\mbox{\Hypertarget{class_time_series_set_ae82ba27c516b80c9f581dc61298cdf09}\label{class_time_series_set_ae82ba27c516b80c9f581dc61298cdf09}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries fields} +\item +\mbox{\Hypertarget{class_time_series_set_a3a63032b26d8ace6e9ab5f9ea1e0acff}\label{class_time_series_set_a3a63032b26d8ace6e9ab5f9ea1e0acff}} +\textbf{ map}$<$ \textbf{ string}, \mbox{\hyperlink{class_time_series}{Time\+Series}} $\ast$ $>$ {\bfseries time\+\_\+series} +\end{DoxyCompactItemize} + + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{class_time_series_set_ad85e413579132f448ec10545f733d452}\label{class_time_series_set_ad85e413579132f448ec10545f733d452}} +\index{TimeSeriesSet@{TimeSeriesSet}!export\_time\_series@{export\_time\_series}} +\index{export\_time\_series@{export\_time\_series}!TimeSeriesSet@{TimeSeriesSet}} +\doxysubsubsection{\texorpdfstring{export\_time\_series()}{export\_time\_series()}} +{\footnotesize\ttfamily void Time\+Series\+Set\+::export\+\_\+time\+\_\+series (\begin{DoxyParamCaption}\item[{\textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&}]{data, }\item[{const \textbf{ vector}$<$ \textbf{ string} $>$ \&}]{requested\+\_\+fields, }\item[{const \textbf{ vector}$<$ \textbf{ string} $>$ \&}]{shift\+\_\+fields, }\item[{\textbf{ int32\+\_\+t}}]{time\+\_\+offset }\end{DoxyParamCaption})} + + + +Time offset $<$ 0 generates input data. + +Do not use the last $<$time\+\_\+offset$>$ values Time offset $>$ 0 generates output data. Do not use the first $<$time\+\_\+offset$>$ values + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +time\+\_\+series/time\+\_\+series.\+hxx\item +time\+\_\+series/time\+\_\+series.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_time_series_sets.tex b/docs/latex/class_time_series_sets.tex new file mode 100644 index 00000000..a92aa8cb --- /dev/null +++ b/docs/latex/class_time_series_sets.tex @@ -0,0 +1,149 @@ +\hypertarget{class_time_series_sets}{}\doxysection{Time\+Series\+Sets Class Reference} +\label{class_time_series_sets}\index{TimeSeriesSets@{TimeSeriesSets}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_time_series_sets_ae456b414586be2e6c2f634b2583f8a1e}\label{class_time_series_sets_ae456b414586be2e6c2f634b2583f8a1e}} +void {\bfseries normalize\+\_\+min\+\_\+max} () +\item +\mbox{\Hypertarget{class_time_series_sets_a3f2ccab1a21a91c64fc3606da7656262}\label{class_time_series_sets_a3f2ccab1a21a91c64fc3606da7656262}} +void {\bfseries normalize\+\_\+min\+\_\+max} (const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+mins, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+maxs) +\item +\mbox{\Hypertarget{class_time_series_sets_a1ff984d07a5531d1781832d57376fd05}\label{class_time_series_sets_a1ff984d07a5531d1781832d57376fd05}} +void {\bfseries normalize\+\_\+avg\+\_\+std\+\_\+dev} () +\item +\mbox{\Hypertarget{class_time_series_sets_ad2acd60c0e4b224fcddd42f982f2e7f7}\label{class_time_series_sets_ad2acd60c0e4b224fcddd42f982f2e7f7}} +void {\bfseries normalize\+\_\+avg\+\_\+std\+\_\+dev} (const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+avgs, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+std\+\_\+devs, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+mins, const \textbf{ map}$<$ \textbf{ string}, double $>$ \&\+\_\+normalize\+\_\+maxs) +\item +\mbox{\Hypertarget{class_time_series_sets_aa405fa31b79ed889be2fc42fd847703f}\label{class_time_series_sets_aa405fa31b79ed889be2fc42fd847703f}} +void {\bfseries split\+\_\+series} (int series, int number\+\_\+slices) +\item +\mbox{\Hypertarget{class_time_series_sets_a25e64a6ebe99d155af40fa3c4827eee0}\label{class_time_series_sets_a25e64a6ebe99d155af40fa3c4827eee0}} +void {\bfseries split\+\_\+all} (int number\+\_\+slices) +\item +\mbox{\Hypertarget{class_time_series_sets_a617cece27b5f4d4cea003b66fb64b2fd}\label{class_time_series_sets_a617cece27b5f4d4cea003b66fb64b2fd}} +void {\bfseries write\+\_\+time\+\_\+series\+\_\+sets} (\textbf{ string} base\+\_\+filename) +\item +\mbox{\Hypertarget{class_time_series_sets_a5c8ac2daba0424ae774be8c9bee789b6}\label{class_time_series_sets_a5c8ac2daba0424ae774be8c9bee789b6}} +void \mbox{\hyperlink{class_time_series_sets_a5c8ac2daba0424ae774be8c9bee789b6}{export\+\_\+time\+\_\+series}} (const \textbf{ vector}$<$ int $>$ \&series\+\_\+indexes, int time\+\_\+offset, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs) +\begin{DoxyCompactList}\small\item\em the series argument is a vector of indexes of the time series that was initially loaded that are to be exported \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_time_series_sets_af7b9f9319c330cb6af8475ce1c2aa545}\label{class_time_series_sets_af7b9f9319c330cb6af8475ce1c2aa545}} +void \mbox{\hyperlink{class_time_series_sets_af7b9f9319c330cb6af8475ce1c2aa545}{export\+\_\+training\+\_\+series}} (int time\+\_\+offset, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs) +\begin{DoxyCompactList}\small\item\em This exports the time series marked as training series by the training\+\_\+indexes vector. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_time_series_sets_a45b61a73ae84cdafce66a10dd9930534}\label{class_time_series_sets_a45b61a73ae84cdafce66a10dd9930534}} +void \mbox{\hyperlink{class_time_series_sets_a45b61a73ae84cdafce66a10dd9930534}{export\+\_\+test\+\_\+series}} (int time\+\_\+offset, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&inputs, \textbf{ vector}$<$ \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ $>$ \&outputs) +\begin{DoxyCompactList}\small\item\em This exports the time series marked as test series by the test\+\_\+indexes vector. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_time_series_sets_a2ee946c4092fa81d3dc2cbf4c65b3388}\label{class_time_series_sets_a2ee946c4092fa81d3dc2cbf4c65b3388}} +void \mbox{\hyperlink{class_time_series_sets_a2ee946c4092fa81d3dc2cbf4c65b3388}{export\+\_\+series\+\_\+by\+\_\+name}} (\textbf{ string} field\+\_\+name, \textbf{ vector}$<$ \textbf{ vector}$<$ double $>$ $>$ \&exported\+\_\+series) +\begin{DoxyCompactList}\small\item\em This exports from all the loaded time series a particular column. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_time_series_sets_af076b335af2db7a42b0f7c08048254be}\label{class_time_series_sets_af076b335af2db7a42b0f7c08048254be}} +double {\bfseries denormalize} (\textbf{ string} field\+\_\+name, double value) +\item +\mbox{\Hypertarget{class_time_series_sets_ac4aae5727651a769f4959898556004a0}\label{class_time_series_sets_ac4aae5727651a769f4959898556004a0}} +\textbf{ string} {\bfseries get\+\_\+normalize\+\_\+type} () const +\item +\mbox{\Hypertarget{class_time_series_sets_a7937d3649074b8a4cddc89f647435109}\label{class_time_series_sets_a7937d3649074b8a4cddc89f647435109}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+mins} () const +\item +\mbox{\Hypertarget{class_time_series_sets_a58557834abac1950e724b3ac784f7b26}\label{class_time_series_sets_a58557834abac1950e724b3ac784f7b26}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+maxs} () const +\item +\mbox{\Hypertarget{class_time_series_sets_a4634322f77e316c48a1169087ebc92a3}\label{class_time_series_sets_a4634322f77e316c48a1169087ebc92a3}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+avgs} () const +\item +\mbox{\Hypertarget{class_time_series_sets_a65e823539a16e15764ed09b9ee10867f}\label{class_time_series_sets_a65e823539a16e15764ed09b9ee10867f}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries get\+\_\+normalize\+\_\+std\+\_\+devs} () const +\item +\mbox{\Hypertarget{class_time_series_sets_a3c957f47e5521db72d60afdfc79e499b}\label{class_time_series_sets_a3c957f47e5521db72d60afdfc79e499b}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries get\+\_\+input\+\_\+parameter\+\_\+names} () const +\item +\mbox{\Hypertarget{class_time_series_sets_a7bab02c0f71aeace479ec18d7345e9f2}\label{class_time_series_sets_a7bab02c0f71aeace479ec18d7345e9f2}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries get\+\_\+output\+\_\+parameter\+\_\+names} () const +\item +\mbox{\Hypertarget{class_time_series_sets_afaaadcc2ee84b7f92e5e8d58a9068194}\label{class_time_series_sets_afaaadcc2ee84b7f92e5e8d58a9068194}} +int {\bfseries get\+\_\+number\+\_\+series} () const +\item +\mbox{\Hypertarget{class_time_series_sets_a9486228ab9068dfd7715f212b10a9fbe}\label{class_time_series_sets_a9486228ab9068dfd7715f212b10a9fbe}} +int {\bfseries get\+\_\+number\+\_\+inputs} () const +\item +\mbox{\Hypertarget{class_time_series_sets_a0dfdafcceb83a78eaf93822d37437f33}\label{class_time_series_sets_a0dfdafcceb83a78eaf93822d37437f33}} +int {\bfseries get\+\_\+number\+\_\+outputs} () const +\item +\mbox{\Hypertarget{class_time_series_sets_ad5fea4bb76a25f3ea308624ac0e3ca44}\label{class_time_series_sets_ad5fea4bb76a25f3ea308624ac0e3ca44}} +void {\bfseries set\+\_\+training\+\_\+indexes} (const \textbf{ vector}$<$ int $>$ \&\+\_\+training\+\_\+indexes) +\item +\mbox{\Hypertarget{class_time_series_sets_aed2bb887ccf5ce0c5d11f3383d6d59cc}\label{class_time_series_sets_aed2bb887ccf5ce0c5d11f3383d6d59cc}} +void {\bfseries set\+\_\+test\+\_\+indexes} (const \textbf{ vector}$<$ int $>$ \&\+\_\+test\+\_\+indexes) +\item +\mbox{\Hypertarget{class_time_series_sets_ac86cd7b9273e43b5196afc28140adfa9}\label{class_time_series_sets_ac86cd7b9273e43b5196afc28140adfa9}} +\mbox{\hyperlink{class_time_series_set}{Time\+Series\+Set}} $\ast$ {\bfseries get\+\_\+set} (\textbf{ int32\+\_\+t} i) +\end{DoxyCompactItemize} +\doxysubsection*{Static Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_time_series_sets_af663701925511022ae92705c35737941}\label{class_time_series_sets_af663701925511022ae92705c35737941}} +static void {\bfseries help\+\_\+message} () +\item +\mbox{\Hypertarget{class_time_series_sets_a9322d26a76489031372e39a0b45fb50b}\label{class_time_series_sets_a9322d26a76489031372e39a0b45fb50b}} +static \mbox{\hyperlink{class_time_series_sets}{Time\+Series\+Sets}} $\ast$ {\bfseries generate\+\_\+from\+\_\+arguments} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&arguments) +\item +\mbox{\Hypertarget{class_time_series_sets_a32a39bf724151744d77dedbcb14b8fee}\label{class_time_series_sets_a32a39bf724151744d77dedbcb14b8fee}} +static \mbox{\hyperlink{class_time_series_sets}{Time\+Series\+Sets}} $\ast$ {\bfseries generate\+\_\+test} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+test\+\_\+filenames, const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+input\+\_\+parameter\+\_\+names, const \textbf{ vector}$<$ \textbf{ string} $>$ \&\+\_\+output\+\_\+parameter\+\_\+names) +\end{DoxyCompactItemize} +\doxysubsection*{Private Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_time_series_sets_a06214294f4a8b5dabca2565b47906b81}\label{class_time_series_sets_a06214294f4a8b5dabca2565b47906b81}} +void {\bfseries parse\+\_\+parameters\+\_\+string} (const \textbf{ vector}$<$ \textbf{ string} $>$ \&p) +\item +\mbox{\Hypertarget{class_time_series_sets_aad6705cac0f8b4dc4b862aa5f24aa04d}\label{class_time_series_sets_aad6705cac0f8b4dc4b862aa5f24aa04d}} +void {\bfseries load\+\_\+time\+\_\+series} () +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_time_series_sets_a8f8134e3f847c2b9891b9eb64757b83a}\label{class_time_series_sets_a8f8134e3f847c2b9891b9eb64757b83a}} +\textbf{ string} {\bfseries normalize\+\_\+type} +\item +\mbox{\Hypertarget{class_time_series_sets_a280d5aaeecc89dda6f589349eda36bd6}\label{class_time_series_sets_a280d5aaeecc89dda6f589349eda36bd6}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries filenames} +\item +\mbox{\Hypertarget{class_time_series_sets_a5aa9e8c442fa1154fc8acb4faa3e97fb}\label{class_time_series_sets_a5aa9e8c442fa1154fc8acb4faa3e97fb}} +\textbf{ vector}$<$ int $>$ {\bfseries training\+\_\+indexes} +\item +\mbox{\Hypertarget{class_time_series_sets_af4b1224bc44b47bdc95d5b406c13c8e9}\label{class_time_series_sets_af4b1224bc44b47bdc95d5b406c13c8e9}} +\textbf{ vector}$<$ int $>$ {\bfseries test\+\_\+indexes} +\item +\mbox{\Hypertarget{class_time_series_sets_afe48a73b7019701cde5ad78b8aae3ed9}\label{class_time_series_sets_afe48a73b7019701cde5ad78b8aae3ed9}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries input\+\_\+parameter\+\_\+names} +\item +\mbox{\Hypertarget{class_time_series_sets_a6e23f09d03dea1d2c1ceb24afd3fb463}\label{class_time_series_sets_a6e23f09d03dea1d2c1ceb24afd3fb463}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries output\+\_\+parameter\+\_\+names} +\item +\mbox{\Hypertarget{class_time_series_sets_ad7303b25502ed6d485135e6b7eaae985}\label{class_time_series_sets_ad7303b25502ed6d485135e6b7eaae985}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries shift\+\_\+parameter\+\_\+names} +\item +\mbox{\Hypertarget{class_time_series_sets_a3d34c7d1dbf0f030b95cd93237613468}\label{class_time_series_sets_a3d34c7d1dbf0f030b95cd93237613468}} +\textbf{ vector}$<$ \textbf{ string} $>$ {\bfseries all\+\_\+parameter\+\_\+names} +\item +\mbox{\Hypertarget{class_time_series_sets_a789620938d75b6b289282d02bfd642e6}\label{class_time_series_sets_a789620938d75b6b289282d02bfd642e6}} +\textbf{ vector}$<$ \mbox{\hyperlink{class_time_series_set}{Time\+Series\+Set}} $\ast$ $>$ {\bfseries time\+\_\+series} +\item +\mbox{\Hypertarget{class_time_series_sets_a074965d69cf655d2c128463ccbf5dbbc}\label{class_time_series_sets_a074965d69cf655d2c128463ccbf5dbbc}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+mins} +\item +\mbox{\Hypertarget{class_time_series_sets_ac490940078d3259eaf7daeb90f450ef3}\label{class_time_series_sets_ac490940078d3259eaf7daeb90f450ef3}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+maxs} +\item +\mbox{\Hypertarget{class_time_series_sets_a1e4987999373b35ec29db9b8fee2a97f}\label{class_time_series_sets_a1e4987999373b35ec29db9b8fee2a97f}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+avgs} +\item +\mbox{\Hypertarget{class_time_series_sets_a421f630f24865a13aa4d4156aa4e57c9}\label{class_time_series_sets_a421f630f24865a13aa4d4156aa4e57c9}} +\textbf{ map}$<$ \textbf{ string}, double $>$ {\bfseries normalize\+\_\+std\+\_\+devs} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +time\+\_\+series/time\+\_\+series.\+hxx\item +time\+\_\+series/time\+\_\+series.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_tracker.tex b/docs/latex/class_tracker.tex new file mode 100644 index 00000000..2a522180 --- /dev/null +++ b/docs/latex/class_tracker.tex @@ -0,0 +1,47 @@ +\hypertarget{class_tracker}{}\doxysection{Tracker Class Reference} +\label{class_tracker}\index{Tracker@{Tracker}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_tracker_a44adefce22937ea5251c59c7ffb9d7f5}\label{class_tracker_a44adefce22937ea5251c59c7ffb9d7f5}} +void {\bfseries track} (double value) +\item +\mbox{\Hypertarget{class_tracker_a1ded60555ced24fbca35560cfae2a8df}\label{class_tracker_a1ded60555ced24fbca35560cfae2a8df}} +double {\bfseries min} () const +\item +\mbox{\Hypertarget{class_tracker_a55975f2920140e38b0e9e767fc5f1871}\label{class_tracker_a55975f2920140e38b0e9e767fc5f1871}} +double {\bfseries max} () const +\item +\mbox{\Hypertarget{class_tracker_a0b3af55643ec9142aea4b0953ad3767b}\label{class_tracker_a0b3af55643ec9142aea4b0953ad3767b}} +double {\bfseries avg} () const +\item +\mbox{\Hypertarget{class_tracker_a5de9582599c863547cb34a1ed9d150cb}\label{class_tracker_a5de9582599c863547cb34a1ed9d150cb}} +double {\bfseries stddev} () +\item +\mbox{\Hypertarget{class_tracker_a039b206b7dce583722070db37241600f}\label{class_tracker_a039b206b7dce583722070db37241600f}} +double {\bfseries correlate} (\mbox{\hyperlink{class_tracker}{Tracker}} \&other) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_tracker_af9f5c0a2e8a5bed6fa229be3c8fef5ef}\label{class_tracker_af9f5c0a2e8a5bed6fa229be3c8fef5ef}} +int32\+\_\+t {\bfseries count} +\item +\mbox{\Hypertarget{class_tracker_a83c7f3040df1f6c1ed0719dc31735fc9}\label{class_tracker_a83c7f3040df1f6c1ed0719dc31735fc9}} +double {\bfseries \+\_\+min} +\item +\mbox{\Hypertarget{class_tracker_a220aa6ee4929baa5deba932567359f8a}\label{class_tracker_a220aa6ee4929baa5deba932567359f8a}} +double {\bfseries sum} +\item +\mbox{\Hypertarget{class_tracker_ac4bbd19641ba52bb54a38733f078c816}\label{class_tracker_ac4bbd19641ba52bb54a38733f078c816}} +double {\bfseries \+\_\+max} +\item +\mbox{\Hypertarget{class_tracker_af52d65a6a19b69bca42d11b513463d60}\label{class_tracker_af52d65a6a19b69bca42d11b513463d60}} +\textbf{ vector}$<$ double $>$ {\bfseries values} +\end{DoxyCompactItemize} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +mpi/tracker.\+hxx\item +mpi/tracker.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_u_g_r_n_n___node.eps b/docs/latex/class_u_g_r_n_n___node.eps new file mode 100644 index 00000000..24fad242 --- /dev/null +++ b/docs/latex/class_u_g_r_n_n___node.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 285.714286 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 1.750000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(UGRNN_Node) cw +(RNN_Node_Interface) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (UGRNN_Node) 0.000000 0.000000 box + (RNN_Node_Interface) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/class_u_g_r_n_n___node.tex b/docs/latex/class_u_g_r_n_n___node.tex new file mode 100644 index 00000000..813f7054 --- /dev/null +++ b/docs/latex/class_u_g_r_n_n___node.tex @@ -0,0 +1,138 @@ +\hypertarget{class_u_g_r_n_n___node}{}\doxysection{U\+G\+R\+N\+N\+\_\+\+Node Class Reference} +\label{class_u_g_r_n_n___node}\index{UGRNN\_Node@{UGRNN\_Node}} +Inheritance diagram for U\+G\+R\+N\+N\+\_\+\+Node\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{class_u_g_r_n_n___node} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a3764b899849150018680e31a6e9bcf0e}\label{class_u_g_r_n_n___node_a3764b899849150018680e31a6e9bcf0e}} +{\bfseries U\+G\+R\+N\+N\+\_\+\+Node} (int \+\_\+innovation\+\_\+number, int \+\_\+type, double \+\_\+depth) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a0d34661f7940c134bbfdca5a510c7285}\label{class_u_g_r_n_n___node_a0d34661f7940c134bbfdca5a510c7285}} +void {\bfseries initialize\+\_\+lamarckian} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double mu, double sigma) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a3422804628e03e4322e330aabc611643}\label{class_u_g_r_n_n___node_a3422804628e03e4322e330aabc611643}} +void {\bfseries initialize\+\_\+xavier} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng1\+\_\+1, double range) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a0023df13a9affc049ac48ee5c70f4550}\label{class_u_g_r_n_n___node_a0023df13a9affc049ac48ee5c70f4550}} +void {\bfseries initialize\+\_\+kaiming} (\textbf{ minstd\+\_\+rand0} \&generator, \mbox{\hyperlink{class_normal_distribution}{Normal\+Distribution}} \&normal\+\_\+distribution, double range) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a5c60b4dddfff9465a3bdd0cf38af1fc1}\label{class_u_g_r_n_n___node_a5c60b4dddfff9465a3bdd0cf38af1fc1}} +void {\bfseries initialize\+\_\+uniform\+\_\+random} (\textbf{ minstd\+\_\+rand0} \&generator, \textbf{ uniform\+\_\+real\+\_\+distribution}$<$ double $>$ \&rng) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_aff29e3f5a1fe8416f8f21328894b7b26}\label{class_u_g_r_n_n___node_aff29e3f5a1fe8416f8f21328894b7b26}} +double {\bfseries get\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a81f5d163fda9d456da8d05cf6cda6911}\label{class_u_g_r_n_n___node_a81f5d163fda9d456da8d05cf6cda6911}} +void {\bfseries print\+\_\+gradient} (\textbf{ string} gradient\+\_\+name) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_aa1ea29490bc1faec78fed644a878cc76}\label{class_u_g_r_n_n___node_aa1ea29490bc1faec78fed644a878cc76}} +void {\bfseries input\+\_\+fired} (int time, double incoming\+\_\+output) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_af4b8c24bf0e32b0edda0fef4167357ac}\label{class_u_g_r_n_n___node_af4b8c24bf0e32b0edda0fef4167357ac}} +void {\bfseries try\+\_\+update\+\_\+deltas} (int time) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_afa4f8c0fd55f91f5a74c69dcfb454f2f}\label{class_u_g_r_n_n___node_afa4f8c0fd55f91f5a74c69dcfb454f2f}} +void {\bfseries error\+\_\+fired} (int time, double error) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_ac03ea962e08046ed985aad74dea29e0d}\label{class_u_g_r_n_n___node_ac03ea962e08046ed985aad74dea29e0d}} +void {\bfseries output\+\_\+fired} (int time, double delta) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_acf582d77ae7bc8aaf0911e5972937a55}\label{class_u_g_r_n_n___node_acf582d77ae7bc8aaf0911e5972937a55}} +uint32\+\_\+t {\bfseries get\+\_\+number\+\_\+weights} () const +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_acea2de86ae7b8e7e7af366ec7eaf4b59}\label{class_u_g_r_n_n___node_acea2de86ae7b8e7e7af366ec7eaf4b59}} +void {\bfseries get\+\_\+weights} (\textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_acc7b262e04d47469f041b981674beae9}\label{class_u_g_r_n_n___node_acc7b262e04d47469f041b981674beae9}} +void {\bfseries set\+\_\+weights} (const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a4549f99ee719b51c4ede52827dbe3ef2}\label{class_u_g_r_n_n___node_a4549f99ee719b51c4ede52827dbe3ef2}} +void {\bfseries get\+\_\+weights} (uint32\+\_\+t \&offset, \textbf{ vector}$<$ double $>$ \¶meters) const +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_adbef72dd872d81476b4b75dca8dc3ad8}\label{class_u_g_r_n_n___node_adbef72dd872d81476b4b75dca8dc3ad8}} +void {\bfseries set\+\_\+weights} (uint32\+\_\+t \&offset, const \textbf{ vector}$<$ double $>$ \¶meters) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a85a3fbc682e58a0b0bb801d3808e5898}\label{class_u_g_r_n_n___node_a85a3fbc682e58a0b0bb801d3808e5898}} +void {\bfseries get\+\_\+gradients} (\textbf{ vector}$<$ double $>$ \&gradients) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a04d8d2fec656055b08f33dd25ceebb3e}\label{class_u_g_r_n_n___node_a04d8d2fec656055b08f33dd25ceebb3e}} +void {\bfseries reset} (int \+\_\+series\+\_\+length) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a413dbf93f5df0fccd811776a4d59d6c5}\label{class_u_g_r_n_n___node_a413dbf93f5df0fccd811776a4d59d6c5}} +void {\bfseries write\+\_\+to\+\_\+stream} (\textbf{ ostream} \&out) +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_adad5484b602d84d3a6bd2fc56d8f6084}\label{class_u_g_r_n_n___node_adad5484b602d84d3a6bd2fc56d8f6084}} +\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$ {\bfseries copy} () const +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a106b71a4e6d4e9cb7fc822f4a613775a}\label{class_u_g_r_n_n___node_a106b71a4e6d4e9cb7fc822f4a613775a}} +double {\bfseries cw} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_abed2f8cb96bb68424b291054b0a3fa35}\label{class_u_g_r_n_n___node_abed2f8cb96bb68424b291054b0a3fa35}} +double {\bfseries ch} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a456763cc730fe9051b0e8f5adda23dd7}\label{class_u_g_r_n_n___node_a456763cc730fe9051b0e8f5adda23dd7}} +double {\bfseries c\+\_\+bias} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a458667ece307ba918ba01c4d5c0b0c6b}\label{class_u_g_r_n_n___node_a458667ece307ba918ba01c4d5c0b0c6b}} +double {\bfseries gw} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_ae593589458b7db43bacbbb9d49f047b3}\label{class_u_g_r_n_n___node_ae593589458b7db43bacbbb9d49f047b3}} +double {\bfseries gh} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_aec5c89dbeb655ed24b21669e1e878148}\label{class_u_g_r_n_n___node_aec5c89dbeb655ed24b21669e1e878148}} +double {\bfseries g\+\_\+bias} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_acddc55613b2cbad64b0830a6f902ec25}\label{class_u_g_r_n_n___node_acddc55613b2cbad64b0830a6f902ec25}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+cw} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a666c4b51b2f8cfd9d9399e4a4ec27996}\label{class_u_g_r_n_n___node_a666c4b51b2f8cfd9d9399e4a4ec27996}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+ch} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_ad8c5949d7e82697e96737ac3d6c1c327}\label{class_u_g_r_n_n___node_ad8c5949d7e82697e96737ac3d6c1c327}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+c\+\_\+bias} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_ad6c348abba03d5be3850917fe075c33b}\label{class_u_g_r_n_n___node_ad6c348abba03d5be3850917fe075c33b}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+gw} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a02a0996f26b3a689643f3655cc28f5c8}\label{class_u_g_r_n_n___node_a02a0996f26b3a689643f3655cc28f5c8}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+gh} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_afc5a04c0d80e76a37dfdb39a2201d34d}\label{class_u_g_r_n_n___node_afc5a04c0d80e76a37dfdb39a2201d34d}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+g\+\_\+bias} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_abec1ce5838aaaf8ec513b9046d86116a}\label{class_u_g_r_n_n___node_abec1ce5838aaaf8ec513b9046d86116a}} +\textbf{ vector}$<$ double $>$ {\bfseries d\+\_\+h\+\_\+prev} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_aafa20e55d19b06bd9612590d4a6b32af}\label{class_u_g_r_n_n___node_aafa20e55d19b06bd9612590d4a6b32af}} +\textbf{ vector}$<$ double $>$ {\bfseries c} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a9cfec643c41dca9c00af8569117e4c15}\label{class_u_g_r_n_n___node_a9cfec643c41dca9c00af8569117e4c15}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+c} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_ac523ab0bc693a7e99f1e54827a3208a6}\label{class_u_g_r_n_n___node_ac523ab0bc693a7e99f1e54827a3208a6}} +\textbf{ vector}$<$ double $>$ {\bfseries g} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a25a0e594c49fcf60bfea66530cfc374c}\label{class_u_g_r_n_n___node_a25a0e594c49fcf60bfea66530cfc374c}} +\textbf{ vector}$<$ double $>$ {\bfseries ld\+\_\+g} +\end{DoxyCompactItemize} +\doxysubsection*{Friends} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_u_g_r_n_n___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}\label{class_u_g_r_n_n___node_a6ed93c885e63e64a8aa0ab2b7f637f0e}} +class {\bfseries R\+N\+N\+\_\+\+Edge} +\end{DoxyCompactItemize} +\doxysubsection*{Additional Inherited Members} + + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +rnn/ugrnn\+\_\+node.\+hxx\item +rnn/ugrnn\+\_\+node.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/class_word_series.tex b/docs/latex/class_word_series.tex new file mode 100644 index 00000000..e0b19115 --- /dev/null +++ b/docs/latex/class_word_series.tex @@ -0,0 +1,158 @@ +\hypertarget{class_word_series}{}\doxysection{Word\+Series Class Reference} +\label{class_word_series}\index{WordSeries@{WordSeries}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{class_word_series_ad0632eb627c4275f0c01877084a265d3}\label{class_word_series_ad0632eb627c4275f0c01877084a265d3}} +{\bfseries Word\+Series} (\textbf{ string} \+\_\+name) +\item +\mbox{\Hypertarget{class_word_series_ac40abcdb2bf29aac109e4663590697cd}\label{class_word_series_ac40abcdb2bf29aac109e4663590697cd}} +void {\bfseries add\+\_\+value} (double value) +\item +\mbox{\Hypertarget{class_word_series_a6b53ae1e5b35716ad4f905b1eb6d665c}\label{class_word_series_a6b53ae1e5b35716ad4f905b1eb6d665c}} +double {\bfseries get\+\_\+value} (int i) +\item +\mbox{\Hypertarget{class_word_series_af40e1cae4cecb85c62a95f96443ebd5a}\label{class_word_series_af40e1cae4cecb85c62a95f96443ebd5a}} +void {\bfseries calculate\+\_\+statistics} () +\item +\mbox{\Hypertarget{class_word_series_af0641d8c6f711ebd3de17507b67997d1}\label{class_word_series_af0641d8c6f711ebd3de17507b67997d1}} +void {\bfseries print\+\_\+statistics} () +\item +\mbox{\Hypertarget{class_word_series_a828c95b1330e9327a207949560885af1}\label{class_word_series_a828c95b1330e9327a207949560885af1}} +int {\bfseries get\+\_\+number\+\_\+values} () const +\item +\mbox{\Hypertarget{class_word_series_a741c2546aa8b032570813b5c6ee137a8}\label{class_word_series_a741c2546aa8b032570813b5c6ee137a8}} +double {\bfseries get\+\_\+min} () const +\item +\mbox{\Hypertarget{class_word_series_abe65b98aea1f4f78e4e55fe8ce6735e3}\label{class_word_series_abe65b98aea1f4f78e4e55fe8ce6735e3}} +double {\bfseries get\+\_\+average} () const +\item +\mbox{\Hypertarget{class_word_series_ac00878158175877d31997cd5cd271198}\label{class_word_series_ac00878158175877d31997cd5cd271198}} +double {\bfseries get\+\_\+max} () const +\item +\mbox{\Hypertarget{class_word_series_a73c60c775afb633cd34064860dbd6d4a}\label{class_word_series_a73c60c775afb633cd34064860dbd6d4a}} +double {\bfseries get\+\_\+std\+\_\+dev} () const +\item +\mbox{\Hypertarget{class_word_series_a175cd6233bb96f88779a22223a819d2d}\label{class_word_series_a175cd6233bb96f88779a22223a819d2d}} +double {\bfseries get\+\_\+variance} () const +\item +\mbox{\Hypertarget{class_word_series_a8d1197bee0719710feb3a4cc0558c7bb}\label{class_word_series_a8d1197bee0719710feb3a4cc0558c7bb}} +double {\bfseries get\+\_\+min\+\_\+change} () const +\item +\mbox{\Hypertarget{class_word_series_ae0c1de06176025d5f8d5460391f5bbcf}\label{class_word_series_ae0c1de06176025d5f8d5460391f5bbcf}} +double {\bfseries get\+\_\+max\+\_\+change} () const +\item +\mbox{\Hypertarget{class_word_series_a2019e6699fc8eb8b18439bdb38a18ed6}\label{class_word_series_a2019e6699fc8eb8b18439bdb38a18ed6}} +void {\bfseries normalize\+\_\+min\+\_\+max} (double \mbox{\hyperlink{class_word_series_a93302deb50a92c6aa6f7b2d8da29fee5}{min}}, double \mbox{\hyperlink{class_word_series_ab61bb7ef53fe10c4ef2077ad70700c25}{max}}) +\item +\mbox{\Hypertarget{class_word_series_ab45a95958471a4255ad7ecf990ab2cd3}\label{class_word_series_ab45a95958471a4255ad7ecf990ab2cd3}} +void {\bfseries normalize\+\_\+avg\+\_\+std\+\_\+dev} (double avg, double \mbox{\hyperlink{class_word_series_a6902b4c4b6bb2f865a59e4c5e27d7f18}{std\+\_\+dev}}, double norm\+\_\+max) +\item +\mbox{\Hypertarget{class_word_series_a89582ef3e5cd7a9fb39ac37e629b0287}\label{class_word_series_a89582ef3e5cd7a9fb39ac37e629b0287}} +void {\bfseries cut} (\textbf{ int32\+\_\+t} start, \textbf{ int32\+\_\+t} stop) +\item +\mbox{\Hypertarget{class_word_series_ae940ee5743cacb86076aac66f6010fa8}\label{class_word_series_ae940ee5743cacb86076aac66f6010fa8}} +double {\bfseries get\+\_\+correlation} (const \mbox{\hyperlink{class_word_series}{Word\+Series}} $\ast$other, \textbf{ int32\+\_\+t} lag) const +\item +\mbox{\Hypertarget{class_word_series_a39fe4ada8a08d95968e4b4e00665a78b}\label{class_word_series_a39fe4ada8a08d95968e4b4e00665a78b}} +\mbox{\hyperlink{class_word_series}{Word\+Series}} $\ast$ {\bfseries copy} () +\item +\mbox{\Hypertarget{class_word_series_aab0949df22d0b2caad5ee6885a196180}\label{class_word_series_aab0949df22d0b2caad5ee6885a196180}} +void {\bfseries copy\+\_\+values} (\textbf{ vector}$<$ double $>$ \&series) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\textbf{ string} \mbox{\hyperlink{class_word_series_ad40f74a195876908084a743b66c7b208}{name}} +\begin{DoxyCompactList}\small\item\em Specifies the string name in the data. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_word_series_a0db2200d32c40a69f28f55457d05afac}\label{class_word_series_a0db2200d32c40a69f28f55457d05afac}} +int \mbox{\hyperlink{class_word_series_a0db2200d32c40a69f28f55457d05afac}{vocab\+\_\+size}} +\begin{DoxyCompactList}\small\item\em Specifies the number of the unique strings in the dataset. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_word_series_a93302deb50a92c6aa6f7b2d8da29fee5}{min}} +\begin{DoxyCompactList}\small\item\em Specifies the minimum of the word series in a particular string. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_word_series_a3422e982c01b71612924ac5f1f0d2181}{average}} +\begin{DoxyCompactList}\small\item\em Specifies the average of the word series in a particular string. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_word_series_ab61bb7ef53fe10c4ef2077ad70700c25}{max}} +\begin{DoxyCompactList}\small\item\em Specifies the maximum of the word series in a particular string. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_word_series_a6902b4c4b6bb2f865a59e4c5e27d7f18}{std\+\_\+dev}} +\begin{DoxyCompactList}\small\item\em Specifies the std. \end{DoxyCompactList}\item +double \mbox{\hyperlink{class_word_series_ad1b8dc0e4f367bf0aa4a46ddb114f8c0}{variance}} +\begin{DoxyCompactList}\small\item\em Specifies the variance of the word series in a particular string. \end{DoxyCompactList}\item +\mbox{\Hypertarget{class_word_series_a56eeff54503c725b99284c6524f704a0}\label{class_word_series_a56eeff54503c725b99284c6524f704a0}} +double {\bfseries min\+\_\+change} +\item +\mbox{\Hypertarget{class_word_series_a9aee1f87534f0fff5c814afb122b69d0}\label{class_word_series_a9aee1f87534f0fff5c814afb122b69d0}} +double {\bfseries max\+\_\+change} +\item +\mbox{\Hypertarget{class_word_series_aed7840684ce132999a2d34a7e26dcd97}\label{class_word_series_aed7840684ce132999a2d34a7e26dcd97}} +\textbf{ vector}$<$ double $>$ \mbox{\hyperlink{class_word_series_aed7840684ce132999a2d34a7e26dcd97}{values}} +\begin{DoxyCompactList}\small\item\em Storers the values in the word series respective of the time in a particular string. \end{DoxyCompactList}\end{DoxyCompactItemize} + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{class_word_series_a3422e982c01b71612924ac5f1f0d2181}\label{class_word_series_a3422e982c01b71612924ac5f1f0d2181}} +\index{WordSeries@{WordSeries}!average@{average}} +\index{average@{average}!WordSeries@{WordSeries}} +\doxysubsubsection{\texorpdfstring{average}{average}} +{\footnotesize\ttfamily double Word\+Series\+::average\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies the average of the word series in a particular string. + +This tells about the words which are used the most. \mbox{\Hypertarget{class_word_series_ab61bb7ef53fe10c4ef2077ad70700c25}\label{class_word_series_ab61bb7ef53fe10c4ef2077ad70700c25}} +\index{WordSeries@{WordSeries}!max@{max}} +\index{max@{max}!WordSeries@{WordSeries}} +\doxysubsubsection{\texorpdfstring{max}{max}} +{\footnotesize\ttfamily double Word\+Series\+::max\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies the maximum of the word series in a particular string. + +This could be 0 or 1 \mbox{\Hypertarget{class_word_series_a93302deb50a92c6aa6f7b2d8da29fee5}\label{class_word_series_a93302deb50a92c6aa6f7b2d8da29fee5}} +\index{WordSeries@{WordSeries}!min@{min}} +\index{min@{min}!WordSeries@{WordSeries}} +\doxysubsubsection{\texorpdfstring{min}{min}} +{\footnotesize\ttfamily double Word\+Series\+::min\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies the minimum of the word series in a particular string. + +This could be 0 or 1 \mbox{\Hypertarget{class_word_series_ad40f74a195876908084a743b66c7b208}\label{class_word_series_ad40f74a195876908084a743b66c7b208}} +\index{WordSeries@{WordSeries}!name@{name}} +\index{name@{name}!WordSeries@{WordSeries}} +\doxysubsubsection{\texorpdfstring{name}{name}} +{\footnotesize\ttfamily \textbf{ string} Word\+Series\+::name\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies the string name in the data. + +All the unique strings in the datasets will have its each word series. \mbox{\Hypertarget{class_word_series_a6902b4c4b6bb2f865a59e4c5e27d7f18}\label{class_word_series_a6902b4c4b6bb2f865a59e4c5e27d7f18}} +\index{WordSeries@{WordSeries}!std\_dev@{std\_dev}} +\index{std\_dev@{std\_dev}!WordSeries@{WordSeries}} +\doxysubsubsection{\texorpdfstring{std\_dev}{std\_dev}} +{\footnotesize\ttfamily double Word\+Series\+::std\+\_\+dev\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies the std. + +deviation of the word series in a particular string. \mbox{\Hypertarget{class_word_series_ad1b8dc0e4f367bf0aa4a46ddb114f8c0}\label{class_word_series_ad1b8dc0e4f367bf0aa4a46ddb114f8c0}} +\index{WordSeries@{WordSeries}!variance@{variance}} +\index{variance@{variance}!WordSeries@{WordSeries}} +\doxysubsubsection{\texorpdfstring{variance}{variance}} +{\footnotesize\ttfamily double Word\+Series\+::variance\hspace{0.3cm}{\ttfamily [private]}} + + + +Specifies the variance of the word series in a particular string. + +This tells about the closeness of a word in the dataset. + +The documentation for this class was generated from the following files\+:\begin{DoxyCompactItemize} +\item +word\+\_\+series/word\+\_\+series.\+hxx\item +word\+\_\+series/word\+\_\+series.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/classconfig__to__arg_1_1_config_to_arg.eps b/docs/latex/classconfig__to__arg_1_1_config_to_arg.eps new file mode 100644 index 00000000..30e614c8 --- /dev/null +++ b/docs/latex/classconfig__to__arg_1_1_config_to_arg.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 229.885057 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 2.175000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(config_to_arg.ConfigToArg) cw +(examm_task.ExammTask) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (config_to_arg.ConfigToArg) 0.000000 1.000000 box + (examm_task.ExammTask) 0.000000 0.000000 box + +% ----- relations ----- + +solid +1 0.000000 0.250000 out +solid +0 0.000000 0.750000 in diff --git a/docs/latex/classconfig__to__arg_1_1_config_to_arg.tex b/docs/latex/classconfig__to__arg_1_1_config_to_arg.tex new file mode 100644 index 00000000..a3df1379 --- /dev/null +++ b/docs/latex/classconfig__to__arg_1_1_config_to_arg.tex @@ -0,0 +1,69 @@ +\hypertarget{classconfig__to__arg_1_1_config_to_arg}{}\doxysection{config\+\_\+to\+\_\+arg.\+Config\+To\+Arg Class Reference} +\label{classconfig__to__arg_1_1_config_to_arg}\index{config\_to\_arg.ConfigToArg@{config\_to\_arg.ConfigToArg}} +Inheritance diagram for config\+\_\+to\+\_\+arg.\+Config\+To\+Arg\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{classconfig__to__arg_1_1_config_to_arg} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +def \mbox{\hyperlink{classconfig__to__arg_1_1_config_to_arg_a0423918c5ac9ce0372cc30c72ae803aa}{\+\_\+\+\_\+init\+\_\+\+\_\+}} (self, config, name, filename, config\+\_\+options=\{\}, defaults=\{\}, types=\{\}, constraints=\{\}) +\item +\mbox{\Hypertarget{classconfig__to__arg_1_1_config_to_arg_a24c44867bb2c637387ca4cb508b79613}\label{classconfig__to__arg_1_1_config_to_arg_a24c44867bb2c637387ca4cb508b79613}} +def {\bfseries to\+\_\+args} (self) +\end{DoxyCompactItemize} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classconfig__to__arg_1_1_config_to_arg_a8cf8295fec60995e7354ebb38fd4c96b}\label{classconfig__to__arg_1_1_config_to_arg_a8cf8295fec60995e7354ebb38fd4c96b}} +{\bfseries config\+\_\+options} +\item +\mbox{\Hypertarget{classconfig__to__arg_1_1_config_to_arg_a085f7121d8a5c5125a28b2131d7ad580}\label{classconfig__to__arg_1_1_config_to_arg_a085f7121d8a5c5125a28b2131d7ad580}} +{\bfseries optional\+\_\+config\+\_\+option\+\_\+defaults} +\item +\mbox{\Hypertarget{classconfig__to__arg_1_1_config_to_arg_ab2d0b438f05da5d9bdaa6f0ee7f184f4}\label{classconfig__to__arg_1_1_config_to_arg_ab2d0b438f05da5d9bdaa6f0ee7f184f4}} +{\bfseries types} +\item +\mbox{\Hypertarget{classconfig__to__arg_1_1_config_to_arg_ab4f530603e6948c4b86e69f7e01112a1}\label{classconfig__to__arg_1_1_config_to_arg_ab4f530603e6948c4b86e69f7e01112a1}} +{\bfseries constraints} +\end{DoxyCompactItemize} + + +\doxysubsection{Detailed Description} +\begin{DoxyVerb}A easily configurable class meant to be used for different configuration sections. +\end{DoxyVerb} + + +\doxysubsection{Constructor \& Destructor Documentation} +\mbox{\Hypertarget{classconfig__to__arg_1_1_config_to_arg_a0423918c5ac9ce0372cc30c72ae803aa}\label{classconfig__to__arg_1_1_config_to_arg_a0423918c5ac9ce0372cc30c72ae803aa}} +\index{config\_to\_arg.ConfigToArg@{config\_to\_arg.ConfigToArg}!\_\_init\_\_@{\_\_init\_\_}} +\index{\_\_init\_\_@{\_\_init\_\_}!config\_to\_arg.ConfigToArg@{config\_to\_arg.ConfigToArg}} +\doxysubsubsection{\texorpdfstring{\_\_init\_\_()}{\_\_init\_\_()}} +{\footnotesize\ttfamily def config\+\_\+to\+\_\+arg.\+Config\+To\+Arg.\+\_\+\+\_\+init\+\_\+\+\_\+ (\begin{DoxyParamCaption}\item[{}]{self, }\item[{}]{config, }\item[{}]{name, }\item[{}]{filename, }\item[{}]{config\+\_\+options = {\ttfamily \{\}}, }\item[{}]{defaults = {\ttfamily \{\}}, }\item[{}]{types = {\ttfamily \{\}}, }\item[{}]{constraints = {\ttfamily \{\}} }\end{DoxyParamCaption})} + +\begin{DoxyVerb}:param config a dictionary from the parsed config file +:param name the name of this configuration section (e.g. examm.) +:param filename name / path of the configuration file + +:param config_options a map from config option name to a lambda used to turn +the parameter into a list of command line arguments. The lambda takes self and the +value of the parameter as arguments. + +:param defaults a map from config option name to a lambda that +returns the default value of a parameter. If a config option does not appear +in this map then it is not optional. If the value of an option is None, +it won't be added to the argument list / it will remain unset. + +:param types maps config option name to a set of the valid types for that config option. + +:param constraints maps config option name to a tuple, the first element is a lambda that +takes the config option value as an argument and returns True if it meets all constraints. +The second value of the tuple is a short description of the constraints. +\end{DoxyVerb} + + +The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} +\item +cli/config\+\_\+to\+\_\+arg.\+py\end{DoxyCompactItemize} diff --git a/docs/latex/classexamm__task_1_1_examm_task.eps b/docs/latex/classexamm__task_1_1_examm_task.eps new file mode 100644 index 00000000..3439d880 --- /dev/null +++ b/docs/latex/classexamm__task_1_1_examm_task.eps @@ -0,0 +1,197 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: ClassName +%%Creator: Doxygen +%%CreationDate: Time +%%For: +%Magnification: 1.00 +%%Orientation: Portrait +%%BoundingBox: 0 0 500 229.885057 +%%Pages: 0 +%%BeginSetup +%%EndSetup +%%EndComments + +% ----- variables ----- + +/boxwidth 0 def +/boxheight 40 def +/fontheight 24 def +/marginwidth 10 def +/distx 20 def +/disty 40 def +/boundaspect 2.175000 def % aspect ratio of the BoundingBox (width/height) +/boundx 500 def +/boundy boundx boundaspect div def +/xspacing 0 def +/yspacing 0 def +/rows 2 def +/cols 1 def +/scalefactor 0 def +/boxfont /Times-Roman findfont fontheight scalefont def + +% ----- procedures ----- + +/dotted { [1 4] 0 setdash } def +/dashed { [5] 0 setdash } def +/solid { [] 0 setdash } def + +/max % result = MAX(arg1,arg2) +{ + /a exch def + /b exch def + a b gt {a} {b} ifelse +} def + +/xoffset % result = MAX(0,(scalefactor-(boxwidth*cols+distx*(cols-1)))/2) +{ + 0 scalefactor boxwidth cols mul distx cols 1 sub mul add sub 2 div max +} def + +/cw % boxwidth = MAX(boxwidth, stringwidth(arg1)) +{ + /str exch def + /boxwidth boxwidth str stringwidth pop max def +} def + +/box % draws a box with text 'arg1' at grid pos (arg2,arg3) +{ gsave + 2 setlinewidth + newpath + exch xspacing mul xoffset add + exch yspacing mul + moveto + boxwidth 0 rlineto + 0 boxheight rlineto + boxwidth neg 0 rlineto + 0 boxheight neg rlineto + closepath + dup stringwidth pop neg boxwidth add 2 div + boxheight fontheight 2 div sub 2 div + rmoveto show stroke + grestore +} def + +/mark +{ newpath + exch xspacing mul xoffset add boxwidth add + exch yspacing mul + moveto + 0 boxheight 4 div rlineto + boxheight neg 4 div boxheight neg 4 div rlineto + closepath + eofill + stroke +} def + +/arrow +{ newpath + moveto + 3 -8 rlineto + -6 0 rlineto + 3 8 rlineto + closepath + eofill + stroke +} def + +/out % draws an output connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight add + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/in % draws an input connector for the block at (arg1,arg2) +{ + newpath + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul disty 2 div sub + /y exch def + /x exch def + x y moveto + 0 disty 2 div rlineto + stroke + 1 eq { x y disty 2 div add arrow } if +} def + +/hedge +{ + exch xspacing mul xoffset add boxwidth 2 div add + exch yspacing mul boxheight 2 div sub + /y exch def + /x exch def + newpath + x y moveto + boxwidth 2 div distx add 0 rlineto + stroke + 1 eq + { newpath x boxwidth 2 div distx add add y moveto + -8 3 rlineto + 0 -6 rlineto + 8 3 rlineto + closepath + eofill + stroke + } if +} def + +/vedge +{ + /ye exch def + /ys exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add dup + ys yspacing mul boxheight 2 div sub + moveto + ye yspacing mul boxheight 2 div sub + lineto + stroke +} def + +/conn % connections the blocks from col 'arg1' to 'arg2' of row 'arg3' +{ + /ys exch def + /xe exch def + /xs exch def + newpath + xs xspacing mul xoffset add boxwidth 2 div add + ys yspacing mul disty 2 div sub + moveto + xspacing xe xs sub mul 0 + rlineto + stroke +} def + +% ----- main ------ + +boxfont setfont +1 boundaspect scale +(examm_task.ExammTask) cw +(config_to_arg.ConfigToArg) cw +/boxwidth boxwidth marginwidth 2 mul add def +/xspacing boxwidth distx add def +/yspacing boxheight disty add def +/scalefactor + boxwidth cols mul distx cols 1 sub mul add + boxheight rows mul disty rows 1 sub mul add boundaspect mul + max def +boundx scalefactor div boundy scalefactor div scale + +% ----- classes ----- + + (examm_task.ExammTask) 0.000000 0.000000 box + (config_to_arg.ConfigToArg) 0.000000 1.000000 box + +% ----- relations ----- + +solid +0 0.000000 0.000000 out +solid +1 0.000000 1.000000 in diff --git a/docs/latex/classexamm__task_1_1_examm_task.tex b/docs/latex/classexamm__task_1_1_examm_task.tex new file mode 100644 index 00000000..d562017c --- /dev/null +++ b/docs/latex/classexamm__task_1_1_examm_task.tex @@ -0,0 +1,166 @@ +\hypertarget{classexamm__task_1_1_examm_task}{}\doxysection{examm\+\_\+task.\+Examm\+Task Class Reference} +\label{classexamm__task_1_1_examm_task}\index{examm\_task.ExammTask@{examm\_task.ExammTask}} +Inheritance diagram for examm\+\_\+task.\+Examm\+Task\+:\begin{figure}[H] +\begin{center} +\leavevmode +\includegraphics[height=2.000000cm]{classexamm__task_1_1_examm_task} +\end{center} +\end{figure} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_a5119a595242db05a6da9cab9e0c1ac71}\label{classexamm__task_1_1_examm_task_a5119a595242db05a6da9cab9e0c1ac71}} +def {\bfseries \+\_\+\+\_\+init\+\_\+\+\_\+} (self, toml\+\_\+object, file\+\_\+path, run\+\_\+number) +\item +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_a8b81653168fb3c8ead9121b5e95d3a6a}\label{classexamm__task_1_1_examm_task_a8b81653168fb3c8ead9121b5e95d3a6a}} +def {\bfseries to\+\_\+command} (self) +\item +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_a2757a4f0790e4fd677a4b61afebe0943}\label{classexamm__task_1_1_examm_task_a2757a4f0790e4fd677a4b61afebe0943}} +def {\bfseries all\+\_\+strings} (self, some\+\_\+list) +\end{DoxyCompactItemize} +\doxysubsection*{Static Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_a42db8070c8a2b98c039c47426593e88a}\label{classexamm__task_1_1_examm_task_a42db8070c8a2b98c039c47426593e88a}} +def {\bfseries glob\+\_\+to\+\_\+all} (paths) +\end{DoxyCompactItemize} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_ab1f9fa55d6ca5621228e55a28a9c13ad}\label{classexamm__task_1_1_examm_task_ab1f9fa55d6ca5621228e55a28a9c13ad}} +{\bfseries examm\+\_\+config} +\item +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_a752f28628fd4411a18f94d6c74a1b6de}\label{classexamm__task_1_1_examm_task_a752f28628fd4411a18f94d6c74a1b6de}} +{\bfseries filename} +\item +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_a36260f518ff0be1f547733e8627beb23}\label{classexamm__task_1_1_examm_task_a36260f518ff0be1f547733e8627beb23}} +{\bfseries version} +\item +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_af0fb2f0fab51ef210a0913abdb6bf44f}\label{classexamm__task_1_1_examm_task_af0fb2f0fab51ef210a0913abdb6bf44f}} +{\bfseries parallelism} +\item +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_a69ced56acbffda148ff50c63c8ffa969}\label{classexamm__task_1_1_examm_task_a69ced56acbffda148ff50c63c8ffa969}} +{\bfseries output\+\_\+directory} +\end{DoxyCompactItemize} +\doxysubsection*{Static Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_af01348dcf6873ad50c8ede43c20ac720}\label{classexamm__task_1_1_examm_task_af01348dcf6873ad50c8ede43c20ac720}} +list {\bfseries A\+L\+L\+\_\+\+N\+O\+D\+E\+\_\+\+T\+Y\+P\+ES} = \mbox{[} \textquotesingle{}simple\textquotesingle{}, \textquotesingle{}U\+G\+R\+NN\textquotesingle{}, \textquotesingle{}M\+GU\textquotesingle{}, \textquotesingle{}G\+RU\textquotesingle{}, \textquotesingle{}delta\textquotesingle{}, \textquotesingle{}L\+S\+TM\textquotesingle{} , \textquotesingle{}E\+N\+A\+RC\textquotesingle{} \mbox{]} +\item +dictionary {\bfseries C\+O\+N\+F\+I\+G\+\_\+\+O\+P\+T\+I\+O\+NS} +\item +dictionary {\bfseries D\+E\+F\+A\+U\+L\+TS} +\item +dictionary {\bfseries T\+Y\+P\+ES} +\item +dictionary {\bfseries C\+O\+N\+S\+T\+R\+A\+I\+N\+TS} +\end{DoxyCompactItemize} + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_aae6eeef6249cb3398c603e22259a8615}\label{classexamm__task_1_1_examm_task_aae6eeef6249cb3398c603e22259a8615}} +\index{examm\_task.ExammTask@{examm\_task.ExammTask}!CONFIG\_OPTIONS@{CONFIG\_OPTIONS}} +\index{CONFIG\_OPTIONS@{CONFIG\_OPTIONS}!examm\_task.ExammTask@{examm\_task.ExammTask}} +\doxysubsubsection{\texorpdfstring{CONFIG\_OPTIONS}{CONFIG\_OPTIONS}} +{\footnotesize\ttfamily dictionary examm\+\_\+task.\+Examm\+Task.\+C\+O\+N\+F\+I\+G\+\_\+\+O\+P\+T\+I\+O\+NS\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{"training\_files"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/training\_filenames'}] + ExammTask.glob\_to\_all(x),} +\DoxyCodeLine{ \textcolor{stringliteral}{"test\_files"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/test\_filenames'}] + ExammTask.glob\_to\_all(x),} +\DoxyCodeLine{ \textcolor{stringliteral}{"time\_offset"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/time\_offset'}, str(x)],} +\DoxyCodeLine{ \textcolor{stringliteral}{"input\_parameters"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/input\_parameter\_names'}] + list(map(str, x)),} +\DoxyCodeLine{ \textcolor{stringliteral}{"output\_parameters"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/output\_parameter\_names'}] + list(map(str, x)),} +\DoxyCodeLine{ \textcolor{stringliteral}{"n\_islands"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/number\_islands'}, str(x)],} +\DoxyCodeLine{ \textcolor{stringliteral}{"population\_size"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/population\_size'}, str(x)],} +\DoxyCodeLine{ \textcolor{stringliteral}{"max\_genomes"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/max\_genomes'}, str(x)],} +\DoxyCodeLine{ \textcolor{stringliteral}{"bp\_iterations"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/bp\_iterations'}, str(x)],} +\DoxyCodeLine{ \textcolor{stringliteral}{"output\_directory"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/output\_directory'}, str(x)],} +\DoxyCodeLine{ \textcolor{stringliteral}{"node\_types"}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/possible\_node\_types'}] + list(map(str, x)),} +\DoxyCodeLine{ \textcolor{stringliteral}{"rec"}: \textcolor{keyword}{lambda} self, x: RecArgs(x, self.filename).to\_args(),} +\DoxyCodeLine{ \textcolor{stringliteral}{"island\_purging"}: \textcolor{keyword}{lambda} self, x: IslandPurgingArgs(x, self.filename).to\_args()} +\DoxyCodeLine{ \}} + +\end{DoxyCode} +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_a22398504cffcc3e032690b7c55e21556}\label{classexamm__task_1_1_examm_task_a22398504cffcc3e032690b7c55e21556}} +\index{examm\_task.ExammTask@{examm\_task.ExammTask}!CONSTRAINTS@{CONSTRAINTS}} +\index{CONSTRAINTS@{CONSTRAINTS}!examm\_task.ExammTask@{examm\_task.ExammTask}} +\doxysubsubsection{\texorpdfstring{CONSTRAINTS}{CONSTRAINTS}} +{\footnotesize\ttfamily dictionary examm\+\_\+task.\+Examm\+Task.\+C\+O\+N\+S\+T\+R\+A\+I\+N\+TS\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{"training\_files"}: (\textcolor{keyword}{lambda} self: self.all\_strings(self.training\_files),} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be a list of strings"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"test\_files"}: (\textcolor{keyword}{lambda} self: self.all\_strings(self.test\_files),} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be a list of strings"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"time\_offset"}: (\textcolor{keyword}{lambda} self: self.time\_offset > 0, \textcolor{stringliteral}{"must a positive integer"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"input\_parameters"}: (\textcolor{keyword}{lambda} self: self.all\_strings(self.input\_parameters),} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be a list of strings"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"output\_parameters"}: (\textcolor{keyword}{lambda} self: self.all\_strings(self.output\_parameters),} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be a list of strings"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"n\_islands"}: (\textcolor{keyword}{lambda} self: self.n\_islands > 0, \textcolor{stringliteral}{"must be a positive integer"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"population\_size"}: (\textcolor{keyword}{lambda} self: self.population\_size > 0, \textcolor{stringliteral}{"must be a positive integer"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"max\_genomes"}: (\textcolor{keyword}{lambda} self: self.max\_genomes > 0, \textcolor{stringliteral}{"must be a positive integer"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"bp\_iterations"}: (\textcolor{keyword}{lambda} self: self.bp\_iterations >= 0, \textcolor{stringliteral}{"must be a non-\/negative integer"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"output\_directory"}: (\textcolor{keyword}{lambda} self: \textcolor{keyword}{True}, \textcolor{stringliteral}{"must be a valid path"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"node\_types"}: (\textcolor{keyword}{lambda} self: self.all\_strings(self.node\_types) \(\backslash\)} +\DoxyCodeLine{ \textcolor{keywordflow}{and} set(self.node\_types).issubset(set(ExammTask.ALL\_NODE\_TYPES)),} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be a subset of "} + str(ALL\_NODE\_TYPES)),} +\DoxyCodeLine{ \textcolor{comment}{\# Subsections should be of type dict}} +\DoxyCodeLine{ \textcolor{stringliteral}{"rec"}: (\textcolor{keyword}{lambda} self: \textcolor{keyword}{True}, \textcolor{stringliteral}{""}),} +\DoxyCodeLine{ \textcolor{stringliteral}{"island\_purging"}: (\textcolor{keyword}{lambda} self: \textcolor{keyword}{True}, \textcolor{stringliteral}{""})} +\DoxyCodeLine{ } +\DoxyCodeLine{ \}} + +\end{DoxyCode} +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_a6a8084b4265f660640f91d59eea44835}\label{classexamm__task_1_1_examm_task_a6a8084b4265f660640f91d59eea44835}} +\index{examm\_task.ExammTask@{examm\_task.ExammTask}!DEFAULTS@{DEFAULTS}} +\index{DEFAULTS@{DEFAULTS}!examm\_task.ExammTask@{examm\_task.ExammTask}} +\doxysubsubsection{\texorpdfstring{DEFAULTS}{DEFAULTS}} +{\footnotesize\ttfamily dictionary examm\+\_\+task.\+Examm\+Task.\+D\+E\+F\+A\+U\+L\+TS\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{"node\_types"}: \textcolor{keyword}{lambda}: ExammTask.ALL\_NODE\_TYPES,} +\DoxyCodeLine{ \textcolor{stringliteral}{"rec"}: \textcolor{keyword}{lambda}: dict(),} +\DoxyCodeLine{ \textcolor{stringliteral}{"island\_purging"}: \textcolor{keyword}{lambda}: dict()} +\DoxyCodeLine{ \}} + +\end{DoxyCode} +\mbox{\Hypertarget{classexamm__task_1_1_examm_task_a94966dcee15238e180cede3c68ac7712}\label{classexamm__task_1_1_examm_task_a94966dcee15238e180cede3c68ac7712}} +\index{examm\_task.ExammTask@{examm\_task.ExammTask}!TYPES@{TYPES}} +\index{TYPES@{TYPES}!examm\_task.ExammTask@{examm\_task.ExammTask}} +\doxysubsubsection{\texorpdfstring{TYPES}{TYPES}} +{\footnotesize\ttfamily dictionary examm\+\_\+task.\+Examm\+Task.\+T\+Y\+P\+ES\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{"training\_files"}: \{list\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"test\_files"}: \{list\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"time\_offset"}: \{int\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"input\_parameters"}: \{list\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"output\_parameters"}: \{list\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"n\_islands"}: \{int\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"population\_size"}: \{int\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"max\_genomes"}: \{int\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"bp\_iterations"}: \{int\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"output\_directory"}: \{str\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"node\_types"}: \{list\},} +\DoxyCodeLine{ \textcolor{comment}{\# Subsections should be of type dict}} +\DoxyCodeLine{ \textcolor{stringliteral}{"rec"}: \{dict\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"island\_purging"}: \{dict\}} +\DoxyCodeLine{ } +\DoxyCodeLine{ \}} + +\end{DoxyCode} + + +The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} +\item +cli/examm\+\_\+task.\+py\end{DoxyCompactItemize} diff --git a/docs/latex/classisland__purging__args_1_1_island_purging_args.tex b/docs/latex/classisland__purging__args_1_1_island_purging_args.tex new file mode 100644 index 00000000..15233452 --- /dev/null +++ b/docs/latex/classisland__purging__args_1_1_island_purging_args.tex @@ -0,0 +1,109 @@ +\hypertarget{classisland__purging__args_1_1_island_purging_args}{}\doxysection{island\+\_\+purging\+\_\+args.\+Island\+Purging\+Args Class Reference} +\label{classisland__purging__args_1_1_island_purging_args}\index{island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classisland__purging__args_1_1_island_purging_args_a4b9638c5cda5ea696b73d0bac5b7b646}\label{classisland__purging__args_1_1_island_purging_args_a4b9638c5cda5ea696b73d0bac5b7b646}} +def {\bfseries \+\_\+\+\_\+init\+\_\+\+\_\+} (self, rec, filename) +\item +\mbox{\Hypertarget{classisland__purging__args_1_1_island_purging_args_a989d7b5c2711f982f060ea0b60a7393a}\label{classisland__purging__args_1_1_island_purging_args_a989d7b5c2711f982f060ea0b60a7393a}} +def {\bfseries to\+\_\+args} (self) +\end{DoxyCompactItemize} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classisland__purging__args_1_1_island_purging_args_a120817e8732b70bd03a800994496540e}\label{classisland__purging__args_1_1_island_purging_args_a120817e8732b70bd03a800994496540e}} +{\bfseries c2a} +\end{DoxyCompactItemize} +\doxysubsection*{Static Public Attributes} +\begin{DoxyCompactItemize} +\item +dictionary {\bfseries S\+E\+L\+E\+C\+T\+I\+O\+N\+\_\+\+M\+E\+T\+H\+O\+D\+\_\+\+M\+AP} +\item +dictionary {\bfseries C\+O\+N\+F\+I\+G\+\_\+\+O\+P\+T\+I\+O\+NS} +\item +dictionary {\bfseries D\+E\+F\+A\+U\+L\+TS} +\item +dictionary {\bfseries T\+Y\+P\+ES} +\item +dictionary {\bfseries C\+O\+N\+S\+T\+R\+A\+I\+N\+TS} +\end{DoxyCompactItemize} + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{classisland__purging__args_1_1_island_purging_args_ac0408cda21769d62b29c0b07080ea541}\label{classisland__purging__args_1_1_island_purging_args_ac0408cda21769d62b29c0b07080ea541}} +\index{island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}!CONFIG\_OPTIONS@{CONFIG\_OPTIONS}} +\index{CONFIG\_OPTIONS@{CONFIG\_OPTIONS}!island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}} +\doxysubsubsection{\texorpdfstring{CONFIG\_OPTIONS}{CONFIG\_OPTIONS}} +{\footnotesize\ttfamily dictionary island\+\_\+purging\+\_\+args.\+Island\+Purging\+Args.\+C\+O\+N\+F\+I\+G\+\_\+\+O\+P\+T\+I\+O\+NS\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{'selection\_method'}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/check\_on\_island\_method'}, IslandPurgingArgs.SELECTION\_METHOD\_MAP[x]],} +\DoxyCodeLine{ \textcolor{stringliteral}{'period'}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/num\_genomes\_check\_worst\_fit'}, str(x)],} +\DoxyCodeLine{ \}} + +\end{DoxyCode} +\mbox{\Hypertarget{classisland__purging__args_1_1_island_purging_args_a688ff9b68d60391090be6f49ee90cc27}\label{classisland__purging__args_1_1_island_purging_args_a688ff9b68d60391090be6f49ee90cc27}} +\index{island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}!CONSTRAINTS@{CONSTRAINTS}} +\index{CONSTRAINTS@{CONSTRAINTS}!island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}} +\doxysubsubsection{\texorpdfstring{CONSTRAINTS}{CONSTRAINTS}} +{\footnotesize\ttfamily dictionary island\+\_\+purging\+\_\+args.\+Island\+Purging\+Args.\+C\+O\+N\+S\+T\+R\+A\+I\+N\+TS\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{'period'}: (\textcolor{keyword}{lambda} self: self.period > 0,} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be a positive integer"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{'selection\_method'}: (\textcolor{keyword}{lambda} self: self.selection\_method \textcolor{keywordflow}{in} \{\textcolor{stringliteral}{'worst\_best'}\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be unset or 'worst\_best'"})} +\DoxyCodeLine{ \}} + +\end{DoxyCode} +\mbox{\Hypertarget{classisland__purging__args_1_1_island_purging_args_af4b7da43c0947f3b0964a07c0df81748}\label{classisland__purging__args_1_1_island_purging_args_af4b7da43c0947f3b0964a07c0df81748}} +\index{island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}!DEFAULTS@{DEFAULTS}} +\index{DEFAULTS@{DEFAULTS}!island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}} +\doxysubsubsection{\texorpdfstring{DEFAULTS}{DEFAULTS}} +{\footnotesize\ttfamily dictionary island\+\_\+purging\+\_\+args.\+Island\+Purging\+Args.\+D\+E\+F\+A\+U\+L\+TS\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{'selection\_method'}: \textcolor{keyword}{lambda}: \textcolor{keywordtype}{None},} +\DoxyCodeLine{ \textcolor{stringliteral}{'period'}: \textcolor{keyword}{lambda}: \textcolor{keywordtype}{None},} +\DoxyCodeLine{ \}} + +\end{DoxyCode} +\mbox{\Hypertarget{classisland__purging__args_1_1_island_purging_args_a3777a6ef4fac9e1e48234b5f76169614}\label{classisland__purging__args_1_1_island_purging_args_a3777a6ef4fac9e1e48234b5f76169614}} +\index{island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}!SELECTION\_METHOD\_MAP@{SELECTION\_METHOD\_MAP}} +\index{SELECTION\_METHOD\_MAP@{SELECTION\_METHOD\_MAP}!island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}} +\doxysubsubsection{\texorpdfstring{SELECTION\_METHOD\_MAP}{SELECTION\_METHOD\_MAP}} +{\footnotesize\ttfamily dictionary island\+\_\+purging\+\_\+args.\+Island\+Purging\+Args.\+S\+E\+L\+E\+C\+T\+I\+O\+N\+\_\+\+M\+E\+T\+H\+O\+D\+\_\+\+M\+AP\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{'worst\_best'}: \textcolor{stringliteral}{'clear\_island\_with\_worst\_best\_genome'}} +\DoxyCodeLine{ \}} + +\end{DoxyCode} +\mbox{\Hypertarget{classisland__purging__args_1_1_island_purging_args_ababdf6b88eef9038a42ca6c03c8337ab}\label{classisland__purging__args_1_1_island_purging_args_ababdf6b88eef9038a42ca6c03c8337ab}} +\index{island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}!TYPES@{TYPES}} +\index{TYPES@{TYPES}!island\_purging\_args.IslandPurgingArgs@{island\_purging\_args.IslandPurgingArgs}} +\doxysubsubsection{\texorpdfstring{TYPES}{TYPES}} +{\footnotesize\ttfamily dictionary island\+\_\+purging\+\_\+args.\+Island\+Purging\+Args.\+T\+Y\+P\+ES\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{'period'}: \{int\},} +\DoxyCodeLine{ \textcolor{stringliteral}{'selection\_method'}: \{str\}} +\DoxyCodeLine{ \}} + +\end{DoxyCode} + + +The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} +\item +cli/island\+\_\+purging\+\_\+args.\+py\end{DoxyCompactItemize} diff --git a/docs/latex/classmin__max__heap.tex b/docs/latex/classmin__max__heap.tex new file mode 100644 index 00000000..e9bc1457 --- /dev/null +++ b/docs/latex/classmin__max__heap.tex @@ -0,0 +1,184 @@ +\hypertarget{classmin__max__heap}{}\doxysection{min\+\_\+max\+\_\+heap$<$ T $>$ Class Template Reference} +\label{classmin__max__heap}\index{min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}} + + +This implementation of a \mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}} was basically ripped from here\+: \href{https://github.com/itsjohncs/minmaxheap-cpp/blob/master/MinMaxHeap.hpp}{\texttt{ https\+://github.\+com/itsjohncs/minmaxheap-\/cpp/blob/master/\+Min\+Max\+Heap.\+hpp}}. + + + + +{\ttfamily \#include $<$min\+\_\+max\+\_\+heap.\+hxx$>$} + +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\hyperlink{classmin__max__heap_a8150a4c81beed0795529f923cdc57751}{min\+\_\+max\+\_\+heap}} (\textbf{ std\+::function}$<$ bool(const T \&, const T \&)$>$ \+\_\+less\+\_\+than, uint32\+\_\+t size\+\_\+hint=-\/1) +\begin{DoxyCompactList}\small\item\em Creates a new \mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}}. \end{DoxyCompactList}\item +\mbox{\Hypertarget{classmin__max__heap_ae1ae5ecbdcec9be68322b8b369beb5a4}\label{classmin__max__heap_ae1ae5ecbdcec9be68322b8b369beb5a4}} +bool {\bfseries empty} () const +\item +\mbox{\Hypertarget{classmin__max__heap_a75d5cef5d4098a9b8c1d5ba27438b44f}\label{classmin__max__heap_a75d5cef5d4098a9b8c1d5ba27438b44f}} +uint32\+\_\+t {\bfseries size} () const +\item +\mbox{\Hypertarget{classmin__max__heap_af3daad04ceab28dbd8f262c251925a9d}\label{classmin__max__heap_af3daad04ceab28dbd8f262c251925a9d}} +void \mbox{\hyperlink{classmin__max__heap_af3daad04ceab28dbd8f262c251925a9d}{enqueue}} (const T \&e) +\begin{DoxyCompactList}\small\item\em Adds the element e to the heap (in the correct order of course). \end{DoxyCompactList}\item +\mbox{\Hypertarget{classmin__max__heap_a44435bb29db7b94beffa474be7a1ea3f}\label{classmin__max__heap_a44435bb29db7b94beffa474be7a1ea3f}} +const T \& {\bfseries find\+\_\+max} () const +\item +\mbox{\Hypertarget{classmin__max__heap_a662b330984f8c17e7af014091b85a5e4}\label{classmin__max__heap_a662b330984f8c17e7af014091b85a5e4}} +const T \& {\bfseries find\+\_\+min} () const +\item +T \mbox{\hyperlink{classmin__max__heap_a9184c94f7909bc98b6097cad8a49f4bc}{pop\+\_\+max}} () +\begin{DoxyCompactList}\small\item\em Returns and removes the maximum item in this min-\/max heap. \end{DoxyCompactList}\item +\mbox{\Hypertarget{classmin__max__heap_a180790e37a1d72856b3cd28a84f53287}\label{classmin__max__heap_a180790e37a1d72856b3cd28a84f53287}} +T {\bfseries pop} () +\item +T \mbox{\hyperlink{classmin__max__heap_aa80b1443a03ec6c6f63382b8d93a4b11}{pop\+\_\+min}} () +\begin{DoxyCompactList}\small\item\em Returns and removes the minimum item in this min-\/max heap. \end{DoxyCompactList}\item +const\+\_\+iterator \mbox{\hyperlink{classmin__max__heap_a42e3ecf3ab992eddf731bba3cdd5ce9d}{cbegin}} () +\begin{DoxyCompactList}\small\item\em returns the const\+\_\+iterator to the beginning of the underlying vector. \end{DoxyCompactList}\item +const\+\_\+iterator \mbox{\hyperlink{classmin__max__heap_afeaa2d61f64081af3645044292dac605}{cend}} () +\begin{DoxyCompactList}\small\item\em returns the const\+\_\+iterator pointing past the end of the underlying vector (i.\+e. \end{DoxyCompactList}\item +\mbox{\Hypertarget{classmin__max__heap_a296ebbc3fe939b962f339d62aff83d15}\label{classmin__max__heap_a296ebbc3fe939b962f339d62aff83d15}} +T \& \mbox{\hyperlink{classmin__max__heap_a296ebbc3fe939b962f339d62aff83d15}{operator\mbox{[}$\,$\mbox{]}}} (size\+\_\+t index) +\begin{DoxyCompactList}\small\item\em Implementation of the indexing operator to allow direct access. \end{DoxyCompactList}\item +\mbox{\Hypertarget{classmin__max__heap_a3296e9f6f5b4eab2818d0ae9f8510e06}\label{classmin__max__heap_a3296e9f6f5b4eab2818d0ae9f8510e06}} +T \mbox{\hyperlink{classmin__max__heap_a3296e9f6f5b4eab2818d0ae9f8510e06}{erase}} (size\+\_\+t index) +\begin{DoxyCompactList}\small\item\em Allows for deletion of an element at a specified index in O(log(n)) time. \end{DoxyCompactList}\item +\mbox{\Hypertarget{classmin__max__heap_a962166c243c1a1278b41b59d397a5189}\label{classmin__max__heap_a962166c243c1a1278b41b59d397a5189}} +void \mbox{\hyperlink{classmin__max__heap_a962166c243c1a1278b41b59d397a5189}{reserve}} (size\+\_\+t n) +\begin{DoxyCompactList}\small\item\em Ensures that the underlying vector can hold at least n elements. \end{DoxyCompactList}\item +\mbox{\Hypertarget{classmin__max__heap_a7bc015a4db5cc82870e23732e0b01c60}\label{classmin__max__heap_a7bc015a4db5cc82870e23732e0b01c60}} +void \mbox{\hyperlink{classmin__max__heap_a7bc015a4db5cc82870e23732e0b01c60}{clear}} () +\begin{DoxyCompactList}\small\item\em Deletes every element in this heap. \end{DoxyCompactList}\end{DoxyCompactItemize} +\doxysubsection*{Private Types} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classmin__max__heap_ac30e7b7215bebb739cd3eb9eba912a85}\label{classmin__max__heap_ac30e7b7215bebb739cd3eb9eba912a85}} +typedef \textbf{ std\+::vector}$<$ T $>$\+::const\+\_\+iterator {\bfseries const\+\_\+iterator} +\end{DoxyCompactItemize} +\doxysubsection*{Private Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classmin__max__heap_a295f6c99a8b902cc1d688eefe5779736}\label{classmin__max__heap_a295f6c99a8b902cc1d688eefe5779736}} +{\footnotesize template$<$bool max\+\_\+level$>$ }\\void {\bfseries trickle\+\_\+up\+\_\+inner} (uint32\+\_\+t z) +\item +\mbox{\Hypertarget{classmin__max__heap_a606a7c04315e6d4f415d0dec3464cf9d}\label{classmin__max__heap_a606a7c04315e6d4f415d0dec3464cf9d}} +void {\bfseries trickle\+\_\+up} (uint32\+\_\+t z) +\item +\mbox{\Hypertarget{classmin__max__heap_a1d893c1254f50fcd2ee84e4d2c9fad46}\label{classmin__max__heap_a1d893c1254f50fcd2ee84e4d2c9fad46}} +{\footnotesize template$<$bool max\+\_\+level$>$ }\\void {\bfseries trickle\+\_\+down\+\_\+inner} (const uint32\+\_\+t z) +\item +\mbox{\Hypertarget{classmin__max__heap_a6fc48c130f871bbcd343141fdfee8176}\label{classmin__max__heap_a6fc48c130f871bbcd343141fdfee8176}} +void {\bfseries trickle\+\_\+down} (uint32\+\_\+t z) +\item +\mbox{\Hypertarget{classmin__max__heap_abf72fb5bec7655ab16fe1c178ebfa06e}\label{classmin__max__heap_abf72fb5bec7655ab16fe1c178ebfa06e}} +uint32\+\_\+t {\bfseries find\+\_\+min\+\_\+index} () const +\item +\mbox{\Hypertarget{classmin__max__heap_af7217da00ac35b2811b624d93a1d8e7a}\label{classmin__max__heap_af7217da00ac35b2811b624d93a1d8e7a}} +T {\bfseries delete\+\_\+element} (uint32\+\_\+t z) +\end{DoxyCompactItemize} +\doxysubsection*{Static Private Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classmin__max__heap_a813d3838b295f27a52f8f986f4d5cdbe}\label{classmin__max__heap_a813d3838b295f27a52f8f986f4d5cdbe}} +static uint32\+\_\+t {\bfseries parent} (uint32\+\_\+t z) +\item +\mbox{\Hypertarget{classmin__max__heap_a6f85d95a22907d30d6a099e75c00518e}\label{classmin__max__heap_a6f85d95a22907d30d6a099e75c00518e}} +static uint32\+\_\+t {\bfseries left\+\_\+child} (uint32\+\_\+t z) +\item +\mbox{\Hypertarget{classmin__max__heap_a2249d6b56d22b8833c15efe07c2dd1d0}\label{classmin__max__heap_a2249d6b56d22b8833c15efe07c2dd1d0}} +static uint32\+\_\+t {\bfseries right\+\_\+child} (uint32\+\_\+t z) +\item +\mbox{\Hypertarget{classmin__max__heap_a59893aed50ba7c69dbec36ce0300ee41}\label{classmin__max__heap_a59893aed50ba7c69dbec36ce0300ee41}} +static bool {\bfseries is\+\_\+on\+\_\+min\+\_\+level} (uint32\+\_\+t z) +\item +\mbox{\Hypertarget{classmin__max__heap_a3f588656c909df8ecfd3686b1f6dcd91}\label{classmin__max__heap_a3f588656c909df8ecfd3686b1f6dcd91}} +static bool {\bfseries is\+\_\+on\+\_\+max\+\_\+level} (uint32\+\_\+t z) +\end{DoxyCompactItemize} +\doxysubsection*{Private Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classmin__max__heap_adfbc5da06a1e984ab32754342df4d46f}\label{classmin__max__heap_adfbc5da06a1e984ab32754342df4d46f}} +vector$<$ T $>$ {\bfseries heap} +\item +\mbox{\Hypertarget{classmin__max__heap_aff5a71e4120a281f3b470fefe0eae260}\label{classmin__max__heap_aff5a71e4120a281f3b470fefe0eae260}} +function$<$ bool(const T \&, const T \&)$>$ {\bfseries less\+\_\+than} +\end{DoxyCompactItemize} + + +\doxysubsection{Detailed Description} +\subsubsection*{template$<$class T$>$\newline +class min\+\_\+max\+\_\+heap$<$ T $>$} + +This implementation of a \mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}} was basically ripped from here\+: \href{https://github.com/itsjohncs/minmaxheap-cpp/blob/master/MinMaxHeap.hpp}{\texttt{ https\+://github.\+com/itsjohncs/minmaxheap-\/cpp/blob/master/\+Min\+Max\+Heap.\+hpp}}. + +Along with this for reference \href{https://en.wikipedia.org/wiki/Min-max_heap}{\texttt{ https\+://en.\+wikipedia.\+org/wiki/\+Min-\/max\+\_\+heap}} + +The first layer (e.\+g. the layer which only contains the root node) contains the maximum element. + +\doxysubsection{Constructor \& Destructor Documentation} +\mbox{\Hypertarget{classmin__max__heap_a8150a4c81beed0795529f923cdc57751}\label{classmin__max__heap_a8150a4c81beed0795529f923cdc57751}} +\index{min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}!min\_max\_heap@{min\_max\_heap}} +\index{min\_max\_heap@{min\_max\_heap}!min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}} +\doxysubsubsection{\texorpdfstring{min\_max\_heap()}{min\_max\_heap()}} +{\footnotesize\ttfamily template$<$class T $>$ \\ +\mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}}$<$ T $>$\+::\mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}} (\begin{DoxyParamCaption}\item[{\textbf{ std\+::function}$<$ bool(const T \&, const T \&)$>$}]{\+\_\+less\+\_\+than, }\item[{uint32\+\_\+t}]{size\+\_\+hint = {\ttfamily -\/1} }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} + + + +Creates a new \mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}}. + +The only parameter is a function that will compare two elements, and determine if the first is less than the second. This could probably be done in a better way with generics but I\textquotesingle{}m not confident in doing so. + +\doxysubsection{Member Function Documentation} +\mbox{\Hypertarget{classmin__max__heap_a42e3ecf3ab992eddf731bba3cdd5ce9d}\label{classmin__max__heap_a42e3ecf3ab992eddf731bba3cdd5ce9d}} +\index{min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}!cbegin@{cbegin}} +\index{cbegin@{cbegin}!min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}} +\doxysubsubsection{\texorpdfstring{cbegin()}{cbegin()}} +{\footnotesize\ttfamily template$<$class T $>$ \\ +const\+\_\+iterator \mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}}$<$ T $>$\+::cbegin (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} + + + +returns the const\+\_\+iterator to the beginning of the underlying vector. + +There is no non-\/const iterator because the ordering of the data structure could potentially be changed. \mbox{\Hypertarget{classmin__max__heap_afeaa2d61f64081af3645044292dac605}\label{classmin__max__heap_afeaa2d61f64081af3645044292dac605}} +\index{min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}!cend@{cend}} +\index{cend@{cend}!min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}} +\doxysubsubsection{\texorpdfstring{cend()}{cend()}} +{\footnotesize\ttfamily template$<$class T $>$ \\ +const\+\_\+iterator \mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}}$<$ T $>$\+::cend (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} + + + +returns the const\+\_\+iterator pointing past the end of the underlying vector (i.\+e. + +this is an invalid reference). There is no non-\/const end iterator because the ordering of the data structure must be preserved. \mbox{\Hypertarget{classmin__max__heap_a9184c94f7909bc98b6097cad8a49f4bc}\label{classmin__max__heap_a9184c94f7909bc98b6097cad8a49f4bc}} +\index{min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}!pop\_max@{pop\_max}} +\index{pop\_max@{pop\_max}!min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}} +\doxysubsubsection{\texorpdfstring{pop\_max()}{pop\_max()}} +{\footnotesize\ttfamily template$<$class T $>$ \\ +T \mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}}$<$ T $>$\+::pop\+\_\+max (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} + + + +Returns and removes the maximum item in this min-\/max heap. + +If the heap is emoty it will throw an underflow\+\_\+error \mbox{\Hypertarget{classmin__max__heap_aa80b1443a03ec6c6f63382b8d93a4b11}\label{classmin__max__heap_aa80b1443a03ec6c6f63382b8d93a4b11}} +\index{min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}!pop\_min@{pop\_min}} +\index{pop\_min@{pop\_min}!min\_max\_heap$<$ T $>$@{min\_max\_heap$<$ T $>$}} +\doxysubsubsection{\texorpdfstring{pop\_min()}{pop\_min()}} +{\footnotesize\ttfamily template$<$class T $>$ \\ +T \mbox{\hyperlink{classmin__max__heap}{min\+\_\+max\+\_\+heap}}$<$ T $>$\+::pop\+\_\+min (\begin{DoxyParamCaption}{ }\end{DoxyParamCaption})\hspace{0.3cm}{\ttfamily [inline]}} + + + +Returns and removes the minimum item in this min-\/max heap. + +If the heap is empty, it will throw an underflow\+\_\+error + +The documentation for this class was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/min\+\_\+max\+\_\+heap.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/classrec__args_1_1_rec_args.tex b/docs/latex/classrec__args_1_1_rec_args.tex new file mode 100644 index 00000000..0af4c5a8 --- /dev/null +++ b/docs/latex/classrec__args_1_1_rec_args.tex @@ -0,0 +1,104 @@ +\hypertarget{classrec__args_1_1_rec_args}{}\doxysection{rec\+\_\+args.\+Rec\+Args Class Reference} +\label{classrec__args_1_1_rec_args}\index{rec\_args.RecArgs@{rec\_args.RecArgs}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classrec__args_1_1_rec_args_ae258306971ce88fe84711f2b45b25ce2}\label{classrec__args_1_1_rec_args_ae258306971ce88fe84711f2b45b25ce2}} +def {\bfseries \+\_\+\+\_\+init\+\_\+\+\_\+} (self, rec, filename) +\item +\mbox{\Hypertarget{classrec__args_1_1_rec_args_adb2ba8e6bea883cedfe9734738288c67}\label{classrec__args_1_1_rec_args_adb2ba8e6bea883cedfe9734738288c67}} +def {\bfseries to\+\_\+args} (self) +\end{DoxyCompactItemize} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{classrec__args_1_1_rec_args_ac2af995aff9e83dcc76135264391505f}\label{classrec__args_1_1_rec_args_ac2af995aff9e83dcc76135264391505f}} +{\bfseries c2a} +\end{DoxyCompactItemize} +\doxysubsection*{Static Public Attributes} +\begin{DoxyCompactItemize} +\item +dictionary {\bfseries C\+O\+N\+F\+I\+G\+\_\+\+O\+P\+T\+I\+O\+NS} +\item +dictionary {\bfseries D\+E\+F\+A\+U\+L\+TS} +\item +dictionary {\bfseries T\+Y\+P\+ES} +\item +dictionary {\bfseries C\+O\+N\+S\+T\+R\+A\+I\+N\+TS} +\end{DoxyCompactItemize} + + +\doxysubsection{Member Data Documentation} +\mbox{\Hypertarget{classrec__args_1_1_rec_args_a91b75b05d716ac2324963ebc39901b23}\label{classrec__args_1_1_rec_args_a91b75b05d716ac2324963ebc39901b23}} +\index{rec\_args.RecArgs@{rec\_args.RecArgs}!CONFIG\_OPTIONS@{CONFIG\_OPTIONS}} +\index{CONFIG\_OPTIONS@{CONFIG\_OPTIONS}!rec\_args.RecArgs@{rec\_args.RecArgs}} +\doxysubsubsection{\texorpdfstring{CONFIG\_OPTIONS}{CONFIG\_OPTIONS}} +{\footnotesize\ttfamily dictionary rec\+\_\+args.\+Rec\+Args.\+C\+O\+N\+F\+I\+G\+\_\+\+O\+P\+T\+I\+O\+NS\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{'min'}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/rec\_delay\_min'}, str(x)],} +\DoxyCodeLine{ \textcolor{stringliteral}{'max'}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/rec\_delay\_max'}, str(x)],} +\DoxyCodeLine{ \textcolor{stringliteral}{'dist'}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/rec\_sampling\_distribution'}, str(x)],} +\DoxyCodeLine{ \textcolor{stringliteral}{'population'}: \textcolor{keyword}{lambda} self, x: [\textcolor{stringliteral}{'-\/-\/rec\_sampling\_population'}, str(x)],} +\DoxyCodeLine{ \}} + +\end{DoxyCode} +\mbox{\Hypertarget{classrec__args_1_1_rec_args_a2146df883b85dde5a5385fb586a5e193}\label{classrec__args_1_1_rec_args_a2146df883b85dde5a5385fb586a5e193}} +\index{rec\_args.RecArgs@{rec\_args.RecArgs}!CONSTRAINTS@{CONSTRAINTS}} +\index{CONSTRAINTS@{CONSTRAINTS}!rec\_args.RecArgs@{rec\_args.RecArgs}} +\doxysubsubsection{\texorpdfstring{CONSTRAINTS}{CONSTRAINTS}} +{\footnotesize\ttfamily dictionary rec\+\_\+args.\+Rec\+Args.\+C\+O\+N\+S\+T\+R\+A\+I\+N\+TS\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{'min'}: (\textcolor{keyword}{lambda} self: self.min < self.max \textcolor{keywordflow}{and} self.min >= 1,} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be less than examm.rec.max and at least 1"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{'max'}: (\textcolor{keyword}{lambda} self: self.max > self.min \textcolor{keywordflow}{and} self.max >= 2,} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be greater than examm.rec.min and at least 2"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{'dist'}: (\textcolor{keyword}{lambda} self: self.dist \textcolor{keywordflow}{in} \{\textcolor{stringliteral}{'uniform'}, \textcolor{stringliteral}{'histogram'}, \textcolor{stringliteral}{'normal'}\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be one of 'uniform', 'histogram', or 'normal'"}),} +\DoxyCodeLine{ \textcolor{stringliteral}{'population'}: (\textcolor{keyword}{lambda} self: self.population \textcolor{keywordflow}{in} \{\textcolor{stringliteral}{'global'}, \textcolor{stringliteral}{'island'}\},} +\DoxyCodeLine{ \textcolor{stringliteral}{"must be either 'global' or 'island'"})} +\DoxyCodeLine{ \}} + +\end{DoxyCode} +\mbox{\Hypertarget{classrec__args_1_1_rec_args_a57018418c93e6e6ed69550fb4dbfbd3f}\label{classrec__args_1_1_rec_args_a57018418c93e6e6ed69550fb4dbfbd3f}} +\index{rec\_args.RecArgs@{rec\_args.RecArgs}!DEFAULTS@{DEFAULTS}} +\index{DEFAULTS@{DEFAULTS}!rec\_args.RecArgs@{rec\_args.RecArgs}} +\doxysubsubsection{\texorpdfstring{DEFAULTS}{DEFAULTS}} +{\footnotesize\ttfamily dictionary rec\+\_\+args.\+Rec\+Args.\+D\+E\+F\+A\+U\+L\+TS\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{'min'}: \textcolor{keyword}{lambda}: 1,} +\DoxyCodeLine{ \textcolor{stringliteral}{'max'}: \textcolor{keyword}{lambda}: 50,} +\DoxyCodeLine{ \textcolor{stringliteral}{'dist'}: \textcolor{keyword}{lambda}: \textcolor{stringliteral}{'uniform'},} +\DoxyCodeLine{ \textcolor{stringliteral}{'population'}: \textcolor{keyword}{lambda}: \textcolor{stringliteral}{'global'},} +\DoxyCodeLine{ \}} + +\end{DoxyCode} +\mbox{\Hypertarget{classrec__args_1_1_rec_args_a446093f75b5a05a2d5968868600288e0}\label{classrec__args_1_1_rec_args_a446093f75b5a05a2d5968868600288e0}} +\index{rec\_args.RecArgs@{rec\_args.RecArgs}!TYPES@{TYPES}} +\index{TYPES@{TYPES}!rec\_args.RecArgs@{rec\_args.RecArgs}} +\doxysubsubsection{\texorpdfstring{TYPES}{TYPES}} +{\footnotesize\ttfamily dictionary rec\+\_\+args.\+Rec\+Args.\+T\+Y\+P\+ES\hspace{0.3cm}{\ttfamily [static]}} + +{\bfseries Initial value\+:} +\begin{DoxyCode}{0} +\DoxyCodeLine{= \{} +\DoxyCodeLine{ \textcolor{stringliteral}{'min'}: \{int\},} +\DoxyCodeLine{ \textcolor{stringliteral}{'max'}: \{int\},} 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-.2ex}% + {1.5ex \@plus .2ex}% + {\raggedright\normalfont\normalsize\bfseries}} +\newcommand\doxyparagraph{\@startsection{paragraph}{4}{\z@}% + {3.25ex \@plus1ex \@minus.2ex}% + {-1em}% + {\raggedright\normalfont\normalsize\bfseries}} +\newcommand\doxysubparagraph{\@startsection{subparagraph}{5}{\parindent}% + {3.25ex \@plus1ex \@minus .2ex}% + {-1em}% + {\raggedright\normalfont\normalsize\bfseries}} +\makeatother +% Define caption that is also suitable in a table +\makeatletter +\def\doxyfigcaption{% +\refstepcounter{figure}% +\@dblarg{\@caption{figure}}} +\makeatother diff --git a/docs/latex/hierarchy.tex b/docs/latex/hierarchy.tex new file mode 100644 index 00000000..f9b5f04e --- /dev/null +++ b/docs/latex/hierarchy.tex @@ -0,0 +1,110 @@ +\doxysection{Class Hierarchy} +This inheritance list is sorted roughly, but not completely, alphabetically\+:\begin{DoxyCompactList} +\item \contentsline{section}{C\+N\+N\+\_\+\+Edge}{\pageref{class_c_n_n___edge}}{} +\item \contentsline{section}{C\+N\+N\+\_\+\+Genome}{\pageref{class_c_n_n___genome}}{} +\item \contentsline{section}{C\+N\+N\+\_\+\+Node}{\pageref{class_c_n_n___node}}{} +\item \contentsline{section}{Coin}{\pageref{struct_coin}}{} +\item \contentsline{section}{Color}{\pageref{struct_color}}{} +\item \contentsline{section}{Color\+Profile}{\pageref{struct_color_profile}}{} +\item \contentsline{section}{Color\+Tree}{\pageref{struct_color_tree}}{} +\item \contentsline{section}{config\+\_\+to\+\_\+arg.\+Config\+To\+Arg}{\pageref{classconfig__to__arg_1_1_config_to_arg}}{} +\begin{DoxyCompactList} +\item \contentsline{section}{examm\+\_\+task.\+Examm\+Task}{\pageref{classexamm__task_1_1_examm_task}}{} +\end{DoxyCompactList} +\item \contentsline{section}{Consolidated\+Statistics}{\pageref{class_consolidated_statistics}}{} +\item \contentsline{section}{Corpus}{\pageref{class_corpus}}{} +\item \contentsline{section}{cs\+\_\+less\+\_\+than\+\_\+avg}{\pageref{structcs__less__than__avg}}{} +\item \contentsline{section}{cs\+\_\+less\+\_\+than\+\_\+max}{\pageref{structcs__less__than__max}}{} +\item \contentsline{section}{cs\+\_\+less\+\_\+than\+\_\+min}{\pageref{structcs__less__than__min}}{} +\item \contentsline{section}{E\+X\+A\+CT}{\pageref{class_e_x_a_c_t}}{} +\item \contentsline{section}{E\+X\+A\+C\+T\+\_\+\+R\+E\+S\+U\+LT}{\pageref{struct_e_x_a_c_t___r_e_s_u_l_t}}{} +\item \contentsline{section}{E\+X\+A\+MM}{\pageref{class_e_x_a_m_m}}{} +\item \contentsline{section}{Hash}{\pageref{struct_hash}}{} +\item \contentsline{section}{Huffman\+Tree}{\pageref{struct_huffman_tree}}{} +\item \contentsline{section}{Image\+Interface}{\pageref{class_image_interface}}{} +\begin{DoxyCompactList} +\item \contentsline{section}{Image}{\pageref{class_image}}{} +\item \contentsline{section}{Large\+Image}{\pageref{class_large_image}}{} +\end{DoxyCompactList} +\item \contentsline{section}{Images\+Interface}{\pageref{class_images_interface}}{} +\begin{DoxyCompactList} +\item \contentsline{section}{Images}{\pageref{class_images}}{} +\item \contentsline{section}{Multi\+Images\+Interface}{\pageref{class_multi_images_interface}}{} +\begin{DoxyCompactList} +\item \contentsline{section}{Large\+Images}{\pageref{class_large_images}}{} +\item \contentsline{section}{Mosaic\+Images}{\pageref{class_mosaic_images}}{} +\end{DoxyCompactList} +\end{DoxyCompactList} +\item \contentsline{section}{Island}{\pageref{class_island}}{} +\item \contentsline{section}{island\+\_\+purging\+\_\+args.\+Island\+Purging\+Args}{\pageref{classisland__purging__args_1_1_island_purging_args}}{} +\item \contentsline{section}{less\+\_\+than\+\_\+avg}{\pageref{structless__than__avg}}{} +\item \contentsline{section}{less\+\_\+than\+\_\+dfm\+\_\+avg}{\pageref{structless__than__dfm__avg}}{} +\item \contentsline{section}{less\+\_\+than\+\_\+dfm\+\_\+max}{\pageref{structless__than__dfm__max}}{} +\item \contentsline{section}{less\+\_\+than\+\_\+dfm\+\_\+min}{\pageref{structless__than__dfm__min}}{} +\item \contentsline{section}{less\+\_\+than\+\_\+max}{\pageref{structless__than__max}}{} +\item \contentsline{section}{less\+\_\+than\+\_\+min}{\pageref{structless__than__min}}{} +\item \contentsline{section}{Line}{\pageref{class_line}}{} +\item \contentsline{section}{Lode\+P\+N\+G\+Color\+Mode}{\pageref{struct_lode_p_n_g_color_mode}}{} +\item \contentsline{section}{Lode\+P\+N\+G\+Compress\+Settings}{\pageref{struct_lode_p_n_g_compress_settings}}{} +\item \contentsline{section}{Lode\+P\+N\+G\+Decoder\+Settings}{\pageref{struct_lode_p_n_g_decoder_settings}}{} +\item \contentsline{section}{Lode\+P\+N\+G\+Decompress\+Settings}{\pageref{struct_lode_p_n_g_decompress_settings}}{} +\item \contentsline{section}{Lode\+P\+N\+G\+Encoder\+Settings}{\pageref{struct_lode_p_n_g_encoder_settings}}{} +\item \contentsline{section}{Lode\+P\+N\+G\+Info}{\pageref{struct_lode_p_n_g_info}}{} +\item \contentsline{section}{Lode\+P\+N\+G\+State}{\pageref{struct_lode_p_n_g_state}}{} +\item \contentsline{section}{Lode\+P\+N\+G\+Time}{\pageref{struct_lode_p_n_g_time}}{} +\item \contentsline{section}{Log}{\pageref{class_log}}{} +\item \contentsline{section}{Log\+File}{\pageref{class_log_file}}{} +\item \contentsline{section}{min\+\_\+max\+\_\+heap$<$ T $>$}{\pageref{classmin__max__heap}}{} +\item \contentsline{section}{Normal\+Distribution}{\pageref{class_normal_distribution}}{} +\item \contentsline{section}{Parse\+Turbines}{\pageref{class_parse_turbines}}{} +\item \contentsline{section}{Point}{\pageref{class_point}}{} +\item \contentsline{section}{rec\+\_\+args.\+Rec\+Args}{\pageref{classrec__args_1_1_rec_args}}{} +\item \contentsline{section}{Rectangle}{\pageref{class_rectangle}}{} +\item \contentsline{section}{Result\+Set}{\pageref{struct_result_set}}{} +\item \contentsline{section}{R\+NN}{\pageref{class_r_n_n}}{} +\item \contentsline{section}{R\+N\+N\+\_\+\+Edge}{\pageref{class_r_n_n___edge}}{} +\item \contentsline{section}{R\+N\+N\+\_\+\+Genome}{\pageref{class_r_n_n___genome}}{} +\item \contentsline{section}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}{\pageref{class_r_n_n___node___interface}}{} +\begin{DoxyCompactList} +\item \contentsline{section}{Delta\+\_\+\+Node}{\pageref{class_delta___node}}{} +\item \contentsline{section}{E\+N\+A\+R\+C\+\_\+\+Node}{\pageref{class_e_n_a_r_c___node}}{} +\item \contentsline{section}{E\+N\+A\+S\+\_\+\+D\+A\+G\+\_\+\+Node}{\pageref{class_e_n_a_s___d_a_g___node}}{} +\item \contentsline{section}{G\+R\+U\+\_\+\+Node}{\pageref{class_g_r_u___node}}{} +\item \contentsline{section}{L\+S\+T\+M\+\_\+\+Node}{\pageref{class_l_s_t_m___node}}{} +\item \contentsline{section}{M\+G\+U\+\_\+\+Node}{\pageref{class_m_g_u___node}}{} +\item \contentsline{section}{R\+A\+N\+D\+O\+M\+\_\+\+D\+A\+G\+\_\+\+Node}{\pageref{class_r_a_n_d_o_m___d_a_g___node}}{} +\item \contentsline{section}{R\+N\+N\+\_\+\+Node}{\pageref{class_r_n_n___node}}{} +\item \contentsline{section}{U\+G\+R\+N\+N\+\_\+\+Node}{\pageref{class_u_g_r_n_n___node}}{} +\end{DoxyCompactList} +\item \contentsline{section}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}{\pageref{class_r_n_n___recurrent___edge}}{} +\item \contentsline{section}{Run\+Statistics}{\pageref{class_run_statistics}}{} +\item \contentsline{section}{Sentence\+Series}{\pageref{class_sentence_series}}{} +\item \contentsline{section}{sort\+\_\+\+C\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+depth}{\pageref{structsort___c_n_n___edges__by__depth}}{} +\item \contentsline{section}{sort\+\_\+\+C\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+innovation}{\pageref{structsort___c_n_n___edges__by__innovation}}{} +\item \contentsline{section}{sort\+\_\+\+C\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+output\+\_\+depth}{\pageref{structsort___c_n_n___edges__by__output__depth}}{} +\item \contentsline{section}{sort\+\_\+\+C\+N\+N\+\_\+\+Nodes\+\_\+by\+\_\+depth}{\pageref{structsort___c_n_n___nodes__by__depth}}{} +\item \contentsline{section}{sort\+\_\+genomes\+\_\+by\+\_\+fitness}{\pageref{structsort__genomes__by__fitness}}{} +\item \contentsline{section}{sort\+\_\+genomes\+\_\+by\+\_\+predictions}{\pageref{structsort__genomes__by__predictions}}{} +\item \contentsline{section}{sort\+\_\+genomes\+\_\+by\+\_\+validation\+\_\+error}{\pageref{structsort__genomes__by__validation__error}}{} +\item \contentsline{section}{sort\+\_\+\+R\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+depth}{\pageref{structsort___r_n_n___edges__by__depth}}{} +\item \contentsline{section}{sort\+\_\+\+R\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+innovation}{\pageref{structsort___r_n_n___edges__by__innovation}}{} +\item \contentsline{section}{sort\+\_\+\+R\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+output\+\_\+depth}{\pageref{structsort___r_n_n___edges__by__output__depth}}{} +\item \contentsline{section}{sort\+\_\+\+R\+N\+N\+\_\+\+Nodes\+\_\+by\+\_\+depth}{\pageref{structsort___r_n_n___nodes__by__depth}}{} +\item \contentsline{section}{sort\+\_\+\+R\+N\+N\+\_\+\+Nodes\+\_\+by\+\_\+innovation}{\pageref{structsort___r_n_n___nodes__by__innovation}}{} +\item \contentsline{section}{sort\+\_\+\+R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edges\+\_\+by\+\_\+depth}{\pageref{structsort___r_n_n___recurrent___edges__by__depth}}{} +\item \contentsline{section}{sort\+\_\+\+R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edges\+\_\+by\+\_\+innovation}{\pageref{structsort___r_n_n___recurrent___edges__by__innovation}}{} +\item \contentsline{section}{sort\+\_\+\+R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edges\+\_\+by\+\_\+output\+\_\+depth}{\pageref{structsort___r_n_n___recurrent___edges__by__output__depth}}{} +\item \contentsline{section}{Speciation\+Strategy}{\pageref{class_speciation_strategy}}{} +\begin{DoxyCompactList} +\item \contentsline{section}{Island\+Speciation\+Strategy}{\pageref{class_island_speciation_strategy}}{} +\item \contentsline{section}{Neat\+Speciation\+Strategy}{\pageref{class_neat_speciation_strategy}}{} +\end{DoxyCompactList} +\item \contentsline{section}{Species}{\pageref{class_species}}{} +\item \contentsline{section}{Time\+Series}{\pageref{class_time_series}}{} +\item \contentsline{section}{Time\+Series\+Set}{\pageref{class_time_series_set}}{} +\item \contentsline{section}{Time\+Series\+Sets}{\pageref{class_time_series_sets}}{} +\item \contentsline{section}{Tracker}{\pageref{class_tracker}}{} +\item \contentsline{section}{ucvector}{\pageref{structucvector}}{} +\item \contentsline{section}{uivector}{\pageref{structuivector}}{} +\item \contentsline{section}{Word\+Series}{\pageref{class_word_series}}{} +\end{DoxyCompactList} diff --git a/docs/latex/longtable_doxygen.sty b/docs/latex/longtable_doxygen.sty new file mode 100644 index 00000000..a0eb314f --- /dev/null +++ b/docs/latex/longtable_doxygen.sty @@ -0,0 +1,448 @@ +%% +%% This is file `longtable.sty', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% longtable.dtx (with options: `package') +%% +%% This is a generated file. +%% +%% The source is maintained by the LaTeX Project team and bug +%% reports for it can be opened at http://latex-project.org/bugs.html +%% (but please observe conditions on bug reports sent to that address!) +%% +%% Copyright 1993-2016 +%% The LaTeX3 Project and any individual authors listed elsewhere +%% in this file. +%% +%% This file was generated from file(s) of the Standard LaTeX `Tools Bundle'. +%% -------------------------------------------------------------------------- +%% +%% It may be distributed and/or modified under the +%% conditions of the LaTeX Project Public License, either version 1.3c +%% of this license or (at your option) any later version. +%% The latest version of this license is in +%% http://www.latex-project.org/lppl.txt +%% and version 1.3c or later is part of all distributions of LaTeX +%% version 2005/12/01 or later. +%% +%% This file may only be distributed together with a copy of the LaTeX +%% `Tools Bundle'. You may however distribute the LaTeX `Tools Bundle' +%% without such generated files. +%% +%% The list of all files belonging to the LaTeX `Tools Bundle' is +%% given in the file `manifest.txt'. +%% +%% File: longtable.dtx Copyright (C) 1990-2001 David Carlisle +\NeedsTeXFormat{LaTeX2e}[1995/06/01] +\ProvidesPackage{longtable_doxygen} + [2014/10/28 v4.11 Multi-page Table package (DPC) - frozen version for doxygen] +\def\LT@err{\PackageError{longtable}} +\def\LT@warn{\PackageWarning{longtable}} +\def\LT@final@warn{% + \AtEndDocument{% + \LT@warn{Table \@width s have changed. Rerun LaTeX.\@gobbletwo}}% + \global\let\LT@final@warn\relax} +\DeclareOption{errorshow}{% + \def\LT@warn{\PackageInfo{longtable}}} +\DeclareOption{pausing}{% + \def\LT@warn#1{% + \LT@err{#1}{This is not really an error}}} +\DeclareOption{set}{} +\DeclareOption{final}{} +\ProcessOptions +\newskip\LTleft \LTleft=\fill +\newskip\LTright \LTright=\fill +\newskip\LTpre \LTpre=\bigskipamount +\newskip\LTpost \LTpost=\bigskipamount +\newcount\LTchunksize \LTchunksize=20 +\let\c@LTchunksize\LTchunksize +\newdimen\LTcapwidth \LTcapwidth=4in +\newbox\LT@head +\newbox\LT@firsthead +\newbox\LT@foot +\newbox\LT@lastfoot +\newcount\LT@cols +\newcount\LT@rows +\newcounter{LT@tables} +\newcounter{LT@chunks}[LT@tables] +\ifx\c@table\undefined + \newcounter{table} + \def\fnum@table{\tablename~\thetable} +\fi +\ifx\tablename\undefined + \def\tablename{Table} +\fi +\newtoks\LT@p@ftn +\mathchardef\LT@end@pen=30000 +\def\longtable{% + \par + \ifx\multicols\@undefined + \else + \ifnum\col@number>\@ne + \@twocolumntrue + \fi + \fi + \if@twocolumn + \LT@err{longtable not in 1-column mode}\@ehc + \fi + \begingroup + \@ifnextchar[\LT@array{\LT@array[x]}} +\def\LT@array[#1]#2{% + \refstepcounter{table}\stepcounter{LT@tables}% + \if l#1% + \LTleft\z@ \LTright\fill + \else\if r#1% + \LTleft\fill \LTright\z@ + \else\if c#1% + \LTleft\fill \LTright\fill + \fi\fi\fi + \let\LT@mcol\multicolumn + \let\LT@@tabarray\@tabarray + \let\LT@@hl\hline + \def\@tabarray{% + \let\hline\LT@@hl + \LT@@tabarray}% + \let\\\LT@tabularcr\let\tabularnewline\\% + \def\newpage{\noalign{\break}}% + \def\pagebreak{\noalign{\ifnum`}=0\fi\@testopt{\LT@no@pgbk-}4}% + \def\nopagebreak{\noalign{\ifnum`}=0\fi\@testopt\LT@no@pgbk4}% + \let\hline\LT@hline \let\kill\LT@kill\let\caption\LT@caption + \@tempdima\ht\strutbox + \let\@endpbox\LT@endpbox + \ifx\extrarowheight\@undefined + \let\@acol\@tabacol + \let\@classz\@tabclassz \let\@classiv\@tabclassiv + \def\@startpbox{\vtop\LT@startpbox}% + \let\@@startpbox\@startpbox + \let\@@endpbox\@endpbox + \let\LT@LL@FM@cr\@tabularcr + \else + \advance\@tempdima\extrarowheight + \col@sep\tabcolsep + \let\@startpbox\LT@startpbox\let\LT@LL@FM@cr\@arraycr + \fi + \setbox\@arstrutbox\hbox{\vrule + \@height \arraystretch \@tempdima + \@depth \arraystretch \dp \strutbox + \@width \z@}% + \let\@sharp##\let\protect\relax + \begingroup + \@mkpream{#2}% + \xdef\LT@bchunk{% + \global\advance\c@LT@chunks\@ne + \global\LT@rows\z@\setbox\z@\vbox\bgroup + \LT@setprevdepth + \tabskip\LTleft \noexpand\halign to\hsize\bgroup + \tabskip\z@ \@arstrut \@preamble \tabskip\LTright \cr}% + \endgroup + \expandafter\LT@nofcols\LT@bchunk&\LT@nofcols + \LT@make@row + \m@th\let\par\@empty + \everycr{}\lineskip\z@\baselineskip\z@ + \LT@bchunk} +\def\LT@no@pgbk#1[#2]{\penalty #1\@getpen{#2}\ifnum`{=0\fi}} +\def\LT@start{% + \let\LT@start\endgraf + \endgraf\penalty\z@\vskip\LTpre + \dimen@\pagetotal + \advance\dimen@ \ht\ifvoid\LT@firsthead\LT@head\else\LT@firsthead\fi + \advance\dimen@ \dp\ifvoid\LT@firsthead\LT@head\else\LT@firsthead\fi + \advance\dimen@ \ht\LT@foot + \dimen@ii\vfuzz + \vfuzz\maxdimen + \setbox\tw@\copy\z@ + \setbox\tw@\vsplit\tw@ to \ht\@arstrutbox + \setbox\tw@\vbox{\unvbox\tw@}% + \vfuzz\dimen@ii + \advance\dimen@ \ht + \ifdim\ht\@arstrutbox>\ht\tw@\@arstrutbox\else\tw@\fi + \advance\dimen@\dp + \ifdim\dp\@arstrutbox>\dp\tw@\@arstrutbox\else\tw@\fi + \advance\dimen@ -\pagegoal + \ifdim \dimen@>\z@\vfil\break\fi + \global\@colroom\@colht + \ifvoid\LT@foot\else + \advance\vsize-\ht\LT@foot + \global\advance\@colroom-\ht\LT@foot + \dimen@\pagegoal\advance\dimen@-\ht\LT@foot\pagegoal\dimen@ + \maxdepth\z@ + \fi + \ifvoid\LT@firsthead\copy\LT@head\else\box\LT@firsthead\fi\nobreak + \output{\LT@output}} +\def\endlongtable{% + \crcr + \noalign{% + \let\LT@entry\LT@entry@chop + \xdef\LT@save@row{\LT@save@row}}% + \LT@echunk + \LT@start + \unvbox\z@ + \LT@get@widths + \if@filesw + {\let\LT@entry\LT@entry@write\immediate\write\@auxout{% + \gdef\expandafter\noexpand + \csname LT@\romannumeral\c@LT@tables\endcsname + {\LT@save@row}}}% + \fi + \ifx\LT@save@row\LT@@save@row + \else + \LT@warn{Column \@width s have changed\MessageBreak + in table \thetable}% + \LT@final@warn + \fi + \endgraf\penalty -\LT@end@pen + \endgroup + \global\@mparbottom\z@ + \pagegoal\vsize + \endgraf\penalty\z@\addvspace\LTpost + \ifvoid\footins\else\insert\footins{}\fi} +\def\LT@nofcols#1&{% + \futurelet\@let@token\LT@n@fcols} +\def\LT@n@fcols{% + \advance\LT@cols\@ne + \ifx\@let@token\LT@nofcols + \expandafter\@gobble + \else + \expandafter\LT@nofcols + \fi} +\def\LT@tabularcr{% + \relax\iffalse{\fi\ifnum0=`}\fi + \@ifstar + {\def\crcr{\LT@crcr\noalign{\nobreak}}\let\cr\crcr + \LT@t@bularcr}% + {\LT@t@bularcr}} +\let\LT@crcr\crcr +\let\LT@setprevdepth\relax +\def\LT@t@bularcr{% + \global\advance\LT@rows\@ne + \ifnum\LT@rows=\LTchunksize + \gdef\LT@setprevdepth{% + \prevdepth\z@\global + \global\let\LT@setprevdepth\relax}% + \expandafter\LT@xtabularcr + \else + \ifnum0=`{}\fi + \expandafter\LT@LL@FM@cr + \fi} +\def\LT@xtabularcr{% + \@ifnextchar[\LT@argtabularcr\LT@ntabularcr} +\def\LT@ntabularcr{% + \ifnum0=`{}\fi + \LT@echunk + \LT@start + \unvbox\z@ + \LT@get@widths + \LT@bchunk} +\def\LT@argtabularcr[#1]{% + \ifnum0=`{}\fi + \ifdim #1>\z@ + \unskip\@xargarraycr{#1}% + \else + \@yargarraycr{#1}% + \fi + \LT@echunk + \LT@start + \unvbox\z@ + \LT@get@widths + \LT@bchunk} +\def\LT@echunk{% + \crcr\LT@save@row\cr\egroup + \global\setbox\@ne\lastbox + \unskip + \egroup} +\def\LT@entry#1#2{% + \ifhmode\@firstofone{&}\fi\omit + \ifnum#1=\c@LT@chunks + \else + \kern#2\relax + \fi} +\def\LT@entry@chop#1#2{% + \noexpand\LT@entry + {\ifnum#1>\c@LT@chunks + 1}{0pt% + \else + #1}{#2% + \fi}} +\def\LT@entry@write{% + \noexpand\LT@entry^^J% + \@spaces} +\def\LT@kill{% + \LT@echunk + \LT@get@widths + \expandafter\LT@rebox\LT@bchunk} +\def\LT@rebox#1\bgroup{% + #1\bgroup + \unvbox\z@ + \unskip + \setbox\z@\lastbox} +\def\LT@blank@row{% + \xdef\LT@save@row{\expandafter\LT@build@blank + \romannumeral\number\LT@cols 001 }} +\def\LT@build@blank#1{% + \if#1m% + \noexpand\LT@entry{1}{0pt}% + \expandafter\LT@build@blank + \fi} +\def\LT@make@row{% + \global\expandafter\let\expandafter\LT@save@row + \csname LT@\romannumeral\c@LT@tables\endcsname + \ifx\LT@save@row\relax + \LT@blank@row + \else + {\let\LT@entry\or + \if!% + \ifcase\expandafter\expandafter\expandafter\LT@cols + \expandafter\@gobble\LT@save@row + \or + \else + \relax + \fi + !% + \else + \aftergroup\LT@blank@row + \fi}% + \fi} +\let\setlongtables\relax +\def\LT@get@widths{% + \setbox\tw@\hbox{% + \unhbox\@ne + \let\LT@old@row\LT@save@row + \global\let\LT@save@row\@empty + \count@\LT@cols + \loop + \unskip + \setbox\tw@\lastbox + \ifhbox\tw@ + \LT@def@row + \advance\count@\m@ne + \repeat}% + \ifx\LT@@save@row\@undefined + \let\LT@@save@row\LT@save@row + \fi} +\def\LT@def@row{% + \let\LT@entry\or + \edef\@tempa{% + \ifcase\expandafter\count@\LT@old@row + \else + {1}{0pt}% + \fi}% + \let\LT@entry\relax + \xdef\LT@save@row{% + \LT@entry + \expandafter\LT@max@sel\@tempa + \LT@save@row}} +\def\LT@max@sel#1#2{% + {\ifdim#2=\wd\tw@ + #1% + \else + \number\c@LT@chunks + \fi}% + {\the\wd\tw@}} +\def\LT@hline{% + \noalign{\ifnum0=`}\fi + \penalty\@M + \futurelet\@let@token\LT@@hline} +\def\LT@@hline{% + \ifx\@let@token\hline + \global\let\@gtempa\@gobble + \gdef\LT@sep{\penalty-\@medpenalty\vskip\doublerulesep}% + \else + \global\let\@gtempa\@empty + \gdef\LT@sep{\penalty-\@lowpenalty\vskip-\arrayrulewidth}% + \fi + \ifnum0=`{\fi}% + \multispan\LT@cols + \unskip\leaders\hrule\@height\arrayrulewidth\hfill\cr + \noalign{\LT@sep}% + \multispan\LT@cols + \unskip\leaders\hrule\@height\arrayrulewidth\hfill\cr + \noalign{\penalty\@M}% + \@gtempa} +\def\LT@caption{% + \noalign\bgroup + \@ifnextchar[{\egroup\LT@c@ption\@firstofone}\LT@capti@n} +\def\LT@c@ption#1[#2]#3{% + \LT@makecaption#1\fnum@table{#3}% + \def\@tempa{#2}% + \ifx\@tempa\@empty\else + {\let\\\space + \addcontentsline{lot}{table}{\protect\numberline{\thetable}{#2}}}% + \fi} +\def\LT@capti@n{% + \@ifstar + {\egroup\LT@c@ption\@gobble[]}% + {\egroup\@xdblarg{\LT@c@ption\@firstofone}}} +\def\LT@makecaption#1#2#3{% + \LT@mcol\LT@cols c{\hbox to\z@{\hss\parbox[t]\LTcapwidth{% + \sbox\@tempboxa{#1{#2: }#3}% + \ifdim\wd\@tempboxa>\hsize + #1{#2: }#3% + \else + \hbox to\hsize{\hfil\box\@tempboxa\hfil}% + \fi + \endgraf\vskip\baselineskip}% + \hss}}} +\def\LT@output{% + \ifnum\outputpenalty <-\@Mi + \ifnum\outputpenalty > -\LT@end@pen + \LT@err{floats and marginpars not allowed in a longtable}\@ehc + \else + \setbox\z@\vbox{\unvbox\@cclv}% + \ifdim \ht\LT@lastfoot>\ht\LT@foot + \dimen@\pagegoal + \advance\dimen@-\ht\LT@lastfoot + \ifdim\dimen@<\ht\z@ + \setbox\@cclv\vbox{\unvbox\z@\copy\LT@foot\vss}% + \@makecol + \@outputpage + \setbox\z@\vbox{\box\LT@head}% + \fi + \fi + \global\@colroom\@colht + \global\vsize\@colht + \vbox + {\unvbox\z@\box\ifvoid\LT@lastfoot\LT@foot\else\LT@lastfoot\fi}% + \fi + \else + \setbox\@cclv\vbox{\unvbox\@cclv\copy\LT@foot\vss}% + \@makecol + \@outputpage + \global\vsize\@colroom + \copy\LT@head\nobreak + \fi} +\def\LT@end@hd@ft#1{% + \LT@echunk + \ifx\LT@start\endgraf + \LT@err + {Longtable head or foot not at start of table}% + {Increase LTchunksize}% + \fi + \setbox#1\box\z@ + \LT@get@widths + \LT@bchunk} +\def\endfirsthead{\LT@end@hd@ft\LT@firsthead} +\def\endhead{\LT@end@hd@ft\LT@head} +\def\endfoot{\LT@end@hd@ft\LT@foot} +\def\endlastfoot{\LT@end@hd@ft\LT@lastfoot} +\def\LT@startpbox#1{% + \bgroup + \let\@footnotetext\LT@p@ftntext + \setlength\hsize{#1}% + \@arrayparboxrestore + \vrule \@height \ht\@arstrutbox \@width \z@} +\def\LT@endpbox{% + \@finalstrut\@arstrutbox + \egroup + \the\LT@p@ftn + \global\LT@p@ftn{}% + \hfil} +\def\LT@p@ftntext#1{% + \edef\@tempa{\the\LT@p@ftn\noexpand\footnotetext[\the\c@footnote]}% + \global\LT@p@ftn\expandafter{\@tempa{#1}}}% + +\@namedef{ver@longtable.sty}{2014/10/28 v4.11 Multi-page Table package (DPC) - frozen version for doxygen} +\endinput +%% +%% End of file `longtable.sty'. diff --git a/docs/latex/md__r_e_a_d_m_e.tex b/docs/latex/md__r_e_a_d_m_e.tex new file mode 100644 index 00000000..dedbcb47 --- /dev/null +++ b/docs/latex/md__r_e_a_d_m_e.tex @@ -0,0 +1,155 @@ +E\+X\+O\+NA has been developed to compile using C\+Make, which should be installed before attempting to compile. To use the M\+PI version, a version of M\+PI (such as Open\+M\+PI) should be installed. \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} currently requires libtiff and libpng The \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} algorithm can also checkpoint to a database, however this is not required. To enable this I recommend installing libmysql-\/dev via apt-\/get on Linux systems, or mysql via \href{https://brew.sh}{\texttt{ homebrew}} on O\+SX. Other than that, E\+X\+A\+C\+T/\+E\+X\+A\+L\+T/\+E\+X\+A\+MM has no prerequesites other than c++11 compatible compiler. + +If you are using O\+SX, to set up the environment\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{\$ brew install cmake} +\DoxyCodeLine{\$ brew install mysql} +\DoxyCodeLine{\$ xcode-\/select -\/-\/install} +\end{DoxyCode} + + +To build\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{\string~/exact \$ mkdir build} +\DoxyCodeLine{\string~/exact \$ cd build} +\DoxyCodeLine{\string~/exact/build \$ cmake ..} +\DoxyCodeLine{\string~/exact/build \$ make} +\end{DoxyCode} + + +You may also want to have graphviz installed so you can generate images of the evolved neural networks. E\+X\+A\+C\+T/\+E\+X\+A\+L\+T/\+E\+X\+A\+MM will write out evolved genomes in a .gv (graphviz) format for this. For example, can generate a pdf from a gv file (assuming graphviz is installed with)\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{\$ dot -\/Tpdf genome.gv -\/o genome.pdf} +\end{DoxyCode} +\hypertarget{md__r_e_a_d_m_e_autotoc_md1}{}\doxysection{E\+X\+A\+M\+M\+: Evolutionary e\+Xploration of Augmenting Memory Models and E\+X\+A\+L\+T\+: Evolutionary e\+Xploration of Augmenting L\+S\+T\+M Topologies}\label{md__r_e_a_d_m_e_autotoc_md1} +Source code for E\+X\+A\+L\+T/\+E\+X\+A\+MM can be found in the rnn subdirectory. E\+X\+A\+LT has been enhanced with the ability to utilize more recurrent memory cells and has been renamed \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}}. The memory cells currently implemented are Delta-\/\+R\+NN, G\+RU, L\+S\+TM, M\+GU, and U\+G\+R\+N\+Ns. Some example time series data has been provided as part of two publications on E\+X\+A\+LT and \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}}, which also provide implementation details\+: + + +\begin{DoxyEnumerate} +\item Alex Ororbia, Abd\+El\+Rahman El\+Said, and Travis Desell. {\bfseries{\href{https://dl.acm.org/citation.cfm?id=3321795}{\texttt{ Investigating Recurrent Neural Network Memory Structures using Neuro-\/\+Evolution}}.}} {\itshape The Genetic and Evolutionary Computation Conference (G\+E\+C\+CO 2019).} Prague, Czech Republic. July 8-\/12, 2019. +\item Abd\+El\+Rahman El\+Said, Steven Benson, Shuchita Patwardhan, David Stadem and Travis Desell. {\bfseries{\href{https://link.springer.com/chapter/10.1007/978-3-030-16692-2_33}{\texttt{ Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters}}.}} {\itshape The 22nd International Conference on the Applications of Evolutionary Computation (Evo\+Star\+: Evo\+Apps 2019).} Leipzig, Germany. April 24-\/26, 2019. +\end{DoxyEnumerate} + +These datasets can be found in the {\itshape datasets} directory, and provide example C\+SV files which you can use with \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}}. \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}} can be run in two different ways, a multithreaded version\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{./multithreaded/examm\_mt -\/-\/number\_threads 9 -\/-\/training\_filenames ../datasets/2018\_coal/burner\_[0-\/9].csv -\/-\/test\_filenames ../datasets/2018\_coal/burner\_1[0-\/1].csv -\/-\/time\_offset 1 -\/-\/input\_parameter\_names Conditioner\_Inlet\_Temp Conditioner\_Outlet\_Temp Coal\_Feeder\_Rate Primary\_Air\_Flow Primary\_Air\_Split System\_Secondary\_Air\_Flow\_Total Secondary\_Air\_Flow Secondary\_Air\_Split Tertiary\_Air\_Split Total\_Comb\_Air\_Flow Supp\_Fuel\_Flow Main\_Flm\_Int -\/-\/output\_parameter\_names Main\_Flm\_Int -\/-\/number\_islands 10 -\/-\/population\_size 10 -\/-\/max\_genomes 2000 -\/-\/bp\_iterations 10 -\/-\/output\_directory "./test\_output" -\/-\/possible\_node\_types simple UGRNN MGU GRU delta LSTM -\/-\/std\_message\_level INFO -\/-\/file\_message\_level INFO} +\end{DoxyCode} + + +And a parallel version using M\+PI\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{\string~/exact/build/ \$ mpirun -\/np 9 ./mpi/examm\_mpi -\/-\/training\_filenames ../datasets/2018\_coal/burner\_[0-\/9].csv -\/-\/test\_filenames ../datasets/2018\_coal/burner\_1[0-\/1].csv -\/-\/time\_offset 1 -\/-\/input\_parameter\_names Conditioner\_Inlet\_Temp Conditioner\_Outlet\_Temp Coal\_Feeder\_Rate Primary\_Air\_Flow Primary\_Air\_Split System\_Secondary\_Air\_Flow\_Total Secondary\_Air\_Flow Secondary\_Air\_Split Tertiary\_Air\_Split Total\_Comb\_Air\_Flow Supp\_Fuel\_Flow Main\_Flm\_Int -\/-\/output\_parameter\_names Main\_Flm\_Int -\/-\/number\_islands 10 -\/-\/population\_size 10 -\/-\/max\_genomes 2000 -\/-\/bp\_iterations 10 -\/-\/output\_directory "./test\_output" -\/-\/possible\_node\_types simple UGRNN MGU GRU delta LSTM -\/-\/std\_message\_level INFO -\/-\/file\_message\_level INFO} +\end{DoxyCode} + + +Which will run \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}} with 9 threads or 9 processes, respectively. Note that \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}} uses one thread/process as the master and this typically just waits on the results of backprop so you if you have 8 processors/cores available you can usually run \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}} with 9 processes/threads for better performance. A performance log of \mbox{\hyperlink{class_r_n_n}{R\+NN}} fitnesses will be exported into fitness\+\_\+log.\+csv, as well as the best found R\+N\+Ns into the specified output directory, in this case $\ast$./test\+\_\+output$\ast$. You can control the level of message logging for standard output with {\itshape --std\+\_\+message\+\_\+level} (options are N\+O\+NE, F\+A\+T\+AL, E\+R\+R\+OR, W\+A\+R\+N\+I\+NG, I\+N\+FO, D\+E\+B\+UG, T\+R\+A\+CE and A\+LL) and message logging to files (which will be placed in the output directory) with {\itshape --file\+\_\+message\+\_\+level}. Separate logging files will be made for each thread/process. + +The aviation data can be run similarly, however it the data should be normalized first (which can be done with the {\itshape --normalize} command line parameter), e.\+g.\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{./multithreaded/examm\_mt -\/-\/number\_threads 9 -\/-\/training\_filenames ../datasets/2018\_ngafid/flight\_[0-\/7].csv -\/-\/test\_filenames ../datasets/2018\_ngafid/flight\_[8-\/9].csv -\/-\/time\_offset 1 -\/-\/input\_parameter\_names "AltAGL" "E1 CHT1" "E1 CHT2" "E1 CHT3" "E1 CHT4" "E1 EGT1" "E1 EGT2" "E1 EGT3" "E1 EGT4" "E1 OilP" "E1 OilT" "E1 RPM" "FQtyL" "FQtyR" "GndSpd" "IAS" "LatAc" "NormAc" "OAT" "Pitch" "Roll" "TAS" "volt1" "volt2" "VSpd" "VSpdG" -\/-\/output\_parameter\_names Pitch -\/-\/number\_islands 10 -\/-\/population\_size 10 -\/-\/max\_genomes 2000 -\/-\/bp\_iterations 10 -\/-\/output\_directory "./test\_output" -\/-\/possible\_node\_types simple UGRNN MGU GRU delta LSTM -\/-\/normalize -\/-\/std\_message\_level INFO -\/-\/file\_message\_level INFO} +\end{DoxyCode} + + +The {\itshape --time\+\_\+offset} parameter specifies how many time steps in the future \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}} should predict for the output parameter(s). The {\itshape --number\+\_\+islands} is the number of islands of populations that \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}} will use, and the {\itshape --population\+\_\+size} parameter specifies how many individuals/genomes are in each island. The {\itshape --bp\+\_\+iterations} specifies how many epochs/iterations backpropagation should be run for each generated \mbox{\hyperlink{class_r_n_n}{R\+NN}} genome. + +The\hypertarget{md__r_e_a_d_m_e_autotoc_md2}{}\doxysection{E\+X\+A\+C\+T\+: Evolutionary Exploration of Augmenting Convolutional Topologies}\label{md__r_e_a_d_m_e_autotoc_md2} +This repository provides source code for the Evolutionary Exploration of Augmenting Convolutional Topologies algorithm. This algorithm progressively evolves convolutional neural networks for image classification problems. The algorithm is asychronous, which allows for easy multithreading and parallelization. Code is provided for running \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} as a B\+O\+I\+NC volunteer computing project, on a cluster or supercomputer using M\+PI or on a desktop or laptop using threads. + +We\textquotesingle{}ve built and run \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} on both an Ubuntu Linux high performance computing cluser and O\+SX laptops and desktops. We have not tried it on Windows. If there are issues with the C\+Make scripts please let us know and we\textquotesingle{}ll update them. + +If you want to set this up on your own B\+O\+I\+NC volunteer computing project, we recommend sending us an email as this is a rather complicated process. + +For more information on \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} please see our following publications\+: + + +\begin{DoxyEnumerate} +\item Travis Desell. {\bfseries{\href{https://arxiv.org/abs/1811.08286}{\texttt{ Accelerating the Evolution of Convolutional Neural Networks with Node-\/\+Level Mutations and Epigenetic Weight Initialization}}.}} {\itshape ar\+Xiv\+: Neural and Evolutionary Computing (cs.\+NE).} November, 2018. +\item Travis Desell. {\bfseries{\href{https://ieeexplore.ieee.org/document/8109119}{\texttt{ Developing a Volunteer Computing Project to Evolve Convolutional Neural Networks and Their Hyperparameters}}.}} {\itshape The 13th I\+E\+EE International Conference on e\+Science (e\+Science 2017).} Auckland, New Zealand. October 24-\/27 2017. +\end{DoxyEnumerate}\hypertarget{md__r_e_a_d_m_e_autotoc_md3}{}\doxysubsection{Setting up Training and Testing Data}\label{md__r_e_a_d_m_e_autotoc_md3} +This version \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} is set up to run using the \href{http://yann.lecun.com/exdb/mnist/}{\texttt{ M\+N\+I\+ST Handwritten Digits Dataset}}. However it expects a different data format where the images and labels are combined. You can download the data and convert it as follows\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{\string~/exact \$ mkdir datasets} +\DoxyCodeLine{\string~/exact \$ cd datasets} +\DoxyCodeLine{} +\DoxyCodeLine{\string~/exact/datasets \$ wget http://yann.lecun.com/exdb/mnist/train-\/images-\/idx3-\/ubyte.gz} +\DoxyCodeLine{\string~/exact/datasets \$ wget http://yann.lecun.com/exdb/mnist/train-\/labels-\/idx1-\/ubyte.gz} +\DoxyCodeLine{\string~/exact/datasets \$ wget http://yann.lecun.com/exdb/mnist/t10k-\/images-\/idx3-\/ubyte.gz} +\DoxyCodeLine{\string~/exact/datasets \$ wget http://yann.lecun.com/exdb/mnist/t10k-\/labels-\/idx1-\/ubyte.gz} +\DoxyCodeLine{} +\DoxyCodeLine{\string~/exact/datasets \$ unzip train-\/images-\/idx3-\/ubyte.gz} +\DoxyCodeLine{\string~/exact/datasets \$ unzip train-\/labels-\/idx1-\/ubyte.gz} +\DoxyCodeLine{\string~/exact/datasets \$ unzip t10k-\/images-\/idx3-\/ubyte.gz} +\DoxyCodeLine{\string~/exact/datasets \$ unzip t10k-\/labels-\/idx1-\/ubyte.gz} +\DoxyCodeLine{} +\DoxyCodeLine{\string~/exact/datasets \$ cd ../build} +\DoxyCodeLine{\string~/exact/build \$ ./image\_tools/convert\_mnist\_data ../datasets/train-\/images-\/idx3-\/ubyte ../datasets/train-\/labels-\/idx1-\/ubyte ../datasets/mnist\_training\_data.bin 60000} +\DoxyCodeLine{\string~/exact/build \$ ./image\_tools/convert\_mnist\_data ../datasets/t10k-\/images-\/idx3-\/ubyte ../datasets/t10k-\/labels-\/idx1-\/ubyte ../datasets/mnist\_testing\_data.bin 10000} +\end{DoxyCode} + + +You can also split up either the training or test data to add a third dataset of validation images for more robust analysis, e.\+g., so you can train on one set, use the validation set to determine the best C\+N\+Ns for \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} to keep, and then after the evolution process is completed you can test the best found genome(s) on the test set which has not been seen before. You can split up the M\+N\+I\+ST training data into a 50k training set and 10k validation set as follows\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{\string~/exact/build/ \$ ./image\_tools/split\_mnist\_data ../datasets/train-\/images-\/idx3-\/ubyte ../datasets/train-\/labels-\/idx1-\/ubyte ../datasets/mnist\_train\_50k.bin ../datasets/mnist\_validation\_10k.bin 60000 1000} +\end{DoxyCode} + + +Where the usage is\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{./image\_tools/split\_mnist\_data } +\end{DoxyCode} + + +So this will take 1000 images of each type from the training data and put them into the specified validation file ({\itshape mnist\+\_\+validation\+\_\+10k.\+bin}) and the rest of the images will be in the training set ({\itshape mnist\+\_\+train\+\_\+50k.\+bin}).\hypertarget{md__r_e_a_d_m_e_autotoc_md4}{}\doxysubsection{Running E\+X\+A\+CT}\label{md__r_e_a_d_m_e_autotoc_md4} +You can then run \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} in a similar manner to \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}}, either using threads or M\+PI. For the threaded version\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{\string~/exact/build/ \$ ./multithreaded/exact\_mt -\/-\/number\_threads 9 -\/-\/padding 2 -\/-\/training\_file ../datasets/mnist\_train\_50k.bin -\/-\/validation\_file ../datasets/mnist\_validation\_10k.bin -\/-\/testing\_file ../datasets/mnist\_testing\_data.bin -\/-\/population\_size 30 -\/-\/max\_epochs 5 -\/-\/use\_sfmp 1 -\/-\/use\_node\_operations 1 -\/-\/max\_genomes 1000 -\/-\/output\_directory ./test\_exact -\/-\/search\_name "test" -\/-\/reset\_edges 0 -\/-\/images\_resize 50000} +\end{DoxyCode} + + +And for the M\+PI version\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{\string~/exact/build/ \$ mpirun -\/np 9 ./mpi/exact\_mpi -\/-\/padding 2 -\/-\/training\_file ../datasets/mnist\_train\_50k.bin -\/-\/validation\_file ../datasets/mnist\_validation\_10k.bin -\/-\/testing\_file ../datasets/mnist\_testing\_data.bin -\/-\/population\_size 30 -\/-\/max\_epochs 5 -\/-\/use\_sfmp 1 -\/-\/use\_node\_operations 1 -\/-\/max\_genomes 1000 -\/-\/output\_directory ./test\_exact -\/-\/search\_name "test" -\/-\/reset\_edges 0 -\/-\/images\_resize 50000} +\end{DoxyCode} + + +Which will run \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} with 9 threads or processes, respectively. The {\itshape --use\+\_\+sfmp} argument turns on or off scaled fractional max pooling (which allows for pooling operations between feature maps of any size), the {\itshape --use\+\_\+node\+\_\+operations} argument turns on or off node level mutations (see the \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} and \mbox{\hyperlink{class_e_x_a_m_m}{E\+X\+A\+MM}} papers), the {\itshape --reset\+\_\+edges} parameter turns on or off Lamarckian weight evolution (turning it on will evolve and train networks faster) and the {\itshape --images\+\_\+resize} parameter allows \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} to train C\+N\+Ns on a subset of the training data to speed the evolution process (e.\+g., --images\+\_\+resize 5000 would train each C\+NN on a different subset of 5k images from the training data, as opposed to the full 50k).\hypertarget{md__r_e_a_d_m_e_autotoc_md5}{}\doxysubsection{Example Genomes from G\+E\+C\+C\+O 2017}\label{md__r_e_a_d_m_e_autotoc_md5} +Our submission to G\+E\+C\+CO describes a set of best found genomes for the M\+N\+I\+ST handwritten digits dataset. These can be found in the genomes subdirectory of the project. Please checkout the tag for the G\+E\+C\+CO paper to use the version of \mbox{\hyperlink{class_e_x_a_c_t}{E\+X\+A\+CT}} these C\+NN genome files were generated with\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{git checkout -\/b exact\_gecco gecco\_2017} +\end{DoxyCode} + + +After compiling this version and setting up the M\+N\+I\+ST training and testing data as described in the previous section, these genomes can be run over the training and testing data for validation as follows\+: + + +\begin{DoxyCode}{0} +\DoxyCodeLine{\string~/exact/build/ \$ ./tests/evaluate\_cnn -\/-\/training\_data ../datasets/mnist\_training\_data.bin -\/-\/testing\_data ../datasets/mnist\_testing\_data.bin -\/-\/genome\_file ../genomes/genome\_46823} +\DoxyCodeLine{\string~/exact/build/ \$ ./tests/evaluate\_cnn -\/-\/training\_data ../datasets/mnist\_training\_data.bin -\/-\/testing\_data ../datasets/mnist\_testing\_data.bin -\/-\/genome\_file ../genomes/genome\_57302} +\DoxyCodeLine{\string~/exact/build/ \$ ./tests/evaluate\_cnn -\/-\/training\_data ../datasets/mnist\_training\_data.bin -\/-\/testing\_data ../datasets/mnist\_testing\_data.bin -\/-\/genome\_file ../genomes/genome\_59455} +\DoxyCodeLine{\string~/exact/build/ \$ ./tests/evaluate\_cnn -\/-\/training\_data ../datasets/mnist\_training\_data.bin -\/-\/testing\_data ../datasets/mnist\_testing\_data.bin -\/-\/genome\_file ../genomes/genome\_59920} +\end{DoxyCode} + \ No newline at end of file diff --git a/docs/latex/namespaceplot__multi__time__series.tex b/docs/latex/namespaceplot__multi__time__series.tex new file mode 100644 index 00000000..20b2871e --- /dev/null +++ b/docs/latex/namespaceplot__multi__time__series.tex @@ -0,0 +1,54 @@ +\hypertarget{namespaceplot__multi__time__series}{}\doxysection{plot\+\_\+multi\+\_\+time\+\_\+series Namespace Reference} +\label{namespaceplot__multi__time__series}\index{plot\_multi\_time\_series@{plot\_multi\_time\_series}} +\doxysubsection*{Variables} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_a97885b5fee72386f66a4ee6a763127eb}\label{namespaceplot__multi__time__series_a97885b5fee72386f66a4ee6a763127eb}} +{\bfseries csv\+\_\+filename} = sys.\+argv\mbox{[}1\mbox{]} +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_a92e6b9a89914d7f9da239fa5bbc1acfe}\label{namespaceplot__multi__time__series_a92e6b9a89914d7f9da239fa5bbc1acfe}} +{\bfseries parameter\+\_\+name} = sys.\+argv\mbox{[}2\mbox{]} +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_a4f43b211d6980967d37938bf82f23fa3}\label{namespaceplot__multi__time__series_a4f43b211d6980967d37938bf82f23fa3}} +{\bfseries xlabel\+\_\+name} = sys.\+argv\mbox{[}3\mbox{]} +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_afcca6556fbb99c4fbd70bc121118c4dd}\label{namespaceplot__multi__time__series_afcca6556fbb99c4fbd70bc121118c4dd}} +{\bfseries output\+\_\+filename} = sys.\+argv\mbox{[}4\mbox{]} +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_a7dc670c7b7bfa2e141cf6d139b60c1aa}\label{namespaceplot__multi__time__series_a7dc670c7b7bfa2e141cf6d139b60c1aa}} +{\bfseries offsets} = sys.\+argv\mbox{[}5\+:\mbox{]} +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_a2e2a6c223dca82e734daffb0d74ad4d3}\label{namespaceplot__multi__time__series_a2e2a6c223dca82e734daffb0d74ad4d3}} +{\bfseries values} = genfromtxt(csv\+\_\+filename, delimiter=\char`\"{},\char`\"{}) +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_aa698bc6b1ecc18309117c6adbc11d76c}\label{namespaceplot__multi__time__series_aa698bc6b1ecc18309117c6adbc11d76c}} +{\bfseries rows} = len(values) +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_aec8d4ebcb56e41ffc414583034915951}\label{namespaceplot__multi__time__series_aec8d4ebcb56e41ffc414583034915951}} +{\bfseries cols} = len(values\mbox{[}0\mbox{]}) +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_a0a178be487d773ac674426e6c9048a74}\label{namespaceplot__multi__time__series_a0a178be487d773ac674426e6c9048a74}} +{\bfseries figsize} +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_a7327695ce7e1f3f6b0a8ac72f329d86b}\label{namespaceplot__multi__time__series_a7327695ce7e1f3f6b0a8ac72f329d86b}} +{\bfseries label} +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_a612556db538d7e149f21512f77799395}\label{namespaceplot__multi__time__series_a612556db538d7e149f21512f77799395}} +{\bfseries linewidth} +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_a444969616e4eb0a685ce5d5f096a463f}\label{namespaceplot__multi__time__series_a444969616e4eb0a685ce5d5f096a463f}} +{\bfseries loc} +\item +\mbox{\Hypertarget{namespaceplot__multi__time__series_a68da059582342c21aa9c664534182f7b}\label{namespaceplot__multi__time__series_a68da059582342c21aa9c664534182f7b}} +{\bfseries bbox\+\_\+inches} +\end{DoxyCompactItemize} + + +\doxysubsection{Detailed Description} +\begin{DoxyVerb}usage examples: + python plot_multi_time_series.py ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/net_plant_heat_rate_predictions.csv "Net Plant Heat Rate" "Hours" ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/net_plant_heat_rate_multi.png 1 2 4 8 + python plot_multi_time_series.py ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/nose_gas_temperature_predictons.csv "Nose Gas Temperature" "Hours" ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/nose_gas_temperature_multi.png 1 2 4 8 + python plot_multi_time_series.py ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_extra_3.csv "Flame Intensity (Plant ~ Fuel)" "Minutes" ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_extra_3.png 1 15 30 60 120 240 480 + python plot_multi_time_series.py ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_plant_fuel_3.csv "Flame Intensity (Plant + Fuel)" "Minutes" ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_plant_fuel_3.png 1 15 30 60 120 240 480 + python plot_multi_time_series.py ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_plant_3.csv "Flame Intensity (Plant Only)" "Minutes" ~/Dropbox/1537\ MTI-RIT/2019_degruyter_results/flame_intensity_plant_3.png 1 15 30 60 120 240 480\end{DoxyVerb} + \ No newline at end of file diff --git a/docs/latex/namespaces.tex b/docs/latex/namespaces.tex new file mode 100644 index 00000000..a90967a3 --- /dev/null +++ b/docs/latex/namespaces.tex @@ -0,0 +1,4 @@ +\doxysection{Namespace List} +Here is a list of all documented namespaces with brief descriptions\+:\begin{DoxyCompactList} +\item\contentsline{section}{\mbox{\hyperlink{namespaceplot__multi__time__series}{plot\+\_\+multi\+\_\+time\+\_\+series}} }{\pageref{namespaceplot__multi__time__series}}{} +\end{DoxyCompactList} diff --git a/docs/latex/refman.tex b/docs/latex/refman.tex new file mode 100644 index 00000000..a4b6b885 --- /dev/null +++ b/docs/latex/refman.tex @@ -0,0 +1,292 @@ +\let\mypdfximage\pdfximage\def\pdfximage{\immediate\mypdfximage}\documentclass[twoside]{book} + +%% moved from doxygen.sty due to workaround for LaTex 2019 version and unmaintained tabu package +\usepackage{ifthen} +\ifx\requestedLaTeXdate\undefined +\usepackage{array} +\else +\usepackage{array}[=2016-10-06] +\fi +%% +% Packages required by doxygen +\usepackage{fixltx2e} +\usepackage{calc} +\usepackage{doxygen} +\usepackage{graphicx} +\usepackage[utf8]{inputenc} +\usepackage{makeidx} +\usepackage{multicol} +\usepackage{multirow} +\PassOptionsToPackage{warn}{textcomp} +\usepackage{textcomp} +\usepackage[nointegrals]{wasysym} +\usepackage[table]{xcolor} +\usepackage{ifpdf,ifxetex} + +% Font selection +\usepackage[T1]{fontenc} +\usepackage[scaled=.90]{helvet} +\usepackage{courier} +\usepackage{amssymb} +\usepackage{sectsty} +\renewcommand{\familydefault}{\sfdefault} +\allsectionsfont{% + \fontseries{bc}\selectfont% + \color{darkgray}% +} +\renewcommand{\DoxyLabelFont}{% + \fontseries{bc}\selectfont% + \color{darkgray}% +} +\newcommand{\+}{\discretionary{\mbox{\scriptsize$\hookleftarrow$}}{}{}} + +% Arguments of doxygenemoji: +% 1) '::' form of the emoji, already "LaTeX"-escaped +% 2) file with the name of the emoji without the .png extension +% in case image exist use this otherwise use the '::' form +\newcommand{\doxygenemoji}[2]{% + \IfFileExists{./#2.png}{\raisebox{-0.1em}{\includegraphics[height=0.9em]{./#2.png}}}{#1}% +} +% Page & text layout +\usepackage{geometry} +\geometry{% + a4paper,% + top=2.5cm,% + bottom=2.5cm,% + left=2.5cm,% + right=2.5cm% +} +\tolerance=750 +\hfuzz=15pt +\hbadness=750 +\setlength{\emergencystretch}{15pt} +\setlength{\parindent}{0cm} +\newcommand{\doxynormalparskip}{\setlength{\parskip}{3ex plus 2ex minus 2ex}} +\newcommand{\doxytocparskip}{\setlength{\parskip}{1ex plus 0ex minus 0ex}} +\doxynormalparskip +\makeatletter +\renewcommand{\paragraph}{% + \@startsection{paragraph}{4}{0ex}{-1.0ex}{1.0ex}{% + \normalfont\normalsize\bfseries\SS@parafont% + }% +} +\renewcommand{\subparagraph}{% + \@startsection{subparagraph}{5}{0ex}{-1.0ex}{1.0ex}{% + \normalfont\normalsize\bfseries\SS@subparafont% + }% +} +\makeatother + +\makeatletter +\newcommand\hrulefilll{\leavevmode\leaders\hrule\hskip 0pt plus 1filll\kern\z@} +\makeatother + +% Headers & footers +\usepackage{fancyhdr} +\pagestyle{fancyplain} +\fancyhead[LE]{\fancyplain{}{\bfseries\thepage}} +\fancyhead[CE]{\fancyplain{}{}} +\fancyhead[RE]{\fancyplain{}{\bfseries\leftmark}} +\fancyhead[LO]{\fancyplain{}{\bfseries\rightmark}} +\fancyhead[CO]{\fancyplain{}{}} +\fancyhead[RO]{\fancyplain{}{\bfseries\thepage}} +\fancyfoot[LE]{\fancyplain{}{}} +\fancyfoot[CE]{\fancyplain{}{}} +\fancyfoot[RE]{\fancyplain{}{\bfseries\scriptsize Generated by Doxygen }} +\fancyfoot[LO]{\fancyplain{}{\bfseries\scriptsize Generated by Doxygen }} +\fancyfoot[CO]{\fancyplain{}{}} +\fancyfoot[RO]{\fancyplain{}{}} +\renewcommand{\footrulewidth}{0.4pt} +\renewcommand{\chaptermark}[1]{% + \markboth{#1}{}% +} +\renewcommand{\sectionmark}[1]{% + \markright{\thesection\ #1}% +} + +% Indices & bibliography +\usepackage{natbib} +\usepackage[titles]{tocloft} +\setcounter{tocdepth}{3} +\setcounter{secnumdepth}{5} +\makeindex + +\usepackage{newunicodechar} + \newunicodechar{⁻}{${}^{-}$}% Superscript minus + \newunicodechar{²}{${}^{2}$}% Superscript two + \newunicodechar{³}{${}^{3}$}% Superscript three + +% Hyperlinks (required, but should be loaded last) +\ifpdf + \usepackage[pdftex,pagebackref=true]{hyperref} +\else + \ifxetex + \usepackage[pagebackref=true]{hyperref} + \else + \usepackage[ps2pdf,pagebackref=true]{hyperref} + \fi +\fi + +\hypersetup{% + colorlinks=true,% + linkcolor=blue,% + citecolor=blue,% + unicode% +} + +% Custom commands +\newcommand{\clearemptydoublepage}{% + \newpage{\pagestyle{empty}\cleardoublepage}% +} + +\usepackage{caption} +\captionsetup{labelsep=space,justification=centering,font={bf},singlelinecheck=off,skip=4pt,position=top} + +\usepackage{etoc} +\etocsettocstyle{\doxytocparskip}{\doxynormalparskip} +\renewcommand{\numberline}[1]{#1~} +%===== C O N T E N T S ===== + +\begin{document} + +% Titlepage & ToC +\hypersetup{pageanchor=false, + bookmarksnumbered=true, + pdfencoding=unicode + } +\pagenumbering{alph} +\begin{titlepage} +\vspace*{7cm} +\begin{center}% +{\Large E\+X\+O\+NA\+: Evolutionary e\+Xploration of Neural Architectures }\\ +\vspace*{1cm} +{\large Generated by Doxygen 1.8.17}\\ +\end{center} +\end{titlepage} +\clearemptydoublepage +\pagenumbering{roman} +\tableofcontents +\clearemptydoublepage +\pagenumbering{arabic} +\hypersetup{pageanchor=true} + +%--- Begin generated contents --- +\chapter{Getting Started and Prerequisites} +\label{md__r_e_a_d_m_e} +\Hypertarget{md__r_e_a_d_m_e} +\input{md__r_e_a_d_m_e} +\chapter{Namespace Index} +\input{namespaces} +\chapter{Hierarchical Index} +\input{hierarchy} +\chapter{Class Index} +\input{annotated} +\chapter{Namespace Documentation} +\input{namespaceplot__multi__time__series} +\chapter{Class Documentation} +\input{class_c_n_n___edge} +\input{class_c_n_n___genome} +\input{class_c_n_n___node} +\input{struct_coin} +\input{struct_color} +\input{struct_color_profile} +\input{struct_color_tree} +\input{classconfig__to__arg_1_1_config_to_arg} +\input{class_consolidated_statistics} +\input{class_corpus} +\input{structcs__less__than__avg} +\input{structcs__less__than__max} +\input{structcs__less__than__min} +\input{class_delta___node} +\input{class_e_n_a_r_c___node} +\input{class_e_n_a_s___d_a_g___node} +\input{class_e_x_a_c_t} +\input{struct_e_x_a_c_t___r_e_s_u_l_t} +\input{class_e_x_a_m_m} +\input{classexamm__task_1_1_examm_task} +\input{class_g_r_u___node} +\input{struct_hash} +\input{struct_huffman_tree} +\input{class_image} +\input{class_image_interface} +\input{class_images} +\input{class_images_interface} +\input{class_island} +\input{classisland__purging__args_1_1_island_purging_args} +\input{class_island_speciation_strategy} +\input{class_large_image} +\input{class_large_images} +\input{structless__than__avg} +\input{structless__than__dfm__avg} +\input{structless__than__dfm__max} +\input{structless__than__dfm__min} +\input{structless__than__max} +\input{structless__than__min} +\input{class_line} +\input{struct_lode_p_n_g_color_mode} +\input{struct_lode_p_n_g_compress_settings} +\input{struct_lode_p_n_g_decoder_settings} +\input{struct_lode_p_n_g_decompress_settings} +\input{struct_lode_p_n_g_encoder_settings} +\input{struct_lode_p_n_g_info} +\input{struct_lode_p_n_g_state} +\input{struct_lode_p_n_g_time} +\input{class_log} +\input{class_log_file} +\input{class_l_s_t_m___node} +\input{class_m_g_u___node} +\input{classmin__max__heap} +\input{class_mosaic_images} +\input{class_multi_images_interface} +\input{class_neat_speciation_strategy} +\input{class_normal_distribution} +\input{class_parse_turbines} +\input{class_point} +\input{class_r_a_n_d_o_m___d_a_g___node} +\input{classrec__args_1_1_rec_args} +\input{class_rectangle} +\input{struct_result_set} +\input{class_r_n_n} +\input{class_r_n_n___edge} +\input{class_r_n_n___genome} +\input{class_r_n_n___node} +\input{class_r_n_n___node___interface} +\input{class_r_n_n___recurrent___edge} +\input{class_run_statistics} +\input{class_sentence_series} +\input{structsort___c_n_n___edges__by__depth} +\input{structsort___c_n_n___edges__by__innovation} +\input{structsort___c_n_n___edges__by__output__depth} +\input{structsort___c_n_n___nodes__by__depth} +\input{structsort__genomes__by__fitness} +\input{structsort__genomes__by__predictions} +\input{structsort__genomes__by__validation__error} +\input{structsort___r_n_n___edges__by__depth} +\input{structsort___r_n_n___edges__by__innovation} +\input{structsort___r_n_n___edges__by__output__depth} +\input{structsort___r_n_n___nodes__by__depth} +\input{structsort___r_n_n___nodes__by__innovation} +\input{structsort___r_n_n___recurrent___edges__by__depth} +\input{structsort___r_n_n___recurrent___edges__by__innovation} +\input{structsort___r_n_n___recurrent___edges__by__output__depth} +\input{class_speciation_strategy} +\input{class_species} +\input{class_time_series} +\input{class_time_series_set} +\input{class_time_series_sets} +\input{class_tracker} +\input{structucvector} +\input{class_u_g_r_n_n___node} +\input{structuivector} +\input{class_word_series} +%--- End generated contents --- + +% Index +\backmatter +\newpage +\phantomsection +\clearemptydoublepage +\addcontentsline{toc}{chapter}{\indexname} +\printindex + +\end{document} diff --git a/docs/latex/struct_coin.tex b/docs/latex/struct_coin.tex new file mode 100644 index 00000000..2d01dd3e --- /dev/null +++ b/docs/latex/struct_coin.tex @@ -0,0 +1,16 @@ +\hypertarget{struct_coin}{}\doxysection{Coin Struct Reference} +\label{struct_coin}\index{Coin@{Coin}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_coin_a1d6bc1176bb9fcbd9ca8c781c92ef432}\label{struct_coin_a1d6bc1176bb9fcbd9ca8c781c92ef432}} +\mbox{\hyperlink{structuivector}{uivector}} {\bfseries symbols} +\item +\mbox{\Hypertarget{struct_coin_a0427f9a678a9ce031a254f40f06b3669}\label{struct_coin_a0427f9a678a9ce031a254f40f06b3669}} +float {\bfseries weight} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+cpp\end{DoxyCompactItemize} diff --git a/docs/latex/struct_color.tex b/docs/latex/struct_color.tex new file mode 100644 index 00000000..8cf6c493 --- /dev/null +++ b/docs/latex/struct_color.tex @@ -0,0 +1,19 @@ +\hypertarget{struct_color}{}\doxysection{Color Struct Reference} +\label{struct_color}\index{Color@{Color}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_color_ab940d17000c033788115329369f856cf}\label{struct_color_ab940d17000c033788115329369f856cf}} +int16\+\_\+t {\bfseries red} +\item +\mbox{\Hypertarget{struct_color_abe1fc56211c830a34bb58fafdbf04dbb}\label{struct_color_abe1fc56211c830a34bb58fafdbf04dbb}} +int16\+\_\+t {\bfseries green} +\item +\mbox{\Hypertarget{struct_color_a25e2945bf418857e52db0a549e7d3f81}\label{struct_color_a25e2945bf418857e52db0a549e7d3f81}} +int16\+\_\+t {\bfseries blue} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +common/color\+\_\+table.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/struct_color_profile.tex b/docs/latex/struct_color_profile.tex new file mode 100644 index 00000000..8afe1069 --- /dev/null +++ b/docs/latex/struct_color_profile.tex @@ -0,0 +1,61 @@ +\hypertarget{struct_color_profile}{}\doxysection{Color\+Profile Struct Reference} +\label{struct_color_profile}\index{ColorProfile@{ColorProfile}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_color_profile_a4060733b0b18c705f404e57fe7074492}\label{struct_color_profile_a4060733b0b18c705f404e57fe7074492}} +unsigned char {\bfseries sixteenbit} +\item +\mbox{\Hypertarget{struct_color_profile_a73c5a699599d34017e7101ae355bafce}\label{struct_color_profile_a73c5a699599d34017e7101ae355bafce}} +unsigned char {\bfseries sixteenbit\+\_\+done} +\item +\mbox{\Hypertarget{struct_color_profile_a5473d538859f232abe8aaf864ff1548e}\label{struct_color_profile_a5473d538859f232abe8aaf864ff1548e}} +unsigned char {\bfseries colored} +\item +\mbox{\Hypertarget{struct_color_profile_ab0c56fe33cb50929702a475288ab8e4c}\label{struct_color_profile_ab0c56fe33cb50929702a475288ab8e4c}} +unsigned char {\bfseries colored\+\_\+done} +\item +\mbox{\Hypertarget{struct_color_profile_ac859f5252253e3e41651d4a40b3f8c07}\label{struct_color_profile_ac859f5252253e3e41651d4a40b3f8c07}} +unsigned char {\bfseries key} +\item +\mbox{\Hypertarget{struct_color_profile_a6bcd7f7c40c3e8763a3f1185414f4932}\label{struct_color_profile_a6bcd7f7c40c3e8763a3f1185414f4932}} +unsigned short {\bfseries key\+\_\+r} +\item +\mbox{\Hypertarget{struct_color_profile_a69145711cec524101e185425b4b34899}\label{struct_color_profile_a69145711cec524101e185425b4b34899}} +unsigned short {\bfseries key\+\_\+g} +\item +\mbox{\Hypertarget{struct_color_profile_a5d9b7502f24a622990f2a532d2f64ac8}\label{struct_color_profile_a5d9b7502f24a622990f2a532d2f64ac8}} +unsigned short {\bfseries key\+\_\+b} +\item +\mbox{\Hypertarget{struct_color_profile_ac2d692c039ecb5974303d8e8240367af}\label{struct_color_profile_ac2d692c039ecb5974303d8e8240367af}} +unsigned char {\bfseries alpha} +\item +\mbox{\Hypertarget{struct_color_profile_a9c8071d69a1a7a7015f3d880ed9faf8b}\label{struct_color_profile_a9c8071d69a1a7a7015f3d880ed9faf8b}} +unsigned char {\bfseries alpha\+\_\+done} +\item +\mbox{\Hypertarget{struct_color_profile_ac38cbfa9237ecaf21cb56730c0c15e19}\label{struct_color_profile_ac38cbfa9237ecaf21cb56730c0c15e19}} +unsigned {\bfseries numcolors} +\item +\mbox{\Hypertarget{struct_color_profile_ad64583d868f74ccd5bda6f94b7bd0a6d}\label{struct_color_profile_ad64583d868f74ccd5bda6f94b7bd0a6d}} +\mbox{\hyperlink{struct_color_tree}{Color\+Tree}} {\bfseries tree} +\item +\mbox{\Hypertarget{struct_color_profile_acf805bc910a23088e71d4cb04dcaf3df}\label{struct_color_profile_acf805bc910a23088e71d4cb04dcaf3df}} +unsigned char $\ast$ {\bfseries palette} +\item +\mbox{\Hypertarget{struct_color_profile_a2edd65be07c3c028a58329e570264980}\label{struct_color_profile_a2edd65be07c3c028a58329e570264980}} +unsigned {\bfseries maxnumcolors} +\item +\mbox{\Hypertarget{struct_color_profile_a7a0f6fe120d02e4573d2c27be20715cb}\label{struct_color_profile_a7a0f6fe120d02e4573d2c27be20715cb}} +unsigned char {\bfseries numcolors\+\_\+done} +\item +\mbox{\Hypertarget{struct_color_profile_a1a1aefe1c04cff8ede43e12b92429519}\label{struct_color_profile_a1a1aefe1c04cff8ede43e12b92429519}} +unsigned {\bfseries greybits} +\item +\mbox{\Hypertarget{struct_color_profile_af3825b320146425e402d7ae42bfaa63e}\label{struct_color_profile_af3825b320146425e402d7ae42bfaa63e}} +unsigned char {\bfseries greybits\+\_\+done} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+cpp\end{DoxyCompactItemize} diff --git a/docs/latex/struct_color_tree.tex b/docs/latex/struct_color_tree.tex new file mode 100644 index 00000000..d997c50e --- /dev/null +++ b/docs/latex/struct_color_tree.tex @@ -0,0 +1,16 @@ +\hypertarget{struct_color_tree}{}\doxysection{Color\+Tree Struct Reference} +\label{struct_color_tree}\index{ColorTree@{ColorTree}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_color_tree_a46a3b1d9239f5fd467ec97cd067b9a96}\label{struct_color_tree_a46a3b1d9239f5fd467ec97cd067b9a96}} +\mbox{\hyperlink{struct_color_tree}{Color\+Tree}} $\ast$ {\bfseries children} \mbox{[}16\mbox{]} +\item +\mbox{\Hypertarget{struct_color_tree_ab3836a4a5981a7cf4ef553d25d9b0361}\label{struct_color_tree_ab3836a4a5981a7cf4ef553d25d9b0361}} +int {\bfseries index} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+cpp\end{DoxyCompactItemize} diff --git a/docs/latex/struct_e_x_a_c_t___r_e_s_u_l_t.tex b/docs/latex/struct_e_x_a_c_t___r_e_s_u_l_t.tex new file mode 100644 index 00000000..40bb0b37 --- /dev/null +++ b/docs/latex/struct_e_x_a_c_t___r_e_s_u_l_t.tex @@ -0,0 +1,13 @@ +\hypertarget{struct_e_x_a_c_t___r_e_s_u_l_t}{}\doxysection{E\+X\+A\+C\+T\+\_\+\+R\+E\+S\+U\+LT Struct Reference} +\label{struct_e_x_a_c_t___r_e_s_u_l_t}\index{EXACT\_RESULT@{EXACT\_RESULT}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_e_x_a_c_t___r_e_s_u_l_t_ab4d3de460084674e520889dd1b85ab34}\label{struct_e_x_a_c_t___r_e_s_u_l_t_ab4d3de460084674e520889dd1b85ab34}} +\textbf{ string} {\bfseries file\+\_\+contents} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +server/exact\+\_\+validation\+\_\+policy.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/struct_hash.tex b/docs/latex/struct_hash.tex new file mode 100644 index 00000000..fbe2c84b --- /dev/null +++ b/docs/latex/struct_hash.tex @@ -0,0 +1,22 @@ +\hypertarget{struct_hash}{}\doxysection{Hash Struct Reference} +\label{struct_hash}\index{Hash@{Hash}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_hash_a0977cf12b1d8e6bbc784b5e0877926f5}\label{struct_hash_a0977cf12b1d8e6bbc784b5e0877926f5}} +int $\ast$ {\bfseries head} +\item +\mbox{\Hypertarget{struct_hash_a66918968854722efdf7ab5f8ac2c6c1d}\label{struct_hash_a66918968854722efdf7ab5f8ac2c6c1d}} +int $\ast$ {\bfseries val} +\item +\mbox{\Hypertarget{struct_hash_abf6ad3db2f652a19cc4ff0792e477899}\label{struct_hash_abf6ad3db2f652a19cc4ff0792e477899}} +unsigned short $\ast$ {\bfseries chain} +\item +\mbox{\Hypertarget{struct_hash_a7247caa3e23eaba8f0d199ec5010c931}\label{struct_hash_a7247caa3e23eaba8f0d199ec5010c931}} +unsigned short $\ast$ {\bfseries zeros} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+cpp\end{DoxyCompactItemize} diff --git a/docs/latex/struct_huffman_tree.tex b/docs/latex/struct_huffman_tree.tex new file mode 100644 index 00000000..bf1b1fc9 --- /dev/null +++ b/docs/latex/struct_huffman_tree.tex @@ -0,0 +1,25 @@ +\hypertarget{struct_huffman_tree}{}\doxysection{Huffman\+Tree Struct Reference} +\label{struct_huffman_tree}\index{HuffmanTree@{HuffmanTree}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_huffman_tree_a91160304cb771d2f9f39ee357c9b05a8}\label{struct_huffman_tree_a91160304cb771d2f9f39ee357c9b05a8}} +unsigned $\ast$ {\bfseries tree2d} +\item +\mbox{\Hypertarget{struct_huffman_tree_a47b3346a25fe0a3222b595c236ad146e}\label{struct_huffman_tree_a47b3346a25fe0a3222b595c236ad146e}} +unsigned $\ast$ {\bfseries tree1d} +\item +\mbox{\Hypertarget{struct_huffman_tree_aef81d45a5c56276c5699a8e9a575021d}\label{struct_huffman_tree_aef81d45a5c56276c5699a8e9a575021d}} +unsigned $\ast$ {\bfseries lengths} +\item +\mbox{\Hypertarget{struct_huffman_tree_adf034ca9ce62a4ebfffaaeaba4378a26}\label{struct_huffman_tree_adf034ca9ce62a4ebfffaaeaba4378a26}} +unsigned {\bfseries maxbitlen} +\item +\mbox{\Hypertarget{struct_huffman_tree_a608df5a24f60d1077a5cde19d5149e1f}\label{struct_huffman_tree_a608df5a24f60d1077a5cde19d5149e1f}} +unsigned {\bfseries numcodes} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+cpp\end{DoxyCompactItemize} diff --git a/docs/latex/struct_lode_p_n_g_color_mode.tex b/docs/latex/struct_lode_p_n_g_color_mode.tex new file mode 100644 index 00000000..bb0eb8c9 --- /dev/null +++ b/docs/latex/struct_lode_p_n_g_color_mode.tex @@ -0,0 +1,34 @@ +\hypertarget{struct_lode_p_n_g_color_mode}{}\doxysection{Lode\+P\+N\+G\+Color\+Mode Struct Reference} +\label{struct_lode_p_n_g_color_mode}\index{LodePNGColorMode@{LodePNGColorMode}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_color_mode_a4f3df7240411abe80546052d197fbe8d}\label{struct_lode_p_n_g_color_mode_a4f3df7240411abe80546052d197fbe8d}} +Lode\+P\+N\+G\+Color\+Type {\bfseries colortype} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_color_mode_ad20010b9561980f65281bc17f7848253}\label{struct_lode_p_n_g_color_mode_ad20010b9561980f65281bc17f7848253}} +unsigned {\bfseries bitdepth} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_color_mode_a54f0a793238009fcb95f081626fae308}\label{struct_lode_p_n_g_color_mode_a54f0a793238009fcb95f081626fae308}} +unsigned char $\ast$ {\bfseries palette} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_color_mode_a407557f056168682d9319aeb60866dcc}\label{struct_lode_p_n_g_color_mode_a407557f056168682d9319aeb60866dcc}} +size\+\_\+t {\bfseries palettesize} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_color_mode_ab9105505c5d56cfc6ce4efe1bb288b54}\label{struct_lode_p_n_g_color_mode_ab9105505c5d56cfc6ce4efe1bb288b54}} +unsigned {\bfseries key\+\_\+defined} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_color_mode_a29e64327bca1f3d16235e9ff471e4d50}\label{struct_lode_p_n_g_color_mode_a29e64327bca1f3d16235e9ff471e4d50}} +unsigned {\bfseries key\+\_\+r} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_color_mode_ad98309f36d289392b0c440baa50af9f6}\label{struct_lode_p_n_g_color_mode_ad98309f36d289392b0c440baa50af9f6}} +unsigned {\bfseries key\+\_\+g} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_color_mode_a93a269405fee0d1c5045a1a671ed1de8}\label{struct_lode_p_n_g_color_mode_a93a269405fee0d1c5045a1a671ed1de8}} +unsigned {\bfseries key\+\_\+b} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+h\end{DoxyCompactItemize} diff --git a/docs/latex/struct_lode_p_n_g_compress_settings.tex b/docs/latex/struct_lode_p_n_g_compress_settings.tex new file mode 100644 index 00000000..ce3fd22c --- /dev/null +++ b/docs/latex/struct_lode_p_n_g_compress_settings.tex @@ -0,0 +1,37 @@ +\hypertarget{struct_lode_p_n_g_compress_settings}{}\doxysection{Lode\+P\+N\+G\+Compress\+Settings Struct Reference} +\label{struct_lode_p_n_g_compress_settings}\index{LodePNGCompressSettings@{LodePNGCompressSettings}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_compress_settings_ac0afeac7276cce01fa9824aa2d5a1ba9}\label{struct_lode_p_n_g_compress_settings_ac0afeac7276cce01fa9824aa2d5a1ba9}} +unsigned {\bfseries btype} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_compress_settings_a37a87bd874376f0298efad2870e70e7e}\label{struct_lode_p_n_g_compress_settings_a37a87bd874376f0298efad2870e70e7e}} +unsigned {\bfseries use\+\_\+lz77} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_compress_settings_a01e77a9db5c2c4dfe6c79bf04f0bf84e}\label{struct_lode_p_n_g_compress_settings_a01e77a9db5c2c4dfe6c79bf04f0bf84e}} +unsigned {\bfseries windowsize} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_compress_settings_a11d89e0ff0c57f1c49dd58cb8347e005}\label{struct_lode_p_n_g_compress_settings_a11d89e0ff0c57f1c49dd58cb8347e005}} +unsigned {\bfseries minmatch} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_compress_settings_a70bc37e21eeffead6e9c8d67e163a591}\label{struct_lode_p_n_g_compress_settings_a70bc37e21eeffead6e9c8d67e163a591}} +unsigned {\bfseries nicematch} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_compress_settings_ad4ffde429dee40a8c314016f5f6fdab5}\label{struct_lode_p_n_g_compress_settings_ad4ffde429dee40a8c314016f5f6fdab5}} +unsigned {\bfseries lazymatching} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_compress_settings_a4a7835f394349f15f1302d11bcb0efa0}\label{struct_lode_p_n_g_compress_settings_a4a7835f394349f15f1302d11bcb0efa0}} +unsigned($\ast$ {\bfseries custom\+\_\+zlib} )(unsigned char $\ast$$\ast$, size\+\_\+t $\ast$, const unsigned char $\ast$, size\+\_\+t, const \mbox{\hyperlink{struct_lode_p_n_g_compress_settings}{Lode\+P\+N\+G\+Compress\+Settings}} $\ast$) +\item +\mbox{\Hypertarget{struct_lode_p_n_g_compress_settings_a55dafebbbe017806fb2bbc32bb40a59b}\label{struct_lode_p_n_g_compress_settings_a55dafebbbe017806fb2bbc32bb40a59b}} +unsigned($\ast$ {\bfseries custom\+\_\+deflate} )(unsigned char $\ast$$\ast$, size\+\_\+t $\ast$, const unsigned char $\ast$, size\+\_\+t, const \mbox{\hyperlink{struct_lode_p_n_g_compress_settings}{Lode\+P\+N\+G\+Compress\+Settings}} $\ast$) +\item +\mbox{\Hypertarget{struct_lode_p_n_g_compress_settings_a62826645ef28e2a84dd2b65f547a2883}\label{struct_lode_p_n_g_compress_settings_a62826645ef28e2a84dd2b65f547a2883}} +const void $\ast$ {\bfseries custom\+\_\+context} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+h\end{DoxyCompactItemize} diff --git a/docs/latex/struct_lode_p_n_g_decoder_settings.tex b/docs/latex/struct_lode_p_n_g_decoder_settings.tex new file mode 100644 index 00000000..23fbf927 --- /dev/null +++ b/docs/latex/struct_lode_p_n_g_decoder_settings.tex @@ -0,0 +1,28 @@ +\hypertarget{struct_lode_p_n_g_decoder_settings}{}\doxysection{Lode\+P\+N\+G\+Decoder\+Settings Struct Reference} +\label{struct_lode_p_n_g_decoder_settings}\index{LodePNGDecoderSettings@{LodePNGDecoderSettings}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_decoder_settings_a9ae8fef9880bef97a3e932f8ea942ed8}\label{struct_lode_p_n_g_decoder_settings_a9ae8fef9880bef97a3e932f8ea942ed8}} +\mbox{\hyperlink{struct_lode_p_n_g_decompress_settings}{Lode\+P\+N\+G\+Decompress\+Settings}} {\bfseries zlibsettings} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_decoder_settings_a6390c403d2a5718242337bbbaf15131d}\label{struct_lode_p_n_g_decoder_settings_a6390c403d2a5718242337bbbaf15131d}} +unsigned {\bfseries ignore\+\_\+crc} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_decoder_settings_abaaeec14170c4a37832543c0af6c44b9}\label{struct_lode_p_n_g_decoder_settings_abaaeec14170c4a37832543c0af6c44b9}} +unsigned {\bfseries fix\+\_\+png} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_decoder_settings_af26f2b29cd338ce4476bee9571a0818a}\label{struct_lode_p_n_g_decoder_settings_af26f2b29cd338ce4476bee9571a0818a}} +unsigned {\bfseries color\+\_\+convert} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_decoder_settings_aa1212905c3f73d9fffef2c04a220d951}\label{struct_lode_p_n_g_decoder_settings_aa1212905c3f73d9fffef2c04a220d951}} +unsigned {\bfseries read\+\_\+text\+\_\+chunks} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_decoder_settings_a8775e4fc539dc457916720f52b442f27}\label{struct_lode_p_n_g_decoder_settings_a8775e4fc539dc457916720f52b442f27}} +unsigned {\bfseries remember\+\_\+unknown\+\_\+chunks} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+h\end{DoxyCompactItemize} diff --git a/docs/latex/struct_lode_p_n_g_decompress_settings.tex b/docs/latex/struct_lode_p_n_g_decompress_settings.tex new file mode 100644 index 00000000..1afb854f --- /dev/null +++ b/docs/latex/struct_lode_p_n_g_decompress_settings.tex @@ -0,0 +1,22 @@ +\hypertarget{struct_lode_p_n_g_decompress_settings}{}\doxysection{Lode\+P\+N\+G\+Decompress\+Settings Struct Reference} +\label{struct_lode_p_n_g_decompress_settings}\index{LodePNGDecompressSettings@{LodePNGDecompressSettings}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_decompress_settings_afab4b919650b51b4d2f175a60ed6c580}\label{struct_lode_p_n_g_decompress_settings_afab4b919650b51b4d2f175a60ed6c580}} +unsigned {\bfseries ignore\+\_\+adler32} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_decompress_settings_a9dd432e46330dbd2ce3ef1929c64337d}\label{struct_lode_p_n_g_decompress_settings_a9dd432e46330dbd2ce3ef1929c64337d}} +unsigned($\ast$ {\bfseries custom\+\_\+zlib} )(unsigned char $\ast$$\ast$, size\+\_\+t $\ast$, const unsigned char $\ast$, size\+\_\+t, const \mbox{\hyperlink{struct_lode_p_n_g_decompress_settings}{Lode\+P\+N\+G\+Decompress\+Settings}} $\ast$) +\item +\mbox{\Hypertarget{struct_lode_p_n_g_decompress_settings_a023aa5946c99934d40280850a4d8b204}\label{struct_lode_p_n_g_decompress_settings_a023aa5946c99934d40280850a4d8b204}} +unsigned($\ast$ {\bfseries custom\+\_\+inflate} )(unsigned char $\ast$$\ast$, size\+\_\+t $\ast$, const unsigned char $\ast$, size\+\_\+t, const \mbox{\hyperlink{struct_lode_p_n_g_decompress_settings}{Lode\+P\+N\+G\+Decompress\+Settings}} $\ast$) +\item +\mbox{\Hypertarget{struct_lode_p_n_g_decompress_settings_a66e3608b541c64bb275c0ac1a80c3ec6}\label{struct_lode_p_n_g_decompress_settings_a66e3608b541c64bb275c0ac1a80c3ec6}} +const void $\ast$ {\bfseries custom\+\_\+context} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+h\end{DoxyCompactItemize} diff --git a/docs/latex/struct_lode_p_n_g_encoder_settings.tex b/docs/latex/struct_lode_p_n_g_encoder_settings.tex new file mode 100644 index 00000000..19426126 --- /dev/null +++ b/docs/latex/struct_lode_p_n_g_encoder_settings.tex @@ -0,0 +1,34 @@ +\hypertarget{struct_lode_p_n_g_encoder_settings}{}\doxysection{Lode\+P\+N\+G\+Encoder\+Settings Struct Reference} +\label{struct_lode_p_n_g_encoder_settings}\index{LodePNGEncoderSettings@{LodePNGEncoderSettings}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_encoder_settings_a2c5928b4172c75e27de467870f2ff946}\label{struct_lode_p_n_g_encoder_settings_a2c5928b4172c75e27de467870f2ff946}} +\mbox{\hyperlink{struct_lode_p_n_g_compress_settings}{Lode\+P\+N\+G\+Compress\+Settings}} {\bfseries zlibsettings} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_encoder_settings_a4cad5feafb33c208706886bedb93d3d4}\label{struct_lode_p_n_g_encoder_settings_a4cad5feafb33c208706886bedb93d3d4}} +Lode\+P\+N\+G\+Auto\+Convert {\bfseries auto\+\_\+convert} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_encoder_settings_a0d82e8f2fabcb6cebbc54b80922945f1}\label{struct_lode_p_n_g_encoder_settings_a0d82e8f2fabcb6cebbc54b80922945f1}} +unsigned {\bfseries filter\+\_\+palette\+\_\+zero} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_encoder_settings_a5e18e4eb941763a2e3e6c65ee9f0729c}\label{struct_lode_p_n_g_encoder_settings_a5e18e4eb941763a2e3e6c65ee9f0729c}} +Lode\+P\+N\+G\+Filter\+Strategy {\bfseries filter\+\_\+strategy} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_encoder_settings_a4446f87b5283f25664802a1be037e76e}\label{struct_lode_p_n_g_encoder_settings_a4446f87b5283f25664802a1be037e76e}} +const unsigned char $\ast$ {\bfseries predefined\+\_\+filters} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_encoder_settings_a04dc9622ccd1d7c74c56291409aa512a}\label{struct_lode_p_n_g_encoder_settings_a04dc9622ccd1d7c74c56291409aa512a}} +unsigned {\bfseries force\+\_\+palette} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_encoder_settings_a893aa542aa7c122c32ee36dd716fbcb2}\label{struct_lode_p_n_g_encoder_settings_a893aa542aa7c122c32ee36dd716fbcb2}} +unsigned {\bfseries add\+\_\+id} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_encoder_settings_a6ffdcb8e85a65ea208fe027be072d710}\label{struct_lode_p_n_g_encoder_settings_a6ffdcb8e85a65ea208fe027be072d710}} +unsigned {\bfseries text\+\_\+compression} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+h\end{DoxyCompactItemize} diff --git a/docs/latex/struct_lode_p_n_g_info.tex b/docs/latex/struct_lode_p_n_g_info.tex new file mode 100644 index 00000000..8a60d2d1 --- /dev/null +++ b/docs/latex/struct_lode_p_n_g_info.tex @@ -0,0 +1,82 @@ +\hypertarget{struct_lode_p_n_g_info}{}\doxysection{Lode\+P\+N\+G\+Info Struct Reference} +\label{struct_lode_p_n_g_info}\index{LodePNGInfo@{LodePNGInfo}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a42bcacd0dbaaea01c04cc87b58ac3c1d}\label{struct_lode_p_n_g_info_a42bcacd0dbaaea01c04cc87b58ac3c1d}} +unsigned {\bfseries compression\+\_\+method} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a5098d6e8aa528d5197f51914439633b9}\label{struct_lode_p_n_g_info_a5098d6e8aa528d5197f51914439633b9}} +unsigned {\bfseries filter\+\_\+method} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a80207e3e53c959b2285636496a3dd3f1}\label{struct_lode_p_n_g_info_a80207e3e53c959b2285636496a3dd3f1}} +unsigned {\bfseries interlace\+\_\+method} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a0af9bab3435084780ce8c1cb69bb2628}\label{struct_lode_p_n_g_info_a0af9bab3435084780ce8c1cb69bb2628}} +\mbox{\hyperlink{struct_lode_p_n_g_color_mode}{Lode\+P\+N\+G\+Color\+Mode}} {\bfseries color} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_aa94c65344af02472adb9c71eae2e765f}\label{struct_lode_p_n_g_info_aa94c65344af02472adb9c71eae2e765f}} +unsigned {\bfseries background\+\_\+defined} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a98b59c3760bda184bb16c9713b430bc3}\label{struct_lode_p_n_g_info_a98b59c3760bda184bb16c9713b430bc3}} +unsigned {\bfseries background\+\_\+r} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_abf638e191edaeaa2b02c371a381e3a89}\label{struct_lode_p_n_g_info_abf638e191edaeaa2b02c371a381e3a89}} +unsigned {\bfseries background\+\_\+g} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a994de0c74ef1092f056ff534e00dfa0d}\label{struct_lode_p_n_g_info_a994de0c74ef1092f056ff534e00dfa0d}} +unsigned {\bfseries background\+\_\+b} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a393e0b3948ca6674232e1cc625db282e}\label{struct_lode_p_n_g_info_a393e0b3948ca6674232e1cc625db282e}} +size\+\_\+t {\bfseries text\+\_\+num} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a0a26147c9673870dd122693f17a69b13}\label{struct_lode_p_n_g_info_a0a26147c9673870dd122693f17a69b13}} +char $\ast$$\ast$ {\bfseries text\+\_\+keys} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_aac321d27e65c54e56d6092d3a6400a81}\label{struct_lode_p_n_g_info_aac321d27e65c54e56d6092d3a6400a81}} +char $\ast$$\ast$ {\bfseries text\+\_\+strings} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a22166bb10c89a4d80e206d6c4736b625}\label{struct_lode_p_n_g_info_a22166bb10c89a4d80e206d6c4736b625}} +size\+\_\+t {\bfseries itext\+\_\+num} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a1b909e03596abf86d564641741b0087f}\label{struct_lode_p_n_g_info_a1b909e03596abf86d564641741b0087f}} +char $\ast$$\ast$ {\bfseries itext\+\_\+keys} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_ae9f9f594e63c910d467a14f550960837}\label{struct_lode_p_n_g_info_ae9f9f594e63c910d467a14f550960837}} +char $\ast$$\ast$ {\bfseries itext\+\_\+langtags} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a93a8e823ac715dbdd625f023d8fdebc2}\label{struct_lode_p_n_g_info_a93a8e823ac715dbdd625f023d8fdebc2}} +char $\ast$$\ast$ {\bfseries itext\+\_\+transkeys} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a7014fd40ffeb1d482f72d33c020cf73e}\label{struct_lode_p_n_g_info_a7014fd40ffeb1d482f72d33c020cf73e}} +char $\ast$$\ast$ {\bfseries itext\+\_\+strings} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a9adb9f74ab90716ae107b99da5384424}\label{struct_lode_p_n_g_info_a9adb9f74ab90716ae107b99da5384424}} +unsigned {\bfseries time\+\_\+defined} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a4d3407acdf79bf87f20a3562f210b393}\label{struct_lode_p_n_g_info_a4d3407acdf79bf87f20a3562f210b393}} +\mbox{\hyperlink{struct_lode_p_n_g_time}{Lode\+P\+N\+G\+Time}} {\bfseries time} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a9b8e29b7e7b4908a2de0275e01a828ed}\label{struct_lode_p_n_g_info_a9b8e29b7e7b4908a2de0275e01a828ed}} +unsigned {\bfseries phys\+\_\+defined} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a1593fa6e1acc93f3b9de51c340bef94d}\label{struct_lode_p_n_g_info_a1593fa6e1acc93f3b9de51c340bef94d}} +unsigned {\bfseries phys\+\_\+x} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a52ad7a105244d00f1e91c489eaf53f97}\label{struct_lode_p_n_g_info_a52ad7a105244d00f1e91c489eaf53f97}} +unsigned {\bfseries phys\+\_\+y} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_ad6f2171d9f87716e5010f6c5352f9855}\label{struct_lode_p_n_g_info_ad6f2171d9f87716e5010f6c5352f9855}} +unsigned {\bfseries phys\+\_\+unit} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a8347476da7fc2fc6af4ec7ed44b638c6}\label{struct_lode_p_n_g_info_a8347476da7fc2fc6af4ec7ed44b638c6}} +unsigned char $\ast$ {\bfseries unknown\+\_\+chunks\+\_\+data} \mbox{[}3\mbox{]} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_info_a25a81d760759bd0383ae5a81ba83911d}\label{struct_lode_p_n_g_info_a25a81d760759bd0383ae5a81ba83911d}} +size\+\_\+t {\bfseries unknown\+\_\+chunks\+\_\+size} \mbox{[}3\mbox{]} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+h\end{DoxyCompactItemize} diff --git a/docs/latex/struct_lode_p_n_g_state.tex b/docs/latex/struct_lode_p_n_g_state.tex new file mode 100644 index 00000000..bf7958d2 --- /dev/null +++ b/docs/latex/struct_lode_p_n_g_state.tex @@ -0,0 +1,25 @@ +\hypertarget{struct_lode_p_n_g_state}{}\doxysection{Lode\+P\+N\+G\+State Struct Reference} +\label{struct_lode_p_n_g_state}\index{LodePNGState@{LodePNGState}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_state_abd2c38ffc68f04b0e4159e1f97ba1f76}\label{struct_lode_p_n_g_state_abd2c38ffc68f04b0e4159e1f97ba1f76}} +\mbox{\hyperlink{struct_lode_p_n_g_decoder_settings}{Lode\+P\+N\+G\+Decoder\+Settings}} {\bfseries decoder} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_state_ac63d91db835129d02eb83bbe81de347e}\label{struct_lode_p_n_g_state_ac63d91db835129d02eb83bbe81de347e}} +\mbox{\hyperlink{struct_lode_p_n_g_encoder_settings}{Lode\+P\+N\+G\+Encoder\+Settings}} {\bfseries encoder} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_state_a597bc08de787147474d43adf8b6ceacf}\label{struct_lode_p_n_g_state_a597bc08de787147474d43adf8b6ceacf}} +\mbox{\hyperlink{struct_lode_p_n_g_color_mode}{Lode\+P\+N\+G\+Color\+Mode}} {\bfseries info\+\_\+raw} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_state_a08d9ac43c995fcf34d72b1d37047b6fa}\label{struct_lode_p_n_g_state_a08d9ac43c995fcf34d72b1d37047b6fa}} +\mbox{\hyperlink{struct_lode_p_n_g_info}{Lode\+P\+N\+G\+Info}} {\bfseries info\+\_\+png} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_state_a1a00a050da588cf3c2b7a6252bebb0cd}\label{struct_lode_p_n_g_state_a1a00a050da588cf3c2b7a6252bebb0cd}} +unsigned {\bfseries error} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+h\end{DoxyCompactItemize} diff --git a/docs/latex/struct_lode_p_n_g_time.tex b/docs/latex/struct_lode_p_n_g_time.tex new file mode 100644 index 00000000..88556381 --- /dev/null +++ b/docs/latex/struct_lode_p_n_g_time.tex @@ -0,0 +1,28 @@ +\hypertarget{struct_lode_p_n_g_time}{}\doxysection{Lode\+P\+N\+G\+Time Struct Reference} +\label{struct_lode_p_n_g_time}\index{LodePNGTime@{LodePNGTime}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_time_a32b68342f39f3d38ba91a721b1149b8f}\label{struct_lode_p_n_g_time_a32b68342f39f3d38ba91a721b1149b8f}} +unsigned {\bfseries year} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_time_a295d890e862d5cd0c444e9d3a96fa9d5}\label{struct_lode_p_n_g_time_a295d890e862d5cd0c444e9d3a96fa9d5}} +unsigned {\bfseries month} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_time_aa3dee3b7b3a1e730fbded7a7b8cf355e}\label{struct_lode_p_n_g_time_aa3dee3b7b3a1e730fbded7a7b8cf355e}} +unsigned {\bfseries day} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_time_ac99cb7f3ce16a85f9f505b7f5f6e0aa7}\label{struct_lode_p_n_g_time_ac99cb7f3ce16a85f9f505b7f5f6e0aa7}} +unsigned {\bfseries hour} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_time_ac3045de79728f29fc61f534b062e0f13}\label{struct_lode_p_n_g_time_ac3045de79728f29fc61f534b062e0f13}} +unsigned {\bfseries minute} +\item +\mbox{\Hypertarget{struct_lode_p_n_g_time_a6c691c5821e828488a8bb8a90751a2f0}\label{struct_lode_p_n_g_time_a6c691c5821e828488a8bb8a90751a2f0}} +unsigned {\bfseries second} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+h\end{DoxyCompactItemize} diff --git a/docs/latex/struct_result_set.tex b/docs/latex/struct_result_set.tex new file mode 100644 index 00000000..b169d840 --- /dev/null +++ b/docs/latex/struct_result_set.tex @@ -0,0 +1,28 @@ +\hypertarget{struct_result_set}{}\doxysection{Result\+Set Struct Reference} +\label{struct_result_set}\index{ResultSet@{ResultSet}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{struct_result_set_a46076d8e456ebd1432632d14c9212d99}\label{struct_result_set_a46076d8e456ebd1432632d14c9212d99}} +int {\bfseries job} +\item +\mbox{\Hypertarget{struct_result_set_a9501814253897a060bd81a8a25f8af3f}\label{struct_result_set_a9501814253897a060bd81a8a25f8af3f}} +double {\bfseries training\+\_\+mae} +\item +\mbox{\Hypertarget{struct_result_set_a29f27bcf55714d86c9089e333f15d0b4}\label{struct_result_set_a29f27bcf55714d86c9089e333f15d0b4}} +double {\bfseries training\+\_\+mse} +\item +\mbox{\Hypertarget{struct_result_set_a189c30d8b7a41de52364f2ddfca5a08e}\label{struct_result_set_a189c30d8b7a41de52364f2ddfca5a08e}} +double {\bfseries test\+\_\+mae} +\item +\mbox{\Hypertarget{struct_result_set_a39fe2f9170de18a62b60171e87c21269}\label{struct_result_set_a39fe2f9170de18a62b60171e87c21269}} +double {\bfseries test\+\_\+mse} +\item +\mbox{\Hypertarget{struct_result_set_a1ec173e479cab71996940492757aece3}\label{struct_result_set_a1ec173e479cab71996940492757aece3}} +long {\bfseries milliseconds} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +mpi/rnn\+\_\+kfold\+\_\+sweep.\+cxx\end{DoxyCompactItemize} diff --git a/docs/latex/structcs__less__than__avg.tex b/docs/latex/structcs__less__than__avg.tex new file mode 100644 index 00000000..7d5745c6 --- /dev/null +++ b/docs/latex/structcs__less__than__avg.tex @@ -0,0 +1,13 @@ +\hypertarget{structcs__less__than__avg}{}\doxysection{cs\+\_\+less\+\_\+than\+\_\+avg Struct Reference} +\label{structcs__less__than__avg}\index{cs\_less\_than\_avg@{cs\_less\_than\_avg}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structcs__less__than__avg_a3f5175ffd950c9178a52fb6cb681864e}\label{structcs__less__than__avg_a3f5175ffd950c9178a52fb6cb681864e}} +bool {\bfseries operator()} (const \mbox{\hyperlink{class_consolidated_statistics}{Consolidated\+Statistics}} $\ast$s1, const \mbox{\hyperlink{class_consolidated_statistics}{Consolidated\+Statistics}} $\ast$s2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structcs__less__than__max.tex b/docs/latex/structcs__less__than__max.tex new file mode 100644 index 00000000..8b04be9c --- /dev/null +++ b/docs/latex/structcs__less__than__max.tex @@ -0,0 +1,13 @@ +\hypertarget{structcs__less__than__max}{}\doxysection{cs\+\_\+less\+\_\+than\+\_\+max Struct Reference} +\label{structcs__less__than__max}\index{cs\_less\_than\_max@{cs\_less\_than\_max}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structcs__less__than__max_adc8849f06b81eecec7ac74bbe334c24a}\label{structcs__less__than__max_adc8849f06b81eecec7ac74bbe334c24a}} +bool {\bfseries operator()} (const \mbox{\hyperlink{class_consolidated_statistics}{Consolidated\+Statistics}} $\ast$s1, const \mbox{\hyperlink{class_consolidated_statistics}{Consolidated\+Statistics}} $\ast$s2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structcs__less__than__min.tex b/docs/latex/structcs__less__than__min.tex new file mode 100644 index 00000000..984dcb0a --- /dev/null +++ b/docs/latex/structcs__less__than__min.tex @@ -0,0 +1,13 @@ +\hypertarget{structcs__less__than__min}{}\doxysection{cs\+\_\+less\+\_\+than\+\_\+min Struct Reference} +\label{structcs__less__than__min}\index{cs\_less\_than\_min@{cs\_less\_than\_min}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structcs__less__than__min_ab532df35c758abfa79398c3fe71af373}\label{structcs__less__than__min_ab532df35c758abfa79398c3fe71af373}} +bool {\bfseries operator()} (const \mbox{\hyperlink{class_consolidated_statistics}{Consolidated\+Statistics}} $\ast$s1, const \mbox{\hyperlink{class_consolidated_statistics}{Consolidated\+Statistics}} $\ast$s2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structless__than__avg.tex b/docs/latex/structless__than__avg.tex new file mode 100644 index 00000000..e42597a4 --- /dev/null +++ b/docs/latex/structless__than__avg.tex @@ -0,0 +1,13 @@ +\hypertarget{structless__than__avg}{}\doxysection{less\+\_\+than\+\_\+avg Struct Reference} +\label{structless__than__avg}\index{less\_than\_avg@{less\_than\_avg}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structless__than__avg_acc7aee282e5828e0a9dbd421936cef43}\label{structless__than__avg_acc7aee282e5828e0a9dbd421936cef43}} +bool {\bfseries operator()} (const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s1, const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structless__than__dfm__avg.tex b/docs/latex/structless__than__dfm__avg.tex new file mode 100644 index 00000000..37dcd990 --- /dev/null +++ b/docs/latex/structless__than__dfm__avg.tex @@ -0,0 +1,13 @@ +\hypertarget{structless__than__dfm__avg}{}\doxysection{less\+\_\+than\+\_\+dfm\+\_\+avg Struct Reference} +\label{structless__than__dfm__avg}\index{less\_than\_dfm\_avg@{less\_than\_dfm\_avg}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structless__than__dfm__avg_a170ef354337c2bc1cdd9bec4e49fe7d9}\label{structless__than__dfm__avg_a170ef354337c2bc1cdd9bec4e49fe7d9}} +bool {\bfseries operator()} (const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s1, const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structless__than__dfm__max.tex b/docs/latex/structless__than__dfm__max.tex new file mode 100644 index 00000000..16e30fcd --- /dev/null +++ b/docs/latex/structless__than__dfm__max.tex @@ -0,0 +1,13 @@ +\hypertarget{structless__than__dfm__max}{}\doxysection{less\+\_\+than\+\_\+dfm\+\_\+max Struct Reference} +\label{structless__than__dfm__max}\index{less\_than\_dfm\_max@{less\_than\_dfm\_max}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structless__than__dfm__max_a438d0fcca06b9a3e6b506d214dcf32e5}\label{structless__than__dfm__max_a438d0fcca06b9a3e6b506d214dcf32e5}} +bool {\bfseries operator()} (const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s1, const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structless__than__dfm__min.tex b/docs/latex/structless__than__dfm__min.tex new file mode 100644 index 00000000..dfe11fbc --- /dev/null +++ b/docs/latex/structless__than__dfm__min.tex @@ -0,0 +1,13 @@ +\hypertarget{structless__than__dfm__min}{}\doxysection{less\+\_\+than\+\_\+dfm\+\_\+min Struct Reference} +\label{structless__than__dfm__min}\index{less\_than\_dfm\_min@{less\_than\_dfm\_min}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structless__than__dfm__min_a38f0bd46d26fd24d5897a7fd9f01ca09}\label{structless__than__dfm__min_a38f0bd46d26fd24d5897a7fd9f01ca09}} +bool {\bfseries operator()} (const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s1, const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structless__than__max.tex b/docs/latex/structless__than__max.tex new file mode 100644 index 00000000..17614613 --- /dev/null +++ b/docs/latex/structless__than__max.tex @@ -0,0 +1,13 @@ +\hypertarget{structless__than__max}{}\doxysection{less\+\_\+than\+\_\+max Struct Reference} +\label{structless__than__max}\index{less\_than\_max@{less\_than\_max}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structless__than__max_ab56ea10ec2c21ec48a748debd1d08ab9}\label{structless__than__max_ab56ea10ec2c21ec48a748debd1d08ab9}} +bool {\bfseries operator()} (const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s1, const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structless__than__min.tex b/docs/latex/structless__than__min.tex new file mode 100644 index 00000000..723f2025 --- /dev/null +++ b/docs/latex/structless__than__min.tex @@ -0,0 +1,13 @@ +\hypertarget{structless__than__min}{}\doxysection{less\+\_\+than\+\_\+min Struct Reference} +\label{structless__than__min}\index{less\_than\_min@{less\_than\_min}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structless__than__min_a738821687cd1c61110b74d48df62b782}\label{structless__than__min_a738821687cd1c61110b74d48df62b782}} +bool {\bfseries operator()} (const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s1, const \mbox{\hyperlink{class_run_statistics}{Run\+Statistics}} $\ast$s2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +mpi/run\+\_\+statistics.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___c_n_n___edges__by__depth.tex b/docs/latex/structsort___c_n_n___edges__by__depth.tex new file mode 100644 index 00000000..e6947de0 --- /dev/null +++ b/docs/latex/structsort___c_n_n___edges__by__depth.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___c_n_n___edges__by__depth}{}\doxysection{sort\+\_\+\+C\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+depth Struct Reference} +\label{structsort___c_n_n___edges__by__depth}\index{sort\_CNN\_Edges\_by\_depth@{sort\_CNN\_Edges\_by\_depth}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___c_n_n___edges__by__depth_a3ed9a9b0a4bc6d45056789d989996c14}\label{structsort___c_n_n___edges__by__depth_a3ed9a9b0a4bc6d45056789d989996c14}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$n1, \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +cnn/cnn\+\_\+edge.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___c_n_n___edges__by__innovation.tex b/docs/latex/structsort___c_n_n___edges__by__innovation.tex new file mode 100644 index 00000000..aac41939 --- /dev/null +++ b/docs/latex/structsort___c_n_n___edges__by__innovation.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___c_n_n___edges__by__innovation}{}\doxysection{sort\+\_\+\+C\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+innovation Struct Reference} +\label{structsort___c_n_n___edges__by__innovation}\index{sort\_CNN\_Edges\_by\_innovation@{sort\_CNN\_Edges\_by\_innovation}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___c_n_n___edges__by__innovation_ae089c0daf22fcc4ef9446ad6a2e3ecd6}\label{structsort___c_n_n___edges__by__innovation_ae089c0daf22fcc4ef9446ad6a2e3ecd6}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$n1, \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +cnn/cnn\+\_\+edge.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___c_n_n___edges__by__output__depth.tex b/docs/latex/structsort___c_n_n___edges__by__output__depth.tex new file mode 100644 index 00000000..3edd68db --- /dev/null +++ b/docs/latex/structsort___c_n_n___edges__by__output__depth.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___c_n_n___edges__by__output__depth}{}\doxysection{sort\+\_\+\+C\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+output\+\_\+depth Struct Reference} +\label{structsort___c_n_n___edges__by__output__depth}\index{sort\_CNN\_Edges\_by\_output\_depth@{sort\_CNN\_Edges\_by\_output\_depth}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___c_n_n___edges__by__output__depth_aea02edda67b14c423ccdcfc59431cf36}\label{structsort___c_n_n___edges__by__output__depth_aea02edda67b14c423ccdcfc59431cf36}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$n1, \mbox{\hyperlink{class_c_n_n___edge}{C\+N\+N\+\_\+\+Edge}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +cnn/cnn\+\_\+edge.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___c_n_n___nodes__by__depth.tex b/docs/latex/structsort___c_n_n___nodes__by__depth.tex new file mode 100644 index 00000000..d1d04c37 --- /dev/null +++ b/docs/latex/structsort___c_n_n___nodes__by__depth.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___c_n_n___nodes__by__depth}{}\doxysection{sort\+\_\+\+C\+N\+N\+\_\+\+Nodes\+\_\+by\+\_\+depth Struct Reference} +\label{structsort___c_n_n___nodes__by__depth}\index{sort\_CNN\_Nodes\_by\_depth@{sort\_CNN\_Nodes\_by\_depth}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___c_n_n___nodes__by__depth_a28052ab84934932f91e338246deadf24}\label{structsort___c_n_n___nodes__by__depth_a28052ab84934932f91e338246deadf24}} +bool {\bfseries operator()} (const \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$n1, const \mbox{\hyperlink{class_c_n_n___node}{C\+N\+N\+\_\+\+Node}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +cnn/cnn\+\_\+node.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___r_n_n___edges__by__depth.tex b/docs/latex/structsort___r_n_n___edges__by__depth.tex new file mode 100644 index 00000000..7dbf495d --- /dev/null +++ b/docs/latex/structsort___r_n_n___edges__by__depth.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___r_n_n___edges__by__depth}{}\doxysection{sort\+\_\+\+R\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+depth Struct Reference} +\label{structsort___r_n_n___edges__by__depth}\index{sort\_RNN\_Edges\_by\_depth@{sort\_RNN\_Edges\_by\_depth}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___r_n_n___edges__by__depth_a7a8ededf12bc9883594faa1741fcff4b}\label{structsort___r_n_n___edges__by__depth_a7a8ededf12bc9883594faa1741fcff4b}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$n1, \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+edge.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___r_n_n___edges__by__innovation.tex b/docs/latex/structsort___r_n_n___edges__by__innovation.tex new file mode 100644 index 00000000..e6f622d6 --- /dev/null +++ b/docs/latex/structsort___r_n_n___edges__by__innovation.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___r_n_n___edges__by__innovation}{}\doxysection{sort\+\_\+\+R\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+innovation Struct Reference} +\label{structsort___r_n_n___edges__by__innovation}\index{sort\_RNN\_Edges\_by\_innovation@{sort\_RNN\_Edges\_by\_innovation}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___r_n_n___edges__by__innovation_a6d1cfc2ba6163fce1cad300f1e6b3f86}\label{structsort___r_n_n___edges__by__innovation_a6d1cfc2ba6163fce1cad300f1e6b3f86}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$n1, \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+edge.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___r_n_n___edges__by__output__depth.tex b/docs/latex/structsort___r_n_n___edges__by__output__depth.tex new file mode 100644 index 00000000..c8ab1aba --- /dev/null +++ b/docs/latex/structsort___r_n_n___edges__by__output__depth.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___r_n_n___edges__by__output__depth}{}\doxysection{sort\+\_\+\+R\+N\+N\+\_\+\+Edges\+\_\+by\+\_\+output\+\_\+depth Struct Reference} +\label{structsort___r_n_n___edges__by__output__depth}\index{sort\_RNN\_Edges\_by\_output\_depth@{sort\_RNN\_Edges\_by\_output\_depth}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___r_n_n___edges__by__output__depth_a9c3ca0231748a742c637cb9facab927b}\label{structsort___r_n_n___edges__by__output__depth_a9c3ca0231748a742c637cb9facab927b}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$n1, \mbox{\hyperlink{class_r_n_n___edge}{R\+N\+N\+\_\+\+Edge}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+edge.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___r_n_n___nodes__by__depth.tex b/docs/latex/structsort___r_n_n___nodes__by__depth.tex new file mode 100644 index 00000000..3eadb9e1 --- /dev/null +++ b/docs/latex/structsort___r_n_n___nodes__by__depth.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___r_n_n___nodes__by__depth}{}\doxysection{sort\+\_\+\+R\+N\+N\+\_\+\+Nodes\+\_\+by\+\_\+depth Struct Reference} +\label{structsort___r_n_n___nodes__by__depth}\index{sort\_RNN\_Nodes\_by\_depth@{sort\_RNN\_Nodes\_by\_depth}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___r_n_n___nodes__by__depth_a87ccc3e6ade2336119c29c318db9cc12}\label{structsort___r_n_n___nodes__by__depth_a87ccc3e6ade2336119c29c318db9cc12}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n1, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+node\+\_\+interface.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___r_n_n___nodes__by__innovation.tex b/docs/latex/structsort___r_n_n___nodes__by__innovation.tex new file mode 100644 index 00000000..61cc775f --- /dev/null +++ b/docs/latex/structsort___r_n_n___nodes__by__innovation.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___r_n_n___nodes__by__innovation}{}\doxysection{sort\+\_\+\+R\+N\+N\+\_\+\+Nodes\+\_\+by\+\_\+innovation Struct Reference} +\label{structsort___r_n_n___nodes__by__innovation}\index{sort\_RNN\_Nodes\_by\_innovation@{sort\_RNN\_Nodes\_by\_innovation}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___r_n_n___nodes__by__innovation_a51e5c470e4897ea95fba81b8ee8daf4d}\label{structsort___r_n_n___nodes__by__innovation_a51e5c470e4897ea95fba81b8ee8daf4d}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n1, \mbox{\hyperlink{class_r_n_n___node___interface}{R\+N\+N\+\_\+\+Node\+\_\+\+Interface}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+node\+\_\+interface.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___r_n_n___recurrent___edges__by__depth.tex b/docs/latex/structsort___r_n_n___recurrent___edges__by__depth.tex new file mode 100644 index 00000000..815a5f6a --- /dev/null +++ b/docs/latex/structsort___r_n_n___recurrent___edges__by__depth.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___r_n_n___recurrent___edges__by__depth}{}\doxysection{sort\+\_\+\+R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edges\+\_\+by\+\_\+depth Struct Reference} +\label{structsort___r_n_n___recurrent___edges__by__depth}\index{sort\_RNN\_Recurrent\_Edges\_by\_depth@{sort\_RNN\_Recurrent\_Edges\_by\_depth}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___r_n_n___recurrent___edges__by__depth_affcf0342282ce0284858e6cebd14d668}\label{structsort___r_n_n___recurrent___edges__by__depth_affcf0342282ce0284858e6cebd14d668}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$n1, \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+recurrent\+\_\+edge.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___r_n_n___recurrent___edges__by__innovation.tex b/docs/latex/structsort___r_n_n___recurrent___edges__by__innovation.tex new file mode 100644 index 00000000..fde5e6ee --- /dev/null +++ b/docs/latex/structsort___r_n_n___recurrent___edges__by__innovation.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___r_n_n___recurrent___edges__by__innovation}{}\doxysection{sort\+\_\+\+R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edges\+\_\+by\+\_\+innovation Struct Reference} +\label{structsort___r_n_n___recurrent___edges__by__innovation}\index{sort\_RNN\_Recurrent\_Edges\_by\_innovation@{sort\_RNN\_Recurrent\_Edges\_by\_innovation}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___r_n_n___recurrent___edges__by__innovation_a4e6f3310647974b9f69dc692b175b3c7}\label{structsort___r_n_n___recurrent___edges__by__innovation_a4e6f3310647974b9f69dc692b175b3c7}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$n1, \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+recurrent\+\_\+edge.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort___r_n_n___recurrent___edges__by__output__depth.tex b/docs/latex/structsort___r_n_n___recurrent___edges__by__output__depth.tex new file mode 100644 index 00000000..dce4846e --- /dev/null +++ b/docs/latex/structsort___r_n_n___recurrent___edges__by__output__depth.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort___r_n_n___recurrent___edges__by__output__depth}{}\doxysection{sort\+\_\+\+R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edges\+\_\+by\+\_\+output\+\_\+depth Struct Reference} +\label{structsort___r_n_n___recurrent___edges__by__output__depth}\index{sort\_RNN\_Recurrent\_Edges\_by\_output\_depth@{sort\_RNN\_Recurrent\_Edges\_by\_output\_depth}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort___r_n_n___recurrent___edges__by__output__depth_aa72fc5151555dbf7ea233e94dc9b05dd}\label{structsort___r_n_n___recurrent___edges__by__output__depth_aa72fc5151555dbf7ea233e94dc9b05dd}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$n1, \mbox{\hyperlink{class_r_n_n___recurrent___edge}{R\+N\+N\+\_\+\+Recurrent\+\_\+\+Edge}} $\ast$n2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+recurrent\+\_\+edge.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort__genomes__by__fitness.tex b/docs/latex/structsort__genomes__by__fitness.tex new file mode 100644 index 00000000..a0cc4b93 --- /dev/null +++ b/docs/latex/structsort__genomes__by__fitness.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort__genomes__by__fitness}{}\doxysection{sort\+\_\+genomes\+\_\+by\+\_\+fitness Struct Reference} +\label{structsort__genomes__by__fitness}\index{sort\_genomes\_by\_fitness@{sort\_genomes\_by\_fitness}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort__genomes__by__fitness_a4938540d4d30d58f87248c4febbaaf18}\label{structsort__genomes__by__fitness_a4938540d4d30d58f87248c4febbaaf18}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$g1, \mbox{\hyperlink{class_r_n_n___genome}{R\+N\+N\+\_\+\+Genome}} $\ast$g2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +rnn/rnn\+\_\+genome.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort__genomes__by__predictions.tex b/docs/latex/structsort__genomes__by__predictions.tex new file mode 100644 index 00000000..eeb3ca68 --- /dev/null +++ b/docs/latex/structsort__genomes__by__predictions.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort__genomes__by__predictions}{}\doxysection{sort\+\_\+genomes\+\_\+by\+\_\+predictions Struct Reference} +\label{structsort__genomes__by__predictions}\index{sort\_genomes\_by\_predictions@{sort\_genomes\_by\_predictions}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort__genomes__by__predictions_abfe8f7bd410d2e866d04cef265e6d171}\label{structsort__genomes__by__predictions_abfe8f7bd410d2e866d04cef265e6d171}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$g1, \mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$g2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +cnn/cnn\+\_\+genome.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structsort__genomes__by__validation__error.tex b/docs/latex/structsort__genomes__by__validation__error.tex new file mode 100644 index 00000000..4ee69e0c --- /dev/null +++ b/docs/latex/structsort__genomes__by__validation__error.tex @@ -0,0 +1,13 @@ +\hypertarget{structsort__genomes__by__validation__error}{}\doxysection{sort\+\_\+genomes\+\_\+by\+\_\+validation\+\_\+error Struct Reference} +\label{structsort__genomes__by__validation__error}\index{sort\_genomes\_by\_validation\_error@{sort\_genomes\_by\_validation\_error}} +\doxysubsection*{Public Member Functions} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structsort__genomes__by__validation__error_abf56a0cb077a78a1ec6f8af9c500e0cc}\label{structsort__genomes__by__validation__error_abf56a0cb077a78a1ec6f8af9c500e0cc}} +bool {\bfseries operator()} (\mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$g1, \mbox{\hyperlink{class_c_n_n___genome}{C\+N\+N\+\_\+\+Genome}} $\ast$g2) +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +cnn/cnn\+\_\+genome.\+hxx\end{DoxyCompactItemize} diff --git a/docs/latex/structucvector.tex b/docs/latex/structucvector.tex new file mode 100644 index 00000000..2bf4f4a5 --- /dev/null +++ b/docs/latex/structucvector.tex @@ -0,0 +1,19 @@ +\hypertarget{structucvector}{}\doxysection{ucvector Struct Reference} +\label{structucvector}\index{ucvector@{ucvector}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structucvector_ace794c5713208c5a20f21762cd87c919}\label{structucvector_ace794c5713208c5a20f21762cd87c919}} +unsigned char $\ast$ {\bfseries data} +\item +\mbox{\Hypertarget{structucvector_a27c99c34de0c5b3ca0c242d402c69499}\label{structucvector_a27c99c34de0c5b3ca0c242d402c69499}} +size\+\_\+t {\bfseries size} +\item +\mbox{\Hypertarget{structucvector_a235168baac13f0c78bd3e309dc170f90}\label{structucvector_a235168baac13f0c78bd3e309dc170f90}} +size\+\_\+t {\bfseries allocsize} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+cpp\end{DoxyCompactItemize} diff --git a/docs/latex/structuivector.tex b/docs/latex/structuivector.tex new file mode 100644 index 00000000..7342f883 --- /dev/null +++ b/docs/latex/structuivector.tex @@ -0,0 +1,19 @@ +\hypertarget{structuivector}{}\doxysection{uivector Struct Reference} +\label{structuivector}\index{uivector@{uivector}} +\doxysubsection*{Public Attributes} +\begin{DoxyCompactItemize} +\item +\mbox{\Hypertarget{structuivector_a427d761df4bb4f0f19b4a973fc224c78}\label{structuivector_a427d761df4bb4f0f19b4a973fc224c78}} +unsigned $\ast$ {\bfseries data} +\item +\mbox{\Hypertarget{structuivector_aa999025945f0c93d0461192475ae2720}\label{structuivector_aa999025945f0c93d0461192475ae2720}} +size\+\_\+t {\bfseries size} +\item +\mbox{\Hypertarget{structuivector_aac0395a9ad397ae7a28219561ab49ffa}\label{structuivector_aac0395a9ad397ae7a28219561ab49ffa}} +size\+\_\+t {\bfseries allocsize} +\end{DoxyCompactItemize} + + +The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} +\item +image\+\_\+tools/lodepng.\+cpp\end{DoxyCompactItemize} diff --git a/docs/latex/tabu_doxygen.sty b/docs/latex/tabu_doxygen.sty new file mode 100644 index 00000000..60fd7e8d --- /dev/null +++ b/docs/latex/tabu_doxygen.sty @@ -0,0 +1,2557 @@ +%% +%% This is file `tabu.sty', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% tabu.dtx (with options: `package') +%% +%% This is a generated file. +%% Copyright (FC) 2010-2011 - lppl +%% +%% tabu : 2011/02/26 v2.8 - tabu : Flexible LaTeX tabulars +%% +%% ********************************************************************************************** +%% \begin{tabu} { preamble } => default target: \linewidth or \linegoal +%% \begin{tabu} to { preamble } => target specified +%% \begin{tabu} spread { preamble } => target relative to the ``natural width'' +%% +%% tabu works in text and in math modes. +%% +%% X columns: automatic width adjustment + horizontal and vertical alignment +%% \begin{tabu} { X[4c] X[1c] X[-2ml] } +%% +%% Horizontal lines and / or leaders: +%% \hline\hline => double horizontal line +%% \firsthline\hline => for nested tabulars +%% \lasthline\hline => for nested tabulars +%% \tabucline[line spec]{column-column} => ``funny'' lines (dash/leader) +%% Automatic lines / leaders : +%% \everyrow{\hline\hline} +%% +%% Vertical lines and / or leaders: +%% \begin{tabu} { |[3pt red] X[4c] X[1c] X[-2ml] |[3pt blue] } +%% \begin{tabu} { |[3pt red] X[4c] X[1c] X[-2ml] |[3pt on 2pt off 4pt blue] } +%% +%% Fixed vertical spacing adjustment: +%% \extrarowheight= \extrarowdepth= +%% or: \extrarowsep= => may be prefixed by \global +%% +%% Dynamic vertical spacing adjustment: +%% \abovetabulinesep= \belowtabulinesep= +%% or: \tabulinesep= => may be prefixed by \global +%% +%% delarray.sty shortcuts: in math and text modes +%% \begin{tabu} .... \({ preamble }\) +%% +%% Algorithms reports: +%% \tracingtabu=1 \tracingtabu=2 +%% +%% ********************************************************************************************** +%% +%% This work may be distributed and/or modified under the +%% conditions of the LaTeX Project Public License, either +%% version 1.3 of this license or (at your option) any later +%% version. The latest version of this license is in +%% http://www.latex-project.org/lppl.txt +%% +%% This work consists of the main source file tabu.dtx +%% and the derived files +%% tabu.sty, tabu.pdf, tabu.ins +%% +%% tabu : Flexible LaTeX tabulars +%% lppl copyright 2010-2011 by FC +%% + +\NeedsTeXFormat{LaTeX2e}[2005/12/01] +\ProvidesPackage{tabu_doxygen}[2011/02/26 v2.8 - flexible LaTeX tabulars (FC), frozen version for doxygen] +\RequirePackage{array}[2008/09/09] +\RequirePackage{varwidth}[2009/03/30] +\AtEndOfPackage{\tabu@AtEnd \let\tabu@AtEnd \@undefined} +\let\tabu@AtEnd\@empty +\def\TMP@EnsureCode#1={% + \edef\tabu@AtEnd{\tabu@AtEnd + \catcode#1 \the\catcode#1}% + \catcode#1=% +}% \TMP@EnsureCode +\TMP@EnsureCode 33 = 12 % ! +\TMP@EnsureCode 58 = 12 % : (for siunitx) +\TMP@EnsureCode124 = 12 % | +\TMP@EnsureCode 36 = 3 % $ = math shift +\TMP@EnsureCode 38 = 4 % & = tab alignment character +\TMP@EnsureCode 32 = 10 % space +\TMP@EnsureCode 94 = 7 % ^ +\TMP@EnsureCode 95 = 8 % _ +%% Constants -------------------------------------------------------- +\newcount \c@taburow \def\thetaburow {\number\c@taburow} +\newcount \tabu@nbcols +\newcount \tabu@cnt +\newcount \tabu@Xcol +\let\tabu@start \@tempcnta +\let\tabu@stop \@tempcntb +\newcount \tabu@alloc \tabu@alloc=\m@ne +\newcount \tabu@nested +\def\tabu@alloc@{\global\advance\tabu@alloc \@ne \tabu@nested\tabu@alloc} +\newdimen \tabu@target +\newdimen \tabu@spreadtarget +\newdimen \tabu@naturalX +\newdimen \tabucolX +\let\tabu@DELTA \@tempdimc +\let\tabu@thick \@tempdima +\let\tabu@on \@tempdimb +\let\tabu@off \@tempdimc +\newdimen \tabu@Xsum +\newdimen \extrarowdepth +\newdimen \abovetabulinesep +\newdimen \belowtabulinesep +\newdimen \tabustrutrule \tabustrutrule \z@ +\newtoks \tabu@thebody +\newtoks \tabu@footnotes +\newsavebox \tabu@box +\newsavebox \tabu@arstrutbox +\newsavebox \tabu@hleads +\newsavebox \tabu@vleads +\newif \iftabu@colortbl +\newif \iftabu@siunitx +\newif \iftabu@measuring +\newif \iftabu@spread +\newif \iftabu@negcoef +\newif \iftabu@everyrow +\def\tabu@everyrowtrue {\global\let\iftabu@everyrow \iftrue} +\def\tabu@everyrowfalse{\global\let\iftabu@everyrow \iffalse} +\newif \iftabu@long +\newif \iftabuscantokens +\def\tabu@rescan {\tabu@verbatim \scantokens } +%% Utilities (for internal usage) ----------------------------------- +\def\tabu@gobblespace #1 {#1} +\def\tabu@gobbletoken #1#2{#1} +\def\tabu@gobbleX{\futurelet\@let@token \tabu@gobblex} +\def\tabu@gobblex{\if ^^J\noexpand\@let@token \expandafter\@gobble + \else\ifx \@sptoken\@let@token + \expandafter\tabu@gobblespace\expandafter\tabu@gobbleX + \fi\fi +}% \tabu@gobblex +\def\tabu@X{^^J} +{\obeyspaces +\global\let\tabu@spxiii= % saves an active space (for \ifx) +\gdef\tabu@@spxiii{ }} +\def\tabu@ifenvir {% only for \multicolumn + \expandafter\tabu@if@nvir\csname\@currenvir\endcsname +}% \tabu@ifenvir +\def\tabu@if@nvir #1{\csname @\ifx\tabu#1first\else + \ifx\longtabu#1first\else + second\fi\fi oftwo\endcsname +}% \tabu@ifenvir +\def\tabu@modulo #1#2{\numexpr\ifnum\numexpr#1=\z@ 0\else #1-(#1-(#2-1)/2)/(#2)*(#2)\fi} +{\catcode`\&=3 +\gdef\tabu@strtrim #1{% #1 = control sequence to trim + \ifodd 1\ifx #1\@empty \else \ifx #1\space \else 0\fi \fi + \let\tabu@c@l@r \@empty \let#1\@empty + \else \expandafter \tabu@trimspaces #1\@nnil + \fi +}% \tabu@strtrim +\gdef\tabu@trimspaces #1\@nnil{\let\tabu@c@l@r=#2\tabu@firstspace .#1& }% +\gdef\tabu@firstspace #1#2#3 &{\tabu@lastspace #2#3&} +\gdef\tabu@lastspace #1{\def #3{#1}% + \ifx #3\tabu@c@l@r \def\tabu@c@l@r{\protect\color{#1}}\expandafter\remove@to@nnil \fi + \tabu@trimspaces #1\@nnil} +}% \catcode +\def\tabu@sanitizearg #1#2{{% + \csname \ifcsname if@safe@actives\endcsname % + @safe@activestrue\else + relax\fi \endcsname + \edef#2{#1}\tabu@strtrim#2\@onelevel@sanitize#2% + \expandafter}\expandafter\def\expandafter#2\expandafter{#2}% +}% \tabu@sanitizearg +\def\tabu@textbar #1{\begingroup \endlinechar\m@ne \scantokens{\def\:{|}}% + \expandafter\endgroup \expandafter#1\:% !!! semi simple group !!! +}% \tabu@textbar +\def\tabu@everyrow@bgroup{\iftabu@everyrow \begingroup \else \noalign{\ifnum0=`}\fi \fi} +\def\tabu@everyrow@egroup{% + \iftabu@everyrow \expandafter \endgroup \the\toks@ + \else \ifnum0=`{\fi}% + \fi +}% \tabu@everyrow@egroup +\def\tabu@arstrut {\global\setbox\@arstrutbox \hbox{\vrule + height \arraystretch \dimexpr\ht\strutbox+\extrarowheight + depth \arraystretch \dimexpr\dp\strutbox+\extrarowdepth + width \z@}% +}% \tabu@arstrut +\def\tabu@rearstrut {% + \@tempdima \arraystretch\dimexpr\ht\strutbox+\extrarowheight \relax + \@tempdimb \arraystretch\dimexpr\dp\strutbox+\extrarowdepth \relax + \ifodd 1\ifdim \ht\@arstrutbox=\@tempdima + \ifdim \dp\@arstrutbox=\@tempdimb 0 \fi\fi + \tabu@mkarstrut + \fi +}% \tabu@rearstrut +\def\tabu@@DBG #1{\ifdim\tabustrutrule>\z@ \color{#1}\fi} +\def\tabu@DBG@arstrut {\global\setbox\@arstrutbox + \hbox to\z@{\hbox to\z@{\hss + {\tabu@DBG{cyan}\vrule + height \arraystretch \dimexpr\ht\strutbox+\extrarowheight + depth \z@ + width \tabustrutrule}\kern-\tabustrutrule + {\tabu@DBG{pink}\vrule + height \z@ + depth \arraystretch \dimexpr\dp\strutbox+\extrarowdepth + width \tabustrutrule}}}% +}% \tabu@DBG@arstrut +\def\tabu@save@decl{\toks\count@ \expandafter{\the\toks\expandafter\count@ + \@nextchar}}% +\def\tabu@savedecl{\ifcat$\d@llarend\else + \let\save@decl \tabu@save@decl \fi % no inversion of tokens in text mode +}% \tabu@savedecl +\def\tabu@finalstrut #1{\unskip\ifhmode\nobreak\fi\vrule height\z@ depth\z@ width\z@} +\newcommand*\tabuDisableCommands {\g@addto@macro\tabu@trialh@@k } +\let\tabu@trialh@@k \@empty +\def\tabu@nowrite #1#{{\afterassignment}\toks@} +\let\tabu@write\write +\let\tabu@immediate\immediate +\def\tabu@WRITE{\begingroup + \def\immediate\write{\aftergroup\endgroup + \tabu@immediate\tabu@write}% +}% \tabu@WRITE +\expandafter\def\expandafter\tabu@GenericError\expandafter{% + \expandafter\tabu@WRITE\GenericError} +\def\tabu@warn{\tabu@WRITE\PackageWarning{tabu}} +\def\tabu@noxfootnote [#1]{\@gobble} +\def\tabu@nocolor #1#{\@gobble} +\newcommand*\tabu@norowcolor[2][]{} +\def\tabu@maybesiunitx #1{\def\tabu@temp{#1}% + \futurelet\@let@token \tabu@m@ybesiunitx} +\def\tabu@m@ybesiunitx #1{\def\tabu@m@ybesiunitx {% + \ifx #1\@let@token \let\tabu@cellleft \@empty \let\tabu@cellright \@empty \fi + \tabu@temp}% \tabu@m@ybesiunitx +}\expandafter\tabu@m@ybesiunitx \csname siunitx_table_collect_begin:Nn\endcsname +\def\tabu@celllalign@def #1{\def\tabu@celllalign{\tabu@maybesiunitx{#1}}}% +%% Fixed vertical spacing adjustment: \extrarowsep ------------------ +\newcommand*\extrarowsep{\edef\tabu@C@extra{\the\numexpr\tabu@C@extra+1}% + \iftabu@everyrow \aftergroup\tabu@Gextra + \else \aftergroup\tabu@n@Gextra + \fi + \@ifnextchar={\tabu@gobbletoken\tabu@extra} \tabu@extra +}% \extrarowsep +\def\tabu@extra {\@ifnextchar_% + {\tabu@gobbletoken{\tabu@setextra\extrarowheight \extrarowdepth}} + {\ifx ^\@let@token \def\tabu@temp{% + \tabu@gobbletoken{\tabu@setextra\extrarowdepth \extrarowheight}}% + \else \let\tabu@temp \@empty + \afterassignment \tabu@setextrasep \extrarowdepth + \fi \tabu@temp}% +}% \tabu@extra +\def\tabu@setextra #1#2{\def\tabu@temp{\tabu@extr@#1#2}\afterassignment\tabu@temp#2} +\def\tabu@extr@ #1#2{\@ifnextchar^% + {\tabu@gobbletoken{\tabu@setextra\extrarowdepth \extrarowheight}} + {\ifx _\@let@token \def\tabu@temp{% + \tabu@gobbletoken{\tabu@setextra\extrarowheight \extrarowdepth}}% + \else \let\tabu@temp \@empty + \tabu@Gsave \tabu@G@extra \tabu@C@extra \extrarowheight \extrarowdepth + \fi \tabu@temp}% +}% \tabu@extr@ +\def\tabu@setextrasep {\extrarowheight=\extrarowdepth + \tabu@Gsave \tabu@G@extra \tabu@C@extra \extrarowheight \extrarowdepth +}% \tabu@setextrasep +\def\tabu@Gextra{\ifx \tabu@G@extra\@empty \else {\tabu@Rextra}\fi} +\def\tabu@n@Gextra{\ifx \tabu@G@extra\@empty \else \noalign{\tabu@Rextra}\fi} +\def\tabu@Rextra{\tabu@Grestore \tabu@G@extra \tabu@C@extra} +\let\tabu@C@extra \z@ +\let\tabu@G@extra \@empty +%% Dynamic vertical spacing adjustment: \tabulinesep ---------------- +\newcommand*\tabulinesep{\edef\tabu@C@linesep{\the\numexpr\tabu@C@linesep+1}% + \iftabu@everyrow \aftergroup\tabu@Glinesep + \else \aftergroup\tabu@n@Glinesep + \fi + \@ifnextchar={\tabu@gobbletoken\tabu@linesep} \tabu@linesep +}% \tabulinesep +\def\tabu@linesep {\@ifnextchar_% + {\tabu@gobbletoken{\tabu@setsep\abovetabulinesep \belowtabulinesep}} + {\ifx ^\@let@token \def\tabu@temp{% + \tabu@gobbletoken{\tabu@setsep\belowtabulinesep \abovetabulinesep}}% + \else \let\tabu@temp \@empty + \afterassignment \tabu@setlinesep \abovetabulinesep + \fi \tabu@temp}% +}% \tabu@linesep +\def\tabu@setsep #1#2{\def\tabu@temp{\tabu@sets@p#1#2}\afterassignment\tabu@temp#2} +\def\tabu@sets@p #1#2{\@ifnextchar^% + {\tabu@gobbletoken{\tabu@setsep\belowtabulinesep \abovetabulinesep}} + {\ifx _\@let@token \def\tabu@temp{% + \tabu@gobbletoken{\tabu@setsep\abovetabulinesep \belowtabulinesep}}% + \else \let\tabu@temp \@empty + \tabu@Gsave \tabu@G@linesep \tabu@C@linesep \abovetabulinesep \belowtabulinesep + \fi \tabu@temp}% +}% \tabu@sets@p +\def\tabu@setlinesep {\belowtabulinesep=\abovetabulinesep + \tabu@Gsave \tabu@G@linesep \tabu@C@linesep \abovetabulinesep \belowtabulinesep +}% \tabu@setlinesep +\def\tabu@Glinesep{\ifx \tabu@G@linesep\@empty \else {\tabu@Rlinesep}\fi} +\def\tabu@n@Glinesep{\ifx \tabu@G@linesep\@empty \else \noalign{\tabu@Rlinesep}\fi} +\def\tabu@Rlinesep{\tabu@Grestore \tabu@G@linesep \tabu@C@linesep} +\let\tabu@C@linesep \z@ +\let\tabu@G@linesep \@empty +%% \global\extrarowsep and \global\tabulinesep ------------------- +\def\tabu@Gsave #1#2#3#4{\xdef#1{#1% + \toks#2{\toks\the\currentgrouplevel{\global#3\the#3\global#4\the#4}}}% +}% \tabu@Gsave +\def\tabu@Grestore#1#2{% + \toks#2{}#1\toks\currentgrouplevel\expandafter{\expandafter}\the\toks#2\relax + \ifcat$\the\toks\currentgrouplevel$\else + \global\let#1\@empty \global\let#2\z@ + \the\toks\currentgrouplevel + \fi +}% \tabu@Grestore +%% Setting code for every row --------------------------------------- +\newcommand*\everyrow{\tabu@everyrow@bgroup + \tabu@start \z@ \tabu@stop \z@ \tabu@evrstartstop +}% \everyrow +\def\tabu@evrstartstop {\@ifnextchar^% + {\afterassignment \tabu@evrstartstop \tabu@stop=}% + {\ifx ^\@let@token + \afterassignment\tabu@evrstartstop \tabu@start=% + \else \afterassignment\tabu@everyr@w \toks@ + \fi}% +}% \tabu@evrstartstop +\def\tabu@everyr@w {% + \xdef\tabu@everyrow{% + \noexpand\tabu@everyrowfalse + \let\noalign \relax + \noexpand\tabu@rowfontreset + \iftabu@colortbl \noexpand\tabu@rc@ \fi % \taburowcolors + \let\noexpand\tabu@docline \noexpand\tabu@docline@evr + \the\toks@ + \noexpand\tabu@evrh@@k + \noexpand\tabu@rearstrut + \global\advance\c@taburow \@ne}% + \iftabu@everyrow \toks@\expandafter + {\expandafter\def\expandafter\tabu@evr@L\expandafter{\the\toks@}\ignorespaces}% + \else \xdef\tabu@evr@G{\the\toks@}% + \fi + \tabu@everyrow@egroup +}% \tabu@everyr@w +\def\tabu@evr {\def\tabu@evrh@@k} % for internal use only +\tabu@evr{} +%% line style and leaders ------------------------------------------- +\newcommand*\newtabulinestyle [1]{% + {\@for \@tempa :=#1\do{\expandafter\tabu@newlinestyle \@tempa==\@nil}}% +}% \newtabulinestyle +\def\tabu@newlinestyle #1=#2=#3\@nil{\tabu@getline {#2}% + \tabu@sanitizearg {#1}\@tempa + \ifodd 1\ifx \@tempa\@empty \ifdefined\tabu@linestyle@ 0 \fi\fi + \global\expandafter\let + \csname tabu@linestyle@\@tempa \endcsname =\tabu@thestyle \fi +}% \tabu@newlinestyle +\newcommand*\tabulinestyle [1]{\tabu@everyrow@bgroup \tabu@getline{#1}% + \iftabu@everyrow + \toks@\expandafter{\expandafter \def \expandafter + \tabu@ls@L\expandafter{\tabu@thestyle}\ignorespaces}% + \gdef\tabu@ls@{\tabu@ls@L}% + \else + \global\let\tabu@ls@G \tabu@thestyle + \gdef\tabu@ls@{\tabu@ls@G}% + \fi + \tabu@everyrow@egroup +}% \tabulinestyle +\newcommand*\taburulecolor{\tabu@everyrow@bgroup \tabu@textbar \tabu@rulecolor} +\def\tabu@rulecolor #1{\toks@{}% + \def\tabu@temp #1##1#1{\tabu@ruledrsc{##1}}\@ifnextchar #1% + \tabu@temp + \tabu@rulearc +}% \tabu@rulecolor +\def\tabu@ruledrsc #1{\edef\tabu@temp{#1}\tabu@strtrim\tabu@temp + \ifx \tabu@temp\@empty \def\tabu@temp{\tabu@rule@drsc@ {}{}}% + \else \edef\tabu@temp{\noexpand\tabu@rule@drsc@ {}{\tabu@temp}}% + \fi + \tabu@temp +}% \tabu@ruledrsc@ +\def\tabu@ruledrsc@ #1#{\tabu@rule@drsc@ {#1}} +\def\tabu@rule@drsc@ #1#2{% + \iftabu@everyrow + \ifx \\#1#2\\\toks@{\let\CT@drsc@ \relax}% + \else \toks@{\def\CT@drsc@{\color #1{#2}}}% + \fi + \else + \ifx \\#1#2\\\global\let\CT@drsc@ \relax + \else \gdef\CT@drsc@{\color #1{#2}}% + \fi + \fi + \tabu@rulearc +}% \tabu@rule@drsc@ +\def\tabu@rulearc #1#{\tabu@rule@arc@ {#1}} +\def\tabu@rule@arc@ #1#2{% + \iftabu@everyrow + \ifx \\#1#2\\\toks@\expandafter{\the\toks@ \def\CT@arc@{}}% + \else \toks@\expandafter{\the\toks@ \def\CT@arc@{\color #1{#2}}}% + \fi + \toks@\expandafter{\the\toks@ + \let\tabu@arc@L \CT@arc@ + \let\tabu@drsc@L \CT@drsc@ + \ignorespaces}% + \else + \ifx \\#1#2\\\gdef\CT@arc@{}% + \else \gdef\CT@arc@{\color #1{#2}}% + \fi + \global\let\tabu@arc@G \CT@arc@ + \global\let\tabu@drsc@G \CT@drsc@ + \fi + \tabu@everyrow@egroup +}% \tabu@rule@arc@ +\def\taburowcolors {\tabu@everyrow@bgroup \@testopt \tabu@rowcolors 1} +\def\tabu@rowcolors [#1]#2#{\tabu@rowc@lors{#1}{#2}} +\def\tabu@rowc@lors #1#2#3{% + \toks@{}\@defaultunits \count@ =\number0#2\relax \@nnil + \@defaultunits \tabu@start =\number0#1\relax \@nnil + \ifnum \count@<\tw@ \count@=\tw@ \fi + \advance\tabu@start \m@ne + \ifnum \tabu@start<\z@ \tabu@start \z@ \fi + \tabu@rowcolorseries #3\in@..\in@ \@nnil +}% \tabu@rowcolors +\def\tabu@rowcolorseries #1..#2\in@ #3\@nnil {% + \ifx \in@#1\relax + \iftabu@everyrow \toks@{\def\tabu@rc@{}\let\tabu@rc@L \tabu@rc@}% + \else \gdef\tabu@rc@{}\global\let\tabu@rc@G \tabu@rc@ + \fi + \else + \ifx \\#2\\\tabu@rowcolorserieserror \fi + \tabu@sanitizearg{#1}\tabu@temp + \tabu@sanitizearg{#2}\@tempa + \advance\count@ \m@ne + \iftabu@everyrow + \def\tabu@rc@ ##1##2##3##4{\def\tabu@rc@{% + \ifnum ##2=\c@taburow + \definecolorseries{tabu@rcseries@\the\tabu@nested}{rgb}{last}{##3}{##4}\fi + \ifnum \c@taburow<##2 \else + \ifnum \tabu@modulo {\c@taburow-##2}{##1+1}=\z@ + \resetcolorseries[{##1}]{tabu@rcseries@\the\tabu@nested}\fi + \xglobal\colorlet{tabu@rc@\the\tabu@nested}{tabu@rcseries@\the\tabu@nested!!+}% + \rowcolor{tabu@rc@\the\tabu@nested}\fi}% + }\edef\x{\noexpand\tabu@rc@ {\the\count@} + {\the\tabu@start} + {\tabu@temp} + {\@tempa}% + }\x + \toks@\expandafter{\expandafter\def\expandafter\tabu@rc@\expandafter{\tabu@rc@}}% + \toks@\expandafter{\the\toks@ \let\tabu@rc@L \tabu@rc@ \ignorespaces}% + \else % inside \noalign + \definecolorseries{tabu@rcseries@\the\tabu@nested}{rgb}{last}{\tabu@temp}{\@tempa}% + \expandafter\resetcolorseries\expandafter[\the\count@]{tabu@rcseries@\the\tabu@nested}% + \xglobal\colorlet{tabu@rc@\the\tabu@nested}{tabu@rcseries@\the\tabu@nested!!+}% + \let\noalign \relax \rowcolor{tabu@rc@\the\tabu@nested}% + \def\tabu@rc@ ##1##2{\gdef\tabu@rc@{% + \ifnum \tabu@modulo {\c@taburow-##2}{##1+1}=\@ne + \resetcolorseries[{##1}]{tabu@rcseries@\the\tabu@nested}\fi + \xglobal\colorlet{tabu@rc@\the\tabu@nested}{tabu@rcseries@\the\tabu@nested!!+}% + \rowcolor{tabu@rc@\the\tabu@nested}}% + }\edef\x{\noexpand\tabu@rc@{\the\count@}{\the\c@taburow}}\x + \global\let\tabu@rc@G \tabu@rc@ + \fi + \fi + \tabu@everyrow@egroup +}% \tabu@rowcolorseries +\tabuDisableCommands {\let\tabu@rc@ \@empty } +\def\tabu@rowcolorserieserror {\PackageError{tabu} + {Invalid syntax for \string\taburowcolors + \MessageBreak Please look at the documentation!}\@ehd +}% \tabu@rowcolorserieserror +\newcommand*\tabureset {% + \tabulinesep=\z@ \extrarowsep=\z@ \extratabsurround=\z@ + \tabulinestyle{}\everyrow{}\taburulecolor||{}\taburowcolors{}% +}% \tabureset +%% Parsing the line styles ------------------------------------------ +\def\tabu@getline #1{\begingroup + \csname \ifcsname if@safe@actives\endcsname % + @safe@activestrue\else + relax\fi \endcsname + \edef\tabu@temp{#1}\tabu@sanitizearg{#1}\@tempa + \let\tabu@thestyle \relax + \ifcsname tabu@linestyle@\@tempa \endcsname + \edef\tabu@thestyle{\endgroup + \def\tabu@thestyle{\expandafter\noexpand + \csname tabu@linestyle@\@tempa\endcsname}% + }\tabu@thestyle + \else \expandafter\tabu@definestyle \tabu@temp \@nil + \fi +}% \tabu@getline +\def\tabu@definestyle #1#2\@nil {\endlinechar \m@ne \makeatletter + \tabu@thick \maxdimen \tabu@on \maxdimen \tabu@off \maxdimen + \let\tabu@c@lon \@undefined \let\tabu@c@loff \@undefined + \ifodd 1\ifcat .#1\else\ifcat\relax #1\else 0\fi\fi % catcode 12 or non expandable cs + \def\tabu@temp{\tabu@getparam{thick}}% + \else \def\tabu@temp{\tabu@getparam{thick}\maxdimen}% + \fi + {% + \let\tabu@ \relax + \def\:{\obeyspaces \tabu@oXIII \tabu@commaXIII \edef\:}% (space active \: happy ;-)) + \scantokens{\:{\tabu@temp #1#2 \tabu@\tabu@}}% + \expandafter}\expandafter + \def\expandafter\:\expandafter{\:}% line spec rewritten now ;-) + \def\;{\def\:}% + \scantokens\expandafter{\expandafter\;\expandafter{\:}}% space is now inactive (catcode 10) + \let\tabu@ \tabu@getcolor \:% all arguments are ready now ;-) + \ifdefined\tabu@c@lon \else \let\tabu@c@lon\@empty \fi + \ifx \tabu@c@lon\@empty \def\tabu@c@lon{\CT@arc@}\fi + \ifdefined\tabu@c@loff \else \let\tabu@c@loff \@empty \fi + \ifdim \tabu@on=\maxdimen \ifdim \tabu@off<\maxdimen + \tabu@on \tabulineon \fi\fi + \ifdim \tabu@off=\maxdimen \ifdim \tabu@on<\maxdimen + \tabu@off \tabulineoff \fi\fi + \ifodd 1\ifdim \tabu@off=\maxdimen \ifdim \tabu@on=\maxdimen 0 \fi\fi + \in@true % + \else \in@false % + \fi + \ifdim\tabu@thick=\maxdimen \def\tabu@thick{\arrayrulewidth}% + \else \edef\tabu@thick{\the\tabu@thick}% + \fi + \edef \tabu@thestyle ##1##2{\endgroup + \def\tabu@thestyle{% + \ifin@ \noexpand\tabu@leadersstyle {\tabu@thick} + {\the\tabu@on}{##1} + {\the\tabu@off}{##2}% + \else \noexpand\tabu@rulesstyle + {##1\vrule width \tabu@thick}% + {##1\leaders \hrule height \tabu@thick \hfil}% + \fi}% + }\expandafter \expandafter + \expandafter \tabu@thestyle \expandafter + \expandafter \expandafter + {\expandafter\tabu@c@lon\expandafter}\expandafter{\tabu@c@loff}% +}% \tabu@definestyle +{\catcode`\O=\active \lccode`\O=`\o \catcode`\,=\active + \lowercase{\gdef\tabu@oXIII {\catcode`\o=\active \let O=\tabu@oxiii}} + \gdef\tabu@commaXIII {\catcode`\,=\active \let ,=\space} +}% \catcode +\def\tabu@oxiii #1{% + \ifcase \ifx n#1\z@ \else + \ifx f#1\@ne\else + \tw@ \fi\fi + \expandafter\tabu@onxiii + \or \expandafter\tabu@ofxiii + \else o% + \fi#1}% +\def\tabu@onxiii #1#2{% + \ifcase \ifx !#2\tw@ \else + \ifcat.\noexpand#2\z@ \else + \ifx \tabu@spxiii#2\@ne\else + \tw@ \fi\fi\fi + \tabu@getparam{on}#2\expandafter\@gobble + \or \expandafter\tabu@onxiii % (space is active) + \else o\expandafter\@firstofone + \fi{#1#2}}% +\def\tabu@ofxiii #1#2{% + \ifx #2f\expandafter\tabu@offxiii + \else o\expandafter\@firstofone + \fi{#1#2}} +\def\tabu@offxiii #1#2{% + \ifcase \ifx !#2\tw@ \else + \ifcat.\noexpand#2\z@ \else + \ifx\tabu@spxiii#2\@ne \else + \tw@ \fi\fi\fi + \tabu@getparam{off}#2\expandafter\@gobble + \or \expandafter\tabu@offxiii % (space is active) + \else o\expandafter\@firstofone + \fi{#1#2}} +\def\tabu@getparam #1{\tabu@ \csname tabu@#1\endcsname=} +\def\tabu@getcolor #1{% \tabu@ <- \tabu@getcolor after \edef + \ifx \tabu@#1\else % no more spec + \let\tabu@theparam=#1\afterassignment \tabu@getc@l@r #1\fi +}% \tabu@getcolor +\def\tabu@getc@l@r #1\tabu@ {% + \def\tabu@temp{#1}\tabu@strtrim \tabu@temp + \ifx \tabu@temp\@empty + \else%\ifcsname \string\color@\tabu@temp \endcsname % if the color exists + \ifx \tabu@theparam \tabu@off \let\tabu@c@loff \tabu@c@l@r + \else \let\tabu@c@lon \tabu@c@l@r + \fi + %\else \tabu@warncolour{\tabu@temp}% + \fi%\fi + \tabu@ % next spec +}% \tabu@getc@l@r +\def\tabu@warncolour #1{\PackageWarning{tabu} + {Color #1 is not defined. Default color used}% +}% \tabu@warncolour +\def\tabu@leadersstyle #1#2#3#4#5{\def\tabu@leaders{{#1}{#2}{#3}{#4}{#5}}% + \ifx \tabu@leaders\tabu@leaders@G \else + \tabu@LEADERS{#1}{#2}{#3}{#4}{#5}\fi +}% \tabu@leadersstyle +\def\tabu@rulesstyle #1#2{\let\tabu@leaders \@undefined + \gdef\tabu@thevrule{#1}\gdef\tabu@thehrule{#2}% +}% \tabu@rulesstyle +%% The leaders boxes ------------------------------------------------ +\def\tabu@LEADERS #1#2#3#4#5{%% width, dash, dash color, gap, gap color + {\let\color \tabu@color % => during trials -> \color = \tabu@nocolor + {% % but the leaders boxes should have colors ! + \def\@therule{\vrule}\def\@thick{height}\def\@length{width}% + \def\@box{\hbox}\def\@unbox{\unhbox}\def\@elt{\wd}% + \def\@skip{\hskip}\def\@ss{\hss}\def\tabu@leads{\tabu@hleads}% + \tabu@l@@d@rs {#1}{#2}{#3}{#4}{#5}% + \global\let\tabu@thehleaders \tabu@theleaders + }% + {% + \def\@therule{\hrule}\def\@thick{width}\def\@length{height}% + \def\@box{\vbox}\def\@unbox{\unvbox}\def\@elt{\ht}% + \def\@skip{\vskip}\def\@ss{\vss}\def\tabu@leads{\tabu@vleads}% + \tabu@l@@d@rs {#1}{#2}{#3}{#4}{#5}% + \global\let\tabu@thevleaders \tabu@theleaders + }% + \gdef\tabu@leaders@G{{#1}{#2}{#3}{#4}{#5}}% + }% +}% \tabu@LEADERS +\def\tabu@therule #1#2{\@therule \@thick#1\@length\dimexpr#2/2 \@depth\z@} +\def\tabu@l@@d@rs #1#2#3#4#5{%% width, dash, dash color, gap, gap color + \global\setbox \tabu@leads=\@box{% + {#3\tabu@therule{#1}{#2}}% + \ifx\\#5\\\@skip#4\else{#5\tabu@therule{#1}{#4*2}}\fi + {#3\tabu@therule{#1}{#2}}}% + \global\setbox\tabu@leads=\@box to\@elt\tabu@leads{\@ss + {#3\tabu@therule{#1}{#2}}\@unbox\tabu@leads}% + \edef\tabu@theleaders ##1{\def\noexpand\tabu@theleaders {% + {##1\tabu@therule{#1}{#2}}% + \xleaders \copy\tabu@leads \@ss + \tabu@therule{0pt}{-#2}{##1\tabu@therule{#1}{#2}}}% + }\tabu@theleaders{#3}% +}% \tabu@l@@d@rs +%% \tabu \endtabu \tabu* \longtabu \endlongtabu \longtabu* ---------- +\newcommand*\tabu {\tabu@longfalse + \ifmmode \def\tabu@ {\array}\def\endtabu {\endarray}% + \else \def\tabu@ {\tabu@tabular}\def\endtabu {\endtabular}\fi + \expandafter\let\csname tabu*\endcsname \tabu + \expandafter\def\csname endtabu*\endcsname{\endtabu}% + \tabu@spreadfalse \tabu@negcoeffalse \tabu@settarget +}% {tabu} +\let\tabu@tabular \tabular % +\expandafter\def\csname tabu*\endcsname{\tabuscantokenstrue \tabu} +\newcommand*\longtabu {\tabu@longtrue + \ifmmode\PackageError{tabu}{longtabu not allowed in math mode}\fi + \def\tabu@{\longtable}\def\endlongtabu{\endlongtable}% + \LTchunksize=\@M + \expandafter\let\csname tabu*\endcsname \tabu + \expandafter\def\csname endlongtabu*\endcsname{\endlongtabu}% + \let\LT@startpbox \tabu@LT@startpbox % \everypar{ array struts } + \tabu@spreadfalse \tabu@negcoeffalse \tabu@settarget +}% {longtabu} +\expandafter\def\csname longtabu*\endcsname{\tabuscantokenstrue \longtabu} +\def\tabu@nolongtabu{\PackageError{tabu} + {longtabu requires the longtable package}\@ehd} +%% Read the target and then : \tabular or \@array ------------------ +\def\tabu@settarget {\futurelet\@let@token \tabu@sett@rget } +\def\tabu@sett@rget {\tabu@target \z@ + \ifcase \ifx \bgroup\@let@token \z@ \else + \ifx \@sptoken\@let@token \@ne \else + \if t\@let@token \tw@ \else + \if s\@let@token \thr@@\else + \z@\fi\fi\fi\fi + \expandafter\tabu@begin + \or \expandafter\tabu@gobblespace\expandafter\tabu@settarget + \or \expandafter\tabu@to + \or \expandafter\tabu@spread + \fi +}% \tabu@sett@rget +\def\tabu@to to{\def\tabu@halignto{to}\tabu@gettarget} +\def\tabu@spread spread{\tabu@spreadtrue\def\tabu@halignto{spread}\tabu@gettarget} +\def\tabu@gettarget {\afterassignment\tabu@linegoaltarget \tabu@target } +\def\tabu@linegoaltarget {\futurelet\tabu@temp \tabu@linegoalt@rget } +\def\tabu@linegoalt@rget {% + \ifx \tabu@temp\LNGL@setlinegoal + \LNGL@setlinegoal \expandafter \@firstoftwo \fi % @gobbles \LNGL@setlinegoal + \tabu@begin +}% \tabu@linegoalt@rget +\def\tabu@begin #1#{% + \iftabu@measuring \expandafter\tabu@nestedmeasure \fi + \ifdim \tabu@target=\z@ \let\tabu@halignto \@empty + \else \edef\tabu@halignto{\tabu@halignto\the\tabu@target}% + \fi + \@testopt \tabu@tabu@ \tabu@aligndefault #1\@nil +}% \tabu@begin +\long\def\tabu@tabu@ [#1]#2\@nil #3{\tabu@setup + \def\tabu@align {#1}\def\tabu@savedpream{\NC@find #3}% + \tabu@ [\tabu@align ]#2{#3\tabu@rewritefirst }% +}% \tabu@tabu@ +\def\tabu@nestedmeasure {% + \ifodd 1\iftabu@spread \else \ifdim\tabu@target=\z@ \else 0 \fi\fi\relax + \tabu@spreadtrue + \else \begingroup \iffalse{\fi \ifnum0=`}\fi + \toks@{}\def\tabu@stack{b}% + \expandafter\tabu@collectbody\expandafter\tabu@quickrule + \expandafter\endgroup + \fi +}% \tabu@nestedmeasure +\def\tabu@quickrule {\indent\vrule height\z@ depth\z@ width\tabu@target} +%% \tabu@setup \tabu@init \tabu@indent +\def\tabu@setup{\tabu@alloc@ + \ifcase \tabu@nested + \ifmmode \else \iftabu@spread\else \ifdim\tabu@target=\z@ + \let\tabu@afterendpar \par + \fi\fi\fi + \def\tabu@aligndefault{c}\tabu@init \tabu@indent + \else % + \def\tabu@aligndefault{t}\let\tabudefaulttarget \linewidth + \fi + \let\tabu@thetarget \tabudefaulttarget \let\tabu@restored \@undefined + \edef\tabu@NC@list{\the\NC@list}\NC@list{\NC@do \tabu@rewritefirst}% + \everycr{}\let\@startpbox \tabu@startpbox % for nested tabu inside longtabu... + \let\@endpbox \tabu@endpbox % idem " " " " " " + \let\@tabarray \tabu@tabarray % idem " " " " " " + \tabu@setcleanup \tabu@setreset +}% \tabu@setup +\def\tabu@init{\tabu@starttimer \tabu@measuringfalse + \edef\tabu@hfuzz {\the\dimexpr\hfuzz+1sp}\global\tabu@footnotes{}% + \let\firsthline \tabu@firsthline \let\lasthline \tabu@lasthline + \let\firstline \tabu@firstline \let\lastline \tabu@lastline + \let\hline \tabu@hline \let\@xhline \tabu@xhline + \let\color \tabu@color \let\@arstrutbox \tabu@arstrutbox + \iftabu@colortbl\else\let\LT@@hline \tabu@LT@@hline \fi + \tabu@trivlist % + \let\@footnotetext \tabu@footnotetext \let\@xfootnotetext \tabu@xfootnotetext + \let\@xfootnote \tabu@xfootnote \let\centering \tabu@centering + \let\raggedright \tabu@raggedright \let\raggedleft \tabu@raggedleft + \let\tabudecimal \tabu@tabudecimal \let\Centering \tabu@Centering + \let\RaggedRight \tabu@RaggedRight \let\RaggedLeft \tabu@RaggedLeft + \let\justifying \tabu@justifying \let\rowfont \tabu@rowfont + \let\fbox \tabu@fbox \let\color@b@x \tabu@color@b@x + \let\tabu@@everycr \everycr \let\tabu@@everypar \everypar + \let\tabu@prepnext@tokORI \prepnext@tok\let\prepnext@tok \tabu@prepnext@tok + \let\tabu@multicolumnORI\multicolumn \let\multicolumn \tabu@multicolumn + \let\tabu@startpbox \@startpbox % for nested tabu inside longtabu pfff !!! + \let\tabu@endpbox \@endpbox % idem " " " " " " " + \let\tabu@tabarray \@tabarray % idem " " " " " " " + \tabu@adl@fix \let\endarray \tabu@endarray % colortbl & arydshln (delarray) + \iftabu@colortbl\CT@everycr\expandafter{\expandafter\iftabu@everyrow \the\CT@everycr \fi}\fi +}% \tabu@init +\def\tabu@indent{% correction for indentation + \ifdim \parindent>\z@\ifx \linewidth\tabudefaulttarget + \everypar\expandafter{% + \the\everypar\everypar\expandafter{\the\everypar}% + \setbox\z@=\lastbox + \ifdim\wd\z@>\z@ \edef\tabu@thetarget + {\the\dimexpr -\wd\z@+\tabudefaulttarget}\fi + \box\z@}% + \fi\fi +}% \tabu@indent +\def\tabu@setcleanup {% saves last global assignments + \ifodd 1\ifmmode \else \iftabu@long \else 0\fi\fi\relax + \def\tabu@aftergroupcleanup{% + \def\tabu@aftergroupcleanup{\aftergroup\tabu@cleanup}}% + \else + \def\tabu@aftergroupcleanup{% + \aftergroup\aftergroup\aftergroup\tabu@cleanup + \let\tabu@aftergroupcleanup \relax}% + \fi + \let\tabu@arc@Gsave \tabu@arc@G + \let\tabu@arc@G \tabu@arc@L % + \let\tabu@drsc@Gsave \tabu@drsc@G + \let\tabu@drsc@G \tabu@drsc@L % + \let\tabu@ls@Gsave \tabu@ls@G + \let\tabu@ls@G \tabu@ls@L % + \let\tabu@rc@Gsave \tabu@rc@G + \let\tabu@rc@G \tabu@rc@L % + \let\tabu@evr@Gsave \tabu@evr@G + \let\tabu@evr@G \tabu@evr@L % + \let\tabu@celllalign@save \tabu@celllalign + \let\tabu@cellralign@save \tabu@cellralign + \let\tabu@cellleft@save \tabu@cellleft + \let\tabu@cellright@save \tabu@cellright + \let\tabu@@celllalign@save \tabu@@celllalign + \let\tabu@@cellralign@save \tabu@@cellralign + \let\tabu@@cellleft@save \tabu@@cellleft + \let\tabu@@cellright@save \tabu@@cellright + \let\tabu@rowfontreset@save \tabu@rowfontreset + \let\tabu@@rowfontreset@save\tabu@@rowfontreset + \let\tabu@rowfontreset \@empty + \edef\tabu@alloc@save {\the\tabu@alloc}% restore at \tabu@reset + \edef\c@taburow@save {\the\c@taburow}% + \edef\tabu@naturalX@save {\the\tabu@naturalX}% + \let\tabu@naturalXmin@save \tabu@naturalXmin + \let\tabu@naturalXmax@save \tabu@naturalXmax + \let\tabu@mkarstrut@save \tabu@mkarstrut + \edef\tabu@clarstrut{% + \extrarowheight \the\dimexpr \ht\@arstrutbox-\ht\strutbox \relax + \extrarowdepth \the\dimexpr \dp\@arstrutbox-\dp\strutbox \relax + \let\noexpand\@arraystretch \@ne \noexpand\tabu@rearstrut}% +}% \tabu@setcleanup +\def\tabu@cleanup {\begingroup + \globaldefs\@ne \tabu@everyrowtrue + \let\tabu@arc@G \tabu@arc@Gsave + \let\CT@arc@ \tabu@arc@G + \let\tabu@drsc@G \tabu@drsc@Gsave + \let\CT@drsc@ \tabu@drsc@G + \let\tabu@ls@G \tabu@ls@Gsave + \let\tabu@ls@ \tabu@ls@G + \let\tabu@rc@G \tabu@rc@Gsave + \let\tabu@rc@ \tabu@rc@G + \let\CT@do@color \relax + \let\tabu@evr@G \tabu@evr@Gsave + \let\tabu@celllalign \tabu@celllalign@save + \let\tabu@cellralign \tabu@cellralign@save + \let\tabu@cellleft \tabu@cellleft@save + \let\tabu@cellright \tabu@cellright@save + \let\tabu@@celllalign \tabu@@celllalign@save + \let\tabu@@cellralign \tabu@@cellralign@save + \let\tabu@@cellleft \tabu@@cellleft@save + \let\tabu@@cellright \tabu@@cellright@save + \let\tabu@rowfontreset \tabu@rowfontreset@save + \let\tabu@@rowfontreset \tabu@@rowfontreset@save + \tabu@naturalX =\tabu@naturalX@save + \let\tabu@naturalXmax \tabu@naturalXmax@save + \let\tabu@naturalXmin \tabu@naturalXmin@save + \let\tabu@mkarstrut \tabu@mkarstrut@save + \c@taburow =\c@taburow@save + \ifcase \tabu@nested \tabu@alloc \m@ne\fi + \endgroup % + \ifcase \tabu@nested + \the\tabu@footnotes \global\tabu@footnotes{}% + \tabu@afterendpar \tabu@elapsedtime + \fi + \tabu@clarstrut + \everyrow\expandafter {\tabu@evr@G}% +}% \tabu@cleanup +\let\tabu@afterendpar \relax +\def\tabu@setreset {% + \edef\tabu@savedparams {% \relax for \tabu@message@save + \ifmmode \col@sep \the\arraycolsep + \else \col@sep \the\tabcolsep \fi \relax + \arrayrulewidth \the\arrayrulewidth \relax + \doublerulesep \the\doublerulesep \relax + \extratabsurround \the\extratabsurround \relax + \extrarowheight \the\extrarowheight \relax + \extrarowdepth \the\extrarowdepth \relax + \abovetabulinesep \the\abovetabulinesep \relax + \belowtabulinesep \the\belowtabulinesep \relax + \def\noexpand\arraystretch{\arraystretch}% + \ifdefined\minrowclearance \minrowclearance\the\minrowclearance\relax\fi}% + \begingroup + \@temptokena\expandafter{\tabu@savedparams}% => only for \savetabu / \usetabu + \ifx \tabu@arc@L\relax \else \tabu@setsave \tabu@arc@L \fi + \ifx \tabu@drsc@L\relax \else \tabu@setsave \tabu@drsc@L \fi + \tabu@setsave \tabu@ls@L \tabu@setsave \tabu@evr@L + \expandafter \endgroup \expandafter + \def\expandafter\tabu@saved@ \expandafter{\the\@temptokena + \let\tabu@arc@G \tabu@arc@L + \let\tabu@drsc@G \tabu@drsc@L + \let\tabu@ls@G \tabu@ls@L + \let\tabu@rc@G \tabu@rc@L + \let\tabu@evr@G \tabu@evr@L}% + \def\tabu@reset{\tabu@savedparams + \tabu@everyrowtrue \c@taburow \z@ + \let\CT@arc@ \tabu@arc@L + \let\CT@drsc@ \tabu@drsc@L + \let\tabu@ls@ \tabu@ls@L + \let\tabu@rc@ \tabu@rc@L + \global\tabu@alloc \tabu@alloc@save + \everyrow\expandafter{\tabu@evr@L}}% +}% \tabu@reset +\def\tabu@setsave #1{\expandafter\tabu@sets@ve #1\@nil{#1}} +\long\def\tabu@sets@ve #1\@nil #2{\@temptokena\expandafter{\the\@temptokena \def#2{#1}}} +%% The Rewriting Process ------------------------------------------- +\def\tabu@newcolumntype #1{% + \expandafter\tabu@new@columntype + \csname NC@find@\string#1\expandafter\endcsname + \csname NC@rewrite@\string#1\endcsname + {#1}% +}% \tabu@newcolumntype +\def\tabu@new@columntype #1#2#3{% + \def#1##1#3{\NC@{##1}}% + \let#2\relax \newcommand*#2% +}% \tabu@new@columntype +\def\tabu@privatecolumntype #1{% + \expandafter\tabu@private@columntype + \csname NC@find@\string#1\expandafter\endcsname + \csname NC@rewrite@\string#1\expandafter\endcsname + \csname tabu@NC@find@\string#1\expandafter\endcsname + \csname tabu@NC@rewrite@\string#1\endcsname + {#1}% +}% \tabu@privatecolumntype +\def\tabu@private@columntype#1#2#3#4{% + \g@addto@macro\tabu@privatecolumns{\let#1#3\let#2#4}% + \tabu@new@columntype#3#4% +}% \tabu@private@columntype +\let\tabu@privatecolumns \@empty +\newcommand*\tabucolumn [1]{\expandafter \def \expandafter + \tabu@highprioritycolumns\expandafter{\tabu@highprioritycolumns + \NC@do #1}}% +\let\tabu@highprioritycolumns \@empty +%% The | ``column'' : rewriting process -------------------------- +\tabu@privatecolumntype |{\tabu@rewritevline} +\newcommand*\tabu@rewritevline[1][]{\tabu@vlinearg{#1}% + \expandafter \NC@find \tabu@rewritten} +\def\tabu@lines #1{% + \ifx|#1\else \tabu@privatecolumntype #1{\tabu@rewritevline}\fi + \NC@list\expandafter{\the\NC@list \NC@do #1}% +}% \tabu@lines@ +\def\tabu@vlinearg #1{% + \ifx\\#1\\\def\tabu@thestyle {\tabu@ls@}% + \else\tabu@getline {#1}% + \fi + \def\tabu@rewritten ##1{\def\tabu@rewritten{!{##1\tabu@thevline}}% + }\expandafter\tabu@rewritten\expandafter{\tabu@thestyle}% + \expandafter \tabu@keepls \tabu@thestyle \@nil +}% \tabu@vlinearg +\def\tabu@keepls #1\@nil{% + \ifcat $\@cdr #1\@nil $% + \ifx \relax#1\else + \ifx \tabu@ls@#1\else + \let#1\relax + \xdef\tabu@mkpreambuffer{\tabu@mkpreambuffer + \tabu@savels\noexpand#1}\fi\fi\fi +}% \tabu@keepls +\def\tabu@thevline {\begingroup + \ifdefined\tabu@leaders + \setbox\@tempboxa=\vtop to\dimexpr + \ht\@arstrutbox+\dp\@arstrutbox{{\tabu@thevleaders}}% + \ht\@tempboxa=\ht\@arstrutbox \dp\@tempboxa=\dp\@arstrutbox + \box\@tempboxa + \else + \tabu@thevrule + \fi \endgroup +}% \tabu@thevline +\def\tabu@savels #1{% + \expandafter\let\csname\string#1\endcsname #1% + \expandafter\def\expandafter\tabu@reset\expandafter{\tabu@reset + \tabu@resetls#1}}% +\def\tabu@resetls #1{\expandafter\let\expandafter#1\csname\string#1\endcsname}% +%% \multicolumn inside tabu environment ----------------------------- +\tabu@newcolumntype \tabu@rewritemulticolumn{% + \aftergroup \tabu@endrewritemulticolumn % after \@mkpream group + \NC@list{\NC@do *}\tabu@textbar \tabu@lines + \tabu@savedecl + \tabu@privatecolumns + \NC@list\expandafter{\the\expandafter\NC@list \tabu@NC@list}% + \let\tabu@savels \relax + \NC@find +}% \tabu@rewritemulticolumn +\def\tabu@endrewritemulticolumn{\gdef\tabu@mkpreambuffer{}\endgroup} +\def\tabu@multicolumn{\tabu@ifenvir \tabu@multic@lumn \tabu@multicolumnORI} +\long\def\tabu@multic@lumn #1#2#3{\multispan{#1}\begingroup + \tabu@everyrowtrue + \NC@list{\NC@do \tabu@rewritemulticolumn}% + \expandafter\@gobbletwo % gobbles \multispan{#1} + \tabu@multicolumnORI{#1}{\tabu@rewritemulticolumn #2}% + {\iftabuscantokens \tabu@rescan \else \expandafter\@firstofone \fi + {#3}}% +}% \tabu@multic@lumn +%% The X column(s): rewriting process ----------------------------- +\tabu@privatecolumntype X[1][]{\begingroup \tabu@siunitx{\endgroup \tabu@rewriteX {#1}}} +\def\tabu@nosiunitx #1{#1{}{}\expandafter \NC@find \tabu@rewritten } +\def\tabu@siunitx #1{\@ifnextchar \bgroup + {\tabu@rewriteX@Ss{#1}} + {\tabu@nosiunitx{#1}}} +\def\tabu@rewriteX@Ss #1#2{\@temptokena{}% + \@defaultunits \let\tabu@temp =#2\relax\@nnil + \ifodd 1\ifx S\tabu@temp \else \ifx s\tabu@temp \else 0 \fi\fi + \def\NC@find{\def\NC@find >####1####2<####3\relax{#1 {####1}{####3}% + }\expandafter\NC@find \the\@temptokena \relax + }\expandafter\NC@rewrite@S \@gobble #2\relax + \else \tabu@siunitxerror + \fi + \expandafter \NC@find \tabu@rewritten +}% \tabu@rewriteX@Ss +\def\tabu@siunitxerror {\PackageError{tabu}{Not a S nor s column ! + \MessageBreak X column can only embed siunitx S or s columns}\@ehd +}% \tabu@siunitxerror +\def\tabu@rewriteX #1#2#3{\tabu@Xarg {#1}{#2}{#3}% + \iftabu@measuring + \else \tabu@measuringtrue % first X column found in the preamble + \let\@halignto \relax \let\tabu@halignto \relax + \iftabu@spread \tabu@spreadtarget \tabu@target \tabu@target \z@ + \else \tabu@spreadtarget \z@ \fi + \ifdim \tabu@target=\z@ + \setlength\tabu@target \tabu@thetarget + \tabu@message{\tabu@message@defaulttarget}% + \else \tabu@message{\tabu@message@target}\fi + \fi +}% \tabu@rewriteX +\def\tabu@rewriteXrestore #1#2#3{\let\@halignto \relax + \def\tabu@rewritten{l}} +\def\tabu@Xarg #1#2#3{% + \advance\tabu@Xcol \@ne \let\tabu@Xlcr \@empty + \let\tabu@Xdisp \@empty \let\tabu@Xmath \@empty + \ifx\\#1\\% + \def\tabu@rewritten{p}\tabucolX \p@ % + \else + \let\tabu@rewritten \@empty \let\tabu@temp \@empty \tabucolX \z@ + \tabu@Xparse {}#1\relax + \fi + \tabu@Xrewritten{#2}{#3}% +}% \tabu@Xarg +\def\tabu@Xparse #1{\futurelet\@let@token \tabu@Xtest} +\expandafter\def\expandafter\tabu@Xparsespace\space{\tabu@Xparse{}} +\def\tabu@Xtest{% + \ifcase \ifx \relax\@let@token \z@ \else + \if ,\@let@token \m@ne\else + \if p\@let@token 1\else + \if m\@let@token 2\else + \if b\@let@token 3\else + \if l\@let@token 4\else + \if c\@let@token 5\else + \if r\@let@token 6\else + \if j\@let@token 7\else + \if L\@let@token 8\else + \if C\@let@token 9\else + \if R\@let@token 10\else + \if J\@let@token 11\else + \ifx \@sptoken\@let@token 12\else + \if .\@let@token 13\else + \if -\@let@token 13\else + \ifcat $\@let@token 14\else + 15\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\relax + \or \tabu@Xtype {p}% + \or \tabu@Xtype {m}% + \or \tabu@Xtype {b}% + \or \tabu@Xalign \raggedright\relax + \or \tabu@Xalign \centering\relax + \or \tabu@Xalign \raggedleft\relax + \or \tabu@Xalign \tabu@justify\relax + \or \tabu@Xalign \RaggedRight\raggedright + \or \tabu@Xalign \Centering\centering + \or \tabu@Xalign \RaggedLeft\raggedleft + \or \tabu@Xalign \justifying\tabu@justify + \or \expandafter \tabu@Xparsespace + \or \expandafter \tabu@Xcoef + \or \expandafter \tabu@Xm@th + \or \tabu@Xcoef{}% + \else\expandafter \tabu@Xparse + \fi +}% \tabu@Xtest +\def\tabu@Xalign #1#2{% + \ifx \tabu@Xlcr\@empty \else \PackageWarning{tabu} + {Duplicate horizontal alignment specification}\fi + \ifdefined#1\def\tabu@Xlcr{#1}\let#1\relax + \else \def\tabu@Xlcr{#2}\let#2\relax\fi + \expandafter\tabu@Xparse +}% \tabu@Xalign +\def\tabu@Xtype #1{% + \ifx \tabu@rewritten\@empty \else \PackageWarning{tabu} + {Duplicate vertical alignment specification}\fi + \def\tabu@rewritten{#1}\expandafter\tabu@Xparse +}% \tabu@Xtype +\def\tabu@Xcoef#1{\edef\tabu@temp{\tabu@temp#1}% + \afterassignment\tabu@Xc@ef \tabu@cnt\number\if-#10\fi +}% \tabu@Xcoef +\def\tabu@Xc@ef{\advance\tabucolX \tabu@temp\the\tabu@cnt\p@ + \tabu@Xparse{}% +}% \tabu@Xc@ef +\def\tabu@Xm@th #1{\futurelet \@let@token \tabu@Xd@sp} +\def\tabu@Xd@sp{\let\tabu@Xmath=$% + \ifx $\@let@token \def\tabu@Xdisp{\displaystyle}% + \expandafter\tabu@Xparse + \else \expandafter\tabu@Xparse\expandafter{\expandafter}% + \fi +}% \tabu@Xd@sp +\def\tabu@Xrewritten {% + \ifx \tabu@rewritten\@empty \def\tabu@rewritten{p}\fi + \ifdim \tabucolX<\z@ \tabu@negcoeftrue + \else\ifdim \tabucolX=\z@ \tabucolX \p@ + \fi\fi + \edef\tabu@temp{{\the\tabu@Xcol}{\tabu@strippt\tabucolX}}% + \edef\tabu@Xcoefs{\tabu@Xcoefs \tabu@ \tabu@temp}% + \edef\tabu@rewritten ##1##2{\def\noexpand\tabu@rewritten{% + >{\tabu@Xlcr \ifx$\tabu@Xmath$\tabu@Xdisp\fi ##1}% + \tabu@rewritten {\tabu@hsize \tabu@temp}% + <{##2\ifx$\tabu@Xmath$\fi}}% + }\tabu@rewritten +}% \tabu@Xrewritten +\def\tabu@hsize #1#2{% + \ifdim #2\p@<\z@ + \ifdim \tabucolX=\maxdimen \tabu@wd{#1}\else + \ifdim \tabu@wd{#1}<-#2\tabucolX \tabu@wd{#1}\else -#2\tabucolX\fi + \fi + \else #2\tabucolX + \fi +}% \tabu@hsize +%% \usetabu and \preamble: rewriting process --------------------- +\tabu@privatecolumntype \usetabu [1]{% + \ifx\\#1\\\tabu@saveerr{}\else + \@ifundefined{tabu@saved@\string#1} + {\tabu@saveerr{#1}} + {\let\tabu@rewriteX \tabu@rewriteXrestore + \csname tabu@saved@\string#1\expandafter\endcsname\expandafter\@ne}% + \fi +}% \NC@rewrite@\usetabu +\tabu@privatecolumntype \preamble [1]{% + \ifx\\#1\\\tabu@saveerr{}\else + \@ifundefined{tabu@saved@\string#1} + {\tabu@saveerr{#1}} + {\csname tabu@saved@\string#1\expandafter\endcsname\expandafter\z@}% + \fi +}% \NC@rewrite@\preamble +%% Controlling the rewriting process ------------------------------- +\tabu@newcolumntype \tabu@rewritefirst{% + \iftabu@long \aftergroup \tabu@longpream % + \else \aftergroup \tabu@pream + \fi + \let\tabu@ \relax \let\tabu@hsize \relax + \let\tabu@Xcoefs \@empty \let\tabu@savels \relax + \tabu@Xcol \z@ \tabu@cnt \tw@ + \gdef\tabu@mkpreambuffer{\tabu@{}}\tabu@measuringfalse + \global\setbox\@arstrutbox \box\@arstrutbox + \NC@list{\NC@do *}\tabu@textbar \tabu@lines + \NC@list\expandafter{\the\NC@list \NC@do X}% + \iftabu@siunitx % + \NC@list\expandafter{\the\NC@list \NC@do S\NC@do s}\fi + \NC@list\expandafter{\the\expandafter\NC@list \tabu@highprioritycolumns}% + \expandafter\def\expandafter\tabu@NC@list\expandafter{% + \the\expandafter\NC@list \tabu@NC@list}% % * | X S + \NC@list\expandafter{\expandafter \NC@do \expandafter\usetabu + \expandafter \NC@do \expandafter\preamble + \the\NC@list \NC@do \tabu@rewritemiddle + \NC@do \tabu@rewritelast}% + \tabu@savedecl + \tabu@privatecolumns + \edef\tabu@prev{\the\@temptokena}\NC@find \tabu@rewritemiddle +}% NC@rewrite@\tabu@rewritefirst +\tabu@newcolumntype \tabu@rewritemiddle{% + \edef\tabu@temp{\the\@temptokena}\NC@find \tabu@rewritelast +}% \NC@rewrite@\tabu@rewritemiddle +\tabu@newcolumntype \tabu@rewritelast{% + \ifx \tabu@temp\tabu@prev \advance\tabu@cnt \m@ne + \NC@list\expandafter{\tabu@NC@list \NC@do \tabu@rewritemiddle + \NC@do \tabu@rewritelast}% + \else \let\tabu@prev\tabu@temp + \fi + \ifcase \tabu@cnt \expandafter\tabu@endrewrite + \else \expandafter\NC@find \expandafter\tabu@rewritemiddle + \fi +}% \NC@rewrite@\tabu@rewritelast +%% Choosing the strategy -------------------------------------------- +\def\tabu@endrewrite {% + \let\tabu@temp \NC@find + \ifx \@arrayright\relax \let\@arrayright \@empty \fi + \count@=% + \ifx \@finalstrut\tabu@finalstrut \z@ % outer in mode 0 print + \iftabu@measuring + \xdef\tabu@mkpreambuffer{\tabu@mkpreambuffer + \tabu@target \csname tabu@\the\tabu@nested.T\endcsname + \tabucolX \csname tabu@\the\tabu@nested.X\endcsname + \edef\@halignto {\ifx\@arrayright\@empty to\tabu@target\fi}}% + \fi + \else\iftabu@measuring 4 % X columns + \xdef\tabu@mkpreambuffer{\tabu@{\tabu@mkpreambuffer + \tabu@target \the\tabu@target + \tabu@spreadtarget \the\tabu@spreadtarget}% + \def\noexpand\tabu@Xcoefs{\tabu@Xcoefs}% + \edef\tabu@halignto{\ifx \@arrayright\@empty to\tabu@target\fi}}% + \let\tabu@Xcoefs \relax + \else\ifcase\tabu@nested \thr@@ % outer, no X + \global\let\tabu@afterendpar \relax + \else \@ne % inner, no X, outer in mode 1 or 2 + \fi + \ifdefined\tabu@usetabu + \else \ifdim\tabu@target=\z@ + \else \let\tabu@temp \tabu@extracolsep + \fi\fi + \fi + \fi + \xdef\tabu@mkpreambuffer{\count@ \the\count@ \tabu@mkpreambuffer}% + \tabu@temp +}% \tabu@endrewrite +\def\tabu@extracolsep{\@defaultunits \expandafter\let + \expandafter\tabu@temp \expandafter=\the\@temptokena \relax\@nnil + \ifx \tabu@temp\@sptoken + \expandafter\tabu@gobblespace \expandafter\tabu@extracolsep + \else + \edef\tabu@temp{\noexpand\NC@find + \if |\noexpand\tabu@temp @% + \else\if !\noexpand\tabu@temp @% + \else !% + \fi\fi + {\noexpand\extracolsep\noexpand\@flushglue}}% + \fi + \tabu@temp +}% \tabu@extrac@lsep +%% Implementing the strategy ---------------------------------------- +\long\def\tabu@pream #1\@preamble {% + \let\tabu@ \tabu@@ \tabu@mkpreambuffer \tabu@aftergroupcleanup + \NC@list\expandafter {\tabu@NC@list}% in case of nesting... + \ifdefined\tabu@usetabu \tabu@usetabu \tabu@target \z@ \fi + \let\tabu@savedpreamble \@preamble + \global\let\tabu@elapsedtime \relax + \tabu@thebody ={#1\tabu@aftergroupcleanup}% + \tabu@thebody =\expandafter{\the\expandafter\tabu@thebody + \@preamble}% + \edef\tabuthepreamble {\the\tabu@thebody}% ( no @ allowed for \scantokens ) + \tabu@select +}% \tabu@pream +\long\def\tabu@longpream #1\LT@bchunk #2\LT@bchunk{% + \let\tabu@ \tabu@@ \tabu@mkpreambuffer \tabu@aftergroupcleanup + \NC@list\expandafter {\tabu@NC@list}% in case of nesting... + \let\tabu@savedpreamble \@preamble + \global\let\tabu@elapsedtime \relax + \tabu@thebody ={#1\LT@bchunk #2\tabu@aftergroupcleanup \LT@bchunk}% + \edef\tabuthepreamble {\the\tabu@thebody}% ( no @ allowed for \scantokens ) + \tabu@select +}% \tabu@longpream +\def\tabu@select {% + \ifnum\tabu@nested>\z@ \tabuscantokensfalse \fi + \ifnum \count@=\@ne \iftabu@measuring \count@=\tw@ \fi\fi + \ifcase \count@ + \global\let\tabu@elapsedtime \relax + \tabu@seteverycr + \expandafter \tabuthepreamble % vertical adjustment (inherited from outer) + \or % exit in vertical measure + struts per cell because no X and outer in mode 3 + \tabu@evr{\tabu@verticalinit}\tabu@celllalign@def{\tabu@verticalmeasure}% + \def\tabu@cellralign{\tabu@verticalspacing}% + \tabu@seteverycr + \expandafter \tabuthepreamble + \or % exit without measure because no X and outer in mode 4 + \tabu@evr{}\tabu@celllalign@def{}\let\tabu@cellralign \@empty + \tabu@seteverycr + \expandafter \tabuthepreamble + \else % needs trials + \tabu@evr{}\tabu@celllalign@def{}\let\tabu@cellralign \@empty + \tabu@savecounters + \expandafter \tabu@setstrategy + \fi +}% \tabu@select +\def\tabu@@ {\gdef\tabu@mkpreambuffer} +%% Protections to set up before trials ------------------------------ +\def\tabu@setstrategy {\begingroup % + \tabu@trialh@@k \tabu@cnt \z@ % number of trials + \hbadness \@M \let\hbadness \@tempcnta + \hfuzz \maxdimen \let\hfuzz \@tempdima + \let\write \tabu@nowrite\let\GenericError \tabu@GenericError + \let\savetabu \@gobble \let\tabudefaulttarget \linewidth + \let\@footnotetext \@gobble \let\@xfootnote \tabu@xfootnote + \let\color \tabu@nocolor\let\rowcolor \tabu@norowcolor + \let\tabu@aftergroupcleanup \relax % only after the last trial + \tabu@mkpreambuffer + \ifnum \count@>\thr@@ \let\@halignto \@empty \tabucolX@init + \def\tabu@lasttry{\m@ne\p@}\fi + \begingroup \iffalse{\fi \ifnum0=`}\fi + \toks@{}\def\tabu@stack{b}\iftabuscantokens \endlinechar=10 \obeyspaces \fi % + \tabu@collectbody \tabu@strategy % +}% \tabu@setstrategy +\def\tabu@savecounters{% + \def\@elt ##1{\csname c@##1\endcsname\the\csname c@##1\endcsname}% + \edef\tabu@clckpt {\begingroup \globaldefs=\@ne \cl@@ckpt \endgroup}\let\@elt \relax +}% \tabu@savecounters +\def\tabucolX@init {% \tabucolX <= \tabu@target / (sum coefs > 0) + \dimen@ \z@ \tabu@Xsum \z@ \tabucolX \z@ \let\tabu@ \tabu@Xinit \tabu@Xcoefs + \ifdim \dimen@>\z@ + \@tempdima \dimexpr \tabu@target *\p@/\dimen@ + \tabu@hfuzz\relax + \ifdim \tabucolX<\@tempdima \tabucolX \@tempdima \fi + \fi +}% \tabucolX@init +\def\tabu@Xinit #1#2{\tabu@Xcol #1 \advance \tabu@Xsum + \ifdim #2\p@>\z@ #2\p@ \advance\dimen@ #2\p@ + \else -#2\p@ \tabu@negcoeftrue + \@tempdima \dimexpr \tabu@target*\p@/\dimexpr-#2\p@\relax \relax + \ifdim \tabucolX<\@tempdima \tabucolX \@tempdima \fi + \tabu@wddef{#1}{0pt}% + \fi +}% \tabu@Xinit +%% Collecting the environment body ---------------------------------- +\long\def\tabu@collectbody #1#2\end #3{% + \edef\tabu@stack{\tabu@pushbegins #2\begin\end\expandafter\@gobble\tabu@stack}% + \ifx \tabu@stack\@empty + \toks@\expandafter{\expandafter\tabu@thebody\expandafter{\the\toks@ #2}% + \def\tabu@end@envir{\end{#3}}% + \iftabuscantokens + \iftabu@long \def\tabu@endenvir {\end{#3}\tabu@gobbleX}% + \else \def\tabu@endenvir {\let\endarray \@empty + \end{#3}\tabu@gobbleX}% + \fi + \else \def\tabu@endenvir {\end{#3}}\fi}% + \let\tabu@collectbody \tabu@endofcollect + \else\def\tabu@temp{#3}% + \ifx \tabu@temp\@empty \toks@\expandafter{\the\toks@ #2\end }% + \else \ifx\tabu@temp\tabu@@spxiii \toks@\expandafter{\the\toks@ #2\end #3}% + \else \ifx\tabu@temp\tabu@X \toks@\expandafter{\the\toks@ #2\end #3}% + \else \toks@\expandafter{\the\toks@ #2\end{#3}}% + \fi\fi\fi + \fi + \tabu@collectbody{#1}% +}% \tabu@collectbody +\long\def\tabu@pushbegins#1\begin#2{\ifx\end#2\else b\expandafter\tabu@pushbegins\fi}% +\def\tabu@endofcollect #1{\ifnum0=`{}\fi + \expandafter\endgroup \the\toks@ #1% +}% \tabu@endofcollect +%% The trials: switching between strategies ------------------------- +\def\tabu@strategy {\relax % stops \count@ assignment ! + \ifcase\count@ % case 0 = print with vertical adjustment (outer is finished) + \expandafter \tabu@endoftrials + \or % case 1 = exit in vertical measure (outer in mode 3) + \expandafter\xdef\csname tabu@\the\tabu@nested.T\endcsname{\the\tabu@target}% + \expandafter\xdef\csname tabu@\the\tabu@nested.X\endcsname{\the\tabucolX}% + \expandafter \tabu@endoftrials + \or % case 2 = exit with a rule replacing the table (outer in mode 4) + \expandafter \tabu@quickend + \or % case 3 = outer is in mode 3 because of no X + \begingroup + \tabu@evr{\tabu@verticalinit}\tabu@celllalign@def{\tabu@verticalmeasure}% + \def\tabu@cellralign{\tabu@verticalspacing}% + \expandafter \tabu@measuring + \else % case 4 = horizontal measure + \begingroup + \global\let\tabu@elapsedtime \tabu@message@etime + \long\def\multicolumn##1##2##3{\multispan{##1}}% + \let\tabu@startpboxORI \@startpbox + \iftabu@spread + \def\tabu@naturalXmax {\z@}% + \let\tabu@naturalXmin \tabu@naturalXmax + \tabu@evr{\global\tabu@naturalX \z@}% + \let\@startpbox \tabu@startpboxmeasure + \else\iftabu@negcoef + \let\@startpbox \tabu@startpboxmeasure + \else \let\@startpbox \tabu@startpboxquick + \fi\fi + \expandafter \tabu@measuring + \fi +}% \tabu@strategy +\def\tabu@measuring{\expandafter \tabu@trial \expandafter + \count@ \the\count@ \tabu@endtrial +}% \tabu@measuring +\def\tabu@trial{\iftabu@long \tabu@longtrial \else \tabu@shorttrial \fi} +\def\tabu@shorttrial {\setbox\tabu@box \hbox\bgroup \tabu@seteverycr + \ifx \tabu@savecounters\relax \else + \let\tabu@savecounters \relax \tabu@clckpt \fi + $\iftabuscantokens \tabu@rescan \else \expandafter\@secondoftwo \fi + \expandafter{\expandafter \tabuthepreamble + \the\tabu@thebody + \csname tabu@adl@endtrial\endcsname + \endarray}$\egroup % got \tabu@box +}% \tabu@shorttrial +\def\tabu@longtrial {\setbox\tabu@box \hbox\bgroup \tabu@seteverycr + \ifx \tabu@savecounters\relax \else + \let\tabu@savecounters \relax \tabu@clckpt \fi + \iftabuscantokens \tabu@rescan \else \expandafter\@secondoftwo \fi + \expandafter{\expandafter \tabuthepreamble + \the\tabu@thebody + \tabuendlongtrial}\egroup % got \tabu@box +}% \tabu@longtrial +\def\tabuendlongtrial{% no @ allowed for \scantokens + \LT@echunk \global\setbox\@ne \hbox{\unhbox\@ne}\kern\wd\@ne + \LT@get@widths +}% \tabuendlongtrial +\def\tabu@adl@endtrial{% + \crcr \noalign{\global\adl@ncol \tabu@nbcols}}% anything global is crap, junky and fails ! +\def\tabu@seteverycr {\tabu@reset + \everycr \expandafter{\the\everycr \tabu@everycr}% + \let\everycr \tabu@noeverycr % +}% \tabu@seteverycr +\def\tabu@noeverycr{{\aftergroup\tabu@restoreeverycr \afterassignment}\toks@} +\def\tabu@restoreeverycr {\let\everycr \tabu@@everycr} +\def\tabu@everycr {\iftabu@everyrow \noalign{\tabu@everyrow}\fi} +\def\tabu@endoftrials {% + \iftabuscantokens \expandafter\@firstoftwo + \else \expandafter\@secondoftwo + \fi + {\expandafter \tabu@closetrialsgroup \expandafter + \tabu@rescan \expandafter{% + \expandafter\tabuthepreamble + \the\expandafter\tabu@thebody + \iftabu@long \else \endarray \fi}} + {\expandafter\tabu@closetrialsgroup \expandafter + \tabuthepreamble + \the\tabu@thebody}% + \tabu@endenvir % Finish ! +}% \tabu@endoftrials +\def\tabu@closetrialsgroup {% + \toks@\expandafter{\tabu@endenvir}% + \edef\tabu@bufferX{\endgroup + \tabucolX \the\tabucolX + \tabu@target \the\tabu@target + \tabu@cnt \the\tabu@cnt + \def\noexpand\tabu@endenvir{\the\toks@}% + %Quid de \@halignto = \tabu@halignto ?? + }% \tabu@bufferX + \tabu@bufferX + \ifcase\tabu@nested % print out (outer in mode 0) + \global\tabu@cnt \tabu@cnt + \tabu@evr{\tabu@verticaldynamicadjustment}% + \tabu@celllalign@def{\everypar{}}\let\tabu@cellralign \@empty + \let\@finalstrut \tabu@finalstrut + \else % vertical measure of nested tabu + \tabu@evr{\tabu@verticalinit}% + \tabu@celllalign@def{\tabu@verticalmeasure}% + \def\tabu@cellralign{\tabu@verticalspacing}% + \fi + \tabu@clckpt \let\@halignto \tabu@halignto + \let\@halignto \@empty + \tabu@seteverycr + \ifdim \tabustrutrule>\z@ \ifnum\tabu@nested=\z@ + \setbox\@arstrutbox \box\voidb@x % force \@arstrutbox to be rebuilt (visible struts) + \fi\fi +}% \tabu@closetrialsgroup +\def\tabu@quickend {\expandafter \endgroup \expandafter + \tabu@target \the\tabu@target \tabu@quickrule + \let\endarray \relax \tabu@endenvir +}% \tabu@quickend +\def\tabu@endtrial {\relax % stops \count@ assignment ! + \ifcase \count@ \tabu@err % case 0 = impossible here + \or \tabu@err % case 1 = impossible here + \or \tabu@err % case 2 = impossible here + \or % case 3 = outer goes into mode 0 + \def\tabu@bufferX{\endgroup}\count@ \z@ + \else % case 4 = outer goes into mode 3 + \iftabu@spread \tabu@spreadarith % inner into mode 1 (outer in mode 3) + \else \tabu@arith % or 2 (outer in mode 4) + \fi + \count@=% + \ifcase\tabu@nested \thr@@ % outer goes into mode 3 + \else\iftabu@measuring \tw@ % outer is in mode 4 + \else \@ne % outer is in mode 3 + \fi\fi + \edef\tabu@bufferX{\endgroup + \tabucolX \the\tabucolX + \tabu@target \the\tabu@target}% + \fi + \expandafter \tabu@bufferX \expandafter + \count@ \the\count@ \tabu@strategy +}% \tabu@endtrial +\def\tabu@err{\errmessage{(tabu) Internal impossible error! (\count@=\the\count@)}} +%% The algorithms: compute the widths / stop or go on --------------- +\def\tabu@arithnegcoef {% + \@tempdima \z@ \dimen@ \z@ \let\tabu@ \tabu@arith@negcoef \tabu@Xcoefs +}% \tabu@arithnegcoef +\def\tabu@arith@negcoef #1#2{% + \ifdim #2\p@>\z@ \advance\dimen@ #2\p@ % saturated by definition + \advance\@tempdima #2\tabucolX + \else + \ifdim -#2\tabucolX <\tabu@wd{#1}% c_i X < natural width <= \tabu@target-> saturated + \advance\dimen@ -#2\p@ + \advance\@tempdima -#2\tabucolX + \else + \advance\@tempdima \tabu@wd{#1}% natural width <= c_i X => neutralised + \ifdim \tabu@wd{#1}<\tabu@target \else % neutralised + \advance\dimen@ -#2\p@ % saturated (natural width = tabu@target) + \fi + \fi + \fi +}% \tabu@arith@negcoef +\def\tabu@givespace #1#2{% here \tabu@DELTA < \z@ + \ifdim \@tempdima=\z@ + \tabu@wddef{#1}{\the\dimexpr -\tabu@DELTA*\p@/\tabu@Xsum}% + \else + \tabu@wddef{#1}{\the\dimexpr \tabu@hsize{#1}{#2} + *(\p@ -\tabu@DELTA*\p@/\@tempdima)/\p@\relax}% + \fi +}% \tabu@givespace +\def\tabu@arith {\advance\tabu@cnt \@ne + \ifnum \tabu@cnt=\@ne \tabu@message{\tabu@titles}\fi + \tabu@arithnegcoef + \@tempdimb \dimexpr \wd\tabu@box -\@tempdima \relax % + \tabu@DELTA = \dimexpr \wd\tabu@box - \tabu@target \relax + \tabu@message{\tabu@message@arith}% + \ifdim \tabu@DELTA <\tabu@hfuzz + \ifdim \tabu@DELTA<\z@ % wd (tabu)<\tabu@target ? + \let\tabu@ \tabu@givespace \tabu@Xcoefs + \advance\@tempdima \@tempdimb \advance\@tempdima -\tabu@DELTA % for message + \else % already converged: nothing to do but nearly impossible... + \fi + \tabucolX \maxdimen + \tabu@measuringfalse + \else % need for narrower X columns + \tabucolX =\dimexpr (\@tempdima -\tabu@DELTA) *\p@/\tabu@Xsum \relax + \tabu@measuringtrue + \@whilesw \iftabu@measuring\fi {% + \advance\tabu@cnt \@ne + \tabu@arithnegcoef + \tabu@DELTA =\dimexpr \@tempdima+\@tempdimb -\tabu@target \relax % always < 0 here + \tabu@message{\tabu@header + \tabu@msgalign \tabucolX { }{ }{ }{ }{ }\@@ + \tabu@msgalign \@tempdima+\@tempdimb { }{ }{ }{ }{ }\@@ + \tabu@msgalign \tabu@target { }{ }{ }{ }{ }\@@ + \tabu@msgalign@PT \dimen@ { }{}{}{}{}{}{}\@@ + \ifdim -\tabu@DELTA<\tabu@hfuzz \tabu@spaces target ok\else + \tabu@msgalign \dimexpr -\tabu@DELTA *\p@/\dimen@ {}{}{}{}{}\@@ + \fi}% + \ifdim -\tabu@DELTA<\tabu@hfuzz + \advance\@tempdima \@tempdimb % for message + \tabu@measuringfalse + \else + \advance\tabucolX \dimexpr -\tabu@DELTA *\p@/\dimen@ \relax + \fi + }% + \fi + \tabu@message{\tabu@message@reached}% + \edef\tabu@bufferX{\endgroup \tabu@cnt \the\tabu@cnt + \tabucolX \the\tabucolX + \tabu@target \the\tabu@target}% +}% \tabu@arith +\def\tabu@spreadarith {% + \dimen@ \z@ \@tempdima \tabu@naturalXmax \let\tabu@ \tabu@spread@arith \tabu@Xcoefs + \edef\tabu@naturalXmin {\the\dimexpr\tabu@naturalXmin*\dimen@/\p@}% + \@tempdimc =\dimexpr \wd\tabu@box -\tabu@naturalXmax+\tabu@naturalXmin \relax + \iftabu@measuring + \tabu@target =\dimexpr \@tempdimc+\tabu@spreadtarget \relax + \edef\tabu@bufferX{\endgroup \tabucolX \the\tabucolX \tabu@target\the\tabu@target}% + \else + \tabu@message{\tabu@message@spreadarith}% + \ifdim \dimexpr \@tempdimc+\tabu@spreadtarget >\tabu@target + \tabu@message{(tabu) spread + \ifdim \@tempdimc>\tabu@target useless here: default target used% + \else too large: reduced to fit default target\fi.}% + \else + \tabu@target =\dimexpr \@tempdimc+\tabu@spreadtarget \relax + \tabu@message{(tabu) spread: New target set to \the\tabu@target^^J}% + \fi + \begingroup \let\tabu@wddef \@gobbletwo + \@tempdimb \@tempdima + \tabucolX@init + \tabu@arithnegcoef + \wd\tabu@box =\dimexpr \wd\tabu@box +\@tempdima-\@tempdimb \relax + \expandafter\endgroup \expandafter\tabucolX \the\tabucolX + \tabu@arith + \fi +}% \tabu@spreadarith +\def\tabu@spread@arith #1#2{% + \ifdim #2\p@>\z@ \advance\dimen@ #2\p@ + \else \advance\@tempdima \tabu@wd{#1}\relax + \fi +}% \tabu@spread@arith +%% Reporting in the .log file --------------------------------------- +\def\tabu@message@defaulttarget{% + \ifnum\tabu@nested=\z@^^J(tabu) Default target: + \ifx\tabudefaulttarget\linewidth \string\linewidth + \ifdim \tabu@thetarget=\linewidth \else + -\the\dimexpr\linewidth-\tabu@thetarget\fi = + \else\ifx\tabudefaulttarget\linegoal\string\linegoal= + \fi\fi + \else (tabu) Default target (nested): \fi + \the\tabu@target \on@line + \ifnum\tabu@nested=\z@ , page \the\c@page\fi} +\def\tabu@message@target {^^J(tabu) Target specified: + \the\tabu@target \on@line, page \the\c@page} +\def\tabu@message@arith {\tabu@header + \tabu@msgalign \tabucolX { }{ }{ }{ }{ }\@@ + \tabu@msgalign \wd\tabu@box { }{ }{ }{ }{ }\@@ + \tabu@msgalign \tabu@target { }{ }{ }{ }{ }\@@ + \tabu@msgalign@PT \dimen@ { }{}{}{}{}{}{}\@@ + \ifdim \tabu@DELTA<\tabu@hfuzz giving space\else + \tabu@msgalign \dimexpr (\@tempdima-\tabu@DELTA) *\p@/\tabu@Xsum -\tabucolX {}{}{}{}{}\@@ + \fi +}% \tabu@message@arith +\def\tabu@message@spreadarith {\tabu@spreadheader + \tabu@msgalign \tabu@spreadtarget { }{ }{ }{ }{}\@@ + \tabu@msgalign \wd\tabu@box { }{ }{ }{ }{}\@@ + \tabu@msgalign -\tabu@naturalXmax { }{}{}{}{}\@@ + \tabu@msgalign \tabu@naturalXmin { }{ }{ }{ }{}\@@ + \tabu@msgalign \ifdim \dimexpr\@tempdimc>\tabu@target \tabu@target + \else \@tempdimc+\tabu@spreadtarget \fi + {}{}{}{}{}\@@} +\def\tabu@message@negcoef #1#2{ + \tabu@spaces\tabu@spaces\space * #1. X[\rem@pt#2]: + \space width = \tabu@wd {#1} + \expandafter\string\csname tabu@\the\tabu@nested.W\number#1\endcsname + \ifdim -\tabu@pt#2\tabucolX<\tabu@target + < \number-\rem@pt#2 X + = \the\dimexpr -\tabu@pt#2\tabucolX \relax + \else + <= \the\tabu@target\space < \number-\rem@pt#2 X\fi} +\def\tabu@message@reached{\tabu@header + ******* Reached Target: + hfuzz = \tabu@hfuzz\on@line\space *******} +\def\tabu@message@etime{\edef\tabu@stoptime{\the\pdfelapsedtime}% + \tabu@message{(tabu)\tabu@spaces Time elapsed during measure: + \the\numexpr(\tabu@stoptime-\tabu@starttime-32767)/65536\relax sec + \the\numexpr\numexpr(\tabu@stoptime-\tabu@starttime) + -\numexpr(\tabu@stoptime-\tabu@starttime-32767)/65536\relax*65536\relax + *1000/65536\relax ms \tabu@spaces(\the\tabu@cnt\space + cycle\ifnum\tabu@cnt>\@ne s\fi)^^J^^J}} +\def\tabu@message@verticalsp {% + \ifdim \@tempdima>\tabu@ht + \ifdim \@tempdimb>\tabu@dp + \expandafter\expandafter\expandafter\string\tabu@ht = + \tabu@msgalign \@tempdima { }{ }{ }{ }{ }\@@ + \expandafter\expandafter\expandafter\string\tabu@dp = + \tabu@msgalign \@tempdimb { }{ }{ }{ }{ }\@@^^J% + \else + \expandafter\expandafter\expandafter\string\tabu@ht = + \tabu@msgalign \@tempdima { }{ }{ }{ }{ }\@@^^J% + \fi + \else\ifdim \@tempdimb>\tabu@dp + \tabu@spaces\tabu@spaces\tabu@spaces + \expandafter\expandafter\expandafter\string\tabu@dp = + \tabu@msgalign \@tempdimb { }{ }{ }{ }{ }\@@^^J\fi + \fi +}% \tabu@message@verticalsp +\edef\tabu@spaces{\@spaces} +\def\tabu@strippt{\expandafter\tabu@pt\the} +{\@makeother\P \@makeother\T\lowercase{\gdef\tabu@pt #1PT{#1}}} +\def\tabu@msgalign{\expandafter\tabu@msg@align\the\dimexpr} +\def\tabu@msgalign@PT{\expandafter\tabu@msg@align\romannumeral-`\0\tabu@strippt} +\def\do #1{% + \def\tabu@msg@align##1.##2##3##4##5##6##7##8##9\@@{% + \ifnum##1<10 #1 #1\else + \ifnum##1<100 #1 \else + \ifnum##1<\@m #1\fi\fi\fi + ##1.##2##3##4##5##6##7##8#1}% + \def\tabu@header{(tabu) \ifnum\tabu@cnt<10 #1\fi\the\tabu@cnt) }% + \def\tabu@titles{\ifnum \tabu@nested=\z@ + (tabu) Try#1 #1 tabu X #1 #1 #1tabu Width #1 #1 Target + #1 #1 #1 Coefs #1 #1 #1 Update^^J\fi}% + \def\tabu@spreadheader{% + (tabu) Try#1 #1 Spread #1 #1 tabu Width #1 #1 #1 Nat. X #1 #1 #1 #1Nat. Min. + #1 New Target^^J% + (tabu) sprd} + \def\tabu@message@save {\begingroup + \def\x ####1{\tabu@msg@align ####1{ }{ }{ }{ }{}\@@} + \def\z ####1{\expandafter\x\expandafter{\romannumeral-`\0\tabu@strippt + \dimexpr####1\p@{ }{ }}}% + \let\color \relax \def\tabu@rulesstyle ####1####2{\detokenize{####1}}% + \let\CT@arc@ \relax \let\@preamble \@gobble + \let\tabu@savedpream \@firstofone + \let\tabu@savedparams \@firstofone + \def\tabu@target ####1\relax {(tabu) target #1 #1 #1 #1 #1 = \x{####1}^^J}% + \def\tabucolX ####1\relax {(tabu) X columns width#1 = \x{####1}^^J}% + \def\tabu@nbcols ####1\relax {(tabu) Number of columns: \z{####1}^^J}% + \def\tabu@aligndefault ####1{(tabu) Default alignment: #1 #1 ####1^^J}% + \def\col@sep ####1\relax {(tabu) column sep #1 #1 #1 = \x{####1}^^J}% + \def\arrayrulewidth ####1\relax{(tabu) arrayrulewidth #1 = \x{####1}}% + \def\doublerulesep ####1\relax { doublerulesep = \x{####1}^^J}% + \def\extratabsurround####1\relax{(tabu) extratabsurround = \x{####1}^^J}% + \def\extrarowheight ####1\relax{(tabu) extrarowheight #1 = \x{####1}}% + \def\extrarowdepth ####1\relax {extrarowdepth = \x{####1}^^J}% + \def\abovetabulinesep####1\relax{(tabu) abovetabulinesep=\x{####1} }% + \def\belowtabulinesep####1\relax{ belowtabulinesep=\x{####1}^^J}% + \def\arraystretch ####1{(tabu) arraystretch #1 #1 = \z{####1}^^J}% + \def\minrowclearance####1\relax{(tabu) minrowclearance #1 = \x{####1}^^J}% + \def\tabu@arc@L ####1{(tabu) taburulecolor #1 #1 = ####1^^J}% + \def\tabu@drsc@L ####1{(tabu) tabudoublerulecolor= ####1^^J}% + \def\tabu@evr@L ####1{(tabu) everyrow #1 #1 #1 #1 = \detokenize{####1}^^J}% + \def\tabu@ls@L ####1{(tabu) line style = \detokenize{####1}^^J}% + \def\NC@find ####1\@nil{(tabu) tabu preamble#1 #1 = \detokenize{####1}^^J}% + \def\tabu@wddef####1####2{(tabu) Natural width ####1 = \x{####2}^^J}% + \let\edef \@gobbletwo \let\def \@empty \let\let \@gobbletwo + \tabu@message{% + (tabu) \string\savetabu{\tabu@temp}: \on@line^^J% + \tabu@usetabu \@nil^^J}% + \endgroup} +}\do{ } +%% Measuring the natural width (varwidth) - store the results ------- +\def\tabu@startpboxmeasure #1{\bgroup % entering \vtop + \edef\tabu@temp{\expandafter\@secondoftwo \ifx\tabu@hsize #1\else\relax\fi}% + \ifodd 1\ifx \tabu@temp\@empty 0 \else % starts with \tabu@hsize ? + \iftabu@spread \else % if spread -> measure + \ifdim \tabu@temp\p@>\z@ 0 \fi\fi\fi% if coef>0 -> do not measure + \let\@startpbox \tabu@startpboxORI % restore immediately (nesting) + \tabu@measuringtrue % for the quick option... + \tabu@Xcol =\expandafter\@firstoftwo\ifx\tabu@hsize #1\fi + \ifdim \tabu@temp\p@>\z@ \ifdim \tabu@temp\tabucolX<\tabu@target + \tabu@target=\tabu@temp\tabucolX \fi\fi + \setbox\tabu@box \hbox \bgroup + \begin{varwidth}\tabu@target + \let\FV@ListProcessLine \tabu@FV@ListProcessLine % \hbox to natural width... + \narrowragged \arraybackslash \parfillskip \@flushglue + \ifdefined\pdfadjustspacing \pdfadjustspacing\z@ \fi + \bgroup \aftergroup\tabu@endpboxmeasure + \ifdefined \cellspacetoplimit \tabu@cellspacepatch \fi + \else \expandafter\@gobble + \tabu@startpboxquick{#1}% \@gobble \bgroup + \fi +}% \tabu@startpboxmeasure +\def\tabu@cellspacepatch{\def\bcolumn##1\@nil{}\let\ecolumn\@empty + \bgroup\color@begingroup} +\def\tabu@endpboxmeasure {% + \@finalstrut \@arstrutbox + \end{varwidth}\egroup % + \ifdim \tabu@temp\p@ <\z@ % neg coef + \ifdim \tabu@wd\tabu@Xcol <\wd\tabu@box + \tabu@wddef\tabu@Xcol {\the\wd\tabu@box}% + \tabu@debug{\tabu@message@endpboxmeasure}% + \fi + \else % spread coef>0 + \global\advance \tabu@naturalX \wd\tabu@box + \@tempdima =\dimexpr \wd\tabu@box *\p@/\dimexpr \tabu@temp\p@\relax \relax + \ifdim \tabu@naturalXmax <\tabu@naturalX + \xdef\tabu@naturalXmax {\the\tabu@naturalX}\fi + \ifdim \tabu@naturalXmin <\@tempdima + \xdef\tabu@naturalXmin {\the\@tempdima}\fi + \fi + \box\tabu@box \egroup % end of \vtop (measure) restore \tabu@target +}% \tabu@endpboxmeasure +\def\tabu@wddef #1{\expandafter\xdef + \csname tabu@\the\tabu@nested.W\number#1\endcsname} +\def\tabu@wd #1{\csname tabu@\the\tabu@nested.W\number#1\endcsname} +\def\tabu@message@endpboxmeasure{\tabu@spaces\tabu@spaces<-> % <-> save natural wd + \the\tabu@Xcol. X[\tabu@temp]: + target = \the\tabucolX \space + \expandafter\expandafter\expandafter\string\tabu@wd\tabu@Xcol + =\tabu@wd\tabu@Xcol +}% \tabu@message@endpboxmeasure +\def\tabu@startpboxquick {\bgroup + \let\@startpbox \tabu@startpboxORI % restore immediately + \let\tabu \tabu@quick % \begin is expanded before... + \expandafter\@gobble \@startpbox % gobbles \bgroup +}% \tabu@startpboxquick +\def\tabu@quick {\begingroup \iffalse{\fi \ifnum0=`}\fi + \toks@{}\def\tabu@stack{b}\tabu@collectbody \tabu@endquick +}% \tabu@quick +\def\tabu@endquick {% + \ifodd 1\ifx\tabu@end@envir\tabu@endtabu \else + \ifx\tabu@end@envir\tabu@endtabus \else 0\fi\fi\relax + \endgroup + \else \let\endtabu \relax + \tabu@end@envir + \fi +}% \tabu@quick +\def\tabu@endtabu {\end{tabu}} +\def\tabu@endtabus {\end{tabu*}} +%% Measuring the heights and depths - store the results ------------- +\def\tabu@verticalmeasure{\everypar{}% + \ifnum \currentgrouptype>12 % 14=semi-simple, 15=math shift group + \setbox\tabu@box =\hbox\bgroup + \let\tabu@verticalspacing \tabu@verticalsp@lcr + \d@llarbegin % after \hbox ... + \else + \edef\tabu@temp{\ifnum\currentgrouptype=5\vtop + \else\ifnum\currentgrouptype=12\vcenter + \else\vbox\fi\fi}% + \setbox\tabu@box \hbox\bgroup$\tabu@temp \bgroup + \let\tabu@verticalspacing \tabu@verticalsp@pmb + \fi +}% \tabu@verticalmeasure +\def\tabu@verticalsp@lcr{% + \d@llarend \egroup % + \@tempdima \dimexpr \ht\tabu@box+\abovetabulinesep + \@tempdimb \dimexpr \dp\tabu@box+\belowtabulinesep \relax + \ifdim\tabustrutrule>\z@ \tabu@debug{\tabu@message@verticalsp}\fi + \ifdim \tabu@ht<\@tempdima \tabu@htdef{\the\@tempdima}\fi + \ifdim \tabu@dp<\@tempdimb \tabu@dpdef{\the\@tempdimb}\fi + \noindent\vrule height\@tempdima depth\@tempdimb +}% \tabu@verticalsp@lcr +\def\tabu@verticalsp@pmb{% inserts struts as needed + \par \expandafter\egroup + \expandafter$\expandafter + \egroup \expandafter + \@tempdimc \the\prevdepth + \@tempdima \dimexpr \ht\tabu@box+\abovetabulinesep + \@tempdimb \dimexpr \dp\tabu@box+\belowtabulinesep \relax + \ifdim\tabustrutrule>\z@ \tabu@debug{\tabu@message@verticalsp}\fi + \ifdim \tabu@ht<\@tempdima \tabu@htdef{\the\@tempdima}\fi + \ifdim \tabu@dp<\@tempdimb \tabu@dpdef{\the\@tempdimb}\fi + \let\@finalstrut \@gobble + \hrule height\@tempdima depth\@tempdimb width\hsize +%% \box\tabu@box +}% \tabu@verticalsp@pmb + +\def\tabu@verticalinit{% + \ifnum \c@taburow=\z@ \tabu@rearstrut \fi % after \tabu@reset ! + \advance\c@taburow \@ne + \tabu@htdef{\the\ht\@arstrutbox}\tabu@dpdef{\the\dp\@arstrutbox}% + \advance\c@taburow \m@ne +}% \tabu@verticalinit +\def\tabu@htdef {\expandafter\xdef \csname tabu@\the\tabu@nested.H\the\c@taburow\endcsname} +\def\tabu@ht {\csname tabu@\the\tabu@nested.H\the\c@taburow\endcsname} +\def\tabu@dpdef {\expandafter\xdef \csname tabu@\the\tabu@nested.D\the\c@taburow\endcsname} +\def\tabu@dp {\csname tabu@\the\tabu@nested.D\the\c@taburow\endcsname} +\def\tabu@verticaldynamicadjustment {% + \advance\c@taburow \@ne + \extrarowheight \dimexpr\tabu@ht - \ht\strutbox + \extrarowdepth \dimexpr\tabu@dp - \dp\strutbox + \let\arraystretch \@empty + \advance\c@taburow \m@ne +}% \tabu@verticaldynamicadjustment +\def\tabuphantomline{\crcr \noalign{% + {\globaldefs \@ne + \setbox\@arstrutbox \box\voidb@x + \let\tabu@@celllalign \tabu@celllalign + \let\tabu@@cellralign \tabu@cellralign + \let\tabu@@cellleft \tabu@cellleft + \let\tabu@@cellright \tabu@cellright + \let\tabu@@thevline \tabu@thevline + \let\tabu@celllalign \@empty + \let\tabu@cellralign \@empty + \let\tabu@cellright \@empty + \let\tabu@cellleft \@empty + \let\tabu@thevline \relax}% + \edef\tabu@temp{\tabu@multispan \tabu@nbcols{\noindent &}}% + \toks@\expandafter{\tabu@temp \noindent\tabu@everyrowfalse \cr + \noalign{\tabu@rearstrut + {\globaldefs\@ne + \let\tabu@celllalign \tabu@@celllalign + \let\tabu@cellralign \tabu@@cellralign + \let\tabu@cellleft \tabu@@cellleft + \let\tabu@cellright \tabu@@cellright + \let\tabu@thevline \tabu@@thevline}}}% + \expandafter}\the\toks@ +}% \tabuphantomline +%% \firsthline and \lasthline corrections --------------------------- +\def\tabu@firstline {\tabu@hlineAZ \tabu@firsthlinecorrection {}} +\def\tabu@firsthline{\tabu@hlineAZ \tabu@firsthlinecorrection \hline} +\def\tabu@lastline {\tabu@hlineAZ \tabu@lasthlinecorrection {}} +\def\tabu@lasthline {\tabu@hlineAZ \tabu@lasthlinecorrection \hline} +\def\tabu@hline {% replaces \hline if no colortbl (see \AtBeginDocument) + \noalign{\ifnum0=`}\fi + {\CT@arc@\hrule height\arrayrulewidth}% + \futurelet \tabu@temp \tabu@xhline +}% \tabu@hline +\def\tabu@xhline{% + \ifx \tabu@temp \hline + {\ifx \CT@drsc@\relax \vskip + \else\ifx \CT@drsc@\@empty \vskip + \else \CT@drsc@\hrule height + \fi\fi + \doublerulesep}% + \fi + \ifnum0=`{\fi}% +}% \tabu@xhline +\def\tabu@hlineAZ #1#2{\noalign{\ifnum0=`}\fi \dimen@ \z@ \count@ \z@ + \toks@{}\def\tabu@hlinecorrection{#1}\def\tabu@temp{#2}% + \tabu@hlineAZsurround +}% \tabu@hlineAZ +\newcommand*\tabu@hlineAZsurround[1][\extratabsurround]{% + \extratabsurround #1\let\tabucline \tabucline@scan + \let\hline \tabu@hlinescan \let\firsthline \hline + \let\cline \tabu@clinescan \let\lasthline \hline + \expandafter \futurelet \expandafter \tabu@temp + \expandafter \tabu@nexthlineAZ \tabu@temp +}% \tabu@hlineAZsurround +\def\tabu@hlinescan {\tabu@thick \arrayrulewidth \tabu@xhlineAZ \hline} +\def\tabu@clinescan #1{\tabu@thick \arrayrulewidth \tabu@xhlineAZ {\cline{#1}}} +\def\tabucline@scan{\@testopt \tabucline@sc@n {}} +\def\tabucline@sc@n #1[#2]{\tabu@xhlineAZ {\tabucline[{#1}]{#2}}} +\def\tabu@nexthlineAZ{% + \ifx \tabu@temp\hline \else + \ifx \tabu@temp\cline \else + \ifx \tabu@temp\tabucline \else + \tabu@hlinecorrection + \fi\fi\fi +}% \tabu@nexthlineAZ +\def\tabu@xhlineAZ #1{% + \toks@\expandafter{\the\toks@ #1}% + \@tempdimc \tabu@thick % The last line width + \ifcase\count@ \@tempdimb \tabu@thick % The first line width + \else \advance\dimen@ \dimexpr \tabu@thick+\doublerulesep \relax + \fi + \advance\count@ \@ne \futurelet \tabu@temp \tabu@nexthlineAZ +}% \tabu@xhlineAZ +\def\tabu@firsthlinecorrection{% \count@ = number of \hline -1 + \@tempdima \dimexpr \ht\@arstrutbox+\dimen@ + \edef\firsthline{% + \omit \hbox to\z@{\hss{\noexpand\tabu@DBG{yellow}\vrule + height \the\dimexpr\@tempdima+\extratabsurround + depth \dp\@arstrutbox + width \tabustrutrule}\hss}\cr + \noalign{\vskip -\the\dimexpr \@tempdima+\@tempdimb + +\dp\@arstrutbox \relax}% + \the\toks@ + }\ifnum0=`{\fi + \expandafter}\firsthline % we are then ! +}% \tabu@firsthlinecorrection +\def\tabu@lasthlinecorrection{% + \@tempdima \dimexpr \dp\@arstrutbox+\dimen@+\@tempdimb+\@tempdimc + \edef\lasthline{% + \the\toks@ + \noalign{\vskip -\the\dimexpr\dimen@+\@tempdimb+\dp\@arstrutbox}% + \omit \hbox to\z@{\hss{\noexpand\tabu@DBG{yellow}\vrule + depth \the\dimexpr \dp\@arstrutbox+\@tempdimb+\dimen@ + +\extratabsurround-\@tempdimc + height \z@ + width \tabustrutrule}\hss}\cr + }\ifnum0=`{\fi + \expandafter}\lasthline % we are then ! +}% \tabu@lasthlinecorrection +\def\tabu@LT@@hline{% + \ifx\LT@next\hline + \global\let\LT@next \@gobble + \ifx \CT@drsc@\relax + \gdef\CT@LT@sep{% + \noalign{\penalty-\@medpenalty\vskip\doublerulesep}}% + \else + \gdef\CT@LT@sep{% + \multispan\LT@cols{% + \CT@drsc@\leaders\hrule\@height\doublerulesep\hfill}\cr}% + \fi + \else + \global\let\LT@next\empty + \gdef\CT@LT@sep{% + \noalign{\penalty-\@lowpenalty\vskip-\arrayrulewidth}}% + \fi + \ifnum0=`{\fi}% + \multispan\LT@cols + {\CT@arc@\leaders\hrule\@height\arrayrulewidth\hfill}\cr + \CT@LT@sep + \multispan\LT@cols + {\CT@arc@\leaders\hrule\@height\arrayrulewidth\hfill}\cr + \noalign{\penalty\@M}% + \LT@next +}% \tabu@LT@@hline +%% Horizontal lines : \tabucline ------------------------------------ +\let\tabu@start \@tempcnta +\let\tabu@stop \@tempcntb +\newcommand*\tabucline{\noalign{\ifnum0=`}\fi \tabu@cline} +\newcommand*\tabu@cline[2][]{\tabu@startstop{#2}% + \ifnum \tabu@stop<\z@ \toks@{}% + \else \tabu@clinearg{#1}\tabu@thestyle + \edef\tabucline{\toks@{% + \ifnum \tabu@start>\z@ \omit + \tabu@multispan\tabu@start {\span\omit}&\fi + \omit \tabu@multispan\tabu@stop {\span\omit}% + \tabu@thehline\cr + }}\tabucline + \tabu@tracinglines{(tabu:tabucline) Style: #1^^J\the\toks@^^J^^J}% + \fi + \futurelet \tabu@temp \tabu@xcline +}% \tabu@cline +\def\tabu@clinearg #1{% + \ifx\\#1\\\let\tabu@thestyle \tabu@ls@ + \else \@defaultunits \expandafter\let\expandafter\@tempa + \romannumeral-`\0#1\relax \@nnil + \ifx \hbox\@tempa \tabu@clinebox{#1}% + \else\ifx \box\@tempa \tabu@clinebox{#1}% + \else\ifx \vbox\@tempa \tabu@clinebox{#1}% + \else\ifx \vtop\@tempa \tabu@clinebox{#1}% + \else\ifx \copy\@tempa \tabu@clinebox{#1}% + \else\ifx \leaders\@tempa \tabu@clineleads{#1}% + \else\ifx \cleaders\@tempa \tabu@clineleads{#1}% + \else\ifx \xleaders\@tempa \tabu@clineleads{#1}% + \else\tabu@getline {#1}% + \fi\fi\fi\fi\fi\fi\fi\fi + \fi +}% \tabu@clinearg +\def\tabu@clinebox #1{\tabu@clineleads{\xleaders#1\hss}} +\def\tabu@clineleads #1{% + \let\tabu@thestyle \relax \let\tabu@leaders \@undefined + \gdef\tabu@thehrule{#1}} +\def\tabu@thehline{\begingroup + \ifdefined\tabu@leaders + \noexpand\tabu@thehleaders + \else \noexpand\tabu@thehrule + \fi \endgroup +}% \tabu@thehline +\def\tabu@xcline{% + \ifx \tabu@temp\tabucline + \toks@\expandafter{\the\toks@ \noalign + {\ifx\CT@drsc@\relax \vskip + \else \CT@drsc@\hrule height + \fi + \doublerulesep}}% + \fi + \tabu@docline +}% \tabu@xcline +\def\tabu@docline {\ifnum0=`{\fi \expandafter}\the\toks@} +\def\tabu@docline@evr {\xdef\tabu@doclineafter{\the\toks@}% + \ifnum0=`{\fi}\aftergroup\tabu@doclineafter} +\def\tabu@multispan #1#2{% + \ifnum\numexpr#1>\@ne #2\expandafter\tabu@multispan + \else \expandafter\@gobbletwo + \fi {#1-1}{#2}% +}% \tabu@multispan +\def\tabu@startstop #1{\tabu@start@stop #1\relax 1-\tabu@nbcols \@nnil} +\def\tabu@start@stop #1-#2\@nnil{% + \@defaultunits \tabu@start\number 0#1\relax \@nnil + \@defaultunits \tabu@stop \number 0#2\relax \@nnil + \tabu@stop \ifnum \tabu@start>\tabu@nbcols \m@ne + \else\ifnum \tabu@stop=\z@ \tabu@nbcols + \else\ifnum \tabu@stop>\tabu@nbcols \tabu@nbcols + \else \tabu@stop + \fi\fi\fi + \advance\tabu@start \m@ne + \ifnum \tabu@start>\z@ \advance\tabu@stop -\tabu@start \fi +}% \tabu@start@stop +%% Numbers: siunitx S columns (and \tabudecimal) ------------------- +\def\tabu@tabudecimal #1{% + \def\tabu@decimal{#1}\@temptokena{}% + \let\tabu@getdecimal@ \tabu@getdecimal@ignorespaces + \tabu@scandecimal +}% \tabu@tabudecimal +\def\tabu@scandecimal{\futurelet \tabu@temp \tabu@getdecimal@} +\def\tabu@skipdecimal#1{#1\tabu@scandecimal} +\def\tabu@getdecimal@ignorespaces{% + \ifcase 0\ifx\tabu@temp\ignorespaces\else + \ifx\tabu@temp\@sptoken1\else + 2\fi\fi\relax + \let\tabu@getdecimal@ \tabu@getdecimal + \expandafter\tabu@skipdecimal + \or \expandafter\tabu@gobblespace\expandafter\tabu@scandecimal + \else \expandafter\tabu@skipdecimal + \fi +}% \tabu@getdecimal@ignorespaces +\def\tabu@get@decimal#1{\@temptokena\expandafter{\the\@temptokena #1}% + \tabu@scandecimal} +\def\do#1{% + \def\tabu@get@decimalspace#1{% + \@temptokena\expandafter{\the\@temptokena #1}\tabu@scandecimal}% +}\do{ } +\let\tabu@@tabudecimal \tabu@tabudecimal +\def\tabu@getdecimal{% + \ifcase 0\ifx 0\tabu@temp\else + \ifx 1\tabu@temp\else + \ifx 2\tabu@temp\else + \ifx 3\tabu@temp\else + \ifx 4\tabu@temp\else + \ifx 5\tabu@temp\else + \ifx 6\tabu@temp\else + \ifx 7\tabu@temp\else + \ifx 8\tabu@temp\else + \ifx 9\tabu@temp\else + \ifx .\tabu@temp\else + \ifx ,\tabu@temp\else + \ifx -\tabu@temp\else + \ifx +\tabu@temp\else + \ifx e\tabu@temp\else + \ifx E\tabu@temp\else + \ifx\tabu@cellleft\tabu@temp1\else + \ifx\ignorespaces\tabu@temp1\else + \ifx\@sptoken\tabu@temp2\else + 3\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\fi\relax + \expandafter\tabu@get@decimal + \or \expandafter\tabu@skipdecimal + \or \expandafter\tabu@get@decimalspace + \else\expandafter\tabu@printdecimal + \fi +}% \tabu@getdecimal +\def\tabu@printdecimal{% + \edef\tabu@temp{\the\@temptokena}% + \ifx\tabu@temp\@empty\else + \ifx\tabu@temp\space\else + \expandafter\tabu@decimal\expandafter{\the\@temptokena}% + \fi\fi +}% \tabu@printdecimal +%% Verbatim inside X columns ---------------------------------------- +\def\tabu@verbatim{% + \let\verb \tabu@verb + \let\FV@DefineCheckEnd \tabu@FV@DefineCheckEnd +}% \tabu@verbatim +\let\tabu@ltx@verb \verb +\def\tabu@verb{\@ifstar {\tabu@ltx@verb*} \tabu@ltx@verb} +\def\tabu@fancyvrb {% + \def\tabu@FV@DefineCheckEnd ##1{% + \def\tabu@FV@DefineCheckEnd{% + ##1% + \let\FV@CheckEnd \tabu@FV@CheckEnd + \let\FV@@CheckEnd \tabu@FV@@CheckEnd + \let\FV@@@CheckEnd \tabu@FV@@@CheckEnd + \edef\FV@EndScanning{% + \def\noexpand\next{\noexpand\end{\FV@EnvironName}}% + \global\let\noexpand\FV@EnvironName\relax + \noexpand\next}% + \xdef\FV@EnvironName{\detokenize\expandafter{\FV@EnvironName}}}% + }\expandafter\tabu@FV@DefineCheckEnd\expandafter{\FV@DefineCheckEnd} +}% \tabu@fancyvrb +\def\tabu@FV@CheckEnd #1{\expandafter\FV@@CheckEnd \detokenize{#1\end{}}\@nil} +\edef\tabu@FV@@@CheckEnd {\detokenize{\end{}}} +\begingroup +\catcode`\[1 \catcode`\]2 +\@makeother\{ \@makeother\} + \edef\x[\endgroup + \def\noexpand\tabu@FV@@CheckEnd ##1\detokenize[\end{]##2\detokenize[}]##3% + ]\x \@nil{\def\@tempa{#2}\def\@tempb{#3}} +\def\tabu@FV@ListProcessLine #1{% + \hbox {%to \hsize{% + \kern\leftmargin + \hbox {%to \linewidth{% + \FV@LeftListNumber + \FV@LeftListFrame + \FancyVerbFormatLine{#1}\hss +%% DG/SR modification begin - Jan. 28, 1998 (for numbers=right add-on) +%% \FV@RightListFrame}% + \FV@RightListFrame + \FV@RightListNumber}% +%% DG/SR modification end + \hss}} +%% \savetabu -------------------------------------------------------- +\newcommand*\savetabu[1]{\noalign{% + \tabu@sanitizearg{#1}\tabu@temp + \ifx \tabu@temp\@empty \tabu@savewarn{}{The tabu will not be saved}\else + \@ifundefined{tabu@saved@\tabu@temp}{}{\tabu@savewarn{#1}{Overwriting}}% + \ifdefined\tabu@restored \expandafter\let + \csname tabu@saved@\tabu@temp \endcsname \tabu@restored + \else {\tabu@save}% + \fi + \fi}% +}% \savetabu +\def\tabu@save {% + \toks0\expandafter{\tabu@saved@}% + \iftabu@negcoef + \let\tabu@wddef \relax \let\tabu@ \tabu@savewd \edef\tabu@savewd{\tabu@Xcoefs}% + \toks0\expandafter{\the\toks\expandafter0\tabu@savewd}\fi + \toks1\expandafter{\tabu@savedpream}% + \toks2\expandafter{\tabu@savedpreamble}% + \let\@preamble \relax + \let\tabu@savedpream \relax \let\tabu@savedparams \relax + \edef\tabu@preamble{% + \def\noexpand\tabu@aligndefault{\tabu@align}% + \def\tabu@savedparams {\noexpand\the\toks0}% + \def\tabu@savedpream {\noexpand\the\toks1}}% + \edef\tabu@usetabu{% + \def\@preamble {\noexpand\the\toks2}% + \tabu@target \the\tabu@target \relax + \tabucolX \the\tabucolX \relax + \tabu@nbcols \the\tabu@nbcols \relax + \def\noexpand\tabu@aligndefault{\tabu@align}% + \def\tabu@savedparams {\noexpand\the\toks0}% + \def\tabu@savedpream {\noexpand\the\toks1}}% + \let\tabu@aligndefault \relax \let\@sharp \relax + \edef\@tempa{\noexpand\tabu@s@ved + {\tabu@usetabu} + {\tabu@preamble} + {\the\toks1}}\@tempa + \tabu@message@save +}% \tabu@save +\long\def\tabu@s@ved #1#2#3{% + \def\tabu@usetabu{#1}% + \expandafter\gdef\csname tabu@saved@\tabu@temp\endcsname ##1{% + \ifodd ##1% \usetabu + \tabu@measuringfalse \tabu@spreadfalse % Just in case... + \gdef\tabu@usetabu {% + \ifdim \tabu@target>\z@ \tabu@warn@usetabu \fi + \global\let\tabu@usetabu \@undefined + \def\@halignto {to\tabu@target}% + #1% + \ifx \tabu@align\tabu@aligndefault@text + \ifnum \tabu@nested=\z@ + \let\tabu@align \tabu@aligndefault \fi\fi}% + \else % \preamble + \gdef\tabu@preamble {% + \global\let\tabu@preamble \@undefined + #2% + \ifx \tabu@align\tabu@aligndefault@text + \ifnum \tabu@nested=\z@ + \let\tabu@align \tabu@aligndefault \fi\fi}% + \fi + #3}% +}% \tabu@s@ved +\def\tabu@aligndefault@text {\tabu@aligndefault}% +\def\tabu@warn@usetabu {\PackageWarning{tabu} + {Specifying a target with \string\usetabu\space is useless + \MessageBreak The target cannot be changed!}} +\def\tabu@savewd #1#2{\ifdim #2\p@<\z@ \tabu@wddef{#1}{\tabu@wd{#1}}\fi} +\def\tabu@savewarn#1#2{\PackageInfo{tabu} + {User-name `#1' already used for \string\savetabu + \MessageBreak #2}}% +\def\tabu@saveerr#1{\PackageError{tabu} + {User-name `#1' is unknown for \string\usetabu + \MessageBreak I cannot restore an unknown preamble!}\@ehd} +%% \rowfont --------------------------------------------------------- +\newskip \tabu@cellskip +\def\tabu@rowfont{\ifdim \baselineskip=\z@\noalign\fi + {\ifnum0=`}\fi \tabu@row@font} +\newcommand*\tabu@row@font[2][]{% + \ifnum7=\currentgrouptype + \global\let\tabu@@cellleft \tabu@cellleft + \global\let\tabu@@cellright \tabu@cellright + \global\let\tabu@@celllalign \tabu@celllalign + \global\let\tabu@@cellralign \tabu@cellralign + \global\let\tabu@@rowfontreset\tabu@rowfontreset + \fi + \global\let\tabu@rowfontreset \tabu@rowfont@reset + \expandafter\gdef\expandafter\tabu@cellleft\expandafter{\tabu@cellleft #2}% + \ifcsname tabu@cell@#1\endcsname % row alignment + \csname tabu@cell@#1\endcsname \fi + \ifnum0=`{\fi}% end of group / noalign group +}% \rowfont +\def\tabu@ifcolorleavevmode #1{\let\color \tabu@leavevmodecolor #1\let\color\tabu@color}% +\def\tabu@rowfont@reset{% + \global\let\tabu@rowfontreset \tabu@@rowfontreset + \global\let\tabu@cellleft \tabu@@cellleft + \global\let\tabu@cellright \tabu@@cellright + \global\let\tabu@cellfont \@empty + \global\let\tabu@celllalign \tabu@@celllalign + \global\let\tabu@cellralign \tabu@@cellralign +}% \tabu@@rowfontreset +\let\tabu@rowfontreset \@empty % overwritten \AtBeginDocument if colortbl +%% \tabu@prepnext@tok ----------------------------------------------- +\newif \iftabu@cellright +\def\tabu@prepnext@tok{% + \ifnum \count@<\z@ % + \@tempcnta \@M % + \tabu@nbcols\z@ + \let\tabu@fornoopORI \@fornoop + \tabu@cellrightfalse + \else + \ifcase \numexpr \count@-\@tempcnta \relax % (case 0): prev. token is left + \advance \tabu@nbcols \@ne + \iftabu@cellright % before-previous token is right and is finished + \tabu@cellrightfalse % + \tabu@righttok + \fi + \tabu@lefttok + \or % (case 1) previous token is right + \tabu@cellrighttrue \let\@fornoop \tabu@lastnoop + \else % special column: do not change the token + \iftabu@cellright % before-previous token is right + \tabu@cellrightfalse + \tabu@righttok + \fi + \fi % \ifcase + \fi + \tabu@prepnext@tokORI +}% \tabu@prepnext@tok +\long\def\tabu@lastnoop#1\@@#2#3{\tabu@lastn@@p #2\@nextchar \in@\in@@} +\def\tabu@lastn@@p #1\@nextchar #2#3\in@@{% + \ifx \in@#2\else + \let\@fornoop \tabu@fornoopORI + \xdef\tabu@mkpreambuffer{\tabu@nbcols\the\tabu@nbcols \tabu@mkpreambuffer}% + \toks0\expandafter{\expandafter\tabu@everyrowtrue \the\toks0}% + \expandafter\prepnext@tok + \fi +}% \tabu@lastnoop +\def\tabu@righttok{% + \advance \count@ \m@ne + \toks\count@\expandafter {\the\toks\count@ \tabu@cellright \tabu@cellralign}% + \advance \count@ \@ne +}% \tabu@righttok +\def\tabu@lefttok{\toks\count@\expandafter{\expandafter\tabu@celllalign + \the\toks\count@ \tabu@cellleft}% after because of $ +}% \tabu@lefttok +%% Neutralisation of glues ------------------------------------------ +\let\tabu@cellleft \@empty +\let\tabu@cellright \@empty +\tabu@celllalign@def{\tabu@cellleft}% +\let\tabu@cellralign \@empty +\def\tabu@cell@align #1#2#3{% + \let\tabu@maybesiunitx \toks@ \tabu@celllalign + \global \expandafter \tabu@celllalign@def \expandafter {\the\toks@ #1}% + \toks@\expandafter{\tabu@cellralign #2}% + \xdef\tabu@cellralign{\the\toks@}% + \toks@\expandafter{\tabu@cellleft #3}% + \xdef\tabu@cellleft{\the\toks@}% +}% \tabu@cell@align +\def\tabu@cell@l{% force alignment to left + \tabu@cell@align + {\tabu@removehfil \raggedright \tabu@cellleft}% left + {\tabu@flush1\tabu@ignorehfil}% right + \raggedright +}% \tabu@cell@l +\def\tabu@cell@c{% force alignment to center + \tabu@cell@align + {\tabu@removehfil \centering \tabu@flush{.5}\tabu@cellleft} + {\tabu@flush{.5}\tabu@ignorehfil} + \centering +}% \tabu@cell@c +\def\tabu@cell@r{% force alignment to right + \tabu@cell@align + {\tabu@removehfil \raggedleft \tabu@flush1\tabu@cellleft} + \tabu@ignorehfil + \raggedleft +}% \tabu@cell@r +\def\tabu@cell@j{% force justification (for p, m, b columns) + \tabu@cell@align + {\tabu@justify\tabu@cellleft} + {} + \tabu@justify +}% \tabu@cell@j +\def\tabu@justify{% + \leftskip\z@skip \@rightskip\leftskip \rightskip\@rightskip + \parfillskip\@flushglue +}% \tabu@justify +%% ragged2e settings +\def\tabu@cell@L{% force alignment to left (ragged2e) + \tabu@cell@align + {\tabu@removehfil \RaggedRight \tabu@cellleft} + {\tabu@flush 1\tabu@ignorehfil} + \RaggedRight +}% \tabu@cell@L +\def\tabu@cell@C{% force alignment to center (ragged2e) + \tabu@cell@align + {\tabu@removehfil \Centering \tabu@flush{.5}\tabu@cellleft} + {\tabu@flush{.5}\tabu@ignorehfil} + \Centering +}% \tabu@cell@C +\def\tabu@cell@R{% force alignment to right (ragged2e) + \tabu@cell@align + {\tabu@removehfil \RaggedLeft \tabu@flush 1\tabu@cellleft} + \tabu@ignorehfil + \RaggedLeft +}% \tabu@cell@R +\def\tabu@cell@J{% force justification (ragged2e) + \tabu@cell@align + {\justifying \tabu@cellleft} + {} + \justifying +}% \tabu@cell@J +\def\tabu@flush#1{% + \iftabu@colortbl % colortbl uses \hfill rather than \hfil + \hskip \ifnum13<\currentgrouptype \stretch{#1}% + \else \ifdim#1pt<\p@ \tabu@cellskip + \else \stretch{#1} + \fi\fi \relax + \else % array.sty + \ifnum 13<\currentgrouptype + \hfil \hskip1sp \relax \fi + \fi +}% \tabu@flush +\let\tabu@hfil \hfil +\let\tabu@hfill \hfill +\let\tabu@hskip \hskip +\def\tabu@removehfil{% + \iftabu@colortbl + \unkern \tabu@cellskip =\lastskip + \ifnum\gluestretchorder\tabu@cellskip =\tw@ \hskip-\tabu@cellskip + \else \tabu@cellskip \z@skip + \fi + \else + \ifdim\lastskip=1sp\unskip\fi + \ifnum\gluestretchorder\lastskip =\@ne + \hfilneg % \hfilneg for array.sty but not for colortbl... + \fi + \fi +}% \tabu@removehfil +\def\tabu@ignorehfil{\aftergroup \tabu@nohfil} +\def\tabu@nohfil{% \hfil -> do nothing + restore original \hfil + \def\hfil{\let\hfil \tabu@hfil}% local to (alignment template) group +}% \tabu@nohfil +\def\tabu@colortblalignments {% if colortbl + \def\tabu@nohfil{% + \def\hfil {\let\hfil \tabu@hfil}% local to (alignment template) group + \def\hfill {\let\hfill \tabu@hfill}% (colortbl uses \hfill) pfff... + \def\hskip ####1\relax{\let\hskip \tabu@hskip}}% local +}% \tabu@colortblalignments +%% Taking care of footnotes and hyperfootnotes ---------------------- +\long\def\tabu@footnotetext #1{% + \edef\@tempa{\the\tabu@footnotes + \noexpand\footnotetext [\the\csname c@\@mpfn\endcsname]}% + \global\tabu@footnotes\expandafter{\@tempa {#1}}}% +\long\def\tabu@xfootnotetext [#1]#2{% + \global\tabu@footnotes\expandafter{\the\tabu@footnotes + \footnotetext [{#1}]{#2}}} +\let\tabu@xfootnote \@xfootnote +\long\def\tabu@Hy@ftntext{\tabu@Hy@ftntxt {\the \c@footnote }} +\long\def\tabu@Hy@xfootnote [#1]{% + \begingroup + \value\@mpfn #1\relax + \protected@xdef \@thefnmark {\thempfn}% + \endgroup + \@footnotemark \tabu@Hy@ftntxt {#1}% +}% \tabu@Hy@xfootnote +\long\def\tabu@Hy@ftntxt #1#2{% + \edef\@tempa{% + \the\tabu@footnotes + \begingroup + \value\@mpfn #1\relax + \noexpand\protected@xdef\noexpand\@thefnmark {\noexpand\thempfn}% + \expandafter \noexpand \expandafter + \tabu@Hy@footnotetext \expandafter{\Hy@footnote@currentHref}% + }% + \global\tabu@footnotes\expandafter{\@tempa {#2}% + \endgroup}% +}% \tabu@Hy@ftntxt +\long\def\tabu@Hy@footnotetext #1#2{% + \H@@footnotetext{% + \ifHy@nesting + \hyper@@anchor {#1}{#2}% + \else + \Hy@raisedlink{% + \hyper@@anchor {#1}{\relax}% + }% + \def\@currentHref {#1}% + \let\@currentlabelname \@empty + #2% + \fi + }% +}% \tabu@Hy@footnotetext +%% No need for \arraybackslash ! ------------------------------------ +\def\tabu@latextwoe {% +\def\tabu@temp##1##2##3{{\toks@\expandafter{##2##3}\xdef##1{\the\toks@}}} +\tabu@temp \tabu@centering \centering \arraybackslash +\tabu@temp \tabu@raggedleft \raggedleft \arraybackslash +\tabu@temp \tabu@raggedright \raggedright \arraybackslash +}% \tabu@latextwoe +\def\tabu@raggedtwoe {% +\def\tabu@temp ##1##2##3{{\toks@\expandafter{##2##3}\xdef##1{\the\toks@}}} +\tabu@temp \tabu@Centering \Centering \arraybackslash +\tabu@temp \tabu@RaggedLeft \RaggedLeft \arraybackslash +\tabu@temp \tabu@RaggedRight \RaggedRight \arraybackslash +\tabu@temp \tabu@justifying \justifying \arraybackslash +}% \tabu@raggedtwoe +\def\tabu@normalcrbackslash{\let\\\@normalcr} +\def\tabu@trivlist{\expandafter\def\expandafter\@trivlist\expandafter{% + \expandafter\tabu@normalcrbackslash \@trivlist}} +%% Utilities: \fbox \fcolorbox and \tabudecimal ------------------- +\def\tabu@fbox {\leavevmode\afterassignment\tabu@beginfbox \setbox\@tempboxa\hbox} +\def\tabu@beginfbox {\bgroup \kern\fboxsep + \bgroup\aftergroup\tabu@endfbox} +\def\tabu@endfbox {\kern\fboxsep\egroup\egroup + \@frameb@x\relax} +\def\tabu@color@b@x #1#2{\leavevmode \bgroup + \def\tabu@docolor@b@x{#1{#2\color@block{\wd\z@}{\ht\z@}{\dp\z@}\box\z@}}% + \afterassignment\tabu@begincolor@b@x \setbox\z@ \hbox +}% \tabu@color@b@x +\def\tabu@begincolor@b@x {\kern\fboxsep \bgroup + \aftergroup\tabu@endcolor@b@x \set@color} +\def\tabu@endcolor@b@x {\kern\fboxsep \egroup + \dimen@\ht\z@ \advance\dimen@ \fboxsep \ht\z@ \dimen@ + \dimen@\dp\z@ \advance\dimen@ \fboxsep \dp\z@ \dimen@ + \tabu@docolor@b@x \egroup +}% \tabu@endcolor@b@x +%% Corrections (arydshln, delarray, colortbl) ----------------------- +\def\tabu@fix@arrayright {%% \@arrayright is missing from \endarray + \iftabu@colortbl + \ifdefined\adl@array % + \def\tabu@endarray{% + \adl@endarray \egroup \adl@arrayrestore \CT@end \egroup % + \@arrayright % + \gdef\@preamble{}}% + \else % + \def\tabu@endarray{% + \crcr \egroup \egroup % + \@arrayright % + \gdef\@preamble{}\CT@end}% + \fi + \else + \ifdefined\adl@array % + \def\tabu@endarray{% + \adl@endarray \egroup \adl@arrayrestore \egroup % + \@arrayright % + \gdef\@preamble{}}% + \else % + \PackageWarning{tabu} + {\string\@arrayright\space is missing from the + \MessageBreak definition of \string\endarray. + \MessageBreak Compatibility with delarray.sty is broken.}% + \fi\fi +}% \tabu@fix@arrayright +\def\tabu@adl@xarraydashrule #1#2#3{% + \ifnum\@lastchclass=\adl@class@start\else + \ifnum\@lastchclass=\@ne\else + \ifnum\@lastchclass=5 \else % @-arg (class 5) and !-arg (class 1) + \adl@leftrulefalse \fi\fi % must be treated the same + \fi + \ifadl@zwvrule\else \ifadl@inactive\else + \@addtopreamble{\vrule\@width\arrayrulewidth + \@height\z@ \@depth\z@}\fi \fi + \ifadl@leftrule + \@addtopreamble{\adl@vlineL{\CT@arc@}{\adl@dashgapcolor}% + {\number#1}#3}% + \else \@addtopreamble{\adl@vlineR{\CT@arc@}{\adl@dashgapcolor}% + {\number#2}#3} + \fi +}% \tabu@adl@xarraydashrule +\def\tabu@adl@act@endpbox {% + \unskip \ifhmode \nobreak \fi \@finalstrut \@arstrutbox + \egroup \egroup + \adl@colhtdp \box\adl@box \hfil +}% \tabu@adl@act@endpbox +\def\tabu@adl@fix {% + \let\adl@xarraydashrule \tabu@adl@xarraydashrule % arydshln + \let\adl@act@endpbox \tabu@adl@act@endpbox % arydshln + \let\adl@act@@endpbox \tabu@adl@act@endpbox % arydshln + \let\@preamerror \@preamerr % arydshln +}% \tabu@adl@fix +%% Correction for longtable' \@startbox definition ------------------ +%% => \everypar is ``missing'' : TeX should be in vertical mode +\def\tabu@LT@startpbox #1{% + \bgroup + \let\@footnotetext\LT@p@ftntext + \setlength\hsize{#1}% + \@arrayparboxrestore + \everypar{% + \vrule \@height \ht\@arstrutbox \@width \z@ + \everypar{}}% +}% \tabu@LT@startpbox +%% \tracingtabu and the package options ------------------ +\DeclareOption{delarray}{\AtEndOfPackage{\RequirePackage{delarray}}} +\DeclareOption{linegoal}{% + \AtEndOfPackage{% + \RequirePackage{linegoal}[2010/12/07]% + \let\tabudefaulttarget \linegoal% \linegoal is \linewidth if not pdfTeX +}} +\DeclareOption{scantokens}{\tabuscantokenstrue} +\DeclareOption{debugshow}{\AtEndOfPackage{\tracingtabu=\tw@}} +\def\tracingtabu {\begingroup\@ifnextchar=% + {\afterassignment\tabu@tracing\count@} + {\afterassignment\tabu@tracing\count@1\relax}} +\def\tabu@tracing{\expandafter\endgroup + \expandafter\tabu@tr@cing \the\count@ \relax +}% \tabu@tracing +\def\tabu@tr@cing #1\relax {% + \ifnum#1>\thr@@ \let\tabu@tracinglines\message + \else \let\tabu@tracinglines\@gobble + \fi + \ifnum#1>\tw@ \let\tabu@DBG \tabu@@DBG + \def\tabu@mkarstrut {\tabu@DBG@arstrut}% + \tabustrutrule 1.5\p@ + \else \let\tabu@DBG \@gobble + \def\tabu@mkarstrut {\tabu@arstrut}% + \tabustrutrule \z@ + \fi + \ifnum#1>\@ne \let\tabu@debug \message + \else \let\tabu@debug \@gobble + \fi + \ifnum#1>\z@ + \let\tabu@message \message + \let\tabu@tracing@save \tabu@message@save + \let\tabu@starttimer \tabu@pdftimer + \else + \let\tabu@message \@gobble + \let\tabu@tracing@save \@gobble + \let\tabu@starttimer \relax + \fi +}% \tabu@tr@cing +%% Setup \AtBeginDocument +\AtBeginDocument{\tabu@AtBeginDocument} +\def\tabu@AtBeginDocument{\let\tabu@AtBeginDocument \@undefined + \ifdefined\arrayrulecolor \tabu@colortbltrue % + \tabu@colortblalignments % different glues are used + \else \tabu@colortblfalse \fi + \ifdefined\CT@arc@ \else \let\CT@arc@ \relax \fi + \ifdefined\CT@drsc@\else \let\CT@drsc@ \relax \fi + \let\tabu@arc@L \CT@arc@ \let\tabu@drsc@L \CT@drsc@ + \ifodd 1\ifcsname siunitx_table_collect_begin:Nn\endcsname % + \expandafter\ifx + \csname siunitx_table_collect_begin:Nn\endcsname\relax 0\fi\fi\relax + \tabu@siunitxtrue + \else \let\tabu@maybesiunitx \@firstofone % + \let\tabu@siunitx \tabu@nosiunitx + \tabu@siunitxfalse + \fi + \ifdefined\adl@array % + \else \let\tabu@adl@fix \relax + \let\tabu@adl@endtrial \@empty \fi + \ifdefined\longtable % + \else \let\longtabu \tabu@nolongtabu \fi + \ifdefined\cellspacetoplimit \tabu@warn@cellspace\fi + \csname\ifcsname ifHy@hyperfootnotes\endcsname % + ifHy@hyperfootnotes\else iffalse\fi\endcsname + \let\tabu@footnotetext \tabu@Hy@ftntext + \let\tabu@xfootnote \tabu@Hy@xfootnote \fi + \ifdefined\FV@DefineCheckEnd% + \tabu@fancyvrb \fi + \ifdefined\color % + \let\tabu@color \color + \def\tabu@leavevmodecolor ##1{% + \def\tabu@leavevmodecolor {\leavevmode ##1}% + }\expandafter\tabu@leavevmodecolor\expandafter{\color}% + \else + \let\tabu@color \tabu@nocolor + \let\tabu@leavevmodecolor \@firstofone \fi + \tabu@latextwoe + \ifdefined\@raggedtwoe@everyselectfont % + \tabu@raggedtwoe + \else + \let\tabu@cell@L \tabu@cell@l + \let\tabu@cell@R \tabu@cell@r + \let\tabu@cell@C \tabu@cell@c + \let\tabu@cell@J \tabu@cell@j \fi + \expandafter\in@ \expandafter\@arrayright \expandafter{\endarray}% + \ifin@ \let\tabu@endarray \endarray + \else \tabu@fix@arrayright \fi% + \everyrow{}% +}% \tabu@AtBeginDocument +\def\tabu@warn@cellspace{% + \PackageWarning{tabu}{% + Package cellspace has some limitations + \MessageBreak And redefines some macros of array.sty. + \MessageBreak Please use \string\tabulinesep\space to control + \MessageBreak vertical spacing of lines inside tabu environment}% +}% \tabu@warn@cellspace +%% tabu Package initialisation +\tabuscantokensfalse +\let\tabu@arc@G \relax +\let\tabu@drsc@G \relax +\let\tabu@evr@G \@empty +\let\tabu@rc@G \@empty +\def\tabu@ls@G {\tabu@linestyle@}% +\let\tabu@@rowfontreset \@empty % +\let\tabu@@celllalign \@empty +\let\tabu@@cellralign \@empty +\let\tabu@@cellleft \@empty +\let\tabu@@cellright \@empty +\def\tabu@naturalXmin {\z@} +\def\tabu@naturalXmax {\z@} +\let\tabu@rowfontreset \@empty +\def\tabulineon {4pt}\let\tabulineoff \tabulineon +\tabu@everyrowtrue +\ifdefined\pdfelapsedtime % + \def\tabu@pdftimer {\xdef\tabu@starttime{\the\pdfelapsedtime}}% +\else \let\tabu@pdftimer \relax \let\tabu@message@etime \relax +\fi +\tracingtabu=\z@ +\newtabulinestyle {=\maxdimen}% creates the 'factory' settings \tabu@linestyle@ +\tabulinestyle{} +\taburowcolors{} +\let\tabudefaulttarget \linewidth +\ProcessOptions* % \ProcessOptions* is quicker ! +\endinput +%% +%% End of file `tabu.sty'. diff --git a/rnn/enas_dag_node.hxx b/rnn/enas_dag_node.hxx index fdbd2af7..2adb9a42 100644 --- a/rnn/enas_dag_node.hxx +++ b/rnn/enas_dag_node.hxx @@ -23,27 +23,49 @@ using std::make_pair; class ENAS_DAG_Node : public RNN_Node_Interface{ private: - // starting node 0 + /** + * Weight of the start node in the memory cell. + */ double rw; + /** + * Weight of the start node in the memory cell. + */ double zw; - // weights for other nodes + + /** + * Weights of the subseqeuent nodes in topological order the memory cell. + */ vector weights; - // gradients of starting node 0 + /** + * Gradient of the weight of the start node in the memory cell. + */ vector d_zw; + /** + * Gradient of the weight of the start node in the memory cell. + */ vector d_rw; - // gradients of other nodes + /** + * Gradient of the weights of the other nodes in the memory cell + */ vector> d_weights; - // gradient of prev output + /** + * Gradient of the previous output i.e. t-1 in the memory cell. + */ vector d_h_prev; - // output of edge between node with weight wj from node with weight wi + /** + * Outputs of the nodes in the memory cell connecting with weight wj from node with weight wi. + */ vector> Nodes; - // derivative of edge between node with weight wj from node with weight wi + + /** + * Derivative of the nodes in the memory cell connecting with weight wj from node with weight wi. + */ vector> l_Nodes; @@ -59,10 +81,44 @@ class ENAS_DAG_Node : public RNN_Node_Interface{ void initialize_uniform_random(minstd_rand0 &generator, uniform_real_distribution &rng); + /** + * Gives the gradients in essence the deriviative of the weights in the memory cell in the network. + * + * \param gradient_name in the weight name. + * + * \return gradient. + */ double get_gradient(string gradient_name); + + /** + * Gives the gradients in essence the deriviative of the weights in the memory cell in the network. + * + * \param gradient_name in the weight name. + * + * prints the gradient. + */ void print_gradient(string gradient_name); + + /** + * Gives the activations of the node in the memory cell in the network. + * + * \param value is the output of the node after multiplying weights + * \param act_operator is the actiation type : sigmoid, tanh, swish, identity + * + * \return output of the node after applying activation + */ double activation(double value, int act_operator); + + /** + * Gives the activations derviative of the node in the memory cell in the network. + * + * \param input is the input of the node before multiplying weights + * \param value is the output of the node after multiplying weights + * \param act_operator is the actiation type : sigmoid, tanh, swish, identity + * + * \return output derivative of the node after applying activation + */ double activation_derivative(double value, double input, int act_operator); void input_fired(int time, double incoming_output); diff --git a/rnn/rnn.hxx b/rnn/rnn.hxx index 2501276b..17ed98df 100644 --- a/rnn/rnn.hxx +++ b/rnn/rnn.hxx @@ -17,7 +17,18 @@ using std::vector; class RNN { private: + + /** + * Specifies the length of the dataset. + * It can be the number of the files in case of time series or number of batches in case of word series + */ int series_length; + + /** + * Specifies whether to use the regression or not. + * It will use regression in case of time series and will not use regression in case of word series + */ + bool use_regression; vector input_nodes; @@ -44,10 +55,31 @@ class RNN { void forward_pass(const vector< vector > &series_data, bool using_dropout, bool training, double dropout_probability); void backward_pass(double error, bool using_dropout, bool training, double dropout_probability); + + /** + * Calculates the sum of cross entropy error by comparing the expected outputs and the predicted outputs. + * + * \param expected_outputs is the expected outputs according the dataset. + * + * \return sum of the cross entropy error. + */ double calculate_error_softmax(const vector< vector > &expected_outputs); double calculate_error_mse(const vector< vector > &expected_outputs); double calculate_error_mae(const vector< vector > &expected_outputs); + + + /** + * Calls the calculate_error_softmax after the forward pass across the rnn genome. + * + * \param series_data is the dataset used for the forward pass. + * \param expected_outputs is the expected outputs according to the dataset. + * \param using_dropout is the probability for the node being disabled while forward pass training and not during testing. + * \param training is to specify whether it is training stage or not to use dropout. + * \param dropout_probability is the probability by which the the nodes will be disabled. will not work during testing stage and without use_droput = true. + * + * \return sum of the cross entropy error over the dataset. + */ double prediction_softmax(const vector< vector > &series_data, const vector< vector > &expected_outputs, bool using_dropout, bool training, double dropout_probability); double prediction_mse(const vector< vector > &series_data, const vector< vector > &expected_outputs, bool using_dropout, bool training, double dropout_probability); double prediction_mae(const vector< vector > &series_data, const vector< vector > &expected_outputs, bool using_dropout, bool training, double dropout_probability); @@ -65,6 +97,20 @@ class RNN { uint32_t get_number_weights(); + + /** + * Updates the analytic gradients after the forward pass across the rnn genome and the backward pass sequentially. + * + * \param test_parameters is the dataset parameters used for the forward pass. + * \param inputs are the inputs according to the dataset that is pass to the for forward pass. + * \param outputs are the expected outputs according to the dataset that is pass to the calculate error for backward pass. + * \param analytic_gradients are the current gradients in the rnn genome that are going to be update after a single pass across the rnn genome. + * \param using_dropout is the probability for the node being disabled while forward pass training and not during testing. + * \param training is to specify whether it is training stage or not to use dropout. + * \param dropout_probability is the probability by which the the nodes will be disabled. will not work during testing stage and without use_droput = true. + * + * \return sum of the cross entropy error over the dataset. + */ void get_analytic_gradient(const vector &test_parameters, const vector< vector > &inputs, const vector< vector > &outputs, double &mse, vector &analytic_gradient, bool using_dropout, bool training, double dropout_probability); void get_empirical_gradient(const vector &test_parameters, const vector< vector > &inputs, const vector< vector > &outputs, double &mae, vector &empirical_gradient, bool using_dropout, bool training, double dropout_probability); diff --git a/rnn/rnn_genome.hxx b/rnn/rnn_genome.hxx index 761c4558..fb12b3f9 100644 --- a/rnn/rnn_genome.hxx +++ b/rnn/rnn_genome.hxx @@ -50,6 +50,11 @@ class RNN_Genome { bool use_low_norm; double low_threshold; + /** + * Specifies whether to use the regression or not. + * It will use regression in case of time series and will not use regression in case of word series + */ + bool use_regression; bool use_dropout; @@ -67,7 +72,11 @@ class RNN_Genome { vector initial_parameters; - + /** + * Specifies the validation error across the dataset. + * It will use mean squared error when regression in case of time series and cross entropy loss when regression not used in case of word series + */ + double best_validation_mse; double best_validation_mae; vector best_parameters; @@ -127,7 +136,16 @@ class RNN_Genome { int32_t get_enabled_node_count(); int32_t get_node_count(); + + /** + * Calculates the fitness of the rnn genome across the dataset. + * This will be the mean squared error in case of regression and cross entropy loss in case of classification. + * + * \return MSE or SOFTMAX ERROR. + */ double get_fitness() const; + + double get_best_validation_softmax() const; double get_best_validation_mse() const; double get_best_validation_mae() const; @@ -206,6 +224,16 @@ class RNN_Genome { void backpropagate_stochastic(const vector< vector< vector > > &inputs, const vector< vector< vector > > &outputs, const vector< vector< vector > > &validation_inputs, const vector< vector< vector > > &validation_outputs); + + /** + * Predicts the softmax error after a single pass across the rnn genome. + * + * \param parameters is the parameters such as dropout, momentum type used for the forward pass. + * \param inputs are the inputs according to the dataset that is pass to the for forward pass. + * \param outputs are the expected outputs according to the dataset that is pass to the calculate error for backward pass. + * + * \return average of the cross entropy error over the dataset. + */ double get_softmax(const vector ¶meters, const vector< vector< vector > > &inputs, const vector< vector< vector > > &outputs); double get_mse(const vector ¶meters, const vector< vector< vector > > &inputs, const vector< vector< vector > > &outputs); double get_mae(const vector ¶meters, const vector< vector< vector > > &inputs, const vector< vector< vector > > &outputs); @@ -213,6 +241,19 @@ class RNN_Genome { vector< vector > get_predictions(const vector ¶meters, const vector< vector< vector > > &inputs, const vector< vector< vector > > &outputs); void write_predictions(string output_directory, const vector &input_filenames, const vector ¶meters, const vector< vector< vector > > &inputs, const vector< vector< vector > > &outputs, TimeSeriesSets *time_series_sets); + + /** + * Predicts the cross entropy loss after a single pass across the rnn genome. + * + * \param output_directory is the file directory that contains all the output files. + * \param inputs_filenames are the inputs files according to the dataset that is pass to the for forward pass. + * \param inputs are the inputs according to the dataset that is pass to the for forward pass. + * \param outputs are the expected outputs according to the dataset that is pass to the calculate error for backward pass. + * \param parameters is the parameters such as dropout, momentum type used for the forward pass. + * \param word_series_sets is the Corpus from the word series dataset. + * + * Calls the write predictions from the rnn class. + */ void write_predictions(string output_directory, const vector &input_filenames, const vector ¶meters, const vector< vector< vector > > &inputs, const vector< vector< vector > > &outputs, Corpus * word_series_sets); void get_mu_sigma(const vector &p, double &mu, double &sigma); diff --git a/rnn/rnn_node_interface.hxx b/rnn/rnn_node_interface.hxx index a5d7c6f1..5b8ac873 100644 --- a/rnn/rnn_node_interface.hxx +++ b/rnn/rnn_node_interface.hxx @@ -20,35 +20,111 @@ using std::vector; class RNN; -#define INPUT_LAYER 0 -#define HIDDEN_LAYER 1 -#define OUTPUT_LAYER 2 +#define INPUT_LAYER 0 /**< Specifices the Input Layer of the rnn network. */ +#define HIDDEN_LAYER 1 /**< Specifices the Hidden Layer of the rnn network. */ +#define OUTPUT_LAYER 2 /**< Specifices the Output Layer of the rnn network. */ extern const int32_t NUMBER_NODE_TYPES; extern const string NODE_TYPES[]; -#define SIMPLE_NODE 0 -#define JORDAN_NODE 1 -#define ELMAN_NODE 2 -#define UGRNN_NODE 3 -#define MGU_NODE 4 -#define GRU_NODE 5 -#define DELTA_NODE 6 -#define LSTM_NODE 7 -#define ENARC_NODE 8 -#define ENAS_DAG_NODE 9 -#define RANDOM_DAG_NODE 10 +#define SIMPLE_NODE 0 /**< Specifices the rnn network to use the Simple Node as the memory cell. */ +#define JORDAN_NODE 1 /**< Specifices the rnn network to use the Jordan Node as the memory cell. */ +#define ELMAN_NODE 2 /**< Specifices the rnn network to use the Elman Node as the memory cell. */ +#define UGRNN_NODE 3 /**< Specifices the rnn network to use the UGRNN Node as the memory cell. */ +#define MGU_NODE 4 /**< Specifices the rnn network to use the MGU Node as the memory cell. */ +#define GRU_NODE 5 /**< Specifices the rnn network to use the GRU Node as the memory cell. */ +#define DELTA_NODE 6 /**< Specifices the rnn network to use the Delta Node as the memory cell. */ +#define LSTM_NODE 7 /**< Specifices the rnn network to use the LSTM Node as the memory cell. */ +#define ENARC_NODE 8 /**< Specifices the rnn network to use the ENARC Node as the memory cell. */ +#define ENAS_DAG_NODE 9 /**< Specifices the rnn network to use the ENAS_DAG Node as the memory cell. */ +#define RANDOM_DAG_NODE 10 /**< Specifices the rnn network to use the Random_DAG Node as the memory cell. */ + + + +/** + * Gives the sigmoid of the value to apply non-linearity in the network. + * + * \param value is the output of the node after multiplying with weights. + * + * \return sigmoid of the value + */ +double sigmoid(double value); +/** + * Gives the swish = (identity * sigmoid) of the value to apply non-linearity in the network. + * + * \param value is the output of the node after multiplying with weights. + * + * \return swish of the value + */ +double swish(double value); + +/** + * Gives the leakyReLU of the value to apply non-linearity in the network. + * + * \param value is the output of the node after multiplying with weights. + * + * \return LeakyReLU of the value + */ + +double leakyReLU(double value); + +/** + * Gives the identity of the value. Linearlty is preserved in this case. + * + * \param value is the output of the node after multiplying with weights. + * + * \return value + */ + +double identity(double value); + + +/** + * Gives the sigmoid derivative of the value used during the backward pass. + * + * \param value is the output of the node after multiplying with weights. + * + * \return sigmoid derivative of the value + */ -double sigmoid(double value); double sigmoid_derivative(double value); + +/** + * Gives the tanh derivative of the value used during the backward pass. + * + * \param value is the output of the node after multiplying with weights. + * + * \return tanh derivative of the value + */ + double tanh_derivative(double value); -double swish(double value); + +/** + * Gives the swish derivative of the value used during the backward pass. + * + * \param input is the input of the node before multiplying with weights. + * \param value is the output of the node after multiplying with weights. + * + * \return swish derivative + */ double swish_derivative(double value, double input); -double leakyReLU(double value); + +/** + * Gives the leakyReLU derivative of the value used during the backward pass. + * + * \param input is the output of the node after multiplying with weights. + * + * \return leakyReLU derivative of the input + */ double leakyReLU_derivative(double input); -double identity(double value); + +/** + * Gives the derivative of the identity used during the backward pass. + * + * \return unit + */ double identity_derivative(); diff --git a/temp b/temp deleted file mode 100644 index e69de29b..00000000 diff --git a/word_series/word_series.hxx b/word_series/word_series.hxx index ca244813..fe9953e4 100644 --- a/word_series/word_series.hxx +++ b/word_series/word_series.hxx @@ -16,17 +16,47 @@ using std::vector; class WordSeries{ private: - string name; - int vocab_size; + /** + * Specifies the string name in the data. + * All the unique strings in the datasets will have its each word series. + */ + string name; + /** + * Specifies the number of the unique strings in the dataset. + */ + int vocab_size; + + /** + * Specifies the minimum of the word series in a particular string. + * This could be 0 or 1 + */ double min; + /** + * Specifies the average of the word series in a particular string. + * This tells about the words which are used the most. + */ double average; + /** + * Specifies the maximum of the word series in a particular string. + * This could be 0 or 1 + */ double max; + /** + * Specifies the std. deviation of the word series in a particular string. + */ double std_dev; + /** + * Specifies the variance of the word series in a particular string. + * This tells about the closeness of a word in the dataset. + */ double variance; double min_change; double max_change; + /** + * Storers the values in the word series respective of the time in a particular string. + */ vector values; WordSeries(); @@ -65,10 +95,27 @@ using std::vector; class SentenceSeries { private: + + /** + * Specifies the number of the rows in the dataset. + * This tells about the number of the unique words in the dataset. + */ int number_rows; + /** + * Specifices the filename for the training or testing data. + */ string filename; + /** + * Maps the word string to index of the word series in a particular string. + */ map vocab; + /** + * Maps the index to word string of the word series in a particular string. + */ vector word_index; + /** + * Maps the word string to the word series in a particular string. + */ map word_series; @@ -117,18 +164,47 @@ class Corpus { private: + /** + * Specifices the batches in which the dataset is to be divided into chunks. + */ uint32_t batch_size; string normalize_type; + /** + * Stores all the filenames including training and testing + */ vector filenames; + /** + * stores the training index of the filenames. + */ vector training_indexes; + + /** + * stores the testing index of the filenames. + */ vector test_indexes; + /** + * stores the input parameter names of the filenames. + * stores the unique words used in the filenames. + */ vector input_parameter_names; + /** + * stores the input parameter names of the filenames. + * stores the unique words used in the filenames. + */ vector output_parameter_names; + /** + * stores the input parameter names of the filenames. + * stores the unique words used in the filenames. + */ vector all_parameter_names; + /** + * stores the Sentence series used in the filenames. + * Each file is divided into sentences and word series is made for each word in the sentence. + */ vector sent_series; map normalize_mins; @@ -137,9 +213,20 @@ class Corpus { map normalize_avgs; map normalize_std_devs; - vector word_index; - map vocab; + /** + * Maps the index to word string of the word series in a particular filename. + */ + vector word_index; + + /** + * Maps the word string to the word series in a particular filename. + */ + map vocab; + + /** + * Loads the filenames, word index and the vocabulary of the files. + */ void load_word_library(); @@ -157,12 +244,36 @@ class Corpus { void normalize_avg_std_dev(); void normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs); + + /** + * Generates the csv data file. + * \param base_filename is the filename to be used. + * + */ void write_sentence_series_sets(string base_filename); void export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); + /** + * Exports Corpus Dataset to be used for training in RNN genome. + * + * \param series_indexes are the indexes of the input parameters to be used in the dataset + * \param word_offset is used to predict the after how many words, the predictions to be done in the future. + * \param inputs is the data used for training in the RNN genome + * \param outpus is the data used for training in the RNN genome + * + */ void export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); + /** + * Exports Corpus Dataset to be used for testing in RNN genome. + * + * \param series_indexes are the indexes of the input parameters to be used in the dataset + * \param word_offset is used to predict the after how many words, the predictions to be done in the future. + * \param inputs is the data used for testing in the RNN genome + * \param outpus is the data used for testing in the RNN genome + * + */ void export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); void export_series_by_name(string field_name, vector< vector > &exported_series); From c5f25fad0ff7c90792f3de7c96b97ed55f356919 Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Mon, 19 Oct 2020 11:01:46 -0400 Subject: [PATCH 04/25] Readme updated --- README.md | 62 +++ images/arch.png | Bin 0 -> 64236 bytes images/domain.png | Bin 0 -> 71265 bytes images/result_comp.png | Bin 0 -> 75898 bytes images/result_enarc.png | Bin 0 -> 68076 bytes main_out | 1114 +++++++++++++++++++++++++++++++++++++++ 6 files changed, 1176 insertions(+) create mode 100644 images/arch.png create mode 100644 images/domain.png create mode 100644 images/result_comp.png create mode 100644 images/result_enarc.png create mode 100644 main_out diff --git a/README.md b/README.md index f9964fb0..0ce35363 100644 --- a/README.md +++ b/README.md @@ -27,6 +27,68 @@ You may also want to have graphviz installed so you can generate images of the e $ dot -Tpdf genome.gv -o genome.pdf ``` +# EXAMM: Neural Architectural Search using Parameter Sharing and Augmenting Memory Models + +The datasets used for this part of the code are PennChar, PennTreeBank and WikiText. These datasets can be found in the *datasets* directory. These datasets can be used in both mpi as well as multithreaded version. + +### To use Pennchar as dataset and use Simple and ENARC as memory cell + + +``` +/multithreaded/examm_nlp --number_threads 9 --training_filenames ../datasets/pennchar/train.txt --test_filenames ../datasets/pennchar/test.txt --word_offset 1 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info + +``` + +And a parallel version using MPI: + +``` +~/exact/build/ $ mpirun -np 9 ./mpi/examm_mpi_nlp --training_filenames ../datasets/pennchar/train.txt --test_filenames ../datasets/pennchar/test.txt --word_offset 1 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info + +``` + +### To use PennTreeBank Word Prediction as dataset and use Simple and ENARC as memory cell + + +``` +/multithreaded/examm_nlp --number_threads 9 --training_filenames ../datasets/penn/train.txt --test_filenames ../datasets/penn/test.txt --word_offset 1 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC--normalize min_max --std_message_level info --file_message_level info + +``` + +And a parallel version using MPI: + +``` +~/exact/build/ $ mpirun -np 9 ./mpi/examm_mpi_nlp --training_filenames ../datasets/pennchar/train.txt --test_filenames ../datasets/pennchar/test.txt --word_offset 1 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info + +``` + +The std_out can be seen in file *main_out* for 20 genomes. The number of genomes can be increase. For ideal results, use 20000 genomes. + + +### To use ENARC as Memory Cell on Coal Dataset + + +``` +./multithreaded/examm_mt --number_threads 9 --training_filenames ../datasets/2018_coal/burner_[0-9].csv --test_filenames ../datasets/2018_coal/burner_1[0-1].csv --time_offset 1 --input_parameter_names Conditioner_Inlet_Temp Conditioner_Outlet_Temp Coal_Feeder_Rate Primary_Air_Flow Primary_Air_Split System_Secondary_Air_Flow_Total Secondary_Air_Flow Secondary_Air_Split Tertiary_Air_Split Total_Comb_Air_Flow Supp_Fuel_Flow Main_Flm_Int --output_parameter_names Main_Flm_Int --number_islands 10 --population_size 10 --max_genomes 2000 --bp_iterations 10 --output_directory "./test_output" --possible_node_types simple ENARC --std_message_level INFO --file_message_level INFO +``` + +![Results on Coal using ENARC cell](https://github.com/travisdesell/exact/tree/enarc/images/result_enarc.png) + +![Results Comparison](https://github.com/travisdesell/exact/tree/enarc/images/result_comp.png) + +![Results Comparison](https://github.com/travisdesell/exact/tree/enarc/images/result_comp.png) + +## To visualize the ENARC memory cell, it in *images* directory as Node.pdf + +### Domain Diagram + +![Domain Diagram](https://github.com/travisdesell/exact/tree/enarc/images/domain.png) + +### Architecture Diagram + +![Architecture Diagram](https://github.com/travisdesell/exact/tree/enarc/images/arch.png) + + + # EXAMM: Evolutionary eXploration of Augmenting Memory Models and EXALT: Evolutionary eXploration of Augmenting LSTM Topologies Source code for EXALT/EXAMM can be found in the rnn subdirectory. EXALT has been enhanced with the ability to utilize more recurrent memory cells and has been renamed EXAMM. The memory cells currently implemented are Delta-RNN, GRU, LSTM, MGU, and UGRNNs. 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-1.000000, max_change: 1.000000, std_dev: 0.110508, variance: 0.012212 +[INFO main ] c stats, min: 0.000000, avg: 0.024202, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.153715, variance: 0.023628 +[INFO main ] d stats, min: 0.000000, avg: 0.026777, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.161471, variance: 0.026073 +[INFO main ] e stats, min: 0.000000, avg: 0.096807, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.295772, variance: 0.087481 +[INFO main ] f stats, min: 0.000000, avg: 0.018538, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.134920, variance: 0.018203 +[INFO main ] g stats, min: 0.000000, avg: 0.015448, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.123358, variance: 0.015217 +[INFO main ] h stats, min: 0.000000, avg: 0.029351, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.168832, variance: 0.028504 +[INFO main ] i stats, min: 0.000000, avg: 0.049949, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.217895, variance: 0.047478 +[INFO main ] j stats, min: 0.000000, avg: 0.001545, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.039284, variance: 0.001543 +[INFO main ] k stats, min: 0.000000, avg: 0.019567, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.138544, variance: 0.019194 +[INFO main ] l stats, min: 0.000000, avg: 0.023687, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.152111, variance: 0.023138 +[INFO main ] m stats, min: 0.000000, avg: 0.018538, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.134920, variance: 0.018203 +[INFO main ] n stats, min: 0.000000, avg: 0.062307, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.241774, variance: 0.058455 +[INFO main ] o stats, min: 0.000000, avg: 0.060247, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.238005, variance: 0.056647 +[INFO main ] p stats, min: 0.000000, avg: 0.016478, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.127337, variance: 0.016215 +[INFO main ] q stats, min: 0.000000, avg: 0.001030, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.032083, variance: 0.001029 +[INFO main ] r stats, min: 0.000000, avg: 0.061792, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.240839, variance: 0.058004 +[INFO main ] s stats, min: 0.000000, avg: 0.060247, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.238005, variance: 0.056647 +[INFO main ] t stats, min: 0.000000, avg: 0.066426, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.249090, variance: 0.062046 +[INFO main ] u stats, min: 0.000000, avg: 0.031411, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.174470, variance: 0.030440 +[INFO main ] v stats, min: 0.000000, avg: 0.005664, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.075067, variance: 0.005635 +[INFO main ] w stats, min: 0.000000, avg: 0.012873, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.112757, variance: 0.012714 +[INFO main ] x stats, min: 0.000000, avg: 0.002575, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.050689, variance: 0.002569 +[INFO main ] y stats, min: 0.000000, avg: 0.012358, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.110508, variance: 0.012212 +[INFO main ] z stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 +[INFO main ] read time series '../datasets/pennchar/input.txt' with number rows: 1942 +[INFO main ] ' stats, min: 0.000000, avg: 0.001545, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.039284, variance: 0.001543 +[INFO main ] - stats, min: 0.000000, avg: 0.001545, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.039284, variance: 0.001543 +[INFO main ] . stats, min: 0.000000, avg: 0.004119, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.064067, variance: 0.004105 +[INFO main ] 0 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 +[INFO main ] 1 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 +[INFO main ] 5 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 +[INFO main ] 9 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 +[INFO main ] < stats, min: 0.000000, avg: 0.009784, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.098453, variance: 0.009693 +[INFO main ] stats, min: 0.000000, avg: 0.008239, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.090417, variance: 0.008175 +[INFO main ] > stats, min: 0.000000, avg: 0.009784, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.098453, variance: 0.009693 +[INFO main ] N stats, min: 0.000000, avg: 0.004634, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.067936, variance: 0.004615 +[INFO main ] _ stats, min: 0.000000, avg: 0.162204, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.368733, variance: 0.135964 +[INFO main ] a stats, min: 0.000000, avg: 0.065396, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.247288, variance: 0.061151 +[INFO main ] b stats, min: 0.000000, avg: 0.012358, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.110508, variance: 0.012212 +[INFO main ] c stats, min: 0.000000, avg: 0.024202, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.153715, variance: 0.023628 +[INFO main ] d stats, min: 0.000000, avg: 0.026777, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.161471, variance: 0.026073 +[INFO main ] e stats, min: 0.000000, avg: 0.096807, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.295772, variance: 0.087481 +[INFO main ] f stats, min: 0.000000, avg: 0.018538, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.134920, variance: 0.018203 +[INFO main ] g stats, min: 0.000000, avg: 0.015448, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.123358, variance: 0.015217 +[INFO main ] h stats, min: 0.000000, avg: 0.029351, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.168832, variance: 0.028504 +[INFO main ] i stats, min: 0.000000, avg: 0.049949, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.217895, variance: 0.047478 +[INFO main ] j stats, min: 0.000000, avg: 0.001545, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.039284, variance: 0.001543 +[INFO main ] k stats, min: 0.000000, avg: 0.019567, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.138544, variance: 0.019194 +[INFO main ] l stats, min: 0.000000, avg: 0.023687, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.152111, variance: 0.023138 +[INFO main ] m stats, min: 0.000000, avg: 0.018538, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.134920, variance: 0.018203 +[INFO main ] n stats, min: 0.000000, avg: 0.062307, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.241774, variance: 0.058455 +[INFO main ] o stats, min: 0.000000, avg: 0.060247, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.238005, variance: 0.056647 +[INFO main ] p stats, min: 0.000000, avg: 0.016478, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.127337, variance: 0.016215 +[INFO main ] q stats, min: 0.000000, avg: 0.001030, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.032083, variance: 0.001029 +[INFO main ] r stats, min: 0.000000, avg: 0.061792, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.240839, variance: 0.058004 +[INFO main ] s stats, min: 0.000000, avg: 0.060247, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.238005, variance: 0.056647 +[INFO main ] t stats, min: 0.000000, avg: 0.066426, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.249090, variance: 0.062046 +[INFO main ] u stats, min: 0.000000, avg: 0.031411, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.174470, variance: 0.030440 +[INFO main ] v stats, min: 0.000000, avg: 0.005664, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.075067, variance: 0.005635 +[INFO main ] w stats, min: 0.000000, avg: 0.012873, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.112757, variance: 0.012714 +[INFO main ] x stats, min: 0.000000, avg: 0.002575, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.050689, variance: 0.002569 +[INFO main ] y stats, min: 0.000000, avg: 0.012358, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.110508, variance: 0.012212 +[INFO main ] z stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 +[INFO main ] read time series '../datasets/pennchar/input.txt' with number rows: 1942 +word_offset:: -1 +training +word_offset:: 1 +testing +486 +486 38 4 +486 +486 38 4 +word_offset:: -1 +training +word_offset:: 1 +testing +486 +486 38 4 +486 +486 38 4 +[INFO main ] exported word series. +[INFO main ] The size of the input is :: 486,38,4 +[INFO main ] number_inputs: 38, number_outputs: 38 +[INFO main ] weight initialize: random +[INFO main ] weight inheritance: lamarckian +[INFO main ] mutated component weight: lamarckian +[INFO main ] Speciation method is: "" (Default is the island-based speciation strategy). +[INFO main ] generating new genome for island[0], island_size: 0, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is initializing! +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: UNEVAL to island 0 +[INFO main ] created copy to insert to island: 0 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f52a02cfac8 +[INFO main ] new best fitness for island: 0! +[INFO main ] genomes.size(): 1, max_size: 2, status: 0 +[INFO main ] generating new genome for island[0], island_size: 1, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is initializing! +[INFO main ] island is not empty, mutating a random genome +[INFO main ] generating new genome by mutation. +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: UNEVAL to island 0 +[INFO main ] potential duplicate for hash '2926_1043290_0', had 1 potential matches. +[INFO main ] on potential match 0 of 1 +[INFO main ] created copy to insert to island: 0 +[INFO main ] inserting genome at index: 1 +[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f529f2cdac8 +[INFO main ] genomes.size(): 2, max_size: 2, status: 1 +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] attempting to merge a node! +[INFO main ] attempting to disable a node! +[INFO main ] disabling node 6 at depth 0.000000 +[INFO main ] generating new genome for island[1], island_size: 0, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is initializing! +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: UNEVAL to island 1 +[INFO main ] created copy to insert to island: 1 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f5296accac8 +[INFO main ] new best fitness for island: 1! +[INFO main ] genomes.size(): 1, max_size: 2, status: 0 +[INFO main ] generating new genome for island[1], island_size: 1, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is initializing! +[INFO main ] island is not empty, mutating a random genome +[INFO main ] generating new genome by mutation. +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: UNEVAL to island 1 +[INFO main ] created copy to insert to island: 1 +[INFO main ] inserting genome at index: 1 +[INFO main ] adding to structure_map[2926_1041910_0] : 0x7f529eaccac8 +[INFO main ] genomes.size(): 2, max_size: 2, status: 1 +[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] attempting to add edge! +[INFO main ] reachable_nodes.size(): 76 +[INFO main ] selected first node 7 with depth 845 +[INFO main ] reachable_nodes.size(): 38 +[INFO main ] selected second node 48 with depth 4628 +[INFO main ] adding edge between nodes 7 and 48 +[INFO main ] edge already exists, not adding. +[INFO main ] attempting to disable a node! +[INFO main ] disabling node 26 at depth 0.000000 +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] attempting to add a node! +[INFO main ] add node, split depth: 0.201243, max_inputs: 21, max_outputs: 1 +[INFO main ] add node recurrent probability: 0.000000 +[INFO main ] CREATING NODE, type: 'simple' +[INFO main ] adding edge between nodes 9 and 78 +[INFO main ] adding edge between nodes 9 and 78, new edge weight: -0.156685 +[INFO main ] adding edge between nodes 30 and 78 +[INFO main ] adding edge between nodes 30 and 78, new edge weight: -0.152126 +[INFO main ] adding edge between nodes 20 and 78 +[INFO main ] adding edge between nodes 20 and 78, new edge weight: 0.040005 +[INFO main ] adding edge between nodes 3 and 78 +[INFO main ] adding edge between nodes 3 and 78, new edge weight: 0.366074 +[INFO main ] adding edge between nodes 4 and 78 +[INFO main ] adding edge between nodes 4 and 78, new edge weight: -0.179001 +[INFO main ] adding edge between nodes 6 and 78 +[INFO main ] adding edge between nodes 6 and 78, new edge weight: -0.483166 +[INFO main ] adding edge between nodes 38 and 78 +[INFO main ] adding edge between nodes 38 and 78, new edge weight: 0.472341 +[INFO main ] adding edge between nodes 14 and 78 +[INFO main ] adding edge between nodes 14 and 78, new edge weight: -0.011813 +[INFO main ] adding edge between nodes 17 and 78 +[INFO main ] adding edge between nodes 17 and 78, new edge weight: 0.147622 +[INFO main ] adding edge between nodes 18 and 78 +[INFO main ] adding edge between nodes 18 and 78, new edge weight: 0.224630 +[INFO main ] adding edge between nodes 1 and 78 +[INFO main ] adding edge between nodes 1 and 78, new edge weight: 0.134234 +[INFO main ] adding edge between nodes 22 and 78 +[INFO main ] adding edge between nodes 22 and 78, new edge weight: 0.006210 +[INFO main ] adding edge between nodes 23 and 78 +[INFO main ] adding edge between nodes 23 and 78, new edge weight: 0.244998 +[INFO main ] adding edge between nodes 25 and 78 +[INFO main ] adding edge between nodes 25 and 78, new edge weight: -0.243366 +[INFO main ] adding edge between nodes 28 and 78 +[INFO main ] adding edge between nodes 28 and 78, new edge weight: -0.387377 +[INFO main ] adding edge between nodes 31 and 78 +[INFO main ] adding edge between nodes 31 and 78, new edge weight: 0.263492 +[INFO main ] adding edge between nodes 32 and 78 +[INFO main ] adding edge between nodes 32 and 78, new edge weight: -0.058932 +[INFO main ] adding edge between nodes 34 and 78 +[INFO main ] adding edge between nodes 34 and 78, new edge weight: -0.080912 +[INFO main ] adding edge between nodes 36 and 78 +[INFO main ] adding edge between nodes 36 and 78, new edge weight: 0.267297 +[INFO main ] adding edge between nodes 37 and 78 +[INFO main ] adding edge between nodes 37 and 78, new edge weight: -0.383394 +[INFO main ] adding edge between nodes 19 and 78 +[INFO main ] adding edge between nodes 19 and 78, new edge weight: -0.025244 +[INFO main ] adding edge between nodes 78 and 60 +[INFO main ] adding edge between nodes 78 and 60, new edge weight: -0.278417 +[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing intra-island crossover +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing intra-island crossover +[INFO genome_4_thread_3 ] iteration 0, mse: 7733.529611, v_mse: 15.912612, bv_mse: 15.912612, lr: 0.000002, norm: 1686668.297044, p_norm: 127.667555, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000013 +[INFO genome_7_thread_4 ] iteration 0, mse: 7326.705177, v_mse: 15.075525, bv_mse: 15.075525, lr: 0.000002, norm: 1689712.887290, p_norm: 128.151778, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000013 +[INFO genome_6_thread_2 ] iteration 0, mse: 7340.652115, v_mse: 15.104222, bv_mse: 15.104222, lr: 0.000002, norm: 1557641.165275, p_norm: 128.151778, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000014 +[INFO genome_9_thread_5 ] iteration 0, mse: 7386.622085, v_mse: 15.198811, bv_mse: 15.198811, lr: 0.000002, norm: 1779943.389892, p_norm: 130.303026, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000012 +[INFO genome_2_thread_1 ] iteration 0, mse: 7326.705177, v_mse: 15.075525, bv_mse: 15.075525, lr: 0.000002, norm: 1689712.887290, p_norm: 128.151778, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000013 +[INFO genome_12_thread_7 ] iteration 0, mse: 7363.670893, v_mse: 15.151586, bv_mse: 15.151586, lr: 0.000002, norm: 1540645.237973, p_norm: 129.563464, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000014 +[INFO genome_14_thread_0 ] iteration 0, mse: 7238.272640, v_mse: 14.893565, bv_mse: 14.893565, lr: 0.000002, norm: 1706117.844996, p_norm: 153.730517, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000013 +[INFO genome_4_thread_3 ] iteration 1, mse: 6298.940161, v_mse: 12.960782, bv_mse: 12.960782, lr: 0.000002, norm: 606591.629021, p_norm: 158.816187, v_norm: 3.470511, OVER THRESHOLD, multiplier: 0.000036 +[INFO genome_11_thread_6 ] iteration 0, mse: 7098.992183, v_mse: 14.606980, bv_mse: 14.606980, lr: 0.000002, norm: 1522644.952689, p_norm: 125.784153, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000014 +[INFO genome_13_thread_8 ] iteration 0, mse: 6802.457521, v_mse: 13.996826, bv_mse: 13.996826, lr: 0.000002, norm: 1200348.173889, p_norm: 148.500648, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000018 +[INFO genome_7_thread_4 ] iteration 1, mse: 6327.792787, v_mse: 13.020150, bv_mse: 13.020150, lr: 0.000002, norm: 613568.932167, p_norm: 162.464627, v_norm: 3.476775, OVER THRESHOLD, multiplier: 0.000036 +[INFO genome_6_thread_2 ] iteration 1, mse: 6330.032413, v_mse: 13.024758, bv_mse: 13.024758, lr: 0.000002, norm: 539654.215121, p_norm: 156.808846, v_norm: 3.205023[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 12.960782 to island 0 +[INFO main ] potential duplicate for hash '2926_1043290_0', had 2 potential matches. +[INFO main ] on potential match 0 of 2 +[INFO main ] on potential match 1 of 2 +[INFO main ] created copy to insert to island: 0 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f529f2cdb48 +[INFO main ] new best fitness for island: 0! +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52900d1ad0, &(*potential_match): 0x7f52980d1ad0 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52900d1ad0, &(*potential_match): 0x7f52900d1ad0 +[INFO main ] potential_matches.size() before erase: 3 +[INFO main ] potential_matches.size() after erase: 2 +[INFO main ] structure_map[2926_1043290_0].size() after erase: 2 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52900d1ad0, &(*potential_match): 0x7f521fde8970 +, OVER THRESHOLD, multiplier: 0.000041 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] UNEVAL UNEVAL 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] UNEVAL UNEVAL 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] UNEVAL UNEVAL 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing intra-island crossover +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 13.020150 to island 1 +[INFO main ] potential duplicate for hash '2926_1043290_0', had 1 potential matches. +[INFO main ] on potential match 0 of 1 +[INFO main ] REPLACING DUPLICATE GENOME, fitness of genome in search: UNEVAL, new fitness: 13.020150 +[INFO main ] duplicate_genome_iterator: 0x7f528c08a700, (*potential_match): 0x7f528c08a700 +[INFO main ] potential_match->get_fitness(): 10000000.000000, duplicate_genome_iterator->get_fitness(): 10000000.000000, new_fitness: 13.020150 +[INFO main ] duplicate_genome_index: 0 +[INFO main ] potential_matches.size() before erase: 1 +[INFO main ] potential_matches.size() after erase: 0 +[INFO main ] structure_map[2926_1043290_0].size() after erase: 0 +[INFO main ] deleting the potential_matches vector for hash '2926_1043290_0' because it was empty. +[INFO genome_9_thread_5 ] iteration 1, mse: 6232.487039, v_mse: 12.824047, bv_mse: 12.824047, lr: 0.000002, norm: 527931.122526, p_norm: 170.004606, v_norm: 3.662435, OVER THRESHOLD, multiplier: 0.000042 +[INFO main ] created copy to insert to island: 1 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f5296accb48 +[INFO main ] new best fitness for island: 1! +[INFO main ] genomes.size(): 2, max_size: 2, status: 1 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] UNEVAL UNEVAL 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] UNEVAL UNEVAL 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] attempting to add a node! +[INFO main ] add node, split depth: 0.560641, max_inputs: 21, max_outputs: 8 +[INFO main ] add node recurrent probability: 0.000000 +[INFO main ] CREATING NODE, type: 'simple' +[INFO main ] adding edge between nodes 9 and 79 +[INFO main ] adding edge between nodes 9 and 79, new edge weight: -0.035635 +[INFO main ] adding edge between nodes 11 and 79 +[INFO main ] adding edge between nodes 11 and 79, new edge weight: -0.262195 +[INFO main ] adding edge between nodes 30 and 79 +[INFO main ] adding edge between nodes 30 and 79, new edge weight: -0.310779 +[INFO main ] adding edge between nodes 20 and 79 +[INFO main ] adding edge between nodes 20 and 79, new edge weight: 0.308397 +[INFO main ] adding edge between nodes 3 and 79 +[INFO main ] adding edge between nodes 3 and 79, new edge weight: -0.131418 +[INFO main ] adding edge between nodes 8 and 79 +[INFO main ] adding edge between nodes 8 and 79, new edge weight: -0.505568 +[INFO main ] adding edge between nodes 38 and 79 +[INFO main ] adding edge between nodes 38 and 79, new edge weight: -0.020145 +[INFO main ] adding edge between nodes 16 and 79 +[INFO main ] adding edge between nodes 16 and 79, new edge weight: 0.221037 +[INFO main ] adding edge between nodes 17 and 79 +[INFO main ] adding edge between nodes 17 and 79, new edge weight: -0.243482 +[INFO main ] adding edge between nodes 18 and 79 +[INFO main ] adding edge between nodes 18 and 79, new edge weight: 0.110130 +[INFO main ] adding edge between nodes 21 and 79 +[INFO main ] adding edge between nodes 21 and 79, new edge weight: -0.159293 +[INFO main ] adding edge between nodes 29 and 79 +[INFO main ] adding edge between nodes 29 and 79, new edge weight: 0.361482 +[INFO main ] adding edge between nodes 10 and 79 +[INFO main ] adding edge between nodes 10 and 79, new edge weight: 0.113179 +[INFO main ] adding edge between nodes 1 and 79 +[INFO main ] adding edge between nodes 1 and 79, new edge weight: -0.250825 +[INFO main ] adding edge between nodes 22 and 79 +[INFO main ] adding edge between nodes 22 and 79, new edge weight: -0.022429 +[INFO main ] adding edge between nodes 24 and 79 +[INFO main ] adding edge between nodes 24 and 79, new edge weight: -0.191066 +[INFO main ] adding edge between nodes 26 and 79 +[INFO main ] adding edge between nodes 26 and 79, new edge weight: -0.449681 +[INFO main ] adding edge between nodes 28 and 79 +[INFO main ] adding edge between nodes 28 and 79, new edge weight: 0.247844 +[INFO main ] adding edge between nodes 31 and 79 +[INFO main ] adding edge between nodes 31 and 79, new edge weight: -0.409950 +[INFO main ] adding edge between nodes 32 and 79 +[INFO main ] adding edge between nodes 32 and 79, new edge weight: -0.033563 +[INFO main ] adding edge between nodes 37 and 79 +[INFO main ] adding edge between nodes 37 and 79, new edge weight: 0.562051 +[INFO main ] adding edge between nodes 79 and 42 +[INFO main ] adding edge between nodes 79 and 42, new edge weight: 0.079454 +[INFO main ] adding edge between nodes 79 and 47 +[INFO main ] adding edge between nodes 79 and 47, new edge weight: -0.221006 +[INFO main ] adding edge between nodes 79 and 51 +[INFO main ] adding edge between nodes 79 and 51, new edge weight: 0.098147 +[INFO main ] adding edge between nodes 79 and 52 +[INFO main ] adding edge between nodes 79 and 52, new edge weight: -0.286536 +[INFO main ] adding edge between nodes 79 and 54 +[INFO main ] adding edge between nodes 79 and 54, new edge weight: 0.050287 +[INFO main ] adding edge between nodes 79 and 62 +[INFO main ] adding edge between nodes 79 and 62, new edge weight: 0.677715 +[INFO main ] adding edge between nodes 79 and 64 +[INFO main ] adding edge between nodes 79 and 64, new edge weight: 0.669388 +[INFO main ] adding edge between nodes 79 and 69 +[INFO main ] adding edge between nodes 79 and 69, new edge weight: -0.773083 +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 13.024758 to island 0 +[INFO main ] created copy to insert to island: 0 +[INFO main ] inserting genome at index: 1 +[INFO main ] adding to structure_map[2920_1016348_0] : 0x7f529faceb48 +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52980d1ad0, &(*potential_match): 0x7f52980d1ad0 +[INFO main ] potential_matches.size() before erase: 2 +[INFO main ] potential_matches.size() after erase: 1 +[INFO main ] structure_map[2926_1043290_0].size() after erase: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52980d1ad0, &(*potential_match): 0x7f521fde8970 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] 13.024758 23.115156 1444 (1444) 0 (0) 0 (76) 75 (76) [disable_node:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] UNEVAL UNEVAL 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing intra-island crossover +[INFO genome_2_thread_1 ] iteration 1, mse: 6327.792787, v_mse: 13.020150, bv_mse: 13.020150, lr: 0.000002, norm: 613568.932167, p_norm: 162.464627, v_norm: 3.476775, OVER THRESHOLD, multiplier: 0.000036 +[INFO genome_12_thread_7 ] iteration 1, mse: 6330.648142, v_mse: 13.026025, bv_mse: 13.026025, lr: 0.000002, norm: 543161.125093, p_norm: 156.190941, v_norm: 3.170052, OVER THRESHOLD, multiplier: 0.000041 +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.824047 to island 1 +[INFO main ] potential duplicate for hash '2926_1041910_0', had 1 potential matches. +[INFO main ] on potential match 0 of 1 +[INFO main ] created copy to insert to island: 1 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[2926_1041910_0] : 0x7f529eaccb48 +[INFO main ] new best fitness for island: 1! +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52800d1ad0, &(*potential_match): 0x7f52800d1ad0 +[INFO main ] potential_matches.size() before erase: 2 +[INFO main ] potential_matches.size() after erase: 1 +[INFO main ] structure_map[2926_1041910_0].size() after erase: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52800d1ad0, &(*potential_match): 0x7f50686cc090 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] 13.024758 23.115156 1444 (1444) 0 (0) 0 (76) 75 (76) [disable_node:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] attempting to add a node! +[INFO main ] add node, split depth: 0.849617, max_inputs: 21, max_outputs: 3 +[INFO main ] add node recurrent probability: 0.000000 +[INFO main ] CREATING NODE, type: 'simple' +[INFO main ] adding edge between nodes 21 and 80 +[INFO main ] adding edge between nodes 21 and 80, new edge weight: -0.246043 +[INFO main ] adding edge between nodes 30 and 80 +[INFO main ] adding edge between nodes 30 and 80, new edge weight: 0.030043 +[INFO main ] adding edge between nodes 3 and 80 +[INFO main ] adding edge between nodes 3 and 80, new edge weight: -0.034008 +[INFO main ] adding edge between nodes 4 and 80 +[INFO main ] adding edge between nodes 4 and 80, new edge weight: 0.369791 +[INFO main ] adding edge between nodes 5 and 80 +[INFO main ] adding edge between nodes 5 and 80, new edge weight: -0.058420 +[INFO main ] adding edge between nodes 2 and 80 +[INFO main ] adding edge between nodes 2 and 80, new edge weight: -0.649818 +[INFO main ] adding edge between nodes 13 and 80 +[INFO main ] adding edge between nodes 13 and 80, new edge weight: -0.206339 +[INFO main ] adding edge between nodes 15 and 80 +[INFO main ] adding edge between nodes 15 and 80, new edge weight: -0.502198 +[INFO main ] adding edge between nodes 18 and 80 +[INFO main ] adding edge between nodes 18 and 80, new edge weight: -0.023026 +[INFO main ] adding edge between nodes 28 and 80 +[INFO main ] adding edge between nodes 28 and 80, new edge weight: -0.183560 +[INFO main ] adding edge between nodes 9 and 80 +[INFO main ] adding edge between nodes 9 and 80, new edge weight: 0.632474 +[INFO main ] adding edge between nodes 10 and 80 +[INFO main ] adding edge between nodes 10 and 80, new edge weight: -0.149075 +[INFO main ] adding edge between nodes 1 and 80 +[INFO main ] adding edge between nodes 1 and 80, new edge weight: 0.121135 +[INFO main ] adding edge between nodes 23 and 80 +[INFO main ] adding edge between nodes 23 and 80, new edge weight: -0.379970 +[INFO main ] adding edge between nodes 24 and 80 +[INFO main ] adding edge between nodes 24 and 80, new edge weight: 0.192683 +[INFO main ] adding edge between nodes 31 and 80 +[INFO main ] adding edge between nodes 31 and 80, new edge weight: -0.150028 +[INFO main ] adding edge between nodes 32 and 80 +[INFO main ] adding edge between nodes 32 and 80, new edge weight: -0.734131 +[INFO main ] adding edge between nodes 34 and 80 +[INFO main ] adding edge between nodes 34 and 80, new edge weight: 0.018146 +[INFO main ] adding edge between nodes 35 and 80 +[INFO main ] adding edge between nodes 35 and 80, new edge weight: 0.431549 +[INFO main ] adding edge between nodes 36 and 80 +[INFO main ] adding edge between nodes 36 and 80, new edge weight: 0.324393 +[INFO main ] adding edge between nodes 11 and 80 +[INFO main ] adding edge between nodes 11 and 80, new edge weight: -0.172874 +[INFO main ] adding edge between nodes 80 and 42 +[INFO main ] adding edge between nodes 80 and 42, new edge weight: -0.193806 +[INFO main ] adding edge between nodes 80 and 52 +[INFO main ] adding edge between nodes 80 and 52, new edge weight: 0.996493 +[INFO main ] adding edge between nodes 80 and 75 +[INFO main ] adding edge between nodes 80 and 75, new edge weight: -0.219244 +[INFO genome_14_thread_0 ] iteration 1, mse: 6264.922622, v_mse: 12.890787, bv_mse: 12.890787, lr: 0.000002, norm: 447479.521429, p_norm: 193.518512, v_norm: 3.510531, OVER THRESHOLD, multiplier: 0.000049 +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 13.020150 to island 0 +[INFO main ] potential duplicate for hash '2926_1043290_0', had 1 potential matches. +[INFO main ] on potential match 0 of 1 +[INFO main ] created copy to insert to island: 0 +[INFO main ] inserting genome at index: 1 +[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f52a02cfb48 +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f521b3ab4d0, &(*potential_match): 0x7f521b3ab4d0 +[INFO main ] potential_matches.size() before erase: 1 +[INFO main ] potential_matches.size() after erase: 0 +[INFO main ] structure_map[2920_1016348_0].size() after erase: 0 +[INFO main ] deleting the potential_matches vector for hash '2920_1016348_0' because it was empty. +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 13.026025 to island 0 +[INFO main ] ignoring genome, fitness: 13.026025 > worst for island[0] fitness: 13.020150 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO genome_13_thread_8 ] iteration 1, mse: 6214.359412, v_mse: 12.786748, bv_mse: 12.786748, lr: 0.000002, norm: 474456.939792, p_norm: 172.678960, v_norm: 2.469852[INFO genome_11_thread_6 ] iteration 1, mse: 6270.977832, v_mse: 12.903247, bv_mse: 12.903247, lr: 0.000002, norm: 568216.793745, p_norm: 157.694918, v_norm: 3.133014, OVER THRESHOLD, multiplier: 0.000039 +, OVER THRESHOLD, multiplier: 0.000046 +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 12.890787 to island 0 +[INFO main ] potential duplicate for hash '2926_1043290_0', had 2 potential matches. +[INFO main ] on potential match 0 of 2 +[INFO main ] on potential match 1 of 2 +[INFO main ] created copy to insert to island: 0 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f52a0ad0b48 +[INFO main ] new best fitness for island: 0! +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4f2c29bae0, &(*potential_match): 0x7f521fde8970 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4f2c29bae0, &(*potential_match): 0x7f4f2c29bae0 +[INFO main ] potential_matches.size() before erase: 3 +[INFO main ] potential_matches.size() after erase: 2 +[INFO main ] structure_map[2926_1043290_0].size() after erase: 2 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4f2c29bae0, &(*potential_match): 0x7f527008a700 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] +[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing intra-island crossover +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.786748 to island 1 +[INFO main ] potential duplicate for hash '2926_1043290_0', had 1 potential matches. +[INFO main ] on potential match 0 of 1 +[INFO main ] created copy to insert to island: 1 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f529d2c9b48 +[INFO main ] new best fitness for island: 1! +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f528c08a700, &(*potential_match): 0x7f528c08a700 +[INFO main ] potential_matches.size() before erase: 2 +[INFO main ] potential_matches.size() after erase: 1 +[INFO main ] structure_map[2926_1043290_0].size() after erase: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f528c08a700, &(*potential_match): 0x7f527c08a700 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.786748 25.846512 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.903247 to island 1 +[INFO main ] ignoring genome, fitness: 12.903247 > worst for island[1] fitness: 12.824047 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.786748 25.846512 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] attempting to add a node! +[INFO main ] add node, split depth: 0.595336, max_inputs: 21, max_outputs: 13 +[INFO main ] add node recurrent probability: 0.000000 +[INFO main ] CREATING NODE, type: 'simple' +[INFO main ] adding edge between nodes 8 and 81 +[INFO main ] adding edge between nodes 8 and 81, new edge weight: -0.136597 +[INFO main ] adding edge between nodes 21 and 81 +[INFO main ] adding edge between nodes 21 and 81, new edge weight: 0.219903 +[INFO main ] adding edge between nodes 29 and 81 +[INFO main ] adding edge between nodes 29 and 81, new edge weight: 0.172819 +[INFO main ] adding edge between nodes 30 and 81 +[INFO main ] adding edge between nodes 30 and 81, new edge weight: 0.355953 +[INFO main ] adding edge between nodes 3 and 81 +[INFO main ] adding edge between nodes 3 and 81, new edge weight: -0.304653 +[INFO main ] adding edge between nodes 5 and 81 +[INFO main ] adding edge between nodes 5 and 81, new edge weight: -0.342059 +[INFO main ] adding edge between nodes 7 and 81 +[INFO main ] adding edge between nodes 7 and 81, new edge weight: 0.373446 +[INFO main ] adding edge between nodes 15 and 81 +[INFO main ] adding edge between nodes 15 and 81, new edge weight: -0.271002 +[INFO main ] adding edge between nodes 16 and 81 +[INFO main ] adding edge between nodes 16 and 81, new edge weight: -0.420364 +[INFO main ] adding edge between nodes 17 and 81 +[INFO main ] adding edge between nodes 17 and 81, new edge weight: 0.135258 +[INFO main ] adding edge between nodes 10 and 81 +[INFO main ] adding edge between nodes 10 and 81, new edge weight: -0.248146 +[INFO main ] adding edge between nodes 22 and 81 +[INFO main ] adding edge between nodes 22 and 81, new edge weight: 0.522175 +[INFO main ] adding edge between nodes 24 and 81 +[INFO main ] adding edge between nodes 24 and 81, new edge weight: 0.124579 +[INFO main ] adding edge between nodes 25 and 81 +[INFO main ] adding edge between nodes 25 and 81, new edge weight: 0.171521 +[INFO main ] adding edge between nodes 27 and 81 +[INFO main ] adding edge between nodes 27 and 81, new edge weight: 0.125798 +[INFO main ] adding edge between nodes 31 and 81 +[INFO main ] adding edge between nodes 31 and 81, new edge weight: -0.005519 +[INFO main ] adding edge between nodes 32 and 81 +[INFO main ] adding edge between nodes 32 and 81, new edge weight: 0.264706 +[INFO main ] adding edge between nodes 34 and 81 +[INFO main ] adding edge between nodes 34 and 81, new edge weight: -0.310501 +[INFO main ] adding edge between nodes 36 and 81 +[INFO main ] adding edge between nodes 36 and 81, new edge weight: 0.139972 +[INFO main ] adding edge between nodes 37 and 81 +[INFO main ] adding edge between nodes 37 and 81, new edge weight: 0.012641 +[INFO main ] adding edge between nodes 11 and 81 +[INFO main ] adding edge between nodes 11 and 81, new edge weight: 0.685450 +[INFO main ] adding edge between nodes 81 and 45 +[INFO main ] adding edge between nodes 81 and 45, new edge weight: -0.361658 +[INFO main ] adding edge between nodes 81 and 43 +[INFO main ] adding edge between nodes 81 and 43, new edge weight: 0.188658 +[INFO main ] adding edge between nodes 81 and 57 +[INFO main ] adding edge between nodes 81 and 57, new edge weight: 0.723042 +[INFO main ] adding edge between nodes 81 and 50 +[INFO main ] adding edge between nodes 81 and 50, new edge weight: 0.016008 +[INFO main ] adding edge between nodes 81 and 53 +[INFO main ] adding edge between nodes 81 and 53, new edge weight: 0.393925 +[INFO main ] adding edge between nodes 81 and 66 +[INFO main ] adding edge between nodes 81 and 66, new edge weight: -0.016428 +[INFO main ] adding edge between nodes 81 and 65 +[INFO main ] adding edge between nodes 81 and 65, new edge weight: 0.075208 +[INFO main ] adding edge between nodes 81 and 63 +[INFO main ] adding edge between nodes 81 and 63, new edge weight: -0.194695 +[INFO main ] adding edge between nodes 81 and 62 +[INFO main ] adding edge between nodes 81 and 62, new edge weight: 0.048143 +[INFO main ] adding edge between nodes 81 and 74 +[INFO main ] adding edge between nodes 81 and 74, new edge weight: 0.033704 +[INFO main ] adding edge between nodes 81 and 70 +[INFO main ] adding edge between nodes 81 and 70, new edge weight: 0.258209 +[INFO main ] adding edge between nodes 81 and 71 +[INFO main ] adding edge between nodes 81 and 71, new edge weight: -0.050983 +[INFO main ] adding edge between nodes 81 and 72 +[INFO main ] adding edge between nodes 81 and 72, new edge weight: 0.277614 +[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] attempting to disable a node! +[INFO main ] disabling node 17 at depth 0.000000 +[INFO genome_15_thread_3 ] iteration 0, mse: 7257.768468, v_mse: 14.933680, bv_mse: 14.933680, lr: 0.000002, norm: 1854846.366558, p_norm: 151.835173, v_norm: 0.000000[INFO genome_17_thread_2 ] iteration 0, mse: 7125.284456, v_mse: 14.661079, bv_mse: 14.661079, lr: 0.000002, norm: 1345813.626528, p_norm: 176.832944, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000012 +, OVER THRESHOLD, multiplier: 0.000016 +[INFO genome_16_thread_4 ] iteration 0, mse: 7339.527342, v_mse: 15.101908, bv_mse: 15.101908, lr: 0.000002, norm: 1510305.755211, p_norm: 131.498007, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000015 +[INFO genome_18_thread_5 ] iteration 0, mse: 7243.038227, v_mse: 14.903371, bv_mse: 14.903371, lr: 0.000002, norm: 2065244.760592, p_norm: 160.383597, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000011 +[INFO genome_15_thread_3 ] iteration 1, mse: 6245.168962, v_mse: 12.850142, bv_mse: 12.850142, lr: 0.000002, norm: 633324.431810, p_norm: 196.589876, v_norm: 3.816556, OVER THRESHOLD, multiplier: 0.000035 +[INFO genome_17_thread_2 ] iteration 1, mse: 6317.668588, v_mse: 12.999318, bv_mse: 12.999318, lr: 0.000002, norm: 415079.723750, p_norm: 199.700853, v_norm: 2.769164, OVER THRESHOLD, multiplier: 0.000053 +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.850142 to island 1 +[INFO main ] ignoring genome, fitness: 12.850142 > worst for island[1] fitness: 12.824047 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO genome_20_thread_7 ] iteration 0, mse: 6799.275863, v_mse: 13.990280, bv_mse: 13.990280, lr: 0.000002, norm: 1350437.295612, p_norm: 162.464627, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000016 +[INFO genome_18_thread_5 ] iteration 1, mse: 6223.957684, v_mse: 12.806497, bv_mse: 12.806497, lr: 0.000002, norm: 478093.744754, p_norm: 219.697699, v_norm: 4.249475, OVER THRESHOLD, multiplier: 0.000046 +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO genome_22_thread_8 ] iteration 0, mse: 7248.078167, v_mse: 14.913741, bv_mse: 14.913741, lr: 0.000002, norm: 2042612.907146, p_norm: 161.770810, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000011 +[INFO genome_19_thread_1 ] iteration 0, mse: 7471.193607, v_mse: 15.372826, bv_mse: 15.372826, lr: 0.000002, norm: 1220041.659055, p_norm: 181.837990, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000018 +[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO genome_16_thread_4 ] iteration 1, mse: 6339.507457, v_mse: 13.044254, bv_mse: 13.044254, lr: 0.000002, norm: 612213.087440, p_norm: 157.878111, v_norm: 3.107625[INFO main ] 12.786748 25.846512 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO genome_21_thread_0 ] iteration 0, mse: 6694.796766, v_mse: 13.775302, bv_mse: 13.775302, lr: 0.000002, norm: 1299363.711451, p_norm: 199.913509, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000017 +[INFO genome_20_thread_7 ] iteration 1, mse: 6183.965405, v_mse: 12.724209, bv_mse: 12.724209, lr: 0.000002, norm: 307411.911338, p_norm: 186.376312, v_norm: 2.778678, OVER THRESHOLD, multiplier: 0.000072 +, OVER THRESHOLD, multiplier: 0.000036 +[INFO genome_23_thread_6 ] iteration 0, mse: 7049.005931, v_mse: 14.504127, bv_mse: 14.504127, lr: 0.000002, norm: 1473281.940970, p_norm: 172.678960, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000015 +[INFO genome_22_thread_8 ] iteration 1, mse: 6140.370933, v_mse: 12.634508, bv_mse: 12.634508, lr: 0.000002, norm: 136914.355993, p_norm: 220.695043, v_norm: 4.202907, OVER THRESHOLD, multiplier: 0.000161 +[INFO genome_21_thread_0 ] iteration 1, mse: 6172.754494, v_mse: 12.701141, bv_mse: 12.701141, lr: 0.000002, norm: 262315.513345, p_norm: 225.552883, v_norm: 2.673588, OVER THRESHOLD, multiplier: 0.000084 +[INFO genome_19_thread_1 ] iteration 1, mse: 6183.387876, v_mse: 12.723020, bv_mse: 12.723020, lr: 0.000002, norm: 601863.179826, p_norm: 199.865040, v_norm: 2.510374, OVER THRESHOLD, multiplier: 0.000037 +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 12.806497 to island 0 +[INFO main ] created copy to insert to island: 0 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[3000_1052552_0] : 0x7f529eaccb48 +[INFO main ] new best fitness for island: 0! +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f521fde8970, &(*potential_match): 0x7f521fde8970 +[INFO main ] potential_matches.size() before erase: 2 +[INFO main ] potential_matches.size() after erase: 1 +[INFO main ] structure_map[2926_1043290_0].size() after erase: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f521fde8970, &(*potential_match): 0x7f527008a700 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.806497 30.599308 1468 (1468) 0 (0) 1 (77) 76 (77) [add_node(simple):1] +[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.786748 25.846512 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.701141 to island 1 +[INFO main ] potential duplicate for hash '2926_1043290_0', had 1 potential matches. +[INFO main ] on potential match 0 of 1 +[INFO main ] created copy to insert to island: 1 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f52a0ad0b48 +[INFO main ] new best fitness for island: 1! +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f50686cc090, &(*potential_match): 0x7f50686cc090 +[INFO main ] potential_matches.size() before erase: 1 +[INFO main ] potential_matches.size() after erase: 0 +[INFO main ] structure_map[2926_1041910_0].size() after erase: 0 +[INFO main ] deleting the potential_matches vector for hash '2926_1041910_0' because it was empty. +[INFO genome_23_thread_6 ] iteration 1, mse: 6252.648207, v_mse: 12.865531, bv_mse: 12.865531, lr: 0.000002, norm: 474364.587553, p_norm: 198.934544, v_norm: 3.031444, OVER THRESHOLD, multiplier: 0.000046 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.806497 30.599308 1468 (1468) 0 (0) 1 (77) 76 (77) [add_node(simple):1] +[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.786748 25.846512 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.723020 to island 1 +[INFO main ] potential duplicate for hash '2926_1043290_0', had 2 potential matches. +[INFO main ] on potential match 0 of 2 +[INFO main ] on potential match 1 of 2 +[INFO main ] created copy to insert to island: 1 +[INFO main ] inserting genome at index: 1 +[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f52a02cfb48 +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f527c08a700, &(*potential_match): 0x7f527c08a700 +[INFO main ] potential_matches.size() before erase: 3 +[INFO main ] potential_matches.size() after erase: 2 +[INFO main ] structure_map[2926_1043290_0].size() after erase: 2 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f527c08a700, &(*potential_match): 0x7f4f2c29bae0 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f527c08a700, &(*potential_match): 0x7f521b3ab4d0 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.806497 30.599308 1468 (1468) 0 (0) 1 (77) 76 (77) [add_node(simple):1] +[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 12.634508 to island 0 +[INFO main ] created copy to insert to island: 0 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[3007_1095565_0] : 0x7f529d2c9b48 +[INFO main ] new best fitness for island: 0! +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f527008a700, &(*potential_match): 0x7f527008a700 +[INFO main ] potential_matches.size() before erase: 1 +[INFO main ] potential_matches.size() after erase: 0 +[INFO main ] structure_map[2926_1043290_0].size() after erase: 0 +[INFO main ] deleting the potential_matches vector for hash '2926_1043290_0' because it was empty. +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] 12.806497 30.599308 1468 (1468) 0 (0) 1 (77) 76 (77) [add_node(simple):1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 12.724209 to island 0 +[INFO main ] created copy to insert to island: 0 +[INFO main ] inserting genome at index: 1 +[INFO main ] adding to structure_map[2926_1041911_0] : 0x7f529dacab48 +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52800d1ad0, &(*potential_match): 0x7f52800d1ad0 +[INFO main ] potential_matches.size() before erase: 1 +[INFO main ] potential_matches.size() after erase: 0 +[INFO main ] structure_map[3000_1052552_0].size() after erase: 0 +[INFO main ] deleting the potential_matches vector for hash '3000_1052552_0' because it was empty. +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.999318 to island 1 +[INFO main ] ignoring genome, fitness: 12.999318 > worst for island[1] fitness: 12.723020 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] +[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] attempting to merge a node! +[INFO main ] attempting to add a node! +[INFO main ] add node, split depth: 0.296837, max_inputs: 21, max_outputs: 20 +[INFO main ] add node recurrent probability: 0.000000 +[INFO main ] CREATING NODE, type: 'simple' +[INFO main ] adding edge between nodes 30 and 82 +[INFO main ] adding edge between nodes 30 and 82, new edge weight: 0.279580 +[INFO main ] adding edge between nodes 20 and 82 +[INFO main ] adding edge between nodes 20 and 82, new edge weight: -0.258901 +[INFO main ] adding edge between nodes 3 and 82 +[INFO main ] adding edge between nodes 3 and 82, new edge weight: -0.324174 +[INFO main ] adding edge between nodes 4 and 82 +[INFO main ] adding edge between nodes 4 and 82, new edge weight: 0.059251 +[INFO main ] adding edge between nodes 6 and 82 +[INFO main ] adding edge between nodes 6 and 82, new edge weight: -0.040719 +[INFO main ] adding edge between nodes 7 and 82 +[INFO main ] adding edge between nodes 7 and 82, new edge weight: -0.133197 +[INFO main ] adding edge between nodes 8 and 82 +[INFO main ] adding edge between nodes 8 and 82, new edge weight: -0.174522 +[INFO main ] adding edge between nodes 13 and 82 +[INFO main ] adding edge between nodes 13 and 82, new edge weight: -0.034339 +[INFO main ] adding edge between nodes 14 and 82 +[INFO main ] adding edge between nodes 14 and 82, new edge weight: 0.070591 +[INFO main ] adding edge between nodes 15 and 82 +[INFO main ] adding edge between nodes 15 and 82, new edge weight: -0.725389 +[INFO main ] adding edge between nodes 16 and 82 +[INFO main ] adding edge between nodes 16 and 82, new edge weight: 0.216987 +[INFO main ] adding edge between nodes 17 and 82 +[INFO main ] adding edge between nodes 17 and 82, new edge weight: 0.324859 +[INFO main ] adding edge between nodes 18 and 82 +[INFO main ] adding edge between nodes 18 and 82, new edge weight: 0.163352 +[INFO main ] adding edge between nodes 2 and 82 +[INFO main ] adding edge between nodes 2 and 82, new edge weight: -0.080235 +[INFO main ] adding edge between nodes 22 and 82 +[INFO main ] adding edge between nodes 22 and 82, new edge weight: -0.339971 +[INFO main ] adding edge between nodes 24 and 82 +[INFO main ] adding edge between nodes 24 and 82, new edge weight: -0.021835 +[INFO main ] adding edge between nodes 25 and 82 +[INFO main ] adding edge between nodes 25 and 82, new edge weight: -0.128952 +[INFO main ] adding edge between nodes 31 and 82 +[INFO main ] adding edge between nodes 31 and 82, new edge weight: -0.695552 +[INFO main ] adding edge between nodes 32 and 82 +[INFO main ] adding edge between nodes 32 and 82, new edge weight: -0.121489 +[INFO main ] adding edge between nodes 33 and 82 +[INFO main ] adding edge between nodes 33 and 82, new edge weight: -0.388146 +[INFO main ] adding edge between nodes 37 and 82 +[INFO main ] adding edge between nodes 37 and 82, new edge weight: 0.611324 +[INFO main ] adding edge between nodes 82 and 39 +[INFO main ] adding edge between nodes 82 and 39, new edge weight: 0.219600 +[INFO main ] adding edge between nodes 82 and 40 +[INFO main ] adding edge between nodes 82 and 40, new edge weight: 0.407370 +[INFO main ] adding edge between nodes 82 and 41 +[INFO main ] adding edge between nodes 82 and 41, new edge weight: -0.063108 +[INFO main ] adding edge between nodes 82 and 43 +[INFO main ] adding edge between nodes 82 and 43, new edge weight: -0.422589 +[INFO main ] adding edge between nodes 82 and 47 +[INFO main ] adding edge between nodes 82 and 47, new edge weight: -0.005029 +[INFO main ] adding edge between nodes 82 and 48 +[INFO main ] adding edge between nodes 82 and 48, new edge weight: 0.495100 +[INFO main ] adding edge between nodes 82 and 56 +[INFO main ] adding edge between nodes 82 and 56, new edge weight: 0.293628 +[INFO main ] adding edge between nodes 82 and 49 +[INFO main ] adding edge between nodes 82 and 49, new edge weight: 0.553851 +[INFO main ] adding edge between nodes 82 and 50 +[INFO main ] adding edge between nodes 82 and 50, new edge weight: 0.436134 +[INFO main ] adding edge between nodes 82 and 51 +[INFO main ] adding edge between nodes 82 and 51, new edge weight: 0.175278 +[INFO main ] adding edge between nodes 82 and 53 +[INFO main ] adding edge between nodes 82 and 53, new edge weight: 0.167612 +[INFO main ] adding edge between nodes 82 and 62 +[INFO main ] adding edge between nodes 82 and 62, new edge weight: 0.003861 +[INFO main ] adding edge between nodes 82 and 63 +[INFO main ] adding edge between nodes 82 and 63, new edge weight: -0.111817 +[INFO main ] adding edge between nodes 82 and 65 +[INFO main ] adding edge between nodes 82 and 65, new edge weight: -0.351988 +[INFO main ] adding edge between nodes 82 and 67 +[INFO main ] adding edge between nodes 82 and 67, new edge weight: -0.461250 +[INFO main ] adding edge between nodes 82 and 75 +[INFO main ] adding edge between nodes 82 and 75, new edge weight: 0.189152 +[INFO main ] adding edge between nodes 82 and 76 +[INFO main ] adding edge between nodes 82 and 76, new edge weight: 0.241920 +[INFO main ] adding edge between nodes 82 and 68 +[INFO main ] adding edge between nodes 82 and 68, new edge weight: 0.332228 +[INFO main ] adding edge between nodes 82 and 72 +[INFO main ] adding edge between nodes 82 and 72, new edge weight: 0.388992 +[INFO main ] adding edge between nodes 82 and 74 +[INFO main ] adding edge between nodes 82 and 74, new edge weight: 0.054794 +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] attempting to add edge! +[INFO main ] reachable_nodes.size(): 77 +[INFO main ] selected first node 28 with depth 4097 +[INFO main ] reachable_nodes.size(): 39 +[INFO main ] selected second node 54 with depth 5885 +[INFO main ] adding edge between nodes 28 and 54 +[INFO main ] edge already exists, not adding. +[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing mutation +[INFO main ] generating new genome by mutation. +[INFO main ] attempting to split a node! +[INFO main ] attempting to add a node! +[INFO main ] add node, split depth: 0.840017, max_inputs: 21, max_outputs: 1 +[INFO main ] add node recurrent probability: 0.000000 +[INFO main ] CREATING NODE, type: 'simple' +[INFO main ] adding edge between nodes 20 and 83 +[INFO main ] adding edge between nodes 20 and 83, new edge weight: -0.210757 +[INFO main ] adding edge between nodes 7 and 83 +[INFO main ] adding edge between nodes 7 and 83, new edge weight: 0.019640 +[INFO main ] adding edge between nodes 38 and 83 +[INFO main ] adding edge between nodes 38 and 83, new edge weight: 0.314756 +[INFO main ] adding edge between nodes 12 and 83 +[INFO main ] adding edge between nodes 12 and 83, new edge weight: 0.207615 +[INFO main ] adding edge between nodes 15 and 83 +[INFO main ] adding edge between nodes 15 and 83, new edge weight: -0.051694 +[INFO main ] adding edge between nodes 16 and 83 +[INFO main ] adding edge between nodes 16 and 83, new edge weight: -0.392008 +[INFO main ] adding edge between nodes 17 and 83 +[INFO main ] adding edge between nodes 17 and 83, new edge weight: 0.251033 +[INFO main ] adding edge between nodes 29 and 83 +[INFO main ] adding edge between nodes 29 and 83, new edge weight: -0.404156 +[INFO main ] adding edge between nodes 2 and 83 +[INFO main ] adding edge between nodes 2 and 83, new edge weight: -0.295159 +[INFO main ] adding edge between nodes 10 and 83 +[INFO main ] adding edge between nodes 10 and 83, new edge weight: 0.210130 +[INFO main ] adding edge between nodes 1 and 83 +[INFO main ] adding edge between nodes 1 and 83, new edge weight: -0.052025 +[INFO main ] adding edge between nodes 22 and 83 +[INFO main ] adding edge between nodes 22 and 83, new edge weight: -0.235988 +[INFO main ] adding edge between nodes 26 and 83 +[INFO main ] adding edge between nodes 26 and 83, new edge weight: -0.168626 +[INFO main ] adding edge between nodes 27 and 83 +[INFO main ] adding edge between nodes 27 and 83, new edge weight: 0.063415 +[INFO main ] adding edge between nodes 28 and 83 +[INFO main ] adding edge between nodes 28 and 83, new edge weight: -0.218475 +[INFO main ] adding edge between nodes 31 and 83 +[INFO main ] adding edge between nodes 31 and 83, new edge weight: 0.069493 +[INFO main ] adding edge between nodes 32 and 83 +[INFO main ] adding edge between nodes 32 and 83, new edge weight: 0.112906 +[INFO main ] adding edge between nodes 34 and 83 +[INFO main ] adding edge between nodes 34 and 83, new edge weight: -0.091466 +[INFO main ] adding edge between nodes 35 and 83 +[INFO main ] adding edge between nodes 35 and 83, new edge weight: 0.011092 +[INFO main ] adding edge between nodes 36 and 83 +[INFO main ] adding edge between nodes 36 and 83, new edge weight: -0.016694 +[INFO main ] adding edge between nodes 19 and 83 +[INFO main ] adding edge between nodes 19 and 83, new edge weight: 0.161173 +[INFO main ] adding edge between nodes 83 and 53 +[INFO main ] adding edge between nodes 83 and 53, new edge weight: -0.618857 +[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 +[INFO main ] island is full +[INFO main ] performing intra-island crossover +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.865531 to island 1 +[INFO main ] ignoring genome, fitness: 12.865531 > worst for island[1] fitness: 12.723020 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 13.044254 to island 0 +[INFO main ] ignoring genome, fitness: 13.044254 > worst for island[0] fitness: 12.724209 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] +[INFO genome_24_thread_3 ] iteration 0, mse: 6977.146138, v_mse: 14.356268, bv_mse: 14.356268, lr: 0.000002, norm: 1749985.520766, p_norm: 193.518512, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000013 +[INFO genome_24_thread_3 ] iteration 1, mse: 6227.171460, v_mse: 12.813110, bv_mse: 12.813110, lr: 0.000002, norm: 368392.591111, p_norm: 233.873892, v_norm: 3.600793, OVER THRESHOLD, multiplier: 0.000060 +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 12.813110 to island 0 +[INFO main ] ignoring genome, fitness: 12.813110 > worst for island[0] fitness: 12.724209 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] +[INFO genome_28_thread_8 ] iteration 0, mse: 6291.566431, v_mse: 12.945610, bv_mse: 12.945610, lr: 0.000002, norm: 700931.712769, p_norm: 186.376312, v_norm: 0.000000[INFO genome_26_thread_0 ] iteration 0, mse: 6221.955994, v_mse: 12.802379, bv_mse: 12.802379, lr: 0.000002, norm: 601356.639195, p_norm: 220.695043, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000031 +, OVER THRESHOLD, multiplier: 0.000037 +[INFO genome_27_thread_1 ] iteration 0, mse: 6374.400669, v_mse: 13.116051, bv_mse: 13.116051, lr: 0.000002, norm: 613164.075958, p_norm: 261.681514, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000036 +[INFO genome_28_thread_8 ] iteration 1, mse: 6099.737200, v_mse: 12.550900, bv_mse: 12.550900, lr: 0.000002, norm: 219989.367748, p_norm: 194.838409, v_norm: 1.442246, OVER THRESHOLD, multiplier: 0.000100 +[INFO genome_26_thread_0 ] iteration 1, mse: 6147.884451, v_mse: 12.649968, bv_mse: 12.649968, lr: 0.000002, norm: 234739.147240, p_norm: 224.938350, v_norm: 1.237359, OVER THRESHOLD, multiplier: 0.000094 +[INFO genome_27_thread_1 ] iteration 1, mse: 6161.125020, v_mse: 12.677212, bv_mse: 12.677212, lr: 0.000002, norm: 316932.774403, p_norm: 266.386952, v_norm: 1.261654, OVER THRESHOLD, multiplier: 0.000070 +[INFO genome_30_thread_2 ] iteration 0, mse: 6617.779869, v_mse: 13.616831, bv_mse: 13.616831, lr: 0.000002, norm: 1188227.503510, p_norm: 237.380107, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000019 +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.677212 to island 1 +[INFO main ] created copy to insert to island: 1 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[3007_1095565_0] : 0x7f52a02cfb48 +[INFO main ] new best fitness for island: 1! +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f521b3ab4d0, &(*potential_match): 0x7f4f2c29bae0 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f521b3ab4d0, &(*potential_match): 0x7f521b3ab4d0 +[INFO main ] potential_matches.size() before erase: 2 +[INFO main ] potential_matches.size() after erase: 1 +[INFO main ] structure_map[2926_1043290_0].size() after erase: 1 +[INFO genome_30_thread_2 ] iteration 1, mse: 6149.999677, v_mse: 12.654320, bv_mse: 12.654320, lr: 0.000002, norm: 400368.692373, p_norm: 256.944696, v_norm: 2.444913, OVER THRESHOLD, multiplier: 0.000055 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 12.550900 to island 0 +[INFO main ] potential duplicate for hash '2926_1041911_0', had 1 potential matches. +[INFO main ] on potential match 0 of 1 +[INFO main ] created copy to insert to island: 0 +[INFO main ] inserting genome at index: 0 +[INFO main ] adding to structure_map[2926_1041911_0] : 0x7f529d2c9b48 +[INFO main ] new best fitness for island: 0! +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4d68a19620, &(*potential_match): 0x7f4d68a19620 +[INFO main ] potential_matches.size() before erase: 2 +[INFO main ] potential_matches.size() after erase: 1 +[INFO main ] structure_map[2926_1041911_0].size() after erase: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4d68a19620, &(*potential_match): 0x7f527008a700 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.550900 31.207737 1443 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 12.649968 to island 0 +[INFO main ] ignoring genome, fitness: 12.649968 > worst for island[0] fitness: 12.634508 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.550900 31.207737 1443 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO main ] inserting genome to island: 0 +[INFO main ] inserting genome with fitness: 12.654320 to island 0 +[INFO main ] ignoring genome, fitness: 12.654320 > worst for island[0] fitness: 12.634508 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.550900 31.207737 1443 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] +[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] +[INFO genome_29_thread_7 ] iteration 0, mse: 6456.630060, v_mse: 13.285247, bv_mse: 13.285247, lr: 0.000002, norm: 803789.890614, p_norm: 226.926870, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000027 +[INFO genome_29_thread_7 ] iteration 1, mse: 6166.705618, v_mse: 12.688695, bv_mse: 12.688695, lr: 0.000002, norm: 236572.743822, p_norm: 235.391483, v_norm: 1.653889, OVER THRESHOLD, multiplier: 0.000093 +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.688695 to island 1 +[INFO main ] created copy to insert to island: 1 +[INFO main ] inserting genome at index: 1 +[INFO main ] adding to structure_map[3009_1078633_0] : 0x7f529dacab48 +[INFO main ] genomes.size(): 3, max_size: 2, status: 1 +[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4f2c29bae0, &(*potential_match): 0x7f4f2c29bae0 +[INFO main ] potential_matches.size() before erase: 1 +[INFO main ] potential_matches.size() after erase: 0 +[INFO main ] structure_map[2926_1043290_0].size() after erase: 0 +[INFO main ] deleting the potential_matches vector for hash '2926_1043290_0' because it was empty. +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.550900 31.207737 1443 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] +[INFO main ] 12.688695 33.741960 1466 (1466) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO genome_25_thread_5 ] iteration 0, mse: 6471.066972, v_mse: 13.314953, bv_mse: 13.314953, lr: 0.000002, norm: 818111.719508, p_norm: 229.745328, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000027 +[INFO genome_25_thread_5 ] iteration 1, mse: 6175.179782, v_mse: 12.706131, bv_mse: 12.706131, lr: 0.000002, norm: 270825.772208, p_norm: 238.658480, v_norm: 1.683357, OVER THRESHOLD, multiplier: 0.000081 +[INFO main ] inserting genome to island: 1 +[INFO main ] inserting genome with fitness: 12.706131 to island 1 +[INFO main ] ignoring genome, fitness: 12.706131 > worst for island[1] fitness: 12.688695 +[INFO main ] Islands: +[INFO main ] Island 0: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.550900 31.207737 1443 (1444) 0 (0) 0 (76) 76 (76) [clone:1] +[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] Island 1: +[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated +[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] +[INFO main ] 12.688695 33.741960 1466 (1466) 0 (0) 1 (77) 77 (77) [add_node(simple):1] +[INFO main ] completed! 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z;;@4v@p0P<_}GmyVB)Z&qFsce)dtkC2EG(VVOoIO>`Vx7@h8lmpO}9EEvxfK;$9vB z;Ti@?FZ6m-!ak4l_6cwySk9j{A#Foz!6Qh_x>zV|KlfrVXrbNs*x?QwwvKNx7_{Q% z^mJu~!>Cp+27{K~4VjVoe_+BODoBr7p~YZ0=nPabg1!d!jj+=#ih~-a#ylY+2AWRpS<}zqNTW|sAZx4Q7z{cM zLOhI};OoaBuy*^!V9-f0TV0h3Pr=I0i@~6W!I6murO`lHD9Go+sIZvWy%-D!&2(XH z)hGYV0Gf7t@1itlIw_eAoBGpHG%eh7Q5tly`N4Yte9Q6t%7v!s`7TO>+5?}tDTR2s z({o@CG>ti6Q5tj>UNk3v1!t7z4wYoVGwHE6c%C-*fx(}sUvM0}AoihEEH>GD-@lNt zD^P55!2W+BV;7^?y>PvnhyKpRJ}B_| zlHLf{YjgPTTKk*Bz8Rvsr!<>?H;k z1oToPq`*Wfp3zIq+}a3VYDn_9o3k%x*HIzFhL*QVah^<)r-~&{Sx-LoA-J*v53XR~ zM)mR6IP;vmfW0nWE(&u$7nIE;8AgUmpEzb$A;g9zj84xJ77n!h9yeF=arTIhygn97 zg%BH>pfW8tr(pp>N#56c4>{qTN1xY|rBn#9p$YcU3giT37{TyO`Q^{x#0{~b33jIx z$q9HE;Yu-g$3}GoAvQE&e+G`6paLVfDVqw2NK+xih9(TXrXZ-o2uV}Qfq)wGSk={mgE8P*EYQ%1ZT6Ii18q)5jL-V_% zZmA*Po&3HD((dO*=65H*i-MehzFmN{KZ5jr(LpF~9w6TxeTnzN-Tl5T*!}kf-)6>i T^u+fhf`4CEBS^-$4tVuHx{`U~ literal 0 HcmV?d00001 From 20124e32cefca69f8fedd0ef65517adce02502e8 Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Sat, 31 Oct 2020 19:33:33 -0400 Subject: [PATCH 07/25] changed batch_size to sequence_length --- .vscode/settings.json | 3 +++ README.md | 4 ++-- datasets/penn/input.txt | 2 +- datasets/pennchar/input.txt | 17 +------------- multithreaded/examm_mt_nlp.cxx | 35 +++++++++++++++++++++++++--- rnn/mse.cxx | 42 ++++++++++++++++++++++++++++++++++ rnn/mse.hxx | 7 ++++-- rnn/rnn.cxx | 39 +++++++++++++++++++++++++------ rnn/rnn.hxx | 6 +++-- rnn/rnn_genome.cxx | 2 +- rnn/rnn_node_interface.hxx | 1 + rnn_tests/gradient_test.cxx | 9 ++++++++ rnn_tests/gradient_test.hxx | 3 ++- word_series/word_series.cxx | 41 +++++++++++++++++---------------- word_series/word_series.hxx | 4 ++-- 15 files changed, 158 insertions(+), 57 deletions(-) create mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..691a8f68 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "C_Cpp.errorSquiggles": "Disabled" +} \ No newline at end of file diff --git a/README.md b/README.md index 0ce35363..ddbeaeed 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ The datasets used for this part of the code are PennChar, PennTreeBank and WikiT ``` -/multithreaded/examm_nlp --number_threads 9 --training_filenames ../datasets/pennchar/train.txt --test_filenames ../datasets/pennchar/test.txt --word_offset 1 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info +./multithreaded/examm_mt_nlp --number_threads 9 --training_filenames ../datasets/pennchar/train.txt --test_filenames ../datasets/pennchar/test.txt --word_offset 1 --sequence_length 4 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level ``` @@ -50,7 +50,7 @@ And a parallel version using MPI: ``` -/multithreaded/examm_nlp --number_threads 9 --training_filenames ../datasets/penn/train.txt --test_filenames ../datasets/penn/test.txt --word_offset 1 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC--normalize min_max --std_message_level info --file_message_level info +./multithreaded/examm_mt_nlp --number_threads 9 --training_filenames ../datasets/penn/train.txt --test_filenames ../datasets/pennchar/test.txt --word_offset 1 --sequence_length 4 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info ``` diff --git a/datasets/penn/input.txt b/datasets/penn/input.txt index 853ba131..f355fa25 100644 --- a/datasets/penn/input.txt +++ b/datasets/penn/input.txt @@ -1 +1 @@ - consumers may move their telephones a little closer \ No newline at end of file +consumers may move their telephones a little closer \ No newline at end of file diff --git a/datasets/pennchar/input.txt b/datasets/pennchar/input.txt index edce96ca..2e8ab459 100644 --- a/datasets/pennchar/input.txt +++ b/datasets/pennchar/input.txt @@ -1,16 +1 @@ -a e r _ b a n k n o t e _ b e r l i t z _ c a l l o w a y _ c e n t r u s t _ c l u e t t _ f r o m s t e i n _ g i t a n o _ g u t e r m a n _ h y d r o - q u e b e c _ i p o _ k i a _ m e m o t e c _ m l x _ n a h b _ p u n t s _ r a k e _ r e g a t t a _ r u b e n s _ s i m _ s n a c k - f o o d _ s s a n g y o n g _ s w a p o _ w a c h t e r - p i e r r e _ < u n k > _ N _ y e a r s _ o l d _ w i l l _ j o i n _ t h e _ b o a r d _ a s _ a _ n o n e x e c u t i v e _ d i r e c t o r _ n o v . _ N - m r . _ < u n k > _ i s _ c h a i r m a n _ o f _ < u n k > _ n . v . _ t h e _ d u t c h _ p u b l i s h i n g _ g r o u p - r u d o l p h _ < u n k > _ N _ y e a r s _ o l d _ a n d _ f o r m e r _ c h a i r m a n _ o f _ c o n s o l i d a t e d _ g o l d _ f i e l d s _ p l c _ w a s _ n a m e d _ a _ n o n e x e c u t i v e _ d i r e c t o r _ o f _ t h i s _ b r i t i s h _ i n d u s t r i a l _ c o n g l o m e r a t e - a _ f o r m _ o f _ a s b e s t o s _ o n c e _ u s e d _ t o _ m a k e _ k e n t _ c i g a r e t t e _ f i l t e r s _ h a s _ c a u s e d _ a _ h i g h _ p e r c e n t a g e _ o f _ c a n c e r _ d e a t h s _ a m o n g _ a _ g r o u p _ o f _ w o r k e r s _ e x p o s e d _ t o _ i t _ m o r e _ t h a n _ N _ y e a r s _ a g o _ r e s e a r c h e r s _ r e p o r t e d - t h e _ a s b e s t o s _ f i b e r _ < u n k > _ i s _ u n u s u a l l y _ < u n k > _ o n c e _ i t _ e n t e r s _ t h e _ < u n k > _ w i t h _ e v e n _ b r i e f _ e x p o s u r e s _ t o _ i t _ c a u s i n g _ s y m p t o m s _ t h a t _ s h o w _ u p _ d e c a d e s _ l a t e r _ r e s e a r c h e r s _ s a i d - < u n k > _ i n c . _ t h e _ u n i t _ o f _ n e w _ y o r k - b a s e d _ < u n k > _ c o r p . _ t h a t _ m a k e s _ k e n t _ c i g a r e t t e s _ s t o p p e d _ u s i n g _ < u n k > _ i n _ i t s _ < u n k > _ c i g a r e t t e _ f i l t e r s _ i n _ N - a l t h o u g h _ p r e l i m i n a r y _ f i n d i n g s _ w e r e _ r e p o r t e d _ m o r e _ t h a n _ a _ y e a r _ a g o _ t h e _ l a t e s t _ r e s u l t s _ a p p e a r _ i n _ t o d a y _ ' s _ n e w _ e n g l a n d _ j o u r n a l _ o f _ m e d i c i n e _ a _ f o r u m _ l i k e l y _ t o _ b r i n g _ n e w _ a t t e n t i o n _ t o _ t h e _ p r o b l e m - a _ < u n k > _ < u n k > _ s a i d _ t h i s _ i s _ a n _ o l d _ s t o r y - w e _ ' r e _ t a l k i n g _ a b o u t _ y e a r s _ a g o _ b e f o r e _ a n y o n e _ h e a r d _ o f _ a s b e s t o s _ h a v i n g _ a n y _ q u e s t i o n a b l e _ p r o p e r t i e s - t h e r e _ i s _ n o _ a s b e s t o s _ i n _ o u r _ p r o d u c t s _ n o w - n e i t h e r _ < u n k > _ n o r _ t h e _ r e s e a r c h e r s _ w h o _ s t u d i e d _ t h e _ w o r k e r s _ w e r e _ a w a r e _ o f _ a n y _ r e s e a r c h _ o n _ s m o k e r s _ o f _ t h e _ k e n t _ c i g a r e t t e s - w e _ h a v e _ n o _ u s e f u l _ i n f o r m a t i o n _ o n _ w h e t h e r _ u s e r s _ a r e _ a t _ r i s k _ s a i d _ j a m e s _ a . _ < u n k > _ o f _ b o s t o n _ ' s _ < u n k > _ c a n c e r _ i n s t i t u t e - d r . _ < u n k > _ l e d _ a _ t e a m _ o f _ r e s e a r c h e r s _ f r o m _ t h e _ n a t i o n a l _ c a n c e r _ i n s t i t u t e _ a n d _ t h e _ m e d i c a l _ s c h o o l s _ o f _ h a r v a r d _ u n i v e r s i t y _ a n d _ b o s t o n _ u n i v e r s i t y - t h e _ < u n k > _ s p o k e s w o m a n _ s a i d _ a s b e s t o s _ w a s _ u s e d _ i n _ v e r y _ m o d e s t _ a m o u n t s _ i n _ m a k i n g _ p a p e r _ f o r _ t h e _ f i l t e r s _ i n _ t h e _ e a r l y _ 1 9 5 0 s _ a n d _ r e p l a c e d _ w i t h _ a _ d i f f e r e n t _ t y p e _ o f _ < u n k > _ i n _ N - f r o m _ N _ t o _ N _ N _ b i l l i o n _ k e n t _ c i g a r e t t e s _ w i t h _ t h e _ f i l t e r s _ w e r e _ s o l d _ t h e _ c o m p a n y _ s a i d \ No newline at end of file +a e r _ b a n k n o t e _ b e r l i t z _ c a l l o w a y \ No newline at end of file diff --git a/multithreaded/examm_mt_nlp.cxx b/multithreaded/examm_mt_nlp.cxx index 2b1b921d..1cfb779f 100644 --- a/multithreaded/examm_mt_nlp.cxx +++ b/multithreaded/examm_mt_nlp.cxx @@ -94,15 +94,44 @@ int main(int argc, char **argv) int32_t word_offset = 1; get_argument(arguments,"--word_offset",true,word_offset); - uint32_t batch_size = 64; - get_argument(arguments,"--batch_size",true,batch_size); + uint32_t sequence_length = 4; + get_argument(arguments,"--sequence_length",false,sequence_length); Corpus* corpus_sets = Corpus::generate_from_arguments(arguments); corpus_sets->export_training_series(word_offset,training_inputs,training_outputs); corpus_sets->export_test_series(word_offset,validation_inputs,validation_outputs); - Log::info("exported word series.\n"); + + for (size_t i = 0; i < training_inputs.size(); i++) + { + for (size_t j = 0; j < training_inputs[i].size(); j++) + { + for (size_t k = 0; k < training_inputs[i][j].size(); k++) + { + std::cout<get_number_inputs(); int number_outputs = corpus_sets->get_number_outputs(); diff --git a/rnn/mse.cxx b/rnn/mse.cxx index 37c085ef..04e95201 100644 --- a/rnn/mse.cxx +++ b/rnn/mse.cxx @@ -110,3 +110,45 @@ void get_mae(RNN *genome, const vector< vector > &expected, double &mae_ } } } + + +void get_se(RNN *genome, const vector< vector > &expected, double &ce_sum, vector< vector > &deltas) { + deltas.assign(genome->output_nodes.size(), vector(expected[0].size(), 0.0)); + + ce_sum = 0.0; + double softmax; + double error; + + for (uint32_t i = 0; i < genome->output_nodes.size(); i++) { + genome->output_nodes[i]->error_values.resize(expected[i].size()); + } + + + for (uint32_t j = 0; j < expected[0].size(); j++) { + double softmax_sum = 0.0; + double cross_entropy = 0.0; + // get sum of all the outputs of the timestep j from all output node i + for (uint32_t i = 0; i < genome->output_nodes.size(); i++) { + softmax_sum += exp(genome->output_nodes[i]->output_values[j]); + } + + // for each + + for (uint32_t i = 0; i < genome->output_nodes.size(); i++) { + softmax = exp(genome->output_nodes[i]->output_values[j]) / softmax_sum; + error = softmax - expected[i][j]; + deltas[i][j] = error; + genome->output_nodes[i]->error_values[j] = error; + cross_entropy = -expected[i][j] * log(softmax); + ce_sum += cross_entropy; + } + } + + + double d_ce = ce_sum * (1.0 / expected[0].size()); + for (uint32_t i = 0; i < genome->output_nodes.size(); i++) { + for (uint32_t j = 0; j < expected[i].size(); j++) { + deltas[i][j] *= d_ce; + } + } +} \ No newline at end of file diff --git a/rnn/mse.hxx b/rnn/mse.hxx index 063dc6f2..7d9da651 100644 --- a/rnn/mse.hxx +++ b/rnn/mse.hxx @@ -7,10 +7,13 @@ using std::vector; #include "rnn.hxx" void get_mse(const vector &output_values, const vector &expected, double &mse, vector &deltas); -void get_mse(RNN* genome, const vector< vector > &expected, double &mse, vector< vector > &deltas); +void get_mse(RNN* genome, const vector< vector > &expected, double &mse_sum, vector< vector > &deltas); void get_mae(const vector &output_values, const vector &expected, double &mae, vector &deltas); -void get_mae(RNN* genome, const vector< vector > &expected, double &mae, vector< vector > &deltas); +void get_mae(RNN* genome, const vector< vector > &expected, double &mae_sum, vector< vector > &deltas); +//Have to do this +//void get_se(const vector &output_values, const vector &expected, double &ce, vector &deltas); +void get_se(RNN* genome, const vector< vector > &expected, double &ce_sum, vector< vector > &deltas); #endif diff --git a/rnn/rnn.cxx b/rnn/rnn.cxx index 2af29caf..d8b79497 100644 --- a/rnn/rnn.cxx +++ b/rnn/rnn.cxx @@ -413,6 +413,16 @@ double RNN::calculate_error_softmax(const vector< vector > &expected_out double error; double softmax = 0.0; + // for (size_t j = 0; j < expected_outputs[0].size(); j++) + // { + + // for (size_t i = 0; i < expected_outputs.size(); i++) + // { + // std::cout << expected_outputs[i][j] << " "; + // } + // std::cout << std::endl; + // } + for (uint32_t i = 0; i < output_nodes.size(); i++) { output_nodes[i]->error_values.resize(expected_outputs[i].size()); } @@ -437,7 +447,7 @@ double RNN::calculate_error_softmax(const vector< vector > &expected_out } } - return cross_entropy_sum; + return cross_entropy_sum; } double RNN::calculate_error_mse(const vector< vector > &expected_outputs) { @@ -638,7 +648,7 @@ void RNN::get_analytic_gradient(const vector &test_parameters, const vec if(use_regression) { mse = calculate_error_mse(outputs); - backward_pass(mse * (1.0 / outputs[0].size())*2.0, using_dropout, training, dropout_probability); + backward_pass(mse * (1.0 / outputs[0].size()) * 2.0, using_dropout, training, dropout_probability); } else { mse = calculate_error_softmax(outputs); @@ -679,11 +689,17 @@ void RNN::get_empirical_gradient(const vector &test_parameters, const ve empirical_gradient.assign(test_parameters.size(), 0.0); vector< vector > deltas; - + double original_mse = 0.0; set_weights(test_parameters); forward_pass(inputs, using_dropout, training, dropout_probability); - double original_mse = calculate_error_mse(outputs); + + + if (use_regression) { + original_mse = calculate_error_mse(outputs); + } else { + original_mse = calculate_error_softmax(outputs); + } double save; double diff = 0.00001; @@ -696,13 +712,22 @@ void RNN::get_empirical_gradient(const vector &test_parameters, const ve parameters[i] = save - diff; set_weights(parameters); forward_pass(inputs, using_dropout, training, dropout_probability); - get_mse(this, outputs, mse1, deltas); + if (use_regression) { + get_mse(this,outputs,mse1,deltas); + } else { + get_se(this,outputs,mse1,deltas); + } + parameters[i] = save + diff; set_weights(parameters); forward_pass(inputs, using_dropout, training, dropout_probability); - get_mse(this, outputs, mse2, deltas); - + + if (use_regression) { + get_mse(this,outputs,mse2,deltas); + } else { + get_se(this,outputs,mse2,deltas); + } empirical_gradient[i] = (mse2 - mse1) / (2.0 * diff); empirical_gradient[i] *= original_mse; diff --git a/rnn/rnn.hxx b/rnn/rnn.hxx index 17ed98df..8d1c2711 100644 --- a/rnn/rnn.hxx +++ b/rnn/rnn.hxx @@ -116,8 +116,10 @@ class RNN { //RNN* copy(); - friend void get_mse(RNN* genome, const vector< vector > &expected, double &mse, vector< vector > &deltas); - friend void get_mae(RNN* genome, const vector< vector > &expected, double &mae, vector< vector > &deltas); + friend void get_mse(RNN* genome, const vector< vector > &expected, double &mse_sum, vector< vector > &deltas); + friend void get_mae(RNN* genome, const vector< vector > &expected, double &mae_sum, vector< vector > &deltas); + friend void get_se(RNN* genome, const vector< vector > &expected, double &ce_sum, vector< vector > &deltas); + }; #endif diff --git a/rnn/rnn_genome.cxx b/rnn/rnn_genome.cxx index 3d114937..23a85d5a 100644 --- a/rnn/rnn_genome.cxx +++ b/rnn/rnn_genome.cxx @@ -963,7 +963,7 @@ void RNN_Genome::backpropagate(const vector< vector< vector > > &inputs, for (int32_t i = 0; i < n_series; i++) { RNN* r = this->get_rnn(); r->enable_use_regression(use_regression); - rnns.push_back( this->get_rnn() ); + rnns.push_back(r); } vector parameters = initial_parameters; diff --git a/rnn/rnn_node_interface.hxx b/rnn/rnn_node_interface.hxx index 5b8ac873..1b08c41b 100644 --- a/rnn/rnn_node_interface.hxx +++ b/rnn/rnn_node_interface.hxx @@ -207,6 +207,7 @@ class RNN_Node_Interface { friend void get_mse(RNN* genome, const vector< vector > &expected, double &mse, vector< vector > &deltas); friend void get_mae(RNN* genome, const vector< vector > &expected, double &mae, vector< vector > &deltas); + friend void get_se(RNN *genome, const vector> &expected, double &mae, vector> &deltas); }; diff --git a/rnn_tests/gradient_test.cxx b/rnn_tests/gradient_test.cxx index c56f8f0e..f444e035 100644 --- a/rnn_tests/gradient_test.cxx +++ b/rnn_tests/gradient_test.cxx @@ -7,6 +7,7 @@ using std::ifstream; #include using std::minstd_rand0; using std::uniform_real_distribution; +using std::uniform_int_distribution; #include using std::string; @@ -49,6 +50,13 @@ void generate_random_vector(int number_parameters, vector &v) { } } +void generate_random_oneHot_vector(int number_parameters, vector &v) { + uniform_int_distribution dist(0,number_parameters); + v.resize(number_parameters,0); + int index = dist(generator); + v[index] = 1.0; +} + void gradient_test(string name, RNN_Genome *genome, const vector< vector > &inputs, const vector< vector > &outputs) { double analytic_mse, empirical_mse; vector parameters; @@ -61,6 +69,7 @@ void gradient_test(string name, RNN_Genome *genome, const vector< vector Log::debug("got genome \n"); + rnn->enable_use_regression(true); for (int32_t i = 0; i < test_iterations; i++) { if (i == 0) Log::debug_no_header("\n"); diff --git a/rnn_tests/gradient_test.hxx b/rnn_tests/gradient_test.hxx index 51645eed..3dc04765 100644 --- a/rnn_tests/gradient_test.hxx +++ b/rnn_tests/gradient_test.hxx @@ -31,7 +31,8 @@ using std::vector; void initialize_generator(); void generate_random_vector(int number_parameters, vector &v); +void generate_random_oneHot_vector(int number_parameters, vector &v); -void gradient_test(string name, RNN_Genome *genome, const vector< vector > &inputs, const vector< vector > &outputs); + void gradient_test(string name, RNN_Genome *genome, const vector> &inputs, const vector> &outputs); #endif diff --git a/word_series/word_series.cxx b/word_series/word_series.cxx index 35c527e5..d9396401 100644 --- a/word_series/word_series.cxx +++ b/word_series/word_series.cxx @@ -527,7 +527,7 @@ Corpus* Corpus::generate_from_arguments(const vector &arguments){ cs->filenames.clear(); cs->word_index.clear(); cs->vocab.clear(); - uint32_t _batch_size; + uint32_t _sequence_length; if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { vector training_filenames; @@ -555,11 +555,9 @@ Corpus* Corpus::generate_from_arguments(const vector &arguments){ exit(1); } + get_argument(arguments, "--sequence_length", true, _sequence_length); - - get_argument(arguments, "--batch_size", true, _batch_size); - - cs->batch_size =_batch_size; + cs->sequence_length = _sequence_length; cs->all_parameter_names.clear(); cs->input_parameter_names.clear(); cs->output_parameter_names.clear(); @@ -853,39 +851,43 @@ void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, co normalize_type = "avg_std_dev"; } -vector > > batchify(int batch_size, vector > > data) { +vector > > split_sequence_length(int sequence_length, vector > > data) { - vector > > batchData; + vector > > sequenceData; int no_features = data[0].size(); - int no_batches = 0; - int batchNo = -1; + int no_sequences = 0; + int sequenceNo = -1; for (int i = 0; i < data.size(); ++i) { - no_batches += (data[i][0].size() -1) / batch_size +1; + no_sequences += (data[i][0].size() - 1) / sequence_length + 1; } - if (data[0][0].size() < batch_size) { + if (data[0][0].size() < sequence_length) + { - batchData.resize(no_batches, vector >(no_features, vector(data[0][0].size()))); - }else { - batchData.resize(no_batches, vector >(no_features, vector(batch_size))); + sequenceData.resize(no_sequences, vector>(no_features, vector(data[0][0].size()))); + } + else + { + sequenceData.resize(no_sequences, vector>(no_features, vector(sequence_length))); } for (int k = 0; k < data.size(); ++k) { for (int j = 0; j < data[k][0].size(); ++j) { for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && j%batch_size == 0) batchNo++; + if (i == 0 && j % sequence_length == 0) + sequenceNo++; - batchData[batchNo][i][j%batch_size] = data[k][i][j]; + sequenceData[sequenceNo][i][j % sequence_length] = data[k][i][j]; } } } - return batchData; + return sequenceData; } @@ -909,9 +911,8 @@ void Corpus::export_sent_series(const vector &series_indexes, int word_offs sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); } - inputs = batchify(batch_size,temp_inputs); - outputs = batchify(batch_size,temp_outputs); - + inputs = split_sequence_length(sequence_length, temp_inputs); + outputs = split_sequence_length(sequence_length, temp_outputs); } /** diff --git a/word_series/word_series.hxx b/word_series/word_series.hxx index fe9953e4..1b223e12 100644 --- a/word_series/word_series.hxx +++ b/word_series/word_series.hxx @@ -165,9 +165,9 @@ class Corpus { private: /** - * Specifices the batches in which the dataset is to be divided into chunks. + * Specifices the chunks in which the dataset is to be divided into chunks. */ - uint32_t batch_size; + uint32_t sequence_length; string normalize_type; /** From 92ea075b9b97cc59aee3c1ef6bfdd22b770d5b73 Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Sun, 1 Nov 2020 13:01:52 -0500 Subject: [PATCH 08/25] minor updates --- README.md | 36 +++-- datasets/penn/input.txt | 1 - datasets/penn/train_small.txt | 22 +++ datasets/penn/valid_small.txt | 3 + datasets/pennchar/input.txt | 1 - datasets/pennchar/train_small.txt | 13 ++ datasets/pennchar/valid_small.txt | 5 + multithreaded/examm_mt_nlp.cxx | 29 +--- multithreaded/examm_nlp_mt.cxx | 227 ++++++++++++++++++++++++++++++ rnn/rnn_genome.cxx | 6 +- 10 files changed, 301 insertions(+), 42 deletions(-) delete mode 100644 datasets/penn/input.txt create mode 100644 datasets/penn/train_small.txt create mode 100644 datasets/penn/valid_small.txt delete mode 100644 datasets/pennchar/input.txt create mode 100644 datasets/pennchar/train_small.txt create mode 100644 datasets/pennchar/valid_small.txt create mode 100644 multithreaded/examm_nlp_mt.cxx diff --git a/README.md b/README.md index ddbeaeed..a6bcc1da 100644 --- a/README.md +++ b/README.md @@ -35,33 +35,51 @@ The datasets used for this part of the code are PennChar, PennTreeBank and WikiT ``` -./multithreaded/examm_mt_nlp --number_threads 9 --training_filenames ../datasets/pennchar/train.txt --test_filenames ../datasets/pennchar/test.txt --word_offset 1 --sequence_length 4 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level +.//multithreaded/examm_mt_nlp --number_threads 9 --training_filenames ../datasets/pennchar/train_small.txt --test_filenames ../datasets/pennchar/valid_small.txt --word_offset 1 --sequence_length 64 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info -``` -And a parallel version using MPI: ``` -~/exact/build/ $ mpirun -np 9 ./mpi/examm_mpi_nlp --training_filenames ../datasets/pennchar/train.txt --test_filenames ../datasets/pennchar/test.txt --word_offset 1 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info -``` ### To use PennTreeBank Word Prediction as dataset and use Simple and ENARC as memory cell ``` -./multithreaded/examm_mt_nlp --number_threads 9 --training_filenames ../datasets/penn/train.txt --test_filenames ../datasets/pennchar/test.txt --word_offset 1 --sequence_length 4 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info +./multithreaded/examm_mt_nlp --number_threads 9 --training_filenames ../datasets/penn/train_small.txt --test_filenames ../datasets/pennchar/valid_small.txt --word_offset 1 --sequence_length 64 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info ``` -And a parallel version using MPI: +The std_out can be seen in file *main_out* for 20 genomes. The number of genomes can be increase. For ideal results, use 20000 genomes. + + +### To check gradients for ENARC cell ``` -~/exact/build/ $ mpirun -np 9 ./mpi/examm_mpi_nlp --training_filenames ../datasets/pennchar/train.txt --test_filenames ../datasets/pennchar/test.txt --word_offset 1 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info +/rnn_tests/test_enarc_gradients --std_message_level info --file_message_level info --output_directory "./test_output" --input_length 2 ``` -The std_out can be seen in file *main_out* for 20 genomes. The number of genomes can be increase. For ideal results, use 20000 genomes. + +### To check gradients for ENAS_DAG cell + +``` +/rnn_tests/test_enas_dag_gradients --std_message_level info --file_message_level info --output_directory "./test_output" --input_length 2 + +``` + + +### To check gradients for Random DAG cell + +``` +/rnn_tests/test_random_dag_gradients --std_message_level info --file_message_level info --output_directory "./test_output" --input_length 2 + +``` + + + + + ### To use ENARC as Memory Cell on Coal Dataset diff --git a/datasets/penn/input.txt b/datasets/penn/input.txt deleted file mode 100644 index f355fa25..00000000 --- a/datasets/penn/input.txt +++ /dev/null @@ -1 +0,0 @@ -consumers may move their telephones a little closer \ No newline at end of file diff --git a/datasets/penn/train_small.txt b/datasets/penn/train_small.txt new file mode 100644 index 00000000..768f07c5 --- /dev/null +++ b/datasets/penn/train_small.txt @@ -0,0 +1,22 @@ + no it was n't black monday + but while the new york stock exchange did n't fall apart friday as the dow jones industrial average plunged N points most of it in the final hour it barely managed to stay this side of chaos + some circuit breakers installed after the october N crash failed their first test traders say unable to cool the selling panic in both stocks and futures + the N stock specialist firms on the big board floor the buyers and sellers of last resort who were criticized after the N crash once again could n't handle the selling pressure + big investment banks refused to step up to the plate to support the beleaguered floor traders by buying big blocks of stock traders say + heavy selling of standard & poor 's 500-stock index futures in chicago beat stocks downward + seven big board stocks ual amr bankamerica walt disney capital cities\/abc philip morris and pacific telesis group stopped trading and never resumed + the has already begun + the equity market was + once again the specialists were not able to handle the imbalances on the floor of the new york stock exchange said christopher senior vice president at securities corp + james chairman of specialists henderson brothers inc. it is easy to say the specialist is n't doing his job + when the dollar is in a even central banks ca n't stop it + speculators are calling for a degree of liquidity that is not there in the market + many money managers and some traders had already left their offices early friday afternoon on a warm autumn day because the stock market was so quiet + then in a plunge the dow jones industrials in barely an hour surrendered about a third of their gains this year up a 190.58-point or N N loss on the day in trading volume + trading accelerated to N million shares a record for the big board + at the end of the day N million shares were traded + the dow jones industrials closed at N + the dow 's decline was second in point terms only to the black monday crash that occurred oct. N N + in percentage terms however the dow 's dive was the ever and the sharpest since the market fell N or N N a week after black monday + the dow fell N N on black monday + shares of ual the parent of united airlines were extremely active all day friday reacting to news and rumors about the proposed $ N billion buy-out of the airline by an group \ No newline at end of file diff --git a/datasets/penn/valid_small.txt b/datasets/penn/valid_small.txt new file mode 100644 index 00000000..12332c2d --- /dev/null +++ b/datasets/penn/valid_small.txt @@ -0,0 +1,3 @@ +we 've been spending a lot of time in los angeles talking to tv production people says mike parks president of call interactive which supplied technology for both abc sports and nbc 's consumer minutes + with the competitiveness of the television market these days everyone is looking for a way to get viewers more excited + one of the leaders behind the expanded use of N numbers is call interactive a joint venture of giants american express co. and american telephone & telegraph co \ No newline at end of file diff --git a/datasets/pennchar/input.txt b/datasets/pennchar/input.txt deleted file mode 100644 index 2e8ab459..00000000 --- a/datasets/pennchar/input.txt +++ /dev/null @@ -1 +0,0 @@ -a e r _ b a n k n o t e _ b e r l i t z _ c a l l o w a y \ No newline at end of file diff --git a/datasets/pennchar/train_small.txt b/datasets/pennchar/train_small.txt new file mode 100644 index 00000000..54be8ba2 --- /dev/null +++ b/datasets/pennchar/train_small.txt @@ -0,0 +1,13 @@ +n o _ i t _ w a s _ n ' t _ b l a c k _ m o n d a y + b u t _ w h i l e _ t h e _ n e w _ y o r k _ s t o c k _ e x c h a n g e _ d i d _ n ' t _ f a l l _ a p a r t _ f r i d a y _ a s _ t h e _ d o w _ j o n e s _ i n d u s t r i a l _ a v e r a g e _ p l u n g e d _ N _ p o i n t s _ m o s t _ o f _ i t _ i n _ t h e _ f i n a l _ h o u r _ i t _ b a r e l y _ m a n a g e d _ t o _ s t a y _ t h i s _ s i d e _ o f _ c h a o s + s o m e _ c i r c u i t _ b r e a k e r s _ i n s t a l l e d _ a f t e r _ t h e _ o c t o b e r _ N _ c r a s h _ f a i l e d _ t h e i r _ f i r s t _ t e s t _ t r a d e r s _ s a y _ u n a b l e _ t o _ c o o l _ t h e _ s e l l i n g _ p a n i c _ i n _ b o t h _ s t o c k s _ a n d _ f u t u r e s + t h e _ N _ s t o c k _ s p e c i a l i s t _ f i r m s _ o n _ t h e _ b i g _ b o a r d _ f l o o r _ t h e _ b u y e r s _ a n d _ s e l l e r s _ o f _ l a s t _ r e s o r t _ w h o _ w e r e _ c r i t i c i z e d _ a f t e r _ t h e _ N _ c r a s h _ o n c e _ a g a i n _ c o u l d _ n ' t _ h a n d l e _ t h e _ s e l l i n g _ p r e s s u r e + b i g _ i n v e s t m e n t _ b a n k s _ r e f u s e d _ t o _ s t e p _ u p _ t o _ t h e _ p l a t e _ t o _ s u p p o r t _ t h e _ b e l e a g u e r e d _ f l o o r _ t r a d e r s _ b y _ b u y i n g _ b i g _ b l o c k s _ o f _ s t o c k _ t r a d e r s _ s a y + h e a v y _ s e l l i n g _ o f _ s t a n d a r d _ & _ p o o r _ ' s _ 5 0 0 - s t o c k _ i n d e x _ f u t u r e s _ i n _ c h i c a g o _ < u n k > _ b e a t _ s t o c k s _ d o w n w a r d + s e v e n _ b i g _ b o a r d _ s t o c k s _ u a l _ a m r _ b a n k a m e r i c a _ w a l t _ d i s n e y _ c a p i t a l _ c i t i e s \ / a b c _ p h i l i p _ m o r r i s _ a n d _ p a c i f i c _ t e l e s i s _ g r o u p _ s t o p p e d _ t r a d i n g _ a n d _ n e v e r _ r e s u m e d + t h e _ < u n k > _ h a s _ a l r e a d y _ b e g u n + t h e _ e q u i t y _ m a r k e t _ w a s _ < u n k > + o n c e _ a g a i n _ t h e _ s p e c i a l i s t s _ w e r e _ n o t _ a b l e _ t o _ h a n d l e _ t h e _ i m b a l a n c e s _ o n _ t h e _ f l o o r _ o f _ t h e _ n e w _ y o r k _ s t o c k _ e x c h a n g e _ s a i d _ c h r i s t o p h e r _ < u n k > _ s e n i o r _ v i c e _ p r e s i d e n t _ a t _ < u n k > _ s e c u r i t i e s _ c o r p + < u n k > _ j a m e s _ < u n k > _ c h a i r m a n _ o f _ s p e c i a l i s t s _ h e n d e r s o n _ b r o t h e r s _ i n c . _ i t _ i s _ e a s y _ t o _ s a y _ t h e _ s p e c i a l i s t _ i s _ n ' t _ d o i n g _ h i s _ j o b + w h e n _ t h e _ d o l l a r _ i s _ i n _ a _ < u n k > _ e v e n _ c e n t r a l _ b a n k s _ c a _ n ' t _ s t o p _ i t + s p e c u l a t o r s _ a r e _ c a l l i n g _ f o r _ a _ d e g r e e _ o f _ l i q u i d i t y _ t h a t _ i s _ n o t _ t h e r e _ i n _ t h e _ m a r k e t \ No newline at end of file diff --git a/datasets/pennchar/valid_small.txt b/datasets/pennchar/valid_small.txt new file mode 100644 index 00000000..10993af4 --- /dev/null +++ b/datasets/pennchar/valid_small.txt @@ -0,0 +1,5 @@ +c o n s u m e r s _ m a y _ w a n t _ t o _ m o v e _ t h e i r _ t e l e p h o n e s _ a _ l i t t l e _ c l o s e r _ t o _ t h e _ t v _ s e t + < u n k > _ < u n k > _ w a t c h i n g _ a b c _ ' s _ m o n d a y _ n i g h t _ f o o t b a l l _ c a n _ n o w _ v o t e _ d u r i n g _ < u n k > _ f o r _ t h e _ g r e a t e s t _ p l a y _ i n _ N _ y e a r s _ f r o m _ a m o n g _ f o u r _ o r _ f i v e _ < u n k > _ < u n k > + t w o _ w e e k s _ a g o _ v i e w e r s _ o f _ s e v e r a l _ n b c _ < u n k > _ c o n s u m e r _ s e g m e n t s _ s t a r t e d _ c a l l i n g _ a _ N _ n u m b e r _ f o r _ a d v i c e _ o n _ v a r i o u s _ < u n k > _ i s s u e s + a n d _ t h e _ n e w _ s y n d i c a t e d _ r e a l i t y _ s h o w _ h a r d _ c o p y _ r e c o r d s _ v i e w e r s _ ' _ o p i n i o n s _ f o r _ p o s s i b l e _ a i r i n g _ o n _ t h e _ n e x t _ d a y _ ' s _ s h o w + i n t e r a c t i v e _ \ No newline at end of file diff --git a/multithreaded/examm_mt_nlp.cxx b/multithreaded/examm_mt_nlp.cxx index 1cfb779f..c1074968 100644 --- a/multithreaded/examm_mt_nlp.cxx +++ b/multithreaded/examm_mt_nlp.cxx @@ -94,7 +94,7 @@ int main(int argc, char **argv) int32_t word_offset = 1; get_argument(arguments,"--word_offset",true,word_offset); - uint32_t sequence_length = 4; + int32_t sequence_length = 64; get_argument(arguments,"--sequence_length",false,sequence_length); Corpus* corpus_sets = Corpus::generate_from_arguments(arguments); @@ -103,33 +103,6 @@ int main(int argc, char **argv) corpus_sets->export_test_series(word_offset,validation_inputs,validation_outputs); - for (size_t i = 0; i < training_inputs.size(); i++) - { - for (size_t j = 0; j < training_inputs[i].size(); j++) - { - for (size_t k = 0; k < training_inputs[i][j].size(); k++) - { - std::cout< + +#include +using std::condition_variable; + +#include +using std::setw; + +#include +using std::mutex; + +#include +using std::string; + +#include +using std::thread; + +#include +using std::vector; + +#include "common/arguments.hxx" +#include "common/log.hxx" + +#include "rnn/examm.hxx" + +#include"word_series/word_series.hxx" +#include "time_series/time_series.hxx" + +mutex examm_mutex; + +vector arguments; + +EXAMM *examm; + + +bool finished = false; + + +vector< vector< vector > > training_inputs; +vector< vector< vector > > training_outputs; +vector< vector< vector > > validation_inputs; +vector< vector< vector > > validation_outputs; + + +void examm_thread(int id) { + + while (true) { + examm_mutex.lock(); + Log::set_id("main"); + RNN_Genome *genome = examm->generate_genome(); + examm_mutex.unlock(); + + if (genome == NULL) break; //generate_individual returns NULL when the search is done + + string log_id = "genome_" + to_string(genome->get_generation_id()) + "_thread_" + to_string(id); + Log::set_id(log_id); + //genome->backpropagate(training_inputs, training_outputs, validation_inputs, validation_outputs); + genome->backpropagate_stochastic(training_inputs, training_outputs, validation_inputs, validation_outputs); + Log::release_id(log_id); + + examm_mutex.lock(); + Log::set_id("main"); + examm->insert_genome(genome); + examm_mutex.unlock(); + + delete genome; + } + +} + +void get_individual_inputs(string str, vector& tokens) { + string word = ""; + for (auto x : str) { + if (x == ',') { + tokens.push_back(word); + word = ""; + }else + word = word + x; + } + tokens.push_back(word); +} + + +int main(int argc, char **argv) +{ + vector arguments = vector(argv, argv + argc); + + Log::initialize(arguments); + Log::set_id("main"); + + int number_threads; + get_argument(arguments, "--number_threads", true, number_threads); + + int32_t word_offset = 1; + get_argument(arguments,"--word_offset",true,word_offset); + + + Corpus* corpus_sets = Corpus::generate_from_arguments(arguments); + + corpus_sets->export_training_series(word_offset,training_inputs,training_outputs); + corpus_sets->export_test_series(word_offset,validation_inputs,validation_outputs); + + Log::info("exported word series.\n"); + + int number_inputs = corpus_sets->get_number_inputs(); + int number_outputs = corpus_sets->get_number_outputs(); + + Log::info("number_inputs: %d, number_outputs: %d\n", number_inputs, number_outputs); + + int32_t population_size; + get_argument(arguments, "--population_size", true, population_size); + + int32_t number_islands; + get_argument(arguments, "--number_islands", true, number_islands); + + int32_t max_genomes; + get_argument(arguments, "--max_genomes", true, max_genomes); + + string speciation_method = ""; + get_argument(arguments, "--speciation_method", false, speciation_method); + + int32_t extinction_event_generation_number = 0; + get_argument(arguments, "--extinction_event_generation_number", false, extinction_event_generation_number); + + int32_t islands_to_exterminate; + get_argument(arguments, "--islands_to_exterminate", false, islands_to_exterminate); + + string island_ranking_method = ""; + get_argument(arguments, "--island_ranking_method", false, island_ranking_method); + + string repopulation_method = ""; + get_argument(arguments, "--repopulation_method", false, repopulation_method); + + int32_t repopulation_mutations = 0; + get_argument(arguments, "--repopulation_mutations", false, repopulation_mutations); + + bool repeat_extinction = argument_exists(arguments, "--repeat_extinction"); + + int32_t bp_iterations; + get_argument(arguments, "--bp_iterations", true, bp_iterations); + + double learning_rate = 0.001; + get_argument(arguments, "--learning_rate", false, learning_rate); + + double high_threshold = 1.0; + bool use_high_threshold = get_argument(arguments, "--high_threshold", false, high_threshold); + + double low_threshold = 0.05; + bool use_low_threshold = get_argument(arguments, "--low_threshold", false, low_threshold); + + double dropout_probability = 0.0; + bool use_dropout = get_argument(arguments, "--dropout_probability", false, dropout_probability); + + string output_directory = ""; + get_argument(arguments, "--output_directory", false, output_directory); + + vector possible_node_types; + get_argument_vector(arguments, "--possible_node_types", false, possible_node_types); + + int32_t min_recurrent_depth = 1; + get_argument(arguments, "--min_recurrent_depth", false, min_recurrent_depth); + + int32_t max_recurrent_depth = 10; + get_argument(arguments, "--max_recurrent_depth", false, max_recurrent_depth); + + int32_t use_regression = 1; + get_argument(arguments,"--use_regression",false,use_regression); + + + RNN_Genome *seed_genome = NULL; + string genome_file_name = ""; + if (get_argument(arguments, "--genome_bin", false, genome_file_name)) { + seed_genome = new RNN_Genome(genome_file_name); + + string transfer_learning_version; + get_argument(arguments, "--transfer_learning_version", true, transfer_learning_version); + + bool epigenetic_weights = argument_exists(arguments, "--epigenetic_weights"); + + seed_genome->transfer_to(corpus_sets->get_input_parameter_names(), corpus_sets->get_output_parameter_names(), transfer_learning_version, epigenetic_weights, min_recurrent_depth, max_recurrent_depth); + } + + bool start_filled = false; + if (genome_file_name != "") { + get_argument(arguments, "--start_filled", false, start_filled); + } + + examm = new EXAMM(population_size, number_islands, max_genomes, extinction_event_generation_number, islands_to_exterminate, island_ranking_method, + repopulation_method, repopulation_mutations, + repeat_extinction, speciation_method, + corpus_sets->get_input_parameter_names(), + corpus_sets->get_output_parameter_names(), + corpus_sets->get_normalize_type(), + corpus_sets->get_normalize_mins(), + corpus_sets->get_normalize_maxs(), + corpus_sets->get_normalize_avgs(), + corpus_sets->get_normalize_std_devs(), + bp_iterations, learning_rate, + use_high_threshold, high_threshold, + use_low_threshold, low_threshold, + use_dropout, dropout_probability, + min_recurrent_depth, max_recurrent_depth, + //use_regression, + output_directory, + seed_genome, + start_filled); + + if (possible_node_types.size() > 0) { + examm->set_possible_node_types(possible_node_types); + } + + vector threads; + for (int32_t i = 0; i < number_threads; i++) { + threads.push_back( thread(examm_thread, i) ); + } + + for (int32_t i = 0; i < number_threads; i++) { + threads[i].join(); + } + + finished = true; + + Log::info("completed!\n"); + Log::release_id("main"); + + return 0; +} \ No newline at end of file diff --git a/rnn/rnn_genome.cxx b/rnn/rnn_genome.cxx index 23a85d5a..c9ada7cf 100644 --- a/rnn/rnn_genome.cxx +++ b/rnn/rnn_genome.cxx @@ -961,9 +961,9 @@ void RNN_Genome::backpropagate(const vector< vector< vector > > &inputs, int32_t n_series = inputs.size(); vector rnns; for (int32_t i = 0; i < n_series; i++) { - RNN* r = this->get_rnn(); - r->enable_use_regression(use_regression); - rnns.push_back(r); + //RNN* r = this->get_rnn(); + //r->enable_use_regression(use_regression); + rnns.push_back(this->get_rnn()); } vector parameters = initial_parameters; From 4cfbb16e63717f0e4411bdb2ba06ea234607997c Mon Sep 17 00:00:00 2001 From: Vinay Jain Date: Sun, 1 Nov 2020 13:03:44 -0500 Subject: [PATCH 09/25] Update README.md Edit pennchar to penn --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a6bcc1da..cb4e5b7f 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ The datasets used for this part of the code are PennChar, PennTreeBank and WikiT ``` -./multithreaded/examm_mt_nlp --number_threads 9 --training_filenames ../datasets/penn/train_small.txt --test_filenames ../datasets/pennchar/valid_small.txt --word_offset 1 --sequence_length 64 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info +./multithreaded/examm_mt_nlp --number_threads 9 --training_filenames ../datasets/penn/train_small.txt --test_filenames ../datasets/penn/valid_small.txt --word_offset 1 --sequence_length 64 --number_islands 2 --population_size 2 --max_genomes 20 --bp_iterations 2 --use_regression 0 --output_directory "./test_output" --possible_node_types simple ENARC --normalize min_max --std_message_level info --file_message_level info ``` From 7846d03a5da820d769785d7b84e50de8b441b2c6 Mon Sep 17 00:00:00 2001 From: Vinay Jain Date: Mon, 2 Nov 2020 13:57:59 -0500 Subject: [PATCH 10/25] Update README.md editing path of testing files. --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index cb4e5b7f..8f831f74 100644 --- a/README.md +++ b/README.md @@ -56,7 +56,7 @@ The std_out can be seen in file *main_out* for 20 genomes. The number of genomes ### To check gradients for ENARC cell ``` -/rnn_tests/test_enarc_gradients --std_message_level info --file_message_level info --output_directory "./test_output" --input_length 2 +./rnn_tests/test_enarc_gradients --std_message_level info --file_message_level info --output_directory "./test_output" --input_length 2 ``` @@ -64,7 +64,7 @@ The std_out can be seen in file *main_out* for 20 genomes. The number of genomes ### To check gradients for ENAS_DAG cell ``` -/rnn_tests/test_enas_dag_gradients --std_message_level info --file_message_level info --output_directory "./test_output" --input_length 2 +./rnn_tests/test_enas_dag_gradients --std_message_level info --file_message_level info --output_directory "./test_output" --input_length 2 ``` @@ -72,7 +72,7 @@ The std_out can be seen in file *main_out* for 20 genomes. The number of genomes ### To check gradients for Random DAG cell ``` -/rnn_tests/test_random_dag_gradients --std_message_level info --file_message_level info --output_directory "./test_output" --input_length 2 +./rnn_tests/test_random_dag_gradients --std_message_level info --file_message_level info --output_directory "./test_output" --input_length 2 ``` From 14b7c0ba4d2a40d8a07577a97158b80d05c16fab Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Thu, 5 Nov 2020 13:37:03 -0500 Subject: [PATCH 11/25] adding softmax output --- datasets/2018_coal/burner_small.csv | 7 ++ multithreaded/examm_mt_nlp.cxx | 4 +- rnn/mse.cxx | 16 +++- rnn/mse.hxx | 3 + rnn/rnn.cxx | 58 +++++++++++--- rnn/rnn_genome.cxx | 27 +++++-- rnn_tests/CMakeLists.txt | 4 + rnn_tests/test_softmax_output.cxx | 119 ++++++++++++++++++++++++++++ 8 files changed, 217 insertions(+), 21 deletions(-) create mode 100644 datasets/2018_coal/burner_small.csv create mode 100644 rnn_tests/test_softmax_output.cxx diff --git a/datasets/2018_coal/burner_small.csv b/datasets/2018_coal/burner_small.csv new file mode 100644 index 00000000..1c0d2694 --- /dev/null +++ b/datasets/2018_coal/burner_small.csv @@ -0,0 +1,7 @@ +Conditioner_Inlet_Temp,Conditioner_Outlet_Temp,Coal_Feeder_Rate,Primary_Air_Flow,Primary_Air_Split,System_Secondary_Air_Flow_Total,Secondary_Air_Flow,Secondary_Air_Split,Tertiary_Air_Split,Total_Comb_Air_Flow,Supp_Fuel_Flow,Main_Flm_Int +0.59995,0.371572,0.920551,0.708776,0.980315,0.8538,0.722619,0.0155477,0.0514978,0.736751,0.00340125,0.73803 +0.602043,0.370189,0.913209,0.709145,0.980433,0.843072,0.714865,0.015333,0.0519952,0.736368,0.00340125,0.777362 +0.598725,0.3653,0.923316,0.703603,0.980458,0.82285,0.700248,0.0151993,0.0526519,0.728916,0.00340125,0.77878 +0.598352,0.372956,0.907022,0.694319,0.980221,0.8082,0.689659,0.0153123,0.0536368,0.71841,0.00340125,0.736105 +0.600201,0.371572,0.920551,0.690012,0.980233,0.792356,0.678207,0.0152009,0.0542537,0.712665,0.00340125,0.776855 +0.598108,0.373892,0.917366,0.68872,0.979981,0.803057,0.685942,0.0154332,0.0546019,0.712764,0.00340125,0.779795 diff --git a/multithreaded/examm_mt_nlp.cxx b/multithreaded/examm_mt_nlp.cxx index c1074968..4ea0244b 100644 --- a/multithreaded/examm_mt_nlp.cxx +++ b/multithreaded/examm_mt_nlp.cxx @@ -53,8 +53,8 @@ void examm_thread(int id) { string log_id = "genome_" + to_string(genome->get_generation_id()) + "_thread_" + to_string(id); Log::set_id(log_id); - genome->backpropagate(training_inputs, training_outputs, validation_inputs, validation_outputs); - //genome->backpropagate_stochastic(training_inputs, training_outputs, validation_inputs, validation_outputs); + //genome->backpropagate(training_inputs, training_outputs, validation_inputs, validation_outputs); + genome->backpropagate_stochastic(training_inputs, training_outputs, validation_inputs, validation_outputs); Log::release_id(log_id); examm_mutex.lock(); diff --git a/rnn/mse.cxx b/rnn/mse.cxx index 04e95201..2388beb1 100644 --- a/rnn/mse.cxx +++ b/rnn/mse.cxx @@ -3,6 +3,9 @@ #include using std::vector; +#include +using std::max; + #include "rnn.hxx" void get_mse(const vector &output_values, const vector &expected, double &mse, vector &deltas) { @@ -123,19 +126,25 @@ void get_se(RNN *genome, const vector< vector > &expected, double &ce_su genome->output_nodes[i]->error_values.resize(expected[i].size()); } - + for (uint32_t j = 0; j < expected[0].size(); j++) { double softmax_sum = 0.0; double cross_entropy = 0.0; // get sum of all the outputs of the timestep j from all output node i + + double max_output_node_value = genome->output_nodes[0]->output_values[j]; + for (uint32_t i = 0; i < genome->output_nodes.size(); i++) { + max_output_node_value = max(max_output_node_value, genome->output_nodes[i]->output_values[j]); + } + for (uint32_t i = 0; i < genome->output_nodes.size(); i++) { - softmax_sum += exp(genome->output_nodes[i]->output_values[j]); + softmax_sum += exp(genome->output_nodes[i]->output_values[j] - max_output_node_value); } // for each for (uint32_t i = 0; i < genome->output_nodes.size(); i++) { - softmax = exp(genome->output_nodes[i]->output_values[j]) / softmax_sum; + softmax = exp(genome->output_nodes[i]->output_values[j] - max_output_node_value) / softmax_sum; error = softmax - expected[i][j]; deltas[i][j] = error; genome->output_nodes[i]->error_values[j] = error; @@ -144,6 +153,7 @@ void get_se(RNN *genome, const vector< vector > &expected, double &ce_su } } + // double d_ce = ce_sum * (1.0 / expected[0].size()); double d_ce = ce_sum * (1.0 / expected[0].size()); for (uint32_t i = 0; i < genome->output_nodes.size(); i++) { diff --git a/rnn/mse.hxx b/rnn/mse.hxx index 7d9da651..7576490c 100644 --- a/rnn/mse.hxx +++ b/rnn/mse.hxx @@ -4,6 +4,9 @@ #include using std::vector; +#include +using std::max; + #include "rnn.hxx" void get_mse(const vector &output_values, const vector &expected, double &mse, vector &deltas); diff --git a/rnn/rnn.cxx b/rnn/rnn.cxx index d8b79497..7c068e48 100644 --- a/rnn/rnn.cxx +++ b/rnn/rnn.cxx @@ -1,6 +1,7 @@ #include using std::sort; using std::upper_bound; +using std::max; #include @@ -413,15 +414,17 @@ double RNN::calculate_error_softmax(const vector< vector > &expected_out double error; double softmax = 0.0; - // for (size_t j = 0; j < expected_outputs[0].size(); j++) - // { + + + for (uint32_t j = 0; j < expected_outputs[0].size(); j++) + { + for (uint32_t i = 0; i < output_nodes.size(); i++) + { - // for (size_t i = 0; i < expected_outputs.size(); i++) - // { - // std::cout << expected_outputs[i][j] << " "; - // } - // std::cout << std::endl; - // } + Log::info("%f ",output_nodes[i]->output_values[j]); + } + Log::info("\n"); + } for (uint32_t i = 0; i < output_nodes.size(); i++) { output_nodes[i]->error_values.resize(expected_outputs[i].size()); @@ -431,15 +434,25 @@ double RNN::calculate_error_softmax(const vector< vector > &expected_out for (uint32_t j = 0; j < expected_outputs[0].size(); j++) { double softmax_sum = 0.0; double cross_entropy = 0.0; + + + //find max at each timestep + double max_output_node_value = output_nodes[0]->output_values[j]; + for (uint32_t i = 0; i < output_nodes.size(); i++){ + max_output_node_value = max(max_output_node_value,output_nodes[i]->output_values[j]); + } + + Log::info("Max value in softmax : %f\n\n\n",max_output_node_value); + // get sum of all the outputs of the timestep j from all output node i for (uint32_t i = 0; i < output_nodes.size(); i++) { - softmax_sum += exp(output_nodes[i]->output_values[j]); + softmax_sum += exp(output_nodes[i]->output_values[j] - max_output_node_value); } // for each for (uint32_t i = 0; i < output_nodes.size(); i++) { - softmax = exp(output_nodes[i]->output_values[j]) / softmax_sum; + softmax = exp(output_nodes[i]->output_values[j] - max_output_node_value) / softmax_sum; error = softmax - expected_outputs[i][j]; output_nodes[i]->error_values[j] = error; cross_entropy = -expected_outputs[i][j] * log(softmax); @@ -454,7 +467,22 @@ double RNN::calculate_error_mse(const vector< vector > &expected_outputs double mse_sum = 0.0; double mse; double error; - + + for (uint32_t j = 0; j < expected_outputs.size(); j++) { + for (uint32_t i = 0; i < expected_outputs[j].size(); i++) { + std::cout<output_values[j]); + } + Log::info("\n"); + } + for (uint32_t i = 0; i < output_nodes.size(); i++) { output_nodes[i]->error_values.resize(expected_outputs[i].size()); @@ -647,10 +675,12 @@ void RNN::get_analytic_gradient(const vector &test_parameters, const vec forward_pass(inputs, using_dropout, training, dropout_probability); if(use_regression) { + Log::trace("Using regression\n"); mse = calculate_error_mse(outputs); backward_pass(mse * (1.0 / outputs[0].size()) * 2.0, using_dropout, training, dropout_probability); } else { + Log::trace("Using classification\n"); mse = calculate_error_softmax(outputs); backward_pass(mse * (1.0 / outputs[0].size()), using_dropout, training, dropout_probability); @@ -696,8 +726,10 @@ void RNN::get_empirical_gradient(const vector &test_parameters, const ve if (use_regression) { + Log::info("Using regression\n"); original_mse = calculate_error_mse(outputs); } else { + Log::trace("Using classification\n"); original_mse = calculate_error_softmax(outputs); } @@ -714,8 +746,10 @@ void RNN::get_empirical_gradient(const vector &test_parameters, const ve forward_pass(inputs, using_dropout, training, dropout_probability); if (use_regression) { + Log::info("Using regression\n"); get_mse(this,outputs,mse1,deltas); } else { + Log::trace("Using classification\n"); get_se(this,outputs,mse1,deltas); } @@ -724,8 +758,10 @@ void RNN::get_empirical_gradient(const vector &test_parameters, const ve forward_pass(inputs, using_dropout, training, dropout_probability); if (use_regression) { + Log::info("Using regression\n"); get_mse(this,outputs,mse2,deltas); } else { + Log::trace("Using classification\n"); get_se(this,outputs,mse2,deltas); } empirical_gradient[i] = (mse2 - mse1) / (2.0 * diff); diff --git a/rnn/rnn_genome.cxx b/rnn/rnn_genome.cxx index c9ada7cf..92adb12a 100644 --- a/rnn/rnn_genome.cxx +++ b/rnn/rnn_genome.cxx @@ -900,9 +900,11 @@ void RNN_Genome::get_analytic_gradient(vector &rnns, const vector vector threads; for (uint32_t i = 0; i < rnns.size(); i++) { if(use_regression) { + Log::info("Using regression\n"); threads.push_back( thread(forward_pass_thread_regression, rnns[i], parameters, inputs[i], outputs[i], i, mses, use_dropout, training, dropout_probability) ); } else { + Log::info("Using classification\n"); threads.push_back( thread(forward_pass_thread_classification, rnns[i], parameters, inputs[i], outputs[i], i, mses, use_dropout, training, dropout_probability) ); } @@ -917,8 +919,11 @@ void RNN_Genome::get_analytic_gradient(vector &rnns, const vector for (uint32_t i = 0; i < rnns.size(); i++) { double d_mse = 0.0; if (use_regression) { + Log::info("Using regression\n"); d_mse = mse_sum * (1.0 / outputs[i][0].size()) * 2.0; } else { + Log::info("Using classification\n"); + d_mse = mse_sum * (1.0 / outputs[i][0].size()); } rnns[i]->backward_pass(d_mse, use_dropout, training, dropout_probability); @@ -961,9 +966,9 @@ void RNN_Genome::backpropagate(const vector< vector< vector > > &inputs, int32_t n_series = inputs.size(); vector rnns; for (int32_t i = 0; i < n_series; i++) { - //RNN* r = this->get_rnn(); - //r->enable_use_regression(use_regression); - rnns.push_back(this->get_rnn()); + RNN* rnn = this->get_rnn(); + rnn->enable_use_regression(use_regression); + rnns.push_back(rnn); } vector parameters = initial_parameters; @@ -1028,8 +1033,10 @@ void RNN_Genome::backpropagate(const vector< vector< vector > > &inputs, this->set_weights(parameters); if (use_regression) { + Log::info("Using regression\n"); validation_mse = get_mse(parameters, validation_inputs, validation_outputs); } else { + Log::info("Using classification\n"); validation_mse = get_softmax(parameters, validation_inputs, validation_outputs); } if (validation_mse < best_validation_mse) { @@ -1220,8 +1227,18 @@ void RNN_Genome::backpropagate_stochastic(const vector< vector< vector > Log::trace("initialized previous values.\n"); //TODO: need to get validation mse on the RNN not the genome - double validation_mse = get_mse(parameters, validation_inputs, validation_outputs); - best_validation_mse = validation_mse; + + double validation_mse = 0; + if (use_regression) { + Log::info("Using regression\n"); + validation_mse = get_mse(parameters, validation_inputs, validation_outputs); + best_validation_mse = validation_mse; + } + else { + Log::info("Using classification\n"); + validation_mse = get_softmax(parameters, validation_inputs, validation_outputs); + best_validation_mse = validation_mse; + } best_validation_mae = get_mae(parameters, validation_inputs, validation_outputs); best_parameters = parameters; diff --git a/rnn_tests/CMakeLists.txt b/rnn_tests/CMakeLists.txt index 46afcebe..10c4ccbe 100644 --- a/rnn_tests/CMakeLists.txt +++ b/rnn_tests/CMakeLists.txt @@ -30,3 +30,7 @@ target_link_libraries(test_random_dag_gradients examm_strategy exact_common exac add_executable(test_enas_dag_gradients test_enas_dag_gradients gradient_test) target_link_libraries(test_enas_dag_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) + +add_executable(test_softmax_output test_softmax_output gradient_test) +target_link_libraries(test_softmax_output examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) + diff --git a/rnn_tests/test_softmax_output.cxx b/rnn_tests/test_softmax_output.cxx new file mode 100644 index 00000000..f9ab1e4b --- /dev/null +++ b/rnn_tests/test_softmax_output.cxx @@ -0,0 +1,119 @@ +#include + +#include +using std::getline; +using std::ifstream; + +#include +using std::minstd_rand0; +using std::uniform_real_distribution; + +#include +using std::string; + +#include +using std::vector; + +#include "common/arguments.hxx" +#include "common/log.hxx" +#include "common/weight_initialize.hxx" + +#include "rnn/lstm_node.hxx" +#include "rnn/rnn_edge.hxx" +#include "rnn/rnn_genome.hxx" +#include "rnn/rnn_node.hxx" +#include "rnn/rnn_node_interface.hxx" + +#include "rnn/generate_nn.hxx" + +#include "time_series/time_series.hxx" + +#include "gradient_test.hxx" + +int main(int argc, char **argv) +{ + vector arguments = vector(argv, argv + argc); + + Log::initialize(arguments); + Log::set_id("main"); + + initialize_generator(); + + RNN_Genome *genome; + + Log::info("TESTING FEED FORWARD\n"); + + vector> inputs; + vector> outputs; + + int input_length = 10; + get_argument(arguments, "--input_length", true, input_length); + + string weight_initialize_string = "random"; + get_argument(arguments, "--weight_initialize", false, weight_initialize_string); + + WeightType weight_initialize; + weight_initialize = get_enum_from_string(weight_initialize_string); + + if (weight_initialize < 0 || weight_initialize >= NUM_WEIGHT_TYPES - 1) + { + Log::fatal("weight initialization method %s is set wrong \n", weight_initialize_string.c_str()); + } + + inputs.resize(1); + outputs.resize(1); + + generate_random_oneHot_vector(input_length, inputs[0]); + generate_random_oneHot_vector(input_length, outputs[0]); + + vector inputs1{"input 1"}; + vector outputs1{"output 1"}; + + vector parameters; + + //Test 1 input, 1 output, no hidden + + Log::info("****************Using Inputs :: ***************************\n"); + + genome = create_lstm(inputs1, 0, 0, outputs1, 1, weight_initialize); + + for (size_t i = 0; i < inputs.size(); i++) + { + for (size_t j = 0; j < inputs[i].size(); j++) + { + Log::info("%f ",inputs[i][j]); + } + Log::info("\n"); + + } + + Log::info("****************Outputs :: ***************************\n"); + + for (size_t i = 0; i < inputs.size(); i++) + { + for (size_t j = 0; j < inputs[i].size(); j++) + { + Log::info("%f ", inputs[i][j]); + } + Log::info("\n"); + } + + RNN *rnn = genome->get_rnn(); + + + generate_random_vector(rnn->get_number_weights(), parameters); + rnn->set_weights(parameters); + + Log::info("****************Using Softmax :: ***************************\n"); + + rnn->enable_use_regression(false); + rnn->forward_pass(inputs, false, true, 0.0); + rnn->calculate_error_softmax(outputs); + + Log::info("****************Using MSE :: ***************************\n"); + + rnn->enable_use_regression(true); + rnn->forward_pass(inputs, false, true, 0.0); + rnn->calculate_error_mse(outputs); +} + From fde1121d172efcadbe0f147f3664a0667a4a4edd Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Fri, 20 Nov 2020 11:34:48 -0500 Subject: [PATCH 12/25] adding softmax gradient check --- rnn/mse.cxx | 16 ++--- rnn/rnn.cxx | 69 +++++++++++--------- rnn/rnn.hxx | 1 + rnn/rnn_genome.cxx | 2 +- rnn_tests/gradient_test.cxx | 12 +++- rnn_tests/gradient_test.hxx | 4 +- rnn_tests/test_enarc_gradients.cxx | 2 +- rnn_tests/test_softmax_output.cxx | 101 +++++++++++++++-------------- 8 files changed, 116 insertions(+), 91 deletions(-) diff --git a/rnn/mse.cxx b/rnn/mse.cxx index 2388beb1..3403c031 100644 --- a/rnn/mse.cxx +++ b/rnn/mse.cxx @@ -80,6 +80,7 @@ void get_mae(const vector &output_values, const vector &expected for (uint32_t j = 0; j < expected.size(); j++) { deltas[j] *= d_mae; } + } void get_mae(RNN *genome, const vector< vector > &expected, double &mae_sum, vector< vector > &deltas) { @@ -146,19 +147,14 @@ void get_se(RNN *genome, const vector< vector > &expected, double &ce_su for (uint32_t i = 0; i < genome->output_nodes.size(); i++) { softmax = exp(genome->output_nodes[i]->output_values[j] - max_output_node_value) / softmax_sum; error = softmax - expected[i][j]; + deltas[i][j] = error; - genome->output_nodes[i]->error_values[j] = error; cross_entropy = -expected[i][j] * log(softmax); - ce_sum += cross_entropy; + ce_sum += cross_entropy ; } + } - // double d_ce = ce_sum * (1.0 / expected[0].size()); + +} - double d_ce = ce_sum * (1.0 / expected[0].size()); - for (uint32_t i = 0; i < genome->output_nodes.size(); i++) { - for (uint32_t j = 0; j < expected[i].size(); j++) { - deltas[i][j] *= d_ce; - } - } -} \ No newline at end of file diff --git a/rnn/rnn.cxx b/rnn/rnn.cxx index 7c068e48..d5c21ff3 100644 --- a/rnn/rnn.cxx +++ b/rnn/rnn.cxx @@ -414,18 +414,6 @@ double RNN::calculate_error_softmax(const vector< vector > &expected_out double error; double softmax = 0.0; - - - for (uint32_t j = 0; j < expected_outputs[0].size(); j++) - { - for (uint32_t i = 0; i < output_nodes.size(); i++) - { - - Log::info("%f ",output_nodes[i]->output_values[j]); - } - Log::info("\n"); - } - for (uint32_t i = 0; i < output_nodes.size(); i++) { output_nodes[i]->error_values.resize(expected_outputs[i].size()); } @@ -457,7 +445,7 @@ double RNN::calculate_error_softmax(const vector< vector > &expected_out output_nodes[i]->error_values[j] = error; cross_entropy = -expected_outputs[i][j] * log(softmax); cross_entropy_sum += cross_entropy; - } + } } return cross_entropy_sum; @@ -468,20 +456,6 @@ double RNN::calculate_error_mse(const vector< vector > &expected_outputs double mse; double error; - for (uint32_t j = 0; j < expected_outputs.size(); j++) { - for (uint32_t i = 0; i < expected_outputs[j].size(); i++) { - std::cout<output_values[j]); - } - Log::info("\n"); - } for (uint32_t i = 0; i < output_nodes.size(); i++) { output_nodes[i]->error_values.resize(expected_outputs[i].size()); @@ -495,6 +469,8 @@ double RNN::calculate_error_mse(const vector< vector > &expected_outputs mse_sum += mse / expected_outputs[i].size(); } + + return mse_sum; } @@ -682,7 +658,7 @@ void RNN::get_analytic_gradient(const vector &test_parameters, const vec } else { Log::trace("Using classification\n"); mse = calculate_error_softmax(outputs); - backward_pass(mse * (1.0 / outputs[0].size()), using_dropout, training, dropout_probability); + backward_pass(mse, using_dropout, training, dropout_probability); } @@ -773,7 +749,42 @@ void RNN::get_empirical_gradient(const vector &test_parameters, const ve mse = original_mse; } -void RNN::initialize_randomly() { +vector> RNN::get_softmax_gradient(const vector &test_parameters, const vector> &inputs, const vector> &outputs, double &mse, vector> &softmax_gradient, bool using_dropout, bool training, double dropout_probability) { + + softmax_gradient.assign(output_nodes.size(), vector(outputs[0].size(), 0.0)); + vector> deltas; + double original_mse = 0.0; + + set_weights(test_parameters); + forward_pass(inputs, using_dropout, training, dropout_probability); + + get_se(this, outputs, original_mse, deltas); + + double save; + double diff = 0.00001; + double mse1, mse2; + + // get gradient for each output node starting from each time step and each output node in each time step + + for (uint32_t timestep = 0; timestep < outputs[0].size(); timestep++) { + for (uint32_t outputNode = 0; outputNode < output_nodes.size(); outputNode++) { + save = output_nodes[outputNode]->output_values[timestep]; + output_nodes[outputNode]->output_values[timestep] = save - diff; + mse1 = calculate_error_softmax(outputs); + output_nodes[outputNode]->output_values[timestep] = save + diff; + mse2 = calculate_error_softmax(outputs); + + softmax_gradient[outputNode][timestep] = (mse2 - mse1) / (2.0 * diff); + output_nodes[outputNode]->output_values[timestep] = save; + } + } + + mse = original_mse; + return deltas; +} + + void RNN::initialize_randomly() +{ int number_of_weights = get_number_weights(); vector parameters(number_of_weights, 0.0); diff --git a/rnn/rnn.hxx b/rnn/rnn.hxx index 8d1c2711..304211de 100644 --- a/rnn/rnn.hxx +++ b/rnn/rnn.hxx @@ -113,6 +113,7 @@ class RNN { */ void get_analytic_gradient(const vector &test_parameters, const vector< vector > &inputs, const vector< vector > &outputs, double &mse, vector &analytic_gradient, bool using_dropout, bool training, double dropout_probability); void get_empirical_gradient(const vector &test_parameters, const vector< vector > &inputs, const vector< vector > &outputs, double &mae, vector &empirical_gradient, bool using_dropout, bool training, double dropout_probability); + vector> get_softmax_gradient(const vector &test_parameters, const vector> &inputs, const vector> &outputs, double &mae, vector> &softmax_gradient, bool using_dropout, bool training, double dropout_probability); //RNN* copy(); diff --git a/rnn/rnn_genome.cxx b/rnn/rnn_genome.cxx index 92adb12a..6be973ff 100644 --- a/rnn/rnn_genome.cxx +++ b/rnn/rnn_genome.cxx @@ -924,7 +924,7 @@ void RNN_Genome::get_analytic_gradient(vector &rnns, const vector } else { Log::info("Using classification\n"); - d_mse = mse_sum * (1.0 / outputs[i][0].size()); + d_mse = mse_sum; } rnns[i]->backward_pass(d_mse, use_dropout, training, dropout_probability); diff --git a/rnn_tests/gradient_test.cxx b/rnn_tests/gradient_test.cxx index f444e035..57cdc1e1 100644 --- a/rnn_tests/gradient_test.cxx +++ b/rnn_tests/gradient_test.cxx @@ -57,6 +57,16 @@ void generate_random_oneHot_vector(int number_parameters, vector &v) { v[index] = 1.0; } +void generate_random_oneHot_matrix(int timesteps, int number_parameters, vector> &v) { + + for (int32_t j = 0; j < timesteps; j++) { + uniform_int_distribution dist(0, number_parameters-1); + int i = dist(generator); + v[i][j] = 1.0; + } + +} + void gradient_test(string name, RNN_Genome *genome, const vector< vector > &inputs, const vector< vector > &outputs) { double analytic_mse, empirical_mse; vector parameters; @@ -69,7 +79,7 @@ void gradient_test(string name, RNN_Genome *genome, const vector< vector Log::debug("got genome \n"); - rnn->enable_use_regression(true); + rnn->enable_use_regression(false); for (int32_t i = 0; i < test_iterations; i++) { if (i == 0) Log::debug_no_header("\n"); diff --git a/rnn_tests/gradient_test.hxx b/rnn_tests/gradient_test.hxx index 3dc04765..5ccf7f7a 100644 --- a/rnn_tests/gradient_test.hxx +++ b/rnn_tests/gradient_test.hxx @@ -32,7 +32,7 @@ using std::vector; void initialize_generator(); void generate_random_vector(int number_parameters, vector &v); void generate_random_oneHot_vector(int number_parameters, vector &v); - - void gradient_test(string name, RNN_Genome *genome, const vector> &inputs, const vector> &outputs); +void generate_random_oneHot_matrix(int timesteps, int number_parameters, vector> &v); + void gradient_test(string name, RNN_Genome * genome, const vector> &inputs, const vector> &outputs); #endif diff --git a/rnn_tests/test_enarc_gradients.cxx b/rnn_tests/test_enarc_gradients.cxx index f21d9f1d..b413d6d4 100644 --- a/rnn_tests/test_enarc_gradients.cxx +++ b/rnn_tests/test_enarc_gradients.cxx @@ -59,7 +59,7 @@ int main(int argc, char **argv) { } - for (int32_t max_recurrent_depth = 1; max_recurrent_depth <= 5; max_recurrent_depth++) { + for (int32_t max_recurrent_depth = 1; max_recurrent_depth <= 2; max_recurrent_depth++) { Log::info("testing with max recurrent depth: %d\n", max_recurrent_depth); inputs.resize(1); diff --git a/rnn_tests/test_softmax_output.cxx b/rnn_tests/test_softmax_output.cxx index f9ab1e4b..c5d48646 100644 --- a/rnn_tests/test_softmax_output.cxx +++ b/rnn_tests/test_softmax_output.cxx @@ -6,10 +6,12 @@ using std::ifstream; #include using std::minstd_rand0; +using std::uniform_int_distribution; using std::uniform_real_distribution; #include using std::string; +using std::to_string; #include using std::vector; @@ -30,6 +32,8 @@ using std::vector; #include "gradient_test.hxx" + + int main(int argc, char **argv) { vector arguments = vector(argv, argv + argc); @@ -41,14 +45,6 @@ int main(int argc, char **argv) RNN_Genome *genome; - Log::info("TESTING FEED FORWARD\n"); - - vector> inputs; - vector> outputs; - - int input_length = 10; - get_argument(arguments, "--input_length", true, input_length); - string weight_initialize_string = "random"; get_argument(arguments, "--weight_initialize", false, weight_initialize_string); @@ -60,60 +56,71 @@ int main(int argc, char **argv) Log::fatal("weight initialization method %s is set wrong \n", weight_initialize_string.c_str()); } - inputs.resize(1); - outputs.resize(1); + Log::info("TESTING FEED FORWARD\n"); - generate_random_oneHot_vector(input_length, inputs[0]); - generate_random_oneHot_vector(input_length, outputs[0]); + vector> inputs; + vector> outputs; - vector inputs1{"input 1"}; - vector outputs1{"output 1"}; + int timesteps = 10; + get_argument(arguments, "--timestep", true, timesteps); - vector parameters; + int input_length = 1; + get_argument(arguments,"--input_length",true,input_length); - //Test 1 input, 1 output, no hidden + inputs.resize(input_length); + for (size_t i = 0; i < inputs.size(); i++) { + inputs[i].resize(timesteps); + } - Log::info("****************Using Inputs :: ***************************\n"); + outputs.resize(input_length); + for (size_t i = 0; i < outputs.size(); i++) { + outputs[i].resize(timesteps); + } - genome = create_lstm(inputs1, 0, 0, outputs1, 1, weight_initialize); + generate_random_oneHot_matrix(timesteps,input_length,inputs); + generate_random_oneHot_matrix(timesteps, input_length, outputs); - for (size_t i = 0; i < inputs.size(); i++) - { - for (size_t j = 0; j < inputs[i].size(); j++) - { - Log::info("%f ",inputs[i][j]); - } - Log::info("\n"); - - } + vector parameters; + double mse; + vector> softmax_gradients, deltas; - Log::info("****************Outputs :: ***************************\n"); - for (size_t i = 0; i < inputs.size(); i++) - { - for (size_t j = 0; j < inputs[i].size(); j++) - { - Log::info("%f ", inputs[i][j]); - } - Log::info("\n"); + vector inputs1; + vector outputs1; + + string input_par = "input"; + string output_par = "output"; + for (int32_t i = 0; i < input_length; i++) { + string temp_input_par = input_par + to_string(i); + string temp_output_par = output_par + to_string(i); + inputs1.push_back(temp_input_par); + outputs1.push_back(temp_output_par); } - RNN *rnn = genome->get_rnn(); + genome = create_lstm(inputs1, 1, 1, outputs1, 3, weight_initialize); + + RNN *rnn = genome->get_rnn(); generate_random_vector(rnn->get_number_weights(), parameters); rnn->set_weights(parameters); - - Log::info("****************Using Softmax :: ***************************\n"); - rnn->enable_use_regression(false); - rnn->forward_pass(inputs, false, true, 0.0); - rnn->calculate_error_softmax(outputs); - - Log::info("****************Using MSE :: ***************************\n"); - - rnn->enable_use_regression(true); - rnn->forward_pass(inputs, false, true, 0.0); - rnn->calculate_error_mse(outputs); + deltas = rnn->get_softmax_gradient(parameters, inputs, outputs, mse, softmax_gradients, false, true, 0.0); + + + for (uint32_t j = 0; j < deltas[0].size(); j++) { + for (uint32_t i = 0; i < deltas.size(); i++) { + double difference = softmax_gradients[i][j] - deltas[i][j]; + if (fabs(difference) > 10e-10) + { + Log::info("\t\tFAILED softmax gradient[%d][%d]: %lf, deltas[%d][%d]: %lf, difference: %lf\n",i, j, softmax_gradients[i][j],i, j, deltas[i][j], difference); + //exit(1); + } + else + { + Log::info("\t\tPASSED softmax gradient[%d][%d]: %lf, deltas[%d][%d]: %lf, difference: %lf\n", i, j, softmax_gradients[i][j], i, j, deltas[i][j], difference); + } + } + } } From 64ccde1e47d571de298cd1ab2df3ad718a231335 Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Wed, 9 Dec 2020 19:33:56 -0500 Subject: [PATCH 13/25] Updated README --- README.md | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 8f831f74..34f67417 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,6 @@ The datasets used for this part of the code are PennChar, PennTreeBank and WikiT ``` - ### To use PennTreeBank Word Prediction as dataset and use Simple and ENARC as memory cell @@ -76,10 +75,18 @@ The std_out can be seen in file *main_out* for 20 genomes. The number of genomes ``` +### To check outputs for Softmax Layer +``` +./rnn_tests/test_softmax_output --std_message_level info --file_message_level info --output_directory "./test_output" --timesteps 2 --output_length 6 +``` +### To check gradients for Softmax Layer +``` +./rnn_tests/test_softmax_gradient --std_message_level debug --file_message_level debug --output_directory "./test_output" --input_length 4 --timesteps 3 --max_recurrent_depth 10 +``` ### To use ENARC as Memory Cell on Coal Dataset From 9aba3a8887c990575ef7a263960315163f45f19e Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Wed, 9 Dec 2020 19:42:22 -0500 Subject: [PATCH 14/25] adding classification backprop --- rnn/rnn.cxx | 121 +++++++++++++++++++++++++++++++++++---------- rnn/rnn.hxx | 15 +++--- rnn/rnn_genome.cxx | 13 ++--- 3 files changed, 111 insertions(+), 38 deletions(-) diff --git a/rnn/rnn.cxx b/rnn/rnn.cxx index d5c21ff3..e9ca73e2 100644 --- a/rnn/rnn.cxx +++ b/rnn/rnn.cxx @@ -407,12 +407,61 @@ void RNN::backward_pass(double error, bool using_dropout, bool training, double } } +void RNN::backward_pass_class(double error, bool using_dropout, bool training, double dropout_probability) +{ + //do a propagate forward for time == (series_length - 1) so that the + // output fired count on each node will be correct for the first pass + //through the RNN + for (uint32_t i = 0; i < recurrent_edges.size(); i++) + { + if (recurrent_edges[i]->is_reachable()) + recurrent_edges[i]->first_propagate_backward(); + } + + for (int32_t time = series_length - 1; time >= 0; time--) + { + + for (uint32_t i = 0; i < output_nodes.size(); i++) + { + //output_nodes[i]->error_fired(time, 1); + + output_nodes[i]->error_fired(time, cross_entropy_losses[time]); + } + + if (using_dropout) + { + for (int32_t i = (int32_t)edges.size() - 1; i >= 0; i--) + { + if (edges[i]->is_reachable()) + edges[i]->propagate_backward(time, training, dropout_probability); + } + } + else + { + for (int32_t i = (int32_t)edges.size() - 1; i >= 0; i--) + { + if (edges[i]->is_reachable()) + edges[i]->propagate_backward(time); + } + } + + for (int32_t i = (int32_t)recurrent_edges.size() - 1; i >= 0; i--) + { + if (recurrent_edges[i]->is_reachable()) + recurrent_edges[i]->propagate_backward(time); + } + } +} + double RNN::calculate_error_softmax(const vector< vector > &expected_outputs) { - double cross_entropy_sum = 0.0; + double cross_entropy_sum = 0.000000001; + double cross_entropy_time_sum = 0.000000001; double error; double softmax = 0.0; + cross_entropy_losses.assign(expected_outputs[0].size(),0.0); + //cross_entropy_losses.resize(expected_outputs[0].size()); for (uint32_t i = 0; i < output_nodes.size(); i++) { output_nodes[i]->error_values.resize(expected_outputs[i].size()); @@ -420,9 +469,9 @@ double RNN::calculate_error_softmax(const vector< vector > &expected_out // for each time step j for (uint32_t j = 0; j < expected_outputs[0].size(); j++) { - double softmax_sum = 0.0; - double cross_entropy = 0.0; - + double softmax_sum = 0.000000001; + double cross_entropy = 0.000000001; + double cross_entropy_time_sum = 0.000000001; //find max at each timestep double max_output_node_value = output_nodes[0]->output_values[j]; @@ -430,7 +479,7 @@ double RNN::calculate_error_softmax(const vector< vector > &expected_out max_output_node_value = max(max_output_node_value,output_nodes[i]->output_values[j]); } - Log::info("Max value in softmax : %f\n\n\n",max_output_node_value); + //Log::info("Max value in softmax : %f\n\n\n",max_output_node_value); // get sum of all the outputs of the timestep j from all output node i for (uint32_t i = 0; i < output_nodes.size(); i++) { @@ -443,11 +492,22 @@ double RNN::calculate_error_softmax(const vector< vector > &expected_out softmax = exp(output_nodes[i]->output_values[j] - max_output_node_value) / softmax_sum; error = softmax - expected_outputs[i][j]; output_nodes[i]->error_values[j] = error; + cross_entropy = -expected_outputs[i][j] * log(softmax); - cross_entropy_sum += cross_entropy; - } + cross_entropy_time_sum += cross_entropy; + + } + cross_entropy_sum += cross_entropy_time_sum; + cross_entropy_losses[j] = cross_entropy_time_sum; + Log::debug("Cross_Entropy_time_sum :%f\n\n", cross_entropy_time_sum); + + for (uint32_t i = 0; i < output_nodes.size(); i++) { + + output_nodes[i]->error_values[j] /=cross_entropy_losses[j]; + } } + Log::debug("Cross_Entropy_sum :%f\n\n",cross_entropy_sum); return cross_entropy_sum; } @@ -658,9 +718,9 @@ void RNN::get_analytic_gradient(const vector &test_parameters, const vec } else { Log::trace("Using classification\n"); mse = calculate_error_softmax(outputs); - backward_pass(mse, using_dropout, training, dropout_probability); - - } + // backward_pass(mse * (1.0 / outputs[0].size()), using_dropout, training, dropout_probability); + backward_pass_class(1, using_dropout, training, dropout_probability); + } vector current_gradients; @@ -722,11 +782,10 @@ void RNN::get_empirical_gradient(const vector &test_parameters, const ve forward_pass(inputs, using_dropout, training, dropout_probability); if (use_regression) { - Log::info("Using regression\n"); get_mse(this,outputs,mse1,deltas); } else { - Log::trace("Using classification\n"); get_se(this,outputs,mse1,deltas); + //Log::trace("MSE:1 = %f\n",mse1); } parameters[i] = save + diff; @@ -734,14 +793,14 @@ void RNN::get_empirical_gradient(const vector &test_parameters, const ve forward_pass(inputs, using_dropout, training, dropout_probability); if (use_regression) { - Log::info("Using regression\n"); get_mse(this,outputs,mse2,deltas); + empirical_gradient[i] = (mse2 - mse1) / (2.0 * diff); + empirical_gradient[i] *= original_mse; } else { - Log::trace("Using classification\n"); get_se(this,outputs,mse2,deltas); + empirical_gradient[i] = (mse2 - mse1) / (2.0 * diff); } - empirical_gradient[i] = (mse2 - mse1) / (2.0 * diff); - empirical_gradient[i] *= original_mse; + parameters[i] = save; } @@ -749,16 +808,25 @@ void RNN::get_empirical_gradient(const vector &test_parameters, const ve mse = original_mse; } -vector> RNN::get_softmax_gradient(const vector &test_parameters, const vector> &inputs, const vector> &outputs, double &mse, vector> &softmax_gradient, bool using_dropout, bool training, double dropout_probability) { +vector> RNN::get_softmax_gradient(const vector> &outputs, const vector> &expected, double &mse, vector> &softmax_gradient) { softmax_gradient.assign(output_nodes.size(), vector(outputs[0].size(), 0.0)); vector> deltas; double original_mse = 0.0; - set_weights(test_parameters); - forward_pass(inputs, using_dropout, training, dropout_probability); + int timesteps = outputs[0].size(); - get_se(this, outputs, original_mse, deltas); + for (uint32_t outputNode = 0; outputNode < output_nodes.size(); outputNode++) { + output_nodes[outputNode]->output_values.assign(timesteps, 0.0); + } + + for (uint32_t timestep = 0; timestep < outputs[0].size(); timestep++) { + for (uint32_t outputNode = 0; outputNode < output_nodes.size(); outputNode++) { + output_nodes[outputNode]->output_values[timestep] = outputs[outputNode][timestep]; + } + } + + get_se(this, expected, original_mse, deltas); double save; double diff = 0.00001; @@ -766,17 +834,20 @@ vector> RNN::get_softmax_gradient(const vector &test_par // get gradient for each output node starting from each time step and each output node in each time step - for (uint32_t timestep = 0; timestep < outputs[0].size(); timestep++) { - for (uint32_t outputNode = 0; outputNode < output_nodes.size(); outputNode++) { + for (uint32_t timestep = 0; timestep < outputs[0].size(); timestep++) + { + for (uint32_t outputNode = 0; outputNode < output_nodes.size(); outputNode++) + { save = output_nodes[outputNode]->output_values[timestep]; output_nodes[outputNode]->output_values[timestep] = save - diff; - mse1 = calculate_error_softmax(outputs); + mse1 = calculate_error_softmax(expected); output_nodes[outputNode]->output_values[timestep] = save + diff; - mse2 = calculate_error_softmax(outputs); + mse2 = calculate_error_softmax(expected); softmax_gradient[outputNode][timestep] = (mse2 - mse1) / (2.0 * diff); + output_nodes[outputNode]->output_values[timestep] = save; - } + } } mse = original_mse; diff --git a/rnn/rnn.hxx b/rnn/rnn.hxx index 304211de..8b9d3515 100644 --- a/rnn/rnn.hxx +++ b/rnn/rnn.hxx @@ -31,6 +31,8 @@ class RNN { bool use_regression; + vector cross_entropy_losses; + vector input_nodes; vector output_nodes; @@ -54,9 +56,9 @@ class RNN { void forward_pass(const vector< vector > &series_data, bool using_dropout, bool training, double dropout_probability); void backward_pass(double error, bool using_dropout, bool training, double dropout_probability); + void backward_pass_class(double error, bool using_dropout, bool training, double dropout_probability); - - /** + /** * Calculates the sum of cross entropy error by comparing the expected outputs and the predicted outputs. * * \param expected_outputs is the expected outputs according the dataset. @@ -113,14 +115,13 @@ class RNN { */ void get_analytic_gradient(const vector &test_parameters, const vector< vector > &inputs, const vector< vector > &outputs, double &mse, vector &analytic_gradient, bool using_dropout, bool training, double dropout_probability); void get_empirical_gradient(const vector &test_parameters, const vector< vector > &inputs, const vector< vector > &outputs, double &mae, vector &empirical_gradient, bool using_dropout, bool training, double dropout_probability); - vector> get_softmax_gradient(const vector &test_parameters, const vector> &inputs, const vector> &outputs, double &mae, vector> &softmax_gradient, bool using_dropout, bool training, double dropout_probability); + vector< vector > get_softmax_gradient(const vector< vector > &outputs, const vector< vector > &expected, double &mse, vector< vector > &softmax_gradient); //RNN* copy(); - friend void get_mse(RNN* genome, const vector< vector > &expected, double &mse_sum, vector< vector > &deltas); - friend void get_mae(RNN* genome, const vector< vector > &expected, double &mae_sum, vector< vector > &deltas); - friend void get_se(RNN* genome, const vector< vector > &expected, double &ce_sum, vector< vector > &deltas); - + friend void get_mse(RNN * genome, const vector> &expected, double &mse_sum, vector> &deltas); + friend void get_mae(RNN * genome, const vector> &expected, double &mae_sum, vector> &deltas); + friend void get_se(RNN * genome, const vector> &expected, double &ce_sum, vector> &deltas); }; #endif diff --git a/rnn/rnn_genome.cxx b/rnn/rnn_genome.cxx index 6be973ff..3a00bc63 100644 --- a/rnn/rnn_genome.cxx +++ b/rnn/rnn_genome.cxx @@ -900,11 +900,11 @@ void RNN_Genome::get_analytic_gradient(vector &rnns, const vector vector threads; for (uint32_t i = 0; i < rnns.size(); i++) { if(use_regression) { - Log::info("Using regression\n"); + threads.push_back( thread(forward_pass_thread_regression, rnns[i], parameters, inputs[i], outputs[i], i, mses, use_dropout, training, dropout_probability) ); } else { - Log::info("Using classification\n"); + threads.push_back( thread(forward_pass_thread_classification, rnns[i], parameters, inputs[i], outputs[i], i, mses, use_dropout, training, dropout_probability) ); } @@ -920,13 +920,14 @@ void RNN_Genome::get_analytic_gradient(vector &rnns, const vector double d_mse = 0.0; if (use_regression) { Log::info("Using regression\n"); - d_mse = mse_sum * (1.0 / outputs[i][0].size()) * 2.0; + d_mse = mse_sum * (1.0 / outputs[i][0].size()) * 2.0; + rnns[i]->backward_pass(d_mse, use_dropout, training, dropout_probability); } else { Log::info("Using classification\n"); - - d_mse = mse_sum; + d_mse = 1; + rnns[i]->backward_pass_class(d_mse, use_dropout, training, dropout_probability); } - rnns[i]->backward_pass(d_mse, use_dropout, training, dropout_probability); + //double d_mae = mse_sum * (1.0 / outputs[i][0].size()); //rnns[i]->backward_pass(d_mae); From 1ce6289d375cc7c0bc056ecd6b171ede7fad93f0 Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Wed, 9 Dec 2020 19:44:26 -0500 Subject: [PATCH 15/25] adding softmax output and gradient --- rnn_tests/CMakeLists.txt | 3 + rnn_tests/gradient_test.cxx | 80 +++++++++++++++++- rnn_tests/gradient_test.hxx | 5 +- rnn_tests/test_softmax_gradient.cxx | 123 ++++++++++++++++++++++++++++ rnn_tests/test_softmax_output.cxx | 62 ++++++++------ 5 files changed, 248 insertions(+), 25 deletions(-) create mode 100644 rnn_tests/test_softmax_gradient.cxx diff --git a/rnn_tests/CMakeLists.txt b/rnn_tests/CMakeLists.txt index 10c4ccbe..c5c9e9a6 100644 --- a/rnn_tests/CMakeLists.txt +++ b/rnn_tests/CMakeLists.txt @@ -34,3 +34,6 @@ target_link_libraries(test_enas_dag_gradients examm_strategy exact_common exact_ add_executable(test_softmax_output test_softmax_output gradient_test) target_link_libraries(test_softmax_output examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +add_executable(test_softmax_gradient test_softmax_gradient gradient_test) +target_link_libraries(test_softmax_gradient examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) + diff --git a/rnn_tests/gradient_test.cxx b/rnn_tests/gradient_test.cxx index 57cdc1e1..1f641b13 100644 --- a/rnn_tests/gradient_test.cxx +++ b/rnn_tests/gradient_test.cxx @@ -67,6 +67,18 @@ void generate_random_oneHot_matrix(int timesteps, int number_parameters, vector< } +void generate_random_matrix(int timesteps, int number_parameters, vector> &v) +{ + + for (int32_t j = 0; j < timesteps; j++) + { + for (int32_t i = 0; i < number_parameters; i++) + { + v[i][j] = rng(generator); + } + } +} + void gradient_test(string name, RNN_Genome *genome, const vector< vector > &inputs, const vector< vector > &outputs) { double analytic_mse, empirical_mse; vector parameters; @@ -79,7 +91,7 @@ void gradient_test(string name, RNN_Genome *genome, const vector< vector Log::debug("got genome \n"); - rnn->enable_use_regression(false); + rnn->enable_use_regression(true); for (int32_t i = 0; i < test_iterations; i++) { if (i == 0) Log::debug_no_header("\n"); @@ -119,3 +131,69 @@ void gradient_test(string name, RNN_Genome *genome, const vector< vector Log::info("SOME FAILED!\n"); } } + +void gradient_test_class(string name, RNN_Genome *genome, const vector> &inputs, const vector> &outputs) +{ + double analytic_mse, empirical_mse; + vector parameters; + vector analytic_gradient, empirical_gradient; + + Log::info("\ttesting gradient on '%s'...\n", name.c_str()); + bool failed = false; + + RNN *rnn = genome->get_rnn(); + + Log::debug("got genome \n"); + + rnn->enable_use_regression(false); + + for (int32_t i = 0; i < test_iterations; i++) + { + if (i == 0) + Log::debug_no_header("\n"); + Log::debug("\tAttempt %d\n", i); + + generate_random_vector(rnn->get_number_weights(), parameters); + Log::debug("DEBUG: firing weights are %d \n", rnn->get_number_weights()); + + rnn->get_analytic_gradient(parameters, inputs, outputs, analytic_mse, analytic_gradient, false, true, 0.0); + rnn->get_empirical_gradient(parameters, inputs, outputs, empirical_mse, empirical_gradient, false, true, 0.0); + + bool iteration_failed = false; + + for (uint32_t j = 0; j < analytic_gradient.size(); j++) + { + double difference = analytic_gradient[j] - empirical_gradient[j]; + + if (fabs(difference) > 10e-10) + { + failed = true; + iteration_failed = true; + Log::info("\t\tFAILED analytic gradient[%d]: %lf, empirical gradient[%d]: %lf, difference: %lf\n", j, analytic_gradient[j], j, empirical_gradient[j], difference); + //exit(1); + } + else + { + Log::debug("\t\tPASSED analytic gradient[%d]: %lf, empirical gradient[%d]: %lf, difference: %lf\n", j, analytic_gradient[j], j, empirical_gradient[j], difference); + } + } + + if (iteration_failed) + { + Log::info("\tITERATION %d FAILED!\n\n", i); + } + else + { + Log::debug("\tITERATION %d PASSED!\n\n", i); + } + } + + if (!failed) + { + Log::info("ALL PASSED!\n"); + } + else + { + Log::info("SOME FAILED!\n"); + } +} diff --git a/rnn_tests/gradient_test.hxx b/rnn_tests/gradient_test.hxx index 5ccf7f7a..3b9fb96e 100644 --- a/rnn_tests/gradient_test.hxx +++ b/rnn_tests/gradient_test.hxx @@ -33,6 +33,9 @@ void initialize_generator(); void generate_random_vector(int number_parameters, vector &v); void generate_random_oneHot_vector(int number_parameters, vector &v); void generate_random_oneHot_matrix(int timesteps, int number_parameters, vector> &v); - void gradient_test(string name, RNN_Genome * genome, const vector> &inputs, const vector> &outputs); +void generate_random_matrix(int timesteps, int number_parameters, vector> &v); + +void gradient_test(string name, RNN_Genome *genome, const vector> &inputs, const vector> &outputs); +void gradient_test_class(string name, RNN_Genome *genome, const vector> &inputs, const vector> &outputs); #endif diff --git a/rnn_tests/test_softmax_gradient.cxx b/rnn_tests/test_softmax_gradient.cxx new file mode 100644 index 00000000..690b3157 --- /dev/null +++ b/rnn_tests/test_softmax_gradient.cxx @@ -0,0 +1,123 @@ +#include + +#include +using std::getline; +using std::ifstream; + +#include +using std::minstd_rand0; +using std::uniform_real_distribution; + +#include +using std::string; + +#include +using std::vector; + +#include "common/arguments.hxx" +#include "common/log.hxx" +#include "common/weight_initialize.hxx" + +#include "rnn/delta_node.hxx" +#include "rnn/enarc_node.hxx" +#include "rnn/enas_dag_node.hxx" +#include "rnn/gru_node.hxx" +#include "rnn/lstm_node.hxx" +#include "rnn/mgu_node.hxx" +#include "rnn/ugrnn_node.hxx" +#include "rnn/rnn_edge.hxx" +#include "rnn/rnn_genome.hxx" +#include "rnn/rnn_node.hxx" +#include "rnn/rnn_node_interface.hxx" + +#include "rnn/generate_nn.hxx" + +#include "time_series/time_series.hxx" + +#include "gradient_test.hxx" + +int main(int argc, char **argv) +{ + vector arguments = vector(argv, argv + argc); + + Log::initialize(arguments); + Log::set_id("main"); + + initialize_generator(); + + RNN_Genome *genome; + + Log::info("TESTING DELTA\n"); + + vector> inputs; + vector> outputs; + + int input_length = 10; + get_argument(arguments, "--input_length", true, input_length); + + int timesteps = 10; + get_argument(arguments, "--timesteps", true, timesteps); + + string weight_initialize_string = "random"; + get_argument(arguments, "--weight_initialize", false, weight_initialize_string); + + WeightType weight_initialize; + weight_initialize = get_enum_from_string(weight_initialize_string); + + if (weight_initialize < 0 || weight_initialize >= NUM_WEIGHT_TYPES - 1) + { + Log::fatal("weight initialization method %s is set wrong \n", weight_initialize_string.c_str()); + } + + int max_recurrent_depth = 10; + get_argument(arguments, "--max_recurrent_depth", true, max_recurrent_depth); + + Log::info("testing with max recurrent depth: %d\n", max_recurrent_depth); + + inputs.resize(input_length); + for (size_t i = 0; i < inputs.size(); i++) + { + inputs[i].resize(timesteps); + } + + outputs.resize(input_length); + for (size_t i = 0; i < outputs.size(); i++) + { + outputs[i].resize(timesteps); + } + + generate_random_matrix(timesteps, input_length, inputs); + generate_random_oneHot_matrix(timesteps, input_length, outputs); + + vector inputs1; + vector outputs1; + + string input_par = "input"; + string output_par = "output"; + for (int32_t i = 0; i < input_length; i++) + { + string temp_input_par = input_par + to_string(i); + string temp_output_par = output_par + to_string(i); + inputs1.push_back(temp_input_par); + outputs1.push_back(temp_output_par); + } + + genome = create_delta(inputs1, 2, 2, outputs1, max_recurrent_depth, weight_initialize); + gradient_test_class("DELTA: 1 Input, 1 Output", genome, inputs, outputs); + delete genome; + + genome = create_enarc(inputs1, 2, 2, outputs1, max_recurrent_depth + 1, weight_initialize); + gradient_test_class("ENARC: 1 Input, 1 Output", genome, inputs, outputs); + delete genome; + + + genome = create_mgu(inputs1, 2, 2, outputs1, max_recurrent_depth + 1, weight_initialize); + gradient_test_class("MGU: 1 Input, 1 Output", genome, inputs, outputs); + delete genome; + + genome = create_ugrnn(inputs1, 2, 2, outputs1, max_recurrent_depth + 1, weight_initialize); + gradient_test_class("UGRNN: 1 Input, 1 Output", genome, inputs, outputs); + delete genome; + + +} diff --git a/rnn_tests/test_softmax_output.cxx b/rnn_tests/test_softmax_output.cxx index c5d48646..73532d12 100644 --- a/rnn_tests/test_softmax_output.cxx +++ b/rnn_tests/test_softmax_output.cxx @@ -1,3 +1,5 @@ +// ./rnn_tests/test_softmax_output --std_message_level info --file_message_level info --output_directory "./test_output" --timestep 4 --input_length 6 + #include #include @@ -19,17 +21,13 @@ using std::vector; #include "common/arguments.hxx" #include "common/log.hxx" #include "common/weight_initialize.hxx" - #include "rnn/lstm_node.hxx" #include "rnn/rnn_edge.hxx" #include "rnn/rnn_genome.hxx" #include "rnn/rnn_node.hxx" #include "rnn/rnn_node_interface.hxx" - #include "rnn/generate_nn.hxx" - #include "time_series/time_series.hxx" - #include "gradient_test.hxx" @@ -56,31 +54,29 @@ int main(int argc, char **argv) Log::fatal("weight initialization method %s is set wrong \n", weight_initialize_string.c_str()); } - Log::info("TESTING FEED FORWARD\n"); - vector> inputs; vector> outputs; + vector> expected; int timesteps = 10; - get_argument(arguments, "--timestep", true, timesteps); + get_argument(arguments, "--timesteps", true, timesteps); - int input_length = 1; - get_argument(arguments,"--input_length",true,input_length); + int output_length = 1; + get_argument(arguments,"--output_length",true,output_length); - inputs.resize(input_length); - for (size_t i = 0; i < inputs.size(); i++) { - inputs[i].resize(timesteps); - } - - outputs.resize(input_length); + outputs.resize(output_length); for (size_t i = 0; i < outputs.size(); i++) { outputs[i].resize(timesteps); } - generate_random_oneHot_matrix(timesteps,input_length,inputs); - generate_random_oneHot_matrix(timesteps, input_length, outputs); + expected.resize(output_length); + for (size_t i = 0; i < expected.size(); i++) { + expected[i].resize(timesteps); + } + + generate_random_matrix(timesteps, output_length, outputs); + generate_random_oneHot_matrix(timesteps, output_length, expected); - vector parameters; double mse; vector> softmax_gradients, deltas; @@ -90,7 +86,7 @@ int main(int argc, char **argv) string input_par = "input"; string output_par = "output"; - for (int32_t i = 0; i < input_length; i++) { + for (int32_t i = 0; i < output_length; i++) { string temp_input_par = input_par + to_string(i); string temp_output_par = output_par + to_string(i); inputs1.push_back(temp_input_par); @@ -98,16 +94,36 @@ int main(int argc, char **argv) } - genome = create_lstm(inputs1, 1, 1, outputs1, 3, weight_initialize); + genome = create_lstm(inputs1, 1, 1, outputs1, timesteps, weight_initialize); RNN *rnn = genome->get_rnn(); - generate_random_vector(rnn->get_number_weights(), parameters); - rnn->set_weights(parameters); + rnn->enable_use_regression(false); - deltas = rnn->get_softmax_gradient(parameters, inputs, outputs, mse, softmax_gradients, false, true, 0.0); + deltas = rnn->get_softmax_gradient(outputs, expected, mse, softmax_gradients); + + for (uint32_t j = 0; j < outputs[0].size(); j++) { + for (uint32_t i = 0; i < outputs.size(); i++) { + std::cout< Date: Wed, 9 Dec 2020 20:34:25 -0500 Subject: [PATCH 16/25] Update README.md Added about work done this semester --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 34f67417..258c2a24 100644 --- a/README.md +++ b/README.md @@ -31,6 +31,8 @@ $ dot -Tpdf genome.gv -o genome.pdf The datasets used for this part of the code are PennChar, PennTreeBank and WikiText. These datasets can be found in the *datasets* directory. These datasets can be used in both mpi as well as multithreaded version. +## Work done this semester : + ### To use Pennchar as dataset and use Simple and ENARC as memory cell @@ -112,7 +114,7 @@ The std_out can be seen in file *main_out* for 20 genomes. The number of genomes ![Architecture Diagram](https://github.com/travisdesell/exact/tree/enarc/images/arch.png) - +## Work done before # EXAMM: Evolutionary eXploration of Augmenting Memory Models and EXALT: Evolutionary eXploration of Augmenting LSTM Topologies From 61b90bb417407ce9189ad301709a51b561fce34e Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Fri, 18 Dec 2020 06:42:26 -0500 Subject: [PATCH 17/25] adding examm_mpi_nlp --- main_out | 1114 ------------------------------------ mpi/examm_mpi.cxx | 2 +- mpi/examm_mpi_nlp.cxx | 146 ++--- rnn/enas_dag_node.cxx | 4 +- rnn/examm.cxx | 5 + rnn/random_dag_node.cxx | 13 +- rnn/rnn_node_interface.cxx | 4 +- run_enarc_nlp.sh | 55 -- run_lstm_nlp.sh | 55 -- untitled.sh | 1 - 10 files changed, 93 insertions(+), 1306 deletions(-) delete mode 100644 main_out delete mode 100644 run_enarc_nlp.sh delete mode 100644 run_lstm_nlp.sh delete mode 100644 untitled.sh diff --git a/main_out b/main_out deleted file mode 100644 index 1f755835..00000000 --- a/main_out +++ /dev/null @@ -1,1114 +0,0 @@ -[INFO main ] ' stats, min: 0.000000, avg: 0.001545, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.039284, variance: 0.001543 -[INFO main ] - stats, min: 0.000000, avg: 0.001545, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.039284, variance: 0.001543 -[INFO main ] . stats, min: 0.000000, avg: 0.004119, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.064067, variance: 0.004105 -[INFO main ] 0 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 -[INFO main ] 1 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 -[INFO main ] 5 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 -[INFO main ] 9 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 -[INFO main ] < stats, min: 0.000000, avg: 0.009784, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.098453, variance: 0.009693 -[INFO main ] stats, min: 0.000000, avg: 0.008239, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.090417, variance: 0.008175 -[INFO main ] > stats, min: 0.000000, avg: 0.009784, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.098453, variance: 0.009693 -[INFO main ] N stats, min: 0.000000, avg: 0.004634, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.067936, variance: 0.004615 -[INFO main ] _ stats, min: 0.000000, avg: 0.162204, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.368733, variance: 0.135964 -[INFO main ] a stats, min: 0.000000, avg: 0.065396, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.247288, variance: 0.061151 -[INFO main ] b stats, min: 0.000000, avg: 0.012358, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.110508, variance: 0.012212 -[INFO main ] c stats, min: 0.000000, avg: 0.024202, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.153715, variance: 0.023628 -[INFO main ] d stats, min: 0.000000, avg: 0.026777, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.161471, variance: 0.026073 -[INFO main ] e stats, min: 0.000000, avg: 0.096807, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.295772, variance: 0.087481 -[INFO main ] f stats, min: 0.000000, avg: 0.018538, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.134920, variance: 0.018203 -[INFO main ] g stats, min: 0.000000, avg: 0.015448, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.123358, variance: 0.015217 -[INFO main ] h stats, min: 0.000000, avg: 0.029351, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.168832, variance: 0.028504 -[INFO main ] i stats, min: 0.000000, avg: 0.049949, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.217895, variance: 0.047478 -[INFO main ] j stats, min: 0.000000, avg: 0.001545, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.039284, variance: 0.001543 -[INFO main ] k stats, min: 0.000000, avg: 0.019567, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.138544, variance: 0.019194 -[INFO main ] l stats, min: 0.000000, avg: 0.023687, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.152111, variance: 0.023138 -[INFO main ] m stats, min: 0.000000, avg: 0.018538, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.134920, variance: 0.018203 -[INFO main ] n stats, min: 0.000000, avg: 0.062307, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.241774, variance: 0.058455 -[INFO main ] o stats, min: 0.000000, avg: 0.060247, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.238005, variance: 0.056647 -[INFO main ] p stats, min: 0.000000, avg: 0.016478, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.127337, variance: 0.016215 -[INFO main ] q stats, min: 0.000000, avg: 0.001030, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.032083, variance: 0.001029 -[INFO main ] r stats, min: 0.000000, avg: 0.061792, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.240839, variance: 0.058004 -[INFO main ] s stats, min: 0.000000, avg: 0.060247, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.238005, variance: 0.056647 -[INFO main ] t stats, min: 0.000000, avg: 0.066426, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.249090, variance: 0.062046 -[INFO main ] u stats, min: 0.000000, avg: 0.031411, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.174470, variance: 0.030440 -[INFO main ] v stats, min: 0.000000, avg: 0.005664, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.075067, variance: 0.005635 -[INFO main ] w stats, min: 0.000000, avg: 0.012873, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.112757, variance: 0.012714 -[INFO main ] x stats, min: 0.000000, avg: 0.002575, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.050689, variance: 0.002569 -[INFO main ] y stats, min: 0.000000, avg: 0.012358, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.110508, variance: 0.012212 -[INFO main ] z stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 -[INFO main ] read time series '../datasets/pennchar/input.txt' with number rows: 1942 -[INFO main ] ' stats, min: 0.000000, avg: 0.001545, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.039284, variance: 0.001543 -[INFO main ] - stats, min: 0.000000, avg: 0.001545, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.039284, variance: 0.001543 -[INFO main ] . stats, min: 0.000000, avg: 0.004119, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.064067, variance: 0.004105 -[INFO main ] 0 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 -[INFO main ] 1 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 -[INFO main ] 5 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 -[INFO main ] 9 stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 -[INFO main ] < stats, min: 0.000000, avg: 0.009784, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.098453, variance: 0.009693 -[INFO main ] stats, min: 0.000000, avg: 0.008239, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.090417, variance: 0.008175 -[INFO main ] > stats, min: 0.000000, avg: 0.009784, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.098453, variance: 0.009693 -[INFO main ] N stats, min: 0.000000, avg: 0.004634, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.067936, variance: 0.004615 -[INFO main ] _ stats, min: 0.000000, avg: 0.162204, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.368733, variance: 0.135964 -[INFO main ] a stats, min: 0.000000, avg: 0.065396, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.247288, variance: 0.061151 -[INFO main ] b stats, min: 0.000000, avg: 0.012358, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.110508, variance: 0.012212 -[INFO main ] c stats, min: 0.000000, avg: 0.024202, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.153715, variance: 0.023628 -[INFO main ] d stats, min: 0.000000, avg: 0.026777, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.161471, variance: 0.026073 -[INFO main ] e stats, min: 0.000000, avg: 0.096807, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.295772, variance: 0.087481 -[INFO main ] f stats, min: 0.000000, avg: 0.018538, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.134920, variance: 0.018203 -[INFO main ] g stats, min: 0.000000, avg: 0.015448, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.123358, variance: 0.015217 -[INFO main ] h stats, min: 0.000000, avg: 0.029351, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.168832, variance: 0.028504 -[INFO main ] i stats, min: 0.000000, avg: 0.049949, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.217895, variance: 0.047478 -[INFO main ] j stats, min: 0.000000, avg: 0.001545, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.039284, variance: 0.001543 -[INFO main ] k stats, min: 0.000000, avg: 0.019567, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.138544, variance: 0.019194 -[INFO main ] l stats, min: 0.000000, avg: 0.023687, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.152111, variance: 0.023138 -[INFO main ] m stats, min: 0.000000, avg: 0.018538, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.134920, variance: 0.018203 -[INFO main ] n stats, min: 0.000000, avg: 0.062307, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.241774, variance: 0.058455 -[INFO main ] o stats, min: 0.000000, avg: 0.060247, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.238005, variance: 0.056647 -[INFO main ] p stats, min: 0.000000, avg: 0.016478, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.127337, variance: 0.016215 -[INFO main ] q stats, min: 0.000000, avg: 0.001030, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.032083, variance: 0.001029 -[INFO main ] r stats, min: 0.000000, avg: 0.061792, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.240839, variance: 0.058004 -[INFO main ] s stats, min: 0.000000, avg: 0.060247, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.238005, variance: 0.056647 -[INFO main ] t stats, min: 0.000000, avg: 0.066426, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.249090, variance: 0.062046 -[INFO main ] u stats, min: 0.000000, avg: 0.031411, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.174470, variance: 0.030440 -[INFO main ] v stats, min: 0.000000, avg: 0.005664, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.075067, variance: 0.005635 -[INFO main ] w stats, min: 0.000000, avg: 0.012873, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.112757, variance: 0.012714 -[INFO main ] x stats, min: 0.000000, avg: 0.002575, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.050689, variance: 0.002569 -[INFO main ] y stats, min: 0.000000, avg: 0.012358, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.110508, variance: 0.012212 -[INFO main ] z stats, min: 0.000000, avg: 0.000515, max: 1.000000, min_change: -1.000000, max_change: 1.000000, std_dev: 0.022692, variance: 0.000515 -[INFO main ] read time series '../datasets/pennchar/input.txt' with number rows: 1942 -word_offset:: -1 -training -word_offset:: 1 -testing -486 -486 38 4 -486 -486 38 4 -word_offset:: -1 -training -word_offset:: 1 -testing -486 -486 38 4 -486 -486 38 4 -[INFO main ] exported word series. -[INFO main ] The size of the input is :: 486,38,4 -[INFO main ] number_inputs: 38, number_outputs: 38 -[INFO main ] weight initialize: random -[INFO main ] weight inheritance: lamarckian -[INFO main ] mutated component weight: lamarckian -[INFO main ] Speciation method is: "" (Default is the island-based speciation strategy). -[INFO main ] generating new genome for island[0], island_size: 0, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is initializing! -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: UNEVAL to island 0 -[INFO main ] created copy to insert to island: 0 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f52a02cfac8 -[INFO main ] new best fitness for island: 0! -[INFO main ] genomes.size(): 1, max_size: 2, status: 0 -[INFO main ] generating new genome for island[0], island_size: 1, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is initializing! -[INFO main ] island is not empty, mutating a random genome -[INFO main ] generating new genome by mutation. -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: UNEVAL to island 0 -[INFO main ] potential duplicate for hash '2926_1043290_0', had 1 potential matches. -[INFO main ] on potential match 0 of 1 -[INFO main ] created copy to insert to island: 0 -[INFO main ] inserting genome at index: 1 -[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f529f2cdac8 -[INFO main ] genomes.size(): 2, max_size: 2, status: 1 -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] attempting to merge a node! -[INFO main ] attempting to disable a node! -[INFO main ] disabling node 6 at depth 0.000000 -[INFO main ] generating new genome for island[1], island_size: 0, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is initializing! -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: UNEVAL to island 1 -[INFO main ] created copy to insert to island: 1 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f5296accac8 -[INFO main ] new best fitness for island: 1! -[INFO main ] genomes.size(): 1, max_size: 2, status: 0 -[INFO main ] generating new genome for island[1], island_size: 1, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is initializing! -[INFO main ] island is not empty, mutating a random genome -[INFO main ] generating new genome by mutation. -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: UNEVAL to island 1 -[INFO main ] created copy to insert to island: 1 -[INFO main ] inserting genome at index: 1 -[INFO main ] adding to structure_map[2926_1041910_0] : 0x7f529eaccac8 -[INFO main ] genomes.size(): 2, max_size: 2, status: 1 -[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] attempting to add edge! -[INFO main ] reachable_nodes.size(): 76 -[INFO main ] selected first node 7 with depth 845 -[INFO main ] reachable_nodes.size(): 38 -[INFO main ] selected second node 48 with depth 4628 -[INFO main ] adding edge between nodes 7 and 48 -[INFO main ] edge already exists, not adding. -[INFO main ] attempting to disable a node! -[INFO main ] disabling node 26 at depth 0.000000 -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] attempting to add a node! -[INFO main ] add node, split depth: 0.201243, max_inputs: 21, max_outputs: 1 -[INFO main ] add node recurrent probability: 0.000000 -[INFO main ] CREATING NODE, type: 'simple' -[INFO main ] adding edge between nodes 9 and 78 -[INFO main ] adding edge between nodes 9 and 78, new edge weight: -0.156685 -[INFO main ] adding edge between nodes 30 and 78 -[INFO main ] adding edge between nodes 30 and 78, new edge weight: -0.152126 -[INFO main ] adding edge between nodes 20 and 78 -[INFO main ] adding edge between nodes 20 and 78, new edge weight: 0.040005 -[INFO main ] adding edge between nodes 3 and 78 -[INFO main ] adding edge between nodes 3 and 78, new edge weight: 0.366074 -[INFO main ] adding edge between nodes 4 and 78 -[INFO main ] adding edge between nodes 4 and 78, new edge weight: -0.179001 -[INFO main ] adding edge between nodes 6 and 78 -[INFO main ] adding edge between nodes 6 and 78, new edge weight: -0.483166 -[INFO main ] adding edge between nodes 38 and 78 -[INFO main ] adding edge between nodes 38 and 78, new edge weight: 0.472341 -[INFO main ] adding edge between nodes 14 and 78 -[INFO main ] adding edge between nodes 14 and 78, new edge weight: -0.011813 -[INFO main ] adding edge between nodes 17 and 78 -[INFO main ] adding edge between nodes 17 and 78, new edge weight: 0.147622 -[INFO main ] adding edge between nodes 18 and 78 -[INFO main ] adding edge between nodes 18 and 78, new edge weight: 0.224630 -[INFO main ] adding edge between nodes 1 and 78 -[INFO main ] adding edge between nodes 1 and 78, new edge weight: 0.134234 -[INFO main ] adding edge between nodes 22 and 78 -[INFO main ] adding edge between nodes 22 and 78, new edge weight: 0.006210 -[INFO main ] adding edge between nodes 23 and 78 -[INFO main ] adding edge between nodes 23 and 78, new edge weight: 0.244998 -[INFO main ] adding edge between nodes 25 and 78 -[INFO main ] adding edge between nodes 25 and 78, new edge weight: -0.243366 -[INFO main ] adding edge between nodes 28 and 78 -[INFO main ] adding edge between nodes 28 and 78, new edge weight: -0.387377 -[INFO main ] adding edge between nodes 31 and 78 -[INFO main ] adding edge between nodes 31 and 78, new edge weight: 0.263492 -[INFO main ] adding edge between nodes 32 and 78 -[INFO main ] adding edge between nodes 32 and 78, new edge weight: -0.058932 -[INFO main ] adding edge between nodes 34 and 78 -[INFO main ] adding edge between nodes 34 and 78, new edge weight: -0.080912 -[INFO main ] adding edge between nodes 36 and 78 -[INFO main ] adding edge between nodes 36 and 78, new edge weight: 0.267297 -[INFO main ] adding edge between nodes 37 and 78 -[INFO main ] adding edge between nodes 37 and 78, new edge weight: -0.383394 -[INFO main ] adding edge between nodes 19 and 78 -[INFO main ] adding edge between nodes 19 and 78, new edge weight: -0.025244 -[INFO main ] adding edge between nodes 78 and 60 -[INFO main ] adding edge between nodes 78 and 60, new edge weight: -0.278417 -[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing intra-island crossover -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing intra-island crossover -[INFO genome_4_thread_3 ] iteration 0, mse: 7733.529611, v_mse: 15.912612, bv_mse: 15.912612, lr: 0.000002, norm: 1686668.297044, p_norm: 127.667555, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000013 -[INFO genome_7_thread_4 ] iteration 0, mse: 7326.705177, v_mse: 15.075525, bv_mse: 15.075525, lr: 0.000002, norm: 1689712.887290, p_norm: 128.151778, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000013 -[INFO genome_6_thread_2 ] iteration 0, mse: 7340.652115, v_mse: 15.104222, bv_mse: 15.104222, lr: 0.000002, norm: 1557641.165275, p_norm: 128.151778, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000014 -[INFO genome_9_thread_5 ] iteration 0, mse: 7386.622085, v_mse: 15.198811, bv_mse: 15.198811, lr: 0.000002, norm: 1779943.389892, p_norm: 130.303026, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000012 -[INFO genome_2_thread_1 ] iteration 0, mse: 7326.705177, v_mse: 15.075525, bv_mse: 15.075525, lr: 0.000002, norm: 1689712.887290, p_norm: 128.151778, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000013 -[INFO genome_12_thread_7 ] iteration 0, mse: 7363.670893, v_mse: 15.151586, bv_mse: 15.151586, lr: 0.000002, norm: 1540645.237973, p_norm: 129.563464, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000014 -[INFO genome_14_thread_0 ] iteration 0, mse: 7238.272640, v_mse: 14.893565, bv_mse: 14.893565, lr: 0.000002, norm: 1706117.844996, p_norm: 153.730517, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000013 -[INFO genome_4_thread_3 ] iteration 1, mse: 6298.940161, v_mse: 12.960782, bv_mse: 12.960782, lr: 0.000002, norm: 606591.629021, p_norm: 158.816187, v_norm: 3.470511, OVER THRESHOLD, multiplier: 0.000036 -[INFO genome_11_thread_6 ] iteration 0, mse: 7098.992183, v_mse: 14.606980, bv_mse: 14.606980, lr: 0.000002, norm: 1522644.952689, p_norm: 125.784153, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000014 -[INFO genome_13_thread_8 ] iteration 0, mse: 6802.457521, v_mse: 13.996826, bv_mse: 13.996826, lr: 0.000002, norm: 1200348.173889, p_norm: 148.500648, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000018 -[INFO genome_7_thread_4 ] iteration 1, mse: 6327.792787, v_mse: 13.020150, bv_mse: 13.020150, lr: 0.000002, norm: 613568.932167, p_norm: 162.464627, v_norm: 3.476775, OVER THRESHOLD, multiplier: 0.000036 -[INFO genome_6_thread_2 ] iteration 1, mse: 6330.032413, v_mse: 13.024758, bv_mse: 13.024758, lr: 0.000002, norm: 539654.215121, p_norm: 156.808846, v_norm: 3.205023[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 12.960782 to island 0 -[INFO main ] potential duplicate for hash '2926_1043290_0', had 2 potential matches. -[INFO main ] on potential match 0 of 2 -[INFO main ] on potential match 1 of 2 -[INFO main ] created copy to insert to island: 0 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f529f2cdb48 -[INFO main ] new best fitness for island: 0! -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52900d1ad0, &(*potential_match): 0x7f52980d1ad0 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52900d1ad0, &(*potential_match): 0x7f52900d1ad0 -[INFO main ] potential_matches.size() before erase: 3 -[INFO main ] potential_matches.size() after erase: 2 -[INFO main ] structure_map[2926_1043290_0].size() after erase: 2 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52900d1ad0, &(*potential_match): 0x7f521fde8970 -, OVER THRESHOLD, multiplier: 0.000041 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] UNEVAL UNEVAL 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] UNEVAL UNEVAL 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] UNEVAL UNEVAL 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing intra-island crossover -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 13.020150 to island 1 -[INFO main ] potential duplicate for hash '2926_1043290_0', had 1 potential matches. -[INFO main ] on potential match 0 of 1 -[INFO main ] REPLACING DUPLICATE GENOME, fitness of genome in search: UNEVAL, new fitness: 13.020150 -[INFO main ] duplicate_genome_iterator: 0x7f528c08a700, (*potential_match): 0x7f528c08a700 -[INFO main ] potential_match->get_fitness(): 10000000.000000, duplicate_genome_iterator->get_fitness(): 10000000.000000, new_fitness: 13.020150 -[INFO main ] duplicate_genome_index: 0 -[INFO main ] potential_matches.size() before erase: 1 -[INFO main ] potential_matches.size() after erase: 0 -[INFO main ] structure_map[2926_1043290_0].size() after erase: 0 -[INFO main ] deleting the potential_matches vector for hash '2926_1043290_0' because it was empty. -[INFO genome_9_thread_5 ] iteration 1, mse: 6232.487039, v_mse: 12.824047, bv_mse: 12.824047, lr: 0.000002, norm: 527931.122526, p_norm: 170.004606, v_norm: 3.662435, OVER THRESHOLD, multiplier: 0.000042 -[INFO main ] created copy to insert to island: 1 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f5296accb48 -[INFO main ] new best fitness for island: 1! -[INFO main ] genomes.size(): 2, max_size: 2, status: 1 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] UNEVAL UNEVAL 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] UNEVAL UNEVAL 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] attempting to add a node! -[INFO main ] add node, split depth: 0.560641, max_inputs: 21, max_outputs: 8 -[INFO main ] add node recurrent probability: 0.000000 -[INFO main ] CREATING NODE, type: 'simple' -[INFO main ] adding edge between nodes 9 and 79 -[INFO main ] adding edge between nodes 9 and 79, new edge weight: -0.035635 -[INFO main ] adding edge between nodes 11 and 79 -[INFO main ] adding edge between nodes 11 and 79, new edge weight: -0.262195 -[INFO main ] adding edge between nodes 30 and 79 -[INFO main ] adding edge between nodes 30 and 79, new edge weight: -0.310779 -[INFO main ] adding edge between nodes 20 and 79 -[INFO main ] adding edge between nodes 20 and 79, new edge weight: 0.308397 -[INFO main ] adding edge between nodes 3 and 79 -[INFO main ] adding edge between nodes 3 and 79, new edge weight: -0.131418 -[INFO main ] adding edge between nodes 8 and 79 -[INFO main ] adding edge between nodes 8 and 79, new edge weight: -0.505568 -[INFO main ] adding edge between nodes 38 and 79 -[INFO main ] adding edge between nodes 38 and 79, new edge weight: -0.020145 -[INFO main ] adding edge between nodes 16 and 79 -[INFO main ] adding edge between nodes 16 and 79, new edge weight: 0.221037 -[INFO main ] adding edge between nodes 17 and 79 -[INFO main ] adding edge between nodes 17 and 79, new edge weight: -0.243482 -[INFO main ] adding edge between nodes 18 and 79 -[INFO main ] adding edge between nodes 18 and 79, new edge weight: 0.110130 -[INFO main ] adding edge between nodes 21 and 79 -[INFO main ] adding edge between nodes 21 and 79, new edge weight: -0.159293 -[INFO main ] adding edge between nodes 29 and 79 -[INFO main ] adding edge between nodes 29 and 79, new edge weight: 0.361482 -[INFO main ] adding edge between nodes 10 and 79 -[INFO main ] adding edge between nodes 10 and 79, new edge weight: 0.113179 -[INFO main ] adding edge between nodes 1 and 79 -[INFO main ] adding edge between nodes 1 and 79, new edge weight: -0.250825 -[INFO main ] adding edge between nodes 22 and 79 -[INFO main ] adding edge between nodes 22 and 79, new edge weight: -0.022429 -[INFO main ] adding edge between nodes 24 and 79 -[INFO main ] adding edge between nodes 24 and 79, new edge weight: -0.191066 -[INFO main ] adding edge between nodes 26 and 79 -[INFO main ] adding edge between nodes 26 and 79, new edge weight: -0.449681 -[INFO main ] adding edge between nodes 28 and 79 -[INFO main ] adding edge between nodes 28 and 79, new edge weight: 0.247844 -[INFO main ] adding edge between nodes 31 and 79 -[INFO main ] adding edge between nodes 31 and 79, new edge weight: -0.409950 -[INFO main ] adding edge between nodes 32 and 79 -[INFO main ] adding edge between nodes 32 and 79, new edge weight: -0.033563 -[INFO main ] adding edge between nodes 37 and 79 -[INFO main ] adding edge between nodes 37 and 79, new edge weight: 0.562051 -[INFO main ] adding edge between nodes 79 and 42 -[INFO main ] adding edge between nodes 79 and 42, new edge weight: 0.079454 -[INFO main ] adding edge between nodes 79 and 47 -[INFO main ] adding edge between nodes 79 and 47, new edge weight: -0.221006 -[INFO main ] adding edge between nodes 79 and 51 -[INFO main ] adding edge between nodes 79 and 51, new edge weight: 0.098147 -[INFO main ] adding edge between nodes 79 and 52 -[INFO main ] adding edge between nodes 79 and 52, new edge weight: -0.286536 -[INFO main ] adding edge between nodes 79 and 54 -[INFO main ] adding edge between nodes 79 and 54, new edge weight: 0.050287 -[INFO main ] adding edge between nodes 79 and 62 -[INFO main ] adding edge between nodes 79 and 62, new edge weight: 0.677715 -[INFO main ] adding edge between nodes 79 and 64 -[INFO main ] adding edge between nodes 79 and 64, new edge weight: 0.669388 -[INFO main ] adding edge between nodes 79 and 69 -[INFO main ] adding edge between nodes 79 and 69, new edge weight: -0.773083 -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 13.024758 to island 0 -[INFO main ] created copy to insert to island: 0 -[INFO main ] inserting genome at index: 1 -[INFO main ] adding to structure_map[2920_1016348_0] : 0x7f529faceb48 -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52980d1ad0, &(*potential_match): 0x7f52980d1ad0 -[INFO main ] potential_matches.size() before erase: 2 -[INFO main ] potential_matches.size() after erase: 1 -[INFO main ] structure_map[2926_1043290_0].size() after erase: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52980d1ad0, &(*potential_match): 0x7f521fde8970 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] 13.024758 23.115156 1444 (1444) 0 (0) 0 (76) 75 (76) [disable_node:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] UNEVAL UNEVAL 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing intra-island crossover -[INFO genome_2_thread_1 ] iteration 1, mse: 6327.792787, v_mse: 13.020150, bv_mse: 13.020150, lr: 0.000002, norm: 613568.932167, p_norm: 162.464627, v_norm: 3.476775, OVER THRESHOLD, multiplier: 0.000036 -[INFO genome_12_thread_7 ] iteration 1, mse: 6330.648142, v_mse: 13.026025, bv_mse: 13.026025, lr: 0.000002, norm: 543161.125093, p_norm: 156.190941, v_norm: 3.170052, OVER THRESHOLD, multiplier: 0.000041 -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.824047 to island 1 -[INFO main ] potential duplicate for hash '2926_1041910_0', had 1 potential matches. -[INFO main ] on potential match 0 of 1 -[INFO main ] created copy to insert to island: 1 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[2926_1041910_0] : 0x7f529eaccb48 -[INFO main ] new best fitness for island: 1! -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52800d1ad0, &(*potential_match): 0x7f52800d1ad0 -[INFO main ] potential_matches.size() before erase: 2 -[INFO main ] potential_matches.size() after erase: 1 -[INFO main ] structure_map[2926_1041910_0].size() after erase: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52800d1ad0, &(*potential_match): 0x7f50686cc090 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] 13.024758 23.115156 1444 (1444) 0 (0) 0 (76) 75 (76) [disable_node:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] attempting to add a node! -[INFO main ] add node, split depth: 0.849617, max_inputs: 21, max_outputs: 3 -[INFO main ] add node recurrent probability: 0.000000 -[INFO main ] CREATING NODE, type: 'simple' -[INFO main ] adding edge between nodes 21 and 80 -[INFO main ] adding edge between nodes 21 and 80, new edge weight: -0.246043 -[INFO main ] adding edge between nodes 30 and 80 -[INFO main ] adding edge between nodes 30 and 80, new edge weight: 0.030043 -[INFO main ] adding edge between nodes 3 and 80 -[INFO main ] adding edge between nodes 3 and 80, new edge weight: -0.034008 -[INFO main ] adding edge between nodes 4 and 80 -[INFO main ] adding edge between nodes 4 and 80, new edge weight: 0.369791 -[INFO main ] adding edge between nodes 5 and 80 -[INFO main ] adding edge between nodes 5 and 80, new edge weight: -0.058420 -[INFO main ] adding edge between nodes 2 and 80 -[INFO main ] adding edge between nodes 2 and 80, new edge weight: -0.649818 -[INFO main ] adding edge between nodes 13 and 80 -[INFO main ] adding edge between nodes 13 and 80, new edge weight: -0.206339 -[INFO main ] adding edge between nodes 15 and 80 -[INFO main ] adding edge between nodes 15 and 80, new edge weight: -0.502198 -[INFO main ] adding edge between nodes 18 and 80 -[INFO main ] adding edge between nodes 18 and 80, new edge weight: -0.023026 -[INFO main ] adding edge between nodes 28 and 80 -[INFO main ] adding edge between nodes 28 and 80, new edge weight: -0.183560 -[INFO main ] adding edge between nodes 9 and 80 -[INFO main ] adding edge between nodes 9 and 80, new edge weight: 0.632474 -[INFO main ] adding edge between nodes 10 and 80 -[INFO main ] adding edge between nodes 10 and 80, new edge weight: -0.149075 -[INFO main ] adding edge between nodes 1 and 80 -[INFO main ] adding edge between nodes 1 and 80, new edge weight: 0.121135 -[INFO main ] adding edge between nodes 23 and 80 -[INFO main ] adding edge between nodes 23 and 80, new edge weight: -0.379970 -[INFO main ] adding edge between nodes 24 and 80 -[INFO main ] adding edge between nodes 24 and 80, new edge weight: 0.192683 -[INFO main ] adding edge between nodes 31 and 80 -[INFO main ] adding edge between nodes 31 and 80, new edge weight: -0.150028 -[INFO main ] adding edge between nodes 32 and 80 -[INFO main ] adding edge between nodes 32 and 80, new edge weight: -0.734131 -[INFO main ] adding edge between nodes 34 and 80 -[INFO main ] adding edge between nodes 34 and 80, new edge weight: 0.018146 -[INFO main ] adding edge between nodes 35 and 80 -[INFO main ] adding edge between nodes 35 and 80, new edge weight: 0.431549 -[INFO main ] adding edge between nodes 36 and 80 -[INFO main ] adding edge between nodes 36 and 80, new edge weight: 0.324393 -[INFO main ] adding edge between nodes 11 and 80 -[INFO main ] adding edge between nodes 11 and 80, new edge weight: -0.172874 -[INFO main ] adding edge between nodes 80 and 42 -[INFO main ] adding edge between nodes 80 and 42, new edge weight: -0.193806 -[INFO main ] adding edge between nodes 80 and 52 -[INFO main ] adding edge between nodes 80 and 52, new edge weight: 0.996493 -[INFO main ] adding edge between nodes 80 and 75 -[INFO main ] adding edge between nodes 80 and 75, new edge weight: -0.219244 -[INFO genome_14_thread_0 ] iteration 1, mse: 6264.922622, v_mse: 12.890787, bv_mse: 12.890787, lr: 0.000002, norm: 447479.521429, p_norm: 193.518512, v_norm: 3.510531, OVER THRESHOLD, multiplier: 0.000049 -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 13.020150 to island 0 -[INFO main ] potential duplicate for hash '2926_1043290_0', had 1 potential matches. -[INFO main ] on potential match 0 of 1 -[INFO main ] created copy to insert to island: 0 -[INFO main ] inserting genome at index: 1 -[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f52a02cfb48 -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f521b3ab4d0, &(*potential_match): 0x7f521b3ab4d0 -[INFO main ] potential_matches.size() before erase: 1 -[INFO main ] potential_matches.size() after erase: 0 -[INFO main ] structure_map[2920_1016348_0].size() after erase: 0 -[INFO main ] deleting the potential_matches vector for hash '2920_1016348_0' because it was empty. -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 13.026025 to island 0 -[INFO main ] ignoring genome, fitness: 13.026025 > worst for island[0] fitness: 13.020150 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO genome_13_thread_8 ] iteration 1, mse: 6214.359412, v_mse: 12.786748, bv_mse: 12.786748, lr: 0.000002, norm: 474456.939792, p_norm: 172.678960, v_norm: 2.469852[INFO genome_11_thread_6 ] iteration 1, mse: 6270.977832, v_mse: 12.903247, bv_mse: 12.903247, lr: 0.000002, norm: 568216.793745, p_norm: 157.694918, v_norm: 3.133014, OVER THRESHOLD, multiplier: 0.000039 -, OVER THRESHOLD, multiplier: 0.000046 -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 12.890787 to island 0 -[INFO main ] potential duplicate for hash '2926_1043290_0', had 2 potential matches. -[INFO main ] on potential match 0 of 2 -[INFO main ] on potential match 1 of 2 -[INFO main ] created copy to insert to island: 0 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f52a0ad0b48 -[INFO main ] new best fitness for island: 0! -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4f2c29bae0, &(*potential_match): 0x7f521fde8970 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4f2c29bae0, &(*potential_match): 0x7f4f2c29bae0 -[INFO main ] potential_matches.size() before erase: 3 -[INFO main ] potential_matches.size() after erase: 2 -[INFO main ] structure_map[2926_1043290_0].size() after erase: 2 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4f2c29bae0, &(*potential_match): 0x7f527008a700 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] 13.020150 23.667082 1444 (1444) 0 (0) 0 (76) 76 (76) [initial:1] -[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing intra-island crossover -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.786748 to island 1 -[INFO main ] potential duplicate for hash '2926_1043290_0', had 1 potential matches. -[INFO main ] on potential match 0 of 1 -[INFO main ] created copy to insert to island: 1 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f529d2c9b48 -[INFO main ] new best fitness for island: 1! -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f528c08a700, &(*potential_match): 0x7f528c08a700 -[INFO main ] potential_matches.size() before erase: 2 -[INFO main ] potential_matches.size() after erase: 1 -[INFO main ] structure_map[2926_1043290_0].size() after erase: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f528c08a700, &(*potential_match): 0x7f527c08a700 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.786748 25.846512 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.903247 to island 1 -[INFO main ] ignoring genome, fitness: 12.903247 > worst for island[1] fitness: 12.824047 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.786748 25.846512 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] attempting to add a node! -[INFO main ] add node, split depth: 0.595336, max_inputs: 21, max_outputs: 13 -[INFO main ] add node recurrent probability: 0.000000 -[INFO main ] CREATING NODE, type: 'simple' -[INFO main ] adding edge between nodes 8 and 81 -[INFO main ] adding edge between nodes 8 and 81, new edge weight: -0.136597 -[INFO main ] adding edge between nodes 21 and 81 -[INFO main ] adding edge between nodes 21 and 81, new edge weight: 0.219903 -[INFO main ] adding edge between nodes 29 and 81 -[INFO main ] adding edge between nodes 29 and 81, new edge weight: 0.172819 -[INFO main ] adding edge between nodes 30 and 81 -[INFO main ] adding edge between nodes 30 and 81, new edge weight: 0.355953 -[INFO main ] adding edge between nodes 3 and 81 -[INFO main ] adding edge between nodes 3 and 81, new edge weight: -0.304653 -[INFO main ] adding edge between nodes 5 and 81 -[INFO main ] adding edge between nodes 5 and 81, new edge weight: -0.342059 -[INFO main ] adding edge between nodes 7 and 81 -[INFO main ] adding edge between nodes 7 and 81, new edge weight: 0.373446 -[INFO main ] adding edge between nodes 15 and 81 -[INFO main ] adding edge between nodes 15 and 81, new edge weight: -0.271002 -[INFO main ] adding edge between nodes 16 and 81 -[INFO main ] adding edge between nodes 16 and 81, new edge weight: -0.420364 -[INFO main ] adding edge between nodes 17 and 81 -[INFO main ] adding edge between nodes 17 and 81, new edge weight: 0.135258 -[INFO main ] adding edge between nodes 10 and 81 -[INFO main ] adding edge between nodes 10 and 81, new edge weight: -0.248146 -[INFO main ] adding edge between nodes 22 and 81 -[INFO main ] adding edge between nodes 22 and 81, new edge weight: 0.522175 -[INFO main ] adding edge between nodes 24 and 81 -[INFO main ] adding edge between nodes 24 and 81, new edge weight: 0.124579 -[INFO main ] adding edge between nodes 25 and 81 -[INFO main ] adding edge between nodes 25 and 81, new edge weight: 0.171521 -[INFO main ] adding edge between nodes 27 and 81 -[INFO main ] adding edge between nodes 27 and 81, new edge weight: 0.125798 -[INFO main ] adding edge between nodes 31 and 81 -[INFO main ] adding edge between nodes 31 and 81, new edge weight: -0.005519 -[INFO main ] adding edge between nodes 32 and 81 -[INFO main ] adding edge between nodes 32 and 81, new edge weight: 0.264706 -[INFO main ] adding edge between nodes 34 and 81 -[INFO main ] adding edge between nodes 34 and 81, new edge weight: -0.310501 -[INFO main ] adding edge between nodes 36 and 81 -[INFO main ] adding edge between nodes 36 and 81, new edge weight: 0.139972 -[INFO main ] adding edge between nodes 37 and 81 -[INFO main ] adding edge between nodes 37 and 81, new edge weight: 0.012641 -[INFO main ] adding edge between nodes 11 and 81 -[INFO main ] adding edge between nodes 11 and 81, new edge weight: 0.685450 -[INFO main ] adding edge between nodes 81 and 45 -[INFO main ] adding edge between nodes 81 and 45, new edge weight: -0.361658 -[INFO main ] adding edge between nodes 81 and 43 -[INFO main ] adding edge between nodes 81 and 43, new edge weight: 0.188658 -[INFO main ] adding edge between nodes 81 and 57 -[INFO main ] adding edge between nodes 81 and 57, new edge weight: 0.723042 -[INFO main ] adding edge between nodes 81 and 50 -[INFO main ] adding edge between nodes 81 and 50, new edge weight: 0.016008 -[INFO main ] adding edge between nodes 81 and 53 -[INFO main ] adding edge between nodes 81 and 53, new edge weight: 0.393925 -[INFO main ] adding edge between nodes 81 and 66 -[INFO main ] adding edge between nodes 81 and 66, new edge weight: -0.016428 -[INFO main ] adding edge between nodes 81 and 65 -[INFO main ] adding edge between nodes 81 and 65, new edge weight: 0.075208 -[INFO main ] adding edge between nodes 81 and 63 -[INFO main ] adding edge between nodes 81 and 63, new edge weight: -0.194695 -[INFO main ] adding edge between nodes 81 and 62 -[INFO main ] adding edge between nodes 81 and 62, new edge weight: 0.048143 -[INFO main ] adding edge between nodes 81 and 74 -[INFO main ] adding edge between nodes 81 and 74, new edge weight: 0.033704 -[INFO main ] adding edge between nodes 81 and 70 -[INFO main ] adding edge between nodes 81 and 70, new edge weight: 0.258209 -[INFO main ] adding edge between nodes 81 and 71 -[INFO main ] adding edge between nodes 81 and 71, new edge weight: -0.050983 -[INFO main ] adding edge between nodes 81 and 72 -[INFO main ] adding edge between nodes 81 and 72, new edge weight: 0.277614 -[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] attempting to disable a node! -[INFO main ] disabling node 17 at depth 0.000000 -[INFO genome_15_thread_3 ] iteration 0, mse: 7257.768468, v_mse: 14.933680, bv_mse: 14.933680, lr: 0.000002, norm: 1854846.366558, p_norm: 151.835173, v_norm: 0.000000[INFO genome_17_thread_2 ] iteration 0, mse: 7125.284456, v_mse: 14.661079, bv_mse: 14.661079, lr: 0.000002, norm: 1345813.626528, p_norm: 176.832944, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000012 -, OVER THRESHOLD, multiplier: 0.000016 -[INFO genome_16_thread_4 ] iteration 0, mse: 7339.527342, v_mse: 15.101908, bv_mse: 15.101908, lr: 0.000002, norm: 1510305.755211, p_norm: 131.498007, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000015 -[INFO genome_18_thread_5 ] iteration 0, mse: 7243.038227, v_mse: 14.903371, bv_mse: 14.903371, lr: 0.000002, norm: 2065244.760592, p_norm: 160.383597, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000011 -[INFO genome_15_thread_3 ] iteration 1, mse: 6245.168962, v_mse: 12.850142, bv_mse: 12.850142, lr: 0.000002, norm: 633324.431810, p_norm: 196.589876, v_norm: 3.816556, OVER THRESHOLD, multiplier: 0.000035 -[INFO genome_17_thread_2 ] iteration 1, mse: 6317.668588, v_mse: 12.999318, bv_mse: 12.999318, lr: 0.000002, norm: 415079.723750, p_norm: 199.700853, v_norm: 2.769164, OVER THRESHOLD, multiplier: 0.000053 -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.850142 to island 1 -[INFO main ] ignoring genome, fitness: 12.850142 > worst for island[1] fitness: 12.824047 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO genome_20_thread_7 ] iteration 0, mse: 6799.275863, v_mse: 13.990280, bv_mse: 13.990280, lr: 0.000002, norm: 1350437.295612, p_norm: 162.464627, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000016 -[INFO genome_18_thread_5 ] iteration 1, mse: 6223.957684, v_mse: 12.806497, bv_mse: 12.806497, lr: 0.000002, norm: 478093.744754, p_norm: 219.697699, v_norm: 4.249475, OVER THRESHOLD, multiplier: 0.000046 -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO genome_22_thread_8 ] iteration 0, mse: 7248.078167, v_mse: 14.913741, bv_mse: 14.913741, lr: 0.000002, norm: 2042612.907146, p_norm: 161.770810, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000011 -[INFO genome_19_thread_1 ] iteration 0, mse: 7471.193607, v_mse: 15.372826, bv_mse: 15.372826, lr: 0.000002, norm: 1220041.659055, p_norm: 181.837990, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000018 -[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.960782 24.488416 1444 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO genome_16_thread_4 ] iteration 1, mse: 6339.507457, v_mse: 13.044254, bv_mse: 13.044254, lr: 0.000002, norm: 612213.087440, p_norm: 157.878111, v_norm: 3.107625[INFO main ] 12.786748 25.846512 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO genome_21_thread_0 ] iteration 0, mse: 6694.796766, v_mse: 13.775302, bv_mse: 13.775302, lr: 0.000002, norm: 1299363.711451, p_norm: 199.913509, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000017 -[INFO genome_20_thread_7 ] iteration 1, mse: 6183.965405, v_mse: 12.724209, bv_mse: 12.724209, lr: 0.000002, norm: 307411.911338, p_norm: 186.376312, v_norm: 2.778678, OVER THRESHOLD, multiplier: 0.000072 -, OVER THRESHOLD, multiplier: 0.000036 -[INFO genome_23_thread_6 ] iteration 0, mse: 7049.005931, v_mse: 14.504127, bv_mse: 14.504127, lr: 0.000002, norm: 1473281.940970, p_norm: 172.678960, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000015 -[INFO genome_22_thread_8 ] iteration 1, mse: 6140.370933, v_mse: 12.634508, bv_mse: 12.634508, lr: 0.000002, norm: 136914.355993, p_norm: 220.695043, v_norm: 4.202907, OVER THRESHOLD, multiplier: 0.000161 -[INFO genome_21_thread_0 ] iteration 1, mse: 6172.754494, v_mse: 12.701141, bv_mse: 12.701141, lr: 0.000002, norm: 262315.513345, p_norm: 225.552883, v_norm: 2.673588, OVER THRESHOLD, multiplier: 0.000084 -[INFO genome_19_thread_1 ] iteration 1, mse: 6183.387876, v_mse: 12.723020, bv_mse: 12.723020, lr: 0.000002, norm: 601863.179826, p_norm: 199.865040, v_norm: 2.510374, OVER THRESHOLD, multiplier: 0.000037 -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 12.806497 to island 0 -[INFO main ] created copy to insert to island: 0 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[3000_1052552_0] : 0x7f529eaccb48 -[INFO main ] new best fitness for island: 0! -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f521fde8970, &(*potential_match): 0x7f521fde8970 -[INFO main ] potential_matches.size() before erase: 2 -[INFO main ] potential_matches.size() after erase: 1 -[INFO main ] structure_map[2926_1043290_0].size() after erase: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f521fde8970, &(*potential_match): 0x7f527008a700 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.806497 30.599308 1468 (1468) 0 (0) 1 (77) 76 (77) [add_node(simple):1] -[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.786748 25.846512 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.824047 27.219320 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.701141 to island 1 -[INFO main ] potential duplicate for hash '2926_1043290_0', had 1 potential matches. -[INFO main ] on potential match 0 of 1 -[INFO main ] created copy to insert to island: 1 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f52a0ad0b48 -[INFO main ] new best fitness for island: 1! -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f50686cc090, &(*potential_match): 0x7f50686cc090 -[INFO main ] potential_matches.size() before erase: 1 -[INFO main ] potential_matches.size() after erase: 0 -[INFO main ] structure_map[2926_1041910_0].size() after erase: 0 -[INFO main ] deleting the potential_matches vector for hash '2926_1041910_0' because it was empty. -[INFO genome_23_thread_6 ] iteration 1, mse: 6252.648207, v_mse: 12.865531, bv_mse: 12.865531, lr: 0.000002, norm: 474364.587553, p_norm: 198.934544, v_norm: 3.031444, OVER THRESHOLD, multiplier: 0.000046 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.806497 30.599308 1468 (1468) 0 (0) 1 (77) 76 (77) [add_node(simple):1] -[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.786748 25.846512 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.723020 to island 1 -[INFO main ] potential duplicate for hash '2926_1043290_0', had 2 potential matches. -[INFO main ] on potential match 0 of 2 -[INFO main ] on potential match 1 of 2 -[INFO main ] created copy to insert to island: 1 -[INFO main ] inserting genome at index: 1 -[INFO main ] adding to structure_map[2926_1043290_0] : 0x7f52a02cfb48 -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f527c08a700, &(*potential_match): 0x7f527c08a700 -[INFO main ] potential_matches.size() before erase: 3 -[INFO main ] potential_matches.size() after erase: 2 -[INFO main ] structure_map[2926_1043290_0].size() after erase: 2 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f527c08a700, &(*potential_match): 0x7f4f2c29bae0 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f527c08a700, &(*potential_match): 0x7f521b3ab4d0 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.806497 30.599308 1468 (1468) 0 (0) 1 (77) 76 (77) [add_node(simple):1] -[INFO main ] 12.890787 26.518804 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 12.634508 to island 0 -[INFO main ] created copy to insert to island: 0 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[3007_1095565_0] : 0x7f529d2c9b48 -[INFO main ] new best fitness for island: 0! -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f527008a700, &(*potential_match): 0x7f527008a700 -[INFO main ] potential_matches.size() before erase: 1 -[INFO main ] potential_matches.size() after erase: 0 -[INFO main ] structure_map[2926_1043290_0].size() after erase: 0 -[INFO main ] deleting the potential_matches vector for hash '2926_1043290_0' because it was empty. -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] 12.806497 30.599308 1468 (1468) 0 (0) 1 (77) 76 (77) [add_node(simple):1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 12.724209 to island 0 -[INFO main ] created copy to insert to island: 0 -[INFO main ] inserting genome at index: 1 -[INFO main ] adding to structure_map[2926_1041911_0] : 0x7f529dacab48 -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f52800d1ad0, &(*potential_match): 0x7f52800d1ad0 -[INFO main ] potential_matches.size() before erase: 1 -[INFO main ] potential_matches.size() after erase: 0 -[INFO main ] structure_map[3000_1052552_0].size() after erase: 0 -[INFO main ] deleting the potential_matches vector for hash '3000_1052552_0' because it was empty. -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.999318 to island 1 -[INFO main ] ignoring genome, fitness: 12.999318 > worst for island[1] fitness: 12.723020 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] -[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] attempting to merge a node! -[INFO main ] attempting to add a node! -[INFO main ] add node, split depth: 0.296837, max_inputs: 21, max_outputs: 20 -[INFO main ] add node recurrent probability: 0.000000 -[INFO main ] CREATING NODE, type: 'simple' -[INFO main ] adding edge between nodes 30 and 82 -[INFO main ] adding edge between nodes 30 and 82, new edge weight: 0.279580 -[INFO main ] adding edge between nodes 20 and 82 -[INFO main ] adding edge between nodes 20 and 82, new edge weight: -0.258901 -[INFO main ] adding edge between nodes 3 and 82 -[INFO main ] adding edge between nodes 3 and 82, new edge weight: -0.324174 -[INFO main ] adding edge between nodes 4 and 82 -[INFO main ] adding edge between nodes 4 and 82, new edge weight: 0.059251 -[INFO main ] adding edge between nodes 6 and 82 -[INFO main ] adding edge between nodes 6 and 82, new edge weight: -0.040719 -[INFO main ] adding edge between nodes 7 and 82 -[INFO main ] adding edge between nodes 7 and 82, new edge weight: -0.133197 -[INFO main ] adding edge between nodes 8 and 82 -[INFO main ] adding edge between nodes 8 and 82, new edge weight: -0.174522 -[INFO main ] adding edge between nodes 13 and 82 -[INFO main ] adding edge between nodes 13 and 82, new edge weight: -0.034339 -[INFO main ] adding edge between nodes 14 and 82 -[INFO main ] adding edge between nodes 14 and 82, new edge weight: 0.070591 -[INFO main ] adding edge between nodes 15 and 82 -[INFO main ] adding edge between nodes 15 and 82, new edge weight: -0.725389 -[INFO main ] adding edge between nodes 16 and 82 -[INFO main ] adding edge between nodes 16 and 82, new edge weight: 0.216987 -[INFO main ] adding edge between nodes 17 and 82 -[INFO main ] adding edge between nodes 17 and 82, new edge weight: 0.324859 -[INFO main ] adding edge between nodes 18 and 82 -[INFO main ] adding edge between nodes 18 and 82, new edge weight: 0.163352 -[INFO main ] adding edge between nodes 2 and 82 -[INFO main ] adding edge between nodes 2 and 82, new edge weight: -0.080235 -[INFO main ] adding edge between nodes 22 and 82 -[INFO main ] adding edge between nodes 22 and 82, new edge weight: -0.339971 -[INFO main ] adding edge between nodes 24 and 82 -[INFO main ] adding edge between nodes 24 and 82, new edge weight: -0.021835 -[INFO main ] adding edge between nodes 25 and 82 -[INFO main ] adding edge between nodes 25 and 82, new edge weight: -0.128952 -[INFO main ] adding edge between nodes 31 and 82 -[INFO main ] adding edge between nodes 31 and 82, new edge weight: -0.695552 -[INFO main ] adding edge between nodes 32 and 82 -[INFO main ] adding edge between nodes 32 and 82, new edge weight: -0.121489 -[INFO main ] adding edge between nodes 33 and 82 -[INFO main ] adding edge between nodes 33 and 82, new edge weight: -0.388146 -[INFO main ] adding edge between nodes 37 and 82 -[INFO main ] adding edge between nodes 37 and 82, new edge weight: 0.611324 -[INFO main ] adding edge between nodes 82 and 39 -[INFO main ] adding edge between nodes 82 and 39, new edge weight: 0.219600 -[INFO main ] adding edge between nodes 82 and 40 -[INFO main ] adding edge between nodes 82 and 40, new edge weight: 0.407370 -[INFO main ] adding edge between nodes 82 and 41 -[INFO main ] adding edge between nodes 82 and 41, new edge weight: -0.063108 -[INFO main ] adding edge between nodes 82 and 43 -[INFO main ] adding edge between nodes 82 and 43, new edge weight: -0.422589 -[INFO main ] adding edge between nodes 82 and 47 -[INFO main ] adding edge between nodes 82 and 47, new edge weight: -0.005029 -[INFO main ] adding edge between nodes 82 and 48 -[INFO main ] adding edge between nodes 82 and 48, new edge weight: 0.495100 -[INFO main ] adding edge between nodes 82 and 56 -[INFO main ] adding edge between nodes 82 and 56, new edge weight: 0.293628 -[INFO main ] adding edge between nodes 82 and 49 -[INFO main ] adding edge between nodes 82 and 49, new edge weight: 0.553851 -[INFO main ] adding edge between nodes 82 and 50 -[INFO main ] adding edge between nodes 82 and 50, new edge weight: 0.436134 -[INFO main ] adding edge between nodes 82 and 51 -[INFO main ] adding edge between nodes 82 and 51, new edge weight: 0.175278 -[INFO main ] adding edge between nodes 82 and 53 -[INFO main ] adding edge between nodes 82 and 53, new edge weight: 0.167612 -[INFO main ] adding edge between nodes 82 and 62 -[INFO main ] adding edge between nodes 82 and 62, new edge weight: 0.003861 -[INFO main ] adding edge between nodes 82 and 63 -[INFO main ] adding edge between nodes 82 and 63, new edge weight: -0.111817 -[INFO main ] adding edge between nodes 82 and 65 -[INFO main ] adding edge between nodes 82 and 65, new edge weight: -0.351988 -[INFO main ] adding edge between nodes 82 and 67 -[INFO main ] adding edge between nodes 82 and 67, new edge weight: -0.461250 -[INFO main ] adding edge between nodes 82 and 75 -[INFO main ] adding edge between nodes 82 and 75, new edge weight: 0.189152 -[INFO main ] adding edge between nodes 82 and 76 -[INFO main ] adding edge between nodes 82 and 76, new edge weight: 0.241920 -[INFO main ] adding edge between nodes 82 and 68 -[INFO main ] adding edge between nodes 82 and 68, new edge weight: 0.332228 -[INFO main ] adding edge between nodes 82 and 72 -[INFO main ] adding edge between nodes 82 and 72, new edge weight: 0.388992 -[INFO main ] adding edge between nodes 82 and 74 -[INFO main ] adding edge between nodes 82 and 74, new edge weight: 0.054794 -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] attempting to add edge! -[INFO main ] reachable_nodes.size(): 77 -[INFO main ] selected first node 28 with depth 4097 -[INFO main ] reachable_nodes.size(): 39 -[INFO main ] selected second node 54 with depth 5885 -[INFO main ] adding edge between nodes 28 and 54 -[INFO main ] edge already exists, not adding. -[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] generating new genome for island[1], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing mutation -[INFO main ] generating new genome by mutation. -[INFO main ] attempting to split a node! -[INFO main ] attempting to add a node! -[INFO main ] add node, split depth: 0.840017, max_inputs: 21, max_outputs: 1 -[INFO main ] add node recurrent probability: 0.000000 -[INFO main ] CREATING NODE, type: 'simple' -[INFO main ] adding edge between nodes 20 and 83 -[INFO main ] adding edge between nodes 20 and 83, new edge weight: -0.210757 -[INFO main ] adding edge between nodes 7 and 83 -[INFO main ] adding edge between nodes 7 and 83, new edge weight: 0.019640 -[INFO main ] adding edge between nodes 38 and 83 -[INFO main ] adding edge between nodes 38 and 83, new edge weight: 0.314756 -[INFO main ] adding edge between nodes 12 and 83 -[INFO main ] adding edge between nodes 12 and 83, new edge weight: 0.207615 -[INFO main ] adding edge between nodes 15 and 83 -[INFO main ] adding edge between nodes 15 and 83, new edge weight: -0.051694 -[INFO main ] adding edge between nodes 16 and 83 -[INFO main ] adding edge between nodes 16 and 83, new edge weight: -0.392008 -[INFO main ] adding edge between nodes 17 and 83 -[INFO main ] adding edge between nodes 17 and 83, new edge weight: 0.251033 -[INFO main ] adding edge between nodes 29 and 83 -[INFO main ] adding edge between nodes 29 and 83, new edge weight: -0.404156 -[INFO main ] adding edge between nodes 2 and 83 -[INFO main ] adding edge between nodes 2 and 83, new edge weight: -0.295159 -[INFO main ] adding edge between nodes 10 and 83 -[INFO main ] adding edge between nodes 10 and 83, new edge weight: 0.210130 -[INFO main ] adding edge between nodes 1 and 83 -[INFO main ] adding edge between nodes 1 and 83, new edge weight: -0.052025 -[INFO main ] adding edge between nodes 22 and 83 -[INFO main ] adding edge between nodes 22 and 83, new edge weight: -0.235988 -[INFO main ] adding edge between nodes 26 and 83 -[INFO main ] adding edge between nodes 26 and 83, new edge weight: -0.168626 -[INFO main ] adding edge between nodes 27 and 83 -[INFO main ] adding edge between nodes 27 and 83, new edge weight: 0.063415 -[INFO main ] adding edge between nodes 28 and 83 -[INFO main ] adding edge between nodes 28 and 83, new edge weight: -0.218475 -[INFO main ] adding edge between nodes 31 and 83 -[INFO main ] adding edge between nodes 31 and 83, new edge weight: 0.069493 -[INFO main ] adding edge between nodes 32 and 83 -[INFO main ] adding edge between nodes 32 and 83, new edge weight: 0.112906 -[INFO main ] adding edge between nodes 34 and 83 -[INFO main ] adding edge between nodes 34 and 83, new edge weight: -0.091466 -[INFO main ] adding edge between nodes 35 and 83 -[INFO main ] adding edge between nodes 35 and 83, new edge weight: 0.011092 -[INFO main ] adding edge between nodes 36 and 83 -[INFO main ] adding edge between nodes 36 and 83, new edge weight: -0.016694 -[INFO main ] adding edge between nodes 19 and 83 -[INFO main ] adding edge between nodes 19 and 83, new edge weight: 0.161173 -[INFO main ] adding edge between nodes 83 and 53 -[INFO main ] adding edge between nodes 83 and 53, new edge weight: -0.618857 -[INFO main ] generating new genome for island[0], island_size: 2, max_island_size: 2, mutation_rate: 0.700000, intra_island_crossover_rate: 0.900000, inter_island_crossover_rate: 1.000000 -[INFO main ] island is full -[INFO main ] performing intra-island crossover -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.865531 to island 1 -[INFO main ] ignoring genome, fitness: 12.865531 > worst for island[1] fitness: 12.723020 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 13.044254 to island 0 -[INFO main ] ignoring genome, fitness: 13.044254 > worst for island[0] fitness: 12.724209 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] -[INFO genome_24_thread_3 ] iteration 0, mse: 6977.146138, v_mse: 14.356268, bv_mse: 14.356268, lr: 0.000002, norm: 1749985.520766, p_norm: 193.518512, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000013 -[INFO genome_24_thread_3 ] iteration 1, mse: 6227.171460, v_mse: 12.813110, bv_mse: 12.813110, lr: 0.000002, norm: 368392.591111, p_norm: 233.873892, v_norm: 3.600793, OVER THRESHOLD, multiplier: 0.000060 -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 12.813110 to island 0 -[INFO main ] ignoring genome, fitness: 12.813110 > worst for island[0] fitness: 12.724209 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] 12.723020 30.346558 1444 (1444) 0 (0) 0 (76) 76 (76) [island_crossover:1] -[INFO genome_28_thread_8 ] iteration 0, mse: 6291.566431, v_mse: 12.945610, bv_mse: 12.945610, lr: 0.000002, norm: 700931.712769, p_norm: 186.376312, v_norm: 0.000000[INFO genome_26_thread_0 ] iteration 0, mse: 6221.955994, v_mse: 12.802379, bv_mse: 12.802379, lr: 0.000002, norm: 601356.639195, p_norm: 220.695043, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000031 -, OVER THRESHOLD, multiplier: 0.000037 -[INFO genome_27_thread_1 ] iteration 0, mse: 6374.400669, v_mse: 13.116051, bv_mse: 13.116051, lr: 0.000002, norm: 613164.075958, p_norm: 261.681514, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000036 -[INFO genome_28_thread_8 ] iteration 1, mse: 6099.737200, v_mse: 12.550900, bv_mse: 12.550900, lr: 0.000002, norm: 219989.367748, p_norm: 194.838409, v_norm: 1.442246, OVER THRESHOLD, multiplier: 0.000100 -[INFO genome_26_thread_0 ] iteration 1, mse: 6147.884451, v_mse: 12.649968, bv_mse: 12.649968, lr: 0.000002, norm: 234739.147240, p_norm: 224.938350, v_norm: 1.237359, OVER THRESHOLD, multiplier: 0.000094 -[INFO genome_27_thread_1 ] iteration 1, mse: 6161.125020, v_mse: 12.677212, bv_mse: 12.677212, lr: 0.000002, norm: 316932.774403, p_norm: 266.386952, v_norm: 1.261654, OVER THRESHOLD, multiplier: 0.000070 -[INFO genome_30_thread_2 ] iteration 0, mse: 6617.779869, v_mse: 13.616831, bv_mse: 13.616831, lr: 0.000002, norm: 1188227.503510, p_norm: 237.380107, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000019 -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.677212 to island 1 -[INFO main ] created copy to insert to island: 1 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[3007_1095565_0] : 0x7f52a02cfb48 -[INFO main ] new best fitness for island: 1! -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f521b3ab4d0, &(*potential_match): 0x7f4f2c29bae0 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f521b3ab4d0, &(*potential_match): 0x7f521b3ab4d0 -[INFO main ] potential_matches.size() before erase: 2 -[INFO main ] potential_matches.size() after erase: 1 -[INFO main ] structure_map[2926_1043290_0].size() after erase: 1 -[INFO genome_30_thread_2 ] iteration 1, mse: 6149.999677, v_mse: 12.654320, bv_mse: 12.654320, lr: 0.000002, norm: 400368.692373, p_norm: 256.944696, v_norm: 2.444913, OVER THRESHOLD, multiplier: 0.000055 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] 12.724209 31.164939 1443 (1444) 0 (0) 0 (76) 76 (76) [disable_edge:1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 12.550900 to island 0 -[INFO main ] potential duplicate for hash '2926_1041911_0', had 1 potential matches. -[INFO main ] on potential match 0 of 1 -[INFO main ] created copy to insert to island: 0 -[INFO main ] inserting genome at index: 0 -[INFO main ] adding to structure_map[2926_1041911_0] : 0x7f529d2c9b48 -[INFO main ] new best fitness for island: 0! -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4d68a19620, &(*potential_match): 0x7f4d68a19620 -[INFO main ] potential_matches.size() before erase: 2 -[INFO main ] potential_matches.size() after erase: 1 -[INFO main ] structure_map[2926_1041911_0].size() after erase: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4d68a19620, &(*potential_match): 0x7f527008a700 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.550900 31.207737 1443 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 12.649968 to island 0 -[INFO main ] ignoring genome, fitness: 12.649968 > worst for island[0] fitness: 12.634508 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.550900 31.207737 1443 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO main ] inserting genome to island: 0 -[INFO main ] inserting genome with fitness: 12.654320 to island 0 -[INFO main ] ignoring genome, fitness: 12.654320 > worst for island[0] fitness: 12.634508 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.550900 31.207737 1443 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] -[INFO main ] 12.701141 32.476173 1444 (1444) 0 (0) 0 (76) 76 (76) [crossover:1] -[INFO genome_29_thread_7 ] iteration 0, mse: 6456.630060, v_mse: 13.285247, bv_mse: 13.285247, lr: 0.000002, norm: 803789.890614, p_norm: 226.926870, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000027 -[INFO genome_29_thread_7 ] iteration 1, mse: 6166.705618, v_mse: 12.688695, bv_mse: 12.688695, lr: 0.000002, norm: 236572.743822, p_norm: 235.391483, v_norm: 1.653889, OVER THRESHOLD, multiplier: 0.000093 -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.688695 to island 1 -[INFO main ] created copy to insert to island: 1 -[INFO main ] inserting genome at index: 1 -[INFO main ] adding to structure_map[3009_1078633_0] : 0x7f529dacab48 -[INFO main ] genomes.size(): 3, max_size: 2, status: 1 -[INFO main ] checking to remove worst from structure_map - &worst: 0x7f4f2c29bae0, &(*potential_match): 0x7f4f2c29bae0 -[INFO main ] potential_matches.size() before erase: 1 -[INFO main ] potential_matches.size() after erase: 0 -[INFO main ] structure_map[2926_1043290_0].size() after erase: 0 -[INFO main ] deleting the potential_matches vector for hash '2926_1043290_0' because it was empty. -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.550900 31.207737 1443 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] -[INFO main ] 12.688695 33.741960 1466 (1466) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO genome_25_thread_5 ] iteration 0, mse: 6471.066972, v_mse: 13.314953, bv_mse: 13.314953, lr: 0.000002, norm: 818111.719508, p_norm: 229.745328, v_norm: 0.000000, OVER THRESHOLD, multiplier: 0.000027 -[INFO genome_25_thread_5 ] iteration 1, mse: 6175.179782, v_mse: 12.706131, bv_mse: 12.706131, lr: 0.000002, norm: 270825.772208, p_norm: 238.658480, v_norm: 1.683357, OVER THRESHOLD, multiplier: 0.000081 -[INFO main ] inserting genome to island: 1 -[INFO main ] inserting genome with fitness: 12.706131 to island 1 -[INFO main ] ignoring genome, fitness: 12.706131 > worst for island[1] fitness: 12.688695 -[INFO main ] Islands: -[INFO main ] Island 0: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.550900 31.207737 1443 (1444) 0 (0) 0 (76) 76 (76) [clone:1] -[INFO main ] 12.634508 33.495271 1478 (1478) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] Island 1: -[INFO main ] MSE MAE Edges Rec Edges Simple Jordan Elman UGRNN MGU GRU Delta LSTM ENARC ENAS_DAG RANDOM_DAG Total Generated -[INFO main ] 12.677212 32.905165 1478 (1478) 0 (0) 1 (77) 77 (77) [island_crossover:1] -[INFO main ] 12.688695 33.741960 1466 (1466) 0 (0) 1 (77) 77 (77) [add_node(simple):1] -[INFO main ] completed! diff --git a/mpi/examm_mpi.cxx b/mpi/examm_mpi.cxx index a2d980b8..dbc7aa9b 100644 --- a/mpi/examm_mpi.cxx +++ b/mpi/examm_mpi.cxx @@ -9,7 +9,7 @@ using std::setprecision; using std::mutex; #include -using std::string; +using std::string; #include using std::thread; diff --git a/mpi/examm_mpi_nlp.cxx b/mpi/examm_mpi_nlp.cxx index b6b0c534..85f4aaad 100644 --- a/mpi/examm_mpi_nlp.cxx +++ b/mpi/examm_mpi_nlp.cxx @@ -1,9 +1,9 @@ #include #include -using std::setw; using std::fixed; using std::setprecision; +using std::setw; #include using std::mutex; @@ -21,11 +21,11 @@ using std::vector; #include "common/arguments.hxx" #include "common/log.hxx" +#include "common/weight_initialize.hxx" #include "rnn/examm.hxx" -#include "time_series/time_series.hxx" -#include "word_series/word_series.hxx" +#include "word_series/word_series.hxx" #define WORK_REQUEST_TAG 1 #define GENOME_LENGTH_TAG 2 @@ -40,10 +40,10 @@ EXAMM *examm; bool finished = false; -vector< vector< vector > > training_inputs; -vector< vector< vector > > training_outputs; -vector< vector< vector > > validation_inputs; -vector< vector< vector > > validation_outputs; +vector>> training_inputs; +vector>> training_outputs; +vector>> validation_inputs; +vector>> validation_outputs; void send_work_request(int target) { int work_request_message[1]; @@ -57,7 +57,7 @@ void receive_work_request(int source) { MPI_Recv(work_request_message, 1, MPI_INT, source, WORK_REQUEST_TAG, MPI_COMM_WORLD, &status); } -RNN_Genome* receive_genome_from(int source) { +RNN_Genome *receive_genome_from(int source) { MPI_Status status; int length_message[1]; MPI_Recv(length_message, 1, MPI_INT, source, GENOME_LENGTH_TAG, MPI_COMM_WORLD, &status); @@ -66,7 +66,7 @@ RNN_Genome* receive_genome_from(int source) { Log::debug("receiving genome of length: %d from: %d\n", length, source); - char* genome_str = new char[length + 1]; + char *genome_str = new char[length + 1]; Log::debug("receiving genome from: %d\n", source); MPI_Recv(genome_str, length, MPI_CHAR, source, GENOME_TAG, MPI_COMM_WORLD, &status); @@ -75,13 +75,13 @@ RNN_Genome* receive_genome_from(int source) { Log::trace("genome_str:\n%s\n", genome_str); - RNN_Genome* genome = new RNN_Genome(genome_str, length); + RNN_Genome *genome = new RNN_Genome(genome_str, length); - delete [] genome_str; + delete[] genome_str; return genome; } -void send_genome_to(int target, RNN_Genome* genome) { +void send_genome_to(int target, RNN_Genome *genome) { char *byte_array; int32_t length; @@ -126,7 +126,6 @@ void master(int max_rank) { int tag = status.MPI_TAG; Log::debug("probe returned message from: %d with tag: %d\n", source, tag); - //if the message is a work request, send a genome if (tag == WORK_REQUEST_TAG) { @@ -136,16 +135,18 @@ void master(int max_rank) { RNN_Genome *genome = examm->generate_genome(); examm_mutex.unlock(); - if (genome == NULL) { //search was completed if it returns NULL for an individual + if (genome == NULL) { + //search was completed if it returns NULL for an individual //send terminate message Log::info("terminating worker: %d\n", source); send_terminate_message(source); terminates_sent++; Log::debug("sent: %d terminates of %d\n", terminates_sent, (max_rank - 1)); - if (terminates_sent >= max_rank - 1) return; - - } else { + if (terminates_sent >= max_rank - 1) + return; + } + else { //genome->write_to_file( examm->get_output_directory() + "/before_send_gen_" + to_string(genome->get_generation_id()) ); //send genome @@ -155,7 +156,8 @@ void master(int max_rank) { //delete this genome as it will not be used again delete genome; } - } else if (tag == GENOME_LENGTH_TAG) { + } + else if (tag == GENOME_LENGTH_TAG) { Log::debug("received genome from: %d\n", source); RNN_Genome *genome = receive_genome_from(source); @@ -166,7 +168,8 @@ void master(int max_rank) { //delete the genome as it won't be used again, a copy was inserted delete genome; //this genome will be deleted if/when removed from population - } else { + } + else { Log::fatal("ERROR: received message from %d with unknown tag: %d", source, tag); MPI_Abort(MPI_COMM_WORLD, 1); } @@ -191,18 +194,15 @@ void worker(int rank) { Log::debug("received terminate tag!\n"); receive_terminate_message(0); break; - - } else if (tag == GENOME_LENGTH_TAG) { + } + else if (tag == GENOME_LENGTH_TAG) { Log::debug("received genome!\n"); - RNN_Genome* genome = receive_genome_from(0); + RNN_Genome *genome = receive_genome_from(0); //have each worker write the backproagation to a separate log file string log_id = "genome_" + to_string(genome->get_generation_id()) + "_worker_" + to_string(rank); Log::set_id(log_id); - - genome->backpropagate(training_inputs, training_outputs, validation_inputs, validation_outputs); - //genome->backpropagate_stochastic(training_inputs, training_outputs, validation_inputs, validation_outputs); - + genome->backpropagate_stochastic(training_inputs, training_outputs, validation_inputs, validation_outputs); Log::release_id(log_id); //go back to the worker's log for MPI communication @@ -211,7 +211,8 @@ void worker(int rank) { send_genome_to(0, genome); delete genome; - } else { + } + else { Log::fatal("ERROR: received message with unknown tag: %d\n", tag); MPI_Abort(MPI_COMM_WORLD, 1); } @@ -227,23 +228,35 @@ void worker(int rank) { // getchar(); // } -int main(int argc, char** argv) { +int main(int argc, char **argv) { + std::cout << "starting up!" << std::endl; MPI_Init(&argc, &argv); + std::cout << "did mpi init!" << std::endl; int rank, max_rank; MPI_Comm_rank(MPI_COMM_WORLD, &rank); MPI_Comm_size(MPI_COMM_WORLD, &max_rank); + std::cout << "got rank " << rank << " and max rank " << max_rank << std::endl; + arguments = vector(argv, argv + argc); + std::cout << "got arguments!" << std::endl; + Log::initialize(arguments); Log::set_rank(rank); Log::set_id("main_" + to_string(rank)); Log::restrict_to_rank(0); + std::cout << "initailized log!" << std::endl; + + int32_t word_offset = 1; + get_argument(arguments, "--word_offset", true, word_offset); + int32_t sequence_length = 64; + get_argument(arguments, "--sequence_length", false, sequence_length); - Corpus* corpus_sets = NULL; + Corpus *corpus_sets = NULL; if (rank == 0) { //only have the master process print TSS info @@ -253,19 +266,13 @@ int main(int argc, char** argv) { get_argument(arguments, "--write_word_series", true, base_filename); corpus_sets->write_sentence_series_sets(base_filename); } - } else { + } + else { corpus_sets = Corpus::generate_from_arguments(arguments); } - - - int32_t word_offset = 1; - get_argument(arguments, "--word_offset", true, word_offset); - - corpus_sets->export_training_series(word_offset,training_inputs,training_outputs); - corpus_sets->export_test_series(word_offset,validation_inputs,validation_outputs); - - Log::info("exported word series.\n"); + corpus_sets->export_training_series(word_offset, training_inputs, training_outputs); + corpus_sets->export_test_series(word_offset, validation_inputs, validation_outputs); int number_inputs = corpus_sets->get_number_inputs(); int number_outputs = corpus_sets->get_number_outputs(); @@ -286,7 +293,7 @@ int main(int argc, char** argv) { int32_t extinction_event_generation_number = 0; get_argument(arguments, "--extinction_event_generation_number", false, extinction_event_generation_number); - + int32_t islands_to_exterminate; get_argument(arguments, "--islands_to_exterminate", false, islands_to_exterminate); @@ -301,7 +308,7 @@ int main(int argc, char** argv) { double species_threshold = 0.0; get_argument(arguments, "--species_threshold", false, species_threshold); - + double fitness_threshold = 100; get_argument(arguments, "--fitness_threshold", false, fitness_threshold); @@ -313,7 +320,6 @@ int main(int argc, char** argv) { double neat_c3 = 1; get_argument(arguments, "--neat_c3", false, neat_c3); - bool repeat_extinction = argument_exists(arguments, "--repeat_extinction"); int32_t bp_iterations; @@ -344,13 +350,13 @@ int main(int argc, char** argv) { get_argument(arguments, "--max_recurrent_depth", false, max_recurrent_depth); //bool use_regression = argument_exists(arguments, "--use_regression"); - bool use_regression = false; //nlp will never use regression + bool use_regression = false; //time series will always use regression string weight_initialize_string = "random"; get_argument(arguments, "--weight_initialize", false, weight_initialize_string); WeightType weight_initialize; weight_initialize = get_enum_from_string(weight_initialize_string); - + string weight_inheritance_string = "lamarckian"; get_argument(arguments, "--weight_inheritance", false, weight_inheritance_string); WeightType weight_inheritance; @@ -361,7 +367,6 @@ int main(int argc, char** argv) { WeightType mutated_component_weight; mutated_component_weight = get_enum_from_string(mutated_component_weight_string); - RNN_Genome *seed_genome = NULL; string genome_file_name = ""; if (get_argument(arguments, "--genome_bin", false, genome_file_name)) { @@ -375,7 +380,6 @@ int main(int argc, char** argv) { seed_genome->transfer_to(corpus_sets->get_input_parameter_names(), corpus_sets->get_output_parameter_names(), transfer_learning_version, epigenetic_weights, min_recurrent_depth, max_recurrent_depth); } - bool start_filled = false; get_argument(arguments, "--start_filled", false, start_filled); @@ -383,34 +387,35 @@ int main(int argc, char** argv) { if (rank == 0) { examm = new EXAMM(population_size, number_islands, max_genomes, extinction_event_generation_number, islands_to_exterminate, island_ranking_method, - repopulation_method, repopulation_mutations, - repeat_extinction, speciation_method, - species_threshold, fitness_threshold, - neat_c1, neat_c2, neat_c3, - corpus_sets->get_input_parameter_names(), - corpus_sets->get_output_parameter_names(), - corpus_sets->get_normalize_type(), - corpus_sets->get_normalize_mins(), - corpus_sets->get_normalize_maxs(), - corpus_sets->get_normalize_avgs(), - corpus_sets->get_normalize_std_devs(), - weight_initialize, weight_inheritance, mutated_component_weight, - bp_iterations, learning_rate, - use_high_threshold, high_threshold, - use_low_threshold, low_threshold, - use_dropout, dropout_probability, - min_recurrent_depth, max_recurrent_depth, - use_regression, - output_directory, - seed_genome, - start_filled); - - if (possible_node_types.size() > 0) { + repopulation_method, repopulation_mutations, repeat_extinction, + speciation_method, + species_threshold, fitness_threshold, + neat_c1, neat_c2, neat_c3, + corpus_sets->get_input_parameter_names(), + corpus_sets->get_output_parameter_names(), + corpus_sets->get_normalize_type(), + corpus_sets->get_normalize_mins(), + corpus_sets->get_normalize_maxs(), + corpus_sets->get_normalize_avgs(), + corpus_sets->get_normalize_std_devs(), + weight_initialize, weight_inheritance, mutated_component_weight, + bp_iterations, learning_rate, + use_high_threshold, high_threshold, + use_low_threshold, low_threshold, + use_dropout, dropout_probability, + min_recurrent_depth, max_recurrent_depth, + use_regression, + output_directory, + seed_genome, + start_filled); + + if (possible_node_types.size() > 0) { examm->set_possible_node_types(possible_node_types); } master(max_rank); - } else { + } + else { worker(rank); } Log::set_id("main_" + to_string(rank)); @@ -422,5 +427,6 @@ int main(int argc, char** argv) { MPI_Finalize(); delete corpus_sets; + return 0; } diff --git a/rnn/enas_dag_node.cxx b/rnn/enas_dag_node.cxx index 4637b37d..b7e14883 100644 --- a/rnn/enas_dag_node.cxx +++ b/rnn/enas_dag_node.cxx @@ -149,8 +149,8 @@ double ENAS_DAG_Node::activation_derivative(double value, double input, int act_ void ENAS_DAG_Node::input_fired(int time, double incoming_output) { - vector connections {0,1,1,1,2,5,3,5,4}; - vector operations {1,1,1,3,3,0,2,1,2}; + vector connections{0, 1, 1, 1, 2, 5, 3, 5, 4}; + vector operations{1, 1, 1, 3, 3, 0, 2, 1, 2}; vector node_output(connections.size(),1); inputs_fired[time]++; input_values[time] += incoming_output; diff --git a/rnn/examm.cxx b/rnn/examm.cxx index 854cae0d..f9ea02f5 100644 --- a/rnn/examm.cxx +++ b/rnn/examm.cxx @@ -42,6 +42,9 @@ using std::to_string; #include "delta_node.hxx" #include "ugrnn_node.hxx" #include "mgu_node.hxx" +#include "enas_dag_node.hxx" +#include "enarc_node.hxx" +#include "random_dag_node.hxx" #include "common/files.hxx" #include "common/log.hxx" @@ -184,6 +187,8 @@ EXAMM::EXAMM( possible_node_types.push_back(LSTM_NODE); possible_node_types.push_back(ENARC_NODE); possible_node_types.push_back(DELTA_NODE); + possible_node_types.push_back(ENAS_DAG_NODE); + possible_node_types.push_back(RANDOM_DAG_NODE); bool node_ops = true; if (node_ops) { diff --git a/rnn/random_dag_node.cxx b/rnn/random_dag_node.cxx index 1606a739..f4d79425 100644 --- a/rnn/random_dag_node.cxx +++ b/rnn/random_dag_node.cxx @@ -160,12 +160,13 @@ void RANDOM_DAG_Node::input_fired(int time, double incoming_output) { {0,0,0,0,0,0,0,1}, {0,0,0,0,0,0,0,0} }; - vector operations {1,1,1,3,3,0,2,1,2}; - int no_of_nodes = connections.size(); - for(int i = 0;i operations{1, 1, 1, 3, 3, 0, 2, 1, 2}; + int no_of_nodes = connections.size(); + for (int i = 0; i < no_of_nodes; i++) { + if (node_output.size() < no_of_nodes) { + node_output.push_back(1); + } + else{ node_output[i] = 1; } } diff --git a/rnn/rnn_node_interface.cxx b/rnn/rnn_node_interface.cxx index b1f0b06b..3853beed 100644 --- a/rnn/rnn_node_interface.cxx +++ b/rnn/rnn_node_interface.cxx @@ -15,8 +15,8 @@ using std::max; #include "common/log.hxx" -extern const int32_t NUMBER_NODE_TYPES = 9; -extern const string NODE_TYPES[] = { "simple" , "jordan", "elman", "UGRNN", "MGU", "GRU", "delta", "LSTM" , "ENARC" , "ENAS_DAG" }; +extern const int32_t NUMBER_NODE_TYPES = 11; +extern const string NODE_TYPES[] = { "simple" , "jordan", "elman", "UGRNN", "MGU", "GRU", "delta", "LSTM" , "ENARC" , "ENAS_DAG" , "RANDOM_DAG"}; double bound(double value) { if (value < -10.0) value = -10.0; diff --git a/run_enarc_nlp.sh b/run_enarc_nlp.sh deleted file mode 100644 index 89b9dd52..00000000 --- a/run_enarc_nlp.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash -l -# NOTE the -l flag! -# - -#SBATCH -J examm_lstm_nlp - -#SBATCH -A examm #just examm examm_thompson is not a valid account - -#SBATCH -o cluster_logs/examm_test_%A.output -#SBATCH -e cluster_logs/examm_test_%A.error - -#SBATCH --mail-user vj6810@rit.edu - -#SBATCH --mail-type=ALL - -#SBATCH -t 5-0 # if you want to run for 4 days 1-72:0:0 - -# Put the job in the "work" partition and request FOUR cores for one task -# "work" is the default partition so it can be omitted without issue. - -## Please not that each node on the cluster is 36 cores -#SBATCH -p tier3 -n 4 - - -# Job memory requirements in MB -#SBATCH --mem-per-cpu=4G # I like to mem with a suffix [K|M|G|T] 5000 - -#module load module_future -#module load openmpi-1.10-x86_64 -module load openmpi -module load gcc -module load libtiff - - -EXAMM="/home/vj6810/EXACT/exact_/exact" - -for folder in 0 1 2 3 4 ; do - exp_name="$EXAMM/nlp_results_2/$folder" - mkdir -p $exp_name - echo "\t Iteration: $exp_name" - $EXAMM/build/multithreaded/examm_nlp --number_threads 9 \ - --training_filenames ../datasets/pennchar/train.txt \ - --test_filenames ../datasets/pennchar/valid.txt \ - --word_offset 1 \ - --number_islands 10 \ - --population_size 10 \ - --max_genomes 2000 \ - --bp_iterations 10 \ - --output_directory $exp_name \ - --possible_node_types simple ENARC \ - --normalize min_max \ - --std_message_level INFO \ - --file_message_level INFO - -done \ No newline at end of file diff --git a/run_lstm_nlp.sh b/run_lstm_nlp.sh deleted file mode 100644 index c452b7f8..00000000 --- a/run_lstm_nlp.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash -l -# NOTE the -l flag! -# - -#SBATCH -J examm_lstm_nlp - -#SBATCH -A examm #just examm examm_thompson is not a valid account - -#SBATCH -o cluster_logs/examm_test_%A.output -#SBATCH -e cluster_logs/examm_test_%A.error - -#SBATCH --mail-user vj6810@rit.edu - -#SBATCH --mail-type=ALL - -#SBATCH -t 5-0 # if you want to run for 4 days 1-72:0:0 - -# Put the job in the "work" partition and request FOUR cores for one task -# "work" is the default partition so it can be omitted without issue. - -## Please not that each node on the cluster is 36 cores -#SBATCH -p tier3 -n 4 - - -# Job memory requirements in MB -#SBATCH --mem-per-cpu=4G # I like to mem with a suffix [K|M|G|T] 5000 - -#module load module_future -#module load openmpi-1.10-x86_64 -module load openmpi -module load gcc -module load libtiff - - -EXAMM="/home/vj6810/EXACT/exact_/exact" - -for folder in 0 1 2 3 4 ; do - exp_name="$EXAMM/nlp_results_1/$folder" - mkdir -p $exp_name - echo "\t Iteration: $exp_name" - $EXAMM/build/multithreaded/examm_nlp --number_threads 9 \ - --training_filenames ../datasets/pennchar/train.txt \ - --test_filenames ../datasets/pennchar/valid.txt \ - --word_offset 1 \ - --number_islands 10 \ - --population_size 10 \ - --max_genomes 2000 \ - --bp_iterations 10 \ - --output_directory $exp_name \ - --possible_node_types simple LSTM \ - --normalize min_max \ - --std_message_level INFO \ - --file_message_level INFO - -done \ No newline at end of file diff --git a/untitled.sh b/untitled.sh deleted file mode 100644 index 05fa1d80..00000000 --- a/untitled.sh +++ /dev/null @@ -1 +0,0 @@ -mpirun -np 2 ./mpi/examm_mpi --training_filenames ../datasets/pennchar/input.txt --test_filenames ../datasets/pennchar/input.txt --word_offset 1 --number_islands 10 --population_size 10 --max_genomes 2000 --bp_iterations 10 --output_directory $exp_name --possible_node_types simple ENARC --normalize min_max --std_message_level INFO --file_message_level INFO \ No newline at end of file From 28f0058560a9c230e13783dbcee9a2f34e03e516 Mon Sep 17 00:00:00 2001 From: vinay-004 Date: Fri, 18 Dec 2020 06:55:48 -0500 Subject: [PATCH 18/25] adding mpi script for nlp --- nlp_scripts/run_all_cells.sh | 57 ++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 nlp_scripts/run_all_cells.sh diff --git a/nlp_scripts/run_all_cells.sh b/nlp_scripts/run_all_cells.sh new file mode 100644 index 00000000..c399189c --- /dev/null +++ b/nlp_scripts/run_all_cells.sh @@ -0,0 +1,57 @@ +#!/bin/bash -l +# NOTE the -l flag! +# + +#SBATCH -J examm_lstm_nlp + +#SBATCH -A examm #just examm examm_thompson is not a valid account + +#SBATCH -o examm_test_%A_%J.output +#SBATCH -e examm_test_%A_%J.error + +#SBATCH --mail-user vj6810@rit.edu + +#SBATCH --mail-type=ALL + +#SBATCH -t 1-0 # if you want to run for 4 days 1-72:0:0 + +# Put the job in the "work" partition and request FOUR cores for one task +# "work" is the default partition so it can be omitted without issue. + +## Please not that each node on the cluster is 36 cores +#SBATCH -p tier3 -n 21 + + +# Job memory requirements in MB +#SBATCH --mem-per-cpu=4G # I like to mem with a suffix [K|M|G|T] 5000 + +#module load module_future +#module load openmpi-1.10-x86_64 +module load openmpi +module load gcc +module load libtiff + + +EXAMM="/home/vj6810/EXAMM/exact" + +for folder in 0 1 2 3 4 ; do + exp_name="$EXAMM/nlp_results/all_cells/$folder" + mkdir -p $exp_name + echo "\t Iteration: $exp_name" + mpirun -np 20 $EXAMM/build/mpi/examm_mpi_nlp \ + --training_filenames $EXAMM/datasets/pennchar/train.txt \ + --test_filenames $EXAMM/datasets/pennchar/valid.txt \ + --word_offset 1 \ + --sequence_length 64 \ + --number_islands 10 \ + --population_size 10 \ + --max_genomes 5000 \ + --bp_iterations 10 \ + --use_regression 0 \ + --output_directory $exp_name \ + --possible_node_types simple UGRNN MGU GRU delta LSTM \ + --normalize min_max \ + --std_message_level ERROR \ + --file_message_level ERROR + +done From 9f14db0775852cf84a2244ec41c5fa585ae0ef48 Mon Sep 17 00:00:00 2001 From: Vinay Jain Date: Tue, 2 Feb 2021 12:40:46 -0500 Subject: [PATCH 19/25] adding nlp_scripts --- .../examm_test_12197645_12197645.error | 23 + .../examm_test_12197645_12197645.output | 781 ++++++++++++++++++ .../examm_test_12197646_12197646.error | 19 + .../examm_test_12197646_12197646.output | 781 ++++++++++++++++++ .../examm_test_12197647_12197647.error | 20 + .../examm_test_12197647_12197647.output | 781 ++++++++++++++++++ .../examm_test_12197648_12197648.error | 19 + .../examm_test_12197648_12197648.output | 781 ++++++++++++++++++ .../examm_test_12197649_12197649.error | 21 + .../examm_test_12197649_12197649.output | 781 ++++++++++++++++++ .../examm_test_12197650_12197650.error | 20 + .../examm_test_12197650_12197650.output | 781 ++++++++++++++++++ nlp_scripts/run_all.sh | 57 ++ nlp_scripts/run_all_cells.sh | 8 +- nlp_scripts/run_delta_cells.sh | 57 ++ nlp_scripts/run_gru_cells.sh | 57 ++ nlp_scripts/run_lstm_cells.sh | 57 ++ nlp_scripts/run_mgu_cells.sh | 57 ++ nlp_scripts/run_ugrnn_cells.sh | 57 ++ 19 files changed, 5154 insertions(+), 4 deletions(-) create mode 100644 nlp_scripts/examm_test_12197645_12197645.error create mode 100644 nlp_scripts/examm_test_12197645_12197645.output create mode 100644 nlp_scripts/examm_test_12197646_12197646.error create mode 100644 nlp_scripts/examm_test_12197646_12197646.output create mode 100644 nlp_scripts/examm_test_12197647_12197647.error create mode 100644 nlp_scripts/examm_test_12197647_12197647.output create mode 100644 nlp_scripts/examm_test_12197648_12197648.error create mode 100644 nlp_scripts/examm_test_12197648_12197648.output create mode 100644 nlp_scripts/examm_test_12197649_12197649.error create mode 100644 nlp_scripts/examm_test_12197649_12197649.output create mode 100644 nlp_scripts/examm_test_12197650_12197650.error create mode 100644 nlp_scripts/examm_test_12197650_12197650.output create mode 100644 nlp_scripts/run_all.sh create mode 100644 nlp_scripts/run_delta_cells.sh create mode 100644 nlp_scripts/run_gru_cells.sh create mode 100644 nlp_scripts/run_lstm_cells.sh create mode 100644 nlp_scripts/run_mgu_cells.sh create mode 100644 nlp_scripts/run_ugrnn_cells.sh diff --git a/nlp_scripts/examm_test_12197645_12197645.error b/nlp_scripts/examm_test_12197645_12197645.error new file mode 100644 index 00000000..5004cdb7 --- /dev/null +++ b/nlp_scripts/examm_test_12197645_12197645.error @@ -0,0 +1,23 @@ +Autoloading ucx/1.4.0-gcc-7.4.0-v2ddfog4 +Autoloading rdma-core/20-gcc-7.4.0-l7z64ilg +Autoloading libnl/3.3.0-gcc-7.4.0-fojudykh +Autoloading numactl/2.0.12-gcc-7.4.0-iz4isxyc +Autoloading hwloc/1.11.11-gcc-7.4.0-mcwlonx7 +Autoloading libpciaccess/0.13.5-gcc-7.4.0-ucgnzc4b +Autoloading libxml2/2.9.8-gcc-7.4.0-thy7vzt2 +Autoloading libiconv/1.15-gcc-7.4.0-sitpcx3p +Autoloading xz/5.2.4-gcc-7.4.0-qh2fwjgx +Autoloading zlib/1.2.11-gcc-7.4.0-ml43muis +Autoloading slurm/18-08-6-2-gcc-7.4.0-24rpjsm2 +Autoloading gmp/6.1.2-gcc-7.4.0-mnaje5ey +Autoloading mpfr/3.1.6-gcc-7.4.0-vijaqsan +Autoloading mpc/1.1.0-gcc-7.4.0-zg6tdmm5 +Autoloading isl/0.18-gcc-7.4.0-yzgnajke +Autoloading libjpeg-turbo/1.5.90-gcc-7.4.0-f4kkdboq +slurmstepd: error: *** STEP 12197645.0 ON skl-a-03 CANCELLED AT 2020-12-23T09:04:00 DUE TO TIME LIMIT *** +srun: Job step aborted: Waiting up to 4 seconds for job step to finish. +slurmstepd: error: *** JOB 12197645 ON skl-a-03 CANCELLED AT 2020-12-23T09:04:00 DUE TO TIME LIMIT *** +srun: error: skl-a-08: task 2: Terminated +srun: error: skl-a-10: task 4: Terminated +srun: error: skl-a-09: task 3: Terminated +srun: error: skl-a-15: task 5: Terminated diff --git a/nlp_scripts/examm_test_12197645_12197645.output b/nlp_scripts/examm_test_12197645_12197645.output new file mode 100644 index 00000000..8db07cb5 --- /dev/null +++ b/nlp_scripts/examm_test_12197645_12197645.output @@ -0,0 +1,781 @@ +\t Iteration: /home/vj6810/EXAMM/exact/nlp_results/all_cells/0 +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 0 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 1 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 3 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 4 and max rank 60 +got arguments! +did mpi init! +initailized log! +did mpi init! +got rank 2 and max rank 60 +got rank 5 and max rank 60 +got arguments! +got arguments! +initailized log! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 7 and max rank 60 +got arguments! +initailized log! +did mpi init! +did mpi init! +got rank 8 and max rank 60 +got arguments! +got rank 6 and max rank 60 +got arguments! +initailized log! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 11 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 16 and max rank 60 +got rank 13 and max rank 60 +got arguments! +got arguments! +did mpi init! +got rank 9 and max rank 60 +got arguments! +did mpi init! +got rank 14 and max rank 60 +got arguments! +did mpi init! +got rank 15 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 10 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 12 and max rank 60 +got arguments! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 49 and max rank 60 +got arguments! +did mpi init! +got rank 51 and max rank 60 +got arguments! +did mpi init! +got rank 50 and max rank 60 +got arguments! +did mpi init! +got rank 52 and max rank 60 +got arguments! +did mpi init! +got rank 46 and max rank 60 +got arguments! +did mpi init! +got rank 47 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 48 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 56 and max rank 60 +got arguments! +did mpi init! +got rank 55 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 53 and max rank 60 +got rank 54 and max rank 60 +got arguments! +got arguments! +did mpi init! +got rank 58 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 57 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 59 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 35 and max rank 60 +got arguments! +did mpi init! +initailized log! +did mpi init! +did mpi init! +got rank 26 and max rank 60 +got rank 37 and max rank 60 +got arguments! +got arguments! +got rank 30 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 25 and max rank 60 +got arguments! +did mpi init! +got rank 34 and max rank 60 +did mpi init! +got rank 28 and max rank 60 +got arguments! +did mpi init! +got rank 38 and max rank 60 +got arguments! +did mpi init! +got rank 29 and max rank 60 +got arguments! +got arguments! +did mpi init! +got rank 27 and max rank 60 +got arguments! +did mpi init! +got rank 33 and max rank 60 +initailized log! +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 32 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 31 and max rank 60 +got arguments! +did mpi init! +got rank 36 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +did mpi init! +got rank 41 and max rank 60 +did mpi init! +got rank 39 and max rank 60 +got arguments! +got arguments! +did mpi init! +got rank 43 and max rank 60 +got arguments! +did mpi init! +got rank 44 and max rank 60 +got arguments! +got rank 42 and max rank 60 +got arguments! +did mpi init! +got rank 45 and max rank 60 +got arguments! +did mpi init! +got rank 40 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 20 and max rank 60 +got arguments! +did mpi init! +got rank 17 and max rank 60 +got arguments! +did mpi init! +got rank 18 and max rank 60 +got arguments! +did mpi init! +got rank 22 and max rank 60 +got arguments! +did mpi init! +got rank 19 and max rank 60 +got arguments! +did mpi init! +got rank 24 and max rank 60 +got arguments! +did mpi init! +got rank 23 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 21 and max rank 60 +got arguments! +initailized log! +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +training +training +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +training +word_offset:: -1 +training +training +training +training +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 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+word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +testing diff --git a/nlp_scripts/examm_test_12197646_12197646.error b/nlp_scripts/examm_test_12197646_12197646.error new file mode 100644 index 00000000..36b0a1c7 --- /dev/null +++ b/nlp_scripts/examm_test_12197646_12197646.error @@ -0,0 +1,19 @@ +Autoloading ucx/1.4.0-gcc-7.4.0-v2ddfog4 +Autoloading rdma-core/20-gcc-7.4.0-l7z64ilg +Autoloading libnl/3.3.0-gcc-7.4.0-fojudykh +Autoloading numactl/2.0.12-gcc-7.4.0-iz4isxyc +Autoloading hwloc/1.11.11-gcc-7.4.0-mcwlonx7 +Autoloading libpciaccess/0.13.5-gcc-7.4.0-ucgnzc4b +Autoloading libxml2/2.9.8-gcc-7.4.0-thy7vzt2 +Autoloading libiconv/1.15-gcc-7.4.0-sitpcx3p +Autoloading xz/5.2.4-gcc-7.4.0-qh2fwjgx +Autoloading zlib/1.2.11-gcc-7.4.0-ml43muis +Autoloading slurm/18-08-6-2-gcc-7.4.0-24rpjsm2 +Autoloading gmp/6.1.2-gcc-7.4.0-mnaje5ey +Autoloading mpfr/3.1.6-gcc-7.4.0-vijaqsan +Autoloading mpc/1.1.0-gcc-7.4.0-zg6tdmm5 +Autoloading isl/0.18-gcc-7.4.0-yzgnajke +Autoloading libjpeg-turbo/1.5.90-gcc-7.4.0-f4kkdboq +slurmstepd: error: *** STEP 12197646.0 ON skl-a-23 CANCELLED AT 2020-12-23T09:05:00 DUE TO TIME LIMIT *** +srun: Job step aborted: Waiting up to 4 seconds for job step to finish. +slurmstepd: error: *** JOB 12197646 ON skl-a-23 CANCELLED AT 2020-12-23T09:05:00 DUE TO TIME LIMIT *** diff --git a/nlp_scripts/examm_test_12197646_12197646.output b/nlp_scripts/examm_test_12197646_12197646.output new file mode 100644 index 00000000..719fcc8a --- /dev/null +++ b/nlp_scripts/examm_test_12197646_12197646.output @@ -0,0 +1,781 @@ +\t Iteration: /home/vj6810/EXAMM/exact/nlp_results/delta/0 +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 0 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 1 and max rank 60 +got arguments! +did mpi init! +got rank 2 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 3 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 4 and max rank 60 +got arguments! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 25 and max rank 60 +got arguments! +did mpi init! +got rank 27 and max rank 60 +got arguments! +did mpi init! +got rank 23 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 26 and max rank 60 +got rank 22 and max rank 60 +got arguments! +got arguments! +did mpi init! +got rank 24 and max rank 60 +got arguments! +did mpi init! +got rank 28 and max rank 60 +got arguments! +did mpi init! +got rank 21 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 29 and max rank 60 +got arguments! +did mpi init! +got rank 31 and max rank 60 +got arguments! +did mpi init! +got rank 32 and max rank 60 +got arguments! +did mpi init! +got rank 33 and max rank 60 +got arguments! +did mpi init! +got rank 34 and max rank 60 +got arguments! +did mpi init! +got rank 36 and max rank 60 +got arguments! +did mpi init! +got rank 30 and max rank 60 +got arguments! +did mpi init! +got rank 35 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 12 and max rank 60 +got arguments! +did mpi init! +got rank 10 and max rank 60 +got arguments! +did mpi init! +got rank 8 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +did mpi init! +did mpi init! +got rank 11 and max rank 60 +got arguments! +got rank 7 and max rank 60 +got arguments! +did mpi init! +got rank 6 and max rank 60 +got arguments! +did mpi init! +got rank 9 and max rank 60 +initailized log! +initailized log! +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 5 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 16 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 54 and max rank 60 +did mpi init! +got arguments! +got rank 18 and max rank 60 +got arguments! +got rank 20 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 13 and max rank 60 +got arguments! +did mpi init! +got rank 15 and max rank 60 +got arguments! +did mpi init! +got rank 17 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 19 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 14 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 58 and max rank 60 +got arguments! +did mpi init! +got rank 56 and max rank 60 +got arguments! +did mpi init! +got rank 57 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 53 and max rank 60 +got arguments! +did mpi init! +initailized log! +got rank 55 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 59 and max rank 60 +got arguments! +initailized log! +did mpi init! +did mpi init! +got rank 50 and max rank 60 +got arguments! +did mpi init! +got rank 45 and max rank 60 +did mpi init! +got rank 47 and max rank 60 +got arguments! +got rank 48 and max rank 60 +got arguments! +did mpi init! +got rank 49 and max rank 60 +did mpi init! +got rank 51 and max rank 60 +got arguments! +got arguments! +got arguments! +did mpi init! +got rank 46 and max rank 60 +got arguments! +did mpi init! +got rank 52 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 44 and max rank 60 +got arguments! +did mpi init! +got rank 40 and max rank 60 +got arguments! +did mpi init! +got rank 42 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 38 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 41 and max rank 60 +got arguments! +did mpi init! +initailized log! +got rank 37 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 39 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 43 and max rank 60 +got arguments! +initailized log! +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +training +word_offset:: -1 +training +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 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1 +testing +word_offset:: 1 +word_offset:: 1 +testing +training +testing +testing +training +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +testing +testing +testing +word_offset:: 1 +testing +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +testing +testing +testing +testing +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +testing diff --git a/nlp_scripts/examm_test_12197647_12197647.error b/nlp_scripts/examm_test_12197647_12197647.error new file mode 100644 index 00000000..000a7fc4 --- /dev/null +++ b/nlp_scripts/examm_test_12197647_12197647.error @@ -0,0 +1,20 @@ +Autoloading ucx/1.4.0-gcc-7.4.0-v2ddfog4 +Autoloading rdma-core/20-gcc-7.4.0-l7z64ilg +Autoloading libnl/3.3.0-gcc-7.4.0-fojudykh +Autoloading numactl/2.0.12-gcc-7.4.0-iz4isxyc +Autoloading hwloc/1.11.11-gcc-7.4.0-mcwlonx7 +Autoloading libpciaccess/0.13.5-gcc-7.4.0-ucgnzc4b +Autoloading libxml2/2.9.8-gcc-7.4.0-thy7vzt2 +Autoloading libiconv/1.15-gcc-7.4.0-sitpcx3p +Autoloading xz/5.2.4-gcc-7.4.0-qh2fwjgx +Autoloading zlib/1.2.11-gcc-7.4.0-ml43muis +Autoloading slurm/18-08-6-2-gcc-7.4.0-24rpjsm2 +Autoloading gmp/6.1.2-gcc-7.4.0-mnaje5ey +Autoloading mpfr/3.1.6-gcc-7.4.0-vijaqsan +Autoloading mpc/1.1.0-gcc-7.4.0-zg6tdmm5 +Autoloading isl/0.18-gcc-7.4.0-yzgnajke +Autoloading libjpeg-turbo/1.5.90-gcc-7.4.0-f4kkdboq +slurmstepd: error: *** JOB 12197647 ON skl-a-27 CANCELLED AT 2020-12-23T09:07:00 DUE TO TIME LIMIT *** +srun: Job step aborted: Waiting up to 4 seconds for job step to finish. +slurmstepd: error: *** STEP 12197647.0 ON skl-a-27 CANCELLED AT 2020-12-23T09:07:00 DUE TO TIME LIMIT *** +srun: error: skl-a-29: task 1: Terminated diff --git a/nlp_scripts/examm_test_12197647_12197647.output b/nlp_scripts/examm_test_12197647_12197647.output new file mode 100644 index 00000000..dfb33d4d --- /dev/null +++ b/nlp_scripts/examm_test_12197647_12197647.output @@ -0,0 +1,781 @@ +\t Iteration: /home/vj6810/EXAMM/exact/nlp_results/GRU/0 +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 0 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 1 and max rank 60 +got arguments! +did mpi init! +got rank 2 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 4 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 3 and max rank 60 +got arguments! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 10 and max rank 60 +got arguments! +did mpi init! +got rank 6 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 9 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 5 and max rank 60 +got arguments! +did mpi init! +initailized log! +got rank 8 and max rank 60 +got arguments! +did mpi init! +got rank 11 and max rank 60 +did mpi init! +got arguments! +got rank 7 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 27 and max rank 60 +got arguments! +did mpi init! +got rank 21 and max rank 60 +got arguments! +did mpi init! +got rank 25 and max rank 60 +got arguments! +did mpi init! +got rank 23 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 26 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 24 and max rank 60 +got arguments! +did mpi init! +got rank 20 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 22 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 40 and max rank 60 +got arguments! +did mpi init! +got rank 36 and max rank 60 +got arguments! +did mpi init! +got rank 37 and max rank 60 +got arguments! +did mpi init! +got rank 42 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 38 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 39 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 41 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 43 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 53 and max rank 60 +got arguments! +did mpi init! +got rank 57 and max rank 60 +got arguments! +did mpi init! +got rank 59 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 55 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 56 and max rank 60 +got rank 54 and max rank 60 +got arguments! +got arguments! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 52 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 58 and max rank 60 +got arguments! +initailized log! +did mpi init! +did mpi init! +got rank 31 and max rank 60 +got arguments! +got rank 33 and max rank 60 +got arguments! +did mpi init! +got rank 29 and max rank 60 +got arguments! +did mpi init! +got rank 30 and max rank 60 +got arguments! +did mpi init! +got rank 34 and max rank 60 +got arguments! +did mpi init! +got rank 35 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 32 and max rank 60 +got arguments! +did mpi init! +got rank 28 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +did mpi init! +got rank 46 and max rank 60 +got rank 44 and max rank 60 +got arguments! +got arguments! +did mpi init! +did mpi init! +did mpi init! +got rank 51 and max rank 60 +got arguments! +got rank 47 and max rank 60 +got arguments! +got rank 49 and max rank 60 +got arguments! +did mpi init! +got rank 50 and max rank 60 +got arguments! +did mpi init! +got rank 45 and max rank 60 +got arguments! +did mpi init! +got rank 48 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 19 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 15 and max rank 60 +got arguments! +did mpi init! +got rank 12 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 13 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 14 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 16 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 17 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 18 and max rank 60 +got arguments! +initailized log! +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 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+word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: -1 +training +word_offset:: 1 +word_offset:: 1 +training +word_offset:: -1 +testing +word_offset:: -1 +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +training +testing +testing +word_offset:: 1 +word_offset:: 1 +training +word_offset:: 1 +word_offset:: -1 +training +testing +testing +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +training +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +testing +testing +testing +testing +testing +testing +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +testing +testing +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +testing diff --git a/nlp_scripts/examm_test_12197648_12197648.error b/nlp_scripts/examm_test_12197648_12197648.error new file mode 100644 index 00000000..70a55b5d --- /dev/null +++ b/nlp_scripts/examm_test_12197648_12197648.error @@ -0,0 +1,19 @@ +Autoloading ucx/1.4.0-gcc-7.4.0-v2ddfog4 +Autoloading rdma-core/20-gcc-7.4.0-l7z64ilg +Autoloading libnl/3.3.0-gcc-7.4.0-fojudykh +Autoloading numactl/2.0.12-gcc-7.4.0-iz4isxyc +Autoloading hwloc/1.11.11-gcc-7.4.0-mcwlonx7 +Autoloading libpciaccess/0.13.5-gcc-7.4.0-ucgnzc4b +Autoloading libxml2/2.9.8-gcc-7.4.0-thy7vzt2 +Autoloading libiconv/1.15-gcc-7.4.0-sitpcx3p +Autoloading xz/5.2.4-gcc-7.4.0-qh2fwjgx +Autoloading zlib/1.2.11-gcc-7.4.0-ml43muis +Autoloading slurm/18-08-6-2-gcc-7.4.0-24rpjsm2 +Autoloading gmp/6.1.2-gcc-7.4.0-mnaje5ey +Autoloading mpfr/3.1.6-gcc-7.4.0-vijaqsan +Autoloading mpc/1.1.0-gcc-7.4.0-zg6tdmm5 +Autoloading isl/0.18-gcc-7.4.0-yzgnajke +Autoloading libjpeg-turbo/1.5.90-gcc-7.4.0-f4kkdboq +slurmstepd: error: *** JOB 12197648 ON skl-a-42 CANCELLED AT 2020-12-23T09:07:00 DUE TO TIME LIMIT *** +srun: Job step aborted: Waiting up to 4 seconds for job step to finish. +slurmstepd: error: *** STEP 12197648.0 ON skl-a-42 CANCELLED AT 2020-12-23T09:07:00 DUE TO TIME LIMIT *** diff --git a/nlp_scripts/examm_test_12197648_12197648.output b/nlp_scripts/examm_test_12197648_12197648.output new file mode 100644 index 00000000..0bd7cd39 --- /dev/null +++ b/nlp_scripts/examm_test_12197648_12197648.output @@ -0,0 +1,781 @@ +\t Iteration: /home/vj6810/EXAMM/exact/nlp_results/LSTM/0 +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 0 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 33 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 34 and max rank 60 +got arguments! +did mpi init! +got rank 36 and max rank 60 +got arguments! +did mpi init! +got rank 37 and max rank 60 +got arguments! +did mpi init! +got rank 38 and max rank 60 +got arguments! +did mpi init! +initailized log! +got rank 35 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 3 and max rank 60 +got arguments! +did mpi init! +got rank 7 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 6 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 8 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 4 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 2 and max rank 60 +got arguments! +did mpi init! +got rank 1 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 5 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 51 and max rank 60 +got arguments! +did mpi init! +got rank 47 and max rank 60 +got arguments! +did mpi init! +got rank 46 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 48 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 49 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 50 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 52 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 25 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 26 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 54 and max rank 60 +did mpi init! +got rank 28 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 55 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 30 and max rank 60 +got arguments! +got arguments! +did mpi init! +got rank 56 and max rank 60 +got arguments! +did mpi init! +got rank 58 and max rank 60 +got arguments! +got rank 57 and max rank 60 +got arguments! +did mpi init! +got rank 59 and max rank 60 +got arguments! +did mpi init! +got rank 53 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 9 and max rank 60 +got arguments! +did mpi init! +got rank 27 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 29 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 10 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 12 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 31 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 13 and max rank 60 +got arguments! +did mpi init! +got rank 32 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 14 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 11 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 15 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 16 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 41 and max rank 60 +got arguments! +did mpi init! +got rank 45 and max rank 60 +got arguments! +did mpi init! +got rank 40 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 42 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 43 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 44 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 39 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 24 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 22 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 17 and max rank 60 +got rank 23 and max rank 60 +got arguments! +got arguments! +did mpi init! +got rank 21 and max rank 60 +did mpi init! +got arguments! +initailized log! +got rank 20 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 18 and max rank 60 +did mpi init! +got rank 19 and max rank 60 +got arguments! +initailized log! +got arguments! +initailized log! +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 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1 +testing diff --git a/nlp_scripts/examm_test_12197649_12197649.error b/nlp_scripts/examm_test_12197649_12197649.error new file mode 100644 index 00000000..44f13d6d --- /dev/null +++ b/nlp_scripts/examm_test_12197649_12197649.error @@ -0,0 +1,21 @@ +Autoloading ucx/1.4.0-gcc-7.4.0-v2ddfog4 +Autoloading rdma-core/20-gcc-7.4.0-l7z64ilg +Autoloading libnl/3.3.0-gcc-7.4.0-fojudykh +Autoloading numactl/2.0.12-gcc-7.4.0-iz4isxyc +Autoloading hwloc/1.11.11-gcc-7.4.0-mcwlonx7 +Autoloading libpciaccess/0.13.5-gcc-7.4.0-ucgnzc4b +Autoloading libxml2/2.9.8-gcc-7.4.0-thy7vzt2 +Autoloading libiconv/1.15-gcc-7.4.0-sitpcx3p +Autoloading xz/5.2.4-gcc-7.4.0-qh2fwjgx +Autoloading zlib/1.2.11-gcc-7.4.0-ml43muis +Autoloading slurm/18-08-6-2-gcc-7.4.0-24rpjsm2 +Autoloading gmp/6.1.2-gcc-7.4.0-mnaje5ey +Autoloading mpfr/3.1.6-gcc-7.4.0-vijaqsan +Autoloading mpc/1.1.0-gcc-7.4.0-zg6tdmm5 +Autoloading isl/0.18-gcc-7.4.0-yzgnajke +Autoloading libjpeg-turbo/1.5.90-gcc-7.4.0-f4kkdboq +slurmstepd: error: *** JOB 12197649 ON skl-a-05 CANCELLED AT 2020-12-23T09:07:00 DUE TO TIME LIMIT *** +srun: Job step aborted: Waiting up to 4 seconds for job step to finish. +slurmstepd: error: *** STEP 12197649.0 ON skl-a-05 CANCELLED AT 2020-12-23T09:07:00 DUE TO TIME LIMIT *** +srun: error: skl-a-60: task 17: Terminated +srun: error: skl-a-63: task 18: Terminated diff --git a/nlp_scripts/examm_test_12197649_12197649.output b/nlp_scripts/examm_test_12197649_12197649.output new file mode 100644 index 00000000..5e45ce35 --- /dev/null +++ b/nlp_scripts/examm_test_12197649_12197649.output @@ -0,0 +1,781 @@ +\t Iteration: /home/vj6810/EXAMM/exact/nlp_results/MGU/0 +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 17 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 18 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 4 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 6 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 0 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 2 and max rank 60 +got arguments! +initailized log! +did mpi init! +did mpi init! +got rank 3 and max rank 60 +got arguments! +did mpi init! +got rank 1 and max rank 60 +got arguments! +initailized log! +initailized log! +got rank 5 and max rank 60 +got arguments! +did mpi init! +initailized log! +got rank 7 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 8 and max rank 60 +got arguments! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 22 and max rank 60 +got arguments! +did mpi init! +got rank 25 and max rank 60 +got arguments! +did mpi init! +got rank 20 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 23 and max rank 60 +did mpi init! +got rank 24 and max rank 60 +got rank 19 and max rank 60 +got arguments! +did mpi init! +got arguments! +got arguments! +got rank 21 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 26 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 10 and max rank 60 +got arguments! +did mpi init! +got rank 9 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 16 and max rank 60 +got rank 12 and max rank 60 +got arguments! +did mpi init! +got rank 14 and max rank 60 +got arguments! +got arguments! +did mpi init! +did mpi init! +did mpi init! +got rank 11 and max rank 60 +got arguments! +got rank 13 and max rank 60 +got arguments! +got rank 15 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +training +training +training +training +training +word_offset:: -1 +training +training +training +training +word_offset:: -1 +word_offset:: -1 +training +training +training +training +training +word_offset:: -1 +training +training +training +training +training +training +training +training +training +training +training +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +did mpi init! +got rank 46 and max rank 60 +got arguments! +did mpi init! +got rank 42 and max rank 60 +got arguments! +did mpi init! +got rank 34 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 56 and max rank 60 +got rank 32 and max rank 60 +did mpi init! +got arguments! +did mpi init! +got arguments! +did mpi init! +got rank 48 and max rank 60 +got arguments! +got rank 57 and max rank 60 +got arguments! +got rank 45 and max rank 60 +did mpi init! +got arguments! +got rank 27 and max rank 60 +got arguments! +did mpi init! +did mpi init! +did mpi init! +got rank 38 and max rank 60 +got arguments! +got rank 28 and max rank 60 +got arguments! +did mpi init! +got rank 31 and max rank 60 +got arguments! +did mpi init! +did mpi init! +did mpi init! +got rank 51 and max rank 60 +got arguments! +did mpi init! +got rank 55 and max rank 60 +got arguments! +did mpi init! +did mpi init! +did mpi init! +did mpi init! +got rank 37 and max rank 60 +got arguments! +got rank 43 and max rank 60 +got arguments! +got rank 47 and max rank 60 +got arguments! +did mpi init! +got rank 30 and max rank 60 +got arguments! +got rank 36 and max rank 60 +got arguments! +word_offset:: 1 +did mpi init! +did mpi init! +got rank 59 and max rank 60 +got arguments! +did mpi init! +got rank 35 and max rank 60 +got arguments! +did mpi init! +got rank 40 and max rank 60 +did mpi init! +got rank 50 and max rank 60 +did mpi init! +got rank 52 and max rank 60 +did mpi init! +got rank 53 and max rank 60 +got rank 58 and max rank 60 +got arguments! +did mpi init! +got rank 29 and max rank 60 +got rank 33 and max rank 60 +got rank 39 and max rank 60 +got arguments! +did mpi init! +got rank 41 and max rank 60 +got arguments! +got arguments! +got arguments! +got arguments! +got arguments! +got arguments! +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +got rank 54 and max rank 60 +initailized log! +initailized log! +initailized log! +initailized log! +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 44 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +initailized log! +word_offset:: 1 +did mpi init! +got rank 49 and max rank 60 +got arguments! +initailized log! +testing +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +testing +testing +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +testing +testing +testing +testing +testing +word_offset:: 1 +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +training +word_offset:: -1 +training +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +training +training +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: 1 +training +training +training +training +training +training +word_offset:: -1 +testing +training +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +testing +word_offset:: 1 +testing +testing +training +training +training +word_offset:: 1 +testing +word_offset:: -1 +word_offset:: -1 +word_offset:: 1 +testing +word_offset:: 1 +training +word_offset:: 1 +word_offset:: 1 +training +word_offset:: 1 +word_offset:: -1 +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +training +testing +word_offset:: 1 +testing +testing +testing +word_offset:: -1 +testing +testing +word_offset:: 1 +testing +testing +training +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +word_offset:: -1 +training +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: 1 +word_offset:: -1 +training +training +word_offset:: -1 +training +word_offset:: -1 +training +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +training +training +testing +training +word_offset:: -1 +word_offset:: -1 +training +training +training +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: 1 +training +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +training +word_offset:: -1 +testing +word_offset:: -1 +word_offset:: -1 +training +training +training +training +training +training +word_offset:: 1 +training +word_offset:: 1 +training +training +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +testing +testing +testing +testing +testing +testing diff --git a/nlp_scripts/examm_test_12197650_12197650.error b/nlp_scripts/examm_test_12197650_12197650.error new file mode 100644 index 00000000..81079c90 --- /dev/null +++ b/nlp_scripts/examm_test_12197650_12197650.error @@ -0,0 +1,20 @@ +Autoloading ucx/1.4.0-gcc-7.4.0-v2ddfog4 +Autoloading rdma-core/20-gcc-7.4.0-l7z64ilg +Autoloading libnl/3.3.0-gcc-7.4.0-fojudykh +Autoloading numactl/2.0.12-gcc-7.4.0-iz4isxyc +Autoloading hwloc/1.11.11-gcc-7.4.0-mcwlonx7 +Autoloading libpciaccess/0.13.5-gcc-7.4.0-ucgnzc4b +Autoloading libxml2/2.9.8-gcc-7.4.0-thy7vzt2 +Autoloading libiconv/1.15-gcc-7.4.0-sitpcx3p +Autoloading xz/5.2.4-gcc-7.4.0-qh2fwjgx +Autoloading zlib/1.2.11-gcc-7.4.0-ml43muis +Autoloading slurm/18-08-6-2-gcc-7.4.0-24rpjsm2 +Autoloading gmp/6.1.2-gcc-7.4.0-mnaje5ey +Autoloading mpfr/3.1.6-gcc-7.4.0-vijaqsan +Autoloading mpc/1.1.0-gcc-7.4.0-zg6tdmm5 +Autoloading isl/0.18-gcc-7.4.0-yzgnajke +Autoloading libjpeg-turbo/1.5.90-gcc-7.4.0-f4kkdboq +slurmstepd: error: *** JOB 12197650 ON skl-a-03 CANCELLED AT 2020-12-24T17:50:22 DUE TO TIME LIMIT *** +srun: Job step aborted: Waiting up to 4 seconds for job step to finish. +slurmstepd: error: *** STEP 12197650.0 ON skl-a-03 CANCELLED AT 2020-12-24T17:50:22 DUE TO TIME LIMIT *** +srun: error: skl-a-38: task 25: Terminated diff --git a/nlp_scripts/examm_test_12197650_12197650.output b/nlp_scripts/examm_test_12197650_12197650.output new file mode 100644 index 00000000..a4c9b5ef --- /dev/null +++ b/nlp_scripts/examm_test_12197650_12197650.output @@ -0,0 +1,781 @@ +\t Iteration: /home/vj6810/EXAMM/exact/nlp_results/UGRNN/0 +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 25 and max rank 60 +got arguments! +initailized log! +starting up! +starting up! +starting up! +did mpi init! +got rank 23 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 24 and max rank 60 +got arguments! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 4 and max rank 60 +got arguments! +starting up! +did mpi init! +starting up! +got rank 6 and max rank 60 +starting up! +got arguments! +starting up! +did mpi init! +got rank 3 and max rank 60 +got arguments! +starting up! +starting up! +starting up! +starting up! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 5 and max rank 60 +got arguments! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 0 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 1 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 2 and max rank 60 +got arguments! +initailized log! +did mpi init! +did mpi init! +got rank 8 and max rank 60 +got arguments! +got rank 7 and max rank 60 +got arguments! +did mpi init! +got rank 9 and max rank 60 +got arguments! +initailized log! +initailized log! +initailized log! +did mpi init! +got rank 10 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 11 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 12 and max rank 60 +got arguments! +initailized log! +starting up! +starting up! +did mpi init! +got rank 14 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 27 and max rank 60 +got arguments! +did mpi init! +got rank 29 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 31 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 33 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 26 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 28 and max rank 60 +did mpi init! +got rank 13 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 30 and max rank 60 +got arguments! +did mpi init! +got rank 32 and max rank 60 +got arguments! +got arguments! +initailized log! +initailized log! +initailized log! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +starting up! +did mpi init! +got rank 15 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 18 and max rank 60 +got rank 35 and max rank 60 +got arguments! +got arguments! +did mpi init! +did mpi init! +did mpi init! +got rank 22 and max rank 60 +got arguments! +got rank 39 and max rank 60 +did mpi init! +got arguments! +got rank 20 and max rank 60 +got rank 34 and max rank 60 +got arguments! +got arguments! +initailized log! +did mpi init! +initailized log! +did mpi init! +did mpi init! +got rank 19 and max rank 60 +got arguments! +initailized log! +got rank 41 and max rank 60 +got arguments! +initailized log! +got rank 36 and max rank 60 +did mpi init! +got rank 21 and max rank 60 +got arguments! +initailized log! +got arguments! +initailized log! +initailized log! +did mpi init! +initailized log! +got rank 17 and max rank 60 +initailized log! +got arguments! +did mpi init! +got rank 37 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 38 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 40 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 16 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 44 and max rank 60 +got arguments! +did mpi init! +got rank 51 and max rank 60 +got arguments! +did mpi init! +did mpi init! +got rank 52 and max rank 60 +got rank 55 and max rank 60 +got arguments! +got arguments! +did mpi init! +got rank 54 and max rank 60 +got arguments! +initailized log! +did mpi init! +initailized log! +initailized log! +initailized log! +got rank 46 and max rank 60 +got arguments! +did mpi init! +got rank 48 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 57 and max rank 60 +did mpi init! +got arguments! +got rank 50 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 43 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 42 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 45 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 47 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 49 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 53 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 56 and max rank 60 +got arguments! +initailized log! +initailized log! +did mpi init! +got rank 58 and max rank 60 +got arguments! +initailized log! +did mpi init! +got rank 59 and max rank 60 +got arguments! +initailized log! +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +training +word_offset:: -1 +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +training +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +testing +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +training +training +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +testing +word_offset:: -1 +training +word_offset:: -1 +training +word_offset:: 1 +word_offset:: -1 +word_offset:: -1 +testing +word_offset:: -1 +word_offset:: -1 +training +training +training +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: 1 +word_offset:: -1 +training +training +word_offset:: -1 +testing +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: 1 +word_offset:: -1 +word_offset:: 1 +training +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +word_offset:: 1 +training +word_offset:: 1 +word_offset:: -1 +testing +word_offset:: -1 +training +training +testing +word_offset:: -1 +training +word_offset:: -1 +training +training +training +word_offset:: -1 +testing +testing +training +training +word_offset:: -1 +training +training +word_offset:: -1 +training +training +word_offset:: -1 +word_offset:: 1 +word_offset:: 1 +training +word_offset:: 1 +word_offset:: -1 +training +testing +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: -1 +testing +word_offset:: -1 +training +testing +testing +word_offset:: -1 +word_offset:: 1 +word_offset:: -1 +testing +word_offset:: -1 +training +word_offset:: 1 +training +word_offset:: 1 +testing +training +word_offset:: 1 +training +training +word_offset:: 1 +testing +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: -1 +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +testing +testing +training +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +testing +testing +testing +word_offset:: 1 +testing +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +training +training +word_offset:: -1 +training +training +training +word_offset:: -1 +word_offset:: -1 +training +word_offset:: -1 +word_offset:: -1 +word_offset:: -1 +training +training +word_offset:: -1 +training +training +training +training +word_offset:: -1 +training +word_offset:: -1 +training +word_offset:: 1 +word_offset:: 1 +training +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +testing +testing +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +word_offset:: 1 +testing +word_offset:: 1 +word_offset:: 1 +word_offset:: 1 +testing +testing +testing +word_offset:: 1 +testing +testing +testing +word_offset:: 1 +testing +testing +word_offset:: 1 +testing diff --git a/nlp_scripts/run_all.sh b/nlp_scripts/run_all.sh new file mode 100644 index 00000000..3ff47356 --- /dev/null +++ b/nlp_scripts/run_all.sh @@ -0,0 +1,57 @@ +#!/bin/bash -l +# NOTE the -l flag! +# + +#SBATCH -J examm_lstm_nlp + +#SBATCH -A examm #just examm examm_thompson is not a valid account + +#SBATCH -o examm_test_%A_%J.output +#SBATCH -e examm_test_%A_%J.error + +#SBATCH --mail-user vj6810@rit.edu + +#SBATCH --mail-type=ALL + +#SBATCH -t 1-0 # if you want to run for 4 days 1-72:0:0 + +# Put the job in the "work" partition and request FOUR cores for one task +# "work" is the default partition so it can be omitted without issue. + +## Please not that each node on the cluster is 36 cores +#SBATCH -p tier3 -n 9 + + +# Job memory requirements in MB +#SBATCH --mem-per-cpu=4G # I like to mem with a suffix [K|M|G|T] 5000 + +#module load module_future +#module load openmpi-1.10-x86_64 +module load openmpi +module load gcc +module load libtiff + + +EXAMM="/home/vj6810/EXAMM/exact" + +for folder in 0 1 2 3 4 ; do + exp_name="$EXAMM/nlp_results/all_cells/$folder" + mkdir -p $exp_name + echo "\t Iteration: $exp_name" + $EXAMM/build/multithreaded/examm_mt_nlp --number_threads 9 \ + --training_filenames $EXAMM/datasets/pennchar/train.txt \ + --test_filenames $EXAMM/datasets/pennchar/valid.txt \ + --word_offset 1 \ + --sequence_length 64 \ + --number_islands 10 \ + --population_size 10 \ + --max_genomes 5000 \ + --bp_iterations 10 \ + --use_regression 0 \ + --output_directory $exp_name \ + --possible_node_types simple UGRNN MGU GRU delta LSTM \ + --normalize min_max \ + --std_message_level ERROR \ + --file_message_level ERROR + +done diff --git a/nlp_scripts/run_all_cells.sh b/nlp_scripts/run_all_cells.sh index c399189c..49e3892e 100644 --- a/nlp_scripts/run_all_cells.sh +++ b/nlp_scripts/run_all_cells.sh @@ -13,17 +13,17 @@ #SBATCH --mail-type=ALL -#SBATCH -t 1-0 # if you want to run for 4 days 1-72:0:0 +#SBATCH -t 4-0 # if you want to run for 4 days 1-72:0:0 # Put the job in the "work" partition and request FOUR cores for one task # "work" is the default partition so it can be omitted without issue. ## Please not that each node on the cluster is 36 cores -#SBATCH -p tier3 -n 21 +#SBATCH -p tier3 -n 61 # Job memory requirements in MB -#SBATCH --mem-per-cpu=4G # I like to mem with a suffix [K|M|G|T] 5000 +#SBATCH --mem-per-cpu=40G # I like to mem with a suffix [K|M|G|T] 5000 #module load module_future #module load openmpi-1.10-x86_64 @@ -38,7 +38,7 @@ for folder in 0 1 2 3 4 ; do exp_name="$EXAMM/nlp_results/all_cells/$folder" mkdir -p $exp_name echo "\t Iteration: $exp_name" - mpirun -np 20 $EXAMM/build/mpi/examm_mpi_nlp \ + srun -n 60 $EXAMM/build/mpi/examm_mpi_nlp \ --training_filenames $EXAMM/datasets/pennchar/train.txt \ --test_filenames $EXAMM/datasets/pennchar/valid.txt \ --word_offset 1 \ diff --git a/nlp_scripts/run_delta_cells.sh b/nlp_scripts/run_delta_cells.sh new file mode 100644 index 00000000..4a4fc677 --- /dev/null +++ b/nlp_scripts/run_delta_cells.sh @@ -0,0 +1,57 @@ +#!/bin/bash -l +# NOTE the -l flag! +# + +#SBATCH -J examm_lstm_nlp + +#SBATCH -A examm #just examm examm_thompson is not a valid account + +#SBATCH -o examm_test_%A_%J.output +#SBATCH -e examm_test_%A_%J.error + +#SBATCH --mail-user vj6810@rit.edu + +#SBATCH --mail-type=ALL + +#SBATCH -t 4-0 # if you want to run for 4 days 1-72:0:0 + +# Put the job in the "work" partition and request FOUR cores for one task +# "work" is the default partition so it can be omitted without issue. + +## Please not that each node on the cluster is 36 cores +#SBATCH -p tier3 -n 61 + + +# Job memory requirements in MB +#SBATCH --mem-per-cpu=40G # I like to mem with a suffix [K|M|G|T] 5000 + +#module load module_future +#module load openmpi-1.10-x86_64 +module load openmpi +module load gcc +module load libtiff + + +EXAMM="/home/vj6810/EXAMM/exact" + +for folder in 0 1 2 3 4 ; do + exp_name="$EXAMM/nlp_results/delta/$folder" + mkdir -p $exp_name + echo "\t Iteration: $exp_name" + srun -n 60 $EXAMM/build/mpi/examm_mpi_nlp \ + --training_filenames $EXAMM/datasets/pennchar/train.txt \ + --test_filenames $EXAMM/datasets/pennchar/valid.txt \ + --word_offset 1 \ + --sequence_length 64 \ + --number_islands 10 \ + --population_size 10 \ + --max_genomes 5000 \ + --bp_iterations 10 \ + --use_regression 0 \ + --output_directory $exp_name \ + --possible_node_types simple delta \ + --normalize min_max \ + --std_message_level ERROR \ + --file_message_level ERROR + +done diff --git a/nlp_scripts/run_gru_cells.sh b/nlp_scripts/run_gru_cells.sh new file mode 100644 index 00000000..c7532b33 --- /dev/null +++ b/nlp_scripts/run_gru_cells.sh @@ -0,0 +1,57 @@ +#!/bin/bash -l +# NOTE the -l flag! +# + +#SBATCH -J examm_lstm_nlp + +#SBATCH -A examm #just examm examm_thompson is not a valid account + +#SBATCH -o examm_test_%A_%J.output +#SBATCH -e examm_test_%A_%J.error + +#SBATCH --mail-user vj6810@rit.edu + +#SBATCH --mail-type=ALL + +#SBATCH -t 4-0 # if you want to run for 4 days 1-72:0:0 + +# Put the job in the "work" partition and request FOUR cores for one task +# "work" is the default partition so it can be omitted without issue. + +## Please not that each node on the cluster is 36 cores +#SBATCH -p tier3 -n 61 + + +# Job memory requirements in MB +#SBATCH --mem-per-cpu=40G # I like to mem with a suffix [K|M|G|T] 5000 + +#module load module_future +#module load openmpi-1.10-x86_64 +module load openmpi +module load gcc +module load libtiff + + +EXAMM="/home/vj6810/EXAMM/exact" + +for folder in 0 1 2 3 4 ; do + exp_name="$EXAMM/nlp_results/GRU/$folder" + mkdir -p $exp_name + echo "\t Iteration: $exp_name" + srun -n 60 $EXAMM/build/mpi/examm_mpi_nlp \ + --training_filenames $EXAMM/datasets/pennchar/train.txt \ + --test_filenames $EXAMM/datasets/pennchar/valid.txt \ + --word_offset 1 \ + --sequence_length 64 \ + --number_islands 10 \ + --population_size 10 \ + --max_genomes 5000 \ + --bp_iterations 10 \ + --use_regression 0 \ + --output_directory $exp_name \ + --possible_node_types simple GRU \ + --normalize min_max \ + --std_message_level ERROR \ + --file_message_level ERROR + +done diff --git a/nlp_scripts/run_lstm_cells.sh b/nlp_scripts/run_lstm_cells.sh new file mode 100644 index 00000000..5dbabe1f --- /dev/null +++ b/nlp_scripts/run_lstm_cells.sh @@ -0,0 +1,57 @@ +#!/bin/bash -l +# NOTE the -l flag! +# + +#SBATCH -J examm_lstm_nlp + +#SBATCH -A examm #just examm examm_thompson is not a valid account + +#SBATCH -o examm_test_%A_%J.output +#SBATCH -e examm_test_%A_%J.error + +#SBATCH --mail-user vj6810@rit.edu + +#SBATCH --mail-type=ALL + +#SBATCH -t 4-0 # if you want to run for 4 days 1-72:0:0 + +# Put the job in the "work" partition and request FOUR cores for one task +# "work" is the default partition so it can be omitted without issue. + +## Please not that each node on the cluster is 36 cores +#SBATCH -p tier3 -n 61 + + +# Job memory requirements in MB +#SBATCH --mem-per-cpu=40G # I like to mem with a suffix [K|M|G|T] 5000 + +#module load module_future +#module load openmpi-1.10-x86_64 +module load openmpi +module load gcc +module load libtiff + + +EXAMM="/home/vj6810/EXAMM/exact" + +for folder in 0 1 2 3 4 ; do + exp_name="$EXAMM/nlp_results/LSTM/$folder" + mkdir -p $exp_name + echo "\t Iteration: $exp_name" + srun -n 60 $EXAMM/build/mpi/examm_mpi_nlp \ + --training_filenames $EXAMM/datasets/pennchar/train.txt \ + --test_filenames $EXAMM/datasets/pennchar/valid.txt \ + --word_offset 1 \ + --sequence_length 64 \ + --number_islands 10 \ + --population_size 10 \ + --max_genomes 5000 \ + --bp_iterations 10 \ + --use_regression 0 \ + --output_directory $exp_name \ + --possible_node_types simple LSTM \ + --normalize min_max \ + --std_message_level ERROR \ + --file_message_level ERROR + +done diff --git a/nlp_scripts/run_mgu_cells.sh b/nlp_scripts/run_mgu_cells.sh new file mode 100644 index 00000000..10b1244d --- /dev/null +++ b/nlp_scripts/run_mgu_cells.sh @@ -0,0 +1,57 @@ +#!/bin/bash -l +# NOTE the -l flag! +# + +#SBATCH -J examm_lstm_nlp + +#SBATCH -A examm #just examm examm_thompson is not a valid account + +#SBATCH -o examm_test_%A_%J.output +#SBATCH -e examm_test_%A_%J.error + +#SBATCH --mail-user vj6810@rit.edu + +#SBATCH --mail-type=ALL + +#SBATCH -t 4-0 # if you want to run for 4 days 1-72:0:0 + +# Put the job in the "work" partition and request FOUR cores for one task +# "work" is the default partition so it can be omitted without issue. + +## Please not that each node on the cluster is 36 cores +#SBATCH -p tier3 -n 61 + + +# Job memory requirements in MB +#SBATCH --mem-per-cpu=40G # I like to mem with a suffix [K|M|G|T] 5000 + +#module load module_future +#module load openmpi-1.10-x86_64 +module load openmpi +module load gcc +module load libtiff + + +EXAMM="/home/vj6810/EXAMM/exact" + +for folder in 0 1 2 3 4 ; do + exp_name="$EXAMM/nlp_results/MGU/$folder" + mkdir -p $exp_name + echo "\t Iteration: $exp_name" + srun -n 60 $EXAMM/build/mpi/examm_mpi_nlp \ + --training_filenames $EXAMM/datasets/pennchar/train.txt \ + --test_filenames $EXAMM/datasets/pennchar/valid.txt \ + --word_offset 1 \ + --sequence_length 64 \ + --number_islands 10 \ + --population_size 10 \ + --max_genomes 5000 \ + --bp_iterations 10 \ + --use_regression 0 \ + --output_directory $exp_name \ + --possible_node_types simple MGU \ + --normalize min_max \ + --std_message_level ERROR \ + --file_message_level ERROR + +done diff --git a/nlp_scripts/run_ugrnn_cells.sh b/nlp_scripts/run_ugrnn_cells.sh new file mode 100644 index 00000000..4121b35f --- /dev/null +++ b/nlp_scripts/run_ugrnn_cells.sh @@ -0,0 +1,57 @@ +#!/bin/bash -l +# NOTE the -l flag! +# + +#SBATCH -J examm_lstm_nlp + +#SBATCH -A examm #just examm examm_thompson is not a valid account + +#SBATCH -o examm_test_%A_%J.output +#SBATCH -e examm_test_%A_%J.error + +#SBATCH --mail-user vj6810@rit.edu + +#SBATCH --mail-type=ALL + +#SBATCH -t 4-0 # if you want to run for 4 days 1-72:0:0 + +# Put the job in the "work" partition and request FOUR cores for one task +# "work" is the default partition so it can be omitted without issue. + +## Please not that each node on the cluster is 36 cores +#SBATCH -p tier3 -n 61 + + +# Job memory requirements in MB +#SBATCH --mem-per-cpu=40G # I like to mem with a suffix [K|M|G|T] 5000 + +#module load module_future +#module load openmpi-1.10-x86_64 +module load openmpi +module load gcc +module load libtiff + + +EXAMM="/home/vj6810/EXAMM/exact" + +for folder in 0 1 2 3 4 ; do + exp_name="$EXAMM/nlp_results/UGRNN/$folder" + mkdir -p $exp_name + echo "\t Iteration: $exp_name" + srun -n 60 $EXAMM/build/mpi/examm_mpi_nlp \ + --training_filenames $EXAMM/datasets/pennchar/train.txt \ + --test_filenames $EXAMM/datasets/pennchar/valid.txt \ + --word_offset 1 \ + --sequence_length 64 \ + --number_islands 10 \ + --population_size 10 \ + --max_genomes 5000 \ + --bp_iterations 10 \ + --use_regression 0 \ + --output_directory $exp_name \ + --possible_node_types simple UGRNN \ + --normalize min_max \ + --std_message_level ERROR \ + --file_message_level ERROR + +done From b062c60ee3a8dbfd8de66d4b3a0edf1ea65732af Mon Sep 17 00:00:00 2001 From: Vinay Jain Date: Tue, 2 Feb 2021 12:43:45 -0500 Subject: [PATCH 20/25] modifying CMakeLists --- CMakeLists.txt | 4 ++-- rnn_examples/CMakeLists.txt | 2 -- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 15eb8feb..c94651c9 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -17,11 +17,11 @@ SET (PLATFORM 64) #SET (CMAKE_CXX_FLAGS "-std=c++11 -Wall -O3 -funroll-loops -msse3 -DNAN_CHECKS") # 1 This line for local -#SET (CMAKE_CXX_FLAGS "-std=gnu++17 -Wall -O3 -funroll-loops -msse3 -fsanitize=address -fno-omit-frame-pointer -D_GLIBCXX_DEBUG") +#SET (CMAKE_CXX_FLAGS "-std=c++17 -Wall -O3 -funroll-loops -msse3 -fsanitize=address -fno-omit-frame-pointer -D_GLIBCXX_DEBUG") #SET (CMAKE_CXX_FLAGS "-std=c++17 -Wall -O3 -funroll-loops -msse3 -fsanitize=address -fno-omit-frame-pointer -D_GLIBCXX_DEBUG") # 2 This line for cluster -#SET (CMAKE_CXX_FLAGS "-std=gnu++17 -Wall -O3 -funroll-loops -msse3 -fno-omit-frame-pointer -D_GLIBCXX_DEBUG") +#SET (CMAKE_CXX_FLAGS "-std=c++17 -Wall -O3 -funroll-loops -msse3 -fno-omit-frame-pointer -D_GLIBCXX_DEBUG") SET (CMAKE_CXX_FLAGS "-std=c++17 -Wall -O3 -funroll-loops -msse3") SET (CMAKE_CXX_FLAGS_DEBUG "-g") diff --git a/rnn_examples/CMakeLists.txt b/rnn_examples/CMakeLists.txt index df8f19ac..020cb1d4 100644 --- a/rnn_examples/CMakeLists.txt +++ b/rnn_examples/CMakeLists.txt @@ -4,8 +4,6 @@ target_link_libraries(jordan_rnn examm_strategy exact_common exact_time_series e add_executable(train_rnn train_rnn) target_link_libraries(train_rnn examm_strategy exact_common exact_time_series exact_word_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) -add_executable(rnn_heatmap rnn_heatmap) -target_link_libraries(rnn_heatmap examm_strategy exact_common exact_time_series exact_word_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} pthread) add_executable(evaluate_rnn evaluate_rnn) target_link_libraries(evaluate_rnn examm_strategy exact_common exact_time_series exact_word_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} pthread) From 1abf0672b7bbe3277489ee212160f4835c802436 Mon Sep 17 00:00:00 2001 From: Vinay Jain Date: Tue, 2 Feb 2021 12:46:50 -0500 Subject: [PATCH 21/25] remove rnn_heatmap --- rnn_examples/rnn_heatmap.cxx | 167 ----------------------------------- 1 file changed, 167 deletions(-) delete mode 100644 rnn_examples/rnn_heatmap.cxx diff --git a/rnn_examples/rnn_heatmap.cxx b/rnn_examples/rnn_heatmap.cxx deleted file mode 100644 index 95004058..00000000 --- a/rnn_examples/rnn_heatmap.cxx +++ /dev/null @@ -1,167 +0,0 @@ -//example usage: -// ./rnn_examples/rnn_heatmap --std_message_level info --file_message_level none --output_directory ./ --input_directory ~/Dropbox/microbeam_cbm/Cyclone_binaries/120_min/ --testing_directory ~/Dropbox/microbeam_cbm/2019_08-09_data/ --time_offset 120 -#include - -#include -using std::condition_variable; - -#include -namespace fs = std::filesystem; - -#include -using std::setw; - -#include -using std::mutex; - -#include -using std::string; - -#include -using std::thread; - -#include -using std::vector; - -#include "common/arguments.hxx" -#include "common/log.hxx" - -#include "rnn/rnn_genome.hxx" - -#include "time_series/time_series.hxx" - - -vector arguments; - -vector< vector< vector > > testing_inputs; -vector< vector< vector > > testing_outputs; - -int main(int argc, char** argv) { - arguments = vector(argv, argv + argc); - - Log::initialize(arguments); - Log::set_id("main"); - - string input_directory; - get_argument(arguments, "--input_directory", true, input_directory); - - string testing_directory; - get_argument(arguments, "--testing_directory", true, testing_directory); - - string output_directory; - get_argument(arguments, "--output_directory", true, output_directory); - - int32_t time_offset = 1; - get_argument(arguments, "--time_offset", true, time_offset); - - Log::info("input directory: '%s'\n", input_directory.c_str()); - Log::info("testing directory: '%s'\n", testing_directory.c_str()); - Log::info("output directory: '%s'\n", output_directory.c_str()); - - string output_filename = output_directory + "heatmap_output.csv"; - ofstream output_file(output_filename); - - for (int cyclone = 1; cyclone <= 12; cyclone++) { - string cyclone_directory = input_directory + "cyclone_" + to_string(cyclone); - Log::info("analyzing cyclone %d with directory: '%s'\n", cyclone, cyclone_directory.c_str()); - - for (int target_cyclone = 1; target_cyclone <= 12; target_cyclone++) { - double average_mae = 0.0; - - for (int repeat = 0; repeat < 20; repeat++) { - string repeat_directory = cyclone_directory + "/" + to_string(repeat); - Log::trace("\tgetting genome file from repeat directory: '%s'\n", repeat_directory.c_str()); - - string genome_filename = ""; - for (const auto &entry : fs::directory_iterator(repeat_directory)) { - Log::trace("\t\trepeat directory entry: '%s'\n", entry.path().c_str()); - - string path = entry.path(); - if (path.find("rnn_genome") != std::string::npos) { - Log::trace("\t\tgot genome file: '%s'\n", path.c_str()); - genome_filename = path; - break; - } - } - - Log::info("\tgenome filename: '%s'\n", genome_filename.c_str()); - RNN_Genome *genome = new RNN_Genome(genome_filename); - - - string testing_filename = testing_directory + "/cyclone_" + to_string(target_cyclone) + "_test.csv"; - - vector testing_filenames; - testing_filenames.push_back(testing_filename); - - TimeSeriesSets *time_series_sets = TimeSeriesSets::generate_test(testing_filenames, genome->get_input_parameter_names(), genome->get_output_parameter_names()); - Log::debug("got time series sets.\n"); - - string normalize_type = genome->get_normalize_type(); - if (normalize_type.compare("min_max") == 0) { - time_series_sets->normalize_min_max(genome->get_normalize_mins(), genome->get_normalize_maxs()); - } else if (normalize_type.compare("avg_std_dev") == 0) { - time_series_sets->normalize_avg_std_dev(genome->get_normalize_avgs(), genome->get_normalize_std_devs(), genome->get_normalize_mins(), genome->get_normalize_maxs()); - } - - Log::info("normalized type: %s \n", normalize_type.c_str()); - - time_series_sets->export_test_series(time_offset, testing_inputs, testing_outputs); - - vector best_parameters = genome->get_best_parameters(); - - //Log::info("MSE: %lf\n", genome->get_mse(best_parameters, testing_inputs, testing_outputs)); - //Log::info("MAE: %lf\n", genome->get_mae(best_parameters, testing_inputs, testing_outputs)); - double mae = genome->get_mae(best_parameters, testing_inputs, testing_outputs); - - cout << "MAE: " << mae << endl; - - average_mae += mae; - - delete time_series_sets; - delete genome; - } - - if (target_cyclone > 1) output_file << ","; - average_mae /= 20.0; - output_file << average_mae; - - cout << "average MAE: " << average_mae << endl << endl; - } - output_file << endl; - } - - - /* - string genome_filename; - get_argument(arguments, "--genome_file", true, genome_filename); - RNN_Genome *genome = new RNN_Genome(genome_filename); - - vector testing_filenames; - get_argument_vector(arguments, "--testing_filenames", true, testing_filenames); - - TimeSeriesSets *time_series_sets = TimeSeriesSets::generate_test(testing_filenames, genome->get_input_parameter_names(), genome->get_output_parameter_names()); - Log::debug("got time series sets.\n"); - - string normalize_type = genome->get_normalize_type(); - if (normalize_type.compare("min_max") == 0) { - time_series_sets->normalize_min_max(genome->get_normalize_mins(), genome->get_normalize_maxs()); - } else if (normalize_type.compare("avg_std_dev") == 0) { - time_series_sets->normalize_avg_std_dev(genome->get_normalize_avgs(), genome->get_normalize_std_devs(), genome->get_normalize_mins(), genome->get_normalize_maxs()); - } - - Log::info("normalized type: %s \n", normalize_type.c_str()); - - int32_t time_offset = 1; - get_argument(arguments, "--time_offset", true, time_offset); - - time_series_sets->export_test_series(time_offset, testing_inputs, testing_outputs); - - vector best_parameters = genome->get_best_parameters(); - Log::info("MSE: %lf\n", genome->get_mse(best_parameters, testing_inputs, testing_outputs)); - Log::info("MAE: %lf\n", genome->get_mae(best_parameters, testing_inputs, testing_outputs)); - genome->write_predictions(output_directory, testing_filenames, best_parameters, testing_inputs, testing_outputs, time_series_sets); - */ - - Log::release_id("main"); - return 0; -} From 622594d50e5f4d30e8fe81e832c7aee1f5a6b469 Mon Sep 17 00:00:00 2001 From: Zimeng Lyu Date: Sat, 20 Feb 2021 16:29:20 -0500 Subject: [PATCH 22/25] fixed loading word series, now each sentenceSeries class is a sentence --- mpi/examm_mpi_nlp.cxx | 13 +- word_series/word_series.cxx | 269 ++++++++---------------------------- word_series/word_series.hxx | 53 +------ 3 files changed, 66 insertions(+), 269 deletions(-) diff --git a/mpi/examm_mpi_nlp.cxx b/mpi/examm_mpi_nlp.cxx index 85f4aaad..43512fed 100644 --- a/mpi/examm_mpi_nlp.cxx +++ b/mpi/examm_mpi_nlp.cxx @@ -229,26 +229,27 @@ void worker(int rank) { // } int main(int argc, char **argv) { - std::cout << "starting up!" << std::endl; + + // Log::info("EXAMM is starting up! \n"); MPI_Init(&argc, &argv); - std::cout << "did mpi init!" << std::endl; + // Log::info("MPI init finished. \n"); int rank, max_rank; MPI_Comm_rank(MPI_COMM_WORLD, &rank); MPI_Comm_size(MPI_COMM_WORLD, &max_rank); - std::cout << "got rank " << rank << " and max rank " << max_rank << std::endl; + // Log::debug("got rank %d and max rank %d \n", rank, max_rank); arguments = vector(argv, argv + argc); - std::cout << "got arguments!" << std::endl; + // Log::info("got arguments! \n"); Log::initialize(arguments); Log::set_rank(rank); Log::set_id("main_" + to_string(rank)); Log::restrict_to_rank(0); - std::cout << "initailized log!" << std::endl; + Log::info("initailized log! \n"); int32_t word_offset = 1; get_argument(arguments, "--word_offset", true, word_offset); @@ -277,7 +278,7 @@ int main(int argc, char **argv) { int number_inputs = corpus_sets->get_number_inputs(); int number_outputs = corpus_sets->get_number_outputs(); - Log::debug("number_inputs: %d, number_outputs: %d\n", number_inputs, number_outputs); + Log::info("number_inputs: %d, number_outputs: %d\n", number_inputs, number_outputs); int32_t population_size; get_argument(arguments, "--population_size", true, population_size); diff --git a/word_series/word_series.cxx b/word_series/word_series.cxx index d9396401..be9627c0 100644 --- a/word_series/word_series.cxx +++ b/word_series/word_series.cxx @@ -41,16 +41,15 @@ using std::regex_replace; #include "word_series.hxx" -WordSeries::WordSeries(string _name){ +WordSeries::WordSeries(string _name) { name = _name; - } -void WordSeries::add_value(double value){ +void WordSeries::add_value(double value) { values.push_back(value); } -double WordSeries::get_value(int i){ +double WordSeries::get_value(int i) { return values[i]; } @@ -60,7 +59,6 @@ void WordSeries::calculate_statistics() { average = 0.0; max = -numeric_limits::max(); max_change = -numeric_limits::max(); - std_dev = 0.0; variance = 0.0; @@ -69,23 +67,18 @@ void WordSeries::calculate_statistics() { if (min > values[i]) min = values[i]; if (max < values[i]) max = values[i]; - if (i > 0) { double diff = values[i] - values[i-1]; - if (diff < min_change) min_change = diff; if (diff > max_change) max_change = diff; } } - average /= values.size(); - for (uint32_t i = 0; i < values.size(); i++) { double diff = values[i] - average; variance += diff * diff; } variance /= values.size() - 1; - std_dev = sqrt(variance); } @@ -132,7 +125,6 @@ void WordSeries::normalize_min_max(double min, double max) { if (values[i] < min) { Log::warning("normalizing series %s, value[%d] %lf was less than min for normalization: %lf\n", name.c_str(), i, values[i], min); } - if (values[i] > max) { Log::warning("normalizing series %s, value[%d] %lf was greater than max for normalization: %lf\n", name.c_str(), i, values[i], max); } @@ -154,17 +146,15 @@ void WordSeries::cut(int32_t start, int32_t stop) { auto first = values.begin() + start; auto last = values.begin() + stop; values = vector(first, last); - //update the statistics after the cut calculate_statistics(); } double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { double other_average = other->get_average(); - int32_t length = fmin(values.size(), other->values.size()) - lag; - double covariance_sum = 0.0; + for (int32_t i = 0; i < length; i++) { covariance_sum += (values[i + lag] - average) * (other->values[i] - other_average); } @@ -176,7 +166,6 @@ double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { } else { correlation = (covariance_sum / sqrt(variance * other_variance)) / length; } - return correlation; } @@ -194,9 +183,7 @@ WordSeries* WordSeries::copy() { ws->variance = variance; ws->min_change = min_change; ws->max_change = max_change; - ws->values = values; - return ws; } @@ -204,11 +191,9 @@ void WordSeries::copy_values(vector &series) { series = values; } - void string_split_word(const string &s, char delim, vector &result) { stringstream ss; ss.str(s); - string item; while (getline(ss, item, delim)) { result.push_back(item); @@ -223,47 +208,29 @@ void SentenceSeries::add_word_series(string name) { } } - - -SentenceSeries::SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab){ - filename = _filename; - word_index = _word_index; - vocab = _vocab; - - ifstream cs_file(filename.c_str()); +SentenceSeries::SentenceSeries(const string _line, const vector & _word_index , const map &_vocab) { + sentence = _line; + word_index = _word_index; + vocab = _vocab; vector file_words; - - for(string line;getline(cs_file,line);){ - - line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',file_words); - file_words.push_back(""); - } - - for(int i = 0; i < word_index.size();i++){ + string_split_word(sentence,' ',file_words); + file_words.push_back(""); + for(int i = 0; i < word_index.size(); i++) { add_word_series(word_index[i]); } - - for (int i = 0; i < file_words.size(); ++i) - { + for (int i = 0; i < file_words.size(); ++i) { int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) - { - if(i != current_word){ + for (int i = 0; i < word_index.size(); ++i) { + if(i != current_word) { word_series[word_index[i]]->add_value(0); - } - else{ + } else { word_series[word_index[i]]->add_value(1); } } } - - - - - - number_rows = file_words.size(); + + number_rows = file_words.size(); for (auto series = word_series.begin(); series != word_series.end(); series++) { series->second->calculate_statistics(); @@ -275,21 +242,14 @@ SentenceSeries::SentenceSeries(const string _filename,const vector & _wo } else { series->second->print_statistics(); } - int series_rows = series->second->get_number_values(); - if (series_rows != number_rows) { Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); } - } - - Log::info("read time series '%s' with number rows: %d\n", filename.c_str(), number_rows); - - } -SentenceSeries::~SentenceSeries(){ +SentenceSeries::~SentenceSeries() { for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) { WordSeries* series = it->second; @@ -298,9 +258,6 @@ SentenceSeries::~SentenceSeries(){ } } - - - int SentenceSeries::get_number_rows() const { return number_rows; } @@ -309,9 +266,9 @@ int SentenceSeries::get_number_columns() const { return word_index.size(); } -string SentenceSeries::get_filename() const { - return filename; -} +// string SentenceSeries::get_filename() const { +// return filename; +// } vector SentenceSeries::get_word_index() const { return word_index; @@ -360,70 +317,56 @@ void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_d double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { const WordSeries *first_series = word_series.at(word1); const WordSeries *second_series = word_series.at(word2); - return first_series->get_correlation(second_series, lag); } -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset){ - +void SentenceSeries::export_word_series(vector< vector > &data , int word_offset) { cout<<"word_offset:: "< (number_rows - fabs(word_offset),0.0)); - - if(word_offset > 0){ + if(word_offset > 0) { cout<<"testing"<get_value(j); } } - } else if (word_offset < 0){ + } else if (word_offset < 0) { cout<<"training"<get_value(j); } } - } else{ - for (int i = 0; i < word_index.size(); ++i) - { - for (int j = 0; j < number_rows; ++j) - { + } else { + for (int i = 0; i < word_index.size(); ++i) { + for (int j = 0; j < number_rows; ++j) { data[i][j] = word_series[word_index[i]]->get_value(j); } } - } - - } -void SentenceSeries::export_word_series(vector< vector > &data ){ +void SentenceSeries::export_word_series(vector< vector > &data) { export_word_series(data , 0); } -SentenceSeries::SentenceSeries(){ +SentenceSeries::SentenceSeries() { } -SentenceSeries* SentenceSeries::copy(){ +SentenceSeries* SentenceSeries::copy() { SentenceSeries* ss = new SentenceSeries(); ss->number_rows = number_rows; - ss->filename = filename; + ss->sentence = sentence; ss->word_index = word_index; ss->vocab= vocab; for (auto series = word_series.begin(); series != word_series.end(); series++) { ss->word_series[series->first] = series->second->copy(); } - return ss; - } void SentenceSeries::select_parameters(const vector ¶meter_names) { @@ -438,27 +381,19 @@ void SentenceSeries::select_parameters(const vector ¶meter_names) { void SentenceSeries::select_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { vector combined_parameters = input_parameter_names; combined_parameters.insert(combined_parameters.end(), output_parameter_names.begin(), output_parameter_names.end()); - select_parameters(combined_parameters); } - - - - - Corpus::Corpus() : normalize_type("none") { } - -Corpus::~Corpus(){ +Corpus::~Corpus() { for (uint32_t i = 0; i < sent_series.size(); i++) { delete sent_series[i]; } } - void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { all_parameter_names.clear(); @@ -475,55 +410,43 @@ void merge_parameter_names_word(const vector &input_parameter_names, con } } - -void Corpus::load_word_library(){ - - - for (int i = 0; i < training_indexes.size(); ++i) - { +void Corpus::load_word_library() { + for (int i = 0; i < training_indexes.size(); ++i) { vector words; string filename = filenames[training_indexes[i]]; ifstream cs_file(filename.c_str()); - for(string line;getline(cs_file,line);){ + for(string line; getline(cs_file,line); ) { line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',words); words.push_back(""); } set word_index_set(words.begin(), words.end()); - - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) - { + for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) { word_index.push_back(*i); vocab[*i] = vocab.size(); - } - } - input_parameter_names = word_index; output_parameter_names = word_index; all_parameter_names = word_index; - - for (uint32_t i = 0; i < filenames.size(); i++) { + for (int i = 0; i < filenames.size(); i++) { Log::debug("\t%s\n", filenames[i].c_str()); - - SentenceSeries *ss = new SentenceSeries(filenames[i], word_index,vocab); - sent_series.push_back( ss ); - + ifstream cs_file(filenames[i].c_str()); + for(string line; getline(cs_file,line); ) { + line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); + SentenceSeries *ss = new SentenceSeries(line, word_index,vocab); + sent_series.push_back( ss ); + } } + Log::info("number of sentences: %d \n", sent_series.size()); } - - - -Corpus* Corpus::generate_from_arguments(const vector &arguments){ +Corpus* Corpus::generate_from_arguments(const vector &arguments) { Corpus *cs = new Corpus(); - cs->filenames.clear(); cs->word_index.clear(); cs->vocab.clear(); @@ -556,40 +479,32 @@ Corpus* Corpus::generate_from_arguments(const vector &arguments){ } get_argument(arguments, "--sequence_length", true, _sequence_length); - cs->sequence_length = _sequence_length; cs->all_parameter_names.clear(); cs->input_parameter_names.clear(); cs->output_parameter_names.clear(); - cs->load_word_library(); - + cs->normalize_type = "none"; return cs; - } Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { Corpus *cs = new Corpus(); cs->filenames = _test_filenames; - cs->training_indexes.clear(); cs->test_indexes.clear(); for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { cs->test_indexes.push_back(i); - } - + } cs->input_parameter_names = _input_parameter_names; cs->output_parameter_names = _output_parameter_names; merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - cs->normalize_mins.clear(); cs->normalize_maxs.clear(); - cs->normalize_avgs.clear(); cs->normalize_std_devs.clear(); - cs->load_word_library(); return cs; @@ -601,26 +516,18 @@ double Corpus::denormalize(string field_name, double value) { } else if (normalize_type.compare("min_max") == 0) { double min = normalize_mins[field_name]; double max = normalize_maxs[field_name]; - value = (value * (max-min)) + min; - return value; - } else if (normalize_type.compare("avg_std_dev") == 0) { double min = normalize_mins[field_name]; double max = normalize_maxs[field_name]; double avg = normalize_avgs[field_name]; double std_dev = normalize_std_devs[field_name]; - double norm_min = (min - avg) / std_dev; double norm_max = (max - avg) / std_dev; - norm_max = fmax(norm_min, norm_max); - value = (value * norm_max * std_dev) + avg; - return value; - } else { Log::fatal("Unknown normalize type on denormalize for '%s' and '%lf', '%s', this should never happen.\n", field_name.c_str(), value, normalize_type.c_str()); exit(1); @@ -629,45 +536,35 @@ double Corpus::denormalize(string field_name, double value) { void Corpus::normalize_min_max() { Log::info("doing min/max normalization:\n"); - for (int i = 0; i < all_parameter_names.size(); i++) { string parameter_name = all_parameter_names[i]; - double min = numeric_limits::max(); double max = -numeric_limits::max(); - //get the min of all series of the same name //get the max of all series of the same name if (normalize_mins.count(parameter_name) > 0) { min = normalize_mins[parameter_name]; max = normalize_maxs[parameter_name]; - Log::info("user specified bounds for "); - } else { for (int j = 0; j < sent_series.size(); j++) { double current_min = sent_series[j]->get_min(parameter_name); double current_max = sent_series[j]->get_max(parameter_name); - if (current_min < min) min = current_min; if (current_max > max) max = current_max; } - normalize_mins[parameter_name] = min; normalize_maxs[parameter_name] = max; - Log::info("calculated bounds for "); } Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); - //for each series, subtract min, divide by (max - min) for (int j = 0; j < sent_series.size(); j++) { sent_series[j]->normalize_min_max(parameter_name, min, max); } } - normalize_type = "min_max"; } @@ -677,7 +574,6 @@ void Corpus::normalize_min_max(const map &_normalize_mins, const for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { string field = all_parameter_names[i]; - if (normalize_mins.count(field) == 0) { //field doesn't exist in the normalize values, report an error Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); @@ -697,13 +593,11 @@ void Corpus::normalize_min_max(const map &_normalize_mins, const } exit(1); } - //for each series, subtract min, divide by (max - min) for (int j = 0; j < sent_series.size(); j++) { sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); } } - normalize_type = "min_max"; } @@ -712,73 +606,53 @@ void Corpus::normalize_avg_std_dev() { for (int i = 0; i < all_parameter_names.size(); i++) { string parameter_name = all_parameter_names[i]; - double min = numeric_limits::max(); double max = -numeric_limits::max(); - double avg = 0.0; double std_dev = 0.0; - if (normalize_avgs.count(parameter_name) > 0) { min = normalize_mins[parameter_name]; max = normalize_maxs[parameter_name]; avg = normalize_avgs[parameter_name]; std_dev = normalize_std_devs[parameter_name]; - Log::info("user specified avg/std dev for "); - } else { double numerator_average = 0.0; long total_values = 0; - for (int j = 0; j < sent_series.size(); j++) { int n_values = sent_series[j]->get_number_rows(); numerator_average += sent_series[j]->get_average(parameter_name) * n_values; total_values += n_values; - double current_min = sent_series[j]->get_min(parameter_name); double current_max = sent_series[j]->get_max(parameter_name); - if (current_min < min) min = current_min; if (current_max > max) max = current_max; } - normalize_mins[parameter_name] = min; normalize_maxs[parameter_name] = max; - avg = numerator_average / total_values; - double numerator_std_dev = 0.0; //get the Bessel-corrected (n-1 denominator) combined standard deviation for (int j = 0; j < sent_series.size(); j++) { int n_values = sent_series[j]->get_number_rows(); - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); } - std_dev = numerator_std_dev / (total_values - 1); - normalize_avgs[parameter_name] = avg; normalize_std_devs[parameter_name] = std_dev; - - Log::info("calculated bounds for "); } double norm_min = (min - avg) / std_dev; double norm_max = (max - avg) / std_dev; - norm_max = fmax(norm_min, norm_max); - Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); - //for each series, subtract min, divide by (max - min) for (int j = 0; j < sent_series.size(); j++) { sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); } } - normalize_type = "avg_std_dev"; } @@ -790,7 +664,6 @@ void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, co for (int32_t i = 0; i < (int32_t)all_parameter_names.size(); i++) { string field = all_parameter_names[i]; - if (normalize_avgs.count(field) == 0) { //field doesn't exist in the normalize values, report an error Log::fatal("ERROR, couldn't find field '%s' in normalize avg values.\n", field.c_str()); @@ -800,7 +673,6 @@ void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, co } exit(1); } - if (normalize_std_devs.count(field) == 0) { //field doesn't exist in the normalize values, report an error Log::fatal("ERROR, couldn't find field '%s' in normalize std_dev values.\n", field.c_str()); @@ -810,7 +682,6 @@ void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, co } exit(1); } - if (normalize_mins.count(field) == 0) { //field doesn't exist in the normalize values, report an error Log::fatal("ERROR, couldn't find field '%s' in normalize min values.\n", field.c_str()); @@ -820,7 +691,6 @@ void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, co } exit(1); } - if (normalize_maxs.count(field) == 0) { //field doesn't exist in the normalize values, report an error Log::fatal("ERROR, couldn't find field '%s' in normalize max values.\n", field.c_str()); @@ -835,60 +705,40 @@ void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, co double max = normalize_maxs[field]; double avg = normalize_avgs[field]; double std_dev = normalize_std_devs[field]; - double norm_min = (min - avg) / std_dev; double norm_max = (max - avg) / std_dev; - norm_max = fmax(norm_min, norm_max); - - //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 for (int j = 0; j < sent_series.size(); j++) { sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); } } - normalize_type = "avg_std_dev"; } vector > > split_sequence_length(int sequence_length, vector > > data) { - - vector > > sequenceData; - int no_features = data[0].size(); int no_sequences = 0; int sequenceNo = -1; - - for (int i = 0; i < data.size(); ++i) { no_sequences += (data[i][0].size() - 1) / sequence_length + 1; } - - if (data[0][0].size() < sequence_length) - { - + if (data[0][0].size() < sequence_length) { sequenceData.resize(no_sequences, vector>(no_features, vector(data[0][0].size()))); - } - else - { + } else { sequenceData.resize(no_sequences, vector>(no_features, vector(sequence_length))); } for (int k = 0; k < data.size(); ++k) { for (int j = 0; j < data[k][0].size(); ++j) { for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && j % sequence_length == 0) - sequenceNo++; - + if (i == 0 && j % sequence_length == 0) sequenceNo++; sequenceData[sequenceNo][i][j % sequence_length] = data[k][i][j]; } } } - - return sequenceData; - } @@ -896,17 +746,12 @@ void Corpus::export_sent_series(const vector &series_indexes, int word_offs vector< vector< vector > > temp_inputs; vector< vector< vector > > temp_outputs; - inputs.resize(series_indexes.size()); outputs.resize(series_indexes.size()); - temp_inputs.resize(series_indexes.size()); temp_outputs.resize(series_indexes.size()); - - for (uint32_t i = 0; i < series_indexes.size(); i++) { int series_index = series_indexes[i]; - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); } @@ -923,7 +768,6 @@ void Corpus::export_training_series(int word_offset, vector< vector< vector Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); exit(1); } - export_sent_series(test_indexes, word_offset, inputs, outputs); } @@ -944,17 +787,14 @@ void Corpus::export_series_by_name(string field_name, vector< vector > & for (int32_t i = 0; i < sent_series.size(); i++) { vector current_series; - sent_series[i]->get_series(field_name, current_series); exported_series.push_back(current_series); } } - void Corpus::write_sentence_series_sets(string base_filename) { for (uint32_t i = 0; i < sent_series.size(); i++) { ofstream outfile(base_filename + to_string(i) + ".csv"); - vector< vector > data; sent_series[i]->export_word_series(data); @@ -971,7 +811,6 @@ void Corpus::write_sentence_series_sets(string base_filename) { if (k > 0) { outfile << ","; } - outfile << data[k][j]; } outfile << endl; diff --git a/word_series/word_series.hxx b/word_series/word_series.hxx index 1b223e12..7bc0c0fe 100644 --- a/word_series/word_series.hxx +++ b/word_series/word_series.hxx @@ -16,7 +16,6 @@ using std::vector; class WordSeries{ private: - /** * Specifies the string name in the data. * All the unique strings in the datasets will have its each word series. @@ -53,7 +52,6 @@ using std::vector; double variance; double min_change; double max_change; - /** * Storers the values in the word series respective of the time in a particular string. */ @@ -62,15 +60,11 @@ using std::vector; public: WordSeries(string _name); - void add_value(double value); double get_value(int i); - void calculate_statistics(); void print_statistics(); - int get_number_values() const; - double get_min() const; double get_average() const; double get_max() const; @@ -78,17 +72,11 @@ using std::vector; double get_variance() const; double get_min_change() const; double get_max_change() const; - - void normalize_min_max(double min, double max); void normalize_avg_std_dev(double avg, double std_dev, double norm_max); - void cut(int32_t start, int32_t stop); - double get_correlation(const WordSeries *other, int32_t lag) const; - WordSeries* copy(); - void copy_values(vector &series); }; @@ -104,7 +92,7 @@ using std::vector; /** * Specifices the filename for the training or testing data. */ - string filename; + string sentence; /** * Maps the word string to index of the word series in a particular string. */ @@ -118,24 +106,17 @@ using std::vector; */ map word_series; - SentenceSeries(); - public: - -SentenceSeries(const string _filename,const vector & _word_index , const map &_vocab); ~SentenceSeries(); + public: + SentenceSeries(const string _sentence,const vector & _word_index , const map &_vocab); + ~SentenceSeries(); void add_word_series(string name); - int get_number_rows() const; int get_number_columns() const; - string get_filename() const; - vector get_word_index() const; - - void get_series(string word_name, vector &series); - double get_min(string word); double get_average(string word); double get_max(string word); @@ -143,27 +124,20 @@ SentenceSeries(const string _filename,const vector & _word_index , const double get_variance(string word); double get_min_change(string word); double get_max_change(string word); - double get_correlation(string word1, string word2, int32_t lag) const; - void normalize_min_max(string word, double min, double max); void normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max); - void export_word_series(vector< vector > &data , int word_offset); void export_word_series(vector< vector > &data ); - SentenceSeries* copy(); - void select_parameters(const vector &input_parameter_names, const vector &output_parameter_names); void select_parameters(const vector ¶meter_names); - }; class Corpus { private: - /** * Specifices the chunks in which the dataset is to be divided into chunks. */ @@ -206,14 +180,11 @@ class Corpus { * Each file is divided into sentences and word series is made for each word in the sentence. */ vector sent_series; - map normalize_mins; map normalize_maxs; - map normalize_avgs; map normalize_std_devs; - /** * Maps the index to word string of the word series in a particular filename. */ @@ -234,26 +205,19 @@ class Corpus { Corpus(); ~Corpus(); - static Corpus* generate_from_arguments(const vector &arguments); static Corpus* generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names); - void normalize_min_max(); void normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs); - void normalize_avg_std_dev(); void normalize_avg_std_dev(const map &_normalize_avgs, const map &_normalize_std_devs, const map &_normalize_mins, const map &_normalize_maxs); - - /** * Generates the csv data file. * \param base_filename is the filename to be used. * */ void write_sentence_series_sets(string base_filename); - void export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); - /** * Exports Corpus Dataset to be used for training in RNN genome. * @@ -264,7 +228,6 @@ class Corpus { * */ void export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); - /** * Exports Corpus Dataset to be used for testing in RNN genome. * @@ -274,29 +237,23 @@ class Corpus { * \param outpus is the data used for testing in the RNN genome * */ - void export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); + void export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); void export_series_by_name(string field_name, vector< vector > &exported_series); - double denormalize(string field_name, double value); - string get_normalize_type() const; map get_normalize_mins() const; map get_normalize_maxs() const; map get_normalize_avgs() const; map get_normalize_std_devs() const; - vector get_input_parameter_names() const; vector get_output_parameter_names() const; int get_number_series() const; - int get_number_inputs() const; int get_number_outputs() const; - void set_training_indexes(const vector &_training_indexes); void set_test_indexes(const vector &_test_indexes); - SentenceSeries *get_set(int32_t i); }; From 54ce9cbe3ec192b6b5119b6b1605ad05a71631fd Mon Sep 17 00:00:00 2001 From: Zimeng Lyu Date: Sat, 20 Feb 2021 18:21:53 -0500 Subject: [PATCH 23/25] renamed word series to character series --- CMakeLists.txt | 2 +- character_series/CMakeLists.txt | 3 + .../character_series.cxx | 292 +++++++++--------- .../character_series.hxx | 106 +++---- mpi/CMakeLists.txt | 12 +- mpi/examm_mpi_nlp.cxx | 21 +- multithreaded/CMakeLists.txt | 6 +- multithreaded/examm_mt_nlp.cxx | 2 +- multithreaded/examm_nlp_mt.cxx | 2 +- rnn/rnn.cxx | 2 +- rnn/rnn.hxx | 2 +- rnn/rnn_genome.hxx | 2 +- rnn_examples/CMakeLists.txt | 10 +- rnn_tests/CMakeLists.txt | 26 +- word_series/CMakeLists.txt | 3 - 15 files changed, 242 insertions(+), 249 deletions(-) create mode 100644 character_series/CMakeLists.txt rename word_series/word_series.cxx => character_series/character_series.cxx (70%) rename word_series/word_series.hxx => character_series/character_series.hxx (61%) delete mode 100644 word_series/CMakeLists.txt diff --git a/CMakeLists.txt b/CMakeLists.txt index c94651c9..c946a7ae 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -95,7 +95,7 @@ ENDIF (COMPILE_CLIENT STREQUAL "YES") add_subdirectory(common) add_subdirectory(image_tools) add_subdirectory(time_series) -add_subdirectory(word_series) +add_subdirectory(character_series) add_subdirectory(cnn) add_subdirectory(rnn) diff --git a/character_series/CMakeLists.txt b/character_series/CMakeLists.txt new file mode 100644 index 00000000..f65a5938 --- /dev/null +++ b/character_series/CMakeLists.txt @@ -0,0 +1,3 @@ +add_library(exact_character_series character_series) + +target_link_libraries(exact_character_series exact_common pthread) diff --git a/word_series/word_series.cxx b/character_series/character_series.cxx similarity index 70% rename from word_series/word_series.cxx rename to character_series/character_series.cxx index be9627c0..86faf5b4 100644 --- a/word_series/word_series.cxx +++ b/character_series/character_series.cxx @@ -39,21 +39,21 @@ using std::regex_replace; #include "../common/arguments.hxx" #include "../common/log.hxx" -#include "word_series.hxx" +#include "character_series.hxx" -WordSeries::WordSeries(string _name) { +CharacterSeries::CharacterSeries(string _name) { name = _name; } -void WordSeries::add_value(double value) { +void CharacterSeries::add_value(double value) { values.push_back(value); } -double WordSeries::get_value(int i) { +double CharacterSeries::get_value(int i) { return values[i]; } -void WordSeries::calculate_statistics() { +void CharacterSeries::calculate_statistics() { min = numeric_limits::max(); min_change = numeric_limits::max(); average = 0.0; @@ -82,43 +82,43 @@ void WordSeries::calculate_statistics() { std_dev = sqrt(variance); } -void WordSeries::print_statistics() { +void CharacterSeries::print_statistics() { Log::info ("\t%25s stats, min: %lf, avg: %lf, max: %lf, min_change: %lf, max_change: %lf, std_dev: %lf, variance: %lf\n", name.c_str(), min, average, max, min_change, max_change, std_dev, variance); } -int WordSeries::get_number_values() const { +int CharacterSeries::get_number_values() const { return values.size(); } -double WordSeries::get_min() const { +double CharacterSeries::get_min() const { return min; } -double WordSeries::get_average() const { +double CharacterSeries::get_average() const { return average; } -double WordSeries::get_max() const { +double CharacterSeries::get_max() const { return max; } -double WordSeries::get_std_dev() const { +double CharacterSeries::get_std_dev() const { return std_dev; } -double WordSeries::get_variance() const { +double CharacterSeries::get_variance() const { return variance; } -double WordSeries::get_min_change() const { +double CharacterSeries::get_min_change() const { return min_change; } -double WordSeries::get_max_change() const { +double CharacterSeries::get_max_change() const { return max_change; } -void WordSeries::normalize_min_max(double min, double max) { +void CharacterSeries::normalize_min_max(double min, double max) { Log::debug("normalizing time series '%s' with min: %lf and max: %lf, series min: %lf, series max: %lf\n", name.c_str(), min, max, this->min, this->max); for (int i = 0; i < values.size(); i++) { @@ -134,7 +134,7 @@ void WordSeries::normalize_min_max(double min, double max) { } //divide by the normalized max to make things between -1 and 1 -void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { +void CharacterSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_max) { Log::debug("normalizing time series '%s' with avg: %lf, std_dev: %lf and normalized max: %lf, series avg: %lf, series std_dev: %lf\n", name.c_str(), avg, std_dev, norm_max, this->average, this->std_dev); for (int i = 0; i < values.size(); i++) { @@ -142,7 +142,7 @@ void WordSeries::normalize_avg_std_dev(double avg, double std_dev, double norm_m } } -void WordSeries::cut(int32_t start, int32_t stop) { +void CharacterSeries::cut(int32_t start, int32_t stop) { auto first = values.begin() + start; auto last = values.begin() + stop; values = vector(first, last); @@ -150,7 +150,7 @@ void WordSeries::cut(int32_t start, int32_t stop) { calculate_statistics(); } -double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { +double CharacterSeries::get_correlation(const CharacterSeries *other, int32_t lag) const { double other_average = other->get_average(); int32_t length = fmin(values.size(), other->values.size()) - lag; double covariance_sum = 0.0; @@ -169,11 +169,11 @@ double WordSeries::get_correlation(const WordSeries *other, int32_t lag) const { return correlation; } -WordSeries::WordSeries() { +CharacterSeries::CharacterSeries() { } -WordSeries* WordSeries::copy() { - WordSeries *ws = new WordSeries(); +CharacterSeries* CharacterSeries::copy() { + CharacterSeries *ws = new CharacterSeries(); ws->name = name; ws->min = min; @@ -187,11 +187,11 @@ WordSeries* WordSeries::copy() { return ws; } -void WordSeries::copy_values(vector &series) { +void CharacterSeries::copy_values(vector &series) { series = values; } -void string_split_word(const string &s, char delim, vector &result) { +void string_split_character(const string &s, char delim, vector &result) { stringstream ss; ss.str(s); string item; @@ -200,60 +200,60 @@ void string_split_word(const string &s, char delim, vector &result) { } } -void SentenceSeries::add_word_series(string name) { - if (word_series.count(name) == 0) { - word_series[name] = new WordSeries(name); +void SentenceSeries::add_character_series(string name) { + if (character_series.count(name) == 0) { + character_series[name] = new CharacterSeries(name); } else { Log::error("ERROR! Trying to add a time series to a time series set with name '%s' which already exists in the set!\n", name.c_str()); } } -SentenceSeries::SentenceSeries(const string _line, const vector & _word_index , const map &_vocab) { +SentenceSeries::SentenceSeries(const string _line, const vector & _character_index , const map &_vocab) { sentence = _line; - word_index = _word_index; + character_index = _character_index; vocab = _vocab; - vector file_words; - string_split_word(sentence,' ',file_words); - file_words.push_back(""); - for(int i = 0; i < word_index.size(); i++) { - add_word_series(word_index[i]); - } - for (int i = 0; i < file_words.size(); ++i) { - int current_word = vocab[file_words[i]]; - for (int i = 0; i < word_index.size(); ++i) { - if(i != current_word) { - word_series[word_index[i]]->add_value(0); + vector file_characters; + string_split_character(sentence,' ',file_characters); + file_characters.push_back(""); + for(int i = 0; i < character_index.size(); i++) { + add_character_series(character_index[i]); + } + for (int i = 0; i < file_characters.size(); ++i) { + int current_character = vocab[file_characters[i]]; + for (int i = 0; i < character_index.size(); ++i) { + if(i != current_character) { + character_series[character_index[i]]->add_value(0); } else { - word_series[word_index[i]]->add_value(1); + character_series[character_index[i]]->add_value(1); } } } - number_rows = file_words.size(); + number_rows = file_characters.size(); - for (auto series = word_series.begin(); series != word_series.end(); series++) { + for (auto series = character_series.begin(); series != character_series.end(); series++) { series->second->calculate_statistics(); if (series->second->get_min_change() == 0 && series->second->get_max_change() == 0) { Log::warning("WARNING: unchanging series: '%s'\n", series->first.c_str()); Log::warning("removing unchanging series: '%s'\n", series->first.c_str()); series->second->print_statistics(); - //word_series.erase(series); + //character_series.erase(series); } else { series->second->print_statistics(); } int series_rows = series->second->get_number_values(); if (series_rows != number_rows) { - Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), word_series.begin()->first.c_str(), word_series.begin()->second->get_number_values()); + Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), character_series.begin()->first.c_str(), character_series.begin()->second->get_number_values()); } } } SentenceSeries::~SentenceSeries() { - for( std::map::iterator it=word_series.begin(); it!=word_series.end(); it=word_series.begin()) + for( std::map::iterator it=character_series.begin(); it!=character_series.end(); it=character_series.begin()) { - WordSeries* series = it->second; - word_series.erase (it); + CharacterSeries* series = it->second; + character_series.erase (it); delete series; } } @@ -263,93 +263,93 @@ int SentenceSeries::get_number_rows() const { } int SentenceSeries::get_number_columns() const { - return word_index.size(); + return character_index.size(); } // string SentenceSeries::get_filename() const { // return filename; // } -vector SentenceSeries::get_word_index() const { - return word_index; +vector SentenceSeries::get_character_index() const { + return character_index; } -void SentenceSeries::get_series(string word_name, vector &series) { - word_series[word_name]->copy_values(series); +void SentenceSeries::get_series(string character_name, vector &series) { + character_series[character_name]->copy_values(series); } -double SentenceSeries::get_min(string word) { - return word_series[word]->get_min(); +double SentenceSeries::get_min(string character) { + return character_series[character]->get_min(); } -double SentenceSeries::get_average(string word) { - return word_series[word]->get_average(); +double SentenceSeries::get_average(string character) { + return character_series[character]->get_average(); } -double SentenceSeries::get_max(string word) { - return word_series[word]->get_max(); +double SentenceSeries::get_max(string character) { + return character_series[character]->get_max(); } -double SentenceSeries::get_std_dev(string word) { - return word_series[word]->get_std_dev(); +double SentenceSeries::get_std_dev(string character) { + return character_series[character]->get_std_dev(); } -double SentenceSeries::get_variance(string word) { - return word_series[word]->get_variance(); +double SentenceSeries::get_variance(string character) { + return character_series[character]->get_variance(); } -double SentenceSeries::get_min_change(string word) { - return word_series[word]->get_min_change(); +double SentenceSeries::get_min_change(string character) { + return character_series[character]->get_min_change(); } -double SentenceSeries::get_max_change(string word) { - return word_series[word]->get_max_change(); +double SentenceSeries::get_max_change(string character) { + return character_series[character]->get_max_change(); } -void SentenceSeries::normalize_min_max(string word, double min, double max) { - word_series[word]->normalize_min_max(min, max); +void SentenceSeries::normalize_min_max(string character, double min, double max) { + character_series[character]->normalize_min_max(min, max); } -void SentenceSeries::normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max) { - word_series[word]->normalize_avg_std_dev(avg, std_dev, norm_max); +void SentenceSeries::normalize_avg_std_dev(string character, double avg, double std_dev, double norm_max) { + character_series[character]->normalize_avg_std_dev(avg, std_dev, norm_max); } -double SentenceSeries::get_correlation(string word1, string word2, int32_t lag) const { - const WordSeries *first_series = word_series.at(word1); - const WordSeries *second_series = word_series.at(word2); +double SentenceSeries::get_correlation(string character1, string character2, int32_t lag) const { + const CharacterSeries *first_series = character_series.at(character1); + const CharacterSeries *second_series = character_series.at(character2); return first_series->get_correlation(second_series, lag); } -void SentenceSeries::export_word_series(vector< vector > &data , int word_offset) { - cout<<"word_offset:: "< > &data , int character_offset) { + cout<<"character_offset:: "< (number_rows - fabs(word_offset),0.0)); + data.resize(character_index.size(),vector (number_rows - fabs(character_offset),0.0)); - if(word_offset > 0) { + if(character_offset > 0) { cout<<"testing"<get_value(j); + for (int i = 0; i < character_index.size(); ++i) { + for (int j = character_offset; j < number_rows; ++j) { + data[i][j - character_offset] = character_series[character_index[i]]->get_value(j); } } - } else if (word_offset < 0) { + } else if (character_offset < 0) { cout<<"training"<get_value(j); + for (int i = 0; i < character_index.size(); ++i){ + for (int j = 0; j < number_rows + character_offset; ++j) { + data[i][j] = character_series[character_index[i]]->get_value(j); } } } else { - for (int i = 0; i < word_index.size(); ++i) { + for (int i = 0; i < character_index.size(); ++i) { for (int j = 0; j < number_rows; ++j) { - data[i][j] = word_series[word_index[i]]->get_value(j); + data[i][j] = character_series[character_index[i]]->get_value(j); } } } } -void SentenceSeries::export_word_series(vector< vector > &data) { - export_word_series(data , 0); +void SentenceSeries::export_character_series(vector< vector > &data) { + export_character_series(data , 0); } SentenceSeries::SentenceSeries() { @@ -360,20 +360,20 @@ SentenceSeries* SentenceSeries::copy() { SentenceSeries* ss = new SentenceSeries(); ss->number_rows = number_rows; ss->sentence = sentence; - ss->word_index = word_index; + ss->character_index = character_index; ss->vocab= vocab; - for (auto series = word_series.begin(); series != word_series.end(); series++) { - ss->word_series[series->first] = series->second->copy(); + for (auto series = character_series.begin(); series != character_series.end(); series++) { + ss->character_series[series->first] = series->second->copy(); } return ss; } void SentenceSeries::select_parameters(const vector ¶meter_names) { - for (auto series = word_series.begin(); series != word_series.end(); series++) { + for (auto series = character_series.begin(); series != character_series.end(); series++) { if (std::find(parameter_names.begin(), parameter_names.end(), series->first) == parameter_names.end()) { Log::info("removing series: '%s'\n", series->first.c_str()); - word_series.erase(series->first); + character_series.erase(series->first); } } } @@ -389,12 +389,12 @@ Corpus::Corpus() : normalize_type("none") { } Corpus::~Corpus() { - for (uint32_t i = 0; i < sent_series.size(); i++) { - delete sent_series[i]; + for (uint32_t i = 0; i < sentence_series.size(); i++) { + delete sentence_series[i]; } } -void merge_parameter_names_word(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { +void merge_parameter_names_character(const vector &input_parameter_names, const vector &output_parameter_names, vector &all_parameter_names) { all_parameter_names.clear(); for (int32_t i = 0; i < (int32_t)input_parameter_names.size(); i++) { @@ -410,45 +410,45 @@ void merge_parameter_names_word(const vector &input_parameter_names, con } } -void Corpus::load_word_library() { +void Corpus::load_character_library() { for (int i = 0; i < training_indexes.size(); ++i) { - vector words; + vector characters; string filename = filenames[training_indexes[i]]; ifstream cs_file(filename.c_str()); for(string line; getline(cs_file,line); ) { line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - string_split_word(line,' ',words); - words.push_back(""); + string_split_character(line,' ',characters); + characters.push_back(""); } - set word_index_set(words.begin(), words.end()); - for (set::iterator i = word_index_set.begin(); i != word_index_set.end(); ++i) { - word_index.push_back(*i); + set character_index_set(characters.begin(), characters.end()); + for (set::iterator i = character_index_set.begin(); i != character_index_set.end(); ++i) { + character_index.push_back(*i); vocab[*i] = vocab.size(); } } - input_parameter_names = word_index; - output_parameter_names = word_index; - all_parameter_names = word_index; + input_parameter_names = character_index; + output_parameter_names = character_index; + all_parameter_names = character_index; for (int i = 0; i < filenames.size(); i++) { Log::debug("\t%s\n", filenames[i].c_str()); ifstream cs_file(filenames[i].c_str()); for(string line; getline(cs_file,line); ) { line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); - SentenceSeries *ss = new SentenceSeries(line, word_index,vocab); - sent_series.push_back( ss ); + SentenceSeries *ss = new SentenceSeries(line, character_index,vocab); + sentence_series.push_back( ss ); } } - Log::info("number of sentences: %d \n", sent_series.size()); + Log::info("number of sentences: %d \n", sentence_series.size()); } Corpus* Corpus::generate_from_arguments(const vector &arguments) { Corpus *cs = new Corpus(); cs->filenames.clear(); - cs->word_index.clear(); + cs->character_index.clear(); cs->vocab.clear(); uint32_t _sequence_length; @@ -483,7 +483,7 @@ Corpus* Corpus::generate_from_arguments(const vector &arguments) { cs->all_parameter_names.clear(); cs->input_parameter_names.clear(); cs->output_parameter_names.clear(); - cs->load_word_library(); + cs->load_character_library(); cs->normalize_type = "none"; return cs; @@ -500,12 +500,12 @@ Corpus* Corpus::generate_test(const vector &_test_filenames, const vecto } cs->input_parameter_names = _input_parameter_names; cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_word(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); + merge_parameter_names_character(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); cs->normalize_mins.clear(); cs->normalize_maxs.clear(); cs->normalize_avgs.clear(); cs->normalize_std_devs.clear(); - cs->load_word_library(); + cs->load_character_library(); return cs; } @@ -548,9 +548,9 @@ void Corpus::normalize_min_max() { max = normalize_maxs[parameter_name]; Log::info("user specified bounds for "); } else { - for (int j = 0; j < sent_series.size(); j++) { - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); + for (int j = 0; j < sentence_series.size(); j++) { + double current_min = sentence_series[j]->get_min(parameter_name); + double current_max = sentence_series[j]->get_max(parameter_name); if (current_min < min) min = current_min; if (current_max > max) max = current_max; } @@ -561,8 +561,8 @@ void Corpus::normalize_min_max() { Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf\n", parameter_name.c_str(), min, max); //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(parameter_name, min, max); + for (int j = 0; j < sentence_series.size(); j++) { + sentence_series[j]->normalize_min_max(parameter_name, min, max); } } normalize_type = "min_max"; @@ -594,8 +594,8 @@ void Corpus::normalize_min_max(const map &_normalize_mins, const exit(1); } //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); + for (int j = 0; j < sentence_series.size(); j++) { + sentence_series[j]->normalize_min_max(field, normalize_mins[field], normalize_maxs[field]); } } normalize_type = "min_max"; @@ -619,12 +619,12 @@ void Corpus::normalize_avg_std_dev() { } else { double numerator_average = 0.0; long total_values = 0; - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - numerator_average += sent_series[j]->get_average(parameter_name) * n_values; + for (int j = 0; j < sentence_series.size(); j++) { + int n_values = sentence_series[j]->get_number_rows(); + numerator_average += sentence_series[j]->get_average(parameter_name) * n_values; total_values += n_values; - double current_min = sent_series[j]->get_min(parameter_name); - double current_max = sent_series[j]->get_max(parameter_name); + double current_min = sentence_series[j]->get_min(parameter_name); + double current_max = sentence_series[j]->get_max(parameter_name); if (current_min < min) min = current_min; if (current_max > max) max = current_max; } @@ -633,10 +633,10 @@ void Corpus::normalize_avg_std_dev() { avg = numerator_average / total_values; double numerator_std_dev = 0.0; //get the Bessel-corrected (n-1 denominator) combined standard deviation - for (int j = 0; j < sent_series.size(); j++) { - int n_values = sent_series[j]->get_number_rows(); - double avg_diff = sent_series[j]->get_average(parameter_name) - avg; - numerator_std_dev += ((n_values - 1) * sent_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); + for (int j = 0; j < sentence_series.size(); j++) { + int n_values = sentence_series[j]->get_number_rows(); + double avg_diff = sentence_series[j]->get_average(parameter_name) - avg; + numerator_std_dev += ((n_values - 1) * sentence_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); } std_dev = numerator_std_dev / (total_values - 1); normalize_avgs[parameter_name] = avg; @@ -649,8 +649,8 @@ void Corpus::normalize_avg_std_dev() { norm_max = fmax(norm_min, norm_max); Log::info_no_header("%30s, min: %22.10lf, max: %22.10lf, norm_max; %22.10lf, combined average: %22.10lf, combined std_dev: %22.10lf\n", parameter_name.c_str(), min, max, avg, norm_max, std_dev); //for each series, subtract min, divide by (max - min) - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); + for (int j = 0; j < sentence_series.size(); j++) { + sentence_series[j]->normalize_avg_std_dev(parameter_name, avg, std_dev, norm_max); } } normalize_type = "avg_std_dev"; @@ -709,8 +709,8 @@ void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, co double norm_max = (max - avg) / std_dev; norm_max = fmax(norm_min, norm_max); //for each series, subtract avg, divide by std_dev; then divide by normalized_max to make between -1 and 1 - for (int j = 0; j < sent_series.size(); j++) { - sent_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); + for (int j = 0; j < sentence_series.size(); j++) { + sentence_series[j]->normalize_avg_std_dev(field, avg, std_dev, norm_max); } } normalize_type = "avg_std_dev"; @@ -742,7 +742,7 @@ vector > > split_sequence_length(int sequence_length, vect } -void Corpus::export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { +void Corpus::export_sentence_series(const vector &series_indexes, int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { vector< vector< vector > > temp_inputs; vector< vector< vector > > temp_outputs; @@ -752,8 +752,8 @@ void Corpus::export_sent_series(const vector &series_indexes, int word_offs temp_outputs.resize(series_indexes.size()); for (uint32_t i = 0; i < series_indexes.size(); i++) { int series_index = series_indexes[i]; - sent_series[series_index]->export_word_series(temp_inputs[i], -word_offset); - sent_series[series_index]->export_word_series(temp_outputs[i], word_offset); + sentence_series[series_index]->export_character_series(temp_inputs[i], -character_offset); + sentence_series[series_index]->export_character_series(temp_outputs[i], character_offset); } inputs = split_sequence_length(sequence_length, temp_inputs); @@ -763,40 +763,40 @@ void Corpus::export_sent_series(const vector &series_indexes, int word_offs /** * This exports the time series marked as training series by the training_indexes vector. */ -void Corpus::export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { +void Corpus::export_training_series(int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { if (training_indexes.size() == 0) { Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); exit(1); } - export_sent_series(training_indexes, word_offset, inputs, outputs); + export_sentence_series(training_indexes, character_offset, inputs, outputs); } /** * This exports the time series marked as test series by the test_indexes vector. */ -void Corpus::export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { +void Corpus::export_test_series(int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { if (test_indexes.size() == 0) { Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); exit(1); } - export_sent_series(test_indexes, word_offset, inputs, outputs); + export_sentence_series(test_indexes, character_offset, inputs, outputs); } void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { exported_series.clear(); - for (int32_t i = 0; i < sent_series.size(); i++) { + for (int32_t i = 0; i < sentence_series.size(); i++) { vector current_series; - sent_series[i]->get_series(field_name, current_series); + sentence_series[i]->get_series(field_name, current_series); exported_series.push_back(current_series); } } void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sent_series.size(); i++) { + for (uint32_t i = 0; i < sentence_series.size(); i++) { ofstream outfile(base_filename + to_string(i) + ".csv"); vector< vector > data; - sent_series[i]->export_word_series(data); + sentence_series[i]->export_character_series(data); for (int j = 0; j < all_parameter_names.size(); j++) { if (j > 0) { @@ -848,7 +848,7 @@ vector Corpus::get_output_parameter_names() const { } int Corpus::get_number_series() const { - return sent_series.size(); + return sentence_series.size(); } int Corpus::get_number_inputs() const { @@ -868,5 +868,5 @@ void Corpus::set_test_indexes(const vector &_test_indexes) { } SentenceSeries* Corpus::get_set(int32_t i) { - return sent_series.at(i); + return sentence_series.at(i); } diff --git a/word_series/word_series.hxx b/character_series/character_series.hxx similarity index 61% rename from word_series/word_series.hxx rename to character_series/character_series.hxx index 7bc0c0fe..e68da285 100644 --- a/word_series/word_series.hxx +++ b/character_series/character_series.hxx @@ -1,5 +1,5 @@ -#ifndef EXAMM_WORD_SERIES_HXX -#define EXAMM_WORD_SERIES_HXX +#ifndef EXAMM_CHARACTER_SERIES_HXX +#define EXAMM_CHARACTER_SERIES_HXX #include using std::ostream; @@ -14,11 +14,11 @@ using std::map; using std::vector; - class WordSeries{ + class CharacterSeries{ private: /** * Specifies the string name in the data. - * All the unique strings in the datasets will have its each word series. + * All the unique strings in the datasets will have its each character series. */ string name; /** @@ -27,39 +27,39 @@ using std::vector; int vocab_size; /** - * Specifies the minimum of the word series in a particular string. + * Specifies the minimum of the character series in a particular string. * This could be 0 or 1 */ double min; /** - * Specifies the average of the word series in a particular string. - * This tells about the words which are used the most. + * Specifies the average of the character series in a particular string. + * This tells about the characters which are used the most. */ double average; /** - * Specifies the maximum of the word series in a particular string. + * Specifies the maximum of the character series in a particular string. * This could be 0 or 1 */ double max; /** - * Specifies the std. deviation of the word series in a particular string. + * Specifies the std. deviation of the character series in a particular string. */ double std_dev; /** - * Specifies the variance of the word series in a particular string. - * This tells about the closeness of a word in the dataset. + * Specifies the variance of the character series in a particular string. + * This tells about the closeness of a character in the dataset. */ double variance; double min_change; double max_change; /** - * Storers the values in the word series respective of the time in a particular string. + * Storers the values in the character series respective of the time in a particular string. */ vector values; - WordSeries(); + CharacterSeries(); public: - WordSeries(string _name); + CharacterSeries(string _name); void add_value(double value); double get_value(int i); void calculate_statistics(); @@ -75,8 +75,8 @@ using std::vector; void normalize_min_max(double min, double max); void normalize_avg_std_dev(double avg, double std_dev, double norm_max); void cut(int32_t start, int32_t stop); - double get_correlation(const WordSeries *other, int32_t lag) const; - WordSeries* copy(); + double get_correlation(const CharacterSeries *other, int32_t lag) const; + CharacterSeries* copy(); void copy_values(vector &series); }; @@ -86,7 +86,7 @@ using std::vector; /** * Specifies the number of the rows in the dataset. - * This tells about the number of the unique words in the dataset. + * This tells about the number of the unique characters in the dataset. */ int number_rows; /** @@ -94,41 +94,41 @@ using std::vector; */ string sentence; /** - * Maps the word string to index of the word series in a particular string. + * Maps the character string to index of the character series in a particular string. */ map vocab; /** - * Maps the index to word string of the word series in a particular string. + * Maps the index to character string of the character series in a particular string. */ - vector word_index; + vector character_index; /** - * Maps the word string to the word series in a particular string. + * Maps the character string to the character series in a particular string. */ - map word_series; + map character_series; SentenceSeries(); public: - SentenceSeries(const string _sentence,const vector & _word_index , const map &_vocab); + SentenceSeries(const string _sentence,const vector & _character_index , const map &_vocab); ~SentenceSeries(); - void add_word_series(string name); + void add_character_series(string name); int get_number_rows() const; int get_number_columns() const; string get_filename() const; - vector get_word_index() const; - void get_series(string word_name, vector &series); - double get_min(string word); - double get_average(string word); - double get_max(string word); - double get_std_dev(string word); - double get_variance(string word); - double get_min_change(string word); - double get_max_change(string word); - double get_correlation(string word1, string word2, int32_t lag) const; - void normalize_min_max(string word, double min, double max); - void normalize_avg_std_dev(string word, double avg, double std_dev, double norm_max); - void export_word_series(vector< vector > &data , int word_offset); - void export_word_series(vector< vector > &data ); + vector get_character_index() const; + void get_series(string character_name, vector &series); + double get_min(string character); + double get_average(string character); + double get_max(string character); + double get_std_dev(string character); + double get_variance(string character); + double get_min_change(string character); + double get_max_change(string character); + double get_correlation(string character1, string character2, int32_t lag) const; + void normalize_min_max(string character, double min, double max); + void normalize_avg_std_dev(string character, double avg, double std_dev, double norm_max); + void export_character_series(vector< vector > &data , int character_offset); + void export_character_series(vector< vector > &data ); SentenceSeries* copy(); void select_parameters(const vector &input_parameter_names, const vector &output_parameter_names); void select_parameters(const vector ¶meter_names); @@ -161,44 +161,44 @@ class Corpus { /** * stores the input parameter names of the filenames. - * stores the unique words used in the filenames. + * stores the unique characters used in the filenames. */ vector input_parameter_names; /** * stores the input parameter names of the filenames. - * stores the unique words used in the filenames. + * stores the unique characters used in the filenames. */ vector output_parameter_names; /** * stores the input parameter names of the filenames. - * stores the unique words used in the filenames. + * stores the unique characters used in the filenames. */ vector all_parameter_names; /** * stores the Sentence series used in the filenames. - * Each file is divided into sentences and word series is made for each word in the sentence. + * Each file is divided into sentences and character series is made for each character in the sentence. */ - vector sent_series; + vector sentence_series; map normalize_mins; map normalize_maxs; map normalize_avgs; map normalize_std_devs; /** - * Maps the index to word string of the word series in a particular filename. + * Maps the index to character string of the character series in a particular filename. */ - vector word_index; + vector character_index; /** - * Maps the word string to the word series in a particular filename. + * Maps the character string to the character series in a particular filename. */ map vocab; /** - * Loads the filenames, word index and the vocabulary of the files. + * Loads the filenames, character index and the vocabulary of the files. */ - void load_word_library(); + void load_character_library(); public: @@ -217,28 +217,28 @@ class Corpus { * */ void write_sentence_series_sets(string base_filename); - void export_sent_series(const vector &series_indexes, int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); + void export_sentence_series(const vector &series_indexes, int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); /** * Exports Corpus Dataset to be used for training in RNN genome. * * \param series_indexes are the indexes of the input parameters to be used in the dataset - * \param word_offset is used to predict the after how many words, the predictions to be done in the future. + * \param character_offset is used to predict the after how many characters, the predictions to be done in the future. * \param inputs is the data used for training in the RNN genome * \param outpus is the data used for training in the RNN genome * */ - void export_training_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); + void export_training_series(int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); /** * Exports Corpus Dataset to be used for testing in RNN genome. * * \param series_indexes are the indexes of the input parameters to be used in the dataset - * \param word_offset is used to predict the after how many words, the predictions to be done in the future. + * \param character_offset is used to predict the after how many characters, the predictions to be done in the future. * \param inputs is the data used for testing in the RNN genome * \param outpus is the data used for testing in the RNN genome * */ - void export_test_series(int word_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); + void export_test_series(int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs); void export_series_by_name(string field_name, vector< vector > &exported_series); double denormalize(string field_name, double value); string get_normalize_type() const; diff --git a/mpi/CMakeLists.txt b/mpi/CMakeLists.txt index f6cdea4f..78d19639 100644 --- a/mpi/CMakeLists.txt +++ b/mpi/CMakeLists.txt @@ -1,5 +1,5 @@ add_executable(process_sweep_results tracker run_statistics process_sweep_results) -target_link_libraries(process_sweep_results examm_strategy exact_common exact_time_series exact_word_series pthread) +target_link_libraries(process_sweep_results examm_strategy exact_common exact_time_series exact_character_series pthread) find_package(MPI) @@ -12,21 +12,21 @@ if (MPI_FOUND) #target_link_libraries(exact_mpi exact_strategy exact_image_tools exact_common ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) add_executable(test_stream_write test_stream_write) - target_link_libraries(test_stream_write examm_strategy exact_time_series exact_word_series exact_common ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) + target_link_libraries(test_stream_write examm_strategy exact_time_series exact_character_series exact_common ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) add_executable(examm_mpi examm_mpi) - target_link_libraries(examm_mpi examm_strategy exact_time_series exact_word_series exact_common ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) + target_link_libraries(examm_mpi examm_strategy exact_time_series exact_character_series exact_common ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) add_executable(examm_mpi_nlp examm_mpi_nlp) - target_link_libraries(examm_mpi_nlp examm_strategy exact_time_series exact_word_series exact_common ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) + target_link_libraries(examm_mpi_nlp examm_strategy exact_time_series exact_character_series exact_common ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) add_executable(examm_mpi_multi examm_mpi_multi) - target_link_libraries(examm_mpi_multi examm_strategy exact_time_series exact_word_series exact_common ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) + target_link_libraries(examm_mpi_multi examm_strategy exact_time_series exact_character_series exact_common ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) set (CMAKE_CXX_COMPILE_FLAGS "${CMAKE_COMPILE_FLAGS} ${MPI_COMPILE_FLAGS}") set (CMAKE_CXX_LINK_FLAGS "${CMAKE_CXX_LINK_FLAGS} ${MPI_LINK_FLAGS}") include_directories(${MPI_INCLUDE_PATH}) add_executable(rnn_kfold_sweep rnn_kfold_sweep) - target_link_libraries(rnn_kfold_sweep examm_strategy exact_common exact_time_series exact_word_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) + target_link_libraries(rnn_kfold_sweep examm_strategy exact_common exact_time_series exact_character_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) endif (MPI_FOUND) diff --git a/mpi/examm_mpi_nlp.cxx b/mpi/examm_mpi_nlp.cxx index 43512fed..c4aa69d6 100644 --- a/mpi/examm_mpi_nlp.cxx +++ b/mpi/examm_mpi_nlp.cxx @@ -25,7 +25,7 @@ using std::vector; #include "rnn/examm.hxx" -#include "word_series/word_series.hxx" +#include "character_series/character_series.hxx" #define WORK_REQUEST_TAG 1 #define GENOME_LENGTH_TAG 2 @@ -230,29 +230,22 @@ void worker(int rank) { int main(int argc, char **argv) { - // Log::info("EXAMM is starting up! \n"); MPI_Init(&argc, &argv); - // Log::info("MPI init finished. \n"); int rank, max_rank; MPI_Comm_rank(MPI_COMM_WORLD, &rank); MPI_Comm_size(MPI_COMM_WORLD, &max_rank); - // Log::debug("got rank %d and max rank %d \n", rank, max_rank); - arguments = vector(argv, argv + argc); - // Log::info("got arguments! \n"); - Log::initialize(arguments); Log::set_rank(rank); Log::set_id("main_" + to_string(rank)); Log::restrict_to_rank(0); - Log::info("initailized log! \n"); - int32_t word_offset = 1; - get_argument(arguments, "--word_offset", true, word_offset); + int32_t character_offset = 1; + get_argument(arguments, "--character_offset", true, character_offset); int32_t sequence_length = 64; get_argument(arguments, "--sequence_length", false, sequence_length); @@ -262,9 +255,9 @@ int main(int argc, char **argv) { if (rank == 0) { //only have the master process print TSS info corpus_sets = Corpus::generate_from_arguments(arguments); - if (argument_exists(arguments, "--write_word_series")) { + if (argument_exists(arguments, "--write_character_series")) { string base_filename; - get_argument(arguments, "--write_word_series", true, base_filename); + get_argument(arguments, "--write_character_series", true, base_filename); corpus_sets->write_sentence_series_sets(base_filename); } } @@ -272,8 +265,8 @@ int main(int argc, char **argv) { corpus_sets = Corpus::generate_from_arguments(arguments); } - corpus_sets->export_training_series(word_offset, training_inputs, training_outputs); - corpus_sets->export_test_series(word_offset, validation_inputs, validation_outputs); + corpus_sets->export_training_series(character_offset, training_inputs, training_outputs); + corpus_sets->export_test_series(character_offset, validation_inputs, validation_outputs); int number_inputs = corpus_sets->get_number_inputs(); int number_outputs = corpus_sets->get_number_outputs(); diff --git a/multithreaded/CMakeLists.txt b/multithreaded/CMakeLists.txt index 17bd13f7..a3971454 100644 --- a/multithreaded/CMakeLists.txt +++ b/multithreaded/CMakeLists.txt @@ -2,10 +2,10 @@ #target_link_libraries(exact_mt exact_strategy exact_image_tools exact_common ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) add_executable(examm_mt_single_series examm_mt_single_series) -target_link_libraries(examm_mt_single_series examm_strategy exact_time_series exact_word_series exact_common pthread) +target_link_libraries(examm_mt_single_series examm_strategy exact_time_series exact_character_series exact_common pthread) add_executable(examm_mt examm_mt) -target_link_libraries(examm_mt examm_strategy exact_time_series exact_word_series exact_common pthread) +target_link_libraries(examm_mt examm_strategy exact_time_series exact_character_series exact_common pthread) add_executable(examm_mt_nlp examm_mt_nlp) -target_link_libraries(examm_mt_nlp examm_strategy exact_time_series exact_word_series exact_common pthread) +target_link_libraries(examm_mt_nlp examm_strategy exact_time_series exact_character_series exact_common pthread) diff --git a/multithreaded/examm_mt_nlp.cxx b/multithreaded/examm_mt_nlp.cxx index 4ea0244b..d96a5ac6 100644 --- a/multithreaded/examm_mt_nlp.cxx +++ b/multithreaded/examm_mt_nlp.cxx @@ -23,7 +23,7 @@ using std::vector; #include "rnn/examm.hxx" -#include "word_series/word_series.hxx" +#include "character_series/character_series.hxx" mutex examm_mutex; diff --git a/multithreaded/examm_nlp_mt.cxx b/multithreaded/examm_nlp_mt.cxx index e7bc8374..b7cb68bd 100644 --- a/multithreaded/examm_nlp_mt.cxx +++ b/multithreaded/examm_nlp_mt.cxx @@ -23,7 +23,7 @@ using std::vector; #include "rnn/examm.hxx" -#include"word_series/word_series.hxx" +#include "character_series/character_series.hxx" #include "time_series/time_series.hxx" mutex examm_mutex; diff --git a/rnn/rnn.cxx b/rnn/rnn.cxx index e9ca73e2..ddf046cf 100644 --- a/rnn/rnn.cxx +++ b/rnn/rnn.cxx @@ -43,7 +43,7 @@ using std::vector; #include "common/log.hxx" #include "time_series/time_series.hxx" -#include "word_series/word_series.hxx" +#include "character_series/character_series.hxx" void RNN::validate_parameters(const vector &input_parameter_names, const vector &output_parameter_names) { Log::debug("validating parameters -- input_parameter_names.size(): %d, output_parameter_names.size(): %d\n", input_parameter_names.size(), output_parameter_names.size()); diff --git a/rnn/rnn.hxx b/rnn/rnn.hxx index 8b9d3515..ab277a1f 100644 --- a/rnn/rnn.hxx +++ b/rnn/rnn.hxx @@ -13,7 +13,7 @@ using std::vector; #include "rnn_recurrent_edge.hxx" #include "time_series/time_series.hxx" -#include "word_series/word_series.hxx" +#include "character_series/character_series.hxx" class RNN { private: diff --git a/rnn/rnn_genome.hxx b/rnn/rnn_genome.hxx index fb12b3f9..709d4bac 100644 --- a/rnn/rnn_genome.hxx +++ b/rnn/rnn_genome.hxx @@ -27,7 +27,7 @@ using std::vector; #include "common/random.hxx" #include "common/weight_initialize.hxx" #include "time_series/time_series.hxx" -#include "word_series/word_series.hxx" +#include "character_series/character_series.hxx" //mysql can't handle the max float value for some reason #define EXAMM_MAX_DOUBLE 10000000 diff --git a/rnn_examples/CMakeLists.txt b/rnn_examples/CMakeLists.txt index 020cb1d4..27ea355b 100644 --- a/rnn_examples/CMakeLists.txt +++ b/rnn_examples/CMakeLists.txt @@ -1,16 +1,16 @@ add_executable(jordan_rnn jordan_rnn) -target_link_libraries(jordan_rnn examm_strategy exact_common exact_time_series exact_word_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) +target_link_libraries(jordan_rnn examm_strategy exact_common exact_time_series exact_character_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) add_executable(train_rnn train_rnn) -target_link_libraries(train_rnn examm_strategy exact_common exact_time_series exact_word_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) +target_link_libraries(train_rnn examm_strategy exact_common exact_time_series exact_character_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} ${TIFF_LIBRARIES} pthread) add_executable(evaluate_rnn evaluate_rnn) -target_link_libraries(evaluate_rnn examm_strategy exact_common exact_time_series exact_word_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} pthread) +target_link_libraries(evaluate_rnn examm_strategy exact_common exact_time_series exact_character_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} pthread) add_executable(evaluate_rnns_multi_offset evaluate_rnns_multi_offset) -target_link_libraries(evaluate_rnns_multi_offset examm_strategy exact_common exact_time_series exact_word_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} pthread) +target_link_libraries(evaluate_rnns_multi_offset examm_strategy exact_common exact_time_series exact_character_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} pthread) add_executable(rnn_statistics rnn_statistics) -target_link_libraries(rnn_statistics examm_strategy exact_common exact_time_series exact_word_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} pthread) +target_link_libraries(rnn_statistics examm_strategy exact_common exact_time_series exact_character_series ${MPI_LIBRARIES} ${MPI_EXTRA} ${MYSQL_LIBRARIES} pthread) diff --git a/rnn_tests/CMakeLists.txt b/rnn_tests/CMakeLists.txt index c5c9e9a6..abdf22e4 100644 --- a/rnn_tests/CMakeLists.txt +++ b/rnn_tests/CMakeLists.txt @@ -1,39 +1,39 @@ add_executable(test_feed_forward_gradients test_feed_forward_gradients gradient_test) -target_link_libraries(test_feed_forward_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_feed_forward_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_elman_gradients test_elman_gradients gradient_test) -target_link_libraries(test_elman_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_elman_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_jordan_gradients test_jordan_gradients gradient_test) -target_link_libraries(test_jordan_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_jordan_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_delta_gradients test_delta_gradients gradient_test) -target_link_libraries(test_delta_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_delta_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_ugrnn_gradients test_ugrnn_gradients gradient_test) -target_link_libraries(test_ugrnn_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_ugrnn_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_gru_gradients test_gru_gradients gradient_test) -target_link_libraries(test_gru_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_gru_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_mgu_gradients test_mgu_gradients gradient_test) -target_link_libraries(test_mgu_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_mgu_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_lstm_gradients test_lstm_gradients gradient_test) -target_link_libraries(test_lstm_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_lstm_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_enarc_gradients test_enarc_gradients gradient_test) -target_link_libraries(test_enarc_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_enarc_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_random_dag_gradients test_random_dag_gradients gradient_test) -target_link_libraries(test_random_dag_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_random_dag_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_enas_dag_gradients test_enas_dag_gradients gradient_test) -target_link_libraries(test_enas_dag_gradients examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_enas_dag_gradients examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_softmax_output test_softmax_output gradient_test) -target_link_libraries(test_softmax_output examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_softmax_output examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) add_executable(test_softmax_gradient test_softmax_gradient gradient_test) -target_link_libraries(test_softmax_gradient examm_strategy exact_common exact_time_series exact_word_series ${MYSQL_LIBRARIES} pthread) +target_link_libraries(test_softmax_gradient examm_strategy exact_common exact_time_series exact_character_series ${MYSQL_LIBRARIES} pthread) diff --git a/word_series/CMakeLists.txt b/word_series/CMakeLists.txt deleted file mode 100644 index 794713db..00000000 --- a/word_series/CMakeLists.txt +++ /dev/null @@ -1,3 +0,0 @@ -add_library(exact_word_series word_series) - -target_link_libraries(exact_word_series exact_common pthread) From 7495bad43380fc120828d7c66ad2b3d19e97693d Mon Sep 17 00:00:00 2001 From: Zimeng Lyu Date: Sun, 21 Feb 2021 02:19:48 -0500 Subject: [PATCH 24/25] fixed code to load training and testing code correctly --- character_series/character_series.cxx | 212 ++++++++++---------------- character_series/character_series.hxx | 54 ++++--- mpi/examm_mpi_nlp.cxx | 8 - 3 files changed, 115 insertions(+), 159 deletions(-) diff --git a/character_series/character_series.cxx b/character_series/character_series.cxx index 86faf5b4..2b06d58b 100644 --- a/character_series/character_series.cxx +++ b/character_series/character_series.cxx @@ -230,7 +230,7 @@ SentenceSeries::SentenceSeries(const string _line, const vector & _chara } } - number_rows = file_characters.size(); + number_chars = file_characters.size(); for (auto series = character_series.begin(); series != character_series.end(); series++) { series->second->calculate_statistics(); @@ -243,7 +243,7 @@ SentenceSeries::SentenceSeries(const string _line, const vector & _chara series->second->print_statistics(); } int series_rows = series->second->get_number_values(); - if (series_rows != number_rows) { + if (series_rows != number_chars) { Log::error("ERROR! number of rows for field '%s' (%d) doesn't equal number of rows in first field '%s' (%d)\n", series->first.c_str(), series->second->get_number_values(), character_series.begin()->first.c_str(), character_series.begin()->second->get_number_values()); } } @@ -258,8 +258,8 @@ SentenceSeries::~SentenceSeries() { } } -int SentenceSeries::get_number_rows() const { - return number_rows; +int SentenceSeries::get_number_chars() const { + return number_chars; } int SentenceSeries::get_number_columns() const { @@ -320,45 +320,53 @@ double SentenceSeries::get_correlation(string character1, string character2, int return first_series->get_correlation(second_series, lag); } -void SentenceSeries::export_character_series(vector< vector > &data , int character_offset) { - cout<<"character_offset:: "< values + * character_offset > 0 generates output data. Do not use the first values + */ +void SentenceSeries::export_character_series(vector< vector > &data, int32_t character_offset) { + Log::debug("clearing data\n"); data.clear(); - data.resize(character_index.size(),vector (number_rows - fabs(character_offset),0.0)); - if(character_offset > 0) { - cout<<"testing"<(number_chars - abs_character_offset, 0.0)); + Log::debug("resized! character_offset = %d\n", character_offset); + + // output data + if(character_offset > 0) { + for (int i = 0; i < character_index.size(); i++) { + for (int j = character_offset; j < number_chars; j++) { data[i][j - character_offset] = character_series[character_index[i]]->get_value(j); } - } + } + // input data } else if (character_offset < 0) { - cout<<"training"<get_value(j); } } } else { - for (int i = 0; i < character_index.size(); ++i) { - for (int j = 0; j < number_rows; ++j) { + for (int i = 0; i < character_index.size(); i++) { + for (int j = 0; j < number_chars; j++) { data[i][j] = character_series[character_index[i]]->get_value(j); } } } } -void SentenceSeries::export_character_series(vector< vector > &data) { - export_character_series(data , 0); -} - SentenceSeries::SentenceSeries() { } SentenceSeries* SentenceSeries::copy() { SentenceSeries* ss = new SentenceSeries(); - ss->number_rows = number_rows; + ss->number_chars = number_chars; ss->sentence = sentence; ss->character_index = character_index; ss->vocab= vocab; @@ -411,9 +419,11 @@ void merge_parameter_names_character(const vector &input_parameter_names } void Corpus::load_character_library() { - for (int i = 0; i < training_indexes.size(); ++i) { + training_sentence_indexes.clear(); + test_sentence_indexes.clear(); + for (int i = 0; i < training_file_indexes.size(); ++i) { vector characters; - string filename = filenames[training_indexes[i]]; + string filename = filenames[training_file_indexes[i]]; ifstream cs_file(filename.c_str()); for(string line; getline(cs_file,line); ) { @@ -433,16 +443,29 @@ void Corpus::load_character_library() { output_parameter_names = character_index; all_parameter_names = character_index; - for (int i = 0; i < filenames.size(); i++) { - Log::debug("\t%s\n", filenames[i].c_str()); - ifstream cs_file(filenames[i].c_str()); + int current = 0; + for (int i = 0; i < training_file_indexes.size(); i++) { + Log::debug("\t%s\n", filenames[training_file_indexes[i]].c_str()); + ifstream cs_file(filenames[training_file_indexes[i]].c_str()); for(string line; getline(cs_file,line); ) { line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); SentenceSeries *ss = new SentenceSeries(line, character_index,vocab); sentence_series.push_back( ss ); + training_sentence_indexes.push_back(current); + current++; + } + } + for (int i = 0; i < test_file_indexes.size(); i++) { + Log::debug("\t%s\n", filenames[test_file_indexes[i]].c_str()); + ifstream cs_file(filenames[test_file_indexes[i]].c_str()); + for(string line; getline(cs_file,line); ) { + line = regex_replace(line, std::regex("^ +| +$|( ) +"), "$1"); + SentenceSeries *ss = new SentenceSeries(line, character_index,vocab); + sentence_series.push_back( ss ); + test_sentence_indexes.push_back(current); + current++; } } - Log::info("number of sentences: %d \n", sentence_series.size()); } Corpus* Corpus::generate_from_arguments(const vector &arguments) { @@ -450,7 +473,6 @@ Corpus* Corpus::generate_from_arguments(const vector &arguments) { cs->filenames.clear(); cs->character_index.clear(); cs->vocab.clear(); - uint32_t _sequence_length; if (argument_exists(arguments, "--training_filenames") && argument_exists(arguments, "--test_filenames")) { vector training_filenames; @@ -462,13 +484,13 @@ Corpus* Corpus::generate_from_arguments(const vector &arguments) { int current = 0; for (int i = 0; i < training_filenames.size(); i++) { cs->filenames.push_back(training_filenames[i]); - cs->training_indexes.push_back(current); + cs->training_file_indexes.push_back(current); current++; } for (int i = 0; i < test_filenames.size(); i++) { cs->filenames.push_back(test_filenames[i]); - cs->test_indexes.push_back(current); + cs->test_file_indexes.push_back(current); current++; } @@ -478,8 +500,6 @@ Corpus* Corpus::generate_from_arguments(const vector &arguments) { exit(1); } - get_argument(arguments, "--sequence_length", true, _sequence_length); - cs->sequence_length = _sequence_length; cs->all_parameter_names.clear(); cs->input_parameter_names.clear(); cs->output_parameter_names.clear(); @@ -489,27 +509,6 @@ Corpus* Corpus::generate_from_arguments(const vector &arguments) { return cs; } -Corpus* Corpus::generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names) { - Corpus *cs = new Corpus(); - - cs->filenames = _test_filenames; - cs->training_indexes.clear(); - cs->test_indexes.clear(); - for (int32_t i = 0; i < (int32_t)cs->filenames.size(); i++) { - cs->test_indexes.push_back(i); - } - cs->input_parameter_names = _input_parameter_names; - cs->output_parameter_names = _output_parameter_names; - merge_parameter_names_character(cs->input_parameter_names, cs->output_parameter_names, cs->all_parameter_names); - cs->normalize_mins.clear(); - cs->normalize_maxs.clear(); - cs->normalize_avgs.clear(); - cs->normalize_std_devs.clear(); - cs->load_character_library(); - - return cs; -} - double Corpus::denormalize(string field_name, double value) { if (normalize_type.compare("none") == 0) { return value; @@ -620,7 +619,7 @@ void Corpus::normalize_avg_std_dev() { double numerator_average = 0.0; long total_values = 0; for (int j = 0; j < sentence_series.size(); j++) { - int n_values = sentence_series[j]->get_number_rows(); + int n_values = sentence_series[j]->get_number_chars(); numerator_average += sentence_series[j]->get_average(parameter_name) * n_values; total_values += n_values; double current_min = sentence_series[j]->get_min(parameter_name); @@ -634,7 +633,7 @@ void Corpus::normalize_avg_std_dev() { double numerator_std_dev = 0.0; //get the Bessel-corrected (n-1 denominator) combined standard deviation for (int j = 0; j < sentence_series.size(); j++) { - int n_values = sentence_series[j]->get_number_rows(); + int n_values = sentence_series[j]->get_number_chars(); double avg_diff = sentence_series[j]->get_average(parameter_name) - avg; numerator_std_dev += ((n_values - 1) * sentence_series[j]->get_variance(parameter_name)) + (n_values * avg_diff * avg_diff); } @@ -716,70 +715,45 @@ void Corpus::normalize_avg_std_dev(const map &_normalize_avgs, co normalize_type = "avg_std_dev"; } -vector > > split_sequence_length(int sequence_length, vector > > data) { - vector > > sequenceData; - int no_features = data[0].size(); - int no_sequences = 0; - int sequenceNo = -1; - for (int i = 0; i < data.size(); ++i) { - no_sequences += (data[i][0].size() - 1) / sequence_length + 1; - } - if (data[0][0].size() < sequence_length) { - sequenceData.resize(no_sequences, vector>(no_features, vector(data[0][0].size()))); - } else { - sequenceData.resize(no_sequences, vector>(no_features, vector(sequence_length))); - } - - for (int k = 0; k < data.size(); ++k) { - for (int j = 0; j < data[k][0].size(); ++j) { - for (int i = 0; i < data[k].size(); ++i) { - if (i == 0 && j % sequence_length == 0) sequenceNo++; - sequenceData[sequenceNo][i][j % sequence_length] = data[k][i][j]; - } - } - } - return sequenceData; -} - - -void Corpus::export_sentence_series(const vector &series_indexes, int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { +void Corpus::export_sentence_series(const vector &sentence_indexes, int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { + inputs.resize(sentence_indexes.size()); + outputs.resize(sentence_indexes.size()); - vector< vector< vector > > temp_inputs; - vector< vector< vector > > temp_outputs; - inputs.resize(series_indexes.size()); - outputs.resize(series_indexes.size()); - temp_inputs.resize(series_indexes.size()); - temp_outputs.resize(series_indexes.size()); - for (uint32_t i = 0; i < series_indexes.size(); i++) { - int series_index = series_indexes[i]; - sentence_series[series_index]->export_character_series(temp_inputs[i], -character_offset); - sentence_series[series_index]->export_character_series(temp_outputs[i], character_offset); + for (int i = 0; i < sentence_indexes.size(); i++) { + int sentence_index = sentence_indexes[i]; + sentence_series[sentence_index]->export_character_series(inputs[i], -character_offset); + sentence_series[sentence_index]->export_character_series(outputs[i], character_offset); } - - inputs = split_sequence_length(sequence_length, temp_inputs); - outputs = split_sequence_length(sequence_length, temp_outputs); } /** * This exports the time series marked as training series by the training_indexes vector. + * vector input: < sentence < character index < value over the sentence (onehot)> > > */ void Corpus::export_training_series(int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (training_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export training time series, however the training_indexes were not specified.\n"); + for (int i = 0; i < training_sentence_indexes.size(); i++) { + Log::error("training sentence indexes: %d\n", training_sentence_indexes[i]); + } + if (training_sentence_indexes.size() == 0) { + Log::fatal("ERROR: attempting to export training time series, however the training_sentence_indexes were not specified.\n"); exit(1); } - export_sentence_series(training_indexes, character_offset, inputs, outputs); + export_sentence_series(training_sentence_indexes, character_offset, inputs, outputs); } /** * This exports the time series marked as test series by the test_indexes vector. + * vector input: < sentence < character index < value over the sentence (onehot)> > > */ void Corpus::export_test_series(int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - if (test_indexes.size() == 0) { - Log::fatal("ERROR: attempting to export test time series, however the test_indexes were not specified.\n"); + for (int i = 0; i < test_sentence_indexes.size(); i++) { + Log::error("test setentence indexes: %d\n", test_sentence_indexes[i]); + } + if (test_sentence_indexes.size() == 0) { + Log::fatal("ERROR: attempting to export test time series, however the test_sentence_indexes were not specified.\n"); exit(1); } - export_sentence_series(test_indexes, character_offset, inputs, outputs); + export_sentence_series(test_sentence_indexes, character_offset, inputs, outputs); } void Corpus::export_series_by_name(string field_name, vector< vector > &exported_series) { @@ -792,32 +766,6 @@ void Corpus::export_series_by_name(string field_name, vector< vector > & } } -void Corpus::write_sentence_series_sets(string base_filename) { - for (uint32_t i = 0; i < sentence_series.size(); i++) { - ofstream outfile(base_filename + to_string(i) + ".csv"); - vector< vector > data; - sentence_series[i]->export_character_series(data); - - for (int j = 0; j < all_parameter_names.size(); j++) { - if (j > 0) { - outfile << ","; - } - outfile << all_parameter_names[j]; - } - outfile << endl; - - for (int j = 0; j < data[0].size(); j++) { - for (int k = 0; k < data.size(); k++) { - if (k > 0) { - outfile << ","; - } - outfile << data[k][j]; - } - outfile << endl; - } - } -} - string Corpus::get_normalize_type() const { return normalize_type; } @@ -859,12 +807,12 @@ int Corpus::get_number_outputs() const { return output_parameter_names.size(); } -void Corpus::set_training_indexes(const vector &_training_indexes) { - training_indexes = _training_indexes; +void Corpus::set_training_file_indexes(const vector &_training_file_indexes) { + training_file_indexes = _training_file_indexes; } -void Corpus::set_test_indexes(const vector &_test_indexes) { - test_indexes = _test_indexes; +void Corpus::set_test_file_indexes(const vector &_test_file_indexes) { + test_file_indexes = _test_file_indexes; } SentenceSeries* Corpus::get_set(int32_t i) { diff --git a/character_series/character_series.hxx b/character_series/character_series.hxx index e68da285..54609c7e 100644 --- a/character_series/character_series.hxx +++ b/character_series/character_series.hxx @@ -21,10 +21,10 @@ using std::vector; * All the unique strings in the datasets will have its each character series. */ string name; - /** - * Specifies the number of the unique strings in the dataset. - */ - int vocab_size; + // /** + // * Specifies the number of the unique strings in the dataset. + // */ + // int vocab_size; /** * Specifies the minimum of the character series in a particular string. @@ -85,10 +85,9 @@ using std::vector; private: /** - * Specifies the number of the rows in the dataset. - * This tells about the number of the unique characters in the dataset. + * the number of chars in a sentence */ - int number_rows; + int number_chars; /** * Specifices the filename for the training or testing data. */ @@ -109,10 +108,10 @@ using std::vector; SentenceSeries(); public: - SentenceSeries(const string _sentence,const vector & _character_index , const map &_vocab); + SentenceSeries(const string _sentence, const vector & _character_index , const map &_vocab); ~SentenceSeries(); void add_character_series(string name); - int get_number_rows() const; + int get_number_chars() const; int get_number_columns() const; string get_filename() const; vector get_character_index() const; @@ -127,8 +126,8 @@ using std::vector; double get_correlation(string character1, string character2, int32_t lag) const; void normalize_min_max(string character, double min, double max); void normalize_avg_std_dev(string character, double avg, double std_dev, double norm_max); - void export_character_series(vector< vector > &data , int character_offset); - void export_character_series(vector< vector > &data ); + void export_character_series(vector< vector > &data , int32_t character_offset); + // void export_character_series(vector< vector > &data ); SentenceSeries* copy(); void select_parameters(const vector &input_parameter_names, const vector &output_parameter_names); void select_parameters(const vector ¶meter_names); @@ -138,10 +137,10 @@ using std::vector; class Corpus { private: - /** - * Specifices the chunks in which the dataset is to be divided into chunks. - */ - uint32_t sequence_length; + // /** + // * Specifices the chunks in which the dataset is to be divided into chunks. + // */ + // uint32_t sequence_length; string normalize_type; /** @@ -152,12 +151,24 @@ class Corpus { /** * stores the training index of the filenames. */ - vector training_indexes; + vector training_file_indexes; /** * stores the testing index of the filenames. */ - vector test_indexes; + vector test_file_indexes; + + /** + * stores the training index of the sentences. + * it's different from the training_file_indexes because 1 file can have many sentences + */ + vector training_sentence_indexes; + + /** + * stores the testing index of the sentences. + * it's different from the test_file_indexes because 1 file can have many sentences + */ + vector test_sentence_indexes; /** * stores the input parameter names of the filenames. @@ -206,6 +217,11 @@ class Corpus { Corpus(); ~Corpus(); static Corpus* generate_from_arguments(const vector &arguments); + /** + * TODO: generate_test function is not implemented. + * Different from time series, which each file is a time series + * For Charater level prediction, need to load the input files and put each sentence into a seperate sentence class + */ static Corpus* generate_test(const vector &_test_filenames, const vector &_input_parameter_names, const vector &_output_parameter_names); void normalize_min_max(); void normalize_min_max(const map &_normalize_mins, const map &_normalize_maxs); @@ -252,8 +268,8 @@ class Corpus { int get_number_series() const; int get_number_inputs() const; int get_number_outputs() const; - void set_training_indexes(const vector &_training_indexes); - void set_test_indexes(const vector &_test_indexes); + void set_training_file_indexes(const vector &_training_file_indexes); + void set_test_file_indexes(const vector &_test_file_indexes); SentenceSeries *get_set(int32_t i); }; diff --git a/mpi/examm_mpi_nlp.cxx b/mpi/examm_mpi_nlp.cxx index c4aa69d6..2f6c7a0b 100644 --- a/mpi/examm_mpi_nlp.cxx +++ b/mpi/examm_mpi_nlp.cxx @@ -247,19 +247,11 @@ int main(int argc, char **argv) { int32_t character_offset = 1; get_argument(arguments, "--character_offset", true, character_offset); - int32_t sequence_length = 64; - get_argument(arguments, "--sequence_length", false, sequence_length); - Corpus *corpus_sets = NULL; if (rank == 0) { //only have the master process print TSS info corpus_sets = Corpus::generate_from_arguments(arguments); - if (argument_exists(arguments, "--write_character_series")) { - string base_filename; - get_argument(arguments, "--write_character_series", true, base_filename); - corpus_sets->write_sentence_series_sets(base_filename); - } } else { corpus_sets = Corpus::generate_from_arguments(arguments); From 5e46d77e441c60599c5e57ab7aab08dca112a649 Mon Sep 17 00:00:00 2001 From: Zimeng Lyu Date: Mon, 22 Feb 2021 00:13:26 -0500 Subject: [PATCH 25/25] made '' an input character as a whole --- character_series/character_series.cxx | 19 ++++++++++++------- datasets/pennchar/train_small.txt | 10 +++++++++- datasets/pennchar/valid_small.txt | 3 ++- 3 files changed, 23 insertions(+), 9 deletions(-) diff --git a/character_series/character_series.cxx b/character_series/character_series.cxx index 2b06d58b..7cfbb15b 100644 --- a/character_series/character_series.cxx +++ b/character_series/character_series.cxx @@ -196,6 +196,13 @@ void string_split_character(const string &s, char delim, vector &result) ss.str(s); string item; while (getline(ss, item, delim)) { + if (item.compare("<") == 0) { + while (item.compare(">") != 0) { + getline(ss, item, delim); + } + result.push_back(""); + continue; + } result.push_back(item); } } @@ -432,10 +439,14 @@ void Corpus::load_character_library() { characters.push_back(""); } + int current = 0; set character_index_set(characters.begin(), characters.end()); + Log::info("there are %d different inputs in training file! \n", character_index_set.size()); for (set::iterator i = character_index_set.begin(); i != character_index_set.end(); ++i) { character_index.push_back(*i); - vocab[*i] = vocab.size(); + vocab[*i] = current; + Log::info("charator %d is %s \n", current, (*i).c_str()); + current ++; } } @@ -731,9 +742,6 @@ void Corpus::export_sentence_series(const vector &sentence_indexes, int cha * vector input: < sentence < character index < value over the sentence (onehot)> > > */ void Corpus::export_training_series(int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - for (int i = 0; i < training_sentence_indexes.size(); i++) { - Log::error("training sentence indexes: %d\n", training_sentence_indexes[i]); - } if (training_sentence_indexes.size() == 0) { Log::fatal("ERROR: attempting to export training time series, however the training_sentence_indexes were not specified.\n"); exit(1); @@ -746,9 +754,6 @@ void Corpus::export_training_series(int character_offset, vector< vector< vector * vector input: < sentence < character index < value over the sentence (onehot)> > > */ void Corpus::export_test_series(int character_offset, vector< vector< vector > > &inputs, vector< vector< vector > > &outputs) { - for (int i = 0; i < test_sentence_indexes.size(); i++) { - Log::error("test setentence indexes: %d\n", test_sentence_indexes[i]); - } if (test_sentence_indexes.size() == 0) { Log::fatal("ERROR: attempting to export test time series, however the test_sentence_indexes were not specified.\n"); exit(1); diff --git a/datasets/pennchar/train_small.txt b/datasets/pennchar/train_small.txt index 54be8ba2..4d98ab4e 100644 --- a/datasets/pennchar/train_small.txt +++ b/datasets/pennchar/train_small.txt @@ -10,4 +10,12 @@ n o _ i t _ w a s _ n ' t _ b l a c k _ m o n d a y o n c e _ a g a i n _ t h e _ s p e c i a l i s t s _ w e r e _ n o t _ a b l e _ t o _ h a n d l e _ t h e _ i m b a l a n c e s _ o n _ t h e _ f l o o r _ o f _ t h e _ n e w _ y o r k _ s t o c k _ e x c h a n g e _ s a i d _ c h r i s t o p h e r _ < u n k > _ s e n i o r _ v i c e _ p r e s i d e n t _ a t _ < u n k > _ s e c u r i t i e s _ c o r p < u n k > _ j a m e s _ < u n k > _ c h a i r m a n _ o f _ s p e c i a l i s t s _ h e n d e r s o n _ b r o t h e r s _ i n c . _ i t _ i s _ e a s y _ t o _ s a y _ t h e _ s p e c i a l i s t _ i s _ n ' t _ d o i n g _ h i s _ j o b w h e n _ t h e _ d o l l a r _ i s _ i n _ a _ < u n k > _ e v e n _ c e n t r a l _ b a n k s _ c a _ n ' t _ s t o p _ i t - s p e c u l a t o r s _ a r e _ c a l l i n g _ f o r _ a _ d e g r e e _ o f _ l i q u i d i t y _ t h a t _ i s _ n o t _ t h e r e _ i n _ t h e _ m a r k e t \ No newline at end of file + s p e c u l a t o r s _ a r e _ c a l l i n g _ f o r _ a _ d e g r e e _ o f _ l i q u i d i t y _ t h a t _ i s _ n o t _ t h e r e _ i n _ t h e _ m a r k e t + i f _ b o e i n g _ g o e s _ a h e a d _ w i t h _ t h e _ l a r g e r _ N _ t h e _ p l a n e _ c o u l d _ h i t _ t h e _ m a r k e t _ i n _ t h e _ m i d - 1 9 9 0 s + m a g n a _ r e c e n t l y _ c u t _ i t s _ q u a r t e r l y _ d i v i d e n d _ i n _ h a l f _ a n d _ t h e _ c o m p a n y _ ' s _ c l a s s _ a _ s h a r e s _ a r e _ < u n k > _ f a r _ b e l o w _ t h e i r _ 5 2 - w e e k _ h i g h _ o f _ N _ c a n a d i a n _ d o l l a r s _ u s $ _ N + t h e _ t h e o r y _ i s _ t h a t _ s e y m o u r _ i s _ t h e _ c h i e f _ d e s i g n e r _ o f _ t h e _ c r a y - 3 _ a n d _ w i t h o u t _ h i m _ i t _ c o u l d _ n o t _ b e _ c o m p l e t e d + s o _ f a r _ a l l _ t h e _ s t u d i e s _ h a v e _ c o n c l u d e d _ t h a t _ r u - 4 8 6 _ i s _ s a f e + i n s t i t u t i o n s _ h a d _ b e c o m e _ a c t i v e _ m a r k e t _ p l a y e r s _ i n _ t h e _ e a r l y _ 1 9 7 0 s _ a n d _ s o u g h t _ e x c h a n g e _ s e a t s _ t o _ h a n d l e _ t h e i r _ o w n _ t r a d e s + p i e r r e _ < u n k > _ N _ y e a r s _ o l d _ w i l l _ j o i n _ t h e _ b o a r d _ a s _ a _ n o n e x e c u t i v e _ d i r e c t o r _ n o v . _ N + t h e _ b r i t i s h _ p a p e r _ p a c k a g i n g _ a n d _ p u b l i s h i n g _ c o n c e r n _ s a i d _ p r o f i t _ f r o m _ c o n t i n u i n g _ l i n e s _ f e l l _ N _ N _ t o _ # _ N _ m i l l i o n _ f r o m _ # _ N _ m i l l i o n + y e a r _ e n d e d _ d e c . _ N _ N _ \ * \ No newline at end of file diff --git a/datasets/pennchar/valid_small.txt b/datasets/pennchar/valid_small.txt index 10993af4..b23f8ca8 100644 --- a/datasets/pennchar/valid_small.txt +++ b/datasets/pennchar/valid_small.txt @@ -2,4 +2,5 @@ c o n s u m e r s _ m a y _ w a n t _ t o _ m o v e _ t h e i r _ t e l e p h o < u n k > _ < u n k > _ w a t c h i n g _ a b c _ ' s _ m o n d a y _ n i g h t _ f o o t b a l l _ c a n _ n o w _ v o t e _ d u r i n g _ < u n k > _ f o r _ t h e _ g r e a t e s t _ p l a y _ i n _ N _ y e a r s _ f r o m _ a m o n g _ f o u r _ o r _ f i v e _ < u n k > _ < u n k > t w o _ w e e k s _ a g o _ v i e w e r s _ o f _ s e v e r a l _ n b c _ < u n k > _ c o n s u m e r _ s e g m e n t s _ s t a r t e d _ c a l l i n g _ a _ N _ n u m b e r _ f o r _ a d v i c e _ o n _ v a r i o u s _ < u n k > _ i s s u e s a n d _ t h e _ n e w _ s y n d i c a t e d _ r e a l i t y _ s h o w _ h a r d _ c o p y _ r e c o r d s _ v i e w e r s _ ' _ o p i n i o n s _ f o r _ p o s s i b l e _ a i r i n g _ o n _ t h e _ n e x t _ d a y _ ' s _ s h o w - i n t e r a c t i v e _ \ No newline at end of file + i n t e r a c t i v e _ + t h e n _ t h e _ w h o l e _ t h i n g _ w i l l _ s t a r t _ t o _ c o l l a p s e _ j u s t _ a s _ i t _ d i d _ i n _ t h e _ 1 9 7 0 s _ a n d _ t h e _ g h o s t s _ a n d _ < u n k > _ w i l l _ b e _ < u n k > _ t h r o u g h _ t h e _ p l a c e _ t u r n i n g _ p e o p l e _ ' s _ h a i r _ w h i t e

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