diff --git a/Programming Assignment/Week_3/Raj_week3/dataset3Params.m b/Programming Assignment/Week_3/Raj_week3/dataset3Params.m new file mode 100644 index 0000000..14cf4e4 --- /dev/null +++ b/Programming Assignment/Week_3/Raj_week3/dataset3Params.m @@ -0,0 +1,51 @@ +function [C, sigma] = dataset3Params(X, y, Xval, yval) +%DATASET3PARAMS returns your choice of C and sigma for Part 3 of the exercise +%where you select the optimal (C, sigma) learning parameters to use for SVM +%with RBF kernel +% [C, sigma] = DATASET3PARAMS(X, y, Xval, yval) returns your choice of C and +% sigma. You should complete this function to return the optimal C and +% sigma based on a cross-validation set. +% + +% You need to return the following variables correctly. +C = 1; +sigma = 0.3; + +% ====================== YOUR CODE HERE ====================== +% Instructions: Fill in this function to return the optimal C and sigma +% learning parameters found using the cross validation set. +% You can use svmPredict to predict the labels on the cross +% validation set. For example, +% predictions = svmPredict(model, Xval); +% will return the predictions on the cross validation set. +% +% Note: You can compute the prediction error using +% mean(double(predictions ~= yval)) +% +TryC = [0.01 0.03 0.1 0.3 1 3 10 30]; +TrySigma = [0.01 0.03 0.1 0.3 1 3 10 30]; +bestC = TryC(1); +bestSigma = TrySigma(1); +minCost = 0; + +for i = 1:length(TryC) + for j = 1:length(TrySigma) + C = TryC(i); + sigma = TrySigma(j); + model = svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma)); + predictions = svmPredict(model,Xval); + cost1 = mean(double(predictions != yval)); + if(i == 1 && j == 1) + minCost = cost1; + elseif (minCost > cost1) + minCost = cost1; + bestC = TryC(i); + bestSigma = TrySigma(j); + endif + end +end +% svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma)) +% ========================================================================= +C = bestC; +sigma = bestSigma; +end diff --git a/Programming Assignment/Week_3/Raj_week3/emailFeatures.m b/Programming Assignment/Week_3/Raj_week3/emailFeatures.m new file mode 100644 index 0000000..284259b --- /dev/null +++ b/Programming Assignment/Week_3/Raj_week3/emailFeatures.m @@ -0,0 +1,64 @@ +function x = emailFeatures(word_indices) +%EMAILFEATURES takes in a word_indices vector and produces a feature vector +%from the word indices +% x = EMAILFEATURES(word_indices) takes in a word_indices vector and +% produces a feature vector from the word indices. + +% Total number of words in the dictionary +n = 1899; + +% You need to return the following variables correctly. +x = zeros(n, 1); +for i = 1:length(word_indices) + x(word_indices(i)) = 1; +end + +% ====================== YOUR CODE HERE ====================== +% Instructions: Fill in this function to return a feature vector for the +% given email (word_indices). To help make it easier to +% process the emails, we have have already pre-processed each +% email and converted each word in the email into an index in +% a fixed dictionary (of 1899 words). The variable +% word_indices contains the list of indices of the words +% which occur in one email. +% +% Concretely, if an email has the text: +% +% The quick brown fox jumped over the lazy dog. +% +% Then, the word_indices vector for this text might look +% like: +% +% 60 100 33 44 10 53 60 58 5 +% +% where, we have mapped each word onto a number, for example: +% +% the -- 60 +% quick -- 100 +% ... +% +% (note: the above numbers are just an example and are not the +% actual mappings). +% +% Your task is take one such word_indices vector and construct +% a binary feature vector that indicates whether a particular +% word occurs in the email. That is, x(i) = 1 when word i +% is present in the email. Concretely, if the word 'the' (say, +% index 60) appears in the email, then x(60) = 1. The feature +% vector should look like: +% +% x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..]; +% +% + + + + + + + + +% ========================================================================= + + +end diff --git a/Programming Assignment/Week_3/Raj_week3/gaussianKernel.m b/Programming Assignment/Week_3/Raj_week3/gaussianKernel.m new file mode 100644 index 0000000..a88bfa6 --- /dev/null +++ b/Programming Assignment/Week_3/Raj_week3/gaussianKernel.m @@ -0,0 +1,25 @@ +function sim = gaussianKernel(x1, x2, sigma) +%RBFKERNEL returns a radial basis function kernel between x1 and x2 +% sim = gaussianKernel(x1, x2) returns a gaussian kernel between x1 and x2 +% and returns the value in sim + +% Ensure that x1 and x2 are column vectors +x1 = x1(:); x2 = x2(:); + +% You need to return the following variables correctly. +sim = exp(-(sum((x1-x2).^2) / (2*sigma^2))); +% ====================== YOUR CODE HERE ====================== +% Instructions: Fill in this function to return the similarity between x1 +% and x2 computed using a Gaussian kernel with bandwidth +% sigma +% +% + + + + + + +% ============================================================= + +end diff --git a/Programming Assignment/Week_3/Raj_week3/processEmail.m b/Programming Assignment/Week_3/Raj_week3/processEmail.m new file mode 100644 index 0000000..606e30d --- /dev/null +++ b/Programming Assignment/Week_3/Raj_week3/processEmail.m @@ -0,0 +1,121 @@ +function word_indices = processEmail(email_contents) +%PROCESSEMAIL preprocesses a the body of an email and +%returns a list of word_indices +% word_indices = PROCESSEMAIL(email_contents) preprocesses +% the body of an email and returns a list of indices of the +% words contained in the email. +% + +% Load Vocabulary +vocabList = getVocabList(); + +% Init return value +word_indices = []; + +% ========================== Preprocess Email =========================== + +% Find the Headers ( \n\n and remove ) +% Uncomment the following lines if you are working with raw emails with the +% full headers + +% hdrstart = strfind(email_contents, ([char(10) char(10)])); +% email_contents = email_contents(hdrstart(1):end); + +% Lower case +email_contents = lower(email_contents); + +% Strip all HTML +% Looks for any expression that starts with < and ends with > and replace +% and does not have any < or > in the tag it with a space +email_contents = regexprep(email_contents, '<[^<>]+>', ' '); + +% Handle Numbers +% Look for one or more characters between 0-9 +email_contents = regexprep(email_contents, '[0-9]+', 'number'); + +% Handle URLS +% Look for strings starting with http:// or https:// +email_contents = regexprep(email_contents, ... + '(http|https)://[^\s]*', 'httpaddr'); + +% Handle Email Addresses +% Look for strings with @ in the middle +email_contents = regexprep(email_contents, '[^\s]+@[^\s]+', 'emailaddr'); + +% Handle $ sign +email_contents = regexprep(email_contents, '[$]+', 'dollar'); + + +% ========================== Tokenize Email =========================== + +% Output the email to screen as well +fprintf('\n==== Processed Email ====\n\n'); + +% Process file +l = 0; + +while ~isempty(email_contents) + + % Tokenize and also get rid of any punctuation + [str, email_contents] = ... + strtok(email_contents, ... + [' @$/#.-:&*+=[]?!(){},''">_<;%' char(10) char(13)]); + + % Remove any non alphanumeric characters + str = regexprep(str, '[^a-zA-Z0-9]', ''); + + % Stem the word + % (the porterStemmer sometimes has issues, so we use a try catch block) + try str = porterStemmer(strtrim(str)); + catch str = ''; continue; + end; + + % Skip the word if it is too short + if length(str) < 1 + continue; + end + + % Look up the word in the dictionary and add to word_indices if + % found + % ====================== YOUR CODE HERE ====================== + % Instructions: Fill in this function to add the index of str to + % word_indices if it is in the vocabulary. At this point + % of the code, you have a stemmed word from the email in + % the variable str. You should look up str in the + % vocabulary list (vocabList). If a match exists, you + % should add the index of the word to the word_indices + % vector. Concretely, if str = 'action', then you should + % look up the vocabulary list to find where in vocabList + % 'action' appears. For example, if vocabList{18} = + % 'action', then, you should add 18 to the word_indices + % vector (e.g., word_indices = [word_indices ; 18]; ). + % + % Note: vocabList{idx} returns a the word with index idx in the + % vocabulary list. + % + % Note: You can use strcmp(str1, str2) to compare two strings (str1 and + % str2). It will return 1 only if the two strings are equivalent. + % + for i = 1:length(vocabList) + if(strcmp(str,vocabList{i})) + word_indices = [word_indices;i]; + break; + end + end + % ============================================================= + + + % Print to screen, ensuring that the output lines are not too long + if (l + length(str) + 1) > 78 + fprintf('\n'); + l = 0; + end + fprintf('%s ', str); + l = l + length(str) + 1; + +end + +% Print footer +fprintf('\n\n=========================\n'); + +end