Csv reader

docs/methods/preprocessing/csv_reader.md

@@ -0,0 +1,24 @@
+# Csv Reader
+
+It reads the csv File provided and accordingly set the data type in that particular column double/string
+
+After computing the data type whole file is converted to string vector with their data types in first row of vector. Those values can be then converted to data-type on the runtime whenever required.
+
+## Parameters
+
+| Name         | Definition                                 | Type         |
+|--------------|--------------------------------------------|--------------|
+| file | Csv file which needs to be read | `ifstream` |
+
+
+## Methods
+
+| Name                                   | Definition                                    | Return value  |
+|----------------------------------------|-----------------------------------------------|---------------|
+| `readCsv(string name,bool index)` | To read csv file and allocate which column is double or string | `vector<vector<string>>` |
+
+## Example
+
+```
+std::vector<std::vector<std::string>>reader = readCsv("../../lol.csv",1);
+```

examples/methods/neighbors/multinomial_nb.cpp

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+// #include "../../src/slowmokit/methods/neighbors/multinomial_nb.hpp"
+// #include "../../src/slowmokit/core.hpp"
+
+// signed main(){
+//     std::vector<std::vector<int>> xTrain{
+//         {0,0,0},
+//         {1,0,0},
+//         {1,0,1},
+//         {1,0,0},
+//         {0,1,1},
+//         {1,0,0},
+//         {1,1,0},
+//         {0,1,1},
+//         {1,0,1},
+//         {1,1,1}
+//     };
+//     std::vector<int> yTrain{2,2,2,2,1,2,2,1,2,0};
+//     std::vector<int> xTest{0,0,0};
+//     std::vector<int> classes{0,1,2};
+//     MultinomialNB<int> multinomial;
+//     std::cout<<multinomial.predict(xTrain,yTrain,classes,xTest);
+//     return 0;
+// }

examples/methods/preprocessing/csv_reader.cpp

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+// #include "../../src/slowmokit/methods/preprocessing/csv_reader.hpp"
+
+// signed main(){
+//     std::vector<std::vector<std::string>>reader = readCsv("../../lol.csv",1);
+//     return 0;
+// }

src/slowmokit.hpp

@@ -20,5 +20,6 @@
 #include "slowmokit/methods/neighbors/bernoulli_nb.hpp"
 #include "slowmokit/methods/neighbors/gaussian_nb.hpp"
 #include "slowmokit/methods/neighbors/knn.hpp"
+#include "slowmokit/methods/neighbors/multinomial_nb.hpp"
 
 #endif // SLOWMOKIT_HPP

src/slowmokit/methods/neighbors/multinomial_nb.hpp

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+/**
+ * @file methods/neighbors/multinomial_nb.hpp
+ *
+ * Easy include for Multinomial Nb algorithm
+ */
+
+
+#ifndef SLOWMOKIT_MULTINOMIAL_NB_HPP
+#define SLOWMOKIT_MULTINOMIAL_NB_HPP
+
+#include "multinomial_nb/multinomial_nb.hpp"
+
+#endif // SLOWMOKIT_MULTINOMIAL_NB_HPP

src/slowmokit/methods/neighbors/multinomial_nb/multinomial_nb.cpp

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+/**
+ * @file methods/neighbors/multinomial_nb/multinomial_nb.cpp
+ *
+ * Implementation of the Multinomial Naive Bayes main program
+ */
+#include "multinomial_nb.hpp"
+
+template <class T>
+double MultinomialNB<T>::priorProb(std::vector<T> yTrain, int label)
+{ // Prior-Probability P(y)
+	int sum = 0;
+	for (int i = 0; i < yTrain.size(); i++)
+	{
+		if (yTrain[i] == label)
+		{
+			sum += 1;
+		}
+	}
+	return sum / double(yTrain.size());
+}
+
+template <class T>
+double MultinomialNB<T>::conditionalProb(std::vector<std::vector<T>> xTrain,
+                                         std::vector<T> yTrain, int featureCol,
+                                         int featureVal, int label)
+{ // Conditional-Probabilty P(x=f1 / y=class)
+	int denominator = 0, numerator = 0;
+	std::set<int> varies_values;
+	for (int i = 0; i < yTrain.size(); i++)
+	{
+		if (yTrain[i] == label)
+		{ // Check if label of feature is same as output looking for
+			denominator++; // All values with output_class we are looking for
+			varies_values.insert(xTrain[i][featureCol]);
+			if (xTrain[i][featureCol] == featureVal)
+			{
+				numerator++; // numerator increase only when that feature
+				             // consists of value same as value in testing
+				             // feature
+			}
+		}
+	}
+	return (numerator + 1) /
+	    double(denominator +
+	           varies_values
+	               .size()); // as it is multinomial nb so to avoid overfitting
+	// numerator+1 -> because if word do not exist in training but exist in
+	// testing denominator + number_of_different_values_in_feature
+}
+
+template <class T>
+int MultinomialNB<T>::fit_predict(std::vector<std::vector<T>> xTrain,
+                                  std::vector<T> yTrain, std::vector<T> classes,
+                                  std::vector<T> xTest)
+{
+	int n_features = xTrain[0].size();
+
+	std::vector<double> postProbs;
+
+	std::vector<int>::iterator label; // different ouput labels
+	for (label = classes.begin(); label != classes.end(); label++)
+	{ // iterating over all labels (P(y=y1,y2,y3....))
+		double likelihood = 1.0;
+		for (int i = 0; i < n_features; i++)
+		{
+			double cond = conditionalProb(xTrain, yTrain, i, xTest[i], *label);
+			likelihood += log10(cond); // Log10 of computed conditionl
+			                           // probabilty => Log10(P(x=x1 / y=label))
+		}
+
+		double prior = priorProb(yTrain, *label);
+		double post = prior * likelihood;
+		postProbs.push_back(post);
+	}
+	double sumpropProbs = 0.0;
+	int max = 0;
+	for (int i = 0; i < postProbs.size(); i++)
+	{ // Iterating over all labels computed values to check if any negative
+	  // value
+		// exists, if so then direclty values are compared else we will compute
+		// value/sum
+		if (postProbs[i] < 0)
+		{
+			sumpropProbs = 1;
+			break;
+		}
+		sumpropProbs += postProbs[i];
+	}
+	for (int i = 0; i < postProbs.size(); i++)
+	{
+		postProbs[i] /= sumpropProbs;
+		if (postProbs[i] > postProbs[max])
+		{
+			max = i;
+		}
+	}
+	return max;
+};

src/slowmokit/methods/neighbors/multinomial_nb/multinomial_nb.hpp

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+/**
+ * @file methods/neighbors/multinomial_nb/multinomial_nb.hpp
+ *
+ * The header file including the Multinomial Naive Bayes algorithm
+ */
+
+#ifndef SLOWMOKIT_MULTINOMIAL_NB_HPP
+#define SLOWMOKIT_MULTINOMIAL_NB_HPP
+
+#include "../../../core.hpp"
+
+template <class T>
+class MultinomialNB
+{
+	private:
+	/**
+	 * @brief Prior probability of multinomial naive bayes
+	 *
+	 * @param yTrain All y training values
+	 * @param label output we are looking in prior probability
+	 * @return double prior Probability
+	 */
+	double priorProb(std::vector<T> yTrain, int label);
+	/**
+	 * @brief Conditional Probability
+	 *
+	 * @param xTrain all x training int values
+	 * @param yTrain all y training int values
+	 * @param featureCol column for which we are computing conditional prob
+	 * @param featureVal value in that column
+	 * @param label y value
+	 * @return double conditional probability
+	 */
+	double conditionalProb(std::vector<std::vector<T>> xTrain,
+	                       std::vector<T> yTrain, int featureCol,
+	                       int featureVal, int label);
+
+	public:
+	/**
+	 * @brief fit_predict function
+	 *
+	 * @param xTrain all x training values
+	 * @param yTrain all y training values int
+	 * @param classes classes of y
+	 * @param xTest testing values int
+	 * @return int output predicted value
+	 */
+	int fit_predict(std::vector<std::vector<T>> xTrain, std::vector<T> yTrain,
+	                std::vector<T> classes, std::vector<T> xTest);
+};
+
+#endif

src/slowmokit/methods/preprocessing/csv_reader.cpp

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+/**
+ * @file methods/preprocessing/csv_reader.cpp
+ *
+ * Implementation of Csv reader
+ */
+
+#include "csv_reader.hpp"
+
+template <class T>
+std::vector<std::vector<std::string>> readCsv(std::string name, bool index)
+{
+	std::ifstream file;
+	file.open(name);
+	std::string output;
+	if (index)
+	{
+		getline(file, output);
+	}
+	getline(file, output);
+	bool duble = true;
+	std::vector<int> isDouble;
+	std::string current = "";
+	for (int i = 0; i < output.size(); i++)
+	{
+		if (output[i] == ',')
+		{
+			if (duble and current.size() > 0)
+			{
+				isDouble.push_back(1);
+			}
+			else
+			{
+				isDouble.push_back(0);
+			}
+			duble = true;
+			current = "";
+		}
+		else
+		{
+			current += output[i];
+			if ((output[i] >= '0' and output[i] <= '9') or output[i] == '.')
+			{
+				continue;
+			}
+			else
+			{
+				duble = false;
+			}
+		}
+	}
+	file.close();
+
+	// Storing all values as string with their data type at top of them
+	std::vector<std::vector<std::string>> csvFile;
+	std::vector<std::string> first(isDouble.size());
+	for (int i = 0; i < isDouble.size(); i++)
+	{
+		first[i] = std::to_string(isDouble[i]);
+	}
+	csvFile.push_back(first);
+	file.open(name);
+
+	while (getline(file, output))
+	{
+		first.clear();
+		current = "";
+		for (int i = 0; i < output.size(); i++)
+		{
+			if (output[i] == ',')
+			{
+				first.push_back(current);
+				current = "";
+			}
+			else
+			{
+				current += output[i];
+			}
+		}
+		csvFile.push_back(first);
+	}
+
+	return csvFile;
+}

src/slowmokit/methods/preprocessing/csv_reader.hpp

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+/**
+ * @file methods/preprocessing/csv_reader.hpp
+ *
+ * Easy include To Return the csv read data
+ */
+
+#ifndef SLOWMOKIT_CSV_READER_HPP
+#define SLOWMOKIT_CSV_READER_HPP
+
+#include "../../core.hpp"
+
+template <class T>
+std::vector<std::vector<std::string>> readCsv(std::string name, bool index);
+
+#endif // SLOWMOKIT_CSV_READER_HPP