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774 lines
30 KiB
C++
774 lines
30 KiB
C++
//
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// Data.cpp
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// MLP
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "data.hpp"
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#include "../lin_alg/lin_alg.h"
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#include "../stat/stat.h"
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#include "../softmax_net/softmax_net.h"
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#include <iostream>
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#include <random>
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#include <cmath>
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#include <fstream>
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#include <sstream>
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#include <algorithm>
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namespace MLPP{
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// Loading Datasets
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std::tuple<std::vector<std::vector<double>>, std::vector<double>> Data::loadBreastCancer(){
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const int BREAST_CANCER_SIZE = 30; // k = 30
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std::vector<std::vector<double>> inputSet;
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std::vector<double> outputSet;
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setData(BREAST_CANCER_SIZE, "MLPP/Data/Datasets/BreastCancer.csv", inputSet, outputSet);
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return {inputSet, outputSet};
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}
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std::tuple<std::vector<std::vector<double>>, std::vector<double>> Data::loadBreastCancerSVC(){
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const int BREAST_CANCER_SIZE = 30; // k = 30
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std::vector<std::vector<double>> inputSet;
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std::vector<double> outputSet;
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setData(BREAST_CANCER_SIZE, "MLPP/Data/Datasets/BreastCancerSVM.csv", inputSet, outputSet);
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return {inputSet, outputSet};
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}
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std::tuple<std::vector<std::vector<double>>, std::vector<std::vector<double>>> Data::loadIris(){
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const int IRIS_SIZE = 4;
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const int ONE_HOT_NUM = 3;
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std::vector<std::vector<double>> inputSet;
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std::vector<double> tempOutputSet;
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setData(IRIS_SIZE, "/Users/marcmelikyan/Desktop/Data/Iris.csv", inputSet, tempOutputSet);
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std::vector<std::vector<double>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
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return {inputSet, outputSet};
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}
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std::tuple<std::vector<std::vector<double>>, std::vector<std::vector<double>>> Data::loadWine(){
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const int WINE_SIZE = 4;
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const int ONE_HOT_NUM = 3;
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std::vector<std::vector<double>> inputSet;
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std::vector<double> tempOutputSet;
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setData(WINE_SIZE, "MLPP/Data/Datasets/Iris.csv", inputSet, tempOutputSet);
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std::vector<std::vector<double>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
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return {inputSet, outputSet};
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}
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std::tuple<std::vector<std::vector<double>>, std::vector<std::vector<double>>> Data::loadMnistTrain(){
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const int MNIST_SIZE = 784;
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const int ONE_HOT_NUM = 10;
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std::vector<std::vector<double>> inputSet;
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std::vector<double> tempOutputSet;
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setData(MNIST_SIZE, "MLPP/Data/Datasets/MnistTrain.csv", inputSet, tempOutputSet);
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std::vector<std::vector<double>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
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return {inputSet, outputSet};
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}
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std::tuple<std::vector<std::vector<double>>, std::vector<std::vector<double>>> Data::loadMnistTest(){
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const int MNIST_SIZE = 784;
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const int ONE_HOT_NUM = 10;
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std::vector<std::vector<double>> inputSet;
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std::vector<double> tempOutputSet;
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setData(MNIST_SIZE, "MLPP/Data/Datasets/MnistTest.csv", inputSet, tempOutputSet);
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std::vector<std::vector<double>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
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return {inputSet, outputSet};
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}
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std::tuple<std::vector<std::vector<double>>, std::vector<double>> Data::loadCaliforniaHousing(){
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const int CALIFORNIA_HOUSING_SIZE = 13; // k = 30
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std::vector<std::vector<double>> inputSet;
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std::vector<double> outputSet;
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setData(CALIFORNIA_HOUSING_SIZE, "MLPP/Data/Datasets/CaliforniaHousing.csv", inputSet, outputSet);
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return {inputSet, outputSet};
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}
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std::tuple<std::vector<double>, std::vector<double>> Data::loadFiresAndCrime(){
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std::vector<double> inputSet; // k is implicitly 1.
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std::vector<double> outputSet;
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setData("MLPP/Data/Datasets/FiresAndCrime.csv", inputSet, outputSet);
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return {inputSet, outputSet};
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}
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std::tuple<std::vector<std::vector<double>>, std::vector<std::vector<double>>, std::vector<std::vector<double>>, std::vector<std::vector<double>>> Data::trainTestSplit(std::vector<std::vector<double>> inputSet, std::vector<std::vector<double>> outputSet, double testSize){
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::shuffle(inputSet.begin(), inputSet.end(), generator); // inputSet random shuffle
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std::shuffle(outputSet.begin(), outputSet.end(), generator); // outputSet random shuffle)
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std::vector<std::vector<double>> inputTestSet;
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std::vector<std::vector<double>> outputTestSet;
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int testInputNumber = testSize * inputSet.size(); // implicit usage of floor
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int testOutputNumber = testSize * outputSet.size(); // implicit usage of floor
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for(int i = 0; i < testInputNumber; i++){
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inputTestSet.push_back(inputSet[i]);
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inputSet.erase(inputSet.begin());
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}
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for(int i = 0; i < testOutputNumber; i++){
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outputTestSet.push_back(outputSet[i]);
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outputSet.erase(outputSet.begin());
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}
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return {inputSet, outputSet, inputTestSet, outputTestSet};
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}
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// MULTIVARIATE SUPERVISED
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void Data::setData(int k, std::string fileName, std::vector<std::vector<double>>& inputSet, std::vector<double>& outputSet){
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LinAlg alg;
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std::string inputTemp;
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std::string outputTemp;
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inputSet.resize(k);
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std::ifstream dataFile(fileName);
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if(!dataFile.is_open()){
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std::cout << fileName << " failed to open." << std::endl;
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}
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std::string line;
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while(std::getline(dataFile, line)){
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std::stringstream ss(line);
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for(int i = 0; i < k; i++){
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std::getline(ss, inputTemp, ',');
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inputSet[i].push_back(std::stod(inputTemp));
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}
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std::getline(ss, outputTemp, ',');
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outputSet.push_back(std::stod(outputTemp));
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}
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inputSet = alg.transpose(inputSet);
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dataFile.close();
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}
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void Data::printData(std::vector <std::string> inputName, std::string outputName, std::vector<std::vector<double>> inputSet, std::vector<double> outputSet){
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LinAlg alg;
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inputSet = alg.transpose(inputSet);
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for(int i = 0; i < inputSet.size(); i++){
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std::cout << inputName[i] << std::endl;
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for(int j = 0; j < inputSet[i].size(); j++){
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std::cout << inputSet[i][j] << std::endl;
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}
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}
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std::cout << outputName << std::endl;
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for(int i = 0; i < outputSet.size(); i++){
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std::cout << outputSet[i] << std::endl;
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}
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}
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// UNSUPERVISED
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void Data::setData(int k, std::string fileName, std::vector<std::vector<double>>& inputSet){
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LinAlg alg;
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std::string inputTemp;
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inputSet.resize(k);
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std::ifstream dataFile(fileName);
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if(!dataFile.is_open()){
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std::cout << fileName << " failed to open." << std::endl;
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}
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std::string line;
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while(std::getline(dataFile, line)){
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std::stringstream ss(line);
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for(int i = 0; i < k; i++){
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std::getline(ss, inputTemp, ',');
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inputSet[i].push_back(std::stod(inputTemp));
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}
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}
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inputSet = alg.transpose(inputSet);
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dataFile.close();
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}
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void Data::printData(std::vector <std::string> inputName, std::vector<std::vector<double>> inputSet){
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LinAlg alg;
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inputSet = alg.transpose(inputSet);
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for(int i = 0; i < inputSet.size(); i++){
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std::cout << inputName[i] << std::endl;
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for(int j = 0; j < inputSet[i].size(); j++){
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std::cout << inputSet[i][j] << std::endl;
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}
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}
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}
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// SIMPLE
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void Data::setData(std::string fileName, std::vector <double>& inputSet, std::vector <double>& outputSet){
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std::string inputTemp, outputTemp;
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std::ifstream dataFile(fileName);
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if(!dataFile.is_open()){
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std::cout << "The file failed to open." << std::endl;
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}
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std::string line;
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while(std::getline(dataFile, line)){
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std::stringstream ss(line);
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std::getline(ss, inputTemp, ',');
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std::getline(ss, outputTemp, ',');
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inputSet.push_back(std::stod(inputTemp));
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outputSet.push_back(std::stod(outputTemp));
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}
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dataFile.close();
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}
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void Data::printData(std::string& inputName, std::string& outputName, std::vector <double>& inputSet, std::vector <double>& outputSet){
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std::cout << inputName << std::endl;
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for(int i = 0; i < inputSet.size(); i++){
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std::cout << inputSet[i] << std::endl;
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}
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std::cout << outputName << std::endl;
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for(int i = 0; i < inputSet.size(); i++){
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std::cout << outputSet[i] << std::endl;
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}
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}
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// Images
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std::vector<std::vector<double>> Data::rgb2gray(std::vector<std::vector<std::vector<double>>> input){
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std::vector<std::vector<double>> grayScale;
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grayScale.resize(input[0].size());
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for(int i = 0; i < grayScale.size(); i++){
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grayScale[i].resize(input[0][i].size());
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}
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for(int i = 0; i < grayScale.size(); i++){
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for(int j = 0; j < grayScale[i].size(); j++){
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grayScale[i][j] = 0.299 * input[0][i][j] + 0.587 * input[1][i][j] + 0.114 * input[2][i][j];
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}
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}
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return grayScale;
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}
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std::vector<std::vector<std::vector<double>>> Data::rgb2ycbcr(std::vector<std::vector<std::vector<double>>> input){
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LinAlg alg;
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std::vector<std::vector<std::vector<double>>> YCbCr;
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YCbCr = alg.resize(YCbCr, input);
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for(int i = 0; i < YCbCr[0].size(); i++){
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for(int j = 0; j < YCbCr[0][i].size(); j++){
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YCbCr[0][i][j] = 0.299 * input[0][i][j] + 0.587 * input[1][i][j] + 0.114 * input[2][i][j];
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YCbCr[1][i][j] = -0.169 * input[0][i][j] - 0.331 * input[1][i][j] + 0.500 * input[2][i][j];
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YCbCr[2][i][j] = 0.500 * input[0][i][j] - 0.419 * input[1][i][j] - 0.081 * input[2][i][j];
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}
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}
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return YCbCr;
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}
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// Conversion formulas available here:
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// https://www.rapidtables.com/convert/color/rgb-to-hsv.html
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std::vector<std::vector<std::vector<double>>> Data::rgb2hsv(std::vector<std::vector<std::vector<double>>> input){
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LinAlg alg;
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std::vector<std::vector<std::vector<double>>> HSV;
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HSV = alg.resize(HSV, input);
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for(int i = 0; i < HSV[0].size(); i++){
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for(int j = 0; j < HSV[0][i].size(); j++){
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double rPrime = input[0][i][j] / 255;
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double gPrime = input[1][i][j] / 255;
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double bPrime = input[2][i][j] / 255;
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double cMax = alg.max({rPrime, gPrime, bPrime});
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double cMin = alg.min({rPrime, gPrime, bPrime});
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double delta = cMax - cMin;
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// H calculation.
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if(delta == 0){
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HSV[0][i][j] = 0;
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}
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else{
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if(cMax == rPrime){
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HSV[0][i][j] = 60 * fmod(((gPrime - bPrime) / delta), 6);
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}
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else if(cMax == gPrime){
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HSV[0][i][j] = 60 * ( (bPrime - rPrime) / delta + 2);
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}
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else{ // cMax == bPrime
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HSV[0][i][j] = 60 * ( (rPrime - gPrime) / delta + 6);
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}
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}
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// S calculation.
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if(cMax == 0){
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HSV[1][i][j] = 0;
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}
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else{ HSV[1][i][j] = delta/cMax; }
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// V calculation.
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HSV[2][i][j] = cMax;
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}
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}
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return HSV;
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}
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// http://machinethatsees.blogspot.com/2013/07/how-to-convert-rgb-to-xyz-or-vice-versa.html
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std::vector<std::vector<std::vector<double>>> Data::rgb2xyz(std::vector<std::vector<std::vector<double>>> input){
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LinAlg alg;
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std::vector<std::vector<std::vector<double>>> XYZ;
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XYZ = alg.resize(XYZ, input);
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std::vector<std::vector<double>> RGB2XYZ = {{0.4124564, 0.3575761, 0.1804375}, {0.2126726, 0.7151522, 0.0721750}, {0.0193339, 0.1191920, 0.9503041}};
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return alg.vector_wise_tensor_product(input, RGB2XYZ);
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}
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std::vector<std::vector<std::vector<double>>> Data::xyz2rgb(std::vector<std::vector<std::vector<double>>> input){
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LinAlg alg;
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std::vector<std::vector<std::vector<double>>> XYZ;
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XYZ = alg.resize(XYZ, input);
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std::vector<std::vector<double>> RGB2XYZ = alg.inverse({{0.4124564, 0.3575761, 0.1804375}, {0.2126726, 0.7151522, 0.0721750}, {0.0193339, 0.1191920, 0.9503041}});
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return alg.vector_wise_tensor_product(input, RGB2XYZ);
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}
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// TEXT-BASED & NLP
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std::string Data::toLower(std::string text){
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for(int i = 0; i < text.size(); i++){
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text[i] = tolower(text[i]);
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}
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return text;
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}
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std::vector<char> Data::split(std::string text){
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std::vector<char> split_data;
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for(int i = 0; i < text.size(); i++){
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split_data.push_back(text[i]);
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}
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return split_data;
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}
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std::vector<std::string> Data::splitSentences(std::string data){
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std::vector<std::string> sentences;
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std::string currentStr = "";
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for(int i = 0; i < data.length(); i++){
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currentStr.push_back(data[i]);
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if(data[i] == '.' && data[i + 1] != '.'){
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sentences.push_back(currentStr);
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currentStr = "";
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i++;
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}
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}
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return sentences;
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}
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std::vector<std::string> Data::removeSpaces(std::vector<std::string> data){
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for(int i = 0; i < data.size(); i++){
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auto it = data[i].begin();
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for(int j = 0; j < data[i].length(); j++){
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if(data[i][j] == ' '){
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data[i].erase(it);
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}
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it++;
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}
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}
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return data;
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}
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std::vector<std::string> Data::removeNullByte(std::vector<std::string> data){
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for(int i = 0; i < data.size(); i++){
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if(data[i] == "\0"){
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data.erase(data.begin() + i);
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}
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}
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return data;
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}
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std::vector<std::string> Data::segment(std::string text){
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std::vector<std::string> segmented_data;
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int prev_delim = 0;
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for(int i = 0; i < text.length(); i++){
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if(text[i] == ' '){
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segmented_data.push_back(text.substr(prev_delim, i - prev_delim));
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prev_delim = i + 1;
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}
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else if(text[i] == ',' || text[i] == '!' || text[i] == '.' || text[i] == '-'){
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segmented_data.push_back(text.substr(prev_delim, i - prev_delim));
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std::string punc;
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punc.push_back(text[i]);
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segmented_data.push_back(punc);
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prev_delim = i + 2;
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i++;
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}
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else if(i == text.length() - 1){
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segmented_data.push_back(text.substr(prev_delim, text.length() - prev_delim)); // hehe oops- forgot this
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}
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}
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return segmented_data;
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}
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std::vector<double> Data::tokenize(std::string text){
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int max_num = 0;
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bool new_num = true;
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std::vector<std::string> segmented_data = segment(text);
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std::vector<double> tokenized_data;
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tokenized_data.resize(segmented_data.size());
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for(int i = 0; i < segmented_data.size(); i++){
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for(int j = i - 1; j >= 0; j--){
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if(segmented_data[i] == segmented_data[j]){
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tokenized_data[i] = tokenized_data[j];
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new_num = false;
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}
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}
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if(!new_num){
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new_num = true;
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}
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else{
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max_num++;
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tokenized_data[i] = max_num;
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}
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}
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return tokenized_data;
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}
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std::vector<std::string> Data::removeStopWords(std::string text){
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std::vector<std::string> stopWords = {"i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself", "yourselves", "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself", "they", "them", "their", "theirs", "themselves", "what", "which", "who", "whom", "this", "that", "these", "those", "am", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having", "do", "does", "did", "doing", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until", "while", "of", "at", "by", "for", "with", "about", "against", "between", "into", "through", "during", "before", "after", "above", "below", "to", "from", "up", "down", "in", "out", "on", "off", "over", "under", "again", "further", "then", "once", "here", "there", "when", "where", "why", "how", "all", "any", "both", "each", "few", "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so", "than", "too", "very", "s", "t", "can", "will", "just", "don", "should", "now"};
|
|
std::vector<std::string> segmented_data = removeSpaces(segment(toLower(text)));
|
|
|
|
for(int i = 0; i < stopWords.size(); i++){
|
|
for(int j = 0; j < segmented_data.size(); j++){
|
|
if(segmented_data[j] == stopWords[i]){
|
|
segmented_data.erase(segmented_data.begin() + j);
|
|
}
|
|
}
|
|
}
|
|
return segmented_data;
|
|
}
|
|
|
|
std::vector<std::string> Data::removeStopWords(std::vector<std::string> segmented_data){
|
|
std::vector<std::string> stopWords = {"i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself", "yourselves", "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself", "they", "them", "their", "theirs", "themselves", "what", "which", "who", "whom", "this", "that", "these", "those", "am", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having", "do", "does", "did", "doing", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until", "while", "of", "at", "by", "for", "with", "about", "against", "between", "into", "through", "during", "before", "after", "above", "below", "to", "from", "up", "down", "in", "out", "on", "off", "over", "under", "again", "further", "then", "once", "here", "there", "when", "where", "why", "how", "all", "any", "both", "each", "few", "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so", "than", "too", "very", "s", "t", "can", "will", "just", "don", "should", "now"};
|
|
for(int i = 0; i < segmented_data.size(); i++){
|
|
for(int j = 0; j < stopWords.size(); j++){
|
|
if(segmented_data[i] == stopWords[j]){
|
|
segmented_data.erase(segmented_data.begin() + i);
|
|
}
|
|
}
|
|
}
|
|
return segmented_data;
|
|
}
|
|
|
|
std::string Data::stemming(std::string text){
|
|
|
|
// Our list of suffixes which we use to compare against
|
|
std::vector<std::string> suffixes = {"eer", "er", "ion", "ity", "ment", "ness", "or", "sion", "ship", "th", "able", "ible", "al", "ant", "ary", "ful", "ic", "ious", "ous", "ive", "less", "y", "ed", "en", "ing", "ize", "ise", "ly", "ward", "wise"};
|
|
int padding_size = 4;
|
|
char padding = ' '; // our padding
|
|
|
|
for(int i = 0; i < padding_size; i++){
|
|
text[text.length() + i] = padding; // ' ' will be our padding value
|
|
}
|
|
|
|
|
|
for(int i = 0; i < text.size(); i++){
|
|
for(int j = 0; j < suffixes.size(); j++){
|
|
if(text.substr(i, suffixes[j].length()) == suffixes[j] && (text[i + suffixes[j].length()] == ' ' || text[i + suffixes[j].length()] == ',' || text[i + suffixes[j].length()] == '-' || text[i + suffixes[j].length()] == '.' || text[i + suffixes[j].length()] == '!')){
|
|
text.erase(i, suffixes[j].length());
|
|
}
|
|
}
|
|
}
|
|
|
|
return text;
|
|
}
|
|
|
|
std::vector<std::vector<double>> Data::BOW(std::vector<std::string> sentences, std::string type){
|
|
/*
|
|
STEPS OF BOW:
|
|
1) To lowercase (done by removeStopWords function by def)
|
|
2) Removing stop words
|
|
3) Obtain a list of the used words
|
|
4) Create a one hot encoded vector of the words and sentences
|
|
5) Sentence.size() x list.size() matrix
|
|
*/
|
|
|
|
std::vector<std::string> wordList = removeNullByte(removeStopWords(createWordList(sentences)));
|
|
|
|
std::vector<std::vector<std::string>> segmented_sentences;
|
|
segmented_sentences.resize(sentences.size());
|
|
|
|
for(int i = 0; i < sentences.size(); i++){
|
|
segmented_sentences[i] = removeStopWords(sentences[i]);
|
|
}
|
|
|
|
std::vector<std::vector<double>> bow;
|
|
|
|
bow.resize(sentences.size());
|
|
for(int i = 0; i < bow.size(); i++){
|
|
bow[i].resize(wordList.size());
|
|
}
|
|
|
|
|
|
for(int i = 0; i < segmented_sentences.size(); i++){
|
|
for(int j = 0; j < segmented_sentences[i].size(); j++){
|
|
for(int k = 0; k < wordList.size(); k++){
|
|
if(segmented_sentences[i][j] == wordList[k]){
|
|
if(type == "Binary"){
|
|
bow[i][k] = 1;
|
|
}
|
|
else{
|
|
bow[i][k]++;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
return bow;
|
|
}
|
|
|
|
std::vector<std::vector<double>> Data::TFIDF(std::vector<std::string> sentences){
|
|
LinAlg alg;
|
|
std::vector<std::string> wordList = removeNullByte(removeStopWords(createWordList(sentences)));
|
|
|
|
std::vector<std::vector<std::string>> segmented_sentences;
|
|
segmented_sentences.resize(sentences.size());
|
|
|
|
for(int i = 0; i < sentences.size(); i++){
|
|
segmented_sentences[i] = removeStopWords(sentences[i]);
|
|
}
|
|
|
|
std::vector<std::vector<double>> TF;
|
|
std::vector<int> frequency;
|
|
frequency.resize(wordList.size());
|
|
TF.resize(segmented_sentences.size());
|
|
for(int i = 0; i < TF.size(); i++){
|
|
TF[i].resize(wordList.size());
|
|
}
|
|
for(int i = 0; i < segmented_sentences.size(); i++){
|
|
std::vector<bool> present(wordList.size(), 0);
|
|
for(int j = 0; j < segmented_sentences[i].size(); j++){
|
|
for(int k = 0; k < wordList.size(); k++){
|
|
if(segmented_sentences[i][j] == wordList[k]){
|
|
TF[i][k]++;
|
|
if(!present[k]){
|
|
frequency[k]++;
|
|
present[k] = true;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
TF[i] = alg.scalarMultiply(double(1) / double(segmented_sentences[i].size()), TF[i]);
|
|
}
|
|
|
|
std::vector<double> IDF;
|
|
IDF.resize(frequency.size());
|
|
|
|
for(int i = 0; i < IDF.size(); i++){
|
|
IDF[i] = std::log((double)segmented_sentences.size() / (double)frequency[i]);
|
|
}
|
|
|
|
std::vector<std::vector<double>> TFIDF;
|
|
TFIDF.resize(segmented_sentences.size());
|
|
for(int i = 0; i < TFIDF.size(); i++){
|
|
TFIDF[i].resize(wordList.size());
|
|
}
|
|
|
|
for(int i = 0; i < TFIDF.size(); i++){
|
|
for(int j = 0; j < TFIDF[i].size(); j++){
|
|
TFIDF[i][j] = TF[i][j] * IDF[j];
|
|
}
|
|
}
|
|
|
|
return TFIDF;
|
|
}
|
|
|
|
std::tuple<std::vector<std::vector<double>>, std::vector<std::string>> Data::word2Vec(std::vector<std::string> sentences, std::string type, int windowSize, int dimension, double learning_rate, int max_epoch){
|
|
std::vector<std::string> wordList = removeNullByte(removeStopWords(createWordList(sentences)));
|
|
|
|
std::vector<std::vector<std::string>> segmented_sentences;
|
|
segmented_sentences.resize(sentences.size());
|
|
|
|
for(int i = 0; i < sentences.size(); i++){
|
|
segmented_sentences[i] = removeStopWords(sentences[i]);
|
|
}
|
|
|
|
std::vector<std::string> inputStrings;
|
|
std::vector<std::string> outputStrings;
|
|
|
|
for(int i = 0; i < segmented_sentences.size(); i++){
|
|
for(int j = 0; j < segmented_sentences[i].size(); j++){
|
|
for(int k = windowSize; k > 0; k--){
|
|
if(j - k >= 0){
|
|
inputStrings.push_back(segmented_sentences[i][j]);
|
|
|
|
outputStrings.push_back(segmented_sentences[i][j - k]);
|
|
}
|
|
if(j + k <= segmented_sentences[i].size() - 1){
|
|
inputStrings.push_back(segmented_sentences[i][j]);
|
|
outputStrings.push_back(segmented_sentences[i][j + k]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
int inputSize = inputStrings.size();
|
|
|
|
inputStrings.insert(inputStrings.end(), outputStrings.begin(), outputStrings.end());
|
|
|
|
std::vector<std::vector<double>> BOW = Data::BOW(inputStrings, "Binary");
|
|
|
|
std::vector<std::vector<double>> inputSet;
|
|
std::vector<std::vector<double>> outputSet;
|
|
|
|
for(int i = 0; i < inputSize; i++){
|
|
inputSet.push_back(BOW[i]);
|
|
}
|
|
|
|
for(int i = inputSize; i < BOW.size(); i++){
|
|
outputSet.push_back(BOW[i]);
|
|
}
|
|
LinAlg alg;
|
|
SoftmaxNet* model;
|
|
if(type == "Skipgram"){
|
|
model = new SoftmaxNet(outputSet, inputSet, dimension);
|
|
}
|
|
else { // else = CBOW. We maintain it is a default.
|
|
model = new SoftmaxNet(inputSet, outputSet, dimension);
|
|
}
|
|
model->gradientDescent(learning_rate, max_epoch, 1);
|
|
|
|
std::vector<std::vector<double>> wordEmbeddings = model->getEmbeddings();
|
|
delete model;
|
|
return {wordEmbeddings, wordList};
|
|
}
|
|
|
|
std::vector<std::vector<double>> Data::LSA(std::vector<std::string> sentences, int dim){
|
|
LinAlg alg;
|
|
std::vector<std::vector<double>> docWordData = BOW(sentences, "Binary");
|
|
|
|
auto [U, S, Vt] = alg.SVD(docWordData);
|
|
std::vector<std::vector<double>> S_trunc = alg.zeromat(dim, dim);
|
|
std::vector<std::vector<double>> Vt_trunc;
|
|
for(int i = 0; i < dim; i++){
|
|
S_trunc[i][i] = S[i][i];
|
|
Vt_trunc.push_back(Vt[i]);
|
|
}
|
|
|
|
std::vector<std::vector<double>> embeddings = alg.matmult(S_trunc, Vt_trunc);
|
|
return embeddings;
|
|
}
|
|
|
|
std::vector<std::string> Data::createWordList(std::vector<std::string> sentences){
|
|
std::string combinedText = "";
|
|
for(int i = 0; i < sentences.size(); i++){
|
|
if(i != 0){ combinedText += " "; }
|
|
combinedText += sentences[i];
|
|
}
|
|
|
|
return removeSpaces(vecToSet(removeStopWords(combinedText)));
|
|
}
|
|
|
|
// EXTRA
|
|
void Data::setInputNames(std::string fileName, std::vector<std::string>& inputNames){
|
|
std::string inputNameTemp;
|
|
std::ifstream dataFile(fileName);
|
|
if(!dataFile.is_open()){
|
|
std::cout << fileName << " failed to open." << std::endl;
|
|
}
|
|
|
|
while (std::getline(dataFile, inputNameTemp))
|
|
{
|
|
inputNames.push_back(inputNameTemp);
|
|
}
|
|
|
|
dataFile.close();
|
|
}
|
|
|
|
std::vector<std::vector<double>> Data::featureScaling(std::vector<std::vector<double>> X){
|
|
LinAlg alg;
|
|
X = alg.transpose(X);
|
|
std::vector<double> max_elements, min_elements;
|
|
max_elements.resize(X.size());
|
|
min_elements.resize(X.size());
|
|
|
|
for(int i = 0; i < X.size(); i++){
|
|
max_elements[i] = alg.max(X[i]);
|
|
min_elements[i] = alg.min(X[i]);
|
|
}
|
|
|
|
for(int i = 0; i < X.size(); i++){
|
|
for(int j = 0; j < X[i].size(); j++){
|
|
X[i][j] = (X[i][j] - min_elements[i]) / (max_elements[i] - min_elements[i]);
|
|
}
|
|
}
|
|
return alg.transpose(X);
|
|
}
|
|
|
|
|
|
std::vector<std::vector<double>> Data::meanNormalization(std::vector<std::vector<double>> X){
|
|
LinAlg alg;
|
|
Stat stat;
|
|
// (X_j - mu_j) / std_j, for every j
|
|
|
|
X = meanCentering(X);
|
|
for(int i = 0; i < X.size(); i++){
|
|
X[i] = alg.scalarMultiply(1/stat.standardDeviation(X[i]), X[i]);
|
|
}
|
|
return X;
|
|
}
|
|
|
|
std::vector<std::vector<double>> Data::meanCentering(std::vector<std::vector<double>> X){
|
|
LinAlg alg;
|
|
Stat stat;
|
|
for(int i = 0; i < X.size(); i++){
|
|
double mean_i = stat.mean(X[i]);
|
|
for(int j = 0; j < X[i].size(); j++){
|
|
X[i][j] -= mean_i;
|
|
}
|
|
}
|
|
return X;
|
|
}
|
|
|
|
std::vector<std::vector<double>> Data::oneHotRep(std::vector<double> tempOutputSet, int n_class){
|
|
std::vector<std::vector<double>> outputSet;
|
|
outputSet.resize(tempOutputSet.size());
|
|
for(int i = 0; i < tempOutputSet.size(); i++){
|
|
for(int j = 0; j <= n_class - 1; j++){
|
|
if(tempOutputSet[i] == j){
|
|
outputSet[i].push_back(1);
|
|
}
|
|
else{
|
|
outputSet[i].push_back(0);
|
|
}
|
|
}
|
|
}
|
|
return outputSet;
|
|
}
|
|
|
|
std::vector<double> Data::reverseOneHot(std::vector<std::vector<double>> tempOutputSet){
|
|
std::vector<double> outputSet;
|
|
int n_class = tempOutputSet[0].size();
|
|
for(int i = 0; i < tempOutputSet.size(); i++){
|
|
int current_class = 1;
|
|
for(int j = 0; j < tempOutputSet[i].size(); j++){
|
|
if(tempOutputSet[i][j] == 1){
|
|
break;
|
|
}
|
|
else{
|
|
current_class++;
|
|
}
|
|
}
|
|
outputSet.push_back(current_class);
|
|
}
|
|
|
|
return outputSet;
|
|
}
|
|
}
|