pmlpp/mlpp/data/data_old.cpp

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//
// Data.cpp
// MLP
//
// Created by Marc Melikyan on 11/4/20.
//
#include "data_old.h"
#include "core/os/file_access.h"
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#include "../lin_alg/lin_alg_old.h"
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#include "../softmax_net/softmax_net_old.h"
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#include "../stat/stat_old.h"
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#include <algorithm>
#include <cmath>
#include <fstream>
#include <iostream>
#include <random>
#include <sstream>
// Loading Datasets
std::tuple<std::vector<std::vector<real_t>>, std::vector<real_t>> MLPPDataOld::loadBreastCancer() {
const int BREAST_CANCER_SIZE = 30; // k = 30
std::vector<std::vector<real_t>> inputSet;
std::vector<real_t> outputSet;
setData(BREAST_CANCER_SIZE, "MLPP/Data/Datasets/BreastCancer.csv", inputSet, outputSet);
return { inputSet, outputSet };
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<real_t>> MLPPDataOld::loadBreastCancerSVC() {
const int BREAST_CANCER_SIZE = 30; // k = 30
std::vector<std::vector<real_t>> inputSet;
std::vector<real_t> outputSet;
setData(BREAST_CANCER_SIZE, "MLPP/Data/Datasets/BreastCancerSVM.csv", inputSet, outputSet);
return { inputSet, outputSet };
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPDataOld::loadIris() {
const int IRIS_SIZE = 4;
const int ONE_HOT_NUM = 3;
std::vector<std::vector<real_t>> inputSet;
std::vector<real_t> tempOutputSet;
setData(IRIS_SIZE, "/Users/marcmelikyan/Desktop/Data/Iris.csv", inputSet, tempOutputSet);
std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
return { inputSet, outputSet };
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPDataOld::loadWine() {
const int WINE_SIZE = 4;
const int ONE_HOT_NUM = 3;
std::vector<std::vector<real_t>> inputSet;
std::vector<real_t> tempOutputSet;
setData(WINE_SIZE, "MLPP/Data/Datasets/Iris.csv", inputSet, tempOutputSet);
std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
return { inputSet, outputSet };
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPDataOld::loadMnistTrain() {
const int MNIST_SIZE = 784;
const int ONE_HOT_NUM = 10;
std::vector<std::vector<real_t>> inputSet;
std::vector<real_t> tempOutputSet;
setData(MNIST_SIZE, "MLPP/Data/Datasets/MnistTrain.csv", inputSet, tempOutputSet);
std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
return { inputSet, outputSet };
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPDataOld::loadMnistTest() {
const int MNIST_SIZE = 784;
const int ONE_HOT_NUM = 10;
std::vector<std::vector<real_t>> inputSet;
std::vector<real_t> tempOutputSet;
setData(MNIST_SIZE, "MLPP/Data/Datasets/MnistTest.csv", inputSet, tempOutputSet);
std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
return { inputSet, outputSet };
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<real_t>> MLPPDataOld::loadCaliforniaHousing() {
const int CALIFORNIA_HOUSING_SIZE = 13; // k = 30
std::vector<std::vector<real_t>> inputSet;
std::vector<real_t> outputSet;
setData(CALIFORNIA_HOUSING_SIZE, "MLPP/Data/Datasets/CaliforniaHousing.csv", inputSet, outputSet);
return { inputSet, outputSet };
}
std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPDataOld::loadFiresAndCrime() {
std::vector<real_t> inputSet; // k is implicitly 1.
std::vector<real_t> outputSet;
setData("MLPP/Data/Datasets/FiresAndCrime.csv", inputSet, outputSet);
return { inputSet, outputSet };
}
// Note that inputs and outputs should be pairs (technically), but this
// implementation will separate them. (My implementation keeps them tied together.)
// Not yet sure whether this is intentional or not (or it's something like a compiler specific difference)
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPDataOld::trainTestSplit(std::vector<std::vector<real_t>> inputSet, std::vector<std::vector<real_t>> outputSet, real_t testSize) {
std::random_device rd;
std::default_random_engine generator(rd());
std::shuffle(inputSet.begin(), inputSet.end(), generator); // inputSet random shuffle
std::shuffle(outputSet.begin(), outputSet.end(), generator); // outputSet random shuffle)
std::vector<std::vector<real_t>> inputTestSet;
std::vector<std::vector<real_t>> outputTestSet;
int testInputNumber = testSize * inputSet.size(); // implicit usage of floor
int testOutputNumber = testSize * outputSet.size(); // implicit usage of floor
for (int i = 0; i < testInputNumber; i++) {
inputTestSet.push_back(inputSet[i]);
inputSet.erase(inputSet.begin());
}
for (int i = 0; i < testOutputNumber; i++) {
outputTestSet.push_back(outputSet[i]);
outputSet.erase(outputSet.begin());
}
return { inputSet, outputSet, inputTestSet, outputTestSet };
}
// MULTIVARIATE SUPERVISED
void MLPPDataOld::setData(int k, std::string fileName, std::vector<std::vector<real_t>> &inputSet, std::vector<real_t> &outputSet) {
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MLPPLinAlgOld alg;
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std::string inputTemp;
std::string outputTemp;
inputSet.resize(k);
std::ifstream dataFile(fileName);
if (!dataFile.is_open()) {
std::cout << fileName << " failed to open." << std::endl;
}
std::string line;
while (std::getline(dataFile, line)) {
std::stringstream ss(line);
for (int i = 0; i < k; i++) {
std::getline(ss, inputTemp, ',');
inputSet[i].push_back(std::stod(inputTemp));
}
std::getline(ss, outputTemp, ',');
outputSet.push_back(std::stod(outputTemp));
}
inputSet = alg.transpose(inputSet);
dataFile.close();
}
void MLPPDataOld::printData(std::vector<std::string> inputName, std::string outputName, std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet) {
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MLPPLinAlgOld alg;
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inputSet = alg.transpose(inputSet);
for (uint32_t i = 0; i < inputSet.size(); i++) {
std::cout << inputName[i] << std::endl;
for (uint32_t j = 0; j < inputSet[i].size(); j++) {
std::cout << inputSet[i][j] << std::endl;
}
}
std::cout << outputName << std::endl;
for (uint32_t i = 0; i < outputSet.size(); i++) {
std::cout << outputSet[i] << std::endl;
}
}
// UNSUPERVISED
void MLPPDataOld::setData(int k, std::string fileName, std::vector<std::vector<real_t>> &inputSet) {
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MLPPLinAlgOld alg;
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std::string inputTemp;
inputSet.resize(k);
std::ifstream dataFile(fileName);
if (!dataFile.is_open()) {
std::cout << fileName << " failed to open." << std::endl;
}
std::string line;
while (std::getline(dataFile, line)) {
std::stringstream ss(line);
for (int i = 0; i < k; i++) {
std::getline(ss, inputTemp, ',');
inputSet[i].push_back(std::stod(inputTemp));
}
}
inputSet = alg.transpose(inputSet);
dataFile.close();
}
void MLPPDataOld::printData(std::vector<std::string> inputName, std::vector<std::vector<real_t>> inputSet) {
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MLPPLinAlgOld alg;
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inputSet = alg.transpose(inputSet);
for (uint32_t i = 0; i < inputSet.size(); i++) {
std::cout << inputName[i] << std::endl;
for (uint32_t j = 0; j < inputSet[i].size(); j++) {
std::cout << inputSet[i][j] << std::endl;
}
}
}
// SIMPLE
void MLPPDataOld::setData(std::string fileName, std::vector<real_t> &inputSet, std::vector<real_t> &outputSet) {
std::string inputTemp, outputTemp;
std::ifstream dataFile(fileName);
if (!dataFile.is_open()) {
std::cout << "The file failed to open." << std::endl;
}
std::string line;
while (std::getline(dataFile, line)) {
std::stringstream ss(line);
std::getline(ss, inputTemp, ',');
std::getline(ss, outputTemp, ',');
inputSet.push_back(std::stod(inputTemp));
outputSet.push_back(std::stod(outputTemp));
}
dataFile.close();
}
void MLPPDataOld::printData(std::string &inputName, std::string &outputName, std::vector<real_t> &inputSet, std::vector<real_t> &outputSet) {
std::cout << inputName << std::endl;
for (uint32_t i = 0; i < inputSet.size(); i++) {
std::cout << inputSet[i] << std::endl;
}
std::cout << outputName << std::endl;
for (uint32_t i = 0; i < inputSet.size(); i++) {
std::cout << outputSet[i] << std::endl;
}
}
// Images
std::vector<std::vector<real_t>> MLPPDataOld::rgb2gray(std::vector<std::vector<std::vector<real_t>>> input) {
std::vector<std::vector<real_t>> grayScale;
grayScale.resize(input[0].size());
for (uint32_t i = 0; i < grayScale.size(); i++) {
grayScale[i].resize(input[0][i].size());
}
for (uint32_t i = 0; i < grayScale.size(); i++) {
for (uint32_t j = 0; j < grayScale[i].size(); j++) {
grayScale[i][j] = 0.299 * input[0][i][j] + 0.587 * input[1][i][j] + 0.114 * input[2][i][j];
}
}
return grayScale;
}
std::vector<std::vector<std::vector<real_t>>> MLPPDataOld::rgb2ycbcr(std::vector<std::vector<std::vector<real_t>>> input) {
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MLPPLinAlgOld alg;
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std::vector<std::vector<std::vector<real_t>>> YCbCr;
YCbCr = alg.resize(YCbCr, input);
for (uint32_t i = 0; i < YCbCr[0].size(); i++) {
for (uint32_t j = 0; j < YCbCr[0][i].size(); j++) {
YCbCr[0][i][j] = 0.299 * input[0][i][j] + 0.587 * input[1][i][j] + 0.114 * input[2][i][j];
YCbCr[1][i][j] = -0.169 * input[0][i][j] - 0.331 * input[1][i][j] + 0.500 * input[2][i][j];
YCbCr[2][i][j] = 0.500 * input[0][i][j] - 0.419 * input[1][i][j] - 0.081 * input[2][i][j];
}
}
return YCbCr;
}
// Conversion formulas available here:
// https://www.rapidtables.com/convert/color/rgb-to-hsv.html
std::vector<std::vector<std::vector<real_t>>> MLPPDataOld::rgb2hsv(std::vector<std::vector<std::vector<real_t>>> input) {
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MLPPLinAlgOld alg;
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std::vector<std::vector<std::vector<real_t>>> HSV;
HSV = alg.resize(HSV, input);
for (uint32_t i = 0; i < HSV[0].size(); i++) {
for (uint32_t j = 0; j < HSV[0][i].size(); j++) {
real_t rPrime = input[0][i][j] / 255;
real_t gPrime = input[1][i][j] / 255;
real_t bPrime = input[2][i][j] / 255;
real_t cMax = alg.max({ rPrime, gPrime, bPrime });
real_t cMin = alg.min({ rPrime, gPrime, bPrime });
real_t delta = cMax - cMin;
// H calculation.
if (delta == 0) {
HSV[0][i][j] = 0;
} else {
if (cMax == rPrime) {
HSV[0][i][j] = 60 * fmod(((gPrime - bPrime) / delta), 6);
} else if (cMax == gPrime) {
HSV[0][i][j] = 60 * ((bPrime - rPrime) / delta + 2);
} else { // cMax == bPrime
HSV[0][i][j] = 60 * ((rPrime - gPrime) / delta + 6);
}
}
// S calculation.
if (cMax == 0) {
HSV[1][i][j] = 0;
} else {
HSV[1][i][j] = delta / cMax;
}
// V calculation.
HSV[2][i][j] = cMax;
}
}
return HSV;
}
// http://machinethatsees.blogspot.com/2013/07/how-to-convert-rgb-to-xyz-or-vice-versa.html
std::vector<std::vector<std::vector<real_t>>> MLPPDataOld::rgb2xyz(std::vector<std::vector<std::vector<real_t>>> input) {
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MLPPLinAlgOld alg;
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std::vector<std::vector<std::vector<real_t>>> XYZ;
XYZ = alg.resize(XYZ, input);
std::vector<std::vector<real_t>> RGB2XYZ = { { 0.4124564, 0.3575761, 0.1804375 }, { 0.2126726, 0.7151522, 0.0721750 }, { 0.0193339, 0.1191920, 0.9503041 } };
return alg.vector_wise_tensor_product(input, RGB2XYZ);
}
std::vector<std::vector<std::vector<real_t>>> MLPPDataOld::xyz2rgb(std::vector<std::vector<std::vector<real_t>>> input) {
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MLPPLinAlgOld alg;
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std::vector<std::vector<std::vector<real_t>>> XYZ;
XYZ = alg.resize(XYZ, input);
std::vector<std::vector<real_t>> RGB2XYZ = alg.inverse({ { 0.4124564, 0.3575761, 0.1804375 }, { 0.2126726, 0.7151522, 0.0721750 }, { 0.0193339, 0.1191920, 0.9503041 } });
return alg.vector_wise_tensor_product(input, RGB2XYZ);
}
// TEXT-BASED & NLP
std::string MLPPDataOld::toLower(std::string text) {
for (uint32_t i = 0; i < text.size(); i++) {
text[i] = tolower(text[i]);
}
return text;
}
std::vector<char> MLPPDataOld::split(std::string text) {
std::vector<char> split_data;
for (uint32_t i = 0; i < text.size(); i++) {
split_data.push_back(text[i]);
}
return split_data;
}
std::vector<std::string> MLPPDataOld::splitSentences(std::string data) {
std::vector<std::string> sentences;
std::string currentStr = "";
for (uint32_t i = 0; i < data.length(); i++) {
currentStr.push_back(data[i]);
if (data[i] == '.' && data[i + 1] != '.') {
sentences.push_back(currentStr);
currentStr = "";
i++;
}
}
return sentences;
}
std::vector<std::string> MLPPDataOld::removeSpaces(std::vector<std::string> data) {
for (uint32_t i = 0; i < data.size(); i++) {
auto it = data[i].begin();
for (uint32_t j = 0; j < data[i].length(); j++) {
if (data[i][j] == ' ') {
data[i].erase(it);
}
it++;
}
}
return data;
}
std::vector<std::string> MLPPDataOld::removeNullByte(std::vector<std::string> data) {
for (uint32_t i = 0; i < data.size(); i++) {
if (data[i] == "\0") {
data.erase(data.begin() + i);
}
}
return data;
}
std::vector<std::string> MLPPDataOld::segment(std::string text) {
std::vector<std::string> segmented_data;
int prev_delim = 0;
for (uint32_t i = 0; i < text.length(); i++) {
if (text[i] == ' ') {
segmented_data.push_back(text.substr(prev_delim, i - prev_delim));
prev_delim = i + 1;
} else if (text[i] == ',' || text[i] == '!' || text[i] == '.' || text[i] == '-') {
segmented_data.push_back(text.substr(prev_delim, i - prev_delim));
std::string punc;
punc.push_back(text[i]);
segmented_data.push_back(punc);
prev_delim = i + 2;
i++;
} else if (i == text.length() - 1) {
segmented_data.push_back(text.substr(prev_delim, text.length() - prev_delim)); // hehe oops- forgot this
}
}
return segmented_data;
}
std::vector<real_t> MLPPDataOld::tokenize(std::string text) {
int max_num = 0;
bool new_num = true;
std::vector<std::string> segmented_data = segment(text);
std::vector<real_t> tokenized_data;
tokenized_data.resize(segmented_data.size());
for (uint32_t i = 0; i < segmented_data.size(); i++) {
for (int j = i - 1; j >= 0; j--) {
if (segmented_data[i] == segmented_data[j]) {
tokenized_data[i] = tokenized_data[j];
new_num = false;
}
}
if (!new_num) {
new_num = true;
} else {
max_num++;
tokenized_data[i] = max_num;
}
}
return tokenized_data;
}
std::vector<std::string> MLPPDataOld::removeStopWords(std::string text) {
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 (uint32_t i = 0; i < stopWords.size(); i++) {
for (uint32_t 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> MLPPDataOld::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 (uint32_t i = 0; i < segmented_data.size(); i++) {
for (uint32_t j = 0; j < stopWords.size(); j++) {
if (segmented_data[i] == stopWords[j]) {
segmented_data.erase(segmented_data.begin() + i);
}
}
}
return segmented_data;
}
std::string MLPPDataOld::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 (uint32_t i = 0; i < text.size(); i++) {
for (uint32_t 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<real_t>> MLPPDataOld::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 (uint32_t i = 0; i < sentences.size(); i++) {
segmented_sentences[i] = removeStopWords(sentences[i]);
}
std::vector<std::vector<real_t>> bow;
bow.resize(sentences.size());
for (uint32_t i = 0; i < bow.size(); i++) {
bow[i].resize(wordList.size());
}
for (uint32_t i = 0; i < segmented_sentences.size(); i++) {
for (uint32_t j = 0; j < segmented_sentences[i].size(); j++) {
for (uint32_t 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<real_t>> MLPPDataOld::TFIDF(std::vector<std::string> sentences) {
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MLPPLinAlgOld alg;
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std::vector<std::string> wordList = removeNullByte(removeStopWords(createWordList(sentences)));
std::vector<std::vector<std::string>> segmented_sentences;
segmented_sentences.resize(sentences.size());
for (uint32_t i = 0; i < sentences.size(); i++) {
segmented_sentences[i] = removeStopWords(sentences[i]);
}
std::vector<std::vector<real_t>> TF;
std::vector<int> frequency;
frequency.resize(wordList.size());
TF.resize(segmented_sentences.size());
for (uint32_t i = 0; i < TF.size(); i++) {
TF[i].resize(wordList.size());
}
for (uint32_t i = 0; i < segmented_sentences.size(); i++) {
std::vector<bool> present(wordList.size(), false);
for (uint32_t j = 0; j < segmented_sentences[i].size(); j++) {
for (uint32_t 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(real_t(1) / real_t(segmented_sentences[i].size()), TF[i]);
}
std::vector<real_t> IDF;
IDF.resize(frequency.size());
for (uint32_t i = 0; i < IDF.size(); i++) {
IDF[i] = std::log((real_t)segmented_sentences.size() / (real_t)frequency[i]);
}
std::vector<std::vector<real_t>> TFIDF;
TFIDF.resize(segmented_sentences.size());
for (uint32_t i = 0; i < TFIDF.size(); i++) {
TFIDF[i].resize(wordList.size());
}
for (uint32_t i = 0; i < TFIDF.size(); i++) {
for (uint32_t j = 0; j < TFIDF[i].size(); j++) {
TFIDF[i][j] = TF[i][j] * IDF[j];
}
}
return TFIDF;
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::string>> MLPPDataOld::word2Vec(std::vector<std::string> sentences, std::string type, int windowSize, int dimension, real_t 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 (uint32_t i = 0; i < sentences.size(); i++) {
segmented_sentences[i] = removeStopWords(sentences[i]);
}
std::vector<std::string> inputStrings;
std::vector<std::string> outputStrings;
for (uint32_t i = 0; i < segmented_sentences.size(); i++) {
for (uint32_t 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]);
}
}
}
}
uint32_t inputSize = inputStrings.size();
inputStrings.insert(inputStrings.end(), outputStrings.begin(), outputStrings.end());
std::vector<std::vector<real_t>> BOW = MLPPDataOld::BOW(inputStrings, "Binary");
std::vector<std::vector<real_t>> inputSet;
std::vector<std::vector<real_t>> outputSet;
for (uint32_t i = 0; i < inputSize; i++) {
inputSet.push_back(BOW[i]);
}
for (uint32_t i = inputSize; i < BOW.size(); i++) {
outputSet.push_back(BOW[i]);
}
MLPPSoftmaxNetOld *model;
if (type == "Skipgram") {
model = new MLPPSoftmaxNetOld(outputSet, inputSet, dimension);
} else { // else = CBOW. We maintain it is a default.
model = new MLPPSoftmaxNetOld(inputSet, outputSet, dimension);
}
model->gradientDescent(learning_rate, max_epoch, true);
std::vector<std::vector<real_t>> wordEmbeddings = model->getEmbeddings();
delete model;
return { wordEmbeddings, wordList };
}
struct WordsToVecResult {
std::vector<std::vector<real_t>> word_embeddings;
std::vector<std::string> word_list;
};
MLPPDataOld::WordsToVecResult MLPPDataOld::word_to_vec(std::vector<std::string> sentences, std::string type, int windowSize, int dimension, real_t learning_rate, int max_epoch) {
WordsToVecResult res;
res.word_list = removeNullByte(removeStopWords(createWordList(sentences)));
std::vector<std::vector<std::string>> segmented_sentences;
segmented_sentences.resize(sentences.size());
for (uint32_t i = 0; i < sentences.size(); i++) {
segmented_sentences[i] = removeStopWords(sentences[i]);
}
std::vector<std::string> inputStrings;
std::vector<std::string> outputStrings;
for (uint32_t i = 0; i < segmented_sentences.size(); i++) {
for (uint32_t 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]);
}
}
}
}
uint32_t inputSize = inputStrings.size();
inputStrings.insert(inputStrings.end(), outputStrings.begin(), outputStrings.end());
std::vector<std::vector<real_t>> BOW = MLPPDataOld::BOW(inputStrings, "Binary");
std::vector<std::vector<real_t>> inputSet;
std::vector<std::vector<real_t>> outputSet;
for (uint32_t i = 0; i < inputSize; i++) {
inputSet.push_back(BOW[i]);
}
for (uint32_t i = inputSize; i < BOW.size(); i++) {
outputSet.push_back(BOW[i]);
}
MLPPSoftmaxNetOld *model;
if (type == "Skipgram") {
model = new MLPPSoftmaxNetOld(outputSet, inputSet, dimension);
} else { // else = CBOW. We maintain it is a default.
model = new MLPPSoftmaxNetOld(inputSet, outputSet, dimension);
}
model->gradientDescent(learning_rate, max_epoch, false);
res.word_embeddings = model->getEmbeddings();
delete model;
return res;
}
std::vector<std::vector<real_t>> MLPPDataOld::LSA(std::vector<std::string> sentences, int dim) {
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MLPPLinAlgOld alg;
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std::vector<std::vector<real_t>> docWordData = BOW(sentences, "Binary");
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MLPPLinAlgOld::SVDResultOld svr_res = alg.SVD(docWordData);
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std::vector<std::vector<real_t>> S_trunc = alg.zeromat(dim, dim);
std::vector<std::vector<real_t>> Vt_trunc;
for (int i = 0; i < dim; i++) {
S_trunc[i][i] = svr_res.S[i][i];
Vt_trunc.push_back(svr_res.Vt[i]);
}
std::vector<std::vector<real_t>> embeddings = alg.matmult(S_trunc, Vt_trunc);
return embeddings;
}
std::vector<std::string> MLPPDataOld::createWordList(std::vector<std::string> sentences) {
std::string combinedText = "";
for (uint32_t i = 0; i < sentences.size(); i++) {
if (i != 0) {
combinedText += " ";
}
combinedText += sentences[i];
}
return removeSpaces(vecToSet(removeStopWords(combinedText)));
}
// EXTRA
void MLPPDataOld::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<real_t>> MLPPDataOld::featureScaling(std::vector<std::vector<real_t>> X) {
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MLPPLinAlgOld alg;
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X = alg.transpose(X);
std::vector<real_t> max_elements, min_elements;
max_elements.resize(X.size());
min_elements.resize(X.size());
for (uint32_t i = 0; i < X.size(); i++) {
max_elements[i] = alg.max(X[i]);
min_elements[i] = alg.min(X[i]);
}
for (uint32_t i = 0; i < X.size(); i++) {
for (uint32_t 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<real_t>> MLPPDataOld::meanNormalization(std::vector<std::vector<real_t>> X) {
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MLPPLinAlgOld alg;
MLPPStatOld stat;
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// (X_j - mu_j) / std_j, for every j
X = meanCentering(X);
for (uint32_t i = 0; i < X.size(); i++) {
X[i] = alg.scalarMultiply(1 / stat.standardDeviation(X[i]), X[i]);
}
return X;
}
std::vector<std::vector<real_t>> MLPPDataOld::meanCentering(std::vector<std::vector<real_t>> X) {
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MLPPStatOld stat;
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for (uint32_t i = 0; i < X.size(); i++) {
real_t mean_i = stat.mean(X[i]);
for (uint32_t j = 0; j < X[i].size(); j++) {
X[i][j] -= mean_i;
}
}
return X;
}
std::vector<std::vector<real_t>> MLPPDataOld::oneHotRep(std::vector<real_t> tempOutputSet, int n_class) {
std::vector<std::vector<real_t>> outputSet;
outputSet.resize(tempOutputSet.size());
for (uint32_t 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<real_t> MLPPDataOld::reverseOneHot(std::vector<std::vector<real_t>> tempOutputSet) {
std::vector<real_t> outputSet;
//uint32_t n_class = tempOutputSet[0].size();
for (uint32_t i = 0; i < tempOutputSet.size(); i++) {
int current_class = 1;
for (uint32_t j = 0; j < tempOutputSet[i].size(); j++) {
if (tempOutputSet[i][j] == 1) {
break;
} else {
current_class++;
}
}
outputSet.push_back(current_class);
}
return outputSet;
}