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Converted some methods in MLPPData.
This commit is contained in:
parent
65854b55fc
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14150405b0
@ -12,11 +12,9 @@
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#include "../lin_alg/lin_alg.h"
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#include "../stat/stat.h"
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#ifdef OLD_CLASSES_ENABLED
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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 "../softmax_net/softmax_net.h"
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#include "../stat/stat_old.h"
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#endif
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#include <algorithm>
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#include <cmath>
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@ -520,7 +518,6 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>, s
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// MULTIVARIATE SUPERVISED
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void MLPPData::setData(int k, std::string fileName, std::vector<std::vector<real_t>> &inputSet, std::vector<real_t> &outputSet) {
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#ifdef OLD_CLASSES_ENABLED
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MLPPLinAlgOld alg;
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std::string inputTemp;
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std::string outputTemp;
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@ -546,11 +543,9 @@ void MLPPData::setData(int k, std::string fileName, std::vector<std::vector<real
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}
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inputSet = alg.transpose(inputSet);
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dataFile.close();
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#endif
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}
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void MLPPData::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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#ifdef OLD_CLASSES_ENABLED
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MLPPLinAlgOld alg;
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inputSet = alg.transpose(inputSet);
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for (uint32_t i = 0; i < inputSet.size(); i++) {
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@ -564,13 +559,11 @@ void MLPPData::printData(std::vector<std::string> inputName, std::string outputN
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for (uint32_t i = 0; i < outputSet.size(); i++) {
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std::cout << outputSet[i] << std::endl;
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}
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#endif
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}
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// UNSUPERVISED
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void MLPPData::setData(int k, std::string fileName, std::vector<std::vector<real_t>> &inputSet) {
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#ifdef OLD_CLASSES_ENABLED
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MLPPLinAlgOld alg;
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std::string inputTemp;
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@ -592,11 +585,9 @@ void MLPPData::setData(int k, std::string fileName, std::vector<std::vector<real
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}
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inputSet = alg.transpose(inputSet);
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dataFile.close();
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#endif
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}
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void MLPPData::printData(std::vector<std::string> inputName, std::vector<std::vector<real_t>> inputSet) {
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#ifdef OLD_CLASSES_ENABLED
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MLPPLinAlgOld alg;
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inputSet = alg.transpose(inputSet);
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for (uint32_t i = 0; i < inputSet.size(); i++) {
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@ -605,7 +596,6 @@ void MLPPData::printData(std::vector<std::string> inputName, std::vector<std::ve
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std::cout << inputSet[i][j] << std::endl;
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}
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}
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#endif
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}
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// SIMPLE
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@ -661,7 +651,6 @@ std::vector<std::vector<real_t>> MLPPData::rgb2gray(std::vector<std::vector<std:
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}
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std::vector<std::vector<std::vector<real_t>>> MLPPData::rgb2ycbcr(std::vector<std::vector<std::vector<real_t>>> input) {
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#ifdef OLD_CLASSES_ENABLED
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MLPPLinAlgOld alg;
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std::vector<std::vector<std::vector<real_t>>> YCbCr;
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YCbCr = alg.resize(YCbCr, input);
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@ -673,15 +662,11 @@ std::vector<std::vector<std::vector<real_t>>> MLPPData::rgb2ycbcr(std::vector<st
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}
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}
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return YCbCr;
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#else
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return std::vector<std::vector<std::vector<real_t>>>();
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#endif
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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<real_t>>> MLPPData::rgb2hsv(std::vector<std::vector<std::vector<real_t>>> input) {
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#ifdef OLD_CLASSES_ENABLED
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MLPPLinAlgOld alg;
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std::vector<std::vector<std::vector<real_t>>> HSV;
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HSV = alg.resize(HSV, input);
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@ -720,34 +705,23 @@ std::vector<std::vector<std::vector<real_t>>> MLPPData::rgb2hsv(std::vector<std:
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}
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}
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return HSV;
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#else
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return std::vector<std::vector<std::vector<real_t>>>();
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#endif
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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<real_t>>> MLPPData::rgb2xyz(std::vector<std::vector<std::vector<real_t>>> input) {
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#ifdef OLD_CLASSES_ENABLED
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MLPPLinAlgOld alg;
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std::vector<std::vector<std::vector<real_t>>> XYZ;
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XYZ = alg.resize(XYZ, input);
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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 } };
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return alg.vector_wise_tensor_product(input, RGB2XYZ);
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#else
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return std::vector<std::vector<std::vector<real_t>>>();
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#endif
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}
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std::vector<std::vector<std::vector<real_t>>> MLPPData::xyz2rgb(std::vector<std::vector<std::vector<real_t>>> input) {
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#ifdef OLD_CLASSES_ENABLED
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MLPPLinAlgOld alg;
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std::vector<std::vector<std::vector<real_t>>> XYZ;
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XYZ = alg.resize(XYZ, input);
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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 } });
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return alg.vector_wise_tensor_product(input, RGB2XYZ);
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#else
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return std::vector<std::vector<std::vector<real_t>>>();
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#endif
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}
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// TEXT-BASED & NLP
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@ -766,54 +740,58 @@ std::vector<char> MLPPData::split(std::string text) {
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return split_data;
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}
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std::vector<std::string> MLPPData::splitSentences(std::string data) {
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std::vector<std::string> sentences;
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std::string currentStr = "";
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Vector<String> MLPPData::split_sentences(String data) {
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Vector<String> sentences;
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for (uint32_t i = 0; i < data.length(); i++) {
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currentStr.push_back(data[i]);
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int start_index = 0;
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for (int i = 0; i < data.length() - 1; ++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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continue;
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}
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if (data[i] == '.') {
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sentences.push_back(data.substr_index(start_index, i));
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start_index = i + 1;
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}
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}
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if (start_index != data.length() - 1) {
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sentences.push_back(data.substr_index(start_index, data.length() - 1));
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}
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return sentences;
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}
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std::vector<std::string> MLPPData::removeSpaces(std::vector<std::string> data) {
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for (uint32_t i = 0; i < data.size(); i++) {
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auto it = data[i].begin();
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for (uint32_t 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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Vector<String> MLPPData::remove_spaces(Vector<String> data) {
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for (int i = 0; i < data.size(); i++) {
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data.write[i] = data[i].replace(" ", "");
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}
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return data;
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}
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std::vector<std::string> MLPPData::removeNullByte(std::vector<std::string> data) {
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for (uint32_t 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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Vector<String> MLPPData::remove_empty(Vector<String> data) {
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for (int i = 0; i < data.size(); ++i) {
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if (data[i].empty()) {
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data.remove(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> MLPPData::segment(std::string text) {
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std::vector<std::string> segmented_data;
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Vector<String> MLPPData::segment(String text) {
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Vector<String> segmented_data;
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int prev_delim = 0;
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for (uint32_t i = 0; i < text.length(); i++) {
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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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} 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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String punc;
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punc += 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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@ -825,16 +803,17 @@ std::vector<std::string> MLPPData::segment(std::string text) {
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return segmented_data;
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}
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std::vector<real_t> MLPPData::tokenize(std::string text) {
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Vector<int> MLPPData::tokenize(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<real_t> tokenized_data;
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Vector<String> segmented_data = segment(text);
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Vector<int> tokenized_data;
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tokenized_data.resize(segmented_data.size());
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for (uint32_t i = 0; i < segmented_data.size(); i++) {
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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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tokenized_data.write[i] = tokenized_data[j];
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new_num = false;
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}
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}
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@ -842,50 +821,49 @@ std::vector<real_t> MLPPData::tokenize(std::string text) {
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new_num = true;
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} else {
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max_num++;
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tokenized_data[i] = max_num;
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tokenized_data.write[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> MLPPData::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" };
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std::vector<std::string> segmented_data = removeSpaces(segment(toLower(text)));
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Vector<String> MLPPData::remove_stop_words(String text) {
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Vector<String> segmented_data = remove_spaces(segment(text.to_lower()));
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for (uint32_t i = 0; i < stopWords.size(); i++) {
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for (uint32_t j = 0; j < segmented_data.size(); j++) {
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if (segmented_data[j] == stopWords[i]) {
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segmented_data.erase(segmented_data.begin() + j);
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for (int i = 0; i < stop_words.size(); i++) {
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for (int j = 0; j < segmented_data.size(); j++) {
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if (segmented_data[j] == stop_words[i]) {
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segmented_data.remove(j);
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--j;
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}
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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<std::string> MLPPData::removeStopWords(std::vector<std::string> segmented_data) {
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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" };
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for (uint32_t i = 0; i < segmented_data.size(); i++) {
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for (uint32_t j = 0; j < stopWords.size(); j++) {
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if (segmented_data[i] == stopWords[j]) {
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segmented_data.erase(segmented_data.begin() + i);
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Vector<String> MLPPData::remove_stop_words_vec(Vector<String> segmented_data) {
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for (int i = 0; i < segmented_data.size(); i++) {
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for (int j = 0; j < stop_words.size(); j++) {
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if (segmented_data[i] == stop_words[j]) {
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segmented_data.remove(i);
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--i;
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}
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}
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}
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return segmented_data;
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}
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std::string MLPPData::stemming(std::string text) {
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// Our list of suffixes which we use to compare against
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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" };
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String MLPPData::stemming(String text) {
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int padding_size = 4;
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char padding = ' '; // our padding
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String padding = " "; // our padding
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for (int i = 0; i < padding_size; i++) {
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text[text.length() + i] = padding; // ' ' will be our padding value
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}
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text += String(padding).repeat(padding_size); // ' ' will be our padding value
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for (uint32_t i = 0; i < text.size(); i++) {
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for (uint32_t j = 0; j < suffixes.size(); j++) {
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for (int i = 0; i < text.length(); i++) {
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for (int j = 0; j < suffixes.size(); j++) {
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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()] == '!')) {
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text.erase(i, suffixes[j].length());
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}
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@ -895,196 +873,130 @@ std::string MLPPData::stemming(std::string text) {
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return text;
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}
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std::vector<std::vector<real_t>> MLPPData::BOW(std::vector<std::string> sentences, std::string type) {
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Ref<MLPPMatrix> MLPPData::bag_of_words(Vector<String> sentences, BagOfWordsType type) {
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/*
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STEPS OF BOW:
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1) To lowercase (done by removeStopWords function by def)
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1) To lowercase (done by remove_stop_words function by def)
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2) Removing stop words
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3) Obtain a list of the used words
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4) Create a one hot encoded vector of the words and sentences
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5) Sentence.size() x list.size() matrix
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*/
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std::vector<std::string> wordList = removeNullByte(removeStopWords(createWordList(sentences)));
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Vector<String> word_list = remove_empty(remove_stop_words_vec(create_word_list(sentences)));
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std::vector<std::vector<std::string>> segmented_sentences;
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Vector<Vector<String>> segmented_sentences;
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segmented_sentences.resize(sentences.size());
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for (uint32_t i = 0; i < sentences.size(); i++) {
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segmented_sentences[i] = removeStopWords(sentences[i]);
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for (int i = 0; i < sentences.size(); i++) {
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segmented_sentences.write[i] = remove_stop_words(sentences[i]);
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}
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std::vector<std::vector<real_t>> bow;
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Ref<MLPPMatrix> bow;
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bow.instance();
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bow->resize(Size2i(word_list.size(), sentences.size()));
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bow->fill(0);
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bow.resize(sentences.size());
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for (uint32_t i = 0; i < bow.size(); i++) {
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bow[i].resize(wordList.size());
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}
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for (uint32_t i = 0; i < segmented_sentences.size(); i++) {
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for (uint32_t j = 0; j < segmented_sentences[i].size(); j++) {
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for (uint32_t k = 0; k < wordList.size(); k++) {
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if (segmented_sentences[i][j] == wordList[k]) {
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if (type == "Binary") {
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bow[i][k] = 1;
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for (int i = 0; i < segmented_sentences.size(); i++) {
|
||||
for (int j = 0; j < segmented_sentences[i].size(); j++) {
|
||||
for (int k = 0; k < word_list.size(); k++) {
|
||||
if (segmented_sentences[i][j] == word_list[k]) {
|
||||
if (type == BAG_OF_WORDS_TYPE_BINARY) {
|
||||
bow->element_set(i, k, 1);
|
||||
} else {
|
||||
bow[i][k]++;
|
||||
bow->element_set(i, k, bow->element_get(i, k) + 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return bow;
|
||||
}
|
||||
|
||||
std::vector<std::vector<real_t>> MLPPData::TFIDF(std::vector<std::string> sentences) {
|
||||
#ifdef OLD_CLASSES_ENABLED
|
||||
MLPPLinAlgOld alg;
|
||||
std::vector<std::string> wordList = removeNullByte(removeStopWords(createWordList(sentences)));
|
||||
Ref<MLPPMatrix> MLPPData::tfidf(Vector<String> sentences) {
|
||||
Vector<String> word_list = remove_empty(remove_stop_words_vec(create_word_list(sentences)));
|
||||
|
||||
std::vector<std::vector<std::string>> segmented_sentences;
|
||||
Vector<Vector<String>> segmented_sentences;
|
||||
segmented_sentences.resize(sentences.size());
|
||||
|
||||
for (uint32_t i = 0; i < sentences.size(); i++) {
|
||||
segmented_sentences[i] = removeStopWords(sentences[i]);
|
||||
for (int i = 0; i < sentences.size(); i++) {
|
||||
segmented_sentences.write[i] = remove_stop_words(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]++;
|
||||
Ref<MLPPMatrix> TF;
|
||||
TF.instance();
|
||||
TF->resize(Size2i(word_list.size(), segmented_sentences.size()));
|
||||
|
||||
Vector<int> frequency;
|
||||
frequency.resize(word_list.size());
|
||||
frequency.fill(0);
|
||||
|
||||
Ref<MLPPVector> TF_row;
|
||||
TF_row.instance();
|
||||
TF_row->resize(word_list.size());
|
||||
|
||||
for (int i = 0; i < segmented_sentences.size(); i++) {
|
||||
Vector<bool> present;
|
||||
present.resize(word_list.size());
|
||||
present.fill(false);
|
||||
|
||||
for (int j = 0; j < segmented_sentences[i].size(); j++) {
|
||||
for (int k = 0; k < word_list.size(); k++) {
|
||||
if (segmented_sentences[i][j] == word_list[k]) {
|
||||
TF->element_set(i, k, TF->element_get(i, k) + 1);
|
||||
|
||||
if (!present[k]) {
|
||||
frequency[k]++;
|
||||
present[k] = true;
|
||||
frequency.write[k]++;
|
||||
present.write[k] = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
TF[i] = alg.scalarMultiply(real_t(1) / real_t(segmented_sentences[i].size()), TF[i]);
|
||||
|
||||
TF->row_get_into_mlpp_vector(i, TF_row);
|
||||
TF_row->scalar_multiply(real_t(1) / real_t(segmented_sentences[i].size()));
|
||||
TF->row_set_mlpp_vector(i, TF_row);
|
||||
}
|
||||
|
||||
std::vector<real_t> IDF;
|
||||
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]);
|
||||
for (int i = 0; i < IDF.size(); i++) {
|
||||
IDF.write[i] = Math::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());
|
||||
}
|
||||
Ref<MLPPMatrix> TFIDF;
|
||||
TFIDF.instance();
|
||||
Size2i tfidf_size = Size2i(word_list.size(), segmented_sentences.size());
|
||||
TFIDF->resize(tfidf_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];
|
||||
for (int i = 0; i < tfidf_size.y; i++) {
|
||||
for (int j = 0; j < tfidf_size.x; j++) {
|
||||
TFIDF->element_set(i, j, TF->element_get(i, j) * IDF[j]);
|
||||
}
|
||||
}
|
||||
|
||||
return TFIDF;
|
||||
#else
|
||||
return std::vector<std::vector<real_t>>();
|
||||
#endif
|
||||
}
|
||||
|
||||
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::string>> MLPPData::word2Vec(std::vector<std::string> sentences, std::string type, int windowSize, int dimension, real_t learning_rate, int max_epoch) {
|
||||
#ifdef OLD_CLASSES_ENABLED
|
||||
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 = MLPPData::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 };
|
||||
#else
|
||||
return std::tuple<std::vector<std::vector<real_t>>, std::vector<std::string>>();
|
||||
#endif
|
||||
}
|
||||
|
||||
struct WordsToVecResult {
|
||||
std::vector<std::vector<real_t>> word_embeddings;
|
||||
std::vector<std::string> word_list;
|
||||
};
|
||||
|
||||
MLPPData::WordsToVecResult MLPPData::word_to_vec(std::vector<std::string> sentences, std::string type, int windowSize, int dimension, real_t learning_rate, int max_epoch) {
|
||||
MLPPData::WordsToVecResult MLPPData::word_to_vec(Vector<String> sentences, WordToVecType type, int windowSize, int dimension, real_t learning_rate, int max_epoch) {
|
||||
WordsToVecResult res;
|
||||
|
||||
#ifdef OLD_CLASSES_ENABLED
|
||||
res.word_list = removeNullByte(removeStopWords(createWordList(sentences)));
|
||||
res.word_list = remove_empty(remove_stop_words_vec(create_word_list(sentences)));
|
||||
|
||||
std::vector<std::vector<std::string>> segmented_sentences;
|
||||
Vector<Vector<String>> segmented_sentences;
|
||||
segmented_sentences.resize(sentences.size());
|
||||
|
||||
for (uint32_t i = 0; i < sentences.size(); i++) {
|
||||
segmented_sentences[i] = removeStopWords(sentences[i]);
|
||||
for (int i = 0; i < sentences.size(); i++) {
|
||||
segmented_sentences.write[i] = remove_stop_words(sentences[i]);
|
||||
}
|
||||
|
||||
std::vector<std::string> inputStrings;
|
||||
std::vector<std::string> outputStrings;
|
||||
Vector<String> inputStrings;
|
||||
Vector<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 i = 0; i < segmented_sentences.size(); i++) {
|
||||
for (int j = 0; j < segmented_sentences[i].size(); j++) {
|
||||
for (int k = windowSize; k > 0; k--) {
|
||||
int jmk = (int)j - k;
|
||||
|
||||
@ -1101,70 +1013,99 @@ MLPPData::WordsToVecResult MLPPData::word_to_vec(std::vector<std::string> senten
|
||||
}
|
||||
}
|
||||
|
||||
uint32_t inputSize = inputStrings.size();
|
||||
int input_size = inputStrings.size();
|
||||
|
||||
inputStrings.insert(inputStrings.end(), outputStrings.begin(), outputStrings.end());
|
||||
inputStrings.append_array(outputStrings);
|
||||
|
||||
std::vector<std::vector<real_t>> BOW = MLPPData::BOW(inputStrings, "Binary");
|
||||
Ref<MLPPMatrix> bow = bag_of_words(inputStrings, BAG_OF_WORDS_TYPE_BINARY);
|
||||
Size2i bow_size = bow->size();
|
||||
|
||||
std::vector<std::vector<real_t>> inputSet;
|
||||
std::vector<std::vector<real_t>> outputSet;
|
||||
Ref<MLPPMatrix> input_set;
|
||||
Ref<MLPPMatrix> output_set;
|
||||
|
||||
for (uint32_t i = 0; i < inputSize; i++) {
|
||||
inputSet.push_back(BOW[i]);
|
||||
input_set.instance();
|
||||
output_set.instance();
|
||||
|
||||
input_set->resize(Size2i(bow_size.x, input_size));
|
||||
|
||||
Ref<MLPPVector> row_tmp;
|
||||
row_tmp.instance();
|
||||
row_tmp->resize(bow_size.x);
|
||||
|
||||
for (int i = 0; i < input_size; i++) {
|
||||
bow->row_get_into_mlpp_vector(i, row_tmp);
|
||||
input_set->row_set_mlpp_vector(i, row_tmp);
|
||||
}
|
||||
|
||||
for (uint32_t i = inputSize; i < BOW.size(); i++) {
|
||||
outputSet.push_back(BOW[i]);
|
||||
output_set->resize(Size2i(bow_size.x, bow_size.y - input_size));
|
||||
Size2i output_set_size = output_set->size();
|
||||
|
||||
for (int i = 0; i < output_set_size.y; i++) {
|
||||
bow->row_get_into_mlpp_vector(i + input_size, row_tmp);
|
||||
input_set->row_set_mlpp_vector(i, row_tmp);
|
||||
}
|
||||
|
||||
MLPPSoftmaxNetOld *model;
|
||||
MLPPSoftmaxNet *model;
|
||||
|
||||
if (type == "Skipgram") {
|
||||
model = new MLPPSoftmaxNetOld(outputSet, inputSet, dimension);
|
||||
if (type == WORD_TO_VEC_TYPE_SKIPGRAM) {
|
||||
model = memnew(MLPPSoftmaxNet(output_set, input_set, dimension));
|
||||
} else { // else = CBOW. We maintain it is a default.
|
||||
model = new MLPPSoftmaxNetOld(inputSet, outputSet, dimension);
|
||||
model = memnew(MLPPSoftmaxNet(input_set, output_set, dimension));
|
||||
}
|
||||
|
||||
model->gradientDescent(learning_rate, max_epoch, false);
|
||||
model->train_gradient_descent(learning_rate, max_epoch);
|
||||
|
||||
res.word_embeddings = model->getEmbeddings();
|
||||
delete model;
|
||||
#endif
|
||||
res.word_embeddings = model->get_embeddings();
|
||||
memdelete(model);
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
std::vector<std::vector<real_t>> MLPPData::LSA(std::vector<std::string> sentences, int dim) {
|
||||
#ifdef OLD_CLASSES_ENABLED
|
||||
MLPPLinAlgOld alg;
|
||||
std::vector<std::vector<real_t>> docWordData = BOW(sentences, "Binary");
|
||||
Ref<MLPPMatrix> MLPPData::lsa(Vector<String> sentences, int dim) {
|
||||
MLPPLinAlg alg;
|
||||
|
||||
Ref<MLPPMatrix> doc_word_data = bag_of_words(sentences, BAG_OF_WORDS_TYPE_BINARY);
|
||||
|
||||
MLPPLinAlg::SVDResult svr_res = alg.svd(doc_word_data);
|
||||
|
||||
Ref<MLPPMatrix> S_trunc = alg.zeromatnm(dim, dim);
|
||||
Ref<MLPPMatrix> Vt_trunc;
|
||||
Vt_trunc.instance();
|
||||
Vt_trunc->resize(Size2i(svr_res.Vt->size().x, dim));
|
||||
|
||||
Ref<MLPPVector> row_rmp;
|
||||
row_rmp.instance();
|
||||
row_rmp->resize(svr_res.Vt->size().x);
|
||||
|
||||
MLPPLinAlgOld::SVDResultOld svr_res = alg.SVD(docWordData);
|
||||
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]);
|
||||
S_trunc->element_set(i, i, svr_res.S->element_get(i, i));
|
||||
|
||||
svr_res.Vt->row_get_into_mlpp_vector(i, row_rmp);
|
||||
Vt_trunc->row_set_mlpp_vector(i, row_rmp);
|
||||
}
|
||||
|
||||
std::vector<std::vector<real_t>> embeddings = alg.matmult(S_trunc, Vt_trunc);
|
||||
Ref<MLPPMatrix> embeddings = S_trunc->multn(Vt_trunc);
|
||||
return embeddings;
|
||||
#else
|
||||
return std::vector<std::vector<real_t>>();
|
||||
#endif
|
||||
}
|
||||
|
||||
std::vector<std::string> MLPPData::createWordList(std::vector<std::string> sentences) {
|
||||
std::string combinedText = "";
|
||||
for (uint32_t i = 0; i < sentences.size(); i++) {
|
||||
struct SVDResult {
|
||||
Ref<MLPPMatrix> U;
|
||||
Ref<MLPPMatrix> S;
|
||||
Ref<MLPPMatrix> Vt;
|
||||
};
|
||||
|
||||
Vector<String> MLPPData::create_word_list(Vector<String> sentences) {
|
||||
String combined_text = "";
|
||||
|
||||
for (int i = 0; i < sentences.size(); i++) {
|
||||
if (i != 0) {
|
||||
combinedText += " ";
|
||||
combined_text += " ";
|
||||
}
|
||||
combinedText += sentences[i];
|
||||
|
||||
combined_text += sentences[i];
|
||||
}
|
||||
|
||||
return removeSpaces(vecToSet(removeStopWords(combinedText)));
|
||||
return remove_spaces(vec_to_set(remove_stop_words(combined_text)));
|
||||
}
|
||||
|
||||
// EXTRA
|
||||
@ -1183,7 +1124,6 @@ void MLPPData::setInputNames(std::string fileName, std::vector<std::string> &inp
|
||||
}
|
||||
|
||||
std::vector<std::vector<real_t>> MLPPData::featureScaling(std::vector<std::vector<real_t>> X) {
|
||||
#ifdef OLD_CLASSES_ENABLED
|
||||
MLPPLinAlgOld alg;
|
||||
X = alg.transpose(X);
|
||||
std::vector<real_t> max_elements, min_elements;
|
||||
@ -1201,13 +1141,9 @@ std::vector<std::vector<real_t>> MLPPData::featureScaling(std::vector<std::vecto
|
||||
}
|
||||
}
|
||||
return alg.transpose(X);
|
||||
#else
|
||||
return std::vector<std::vector<real_t>>();
|
||||
#endif
|
||||
}
|
||||
|
||||
std::vector<std::vector<real_t>> MLPPData::meanNormalization(std::vector<std::vector<real_t>> X) {
|
||||
#ifdef OLD_CLASSES_ENABLED
|
||||
MLPPLinAlgOld alg;
|
||||
MLPPStatOld stat;
|
||||
// (X_j - mu_j) / std_j, for every j
|
||||
@ -1217,13 +1153,9 @@ std::vector<std::vector<real_t>> MLPPData::meanNormalization(std::vector<std::ve
|
||||
X[i] = alg.scalarMultiply(1 / stat.standardDeviation(X[i]), X[i]);
|
||||
}
|
||||
return X;
|
||||
#else
|
||||
return std::vector<std::vector<real_t>>();
|
||||
#endif
|
||||
}
|
||||
|
||||
std::vector<std::vector<real_t>> MLPPData::meanCentering(std::vector<std::vector<real_t>> X) {
|
||||
#ifdef OLD_CLASSES_ENABLED
|
||||
MLPPStatOld stat;
|
||||
for (uint32_t i = 0; i < X.size(); i++) {
|
||||
real_t mean_i = stat.mean(X[i]);
|
||||
@ -1232,9 +1164,6 @@ std::vector<std::vector<real_t>> MLPPData::meanCentering(std::vector<std::vector
|
||||
}
|
||||
}
|
||||
return X;
|
||||
#else
|
||||
return std::vector<std::vector<real_t>>();
|
||||
#endif
|
||||
}
|
||||
|
||||
std::vector<std::vector<real_t>> MLPPData::oneHotRep(std::vector<real_t> tempOutputSet, int n_class) {
|
||||
@ -1320,6 +1249,15 @@ Ref<MLPPMatrix> MLPPData::one_hot_rep(const Ref<MLPPVector> &temp_output_set, in
|
||||
return output_set;
|
||||
}
|
||||
|
||||
void MLPPData::load_default_suffixes() {
|
||||
// Our list of suffixes which we use to compare against
|
||||
suffixes = String("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").split_spaces();
|
||||
}
|
||||
|
||||
void MLPPData::load_default_stop_words() {
|
||||
stop_words = String("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").split_spaces();
|
||||
}
|
||||
|
||||
void MLPPData::_bind_methods() {
|
||||
ClassDB::bind_method(D_METHOD("load_breast_cancer", "path"), &MLPPData::load_breast_cancer);
|
||||
ClassDB::bind_method(D_METHOD("load_breast_cancer_svc", "path"), &MLPPData::load_breast_cancer_svc);
|
||||
|
||||
@ -140,31 +140,39 @@ public:
|
||||
// Text-Based & NLP
|
||||
std::string toLower(std::string text);
|
||||
std::vector<char> split(std::string text);
|
||||
std::vector<std::string> splitSentences(std::string data);
|
||||
std::vector<std::string> removeSpaces(std::vector<std::string> data);
|
||||
std::vector<std::string> removeNullByte(std::vector<std::string> data);
|
||||
std::vector<std::string> segment(std::string text);
|
||||
std::vector<real_t> tokenize(std::string text);
|
||||
std::vector<std::string> removeStopWords(std::string text);
|
||||
std::vector<std::string> removeStopWords(std::vector<std::string> segmented_data);
|
||||
Vector<String> split_sentences(String data);
|
||||
Vector<String> remove_spaces(Vector<String> data);
|
||||
Vector<String> remove_empty(Vector<String> data);
|
||||
Vector<String> segment(String text);
|
||||
Vector<int> tokenize(String text);
|
||||
Vector<String> remove_stop_words(String text);
|
||||
Vector<String> remove_stop_words_vec(Vector<String> segmented_data);
|
||||
|
||||
std::string stemming(std::string text);
|
||||
String stemming(String text);
|
||||
|
||||
std::vector<std::vector<real_t>> BOW(std::vector<std::string> sentences, std::string = "Default");
|
||||
std::vector<std::vector<real_t>> TFIDF(std::vector<std::string> sentences);
|
||||
|
||||
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::string>> word2Vec(std::vector<std::string> sentences, std::string type, int windowSize, int dimension, real_t learning_rate, int max_epoch);
|
||||
|
||||
struct WordsToVecResult {
|
||||
std::vector<std::vector<real_t>> word_embeddings;
|
||||
std::vector<std::string> word_list;
|
||||
enum BagOfWordsType {
|
||||
BAG_OF_WORDS_TYPE_DEFAULT = 0,
|
||||
BAG_OF_WORDS_TYPE_BINARY,
|
||||
};
|
||||
|
||||
WordsToVecResult word_to_vec(std::vector<std::string> sentences, std::string type, int windowSize, int dimension, real_t learning_rate, int max_epoch);
|
||||
Ref<MLPPMatrix> bag_of_words(Vector<String> sentences, BagOfWordsType type = BAG_OF_WORDS_TYPE_DEFAULT);
|
||||
Ref<MLPPMatrix> tfidf(Vector<String> sentences);
|
||||
|
||||
std::vector<std::vector<real_t>> LSA(std::vector<std::string> sentences, int dim);
|
||||
struct WordsToVecResult {
|
||||
Ref<MLPPMatrix> word_embeddings;
|
||||
Vector<String> word_list;
|
||||
};
|
||||
|
||||
std::vector<std::string> createWordList(std::vector<std::string> sentences);
|
||||
enum WordToVecType {
|
||||
WORD_TO_VEC_TYPE_CBOW = 0,
|
||||
WORD_TO_VEC_TYPE_SKIPGRAM,
|
||||
};
|
||||
|
||||
WordsToVecResult word_to_vec(Vector<String> sentences, WordToVecType type, int windowSize, int dimension, real_t learning_rate, int max_epoch);
|
||||
|
||||
Ref<MLPPMatrix> lsa(Vector<String> sentences, int dim);
|
||||
|
||||
Vector<String> create_word_list(Vector<String> sentences);
|
||||
|
||||
// Extra
|
||||
void setInputNames(std::string fileName, std::vector<std::string> &inputNames);
|
||||
@ -239,6 +247,12 @@ public:
|
||||
return ret;
|
||||
}
|
||||
|
||||
void load_default_suffixes();
|
||||
void load_default_stop_words();
|
||||
|
||||
Vector<String> suffixes;
|
||||
Vector<String> stop_words;
|
||||
|
||||
protected:
|
||||
static void _bind_methods();
|
||||
};
|
||||
|
||||
Loading…
Reference in New Issue
Block a user