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https://github.com/Relintai/pmlpp.git
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Fixed warnings in MLPPData.
This commit is contained in:
parent
69bc0f6f26
commit
bee57218a2
@ -543,15 +543,15 @@ void MLPPData::setData(int k, std::string fileName, std::vector<std::vector<real
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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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MLPPLinAlg 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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for (uint32_t 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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for (uint32_t 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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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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}
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@ -585,9 +585,9 @@ void MLPPData::setData(int k, std::string fileName, std::vector<std::vector<real
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void MLPPData::printData(std::vector<std::string> inputName, std::vector<std::vector<real_t>> inputSet) {
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MLPPLinAlg 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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for (uint32_t 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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for (uint32_t 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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@ -620,12 +620,12 @@ void MLPPData::setData(std::string fileName, std::vector<real_t> &inputSet, std:
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void MLPPData::printData(std::string &inputName, std::string &outputName, std::vector<real_t> &inputSet, std::vector<real_t> &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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for (uint32_t 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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for (uint32_t 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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@ -634,11 +634,11 @@ void MLPPData::printData(std::string &inputName, std::string &outputName, std::v
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std::vector<std::vector<real_t>> MLPPData::rgb2gray(std::vector<std::vector<std::vector<real_t>>> input) {
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std::vector<std::vector<real_t>> 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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for (uint32_t 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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for (uint32_t i = 0; i < grayScale.size(); i++) {
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for (uint32_t 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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@ -649,8 +649,8 @@ std::vector<std::vector<std::vector<real_t>>> MLPPData::rgb2ycbcr(std::vector<st
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MLPPLinAlg 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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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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for (uint32_t i = 0; i < YCbCr[0].size(); i++) {
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for (uint32_t 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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@ -665,8 +665,8 @@ std::vector<std::vector<std::vector<real_t>>> MLPPData::rgb2hsv(std::vector<std:
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MLPPLinAlg 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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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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for (uint32_t i = 0; i < HSV[0].size(); i++) {
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for (uint32_t j = 0; j < HSV[0][i].size(); j++) {
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real_t rPrime = input[0][i][j] / 255;
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real_t gPrime = input[1][i][j] / 255;
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real_t bPrime = input[2][i][j] / 255;
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@ -721,7 +721,7 @@ std::vector<std::vector<std::vector<real_t>>> MLPPData::xyz2rgb(std::vector<std:
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// TEXT-BASED & NLP
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std::string MLPPData::toLower(std::string text) {
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for (int i = 0; i < text.size(); i++) {
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for (uint32_t 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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@ -729,7 +729,7 @@ std::string MLPPData::toLower(std::string text) {
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std::vector<char> MLPPData::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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for (uint32_t 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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@ -739,7 +739,7 @@ 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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for (int i = 0; i < data.length(); i++) {
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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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if (data[i] == '.' && data[i + 1] != '.') {
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sentences.push_back(currentStr);
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@ -751,9 +751,9 @@ std::vector<std::string> MLPPData::splitSentences(std::string data) {
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}
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std::vector<std::string> MLPPData::removeSpaces(std::vector<std::string> data) {
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for (int i = 0; i < data.size(); i++) {
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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 (int j = 0; j < data[i].length(); j++) {
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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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@ -764,7 +764,7 @@ std::vector<std::string> MLPPData::removeSpaces(std::vector<std::string> 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 (int i = 0; i < data.size(); i++) {
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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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}
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@ -775,7 +775,7 @@ std::vector<std::string> MLPPData::removeNullByte(std::vector<std::string> data)
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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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int prev_delim = 0;
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for (int i = 0; i < text.length(); i++) {
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for (uint32_t 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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@ -800,7 +800,7 @@ std::vector<real_t> MLPPData::tokenize(std::string text) {
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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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tokenized_data.resize(segmented_data.size());
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for (int i = 0; i < segmented_data.size(); i++) {
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for (uint32_t 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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@ -821,8 +821,8 @@ 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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for (int i = 0; i < stopWords.size(); i++) {
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for (int j = 0; j < segmented_data.size(); j++) {
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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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}
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@ -833,8 +833,8 @@ std::vector<std::string> MLPPData::removeStopWords(std::string text) {
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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 (int i = 0; i < segmented_data.size(); i++) {
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for (int j = 0; j < stopWords.size(); j++) {
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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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}
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@ -853,8 +853,8 @@ std::string MLPPData::stemming(std::string text) {
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text[text.length() + i] = padding; // ' ' will be our padding value
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}
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for (int i = 0; i < text.size(); i++) {
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for (int j = 0; j < suffixes.size(); j++) {
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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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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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@ -879,20 +879,20 @@ std::vector<std::vector<real_t>> MLPPData::BOW(std::vector<std::string> sentence
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std::vector<std::vector<std::string>> segmented_sentences;
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segmented_sentences.resize(sentences.size());
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for (int i = 0; i < sentences.size(); i++) {
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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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}
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std::vector<std::vector<real_t>> bow;
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bow.resize(sentences.size());
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for (int i = 0; i < bow.size(); i++) {
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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 (int i = 0; i < segmented_sentences.size(); i++) {
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for (int j = 0; j < segmented_sentences[i].size(); j++) {
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for (int k = 0; k < wordList.size(); k++) {
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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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@ -913,7 +913,7 @@ std::vector<std::vector<real_t>> MLPPData::TFIDF(std::vector<std::string> senten
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std::vector<std::vector<std::string>> segmented_sentences;
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segmented_sentences.resize(sentences.size());
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for (int i = 0; i < sentences.size(); i++) {
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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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}
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@ -921,13 +921,13 @@ std::vector<std::vector<real_t>> MLPPData::TFIDF(std::vector<std::string> senten
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std::vector<int> frequency;
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frequency.resize(wordList.size());
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TF.resize(segmented_sentences.size());
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for (int i = 0; i < TF.size(); i++) {
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for (uint32_t i = 0; i < TF.size(); i++) {
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TF[i].resize(wordList.size());
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}
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for (int i = 0; i < segmented_sentences.size(); i++) {
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std::vector<bool> present(wordList.size(), 0);
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for (int j = 0; j < segmented_sentences[i].size(); j++) {
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for (int k = 0; k < wordList.size(); k++) {
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for (uint32_t i = 0; i < segmented_sentences.size(); i++) {
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std::vector<bool> present(wordList.size(), false);
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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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TF[i][k]++;
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if (!present[k]) {
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@ -943,18 +943,18 @@ std::vector<std::vector<real_t>> MLPPData::TFIDF(std::vector<std::string> senten
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std::vector<real_t> IDF;
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IDF.resize(frequency.size());
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for (int i = 0; i < IDF.size(); i++) {
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for (uint32_t i = 0; i < IDF.size(); i++) {
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IDF[i] = std::log((real_t)segmented_sentences.size() / (real_t)frequency[i]);
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}
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std::vector<std::vector<real_t>> TFIDF;
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TFIDF.resize(segmented_sentences.size());
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for (int i = 0; i < TFIDF.size(); i++) {
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for (uint32_t i = 0; i < TFIDF.size(); i++) {
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TFIDF[i].resize(wordList.size());
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}
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for (int i = 0; i < TFIDF.size(); i++) {
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for (int j = 0; j < TFIDF[i].size(); j++) {
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for (uint32_t i = 0; i < TFIDF.size(); i++) {
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for (uint32_t j = 0; j < TFIDF[i].size(); j++) {
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TFIDF[i][j] = TF[i][j] * IDF[j];
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}
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}
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@ -968,15 +968,15 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::string>> MLPPData:
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std::vector<std::vector<std::string>> segmented_sentences;
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segmented_sentences.resize(sentences.size());
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for (int i = 0; i < sentences.size(); i++) {
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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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}
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std::vector<std::string> inputStrings;
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std::vector<std::string> outputStrings;
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for (int i = 0; i < segmented_sentences.size(); i++) {
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for (int j = 0; j < segmented_sentences[i].size(); j++) {
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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 (int k = windowSize; k > 0; k--) {
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if (j - k >= 0) {
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inputStrings.push_back(segmented_sentences[i][j]);
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@ -991,7 +991,7 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::string>> MLPPData:
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}
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}
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int inputSize = inputStrings.size();
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uint32_t inputSize = inputStrings.size();
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inputStrings.insert(inputStrings.end(), outputStrings.begin(), outputStrings.end());
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@ -1000,21 +1000,23 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::string>> MLPPData:
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std::vector<std::vector<real_t>> inputSet;
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std::vector<std::vector<real_t>> outputSet;
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for (int i = 0; i < inputSize; i++) {
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for (uint32_t i = 0; i < inputSize; i++) {
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inputSet.push_back(BOW[i]);
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}
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for (int i = inputSize; i < BOW.size(); i++) {
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for (uint32_t i = inputSize; i < BOW.size(); i++) {
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outputSet.push_back(BOW[i]);
|
||||
}
|
||||
MLPPLinAlg alg;
|
||||
|
||||
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, 1);
|
||||
|
||||
model->gradientDescent(learning_rate, max_epoch, true);
|
||||
|
||||
std::vector<std::vector<real_t>> wordEmbeddings = model->getEmbeddings();
|
||||
delete model;
|
||||
@ -1034,15 +1036,15 @@ MLPPData::WordsToVecResult MLPPData::word_to_vec(std::vector<std::string> senten
|
||||
std::vector<std::vector<std::string>> segmented_sentences;
|
||||
segmented_sentences.resize(sentences.size());
|
||||
|
||||
for (int i = 0; i < sentences.size(); i++) {
|
||||
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 (int i = 0; i < segmented_sentences.size(); i++) {
|
||||
for (int j = 0; j < segmented_sentences[i].size(); j++) {
|
||||
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]);
|
||||
@ -1057,7 +1059,7 @@ MLPPData::WordsToVecResult MLPPData::word_to_vec(std::vector<std::string> senten
|
||||
}
|
||||
}
|
||||
|
||||
int inputSize = inputStrings.size();
|
||||
uint32_t inputSize = inputStrings.size();
|
||||
|
||||
inputStrings.insert(inputStrings.end(), outputStrings.begin(), outputStrings.end());
|
||||
|
||||
@ -1066,20 +1068,22 @@ MLPPData::WordsToVecResult MLPPData::word_to_vec(std::vector<std::string> senten
|
||||
std::vector<std::vector<real_t>> inputSet;
|
||||
std::vector<std::vector<real_t>> outputSet;
|
||||
|
||||
for (int i = 0; i < inputSize; i++) {
|
||||
for (uint32_t i = 0; i < inputSize; i++) {
|
||||
inputSet.push_back(BOW[i]);
|
||||
}
|
||||
|
||||
for (int i = inputSize; i < BOW.size(); i++) {
|
||||
for (uint32_t i = inputSize; i < BOW.size(); i++) {
|
||||
outputSet.push_back(BOW[i]);
|
||||
}
|
||||
MLPPLinAlg alg;
|
||||
|
||||
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();
|
||||
@ -1106,7 +1110,7 @@ std::vector<std::vector<real_t>> MLPPData::LSA(std::vector<std::string> sentence
|
||||
|
||||
std::vector<std::string> MLPPData::createWordList(std::vector<std::string> sentences) {
|
||||
std::string combinedText = "";
|
||||
for (int i = 0; i < sentences.size(); i++) {
|
||||
for (uint32_t i = 0; i < sentences.size(); i++) {
|
||||
if (i != 0) {
|
||||
combinedText += " ";
|
||||
}
|
||||
@ -1138,13 +1142,13 @@ std::vector<std::vector<real_t>> MLPPData::featureScaling(std::vector<std::vecto
|
||||
max_elements.resize(X.size());
|
||||
min_elements.resize(X.size());
|
||||
|
||||
for (int i = 0; i < X.size(); i++) {
|
||||
for (uint32_t 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++) {
|
||||
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]);
|
||||
}
|
||||
}
|
||||
@ -1157,18 +1161,17 @@ std::vector<std::vector<real_t>> MLPPData::meanNormalization(std::vector<std::ve
|
||||
// (X_j - mu_j) / std_j, for every j
|
||||
|
||||
X = meanCentering(X);
|
||||
for (int i = 0; i < X.size(); i++) {
|
||||
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>> MLPPData::meanCentering(std::vector<std::vector<real_t>> X) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPStat stat;
|
||||
for (int i = 0; i < X.size(); i++) {
|
||||
for (uint32_t i = 0; i < X.size(); i++) {
|
||||
real_t mean_i = stat.mean(X[i]);
|
||||
for (int j = 0; j < X[i].size(); j++) {
|
||||
for (uint32_t j = 0; j < X[i].size(); j++) {
|
||||
X[i][j] -= mean_i;
|
||||
}
|
||||
}
|
||||
@ -1178,7 +1181,7 @@ std::vector<std::vector<real_t>> MLPPData::meanCentering(std::vector<std::vector
|
||||
std::vector<std::vector<real_t>> MLPPData::oneHotRep(std::vector<real_t> tempOutputSet, int n_class) {
|
||||
std::vector<std::vector<real_t>> outputSet;
|
||||
outputSet.resize(tempOutputSet.size());
|
||||
for (int i = 0; i < tempOutputSet.size(); i++) {
|
||||
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);
|
||||
@ -1192,10 +1195,10 @@ std::vector<std::vector<real_t>> MLPPData::oneHotRep(std::vector<real_t> tempOut
|
||||
|
||||
std::vector<real_t> MLPPData::reverseOneHot(std::vector<std::vector<real_t>> tempOutputSet) {
|
||||
std::vector<real_t> outputSet;
|
||||
int n_class = tempOutputSet[0].size();
|
||||
for (int i = 0; i < tempOutputSet.size(); i++) {
|
||||
//uint32_t n_class = tempOutputSet[0].size();
|
||||
for (uint32_t i = 0; i < tempOutputSet.size(); i++) {
|
||||
int current_class = 1;
|
||||
for (int j = 0; j < tempOutputSet[i].size(); j++) {
|
||||
for (uint32_t j = 0; j < tempOutputSet[i].size(); j++) {
|
||||
if (tempOutputSet[i][j] == 1) {
|
||||
break;
|
||||
} else {
|
||||
@ -1209,7 +1212,6 @@ std::vector<real_t> MLPPData::reverseOneHot(std::vector<std::vector<real_t>> tem
|
||||
}
|
||||
|
||||
Ref<MLPPMatrix> MLPPData::mean_centering(const Ref<MLPPMatrix> &p_X) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPStat stat;
|
||||
|
||||
Ref<MLPPMatrix> X;
|
||||
@ -1259,6 +1261,7 @@ Ref<MLPPMatrix> MLPPData::one_hot_rep(const Ref<MLPPVector> &temp_output_set, in
|
||||
return output_set;
|
||||
}
|
||||
|
||||
|
||||
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);
|
||||
|
||||
@ -180,9 +180,9 @@ public:
|
||||
template <class T>
|
||||
std::vector<T> vecToSet(std::vector<T> inputSet) {
|
||||
std::vector<T> setInputSet;
|
||||
for (int i = 0; i < inputSet.size(); i++) {
|
||||
for (uint32_t i = 0; i < inputSet.size(); i++) {
|
||||
bool new_element = true;
|
||||
for (int j = 0; j < setInputSet.size(); j++) {
|
||||
for (uint32_t j = 0; j < setInputSet.size(); j++) {
|
||||
if (setInputSet[j] == inputSet[i]) {
|
||||
new_element = false;
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user