Relintai/pmlpp
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Fix build when both tests and old classes are disabled.

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Relintai 2023-04-27 18:37:59 +02:00
parent 097ad002c9
commit dc8a89c3df
1 changed files with 54 additions and 2 deletions
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56
mlpp/data/data.cpp
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@ -10,10 +10,13 @@
#include "core/os/file_access.h"
#include "../lin_alg/lin_alg.h"
#include "../stat/stat.h"
#ifdef OLD_CLASSES_ENABLED
#include "../lin_alg/lin_alg_old.h"
#include "../softmax_net/softmax_net_old.h"
#include "../stat/stat.h"
#include "../stat/stat_old.h"
#endif
#include <algorithm>
#include <cmath>
@ -515,6 +518,7 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>, s
// MULTIVARIATE SUPERVISED
void MLPPData::setData(int k, std::string fileName, std::vector<std::vector<real_t>> &inputSet, std::vector<real_t> &outputSet) {
#ifdef OLD_CLASSES_ENABLED
MLPPLinAlgOld alg;
std::string inputTemp;
std::string outputTemp;
@ -540,9 +544,11 @@ void MLPPData::setData(int k, std::string fileName, std::vector<std::vector<real
}
inputSet = alg.transpose(inputSet);
dataFile.close();
#endif
}
void MLPPData::printData(std::vector<std::string> inputName, std::string outputName, std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet) {
#ifdef OLD_CLASSES_ENABLED
MLPPLinAlgOld alg;
inputSet = alg.transpose(inputSet);
for (uint32_t i = 0; i < inputSet.size(); i++) {
@ -556,11 +562,13 @@ void MLPPData::printData(std::vector<std::string> inputName, std::string outputN
for (uint32_t i = 0; i < outputSet.size(); i++) {
std::cout << outputSet[i] << std::endl;
}
#endif
}
// UNSUPERVISED
void MLPPData::setData(int k, std::string fileName, std::vector<std::vector<real_t>> &inputSet) {
#ifdef OLD_CLASSES_ENABLED
MLPPLinAlgOld alg;
std::string inputTemp;
@ -582,9 +590,11 @@ void MLPPData::setData(int k, std::string fileName, std::vector<std::vector<real
}
inputSet = alg.transpose(inputSet);
dataFile.close();
#endif
}
void MLPPData::printData(std::vector<std::string> inputName, std::vector<std::vector<real_t>> inputSet) {
#ifdef OLD_CLASSES_ENABLED
MLPPLinAlgOld alg;
inputSet = alg.transpose(inputSet);
for (uint32_t i = 0; i < inputSet.size(); i++) {
@ -593,6 +603,7 @@ void MLPPData::printData(std::vector<std::string> inputName, std::vector<std::ve
std::cout << inputSet[i][j] << std::endl;
}
}
#endif
}
// SIMPLE
@ -648,6 +659,7 @@ std::vector<std::vector<real_t>> MLPPData::rgb2gray(std::vector<std::vector<std:
}
std::vector<std::vector<std::vector<real_t>>> MLPPData::rgb2ycbcr(std::vector<std::vector<std::vector<real_t>>> input) {
#ifdef OLD_CLASSES_ENABLED
MLPPLinAlgOld alg;
std::vector<std::vector<std::vector<real_t>>> YCbCr;
YCbCr = alg.resize(YCbCr, input);
@ -659,11 +671,15 @@ std::vector<std::vector<std::vector<real_t>>> MLPPData::rgb2ycbcr(std::vector<st
}
}
return YCbCr;
#else
return std::vector<std::vector<std::vector<real_t>>>();
#endif
}
// Conversion formulas available here:
// https://www.rapidtables.com/convert/color/rgb-to-hsv.html
std::vector<std::vector<std::vector<real_t>>> MLPPData::rgb2hsv(std::vector<std::vector<std::vector<real_t>>> input) {
#ifdef OLD_CLASSES_ENABLED
MLPPLinAlgOld alg;
std::vector<std::vector<std::vector<real_t>>> HSV;
HSV = alg.resize(HSV, input);
@ -702,23 +718,34 @@ std::vector<std::vector<std::vector<real_t>>> MLPPData::rgb2hsv(std::vector<std:
}
}
return HSV;
#else
return std::vector<std::vector<std::vector<real_t>>>();
#endif
}
// http://machinethatsees.blogspot.com/2013/07/how-to-convert-rgb-to-xyz-or-vice-versa.html
std::vector<std::vector<std::vector<real_t>>> MLPPData::rgb2xyz(std::vector<std::vector<std::vector<real_t>>> input) {
#ifdef OLD_CLASSES_ENABLED
MLPPLinAlgOld alg;
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);
#else
return std::vector<std::vector<std::vector<real_t>>>();
#endif
}
std::vector<std::vector<std::vector<real_t>>> MLPPData::xyz2rgb(std::vector<std::vector<std::vector<real_t>>> input) {
#ifdef OLD_CLASSES_ENABLED
MLPPLinAlgOld alg;
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);
#else
return std::vector<std::vector<std::vector<real_t>>>();
#endif
}
// TEXT-BASED & NLP
@ -909,6 +936,7 @@ std::vector<std::vector<real_t>> MLPPData::BOW(std::vector<std::string> sentence
}
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)));
@ -962,9 +990,13 @@ std::vector<std::vector<real_t>> MLPPData::TFIDF(std::vector<std::string> senten
}
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;
@ -1023,6 +1055,9 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::string>> MLPPData:
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 {
@ -1033,6 +1068,7 @@ struct WordsToVecResult {
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) {
WordsToVecResult res;
#ifdef OLD_CLASSES_ENABLED
res.word_list = removeNullByte(removeStopWords(createWordList(sentences)));
std::vector<std::vector<std::string>> segmented_sentences;
@ -1090,11 +1126,13 @@ MLPPData::WordsToVecResult MLPPData::word_to_vec(std::vector<std::string> senten
res.word_embeddings = model->getEmbeddings();
delete model;
#endif
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");
@ -1108,6 +1146,9 @@ std::vector<std::vector<real_t>> MLPPData::LSA(std::vector<std::string> sentence
std::vector<std::vector<real_t>> embeddings = alg.matmult(S_trunc, Vt_trunc);
return embeddings;
#else
return std::vector<std::vector<real_t>>();
#endif
}
std::vector<std::string> MLPPData::createWordList(std::vector<std::string> sentences) {
@ -1138,6 +1179,7 @@ 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;
@ -1155,9 +1197,13 @@ 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
@ -1167,9 +1213,13 @@ 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]);
@ -1178,6 +1228,9 @@ 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) {
@ -1263,7 +1316,6 @@ 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);