pmlpp/mlpp/data/data.cpp

//
//  Data.cpp
//  MLP
//
//  Created by Marc Melikyan on 11/4/20.
//

#include "data.h"

#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_old.h"
#endif

#include <algorithm>
#include <cmath>
#include <fstream>
#include <iostream>
#include <random>
#include <sstream>

Ref<MLPPVector> MLPPDataESimple::get_input() {
	return _input;
}
void MLPPDataESimple::set_input(const Ref<MLPPVector> &val) {
	_input = val;
}

Ref<MLPPVector> MLPPDataESimple::get_output() {
	return _output;
}
void MLPPDataESimple::set_output(const Ref<MLPPVector> &val) {
	_output = val;
}

void MLPPDataESimple::instance_data() {
	_input.instance();
	_output.instance();
}

void MLPPDataESimple::_bind_methods() {
	ClassDB::bind_method(D_METHOD("get_input"), &MLPPDataESimple::get_input);
	ClassDB::bind_method(D_METHOD("set_input", "val"), &MLPPDataESimple::set_input);
	ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_input", "get_input");

	ClassDB::bind_method(D_METHOD("get_output"), &MLPPDataESimple::get_input);
	ClassDB::bind_method(D_METHOD("set_output", "val"), &MLPPDataESimple::set_output);
	ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_output", "get_output");

	ClassDB::bind_method(D_METHOD("instance_data"), &MLPPDataESimple::instance_data);
}

Ref<MLPPMatrix> MLPPDataSimple::get_input() {
	return _input;
}
void MLPPDataSimple::set_input(const Ref<MLPPMatrix> &val) {
	_input = val;
}

Ref<MLPPVector> MLPPDataSimple::get_output() {
	return _output;
}
void MLPPDataSimple::set_output(const Ref<MLPPVector> &val) {
	_output = val;
}

void MLPPDataSimple::instance_data() {
	_input.instance();
	_output.instance();
}

void MLPPDataSimple::_bind_methods() {
	ClassDB::bind_method(D_METHOD("get_input"), &MLPPDataSimple::get_input);
	ClassDB::bind_method(D_METHOD("set_input", "val"), &MLPPDataSimple::set_input);
	ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input", "get_input");

	ClassDB::bind_method(D_METHOD("get_output"), &MLPPDataSimple::get_input);
	ClassDB::bind_method(D_METHOD("set_output", "val"), &MLPPDataSimple::set_output);
	ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_output", "get_output");

	ClassDB::bind_method(D_METHOD("instance_data"), &MLPPDataSimple::instance_data);
}

Ref<MLPPMatrix> MLPPDataComplex::get_input() {
	return _input;
}
void MLPPDataComplex::set_input(const Ref<MLPPMatrix> &val) {
	_input = val;
}

Ref<MLPPMatrix> MLPPDataComplex::get_output() {
	return _output;
}
void MLPPDataComplex::set_output(const Ref<MLPPMatrix> &val) {
	_output = val;
}

void MLPPDataComplex::instance_data() {
	_input.instance();
	_output.instance();
}

void MLPPDataComplex::_bind_methods() {
	ClassDB::bind_method(D_METHOD("get_input"), &MLPPDataComplex::get_input);
	ClassDB::bind_method(D_METHOD("set_input", "val"), &MLPPDataComplex::set_input);
	ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input", "get_input");

	ClassDB::bind_method(D_METHOD("get_output"), &MLPPDataComplex::get_input);
	ClassDB::bind_method(D_METHOD("set_output", "val"), &MLPPDataComplex::set_output);
	ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_output", "get_output");

	ClassDB::bind_method(D_METHOD("instance_data"), &MLPPDataComplex::instance_data);
}

// Loading Datasets
Ref<MLPPDataSimple> MLPPData::load_breast_cancer(const String &path) {
	const int BREAST_CANCER_SIZE = 30; // k = 30

	Ref<MLPPDataSimple> data;
	data.instance();
	data->instance_data();

	set_data_supervised(BREAST_CANCER_SIZE, path, data->get_input(), data->get_output());

	return data;
}

Ref<MLPPDataSimple> MLPPData::load_breast_cancer_svc(const String &path) {
	const int BREAST_CANCER_SIZE = 30; // k = 30

	Ref<MLPPDataSimple> data;
	data.instance();
	data->instance_data();

	set_data_supervised(BREAST_CANCER_SIZE, path, data->get_input(), data->get_output());

	return data;
}

Ref<MLPPDataComplex> MLPPData::load_iris(const String &path) {
	const int IRIS_SIZE = 4;
	const int ONE_HOT_NUM = 3;

	Ref<MLPPVector> temp_output_set;
	temp_output_set.instance();

	Ref<MLPPDataComplex> data;
	data.instance();
	data->instance_data();

	set_data_supervised(IRIS_SIZE, path, data->get_input(), temp_output_set);
	data->set_output(one_hot_rep(temp_output_set, ONE_HOT_NUM));

	return data;
}

Ref<MLPPDataComplex> MLPPData::load_wine(const String &path) {
	const int WINE_SIZE = 4;
	const int ONE_HOT_NUM = 3;

	Ref<MLPPVector> temp_output_set;
	temp_output_set.instance();

	Ref<MLPPDataComplex> data;
	data.instance();
	data->instance_data();

	set_data_supervised(WINE_SIZE, path, data->get_input(), temp_output_set);
	data->set_output(one_hot_rep(temp_output_set, ONE_HOT_NUM));

	return data;
}

Ref<MLPPDataComplex> MLPPData::load_mnist_train(const String &path) {
	const int MNIST_SIZE = 784;
	const int ONE_HOT_NUM = 10;

	Ref<MLPPVector> temp_output_set;
	temp_output_set.instance();

	Ref<MLPPDataComplex> data;
	data.instance();
	data->instance_data();

	set_data_supervised(MNIST_SIZE, path, data->get_input(), temp_output_set);
	data->set_output(one_hot_rep(temp_output_set, ONE_HOT_NUM));

	return data;
}

Ref<MLPPDataComplex> MLPPData::load_mnist_test(const String &path) {
	const int MNIST_SIZE = 784;
	const int ONE_HOT_NUM = 10;

	Ref<MLPPVector> temp_output_set;
	temp_output_set.instance();

	Ref<MLPPDataComplex> data;
	data.instance();
	data->instance_data();

	set_data_supervised(MNIST_SIZE, path, data->get_input(), temp_output_set);
	data->set_output(one_hot_rep(temp_output_set, ONE_HOT_NUM));

	return data;
}

Ref<MLPPDataSimple> MLPPData::load_california_housing(const String &path) {
	const int CALIFORNIA_HOUSING_SIZE = 13; // k = 30

	Ref<MLPPDataSimple> data;
	data.instance();
	data->instance_data();

	set_data_supervised(CALIFORNIA_HOUSING_SIZE, path, data->get_input(), data->get_output());

	return data;
}

Ref<MLPPDataESimple> MLPPData::load_fires_and_crime(const String &path) {
	// k is implicitly 1.

	Ref<MLPPDataESimple> data;
	data.instance();
	data->instance_data();

	set_data_simple(path, data->get_input(), data->get_output());

	return data;
}

// MULTIVARIATE SUPERVISED

void MLPPData::set_data_supervised(int k, const String &file_name, Ref<MLPPMatrix> input_set, Ref<MLPPVector> output_set) {
	ERR_FAIL_COND(!input_set.is_valid() || !output_set.is_valid());

	MLPPLinAlg alg;

	Vector<Vector<real_t>> input_set_tmp;
	input_set_tmp.resize(k);

	Vector<real_t> output_set_tmp;

	FileAccess *file = FileAccess::open(file_name, FileAccess::READ);

	ERR_FAIL_COND(!file);

	while (!file->eof_reached()) {
		Vector<String> ll = file->get_csv_line();

		for (int i = 0; i < k; ++i) {
			input_set_tmp.write[i].push_back(static_cast<real_t>(ll[i].to_double()));
		}

		output_set_tmp.push_back(static_cast<real_t>(ll[k].to_double()));
	}

	file->close();
	memdelete(file);

	output_set->set_from_vector(output_set_tmp);

	input_set->set_from_vectors(input_set_tmp);
	input_set = alg.transposenm(input_set);
}

void MLPPData::set_data_unsupervised(int k, const String &file_name, Ref<MLPPMatrix> input_set) {
	ERR_FAIL_COND(!input_set.is_valid());

	MLPPLinAlg alg;

	Vector<Vector<real_t>> input_set_tmp;
	input_set_tmp.resize(k);

	FileAccess *file = FileAccess::open(file_name, FileAccess::READ);

	ERR_FAIL_COND(!file);

	while (!file->eof_reached()) {
		Vector<String> ll = file->get_csv_line();

		for (int i = 0; i < k; ++i) {
			input_set_tmp.write[i].push_back(static_cast<real_t>(ll[i].to_double()));
		}
	}

	file->close();
	memdelete(file);

	input_set->set_from_vectors(input_set_tmp);
	input_set = alg.transposenm(input_set);
}

void MLPPData::set_data_simple(const String &file_name, Ref<MLPPVector> input_set, Ref<MLPPVector> output_set) {
	ERR_FAIL_COND(!input_set.is_valid() || !output_set.is_valid());

	FileAccess *file = FileAccess::open(file_name, FileAccess::READ);

	ERR_FAIL_COND(!file);

	Vector<real_t> input_set_tmp;
	Vector<real_t> output_set_tmp;

	while (!file->eof_reached()) {
		Vector<String> ll = file->get_csv_line();

		for (int i = 0; i < ll.size(); i += 2) {
			input_set_tmp.push_back(static_cast<real_t>(ll[i].to_double()));
			output_set_tmp.push_back(static_cast<real_t>(ll[i + 1].to_double()));
		}
	}

	file->close();
	memdelete(file);

	input_set->set_from_vector(input_set_tmp);
	output_set->set_from_vector(output_set_tmp);
}

MLPPData::SplitComplexData MLPPData::train_test_split(Ref<MLPPDataComplex> data, real_t test_size) {
	SplitComplexData res;

	res.train.instance();
	res.train->instance_data();
	res.test.instance();
	res.test->instance_data();

	ERR_FAIL_COND_V(!data.is_valid(), res);

	Ref<MLPPMatrix> orig_input = data->get_input();
	Ref<MLPPMatrix> orig_output = data->get_output();

	Size2i orig_input_size = orig_input->size();
	Size2i orig_output_size = orig_output->size();

	int is = MIN(orig_input_size.y, orig_output_size.y);

	Array indices;
	indices.resize(is);

	for (int i = 0; i < is; ++i) {
		indices[i] = i;
	}

	indices.shuffle();

	Ref<MLPPVector> orig_input_row_tmp;
	orig_input_row_tmp.instance();
	orig_input_row_tmp->resize(orig_input_size.x);

	Ref<MLPPVector> orig_output_row_tmp;
	orig_output_row_tmp.instance();
	orig_output_row_tmp->resize(orig_output_size.x);

	int test_input_number = test_size * is; // implicit usage of floor

	Ref<MLPPMatrix> res_test_input = res.test->get_input();
	Ref<MLPPMatrix> res_test_output = res.test->get_output();

	res_test_input->resize(Size2i(orig_input_size.x, test_input_number));
	res_test_output->resize(Size2i(orig_output_size.x, test_input_number));

	for (int i = 0; i < test_input_number; ++i) {
		int index = indices[i];

		orig_input->get_row_into_mlpp_vector(index, orig_input_row_tmp);
		orig_output->get_row_into_mlpp_vector(index, orig_output_row_tmp);

		res_test_input->set_row_mlpp_vector(i, orig_input);
		res_test_output->set_row_mlpp_vector(i, orig_output);
	}

	Ref<MLPPMatrix> res_train_input = res.train->get_input();
	Ref<MLPPMatrix> res_train_output = res.train->get_output();

	int train_input_number = is - test_input_number;

	res_train_input->resize(Size2i(orig_input_size.x, train_input_number));
	res_train_output->resize(Size2i(orig_output_size.x, train_input_number));

	for (int i = 0; i < train_input_number; ++i) {
		int index = indices[train_input_number + i];

		orig_input->get_row_into_mlpp_vector(index, orig_input_row_tmp);
		orig_output->get_row_into_mlpp_vector(index, orig_output_row_tmp);

		res_train_input->set_row_mlpp_vector(i, orig_input);
		res_train_output->set_row_mlpp_vector(i, orig_output);
	}

	return res;
}
Array MLPPData::train_test_split_bind(const Ref<MLPPDataComplex> &data, real_t test_size) {
	SplitComplexData res = train_test_split(data, test_size);

	Array arr;
	arr.push_back(res.train);
	arr.push_back(res.test);

	return arr;
}

// Loading Datasets
std::tuple<std::vector<std::vector<real_t>>, std::vector<real_t>> MLPPData::loadBreastCancer() {
	const int BREAST_CANCER_SIZE = 30; // k = 30
	std::vector<std::vector<real_t>> inputSet;
	std::vector<real_t> outputSet;

	setData(BREAST_CANCER_SIZE, "MLPP/Data/Datasets/BreastCancer.csv", inputSet, outputSet);
	return { inputSet, outputSet };
}

std::tuple<std::vector<std::vector<real_t>>, std::vector<real_t>> MLPPData::loadBreastCancerSVC() {
	const int BREAST_CANCER_SIZE = 30; // k = 30
	std::vector<std::vector<real_t>> inputSet;
	std::vector<real_t> outputSet;

	setData(BREAST_CANCER_SIZE, "MLPP/Data/Datasets/BreastCancerSVM.csv", inputSet, outputSet);
	return { inputSet, outputSet };
}

std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPData::loadIris() {
	const int IRIS_SIZE = 4;
	const int ONE_HOT_NUM = 3;
	std::vector<std::vector<real_t>> inputSet;
	std::vector<real_t> tempOutputSet;

	setData(IRIS_SIZE, "/Users/marcmelikyan/Desktop/Data/Iris.csv", inputSet, tempOutputSet);
	std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
	return { inputSet, outputSet };
}

std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPData::loadWine() {
	const int WINE_SIZE = 4;
	const int ONE_HOT_NUM = 3;
	std::vector<std::vector<real_t>> inputSet;
	std::vector<real_t> tempOutputSet;

	setData(WINE_SIZE, "MLPP/Data/Datasets/Iris.csv", inputSet, tempOutputSet);
	std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
	return { inputSet, outputSet };
}

std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPData::loadMnistTrain() {
	const int MNIST_SIZE = 784;
	const int ONE_HOT_NUM = 10;
	std::vector<std::vector<real_t>> inputSet;
	std::vector<real_t> tempOutputSet;

	setData(MNIST_SIZE, "MLPP/Data/Datasets/MnistTrain.csv", inputSet, tempOutputSet);
	std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
	return { inputSet, outputSet };
}

std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPData::loadMnistTest() {
	const int MNIST_SIZE = 784;
	const int ONE_HOT_NUM = 10;
	std::vector<std::vector<real_t>> inputSet;
	std::vector<real_t> tempOutputSet;

	setData(MNIST_SIZE, "MLPP/Data/Datasets/MnistTest.csv", inputSet, tempOutputSet);
	std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
	return { inputSet, outputSet };
}

std::tuple<std::vector<std::vector<real_t>>, std::vector<real_t>> MLPPData::loadCaliforniaHousing() {
	const int CALIFORNIA_HOUSING_SIZE = 13; // k = 30
	std::vector<std::vector<real_t>> inputSet;
	std::vector<real_t> outputSet;

	setData(CALIFORNIA_HOUSING_SIZE, "MLPP/Data/Datasets/CaliforniaHousing.csv", inputSet, outputSet);
	return { inputSet, outputSet };
}

std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPData::loadFiresAndCrime() {
	std::vector<real_t> inputSet; // k is implicitly 1.
	std::vector<real_t> outputSet;

	setData("MLPP/Data/Datasets/FiresAndCrime.csv", inputSet, outputSet);
	return { inputSet, outputSet };
}

// Note that inputs and outputs should be pairs (technically), but this
// implementation will separate them. (My implementation keeps them tied together.)
// Not yet sure whether this is intentional or not (or it's something like a compiler specific difference)
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPData::trainTestSplit(std::vector<std::vector<real_t>> inputSet, std::vector<std::vector<real_t>> outputSet, real_t testSize) {
	std::random_device rd;
	std::default_random_engine generator(rd());

	std::shuffle(inputSet.begin(), inputSet.end(), generator); // inputSet random shuffle
	std::shuffle(outputSet.begin(), outputSet.end(), generator); // outputSet random shuffle)

	std::vector<std::vector<real_t>> inputTestSet;
	std::vector<std::vector<real_t>> outputTestSet;

	int testInputNumber = testSize * inputSet.size(); // implicit usage of floor
	int testOutputNumber = testSize * outputSet.size(); // implicit usage of floor

	for (int i = 0; i < testInputNumber; i++) {
		inputTestSet.push_back(inputSet[i]);
		inputSet.erase(inputSet.begin());
	}

	for (int i = 0; i < testOutputNumber; i++) {
		outputTestSet.push_back(outputSet[i]);
		outputSet.erase(outputSet.begin());
	}

	return { inputSet, outputSet, inputTestSet, outputTestSet };
}

// MULTIVARIATE SUPERVISED

void 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;

	inputSet.resize(k);

	std::ifstream dataFile(fileName);
	if (!dataFile.is_open()) {
		std::cout << fileName << " failed to open." << std::endl;
	}

	std::string line;
	while (std::getline(dataFile, line)) {
		std::stringstream ss(line);

		for (int i = 0; i < k; i++) {
			std::getline(ss, inputTemp, ',');
			inputSet[i].push_back(std::stod(inputTemp));
		}

		std::getline(ss, outputTemp, ',');
		outputSet.push_back(std::stod(outputTemp));
	}
	inputSet = alg.transpose(inputSet);
	dataFile.close();
#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++) {
		std::cout << inputName[i] << std::endl;
		for (uint32_t j = 0; j < inputSet[i].size(); j++) {
			std::cout << inputSet[i][j] << std::endl;
		}
	}

	std::cout << outputName << std::endl;
	for (uint32_t i = 0; i < outputSet.size(); i++) {
		std::cout << outputSet[i] << std::endl;
	}
#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;

	inputSet.resize(k);

	std::ifstream dataFile(fileName);
	if (!dataFile.is_open()) {
		std::cout << fileName << " failed to open." << std::endl;
	}

	std::string line;
	while (std::getline(dataFile, line)) {
		std::stringstream ss(line);

		for (int i = 0; i < k; i++) {
			std::getline(ss, inputTemp, ',');
			inputSet[i].push_back(std::stod(inputTemp));
		}
	}
	inputSet = alg.transpose(inputSet);
	dataFile.close();
#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++) {
		std::cout << inputName[i] << std::endl;
		for (uint32_t j = 0; j < inputSet[i].size(); j++) {
			std::cout << inputSet[i][j] << std::endl;
		}
	}
#endif
}

// SIMPLE

void MLPPData::setData(std::string fileName, std::vector<real_t> &inputSet, std::vector<real_t> &outputSet) {
	std::string inputTemp, outputTemp;

	std::ifstream dataFile(fileName);
	if (!dataFile.is_open()) {
		std::cout << "The file failed to open." << std::endl;
	}

	std::string line;

	while (std::getline(dataFile, line)) {
		std::stringstream ss(line);

		std::getline(ss, inputTemp, ',');
		std::getline(ss, outputTemp, ',');

		inputSet.push_back(std::stod(inputTemp));
		outputSet.push_back(std::stod(outputTemp));
	}

	dataFile.close();
}

void MLPPData::printData(std::string &inputName, std::string &outputName, std::vector<real_t> &inputSet, std::vector<real_t> &outputSet) {
	std::cout << inputName << std::endl;
	for (uint32_t i = 0; i < inputSet.size(); i++) {
		std::cout << inputSet[i] << std::endl;
	}

	std::cout << outputName << std::endl;
	for (uint32_t i = 0; i < inputSet.size(); i++) {
		std::cout << outputSet[i] << std::endl;
	}
}

// Images
std::vector<std::vector<real_t>> MLPPData::rgb2gray(std::vector<std::vector<std::vector<real_t>>> input) {
	std::vector<std::vector<real_t>> grayScale;
	grayScale.resize(input[0].size());
	for (uint32_t i = 0; i < grayScale.size(); i++) {
		grayScale[i].resize(input[0][i].size());
	}
	for (uint32_t i = 0; i < grayScale.size(); i++) {
		for (uint32_t j = 0; j < grayScale[i].size(); j++) {
			grayScale[i][j] = 0.299 * input[0][i][j] + 0.587 * input[1][i][j] + 0.114 * input[2][i][j];
		}
	}
	return grayScale;
}

std::vector<std::vector<std::vector<real_t>>> 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);
	for (uint32_t i = 0; i < YCbCr[0].size(); i++) {
		for (uint32_t j = 0; j < YCbCr[0][i].size(); j++) {
			YCbCr[0][i][j] = 0.299 * input[0][i][j] + 0.587 * input[1][i][j] + 0.114 * input[2][i][j];
			YCbCr[1][i][j] = -0.169 * input[0][i][j] - 0.331 * input[1][i][j] + 0.500 * input[2][i][j];
			YCbCr[2][i][j] = 0.500 * input[0][i][j] - 0.419 * input[1][i][j] - 0.081 * input[2][i][j];
		}
	}
	return YCbCr;
#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);
	for (uint32_t i = 0; i < HSV[0].size(); i++) {
		for (uint32_t j = 0; j < HSV[0][i].size(); j++) {
			real_t rPrime = input[0][i][j] / 255;
			real_t gPrime = input[1][i][j] / 255;
			real_t bPrime = input[2][i][j] / 255;

			real_t cMax = alg.max({ rPrime, gPrime, bPrime });
			real_t cMin = alg.min({ rPrime, gPrime, bPrime });
			real_t delta = cMax - cMin;

			// H calculation.
			if (delta == 0) {
				HSV[0][i][j] = 0;
			} else {
				if (cMax == rPrime) {
					HSV[0][i][j] = 60 * fmod(((gPrime - bPrime) / delta), 6);
				} else if (cMax == gPrime) {
					HSV[0][i][j] = 60 * ((bPrime - rPrime) / delta + 2);
				} else { // cMax == bPrime
					HSV[0][i][j] = 60 * ((rPrime - gPrime) / delta + 6);
				}
			}

			// S calculation.
			if (cMax == 0) {
				HSV[1][i][j] = 0;
			} else {
				HSV[1][i][j] = delta / cMax;
			}

			// V calculation.
			HSV[2][i][j] = cMax;
		}
	}
	return HSV;
#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
std::string MLPPData::toLower(std::string text) {
	for (uint32_t i = 0; i < text.size(); i++) {
		text[i] = tolower(text[i]);
	}
	return text;
}

std::vector<char> MLPPData::split(std::string text) {
	std::vector<char> split_data;
	for (uint32_t i = 0; i < text.size(); i++) {
		split_data.push_back(text[i]);
	}
	return split_data;
}

std::vector<std::string> MLPPData::splitSentences(std::string data) {
	std::vector<std::string> sentences;
	std::string currentStr = "";

	for (uint32_t i = 0; i < data.length(); i++) {
		currentStr.push_back(data[i]);
		if (data[i] == '.' && data[i + 1] != '.') {
			sentences.push_back(currentStr);
			currentStr = "";
			i++;
		}
	}
	return sentences;
}

std::vector<std::string> MLPPData::removeSpaces(std::vector<std::string> data) {
	for (uint32_t i = 0; i < data.size(); i++) {
		auto it = data[i].begin();
		for (uint32_t j = 0; j < data[i].length(); j++) {
			if (data[i][j] == ' ') {
				data[i].erase(it);
			}
			it++;
		}
	}
	return data;
}

std::vector<std::string> MLPPData::removeNullByte(std::vector<std::string> data) {
	for (uint32_t i = 0; i < data.size(); i++) {
		if (data[i] == "\0") {
			data.erase(data.begin() + i);
		}
	}
	return data;
}

std::vector<std::string> MLPPData::segment(std::string text) {
	std::vector<std::string> segmented_data;
	int prev_delim = 0;
	for (uint32_t i = 0; i < text.length(); i++) {
		if (text[i] == ' ') {
			segmented_data.push_back(text.substr(prev_delim, i - prev_delim));
			prev_delim = i + 1;
		} else if (text[i] == ',' || text[i] == '!' || text[i] == '.' || text[i] == '-') {
			segmented_data.push_back(text.substr(prev_delim, i - prev_delim));
			std::string punc;
			punc.push_back(text[i]);
			segmented_data.push_back(punc);
			prev_delim = i + 2;
			i++;
		} else if (i == text.length() - 1) {
			segmented_data.push_back(text.substr(prev_delim, text.length() - prev_delim)); // hehe oops- forgot this
		}
	}

	return segmented_data;
}

std::vector<real_t> MLPPData::tokenize(std::string text) {
	int max_num = 0;
	bool new_num = true;
	std::vector<std::string> segmented_data = segment(text);
	std::vector<real_t> tokenized_data;
	tokenized_data.resize(segmented_data.size());
	for (uint32_t i = 0; i < segmented_data.size(); i++) {
		for (int j = i - 1; j >= 0; j--) {
			if (segmented_data[i] == segmented_data[j]) {
				tokenized_data[i] = tokenized_data[j];
				new_num = false;
			}
		}
		if (!new_num) {
			new_num = true;
		} else {
			max_num++;
			tokenized_data[i] = max_num;
		}
	}
	return tokenized_data;
}

std::vector<std::string> MLPPData::removeStopWords(std::string text) {
	std::vector<std::string> stopWords = { "i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself", "yourselves", "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself", "they", "them", "their", "theirs", "themselves", "what", "which", "who", "whom", "this", "that", "these", "those", "am", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having", "do", "does", "did", "doing", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until", "while", "of", "at", "by", "for", "with", "about", "against", "between", "into", "through", "during", "before", "after", "above", "below", "to", "from", "up", "down", "in", "out", "on", "off", "over", "under", "again", "further", "then", "once", "here", "there", "when", "where", "why", "how", "all", "any", "both", "each", "few", "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so", "than", "too", "very", "s", "t", "can", "will", "just", "don", "should", "now" };
	std::vector<std::string> segmented_data = removeSpaces(segment(toLower(text)));

	for (uint32_t i = 0; i < stopWords.size(); i++) {
		for (uint32_t j = 0; j < segmented_data.size(); j++) {
			if (segmented_data[j] == stopWords[i]) {
				segmented_data.erase(segmented_data.begin() + j);
			}
		}
	}
	return segmented_data;
}

std::vector<std::string> MLPPData::removeStopWords(std::vector<std::string> segmented_data) {
	std::vector<std::string> stopWords = { "i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself", "yourselves", "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself", "they", "them", "their", "theirs", "themselves", "what", "which", "who", "whom", "this", "that", "these", "those", "am", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having", "do", "does", "did", "doing", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until", "while", "of", "at", "by", "for", "with", "about", "against", "between", "into", "through", "during", "before", "after", "above", "below", "to", "from", "up", "down", "in", "out", "on", "off", "over", "under", "again", "further", "then", "once", "here", "there", "when", "where", "why", "how", "all", "any", "both", "each", "few", "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so", "than", "too", "very", "s", "t", "can", "will", "just", "don", "should", "now" };
	for (uint32_t i = 0; i < segmented_data.size(); i++) {
		for (uint32_t j = 0; j < stopWords.size(); j++) {
			if (segmented_data[i] == stopWords[j]) {
				segmented_data.erase(segmented_data.begin() + i);
			}
		}
	}
	return segmented_data;
}

std::string MLPPData::stemming(std::string text) {
	// Our list of suffixes which we use to compare against
	std::vector<std::string> suffixes = { "eer", "er", "ion", "ity", "ment", "ness", "or", "sion", "ship", "th", "able", "ible", "al", "ant", "ary", "ful", "ic", "ious", "ous", "ive", "less", "y", "ed", "en", "ing", "ize", "ise", "ly", "ward", "wise" };
	int padding_size = 4;
	char padding = ' '; // our padding

	for (int i = 0; i < padding_size; i++) {
		text[text.length() + i] = padding; // ' ' will be our padding value
	}

	for (uint32_t i = 0; i < text.size(); i++) {
		for (uint32_t j = 0; j < suffixes.size(); j++) {
			if (text.substr(i, suffixes[j].length()) == suffixes[j] && (text[i + suffixes[j].length()] == ' ' || text[i + suffixes[j].length()] == ',' || text[i + suffixes[j].length()] == '-' || text[i + suffixes[j].length()] == '.' || text[i + suffixes[j].length()] == '!')) {
				text.erase(i, suffixes[j].length());
			}
		}
	}

	return text;
}

std::vector<std::vector<real_t>> MLPPData::BOW(std::vector<std::string> sentences, std::string type) {
	/*
	STEPS OF BOW:
		1) To lowercase (done by removeStopWords function by def)
		2) Removing stop words
		3) Obtain a list of the used words
		4) Create a one hot encoded vector of the words and sentences
		5) Sentence.size() x list.size() matrix
	*/

	std::vector<std::string> wordList = removeNullByte(removeStopWords(createWordList(sentences)));

	std::vector<std::vector<std::string>> segmented_sentences;
	segmented_sentences.resize(sentences.size());

	for (uint32_t i = 0; i < sentences.size(); i++) {
		segmented_sentences[i] = removeStopWords(sentences[i]);
	}

	std::vector<std::vector<real_t>> bow;

	bow.resize(sentences.size());
	for (uint32_t i = 0; i < bow.size(); i++) {
		bow[i].resize(wordList.size());
	}

	for (uint32_t i = 0; i < segmented_sentences.size(); i++) {
		for (uint32_t j = 0; j < segmented_sentences[i].size(); j++) {
			for (uint32_t k = 0; k < wordList.size(); k++) {
				if (segmented_sentences[i][j] == wordList[k]) {
					if (type == "Binary") {
						bow[i][k] = 1;
					} else {
						bow[i][k]++;
					}
				}
			}
		}
	}
	return bow;
}

std::vector<std::vector<real_t>> MLPPData::TFIDF(std::vector<std::string> sentences) {
#ifdef OLD_CLASSES_ENABLED
	MLPPLinAlgOld alg;
	std::vector<std::string> wordList = removeNullByte(removeStopWords(createWordList(sentences)));

	std::vector<std::vector<std::string>> segmented_sentences;
	segmented_sentences.resize(sentences.size());

	for (uint32_t i = 0; i < sentences.size(); i++) {
		segmented_sentences[i] = removeStopWords(sentences[i]);
	}

	std::vector<std::vector<real_t>> TF;
	std::vector<int> frequency;
	frequency.resize(wordList.size());
	TF.resize(segmented_sentences.size());
	for (uint32_t i = 0; i < TF.size(); i++) {
		TF[i].resize(wordList.size());
	}
	for (uint32_t i = 0; i < segmented_sentences.size(); i++) {
		std::vector<bool> present(wordList.size(), false);
		for (uint32_t j = 0; j < segmented_sentences[i].size(); j++) {
			for (uint32_t k = 0; k < wordList.size(); k++) {
				if (segmented_sentences[i][j] == wordList[k]) {
					TF[i][k]++;
					if (!present[k]) {
						frequency[k]++;
						present[k] = true;
					}
				}
			}
		}
		TF[i] = alg.scalarMultiply(real_t(1) / real_t(segmented_sentences[i].size()), TF[i]);
	}

	std::vector<real_t> IDF;
	IDF.resize(frequency.size());

	for (uint32_t i = 0; i < IDF.size(); i++) {
		IDF[i] = std::log((real_t)segmented_sentences.size() / (real_t)frequency[i]);
	}

	std::vector<std::vector<real_t>> TFIDF;
	TFIDF.resize(segmented_sentences.size());
	for (uint32_t i = 0; i < TFIDF.size(); i++) {
		TFIDF[i].resize(wordList.size());
	}

	for (uint32_t i = 0; i < TFIDF.size(); i++) {
		for (uint32_t j = 0; j < TFIDF[i].size(); j++) {
			TFIDF[i][j] = TF[i][j] * IDF[j];
		}
	}

	return TFIDF;
#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) {
	WordsToVecResult res;

#ifdef OLD_CLASSES_ENABLED
	res.word_list = removeNullByte(removeStopWords(createWordList(sentences)));

	std::vector<std::vector<std::string>> segmented_sentences;
	segmented_sentences.resize(sentences.size());

	for (uint32_t i = 0; i < sentences.size(); i++) {
		segmented_sentences[i] = removeStopWords(sentences[i]);
	}

	std::vector<std::string> inputStrings;
	std::vector<std::string> outputStrings;

	for (uint32_t i = 0; i < segmented_sentences.size(); i++) {
		for (uint32_t j = 0; j < segmented_sentences[i].size(); j++) {
			for (int k = windowSize; k > 0; k--) {
				if (j - k >= 0) {
					inputStrings.push_back(segmented_sentences[i][j]);

					outputStrings.push_back(segmented_sentences[i][j - k]);
				}
				if (j + k <= segmented_sentences[i].size() - 1) {
					inputStrings.push_back(segmented_sentences[i][j]);
					outputStrings.push_back(segmented_sentences[i][j + k]);
				}
			}
		}
	}

	uint32_t inputSize = inputStrings.size();

	inputStrings.insert(inputStrings.end(), outputStrings.begin(), outputStrings.end());

	std::vector<std::vector<real_t>> BOW = 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, false);

	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");

	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]);
	}

	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) {
	std::string combinedText = "";
	for (uint32_t i = 0; i < sentences.size(); i++) {
		if (i != 0) {
			combinedText += " ";
		}
		combinedText += sentences[i];
	}

	return removeSpaces(vecToSet(removeStopWords(combinedText)));
}

// EXTRA
void MLPPData::setInputNames(std::string fileName, std::vector<std::string> &inputNames) {
	std::string inputNameTemp;
	std::ifstream dataFile(fileName);
	if (!dataFile.is_open()) {
		std::cout << fileName << " failed to open." << std::endl;
	}

	while (std::getline(dataFile, inputNameTemp)) {
		inputNames.push_back(inputNameTemp);
	}

	dataFile.close();
}

std::vector<std::vector<real_t>> 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;
	max_elements.resize(X.size());
	min_elements.resize(X.size());

	for (uint32_t i = 0; i < X.size(); i++) {
		max_elements[i] = alg.max(X[i]);
		min_elements[i] = alg.min(X[i]);
	}

	for (uint32_t i = 0; i < X.size(); i++) {
		for (uint32_t j = 0; j < X[i].size(); j++) {
			X[i][j] = (X[i][j] - min_elements[i]) / (max_elements[i] - min_elements[i]);
		}
	}
	return alg.transpose(X);
#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

	X = meanCentering(X);
	for (uint32_t i = 0; i < X.size(); i++) {
		X[i] = alg.scalarMultiply(1 / stat.standardDeviation(X[i]), X[i]);
	}
	return X;
#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]);
		for (uint32_t j = 0; j < X[i].size(); j++) {
			X[i][j] -= mean_i;
		}
	}
	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) {
	std::vector<std::vector<real_t>> outputSet;
	outputSet.resize(tempOutputSet.size());
	for (uint32_t i = 0; i < tempOutputSet.size(); i++) {
		for (int j = 0; j <= n_class - 1; j++) {
			if (tempOutputSet[i] == j) {
				outputSet[i].push_back(1);
			} else {
				outputSet[i].push_back(0);
			}
		}
	}
	return outputSet;
}

std::vector<real_t> MLPPData::reverseOneHot(std::vector<std::vector<real_t>> tempOutputSet) {
	std::vector<real_t> outputSet;
	//uint32_t n_class = tempOutputSet[0].size();
	for (uint32_t i = 0; i < tempOutputSet.size(); i++) {
		int current_class = 1;
		for (uint32_t j = 0; j < tempOutputSet[i].size(); j++) {
			if (tempOutputSet[i][j] == 1) {
				break;
			} else {
				current_class++;
			}
		}
		outputSet.push_back(current_class);
	}

	return outputSet;
}

Ref<MLPPMatrix> MLPPData::mean_centering(const Ref<MLPPMatrix> &p_X) {
	MLPPStat stat;

	Ref<MLPPMatrix> X;
	X.instance();
	X->resize(p_X->size());

	Size2i x_size = X->size();

	Ref<MLPPVector> x_row_tmp;
	x_row_tmp.instance();
	x_row_tmp->resize(x_size.x);

	for (int i = 0; i < x_size.y; ++i) {
		X->get_row_into_mlpp_vector(i, x_row_tmp);

		real_t mean_i = stat.meanv(x_row_tmp);

		for (int j = 0; j < x_size.x; ++j) {
			X->element_set(i, j, p_X->element_get(i, j) - mean_i);
		}
	}

	return X;
}

Ref<MLPPMatrix> MLPPData::one_hot_rep(const Ref<MLPPVector> &temp_output_set, int n_class) {
	ERR_FAIL_COND_V(!temp_output_set.is_valid(), Ref<MLPPMatrix>());

	Ref<MLPPMatrix> output_set;
	output_set.instance();

	int temp_output_set_size = temp_output_set->size();
	const real_t *temp_output_set_ptr = temp_output_set->ptr();

	output_set->resize(Size2i(n_class, temp_output_set_size));

	for (int i = 0; i < temp_output_set_size; ++i) {
		for (int j = 0; j <= n_class - 1; ++j) {
			if (static_cast<int>(temp_output_set_ptr[i]) == j) {
				output_set->element_set(i, j, 1);
			} else {
				output_set->element_set(i, j, 0);
			}
		}
	}

	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);
	ClassDB::bind_method(D_METHOD("load_iris", "path"), &MLPPData::load_iris);
	ClassDB::bind_method(D_METHOD("load_wine", "path"), &MLPPData::load_wine);
	ClassDB::bind_method(D_METHOD("load_mnist_train", "path"), &MLPPData::load_mnist_train);
	ClassDB::bind_method(D_METHOD("load_mnist_test", "path"), &MLPPData::load_mnist_test);
	ClassDB::bind_method(D_METHOD("load_california_housing", "path"), &MLPPData::load_california_housing);
	ClassDB::bind_method(D_METHOD("load_fires_and_crime", "path"), &MLPPData::load_fires_and_crime);

	ClassDB::bind_method(D_METHOD("train_test_split", "data", "test_size"), &MLPPData::train_test_split_bind);
}