pmlpp/mlpp/utilities/utilities_old.cpp

//
//  Reg.cpp
//
//  Created by Marc Melikyan on 1/16/21.
//

#include "utilities_old.h"

#include <fstream>
#include <iostream>
#include <random>
#include <string>

std::vector<real_t> MLPPUtilitiesOld::weightInitialization(int n, std::string type) {
	std::random_device rd;
	std::default_random_engine generator(rd());

	std::vector<real_t> weights;
	for (int i = 0; i < n; i++) {
		if (type == "XavierNormal") {
			std::normal_distribution<real_t> distribution(0, sqrt(2 / (n + 1)));
			weights.push_back(distribution(generator));
		} else if (type == "XavierUniform") {
			std::uniform_real_distribution<real_t> distribution(-sqrt(6 / (n + 1)), sqrt(6 / (n + 1)));
			weights.push_back(distribution(generator));
		} else if (type == "HeNormal") {
			std::normal_distribution<real_t> distribution(0, sqrt(2 / n));
			weights.push_back(distribution(generator));
		} else if (type == "HeUniform") {
			std::uniform_real_distribution<real_t> distribution(-sqrt(6 / n), sqrt(6 / n));
			weights.push_back(distribution(generator));
		} else if (type == "LeCunNormal") {
			std::normal_distribution<real_t> distribution(0, sqrt(1 / n));
			weights.push_back(distribution(generator));
		} else if (type == "LeCunUniform") {
			std::uniform_real_distribution<real_t> distribution(-sqrt(3 / n), sqrt(3 / n));
			weights.push_back(distribution(generator));
		} else if (type == "Uniform") {
			std::uniform_real_distribution<real_t> distribution(-1 / sqrt(n), 1 / sqrt(n));
			weights.push_back(distribution(generator));
		} else {
			std::uniform_real_distribution<real_t> distribution(0, 1);
			weights.push_back(distribution(generator));
		}
	}
	return weights;
}

real_t MLPPUtilitiesOld::biasInitialization() {
	std::random_device rd;
	std::default_random_engine generator(rd());
	std::uniform_real_distribution<real_t> distribution(0, 1);

	return distribution(generator);
}

std::vector<std::vector<real_t>> MLPPUtilitiesOld::weightInitialization(int n, int m, std::string type) {
	std::random_device rd;
	std::default_random_engine generator(rd());

	std::vector<std::vector<real_t>> weights;
	weights.resize(n);

	for (int i = 0; i < n; i++) {
		for (int j = 0; j < m; j++) {
			if (type == "XavierNormal") {
				std::normal_distribution<real_t> distribution(0, sqrt(2 / (n + m)));
				weights[i].push_back(distribution(generator));
			} else if (type == "XavierUniform") {
				std::uniform_real_distribution<real_t> distribution(-sqrt(6 / (n + m)), sqrt(6 / (n + m)));
				weights[i].push_back(distribution(generator));
			} else if (type == "HeNormal") {
				std::normal_distribution<real_t> distribution(0, sqrt(2 / n));
				weights[i].push_back(distribution(generator));
			} else if (type == "HeUniform") {
				std::uniform_real_distribution<real_t> distribution(-sqrt(6 / n), sqrt(6 / n));
				weights[i].push_back(distribution(generator));
			} else if (type == "LeCunNormal") {
				std::normal_distribution<real_t> distribution(0, sqrt(1 / n));
				weights[i].push_back(distribution(generator));
			} else if (type == "LeCunUniform") {
				std::uniform_real_distribution<real_t> distribution(-sqrt(3 / n), sqrt(3 / n));
				weights[i].push_back(distribution(generator));
			} else if (type == "Uniform") {
				std::uniform_real_distribution<real_t> distribution(-1 / sqrt(n), 1 / sqrt(n));
				weights[i].push_back(distribution(generator));
			} else {
				std::uniform_real_distribution<real_t> distribution(0, 1);
				weights[i].push_back(distribution(generator));
			}
		}
	}
	return weights;
}

std::vector<real_t> MLPPUtilitiesOld::biasInitialization(int n) {
	std::vector<real_t> bias;
	std::random_device rd;
	std::default_random_engine generator(rd());
	std::uniform_real_distribution<real_t> distribution(0, 1);

	for (int i = 0; i < n; i++) {
		bias.push_back(distribution(generator));
	}
	return bias;
}

real_t MLPPUtilitiesOld::performance(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
	real_t correct = 0;
	for (uint32_t i = 0; i < y_hat.size(); i++) {
		if (std::round(y_hat[i]) == outputSet[i]) {
			correct++;
		}
	}
	return correct / y_hat.size();
}

real_t MLPPUtilitiesOld::performance(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
	real_t correct = 0;
	for (uint32_t i = 0; i < y_hat.size(); i++) {
		uint32_t sub_correct = 0;
		for (uint32_t j = 0; j < y_hat[i].size(); j++) {
			if (std::round(y_hat[i][j]) == y[i][j]) {
				sub_correct++;
			}
			if (sub_correct == y_hat[0].size()) {
				correct++;
			}
		}
	}
	return correct / y_hat.size();
}

void MLPPUtilitiesOld::saveParameters(std::string fileName, std::vector<real_t> weights, real_t bias, bool app, int layer) {
	std::string layer_info = "";
	std::ofstream saveFile;

	if (layer > -1) {
		layer_info = " for layer " + std::to_string(layer);
	}

	if (app) {
		saveFile.open(fileName.c_str(), std::ios_base::app);
	} else {
		saveFile.open(fileName.c_str());
	}

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

	saveFile << "Weight(s)" << layer_info << std::endl;
	for (uint32_t i = 0; i < weights.size(); i++) {
		saveFile << weights[i] << std::endl;
	}
	saveFile << "Bias" << layer_info << std::endl;
	saveFile << bias << std::endl;

	saveFile.close();
}

void MLPPUtilitiesOld::saveParameters(std::string fileName, std::vector<real_t> weights, std::vector<real_t> initial, real_t bias, bool app, int layer) {
	std::string layer_info = "";
	std::ofstream saveFile;

	if (layer > -1) {
		layer_info = " for layer " + std::to_string(layer);
	}

	if (app) {
		saveFile.open(fileName.c_str(), std::ios_base::app);
	} else {
		saveFile.open(fileName.c_str());
	}

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

	saveFile << "Weight(s)" << layer_info << std::endl;
	for (uint32_t i = 0; i < weights.size(); i++) {
		saveFile << weights[i] << std::endl;
	}

	saveFile << "Initial(s)" << layer_info << std::endl;
	for (uint32_t i = 0; i < initial.size(); i++) {
		saveFile << initial[i] << std::endl;
	}

	saveFile << "Bias" << layer_info << std::endl;
	saveFile << bias << std::endl;

	saveFile.close();
}

void MLPPUtilitiesOld::saveParameters(std::string fileName, std::vector<std::vector<real_t>> weights, std::vector<real_t> bias, bool app, int layer) {
	std::string layer_info = "";
	std::ofstream saveFile;

	if (layer > -1) {
		layer_info = " for layer " + std::to_string(layer);
	}

	if (app) {
		saveFile.open(fileName.c_str(), std::ios_base::app);
	} else {
		saveFile.open(fileName.c_str());
	}

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

	saveFile << "Weight(s)" << layer_info << std::endl;
	for (uint32_t i = 0; i < weights.size(); i++) {
		for (uint32_t j = 0; j < weights[i].size(); j++) {
			saveFile << weights[i][j] << std::endl;
		}
	}
	saveFile << "Bias(es)" << layer_info << std::endl;
	for (uint32_t i = 0; i < bias.size(); i++) {
		saveFile << bias[i] << std::endl;
	}

	saveFile.close();
}

void MLPPUtilitiesOld::UI(std::vector<real_t> weights, real_t bias) {
	std::cout << "Values of the weight(s):" << std::endl;
	for (uint32_t i = 0; i < weights.size(); i++) {
		std::cout << weights[i] << std::endl;
	}
	std::cout << "Value of the bias:" << std::endl;
	std::cout << bias << std::endl;
}

void MLPPUtilitiesOld::UI(std::vector<std::vector<real_t>> weights, std::vector<real_t> bias) {
	std::cout << "Values of the weight(s):" << std::endl;
	for (uint32_t i = 0; i < weights.size(); i++) {
		for (uint32_t j = 0; j < weights[i].size(); j++) {
			std::cout << weights[i][j] << std::endl;
		}
	}
	std::cout << "Value of the biases:" << std::endl;
	for (uint32_t i = 0; i < bias.size(); i++) {
		std::cout << bias[i] << std::endl;
	}
}

void MLPPUtilitiesOld::UI(std::vector<real_t> weights, std::vector<real_t> initial, real_t bias) {
	std::cout << "Values of the weight(s):" << std::endl;
	for (uint32_t i = 0; i < weights.size(); i++) {
		std::cout << weights[i] << std::endl;
	}
	std::cout << "Values of the initial(s):" << std::endl;
	for (uint32_t i = 0; i < initial.size(); i++) {
		std::cout << initial[i] << std::endl;
	}
	std::cout << "Value of the bias:" << std::endl;
	std::cout << bias << std::endl;
}

void MLPPUtilitiesOld::CostInfo(int epoch, real_t cost_prev, real_t Cost) {
	std::cout << "-----------------------------------" << std::endl;
	std::cout << "This is epoch: " << epoch << std::endl;
	std::cout << "The cost function has been minimized by " << cost_prev - Cost << std::endl;
	std::cout << "Current Cost:" << std::endl;
	std::cout << Cost << std::endl;
}

std::vector<std::vector<std::vector<real_t>>> MLPPUtilitiesOld::createMiniBatches(std::vector<std::vector<real_t>> inputSet, int n_mini_batch) {
	int n = inputSet.size();

	std::vector<std::vector<std::vector<real_t>>> inputMiniBatches;

	// Creating the mini-batches
	for (int i = 0; i < n_mini_batch; i++) {
		std::vector<std::vector<real_t>> currentInputSet;
		for (int j = 0; j < n / n_mini_batch; j++) {
			currentInputSet.push_back(inputSet[n / n_mini_batch * i + j]);
		}
		inputMiniBatches.push_back(currentInputSet);
	}

	if (real_t(n) / real_t(n_mini_batch) - int(n / n_mini_batch) != 0) {
		for (int i = 0; i < n - n / n_mini_batch * n_mini_batch; i++) {
			inputMiniBatches[n_mini_batch - 1].push_back(inputSet[n / n_mini_batch * n_mini_batch + i]);
		}
	}
	return inputMiniBatches;
}

std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<std::vector<real_t>>> MLPPUtilitiesOld::createMiniBatches(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_mini_batch) {
	int n = inputSet.size();

	std::vector<std::vector<std::vector<real_t>>> inputMiniBatches;
	std::vector<std::vector<real_t>> outputMiniBatches;

	for (int i = 0; i < n_mini_batch; i++) {
		std::vector<std::vector<real_t>> currentInputSet;
		std::vector<real_t> currentOutputSet;
		for (int j = 0; j < n / n_mini_batch; j++) {
			currentInputSet.push_back(inputSet[n / n_mini_batch * i + j]);
			currentOutputSet.push_back(outputSet[n / n_mini_batch * i + j]);
		}
		inputMiniBatches.push_back(currentInputSet);
		outputMiniBatches.push_back(currentOutputSet);
	}

	if (real_t(n) / real_t(n_mini_batch) - int(n / n_mini_batch) != 0) {
		for (int i = 0; i < n - n / n_mini_batch * n_mini_batch; i++) {
			inputMiniBatches[n_mini_batch - 1].push_back(inputSet[n / n_mini_batch * n_mini_batch + i]);
			outputMiniBatches[n_mini_batch - 1].push_back(outputSet[n / n_mini_batch * n_mini_batch + i]);
		}
	}
	return { inputMiniBatches, outputMiniBatches };
}

std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<std::vector<std::vector<real_t>>>> MLPPUtilitiesOld::createMiniBatches(std::vector<std::vector<real_t>> inputSet, std::vector<std::vector<real_t>> outputSet, int n_mini_batch) {
	int n = inputSet.size();

	std::vector<std::vector<std::vector<real_t>>> inputMiniBatches;
	std::vector<std::vector<std::vector<real_t>>> outputMiniBatches;

	for (int i = 0; i < n_mini_batch; i++) {
		std::vector<std::vector<real_t>> currentInputSet;
		std::vector<std::vector<real_t>> currentOutputSet;
		for (int j = 0; j < n / n_mini_batch; j++) {
			currentInputSet.push_back(inputSet[n / n_mini_batch * i + j]);
			currentOutputSet.push_back(outputSet[n / n_mini_batch * i + j]);
		}
		inputMiniBatches.push_back(currentInputSet);
		outputMiniBatches.push_back(currentOutputSet);
	}

	if (real_t(n) / real_t(n_mini_batch) - int(n / n_mini_batch) != 0) {
		for (int i = 0; i < n - n / n_mini_batch * n_mini_batch; i++) {
			inputMiniBatches[n_mini_batch - 1].push_back(inputSet[n / n_mini_batch * n_mini_batch + i]);
			outputMiniBatches[n_mini_batch - 1].push_back(outputSet[n / n_mini_batch * n_mini_batch + i]);
		}
	}
	return { inputMiniBatches, outputMiniBatches };
}

std::tuple<real_t, real_t, real_t, real_t> MLPPUtilitiesOld::TF_PN(std::vector<real_t> y_hat, std::vector<real_t> y) {
	real_t TP = 0;
	real_t FP = 0;
	real_t TN = 0;
	real_t FN = 0;
	for (uint32_t i = 0; i < y_hat.size(); i++) {
		if (y_hat[i] == y[i]) {
			if (y_hat[i] == 1) {
				TP++;
			} else {
				TN++;
			}
		} else {
			if (y_hat[i] == 1) {
				FP++;
			} else {
				FN++;
			}
		}
	}
	return { TP, FP, TN, FN };
}

real_t MLPPUtilitiesOld::recall(std::vector<real_t> y_hat, std::vector<real_t> y) {
	auto res = TF_PN(y_hat, y);
	auto TP = std::get<0>(res);
	//auto FP = std::get<1>(res);
	//auto TN = std::get<2>(res);
	auto FN = std::get<3>(res);

	return TP / (TP + FN);
}

real_t MLPPUtilitiesOld::precision(std::vector<real_t> y_hat, std::vector<real_t> y) {
	auto res = TF_PN(y_hat, y);
	auto TP = std::get<0>(res);
	auto FP = std::get<1>(res);
	//auto TN = std::get<2>(res);
	//auto FN = std::get<3>(res);

	return TP / (TP + FP);
}

real_t MLPPUtilitiesOld::accuracy(std::vector<real_t> y_hat, std::vector<real_t> y) {
	auto res = TF_PN(y_hat, y);
	auto TP = std::get<0>(res);
	auto FP = std::get<1>(res);
	auto TN = std::get<2>(res);
	auto FN = std::get<3>(res);

	return (TP + TN) / (TP + FP + FN + TN);
}
real_t MLPPUtilitiesOld::f1_score(std::vector<real_t> y_hat, std::vector<real_t> y) {
	return 2 * precision(y_hat, y) * recall(y_hat, y) / (precision(y_hat, y) + recall(y_hat, y));
}