mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-11-09 13:22:09 +01:00
383 lines
12 KiB
C++
383 lines
12 KiB
C++
//
|
|
// Reg.cpp
|
|
//
|
|
// Created by Marc Melikyan on 1/16/21.
|
|
//
|
|
|
|
#include "utilities.h"
|
|
#include <fstream>
|
|
#include <iostream>
|
|
#include <random>
|
|
#include <string>
|
|
|
|
|
|
|
|
std::vector<double> MLPPUtilities::weightInitialization(int n, std::string type) {
|
|
std::random_device rd;
|
|
std::default_random_engine generator(rd());
|
|
|
|
std::vector<double> weights;
|
|
for (int i = 0; i < n; i++) {
|
|
if (type == "XavierNormal") {
|
|
std::normal_distribution<double> distribution(0, sqrt(2 / (n + 1)));
|
|
weights.push_back(distribution(generator));
|
|
} else if (type == "XavierUniform") {
|
|
std::uniform_real_distribution<double> distribution(-sqrt(6 / (n + 1)), sqrt(6 / (n + 1)));
|
|
weights.push_back(distribution(generator));
|
|
} else if (type == "HeNormal") {
|
|
std::normal_distribution<double> distribution(0, sqrt(2 / n));
|
|
weights.push_back(distribution(generator));
|
|
} else if (type == "HeUniform") {
|
|
std::uniform_real_distribution<double> distribution(-sqrt(6 / n), sqrt(6 / n));
|
|
weights.push_back(distribution(generator));
|
|
} else if (type == "LeCunNormal") {
|
|
std::normal_distribution<double> distribution(0, sqrt(1 / n));
|
|
weights.push_back(distribution(generator));
|
|
} else if (type == "LeCunUniform") {
|
|
std::uniform_real_distribution<double> distribution(-sqrt(3 / n), sqrt(3 / n));
|
|
weights.push_back(distribution(generator));
|
|
} else if (type == "Uniform") {
|
|
std::uniform_real_distribution<double> distribution(-1 / sqrt(n), 1 / sqrt(n));
|
|
weights.push_back(distribution(generator));
|
|
} else {
|
|
std::uniform_real_distribution<double> distribution(0, 1);
|
|
weights.push_back(distribution(generator));
|
|
}
|
|
}
|
|
return weights;
|
|
}
|
|
|
|
double MLPPUtilities::biasInitialization() {
|
|
std::random_device rd;
|
|
std::default_random_engine generator(rd());
|
|
std::uniform_real_distribution<double> distribution(0, 1);
|
|
|
|
return distribution(generator);
|
|
}
|
|
|
|
std::vector<std::vector<double>> MLPPUtilities::weightInitialization(int n, int m, std::string type) {
|
|
std::random_device rd;
|
|
std::default_random_engine generator(rd());
|
|
|
|
std::vector<std::vector<double>> weights;
|
|
weights.resize(n);
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
for (int j = 0; j < m; j++) {
|
|
if (type == "XavierNormal") {
|
|
std::normal_distribution<double> distribution(0, sqrt(2 / (n + m)));
|
|
weights[i].push_back(distribution(generator));
|
|
} else if (type == "XavierUniform") {
|
|
std::uniform_real_distribution<double> distribution(-sqrt(6 / (n + m)), sqrt(6 / (n + m)));
|
|
weights[i].push_back(distribution(generator));
|
|
} else if (type == "HeNormal") {
|
|
std::normal_distribution<double> distribution(0, sqrt(2 / n));
|
|
weights[i].push_back(distribution(generator));
|
|
} else if (type == "HeUniform") {
|
|
std::uniform_real_distribution<double> distribution(-sqrt(6 / n), sqrt(6 / n));
|
|
weights[i].push_back(distribution(generator));
|
|
} else if (type == "LeCunNormal") {
|
|
std::normal_distribution<double> distribution(0, sqrt(1 / n));
|
|
weights[i].push_back(distribution(generator));
|
|
} else if (type == "LeCunUniform") {
|
|
std::uniform_real_distribution<double> distribution(-sqrt(3 / n), sqrt(3 / n));
|
|
weights[i].push_back(distribution(generator));
|
|
} else if (type == "Uniform") {
|
|
std::uniform_real_distribution<double> distribution(-1 / sqrt(n), 1 / sqrt(n));
|
|
weights[i].push_back(distribution(generator));
|
|
} else {
|
|
std::uniform_real_distribution<double> distribution(0, 1);
|
|
weights[i].push_back(distribution(generator));
|
|
}
|
|
}
|
|
}
|
|
return weights;
|
|
}
|
|
|
|
std::vector<double> MLPPUtilities::biasInitialization(int n) {
|
|
std::vector<double> bias;
|
|
std::random_device rd;
|
|
std::default_random_engine generator(rd());
|
|
std::uniform_real_distribution<double> distribution(0, 1);
|
|
|
|
for (int i = 0; i < n; i++) {
|
|
bias.push_back(distribution(generator));
|
|
}
|
|
return bias;
|
|
}
|
|
|
|
double MLPPUtilities::performance(std::vector<double> y_hat, std::vector<double> outputSet) {
|
|
double correct = 0;
|
|
for (int i = 0; i < y_hat.size(); i++) {
|
|
if (std::round(y_hat[i]) == outputSet[i]) {
|
|
correct++;
|
|
}
|
|
}
|
|
return correct / y_hat.size();
|
|
}
|
|
|
|
double MLPPUtilities::performance(std::vector<std::vector<double>> y_hat, std::vector<std::vector<double>> y) {
|
|
double correct = 0;
|
|
for (int i = 0; i < y_hat.size(); i++) {
|
|
int sub_correct = 0;
|
|
for (int 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 MLPPUtilities::saveParameters(std::string fileName, std::vector<double> weights, double 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 (int i = 0; i < weights.size(); i++) {
|
|
saveFile << weights[i] << std::endl;
|
|
}
|
|
saveFile << "Bias" << layer_info << std::endl;
|
|
saveFile << bias << std::endl;
|
|
|
|
saveFile.close();
|
|
}
|
|
|
|
void MLPPUtilities::saveParameters(std::string fileName, std::vector<double> weights, std::vector<double> initial, double 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 (int i = 0; i < weights.size(); i++) {
|
|
saveFile << weights[i] << std::endl;
|
|
}
|
|
|
|
saveFile << "Initial(s)" << layer_info << std::endl;
|
|
for (int i = 0; i < initial.size(); i++) {
|
|
saveFile << initial[i] << std::endl;
|
|
}
|
|
|
|
saveFile << "Bias" << layer_info << std::endl;
|
|
saveFile << bias << std::endl;
|
|
|
|
saveFile.close();
|
|
}
|
|
|
|
void MLPPUtilities::saveParameters(std::string fileName, std::vector<std::vector<double>> weights, std::vector<double> 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 (int i = 0; i < weights.size(); i++) {
|
|
for (int j = 0; j < weights[i].size(); j++) {
|
|
saveFile << weights[i][j] << std::endl;
|
|
}
|
|
}
|
|
saveFile << "Bias(es)" << layer_info << std::endl;
|
|
for (int i = 0; i < bias.size(); i++) {
|
|
saveFile << bias[i] << std::endl;
|
|
}
|
|
|
|
saveFile.close();
|
|
}
|
|
|
|
void MLPPUtilities::UI(std::vector<double> weights, double bias) {
|
|
std::cout << "Values of the weight(s):" << std::endl;
|
|
for (int 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 MLPPUtilities::UI(std::vector<std::vector<double>> weights, std::vector<double> bias) {
|
|
std::cout << "Values of the weight(s):" << std::endl;
|
|
for (int i = 0; i < weights.size(); i++) {
|
|
for (int j = 0; j < weights[i].size(); j++) {
|
|
std::cout << weights[i][j] << std::endl;
|
|
}
|
|
}
|
|
std::cout << "Value of the biases:" << std::endl;
|
|
for (int i = 0; i < bias.size(); i++) {
|
|
std::cout << bias[i] << std::endl;
|
|
}
|
|
}
|
|
|
|
void MLPPUtilities::UI(std::vector<double> weights, std::vector<double> initial, double bias) {
|
|
std::cout << "Values of the weight(s):" << std::endl;
|
|
for (int i = 0; i < weights.size(); i++) {
|
|
std::cout << weights[i] << std::endl;
|
|
}
|
|
std::cout << "Values of the initial(s):" << std::endl;
|
|
for (int 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 MLPPUtilities::CostInfo(int epoch, double cost_prev, double 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<double>>> MLPPUtilities::createMiniBatches(std::vector<std::vector<double>> inputSet, int n_mini_batch) {
|
|
int n = inputSet.size();
|
|
|
|
std::vector<std::vector<std::vector<double>>> inputMiniBatches;
|
|
|
|
// Creating the mini-batches
|
|
for (int i = 0; i < n_mini_batch; i++) {
|
|
std::vector<std::vector<double>> 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 (double(n) / double(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<double>>>, std::vector<std::vector<double>>> MLPPUtilities::createMiniBatches(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet, int n_mini_batch) {
|
|
int n = inputSet.size();
|
|
|
|
std::vector<std::vector<std::vector<double>>> inputMiniBatches;
|
|
std::vector<std::vector<double>> outputMiniBatches;
|
|
|
|
for (int i = 0; i < n_mini_batch; i++) {
|
|
std::vector<std::vector<double>> currentInputSet;
|
|
std::vector<double> 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 (double(n) / double(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<double>>>, std::vector<std::vector<std::vector<double>>>> MLPPUtilities::createMiniBatches(std::vector<std::vector<double>> inputSet, std::vector<std::vector<double>> outputSet, int n_mini_batch) {
|
|
int n = inputSet.size();
|
|
|
|
std::vector<std::vector<std::vector<double>>> inputMiniBatches;
|
|
std::vector<std::vector<std::vector<double>>> outputMiniBatches;
|
|
|
|
for (int i = 0; i < n_mini_batch; i++) {
|
|
std::vector<std::vector<double>> currentInputSet;
|
|
std::vector<std::vector<double>> 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 (double(n) / double(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<double, double, double, double> MLPPUtilities::TF_PN(std::vector<double> y_hat, std::vector<double> y) {
|
|
double TP, FP, TN, FN = 0;
|
|
for (int 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 };
|
|
}
|
|
|
|
double MLPPUtilities::recall(std::vector<double> y_hat, std::vector<double> y) {
|
|
auto [TP, FP, TN, FN] = TF_PN(y_hat, y);
|
|
return TP / (TP + FN);
|
|
}
|
|
|
|
double MLPPUtilities::precision(std::vector<double> y_hat, std::vector<double> y) {
|
|
auto [TP, FP, TN, FN] = TF_PN(y_hat, y);
|
|
return TP / (TP + FP);
|
|
}
|
|
|
|
double MLPPUtilities::accuracy(std::vector<double> y_hat, std::vector<double> y) {
|
|
auto [TP, FP, TN, FN] = TF_PN(y_hat, y);
|
|
return (TP + TN) / (TP + FP + FN + TN);
|
|
}
|
|
double MLPPUtilities::f1_score(std::vector<double> y_hat, std::vector<double> y) {
|
|
return 2 * precision(y_hat, y) * recall(y_hat, y) / (precision(y_hat, y) + recall(y_hat, y));
|
|
}
|