pmlpp/mlpp/exp_reg/exp_reg_old.cpp

248 lines
6.6 KiB
C++

//
// ExpReg.cpp
//
// Created by Marc Melikyan on 10/2/20.
//
#include "exp_reg_old.h"
#include "../cost/cost.h"
#include "../lin_alg/lin_alg.h"
#include "../regularization/reg.h"
#include "../stat/stat.h"
#include "../utilities/utilities.h"
#include <iostream>
#include <random>
MLPPExpRegOld::MLPPExpRegOld(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, std::string p_reg, real_t p_lambda, real_t p_alpha) {
inputSet = p_inputSet;
outputSet = p_outputSet;
n = p_inputSet.size();
k = p_inputSet[0].size();
reg = p_reg;
lambda = p_lambda;
alpha = p_alpha;
y_hat.resize(n);
weights = MLPPUtilities::weightInitialization(k);
initial = MLPPUtilities::weightInitialization(k);
bias = MLPPUtilities::biasInitialization();
}
std::vector<real_t> MLPPExpRegOld::modelSetTest(std::vector<std::vector<real_t>> X) {
return Evaluate(X);
}
real_t MLPPExpRegOld::modelTest(std::vector<real_t> x) {
return Evaluate(x);
}
void MLPPExpRegOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
forwardPass();
while (true) {
cost_prev = Cost(y_hat, outputSet);
std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
for (int i = 0; i < k; i++) {
// Calculating the weight gradient
real_t sum = 0;
for (int j = 0; j < n; j++) {
sum += error[j] * inputSet[j][i] * std::pow(weights[i], inputSet[j][i] - 1);
}
real_t w_gradient = sum / n;
// Calculating the initial gradient
real_t sum2 = 0;
for (int j = 0; j < n; j++) {
sum2 += error[j] * std::pow(weights[i], inputSet[j][i]);
}
real_t i_gradient = sum2 / n;
// Weight/initial updation
weights[i] -= learning_rate * w_gradient;
initial[i] -= learning_rate * i_gradient;
}
weights = regularization.regWeights(weights, lambda, alpha, reg);
// Calculating the bias gradient
real_t sum = 0;
for (int j = 0; j < n; j++) {
sum += (y_hat[j] - outputSet[j]);
}
real_t b_gradient = sum / n;
// bias updation
bias -= learning_rate * b_gradient;
forwardPass();
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
MLPPUtilities::UI(weights, bias);
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
}
void MLPPExpRegOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
while (true) {
std::random_device rd;
std::default_random_engine generator(rd());
std::uniform_int_distribution<int> distribution(0, int(n - 1));
int outputIndex = distribution(generator);
real_t y_hat = Evaluate(inputSet[outputIndex]);
cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
for (int i = 0; i < k; i++) {
// Calculating the weight gradients
real_t w_gradient = (y_hat - outputSet[outputIndex]) * inputSet[outputIndex][i] * std::pow(weights[i], inputSet[outputIndex][i] - 1);
real_t i_gradient = (y_hat - outputSet[outputIndex]) * std::pow(weights[i], inputSet[outputIndex][i]);
// Weight/initial updation
weights[i] -= learning_rate * w_gradient;
initial[i] -= learning_rate * i_gradient;
}
weights = regularization.regWeights(weights, lambda, alpha, reg);
// Calculating the bias gradients
real_t b_gradient = (y_hat - outputSet[outputIndex]);
// Bias updation
bias -= learning_rate * b_gradient;
y_hat = Evaluate({ inputSet[outputIndex] });
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
MLPPUtilities::UI(weights, bias);
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
forwardPass();
}
void MLPPExpRegOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
// Creating the mini-batches
int n_mini_batch = n / mini_batch_size;
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
auto inputMiniBatches = std::get<0>(batches);
auto outputMiniBatches = std::get<1>(batches);
while (true) {
for (int i = 0; i < n_mini_batch; i++) {
std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
cost_prev = Cost(y_hat, outputMiniBatches[i]);
std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
for (int j = 0; j < k; j++) {
// Calculating the weight gradient
real_t sum = 0;
for (uint32_t k = 0; k < outputMiniBatches[i].size(); k++) {
sum += error[k] * inputMiniBatches[i][k][j] * std::pow(weights[j], inputMiniBatches[i][k][j] - 1);
}
real_t w_gradient = sum / outputMiniBatches[i].size();
// Calculating the initial gradient
real_t sum2 = 0;
for (uint32_t k = 0; k < outputMiniBatches[i].size(); k++) {
sum2 += error[k] * std::pow(weights[j], inputMiniBatches[i][k][j]);
}
real_t i_gradient = sum2 / outputMiniBatches[i].size();
// Weight/initial updation
weights[j] -= learning_rate * w_gradient;
initial[j] -= learning_rate * i_gradient;
}
weights = regularization.regWeights(weights, lambda, alpha, reg);
// Calculating the bias gradient
//real_t sum = 0;
//for (uint32_t j = 0; j < outputMiniBatches[i].size(); j++) {
// sum += (y_hat[j] - outputMiniBatches[i][j]);
//}
//real_t b_gradient = sum / outputMiniBatches[i].size();
y_hat = Evaluate(inputMiniBatches[i]);
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
MLPPUtilities::UI(weights, bias);
}
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
forwardPass();
}
real_t MLPPExpRegOld::score() {
MLPPUtilities util;
return util.performance(y_hat, outputSet);
}
void MLPPExpRegOld::save(std::string fileName) {
MLPPUtilities util;
util.saveParameters(fileName, weights, initial, bias);
}
real_t MLPPExpRegOld::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
MLPPReg regularization;
class MLPPCost cost;
return cost.MSE(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg);
}
std::vector<real_t> MLPPExpRegOld::Evaluate(std::vector<std::vector<real_t>> X) {
std::vector<real_t> y_hat;
y_hat.resize(X.size());
for (uint32_t i = 0; i < X.size(); i++) {
y_hat[i] = 0;
for (uint32_t j = 0; j < X[i].size(); j++) {
y_hat[i] += initial[j] * std::pow(weights[j], X[i][j]);
}
y_hat[i] += bias;
}
return y_hat;
}
real_t MLPPExpRegOld::Evaluate(std::vector<real_t> x) {
real_t y_hat = 0;
for (uint32_t i = 0; i < x.size(); i++) {
y_hat += initial[i] * std::pow(weights[i], x[i]);
}
return y_hat + bias;
}
// a * w^x + b
void MLPPExpRegOld::forwardPass() {
y_hat = Evaluate(inputSet);
}