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312 lines
8.1 KiB
C++
312 lines
8.1 KiB
C++
//
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// ExpReg.cpp
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//
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// Created by Marc Melikyan on 10/2/20.
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//
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#include "exp_reg.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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#include "../regularization/reg.h"
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#include "../stat/stat.h"
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#include "../utilities/utilities.h"
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#include <iostream>
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#include <random>
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Ref<MLPPVector> MLPPExpReg::model_set_test(const Ref<MLPPMatrix> &X) {
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return evaluatem(X);
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}
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real_t MLPPExpReg::model_test(const Ref<MLPPVector> &x) {
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return evaluatev(x);
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}
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void MLPPExpReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forward_pass();
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while (true) {
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cost_prev = cost(_y_hat, _output_set);
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Ref<MLPPVector> error = alg.subtractionnv(_y_hat, _output_set);
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for (int i = 0; i < _k; i++) {
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// Calculating the weight gradient
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real_t sum = 0;
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for (int j = 0; j < _n; j++) {
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sum += error->get_element(j) * _input_set->get_element(j, i) * Math::pow(_weights->get_element(i), _input_set->get_element(j, i) - 1);
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}
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real_t w_gradient = sum / _n;
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// Calculating the initial gradient
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real_t sum2 = 0;
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for (int j = 0; j < _n; j++) {
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sum2 += error->get_element(j) * Math::pow(_weights->get_element(i), _input_set->get_element(j, i));
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}
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real_t i_gradient = sum2 / _n;
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// Weight/initial updation
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_weights->set_element(i, _weights->get_element(i) - learning_rate * w_gradient);
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_initial->set_element(i, _initial->get_element(i) - learning_rate * i_gradient);
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}
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradient
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real_t sum = 0;
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for (int j = 0; j < _n; j++) {
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sum += (_y_hat->get_element(j) - _output_set->get_element(j));
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}
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real_t b_gradient = sum / _n;
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// bias updation
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_bias -= learning_rate * b_gradient;
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forward_pass();
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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}
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void MLPPExpReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(_n - 1));
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Ref<MLPPVector> input_set_row_tmp;
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input_set_row_tmp.instance();
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input_set_row_tmp->resize(_input_set->size().x);
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Ref<MLPPVector> y_hat_row_tmp;
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y_hat_row_tmp.instance();
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y_hat_row_tmp->resize(1);
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Ref<MLPPVector> output_set_row_tmp;
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output_set_row_tmp.instance();
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output_set_row_tmp->resize(1);
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while (true) {
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int output_index = distribution(generator);
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_input_set->get_row_into_mlpp_vector(output_index, input_set_row_tmp);
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real_t output_set_element = _output_set->get_element(output_index);
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output_set_row_tmp->set_element(0, output_set_element);
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real_t y_hat = evaluatev(input_set_row_tmp);
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y_hat_row_tmp->set_element(0, y_hat);
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cost_prev = cost(y_hat_row_tmp, output_set_row_tmp);
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for (int i = 0; i < _k; i++) {
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// Calculating the weight gradients
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real_t w_gradient = (y_hat - output_set_element) * input_set_row_tmp->get_element(i) * Math::pow(_weights->get_element(i), _input_set->get_element(output_index, i) - 1);
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real_t i_gradient = (y_hat - output_set_element) * Math::pow(_weights->get_element(i), _input_set->get_element(output_index, i));
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// Weight/initial updation
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_weights->set_element(i, _weights->get_element(i) - learning_rate * w_gradient);
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_initial->set_element(i, _initial->get_element(i) - learning_rate * i_gradient);
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}
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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real_t b_gradient = (y_hat - output_set_element);
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// Bias updation
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_bias -= learning_rate * b_gradient;
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y_hat = evaluatev(input_set_row_tmp);
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat_row_tmp, output_set_row_tmp));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forward_pass();
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}
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void MLPPExpReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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// Creating the mini-batches
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int n_mini_batch = _n / mini_batch_size;
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MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
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Ref<MLPPVector> current_output_batch = batches.output_sets[i];
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Ref<MLPPVector> y_hat = evaluatem(current_input_batch);
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cost_prev = cost(y_hat, current_output_batch);
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Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_batch);
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for (int j = 0; j < _k; j++) {
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// Calculating the weight gradient
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real_t sum = 0;
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for (int k = 0; k < current_output_batch->size(); k++) {
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sum += error->get_element(k) * current_input_batch->get_element(k, j) * Math::pow(_weights->get_element(j), current_input_batch->get_element(k, j) - 1);
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}
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real_t w_gradient = sum / current_output_batch->size();
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// Calculating the initial gradient
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real_t sum2 = 0;
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for (int k = 0; k < current_output_batch->size(); k++) {
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sum2 += error->get_element(k) * Math::pow(_weights->get_element(j), current_input_batch->get_element(k, j));
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}
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real_t i_gradient = sum2 / current_output_batch->size();
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// Weight/initial updation
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_weights->set_element(i, _weights->get_element(i) - learning_rate * w_gradient);
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_initial->set_element(i, _initial->get_element(i) - learning_rate * i_gradient);
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}
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradient
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//real_t sum = 0;
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//for (int j = 0; j < current_output_batch->size(); j++) {
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// sum += (y_hat->get_element(j) - current_output_batch->get_element(j));
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//}
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//real_t b_gradient = sum / output_mini_batches[i].size();
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y_hat = evaluatem(current_input_batch);
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_batch));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forward_pass();
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}
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real_t MLPPExpReg::score() {
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MLPPUtilities util;
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return util.performance_vec(_y_hat, _output_set);
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}
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void MLPPExpReg::save(const String &file_name) {
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MLPPUtilities util;
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//util.saveParameters(file_name, _weights, _initial, _bias);
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}
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MLPPExpReg::MLPPExpReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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_n = p_input_set->size().y;
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_k = p_input_set->size().x;
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_reg = p_reg;
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_lambda = p_lambda;
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_alpha = p_alpha;
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_y_hat.instance();
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_y_hat->resize(_n);
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MLPPUtilities util;
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_weights.instance();
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_weights->resize(_k);
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util.weight_initializationv(_weights);
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_initial.instance();
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_initial->resize(_k);
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util.weight_initializationv(_initial);
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_bias = util.bias_initializationr();
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}
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MLPPExpReg::MLPPExpReg() {
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}
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MLPPExpReg::~MLPPExpReg() {
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}
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real_t MLPPExpReg::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
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MLPPReg regularization;
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MLPPCost mlpp_cost;
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return mlpp_cost.msev(y_hat, y) + regularization.reg_termv(_weights, _lambda, _alpha, _reg);
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}
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real_t MLPPExpReg::evaluatev(const Ref<MLPPVector> &x) {
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real_t y_hat = 0;
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for (int i = 0; i < x->size(); i++) {
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y_hat += _initial->get_element(i) * Math::pow(_weights->get_element(i), x->get_element(i));
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}
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return y_hat + _bias;
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}
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Ref<MLPPVector> MLPPExpReg::evaluatem(const Ref<MLPPMatrix> &X) {
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Ref<MLPPVector> y_hat;
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y_hat.instance();
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y_hat->resize(X->size().y);
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for (int i = 0; i < X->size().y; i++) {
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real_t y = 0;
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for (int j = 0; j < X->size().x; j++) {
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y += _initial->get_element(j) * Math::pow(_weights->get_element(j), X->get_element(i, j));
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}
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y += _bias;
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y_hat->set_element(i, y);
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}
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return y_hat;
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}
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// a * w^x + b
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void MLPPExpReg::forward_pass() {
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_y_hat = evaluatem(_input_set);
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}
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void MLPPExpReg::_bind_methods() {
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}
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