mirror of https://github.com/Relintai/pmlpp.git
333 lines
10 KiB
C++
333 lines
10 KiB
C++
/*************************************************************************/
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/* exp_reg.cpp */
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/*************************************************************************/
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/* This file is part of: */
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/* PMLPP Machine Learning Library */
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/* https://github.com/Relintai/pmlpp */
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/*************************************************************************/
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/* Copyright (c) 2023-present Péter Magyar. */
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/* Copyright (c) 2022-2023 Marc Melikyan */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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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 "../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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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 = _y_hat->subn(_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->element_get(j) * _input_set->element_get(j, i) * Math::pow(_weights->element_get(i), _input_set->element_get(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->element_get(j) * Math::pow(_weights->element_get(i), _input_set->element_get(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->element_set(i, _weights->element_get(i) - learning_rate * w_gradient);
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_initial->element_set(i, _initial->element_get(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->element_get(j) - _output_set->element_get(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->row_get_into_mlpp_vector(output_index, input_set_row_tmp);
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real_t output_element_set = _output_set->element_get(output_index);
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output_set_row_tmp->element_set(0, output_element_set);
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real_t y_hat = evaluatev(input_set_row_tmp);
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y_hat_row_tmp->element_set(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_element_set) * input_set_row_tmp->element_get(i) * Math::pow(_weights->element_get(i), _input_set->element_get(output_index, i) - 1);
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real_t i_gradient = (y_hat - output_element_set) * Math::pow(_weights->element_get(i), _input_set->element_get(output_index, i));
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// Weight/initial updation
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_weights->element_set(i, _weights->element_get(i) - learning_rate * w_gradient);
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_initial->element_set(i, _initial->element_get(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_element_set);
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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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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 = y_hat->subn(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->element_get(k) * current_input_batch->element_get(k, j) * Math::pow(_weights->element_get(j), current_input_batch->element_get(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->element_get(k) * Math::pow(_weights->element_get(j), current_input_batch->element_get(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->element_set(i, _weights->element_get(i) - learning_rate * w_gradient);
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_initial->element_set(i, _initial->element_get(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->element_get(j) - current_output_batch->element_get(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->element_get(i) * Math::pow(_weights->element_get(i), x->element_get(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->element_get(j) * Math::pow(_weights->element_get(j), X->element_get(i, j));
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}
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y += _bias;
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y_hat->element_set(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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