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https://github.com/Relintai/pmlpp.git
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326 lines
10 KiB
C++
326 lines
10 KiB
C++
/*************************************************************************/
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/* dual_svc.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 "dual_svc.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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#include <random>
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Ref<MLPPVector> MLPPDualSVC::model_set_test(const Ref<MLPPMatrix> &X) {
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return evaluatem(X);
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}
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real_t MLPPDualSVC::model_test(const Ref<MLPPVector> &x) {
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return evaluatev(x);
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}
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void MLPPDualSVC::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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MLPPCost mlpp_cost;
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MLPPActivation avn;
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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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Ref<MLPPVector> input_set_i_row_tmp;
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input_set_i_row_tmp.instance();
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input_set_i_row_tmp->resize(_input_set->size().x);
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Ref<MLPPVector> input_set_j_row_tmp;
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input_set_j_row_tmp.instance();
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input_set_j_row_tmp->resize(_input_set->size().x);
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while (true) {
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cost_prev = cost(_alpha, _input_set, _output_set);
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_alpha->sub(mlpp_cost.dual_form_svm_deriv(_alpha, _input_set, _output_set)->scalar_multiplyn(learning_rate));
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alpha_projection();
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// Calculating the bias
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real_t biasGradient = 0;
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for (int i = 0; i < _alpha->size(); i++) {
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real_t sum = 0;
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if (_alpha->element_get(i) < _C && _alpha->element_get(i) > 0) {
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for (int j = 0; j < _alpha->size(); j++) {
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if (_alpha->element_get(j) > 0) {
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_input_set->row_get_into_mlpp_vector(i, input_set_i_row_tmp);
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_input_set->row_get_into_mlpp_vector(j, input_set_j_row_tmp);
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sum += _alpha->element_get(j) * _output_set->element_get(j) * input_set_j_row_tmp->dot(input_set_i_row_tmp); // TO DO: DON'T forget to add non-linear kernelizations.
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}
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}
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}
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biasGradient = (1 - _output_set->element_get(i) * sum) / _output_set->element_get(i);
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break;
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}
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_bias -= biasGradient * learning_rate;
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forward_pass();
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// UI PORTION
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(_alpha, _input_set, _output_set));
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MLPPUtilities::print_ui_vb(_alpha, _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 MLPPDualSVC::SGD(real_t learning_rate, int max_epoch, bool UI){
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// class MLPPCost cost;
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// MLPPActivation avn;
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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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// while(true){
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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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// int outputIndex = distribution(generator);
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// cost_prev = Cost(alpha, _input_set[outputIndex], _output_set[outputIndex]);
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// // Bias updation
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// bias -= learning_rate * costDeriv;
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// y_hat = Evaluate({_input_set[outputIndex]});
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// if(UI) {
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// MLPPUtilities::CostInfo(epoch, cost_prev, Cost(alpha));
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// MLPPUtilities::UI(weights, bias);
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// }
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// epoch++;
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// if(epoch > max_epoch) { break; }
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// }
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// forwardPass();
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// }
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// void MLPPDualSVC::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI){
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// class MLPPCost cost;
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// MLPPActivation avn;
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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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// auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(_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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// std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
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// std::vector<real_t> z = propagate(inputMiniBatches[i]);
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// cost_prev = Cost(z, outputMiniBatches[i], weights, C);
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// // Calculating the weight gradients
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// weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate/n, alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), cost.HingeLossDeriv(z, outputMiniBatches[i], C))));
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// weights = regularization.regWeights(weights, learning_rate/n, 0, "Ridge");
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// // Calculating the bias gradients
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// bias -= learning_rate * alg.sum_elements(cost.HingeLossDeriv(y_hat, outputMiniBatches[i], C)) / n;
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// forwardPass();
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// y_hat = Evaluate(inputMiniBatches[i]);
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// if(UI) {
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// MLPPUtilities::CostInfo(epoch, cost_prev, Cost(z, outputMiniBatches[i], weights, C));
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// MLPPUtilities::UI(weights, bias);
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// }
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// }
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// epoch++;
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// if(epoch > max_epoch) { break; }
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// }
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// forwardPass();
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// }
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real_t MLPPDualSVC::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 MLPPDualSVC::save(const String &file_name) {
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MLPPUtilities util;
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//util.saveParameters(file_name, _alpha, _bias);
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}
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MLPPDualSVC::MLPPDualSVC(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, real_t p_C, KernelMethod p_kernel) {
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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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_C = p_C;
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_kernel = p_kernel;
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_z.instance();
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_y_hat.instance();
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_alpha.instance();
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_y_hat->resize(_n);
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MLPPUtilities utils;
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_bias = utils.bias_initializationr();
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_alpha->resize(_n);
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utils.weight_initializationv(_alpha); // One alpha for all training examples, as per the lagrangian multipliers.
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_K = kernel_functionm(_input_set, _input_set, _kernel); // For now this is unused. When non-linear kernels are added, the K will be manipulated.
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}
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MLPPDualSVC::MLPPDualSVC() {
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}
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MLPPDualSVC::~MLPPDualSVC() {
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}
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real_t MLPPDualSVC::cost(const Ref<MLPPVector> &alpha, const Ref<MLPPMatrix> &X, const Ref<MLPPVector> &y) {
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class MLPPCost cost;
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return cost.dual_form_svm(alpha, X, y);
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}
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real_t MLPPDualSVC::evaluatev(const Ref<MLPPVector> &x) {
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MLPPActivation avn;
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return avn.sign_normr(propagatev(x));
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}
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real_t MLPPDualSVC::propagatev(const Ref<MLPPVector> &x) {
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real_t z = 0;
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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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for (int j = 0; j < _alpha->size(); j++) {
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if (_alpha->element_get(j) != 0) {
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_input_set->row_get_into_mlpp_vector(j, input_set_row_tmp);
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z += _alpha->element_get(j) * _output_set->element_get(j) * input_set_row_tmp->dot(x); // TO DO: DON'T forget to add non-linear kernelizations.
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}
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}
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z += _bias;
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return z;
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}
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Ref<MLPPVector> MLPPDualSVC::evaluatem(const Ref<MLPPMatrix> &X) {
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MLPPActivation avn;
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return avn.sign_normv(propagatem(X));
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}
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Ref<MLPPVector> MLPPDualSVC::propagatem(const Ref<MLPPMatrix> &X) {
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Ref<MLPPVector> z;
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z.instance();
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z->resize(X->size().y);
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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> x_row_tmp;
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x_row_tmp.instance();
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x_row_tmp->resize(X->size().x);
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for (int i = 0; i < X->size().y; i++) {
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real_t sum = 0;
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for (int j = 0; j < _alpha->size(); j++) {
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if (_alpha->element_get(j) != 0) {
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_input_set->row_get_into_mlpp_vector(j, input_set_row_tmp);
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X->row_get_into_mlpp_vector(i, x_row_tmp);
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sum += _alpha->element_get(j) * _output_set->element_get(j) * input_set_row_tmp->dot(x_row_tmp); // TO DO: DON'T forget to add non-linear kernelizations.
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}
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}
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sum += _bias;
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z->element_set(i, sum);
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}
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return z;
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}
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void MLPPDualSVC::forward_pass() {
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MLPPActivation avn;
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_z = propagatem(_input_set);
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_y_hat = avn.sign_normv(_z);
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}
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void MLPPDualSVC::alpha_projection() {
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for (int i = 0; i < _alpha->size(); i++) {
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if (_alpha->element_get(i) > _C) {
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_alpha->element_set(i, _C);
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} else if (_alpha->element_get(i) < 0) {
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_alpha->element_set(i, 0);
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}
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}
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}
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real_t MLPPDualSVC::kernel_functionv(const Ref<MLPPVector> &v, const Ref<MLPPVector> &u, KernelMethod kernel) {
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if (kernel == KERNEL_METHOD_LINEAR) {
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return u->dot(v);
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}
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return 0;
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}
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Ref<MLPPMatrix> MLPPDualSVC::kernel_functionm(const Ref<MLPPMatrix> &U, const Ref<MLPPMatrix> &V, KernelMethod kernel) {
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if (kernel == KERNEL_METHOD_LINEAR) {
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return _input_set->multn(_input_set->transposen());
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}
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Ref<MLPPMatrix> m;
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m.instance();
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return m;
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}
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void MLPPDualSVC::_bind_methods() {
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}
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