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391 lines
11 KiB
C++
391 lines
11 KiB
C++
//
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// SVC.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 "svc.h"
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#include "../activation/activation.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 "../utilities/utilities.h"
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#include <random>
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Ref<MLPPMatrix> MLPPSVC::get_input_set() const {
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return _input_set;
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}
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void MLPPSVC::set_input_set(const Ref<MLPPMatrix> &val) {
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_input_set = val;
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}
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Ref<MLPPVector> MLPPSVC::get_output_set() const {
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return _output_set;
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}
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void MLPPSVC::set_output_set(const Ref<MLPPMatrix> &val) {
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_output_set = val;
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}
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real_t MLPPSVC::get_c() const {
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return _c;
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}
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void MLPPSVC::set_c(const real_t val) {
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_c = val;
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}
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Ref<MLPPVector> MLPPSVC::data_z_get() const {
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return _z;
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}
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void MLPPSVC::data_z_set(const Ref<MLPPVector> &val) {
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_z = val;
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}
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Ref<MLPPVector> MLPPSVC::data_y_hat_get() const {
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return _y_hat;
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}
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void MLPPSVC::data_y_hat_set(const Ref<MLPPVector> &val) {
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_y_hat = val;
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}
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Ref<MLPPVector> MLPPSVC::data_weights_get() const {
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return _weights;
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}
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void MLPPSVC::data_weights_set(const Ref<MLPPVector> &val) {
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_weights = val;
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}
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real_t MLPPSVC::data_bias_get() const {
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return _bias;
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}
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void MLPPSVC::data_bias_set(const real_t val) {
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_bias = val;
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}
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Ref<MLPPVector> MLPPSVC::model_set_test(const Ref<MLPPMatrix> &X) {
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ERR_FAIL_COND_V(needs_init(), Ref<MLPPVector>());
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return evaluatem(X);
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}
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real_t MLPPSVC::model_test(const Ref<MLPPVector> &x) {
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ERR_FAIL_COND_V(needs_init(), 0);
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return evaluatev(x);
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}
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void MLPPSVC::train_gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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int n = _input_set->size().y;
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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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while (true) {
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cost_prev = cost(_y_hat, _output_set, _weights, _c);
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_weights->sub(_input_set->transposen()->mult_vec(mlpp_cost.hinge_loss_derivwv(_z, _output_set, _c))->scalar_multiplyn(learning_rate / n));
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_weights = regularization.reg_weightsv(_weights, learning_rate / n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
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// Calculating the bias gradients
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_bias += learning_rate * mlpp_cost.hinge_loss_derivwv(_y_hat, _output_set, _c)->sum_elements() / n;
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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(_y_hat, _output_set, _weights, _c));
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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 MLPPSVC::train_sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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int n = _input_set->size().y;
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MLPPCost mlpp_cost;
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MLPPActivation avn;
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MLPPReg regularization;
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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> 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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Ref<MLPPVector> z_row_tmp;
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z_row_tmp.instance();
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z_row_tmp->resize(1);
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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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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_set_indx = _output_set->element_get(output_index);
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output_set_row_tmp->element_set(0, output_set_indx);
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//real_t y_hat = Evaluate(input_set_row_tmp);
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real_t z = propagatev(input_set_row_tmp);
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z_row_tmp->element_set(0, z);
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cost_prev = cost(z_row_tmp, output_set_row_tmp, _weights, _c);
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Ref<MLPPVector> cost_deriv_vec = mlpp_cost.hinge_loss_derivwv(z_row_tmp, output_set_row_tmp, _c);
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real_t cost_deriv = cost_deriv_vec->element_get(0);
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// Weight Updation
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_weights->sub(input_set_row_tmp->scalar_multiplyn(learning_rate * cost_deriv));
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_weights = regularization.reg_weightsv(_weights, learning_rate, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
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// Bias updation
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_bias -= learning_rate * cost_deriv;
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//y_hat = Evaluate({ _input_set[output_index] });
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(z_row_tmp, output_set_row_tmp, _weights, _c));
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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 MLPPSVC::train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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int n = _input_set->size().y;
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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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// 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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forward_pass();
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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_entry = batches.input_sets[i];
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Ref<MLPPVector> current_output_batch_entry = batches.output_sets[i];
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Ref<MLPPVector> y_hat = evaluatem(current_input_batch_entry);
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Ref<MLPPVector> z = propagatem(current_input_batch_entry);
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cost_prev = cost(z, current_output_batch_entry, _weights, _c);
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// Calculating the weight gradients
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_weights->subn(current_input_batch_entry->transposen()->mult_vec(mlpp_cost.hinge_loss_derivwv(z, current_output_batch_entry, _c))->scalar_multiplyn(learning_rate / n));
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_weights = regularization.reg_weightsv(_weights, learning_rate / n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
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// Calculating the bias gradients
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_bias -= learning_rate * mlpp_cost.hinge_loss_derivwv(y_hat, current_output_batch_entry, _c)->sum_elements() / n;
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forward_pass();
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y_hat = evaluatem(current_input_batch_entry);
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(z, current_output_batch_entry, _weights, _c));
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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 MLPPSVC::score() {
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ERR_FAIL_COND_V(needs_init(), 0);
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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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bool MLPPSVC::needs_init() const {
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if (!_input_set.is_valid()) {
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return true;
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}
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if (!_output_set.is_valid()) {
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return true;
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}
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int n = _input_set->size().y;
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int k = _input_set->size().x;
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if (_y_hat->size() != n) {
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return true;
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}
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if (_weights->size() != k) {
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return true;
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}
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return false;
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}
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void MLPPSVC::initialize() {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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int n = _input_set->size().y;
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int k = _input_set->size().x;
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_y_hat->resize(n);
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MLPPUtilities util;
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_weights->resize(k);
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util.weight_initializationv(_weights);
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_bias = util.bias_initializationr();
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}
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MLPPSVC::MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c) {
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_input_set = input_set;
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_output_set = output_set;
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_c = c;
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_z.instance();
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_y_hat.instance();
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_weights.instance();
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_bias = 0;
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initialize();
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}
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MLPPSVC::MLPPSVC() {
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_c = 0;
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_z.instance();
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_y_hat.instance();
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_weights.instance();
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_bias = 0;
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}
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MLPPSVC::~MLPPSVC() {
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}
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real_t MLPPSVC::cost(const Ref<MLPPVector> &z, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t c) {
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MLPPCost mlpp_cost;
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return mlpp_cost.hinge_losswv(z, y, weights, c);
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}
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Ref<MLPPVector> MLPPSVC::evaluatem(const Ref<MLPPMatrix> &X) {
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MLPPActivation avn;
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return avn.sign_normv(X->mult_vec(_weights)->scalar_addn(_bias));
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}
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Ref<MLPPVector> MLPPSVC::propagatem(const Ref<MLPPMatrix> &X) {
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return X->mult_vec(_weights)->scalar_addn(_bias);
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}
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real_t MLPPSVC::evaluatev(const Ref<MLPPVector> &x) {
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MLPPActivation avn;
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return avn.sign_normr(_weights->dot(x) + _bias);
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}
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real_t MLPPSVC::propagatev(const Ref<MLPPVector> &x) {
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MLPPActivation avn;
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return _weights->dot(x) + _bias;
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}
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// sign ( wTx + b )
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void MLPPSVC::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 MLPPSVC::_bind_methods() {
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ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPSVC::get_input_set);
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ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPSVC::set_input_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
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ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPSVC::get_output_set);
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ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPSVC::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_output_set", "get_output_set");
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ClassDB::bind_method(D_METHOD("get_c"), &MLPPSVC::get_c);
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ClassDB::bind_method(D_METHOD("set_c", "val"), &MLPPSVC::set_c);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "c"), "set_c", "get_c");
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ClassDB::bind_method(D_METHOD("data_z_get"), &MLPPSVC::data_z_get);
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ClassDB::bind_method(D_METHOD("data_z_set", "val"), &MLPPSVC::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_z", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_z_set", "data_z_get");
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ClassDB::bind_method(D_METHOD("data_y_hat_get"), &MLPPSVC::data_y_hat_get);
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ClassDB::bind_method(D_METHOD("data_y_hat_set", "val"), &MLPPSVC::data_y_hat_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_y_hat", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_y_hat_set", "data_y_hat_get");
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ClassDB::bind_method(D_METHOD("data_weights_get"), &MLPPSVC::data_weights_get);
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ClassDB::bind_method(D_METHOD("data_weights_set", "val"), &MLPPSVC::data_weights_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_weights", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_weights_set", "data_weights_get");
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ClassDB::bind_method(D_METHOD("data_bias_get"), &MLPPSVC::data_bias_get);
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ClassDB::bind_method(D_METHOD("data_bias_set", "val"), &MLPPSVC::data_bias_set);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "data_bias"), "data_bias_set", "data_bias_get");
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ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPSVC::model_set_test);
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ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPSVC::model_test);
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ClassDB::bind_method(D_METHOD("train_gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSVC::train_gradient_descent, false);
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ClassDB::bind_method(D_METHOD("train_sgd", "learning_rate", "max_epoch", "ui"), &MLPPSVC::train_sgd, false);
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ClassDB::bind_method(D_METHOD("train_mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSVC::train_mbgd, false);
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ClassDB::bind_method(D_METHOD("score"), &MLPPSVC::score);
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ClassDB::bind_method(D_METHOD("needs_init"), &MLPPSVC::needs_init);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPSVC::initialize);
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}
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