pmlpp/mlpp/svc/svc.cpp
2023-04-22 14:23:51 +02:00

366 lines
9.3 KiB
C++

//
// SVC.cpp
//
// Created by Marc Melikyan on 10/2/20.
//
#include "svc.h"
#include "../activation/activation.h"
#include "../cost/cost.h"
#include "../lin_alg/lin_alg.h"
#include "../regularization/reg.h"
#include "../utilities/utilities.h"
#include <random>
Ref<MLPPMatrix> MLPPSVC::get_input_set() {
return _input_set;
}
void MLPPSVC::set_input_set(const Ref<MLPPMatrix> &val) {
_input_set = val;
_initialized = false;
}
Ref<MLPPVector> MLPPSVC::get_output_set() {
return _output_set;
}
void MLPPSVC::set_output_set(const Ref<MLPPMatrix> &val) {
_output_set = val;
_initialized = false;
}
real_t MLPPSVC::get_c() {
return _c;
}
void MLPPSVC::set_c(const real_t val) {
_c = val;
_initialized = false;
}
Ref<MLPPVector> MLPPSVC::model_set_test(const Ref<MLPPMatrix> &X) {
ERR_FAIL_COND_V(!_initialized, Ref<MLPPVector>());
return evaluatem(X);
}
real_t MLPPSVC::model_test(const Ref<MLPPVector> &x) {
ERR_FAIL_COND_V(!_initialized, 0);
return evaluatev(x);
}
void MLPPSVC::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
ERR_FAIL_COND(!_initialized);
MLPPCost mlpp_cost;
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
forward_pass();
while (true) {
cost_prev = cost(_y_hat, _output_set, _weights, _c);
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposenm(_input_set), mlpp_cost.hinge_loss_derivwv(_z, _output_set, _c))));
_weights = regularization.reg_weightsv(_weights, learning_rate / _n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
// Calculating the bias gradients
_bias += learning_rate * alg.sum_elementsv(mlpp_cost.hinge_loss_derivwv(_y_hat, _output_set, _c)) / _n;
forward_pass();
// UI PORTION
if (ui) {
MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set, _weights, _c));
MLPPUtilities::print_ui_vb(_weights, _bias);
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
}
void MLPPSVC::sgd(real_t learning_rate, int max_epoch, bool ui) {
ERR_FAIL_COND(!_initialized);
MLPPCost mlpp_cost;
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
std::random_device rd;
std::default_random_engine generator(rd());
std::uniform_int_distribution<int> distribution(0, int(_n - 1));
Ref<MLPPVector> input_set_row_tmp;
input_set_row_tmp.instance();
input_set_row_tmp->resize(_input_set->size().x);
Ref<MLPPVector> output_set_row_tmp;
output_set_row_tmp.instance();
output_set_row_tmp->resize(1);
Ref<MLPPVector> z_row_tmp;
z_row_tmp.instance();
z_row_tmp->resize(1);
real_t cost_prev = 0;
int epoch = 1;
forward_pass();
while (true) {
int output_index = distribution(generator);
_input_set->get_row_into_mlpp_vector(output_index, input_set_row_tmp);
real_t output_set_indx = _output_set->get_element(output_index);
output_set_row_tmp->set_element(0, output_set_indx);
//real_t y_hat = Evaluate(input_set_row_tmp);
real_t z = propagatev(input_set_row_tmp);
z_row_tmp->set_element(0, z);
cost_prev = cost(z_row_tmp, output_set_row_tmp, _weights, _c);
Ref<MLPPVector> cost_deriv_vec = mlpp_cost.hinge_loss_derivwv(z_row_tmp, output_set_row_tmp, _c);
real_t cost_deriv = cost_deriv_vec->get_element(0);
// Weight Updation
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate * cost_deriv, input_set_row_tmp));
_weights = regularization.reg_weightsv(_weights, learning_rate, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
// Bias updation
_bias -= learning_rate * cost_deriv;
//y_hat = Evaluate({ _input_set[output_index] });
if (ui) {
MLPPUtilities::cost_info(epoch, cost_prev, cost(z_row_tmp, output_set_row_tmp, _weights, _c));
MLPPUtilities::print_ui_vb(_weights, _bias);
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
forward_pass();
}
void MLPPSVC::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
ERR_FAIL_COND(!_initialized);
MLPPCost mlpp_cost;
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
// Creating the mini-batches
int n_mini_batch = _n / mini_batch_size;
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
forward_pass();
while (true) {
for (int i = 0; i < n_mini_batch; i++) {
Ref<MLPPMatrix> current_input_batch_entry = batches.input_sets[i];
Ref<MLPPVector> current_output_batch_entry = batches.output_sets[i];
Ref<MLPPVector> y_hat = evaluatem(current_input_batch_entry);
Ref<MLPPVector> z = propagatem(current_input_batch_entry);
cost_prev = cost(z, current_output_batch_entry, _weights, _c);
// Calculating the weight gradients
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposenm(current_input_batch_entry), mlpp_cost.hinge_loss_derivwv(z, current_output_batch_entry, _c))));
_weights = regularization.reg_weightsv(_weights, learning_rate / _n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
// Calculating the bias gradients
_bias -= learning_rate * alg.sum_elementsv(mlpp_cost.hinge_loss_derivwv(y_hat, current_output_batch_entry, _c)) / _n;
forward_pass();
y_hat = evaluatem(current_input_batch_entry);
if (ui) {
MLPPUtilities::cost_info(epoch, cost_prev, cost(z, current_output_batch_entry, _weights, _c));
MLPPUtilities::print_ui_vb(_weights, _bias);
}
}
epoch++;
if (epoch > max_epoch) {
break;
}
}
forward_pass();
}
real_t MLPPSVC::score() {
ERR_FAIL_COND_V(!_initialized, 0);
MLPPUtilities util;
return util.performance_vec(_y_hat, _output_set);
}
void MLPPSVC::save(const String &file_name) {
ERR_FAIL_COND(!_initialized);
MLPPUtilities util;
//util.saveParameters(_file_name, _weights, _bias);
}
bool MLPPSVC::is_initialized() {
return _initialized;
}
void MLPPSVC::initialize() {
if (_initialized) {
return;
}
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
_n = _input_set->size().y;
_k = _input_set->size().x;
if (!_y_hat.is_valid()) {
_y_hat.instance();
}
_y_hat->resize(_n);
MLPPUtilities util;
if (!_weights.is_valid()) {
_weights.instance();
}
_weights->resize(_k);
util.weight_initializationv(_weights);
_bias = util.bias_initializationr();
_initialized = true;
}
MLPPSVC::MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c) {
_input_set = input_set;
_output_set = output_set;
_n = _input_set->size().y;
_k = _input_set->size().x;
_c = c;
_y_hat.instance();
_y_hat->resize(_n);
MLPPUtilities util;
_weights.instance();
_weights->resize(_k);
util.weight_initializationv(_weights);
_bias = util.bias_initializationr();
_initialized = true;
}
MLPPSVC::MLPPSVC() {
_y_hat.instance();
_weights.instance();
_c = 0;
_n = 0;
_k = 0;
_initialized = false;
}
MLPPSVC::~MLPPSVC() {
}
real_t MLPPSVC::cost(const Ref<MLPPVector> &z, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t c) {
MLPPCost mlpp_cost;
return mlpp_cost.hinge_losswv(z, y, weights, c);
}
Ref<MLPPVector> MLPPSVC::evaluatem(const Ref<MLPPMatrix> &X) {
MLPPLinAlg alg;
MLPPActivation avn;
return avn.sign_normv(alg.scalar_addnv(_bias, alg.mat_vec_multv(X, _weights)));
}
Ref<MLPPVector> MLPPSVC::propagatem(const Ref<MLPPMatrix> &X) {
MLPPLinAlg alg;
MLPPActivation avn;
return alg.scalar_addnv(_bias, alg.mat_vec_multv(X, _weights));
}
real_t MLPPSVC::evaluatev(const Ref<MLPPVector> &x) {
MLPPLinAlg alg;
MLPPActivation avn;
return avn.sign_normr(alg.dotv(_weights, x) + _bias);
}
real_t MLPPSVC::propagatev(const Ref<MLPPVector> &x) {
MLPPLinAlg alg;
MLPPActivation avn;
return alg.dotv(_weights, x) + _bias;
}
// sign ( wTx + b )
void MLPPSVC::forward_pass() {
MLPPActivation avn;
_z = propagatem(_input_set);
_y_hat = avn.sign_normv(_z);
}
void MLPPSVC::_bind_methods() {
ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPSVC::get_input_set);
ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPSVC::set_input_set);
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPSVC::get_output_set);
ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPSVC::set_output_set);
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_output_set", "get_output_set");
ClassDB::bind_method(D_METHOD("get_c"), &MLPPSVC::get_c);
ClassDB::bind_method(D_METHOD("set_c", "val"), &MLPPSVC::set_c);
ADD_PROPERTY(PropertyInfo(Variant::REAL, "c"), "set_c", "get_c");
ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPSVC::model_set_test);
ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPSVC::model_test);
ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSVC::gradient_descent, false);
ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPSVC::sgd, false);
ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSVC::mbgd, false);
ClassDB::bind_method(D_METHOD("score"), &MLPPSVC::score);
ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPSVC::save);
ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPSVC::is_initialized);
ClassDB::bind_method(D_METHOD("initialize"), &MLPPSVC::initialize);
}