pmlpp/mlpp/svc/svc.h
2023-02-10 14:03:48 +01:00

84 lines
1.9 KiB
C++

#ifndef MLPP_SVC_H
#define MLPP_SVC_H
//
// SVC.hpp
//
// Created by Marc Melikyan on 10/2/20.
//
// https://towardsdatascience.com/svm-implementation-from-scratch-python-2db2fc52e5c2
// Illustratd a practical definition of the Hinge Loss function and its gradient when optimizing with SGD.
#include "core/math/math_defs.h"
#include "core/object/reference.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include "../regularization/reg.h"
class MLPPSVC : public Reference {
GDCLASS(MLPPSVC, Reference);
public:
Ref<MLPPMatrix> get_input_set();
void set_input_set(const Ref<MLPPMatrix> &val);
Ref<MLPPVector> get_output_set();
void set_output_set(const Ref<MLPPMatrix> &val);
real_t get_c();
void set_c(const real_t val);
Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
real_t model_test(const Ref<MLPPVector> &x);
void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
void sgd(real_t learning_rate, int max_epoch, bool ui = false);
void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
real_t score();
void save(const String &file_name);
bool is_initialized();
void initialize();
MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c);
MLPPSVC();
~MLPPSVC();
protected:
real_t cost(const Ref<MLPPVector> &z, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t c);
Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
Ref<MLPPVector> propagatem(const Ref<MLPPMatrix> &X);
real_t evaluatev(const Ref<MLPPVector> &x);
real_t propagatev(const Ref<MLPPVector> &x);
void forward_pass();
static void _bind_methods();
Ref<MLPPMatrix> _input_set;
Ref<MLPPVector> _output_set;
Ref<MLPPVector> _z;
Ref<MLPPVector> _y_hat;
Ref<MLPPVector> _weights;
real_t _bias;
real_t _c;
int _n;
int _k;
bool _initialized;
};
#endif /* SVC_hpp */