pmlpp/mlpp/log_reg/log_reg.h

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#ifndef MLPP_LOG_REG_H
#define MLPP_LOG_REG_H
//
// LogReg.hpp
//
// Created by Marc Melikyan on 10/2/20.
//
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include "../regularization/reg.h"
#include <string>
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#include <vector>
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class MLPPLogReg : public Reference {
GDCLASS(MLPPLogReg, Reference);
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public:
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/*
Ref<MLPPMatrix> get_input_set();
void set_input_set(const Ref<MLPPMatrix> &val);
Ref<MLPPVector> get_output_set();
void set_output_set(const Ref<MLPPVector> &val);
MLPPReg::RegularizationType get_reg();
void set_reg(const MLPPReg::RegularizationType val);
real_t get_lambda();
void set_lambda(const real_t val);
real_t get_alpha();
void set_alpha(const real_t val);
*/
std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
real_t model_test(std::vector<real_t> x);
void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
void mle(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);
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real_t score();
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void save(std::string file_name);
bool is_initialized();
void initialize();
//MLPPSoftmaxReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
MLPPLogReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg = "None", real_t p_lambda = 0.5, real_t p_alpha = 0.5);
MLPPLogReg();
~MLPPLogReg();
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protected:
real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t evaluatev(std::vector<real_t> x);
std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
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void forward_pass();
static void _bind_methods();
std::vector<std::vector<real_t>> _input_set;
std::vector<real_t> _output_set;
std::vector<real_t> _y_hat;
std::vector<real_t> _weights;
real_t _bias;
int _n;
int _k;
real_t _learning_rate;
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// Regularization Params
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std::string _reg;
real_t _lambda; /* Regularization Parameter */
real_t _alpha; /* This is the controlling param for Elastic Net*/
bool _initialized;
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};
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#endif /* LogReg_hpp */