pmlpp/mlpp/lin_reg/lin_reg.h

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#ifndef MLPP_LIN_REG_H
#define MLPP_LIN_REG_H
//
// LinReg.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"
class MLPPLinReg : public Reference {
GDCLASS(MLPPLinReg, 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);
*/
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Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
real_t model_test(const Ref<MLPPVector> &x);
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void newton_raphson(real_t learning_rate, int max_epoch, bool ui = false);
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 momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool ui = false);
void nag(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool ui = false);
void adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool ui = false);
void adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool ui = false);
void adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
void adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
void nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
void normal_equation();
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real_t score();
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void save(const String &file_name);
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bool is_initialized();
void initialize();
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MLPPLinReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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MLPPLinReg();
~MLPPLinReg();
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protected:
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real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t evaluatev(const Ref<MLPPVector> &x);
Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
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void forward_pass();
static void _bind_methods();
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Ref<MLPPMatrix> _input_set;
Ref<MLPPVector> _output_set;
Ref<MLPPVector> _y_hat;
Ref<MLPPVector> _weights;
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real_t _bias;
int _n;
int _k;
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// Regularization Params
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MLPPReg::RegularizationType _reg;
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int _lambda;
int _alpha; /* This is the controlling param for Elastic Net*/
bool _initialized;
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};
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#endif /* LinReg_hpp */