#ifndef MLPP_LOG_REG_H #define MLPP_LOG_REG_H // // LogReg.hpp // // Created by Marc Melikyan on 10/2/20. // #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 MLPPLogReg : public Reference { GDCLASS(MLPPLogReg, Reference); public: /* Ref get_input_set(); void set_input_set(const Ref &val); Ref get_output_set(); void set_output_set(const Ref &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); */ Ref model_set_test(const Ref &X); real_t model_test(const Ref &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); real_t score(); void save(std::string file_name); bool is_initialized(); void initialize(); MLPPLogReg(const Ref &p_input_set, const Ref &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5); MLPPLogReg(); ~MLPPLogReg(); protected: real_t cost(const Ref &y_hat, const Ref &y); real_t evaluatev(const Ref &x); Ref evaluatem(const Ref &X); void forward_pass(); static void _bind_methods(); Ref _input_set; Ref _output_set; Ref _y_hat; Ref _weights; real_t _bias; int _n; int _k; //real_t _learning_rate; // Regularization Params MLPPReg::RegularizationType _reg; real_t _lambda; /* Regularization Parameter */ real_t _alpha; /* This is the controlling param for Elastic Net*/ bool _initialized; }; #endif /* LogReg_hpp */