pmlpp/mlpp/tanh_reg/tanh_reg_old.h
2023-02-10 21:01:12 +01:00

56 lines
1.5 KiB
C++

#ifndef MLPP_TANH_REG_OLD_H
#define MLPP_TANH_REG_OLD_H
//
// TanhReg.hpp
//
// Created by Marc Melikyan on 10/2/20.
//
#include "core/math/math_defs.h"
#include <string>
#include <vector>
class MLPPTanhRegOld {
public:
MLPPTanhRegOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
real_t modelTest(std::vector<real_t> x);
void gradientDescent(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 fileName);
private:
real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
std::vector<real_t> propagate(std::vector<std::vector<real_t>> X);
real_t Evaluate(std::vector<real_t> x);
real_t propagate(std::vector<real_t> x);
void forwardPass();
std::vector<std::vector<real_t>> inputSet;
std::vector<real_t> outputSet;
std::vector<real_t> z;
std::vector<real_t> y_hat;
std::vector<real_t> weights;
real_t bias;
int n;
int k;
// UI Portion
void UI(int epoch, real_t cost_prev);
// Regularization Params
std::string reg;
real_t lambda;
real_t alpha; /* This is the controlling param for Elastic Net*/
};
#endif /* TanhReg_hpp */