pmlpp/mlpp/probit_reg/probit_reg_old.h

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2023-02-09 17:12:14 +01:00
#ifndef MLPP_PROBIT_REG_OLD_H
#define MLPP_PROBIT_REG_OLD_H
//
// ProbitReg.hpp
//
// Created by Marc Melikyan on 10/2/20.
//
#include "core/math/math_defs.h"
#include <string>
#include <vector>
class MLPPProbitRegOld {
public:
MLPPProbitRegOld(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 = 0, bool UI = false);
void MLE(real_t learning_rate, int max_epoch = 0, bool UI = false);
void SGD(real_t learning_rate, int max_epoch = 0, 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;
// Regularization Params
std::string reg;
real_t lambda;
real_t alpha; /* This is the controlling param for Elastic Net*/
};
#endif /* ProbitReg_hpp */