mirror of https://github.com/Relintai/pmlpp.git
53 lines
1.3 KiB
C++
53 lines
1.3 KiB
C++
|
|
#ifndef MLPP_LOG_REG_H
|
|
#define MLPP_LOG_REG_H
|
|
|
|
//
|
|
// LogReg.hpp
|
|
//
|
|
// Created by Marc Melikyan on 10/2/20.
|
|
//
|
|
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
|
|
|
|
class MLPPLogReg {
|
|
public:
|
|
MLPPLogReg(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet, std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
|
|
std::vector<double> modelSetTest(std::vector<std::vector<double>> X);
|
|
double modelTest(std::vector<double> x);
|
|
void gradientDescent(double learning_rate, int max_epoch, bool UI = 1);
|
|
void MLE(double learning_rate, int max_epoch, bool UI = 1);
|
|
void SGD(double learning_rate, int max_epoch, bool UI = 1);
|
|
void MBGD(double learning_rate, int max_epoch, int mini_batch_size, bool UI = 1);
|
|
double score();
|
|
void save(std::string fileName);
|
|
|
|
private:
|
|
double Cost(std::vector<double> y_hat, std::vector<double> y);
|
|
|
|
std::vector<double> Evaluate(std::vector<std::vector<double>> X);
|
|
double Evaluate(std::vector<double> x);
|
|
void forwardPass();
|
|
|
|
std::vector<std::vector<double>> inputSet;
|
|
std::vector<double> outputSet;
|
|
std::vector<double> y_hat;
|
|
std::vector<double> weights;
|
|
double bias;
|
|
|
|
int n;
|
|
int k;
|
|
double learning_rate;
|
|
|
|
// Regularization Params
|
|
std::string reg;
|
|
double lambda; /* Regularization Parameter */
|
|
double alpha; /* This is the controlling param for Elastic Net*/
|
|
};
|
|
|
|
|
|
#endif /* LogReg_hpp */
|