pmlpp/mlpp/probit_reg/probit_reg.h

#ifndef MLPP_PROBIT_REG_H
#define MLPP_PROBIT_REG_H

//
//  ProbitReg.hpp
//
//  Created by Marc Melikyan on 10/2/20.
//

#include <string>
#include <vector>



class MLPPProbitReg {
public:
	MLPPProbitReg(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 = 0, bool UI = 1);
	void MLE(double learning_rate, int max_epoch = 0, bool UI = 1);
	void SGD(double learning_rate, int max_epoch = 0, 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);
	std::vector<double> propagate(std::vector<std::vector<double>> X);
	double Evaluate(std::vector<double> x);
	double propagate(std::vector<double> x);
	void forwardPass();

	std::vector<std::vector<double>> inputSet;
	std::vector<double> outputSet;
	std::vector<double> z;
	std::vector<double> y_hat;
	std::vector<double> weights;
	double bias;

	int n;
	int k;

	// Regularization Params
	std::string reg;
	double lambda;
	double alpha; /* This is the controlling param for Elastic Net*/
};


#endif /* ProbitReg_hpp */