pmlpp/mlpp/softmax_reg/softmax_reg.h

#ifndef MLPP_SOFTMAX_REG_H
#define MLPP_SOFTMAX_REG_H

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

#include <string>
#include <vector>



class SoftmaxReg {
public:
	SoftmaxReg(std::vector<std::vector<double>> inputSet, std::vector<std::vector<double>> outputSet, std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
	std::vector<double> modelTest(std::vector<double> x);
	std::vector<std::vector<double>> modelSetTest(std::vector<std::vector<double>> X);
	void gradientDescent(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<std::vector<double>> y_hat, std::vector<std::vector<double>> y);

	std::vector<std::vector<double>> Evaluate(std::vector<std::vector<double>> X);
	std::vector<double> Evaluate(std::vector<double> x);
	void forwardPass();

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

	int n;
	int k;
	int n_class;

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


#endif /* SoftmaxReg_hpp */