pmlpp/mlpp/mann/mann.h

#ifndef MLPP_MANN_H
#define MLPP_MANN_H

//
//  MANN.hpp
//
//  Created by Marc Melikyan on 11/4/20.
//

#include "../hidden_layer/hidden_layer.h"
#include "../multi_output_layer/multi_output_layer.h"

#include <string>
#include <vector>



class MLPPMANN {
public:
	MLPPMANN(std::vector<std::vector<double>> inputSet, std::vector<std::vector<double>> outputSet);
	~MLPPMANN();
	std::vector<std::vector<double>> modelSetTest(std::vector<std::vector<double>> X);
	std::vector<double> modelTest(std::vector<double> x);
	void gradientDescent(double learning_rate, int max_epoch, bool UI = 1);
	double score();
	void save(std::string fileName);

	void addLayer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
	void addOutputLayer(std::string activation, std::string loss, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5);

private:
	double Cost(std::vector<std::vector<double>> y_hat, std::vector<std::vector<double>> y);
	void forwardPass();

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

	std::vector<MLPPHiddenLayer> network;
	MLPPMultiOutputLayer *outputLayer;

	int n;
	int k;
	int n_output;
};


#endif /* MANN_hpp */