#ifndef MLPP_SOFTMAX_NET_H
#define MLPP_SOFTMAX_NET_H

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

#include <string>
#include <vector>

namespace MLPP {

class SoftmaxNet {
public:
	SoftmaxNet(std::vector<std::vector<double>> inputSet, std::vector<std::vector<double>> outputSet, int n_hidden, 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);

	std::vector<std::vector<double>> getEmbeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings.
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::tuple<std::vector<std::vector<double>>, std::vector<std::vector<double>>> propagate(std::vector<std::vector<double>> X);
	std::vector<double> Evaluate(std::vector<double> x);
	std::tuple<std::vector<double>, std::vector<double>> propagate(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>> weights1;
	std::vector<std::vector<double>> weights2;

	std::vector<double> bias1;
	std::vector<double> bias2;

	std::vector<std::vector<double>> z2;
	std::vector<std::vector<double>> a2;

	int n;
	int k;
	int n_class;
	int n_hidden;

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

#endif /* SoftmaxNet_hpp */