#ifndef MLPP_EXP_REG_H
#define MLPP_EXP_REG_H

/*************************************************************************/
/*  exp_reg.h                                                            */
/*************************************************************************/
/*                         This file is part of:                         */
/*                    PMLPP Machine Learning Library                     */
/*                   https://github.com/Relintai/pmlpp                   */
/*************************************************************************/
/* Copyright (c) 2023-present Péter Magyar.                              */
/* Copyright (c) 2022-2023 Marc Melikyan                                 */
/*                                                                       */
/* Permission is hereby granted, free of charge, to any person obtaining */
/* a copy of this software and associated documentation files (the       */
/* "Software"), to deal in the Software without restriction, including   */
/* without limitation the rights to use, copy, modify, merge, publish,   */
/* distribute, sublicense, and/or sell copies of the Software, and to    */
/* permit persons to whom the Software is furnished to do so, subject to */
/* the following conditions:                                             */
/*                                                                       */
/* The above copyright notice and this permission notice shall be        */
/* included in all copies or substantial portions of the Software.       */
/*                                                                       */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,       */
/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF    */
/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY  */
/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,  */
/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE     */
/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.                */
/*************************************************************************/

#ifdef USING_SFW
#include "sfw.h"
#else
#include "core/math/math_defs.h"

#include "core/object/reference.h"
#endif

#include "../regularization/reg.h"

#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"

class MLPPExpReg : public Reference {
	GDCLASS(MLPPExpReg, Reference);

public:
	Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
	real_t model_test(const Ref<MLPPVector> &x);

	void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
	void sgd(real_t learning_rate, int max_epoch, bool ui = false);
	void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);

	real_t score();

	void save(const String &file_name);

	MLPPExpReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);

	MLPPExpReg();
	~MLPPExpReg();

protected:
	real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);

	real_t evaluatev(const Ref<MLPPVector> &x);
	Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);

	void forward_pass();

	static void _bind_methods();

	Ref<MLPPMatrix> _input_set;
	Ref<MLPPVector> _output_set;
	Ref<MLPPVector> _y_hat;
	Ref<MLPPVector> _weights;
	Ref<MLPPVector> _initial;
	real_t _bias;

	int _n;
	int _k;

	// Regularization Params
	MLPPReg::RegularizationType _reg;
	real_t _lambda;
	real_t _alpha; /* This is the controlling param for Elastic Net*/
};

#endif /* ExpReg_hpp */