/**++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 *
 *                                              PANIC
 *                              Portable Algorithms and Numerics In C++
 *
 *                       Scientific computing from scratch, with feeling.
 *
 * Copyright (c) 2026 Michelle Bausager
 *
 * This file is part of PANIC.
 *
 * PANIC is free software licensed under the GNU General Public License v3.0 or later.
 * You may redistribute and/or modify it under the terms of the GPL.
 *
 * PANIC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
 * without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 * See the LICENSE file for the full license text.
 *
 * SPDX-License-Identifier: GPL-3.0-or-later
 *
 *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 *
 * Project Name:      PANIC
 * Module Name:       neural_network
 * File Name:         layer_dense.hpp
 * Revision:          0.1.0
 * Date:              28-08-2026
 * Author:            Michelle Bausager
 *
 * Description:
 * Defines the dense layers used in neural network
 *
 *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++*/
#pragma once

//---------------------------------------------------------------------------------------------------------------------------
// INCLUDE DESCRIPTION
//---------------------------------------------------------------------------------------------------------------------------
#include <config/types.hpp> // panic::uint_t, panic::int_t, and panic::real_t
#include <neural_network/layer/layer.hpp> // for base layer struct
#include <tensor/vector.hpp>
#include <tensor/matrix.hpp>


namespace panic{
	namespace neural_network{

/**
 * @brief struct for dense layer object used in neural networks
 * 
 * Computes:
 * @code
 * panic::neural_network::layer_dense myDenseLayer(3, 5);
 * myDenseLayer.forward(inputMatrix);
 * @endcode
 *
 * The struct is used in PANIC nural_network library.
 */
struct layer_dense : public layer{

     /**
     * @brief Emphty matrix to store input data
     *
     */
    panic::tensor::real_matrix ipnuts;

	 /**
     * @brief Emphty weight matrix to store layer weights
     *
     * Weight shape:
     * input_size x neuron_count
     */
	panic::tensor::real_matrix weights;
    panic::tensor::real_matrix dweights;

	 /**
     * @brief Emphty bias vector to store layer bias
     *
     * Bias shape:
     * 1 x neuron_count
     */
	panic::tensor::real_vector biases;
    panic::tensor::real_vector dbiases;

    /**
     * @brief Empthy constructor
     *
     */
	layer_dense();

    /**
     * @brief Constructor with input size and amount of neurons
     *
     * @param input_size Input size of data to the network.
     * @param neurons Amount of neurons in the layer
     * 
     */
	layer_dense(panic::types::uint_t input_size, panic::types::uint_t neurons);

    /**
     * @brief Default de-constructor
     *
     */
	~layer_dense() = default;

    /**
     * @brief Forward function for layer
     *
     * @param inputs Data input for forward pass.
     * 
     * @Note Calculates -> outputs = inputs * weights + biases
     */
	bool forward(const panic::tensor::real_matrix& input_data);

    /**
     * @brief Backward function for layer
     *
     * @param inputs Data input for bacward pass.
     * 
     * @Note Calculates derivative of forward function.
     */
	bool backward(const panic::tensor::real_matrix& dvalues);
};

	} // namespace tensor
} // namespace panic

//---------------------------------------------------------------------------------------------------------------------------
// VARIABLE DESCRIPTION
//---------------------------------------------------------------------------------------------------------------------------

//---------------------------------------------------------------------------------------------------------------------------
// FUNCTION PROTOTYPE
//---------------------------------------------------------------------------------------------------------------------------