/**++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 *
 *                                              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.cpp
 * Revision:          0.1.0
 * Date:              28-07-2026
 * Author:            Michelle Bausager
 *
 * Description:
 * Defines the dense layers used in neural network
 *
 *++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++*/

//---------------------------------------------------------------------------------------------------------------------------
// INCLUDE DESCRIPTION
//---------------------------------------------------------------------------------------------------------------------------
#include <neural_network/layer/layer_dense.hpp>
#include <config/omp.hpp>

#include <math/matmul.hpp>
#include <math/mul.hpp>
#include <math/add.hpp>
#include <random/uniform.hpp>

#include <math/transpose.hpp>
#include <math/sum.hpp>

//---------------------------------------------------------------------------------------------------------------------------
// PRIVATE CONSTANTS
//---------------------------------------------------------------------------------------------------------------------------
/**
 * @brief Minimum number of element operations before using the OpenMP-enabled loop.
 *
 * Small vectors and matrices are kept serial because the overhead of starting
 * worker threads can be larger than the work itself.
 */
static const panic::types::uint_t layer_dense_omp_min_size = 500;
//---------------------------------------------------------------------------------------------------------------------------
// INPLEMENTATION
//---------------------------------------------------------------------------------------------------------------------------

namespace panic{
	namespace neural_network{


//--------------------------------------------------------------------------------------------------------------------------
// Constructor Name : panic::neural_network::layer_dense
//
// Description:
//		Creates an empty layer.
//--------------------------------------------------------------------------------------------------------------------------
layer_dense::layer_dense() {
	weights.resize(0,0);
	biases.resize(0);
	dweights.resize(0,0);
	dbiases.resize(0);
}

//--------------------------------------------------------------------------------------------------------------------------
// Constructor Name : panic::neural_network::layer_dense
//
// Description:
//		Creates an empty layer with neurons.
//--------------------------------------------------------------------------------------------------------------------------
layer_dense::layer_dense(panic::types::uint_t input_size, panic::types::uint_t neurons) {
	weights.resize(input_size, neurons);
	panic::random::uniform(weights);
	panic::math::mul(weights, static_cast<panic::types::real_t>(0.01), weights);

	biases.resize(neurons);
	biases.fill(0);
	
}


//--------------------------------------------------------------------------------------------------------------------------
// Function Name : panic::neural_network::layer_dense.forward
//
// Description:
//		Calculated the forward pass:
//		outputs = inputs * weights + biases
//--------------------------------------------------------------------------------------------------------------------------
bool layer_dense::forward(const panic::tensor::real_matrix& input_data){

	inputs = input_data;

	if (inputs.cols() != weights.rows()){
		return false;
	}

	if (!outputs.resize(inputs.rows(), weights.cols())){
		return false;
	}

	if (!panic::math::matmul(inputs, weights, outputs)){
		return false;
	}

	if (!panic::math::add_rowwise(outputs, biases, outputs)){
		return false;
	}

	return true;
}


//--------------------------------------------------------------------------------------------------------------------------
// Function Name : panic::neural_network::layer_dense.backward
//
// Description:
//		Calculated the backward pass:
//		??
//--------------------------------------------------------------------------------------------------------------------------
bool layer_dense::backward(const panic::tensor::real_matrix& dvalues){


	// Gradients on parameters
	dweights = panic::math::matmul(panic::math::transpose(inputs), dvalues);
	dbiases = panic::math::sum_colwise(dvalues);

	// Gradients on values
	dinputs = panic::math::matmul(dvalues, panic::math::transpose(weights));

	return true;
}


	} // namespace tensor
} // namespace panic