#pragma once

#include <vector>
#include <cmath>

#include <NeuralNetwork/FeedForward/Network.h>
#include "CorrectionFunction/Linear.h"

namespace NeuralNetwork {
namespace Learning {

	/** @class BackPropagation
	 * @brief 
	 */
	class BackPropagation {

		public:
			inline BackPropagation(FeedForward::Network &feedForwardNetwork, CorrectionFunction::CorrectionFunction *correction = new CorrectionFunction::Linear()):
									network(feedForwardNetwork), correctionFunction(correction),learningCoefficient(0.4), slopes() {
				resize();
			}

			virtual ~BackPropagation() {
				delete correctionFunction;
			}

			BackPropagation(const BackPropagation&)=delete;
			BackPropagation& operator=(const NeuralNetwork::Learning::BackPropagation&) = delete;

			void teach(const std::vector<float> &input, const std::vector<float> &output);

			inline virtual void setLearningCoefficient (const float& coefficient) { learningCoefficient=coefficient; }

		protected:

			virtual inline void resize() {
				if(slopes.size()!=network.size())
					slopes.resize(network.size());

				for(std::size_t i=0; i < network.size(); i++) {
					if(slopes[i].size()!=network[i].size())
						slopes[i].resize(network[i].size());
				}
			}

			virtual void updateWeights(const std::vector<float> &input);

			virtual void computeDeltas(const std::vector<float> &expectation);

			FeedForward::Network &network;

			CorrectionFunction::CorrectionFunction *correctionFunction;

			float learningCoefficient;

			std::vector<std::vector<float>> slopes;
	};
}
}