#pragma once

#include "BatchPropagation.h"

namespace NeuralNetwork {
namespace Learning {

	/** @class BackPropagation
	 * @brief 
	 */
	class BackPropagation : public BatchPropagation {

		public:
			BackPropagation(FeedForward::Network &feedForwardNetwork, std::shared_ptr<CorrectionFunction::CorrectionFunction> correction = std::make_shared<CorrectionFunction::Linear>()):
				BatchPropagation(feedForwardNetwork,correction), learningCoefficient(0.4) {
				resize();
			}

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

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

			float getMomentumWeight() const {
				return momentumWeight;
			}

			void setMomentumWeight(const float& m) {
				momentumWeight=m;
				resize();
			}

			float getWeightDecay() const {
				return weightDecay;
			}

			void setWeightDecay(const float& wd) {
				weightDecay=wd;
			}

		protected:

			virtual inline void resize() override {
				BatchPropagation::resize();
				if(momentumWeight > 0.0) {
					_lastDeltas = _gradients;
				}
			}

			virtual void updateWeightsAndEndBatch() override;

			float learningCoefficient;
			float momentumWeight = 0.0;
			float weightDecay = 0.0;

			std::vector<std::vector<std::vector<float>>> _lastDeltas = {};

	};
}
}