#pragma once


#include "BatchPropagation.h"

namespace NeuralNetwork {
	namespace Learning {

		/** @class Resilient Propagation
		 * @brief
		 */
		class RProp : public BatchPropagation {

			public:
				RProp(FeedForward::Network &feedForwardNetwork, std::shared_ptr<CorrectionFunction::CorrectionFunction> correction = std::make_shared<CorrectionFunction::Linear>()):
					BatchPropagation(feedForwardNetwork, correction) {
				}

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

				void setInitialWeightChange(float initVal) {
					initialWeightChange=initVal;
				}
				void setLearningCoefficient(float) {

				}
			protected:

				virtual inline void resize() override {
					BatchPropagation::resize();

					_lastGradients =_gradients;

					_changesOfWeightChanges = _lastGradients;
					for(std::size_t i = 1; i < _network.size(); i++) {
						for(std::size_t j = 0; j < _changesOfWeightChanges[i].size(); j++) {
							std::fill(_changesOfWeightChanges[i][j].begin(),_changesOfWeightChanges[i][j].end(),initialWeightChange);
						}
					}
					_lastWeightChanges = _lastGradients;
					for(std::size_t i = 1; i < _network.size(); i++) {
						for(std::size_t j = 0; j < _lastWeightChanges[i].size(); j++) {
							std::fill(_lastWeightChanges[i][j].begin(),_lastWeightChanges[i][j].end(),0.1);
						}
					}
				}

				void updateWeightsAndEndBatch() override;

				std::vector<std::vector<std::vector<float>>> _lastGradients = {};
				std::vector<std::vector<std::vector<float>>> _lastWeightChanges = {};
				std::vector<std::vector<std::vector<float>>> _changesOfWeightChanges = {};

				float maxChangeOfWeights = 50;
				float minChangeOfWeights = 0.0001;

				float initialWeightChange=0.02;
				float weightChangePlus=1.2;
				float weightChangeMinus=0.5;
		};
	}
}