#include <NeuralNetwork/Learning/RProp.h>

void NeuralNetwork::Learning::RProp::updateWeightsAndEndBatch() {

	for(std::size_t layerIndex=1;layerIndex<_network.size();layerIndex++) {
		auto &layer = _network[layerIndex];
		auto &prevLayer = _network[layerIndex - 1];

		std::size_t prevLayerSize = prevLayer.size();
		std::size_t layerSize = layer.size();

		for(std::size_t j = 1; j < layerSize; j++) {
			for(std::size_t k = 0; k < prevLayerSize; k++) {
				float gradient = _gradients[layerIndex][j][k];
				float lastGradient = _lastGradients[layerIndex][j][k];

				_lastGradients[layerIndex][j][k] = gradient;

				float weightChangeDelta = _lastWeightChanges[layerIndex][j][k];

				if(gradient * lastGradient > 0) {
					weightChangeDelta = std::min(weightChangeDelta*weightChangePlus,maxChangeOfWeights);
				} else if (gradient * lastGradient < 0) {
					weightChangeDelta = std::max(weightChangeDelta*weightChangeMinus,minChangeOfWeights);
				} else {
					weightChangeDelta = _lastWeightChanges[layerIndex][j][k];
				}

				_lastWeightChanges[layerIndex][j][k] = weightChangeDelta;

				if(gradient > 0) {
					layer[j].weight(k) += weightChangeDelta;
				} else if (gradient < 0){
					layer[j].weight(k) -= weightChangeDelta;
				}
			}
		}
	}
}