refactored propagation

include/NeuralNetwork/Learning/QuickPropagation.h

@@ -15,46 +15,24 @@ namespace NeuralNetwork {
 		class QuickPropagation : public BackPropagation {
 
 		public:
-			inline QuickPropagation(FeedForward::Network &feedForwardNetwork, CorrectionFunction::CorrectionFunction *correction = new CorrectionFunction::Linear()):
-				BackPropagation(feedForwardNetwork,correction),previousSlopes() {
-				resize();
+			inline QuickPropagation(FeedForward::Network &feedForwardNetwork, std::shared_ptr<CorrectionFunction::CorrectionFunction> correction = std::make_shared<CorrectionFunction::Linear>()):
+				BackPropagation(feedForwardNetwork,correction) {
 			}
 
 			virtual ~QuickPropagation() {
 			}
 
 		protected:
+
 			float _maxChange=1.75;
 			float _epsilon=0.5;
 
 			virtual inline void resize() override {
-				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());
-				}
-
-				if(deltas.size()!=network.size())
-					deltas.resize(network.size());
-
-				for(std::size_t i=0; i < network.size(); i++) {
-					if(deltas[i].size()!=network[i].size())
-						deltas[i].resize(network[i].size());
-
-					for(std::size_t j=0; j < previousSlopes[i].size(); j++) {
-						deltas[i][j]=1.0;
-					}
-				}
-				weightChange= deltas;
+				BackPropagation::resize();
+				_previousSlopes = _slopes;
 			}
 
-			virtual void updateWeights(const std::vector<float> &input) override;
-
-			std::vector<std::vector<float>> previousSlopes ={};
-			std::vector<std::vector<float>> deltas ={};
-			std::vector<std::vector<float>> weightChange ={};
+			std::vector<std::vector<float>> _previousSlopes ={};
 		};
 	}
 }