quickProapagtion and tests

include/NeuralNetwork/Learning/BackPropagation.h

@@ -33,7 +33,7 @@ namespace Learning {
 
 		protected:
 
-			inline void resize() {
+			virtual inline void resize() {
 				if(deltas.size()!=network.size())
 					deltas.resize(network.size());
 
@@ -43,6 +43,8 @@ namespace Learning {
 				}
 			}
 
+			virtual void updateWeights(const std::vector<float> &input);
+
 			FeedForward::Network &network;
 
 			CorrectionFunction::CorrectionFunction *correctionFunction;

include/NeuralNetwork/Learning/QuickPropagation.h

@@ -0,0 +1,55 @@
+#pragma once
+
+#include <vector>
+#include <cmath>
+
+#include <NeuralNetwork/FeedForward/Network.h>
+#include "BackPropagation.h"
+
+namespace NeuralNetwork {
+	namespace Learning {
+
+		/** @class QuickPropagation
+		 * @brief
+		 */
+		class QuickPropagation : public BackPropagation {
+
+		public:
+			inline QuickPropagation(FeedForward::Network &feedForwardNetwork, CorrectionFunction::CorrectionFunction *correction = new CorrectionFunction::Linear()):
+				BackPropagation(feedForwardNetwork,correction),deltasPrev() {
+				resize();
+			}
+
+			virtual ~QuickPropagation() {
+			}
+
+		protected:
+
+			virtual inline void resize() override {
+				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());
+				}
+
+				if(deltasPrev.size()!=network.size())
+					deltasPrev.resize(network.size());
+
+				for(std::size_t i=0; i < network.size(); i++) {
+					if(deltasPrev[i].size()!=network[i].size())
+						deltasPrev[i].resize(network[i].size());
+
+					for(std::size_t j=0; j < deltasPrev[i].size(); j++) {
+						deltasPrev[i][j]=1.0;
+					}
+				}
+			}
+
+			virtual void updateWeights(const std::vector<float> &input) override;
+
+			std::vector<std::vector<float>> deltasPrev;
+		};
+	}
+}