quickProapagtion and tests

src/NeuralNetwork/Learning/BackPropagation.cpp

@@ -30,6 +30,11 @@ void NeuralNetwork::Learning::BackPropagation::teach(const std::vector<float> &i
 		}
 	}
 
+	updateWeights(input);
+}
+
+void NeuralNetwork::Learning::BackPropagation::updateWeights(const std::vector<float> &input) {
+
 	for(std::size_t layerIndex=1;layerIndex<network.size();layerIndex++) {
 		auto &layer=network[layerIndex];
 		auto &prevLayer=network[layerIndex-1];
@@ -52,4 +57,5 @@ void NeuralNetwork::Learning::BackPropagation::teach(const std::vector<float> &i
 			}
 		}
 	}
-}
+
+}

src/NeuralNetwork/Learning/QuickPropagation.cpp

@@ -0,0 +1,35 @@
+#include <NeuralNetwork/Learning/QuickPropagation.h>
+
+#include <cassert>
+#include <immintrin.h>
+
+void NeuralNetwork::Learning::QuickPropagation::updateWeights(const std::vector<float> &input) {
+
+	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++)  {
+
+			//TODO: is this correct??
+			float delta=deltas[layerIndex][j]/(deltasPrev[layerIndex][j]-deltas[layerIndex][j]);
+
+			deltas[layerIndex][j]=delta;
+
+			layer[j].weight(0)+=delta;
+
+			for(std::size_t k=1;k<prevLayerSize;k++) {
+				if(layerIndex==1) {
+					layer[j].weight(k)+=delta*input[k-1];
+				} else {
+					layer[j].weight(k)+=delta*prevLayer[k].output();
+				}
+			}
+		}
+	}
+
+	deltas.swap(deltasPrev);
+}