added new tests

tests/backpropagation.cpp

@@ -41,4 +41,76 @@ int main() {
 			assert(ret[0] < 0.1);
 		}
 	}
+	{	// AND problem
+		NeuralNetwork::FeedForward::Network n(2);
+		NeuralNetwork::ActivationFunction::Sigmoid a(-1);
+		n.appendLayer(2,a);
+		n.appendLayer(1,a);
+
+		n.randomizeWeights();
+
+		NeuralNetwork::Learning::BackPropagation prop;
+		for(int i=0;i<10000;i++) {
+			prop.teach(n,{1,1},{1});
+			prop.teach(n,{0,0},{0});
+			prop.teach(n,{0,1},{0});
+			prop.teach(n,{1,0},{0});
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({1,1});
+			assert(ret[0] > 0.9);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({0,1});
+			assert(ret[0] < 0.1);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({1,0});
+			assert(ret[0] < 0.1);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({0,0});
+			assert(ret[0] < 0.1);
+		}
+	}
+	{	// NOT AND problem
+		NeuralNetwork::FeedForward::Network n(2);
+		NeuralNetwork::ActivationFunction::Sigmoid a(-1);
+		n.appendLayer(2,a);
+		n.appendLayer(1,a);
+
+		n.randomizeWeights();
+
+		NeuralNetwork::Learning::BackPropagation prop;
+		for(int i=0;i<10000;i++) {
+			prop.teach(n,{1,1},{0});
+			prop.teach(n,{0,0},{1});
+			prop.teach(n,{0,1},{1});
+			prop.teach(n,{1,0},{1});
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({1,1});
+			assert(ret[0] < 0.1);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({0,1});
+			assert(ret[0] > 0.9);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({1,0});
+			assert(ret[0] > 0.9);
+		}
+
+		{
+			std::vector<float> ret =n.computeOutput({0,0});
+			assert(ret[0] > 0.9);
+		}
+	}
 }