refactoring recurent

src/Makefile

@@ -29,4 +29,6 @@ $(LIBNAME).a: $(OBJFILES)
 	nm --demangle $(LIBNAME).a > $(LIBNAME).nm
 
 clean:
-	@rm -f ./*.o ./*.so ./*.a ./*.nm ./*/*.o
+	@rm -rf *.o ./*.so ./*.a ./*.nm ./*/*.o
+	@rm -rf *.o ./*.so ./*.a ./*.nm ./*/*.o
+	@rm -f $(OBJFILES)

src/NeuralNetwork/Recurrent/Network.cpp

@@ -5,24 +5,24 @@ std::vector<float> NeuralNetwork::Recurrent::Network::computeOutput(const std::v
 
 	std::vector<float> outputs(neuronSize);
 	for(size_t i=0;i<inputSize;i++) {
-		outputs[i]=input[i];
+		outputs[i+1]=input[i];
 	}
 
 	for(unsigned int iter=0;iter< iterations;iter++) {
 
-		for(size_t i=inputSize;i<neuronSize;i++) {
-			outputs[i]=neurons[i].output();
+		for(size_t i=inputSize+1;i<neuronSize;i++) {
+			outputs[i]=neurons[i]->output();
 		}
 
 		// update neurons
-		for(size_t i=inputSize;i<neuronSize;i++) {
-			neurons[i](outputs);
+		for(size_t i=inputSize+1;i<neuronSize;i++) {
+			neurons[i]->operator()(outputs);
 		}
 	}
 
 	std::vector<float> ret;
 	for(size_t i=0;i<outputSize;i++) {
-		ret.push_back(neurons[i+inputSize].output());
+		ret.push_back(neurons[i+inputSize+1]->output());
 	}
 
 	return ret;
@@ -36,15 +36,47 @@ void NeuralNetwork::Recurrent::Network::stringify(std::ostream& out) const {
 	out << "\t\"inputs\":" << inputSize << ",\n";
 	out << "\t\"outputs\":" << outputSize << ",\n";
 
-	out << "\t\"neurons\":[";
+	out << "\t\"neurons\":[\n";
 
 	for(size_t i=0;i<neurons.size();i++) {
 		if(i!=0)
 			out << ",\n";
-
-		out << neurons[i].stringify("\t\t");
+		out << neurons[i]->stringify("\t\t");
 	}
 	out << "\n\t]\n";
 
 	out <<"}";
-}
+}
+
+NeuralNetwork::Recurrent::Network NeuralNetwork::Recurrent::Network::connectWith(const NeuralNetwork::Recurrent::Network &r) const {
+
+}
+/*
+NeuralNetwork::Recurrent::Network NeuralNetwork::Recurrent::Network::connectWith(const NeuralNetwork::Recurrent::Network &r) const {
+	if(outputSize!=r.inputSize) {
+		//TODO: throw exception
+	}
+
+	NeuralNetwork::Recurrent::Network newNetwork(inputSize,r.outputSize,(neurons.size()-1-inputSize)+(r.neurons.size()-1-r.inputSize-r.outputSize));
+
+	// update output neurons first
+	for(size_t i=0;i<r.outputSize;i++) {
+		size_t index=1+newNetwork.inputSize+i;
+
+		delete newNetwork.neurons[index];
+		newNetwork.neurons[index]= r.neurons[1+r.inputSize+i]->clone();
+
+		Neuron* n= newNetwork.neurons[index];
+
+		for(int i=0;i<newNetwork.inputSize;i++) {
+			n->setWeight(newNetwork.+i,0.0);
+		}
+
+		for(int i=0;i<r.inputSize;i++) {
+			n->setWeight(1+newNetwork.inputSize+newNetwork.outputSize,0.0);
+		}
+	}
+
+	return newNetwork;
+}
+*/

src/NeuralNetwork/Recurrent/Neuron.cpp

@@ -3,7 +3,6 @@
 std::string NeuralNetwork::Recurrent::Neuron::stringify(const std::string &prefix) const {
 	std::ostringstream out;
 	out.precision(std::numeric_limits<float>::digits10+1);
-	out <<std::setprecision(std::numeric_limits<float>::digits10+1);
 
 	out << prefix << "{\n";
 	out << prefix << "\t\"class\": \"NeuralNetwork::Recurrent::Neuron\",\n";
@@ -13,13 +12,17 @@ std::string NeuralNetwork::Recurrent::Neuron::stringify(const std::string &prefi
 	out << prefix << "\t\"value\": " << value() << ",\n";
 	out << prefix << "\t\"activationFunction\": " << activation->stringify() <<",\n";
 	out << prefix << "\t\"basisFunction\": " << basis->stringify() <<",\n";
-	out << prefix << "\t\"weights\": [";
+	out << prefix << "\t\"weights\": {";
+	bool first=true;
 	for(size_t j=0;j<weights.size();j++) {
-		if(j!=0)
-			out << ", ";
-		out << weights[j];
+		if(weights[j]!= 0.0) {
+			if(!first)
+				out << ", ";
+			first=false;
+			out << "\"" << j << "\": " << weights[j];
+		}
 	}
-	out << "]\n";
+	out << "}\n";
 	out << prefix << "}";
 	return out.str();
 }