refactoring recurent

include/NeuralNetwork/Recurrent/Network.h

@@ -26,7 +26,13 @@ namespace Recurrent {
 			 * @param hiddenUnits is number of hiddenUnits to be created
 			 */
 			inline Network(size_t _inputSize, size_t _outputSize,size_t hiddenUnits=0):NeuralNetwork::Network(),inputSize(_inputSize),outputSize(_outputSize), neurons(0) {
-				for(size_t i=0;i<_inputSize+_outputSize;i++) {
+				neurons.push_back(new NeuralNetwork::BiasNeuron());
+
+				for(size_t i=0;i<_inputSize;i++) {
+					neurons.push_back(new NeuralNetwork::InputNeuron(neurons.size()));
+				}
+
+				for(size_t i=0;i<_outputSize;i++) {
 					addNeuron();
 				}
 
@@ -35,14 +41,14 @@ namespace Recurrent {
 				}
 			};
 
-			// todo: implement
-			inline Network(const std::string &json) {
-				
-			}
 			/**
 			 * @brief Virtual destructor for Network
 			 */
-			virtual ~Network() {};
+			virtual ~Network() {
+				for(auto& a:neurons) {
+					delete a;
+				}
+			};
 
 			/**
 			 * @brief  This is a function to compute one iterations of network
@@ -61,7 +67,7 @@ namespace Recurrent {
 			 */
 			std::vector<float> computeOutput(const std::vector<float>& input, unsigned int iterations);
 
-			std::vector<Neuron>& getNeurons () {
+			std::vector<NeuralNetwork::Neuron*>& getNeurons () {
 				return neurons;
 			}
 
@@ -69,20 +75,27 @@ namespace Recurrent {
 
 			void stringify(std::ostream& out) const override;
 
-			Neuron& addNeuron() {
-				neurons.push_back(Recurrent::Neuron(neurons.size()));
-				Neuron &newNeuron=neurons.back();
+			NeuralNetwork::Neuron& addNeuron() {
+				neurons.push_back(new Recurrent::Neuron(neurons.size()));
+				NeuralNetwork::Neuron *newNeuron=neurons.back();
 				for(size_t i=0;i<neurons.size();i++) {
-					neurons[i].setWeight(newNeuron,0.0);
+					neurons[i]->setWeight(*newNeuron,0.0);
 				}
-				return newNeuron;
+				return *newNeuron;
 			}
 
+			/**
+			 * @brief  creates new network from joining two
+			 * @param r is network that is connected to outputs of this network
+			 * @returns network of constructed from two networks
+			 */
+			NeuralNetwork::Recurrent::Network connectWith(const NeuralNetwork::Recurrent::Network &r) const;
+
 		protected:
 			size_t inputSize=0;
 			size_t outputSize=0;
 
-			std::vector<Recurrent::Neuron> neurons;
+			std::vector<NeuralNetwork::Neuron*> neurons;
 	};
 }
 }