reffactored and recurrent implementation

include/NeuralNetwork/Recurrent/Network.h

@@ -0,0 +1,88 @@
+#pragma once
+
+#include "../Network.h"
+#include "Neuron.h"
+
+#include <vector>
+
+#include <sstream>
+#include <iomanip>
+#include <limits>
+
+namespace NeuralNetwork {
+namespace Recurrent {
+
+	/**
+	* @author Tomas Cernik (Tom.Cernik@gmail.com)
+	* @brief Reccurent model of Artifical neural network
+	*/
+	class Network: public NeuralNetwork::Network {
+		public:
+
+			/**
+			 * @brief Constructor for Network
+			 * @param _inputSize is number of inputs to network
+			 * @param _outputSize is size of output from network
+			 * @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++) {
+					addNeuron();
+				}
+
+				for(size_t i=0;i<hiddenUnits;i++) {
+					addNeuron();
+				}
+			};
+
+			// todo: implement
+			inline Network(const std::string &json) {
+				
+			}
+			/**
+			 * @brief Virtual destructor for Network
+			 */
+			virtual ~Network() {};
+
+			/**
+			 * @brief  This is a function to compute one iterations of network
+			 * @param input is input of network
+			 * @returns output of network
+			 */
+			inline virtual std::vector<float> computeOutput(const std::vector<float>& input) override {
+				return computeOutput(input,1);
+			}
+
+			/**
+			 * @brief  This is a function to compute iterations of network
+			 * @param input is input of network
+			 * @param iterations is number of iterations
+			 * @returns output of network
+			 */
+			std::vector<float> computeOutput(const std::vector<float>& input, unsigned int iterations);
+
+			std::vector<Neuron>& getNeurons () {
+				return neurons;
+			}
+
+			using NeuralNetwork::Network::stringify;
+
+			void stringify(std::ostream& out) const override;
+
+			Neuron& addNeuron() {
+				neurons.push_back(Recurrent::Neuron(neurons.size()));
+				Neuron &newNeuron=neurons.back();
+				for(size_t i=0;i<neurons.size();i++) {
+					neurons[i].setWeight(newNeuron,0.0);
+				}
+				return newNeuron;
+			}
+
+		protected:
+			size_t inputSize=0;
+			size_t outputSize=0;
+
+			std::vector<Recurrent::Neuron> neurons;
+	};
+}
+}