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;
 	};
 }
 }

include/NeuralNetwork/Recurrent/Neuron.h

@@ -22,14 +22,15 @@ namespace Recurrent {
 	class Neuron : public NeuralNetwork::Neuron
 	{
 		public:
-			Neuron(unsigned long _id=0,const float& _bias = 0): NeuralNetwork::Neuron(), basis(new BasisFunction::Linear),
+			Neuron(unsigned long _id=0): NeuralNetwork::Neuron(), basis(new BasisFunction::Linear),
 																activation(new ActivationFunction::Sigmoid(-4.9)),
-																id_(_id),bias(_bias),weights(_id+1),_output(0),_value(0) {
+																id_(_id),weights(_id+1),_output(0),_value(0) {
 			}
 
 			Neuron(const Neuron &r): NeuralNetwork::Neuron(), basis(r.basis->clone()), activation(r.activation->clone()),id_(r.id_),
-									 bias(r.bias), weights(r.weights), _output(r._output), _value(r._value) {
+									 weights(r.weights), _output(r._output), _value(r._value) {
 			}
+
 			virtual ~Neuron() {
 				delete basis;
 				delete activation;
@@ -39,7 +40,6 @@ namespace Recurrent {
 
 			Recurrent::Neuron& operator=(const NeuralNetwork::Recurrent::Neuron&r) {
 				id_=r.id_;
-				bias=r.bias;
 				weights=r.weights;
 				basis=r.basis->clone();
 				activation=r.activation->clone();
@@ -89,19 +89,19 @@ namespace Recurrent {
 			 * @param _bias is new bias (initial value for neuron)
 			 */
 			virtual void setBias(const float &_bias) override {
-				bias=_bias;
+				weights[0]=_bias;
 			}
 
 			/**
 			 * @brief Function returns bias for neuron
 			 */
 			virtual float getBias() const override {
-				return bias;
+				return weights[0];
 			}
 
 			float operator()(const std::vector<float>& inputs) {
 				//compute value
-				_value=basis->operator()(weights,inputs)+bias;
+				_value=basis->operator()(weights,inputs);
 
 				//compute output
 				_output=activation->operator()(_value);
@@ -109,15 +109,83 @@ namespace Recurrent {
 				return _output;
 			}
 
+			virtual Neuron* clone() const override {
+				Neuron *n = new Recurrent::Neuron;
+				*n=*this;
+				return n;
+			}
 		protected:
 			BasisFunction::BasisFunction *basis;
 			ActivationFunction::ActivationFunction *activation;
 
 			unsigned long id_;
-			float bias;
 			std::vector<float> weights;
 			float _output;
 			float _value;
 	};
+
+	/**
+	 * @author Tomas Cernik (Tom.Cernik@gmail.com)
+	 * @brief Class of LSTM unit
+	 */
+	// input + input gate + forget gate + ouput gate
+	// https://en.wikipedia.org/wiki/Long_short-term_memory
+
+	class LSTMNeuron : public Neuron
+	{
+		public:
+			LSTMNeuron(unsigned long _id=0): Neuron(_id) {
+			}
+
+			LSTMNeuron(const Neuron &r): Neuron(r) {
+			}
+
+			virtual ~LSTMNeuron() {
+				
+			};
+
+			virtual std::string stringify(const std::string &prefix="") const override;
+
+			LSTMNeuron& operator=(const LSTMNeuron&r) {
+				this->Neuron::operator=(r);
+
+				return *this;
+			}
+
+			/**
+			 * @brief Returns output of neuron
+			 */
+			virtual float output() const override {
+				return _output;
+			}
+
+			/**
+			 * @brief Returns input of neuron
+			 */
+			virtual float value() const override {
+				return _value;
+			}
+
+			float operator()(const std::vector<float>& inputs) override {
+
+				//compute value
+				_value=basis->operator()(weights,inputs);
+
+				//compute output
+				_output=activation->operator()(_value);
+
+				return _output;
+			}
+
+			virtual Recurrent::LSTMNeuron* clone() const override {
+				LSTMNeuron *n = new Recurrent::LSTMNeuron;
+				*n=*this;
+				return n;
+			}
+		protected:
+			std::vector<float> forgetWeights;
+			std::vector<float> outputWeights;
+			
+	};
 }
 }