documentation progress

src/NeuralNetwork/Network.h

@@ -18,11 +18,10 @@ namespace NeuralNetwork
 	 */
 	const float lambda=0.8;
 
-/**
+	/**
 	 * @author Tomas Cernik (Tom.Cernik@gmail.com)
 	 * @brief Abstract class for all Layers of neurons
 	 */
-
 	class Layer
 	{
 		public:
@@ -45,7 +44,6 @@ namespace NeuralNetwork
 /**
  * @author Tomas Cernik (Tom.Cernik@gmail.com)
  * @brief Abstract model of simple Network
- * 
  */
 	class Network
 	{

src/NeuralNetwork/Neuron.h

@@ -7,19 +7,55 @@ namespace Shin
 {
 namespace NeuralNetwork
 {
+	/**
+	 * @author Tomas Cernik (Tom.Cernik@gmail.com)
+	 * @brief Abstract class of neuron. All Neuron classes should derive from this on
+	 */
 	class Neuron
 	{
 		public:
-			Neuron() {};
+			/**
+			 * @brief virtual destructor for Neuron
+			 */
 			virtual ~Neuron() {};
+
+			/**
+			 * @brief Returns potential of neuron
+			 */
 			virtual float getPotential() const =0;
+
+			/**
+			 * @brief Sets potential of neuron
+			 * @param p is new pontential
+			 */
 			virtual void setPotential(const float &p) =0;
 
-			virtual float getWeight(const size_t&) const =0;
+			/**
-			virtual void setWeight(const size_t& i,const float &p) =0;
+			 * @brief Returns weight for w-th input neuron
+			 * @param w is weight of neuron number w
+			 */
+			virtual float getWeight(const size_t &w) const =0;
 
+			/**
+			 * @brief Sets weight
+			 * @param i is number of neuron
+			 * @param p is new weight for input neuron i
+			 */
+			virtual void setWeight(const size_t& i ,const float &p) =0;
+
+			/**
+			 * @brief Returns output of neuron
+			 */
 			virtual float output() const =0;
+
+			/**
+			 * @brief Returns input of neuron
+			 */
 			virtual float input() const=0;
+
+			/**
+			 * @brief Returns value for derivation of activation function
+			 */
 			virtual float derivatedOutput() const=0;
 		protected:
 	};