preparing documentation

src/NeuralNetwork/FeedForward.cpp

@@ -33,7 +33,7 @@ FFNeuron& FFLayer::operator[](const size_t& neuron)
 
 }
 
-FeedForward::FeedForward(std::initializer_list<size_t> s, double lam, std::function<float(const size_t&layer, const size_t &neuron, const size_t &weight)> weightInit): ACyclicNetwork(lam),layers(s.size())
+FeedForward::FeedForward(std::initializer_list<size_t> s, double lam, FeedForwardInitializer weightInit): ACyclicNetwork(lam),layers(s.size())
 {
 	transfer = new TransferFunction::TransferFunction*[s.size()];
 	weights= new float**[s.size()];

src/NeuralNetwork/FeedForward.h

@@ -4,9 +4,9 @@
 #include "../Problem"
 #include "../Solution"
 #include "Network"
+
 #include "TransferFunction/Sigmoid.h"
 #include "TransferFunction/TransferFunction.h"
-#include "TransferFunction/HyperbolicTangent.h"
 
 #include <vector>
 #include <initializer_list>
@@ -78,16 +78,38 @@ namespace NeuralNetwork
 			float lambda;
 	};
 
+	/**
+	 * @brief typedef for FeedForward network initializating function
+	 */
+	typedef std::function<float(const size_t&layer, const size_t &neuron, const size_t &weight)> FeedForwardInitializer;
+
+	/**
+	 * @author Tomas Cernik (Tom.Cernik@gmail.com)
+	 * @brief Class representing FeedForward network
+	 * @see ACyclicNetwork
+	 */
 	class FeedForward:public ACyclicNetwork
 	{
 		public:
+			/**
+			 * @brief Constructor for FeedForward
+			 * @param s is initiaizer for layers (it's sizes)
+			 * @param lam is parametr for TransferFunction
+			 * @param weightInit is weight initializer function
+			 */
 			FeedForward(std::initializer_list<size_t> s, double lam=Shin::NeuralNetwork::lambda,
-				std::function<float(const size_t&layer, const size_t &neuron, const size_t &weight)> weightInit=
+			FeedForwardInitializer weightInit=
 					[](const size_t&, const size_t &, const size_t &)->float{ return 1.0-((float)(rand()%2001))/1000.0;}
 			);
 			virtual ~FeedForward();
 
+			/**
+			 * @brief we don't want to allow network to be copied
+			 */
 			FeedForward(const FeedForward &f) = delete; //TODO
+			/**
+			 * @brief we don't want to allow network to be copied
+			 */
 			FeedForward operator=(const FeedForward &f)=delete;
 
 			virtual Solution solve(const Problem& p) override;
@@ -103,7 +125,7 @@ namespace NeuralNetwork
 			float **inputs=nullptr;
 			TransferFunction::TransferFunction **transfer=nullptr;
 			size_t *layerSizes=nullptr;
-			size_t layers;
+			size_t layers;/**< Number of layers */
 	};
 
 }