www.pudn.com > 基于VC的神经网络开发程序包(源码).rar > RadialBasisNetwork.h

#ifndef _RADIALBASISNETWORK_H 
#define _RADIALBASISNETWORK_H 
 
#include "Network.h" 
#include "SimpleNeuron.h" 
#include "CenterNeuron.h" 
#include "InputLayer.h" 
#include "TrainingSet.h" 
 
namespace annie 
{ 
 
/** A Radial Basis Function Neural Network. 
  * 
  * The network consists of a layer of N-inputs, then h-(N-dimensional) centers, and  
  * some outputs. 
  * The default activation function for the centers is gaussian(), which is the gaussian 
  * distribution function with sigma = 1. If you want to change that then you'll have 
  * to write your own activation function and change it with setCenterActivationFunction(). 
  * The output neurons use the identity() function as the activation function, thus the  
  * output is simply the weighted sum of the outputs of the centers. 
  * 
  * There are atleast two ways to train a radial basis network. 
  * 
  * One involves fixed centers and training the weights only in order to minimize 
  * the error over a training set. The other involves using the gradient descent 
  * rule to adjust both centers and weights. Currently only the former is implemented 
  * in annie using trainWeights(). 
  * 
  * @see trainWeights 
  * \todo Implement gradient-descent rule based updation of centers and weights 
  * \todo The copy constructor 
  */ 
class RadialBasisNetwork : public Network 
{ 
protected: 
	/** Number of centers in the network. 
	  * If you plan to extend this class, then the onus of keeping this value 
	  * consistent lies on you 
	  */ 
	int _nCenters; 
 
	/// Layer of input. Each member is an InputNeuron 
	InputLayer *_inputLayer; 
 
	/// Layer of centers, each member is a CenterNEuron 
	Layer *_centerLayer; 
 
	/// Layer of output, each member if a SimpleNeuron 
	Layer *_outputLayer; 
 
public: 
	/** Creates a Radial basis function network. All the outputs will have a bias. 
	  * @param inputs Number of inputs taken in by the network 
	  * @param centers Number of centers the network has. Each center will be  
	  *			an inputs-dimensional point 
	  * @param outputs The number of outputs given by the neuron. All of them will have 
	  *			a bias 
	  */ 
	RadialBasisNetwork(int inputs, int centers, int outputs); 
 
	/** Loads a network from a text file 
	  * @see save 
	  * @param filename Name of the file from which to load network structure 
	  * @throws Exception On any error 
	  */ 
	RadialBasisNetwork(const char *filename); 
 
	/// Copy constructor, NOT YET IMPLEMENTED 
	RadialBasisNetwork(RadialBasisNetwork &srcNet); 
 
	virtual ~RadialBasisNetwork(); 
 
	/** Sets the ith center point to the given point. 
	  * @param i The center that is to be changed 
	  * @param center The getInputCount() dimensional point 
	  */ 
	virtual void setCenter(int i, VECTOR ¢er); 
 
	/** Sets the ith center point to the given point. 
	  * @param i The center that is to be changed 
	  * @param center The getInputCount() dimensional point 
	  */ 
	virtual void setCenter(int i, real *center); 
 
	/** Returns the point corresponding to the ith center. 
	  * @param i The center whose point is wanted 
	  * @return The getInputCount() dimensional point corresponding to the  
	  *		ith center 
	  */ 
	virtual VECTOR getCenter(int i); 
 
	/// The number of centers in the network 
	virtual int getCenterCount(); 
 
	/** Sets the bias of the ith output. 
	  * @param i The index of the output (0<=i