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

#ifndef _SIMPLENEURON_H 
#define _SIMPLENEURON_H 
 
#include "Neuron.h" 
#include "defines.h" 
 
namespace annie 
{ 
 
/** A simple perceptron - i.e., it takes as input the weighted sum of the 
  * outputs of the neurons connected to it. 
  * Probably the first type of neuron you would come across in any  
  * introductory literature of neural networks. 
  */ 
class SimpleNeuron : public Neuron 
{ 
protected: 
	/// Is this neuron allowed to have a bias? 
	bool _hasBias; 
 
	/// If allowed to have a bias then the bias, otherwise 0.0 
	real _bias; 
 
	/// The change in bias, calculated using the gradient descent rule 
	real _deltaBias; 
 
	/// The derivative of the activation function, required for gradient descent training 
	ActivationFunction _dActivationFunction; 
 
	/// @see Neuron::_recacheOutput 
	virtual void _recacheOutput(); 
 
	/// @see Neuron::_recacheError 
	virtual void _recacheError(); 
 
public: 
	/** Creates a simple neuron with the given label. 
	  * @param label The label to be given to the neuron 
	  * @param hasBias true if the neuron is allowed to have a bias, false otherwise. Default is true 
	  * @see removeBias 
	  */ 
	SimpleNeuron(int label, bool hasBias = true); 
 
	/** Sets the bias of the neuron. 
	  * @param bias The bias to be given to the neuron 
	  * @throws Exception if the neuron is not allowed to have a bias 
	  */ 
	virtual void setBias(real bias); 
	 
	/// Is the neuron allowed to have a bias? 
	virtual bool hasBias(); 
	 
	/// The bias of the neuron, 0.0 if it's not allowed to have a bias 
	virtual real getBias(); 
 
	/// Sets bias to 0.0 and prevents the neuron from having a bias 
	virtual void removeBias(); 
 
	/** Sets the desired output of the neuron. 
	  * Should be called only for output neurons, i.e., those whose output is not 
	  * connected to anyone else. Setting the desired output at these neurons 
	  * will form the basis of error backpropagation 
	  * @param desired The desired output of this neuron 
	  * @throws Exception if the neuron is not an output neuron 
	  */ 
	virtual void setDesiredOutput(real desired); 
 
	/** Sets the activation function and its derivative (required for error backpropagation) 
	  * @param f The activation function to be used 
	  * @param df The derivative of the activation function 
	  */ 
	virtual void setActivationFunction(ActivationFunction f, ActivationFunction df); 
 
	/** Connects the given neuron to this one, i.e., the output of the supplied neuron 
	  * will be given as input to this neuron. A random weight is provided to the link 
	  * @param from The neuron whose output is to be taken is as input 
	  */ 
	virtual void connect(Neuron *from); 
 
	/** Connects the given neuron to this one, i.e., the output of the supplied neuron 
	  * will be given as input to this neuron. The weight of the link will be the one 
	  * supplied. 
	  * @param from The neuron whose output is to be taken as input 
	  * @param weight The weight of the connection 
	  */ 
	virtual void connect(Neuron *from, real weight); 
	 
	/** Calculates the adjustment to incoming weights based on the gradient 
	  * descent rule (backpropagation). 
	  * Doesn't actually update the weights, just sets the value. 
	  * @see update 
	  */ 
	virtual void calculateNewWeights(real learningRate, real momentum=0.0); 
 
	/** Updates the weights of incoming connections according to the values 
	  * calculated using the gradient descent rule. 
	  * @see calculateNewWeights 
	  */ 
	virtual void update(); 
 
	/// @see Neuron::toString 
	virtual std::string toString(); 
 
	/// Returns "SimpleNeuron" 
	virtual const char *getClassName(); 
 
	/** Returns the weight of the link between the provided neuron and this neuron. 
	  * @param from The neuron whose output is connected to this neuron's input 
	  * @return The weight of the connection, 0.0 if no connection exists 
	  */ 
	virtual real getWeight(Neuron *from); 
}; 
 
}; //namespace annie 
#endif // define _SIMPLENEURON_H