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

#include "../include/Exception.h" 
#include "../include/RecurrentNeuron.h" 
 
#include  
using namespace std; 
 
namespace annie 
{ 
 
RecurrentNeuron::RecurrentNeuron(int label, bool hasBias) : SimpleNeuron(label,hasBias) 
{ 
	_time=0; 
	_lastOutput = _initialOutput = 0.0; 
	_classHeirarchy.push_back(_RECURRENT_NEURON_STRING); 
} 
 
void 
RecurrentNeuron::reset(real initActivation) 
{ 
	_time = 0; 
	_initialOutput = _lastOutput = _activationCache = initActivation; 
	_outputCache = _activationFunction(_activationCache); 
	_outputCacheValid = true; 
} 
 
void 
RecurrentNeuron::step() 
{ 
	invalidateOutputCache(); 
	_time++; 
	LINKS::iterator it; 
	for (it = _inputLinks.begin(); it != _inputLinks.end(); it++) 
	{ 
		Link *l = (Link*)(*it); 
		Neuron *from = l->getSource(); 
		if (from->instanceOf(_RECURRENT_NEURON_STRING)) 
		{ 
			RecurrentNeuron *nrn = (RecurrentNeuron*)from; 
			while (nrn->getTime() != _time) 
				nrn->step(); 
		} 
	} 
} 
 
real 
RecurrentNeuron::getNextOutput() 
{ 
	step(); 
	return getOutput(); 
} 
 
int 
RecurrentNeuron::getTime() 
{	return _time;	} 
 
const char * 
RecurrentNeuron::getClassName() 
{	return _RECURRENT_NEURON_STRING; } 
 
void  
RecurrentNeuron::calculateNewWeights(real learningRate) 
{ 
	string s(getClassName()); 
	s = s + "::calculateNewWeights() - Method not yet implemented"; 
	throw Exception(s); 
} 
void  
RecurrentNeuron::update() 
{ 
	string s(getClassName()); 
	s = s + "::update() - Method not yet implemented"; 
	throw Exception(s); 
} 
 
void  
RecurrentNeuron::setDesiredOutput(real desired) 
{ 
	string s(getClassName()); 
	s = s + "::setDesiredOutput() - Method not yet implemented"; 
	throw Exception(s); 
} 
 
real 
RecurrentNeuron::getOutput() 
{ 
	if (_time == 0) 
		return _initialOutput; 
	return SimpleNeuron::getOutput(); 
} 
 
real 
RecurrentNeuron::getLastOutput() 
{	return _lastOutput;	} 
 
void 
RecurrentNeuron::_recacheOutput() 
{ 
	if (_outputCacheValid) 
		return; 
	LINKS::iterator it; 
 
	_lastOutput = _outputCache; 
 
	_activationCache = 0; 
	for (it=_inputLinks.begin();it!=_inputLinks.end();it++) 
	{ 
		Link *l = (Link *)(*it); 
		Neuron *source = l->getSource(); 
		if (source->instanceOf(_RECURRENT_NEURON_STRING)) 
			_activationCache+=((RecurrentNeuron*)source)->getLastOutput()*l->getWeight(); 
		else 
			_activationCache+=l->getSource()->getOutput()*l->getWeight(); 
	} 
	if (_hasBias) 
		_activationCache+=_bias; 
	_outputCache = _activationFunction(_activationCache); 
	_outputCacheValid = true; 
} 
 
}; //namespace annie