override public void fire(InputPattern I)
{
float [] input = I.getInputPattern();
double sum = 0;
for (int i = 0; i < inputSynapses.GetLength(0); i++)
{
sum += inputSynapses[i].getSynapseWeight() * input[i];
}
base.output = (float)sum;
}
override public void fire(InputPattern I)
{
//z = -sqrt(sum(w-p)^2)
float sum = 0.0F;
for (int i = 0; i < inputSynapses.Length; i++)
{
sum += (float)Math.Pow(inputSynapses[i].getSynapseWeight() - I.getInputValue(i), 2);
}
base.output = (float)(-1 * Math.Sqrt(sum));
}
public OutputPattern execute(InputPattern I)
{
IEnumerator IEnum = (layerOne.getHiddenLayerNeurons()).GetEnumerator();
while (IEnum.MoveNext())
{
((HiddenLayerNeuron)IEnum.Current).fire(I);
}
float [] x = competitionBox.compete(layerOne);
I = new InputPattern(x);
IEnum = (layerTwo.getOutputNeuronsList()).GetEnumerator();
OutputPattern o = new OutputPattern(layerTwo.getOutputNeuronsList().Count);
int i = 0;
while (IEnum.MoveNext())
{
((OutputLayerNeuron)IEnum.Current).fire(I);
o.setOutput(((OutputLayerNeuron)IEnum.Current).getOutput(), i++);
}
return(o);
}
virtual public void fire(InputPattern I)
{
}
public OutputPattern feedInput(InputPattern input)
{
return(lvqNet.execute(input));
}
