public void oneStep(double x1, double x2, double Z)
{
//Set up input for hidden layer
List <double> outPutHidden = new List <double> {
1
}; //{1 is for bias}
for (int ne = 0; ne < 5; ne++)
{
if (listNeu[ne] != null)
{
listNeu[ne].inputs = new double[] { 1, x1, x2 };
outPutHidden.Add(listNeu[ne].output());
}
else
{
outPutHidden.Add(0);
}
}
//output Hidden is the input of the output layer
listNeu[5].inputs = outPutHidden.ToArray();
double outPut = listNeu[5].output();
//Error
listNeu[5].diff = Sigmoid.derivative(outPut) * (Z - outPut); // 5 ~ output neuron
listNeu[5].adjustWeights();
for (int ne = 0; ne < 5; ne++)
{
if (listNeu[ne] != null)
{
listNeu[ne].diff = Sigmoid.derivative(outPutHidden[ne + 1]) * listNeu[5].diff * listNeu[5].weights[ne + 1];
listNeu[ne].adjustWeights();
}
}
//Error function (1/2 (output-targe)^2)
lstErro.Add(Math.Pow((Z - outPut), 2) / 2);
if (currentEpock == maxEpoch)
{
lstOutPut.Add(outPut);
}
}
public override double output()
{
outPut = Sigmoid.output(weights[0] + weights[1] * inputs[1] + weights[2] * inputs[2] + weights[3] * inputs[3] + weights[4] * inputs[4] + weights[5] * inputs[5]);
return(outPut);
}
