internal double [] dueronomy(double[][] helix)
{
{
double[] add = new double[max];
double[] ans = new double[unit];
double[][] question = new double[NaturalSelection.vN][];
int passover = 0;
int addendum;
byte num = 0;
for (byte b = 0; b < NaturalSelection.vAN; b++)
{
foreach (double[] d in helix)
{
question[num] = this.BinaryRedux(d[b]);
fuzzify(ref question[num]);
addendum = unit + passover;
for (int c = passover; c < addendum; c++)
{
byte slider = 0;
add[c] = question[num][slider];
slider++;
}
passover += unit;
num++;
}
}
NeuralNet.PreparePerceptionLayerForPulse(precog, add);
precog.Pulse();
precog.ApplyLearning();
return(ans);
}
}
public static void BackPropogation_TrainingSession(NeuralNet net, double[] input, double[] desiredResult)
{
PreparePerceptionLayerForPulse(net, input);
net.Pulse();
CalculateErrors(net, desiredResult);
CalculateAndAppendTransformation(net);
}
public static void BackPropogation_TrainingSession(NeuralNet net, double[] input, double[] desiredResult)
{
PreparePerceptionLayerForPulse(net, input);
net.Pulse();
CalculateErrors(net, desiredResult);
CalculateAndAppendTransformation(net);
}
