public void preparePointList(TrainingResultP trainingResult, TrainingSetings.ActiviationFunction activationFunction)
{
float x1 = 0.01f;
float x2 = 0.01f;
for (int x = 0; x <= 100; x++)
{
x2 = 0.01f;
for (int y = 0; y <= 100; y++)
{
PointInfo point = preparePoint(trainingResult, x1, x2, activationFunction);
if (point.color == 0.00f)
{
list0.Add(point);
}
else
{
list1.Add(point);
}
x2 += 0.01f;
}
x1 += 0.01f;
}
}
public void preparePointListForXor(XorTrResult xorTrResult, TrainingSetings.ActiviationFunction activationFunction)
{
float x1 = 0.01f;
float x2 = 0.01f;
for (int x = 0; x <= 100; x++)
{
x2 = 0.01f;
for (int y = 0; y <= 100; y++)
{
PointInfo point = preparePointForXor(xorTrResult, x1, x2, activationFunction);
point.color = activatonFunctions.correctionForSigmoid(point.color);
if (point.color == 0.00f)
{
list0.Add(point);
}
else
{
list1.Add(point);
}
x2 += 0.01f;
}
x1 += 0.01f;
}
}
public PointInfo preparePoint(TrainingResultP trainingResult, float x, float y, TrainingSetings.ActiviationFunction activiationFunction)
{
float e = x * trainingResult.w1 + y * trainingResult.w1 + trainingResult.biasI;
float result = 0.00f;
if (activiationFunction == TrainingSetings.ActiviationFunction.JUMP)
{
result = activatonFunctions.jumpActivationFuntion(e);
}
else
{
result = activatonFunctions.sigmoidActivationFunction(e);
result = activatonFunctions.correctionForSigmoid(result);
}
return(new PointInfo(x, y, result));
}
public PointInfo preparePointForXor(XorTrResult xorTrResult, float x, float y, TrainingSetings.ActiviationFunction activiationFunction)
{
xorTrResult.hNeuron1.inputs = new float[] { x, y };
xorTrResult.hNeuron2.inputs = new float[] { x, y };
xorTrResult.oNeuron.inputs = new float[] { xorTrResult.hNeuron1.output(), xorTrResult.hNeuron2.output() };
return(new PointInfo(x, y, xorTrResult.oNeuron.output()));
}
