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;
}
}
private void TrainingStartButton_Click(object sender, EventArgs e)
{
Debug.WriteLine("Click");
try
{
trainingSetings.learningRate = float.Parse(LearningRateTB.Text, CultureInfo.InvariantCulture);
trainingSetings.maxError = float.Parse(MaxErrorTB.Text, CultureInfo.InvariantCulture);
Debug.WriteLine("Jestem w try");
if (trainingSetings.gateType == TrainingSetings.GateType.XOR)
{
Debug.WriteLine("Xor training in Form1");
xorTrResult = trainingService.trainXOR(trainingSetings, gateTrValuesContainer);
showTrainingRaport(xorTrResult.raport);
}
else
{
trainingResult = trainingService.train(trainingSetings, gateTrValuesContainer);
showTrainingRaport(trainingResult.raport);
}
}
catch (NotImplementedException exception)
{
MessageBox.Show(exception.Message);
}
catch (ArgumentException exception)
{
MessageBox.Show(exception.Message);
}
catch (FormatException exception)
{
MessageBox.Show(exception.Message);
}
}
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()));
}
public XorTrResult trainXOR(TrainingSetings settings, GateTrainingValuesContainer valuesContainer)
{
this.trainingSetings = settings;
this.valuesContainer = valuesContainer;
activationFunctions.maxError = trainingSetings.maxError;
firstLineBuilder = new StringBuilder();
firstLineBuilder.Append("Trenowanie: ");
firstLineBuilder.Append(" bramka: " + trainingSetings.gateType.ToString() + ", ");
XorTrResult xorTrResult = trainByTwoNeuronsBySigmoid();
return(xorTrResult);
}
public void showChartForXor(XorTrResult xorTrResult, TrainingSetings trainSettings)
{
list0 = new List <PointInfo>();
list1 = new List <PointInfo>();
activatonFunctions.maxError = trainSettings.maxError;
preparePointListForXor(xorTrResult, trainSettings.activiationFunction);
float[,] table0 = changeListToArray(list0);
float[,] table1 = changeListToArray(list1);
ChartForm chartForm = new ChartForm(table0, table1);
chartForm.Show();
}
