public TrainingResultP train(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() + ", ");
switch (settings.activiationFunction)
{
case TrainingSetings.ActiviationFunction.JUMP:
firstLineBuilder.Append(" funkcja skokowa, ");
trainByJumpFunction();
break;
case TrainingSetings.ActiviationFunction.SIGMOID:
firstLineBuilder.Append(" funkcja sigmoidowa, ");
trainBySigmoidFunction();
break;
default:
throw new ArgumentException("Brak wybranej fukcji");
}
return(tResult);
}
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();
}
