/// <summary>
/// Used internally to calculate the error for a training set.
/// </summary>
/// <param name="trainingSet"> The training set to calculate for. </param>
/// <returns> The error value. </returns>
private double determineError(DataSet trainingSet)
{
double result = 0d;
IEnumerator <DataSetRow> iterator = trainingSet.iterator();
while (iterator.MoveNext() && !Stopped)
{
DataSetRow trainingSetRow = iterator.Current;
double[] input = trainingSetRow.Input;
Network.Input = input;
Network.calculate();
double[] output = Network.Output;
double[] desiredOutput = trainingSetRow.DesiredOutput;
double[] patternError = ErrorFunction.calculatePatternError(desiredOutput, output);
double sqrErrorSum = 0;
foreach (double error in patternError)
{
sqrErrorSum += (error * error);
}
result += sqrErrorSum / (2 * patternError.Length);
}
return(result);
}
/// <summary>
/// Trains network with the input and desired output pattern from the specified training element
/// </summary>
/// <param name="trainingElement"> supervised training element which contains input and desired output </param>
protected internal virtual void learnPattern(DataSetRow trainingElement)
{
double[] input = trainingElement.Input;
this.neuralNetwork.Input = input;
this.neuralNetwork.calculate();
double[] output = this.neuralNetwork.Output;
double[] desiredOutput = trainingElement.DesiredOutput;
double[] patternError = errorFunction.calculatePatternError(output, desiredOutput);
this.updateNetworkWeights(patternError);
}