/// <summary>
/// Glowna procedura uczenia
/// </summary>
/// <param name="docs">dane uczace</param>
/// <returns></returns>
public override bool Learn(Object obj)
{
if (obj == null)
{
throw new NullReferenceException("docList puste");
}
if (!(obj is DocumentList))
{
throw new ArgumentException("Argument nie jest typu: DocumentList !");
}
double num1 = 0, num2 = 0;
DocumentList docList = obj as DocumentList;
docList.ToString();
this.learningData = docList;
documentList = docList.GetAllDataRandomized();
PreSelect();
if (documentList == null || documentList.Count == 0)
{
throw new NullReferenceException("DocumentList pusty");
}
List <double[]> desiredOutputData = PrepareOutputData(documentList);
CoverSpaceByNeuronCells(docList.GetMinValues().ToArray(), docList.GetMaxValues().ToArray(), this.neuronOutputLayer);
do
{
double num3 = 0, num4 = 0;
OutputLayerLearning(desiredOutputData);
do
{
HiddenLayerLearning();
PrintNeuronsInfo();
num4 = num3;
num3 = LearnCheck();
//if (bWorker != null && bWorker.CancellationPending)dodałem tomek
// return false;
} while (num3 > num4);
foreach (RadialNeuron n in neuronHiddenLayer)
{
n.BackToPrevWeights();
}
num2 = num1;
num1 = LearnCheck();
} while (num1 > num2);
//MessageBox.Show("Koniec nauki");
foreach (LinearNeuron n in neuronOutputLayer)
{
n.BackToPrevWeights();
}
return(true);
}