/// <summary>
/// Trains network with the pattern from the specified training element
/// </summary>
/// <param name="trainingElement">unsupervised training element which contains network input</param>
protected void LearnPattern(TrainingElement trainingElement)
{
double[] input = trainingElement.Input;
this.NeuralNetwork.SetInput(input);
this.NeuralNetwork.Calculate();
this.AdjustWeights();
}
private void LearnPattern(TrainingElement tE, int neighborhood)
{
this.NeuralNetwork.SetInput(tE.Input);
this.NeuralNetwork.Calculate();
Neuron winner = GetClosest();
if (winner.Output == 0)
return; // ako je vec istrenirana jedna celija, izadji
Layer mapLayer = this.NeuralNetwork.GetLayerAt(1);
int winnerIdx = mapLayer.IndexOf(winner);
AdjustCellWeights(winner, 0);
int cellNum = mapLayer.NeuronsCount;
for (int p = 0; p < cellNum; p++)
{
if (p == winnerIdx)
continue;
if (IsNeighbor(winnerIdx, p, neighborhood))
{
Neuron cell = mapLayer.GetNeuronAt(p);
AdjustCellWeights(cell, 1);
} // if
} // for
}
