private void CorrectionLayerWeight(Neuron[] layer, double[] layerError)
{
const double learningSpeed = 0.8;
for (int j = 0; j < layer.Count(); j++)
{
for (int i = 0; i < layer[j].EntrancesWeight.Count(); i++)
{
var delta = (-1) * learningSpeed * layerError[j] * layer[j].EntranceValue[i];
layer[j].EntrancesWeight[i] += delta;
}
}
}
private double[] ErrorInLayer(double alpha, Neuron[] currentLayer, Neuron[] nextLayer, double[] nextLayerError)
{
var layerError = new double[currentLayer.Count()];
for (int i = 0; i < layerError.Count(); i++)
{
double weight = 0;
for (int j = 0; j < nextLayer.Count(); j++)
{
weight += nextLayer[j].EntrancesWeight[i] * nextLayerError[j];
}
layerError[i] = alpha * currentLayer[i].ActivateFunction() * (1 - currentLayer[i].ActivateFunction()) * weight;
}
return layerError;
}
private double[] GetLayerEntrance(Neuron[] layer, double[] inputValue)
{
foreach (var value in layer)
value.Entrance(inputValue);
var intermediateResult = new double[layer.Count()];
for (int i = 0; i < layer.Count(); i++)
intermediateResult[i] = layer[i].ActivateFunction();
return intermediateResult;
}
private void FillNeyronLayer(Neuron[] layer, Random rnd, int entrancesNumber)
{
for (int i = 0; i < layer.Count(); i++)
layer[i] = new Neuron(entrancesNumber, rnd.Next(10000));
}
