private void Initialise()
{
outputs = new Neuron[length,length];
for (int i = 0; i < length; i++)
{
for (int j = 0; j < length; j++)
{
outputs[i, j] = new Neuron(i, j, length);
outputs[i, j].Weights = new double[dimensions];
for (int k = 0; k < dimensions; k++)
{
outputs[i, j].Weights[k] = rnd.NextDouble();
}
}
}
}
public double UpdateWeights(double[] pattern, Neuron winner, int it)
{
double sum = 0;
for (int i = 0; i < Weights.Length; i++)
{
double delta = LearningRate(it)*Gauss(winner, it)*(pattern[i] - Weights[i]);
Weights[i] += delta;
sum += delta;
}
return sum/Weights.Length;
}
private double Gauss(Neuron win, int it)
{
double distance = Math.Sqrt(Math.Pow(win.X - X, 2) + Math.Pow(win.Y - Y, 2));
return Math.Exp(-Math.Pow(distance, 2)/(Math.Pow(Strength(it), 2)));
}
private double Gauss(Neuron win, int it)
{
double distance = Math.Sqrt(Math.Pow(win.X - X, 2) + Math.Pow(win.Y - Y, 2));
return(Math.Exp(-Math.Pow(distance, 2) / (Math.Pow(Strength(it), 2))));
}
