/// <summary>
/// Updates the sigma units depending on the current error by backpropogation, weights and the expected outputs
/// Makes use of sigmoid function and the derivated version of the sigmoid function
/// </summary>
/// <param name="desiredOutput"></param>
void Adjust_Sigmas(double[] desiredOutput)
{
Sigmas = new List <double[]>();
for (int i = 0; i < Layers.Count; i++)
{
Sigmas.Add(new double[Layers[i].PerceptronCount]);
}
for (int i = Layers.Count - 1; i >= 0; i--)
{
for (int j = 0; j < Layers[i].PerceptronCount; j++)
{
if (i == Layers.Count - 1)
{
double y = Layers[i].Layer_Neurons[j].Output;
Sigmas[i][j] = (Perceptron.Sigmoid(y) - desiredOutput[j]) * Perceptron.Sigmoid_D(y);
}
else
{
double sum = 0;
for (int k = 0; k < Layers[i + 1].PerceptronCount; k++)
{
sum += Layers[i + 1].Layer_Neurons[k].Weights[j] * Sigmas[i + 1][k];
}
Sigmas[i][j] = Perceptron.Sigmoid_D(Layers[i].Layer_Neurons[j].Output) * sum;
}
}
}
}
/// <summary>
/// Updates delta values depending on the sigma values
/// </summary>
private void Update_Delta()
{
for (int i = 1; i < Layers.Count; i++)
{
for (int j = 0; j < Layers[i].PerceptronCount; j++)
{
for (int k = 0; k < Layers[i].Layer_Neurons[j].Weights.Length; k++)
{
Delta[i][j, k] += Sigmas[i][j] * Perceptron.Sigmoid(Layers[i - 1].Layer_Neurons[k].Output);
}
}
}
}