/// <summary>
/// Get the magnitude of the specified input.
/// </summary>
/// <param name="input">The input to calculate the magnitude for.</param>
/// <returns>The magnitude of the specified pattern.</returns>
public double Magnitude(BiPolarNeuralData input)
{
double result;
result = 0;
for (int i = 0; i < this.layerF1.NeuronCount; i++)
{
result += input.GetBoolean(i) ? 1 : 0;
}
return result;
}
/// <summary>
/// Set the input to the neural network.
/// </summary>
/// <param name="input">The input.</param>
private void SetInput(BiPolarNeuralData input)
{
double activation;
for (int i = 0; i < this.layerF1.NeuronCount; i++)
{
activation = (input.GetBoolean(i) ? 1 : 0)
/ (1 + this.a1 * ((input.GetBoolean(i) ? 1 : 0) + this.b1) + this.c1);
this.outputF1.SetBoolean(i, (activation > 0));
}
}
/// <summary>
/// Compute the output from the F1 layer.
/// </summary>
/// <param name="input">The input to the F1 layer.</param>
private void ComputeF1(BiPolarNeuralData input)
{
double sum, activation;
for (int i = 0; i < this.layerF1.NeuronCount; i++)
{
sum = this.synapseF1toF2.WeightMatrix[i, this.winner]
* (this.outputF2.GetBoolean(this.winner) ? 1 : 0);
activation = ((input.GetBoolean(i) ? 1 : 0) + this.d1 * sum - this.b1)
/ (1 + this.a1
* ((input.GetBoolean(i) ? 1 : 0) + this.d1 * sum) + this.c1);
this.outputF1.SetBoolean(i, activation > 0);
}
}
