/// <summary>
/// Computes the activation of node n. We initially start with output neurons, and work
/// our way backward.
/// </summary>
private void ComputeActivation(Node n, List <Node> previousNodeRequests)
{
if (n.Role == NodeRole.Input || n.Role == NodeRole.Bias)
{
// It doesn't make sense to compute the activation of Input and Bias nodes.
// The activation of the Bias node is always 1
// The activation of the input node comes from an external input vector.
return;
}
n.IncomingActivity = 0;
foreach (Link l in n.LinksIncoming.Values)
{
double activationContribution = 0;
if (previousNodeRequests.Contains(l.NodeIn))
{
// We've already asked about this node, so we must have hit a recurrent loop.
// Use the previous activation, and stop asking.
activationContribution = l.NodeIn.ActivationPrevious * l.Weight;
}
else
{
previousNodeRequests.Add(l.NodeIn);
ComputeActivation(l.NodeIn, previousNodeRequests);
// After computing, pop the last item off the list
previousNodeRequests.RemoveAt(previousNodeRequests.Count - 1);
activationContribution = l.NodeIn.ActivationCurrent * l.Weight;
}
n.IncomingActivity += activationContribution;
}
n.ActivationPrevious = n.ActivationCurrent;
n.ActivationCurrent = ActivationFunctions.Sigmoidal_0_1(n.IncomingActivity);
}
