public static Synapse Connect(Neuron n1, Neuron n2, float weight, int delay, ILearningRule learningRule)
{
Synapse syn = new Synapse(n1, n2, weight, delay, learningRule.GetCopy());
n1.AddPostSynapse(syn);
n2.AddPreSynapse(syn);
return(syn);
}
public static void MakeConnection(Group sourceGroup, Group destinationGroup, float weight, int delay,
ConnectionPatternDelegate connectionPattern,
ILearningRule learningRule)
{
for (int i = 0; i < sourceGroup.Count; i++)
{
for (int j = 0; j < destinationGroup.Count; j++)
{
if (connectionPattern(i, j))
{
Synapse syn = new Synapse(sourceGroup[i], destinationGroup[j],
weight, delay, learningRule.GetCopy());
sourceGroup[i].AddPostSynapse(syn);
destinationGroup[j].AddPreSynapse(syn);
}
}
}
}
public static void MakeRandomValueConnection(Group sourceGroup, Group destinationGroup,
float minWeight, float maxWeight, int minDelay, int maxDelay,
ConnectionPatternDelegate connectionPattern, ILearningRule learningRule)
{
float range = maxWeight - minWeight;
for (int i = 0; i < sourceGroup.Count; i++)
{
for (int j = 0; j < destinationGroup.Count; j++)
{
if (connectionPattern(i, j))
{
float randWeight = (float)rand.NextDouble() * range + minWeight;
int randDelay = rand.Next(minDelay, maxDelay + 1);
Synapse syn = new Synapse(sourceGroup[i], destinationGroup[j],
randWeight, randDelay, learningRule.GetCopy());
sourceGroup[i].AddPostSynapse(syn);
destinationGroup[j].AddPreSynapse(syn);
}
}
}
}