internal void AddConnection(INeuralConnection connection, ILearningRule rule)
{
Contract.Requires(connection != null);
Contract.Requires(rule != null);
Contract.Requires(rule.AlgorithmType == GetType());
connectionList.AddLast(new LearningConnection(connection, rule));
}
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);
}
internal LearningConnection(INeuralConnection connection, ILearningRule rule)
{
Contract.Requires(connection != null);
Contract.Requires(rule != null);
Connection = connection;
Rule = rule;
}
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);
}
}
}
}
private void AddLearningAlgo(ILearningConnection connection, ILearningRule rule, Dictionary<Tuple<Type, int>, LearningAlgorithm> algos)
{
var key = Tuple.Create(rule.AlgorithmType, rule.GroupID);
LearningAlgorithm algo;
if (!algos.TryGetValue(key, out algo))
{
algo = (LearningAlgorithm)Activator.CreateInstance(rule.AlgorithmType);
algo.BeginInit();
algos.Add(key, algo);
if (algo.BackwardIterationMode == BackwardIterationMode.BackPropagation) doBackpropagation = true;
}
algo.AddConnection(connection, rule);
}
public Synapse(Neuron pre, Neuron post,
float synapseWeight, int synapseDelay, ILearningRule synapseLearningRule) :
this(pre, post, new SynapseCore(synapseWeight, synapseDelay, synapseLearningRule))
{
}
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);
}
}
}
}
public SynapseCore(float synapseWeight, int synapseDelay, ILearningRule synapseLearningRule)
{
Weight = synapseWeight;
Delay = synapseDelay;
learningRule = synapseLearningRule;
}
