public void AddNeuron()
{
if (!AllSynapses.Any())
{
AddSynapse();
return;
}
//Debug.Log("Poczatek add neuron");
int tmp = RandomGenerator.Next(AllSynapses.Count);
Synapse oldSyn = AllSynapses.ToList()[tmp].Value;
AllSynapses.Remove(oldSyn.InnovationNo);
oldSyn.InputNeuron.OutputSynapses.Remove(oldSyn);
oldSyn.OutputNeuron.InputSynapses.Remove(oldSyn);
Neuron neuron = new Neuron(NeuronInnovationNo);
Synapse newSyn1 = new Synapse(oldSyn.InputNeuron, neuron, SynapseInnovationNo);
newSyn1.Weight = 1;
Synapse newSyn2 = new Synapse(neuron, oldSyn.OutputNeuron, SynapseInnovationNo);
newSyn2.Weight = oldSyn.Weight;
HiddenLayers.Add(neuron.InnovationNo, neuron);
AllSynapses.Add(newSyn1.InnovationNo, newSyn1);
AllSynapses.Add(newSyn2.InnovationNo, newSyn2);
}
public float Distance(NEAT partner)
{
if (partner == null)
{
Debug.Log("NULL partner");
Debug.Break();
}
//var synapseIenumerator = AllSynapses.GetEnumerator();
//var partnerSynapseIenumerator = partner.AllSynapses.GetEnumerator();
//synapseIenumerator.MoveNext();
float result = 0;
if (AllSynapses.Any() || partner.AllSynapses.Any())
{
int disjointGenes = 0;
int excessGenes = 0;
float weightDifference = 0;
float N = Math.Max(AllSynapses.Count, partner.AllSynapses.Count);
disjointGenes = 2 * AllSynapses.Keys.Union(partner.AllSynapses.Keys).Count() - AllSynapses.Count - partner.AllSynapses.Count;
foreach (var synapseInnovationNo in AllSynapses.Keys.Intersect(partner.AllSynapses.Keys))
{
weightDifference += Math.Abs((float)(AllSynapses[synapseInnovationNo].Weight - partner.AllSynapses[synapseInnovationNo].Weight));
//if (Math.Abs((float)(AllSynapses[synapseInnovationNo].Weight - partner.AllSynapses[synapseInnovationNo].Weight)) > Constants.Con.synapse_difference_threshold)
// disjointGenes++;
}
result += (Constants.Con.c1 * excessGenes + Constants.Con.c2 * disjointGenes + Constants.Con.c3 * weightDifference) / N;
}
//if(HiddenLayers.Any() || partner.HiddenLayers.Any()) OutputLayer.Count > 0 always
{
int disjointGenes = 0;
int excessGenes = 0;
float weightDifference = 0;
float N = Math.Max(HiddenLayers.Count, partner.HiddenLayers.Count) + OutputLayer.Count;
foreach (var neuronPair in OutputLayer.Zip(partner.OutputLayer))
{
weightDifference += Mathf.Abs((float)(neuronPair.Key.Bias - neuronPair.Value.Bias));
//if (Mathf.Abs((float)(neuronPair.Key.Bias - neuronPair.Value.Bias)) > Constants.Con.bias_difference_threshold)
// disjointGenes++;
}
disjointGenes = 2 * HiddenLayers.Keys.Union(partner.HiddenLayers.Keys).Count() - HiddenLayers.Count - partner.HiddenLayers.Count;
foreach (var neuronInnovationNo in HiddenLayers.Keys.Intersect(partner.HiddenLayers.Keys))
{
weightDifference += Math.Abs((float)(HiddenLayers[neuronInnovationNo].Bias - partner.HiddenLayers[neuronInnovationNo].Bias));
//if (Math.Abs((float)(HiddenLayers[neuronInnovationNo].Bias - partner.HiddenLayers[neuronInnovationNo].Bias)) > Constants.Con.bias_difference_threshold)
// disjointGenes++;
}
result += (Constants.Con.c1 * excessGenes + Constants.Con.c2 * disjointGenes + Constants.Con.c3 * weightDifference) / N;
}
return(result);
//if (result < Constants.Con.delta_t)
// return true;
//return false;
}
private void DelSynapse()
{
if (!AllSynapses.Any())
{
return;
}
int tmp = RandomGenerator.Next(AllSynapses.Count);
Synapse oldSyn = AllSynapses.Values.ToList()[tmp];
oldSyn.InputNeuron.OutputSynapses.Remove(oldSyn);
oldSyn.OutputNeuron.InputSynapses.Remove(oldSyn);
AllSynapses.Remove(oldSyn.InnovationNo);
}
