/// <summary>
/// Determins if two axons share similar topology. That is, if the axons have
/// the same inputs and outputs. It dose not consider the weight of the axons.
/// </summary>
/// <param name="other">An axon to compare</param>
/// <returns>True if the axons share the same topology</returns>
public bool IsSimilar(AxonComp other)
{
if (this.Input != other.Input)
{
return(false);
}
if (this.Output != other.Output)
{
return(false);
}
return(true);
}
public double Compare(NetworkComp genome)
{
//calculates the maximum number of nurons and axons
//this is nessary incase the genomes have diffrent numbers
int nmax = Math.Min(this.NuronCount, genome.NuronCount);
int amax = Math.Min(this.AxonCount, genome.AxonCount);
//used in computing the distance
int disjoint = 0;
double wbar = 0.0;
for (int i = 0; i < amax; i++)
{
AxonComp source = this.axons[i];
AxonComp target = genome.axons[i];
if (source.IsSimilar(target))
{
//computes the distance between the weights
double w1 = source.Weight;
double w2 = target.Weight;
wbar += Math.Abs(w1 - w2);
}
else
{
//counts the number of disjoint axons
disjoint++;
}
}
//computes the number of matches
double match = amax - disjoint;
//couputes the distance for specisation
double dist = (C0 * disjoint) / (double)amax;
dist += C1 * (wbar / (double)match);
return(dist);
}
public void Crossover(VRandom rng, NetworkComp genome)
{
//determins the rate of crossover
double rate = rng.RandDouble(0.25, 0.75);
//calculates the maximum number of nurons and axons
//this is nessary incase the genomes have diffrent numbers
int nmax = Math.Min(this.NuronCount, genome.NuronCount);
int amax = Math.Min(this.AxonCount, genome.AxonCount);
for (int i = 0; i < nmax; i++)
{
if (rng.NextDouble() > rate)
{
continue;
}
NuronComp copy = genome.nurons[i];
nurons[i].Funciton = copy.Funciton;
}
for (int i = 0; i < amax; i++)
{
if (rng.NextDouble() > rate)
{
continue;
}
//NOTE: should we copy the entier axon or just the weight?
AxonComp copy = genome.axons[i];
axons[i].Weight = copy.Weight;
axons[i].Enabled = copy.Enabled;
axons[i].Input = copy.Input;
axons[i].Output = copy.Output;
}
throw new NotImplementedException();
}
