/// <summary>
/// Create an initial random population.
/// </summary>
public void Reset()
{
// create the genome factory
if (IsHyperNEAT)
{
CODEC = new HyperNEATCODEC();
GenomeFactory = new FactorHyperNEATGenome();
}
else
{
CODEC = new NEATCODEC();
GenomeFactory = new FactorNEATGenome();
}
// create the new genomes
Species.Clear();
// reset counters
GeneIdGenerate.CurrentID = 1;
InnovationIDGenerate.CurrentID = 1;
EncogRandom rnd = RandomNumberFactory.Factor();
// create one default species
BasicSpecies defaultSpecies = new BasicSpecies();
defaultSpecies.Population = this;
// create the initial population
for (int i = 0; i < PopulationSize; i++)
{
NEATGenome genome = GenomeFactory.Factor(rnd, this,
InputCount, OutputCount,
InitialConnectionDensity);
defaultSpecies.Add(genome);
}
defaultSpecies.Leader = defaultSpecies.Members[0];
Species.Add(defaultSpecies);
// create initial innovations
Innovations = new NEATInnovationList(this);
}
/// <summary>
/// Create an initial random population.
/// </summary>
public void Reset()
{
// create the genome factory
if (IsHyperNEAT)
{
CODEC = new HyperNEATCODEC();
GenomeFactory = new FactorHyperNEATGenome();
}
else
{
CODEC = new NEATCODEC();
GenomeFactory = new FactorNEATGenome();
}
// create the new genomes
Species.Clear();
// reset counters
GeneIdGenerate.CurrentID = 1;
InnovationIDGenerate.CurrentID = 1;
EncogRandom rnd = RandomNumberFactory.Factor();
// create one default species
BasicSpecies defaultSpecies = new BasicSpecies();
defaultSpecies.Population = this;
// create the initial population
for (int i = 0; i < PopulationSize; i++)
{
NEATGenome genome = GenomeFactory.Factor(rnd, this ,
InputCount, OutputCount,
InitialConnectionDensity);
defaultSpecies.Add(genome);
}
defaultSpecies.Leader = defaultSpecies.Members[0];
Species.Add(defaultSpecies);
// create initial innovations
Innovations = new NEATInnovationList(this);
}
/// <inheritdoc/>
public IMLMethod Decode(NEATPopulation pop, Substrate.Substrate substrate,
IGenome genome)
{
// obtain the CPPN
NEATCODEC neatCodec = new NEATCODEC();
NEATNetwork cppn = (NEATNetwork)neatCodec.Decode(genome);
List<NEATLink> linkList = new List<NEATLink>();
IActivationFunction[] afs = new IActivationFunction[substrate.NodeCount];
IActivationFunction af = new ActivationSteepenedSigmoid();
// all activation functions are the same
for (int i = 0; i < afs.Length; i++)
{
afs[i] = af;
}
double c = this.MaxWeight / (1.0 - this.MinWeight);
BasicMLData input = new BasicMLData(cppn.InputCount);
// First create all of the non-bias links.
foreach (SubstrateLink link in substrate.Links)
{
SubstrateNode source = link.Source;
SubstrateNode target = link.Target;
int index = 0;
foreach (double d in source.Location)
{
input.Data[index++] = d;
}
foreach (double d in target.Location)
{
input.Data[index++] = d;
}
IMLData output = cppn.Compute(input);
double weight = output[0];
if (Math.Abs(weight) > this.MinWeight)
{
weight = (Math.Abs(weight) - this.MinWeight) * c
* Math.Sign(weight);
linkList.Add(new NEATLink(source.ID, target.ID,
weight));
}
}
// now create biased links
input.Clear();
int d2 = substrate.Dimensions;
IList<SubstrateNode> biasedNodes = substrate.GetBiasedNodes();
foreach (SubstrateNode target in biasedNodes)
{
for (int i = 0; i < d2; i++)
{
input.Data[d2 + i] = target.Location[i];
}
IMLData output = cppn.Compute(input);
double biasWeight = output[1];
if (Math.Abs(biasWeight) > this.MinWeight)
{
biasWeight = (Math.Abs(biasWeight) - this.MinWeight) * c
* Math.Sign(biasWeight);
linkList.Add(new NEATLink(0, target.ID, biasWeight));
}
}
// check for invalid neural network
if (linkList.Count == 0)
{
return null;
}
linkList.Sort();
NEATNetwork network = new NEATNetwork(substrate.InputCount,
substrate.OutputCount, linkList, afs);
network.ActivationCycles = substrate.ActivationCycles;
return network;
}
