/// <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;
}
/// <summary>
/// Construct a starting NEAT population. does not generate the initial
/// random population of genomes.
/// </summary>
/// <param name="inputCount">The input neuron count.</param>
/// <param name="outputCount">The output neuron count.</param>
/// <param name="populationSize">The population size.</param>
public NEATPopulation(int inputCount, int outputCount,
int populationSize)
: base(populationSize, null)
{
SurvivalRate = DefaultSurvivalRate;
WeightRange = 5;
InitialConnectionDensity = 0.1;
RandomNumberFactory = EncogFramework.Instance
.RandomFactory.FactorFactory();
InputCount = inputCount;
OutputCount = outputCount;
NEATActivationFunction = new ActivationSteepenedSigmoid();
if (populationSize == 0)
{
throw new NeuralNetworkError(
"Population must have more than zero genomes.");
}
}
void AddLayers(List<LayerConfig> gen)
{
foreach (var g in gen)
{
IActivationFunction act;
if (g.ActivationType == 0)
{
act = new ActivationBiPolar();
}
switch (g.ActivationType )
{
case 0:
act = new ActivationBiPolar();
break;
case 1:
act = new ActivationBipolarSteepenedSigmoid ();
break;
case 2:
act = new ActivationClippedLinear();
break;
case 3:
act = new ActivationCompetitive();
break;
case 4:
act = new ActivationElliott();
break;
case 5:
act = new ActivationElliottSymmetric();
break;
case 6:
act = new ActivationGaussian();
break;
case 7:
act = new ActivationLinear();
break;
case 8:
act = new ActivationLOG();
break;
case 9:
act = new ActivationRamp();
break;
case 10:
act = new ActivationRamp();
break;
case 11:
act = new ActivationSigmoid();
break;
case 12:
act = new ActivationSIN();
break;
case 13:
act = new ActivationSoftMax();
break;
case 14:
act = new ActivationSteepenedSigmoid();
break;
case 15:
act = new ActivationStep();
break;
case 16:
act = new ActivationTANH();
break;
default:
act = new ActivationSoftMax();
break;
}
network.AddLayer(new BasicLayer(act, g.hasBias, g.neurons));
}
}
