/// <summary>
/// Construct a starting NEAT population.
/// </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)
{
_neatActivationFunction = new ActivationSigmoid();
_outputActivationFunction = new ActivationLinear();
InputCount = inputCount;
OutputCount = outputCount;
if (populationSize == 0)
{
throw new NeuralNetworkError(
"Population must have more than zero genomes.");
}
// create the initial population
for (int i = 0; i < populationSize; i++)
{
var genome = new NEATGenome(AssignGenomeID(), inputCount,
outputCount);
Add(genome);
}
// create initial innovations
var genome2 = (NEATGenome)Genomes[0];
Innovations = new NEATInnovationList(this, genome2.Links,
genome2.Neurons);
}
/// <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>
/// Read the object.
/// </summary>
/// <param name="mask0">The stream to read the object from.</param>
/// <returns>The object that was loaded.</returns>
public virtual Object Read(Stream mask0)
{
var result = new NEATPopulation();
var innovationList = new NEATInnovationList {
Population = result
};
result.Innovations = innovationList;
var ins0 = new EncogReadHelper(mask0);
IDictionary <Int32, ISpecies> speciesMap = new Dictionary <Int32, ISpecies>();
IDictionary <ISpecies, Int32> leaderMap = new Dictionary <ISpecies, Int32>();
IDictionary <Int32, IGenome> genomeMap = new Dictionary <Int32, IGenome>();
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("INNOVATIONS"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
var innovation = new NEATInnovation
{
InnovationID = Int32.Parse(cols[0]),
InnovationType = StringToInnovationType(cols[1]),
NeuronType = StringToNeuronType(cols[2]),
SplitX = CSVFormat.EgFormat.Parse(cols[3]),
SplitY = CSVFormat.EgFormat.Parse(cols[4]),
NeuronID = Int32.Parse(cols[5]),
FromNeuronID = Int32.Parse(cols[6]),
ToNeuronID = Int32.Parse(cols[7])
};
result.Innovations.Add(innovation);
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("SPECIES"))
{
foreach (String line in section.Lines)
{
String[] cols = line.Split(',');
var species = new BasicSpecies
{
SpeciesID = Int32.Parse(cols[0]),
Age = Int32.Parse(cols[1]),
BestScore = CSVFormat.EgFormat.Parse(cols[2]),
GensNoImprovement = Int32.Parse(cols[3]),
SpawnsRequired = CSVFormat.EgFormat
.Parse(cols[4])
};
species.SpawnsRequired = CSVFormat.EgFormat
.Parse(cols[5]);
leaderMap[(species)] = (Int32.Parse(cols[6]));
result.Species.Add(species);
speciesMap[((int)species.SpeciesID)] = (species);
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("GENOMES"))
{
NEATGenome lastGenome = null;
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
if (cols[0].Equals("g", StringComparison.InvariantCultureIgnoreCase))
{
lastGenome = new NEATGenome
{
NeuronsChromosome = new Chromosome(),
LinksChromosome = new Chromosome()
};
lastGenome.Chromosomes.Add(lastGenome.NeuronsChromosome);
lastGenome.Chromosomes.Add(lastGenome.LinksChromosome);
lastGenome.GenomeID = Int32.Parse(cols[1]);
lastGenome.SpeciesID = Int32.Parse(cols[2]);
lastGenome.AdjustedScore = CSVFormat.EgFormat
.Parse(cols[3]);
lastGenome.AmountToSpawn = CSVFormat.EgFormat
.Parse(cols[4]);
lastGenome.NetworkDepth = Int32.Parse(cols[5]);
lastGenome.Score = CSVFormat.EgFormat.Parse(cols[6]);
result.Add(lastGenome);
genomeMap[(int)lastGenome.GenomeID] = lastGenome;
}
else if (cols[0].Equals("n", StringComparison.InvariantCultureIgnoreCase))
{
var neuronGene = new NEATNeuronGene
{
Id = Int32.Parse(cols[1]),
NeuronType = StringToNeuronType(cols[2]),
Enabled = Int32.Parse(cols[3]) > 0,
InnovationId = Int32.Parse(cols[4]),
ActivationResponse = CSVFormat.EgFormat
.Parse(cols[5]),
SplitX = CSVFormat.EgFormat.Parse(cols[6]),
SplitY = CSVFormat.EgFormat.Parse(cols[7])
};
lastGenome.Neurons.Add(neuronGene);
}
else if (cols[0].Equals("l", StringComparison.InvariantCultureIgnoreCase))
{
var linkGene = new NEATLinkGene();
linkGene.Id = Int32.Parse(cols[1]);
linkGene.Enabled = Int32.Parse(cols[2]) > 0;
linkGene.Recurrent = Int32.Parse(cols[3]) > 0;
linkGene.FromNeuronID = Int32.Parse(cols[4]);
linkGene.ToNeuronID = Int32.Parse(cols[5]);
linkGene.Weight = CSVFormat.EgFormat.Parse(cols[6]);
linkGene.InnovationId = Int32.Parse(cols[7]);
lastGenome.Links.Add(linkGene);
}
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("CONFIG"))
{
IDictionary <String, String> paras = section.ParseParams();
result.NeatActivationFunction = EncogFileSection
.ParseActivationFunction(paras,
NEATPopulation.PropertyNEATActivation);
result.OutputActivationFunction = EncogFileSection
.ParseActivationFunction(paras,
NEATPopulation.PropertyOutputActivation);
result.Snapshot = EncogFileSection.ParseBoolean(paras,
PersistConst.Snapshot);
result.InputCount = EncogFileSection.ParseInt(paras,
PersistConst.InputCount);
result.OutputCount = EncogFileSection.ParseInt(paras,
PersistConst.OutputCount);
result.OldAgePenalty = EncogFileSection.ParseDouble(paras,
PopulationConst.PropertyOldAgePenalty);
result.OldAgeThreshold = EncogFileSection.ParseInt(paras,
PopulationConst.PropertyOldAgeThreshold);
result.PopulationSize = EncogFileSection.ParseInt(paras,
PopulationConst.PropertyPopulationSize);
result.SurvivalRate = EncogFileSection.ParseDouble(paras,
PopulationConst.PropertySurvivalRate);
result.YoungBonusAgeThreshhold = EncogFileSection.ParseInt(
paras, PopulationConst.PropertyYoungAgeThreshold);
result.YoungScoreBonus = EncogFileSection.ParseDouble(paras,
PopulationConst.PropertyYoungAgeBonus);
result.GenomeIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextGenomeID);
result.InnovationIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextInnovationID);
result.GeneIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextGeneID);
result.SpeciesIDGenerate.CurrentID = EncogFileSection.ParseInt(paras,
PopulationConst.
PropertyNextSpeciesID);
}
}
// now link everything up
// first put all the genomes into correct species
foreach (IGenome genome in result.Genomes)
{
var neatGenome = (NEATGenome)genome;
var speciesId = (int)neatGenome.SpeciesID;
if (speciesMap.ContainsKey(speciesId))
{
ISpecies s = speciesMap[speciesId];
s.Members.Add(neatGenome);
}
neatGenome.InputCount = result.InputCount;
neatGenome.OutputCount = result.OutputCount;
}
// set the species leader links
foreach (ISpecies species in leaderMap.Keys)
{
int leaderID = leaderMap[species];
IGenome leader = genomeMap[leaderID];
species.Leader = leader;
((BasicSpecies)species).Population = result;
}
return(result);
}
/// <inheritdoc/>
public Object Read(Stream istream)
{
long nextInnovationId = 0;
long nextGeneId = 0;
var result = new NEATPopulation();
var innovationList = new NEATInnovationList {
Population = result
};
result.Innovations = innovationList;
var reader = new EncogReadHelper(istream);
EncogFileSection section;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("INNOVATIONS"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection
.SplitColumns(line);
var innovation = new NEATInnovation();
var innovationId = int.Parse(cols[1]);
innovation.InnovationId = innovationId;
innovation.NeuronId = int.Parse(cols[2]);
result.Innovations.Innovations[cols[0]] = innovation;
nextInnovationId = Math.Max(nextInnovationId, innovationId + 1);
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("SPECIES"))
{
NEATGenome lastGenome = null;
BasicSpecies lastSpecies = null;
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
if (String.Compare(cols[0], "s", StringComparison.OrdinalIgnoreCase) == 0)
{
lastSpecies = new BasicSpecies
{
Population = result,
Age = int.Parse(cols[1]),
BestScore = CSVFormat.EgFormat.Parse(cols[2]),
GensNoImprovement = int.Parse(cols[3])
};
result.Species.Add(lastSpecies);
}
else if (String.Compare(cols[0], "g", StringComparison.OrdinalIgnoreCase) == 0)
{
bool isLeader = lastGenome == null;
lastGenome = new NEATGenome
{
InputCount = result.InputCount,
OutputCount = result.OutputCount,
Species = lastSpecies,
AdjustedScore = CSVFormat.EgFormat.Parse(cols[1]),
Score = CSVFormat.EgFormat.Parse(cols[2]),
BirthGeneration = int.Parse(cols[3])
};
lastSpecies.Add(lastGenome);
if (isLeader)
{
lastSpecies.Leader = lastGenome;
}
}
else if (String.Compare(cols[0], "n", StringComparison.OrdinalIgnoreCase) == 0)
{
var neuronGene = new NEATNeuronGene();
int geneId = int.Parse(cols[1]);
neuronGene.Id = geneId;
IActivationFunction af = EncogFileSection.ParseActivationFunction(cols[2]);
neuronGene.ActivationFunction = af;
neuronGene.NeuronType = PersistNEATPopulation.StringToNeuronType(cols[3]);
neuronGene.InnovationId = int.Parse(cols[4]);
lastGenome.NeuronsChromosome.Add(neuronGene);
nextGeneId = Math.Max(geneId + 1, nextGeneId);
}
else if (String.Compare(cols[0], "l", StringComparison.OrdinalIgnoreCase) == 0)
{
var linkGene = new NEATLinkGene
{
Id = int.Parse(cols[1]),
Enabled = (int.Parse(cols[2]) > 0),
FromNeuronId = int.Parse(cols[3]),
ToNeuronId = int.Parse(cols[4]),
Weight = CSVFormat.EgFormat.Parse(cols[5]),
InnovationId = int.Parse(cols[6])
};
lastGenome.LinksChromosome.Add(linkGene);
}
}
}
else if (section.SectionName.Equals("NEAT-POPULATION") &&
section.SubSectionName.Equals("CONFIG"))
{
IDictionary <string, string> prm = section.ParseParams();
string afStr = prm[NEATPopulation.PropertyNEATActivation];
if (String.Compare(afStr, TypeCppn, StringComparison.OrdinalIgnoreCase) == 0)
{
HyperNEATGenome.BuildCPPNActivationFunctions(result.ActivationFunctions);
}
else
{
result.NEATActivationFunction = EncogFileSection.ParseActivationFunction(prm,
NEATPopulation.PropertyNEATActivation);
}
result.ActivationCycles = EncogFileSection.ParseInt(prm,
PersistConst.ActivationCycles);
result.InputCount = EncogFileSection.ParseInt(prm,
PersistConst.InputCount);
result.OutputCount = EncogFileSection.ParseInt(prm,
PersistConst.OutputCount);
result.PopulationSize = EncogFileSection.ParseInt(prm,
NEATPopulation.PropertyPopulationSize);
result.SurvivalRate = EncogFileSection.ParseDouble(prm,
NEATPopulation.PropertySurvivalRate);
result.ActivationCycles = EncogFileSection.ParseInt(prm,
NEATPopulation.PropertyCycles);
}
}
// set factories
if (result.IsHyperNEAT)
{
result.GenomeFactory = new FactorHyperNEATGenome();
result.CODEC = new HyperNEATCODEC();
}
else
{
result.GenomeFactory = new FactorNEATGenome();
result.CODEC = new NEATCODEC();
}
// set the next ID's
result.InnovationIDGenerate.CurrentID = nextInnovationId;
result.GeneIdGenerate.CurrentID = nextGeneId;
// find first genome, which should be the best genome
if (result.Species.Count > 0)
{
ISpecies species = result.Species[0];
if (species.Members.Count > 0)
{
result.BestGenome = species.Members[0];
}
}
return(result);
}
