/// <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>
/// Determine the species.
/// </summary>
/// <param name="genomes">The genomes to speciate.</param>
private void SpeciateAndCalculateSpawnLevels(IList <IGenome> genomes)
{
double maxScore = 0;
if (genomes.Count == 0)
{
throw new EncogError("Can't speciate, the population is empty.");
}
IList <ISpecies> speciesCollection = _population.Species;
if (speciesCollection.Count == 0)
{
throw new EncogError("Can't speciate, there are no species.1");
}
// calculate compatibility between genomes and species
AdjustCompatibilityThreshold();
// assign genomes to species (if any exist)
foreach (IGenome g in genomes)
{
ISpecies currentSpecies = null;
IGenome genome = g;
if (!Double.IsNaN(genome.AdjustedScore) &&
!double.IsInfinity(genome.AdjustedScore))
{
maxScore = Math.Max(genome.AdjustedScore, maxScore);
}
foreach (ISpecies s in speciesCollection)
{
double compatibility = GetCompatibilityScore(genome,
s.Leader);
if (compatibility <= _compatibilityThreshold)
{
currentSpecies = s;
AddSpeciesMember(s, genome);
genome.Species = s;
break;
}
}
// if this genome did not fall into any existing species, create a
// new species
if (currentSpecies == null)
{
currentSpecies = new BasicSpecies(_population, genome);
_population.Species.Add(currentSpecies);
}
}
//
double totalSpeciesScore = speciesCollection.Sum(species => species.CalculateShare(_owner.ScoreFunction.ShouldMinimize, maxScore));
if (speciesCollection.Count == 0)
{
throw new EncogError("Can't speciate, there are no species.2");
}
if (totalSpeciesScore < EncogFramework.DefaultDoubleEqual)
{
// This should not happen much, or if it does, only in the
// beginning.
// All species scored zero. So they are all equally bad. Just divide
// up the right to produce offspring evenly.
DivideEven(speciesCollection);
}
else
{
// Divide up the number of offspring produced to the most fit
// species.
DivideByFittestSpecies(speciesCollection, totalSpeciesScore);
}
LevelOff();
}
/// <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;
}
/// <inheritdoc />
public ISpecies CreateSpecies()
{
ISpecies species = new BasicSpecies();
species.Population = this;
_species.Add(species);
return species;
}
/// <inheritdoc />
public Object Read(Stream istream)
{
var context = new EncogProgramContext();
var result = new PrgPopulation(context, 0);
var reader = new EncogReadHelper(istream);
EncogFileSection section;
int count = 0;
ISpecies lastSpecies = null;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("PARAMS"))
{
IDictionary<string, string> prms = section.ParseParams();
EngineArray.PutAll(prms, result.Properties);
}
else if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("EPL-POPULATION"))
{
foreach (string line in section.Lines)
{
IList<String> cols = EncogFileSection.SplitColumns(line);
if (String.Compare(cols[0], "s", StringComparison.OrdinalIgnoreCase) == 0)
{
lastSpecies = new BasicSpecies
{
Age = int.Parse(cols[1]),
BestScore = CSVFormat.EgFormat.Parse(cols[2]),
Population = result,
GensNoImprovement = int.Parse(cols[3])
};
result.Species.Add(lastSpecies);
}
else if (cols[0].Equals("p"))
{
double score;
double adjustedScore;
if (String.Compare(cols[1], "nan", StringComparison.OrdinalIgnoreCase) == 0
|| String.Compare(cols[2], "nan", StringComparison.OrdinalIgnoreCase) == 0)
{
score = Double.NaN;
adjustedScore = Double.NaN;
}
else
{
score = CSVFormat.EgFormat.Parse(cols[1]);
adjustedScore = CSVFormat.EgFormat.Parse(cols[2]);
}
String code = cols[3];
var prg = new EncogProgram(context);
prg.CompileEPL(code);
prg.Score = score;
prg.Species = lastSpecies;
prg.AdjustedScore = adjustedScore;
if (lastSpecies == null)
{
throw new EncogError(
"Have not defined a species yet");
}
lastSpecies.Add(prg);
count++;
}
}
}
else if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("EPL-OPCODES"))
{
foreach (String line in section.Lines)
{
IList<string> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
int args = int.Parse(cols[1]);
result.Context.Functions.AddExtension(name, args);
}
}
else if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("EPL-SYMBOLIC"))
{
bool first = true;
foreach (string line in section.Lines)
{
if (!first)
{
IList<String> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
String t = cols[1];
var vt = EPLValueType.Unknown;
if (string.Compare(t, "f", true) == 0)
{
vt = EPLValueType.FloatingType;
}
else if (string.Compare(t, "b", true) == 0)
{
vt = EPLValueType.BooleanType;
}
else if (string.Compare(t, "i", true) == 0)
{
vt = EPLValueType.IntType;
}
else if (string.Compare(t, "s", true) == 0)
{
vt = EPLValueType.StringType;
}
else if (string.Compare(t, "e", true) == 0)
{
vt = EPLValueType.EnumType;
}
int enumType = int.Parse(cols[2]);
int enumCount = int.Parse(cols[3]);
var mapping = new VariableMapping(
name, vt, enumType, enumCount);
if (mapping.Name.Length > 0)
{
result.Context.DefineVariable(mapping);
}
else
{
result.Context.Result = mapping;
}
}
else
{
first = false;
}
}
}
}
result.PopulationSize = count;
// set the best genome, should be the first genome in the first species
if (result.Species.Count > 0)
{
ISpecies species = result.Species[0];
if (species.Members.Count > 0)
{
result.BestGenome = species.Members[0];
}
// set the leaders
foreach (ISpecies sp in result.Species)
{
if (sp.Members.Count > 0)
{
sp.Leader = sp.Members[0];
}
}
}
return result;
}
/// <summary>
/// Determine the species.
/// </summary>
/// <param name="genomes">The genomes to speciate.</param>
private void SpeciateAndCalculateSpawnLevels(IList<IGenome> genomes)
{
double maxScore = 0;
if (genomes.Count == 0)
{
throw new EncogError("Can't speciate, the population is empty.");
}
IList<ISpecies> speciesCollection = _population.Species;
if (speciesCollection.Count == 0)
{
throw new EncogError("Can't speciate, there are no species.1");
}
// calculate compatibility between genomes and species
AdjustCompatibilityThreshold();
// assign genomes to species (if any exist)
foreach (IGenome g in genomes)
{
ISpecies currentSpecies = null;
IGenome genome = g;
if (!Double.IsNaN(genome.AdjustedScore)
&& !double.IsInfinity(genome.AdjustedScore))
{
maxScore = Math.Max(genome.AdjustedScore, maxScore);
}
foreach (ISpecies s in speciesCollection)
{
double compatibility = GetCompatibilityScore(genome,
s.Leader);
if (compatibility <= _compatibilityThreshold)
{
currentSpecies = s;
AddSpeciesMember(s, genome);
genome.Species = s;
break;
}
}
// if this genome did not fall into any existing species, create a
// new species
if (currentSpecies == null)
{
currentSpecies = new BasicSpecies(_population, genome);
_population.Species.Add(currentSpecies);
}
}
//
double totalSpeciesScore = speciesCollection.Sum(species => species.CalculateShare(_owner.ScoreFunction.ShouldMinimize, maxScore));
if (speciesCollection.Count == 0)
{
throw new EncogError("Can't speciate, there are no species.2");
}
if (totalSpeciesScore < EncogFramework.DefaultDoubleEqual)
{
// This should not happen much, or if it does, only in the
// beginning.
// All species scored zero. So they are all equally bad. Just divide
// up the right to produce offspring evenly.
DivideEven(speciesCollection);
}
else
{
// Divide up the number of offspring produced to the most fit
// species.
DivideByFittestSpecies(speciesCollection, totalSpeciesScore);
}
LevelOff();
}
private IPopulation initPopulation()
{
IPopulation result = new BasicPopulation(POPULATION_SIZE, null);
BasicSpecies defaultSpecies = new BasicSpecies();
defaultSpecies.Population = result;
for (int i = 0; i < POPULATION_SIZE; i++) {
IntegerArrayGenome genome = RandomGenome();
defaultSpecies.Members.Add(genome);
}
result.GenomeFactory = new IntegerArrayGenomeFactory(cities.Length);
result.Species.Add(defaultSpecies);
return result;
}
