/// <summary>
/// Create a link between two neuron id's. Create or find any necessary
/// innovation records.
/// </summary>
/// <param name="target">The target genome.</param>
/// <param name="neuron1Id">The id of the source neuron.</param>
/// <param name="neuron2Id">The id of the target neuron.</param>
/// <param name="weight">The weight of this new link.</param>
public void CreateLink(NEATGenome target, long neuron1Id,
long neuron2Id, double weight)
{
// first, does this link exist? (and if so, hopefully disabled,
// otherwise we have a problem)
foreach (NEATLinkGene linkGene in target.LinksChromosome)
{
if ((linkGene.FromNeuronId == neuron1Id) &&
(linkGene.ToNeuronId == neuron2Id))
{
// bring the link back, at the new weight
linkGene.Enabled = true;
linkGene.Weight = weight;
return;
}
}
// check to see if this innovation has already been tried
NEATInnovation innovation = ((NEATPopulation)target
.Population).Innovations.FindInnovation(neuron1Id,
neuron2Id);
// now create this link
var lg = new NEATLinkGene(neuron1Id, neuron2Id,
true, innovation.InnovationId, weight);
target.LinksChromosome.Add(lg);
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
var output = new EncogWriteHelper(os);
var pop = (NEATPopulation)obj;
output.AddSection("NEAT-POPULATION");
output.AddSubSection("CONFIG");
output.WriteProperty(PersistConst.ActivationCycles, pop.ActivationCycles);
if (pop.IsHyperNEAT)
{
output.WriteProperty(NEATPopulation.PropertyNEATActivation, TypeCppn);
}
else
{
IActivationFunction af = pop.ActivationFunctions.Contents[0].obj;
output.WriteProperty(NEATPopulation.PropertyNEATActivation, af);
}
output.WriteProperty(PersistConst.InputCount, pop.InputCount);
output.WriteProperty(PersistConst.OutputCount, pop.OutputCount);
output.WriteProperty(NEATPopulation.PropertyCycles, pop.ActivationCycles);
output.WriteProperty(NEATPopulation.PropertyPopulationSize, pop.PopulationSize);
output.WriteProperty(NEATPopulation.PropertySurvivalRate, pop.SurvivalRate);
output.AddSubSection("INNOVATIONS");
if (pop.Innovations != null)
{
foreach (string key in pop.Innovations.Innovations.Keys)
{
NEATInnovation innovation = pop.Innovations.Innovations[key];
output.AddColumn(key);
output.AddColumn(innovation.InnovationId);
output.AddColumn(innovation.NeuronId);
output.WriteLine();
}
}
output.AddSubSection("SPECIES");
// make sure the best species goes first
ISpecies bestSpecies = pop.DetermineBestSpecies();
if (bestSpecies != null)
{
SaveSpecies(output, bestSpecies);
}
// now write the other species, other than the best one
foreach (ISpecies species in pop.Species)
{
if (species != bestSpecies)
{
SaveSpecies(output, species);
}
}
output.Flush();
}
/// <inheritdoc/>
public override void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var target = ObtainGenome(parents, parentIndex, offspring,
offspringIndex);
var countTrysToFindOldLink = Owner.MaxTries;
var pop = ((NEATPopulation)target.Population);
// the link to split
NEATLinkGene splitLink = null;
int sizeBias = ((NEATGenome)parents[0]).InputCount
+ ((NEATGenome)parents[0]).OutputCount + 10;
// if there are not at least
int upperLimit;
if (target.LinksChromosome.Count < sizeBias)
{
upperLimit = target.NumGenes - 1
- (int)Math.Sqrt(target.NumGenes);
}
else
{
upperLimit = target.NumGenes - 1;
}
while ((countTrysToFindOldLink--) > 0)
{
// choose a link, use the square root to prefer the older links
int i = RangeRandomizer.RandomInt(0, upperLimit);
NEATLinkGene link = target.LinksChromosome[i];
// get the from neuron
long fromNeuron = link.FromNeuronId;
if ((link.Enabled) &&
(target.NeuronsChromosome
[GetElementPos(target, fromNeuron)]
.NeuronType != NEATNeuronType.Bias))
{
splitLink = link;
break;
}
}
if (splitLink == null)
{
return;
}
splitLink.Enabled = false;
long from = splitLink.FromNeuronId;
long to = splitLink.ToNeuronId;
NEATInnovation innovation = ((NEATPopulation)Owner.Population).Innovations
.FindInnovationSplit(from, to);
// add the splitting neuron
IActivationFunction af = ((NEATPopulation)Owner.Population).ActivationFunctions.Pick(new Random());
target.NeuronsChromosome.Add(
new NEATNeuronGene(NEATNeuronType.Hidden, af, innovation
.NeuronId, innovation.InnovationId));
// add the other two sides of the link
CreateLink(target, from, innovation.NeuronId,
splitLink.Weight);
CreateLink(target, innovation.NeuronId, to, pop.WeightRange);
target.SortGenes();
}
/// <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);
}
