/// <summary>
/// Construct a genome by copying another.
/// </summary>
/// <param name="other">The other genome.</param>
public NEATGenome(NEATGenome other)
{
NetworkDepth = other.NetworkDepth;
Population = other.Population;
Score = other.Score;
AdjustedScore = other.AdjustedScore;
InputCount = other.InputCount;
OutputCount = other.OutputCount;
Species = other.Species;
// copy neurons
foreach (NEATNeuronGene oldGene in other.NeuronsChromosome)
{
var newGene = new NEATNeuronGene(oldGene);
_neuronsList.Add(newGene);
}
// copy links
foreach (var oldGene in other.LinksChromosome)
{
var newGene = new NEATLinkGene(
oldGene.FromNeuronId, oldGene.ToNeuronId,
oldGene.Enabled, oldGene.InnovationId,
oldGene.Weight);
_linksList.Add(newGene);
}
}
/// <summary>
/// Copy from another gene.
/// </summary>
/// <param name="gene">The other gene.</param>
public void Copy(NEATLinkGene gene)
{
NEATLinkGene other = gene;
Enabled = other.Enabled;
FromNeuronId = other.FromNeuronId;
ToNeuronId = other.ToNeuronId;
InnovationId = other.InnovationId;
Weight = other.Weight;
}
/// <summary>
/// Create a new genome with the specified connection density. This
/// constructor is typically used to create the initial population.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="pop">The population.</param>
/// <param name="inputCount">The input count.</param>
/// <param name="outputCount">The output count.</param>
/// <param name="connectionDensity">The connection density.</param>
public NEATGenome(EncogRandom rnd, NEATPopulation pop,
int inputCount, int outputCount,
double connectionDensity)
{
AdjustedScore = 0;
InputCount = inputCount;
OutputCount = outputCount;
// get the activation function
IActivationFunction af = pop.ActivationFunctions.PickFirst();
// first bias
int innovationId = 0;
var biasGene = new NEATNeuronGene(NEATNeuronType.Bias, af,
inputCount, innovationId++);
_neuronsList.Add(biasGene);
// then inputs
for (var i = 0; i < inputCount; i++)
{
var gene = new NEATNeuronGene(NEATNeuronType.Input, af,
i, innovationId++);
_neuronsList.Add(gene);
}
// then outputs
for (int i = 0; i < outputCount; i++)
{
var gene = new NEATNeuronGene(NEATNeuronType.Output, af,
i + inputCount + 1, innovationId++);
_neuronsList.Add(gene);
}
// and now links
for (var i = 0; i < inputCount + 1; i++)
{
for (var j = 0; j < outputCount; j++)
{
// make sure we have at least one connection
if (_linksList.Count < 1 ||
rnd.NextDouble() < connectionDensity)
{
long fromId = this._neuronsList[i].Id;
long toId = this._neuronsList[inputCount + j + 1].Id;
double w = RangeRandomizer.Randomize(rnd, -pop.WeightRange, pop.WeightRange);
var gene = new NEATLinkGene(fromId, toId, true,
innovationId++, w);
_linksList.Add(gene);
}
}
}
}
public NEATGenome(NEATGenome other)
{
goto Label_0182;
Label_0017:
this.inputCount = other.inputCount;
this.outputCount = other.outputCount;
Label_002F:
this.speciesID = other.speciesID;
foreach (IGene gene in other.Neurons.Genes)
{
NEATNeuronGene gene2 = (NEATNeuronGene) gene;
if (0xff != 0)
{
NEATNeuronGene gene3 = new NEATNeuronGene(gene2.NeuronType, gene2.Id, gene2.SplitY, gene2.SplitX, gene2.Recurrent, gene2.ActivationResponse);
this.Neurons.Add(gene3);
}
}
foreach (IGene gene4 in other.Links.Genes)
{
NEATLinkGene gene5 = (NEATLinkGene) gene4;
NEATLinkGene gene6 = new NEATLinkGene((long) gene5.FromNeuronID, (long) gene5.ToNeuronID, gene5.Enabled, gene5.InnovationId, gene5.Weight, gene5.Recurrent);
this.Links.Add(gene6);
}
return;
Label_0182:
this.neuronsChromosome = new Chromosome();
this.linksChromosome = new Chromosome();
base.GA = other.GA;
base.Chromosomes.Add(this.neuronsChromosome);
if (0 != 0)
{
goto Label_002F;
}
base.Chromosomes.Add(this.linksChromosome);
base.GenomeID = other.GenomeID;
this.networkDepth = other.networkDepth;
base.Population = other.Population;
base.Score = other.Score;
if (0x7fffffff == 0)
{
goto Label_0017;
}
base.AdjustedScore = other.AdjustedScore;
if (0 == 0)
{
base.AmountToSpawn = other.AmountToSpawn;
goto Label_0017;
}
goto Label_0182;
}
/// <summary>
/// Construct a genome by copying another.
/// </summary>
///
/// <param name="other">The other genome.</param>
public NEATGenome(NEATGenome other)
{
neuronsChromosome = new Chromosome();
linksChromosome = new Chromosome();
GA = other.GA;
Chromosomes.Add(neuronsChromosome);
Chromosomes.Add(linksChromosome);
GenomeID = other.GenomeID;
networkDepth = other.networkDepth;
Population = other.Population;
Score = other.Score;
AdjustedScore = other.AdjustedScore;
AmountToSpawn = other.AmountToSpawn;
inputCount = other.inputCount;
outputCount = other.outputCount;
speciesID = other.speciesID;
// copy neurons
foreach (IGene gene in other.Neurons.Genes)
{
var oldGene = (NEATNeuronGene)gene;
var newGene = new NEATNeuronGene(
oldGene.NeuronType, oldGene.Id,
oldGene.SplitY, oldGene.SplitX,
oldGene.Recurrent, oldGene.ActivationResponse);
Neurons.Add(newGene);
}
// copy links
foreach (IGene gene_0 in other.Links.Genes)
{
var oldGene_1 = (NEATLinkGene)gene_0;
var newGene_2 = new NEATLinkGene(
oldGene_1.FromNeuronID, oldGene_1.ToNeuronID,
oldGene_1.Enabled, oldGene_1.InnovationId,
oldGene_1.Weight, oldGene_1.Recurrent);
Links.Add(newGene_2);
}
}
/// <summary>
/// Copy from another gene.
/// </summary>
/// <param name="gene">The other gene.</param>
public void Copy(NEATLinkGene gene)
{
NEATLinkGene other = gene;
Enabled = other.Enabled;
FromNeuronId = other.FromNeuronId;
ToNeuronId = other.ToNeuronId;
InnovationId = other.InnovationId;
Weight = other.Weight;
}
internal void AddNeuron(double mutationRate, int numTrysToFindOldLink)
{
int num;
NEATLinkGene gene;
int num2;
int num3;
int num4;
NEATLinkGene gene2;
long num5;
double weight;
long fromNeuronID;
long toNeuronID;
NEATNeuronGene gene3;
NEATNeuronGene gene4;
double num9;
double num10;
NEATInnovation innovation;
long num11;
long num12;
long num13;
long num14;
long neuronID;
NEATInnovation innovation2;
NEATInnovation innovation3;
NEATLinkGene gene7;
NEATLinkGene gene8;
NEATNeuronGene gene9;
if (ThreadSafeRandom.NextDouble() <= mutationRate)
{
goto Label_05EE;
}
return;
Label_0043:
this.linksChromosome.Add(gene7);
this.linksChromosome.Add(gene8);
Label_005D:
gene9 = new NEATNeuronGene(NEATNeuronType.Hidden, neuronID, num9, num10);
this.neuronsChromosome.Add(gene9);
if ((((uint) num2) & 0) == 0)
{
if ((((uint) num14) + ((uint) num14)) <= uint.MaxValue)
{
return;
}
if ((((uint) num10) - ((uint) num12)) >= 0)
{
goto Label_0131;
}
goto Label_00EC;
}
Label_008C:
if ((((uint) num11) & 0) != 0)
{
goto Label_0043;
}
Label_00A9:
throw new NeuralNetworkError("NEAT Error");
Label_00EC:
innovation3 = ((NEATTraining) base.GA).Innovations.CheckInnovation(neuronID, toNeuronID, NEATInnovationType.NewLink);
if ((((uint) numTrysToFindOldLink) - ((uint) num4)) < 0)
{
goto Label_0332;
}
if (innovation2 != null)
{
if (innovation3 == null)
{
if ((((uint) num) & 0) != 0)
{
return;
}
goto Label_008C;
}
gene7 = new NEATLinkGene(fromNeuronID, neuronID, true, innovation2.InnovationID, 1.0, false);
gene8 = new NEATLinkGene(neuronID, toNeuronID, true, innovation3.InnovationID, weight, false);
goto Label_0043;
}
goto Label_00A9;
Label_0131:
if (innovation != null)
{
neuronID = innovation.NeuronID;
innovation2 = ((NEATTraining) base.GA).Innovations.CheckInnovation(fromNeuronID, neuronID, NEATInnovationType.NewLink);
goto Label_00EC;
}
do
{
num12 = ((NEATTraining) base.GA).Innovations.CreateNewInnovation(fromNeuronID, toNeuronID, NEATInnovationType.NewNeuron, NEATNeuronType.Hidden, num10, num9);
this.neuronsChromosome.Add(new NEATNeuronGene(NEATNeuronType.Hidden, num12, num9, num10));
num13 = base.GA.Population.AssignInnovationID();
if ((((uint) fromNeuronID) | 15) == 0)
{
goto Label_0534;
}
((NEATTraining) base.GA).Innovations.CreateNewInnovation(fromNeuronID, num12, NEATInnovationType.NewLink);
NEATLinkGene gene5 = new NEATLinkGene(fromNeuronID, num12, true, num13, 1.0, false);
this.linksChromosome.Add(gene5);
if (((uint) num4) <= uint.MaxValue)
{
num14 = base.GA.Population.AssignInnovationID();
((NEATTraining) base.GA).Innovations.CreateNewInnovation(num12, toNeuronID, NEATInnovationType.NewLink);
NEATLinkGene gene6 = new NEATLinkGene(num12, toNeuronID, true, num14, weight, false);
this.linksChromosome.Add(gene6);
return;
}
}
while ((((uint) num14) + ((uint) num10)) > uint.MaxValue);
Label_029B:
if (((uint) num) > uint.MaxValue)
{
goto Label_05EE;
}
goto Label_0131;
Label_0332:
num10 = (gene3.SplitX + gene4.SplitX) / 2.0;
innovation = ((NEATTraining) base.GA).Innovations.CheckInnovation(fromNeuronID, toNeuronID, NEATInnovationType.NewNeuron);
if ((((uint) num9) <= uint.MaxValue) && (innovation == null))
{
if (((uint) num5) > uint.MaxValue)
{
goto Label_00EC;
}
if ((((uint) weight) & 0) != 0)
{
goto Label_005D;
}
if (((uint) num) >= 0)
{
if ((((uint) num) + ((uint) mutationRate)) <= uint.MaxValue)
{
if ((((uint) num9) - ((uint) weight)) < 0)
{
return;
}
goto Label_029B;
}
goto Label_04BE;
}
}
else
{
num11 = innovation.NeuronID;
if (this.AlreadyHaveThisNeuronID(num11))
{
innovation = null;
}
goto Label_0131;
}
Label_03A3:
if ((((uint) neuronID) - ((uint) num10)) > uint.MaxValue)
{
if (((uint) num5) >= 0)
{
goto Label_04BE;
}
if ((((uint) num5) & 0) == 0)
{
goto Label_04DC;
}
goto Label_0534;
}
gene4 = (NEATNeuronGene) this.Neurons.Get(this.GetElementPos(toNeuronID));
num9 = (gene3.SplitY + gene4.SplitY) / 2.0;
goto Label_0332;
Label_03FD:
gene3 = (NEATNeuronGene) this.Neurons.Get(this.GetElementPos(fromNeuronID));
if ((((uint) num10) - ((uint) num9)) >= 0)
{
goto Label_03A3;
}
if (((uint) num5) <= uint.MaxValue)
{
return;
}
Label_0441:
gene.Enabled = false;
weight = gene.Weight;
fromNeuronID = gene.FromNeuronID;
toNeuronID = gene.ToNeuronID;
if (((uint) num13) >= 0)
{
goto Label_03FD;
}
return;
Label_0475:
if (gene == null)
{
return;
}
if ((((uint) num9) - ((uint) weight)) >= 0)
{
goto Label_0441;
}
goto Label_03FD;
Label_0497:
if (num-- > 0)
{
goto Label_0534;
}
if ((((uint) num10) | 3) != 0)
{
goto Label_0475;
}
return;
Label_04BE:
if (((uint) num12) <= uint.MaxValue)
{
goto Label_0497;
}
Label_04DC:
if (((NEATNeuronGene) this.Neurons.Get(this.GetElementPos(num5))).NeuronType == NEATNeuronType.Bias)
{
goto Label_0497;
}
gene = gene2;
goto Label_0475;
Label_0534:
num4 = RangeRandomizer.RandomInt(0, num3);
gene2 = (NEATLinkGene) this.linksChromosome.Get(num4);
num5 = gene2.FromNeuronID;
if (!gene2.Enabled || gene2.Recurrent)
{
goto Label_0497;
}
goto Label_04DC;
Label_0575:
num3 = this.NumGenes - 1;
goto Label_0497;
Label_05EE:
num = numTrysToFindOldLink;
if ((((uint) num9) + ((uint) num13)) > uint.MaxValue)
{
if ((((uint) num4) - ((uint) num3)) > uint.MaxValue)
{
goto Label_00A9;
}
goto Label_0575;
}
gene = null;
num2 = (this.inputCount + this.outputCount) + 10;
if ((((uint) toNeuronID) & 0) != 0)
{
goto Label_04DC;
}
if (this.linksChromosome.Size() >= num2)
{
goto Label_0575;
}
num3 = (this.NumGenes - 1) - ((int) Math.Sqrt((double) this.NumGenes));
goto Label_0497;
}
/// <summary>
/// Construct from another gene.
/// </summary>
/// <param name="other">The other gene.</param>
public NEATLinkGene(NEATLinkGene other)
{
Copy(other);
}
/// <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;
}
/// <summary>
/// Construct from another gene.
/// </summary>
/// <param name="other">The other gene.</param>
public NEATLinkGene(NEATLinkGene other)
{
Copy(other);
}
/// <summary>
/// Perform a cross over.
/// </summary>
/// <param name="mom">The mother genome.</param>
/// <param name="dad">The father genome.</param>
/// <returns></returns>
public new NEATGenome Crossover(NEATGenome mom, NEATGenome dad)
{
NEATParent best;
// first determine who is more fit, the mother or the father?
if (mom.Score == dad.Score)
{
if (mom.NumGenes == dad.NumGenes)
{
if (ThreadSafeRandom.NextDouble() > 0)
{
best = NEATParent.Mom;
}
else
{
best = NEATParent.Dad;
}
}
else
{
if (mom.NumGenes < dad.NumGenes)
{
best = NEATParent.Mom;
}
else
{
best = NEATParent.Dad;
}
}
}
else
{
if (Comparator.IsBetterThan(mom.Score, dad.Score))
{
best = NEATParent.Mom;
}
else
{
best = NEATParent.Dad;
}
}
var babyNeurons = new Chromosome();
var babyGenes = new Chromosome();
var vecNeurons = new List <long>();
int curMom = 0;
int curDad = 0;
NEATLinkGene momGene;
NEATLinkGene dadGene;
NEATLinkGene selectedGene = null;
while ((curMom < mom.NumGenes) || (curDad < dad.NumGenes))
{
if (curMom < mom.NumGenes)
{
momGene = (NEATLinkGene)mom.Links.Get(curMom);
}
else
{
momGene = null;
}
if (curDad < dad.NumGenes)
{
dadGene = (NEATLinkGene)dad.Links.Get(curDad);
}
else
{
dadGene = null;
}
if ((momGene == null) && (dadGene != null))
{
if (best == NEATParent.Dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if ((dadGene == null) && (momGene != null))
{
if (best == NEATParent.Mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (momGene.InnovationId < dadGene.InnovationId)
{
if (best == NEATParent.Mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (dadGene.InnovationId < momGene.InnovationId)
{
if (best == NEATParent.Dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if (dadGene.InnovationId == momGene.InnovationId)
{
if (ThreadSafeRandom.NextDouble() < 0.5f)
{
selectedGene = momGene;
}
else
{
selectedGene = dadGene;
}
curMom++;
curDad++;
}
if (babyGenes.Size() == 0)
{
babyGenes.Add(selectedGene);
}
else
{
if (((NEATLinkGene)babyGenes.Get(babyGenes.Size() - 1))
.InnovationId != selectedGene.InnovationId)
{
babyGenes.Add(selectedGene);
}
}
// Check if we already have the nodes referred to in SelectedGene.
// If not, they need to be added.
AddNeuronID(selectedGene.FromNeuronID, vecNeurons);
AddNeuronID(selectedGene.ToNeuronID, vecNeurons);
} // end while
// now create the required nodes. First sort them into order
vecNeurons.Sort();
for (int i = 0; i < vecNeurons.Count; i++)
{
babyNeurons.Add(Innovations.CreateNeuronFromID(
vecNeurons[i]));
}
// finally, create the genome
var babyGenome = new NEATGenome(Population
.AssignGenomeID(), babyNeurons, babyGenes, mom.InputCount,
mom.OutputCount);
babyGenome.GA = this;
babyGenome.Population = Population;
return(babyGenome);
}
/// <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;
}
/// <summary>
/// Mutate the genome by adding a neuron.
/// </summary>
///
/// <param name="mutationRate">The mutation rate.</param>
/// <param name="numTrysToFindOldLink">The number of tries to find a link to split.</param>
internal void AddNeuron(double mutationRate, int numTrysToFindOldLink)
{
// should we add a neuron?
if (ThreadSafeRandom.NextDouble() > mutationRate)
{
return;
}
int countTrysToFindOldLink = numTrysToFindOldLink;
// the link to split
NEATLinkGene splitLink = null;
int sizeBias = inputCount + outputCount + 10;
// if there are not at least
int upperLimit;
if (linksChromosome.Size() < sizeBias)
{
upperLimit = NumGenes - 1 - (int)Math.Sqrt(NumGenes);
}
else
{
upperLimit = NumGenes - 1;
}
while ((countTrysToFindOldLink--) > 0)
{
// choose a link, use the square root to prefer the older links
int i = RangeRandomizer.RandomInt(0, upperLimit);
var link = (NEATLinkGene)linksChromosome
.Get(i);
// get the from neuron
long fromNeuron = link.FromNeuronID;
if ((link.Enabled) &&
(!link.Recurrent) &&
(((NEATNeuronGene)Neurons.Get(
GetElementPos(fromNeuron))).NeuronType != NEATNeuronType.Bias))
{
splitLink = link;
break;
}
}
if (splitLink == null)
{
return;
}
splitLink.Enabled = false;
double originalWeight = splitLink.Weight;
long from = splitLink.FromNeuronID;
long to = splitLink.ToNeuronID;
var fromGene = (NEATNeuronGene)Neurons.Get(
GetElementPos(from));
var toGene = (NEATNeuronGene)Neurons.Get(
GetElementPos(to));
double newDepth = (fromGene.SplitY + toGene.SplitY) / 2;
double newWidth = (fromGene.SplitX + toGene.SplitX) / 2;
// has this innovation already been tried?
NEATInnovation innovation = ((NEATTraining)GA).Innovations.CheckInnovation(from, to,
NEATInnovationType
.NewNeuron);
// prevent chaining
if (innovation != null)
{
long neuronID = innovation.NeuronID;
if (AlreadyHaveThisNeuronID(neuronID))
{
innovation = null;
}
}
if (innovation == null)
{
// this innovation has not been tried, create it
long newNeuronID = ((NEATTraining)GA).Innovations.CreateNewInnovation(from, to,
NEATInnovationType.
NewNeuron,
NEATNeuronType.
Hidden,
newWidth, newDepth);
neuronsChromosome.Add(new NEATNeuronGene(
NEATNeuronType.Hidden, newNeuronID, newDepth, newWidth));
// add the first link
long link1ID = (GA).Population.AssignInnovationID();
((NEATTraining)GA).Innovations
.CreateNewInnovation(from, newNeuronID,
NEATInnovationType.NewLink);
var link1 = new NEATLinkGene(from, newNeuronID,
true, link1ID, 1.0d, false);
linksChromosome.Add(link1);
// add the second link
long link2ID = (GA).Population.AssignInnovationID();
((NEATTraining)GA).Innovations
.CreateNewInnovation(newNeuronID, to,
NEATInnovationType.NewLink);
var link2 = new NEATLinkGene(newNeuronID, to, true,
link2ID, originalWeight, false);
linksChromosome.Add(link2);
}
else
{
// existing innovation
long newNeuronID_0 = innovation.NeuronID;
NEATInnovation innovationLink1 = ((NEATTraining)GA).Innovations.CheckInnovation(from,
newNeuronID_0,
NEATInnovationType
.
NewLink);
NEATInnovation innovationLink2 =
((NEATTraining)GA).Innovations.CheckInnovation(newNeuronID_0, to,
NEATInnovationType.NewLink);
if ((innovationLink1 == null) || (innovationLink2 == null))
{
throw new NeuralNetworkError("NEAT Error");
}
var link1_1 = new NEATLinkGene(from, newNeuronID_0,
true, innovationLink1.InnovationID, 1.0d, false);
var link2_2 = new NEATLinkGene(newNeuronID_0, to, true,
innovationLink2.InnovationID, originalWeight, false);
linksChromosome.Add(link1_1);
linksChromosome.Add(link2_2);
var newNeuron = new NEATNeuronGene(
NEATNeuronType.Hidden, newNeuronID_0, newDepth, newWidth);
neuronsChromosome.Add(newNeuron);
}
return;
}
/// <summary>
/// Create a new genome with the specified connection density. This
/// constructor is typically used to create the initial population.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="pop">The population.</param>
/// <param name="inputCount">The input count.</param>
/// <param name="outputCount">The output count.</param>
/// <param name="connectionDensity">The connection density.</param>
public NEATGenome(EncogRandom rnd, NEATPopulation pop,
int inputCount, int outputCount,
double connectionDensity)
{
AdjustedScore = 0;
InputCount = inputCount;
OutputCount = outputCount;
// get the activation function
IActivationFunction af = pop.ActivationFunctions.PickFirst();
// first bias
int innovationId = 0;
var biasGene = new NEATNeuronGene(NEATNeuronType.Bias, af,
inputCount, innovationId++);
_neuronsList.Add(biasGene);
// then inputs
for (var i = 0; i < inputCount; i++)
{
var gene = new NEATNeuronGene(NEATNeuronType.Input, af,
i, innovationId++);
_neuronsList.Add(gene);
}
// then outputs
for (int i = 0; i < outputCount; i++)
{
var gene = new NEATNeuronGene(NEATNeuronType.Output, af,
i + inputCount + 1, innovationId++);
_neuronsList.Add(gene);
}
// and now links
for (var i = 0; i < inputCount + 1; i++)
{
for (var j = 0; j < outputCount; j++)
{
// make sure we have at least one connection
if (_linksList.Count < 1
|| rnd.NextDouble() < connectionDensity)
{
long fromId = this._neuronsList[i].Id;
long toId = this._neuronsList[inputCount + j + 1].Id;
double w = RangeRandomizer.Randomize(rnd, -pop.WeightRange, pop.WeightRange);
var gene = new NEATLinkGene(fromId, toId, true,
innovationId++, w);
_linksList.Add(gene);
}
}
}
}
internal void AddLink(double mutationRate, double chanceOfLooped, int numTrysToFindLoop, int numTrysToAddLink)
{
int num;
int num2;
long id;
long num4;
bool flag;
NEATNeuronGene gene;
NEATNeuronGene gene2;
NEATNeuronGene gene3;
NEATInnovation innovation;
NEATNeuronGene gene4;
long num5;
NEATLinkGene gene5;
if (ThreadSafeRandom.NextDouble() <= mutationRate)
{
num = numTrysToFindLoop;
if ((((uint) num5) - ((uint) chanceOfLooped)) > uint.MaxValue)
{
if ((((uint) num4) + ((uint) num2)) < 0)
{
goto Label_01D4;
}
if ((((uint) numTrysToAddLink) - ((uint) num2)) < 0)
{
goto Label_0327;
}
goto Label_01BA;
}
num2 = numTrysToFindLoop;
id = -1L;
num4 = -1L;
flag = false;
goto Label_01EE;
}
return;
Label_0057:
if (innovation == null)
{
((NEATTraining) base.GA).Innovations.CreateNewInnovation(id, num4, NEATInnovationType.NewLink);
num5 = base.GA.Population.AssignInnovationID();
if (15 != 0)
{
gene5 = new NEATLinkGene(id, num4, true, num5, RangeRandomizer.Randomize(-1.0, 1.0), flag);
if ((((uint) num) + ((uint) num5)) > uint.MaxValue)
{
goto Label_00B2;
}
}
}
else
{
NEATLinkGene gene6 = new NEATLinkGene(id, num4, true, innovation.InnovationID, RangeRandomizer.Randomize(-1.0, 1.0), flag);
this.linksChromosome.Add(gene6);
if (3 != 0)
{
return;
}
goto Label_0123;
}
Label_0063:
this.linksChromosome.Add(gene5);
return;
Label_00B2:
gene4 = (NEATNeuronGene) this.neuronsChromosome.Get(this.GetElementPos(id));
if (gene4.SplitY > gene4.SplitY)
{
flag = true;
}
goto Label_0057;
Label_011E:
if (id < 0L)
{
return;
}
Label_0123:
if (num4 >= 0L)
{
innovation = ((NEATTraining) base.GA).Innovations.CheckInnovation(id, id, NEATInnovationType.NewLink);
if ((((uint) numTrysToAddLink) + ((uint) flag)) <= uint.MaxValue)
{
goto Label_00B2;
}
goto Label_0063;
}
return;
Label_0198:
if (num2-- > 0)
{
gene2 = this.ChooseRandomNeuron(true);
gene3 = this.ChooseRandomNeuron(false);
goto Label_01CA;
}
if ((((uint) mutationRate) + ((uint) chanceOfLooped)) < 0)
{
if ((((uint) id) | 4) == 0)
{
goto Label_0057;
}
goto Label_01EE;
}
goto Label_011E;
Label_01BA:
if (gene3.NeuronType != NEATNeuronType.Bias)
{
id = gene2.Id;
if ((((uint) mutationRate) + ((uint) numTrysToAddLink)) > uint.MaxValue)
{
goto Label_01CA;
}
num4 = gene3.Id;
if ((((uint) num5) + ((uint) num4)) > uint.MaxValue)
{
goto Label_0311;
}
if ((((uint) num) - ((uint) id)) < 0)
{
return;
}
if (0 == 0)
{
goto Label_011E;
}
goto Label_02DF;
}
goto Label_0198;
Label_01CA:
if (this.IsDuplicateLink(id, num4))
{
goto Label_0198;
}
Label_01D4:
if (((((uint) num5) - ((uint) chanceOfLooped)) <= uint.MaxValue) && (gene2.Id != gene3.Id))
{
goto Label_01BA;
}
goto Label_0198;
Label_01EE:
if (ThreadSafeRandom.NextDouble() >= chanceOfLooped)
{
goto Label_0198;
}
Label_02DF:
if (num-- > 0)
{
gene = this.ChooseRandomNeuron(false);
}
else
{
goto Label_011E;
}
Label_0311:
if ((!gene.Recurrent && (gene.NeuronType != NEATNeuronType.Bias)) && (gene.NeuronType != NEATNeuronType.Input))
{
if (-1 == 0)
{
goto Label_032F;
}
id = gene.Id;
}
else
{
goto Label_02DF;
}
Label_0327:
num4 = gene.Id;
Label_032F:
gene.Recurrent = true;
flag = true;
num = 0;
goto Label_02DF;
}
/// <summary>
/// Mutate the genome by adding a link to this genome.
/// </summary>
///
/// <param name="mutationRate">The mutation rate.</param>
/// <param name="chanceOfLooped">The chance of a self-connected neuron.</param>
/// <param name="numTrysToFindLoop">The number of tries to find a loop.</param>
/// <param name="numTrysToAddLink">The number of tries to add a link.</param>
internal void AddLink(double mutationRate, double chanceOfLooped,
int numTrysToFindLoop, int numTrysToAddLink)
{
// should we even add the link
if (ThreadSafeRandom.NextDouble() > mutationRate)
{
return;
}
int countTrysToFindLoop = numTrysToFindLoop;
int countTrysToAddLink = numTrysToFindLoop;
// the link will be between these two neurons
long neuron1ID = -1;
long neuron2ID = -1;
bool recurrent = false;
// a self-connected loop?
if (ThreadSafeRandom.NextDouble() < chanceOfLooped)
{
// try to find(randomly) a neuron to add a self-connected link to
while ((countTrysToFindLoop--) > 0)
{
NEATNeuronGene neuronGene = ChooseRandomNeuron(false);
// no self-links on input or bias neurons
if (!neuronGene.Recurrent &&
(neuronGene.NeuronType != NEATNeuronType.Bias) &&
(neuronGene.NeuronType != NEATNeuronType.Input))
{
neuron1ID = neuronGene.Id;
neuron2ID = neuronGene.Id;
neuronGene.Recurrent = true;
recurrent = true;
countTrysToFindLoop = 0;
}
}
}
else
{
// try to add a regular link
while ((countTrysToAddLink--) > 0)
{
NEATNeuronGene neuron1 = ChooseRandomNeuron(true);
NEATNeuronGene neuron2 = ChooseRandomNeuron(false);
if (!IsDuplicateLink(neuron1ID, neuron2ID) &&
(neuron1.Id != neuron2.Id) &&
(neuron2.NeuronType != NEATNeuronType.Bias))
{
neuron1ID = neuron1.Id;
neuron2ID = neuron2.Id;
break;
}
}
}
// did we fail to find a link
if ((neuron1ID < 0) || (neuron2ID < 0))
{
return;
}
// check to see if this innovation has already been tried
NEATInnovation innovation = ((NEATTraining)GA).Innovations.CheckInnovation(neuron1ID,
neuron1ID,
NEATInnovationType
.NewLink);
// see if this is a recurrent(backwards) link
var neuronGene_0 = (NEATNeuronGene)neuronsChromosome
.Get(GetElementPos(neuron1ID));
if (neuronGene_0.SplitY > neuronGene_0.SplitY)
{
recurrent = true;
}
// is this a new innovation?
if (innovation == null)
{
// new innovation
((NEATTraining)GA).Innovations
.CreateNewInnovation(neuron1ID, neuron2ID,
NEATInnovationType.NewLink);
long id2 = GA.Population.AssignInnovationID();
var linkGene = new NEATLinkGene(neuron1ID,
neuron2ID, true, id2, RangeRandomizer.Randomize(-1, 1),
recurrent);
linksChromosome.Add(linkGene);
}
else
{
// existing innovation
var linkGene_1 = new NEATLinkGene(neuron1ID,
neuron2ID, true, innovation.InnovationID,
RangeRandomizer.Randomize(-1, 1), recurrent);
linksChromosome.Add(linkGene_1);
}
}
/// <summary>
/// Mutate the genome by adding a neuron.
/// </summary>
///
/// <param name="mutationRate">The mutation rate.</param>
/// <param name="numTrysToFindOldLink">The number of tries to find a link to split.</param>
internal void AddNeuron(double mutationRate, int numTrysToFindOldLink)
{
// should we add a neuron?
if (ThreadSafeRandom.NextDouble() > mutationRate)
{
return;
}
int countTrysToFindOldLink = numTrysToFindOldLink;
// the link to split
NEATLinkGene splitLink = null;
int sizeBias = inputCount + outputCount + 10;
// if there are not at least
int upperLimit;
if (linksChromosome.Size() < sizeBias)
{
upperLimit = NumGenes - 1 - (int) Math.Sqrt(NumGenes);
}
else
{
upperLimit = NumGenes - 1;
}
while ((countTrysToFindOldLink--) > 0)
{
// choose a link, use the square root to prefer the older links
int i = RangeRandomizer.RandomInt(0, upperLimit);
var link = (NEATLinkGene) linksChromosome
.Get(i);
// get the from neuron
long fromNeuron = link.FromNeuronID;
if ((link.Enabled)
&& (!link.Recurrent)
&& (((NEATNeuronGene) Neurons.Get(
GetElementPos(fromNeuron))).NeuronType != NEATNeuronType.Bias))
{
splitLink = link;
break;
}
}
if (splitLink == null)
{
return;
}
splitLink.Enabled = false;
double originalWeight = splitLink.Weight;
long from = splitLink.FromNeuronID;
long to = splitLink.ToNeuronID;
var fromGene = (NEATNeuronGene) Neurons.Get(
GetElementPos(from));
var toGene = (NEATNeuronGene) Neurons.Get(
GetElementPos(to));
double newDepth = (fromGene.SplitY + toGene.SplitY)/2;
double newWidth = (fromGene.SplitX + toGene.SplitX)/2;
// has this innovation already been tried?
NEATInnovation innovation = ((NEATTraining) GA).Innovations.CheckInnovation(from, to,
NEATInnovationType
.NewNeuron);
// prevent chaining
if (innovation != null)
{
long neuronID = innovation.NeuronID;
if (AlreadyHaveThisNeuronID(neuronID))
{
innovation = null;
}
}
if (innovation == null)
{
// this innovation has not been tried, create it
long newNeuronID = ((NEATTraining) GA).Innovations.CreateNewInnovation(from, to,
NEATInnovationType.
NewNeuron,
NEATNeuronType.
Hidden,
newWidth, newDepth);
neuronsChromosome.Add(new NEATNeuronGene(
NEATNeuronType.Hidden, newNeuronID, newDepth, newWidth));
// add the first link
long link1ID = (GA).Population.AssignInnovationID();
((NEATTraining) GA).Innovations
.CreateNewInnovation(from, newNeuronID,
NEATInnovationType.NewLink);
var link1 = new NEATLinkGene(from, newNeuronID,
true, link1ID, 1.0d, false);
linksChromosome.Add(link1);
// add the second link
long link2ID = (GA).Population.AssignInnovationID();
((NEATTraining) GA).Innovations
.CreateNewInnovation(newNeuronID, to,
NEATInnovationType.NewLink);
var link2 = new NEATLinkGene(newNeuronID, to, true,
link2ID, originalWeight, false);
linksChromosome.Add(link2);
}
else
{
// existing innovation
long newNeuronID_0 = innovation.NeuronID;
NEATInnovation innovationLink1 = ((NEATTraining) GA).Innovations.CheckInnovation(from,
newNeuronID_0,
NEATInnovationType
.
NewLink);
NEATInnovation innovationLink2 =
((NEATTraining) GA).Innovations.CheckInnovation(newNeuronID_0, to,
NEATInnovationType.NewLink);
if ((innovationLink1 == null) || (innovationLink2 == null))
{
throw new NeuralNetworkError("NEAT Error");
}
var link1_1 = new NEATLinkGene(from, newNeuronID_0,
true, innovationLink1.InnovationID, 1.0d, false);
var link2_2 = new NEATLinkGene(newNeuronID_0, to, true,
innovationLink2.InnovationID, originalWeight, false);
linksChromosome.Add(link1_1);
linksChromosome.Add(link2_2);
var newNeuron = new NEATNeuronGene(
NEATNeuronType.Hidden, newNeuronID_0, newDepth, newWidth);
neuronsChromosome.Add(newNeuron);
}
return;
}
/// <summary>
/// Mutate the genome by adding a link to this genome.
/// </summary>
///
/// <param name="mutationRate">The mutation rate.</param>
/// <param name="chanceOfLooped">The chance of a self-connected neuron.</param>
/// <param name="numTrysToFindLoop">The number of tries to find a loop.</param>
/// <param name="numTrysToAddLink">The number of tries to add a link.</param>
internal void AddLink(double mutationRate, double chanceOfLooped,
int numTrysToFindLoop, int numTrysToAddLink)
{
// should we even add the link
if (ThreadSafeRandom.NextDouble() > mutationRate)
{
return;
}
int countTrysToFindLoop = numTrysToFindLoop;
int countTrysToAddLink = numTrysToFindLoop;
// the link will be between these two neurons
long neuron1ID = -1;
long neuron2ID = -1;
bool recurrent = false;
// a self-connected loop?
if (ThreadSafeRandom.NextDouble() < chanceOfLooped)
{
// try to find(randomly) a neuron to add a self-connected link to
while ((countTrysToFindLoop--) > 0)
{
NEATNeuronGene neuronGene = ChooseRandomNeuron(false);
// no self-links on input or bias neurons
if (!neuronGene.Recurrent
&& (neuronGene.NeuronType != NEATNeuronType.Bias)
&& (neuronGene.NeuronType != NEATNeuronType.Input))
{
neuron1ID = neuronGene.Id;
neuron2ID = neuronGene.Id;
neuronGene.Recurrent = true;
recurrent = true;
countTrysToFindLoop = 0;
}
}
}
else
{
// try to add a regular link
while ((countTrysToAddLink--) > 0)
{
NEATNeuronGene neuron1 = ChooseRandomNeuron(true);
NEATNeuronGene neuron2 = ChooseRandomNeuron(false);
if (!IsDuplicateLink(neuron1ID, neuron2ID)
&& (neuron1.Id != neuron2.Id)
&& (neuron2.NeuronType != NEATNeuronType.Bias))
{
neuron1ID = neuron1.Id;
neuron2ID = neuron2.Id;
break;
}
}
}
// did we fail to find a link
if ((neuron1ID < 0) || (neuron2ID < 0))
{
return;
}
// check to see if this innovation has already been tried
NEATInnovation innovation = ((NEATTraining) GA).Innovations.CheckInnovation(neuron1ID,
neuron1ID,
NEATInnovationType
.NewLink);
// see if this is a recurrent(backwards) link
var neuronGene_0 = (NEATNeuronGene) neuronsChromosome
.Get(GetElementPos(neuron1ID));
if (neuronGene_0.SplitY > neuronGene_0.SplitY)
{
recurrent = true;
}
// is this a new innovation?
if (innovation == null)
{
// new innovation
((NEATTraining) GA).Innovations
.CreateNewInnovation(neuron1ID, neuron2ID,
NEATInnovationType.NewLink);
long id2 = GA.Population.AssignInnovationID();
var linkGene = new NEATLinkGene(neuron1ID,
neuron2ID, true, id2, RangeRandomizer.Randomize(-1, 1),
recurrent);
linksChromosome.Add(linkGene);
}
else
{
// existing innovation
var linkGene_1 = new NEATLinkGene(neuron1ID,
neuron2ID, true, innovation.InnovationID,
RangeRandomizer.Randomize(-1, 1), recurrent);
linksChromosome.Add(linkGene_1);
}
}
/// <summary>
/// Construct a genome by copying another.
/// </summary>
///
/// <param name="other">The other genome.</param>
public NEATGenome(NEATGenome other)
{
neuronsChromosome = new Chromosome();
linksChromosome = new Chromosome();
GA = other.GA;
Chromosomes.Add(neuronsChromosome);
Chromosomes.Add(linksChromosome);
GenomeID = other.GenomeID;
networkDepth = other.networkDepth;
Population = other.Population;
Score = other.Score;
AdjustedScore = other.AdjustedScore;
AmountToSpawn = other.AmountToSpawn;
inputCount = other.inputCount;
outputCount = other.outputCount;
speciesID = other.speciesID;
// copy neurons
foreach (IGene gene in other.Neurons.Genes)
{
var oldGene = (NEATNeuronGene) gene;
var newGene = new NEATNeuronGene(
oldGene.NeuronType, oldGene.Id,
oldGene.SplitY, oldGene.SplitX,
oldGene.Recurrent, oldGene.ActivationResponse);
Neurons.Add(newGene);
}
// copy links
foreach (IGene gene_0 in other.Links.Genes)
{
var oldGene_1 = (NEATLinkGene) gene_0;
var newGene_2 = new NEATLinkGene(
oldGene_1.FromNeuronID, oldGene_1.ToNeuronID,
oldGene_1.Enabled, oldGene_1.InnovationId,
oldGene_1.Weight, oldGene_1.Recurrent);
Links.Add(newGene_2);
}
}
public virtual object Read(Stream mask0)
{
IDictionary<ISpecies, int> dictionary2;
IDictionary<int, IGenome> dictionary3;
EncogFileSection section;
IDictionary<string, string> dictionary4;
int num;
int num2;
NEATPopulation population = new NEATPopulation();
NEATInnovationList list = new NEATInnovationList {
Population = population
};
population.Innovations = list;
EncogReadHelper helper = new EncogReadHelper(mask0);
IDictionary<int, ISpecies> dictionary = new Dictionary<int, ISpecies>();
goto Label_0BD6;
Label_0023:
if ((section = helper.ReadNextSection()) != null)
{
if (!section.SectionName.Equals("NEAT-POPULATION"))
{
goto Label_085C;
}
if (section.SubSectionName.Equals("INNOVATIONS"))
{
using (IEnumerator<string> enumerator = section.Lines.GetEnumerator())
{
string str;
IList<string> list2;
NEATInnovation innovation;
NEATInnovation innovation2;
goto Label_0A6C;
Label_0A43:
innovation = innovation2;
if ((((uint) num2) - ((uint) num)) <= uint.MaxValue)
{
}
population.Innovations.Add(innovation);
Label_0A6C:
if (enumerator.MoveNext())
{
goto Label_0B54;
}
goto Label_0AD7;
Label_0A7A:
innovation2.SplitY = CSVFormat.EgFormat.Parse(list2[4]);
innovation2.NeuronID = int.Parse(list2[5]);
innovation2.FromNeuronID = int.Parse(list2[6]);
innovation2.ToNeuronID = int.Parse(list2[7]);
goto Label_0A43;
Label_0AD7:
if ((((uint) num2) - ((uint) num)) >= 0)
{
goto Label_0023;
}
Label_0AEF:
innovation2 = new NEATInnovation();
innovation2.InnovationID = int.Parse(list2[0]);
innovation2.InnovationType = StringToInnovationType(list2[1]);
innovation2.NeuronType = StringToNeuronType(list2[2]);
innovation2.SplitX = CSVFormat.EgFormat.Parse(list2[3]);
if (0 == 0)
{
goto Label_0A7A;
}
goto Label_0023;
Label_0B54:
str = enumerator.Current;
do
{
list2 = EncogFileSection.SplitColumns(str);
}
while (0 != 0);
goto Label_0AEF;
}
}
if (((uint) num) <= uint.MaxValue)
{
goto Label_085C;
}
goto Label_030B;
}
using (IEnumerator<IGenome> enumerator4 = population.Genomes.GetEnumerator())
{
IGenome genome3;
NEATGenome genome4;
ISpecies species3;
Label_0040:
if (enumerator4.MoveNext())
{
goto Label_00D6;
}
goto Label_0102;
Label_0051:
genome4.OutputCount = population.OutputCount;
if ((((uint) num) - ((uint) num2)) >= 0)
{
goto Label_0040;
}
Label_007D:
if (dictionary.ContainsKey(num))
{
goto Label_00BA;
}
Label_0087:
genome4.InputCount = population.InputCount;
goto Label_0051;
Label_0096:
num = (int) genome4.SpeciesID;
if ((((uint) num2) + ((uint) num2)) <= uint.MaxValue)
{
}
goto Label_007D;
Label_00BA:
species3 = dictionary[num];
species3.Members.Add(genome4);
goto Label_0087;
Label_00D6:
genome3 = enumerator4.Current;
genome4 = (NEATGenome) genome3;
goto Label_0096;
}
Label_0102:
using (IEnumerator<ISpecies> enumerator5 = dictionary2.Keys.GetEnumerator())
{
ISpecies current;
goto Label_011F;
Label_0112:
((BasicSpecies) current).Population = population;
Label_011F:
if (enumerator5.MoveNext())
{
current = enumerator5.Current;
num2 = dictionary2[current];
do
{
IGenome genome5 = dictionary3[num2];
current.Leader = genome5;
}
while (-1 == 0);
goto Label_0112;
}
return population;
}
Label_016E:
population.YoungScoreBonus = EncogFileSection.ParseDouble(dictionary4, "youngAgeBonus");
population.GenomeIDGenerate.CurrentID = EncogFileSection.ParseInt(dictionary4, "nextGenomeID");
population.InnovationIDGenerate.CurrentID = EncogFileSection.ParseInt(dictionary4, "nextInnovationID");
population.GeneIDGenerate.CurrentID = EncogFileSection.ParseInt(dictionary4, "nextGeneID");
population.SpeciesIDGenerate.CurrentID = EncogFileSection.ParseInt(dictionary4, "nextSpeciesID");
goto Label_0023;
Label_0201:
population.SurvivalRate = EncogFileSection.ParseDouble(dictionary4, "survivalRate");
if (0 != 0)
{
goto Label_03AA;
}
goto Label_02E9;
Label_0242:
population.OldAgePenalty = EncogFileSection.ParseDouble(dictionary4, "oldAgePenalty");
population.OldAgeThreshold = EncogFileSection.ParseInt(dictionary4, "oldAgeThreshold");
Label_0266:
population.PopulationSize = EncogFileSection.ParseInt(dictionary4, "populationSize");
if (((uint) num2) <= uint.MaxValue)
{
if ((((uint) num2) - ((uint) num2)) >= 0)
{
if ((((uint) num) + ((uint) num2)) > uint.MaxValue)
{
goto Label_0242;
}
goto Label_0201;
}
goto Label_02E9;
}
Label_028A:
population.OutputActivationFunction = EncogFileSection.ParseActivationFunction(dictionary4, "outAct");
if ((((uint) num2) + ((uint) num2)) < 0)
{
goto Label_0BD6;
}
population.Snapshot = EncogFileSection.ParseBoolean(dictionary4, "snapshot");
population.InputCount = EncogFileSection.ParseInt(dictionary4, "inputCount");
population.OutputCount = EncogFileSection.ParseInt(dictionary4, "outputCount");
goto Label_0242;
Label_02E9:
if ((((uint) num2) - ((uint) num)) >= 0)
{
population.YoungBonusAgeThreshhold = EncogFileSection.ParseInt(dictionary4, "youngAgeThreshold");
}
if (0xff != 0)
{
goto Label_016E;
}
Label_030B:
population.NeatActivationFunction = EncogFileSection.ParseActivationFunction(dictionary4, "neatAct");
goto Label_028A;
Label_03AA:
if (!section.SectionName.Equals("NEAT-POPULATION") || !section.SubSectionName.Equals("CONFIG"))
{
goto Label_0023;
}
if ((((uint) num2) + ((uint) num2)) <= uint.MaxValue)
{
if ((((uint) num2) + ((uint) num2)) <= uint.MaxValue)
{
dictionary4 = section.ParseParams();
}
goto Label_030B;
}
Label_0821:
if (section.SectionName.Equals("NEAT-POPULATION"))
{
if (section.SubSectionName.Equals("GENOMES"))
{
NEATGenome genome = null;
using (IEnumerator<string> enumerator3 = section.Lines.GetEnumerator())
{
string str3;
IList<string> list3;
NEATGenome genome2;
NEATNeuronGene gene;
NEATNeuronGene gene2;
NEATLinkGene gene3;
goto Label_0402;
Label_03DA:
genome.Links.Add(gene3);
goto Label_0402;
Label_03EA:
if (list3[0].Equals("l", StringComparison.InvariantCultureIgnoreCase))
{
goto Label_04EA;
}
Label_0402:
if (enumerator3.MoveNext())
{
goto Label_0770;
}
if ((((uint) num2) | 0x80000000) != 0)
{
goto Label_0023;
}
Label_0429:
gene3.Enabled = int.Parse(list3[2]) > 0;
Label_0440:
gene3.Recurrent = int.Parse(list3[3]) > 0;
gene3.FromNeuronID = int.Parse(list3[4]);
gene3.ToNeuronID = int.Parse(list3[5]);
gene3.Weight = CSVFormat.EgFormat.Parse(list3[6]);
gene3.InnovationId = int.Parse(list3[7]);
goto Label_03DA;
Label_04B4:
if ((((uint) num) & 0) != 0)
{
goto Label_03DA;
}
gene3.Id = int.Parse(list3[1]);
goto Label_0429;
Label_04EA:
gene3 = new NEATLinkGene();
goto Label_04B4;
Label_04F6:
gene2.SplitY = CSVFormat.EgFormat.Parse(list3[7]);
Label_050F:
gene = gene2;
genome.Neurons.Add(gene);
if ((((uint) num2) & 0) != 0)
{
goto Label_0782;
}
goto Label_0402;
Label_053D:
gene2.Enabled = int.Parse(list3[3]) > 0;
gene2.InnovationId = int.Parse(list3[4]);
if (0xff == 0)
{
goto Label_050F;
}
gene2.ActivationResponse = CSVFormat.EgFormat.Parse(list3[5]);
gene2.SplitX = CSVFormat.EgFormat.Parse(list3[6]);
goto Label_04F6;
Label_05A7:
gene2.Id = int.Parse(list3[1]);
gene2.NeuronType = StringToNeuronType(list3[2]);
goto Label_053D;
Label_05DA:
if (!list3[0].Equals("n", StringComparison.InvariantCultureIgnoreCase))
{
goto Label_03EA;
}
if (3 == 0)
{
goto Label_0440;
}
gene2 = new NEATNeuronGene();
goto Label_07C8;
Label_0608:
population.Add(genome);
dictionary3[(int) genome.GenomeID] = genome;
if (((uint) num) >= 0)
{
goto Label_0402;
}
goto Label_06B0;
Label_0638:
genome.AmountToSpawn = CSVFormat.EgFormat.Parse(list3[4]);
genome.NetworkDepth = int.Parse(list3[5]);
if ((((uint) num2) | 0x80000000) == 0)
{
goto Label_07AD;
}
if (((uint) num2) < 0)
{
goto Label_0023;
}
genome.Score = CSVFormat.EgFormat.Parse(list3[6]);
goto Label_06F8;
Label_06B0:
genome.GenomeID = int.Parse(list3[1]);
genome.SpeciesID = int.Parse(list3[2]);
genome.AdjustedScore = CSVFormat.EgFormat.Parse(list3[3]);
goto Label_0638;
Label_06F8:
if (3 != 0)
{
goto Label_0608;
}
goto Label_0402;
Label_0704:
genome.Chromosomes.Add(genome.LinksChromosome);
if ((((uint) num2) + ((uint) num2)) >= 0)
{
goto Label_06B0;
}
goto Label_0402;
Label_0737:
if (8 == 0)
{
goto Label_050F;
}
genome = genome2;
genome.Chromosomes.Add(genome.NeuronsChromosome);
if ((((uint) num) - ((uint) num2)) <= uint.MaxValue)
{
goto Label_0704;
}
Label_0770:
str3 = enumerator3.Current;
list3 = EncogFileSection.SplitColumns(str3);
Label_0782:
if (!list3[0].Equals("g", StringComparison.InvariantCultureIgnoreCase))
{
goto Label_05DA;
}
genome2 = new NEATGenome {
NeuronsChromosome = new Chromosome()
};
Label_07AD:
genome2.LinksChromosome = new Chromosome();
goto Label_0737;
Label_07C8:
if ((((uint) num2) - ((uint) num)) <= uint.MaxValue)
{
goto Label_05A7;
}
if (0 == 0)
{
goto Label_04EA;
}
goto Label_04B4;
}
}
if ((((uint) num2) < 0) || ((((uint) num2) - ((uint) num2)) > uint.MaxValue))
{
goto Label_030B;
}
}
goto Label_03AA;
Label_085C:
if (section.SectionName.Equals("NEAT-POPULATION"))
{
if ((((uint) num) | 1) == 0)
{
goto Label_0266;
}
if (section.SubSectionName.Equals("SPECIES"))
{
if ((((uint) num) | 8) == 0)
{
goto Label_0201;
}
using (IEnumerator<string> enumerator2 = section.Lines.GetEnumerator())
{
string str2;
string[] strArray;
BasicSpecies species;
BasicSpecies species2;
goto Label_0913;
Label_08CB:
species = species2;
species.SpawnsRequired = CSVFormat.EgFormat.Parse(strArray[5]);
dictionary2[species] = int.Parse(strArray[6]);
population.Species.Add(species);
dictionary[(int) species.SpeciesID] = species;
Label_0913:
if (enumerator2.MoveNext())
{
goto Label_09D6;
}
goto Label_0023;
Label_0924:
if ((((uint) num2) & 0) != 0)
{
goto Label_0969;
}
if (2 == 0)
{
goto Label_0924;
}
goto Label_09BE;
Label_0941:
species2 = new BasicSpecies();
species2.SpeciesID = int.Parse(strArray[0]);
species2.Age = int.Parse(strArray[1]);
Label_0969:
species2.BestScore = CSVFormat.EgFormat.Parse(strArray[2]);
species2.GensNoImprovement = int.Parse(strArray[3]);
species2.SpawnsRequired = CSVFormat.EgFormat.Parse(strArray[4]);
if ((((uint) num) + ((uint) num2)) >= 0)
{
goto Label_0924;
}
Label_09BE:
if ((((uint) num2) + ((uint) num)) <= uint.MaxValue)
{
goto Label_09FD;
}
Label_09D6:
str2 = enumerator2.Current;
strArray = str2.Split(new char[] { ',' });
goto Label_0941;
Label_09FD:
if ((((uint) num) - ((uint) num)) <= uint.MaxValue)
{
goto Label_08CB;
}
goto Label_0023;
}
}
}
goto Label_0821;
Label_0BD6:
dictionary2 = new Dictionary<ISpecies, int>();
dictionary3 = new Dictionary<int, IGenome>();
goto Label_0023;
}
/// <summary>
/// Construct a genome by copying another.
/// </summary>
/// <param name="other">The other genome.</param>
public NEATGenome(NEATGenome other)
{
NetworkDepth = other.NetworkDepth;
Population = other.Population;
Score = other.Score;
AdjustedScore = other.AdjustedScore;
InputCount = other.InputCount;
OutputCount = other.OutputCount;
Species = other.Species;
// copy neurons
foreach (NEATNeuronGene oldGene in other.NeuronsChromosome)
{
var newGene = new NEATNeuronGene(oldGene);
_neuronsList.Add(newGene);
}
// copy links
foreach (var oldGene in other.LinksChromosome)
{
var newGene = new NEATLinkGene(
oldGene.FromNeuronId, oldGene.ToNeuronId,
oldGene.Enabled, oldGene.InnovationId,
oldGene.Weight);
_linksList.Add(newGene);
}
}