/// <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); } }
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> /// 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; }
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; }