public void TestGenotypeStream()
{
var stream = new GenotypeStream(protoGenotype);
var genotypes = stream.Take(5);
// Correct count
Assert.AreEqual(5, genotypes.Count());
// Each unique
var distinct = genotypes.Distinct(new GenotypeEqualityComparer());
Assert.AreEqual(5, distinct.Count());
// Same shape
var shape = genotypes.Distinct(new GenotypeShapeEqualityComparer());
Assert.AreEqual(1, shape.Count());
}
IEnumerator Start()
{
var logPath = string.Format("logs_{0}", DateTime.Now.Ticks);
Debug.LogFormat("Logging to {0}", logPath);
if (!Directory.Exists(logPath)) {
Directory.CreateDirectory(logPath);
}
elitesLog = File.CreateText(Path.Combine(logPath, "elites.csv"));
populationLog = File.CreateText(Path.Combine(logPath, "populations.csv"));
generationLog = File.CreateText(Path.Combine(logPath, "generations.csv"));
speciesLog = File.CreateText(Path.Combine(logPath, "species.csv"));
var populationSize = 100;
var innovations = new InnovationCollection();
var mutations = new MutationCollection();
mutations.Add(0.01f, new AddNeuronMutator(innovations)); // 0.1%
mutations.Add(0.05f, new AddSynapseMutator(innovations)); // 1%
mutations.Add(0.05f, new ToggleSynapseMutator(0.125f));
mutations.Add(0.20f, new PerturbNeuronMutator(0.5f, 0.25f)); // 98% vvv
mutations.Add(0.20f, new PerturbSynapseMutator(0.5f, 0.25f));
mutations.Add(0.20f, new ReplaceNeuronMutator(0.5f));
mutations.Add(0.20f, new ReplaceSynapseMutator(0.5f));
var eliteSelector = new EliteSelector();
var crossover = new MultipointCrossover();
var offspringSelector = new OffspringSelector(crossover);
var distanceMetric = new DistanceMetric(2.0f, 2.0f, 1.0f);
var speciation = new Speciation(10, 6.0f, 0.3f, distanceMetric);
var neuronGenes = new []{
new NeuronGene(innovations.GetInitialNeuronInnovationId(0), NeuronType.UpperNeuron),
new NeuronGene(innovations.GetInitialNeuronInnovationId(1), NeuronType.LowerNeuron),
new NeuronGene(innovations.GetInitialNeuronInnovationId(2), NeuronType.PositionNeuron),
new NeuronGene(innovations.GetInitialNeuronInnovationId(3), NeuronType.SpeedNeuron),
}.ToGeneList();
var synapseGenes = new GeneList<SynapseGene>();
var protoGenotype = new Genotype(neuronGenes, synapseGenes);
var genotypes = new GenotypeStream(protoGenotype)
.Take(populationSize).ToArray();
var species = new Specie[0];
var generation = 0;
var elitePhenotypes = new List<Phenotype>();
var offspringPhenotypes = genotypes.Select(gt => new Phenotype(gt)).ToList();
while (true) {
yield return StartCoroutine(EvaluatePopulation(offspringPhenotypes));
var phenotypes = new List<Phenotype>(elitePhenotypes.Count + offspringPhenotypes.Count);
phenotypes.AddRange(elitePhenotypes);
phenotypes.AddRange(offspringPhenotypes);
Assert.AreEqual(phenotypes.Count, populationSize,
"Population size must remain constant");
var longest = phenotypes.OrderByDescending(pt => pt.BestDuration).First();
Debug.LogFormat("[{0}] Fitness: {1}, Duration: {2}s ({3}, {4}) (Longest)",
generation, longest.MeanFitness, longest.MeanDuration,
longest.Genotype.NeuronCount,
longest.Genotype.SynapseCount);
var best = phenotypes.OrderByDescending(pt => pt.Fitness).First();
Debug.LogFormat("[{0}] Fitness: {1}, Duration: {2}s ({3}, {4}) (Best)",
generation, best.MeanFitness, best.MeanDuration,
best.Genotype.NeuronCount,
best.Genotype.SynapseCount);
elitesLog.WriteLine(string.Join(",", new string[]{
generation.ToString(),
best.Fitness.ToString(),
best.BestDuration.ToString(),
JSON.Serialize(best.Genotype.ToJSON()),
}));
var meanComplexity = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + (float)pt.Genotype.Complexity,
(sum) => sum / (float)phenotypes.Count);
var meanNeuronCount = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + (float)pt.Genotype.NeuronCount,
(sum) => sum / (float)phenotypes.Count);
var meanSynapseCount = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + (float)pt.Genotype.SynapseCount,
(sum) => sum / (float)phenotypes.Count);
var meanFitness = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + (float)pt.Fitness,
(sum) => sum / (float)phenotypes.Count);
var stdevFitness = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + Mathf.Pow(pt.Fitness - meanFitness, 2.0f),
(sum) => Mathf.Sqrt(sum / (float)phenotypes.Count));
var stdevComplexity = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + Mathf.Pow((float)pt.Genotype.Complexity - meanComplexity, 2.0f),
(sum) => Mathf.Sqrt(sum / (float)phenotypes.Count));
var stdevNeuronCount = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + Mathf.Pow((float)pt.Genotype.NeuronCount - meanNeuronCount, 2.0f),
(sum) => Mathf.Sqrt(sum / (float)phenotypes.Count));
var stdevSynapseCount = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + Mathf.Pow((float)pt.Genotype.SynapseCount - meanSynapseCount, 2.0f),
(sum) => Mathf.Sqrt(sum / (float)phenotypes.Count));
species = speciation.Speciate(species, phenotypes.ToArray());
Debug.LogFormat("[{0}] Species Count: {1} (Threshold: {2})",
generation, species.Length, speciation.DistanceThreshold);
var meanAdjustedFitness = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + (float)pt.AdjustedFitness,
(sum) => sum / (float)phenotypes.Count);
// standard deviation:
// take the square root of the average of the squared deviations of the values from their average value
var stdevAdjustedFitness = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + Mathf.Pow(pt.AdjustedFitness - meanAdjustedFitness, 2.0f),
(sum) => Mathf.Sqrt(sum / (float)phenotypes.Count));
generationLog.WriteLine(new []{
generation,
best.Fitness,
best.MeanDuration,
meanAdjustedFitness, stdevAdjustedFitness,
meanFitness, stdevFitness,
meanComplexity, stdevComplexity,
meanNeuronCount, stdevNeuronCount,
meanSynapseCount, stdevSynapseCount
}.Stringify());
foreach (var sp in species) {
speciesLog.WriteLine(new []{
generation,
sp.SpeciesId, sp.Count,
sp.BestFitness,
sp.MeanFitness,
sp.MeanAdjustedFitness,
sp.MeanComplexity,
}.Stringify());
foreach (var pt in sp) {
populationLog.WriteLine(new []{
generation,
sp.SpeciesId,
pt.Fitness,
pt.AdjustedFitness,
pt.BestDuration,
pt.Genotype.Complexity,
pt.Genotype.NeuronCount,
pt.Genotype.SynapseCount,
}.Stringify());
}
}
var eliteCount = species.Length;
elitePhenotypes = eliteSelector.Select(species, eliteCount);
var offspringCount = populationSize - elitePhenotypes.Count;
var offspringGenotypes = offspringSelector.Select(species, offspringCount);
var mutationResults = mutations.Mutate(offspringGenotypes);
Debug.LogFormat("[{0}] Mutations: Added Neurons: {1}, Added Synapses: {2}, Perturbed Neurons: {3}, Perturbed Synapses: {4}, Replaced Neurons: {5}, Replaced Synapses: {6}, Toggled Synapses: {7}, Pruned Synapses: {8}, Orphaned Neurons: {9}",
generation,
mutationResults.addedNeurons,
mutationResults.addedSynapses,
mutationResults.perturbedNeurons,
mutationResults.perturbedSynapses,
mutationResults.replacedNeurons,
mutationResults.replacedSynapses,
mutationResults.toggledSynapses,
mutationResults.prunedSynapses,
mutationResults.orphanedNeurons);
offspringPhenotypes = offspringGenotypes.Select(gt => new Phenotype(gt)).ToList();
generation++;
// Flush these so we can preview results while it runs
generationLog.Flush();
populationLog.Flush();
speciesLog.Flush();
}
}
IEnumerator Start()
{
var logPath = string.Format("logs_{0}", DateTime.Now.Ticks);
Debug.LogFormat("Logging to {0}", logPath);
if (!Directory.Exists(logPath))
{
Directory.CreateDirectory(logPath);
}
elitesLog = File.CreateText(Path.Combine(logPath, "elites.csv"));
populationLog = File.CreateText(Path.Combine(logPath, "populations.csv"));
generationLog = File.CreateText(Path.Combine(logPath, "generations.csv"));
speciesLog = File.CreateText(Path.Combine(logPath, "species.csv"));
var populationSize = 100;
var innovations = new InnovationCollection();
var mutations = new MutationCollection();
mutations.Add(0.01f, new AddNeuronMutator(innovations)); // 0.1%
mutations.Add(0.05f, new AddSynapseMutator(innovations)); // 1%
mutations.Add(0.05f, new ToggleSynapseMutator(0.125f));
mutations.Add(0.20f, new PerturbNeuronMutator(0.5f, 0.25f)); // 98% vvv
mutations.Add(0.20f, new PerturbSynapseMutator(0.5f, 0.25f));
mutations.Add(0.20f, new ReplaceNeuronMutator(0.5f));
mutations.Add(0.20f, new ReplaceSynapseMutator(0.5f));
var eliteSelector = new EliteSelector();
var crossover = new MultipointCrossover();
var offspringSelector = new OffspringSelector(crossover);
var distanceMetric = new DistanceMetric(2.0f, 2.0f, 1.0f);
var speciation = new Speciation(10, 6.0f, 0.3f, distanceMetric);
var neuronGenes = new [] {
new NeuronGene(innovations.GetInitialNeuronInnovationId(0), NeuronType.UpperNeuron),
new NeuronGene(innovations.GetInitialNeuronInnovationId(1), NeuronType.LowerNeuron),
new NeuronGene(innovations.GetInitialNeuronInnovationId(2), NeuronType.PositionNeuron),
new NeuronGene(innovations.GetInitialNeuronInnovationId(3), NeuronType.SpeedNeuron),
}.ToGeneList();
var synapseGenes = new GeneList <SynapseGene>();
var protoGenotype = new Genotype(neuronGenes, synapseGenes);
var genotypes = new GenotypeStream(protoGenotype)
.Take(populationSize).ToArray();
var species = new Specie[0];
var generation = 0;
var elitePhenotypes = new List <Phenotype>();
var offspringPhenotypes = genotypes.Select(gt => new Phenotype(gt)).ToList();
while (true)
{
yield return(StartCoroutine(EvaluatePopulation(offspringPhenotypes)));
var phenotypes = new List <Phenotype>(elitePhenotypes.Count + offspringPhenotypes.Count);
phenotypes.AddRange(elitePhenotypes);
phenotypes.AddRange(offspringPhenotypes);
Assert.AreEqual(phenotypes.Count, populationSize,
"Population size must remain constant");
var longest = phenotypes.OrderByDescending(pt => pt.BestDuration).First();
Debug.LogFormat("[{0}] Fitness: {1}, Duration: {2}s ({3}, {4}) (Longest)",
generation, longest.MeanFitness, longest.MeanDuration,
longest.Genotype.NeuronCount,
longest.Genotype.SynapseCount);
var best = phenotypes.OrderByDescending(pt => pt.Fitness).First();
Debug.LogFormat("[{0}] Fitness: {1}, Duration: {2}s ({3}, {4}) (Best)",
generation, best.MeanFitness, best.MeanDuration,
best.Genotype.NeuronCount,
best.Genotype.SynapseCount);
elitesLog.WriteLine(string.Join(",", new string[] {
generation.ToString(),
best.Fitness.ToString(),
best.BestDuration.ToString(),
JSON.Serialize(best.Genotype.ToJSON()),
}));
var meanComplexity = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + (float)pt.Genotype.Complexity,
(sum) => sum / (float)phenotypes.Count);
var meanNeuronCount = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + (float)pt.Genotype.NeuronCount,
(sum) => sum / (float)phenotypes.Count);
var meanSynapseCount = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + (float)pt.Genotype.SynapseCount,
(sum) => sum / (float)phenotypes.Count);
var meanFitness = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + (float)pt.Fitness,
(sum) => sum / (float)phenotypes.Count);
var stdevFitness = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + Mathf.Pow(pt.Fitness - meanFitness, 2.0f),
(sum) => Mathf.Sqrt(sum / (float)phenotypes.Count));
var stdevComplexity = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + Mathf.Pow((float)pt.Genotype.Complexity - meanComplexity, 2.0f),
(sum) => Mathf.Sqrt(sum / (float)phenotypes.Count));
var stdevNeuronCount = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + Mathf.Pow((float)pt.Genotype.NeuronCount - meanNeuronCount, 2.0f),
(sum) => Mathf.Sqrt(sum / (float)phenotypes.Count));
var stdevSynapseCount = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + Mathf.Pow((float)pt.Genotype.SynapseCount - meanSynapseCount, 2.0f),
(sum) => Mathf.Sqrt(sum / (float)phenotypes.Count));
species = speciation.Speciate(species, phenotypes.ToArray());
Debug.LogFormat("[{0}] Species Count: {1} (Threshold: {2})",
generation, species.Length, speciation.DistanceThreshold);
var meanAdjustedFitness = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + (float)pt.AdjustedFitness,
(sum) => sum / (float)phenotypes.Count);
// standard deviation:
// take the square root of the average of the squared deviations of the values from their average value
var stdevAdjustedFitness = phenotypes.Aggregate(0.0f,
(sum, pt) => sum + Mathf.Pow(pt.AdjustedFitness - meanAdjustedFitness, 2.0f),
(sum) => Mathf.Sqrt(sum / (float)phenotypes.Count));
generationLog.WriteLine(new [] {
generation,
best.Fitness,
best.MeanDuration,
meanAdjustedFitness, stdevAdjustedFitness,
meanFitness, stdevFitness,
meanComplexity, stdevComplexity,
meanNeuronCount, stdevNeuronCount,
meanSynapseCount, stdevSynapseCount
}.Stringify());
foreach (var sp in species)
{
speciesLog.WriteLine(new [] {
generation,
sp.SpeciesId, sp.Count,
sp.BestFitness,
sp.MeanFitness,
sp.MeanAdjustedFitness,
sp.MeanComplexity,
}.Stringify());
foreach (var pt in sp)
{
populationLog.WriteLine(new [] {
generation,
sp.SpeciesId,
pt.Fitness,
pt.AdjustedFitness,
pt.BestDuration,
pt.Genotype.Complexity,
pt.Genotype.NeuronCount,
pt.Genotype.SynapseCount,
}.Stringify());
}
}
var eliteCount = species.Length;
elitePhenotypes = eliteSelector.Select(species, eliteCount);
var offspringCount = populationSize - elitePhenotypes.Count;
var offspringGenotypes = offspringSelector.Select(species, offspringCount);
var mutationResults = mutations.Mutate(offspringGenotypes);
Debug.LogFormat("[{0}] Mutations: Added Neurons: {1}, Added Synapses: {2}, Perturbed Neurons: {3}, Perturbed Synapses: {4}, Replaced Neurons: {5}, Replaced Synapses: {6}, Toggled Synapses: {7}, Pruned Synapses: {8}, Orphaned Neurons: {9}",
generation,
mutationResults.addedNeurons,
mutationResults.addedSynapses,
mutationResults.perturbedNeurons,
mutationResults.perturbedSynapses,
mutationResults.replacedNeurons,
mutationResults.replacedSynapses,
mutationResults.toggledSynapses,
mutationResults.prunedSynapses,
mutationResults.orphanedNeurons);
offspringPhenotypes = offspringGenotypes.Select(gt => new Phenotype(gt)).ToList();
generation++;
// Flush these so we can preview results while it runs
generationLog.Flush();
populationLog.Flush();
speciesLog.Flush();
}
}
