public void TestAddSynapseMutator()
{
var mutator = new AddSynapseMutator(innovations);
var mutatedGenotype = new Genotype(genotype);
var mutationResults = new MutationResults();
mutator.Mutate(mutatedGenotype, mutationResults);
Assert.AreEqual(1, mutationResults.addedSynapses);
// Compare gene counts
Assert.AreEqual(mutatedGenotype.NeuronCount, genotype.NeuronCount);
Assert.AreEqual(1, mutatedGenotype.SynapseCount - genotype.SynapseCount);
// Compare innovation ids
var aN = new [] { 0, 1, 2 };
var bN = mutatedGenotype.NeuronGenes.Select(g => g.InnovationId);
Assert.That(aN.SequenceEqual(bN));
var aS = new [] { 3, 4, 5, 6 };
var bS = mutatedGenotype.SynapseGenes.Select(g => g.InnovationId);
Assert.That(aS.SequenceEqual(bS));
}
public void Mutate(Genotype genotype, MutationResults results)
{
var neuronIndexA = Random.Range(0, genotype.NeuronCount);
var neuronGeneA = genotype.NeuronGenes.ElementAt(neuronIndexA);
var candidates = new List<NeuronGene>(genotype.NeuronGenes);
candidates.Shuffle();
NeuronGene neuronGeneB = default(NeuronGene);
bool foundNeuron = false;
for (var i = 0; i < candidates.Count; i++) {
neuronGeneB = candidates[i];
var exists = genotype.SynapseGenes.Any(s =>
neuronGeneA.InnovationId == s.fromNeuronId &&
neuronGeneB.InnovationId == s.toNeuronId);
if (!exists) {
foundNeuron = true;
break;
}
}
if (foundNeuron) {
var synapseInnovationId = innovations.GetSynapseInnovationId(neuronGeneA.InnovationId, neuronGeneB.InnovationId);
var synapseGene = new SynapseGene(synapseInnovationId, neuronGeneA.InnovationId, neuronGeneB.InnovationId, true);
var synapseGenes = new GeneList<SynapseGene>(genotype.SynapseGenes);
synapseGenes.Add(synapseGene);
genotype.SynapseGenes = synapseGenes;
results.addedSynapses += 1;
}
}
public void Init()
{
var innovations = new InnovationCollection();
var neuronGenes = Enumerable.Range(0, 3).Select(i => {
var inId = innovations.GetInitialNeuronInnovationId(i);
return(new NeuronGene(inId, NeuronType.HiddenNeuron));
}).ToGeneList();
var synapseGenes = Enumerable.Range(0, 3).Select(i => {
var inId = innovations.GetSynapseInnovationId(i, i + 1);
return(new SynapseGene(inId, i, i + 1, true));
}).ToGeneList();
var protoGenotype = new Genotype(neuronGenes, synapseGenes);
var phenotypes = Enumerable.Range(0, 100).Select(_ => {
var gt = Genotype.FromPrototype(protoGenotype);
var pt = new Phenotype(gt);
return(pt);
}).ToArray();
var distanceMetric = new NEAT.DistanceMetric(0.0f, 0.0f, 35.0f);
var speciation = new NEAT.Speciation(10, 50.0f, 0.2f, distanceMetric);
species = speciation.Speciate(new NEAT.Specie[0], phenotypes);
}
public void Mutate(Genotype genotype, MutationResults results)
{
var prunedSynapses = genotype.SynapseGenes
.Where(_ => Random.value < p)
.Where(g => !g.isEnabled)
.ToList();
var synapseGenes = genotype.SynapseGenes.Except(prunedSynapses)
.ToGeneList();
genotype.SynapseGenes = synapseGenes;
// An orphan is a neuron with no connecting synapses
// For each neuron: check if any synapse connects to it
// Exclude IO neurons
var orphanedNeurons = genotype.HiddenNeurons.Where(g =>
synapseGenes.None(s =>
s.fromNeuronId == g.InnovationId ||
s.toNeuronId == g.InnovationId)).ToList();
var neuronGenes = genotype.NeuronGenes.Except(orphanedNeurons)
.ToGeneList();
genotype.NeuronGenes = neuronGenes;
results.orphanedNeurons += orphanedNeurons.Count;
results.prunedSynapses += prunedSynapses.Count;
}
public void Init()
{
var innovations = new InnovationCollection();
var neuronGenes = Enumerable.Range(0, 3).Select(i => {
var inId = innovations.GetInitialNeuronInnovationId(i);
return new NeuronGene(inId, NeuronType.HiddenNeuron);
}).ToGeneList();
var synapseGenes = Enumerable.Range(0, 3).Select(i => {
var inId = innovations.GetSynapseInnovationId(i, i + 1);
return new SynapseGene(inId, i, i + 1, true);
}).ToGeneList();
var protoGenotype = new Genotype(neuronGenes, synapseGenes);
var phenotypes = Enumerable.Range(0, 100).Select(_ => {
var gt = Genotype.FromPrototype(protoGenotype);
var pt = new Phenotype(gt);
return pt;
}).ToArray();
var distanceMetric = new NEAT.DistanceMetric(0.0f, 0.0f, 35.0f);
var speciation = new NEAT.Speciation(10, 50.0f, 0.2f, distanceMetric);
species = speciation.Speciate(new NEAT.Specie[0], phenotypes);
}
public void Mutate(Genotype genotype, MutationResults results)
{
var prunedSynapses = genotype.SynapseGenes
.Where(_ => Random.value < p)
.Where(g => !g.isEnabled)
.ToList();
var synapseGenes = genotype.SynapseGenes.Except(prunedSynapses)
.ToGeneList();
genotype.SynapseGenes = synapseGenes;
// An orphan is a neuron with no connecting synapses
// For each neuron: check if any synapse connects to it
// Exclude IO neurons
var orphanedNeurons = genotype.HiddenNeurons.Where(g =>
synapseGenes.None(s =>
s.fromNeuronId == g.InnovationId ||
s.toNeuronId == g.InnovationId)).ToList();
var neuronGenes = genotype.NeuronGenes.Except(orphanedNeurons)
.ToGeneList();
genotype.NeuronGenes = neuronGenes;
results.orphanedNeurons += orphanedNeurons.Count;
results.prunedSynapses += prunedSynapses.Count;
}
public Specie(int speciesId, Genotype representative, int age, int bestAge, float pastBestAdjustedFitness) : base()
{
this.speciesId = speciesId;
this.representative = representative;
this.age = age;
this.bestAge = bestAge;
this.pastBestAdjustedFitness = pastBestAdjustedFitness;
}
public void TestGenotype_RoundTripJSON()
{
var json = "{\"neurons\":[{\"innovation\":0,\"type\":4,\"mean\":0.5,\"sigma\":0,\"a\":0.5,\"b\":0.5,\"c\":0.5,\"d\":0.5},{\"innovation\":1,\"type\":4,\"mean\":0.5,\"sigma\":0,\"a\":0.5,\"b\":0.5,\"c\":0.5,\"d\":0.5},{\"innovation\":2,\"type\":4,\"mean\":0.5,\"sigma\":0,\"a\":0.5,\"b\":0.5,\"c\":0.5,\"d\":0.5}],\"synapses\":[{\"innovation\":0,\"weight\":0.5,\"from\":0,\"to\":1,\"enabled\":true},{\"innovation\":1,\"weight\":0.5,\"from\":0,\"to\":1,\"enabled\":true},{\"innovation\":2,\"weight\":0.5,\"from\":0,\"to\":1,\"enabled\":true}]}";
var deserializedGenotype = Genotype.FromJSON(JSON.Deserialize(json));
var serializedGenotype = JSON.Serialize(deserializedGenotype.ToJSON());
Assert.AreEqual(json, serializedGenotype, "JSON can convert round-trip");
}
public void TestGenotype_FromJSON()
{
var json = "{\"neurons\":[{\"innovation\":0,\"type\":4,\"mean\":0.5,\"sigma\":0,\"a\":0.5,\"b\":0.5,\"c\":0.5,\"d\":0.5},{\"innovation\":1,\"type\":4,\"mean\":0.5,\"sigma\":0,\"a\":0.5,\"b\":0.5,\"c\":0.5,\"d\":0.5},{\"innovation\":2,\"type\":4,\"mean\":0.5,\"sigma\":0,\"a\":0.5,\"b\":0.5,\"c\":0.5,\"d\":0.5}],\"synapses\":[{\"innovation\":0,\"weight\":0.5,\"from\":0,\"to\":1,\"enabled\":true},{\"innovation\":1,\"weight\":0.5,\"from\":0,\"to\":1,\"enabled\":true},{\"innovation\":2,\"weight\":0.5,\"from\":0,\"to\":1,\"enabled\":true}]}";
var deserializedGenotype = Genotype.FromJSON(JSON.Deserialize(json));
var comparer = new GenotypeEqualityComparer();
Assert.That(comparer.Equals(genotype, deserializedGenotype));
}
public Genotype(Genotype other)
{
this.neuronGenes = other.NeuronGenes
.Select(g => new NeuronGene(g))
.ToGeneList();
this.synapseGenes = other.SynapseGenes
.Select(g => new SynapseGene(g))
.ToGeneList();
}
public Genotype(Genotype other)
{
this.neuronGenes = other.NeuronGenes
.Select(g => new NeuronGene(g))
.ToGeneList();
this.synapseGenes = other.SynapseGenes
.Select(g => new SynapseGene(g))
.ToGeneList();
}
public void TestGenotype_Clone()
{
var clonedGenotype = new Genotype(genotype);
Assert.AreNotSame(genotype, clonedGenotype);
Assert.AreNotSame(genotype.NeuronGenes, clonedGenotype.NeuronGenes);
Assert.AreNotSame(genotype.SynapseGenes, clonedGenotype.SynapseGenes);
var comparer = new GenotypeEqualityComparer();
Assert.That(comparer.Equals(genotype, clonedGenotype));
}
public void Init()
{
var neuronGenes = Enumerable.Range(0, 3)
.Select(i => new NeuronGene(i, NeuronType.HiddenNeuron))
.ToGeneList();
var synapseGenes = Enumerable.Range(0, 3)
.Select(i => new SynapseGene(i, 0, 1, true, 0.5f))
.ToGeneList();
genotype = new Genotype(neuronGenes, synapseGenes);
}
public void Init()
{
var neuronGenes = Enumerable.Range(0, 3)
.Select(i => new NeuronGene(i, NeuronType.HiddenNeuron))
.ToGeneList();
var synapseGenes = Enumerable.Range(0, 3)
.Select(i => new SynapseGene(i, 0, 1, true, 0.5f))
.ToGeneList();
genotype = new Genotype(neuronGenes, synapseGenes);
}
public void Mutate(Genotype genotype, MutationResults results)
{
var innovationIds = genotype.NeuronGenes.InnovationIds;
for (int i = 0; i < innovationIds.Count; i++) {
if (Random.value < p) {
var innovationId = innovationIds[i];
genotype.NeuronGenes[innovationId] = NeuronGene.Random(innovationId);
results.replacedNeurons++;
}
}
}
public static Genotype FromPrototype(Genotype protoGenotype)
{
var neuronGenes = protoGenotype.NeuronGenes
.Select(g => NeuronGene.FromPrototype(g))
.ToGeneList();
var synapseGenes = protoGenotype.SynapseGenes
.Select(g => SynapseGene.FromPrototype(g))
.ToGeneList();
return(new Genotype(neuronGenes, synapseGenes));
}
public void Mutate(Genotype genotype, MutationResults results)
{
var innovationIds = genotype.SynapseGenes.InnovationIds;
for (int i = 0; i < innovationIds.Count; i++) {
if (Random.value < p) {
var synapseGene = genotype.SynapseGenes[innovationIds[i]];
synapseGene.weight = Mathf.Clamp01(RandomHelper.NextCauchy(synapseGene.weight, mutationScale));
genotype.SynapseGenes[synapseGene.InnovationId] = synapseGene;
results.perturbedSynapses++;
}
}
}
public void Mutate(Genotype genotype, MutationResults results)
{
var innovationIds = genotype.SynapseGenes.InnovationIds;
for (int i = 0; i < innovationIds.Count; i++) {
if (Random.value < p) {
var synapseGene = genotype.SynapseGenes[innovationIds[i]];
synapseGene.isEnabled = !synapseGene.isEnabled;
genotype.SynapseGenes[synapseGene.InnovationId] = synapseGene;
results.toggledSynapses++;
}
}
}
public void Mutate(Genotype genotype, MutationResults results)
{
var innovationIds = genotype.SynapseGenes.InnovationIds;
for (int i = 0; i < innovationIds.Count; i++) {
if (Random.value < p) {
var synapseGene = genotype.SynapseGenes[innovationIds[i]];
synapseGene.weight = Random.value;
genotype.SynapseGenes[synapseGene.InnovationId] = synapseGene;
results.replacedSynapses++;
}
}
}
public void Mutate(Genotype genotype, MutationResults results)
{
// We require a synapse to split
if (genotype.SynapseCount == 0)
{
return;
}
// Pick a synapse at random
var synapseIndex = Random.Range(0, genotype.SynapseCount);
var synapseGene = genotype.SynapseGenes.ElementAt(synapseIndex);
// Generate an innovation id for the operation
var innovationId = innovations.GetNeuronInnovationId(
synapseGene.fromNeuronId,
synapseGene.toNeuronId,
synapseGene.InnovationId
);
// If the genotype already contains the innovation id, bail
if (genotype.Contains(innovationId))
{
return;
}
var neuronGene = NeuronGene.Random(innovationId);
var neuronGenes = new GeneList <NeuronGene>(genotype.NeuronGenes);
neuronGenes.Add(neuronGene);
genotype.NeuronGenes = neuronGenes;
var synapseGenes = new GeneList <SynapseGene>(genotype.SynapseGenes);
var synapseGene1 = new SynapseGene(innovationId + 0,
synapseGene.fromNeuronId,
neuronGene.InnovationId,
true, 0.5f);
synapseGenes.Add(synapseGene1);
var synapseGene2 = new SynapseGene(innovationId + 1,
neuronGene.InnovationId,
synapseGene.toNeuronId,
true, 0.5f);
synapseGenes.Add(synapseGene2);
genotype.SynapseGenes = synapseGenes;
results.addedNeurons += 1;
results.addedSynapses += 2;
}
public void TestGenotype_Clone()
{
var clonedGenotype = new Genotype(genotype);
Assert.AreNotSame(genotype, clonedGenotype);
Assert.AreNotSame(genotype.NeuronGenes, clonedGenotype.NeuronGenes);
Assert.AreNotSame(genotype.SynapseGenes, clonedGenotype.SynapseGenes);
var comparer = new GenotypeEqualityComparer();
Assert.That(comparer.Equals(genotype, clonedGenotype));
}
public void TestPerturbSynapseMutator()
{
var mutator = new PerturbSynapseMutator(1.0f, 0.25f);
var mutatedGenotype = new Genotype(genotype);
var mutationResults = new MutationResults();
mutator.Mutate(mutatedGenotype, mutationResults);
Assert.AreEqual(3, mutationResults.perturbedSynapses);
// Compare gene counts
var comparer = new GenotypeShapeEqualityComparer();
Assert.That(comparer.Equals(mutatedGenotype, genotype));
}
public Genotype Crossover(Phenotype a, Phenotype b)
{
if (a == b)
{
return(new Genotype(a.Genotype));
}
var aGenotype = new Genotype(a.Genotype);
var bGenotype = new Genotype(b.Genotype);
var newNeuronGenes = CrossoverGenes <NeuronGene>(aGenotype.NeuronGenes, bGenotype.NeuronGenes, a.AdjustedFitness, b.AdjustedFitness);
var newSynapseGenes = CrossoverGenes <SynapseGene>(aGenotype.SynapseGenes, bGenotype.SynapseGenes, a.AdjustedFitness, b.AdjustedFitness);
return(new Genotype(newNeuronGenes, newSynapseGenes));
}
public void Mutate(Genotype genotype, MutationResults results)
{
var innovationIds = genotype.NeuronGenes.InnovationIds;
for (int i = 0; i < innovationIds.Count; i++)
{
if (Random.value < p)
{
var innovationId = innovationIds[i];
genotype.NeuronGenes[innovationId] = NeuronGene.Random(innovationId);
results.replacedNeurons++;
}
}
}
public void TestReplaceSynapseMutator()
{
var mutator = new ReplaceSynapseMutator(1.0f);
var mutatedGenotype = new Genotype(genotype);
var mutationResults = new MutationResults();
mutator.Mutate(mutatedGenotype, mutationResults);
Assert.AreEqual(mutationResults.replacedSynapses, 3);
// Compare gene counts
var comparer = new GenotypeShapeEqualityComparer();
Assert.That(comparer.Equals(mutatedGenotype, genotype));
}
public void Mutate(Genotype genotype, MutationResults results)
{
var innovationIds = genotype.SynapseGenes.InnovationIds;
for (int i = 0; i < innovationIds.Count; i++)
{
if (Random.value < p)
{
var synapseGene = genotype.SynapseGenes[innovationIds[i]];
synapseGene.weight = Mathf.Clamp01(RandomHelper.NextCauchy(synapseGene.weight, mutationScale));
genotype.SynapseGenes[synapseGene.InnovationId] = synapseGene;
results.perturbedSynapses++;
}
}
}
public void TestMutationCollection()
{
var genotypes = Enumerable.Range(0, 100).Select(_ =>
Genotype.FromPrototype(protoGenotype)).ToArray();
var mutations = new NEAT.MutationCollection();
mutations.Add(0.75f, new NEAT.AddNeuronMutator(innovations));
mutations.Add(0.25f, new NEAT.AddSynapseMutator(innovations));
var results = mutations.Mutate(genotypes);
Assert.AreEqual(75, results.addedNeurons, 75 * 0.15f);
Assert.AreEqual(175, results.addedSynapses, 175 * 0.25f);
}
public void Mutate(Genotype genotype, MutationResults results)
{
var innovationIds = genotype.SynapseGenes.InnovationIds;
for (int i = 0; i < innovationIds.Count; i++)
{
if (Random.value < p)
{
var synapseGene = genotype.SynapseGenes[innovationIds[i]];
synapseGene.weight = Random.value;
genotype.SynapseGenes[synapseGene.InnovationId] = synapseGene;
results.replacedSynapses++;
}
}
}
public void Mutate(Genotype genotype, MutationResults results)
{
var innovationIds = genotype.SynapseGenes.InnovationIds;
for (int i = 0; i < innovationIds.Count; i++)
{
if (Random.value < p)
{
var synapseGene = genotype.SynapseGenes[innovationIds[i]];
synapseGene.isEnabled = !synapseGene.isEnabled;
genotype.SynapseGenes[synapseGene.InnovationId] = synapseGene;
results.toggledSynapses++;
}
}
}
public void Mutate(Genotype genotype, MutationResults results)
{
// We require a synapse to split
if (genotype.SynapseCount == 0) {
return;
}
// Pick a synapse at random
var synapseIndex = Random.Range(0, genotype.SynapseCount);
var synapseGene = genotype.SynapseGenes.ElementAt(synapseIndex);
// Generate an innovation id for the operation
var innovationId = innovations.GetNeuronInnovationId(
synapseGene.fromNeuronId,
synapseGene.toNeuronId,
synapseGene.InnovationId
);
// If the genotype already contains the innovation id, bail
if (genotype.Contains(innovationId)) {
return;
}
var neuronGene = NeuronGene.Random(innovationId);
var neuronGenes = new GeneList<NeuronGene>(genotype.NeuronGenes);
neuronGenes.Add(neuronGene);
genotype.NeuronGenes = neuronGenes;
var synapseGenes = new GeneList<SynapseGene>(genotype.SynapseGenes);
var synapseGene1 = new SynapseGene(innovationId + 0,
synapseGene.fromNeuronId,
neuronGene.InnovationId,
true, 0.5f);
synapseGenes.Add(synapseGene1);
var synapseGene2 = new SynapseGene(innovationId + 1,
neuronGene.InnovationId,
synapseGene.toNeuronId,
true, 0.5f);
synapseGenes.Add(synapseGene2);
genotype.SynapseGenes = synapseGenes;
results.addedNeurons += 1;
results.addedSynapses += 2;
}
public void Init()
{
var innovations = new InnovationCollection();
var neuronGenes = Enumerable.Range(0, 3).Select(i => {
var inId = innovations.GetInitialNeuronInnovationId(i);
return(new NeuronGene(inId, NeuronType.HiddenNeuron));
}).ToGeneList();
var synapseGenes = Enumerable.Range(0, 3).Select(i => {
var inId = innovations.GetSynapseInnovationId(i, i + 1);
return(new SynapseGene(inId, i, i + 1, true));
}).ToGeneList();
protoGenotype = new Genotype(neuronGenes, synapseGenes);
}
public MutationResults Mutate(Genotype[] genotypes)
{
var orderedMutators = mutators.OrderBy(m => m.Key);
var results = new MutationResults();
foreach (var genotype in genotypes) {
foreach (var p_mutator in orderedMutators) {
var p = p_mutator.Key;
var mutator = p_mutator.Value;
if (Random.value < p) {
mutator.Mutate(genotype, results);
break;
}
}
}
return results;
}
public void TestPerturbNeuronMutator()
{
var mutator = new PerturbNeuronMutator(1.0f, 0.25f);
var mutatedGenotype = new Genotype(genotype);
var mutationResults = new MutationResults();
mutator.Mutate(mutatedGenotype, mutationResults);
Assert.AreEqual(3, mutationResults.perturbedNeurons);
// Compare gene counts
var comparer = new GenotypeShapeEqualityComparer();
Assert.That(comparer.Equals(mutatedGenotype, genotype));
}
public void Init()
{
var innovations = new InnovationCollection();
var neuronGenes = Enumerable.Range(0, 3).Select(i => {
var inId = innovations.GetInitialNeuronInnovationId(i);
return new NeuronGene(inId, NeuronType.HiddenNeuron);
}).ToGeneList();
var synapseGenes = Enumerable.Range(0, 3).Select(i => {
var inId = innovations.GetSynapseInnovationId(i, i + 1);
return new SynapseGene(inId, i, i + 1, true);
}).ToGeneList();
genotype = new Genotype(neuronGenes, synapseGenes);
}
public void TestReplaceSynapseMutator()
{
var mutator = new ReplaceSynapseMutator(1.0f);
var mutatedGenotype = new Genotype(genotype);
var mutationResults = new MutationResults();
mutator.Mutate(mutatedGenotype, mutationResults);
Assert.AreEqual(mutationResults.replacedSynapses, 3);
// Compare gene counts
var comparer = new GenotypeShapeEqualityComparer();
Assert.That(comparer.Equals(mutatedGenotype, genotype));
}
public void Mutate(Genotype genotype, MutationResults results)
{
var innovationIds = genotype.NeuronGenes.InnovationIds;
for (int i = 0; i < innovationIds.Count; i++) {
if (Random.value < p) {
var neuronGene = genotype.NeuronGenes[innovationIds[i]];
neuronGene.a = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.a, mutationScale));
neuronGene.b = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.b, mutationScale));
neuronGene.c = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.c, mutationScale));
neuronGene.d = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.d, mutationScale));
neuronGene.mean = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.mean, mutationScale));
neuronGene.sigma = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.sigma, mutationScale));
genotype.NeuronGenes[neuronGene.InnovationId] = neuronGene;
results.perturbedNeurons++;
}
}
}
public void TestSpecie()
{
var specie = new Specie(13, protoGenotype, 0, 0, 0.0f);
for (int i = 0; i < 100; i++)
{
var gt = Genotype.FromPrototype(protoGenotype);
var pt = new Phenotype(gt);
specie.Add(pt);
}
foreach (var pt in specie)
{
pt.AdjustedFitness = 1.0f / (float)specie.Count;
}
Assert.AreEqual(0.0f, specie.MeanFitness, 0.001f);
Assert.AreEqual(0.01f, specie.MeanAdjustedFitness, 0.001f);
Assert.AreEqual(100, specie.Count);
}
public void Mutate(Genotype genotype, MutationResults results)
{
var innovationIds = genotype.NeuronGenes.InnovationIds;
for (int i = 0; i < innovationIds.Count; i++)
{
if (Random.value < p)
{
var neuronGene = genotype.NeuronGenes[innovationIds[i]];
neuronGene.a = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.a, mutationScale));
neuronGene.b = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.b, mutationScale));
neuronGene.c = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.c, mutationScale));
neuronGene.d = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.d, mutationScale));
neuronGene.mean = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.mean, mutationScale));
neuronGene.sigma = Mathf.Clamp01(RandomHelper.NextCauchy(neuronGene.sigma, mutationScale));
genotype.NeuronGenes[neuronGene.InnovationId] = neuronGene;
results.perturbedNeurons++;
}
}
}
public void Init()
{
var neuronGenesA = Enumerable.Range(0, 3)
.Select(i => new NeuronGene(i, NeuronType.HiddenNeuron))
.ToGeneList();
var synapseGenesA = Enumerable.Range(0, 3)
.Select(i => new SynapseGene(i, 0, 1, true, 0.5f))
.ToGeneList();
genotypeA = new Genotype(neuronGenesA, synapseGenesA);
genotypeB = new Genotype(genotypeA);
var neuronGenesC = Enumerable.Range(0, 3)
.Select(i => new NeuronGene(i, NeuronType.HiddenNeuron))
.ToGeneList();
var synapseGenesC = Enumerable.Range(0, 3)
.Select(i => new SynapseGene(i + 1, -1, 1, true, 0.0f))
.ToGeneList();
genotypeC = new Genotype(neuronGenesC, synapseGenesC);
}
public void Init()
{
var neuronGenesA = Enumerable.Range(0, 3)
.Select(i => new NeuronGene(i, NeuronType.HiddenNeuron))
.ToGeneList();
var synapseGenesA = Enumerable.Range(0, 3)
.Select(i => new SynapseGene(i, 0, 1, true, 0.5f))
.ToGeneList();
genotypeA = new Genotype(neuronGenesA, synapseGenesA);
genotypeB = new Genotype(genotypeA);
var neuronGenesC = Enumerable.Range(0, 3)
.Select(i => new NeuronGene(i, NeuronType.HiddenNeuron))
.ToGeneList();
var synapseGenesC = Enumerable.Range(0, 3)
.Select(i => new SynapseGene(i + 1, -1, 1, true, 0.0f))
.ToGeneList();
genotypeC = new Genotype(neuronGenesC, synapseGenesC);
}
public void Mutate(Genotype genotype, MutationResults results)
{
var neuronIndexA = Random.Range(0, genotype.InputNeurons.Count);
var neuronGeneA = genotype.InputNeurons[neuronIndexA];
var validNeurons = genotype.OutputNeurons
.Where(_ => Random.value < p)
.Where(neuronGeneB => genotype.SynapseGenes.None(s =>
neuronGeneA.InnovationId == s.fromNeuronId &&
neuronGeneB.InnovationId == s.toNeuronId));
foreach (var neuronGeneB in validNeurons) {
var synapseInnovationId = innovations.GetSynapseInnovationId(neuronGeneA.InnovationId, neuronGeneB.InnovationId);
var synapseGene = SynapseGene.Random(synapseInnovationId, neuronGeneA.InnovationId, neuronGeneB.InnovationId, true);
var synapseGenes = new GeneList<SynapseGene>(genotype.SynapseGenes);
synapseGenes.Add(synapseGene);
genotype.SynapseGenes = synapseGenes;
results.addedSynapses += 1;
}
}
public void Init()
{
var innovations = new InnovationCollection();
var neuronGenes = Enumerable.Range(0, 3).Select(i => {
var inId = innovations.GetInitialNeuronInnovationId(i);
return new NeuronGene(inId, NeuronType.HiddenNeuron);
}).ToGeneList();
var synapseGenes = Enumerable.Range(0, 3).Select(i => {
var inId = innovations.GetSynapseInnovationId(i, i + 1);
return new SynapseGene(inId, i, i + 1, true);
}).ToGeneList();
var protoGenotype = new Genotype(neuronGenes, synapseGenes);
phenotypes = Enumerable.Range(0, 100).Select(_ => {
var gt = Genotype.FromPrototype(protoGenotype);
var pt = new Phenotype(gt);
return pt;
}).ToArray();
}
public void Mutate(Genotype genotype, MutationResults results)
{
var neuronIndexA = Random.Range(0, genotype.NeuronCount);
var neuronGeneA = genotype.NeuronGenes.ElementAt(neuronIndexA);
var candidates = new List <NeuronGene>(genotype.NeuronGenes);
candidates.Shuffle();
NeuronGene neuronGeneB = default(NeuronGene);
bool foundNeuron = false;
for (var i = 0; i < candidates.Count; i++)
{
neuronGeneB = candidates[i];
var exists = genotype.SynapseGenes.Any(s =>
neuronGeneA.InnovationId == s.fromNeuronId &&
neuronGeneB.InnovationId == s.toNeuronId);
if (!exists)
{
foundNeuron = true;
break;
}
}
if (foundNeuron)
{
var synapseInnovationId = innovations.GetSynapseInnovationId(neuronGeneA.InnovationId, neuronGeneB.InnovationId);
var synapseGene = new SynapseGene(synapseInnovationId, neuronGeneA.InnovationId, neuronGeneB.InnovationId, true);
var synapseGenes = new GeneList <SynapseGene>(genotype.SynapseGenes);
synapseGenes.Add(synapseGene);
genotype.SynapseGenes = synapseGenes;
results.addedSynapses += 1;
}
}
public void TestAddSynapseMutator()
{
var mutator = new AddSynapseMutator(innovations);
var mutatedGenotype = new Genotype(genotype);
var mutationResults = new MutationResults();
mutator.Mutate(mutatedGenotype, mutationResults);
Assert.AreEqual(1, mutationResults.addedSynapses);
// Compare gene counts
Assert.AreEqual(mutatedGenotype.NeuronCount, genotype.NeuronCount);
Assert.AreEqual(1, mutatedGenotype.SynapseCount - genotype.SynapseCount);
// Compare innovation ids
var aN = new []{0, 1, 2};
var bN = mutatedGenotype.NeuronGenes.Select(g => g.InnovationId);
Assert.That(aN.SequenceEqual(bN));
var aS = new []{3, 4, 5, 6};
var bS = mutatedGenotype.SynapseGenes.Select(g => g.InnovationId);
Assert.That(aS.SequenceEqual(bS));
}
public void Mutate(Genotype genotype, MutationResults results)
{
var neuronIndexA = Random.Range(0, genotype.InputNeurons.Count);
var neuronGeneA = genotype.InputNeurons[neuronIndexA];
var validNeurons = genotype.OutputNeurons
.Where(_ => Random.value < p)
.Where(neuronGeneB => genotype.SynapseGenes.None(s =>
neuronGeneA.InnovationId == s.fromNeuronId &&
neuronGeneB.InnovationId == s.toNeuronId));
foreach (var neuronGeneB in validNeurons)
{
var synapseInnovationId = innovations.GetSynapseInnovationId(neuronGeneA.InnovationId, neuronGeneB.InnovationId);
var synapseGene = SynapseGene.Random(synapseInnovationId, neuronGeneA.InnovationId, neuronGeneB.InnovationId, true);
var synapseGenes = new GeneList <SynapseGene>(genotype.SynapseGenes);
synapseGenes.Add(synapseGene);
genotype.SynapseGenes = synapseGenes;
results.addedSynapses += 1;
}
}
public void TestMultipointCrossover()
{
var innovationIdsA = new int[] { 1, 2, 3, 4, 5, 8 };
var neuronGenesA = innovationIdsA.Select(i => new NeuronGene(i, NeuronType.HiddenNeuron)).ToGeneList();
var innovationIdsB = new int[] { 1, 2, 3, 4, 5, 6, 7, 9, 10 };
var neuronGenesB = innovationIdsB.Select(i => new NeuronGene(i, NeuronType.HiddenNeuron)).ToGeneList();
var expectedInnovationIds = new int[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
var genotypeA = new Genotype(neuronGenesA, new GeneList <SynapseGene>(0));
var genotypeB = new Genotype(neuronGenesB, new GeneList <SynapseGene>(0));
var phenotypeA = new Phenotype(genotypeA);
var phenotypeB = new Phenotype(genotypeB);
var crossover = new MultipointCrossover();
var offspring = crossover.Crossover(phenotypeA, phenotypeB);
var offspringInnovationIds = offspring.NeuronGenes.Select(g =>
g.InnovationId).ToArray();
Assert.AreEqual(expectedInnovationIds, offspringInnovationIds);
}
public void TestMultipointCrossover()
{
var innovationIdsA = new int[]{1, 2, 3, 4, 5, 8};
var neuronGenesA = innovationIdsA.Select(i => new NeuronGene(i, NeuronType.HiddenNeuron)).ToGeneList();
var innovationIdsB = new int[]{1, 2, 3, 4, 5, 6, 7, 9, 10};
var neuronGenesB = innovationIdsB.Select(i => new NeuronGene(i, NeuronType.HiddenNeuron)).ToGeneList();
var expectedInnovationIds = new int[]{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
var genotypeA = new Genotype(neuronGenesA, new GeneList<SynapseGene>(0));
var genotypeB = new Genotype(neuronGenesB, new GeneList<SynapseGene>(0));
var phenotypeA = new Phenotype(genotypeA);
var phenotypeB = new Phenotype(genotypeB);
var crossover = new MultipointCrossover();
var offspring = crossover.Crossover(phenotypeA, phenotypeB);
var offspringInnovationIds = offspring.NeuronGenes.Select(g =>
g.InnovationId).ToArray();
Assert.AreEqual(expectedInnovationIds, offspringInnovationIds);
}
public Phenotype(Genotype genotype)
{
this.genotype = genotype;
}
public bool MeasureDistance(Genotype a, Genotype b, float threshold)
{
return(MeasureDistance(a, b) <= threshold);
}
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();
}
}
public bool MeasureDistance(Genotype a, Genotype b, float threshold)
{
return MeasureDistance(a, b) <= threshold;
}
public float MeasureDistance(Genotype a, Genotype b)
{
var neuronCount = Mathf.Max(a.NeuronCount, b.NeuronCount);
if (neuronCount == 0) { // Don't divide by zero
neuronCount = 1;
}
var neuronDiff = 0.0f;
var neuronExcess = 0.0f;
var neuronDisjoint = 0.0f;
foreach (var t in new GeneEnumerable<NeuronGene>(a.NeuronGenes, b.NeuronGenes)) {
switch (t.First) {
case HistoricalGeneTypes.Aligned:
neuronDiff += Mathf.Abs(a.NeuronGenes[t.Second].a - b.NeuronGenes[t.Third].a);
neuronDiff += Mathf.Abs(a.NeuronGenes[t.Second].b - b.NeuronGenes[t.Third].b);
neuronDiff += Mathf.Abs(a.NeuronGenes[t.Second].c - b.NeuronGenes[t.Third].c);
neuronDiff += Mathf.Abs(a.NeuronGenes[t.Second].d - b.NeuronGenes[t.Third].d);
break;
case HistoricalGeneTypes.DisjointA:
case HistoricalGeneTypes.DisjointB:
neuronDisjoint++;
break;
case HistoricalGeneTypes.ExcessA:
case HistoricalGeneTypes.ExcessB:
neuronExcess++;
break;
}
}
var synapseCount = Mathf.Max(a.SynapseCount, b.SynapseCount);
if (synapseCount == 0) { // Don't divide by zero
synapseCount = 1;
}
var synapseDiff = 0.0f;
var synapseExcess = 0.0f;
var synapseDisjoint = 0.0f;
foreach (var t in new GeneEnumerable<SynapseGene>(a.SynapseGenes, b.SynapseGenes)) {
switch (t.First) {
case HistoricalGeneTypes.Aligned:
synapseDiff += Mathf.Abs(a.SynapseGenes[t.Second].weight - b.SynapseGenes[t.Third].weight);
break;
case HistoricalGeneTypes.DisjointA:
case HistoricalGeneTypes.DisjointB:
synapseDisjoint++;
break;
case HistoricalGeneTypes.ExcessA:
case HistoricalGeneTypes.ExcessB:
synapseExcess++;
break;
}
}
neuronDiff /= (float)neuronCount;
synapseDiff /= (float)synapseCount;
return (excessCoeff * neuronExcess) +
(excessCoeff * synapseExcess) +
(disjointCoeff * neuronDisjoint) +
(disjointCoeff * synapseDisjoint) +
(mutationCoeff * neuronDiff) +
(mutationCoeff * synapseDiff);
}
public static Genotype FromPrototype(Genotype protoGenotype)
{
var neuronGenes = protoGenotype.NeuronGenes
.Select(g => NeuronGene.FromPrototype(g))
.ToGeneList();
var synapseGenes = protoGenotype.SynapseGenes
.Select(g => SynapseGene.FromPrototype(g))
.ToGeneList();
return new Genotype(neuronGenes, synapseGenes);
}
public Phenotype(Genotype genotype)
{
this.genotype = genotype;
}
public static NetworkPorts FromGenotype(NEAT.Genotype genotype)
{
var neuronGenes = genotype.NeuronGenes.ToList();
var synapseGenes = genotype.SynapseGenes.ToList();
var network = new Neural.Network(20ul);
var upperNeurons = new Dictionary <int, IReceptiveField>();
var lowerNeurons = new Dictionary <int, IReceptiveField>();
var positionNeurons = new Dictionary <int, IReceptiveField>();
var speedNeurons = new Dictionary <int, IReceptiveField>();
foreach (var neuronGene in neuronGenes)
{
float a = NumberHelper.Scale(neuronGene.a, 0.02f, 0.1f); // 0.1
float b = NumberHelper.Scale(neuronGene.b, 0.2f, 0.25f); // 0.2
float c = NumberHelper.Scale(neuronGene.c, -65.0f, -50.0f); // -65.0
float d = NumberHelper.Scale(neuronGene.d, 0.05f, 8.0f); // 2.0
try {
var id = (int)network.AddNeuron(Neural.IzhikevichConfig.Of(a, b, c, d));
var mean = 0.0f;
var sigma = 0.0f;
switch (neuronGene.type)
{
case NeuronType.UpperNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -180.0f, 180.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 180.0f);
upperNeurons[id] = new SignReceptiveField(mean, sigma);
break;
case NeuronType.LowerNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -180.0f, 180.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 180.0f);
lowerNeurons[id] = new SignReceptiveField(mean, sigma);
break;
case NeuronType.PositionNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -12.0f, 12.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 12.0f);
positionNeurons[id] = new SignReceptiveField(mean, sigma);
break;
case NeuronType.SpeedNeuron:
mean = NumberHelper.Scale(neuronGene.mean, -1.0f, 1.0f);
sigma = NumberHelper.Scale(neuronGene.sigma, 0.0f, 1000.0f);
speedNeurons[id] = new MulReceptiveField(mean, sigma);
break;
}
} catch (Exception e) {
Debug.LogException(e);
}
}
// Connect each input neuron to the output neuron.
foreach (var synapseGene in synapseGenes)
{
if (!synapseGene.isEnabled)
{
continue;
}
var fromNeuronId = neuronGenes.FindIndex(n => n.InnovationId == synapseGene.fromNeuronId);
var toNeuronId = neuronGenes.FindIndex(n => n.InnovationId == synapseGene.toNeuronId);
Assert.AreNotEqual(fromNeuronId, -1, "Must find from-neuron id");
Assert.AreNotEqual(toNeuronId, -1, "Must find to-neuron id");
float weight = NumberHelper.Scale(synapseGene.weight, -40.0f, 40.0f);
try {
network.AddSynapse((ulong)fromNeuronId, (ulong)toNeuronId, Neural.STDPConfig.Of(weight, -40.0f, 40.0f));
} catch (Exception e) {
Debug.LogException(e);
}
}
return(new NetworkPorts(network, upperNeurons, lowerNeurons, positionNeurons, speedNeurons));
}
public float MeasureDistance(Genotype a, Genotype b)
{
var neuronCount = Mathf.Max(a.NeuronCount, b.NeuronCount);
if (neuronCount == 0) // Don't divide by zero
{
neuronCount = 1;
}
var neuronDiff = 0.0f;
var neuronExcess = 0.0f;
var neuronDisjoint = 0.0f;
foreach (var t in new GeneEnumerable <NeuronGene>(a.NeuronGenes, b.NeuronGenes))
{
switch (t.First)
{
case HistoricalGeneTypes.Aligned:
neuronDiff += Mathf.Abs(a.NeuronGenes[t.Second].a - b.NeuronGenes[t.Third].a);
neuronDiff += Mathf.Abs(a.NeuronGenes[t.Second].b - b.NeuronGenes[t.Third].b);
neuronDiff += Mathf.Abs(a.NeuronGenes[t.Second].c - b.NeuronGenes[t.Third].c);
neuronDiff += Mathf.Abs(a.NeuronGenes[t.Second].d - b.NeuronGenes[t.Third].d);
break;
case HistoricalGeneTypes.DisjointA:
case HistoricalGeneTypes.DisjointB:
neuronDisjoint++;
break;
case HistoricalGeneTypes.ExcessA:
case HistoricalGeneTypes.ExcessB:
neuronExcess++;
break;
}
}
var synapseCount = Mathf.Max(a.SynapseCount, b.SynapseCount);
if (synapseCount == 0) // Don't divide by zero
{
synapseCount = 1;
}
var synapseDiff = 0.0f;
var synapseExcess = 0.0f;
var synapseDisjoint = 0.0f;
foreach (var t in new GeneEnumerable <SynapseGene>(a.SynapseGenes, b.SynapseGenes))
{
switch (t.First)
{
case HistoricalGeneTypes.Aligned:
synapseDiff += Mathf.Abs(a.SynapseGenes[t.Second].weight - b.SynapseGenes[t.Third].weight);
break;
case HistoricalGeneTypes.DisjointA:
case HistoricalGeneTypes.DisjointB:
synapseDisjoint++;
break;
case HistoricalGeneTypes.ExcessA:
case HistoricalGeneTypes.ExcessB:
synapseExcess++;
break;
}
}
neuronDiff /= (float)neuronCount;
synapseDiff /= (float)synapseCount;
return((excessCoeff * neuronExcess) +
(excessCoeff * synapseExcess) +
(disjointCoeff * neuronDisjoint) +
(disjointCoeff * synapseDisjoint) +
(mutationCoeff * neuronDiff) +
(mutationCoeff * synapseDiff));
}
