public CPPNNEATGenome(ICPPNNEATGA parentGA, CPPNNEATGenome <PType> parent, CPPNNEATGenome <PType> partner)
: this(parentGA)
{
var differences = new DifferenceAnalysis(parent.GeneCollection, partner.GeneCollection);
var disjointAndExcessSource = differences.FirstCollection;
if (partner.Score > parent.Score)
{
disjointAndExcessSource = differences.SecondCollection;
}
else if (partner.Score == parent.Score)
{
disjointAndExcessSource = (MathExtensions.RandomNumber() <= 0.5) ? differences.FirstCollection :
differences.SecondCollection;
}
Func <CPPNNEATLinkGene, CPPNNEATLinkGene, Complex> weightSelector = null;
if (MathExtensions.RandomNumber() <= parentGA.MateByAveragingRate)
{
weightSelector = (first, second) => (first.Weight + second.Weight) / 2;
}
else
{
weightSelector = (first, second) => (MathExtensions.RandomNumber() <= 0.5) ? first.Weight : second.Weight;
}
foreach (var match in differences.Matches)
{
var geneToCopy = match.FirstCollection;
var newGene = geneToCopy.Copy();
newGene.Enabled = true;
newGene.Weight = weightSelector(match.FirstCollection, match.SecondCollection);
GeneCollection.TryAddLinkGene(newGene);
if ((!match.FirstCollection.Enabled || !match.SecondCollection.Enabled) &&
MathExtensions.RandomNumber() <= parentGA.DisableGeneRate)
{
GeneCollection.DisableLinkGene(newGene.InnovationNumber);
}
}
foreach (var linkGene in disjointAndExcessSource
.Disjoint.Union(disjointAndExcessSource.Excess))
{
GeneCollection.TryAddLinkGene(linkGene.Copy());
}
}
protected override void InnerMutate()
{
var parent = Parent as ICPPNNEATGA;
var mutationProbabilities = new double[TOTAL_MUTATIONS];
mutationProbabilities[LINK_MUTATION_INDEX] = parent.NewLinkRate;
mutationProbabilities[NEURON_MUTATION_INDEX] = parent.NewNeuronRate;
mutationProbabilities[WEIGHT_MUTATION_INDEX] = parent.WeightMutationRate;
var selected = Enumerable.Range(0, TOTAL_MUTATIONS).RouletteWheelSingle(i => mutationProbabilities[i]);
switch (selected)
{
case LINK_MUTATION_INDEX:
GeneCollection.TryCreateLinkGene();
break;
case NEURON_MUTATION_INDEX:
GeneCollection.TryCreateNeuronGene();
break;
case WEIGHT_MUTATION_INDEX:
foreach (var link in GeneCollection.LinkGenes)
{
if (MathExtensions.RandomNumber() <= parent.WeightMutationRate)
{
if (MathExtensions.RandomNumber() <= parent.WeightPertubationRate)
{
link.Weight += MathExtensions.ComplexRandom(-parent.MaxPerturbation,
parent.MaxPerturbation);
}
else
{
link.Weight = parent.GetRandomWeight();
}
}
}
break;
}
}
public override void Initialise()
{
GeneCollection.Initialise();
}
protected CPPNNetwork GetNetwork()
{
GeneCollection.Update();
return(GeneCollection.Phenome);
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object can be used to retrieve data from input parameters and
/// to store data in output parameters.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
Curve boundary = new PolylineCurve();
bool shouldInstantiateRooms = false;
adjStrList = new List <string>();
List <Line> adjLines = new List <Line>();
bool shouldClearGenes = false;
bool springCollAllGenes = false;
bool adjustArea = false;
DA.GetData("Reset", ref shouldInstantiateRooms);
DA.GetData("Boundary", ref boundary);
currentInputRooms.Clear();
DA.GetDataList(1, currentInputRooms);
if (shouldInstantiateRooms || rooms.Count == 0 || boundaryArea != AreaMassProperties.Compute(boundary).Area ||
originalInputRooms == null || !RoomListsAreEqual(originalInputRooms, currentInputRooms))
{
shouldClearGenes = true;
boundaryArea = AreaMassProperties.Compute(boundary).Area;
rooms.Clear();
originalInputRooms.Clear();
DA.GetDataList(1, rooms);
DA.GetDataList(1, originalInputRooms);
}
DA.GetData("ProportionThreshold", ref proportionThreshold);
DA.GetData("FF Balance", ref Gene.fitnessFunctionBalance);
DA.GetDataList("Adjacencies", adjStrList);
DA.GetData("SpringCollAllGenes", ref springCollAllGenes);
DA.GetData("AdjustArea", ref adjustArea);
int[,] adjArray = new int[adjStrList.Count, 2];
for (int i = 0; i < adjStrList.Count; i++)
{
adjArray[i, 0] = Int32.Parse((adjStrList[i].Split(new string[] { "-" }, StringSplitOptions.RemoveEmptyEntries)[0]));
adjArray[i, 1] = Int32.Parse((adjStrList[i].Split(new string[] { "-" }, StringSplitOptions.RemoveEmptyEntries)[1]));
}
Gene.proportionThreshold = proportionThreshold;
Gene.adjacencyList = adjArray;
if (shouldClearGenes)
{
// Adapt the summarized rooms area to the boundary area if it is needed
if (adjustArea)
{
double roomSumArea = 0;
foreach (Curve room in rooms)
{
roomSumArea += AreaMassProperties.Compute(room).Area;
}
foreach (Curve room in rooms)
{
room.Transform(Transform.Scale(room.GetBoundingBox(false).Center, Math.Sqrt(boundaryArea / roomSumArea)));// AreaMassProperties.Compute(room).Area;
}
}
shouldClearGenes = false;
geneCollection = new GeneCollection(15, boundary);
// Let's create some starting genes
foreach (Gene gene in geneCollection.genes)
{
if (Menu_ShuffleRoomsAtFirst)
{
gene.InstantiateRandomly(rooms);
}
else
{
gene.Instantiate(rooms);
}
}
}
geneCollection.Iterate();
if (springCollAllGenes)
{
for (int l = 0; l < geneCollection.genes.Count; l++)
{
Gene gene = geneCollection.genes[l];
rooms = gene.GetCurves();
rooms = CollisionDetectionMain(rooms, boundary);
gene.collection.Clear();
gene.Instantiate(rooms);
}
}
rooms = geneCollection.GetBest();
AdjacentContraction(rooms, adjStrList, boundary, out adjLines);
List <GH_Curve> GH_rooms = new List <GH_Curve>();
foreach (Curve c in rooms)
{
GH_rooms.Add(new GH_Curve(c));
}
DA.SetDataList(0, GH_rooms);
DA.SetDataList(1, adjLines);
}
