/// <inheritdoc/>
public override void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var target = ObtainGenome(parents, parentIndex, offspring,
offspringIndex);
if (target.LinksChromosome.Count < MinLink)
{
// don't remove from small genomes
return;
}
// determine the target and remove
var index = RangeRandomizer.RandomInt(0, target
.LinksChromosome.Count - 1);
NEATLinkGene targetGene = target.LinksChromosome[index];
target.LinksChromosome.Remove(targetGene);
// if this orphaned any nodes, then kill them too!
if (!IsNeuronNeeded(target, targetGene.FromNeuronId))
{
RemoveNeuron(target, targetGene.FromNeuronId);
}
if (!IsNeuronNeeded(target, targetGene.ToNeuronId))
{
RemoveNeuron(target, targetGene.ToNeuronId);
}
}
private void NewCase_Click(object sender, RoutedEventArgs e)
{
EncogRandom r = new EncogRandom();
this.resolution = int.Parse(((ComboBoxItem)Resolution.SelectedValue).Content.ToString());
this.testCase.InitTestCase(r.Next(3));
Render();
}
/// <inheritdoc/>
public NEATGenome Factor(EncogRandom rnd, NEATPopulation pop,
int inputCount, int outputCount,
double connectionDensity)
{
return new NEATGenome(rnd, pop, inputCount, outputCount,
connectionDensity);
}
/// <inheritdoc />
public void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
IEvolutionaryOperator opp = _components.Pick(rnd);
opp.PerformOperation(rnd, parents, parentIndex, offspring,
offspringIndex);
}
/// <summary>
/// Construct the EA worker.
/// </summary>
/// <param name="theTrain">The trainer.</param>
/// <param name="theSpecies">The species.</param>
public EAWorker(BasicEA theTrain, ISpecies theSpecies)
{
_train = theTrain;
_species = theSpecies;
_rnd = _train.RandomNumberFactory.Factor();
_parents = new IGenome[_train.Operators.MaxParents()];
_children = new IGenome[_train.Operators.MaxOffspring()];
}
/// <inheritdoc />
public void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var program = (EncogProgram) parents[0];
EncogProgramContext context = program.Context;
EncogProgram result = context.CloneProgram(program);
MutateNode(rnd, result.RootNode);
offspring[0] = result;
}
/// <inheritdoc/>
public override void PerformOperation(EncogRandom rnd, IGenome[] parents, int parentIndex,
IGenome[] offspring, int offspringIndex)
{
int countTrysToAddLink = Owner.MaxTries;
NEATGenome target = ObtainGenome(parents, parentIndex, offspring,
offspringIndex);
// the link will be between these two neurons
long neuron1Id = -1;
long neuron2Id = -1;
// try to add a link
while ((countTrysToAddLink--) > 0)
{
NEATNeuronGene neuron1 = ChooseRandomNeuron(target, true);
NEATNeuronGene neuron2 = ChooseRandomNeuron(target, false);
if (neuron1 == null || neuron2 == null)
{
return;
}
// do not duplicate
// do not go to a bias neuron
// do not go from an output neuron
// do not go to an input neuron
if (!IsDuplicateLink(target, neuron1.Id, neuron2.Id)
&& (neuron2.NeuronType != NEATNeuronType.Bias)
&& (neuron2.NeuronType != NEATNeuronType.Input))
{
if (((NEATPopulation)Owner.Population).ActivationCycles != 1
|| neuron1.NeuronType != NEATNeuronType.Output)
{
neuron1Id = neuron1.Id;
neuron2Id = neuron2.Id;
break;
}
}
}
// did we fail to find a link
if ((neuron1Id < 0) || (neuron2Id < 0))
{
return;
}
double r = ((NEATPopulation)target.Population).WeightRange;
CreateLink(target, neuron1Id, neuron2Id,
RangeRandomizer.Randomize(rnd, -r, r));
target.SortGenes();
}
/// <inheritdoc />
public override ProgramNode CreateNode(EncogRandom rnd, EncogProgram program,
int depthRemaining, IList<EPLValueType> types)
{
int actualDepthRemaining = depthRemaining;
if (rnd.NextDouble() > 0.5)
{
return _fullGenerator.CreateNode(rnd, program,
actualDepthRemaining, types);
}
return _growGenerator.CreateNode(rnd, program,
actualDepthRemaining, types);
}
/// <inheritdoc />
public void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var parent1 = (EncogProgram) parents[0];
var parent2 = (EncogProgram) parents[1];
offspring[0] = null;
EncogProgramContext context = parent1.Context;
int size1 = parent1.RootNode.Count;
int size2 = parent2.RootNode.Count;
bool done = false;
int tries = 100;
while (!done)
{
int p1Index = rnd.Next(size1);
int p2Index = rnd.Next(size2);
var holder1 = new LevelHolder(p1Index);
var holder2 = new LevelHolder(p2Index);
IList<EPLValueType> types = new List<EPLValueType>();
types.Add(context.Result.VariableType);
FindNode(rnd, parent1.RootNode, types, holder1);
FindNode(rnd, parent2.RootNode, types, holder2);
if (LevelHolder.CompatibleTypes(holder1.Types,
holder2.Types))
{
EncogProgram result = context.CloneProgram(parent1);
ProgramNode resultNode = parent1.FindNode(p1Index);
ProgramNode p2Node = parent2.FindNode(p2Index);
ProgramNode newInsert = context.CloneBranch(result,
p2Node);
result.ReplaceNode(resultNode, newInsert);
offspring[0] = result;
done = true;
}
else
{
tries--;
if (tries < 0)
{
done = true;
}
}
}
}
/// <inheritdoc/>
public void PerformOperation(EncogRandom rnd, IGenome[] parents, int parentIndex,
IGenome[] offspring, int offspringIndex)
{
DoubleArrayGenome parent = (DoubleArrayGenome)parents[parentIndex];
offspring[offspringIndex] = parent.Population.GenomeFactory.Factor();
DoubleArrayGenome child = (DoubleArrayGenome)offspring[offspringIndex];
for (int i = 0; i < parent.Size; i++)
{
double value = parent.Data[i];
value += (perturbAmount - (rnd.NextDouble() * perturbAmount * 2));
child.Data[i] = value;
}
}
/// <inheritdoc />
public void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var program = (EncogProgram) parents[0];
EncogProgramContext context = program.Context;
EncogProgram result = context.CloneProgram(program);
IList<EPLValueType> types = new List<EPLValueType>();
types.Add(context.Result.VariableType);
var globalIndex = new int[1];
globalIndex[0] = rnd.Next(result.RootNode.Count);
FindNode(rnd, result, result.RootNode, types, globalIndex);
offspring[0] = result;
}
/// <inheritdoc/>
public void PerformOperation(EncogRandom rnd, IGenome[] parents, int parentIndex,
IGenome[] offspring, int offspringIndex)
{
IArrayGenome parent = (IArrayGenome)parents[parentIndex];
offspring[offspringIndex] = this.owner.Population.GenomeFactory.Factor();
IArrayGenome child = (IArrayGenome)offspring[offspringIndex];
child.Copy(parent);
int length = parent.Size;
int iswap1 = (int)(rnd.NextDouble() * length);
int iswap2 = (int)(rnd.NextDouble() * length);
// can't be equal
if (iswap1 == iswap2)
{
// move to the next, but
// don't go out of bounds
if (iswap1 > 0)
{
iswap1--;
}
else
{
iswap1++;
}
}
// make sure they are in the right order
if (iswap1 > iswap2)
{
int temp = iswap1;
iswap1 = iswap2;
iswap2 = temp;
}
child.Swap(iswap1, iswap2);
}
/// <inheritdoc/>
public void PerformOperation(EncogRandom rnd, IGenome[] parents, int parentIndex,
IGenome[] offspring, int offspringIndex)
{
IArrayGenome mother = (IArrayGenome)parents[parentIndex];
IArrayGenome father = (IArrayGenome)parents[parentIndex + 1];
IArrayGenome offspring1 = (IArrayGenome)this.owner.Population.GenomeFactory.Factor();
IArrayGenome offspring2 = (IArrayGenome)this.owner.Population.GenomeFactory.Factor();
offspring[offspringIndex] = offspring1;
offspring[offspringIndex + 1] = offspring2;
int geneLength = mother.Size;
// the chromosome must be cut at two positions, determine them
int cutpoint1 = (int)(rnd.Next(geneLength - this.cutLength));
int cutpoint2 = cutpoint1 + this.cutLength;
// handle cut section
for (int i = 0; i < geneLength; i++)
{
if (!((i < cutpoint1) || (i > cutpoint2)))
{
offspring1.Copy(father, i, i);
offspring2.Copy(mother, i, i);
}
}
// handle outer sections
for (int i = 0; i < geneLength; i++)
{
if ((i < cutpoint1) || (i > cutpoint2))
{
offspring1.Copy(mother, i, i);
offspring2.Copy(father, i, i);
}
}
}
/// <inheritdoc/>
public override void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var target = ObtainGenome(parents, parentIndex, offspring,
offspringIndex);
var countTrysToFindOldLink = Owner.MaxTries;
var pop = ((NEATPopulation)target.Population);
// the link to split
NEATLinkGene splitLink = null;
int sizeBias = ((NEATGenome)parents[0]).InputCount
+ ((NEATGenome)parents[0]).OutputCount + 10;
// if there are not at least
int upperLimit;
if (target.LinksChromosome.Count < sizeBias)
{
upperLimit = target.NumGenes - 1
- (int)Math.Sqrt(target.NumGenes);
}
else
{
upperLimit = target.NumGenes - 1;
}
while ((countTrysToFindOldLink--) > 0)
{
// choose a link, use the square root to prefer the older links
int i = RangeRandomizer.RandomInt(0, upperLimit);
NEATLinkGene link = target.LinksChromosome[i];
// get the from neuron
long fromNeuron = link.FromNeuronId;
if ((link.Enabled)
&& (target.NeuronsChromosome
[GetElementPos(target, fromNeuron)]
.NeuronType != NEATNeuronType.Bias))
{
splitLink = link;
break;
}
}
if (splitLink == null)
{
return;
}
splitLink.Enabled = false;
long from = splitLink.FromNeuronId;
long to = splitLink.ToNeuronId;
NEATInnovation innovation = ((NEATPopulation)Owner.Population).Innovations
.FindInnovationSplit(from, to);
// add the splitting neuron
IActivationFunction af = ((NEATPopulation)Owner.Population).ActivationFunctions.Pick(new Random());
target.NeuronsChromosome.Add(
new NEATNeuronGene(NEATNeuronType.Hidden, af, innovation
.NeuronId, innovation.InnovationId));
// add the other two sides of the link
CreateLink(target, from, innovation.NeuronId,
splitLink.Weight);
CreateLink(target, innovation.NeuronId, to, pop.WeightRange);
target.SortGenes();
}
/// <inheritdoc />
public override ProgramNode CreateNode(EncogRandom rnd, EncogProgram program,
int depthRemaining, IList<EPLValueType> types)
{
return CreateRandomNode(rnd, program, depthRemaining, types, true, true);
}
/// <inheritdoc/>
public void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
var mom = (NEATGenome)parents[parentIndex + 0];
var dad = (NEATGenome)parents[parentIndex + 1];
var best = FavorParent(rnd, mom, dad);
var notBest = (best == mom) ? mom : dad;
var selectedLinks = new List<NEATLinkGene>();
var selectedNeurons = new List<NEATNeuronGene>();
int curMom = 0; // current gene index from mom
int curDad = 0; // current gene index from dad
NEATLinkGene selectedGene = null;
// add in the input and bias, they should always be here
int alwaysCount = ((NEATGenome)parents[0]).InputCount
+ ((NEATGenome)parents[0]).OutputCount + 1;
for (int i = 0; i < alwaysCount; i++)
{
AddNeuronId(i, selectedNeurons, best, notBest);
}
while ((curMom < mom.NumGenes) || (curDad < dad.NumGenes))
{
NEATLinkGene momGene = null; // the mom gene object
NEATLinkGene dadGene = null; // the dad gene object
long momInnovation = -1;
long dadInnovation = -1;
// grab the actual objects from mom and dad for the specified
// indexes
// if there are none, then null
if (curMom < mom.NumGenes)
{
momGene = mom.LinksChromosome[curMom];
momInnovation = momGene.InnovationId;
}
if (curDad < dad.NumGenes)
{
dadGene = dad.LinksChromosome[curDad];
dadInnovation = dadGene.InnovationId;
}
// now select a gene for mom or dad. This gene is for the baby
if ((momGene == null) && (dadGene != null))
{
if (best == dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if ((dadGene == null) && (momGene != null))
{
if (best == mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (momInnovation < dadInnovation)
{
if (best == mom)
{
selectedGene = momGene;
}
curMom++;
}
else if (dadInnovation < momInnovation)
{
if (best == dad)
{
selectedGene = dadGene;
}
curDad++;
}
else if (dadInnovation == momInnovation)
{
selectedGene = rnd.NextDouble() < 0.5f ? momGene : dadGene;
curMom++;
curDad++;
}
if (selectedGene != null)
{
if (selectedLinks.Count == 0)
{
selectedLinks.Add(selectedGene);
}
else
{
if (selectedLinks[selectedLinks.Count - 1]
.InnovationId != selectedGene
.InnovationId)
{
selectedLinks.Add(selectedGene);
}
}
// Check if we already have the nodes referred to in
// SelectedGene.
// If not, they need to be added.
AddNeuronId(selectedGene.FromNeuronId, selectedNeurons,
best, notBest);
AddNeuronId(selectedGene.ToNeuronId, selectedNeurons,
best, notBest);
}
}
// now create the required nodes. First sort them into order
selectedNeurons.Sort();
// finally, create the genome
var factory = (INEATGenomeFactory)_owner
.Population.GenomeFactory;
var babyGenome = factory.Factor(selectedNeurons,
selectedLinks, mom.InputCount, mom.OutputCount);
babyGenome.BirthGeneration = _owner.IterationNumber;
babyGenome.Population = _owner.Population;
babyGenome.SortGenes();
offspring[offspringIndex] = babyGenome;
}
/// <summary>
/// Choose a parent to favor.
/// </summary>
/// <param name="rnd">Random generator.</param>
/// <param name="mom">The mother.</param>
/// <param name="dad">The father</param>
/// <returns></returns>
private NEATGenome FavorParent(EncogRandom rnd, NEATGenome mom, NEATGenome dad)
{
// first determine who is more fit, the mother or the father?
// see if mom and dad are the same fitness
if (Math.Abs(mom.Score - dad.Score) < EncogFramework.DefaultDoubleEqual)
{
// are mom and dad the same fitness
if (mom.NumGenes == dad.NumGenes)
{
// if mom and dad are the same fitness and have the same number
// of genes,
// then randomly pick mom or dad as the most fit.
if (rnd.NextDouble() > 0.5)
{
return mom;
}
return dad;
}
// mom and dad are the same fitness, but different number of genes
// favor the parent with fewer genes
if (mom.NumGenes < dad.NumGenes)
{
return mom;
}
return dad;
}
// mom and dad have different scores, so choose the better score.
// important to note, better score COULD BE the larger or smaller
// score.
if (_owner.SelectionComparer.Compare(mom, dad) < 0)
{
return mom;
}
return dad;
}
/// <inheritdoc/>
public override void PerformOperation(EncogRandom rnd, IGenome[] parents,
int parentIndex, IGenome[] offspring,
int offspringIndex)
{
NEATGenome target = ObtainGenome(parents, parentIndex, offspring,
offspringIndex);
double weightRange = ((NEATPopulation)Owner.Population).WeightRange;
IList<NEATLinkGene> list = _linkSelection.SelectLinks(rnd,
target);
foreach (NEATLinkGene gene in list)
{
_weightMutation.MutateWeight(rnd, gene, weightRange);
}
}
/// <inheritdoc />
public void Randomize(EncogRandom rnd, IList<EPLValueType> desiredType, ProgramNode actual, double minValue,
double maxValue)
{
if (_delRandomize != null)
{
_delRandomize(rnd, desiredType, actual, minValue, maxValue);
}
}
/// <summary>
/// This method is called reflexivly as we iterate downward. Once we reach
/// the desired point (when current level drops to zero), the operation is
/// performed.
/// </summary>
/// <param name="rnd">A random number generator.</param>
/// <param name="result">The parent node.</param>
/// <param name="parentNode"></param>
/// <param name="types">The desired node</param>
/// <param name="globalIndex">The level holder.</param>
private void FindNode(EncogRandom rnd, EncogProgram result,
ProgramNode parentNode, IList<EPLValueType> types,
int[] globalIndex)
{
if (globalIndex[0] == 0)
{
globalIndex[0]--;
ProgramNode newInsert = Generator.CreateNode(rnd,
result, _maxDepth, types);
result.ReplaceNode(parentNode, newInsert);
}
else
{
globalIndex[0]--;
for (int i = 0; i < parentNode.Template.ChildNodeCount; i++)
{
ProgramNode childNode = parentNode.GetChildNode(i);
IList<EPLValueType> childTypes = parentNode.Template.Params[i].DetermineArgumentTypes(types);
FindNode(rnd, result, childNode, childTypes, globalIndex);
}
}
}
/// <inheritdoc/>
public abstract void PerformOperation(EncogRandom rnd, IGenome[] parents, int parentIndex,
IGenome[] offspring, int offspringIndex);
/// <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);
}
}
}
}
/// <inheritdoc/>
public void MutateWeight(EncogRandom rnd, NEATLinkGene linkGene,
double weightRange)
{
double delta = rnd.NextGaussian() * _sigma;
double w = linkGene.Weight + delta;
w = NEATPopulation.ClampWeight(w, weightRange);
linkGene.Weight = w;
}
/// <summary>
/// Called for each node in the progrmam. If this is a const node, then
/// mutate it according to the frequency and sigma specified.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="node">The node to mutate.</param>
private void MutateNode(EncogRandom rnd, ProgramNode node)
{
if (node.Template == StandardExtensions.EXTENSION_CONST_SUPPORT)
{
if (rnd.NextDouble() < _frequency)
{
ExpressionValue v = node.Data[0];
if (v.IsFloat)
{
double adj = rnd.NextGaussian()*_sigma;
node.Data[0] = new ExpressionValue(v.ToFloatValue()
+ adj);
}
}
}
foreach (ITreeNode n in node.ChildNodes)
{
var childNode = (ProgramNode) n;
MutateNode(rnd, childNode);
}
}
/// <summary>
/// Construct a random HyperNEAT genome.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="pop">The target population.</param>
/// <param name="inputCount">The input count.</param>
/// <param name="outputCount">The output count.</param>
/// <param name="connectionDensity">The connection densitoy, 1.0 for fully connected.</param>
public HyperNEATGenome(EncogRandom rnd, NEATPopulation pop,
int inputCount, int outputCount,
double connectionDensity)
: base(rnd, pop, inputCount, outputCount, connectionDensity)
{
}
/// <summary>
/// This method is called reflexivly as we iterate downward. Once we reach
/// the desired point (when current level drops to zero), the operation is
/// performed.
/// </summary>
/// <param name="rnd">A random number generator.</param>
/// <param name="parentNode">The parent node.</param>
/// <param name="types">The desired node.</param>
/// <param name="holder">The level holder.</param>
private void FindNode(EncogRandom rnd, ProgramNode parentNode,
IList<EPLValueType> types, LevelHolder holder)
{
if (holder.CurrentLevel == 0)
{
holder.DecreaseLevel();
holder.Types = types;
holder.NodeFound = parentNode;
}
else
{
holder.DecreaseLevel();
for (int i = 0; i < parentNode.Template.ChildNodeCount; i++)
{
ProgramNode childNode = parentNode.GetChildNode(i);
IList<EPLValueType> childTypes = parentNode.Template
.Params[i].DetermineArgumentTypes(types);
FindNode(rnd, childNode, childTypes, holder);
}
}
}
/// <inheritdoc />
public override int DetermineMaxDepth(EncogRandom rnd)
{
int range = MaxDepth - _minDepth;
return rnd.Next(range) + _minDepth;
}
/// <inheritdoc/>
public void PerformOperation(EncogRandom rnd, IGenome[] parents, int parentIndex,
IGenome[] offspring, int offspringIndex)
{
IntegerArrayGenome mother = (IntegerArrayGenome)parents[parentIndex];
IntegerArrayGenome father = (IntegerArrayGenome)parents[parentIndex + 1];
IntegerArrayGenome offspring1 = (IntegerArrayGenome)this.owner.Population.GenomeFactory.Factor();
IntegerArrayGenome offspring2 = (IntegerArrayGenome)this.owner.Population.GenomeFactory.Factor();
offspring[offspringIndex] = offspring1;
offspring[offspringIndex + 1] = offspring2;
int geneLength = mother.Size;
// the chromosome must be cut at two positions, determine them
int cutpoint1 = (int)(rnd.Next(geneLength - this.cutLength));
int cutpoint2 = cutpoint1 + this.cutLength;
// keep track of which genes have been taken in each of the two
// offspring, defaults to false.
HashSet<int> taken1 = new HashSet<int>();
HashSet<int> taken2 = new HashSet<int>();
// handle cut section
for (int i = 0; i < geneLength; i++)
{
if (!((i < cutpoint1) || (i > cutpoint2)))
{
offspring1.Copy(father, i, i);
offspring2.Copy(mother, i, i);
taken1.Add(father.Data[i]);
taken2.Add(mother.Data[i]);
}
}
// handle outer sections
for (int i = 0; i < geneLength; i++)
{
if ((i < cutpoint1) || (i > cutpoint2))
{
offspring1.Data[i] = SpliceNoRepeat.GetNotTaken(mother, taken1);
offspring2.Data[i] = SpliceNoRepeat.GetNotTaken(father, taken2);
}
}
}
/// <summary>
/// Select a random variable from the defined variables.
/// </summary>
/// <param name="rnd">A random number generator.</param>
/// <param name="desiredTypes">The desired types that the variable can be.</param>
/// <returns>The index of the defined variable, or -1 if unable to define.</returns>
public int SelectRandomVariable(EncogRandom rnd,
IList<EPLValueType> desiredTypes)
{
IList<VariableMapping> selectionSet = _context
.FindVariablesByTypes(desiredTypes);
if (selectionSet.Count == 0
&& desiredTypes.Contains(EPLValueType.IntType))
{
IList<EPLValueType> floatList = new List<EPLValueType>();
floatList.Add(EPLValueType.FloatingType);
selectionSet = _context.FindVariablesByTypes(floatList);
}
if (selectionSet.Count == 0)
{
return -1;
}
VariableMapping selected = selectionSet[rnd.Next(selectionSet.Count)];
return Context.DefinedVariables.IndexOf(selected);
}
/// <inheritdoc/>
public IList<NEATLinkGene> SelectLinks(EncogRandom rnd,
NEATGenome genome)
{
IList<NEATLinkGene> result = new List<NEATLinkGene>();
int cnt = Math.Min(_linkCount, genome.LinksChromosome.Count);
while (result.Count < cnt)
{
int idx = rnd.Next(genome.LinksChromosome.Count);
NEATLinkGene link = genome.LinksChromosome[idx];
if (!result.Contains(link))
{
result.Add(link);
}
}
return result;
}
