/// <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);
}
}
/// <summary>
/// Choose a random neuron.
/// </summary>
/// <param name="includeInput">Should the input neurons be included.</param>
/// <returns>The random neuron.</returns>
private NEATNeuronGene ChooseRandomNeuron(bool includeInput)
{
int start;
if (includeInput)
{
start = 0;
}
else
{
start = inputCount + 1;
}
int neuronPos = RangeRandomizer.RandomInt(start, Neurons
.Genes.Count - 1);
NEATNeuronGene neuronGene = (NEATNeuronGene)neuronsChromosome.Genes[neuronPos];
return(neuronGene);
}
/// <summary>
/// Get the next row from the underlying CSV file.
/// </summary>
/// <param name="csv">The underlying CSV file.</param>
/// <returns>The loaded row.</returns>
private LoadedRow GetNextRow(ReadCSV csv)
{
if (_remaining == 0)
{
LoadBuffer(csv);
}
while (_remaining > 0)
{
int index = RangeRandomizer.RandomInt(0, _bufferSize - 1);
if (_buffer[index] != null)
{
LoadedRow result = _buffer[index];
_buffer[index] = null;
_remaining--;
return(result);
}
}
return(null);
}
/// <summary>
/// Choose a random neuron.
/// </summary>
/// <param name="target">The target genome. Should the input and bias neurons be
/// included.</param>
/// <param name="choosingFrom">True if we are chosing from all neurons, false if we exclude
/// the input and bias.</param>
/// <returns>The random neuron.</returns>
public NEATNeuronGene ChooseRandomNeuron(NEATGenome target,
bool choosingFrom)
{
int start;
if (choosingFrom)
{
start = 0;
}
else
{
start = target.InputCount + 1;
}
// if this network will not "cycle" then output neurons cannot be source
// neurons
if (!choosingFrom)
{
int ac = ((NEATPopulation)target.Population)
.ActivationCycles;
if (ac == 1)
{
start += target.OutputCount;
}
}
int end = target.NeuronsChromosome.Count - 1;
// no neurons to pick!
if (start > end)
{
return(null);
}
int neuronPos = RangeRandomizer.RandomInt(start, end);
NEATNeuronGene neuronGene = target.NeuronsChromosome[neuronPos];
return(neuronGene);
}
/// <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>
public void AddNeuron(double mutationRate, int numTrysToFindOldLink)
{
// should we add a neuron?
if (ThreadSafeRandom.NextDouble() > mutationRate)
{
return;
}
// the link to split
NEATLinkGene splitLink = null;
int sizeThreshold = inputCount + outputCount + 10;
// if there are not at least
int upperLimit;
if (linksChromosome.Genes.Count < sizeThreshold)
{
upperLimit = NumGenes - 1 - (int)Math.Sqrt(NumGenes);
}
else
{
upperLimit = NumGenes - 1;
}
while ((numTrysToFindOldLink--) > 0)
{
// choose a link, use the square root to prefer the older links
int i = RangeRandomizer.RandomInt(0, upperLimit);
NEATLinkGene link = (NEATLinkGene)linksChromosome.Genes[i];
// get the from neuron
long fromNeuron = link.FromNeuronID;
if ((link.Enabled) &&
(!link.IsRecurrent) &&
(((NEATNeuronGene)Neurons.Genes[
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;
NEATNeuronGene fromGene = (NEATNeuronGene)Neurons.Genes[
GetElementPos(from)];
NEATNeuronGene toGene = (NEATNeuronGene)Neurons.Genes[
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.Genes.Add(new NEATNeuronGene(NEATNeuronType.Hidden,
newNeuronID, newDepth, newWidth));
// add the first link
long link1ID = ((NEATTraining)GA).Population.AssignInnovationID();
((NEATTraining)GA).Innovations.CreateNewInnovation(from, newNeuronID,
NEATInnovationType.NewLink);
NEATLinkGene link1 = new NEATLinkGene(from, newNeuronID,
true, link1ID, 1.0, false);
linksChromosome.Genes.Add(link1);
// add the second link
long link2ID = ((NEATTraining)GA).Population.AssignInnovationID();
((NEATTraining)GA).Innovations.CreateNewInnovation(newNeuronID, to,
NEATInnovationType.NewLink);
NEATLinkGene link2 = new NEATLinkGene(newNeuronID, to, true,
link2ID, originalWeight, false);
linksChromosome.Genes.Add(link2);
}
else
{
// existing innovation
long newNeuronID = innovation.NeuronID;
NEATInnovation innovationLink1 = ((NEATTraining)GA).Innovations
.CheckInnovation(from, newNeuronID,
NEATInnovationType.NewLink);
NEATInnovation innovationLink2 = ((NEATTraining)GA).Innovations
.CheckInnovation(newNeuronID, to,
NEATInnovationType.NewLink);
if ((innovationLink1 == null) || (innovationLink2 == null))
{
throw new NeuralNetworkError("NEAT Error");
}
NEATLinkGene link1 = new NEATLinkGene(from, newNeuronID,
true, innovationLink1.InnovationID, 1.0, false);
NEATLinkGene link2 = new NEATLinkGene(newNeuronID, to, true,
innovationLink2.InnovationID, originalWeight, false);
linksChromosome.Genes.Add(link1);
linksChromosome.Genes.Add(link2);
NEATNeuronGene newNeuron = new NEATNeuronGene(
NEATNeuronType.Hidden, newNeuronID, newDepth, newWidth);
neuronsChromosome.Genes.Add(newNeuron);
}
return;
}
/// <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();
}