/// <summary>
/// Create a weight mutation strategy that applies deltas to a random subset of weights, with deltas sampled from a uniform distribution.
/// </summary>
/// <param name="selectionStrategy">Weight selection strategy.</param>
/// <param name="weightScale">The uniform distribution scale.</param>
/// <returns>A new instance of <see cref="DeltaWeightMutationStrategy"/>.</returns>
public static DeltaWeightMutationStrategy CreateUniformDeltaStrategy(
ISubsetSelectionStrategy selectionStrategy,
double weightScale)
{
var sampler = UniformDistributionSamplerFactory.CreateStatelessSampler <double>(weightScale, true);
return(new DeltaWeightMutationStrategy(selectionStrategy, sampler));
}
/// <summary>
/// Create a weight mutation strategy that replaces a random subset of weights, with new weights sampled
/// from a uniform distribution.
/// </summary>
/// <param name="selectionStrategy">Weight selection strategy.</param>
/// <param name="weightScale">The uniform distribution scale.</param>
/// <returns>A new instance of <see cref="ResetWeightMutationStrategy{T}"/>.</returns>
public static ResetWeightMutationStrategy <T> CreateUniformResetStrategy(
ISubsetSelectionStrategy selectionStrategy,
double weightScale)
{
var sampler = UniformDistributionSamplerFactory.CreateStatelessSampler <T>(weightScale, true);
return(new ResetWeightMutationStrategy <T>(selectionStrategy, sampler));
}
/// <summary>
/// Construct a new instance.
/// </summary>
/// <param name="metaNeatGenome">NEAT genome metadata.</param>
/// <param name="genomeBuilder">NeatGenome builder.</param>
/// <param name="genomeIdSeq">Genome ID sequence; for obtaining new genome IDs.</param>
/// <param name="generationSeq">Generation sequence; for obtaining the current generation number.</param>
public AddCyclicConnectionStrategy(
MetaNeatGenome <T> metaNeatGenome,
INeatGenomeBuilder <T> genomeBuilder,
Int32Sequence genomeIdSeq,
Int32Sequence generationSeq)
{
_metaNeatGenome = metaNeatGenome;
_genomeBuilder = genomeBuilder;
_genomeIdSeq = genomeIdSeq;
_generationSeq = generationSeq;
_weightSamplerA = UniformDistributionSamplerFactory.CreateStatelessSampler <T>(metaNeatGenome.ConnectionWeightScale, true);
_weightSamplerB = UniformDistributionSamplerFactory.CreateStatelessSampler <T>(metaNeatGenome.ConnectionWeightScale * 0.01, true);
}
private NeatPopulationFactory(
MetaNeatGenome <T> metaNeatGenome,
double connectionsProportion,
IRandomSource rng)
{
_metaNeatGenome = metaNeatGenome;
_genomeBuilder = NeatGenomeBuilderFactory <T> .Create(metaNeatGenome);
_connectionsProportion = connectionsProportion;
// Define the set of all possible connections between the input and output nodes (fully interconnected).
int inputCount = metaNeatGenome.InputNodeCount;
int outputCount = metaNeatGenome.OutputNodeCount;
_connectionDefArr = new DirectedConnection[inputCount * outputCount];
// Notes.
// Nodes are assigned innovation IDs. By convention the input nodes are assigned IDs first starting at zero, then the output nodes.
// Thus, because all of the evolved networks have a fixed number of inputs and outputs, the IDs of these nodes are always fixed.
int firstOutputNodeId = inputCount;
for (int srcId = 0, i = 0; srcId < inputCount; srcId++)
{
for (int tgtIdx = 0; tgtIdx < outputCount; tgtIdx++)
{
_connectionDefArr[i++] = new DirectedConnection(srcId, firstOutputNodeId + tgtIdx);
}
}
// Init RNG and ID sequences.
_rng = rng;
_genomeIdSeq = new Int32Sequence();
int nextInnovationId = inputCount + outputCount;
_innovationIdSeq = new Int32Sequence(nextInnovationId);
// Init random connection weight source.
_connWeightDist = UniformDistributionSamplerFactory.CreateStatelessSampler <T>(_metaNeatGenome.ConnectionWeightScale, true);
}