Uses of Class
net.bmahe.genetics4j.core.spec.EvolutionResult

Packages that use EvolutionResult

Package	Description
net.bmahe.genetics4j.core
net.bmahe.genetics4j.core.spec
net.bmahe.genetics4j.samples.mixturemodel

Uses of EvolutionResult in net.bmahe.genetics4j.core

Methods in net.bmahe.genetics4j.core that return EvolutionResult

Modifier and Type	Method	Description
EvolutionResult<T>	EASystem.evolve()	Triggers the evolutionary process

Uses of EvolutionResult in net.bmahe.genetics4j.core.spec

Subclasses of EvolutionResult in net.bmahe.genetics4j.core.spec

Modifier and Type	Class	Description
final class	ImmutableEvolutionResult<T extends Comparable<T>>	Immutable implementation of EvolutionResult.

Methods in net.bmahe.genetics4j.core.spec with parameters of type EvolutionResult

Modifier and Type	Method	Description
static <T extends Comparable<T>> ImmutableEvolutionResult<T>	ImmutableEvolutionResult.copyOf(EvolutionResult<T> instance)	Creates an immutable copy of a EvolutionResult value.
final ImmutableEvolutionResult.Builder<T>	ImmutableEvolutionResult.Builder.from(EvolutionResult<T> instance)	Fill a builder with attribute values from the provided EvolutionResult instance.

Uses of EvolutionResult in net.bmahe.genetics4j.samples.mixturemodel

Methods in net.bmahe.genetics4j.samples.mixturemodel that return EvolutionResult

Modifier and Type	Method	Description
EvolutionResult<FitnessVector<Float>>	MooCPU.run(int maxPossibleDistributions, double[][] samples, float[] x, float[] y, String algorithmName, Collection<Genotype> seedPopulation)
EvolutionResult<Double>	SingleObjectiveMethod.run(int maxPossibleDistributions, double[][] samples, float[] x, float[] y, String algorithmName, Collection<Genotype> seedPopulation)

Methods in net.bmahe.genetics4j.samples.mixturemodel with parameters of type EvolutionResult

Modifier and Type	Method	Description
static void	ClusteringUtils.categorizeByNumClusters(int distributionNumParameters, int maxPossibleDistributions, float[] x, float[] y, double[][] samplesDouble, EvolutionResult<FitnessVector<Float>> evolutionResult, String baseDir, String type)
static Map<Integer,Individual<FitnessVector<Float>>>	ClusteringUtils.groupByNumClusters(double[][] samplesDouble, EvolutionResult<FitnessVector<Float>> evolutionResult)
void	MooGPU.run(int maxPossibleDistributions, int numDistributions, double[][] samplesDouble, float[][] samples, float[] x, float[] y, String algorithmName, Collection<Genotype> seedPopulation, EvolutionResult<FitnessVector<Float>> bestCPUResult)