Uses of Interface
org.apache.commons.rng.sampling.distribution.DiscreteSampler
-
Packages that use DiscreteSampler Package Description org.apache.commons.rng.sampling This package provides sampling utilities.org.apache.commons.rng.sampling.distribution This package contains classes for sampling from statistical distributions. -
-
Uses of DiscreteSampler in org.apache.commons.rng.sampling
Classes in org.apache.commons.rng.sampling that implement DiscreteSampler Modifier and Type Class Description private static class
CompositeSamplers.DiscreteSamplerFactory.CompositeDiscreteSampler
A composite discrete sampler.private static class
CompositeSamplers.SharedStateDiscreteProbabilitySampler
A class to implement the SharedStateDiscreteSampler interface for a discrete probability sampler given a factory and the probability distribution.private static class
CompositeSamplers.SharedStateDiscreteSamplerFactory.CompositeSharedStateDiscreteSampler
A composite discrete sampler with shared state support.Fields in org.apache.commons.rng.sampling declared as DiscreteSampler Modifier and Type Field Description protected DiscreteSampler
CompositeSamplers.CompositeSampler. discreteSampler
Continuous sampler to choose the individual sampler to sample.private DiscreteSampler
CompositeSamplers.SharedStateDiscreteProbabilitySampler. sampler
The sampler.Methods in org.apache.commons.rng.sampling that return DiscreteSampler Modifier and Type Method Description DiscreteSampler
CompositeSamplers.DiscreteProbabilitySamplerFactory. create(UniformRandomProvider rng, double[] probabilities)
Creates the sampler.private DiscreteSampler
CompositeSamplers.SamplerBuilder. createDiscreteSampler(UniformRandomProvider rng, double[] weights)
Creates the discrete sampler of the enumerated probability distribution.DiscreteSampler
CompositeSamplers.DiscreteSamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<DiscreteSampler> samplers)
Methods in org.apache.commons.rng.sampling that return types with arguments of type DiscreteSampler Modifier and Type Method Description static CompositeSamplers.Builder<DiscreteSampler>
CompositeSamplers. newDiscreteSamplerBuilder()
Create a new builder for a compositeDiscreteSampler
.Methods in org.apache.commons.rng.sampling with parameters of type DiscreteSampler Modifier and Type Method Description ContinuousSampler
CompositeSamplers.ContinuousSamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<ContinuousSampler> samplers)
DiscreteSampler
CompositeSamplers.DiscreteSamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<DiscreteSampler> samplers)
LongSampler
CompositeSamplers.LongSamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<LongSampler> samplers)
ObjectSampler<T>
CompositeSamplers.ObjectSamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<ObjectSampler<T>> samplers)
S
CompositeSamplers.SamplerBuilder.SamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<S> samplers)
Creates a new composite sampler.SharedStateContinuousSampler
CompositeSamplers.SharedStateContinuousSamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<SharedStateContinuousSampler> samplers)
SharedStateDiscreteSampler
CompositeSamplers.SharedStateDiscreteSamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<SharedStateDiscreteSampler> samplers)
SharedStateLongSampler
CompositeSamplers.SharedStateLongSamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<SharedStateLongSampler> samplers)
SharedStateObjectSampler<T>
CompositeSamplers.SharedStateObjectSamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<SharedStateObjectSampler<T>> samplers)
Method parameters in org.apache.commons.rng.sampling with type arguments of type DiscreteSampler Modifier and Type Method Description DiscreteSampler
CompositeSamplers.DiscreteSamplerFactory. createSampler(DiscreteSampler discreteSampler, java.util.List<DiscreteSampler> samplers)
Constructors in org.apache.commons.rng.sampling with parameters of type DiscreteSampler Constructor Description CompositeContinuousSampler(DiscreteSampler discreteSampler, java.util.List<ContinuousSampler> samplers)
CompositeDiscreteSampler(DiscreteSampler discreteSampler, java.util.List<DiscreteSampler> samplers)
CompositeLongSampler(DiscreteSampler discreteSampler, java.util.List<LongSampler> samplers)
CompositeObjectSampler(DiscreteSampler discreteSampler, java.util.List<ObjectSampler<T>> samplers)
CompositeSampler(DiscreteSampler discreteSampler, java.util.List<S> samplers)
SharedStateDiscreteProbabilitySampler(DiscreteSampler sampler, CompositeSamplers.DiscreteProbabilitySamplerFactory factory, double[] probabilities)
Constructor parameters in org.apache.commons.rng.sampling with type arguments of type DiscreteSampler Constructor Description CompositeDiscreteSampler(DiscreteSampler discreteSampler, java.util.List<DiscreteSampler> samplers)
-
Uses of DiscreteSampler in org.apache.commons.rng.sampling.distribution
Subinterfaces of DiscreteSampler in org.apache.commons.rng.sampling.distribution Modifier and Type Interface Description interface
SharedStateDiscreteSampler
Sampler that generates values of typeint
and can create new instances to sample from the same state with a given source of randomness.Classes in org.apache.commons.rng.sampling.distribution that implement DiscreteSampler Modifier and Type Class Description class
AliasMethodDiscreteSampler
Distribution sampler that uses the Alias method.private static class
AliasMethodDiscreteSampler.SmallTableAliasMethodDiscreteSampler
Sample from the computed tables exploiting the small power-of-two table size.class
DiscreteUniformSampler
Discrete uniform distribution sampler.private static class
DiscreteUniformSampler.AbstractDiscreteUniformSampler
Base class for a sampler from a discrete uniform distribution.private static class
DiscreteUniformSampler.FixedDiscreteUniformSampler
Discrete uniform distribution sampler when the sample value is fixed.private static class
DiscreteUniformSampler.LargeRangeDiscreteUniformSampler
Discrete uniform distribution sampler when the range between lower and upper is too large to fit in a positive integer.private static class
DiscreteUniformSampler.OffsetDiscreteUniformSampler
Adds an offset to an underlying discrete sampler.private static class
DiscreteUniformSampler.PowerOf2RangeDiscreteUniformSampler
Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.private static class
DiscreteUniformSampler.SmallRangeDiscreteUniformSampler
Discrete uniform distribution sampler when the range is small enough to fit in a positive integer.class
FastLoadedDiceRollerDiscreteSampler
Distribution sampler that uses the Fast Loaded Dice Roller (FLDR).private static class
FastLoadedDiceRollerDiscreteSampler.FixedValueDiscreteSampler
Class to handle the edge case of observations in only one category.private static class
FastLoadedDiceRollerDiscreteSampler.FLDRSampler
Class to implement the FLDR sample algorithm.private static class
GeometricSampler.GeometricExponentialSampler
Sample from the geometric distribution by using a related exponential distribution.private static class
GeometricSampler.GeometricP1Sampler
Sample from the geometric distribution when the probability of success is 1.class
GuideTableDiscreteSampler
Compute a sample fromn
values each with an associated probability.class
InverseTransformDiscreteSampler
Distribution sampler that uses the inversion method.class
KempSmallMeanPoissonSampler
Sampler for the Poisson distribution.class
LargeMeanPoissonSampler
Sampler for the Poisson distribution.private static class
MarsagliaTsangWangDiscreteSampler.AbstractMarsagliaTsangWangDiscreteSampler
The base class for Marsaglia-Tsang-Wang samplers.private static class
MarsagliaTsangWangDiscreteSampler.Binomial.MarsagliaTsangWangFixedResultBinomialSampler
Return a fixed result for the Binomial distribution.private static class
MarsagliaTsangWangDiscreteSampler.Binomial.MarsagliaTsangWangInversionBinomialSampler
Return an inversion result for the Binomial distribution.private static class
MarsagliaTsangWangDiscreteSampler.MarsagliaTsangWangBase64Int16DiscreteSampler
An implementation for the sample algorithm based on the decomposition of the index in the range[0,2^30)
into 5 base-64 digits with 16-bit backing storage.private static class
MarsagliaTsangWangDiscreteSampler.MarsagliaTsangWangBase64Int32DiscreteSampler
An implementation for the sample algorithm based on the decomposition of the index in the range[0,2^30)
into 5 base-64 digits with 32-bit backing storage.private static class
MarsagliaTsangWangDiscreteSampler.MarsagliaTsangWangBase64Int8DiscreteSampler
An implementation for the sample algorithm based on the decomposition of the index in the range[0,2^30)
into 5 base-64 digits with 8-bit backing storage.class
PoissonSampler
Sampler for the Poisson distribution.class
RejectionInversionZipfSampler
Implementation of the Zipf distribution.private static class
RejectionInversionZipfSampler.RejectionInversionZipfSamplerImpl
Implements the rejection-inversion method for the Zipf distribution.class
SmallMeanPoissonSampler
Sampler for the Poisson distribution.Methods in org.apache.commons.rng.sampling.distribution that return DiscreteSampler Modifier and Type Method Description DiscreteSampler
PoissonSamplerCache. createPoissonSampler(UniformRandomProvider rng, double mean)
-