Uses of Interface
org.apache.commons.rng.sampling.SharedStateSampler

Packages that use SharedStateSampler

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.
org.apache.commons.rng.sampling.shape	This package contains classes for sampling coordinates from shapes, for example a unit ball.

Uses of SharedStateSampler in org.apache.commons.rng.sampling

Subinterfaces of SharedStateSampler in org.apache.commons.rng.sampling

Modifier and Type	Interface	Description
interface	SharedStateObjectSampler<T>	Sampler that generates values of a specified type and can create new instances to sample from the same state with a given source of randomness.

Classes in org.apache.commons.rng.sampling that implement SharedStateSampler

Modifier and Type	Class	Description
class	CollectionSampler<T>	Sampling from a Collection.
class	CombinationSampler	Class for representing combinations of a sequence of integers.
private static final class	CompositeSamplers.SharedStateContinuousSamplerFactory.CompositeSharedStateContinuousSampler	A composite continuous sampler with shared state support.
private static final class	CompositeSamplers.SharedStateDiscreteProbabilitySampler	A class to implement the SharedStateDiscreteSampler interface for a discrete probability sampler given a factory and the probability distribution.
private static final class	CompositeSamplers.SharedStateDiscreteSamplerFactory.CompositeSharedStateDiscreteSampler	A composite discrete sampler with shared state support.
private static final class	CompositeSamplers.SharedStateLongSamplerFactory.CompositeSharedStateLongSampler	A composite long sampler with shared state support.
private static final class	CompositeSamplers.SharedStateObjectSamplerFactory.CompositeSharedStateObjectSampler<T>	A composite object sampler with shared state support.
class	DiscreteProbabilityCollectionSampler<T>	Sampling from a collection of items with user-defined probabilities.
class	PermutationSampler	Class for representing permutations of a sequence of integers.
class	UnitSphereSampler	Generate vectors isotropically located on the surface of a sphere.
private static final class	UnitSphereSampler.UnitSphereSampler1D	Sample uniformly from the ends of a 1D unit line.
private static final class	UnitSphereSampler.UnitSphereSampler2D	Sample uniformly from a 2D unit circle.
private static final class	UnitSphereSampler.UnitSphereSampler3D	Sample uniformly from a 3D unit sphere.
private static final class	UnitSphereSampler.UnitSphereSamplerND	Sample uniformly from a ND unit sphere.

Methods in org.apache.commons.rng.sampling with type parameters of type SharedStateSampler

Modifier and Type	Method	Description
private static <T extends SharedStateSampler<T>> List<T>	CompositeSamplers.copy(List<T> samplers, UniformRandomProvider rng)	Create a copy instance of each sampler in the list of samplers using the given uniform random provider as the source of randomness.

Uses of SharedStateSampler in org.apache.commons.rng.sampling.distribution

Subinterfaces of SharedStateSampler in org.apache.commons.rng.sampling.distribution

Modifier and Type	Interface	Description
interface	SharedStateContinuousSampler	Sampler that generates values of type double and can create new instances to sample from the same state with a given source of randomness.
interface	SharedStateDiscreteSampler	Sampler that generates values of type int and can create new instances to sample from the same state with a given source of randomness.
interface	SharedStateLongSampler	Sampler that generates values of type long 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 SharedStateSampler

Modifier and Type	Class	Description
class	AhrensDieterExponentialSampler	Sampling from an exponential distribution.
class	AhrensDieterMarsagliaTsangGammaSampler	Sampling from the gamma distribution.
private static final class	AhrensDieterMarsagliaTsangGammaSampler.AhrensDieterGammaSampler	Class to sample from the Gamma distribution when 0 < alpha < 1.
private static class	AhrensDieterMarsagliaTsangGammaSampler.BaseGammaSampler	Base class for a sampler from the Gamma distribution.
private static final class	AhrensDieterMarsagliaTsangGammaSampler.MarsagliaTsangGammaSampler	Class to sample from the Gamma distribution when the alpha >= 1.
class	AliasMethodDiscreteSampler	Distribution sampler that uses the Alias method.
private static final class	AliasMethodDiscreteSampler.SmallTableAliasMethodDiscreteSampler	Sample from the computed tables exploiting the small power-of-two table size.
class	BoxMullerNormalizedGaussianSampler	Box-Muller algorithm for sampling from Gaussian distribution with mean 0 and standard deviation 1.
class	ChengBetaSampler	Sampling from a beta distribution.
private static class	ChengBetaSampler.BaseChengBetaSampler	Base class to implement Cheng's algorithms for the beta distribution.
private static final class	ChengBetaSampler.ChengBBBetaSampler	Computes one sample using Cheng's BB algorithm, when beta distribution alpha and beta shape parameters are both larger than 1.
private static final class	ChengBetaSampler.ChengBCBetaSampler	Computes one sample using Cheng's BC algorithm, when at least one of beta distribution alpha or beta shape parameters is smaller than 1.
class	ContinuousUniformSampler	Sampling from a uniform distribution.
private static final class	ContinuousUniformSampler.OpenIntervalContinuousUniformSampler	Specialization to sample from an open interval (lo, hi).
class	DirichletSampler	Sampling from a Dirichlet distribution.
private static final class	DirichletSampler.GeneralDirichletSampler	Sample from a Dirichlet distribution with different concentration parameters for each category.
private static final class	DirichletSampler.SymmetricDirichletSampler	Sample from a symmetric Dirichlet distribution with the same concentration parameter for each category.
class	DiscreteUniformSampler	Discrete uniform distribution sampler.
private static class	DiscreteUniformSampler.AbstractDiscreteUniformSampler	Base class for a sampler from a discrete uniform distribution.
private static final class	DiscreteUniformSampler.FixedDiscreteUniformSampler	Discrete uniform distribution sampler when the sample value is fixed.
private static final 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 final class	DiscreteUniformSampler.OffsetDiscreteUniformSampler	Adds an offset to an underlying discrete sampler.
private static final class	DiscreteUniformSampler.PowerOf2RangeDiscreteUniformSampler	Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.
private static final 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 final class	FastLoadedDiceRollerDiscreteSampler.FixedValueDiscreteSampler	Class to handle the edge case of observations in only one category.
private static final class	FastLoadedDiceRollerDiscreteSampler.FLDRSampler	Class to implement the FLDR sample algorithm.
class	GaussianSampler	Sampling from a Gaussian distribution with given mean and standard deviation.
private static final class	GeometricSampler.GeometricExponentialSampler	Sample from the geometric distribution by using a related exponential distribution.
private static final class	GeometricSampler.GeometricP1Sampler	Sample from the geometric distribution when the probability of success is 1.
final class	GuideTableDiscreteSampler	Compute a sample from n values each with an associated probability.
class	InverseTransformContinuousSampler	Distribution sampler that uses the inversion method.
class	InverseTransformDiscreteSampler	Distribution sampler that uses the inversion method.
class	InverseTransformParetoSampler	Sampling from a Pareto distribution.
final class	KempSmallMeanPoissonSampler	Sampler for the Poisson distribution.
class	LargeMeanPoissonSampler	Sampler for the Poisson distribution.
final class	LevySampler	Sampling from a Lévy distribution.
class	LogNormalSampler	Sampling from a log-normal distribution.
class	MarsagliaNormalizedGaussianSampler	Marsaglia polar method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.
private static class	MarsagliaTsangWangDiscreteSampler.AbstractMarsagliaTsangWangDiscreteSampler	The base class for Marsaglia-Tsang-Wang samplers.
private static final class	MarsagliaTsangWangDiscreteSampler.Binomial.MarsagliaTsangWangFixedResultBinomialSampler	Return a fixed result for the Binomial distribution.
private static final class	MarsagliaTsangWangDiscreteSampler.Binomial.MarsagliaTsangWangInversionBinomialSampler	Return an inversion result for the Binomial distribution.
private static final 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 final 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 final 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 final class	RejectionInversionZipfSampler.RejectionInversionZipfSamplerImpl	Implements the rejection-inversion method for the Zipf distribution.
class	SmallMeanPoissonSampler	Sampler for the Poisson distribution.
class	StableSampler	Samples from a stable distribution.
(package private) static class	StableSampler.Alpha1CMSStableSampler	Implement the stable distribution case: alpha == 1 and beta != 0.
private static class	StableSampler.BaseStableSampler	Base class for implementations of a stable distribution that requires an exponential random deviate.
(package private) static class	StableSampler.Beta0CMSStableSampler	Implement the generic stable distribution case: alpha < 2 and beta == 0.
(package private) static class	StableSampler.Beta0WeronStableSampler	Implement the generic stable distribution case: alpha < 2 and beta == 0.
private static final class	StableSampler.CauchyStableSampler	Implement the alpha = 1 and beta = 0 stable distribution case (Cauchy distribution).
(package private) static class	StableSampler.CMSStableSampler	Implement the generic stable distribution case: alpha < 2 and beta != 0.
private static final class	StableSampler.GaussianStableSampler	Implement the alpha = 2 stable distribution case (Gaussian distribution).
private static final class	StableSampler.LevyStableSampler	Implement the alpha = 0.5 and beta = 1 stable distribution case (Levy distribution).
private static final class	StableSampler.TransformedStableSampler	Class for implementations of a stable distribution transformed by scale and location.
(package private) static class	StableSampler.WeronStableSampler	Implement the generic stable distribution case: alpha < 2 and beta != 0.
class	TSampler	Sampling from a T distribution.
private static final class	TSampler.NormalTSampler	Sample from a t-distribution using a normal distribution.
private static final class	TSampler.StudentsTSampler	Sample from a t-distribution using Bailey's algorithm.
class	UniformLongSampler	Discrete uniform distribution sampler generating values of type long.
private static final class	UniformLongSampler.FixedUniformLongSampler	Discrete uniform distribution sampler when the sample value is fixed.
private static final class	UniformLongSampler.LargeRangeUniformLongSampler	Discrete uniform distribution sampler when the range between lower and upper is too large to fit in a positive long.
private static final class	UniformLongSampler.OffsetUniformLongSampler	Adds an offset to an underlying discrete sampler.
private static final class	UniformLongSampler.PowerOf2RangeUniformLongSampler	Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.
private static final class	UniformLongSampler.SmallRangeUniformLongSampler	Discrete uniform distribution sampler when the range is small enough to fit in a positive long.
class	ZigguratNormalizedGaussianSampler	Marsaglia and Tsang "Ziggurat" method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.
class	ZigguratSampler	Modified ziggurat method for sampling from Gaussian and exponential distributions.
static class	ZigguratSampler.Exponential	Modified ziggurat method for sampling from an exponential distribution.
private static final class	ZigguratSampler.Exponential.ExponentialMean	Specialisation which multiplies the standard exponential result by a specified mean.
static final class	ZigguratSampler.NormalizedGaussian	Modified ziggurat method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.

Uses of SharedStateSampler in org.apache.commons.rng.sampling.shape

Classes in org.apache.commons.rng.sampling.shape that implement SharedStateSampler

Modifier and Type	Class	Description
class	BoxSampler	Generate points uniformly distributed within a n-dimension box (hyperrectangle).
private static final class	BoxSampler.BoxSampler2D	Sample uniformly from a box in 2D.
private static final class	BoxSampler.BoxSampler3D	Sample uniformly from a box in 3D.
private static final class	BoxSampler.BoxSamplerND	Sample uniformly from a box in ND.
class	LineSampler	Generate points uniformly distributed on a line.
private static final class	LineSampler.LineSampler1D	Sample uniformly from a line in 1D.
private static final class	LineSampler.LineSampler2D	Sample uniformly from a line in 2D.
private static final class	LineSampler.LineSampler3D	Sample uniformly from a line in 3D.
private static final class	LineSampler.LineSamplerND	Sample uniformly from a line in ND.
class	TetrahedronSampler	Generate points uniformly distributed within a tetrahedron.
class	TriangleSampler	Generate points uniformly distributed within a triangle.
private static final class	TriangleSampler.TriangleSampler2D	Sample uniformly from a triangle in 2D.
private static final class	TriangleSampler.TriangleSampler3D	Sample uniformly from a triangle in 3D.
private static final class	TriangleSampler.TriangleSamplerND	Sample uniformly from a triangle in ND.
class	UnitBallSampler	Generate coordinates uniformly distributed within the unit n-ball.
private static final class	UnitBallSampler.UnitBallSampler1D	Sample uniformly from a 1D unit line.
private static final class	UnitBallSampler.UnitBallSampler2D	Sample uniformly from a 2D unit disk.
private static final class	UnitBallSampler.UnitBallSampler3D	Sample uniformly from a 3D unit ball.
private static final class	UnitBallSampler.UnitBallSamplerND	Sample using ball point picking.