Uses of Interface
org.apache.commons.rng.sampling.SharedStateSampler
Packages that use SharedStateSampler
Package
Description
This package provides sampling utilities.
This package contains classes for sampling from statistical distributions.
This package contains classes for sampling coordinates from shapes, for example a unit ball.
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Uses of SharedStateSampler in org.apache.commons.rng.sampling
Subinterfaces of SharedStateSampler in org.apache.commons.rng.samplingModifier and TypeInterfaceDescriptioninterface
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 SharedStateSamplerModifier and TypeClassDescriptionclass
Sampling from aCollection
.class
Class for representing combinations of a sequence of integers.private static final class
A composite continuous sampler with shared state support.private static final class
A class to implement the SharedStateDiscreteSampler interface for a discrete probability sampler given a factory and the probability distribution.private static final class
A composite discrete sampler with shared state support.private static final class
A composite long sampler with shared state support.private static final class
A composite object sampler with shared state support.class
Sampling from a collection of items with user-defined probabilities.class
Class for representing permutations of a sequence of integers.class
Generate vectors isotropically located on the surface of a sphere.private static final class
Sample uniformly from the ends of a 1D unit line.private static final class
Sample uniformly from a 2D unit circle.private static final class
Sample uniformly from a 3D unit sphere.private static final class
Sample uniformly from a ND unit sphere.Methods in org.apache.commons.rng.sampling with type parameters of type SharedStateSamplerModifier and TypeMethodDescriptionprivate 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.distributionModifier and TypeInterfaceDescriptioninterface
Sampler that generates values of typedouble
and can create new instances to sample from the same state with a given source of randomness.interface
Sampler that generates values of typeint
and can create new instances to sample from the same state with a given source of randomness.interface
Sampler that generates values of typelong
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 SharedStateSamplerModifier and TypeClassDescriptionclass
Sampling from an exponential distribution.class
Sampling from the gamma distribution.private static final class
Class to sample from the Gamma distribution when0 < alpha < 1
.private static class
Base class for a sampler from the Gamma distribution.private static final class
Class to sample from the Gamma distribution when thealpha >= 1
.class
Distribution sampler that uses the Alias method.private static final class
Sample from the computed tables exploiting the small power-of-two table size.class
Box-Muller algorithm for sampling from Gaussian distribution with mean 0 and standard deviation 1.class
Sampling from a beta distribution.private static class
Base class to implement Cheng's algorithms for the beta distribution.private static final class
Computes one sample using Cheng's BB algorithm, when beta distributionalpha
andbeta
shape parameters are both larger than 1.private static final class
Computes one sample using Cheng's BC algorithm, when at least one of beta distributionalpha
orbeta
shape parameters is smaller than 1.class
Sampling from a uniform distribution.private static final class
Specialization to sample from an open interval(lo, hi)
.class
Sampling from a Dirichlet distribution.private static final class
Sample from a Dirichlet distribution with different concentration parameters for each category.private static final class
Sample from a symmetric Dirichlet distribution with the same concentration parameter for each category.class
Discrete uniform distribution sampler.private static class
Base class for a sampler from a discrete uniform distribution.private static final class
Discrete uniform distribution sampler when the sample value is fixed.private static final class
Discrete uniform distribution sampler when the range between lower and upper is too large to fit in a positive integer.private static final class
Adds an offset to an underlying discrete sampler.private static final class
Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.private static final class
Discrete uniform distribution sampler when the range is small enough to fit in a positive integer.class
Distribution sampler that uses the Fast Loaded Dice Roller (FLDR).private static final class
Class to handle the edge case of observations in only one category.private static final class
Class to implement the FLDR sample algorithm.class
Sampling from a Gaussian distribution with given mean and standard deviation.private static final class
Sample from the geometric distribution by using a related exponential distribution.private static final class
Sample from the geometric distribution when the probability of success is 1.final class
Compute a sample fromn
values each with an associated probability.class
Distribution sampler that uses the inversion method.class
Distribution sampler that uses the inversion method.class
Sampling from a Pareto distribution.final class
Sampler for the Poisson distribution.class
Sampler for the Poisson distribution.final class
Sampling from a Lévy distribution.class
Sampling from a log-normal distribution.class
Marsaglia polar method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.private static class
The base class for Marsaglia-Tsang-Wang samplers.private static final class
Return a fixed result for the Binomial distribution.private static final class
Return an inversion result for the Binomial distribution.private static final class
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
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
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
Sampler for the Poisson distribution.class
Implementation of the Zipf distribution.private static final class
Implements the rejection-inversion method for the Zipf distribution.class
Sampler for the Poisson distribution.class
Samples from a stable distribution.(package private) static class
Implement the stable distribution case:alpha == 1
andbeta != 0
.private static class
Base class for implementations of a stable distribution that requires an exponential random deviate.(package private) static class
Implement the generic stable distribution case:alpha < 2
andbeta == 0
.(package private) static class
Implement the generic stable distribution case:alpha < 2
andbeta == 0
.private static final class
Implement thealpha = 1
andbeta = 0
stable distribution case (Cauchy distribution).(package private) static class
Implement the generic stable distribution case:alpha < 2
andbeta != 0
.private static final class
Implement thealpha = 2
stable distribution case (Gaussian distribution).private static final class
Implement thealpha = 0.5
andbeta = 1
stable distribution case (Levy distribution).private static final class
Class for implementations of a stable distribution transformed by scale and location.(package private) static class
Implement the generic stable distribution case:alpha < 2
andbeta != 0
.class
Sampling from a T distribution.private static final class
Sample from a t-distribution using a normal distribution.private static final class
Sample from a t-distribution using Bailey's algorithm.class
Discrete uniform distribution sampler generating values of typelong
.private static final class
Discrete uniform distribution sampler when the sample value is fixed.private static final class
Discrete uniform distribution sampler when the range between lower and upper is too large to fit in a positive long.private static final class
Adds an offset to an underlying discrete sampler.private static final class
Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.private static final class
Discrete uniform distribution sampler when the range is small enough to fit in a positive long.class
Marsaglia and Tsang "Ziggurat" method for sampling from a Gaussian distribution with mean 0 and standard deviation 1.class
Modified ziggurat method for sampling from Gaussian and exponential distributions.static class
Modified ziggurat method for sampling from an exponential distribution.private static final class
Specialisation which multiplies the standard exponential result by a specified mean.static final class
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 SharedStateSamplerModifier and TypeClassDescriptionclass
Generate points uniformly distributed within a n-dimension box (hyperrectangle).private static final class
Sample uniformly from a box in 2D.private static final class
Sample uniformly from a box in 3D.private static final class
Sample uniformly from a box in ND.class
Generate points uniformly distributed on a line.private static final class
Sample uniformly from a line in 1D.private static final class
Sample uniformly from a line in 2D.private static final class
Sample uniformly from a line in 3D.private static final class
Sample uniformly from a line in ND.class
Generate points uniformly distributed within a tetrahedron.class
Generate points uniformly distributed within a triangle.private static final class
Sample uniformly from a triangle in 2D.private static final class
Sample uniformly from a triangle in 3D.private static final class
Sample uniformly from a triangle in ND.class
Generate coordinates uniformly distributed within the unit n-ball.private static final class
Sample uniformly from a 1D unit line.private static final class
Sample uniformly from a 2D unit disk.private static final class
Sample uniformly from a 3D unit ball.private static final class
Sample using ball point picking.