All Classes Interface Summary Class Summary Enum Summary
Class |
Description |
AbstractL128 |
This abstract class is a base for algorithms from the LXM family of
generators with a 128-bit LCG sub-generator.
|
AbstractL64 |
This abstract class is a base for algorithms from the LXM family of
generators with a 64-bit LCG sub-generator.
|
AbstractL64X128 |
This abstract class is a base for algorithms from the LXM family of
generators with a 64-bit LCG and 128-bit XBG sub-generator.
|
AbstractPcg6432 |
This abstract class is a base for algorithms from the Permuted Congruential Generator (PCG)
family that use an internal 64-bit Linear Congruential Generator (LCG) and output 32-bits
per cycle.
|
AbstractPcgMcg6432 |
This abstract class is a base for algorithms from the Permuted Congruential Generator (PCG)
family that use an internal 64-bit Multiplicative Congruential Generator (MCG) and output
32-bits per cycle.
|
AbstractWell |
This abstract class implements the WELL class of pseudo-random number
generator from François Panneton, Pierre L'Ecuyer and Makoto
Matsumoto.
|
AbstractWell.IndexTable |
Inner class used to store the indirection index table which is fixed for a given
type of WELL class of pseudo-random number generator.
|
AbstractXoRoShiRo1024 |
This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 64-bit
generators with 1024-bits of state.
|
AbstractXoRoShiRo128 |
This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 64-bit
generators with 128-bits of state.
|
AbstractXoRoShiRo64 |
This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 32-bit
generators with 64-bits of state.
|
AbstractXoShiRo128 |
This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 32-bit
generators with 128-bits of state.
|
AbstractXoShiRo256 |
This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 64-bit
generators with 256-bits of state.
|
AbstractXoShiRo512 |
This abstract class is a base for algorithms from the Xor-Shift-Rotate family of 64-bit
generators with 512-bits of state.
|
AhrensDieterExponentialSampler |
|
AhrensDieterMarsagliaTsangGammaSampler |
|
AhrensDieterMarsagliaTsangGammaSampler.AhrensDieterGammaSampler |
Class to sample from the Gamma distribution when 0 < alpha < 1 .
|
AhrensDieterMarsagliaTsangGammaSampler.BaseGammaSampler |
Base class for a sampler from the Gamma distribution.
|
AhrensDieterMarsagliaTsangGammaSampler.MarsagliaTsangGammaSampler |
Class to sample from the Gamma distribution when the alpha >= 1 .
|
AliasMethodDiscreteSampler |
|
AliasMethodDiscreteSampler.SmallTableAliasMethodDiscreteSampler |
Sample from the computed tables exploiting the small power-of-two table size.
|
ArraySampler |
Utilities for shuffling an array in-place.
|
BaseProvider |
Base class with default implementation for common methods.
|
BoxMullerGaussianSampler |
Deprecated.
|
BoxMullerLogNormalSampler |
Deprecated.
|
BoxMullerNormalizedGaussianSampler |
|
BoxSampler |
Generate points uniformly distributed within a n-dimension box (hyperrectangle).
|
BoxSampler.BoxSampler2D |
Sample uniformly from a box in 2D.
|
BoxSampler.BoxSampler3D |
Sample uniformly from a box in 3D.
|
BoxSampler.BoxSamplerND |
Sample uniformly from a box in ND.
|
ByteArray2IntArray |
Creates a int[] from a byte[] .
|
ByteArray2LongArray |
Creates a long[] from a byte[] .
|
ChengBetaSampler |
|
ChengBetaSampler.BaseChengBetaSampler |
Base class to implement Cheng's algorithms for the beta distribution.
|
ChengBetaSampler.ChengBBBetaSampler |
Computes one sample using Cheng's BB algorithm, when beta distribution alpha and
beta shape parameters are both larger than 1.
|
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.
|
CollectionSampler<T> |
Sampling from a Collection .
|
CombinationSampler |
Class for representing combinations
of a sequence of integers.
|
CompositeSamplers |
Factory class to create a sampler that combines sampling from multiple samplers.
|
CompositeSamplers.Builder<S> |
Builds a composite sampler.
|
CompositeSamplers.CompositeSampler<S> |
A composite sampler.
|
CompositeSamplers.ContinuousSamplerFactory |
A factory for creating a composite ContinuousSampler.
|
CompositeSamplers.ContinuousSamplerFactory.CompositeContinuousSampler |
A composite continuous sampler.
|
CompositeSamplers.DiscreteProbabilitySampler |
|
CompositeSamplers.DiscreteProbabilitySamplerFactory |
|
CompositeSamplers.DiscreteSamplerFactory |
A factory for creating a composite DiscreteSampler.
|
CompositeSamplers.DiscreteSamplerFactory.CompositeDiscreteSampler |
A composite discrete sampler.
|
CompositeSamplers.LongSamplerFactory |
A factory for creating a composite LongSampler.
|
CompositeSamplers.LongSamplerFactory.CompositeLongSampler |
A composite long sampler.
|
CompositeSamplers.ObjectSamplerFactory<T> |
A factory for creating a composite ObjectSampler.
|
CompositeSamplers.ObjectSamplerFactory.CompositeObjectSampler<T> |
A composite object sampler.
|
CompositeSamplers.SamplerBuilder<S> |
Builds a composite sampler.
|
CompositeSamplers.SamplerBuilder.SamplerFactory<S> |
A factory for creating composite samplers.
|
CompositeSamplers.SamplerBuilder.Specialisation |
The specialisation of composite sampler to build.
|
CompositeSamplers.SamplerBuilder.WeightedSampler<S> |
Contains a weighted sampler.
|
CompositeSamplers.SharedStateContinuousSamplerFactory |
A factory for creating a composite SharedStateContinuousSampler.
|
CompositeSamplers.SharedStateContinuousSamplerFactory.CompositeSharedStateContinuousSampler |
A composite continuous sampler with shared state support.
|
CompositeSamplers.SharedStateDiscreteProbabilitySampler |
A class to implement the SharedStateDiscreteSampler interface for a discrete probability
sampler given a factory and the probability distribution.
|
CompositeSamplers.SharedStateDiscreteSamplerFactory |
A factory for creating a composite SharedStateDiscreteSampler.
|
CompositeSamplers.SharedStateDiscreteSamplerFactory.CompositeSharedStateDiscreteSampler |
A composite discrete sampler with shared state support.
|
CompositeSamplers.SharedStateLongSamplerFactory |
A factory for creating a composite SharedStateLongSampler.
|
CompositeSamplers.SharedStateLongSamplerFactory.CompositeSharedStateLongSampler |
A composite long sampler with shared state support.
|
CompositeSamplers.SharedStateObjectSamplerFactory<T> |
A factory for creating a composite SharedStateObjectSampler.
|
CompositeSamplers.SharedStateObjectSamplerFactory.CompositeSharedStateObjectSampler<T> |
A composite object sampler with shared state support.
|
ContinuousInverseCumulativeProbabilityFunction |
Interface for a continuous distribution that can be sampled using
the
inversion method.
|
ContinuousSampler |
Sampler that generates values of type double .
|
ContinuousUniformSampler |
Sampling from a uniform distribution.
|
ContinuousUniformSampler.OpenIntervalContinuousUniformSampler |
Specialization to sample from an open interval (lo, hi) .
|
Conversions |
Performs seed conversions.
|
Coordinates |
Utility class for common coordinate operations for shape samplers.
|
DirichletSampler |
|
DirichletSampler.GeneralDirichletSampler |
Sample from a Dirichlet distribution with different concentration parameters
for each category.
|
DirichletSampler.SymmetricDirichletSampler |
Sample from a symmetric Dirichlet distribution with the same concentration parameter
for each category.
|
DiscreteInverseCumulativeProbabilityFunction |
Interface for a discrete distribution that can be sampled using
the
inversion method.
|
DiscreteProbabilityCollectionSampler<T> |
Sampling from a collection of items with user-defined
probabilities.
|
DiscreteSampler |
Sampler that generates values of type int .
|
DiscreteUniformSampler |
Discrete uniform distribution sampler.
|
DiscreteUniformSampler.AbstractDiscreteUniformSampler |
Base class for a sampler from a discrete uniform distribution.
|
DiscreteUniformSampler.FixedDiscreteUniformSampler |
Discrete uniform distribution sampler when the sample value is fixed.
|
DiscreteUniformSampler.LargeRangeDiscreteUniformSampler |
Discrete uniform distribution sampler when the range between lower and upper is too large
to fit in a positive integer.
|
DiscreteUniformSampler.OffsetDiscreteUniformSampler |
Adds an offset to an underlying discrete sampler.
|
DiscreteUniformSampler.PowerOf2RangeDiscreteUniformSampler |
Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.
|
DiscreteUniformSampler.SmallRangeDiscreteUniformSampler |
Discrete uniform distribution sampler when the range is small
enough to fit in a positive integer.
|
DotyHumphreySmallFastCounting32 |
Implement the Small, Fast, Counting (SFC) 32-bit generator of Chris Doty-Humphrey.
|
DotyHumphreySmallFastCounting64 |
Implement the Small, Fast, Counting (SFC) 64-bit generator of Chris Doty-Humphrey.
|
FastLoadedDiceRollerDiscreteSampler |
Distribution sampler that uses the Fast Loaded Dice Roller (FLDR).
|
FastLoadedDiceRollerDiscreteSampler.FixedValueDiscreteSampler |
Class to handle the edge case of observations in only one category.
|
FastLoadedDiceRollerDiscreteSampler.FLDRSampler |
Class to implement the FLDR sample algorithm.
|
GaussianSampler |
Sampling from a Gaussian distribution with given mean and
standard deviation.
|
GeometricSampler |
|
GeometricSampler.GeometricExponentialSampler |
Sample from the geometric distribution by using a related exponential distribution.
|
GeometricSampler.GeometricP1Sampler |
Sample from the geometric distribution when the probability of success is 1.
|
GuideTableDiscreteSampler |
Compute a sample from n values each with an associated probability.
|
Int2Long |
Converts a Integer to an Long .
|
IntArray2Int |
Creates a single value by "xor" of all the values in the input array.
|
IntArray2LongArray |
Creates a long[] from an int[] .
|
InternalGamma |
Adapted and stripped down copy of class
"org.apache.commons.math4.special.Gamma" .
|
InternalUtils |
Functions used by some of the samplers.
|
InternalUtils.FactorialLog |
Class for computing the natural logarithm of the factorial of n .
|
IntProvider |
Base class for all implementations that provide an int -based
source randomness.
|
InverseTransformContinuousSampler |
|
InverseTransformDiscreteSampler |
|
InverseTransformParetoSampler |
|
ISAACRandom |
A fast cryptographic pseudo-random number generator.
|
JDKRandom |
A provider that uses the Random.nextInt() method of the JDK's
Random class as the source of randomness.
|
JDKRandom.ValidatingObjectInputStream |
An ObjectInputStream that's restricted to deserialize
only Random using look-ahead deserialization.
|
JDKRandomBridge |
|
JDKRandomWrapper |
|
JenkinsSmallFast32 |
Implement Bob Jenkins's small fast (JSF) 32-bit generator.
|
JenkinsSmallFast64 |
Implement Bob Jenkins's small fast (JSF) 64-bit generator.
|
JumpableUniformRandomProvider |
Applies to generators that can be advanced a large number of
steps of the output sequence in a single operation.
|
KempSmallMeanPoissonSampler |
|
KISSRandom |
|
L128X1024Mix |
A 64-bit all purpose generator.
|
L128X128Mix |
A 64-bit all purpose generator.
|
L128X256Mix |
A 64-bit all purpose generator.
|
L32X64Mix |
A 32-bit all purpose generator.
|
L64X1024Mix |
A 64-bit all purpose generator.
|
L64X128Mix |
A 64-bit all purpose generator.
|
L64X128StarStar |
A 64-bit all purpose generator.
|
L64X256Mix |
A 64-bit all purpose generator.
|
LargeMeanPoissonSampler |
|
LargeMeanPoissonSampler.LargeMeanPoissonSamplerState |
Encapsulate the state of the sampler.
|
LevySampler |
Sampling from a Lévy distribution.
|
LineSampler |
Generate points uniformly distributed on a line.
|
LineSampler.LineSampler1D |
Sample uniformly from a line in 1D.
|
LineSampler.LineSampler2D |
Sample uniformly from a line in 2D.
|
LineSampler.LineSampler3D |
Sample uniformly from a line in 3D.
|
LineSampler.LineSamplerND |
Sample uniformly from a line in ND.
|
ListSampler |
Sampling from a List .
|
LogNormalSampler |
Sampling from a log-normal distribution.
|
Long2Int |
Converts a Long to an Integer .
|
Long2IntArray |
Uses a long value to seed a
SplitMix64 RNG and
create a int[] with the requested number of random
values.
|
Long2LongArray |
Uses a Long value to seed a
SplitMix64 RNG and
create a long[] with the requested number of random
values.
|
LongArray2IntArray |
Creates an int[] from a long[] .
|
LongArray2Long |
Creates a single value by "xor" of all the values in the input array.
|
LongJumpableUniformRandomProvider |
Applies to generators that can be advanced a very large number of
steps of the output sequence in a single operation.
|
LongProvider |
Base class for all implementations that provide a long -based
source randomness.
|
LongSampler |
Sampler that generates values of type long .
|
LXMSupport |
Utility support for the LXM family of generators.
|
LXMSupport |
Utility support for the LXM family of generators.
|
MarsagliaNormalizedGaussianSampler |
|
MarsagliaTsangWangDiscreteSampler |
Sampler for a discrete distribution using an optimised look-up table.
|
MarsagliaTsangWangDiscreteSampler.AbstractMarsagliaTsangWangDiscreteSampler |
The base class for Marsaglia-Tsang-Wang samplers.
|
MarsagliaTsangWangDiscreteSampler.Binomial |
Create a sampler for the Binomial distribution.
|
MarsagliaTsangWangDiscreteSampler.Binomial.MarsagliaTsangWangFixedResultBinomialSampler |
Return a fixed result for the Binomial distribution.
|
MarsagliaTsangWangDiscreteSampler.Binomial.MarsagliaTsangWangInversionBinomialSampler |
Return an inversion result for the Binomial distribution.
|
MarsagliaTsangWangDiscreteSampler.Enumerated |
Create a sampler for an enumerated distribution of n values each with an
associated probability.
|
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.
|
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.
|
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.
|
MarsagliaTsangWangDiscreteSampler.Poisson |
Create a sampler for the Poisson distribution.
|
MersenneTwister |
This class implements a powerful pseudo-random number generator
developed by Makoto Matsumoto and Takuji Nishimura during
1996-1997.
|
MersenneTwister64 |
This class provides the 64-bits version of the originally 32-bits
Mersenne Twister .
|
MiddleSquareWeylSequence |
Middle Square Weyl Sequence Random Number Generator.
|
MixFunctions |
Performs mixing of bits.
|
MultiplyWithCarry256 |
|
NativeSeedType |
The native seed type.
|
NoOpConverter<SEED> |
Dummy converter that simply passes on its input.
|
NormalizedGaussianSampler |
|
NumberFactory |
Utility for creating number types from one or two int values
or one long value, or a sequence of bytes.
|
ObjectSampler<T> |
Sampler that generates values of a specified type.
|
PcgMcgXshRr32 |
A Permuted Congruential Generator (PCG) that is composed of a 64-bit Multiplicative Congruential
Generator (MCG) combined with the XSH-RR (xorshift; random rotate) output
transformation to create 32-bit output.
|
PcgMcgXshRs32 |
A Permuted Congruential Generator (PCG) that is composed of a 64-bit Multiplicative Congruential
Generator (MCG) combined with the XSH-RS (xorshift; random shift) output
transformation to create 32-bit output.
|
PcgRxsMXs64 |
A Permuted Congruential Generator (PCG) that is composed of a 64-bit Linear Congruential
Generator (LCG) combined with the RXS-M-XS (random xorshift; multiply; xorshift) output
transformation to create 64-bit output.
|
PcgXshRr32 |
A Permuted Congruential Generator (PCG) that is composed of a 64-bit Linear Congruential
Generator (LCG) combined with the XSH-RR (xorshift; random rotate) output
transformation to create 32-bit output.
|
PcgXshRs32 |
A Permuted Congruential Generator (PCG) that is composed of a 64-bit Linear Congruential
Generator (LCG) combined with the XSH-RS (xorshift; random shift) output
transformation to create 32-bit output.
|
PermutationSampler |
Class for representing permutations
of a sequence of integers.
|
PoissonSampler |
|
PoissonSamplerCache |
|
ProviderBuilder |
RNG builder.
|
ProviderBuilder.RandomSourceInternal |
Identifiers of the generators.
|
RandomIntSource |
Source of randomness that generates values of type int .
|
RandomLongSource |
Source of randomness that generates values of type long .
|
RandomProviderDefaultState |
Wraps the internal state of a generator instance.
|
RandomProviderState |
Marker interface for objects that represents the state of a random
generator.
|
RandomSource |
This class provides the API for creating generators of random numbers.
|
RandomStreams |
Utility for creating streams using a source of randomness.
|
RandomStreams.SeededObjectFactory<T> |
A factory for creating objects using a seed and a using a source of randomness.
|
RandomStreams.SeededObjectSpliterator<T> |
Spliterator for streams of a given object type that can be created from a seed
and source of randomness.
|
RejectionInversionZipfSampler |
|
RejectionInversionZipfSampler.RejectionInversionZipfSamplerImpl |
Implements the rejection-inversion method for the Zipf distribution.
|
RestorableUniformRandomProvider |
Applies to generators whose internal state can be saved and restored.
|
SamplerBase |
Deprecated.
|
Seed2ArrayConverter<IN,OUT> |
Seed converter to create an output array type.
|
SeedConverter<IN,OUT> |
Seed converter.
|
SeedConverterComposer<IN,TRANS,OUT> |
|
SeedFactory |
Utilities related to seeding.
|
SeedUtils |
Utility for creating seeds.
|
SeedUtils.UnsignedByteProvider |
Provider of unsigned 8-bit integers.
|
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.
|
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.
|
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.
|
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.
|
SharedStateSampler<R> |
Applies to samplers that can share state between instances.
|
SmallMeanPoissonSampler |
|
SplitMix64 |
A fast RNG, with 64 bits of state, that can be used to initialize the
state of other generators.
|
SplittableUniformRandomProvider |
Applies to generators that can be split into two objects (the original and a new instance)
each of which implements the same interface (and can be recursively split indefinitely).
|
StableSampler |
Samples from a stable distribution.
|
StableSampler.Alpha1CMSStableSampler |
Implement the stable distribution case: alpha == 1 and beta != 0 .
|
StableSampler.BaseStableSampler |
Base class for implementations of a stable distribution that requires an exponential
random deviate.
|
StableSampler.Beta0CMSStableSampler |
Implement the generic stable distribution case: alpha < 2 and beta == 0 .
|
StableSampler.Beta0WeronStableSampler |
Implement the generic stable distribution case: alpha < 2 and beta == 0 .
|
StableSampler.CauchyStableSampler |
Implement the alpha = 1 and beta = 0 stable distribution case
(Cauchy distribution).
|
StableSampler.CMSStableSampler |
Implement the generic stable distribution case: alpha < 2 and
beta != 0 .
|
StableSampler.GaussianStableSampler |
Implement the alpha = 2 stable distribution case (Gaussian distribution).
|
StableSampler.LevyStableSampler |
Implement the alpha = 0.5 and beta = 1 stable distribution case
(Levy distribution).
|
StableSampler.SpecialMath |
Implement special math functions required by the CMS algorithm.
|
StableSampler.TransformedStableSampler |
Class for implementations of a stable distribution transformed by scale and location.
|
StableSampler.WeronStableSampler |
Implement the generic stable distribution case: alpha < 2 and
beta != 0 .
|
SubsetSamplerUtils |
Utility class for selecting a subset of a sequence of integers.
|
TetrahedronSampler |
Generate points uniformly distributed within a
tetrahedron.
|
ThreadLocalRandomSource |
|
ThreadLocalRandomSource.ThreadLocalRng |
Extend the ThreadLocal to allow creation of the desired RandomSource .
|
TriangleSampler |
|
TriangleSampler.TriangleSampler2D |
Sample uniformly from a triangle in 2D.
|
TriangleSampler.TriangleSampler3D |
Sample uniformly from a triangle in 3D.
|
TriangleSampler.TriangleSamplerND |
Sample uniformly from a triangle in ND.
|
TSampler |
Sampling from a T distribution.
|
TSampler.NormalTSampler |
Sample from a t-distribution using a normal distribution.
|
TSampler.StudentsTSampler |
Sample from a t-distribution using Bailey's algorithm.
|
TwoCmres |
Random number generator designed by Mark D. Overton.
|
TwoCmres.Cmres |
Subcycle generator.
|
TwoCmres.Cmres.Factory |
Factory.
|
UniformLongSampler |
Discrete uniform distribution sampler generating values of type long .
|
UniformLongSampler.FixedUniformLongSampler |
Discrete uniform distribution sampler when the sample value is fixed.
|
UniformLongSampler.LargeRangeUniformLongSampler |
Discrete uniform distribution sampler when the range between lower and upper is too large
to fit in a positive long.
|
UniformLongSampler.OffsetUniformLongSampler |
Adds an offset to an underlying discrete sampler.
|
UniformLongSampler.PowerOf2RangeUniformLongSampler |
Discrete uniform distribution sampler when the range is a power of 2 and greater than 1.
|
UniformLongSampler.SmallRangeUniformLongSampler |
Discrete uniform distribution sampler when the range is small
enough to fit in a positive long.
|
UniformRandomProvider |
Applies to generators of random number sequences that follow a uniform
distribution.
|
UniformRandomProviderSupport |
|
UniformRandomProviderSupport.ProviderDoublesSpliterator |
Spliterator for streams of double values that may be recursively split.
|
UniformRandomProviderSupport.ProviderIntsSpliterator |
Spliterator for streams of int values that may be recursively split.
|
UniformRandomProviderSupport.ProviderLongsSpliterator |
Spliterator for streams of long values that may be recursively split.
|
UniformRandomProviderSupport.ProviderSpliterator |
Base class for spliterators for streams of values.
|
UniformRandomProviderSupport.ProviderSplitsSpliterator |
Spliterator for streams of SplittableUniformRandomProvider.
|
UnitBallSampler |
|
UnitBallSampler.UnitBallSampler1D |
Sample uniformly from a 1D unit line.
|
UnitBallSampler.UnitBallSampler2D |
Sample uniformly from a 2D unit disk.
|
UnitBallSampler.UnitBallSampler3D |
Sample uniformly from a 3D unit ball.
|
UnitBallSampler.UnitBallSamplerND |
Sample using ball point picking.
|
UnitSphereSampler |
|
UnitSphereSampler.UnitSphereSampler1D |
Sample uniformly from the ends of a 1D unit line.
|
UnitSphereSampler.UnitSphereSampler2D |
Sample uniformly from a 2D unit circle.
|
UnitSphereSampler.UnitSphereSampler3D |
Sample uniformly from a 3D unit sphere.
|
UnitSphereSampler.UnitSphereSamplerND |
Sample uniformly from a ND unit sphere.
|
Well1024a |
This class implements the WELL1024a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
Well19937a |
This class implements the WELL19937a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
Well19937c |
This class implements the WELL19937c pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
Well44497a |
This class implements the WELL44497a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
Well44497b |
This class implements the WELL44497b pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
Well512a |
This class implements the WELL512a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
|
XoRoShiRo1024PlusPlus |
A large-state all-purpose 64-bit generator.
|
XoRoShiRo1024Star |
A large-state 64-bit generator suitable for double generation.
|
XoRoShiRo1024StarStar |
A large-state all-purpose 64-bit generator.
|
XoRoShiRo128Plus |
A fast 64-bit generator suitable for double generation.
|
XoRoShiRo128PlusPlus |
A fast all-purpose 64-bit generator.
|
XoRoShiRo128StarStar |
A fast all-purpose 64-bit generator.
|
XoRoShiRo64Star |
A fast 32-bit generator suitable for float generation.
|
XoRoShiRo64StarStar |
A fast all-purpose 32-bit generator.
|
XorShift1024Star |
A fast RNG implementing the XorShift1024* algorithm.
|
XorShift1024StarPhi |
A fast RNG implementing the XorShift1024* algorithm.
|
XoShiRo128Plus |
A fast 32-bit generator suitable for float generation.
|
XoShiRo128PlusPlus |
A fast all-purpose 32-bit generator.
|
XoShiRo128StarStar |
A fast all-purpose 32-bit generator.
|
XoShiRo256Plus |
A fast 64-bit generator suitable for double generation.
|
XoShiRo256PlusPlus |
A fast all-purpose 64-bit generator.
|
XoShiRo256StarStar |
A fast all-purpose 64-bit generator.
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XoShiRo512Plus |
A fast 64-bit generator suitable for double generation.
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XoShiRo512PlusPlus |
A fast all-purpose generator.
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XoShiRo512StarStar |
A fast all-purpose generator.
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ZigguratNormalizedGaussianSampler |
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ZigguratSampler |
Modified ziggurat method for sampling from Gaussian and exponential distributions.
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ZigguratSampler.Exponential |
Modified ziggurat method for sampling from an exponential distribution.
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ZigguratSampler.Exponential.ExponentialMean |
Specialisation which multiplies the standard exponential result by a specified mean.
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ZigguratSampler.NormalizedGaussian |
Modified ziggurat method for sampling from a Gaussian distribution with
mean 0 and standard deviation 1.
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