All Classes and Interfaces
Class
Description
This abstract class implements the WELL class of pseudo-random number
generator from François Panneton, Pierre L'Ecuyer and Makoto
Matsumoto.
Inner class used to store the indirection index table which is fixed for a given
type of WELL class of pseudo-random number generator.
Sampling from an exponential distribution.
Sampling from the gamma distribution.
Distribution sampler that uses the Alias method.
Utilities for shuffling an array in-place.
Base class with default implementation for common methods.
Deprecated.
Since version 1.1.
Deprecated.
Since version 1.1.
Box-Muller algorithm for sampling from Gaussian distribution with
mean 0 and standard deviation 1.
Generate points uniformly distributed within a n-dimension box (hyperrectangle).
Creates a
int[] from a byte[].Creates a
long[] from a byte[].Sampling from a beta distribution.
Sampling from a
Collection.Class for representing combinations
of a sequence of integers.
Factory class to create a sampler that combines sampling from multiple samplers.
Builds a composite sampler.
The DiscreteProbabilitySampler class defines implementations that sample from a user-defined
discrete probability distribution.
A factory for creating a sampler of a user-defined
discrete probability distribution.
Interface for a continuous distribution that can be sampled using
the
inversion method.
Sampler that generates values of type
double.Sampling from a uniform distribution.
Sampling from a Dirichlet
distribution.
Interface for a discrete distribution that can be sampled using
the
inversion method.
Sampling from a collection of items with user-defined
probabilities.
Sampler that generates values of type
int.Discrete uniform distribution sampler.
Implement the Small, Fast, Counting (SFC) 32-bit generator of Chris Doty-Humphrey.
Implement the Small, Fast, Counting (SFC) 64-bit generator of Chris Doty-Humphrey.
Distribution sampler that uses the Fast Loaded Dice Roller (FLDR).
Sampling from a Gaussian distribution with given mean and
standard deviation.
Sampling from a geometric
distribution.
Compute a sample from
n values each with an associated probability.Converts a
Integer to an Long.Creates a single value by "xor" of all the values in the input array.
Creates a
long[] from an int[].Base class for all implementations that provide an
int-based
source randomness.Distribution sampler that uses the
inversion method.
Distribution sampler that uses the
inversion method.
Sampling from a Pareto distribution.
A fast cryptographic pseudo-random number generator.
A provider that uses the
Random.nextInt() method of the JDK's
Random class as the source of randomness.Subclass of
Random that delegates to a
RestorableUniformRandomProvider instance but will otherwise rely
on the base class for generating all the random types.Wraps a
Random instance to implement UniformRandomProvider.Implement Bob Jenkins's small fast (JSF) 32-bit generator.
Implement Bob Jenkins's small fast (JSF) 64-bit generator.
Applies to generators that can be advanced a large number of
steps of the output sequence in a single operation.
Sampler for the Poisson
distribution.
Port from Marsaglia's
"KISS" algorithm.
A 64-bit all purpose generator.
A 64-bit all purpose generator.
A 64-bit all purpose generator.
A 32-bit all purpose generator.
A 64-bit all purpose generator.
A 64-bit all purpose generator.
A 64-bit all purpose generator.
A 64-bit all purpose generator.
Sampler for the Poisson distribution.
Sampling from a Lévy distribution.
Generate points uniformly distributed on a line.
Sampling from a
List.Sampling from a log-normal distribution.
Converts a
Long to an Integer.Uses a
long value to seed a
SplitMix64 RNG and
create a int[] with the requested number of random
values.Uses a
Long value to seed a
SplitMix64 RNG and
create a long[] with the requested number of random
values.Creates an
int[] from a long[].Creates a single value by "xor" of all the values in the input array.
Applies to generators that can be advanced a very large number of
steps of the output sequence in a single operation.
Base class for all implementations that provide a
long-based
source randomness.Sampler that generates values of type
long.
Marsaglia polar method for sampling from a Gaussian distribution
with mean 0 and standard deviation 1.
Sampler for a discrete distribution using an optimised look-up table.
Create a sampler for the Binomial distribution.
Create a sampler for an enumerated distribution of
n values each with an
associated probability.Create a sampler for the Poisson distribution.
This class implements a powerful pseudo-random number generator
developed by Makoto Matsumoto and Takuji Nishimura during
1996-1997.
This class provides the 64-bits version of the originally 32-bits
Mersenne Twister.Middle Square Weyl Sequence Random Number Generator.
Port from Marsaglia's
"Multiply-With-Carry" algorithm.
The native seed type.
Dummy converter that simply passes on its input.
Marker interface for a sampler that generates values from an N(0,1)
Gaussian distribution.
Utility for creating number types from one or two
int values
or one long value, or a sequence of bytes.Sampler that generates values of a specified type.
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.
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.
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.
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.
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.
Class for representing permutations
of a sequence of integers.
Sampler for the Poisson distribution.
Create a sampler for the
Poisson
distribution using a cache to minimise construction cost.
RNG builder.
Identifiers of the generators.
Source of randomness that generates values of type
int.Source of randomness that generates values of type
long.Wraps the internal state of a generator instance.
Marker interface for objects that represents the state of a random
generator.
This class provides the API for creating generators of random numbers.
Utility for creating streams using a source of randomness.
A factory for creating objects using a seed and a using a source of randomness.
Implementation of the Zipf distribution.
Applies to generators whose internal state can be saved and restored.
Deprecated.
Since version 1.1.
Seed converter to create an output array type.
Seed converter.
Composes two
converters.Utilities related to seeding.
Sampler that generates values of type
double and can create new instances to sample
from the same state with a given source of randomness.Sampler that generates values of type
int and can create new instances to sample
from the same state with a given source of randomness.Sampler that generates values of type
long and can create new instances to sample
from the same state with a given source of randomness.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.
Applies to samplers that can share state between instances.
Sampler for the Poisson distribution.
A fast RNG, with 64 bits of state, that can be used to initialize the
state of other generators.
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).
Samples from a stable distribution.
Generate points uniformly distributed within a
tetrahedron.
This class provides a thread-local
UniformRandomProvider.Generate points
uniformly distributed within a triangle.
Sampling from a T distribution.
Random number generator designed by Mark D. Overton.
Discrete uniform distribution sampler generating values of type
long.Applies to generators of random number sequences that follow a uniform
distribution.
Generate coordinates
uniformly distributed within the unit n-ball.
Generate vectors
isotropically located on the surface of a sphere.
This class implements the WELL1024a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
This class implements the WELL19937a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
This class implements the WELL19937c pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
This class implements the WELL44497a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
This class implements the WELL44497b pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
This class implements the WELL512a pseudo-random number generator
from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
A large-state all-purpose 64-bit generator.
A large-state 64-bit generator suitable for
double generation.A large-state all-purpose 64-bit generator.
A fast 64-bit generator suitable for
double generation.A fast all-purpose 64-bit generator.
A fast all-purpose 64-bit generator.
A fast 32-bit generator suitable for
float generation.A fast all-purpose 32-bit generator.
A fast RNG implementing the
XorShift1024* algorithm.A fast RNG implementing the
XorShift1024* algorithm.A fast 32-bit generator suitable for
float generation.A fast all-purpose 32-bit generator.
A fast all-purpose 32-bit generator.
A fast 64-bit generator suitable for
double generation.A fast all-purpose 64-bit generator.
A fast all-purpose 64-bit generator.
A fast 64-bit generator suitable for
double generation.A fast all-purpose generator.
A fast all-purpose generator.
Marsaglia and Tsang "Ziggurat" method for sampling from a Gaussian
distribution with mean 0 and standard deviation 1.
Modified ziggurat method for sampling from Gaussian and exponential distributions.
Modified ziggurat method for sampling from an exponential distribution.
Modified ziggurat method for sampling from a Gaussian distribution with
mean 0 and standard deviation 1.