Uses of Interface org.apache.commons.math3.random.RandomGenerator (Apache Commons Math 3.3 API)

## Uses of Interface
org.apache.commons.math3.random.RandomGenerator

- Packages that use RandomGenerator
  Package | Description
  --- | ---
  org.apache.commons.math3.distribution | Implementations of common discrete and continuous distributions.
  org.apache.commons.math3.genetics | This package provides Genetic Algorithms components and implementations.
  org.apache.commons.math3.ml.clustering | Clustering algorithms.
  org.apache.commons.math3.ml.neuralnet | Neural networks.
  org.apache.commons.math3.optim.nonlinear.scalar.noderiv | This package provides optimization algorithms that do not require derivatives.
  org.apache.commons.math3.optim.univariate | One-dimensional optimization algorithms.
  org.apache.commons.math3.optimization.direct | This package provides optimization algorithms that don't require derivatives.
  org.apache.commons.math3.optimization.univariate | Univariate real functions minimum finding algorithms.
  org.apache.commons.math3.random | Random number and random data generators.
  org.apache.commons.math3.stat.inference | Classes providing hypothesis testing.
  org.apache.commons.math3.stat.ranking | Classes providing rank transformations.
  org.apache.commons.math3.util | Convenience routines and common data structures used throughout the commons-math library.

- - ### Uses of RandomGenerator in org.apache.commons.math3.distribution

    Fields in org.apache.commons.math3.distribution declared as RandomGenerator
    Modifier and Type | Field and Description
    --- | ---
    `protected RandomGenerator` | AbstractIntegerDistribution.`random` RNG instance used to generate samples from the distribution.
    `protected RandomGenerator` | AbstractMultivariateRealDistribution.`random` RNG instance used to generate samples from the distribution.
    `protected RandomGenerator` | EnumeratedDistribution.`random` RNG instance used to generate samples from the distribution.
    `protected RandomGenerator` | AbstractRealDistribution.`random` RNG instance used to generate samples from the distribution.

    Constructors in org.apache.commons.math3.distribution with parameters of type RandomGenerator
    Constructor and Description
    `AbstractIntegerDistribution(RandomGenerator rng)`
    `AbstractMultivariateRealDistribution(RandomGenerator rng, int n)`
    `AbstractRealDistribution(RandomGenerator rng)`
    `BetaDistribution(RandomGenerator rng, double alpha, double beta)` Creates a β distribution.
    `BetaDistribution(RandomGenerator rng, double alpha, double beta, double inverseCumAccuracy)` Creates a β distribution.
    `BinomialDistribution(RandomGenerator rng, int trials, double p)` Creates a binomial distribution.
    `CauchyDistribution(RandomGenerator rng, double median, double scale)` Creates a Cauchy distribution.
    `CauchyDistribution(RandomGenerator rng, double median, double scale, double inverseCumAccuracy)` Creates a Cauchy distribution.
    `ChiSquaredDistribution(RandomGenerator rng, double degreesOfFreedom)` Create a Chi-Squared distribution with the given degrees of freedom.
    `ChiSquaredDistribution(RandomGenerator rng, double degreesOfFreedom, double inverseCumAccuracy)` Create a Chi-Squared distribution with the given degrees of freedom and inverse cumulative probability accuracy.
    `EnumeratedDistribution(RandomGenerator rng, List<Pair<T,Double>> pmf)` Create an enumerated distribution using the given random number generator and probability mass function enumeration.
    `EnumeratedIntegerDistribution(RandomGenerator rng, int[] singletons, double[] probabilities)` Create a discrete distribution using the given random number generator and probability mass function definition.
    `EnumeratedRealDistribution(RandomGenerator rng, double[] singletons, double[] probabilities)` Create a discrete distribution using the given random number generator and probability mass function enumeration.
    `ExponentialDistribution(RandomGenerator rng, double mean)` Creates an exponential distribution.
    `ExponentialDistribution(RandomGenerator rng, double mean, double inverseCumAccuracy)` Creates an exponential distribution.
    `FDistribution(RandomGenerator rng, double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom)` Creates an F distribution.
    `FDistribution(RandomGenerator rng, double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom, double inverseCumAccuracy)` Creates an F distribution.
    `GammaDistribution(RandomGenerator rng, double shape, double scale)` Creates a Gamma distribution.
    `GammaDistribution(RandomGenerator rng, double shape, double scale, double inverseCumAccuracy)` Creates a Gamma distribution.
    `GeometricDistribution(RandomGenerator rng, double p)` Creates a geometric distribution.
    `HypergeometricDistribution(RandomGenerator rng, int populationSize, int numberOfSuccesses, int sampleSize)` Creates a new hypergeometric distribution.
    `LevyDistribution(RandomGenerator rng, double mu, double c)` Creates a LevyDistribution.
    `LogNormalDistribution(RandomGenerator rng, double scale, double shape)` Creates a log-normal distribution.
    `LogNormalDistribution(RandomGenerator rng, double scale, double shape, double inverseCumAccuracy)` Creates a log-normal distribution.
    `MixtureMultivariateNormalDistribution(RandomGenerator rng, List<Pair<Double,MultivariateNormalDistribution>> components)` Creates a mixture model from a list of distributions and their associated weights.
    `MixtureMultivariateRealDistribution(RandomGenerator rng, List<Pair<Double,T>> components)` Creates a mixture model from a list of distributions and their associated weights.
    `MultivariateNormalDistribution(RandomGenerator rng, double[] means, double[][] covariances)` Creates a multivariate normal distribution with the given mean vector and covariance matrix.
    `NormalDistribution(RandomGenerator rng, double mean, double sd)` Creates a normal distribution.
    `NormalDistribution(RandomGenerator rng, double mean, double sd, double inverseCumAccuracy)` Creates a normal distribution.
    `ParetoDistribution(RandomGenerator rng, double scale, double shape)` Creates a log-normal distribution.
    `ParetoDistribution(RandomGenerator rng, double scale, double shape, double inverseCumAccuracy)` Creates a log-normal distribution.
    `PascalDistribution(RandomGenerator rng, int r, double p)` Create a Pascal distribution with the given number of successes and probability of success.
    `PoissonDistribution(RandomGenerator rng, double p, double epsilon, int maxIterations)` Creates a new Poisson distribution with specified mean, convergence criterion and maximum number of iterations.
    `TDistribution(RandomGenerator rng, double degreesOfFreedom)` Creates a t distribution.
    `TDistribution(RandomGenerator rng, double degreesOfFreedom, double inverseCumAccuracy)` Creates a t distribution.
    `TriangularDistribution(RandomGenerator rng, double a, double c, double b)` Creates a triangular distribution.
    `UniformIntegerDistribution(RandomGenerator rng, int lower, int upper)` Creates a new uniform integer distribution using the given lower and upper bounds (both inclusive).
    `UniformRealDistribution(RandomGenerator rng, double lower, double upper)` Creates a uniform distribution.
    `UniformRealDistribution(RandomGenerator rng, double lower, double upper, double inverseCumAccuracy)` Deprecated. as of 3.2, inverse CDF is now calculated analytically, use `UniformRealDistribution.UniformRealDistribution(RandomGenerator, double, double)` instead.
    `WeibullDistribution(RandomGenerator rng, double alpha, double beta)` Creates a Weibull distribution.
    `WeibullDistribution(RandomGenerator rng, double alpha, double beta, double inverseCumAccuracy)` Creates a Weibull distribution.
    `ZipfDistribution(RandomGenerator rng, int numberOfElements, double exponent)` Creates a Zipf distribution.

  - ### Uses of RandomGenerator in org.apache.commons.math3.genetics

    Methods in org.apache.commons.math3.genetics that return RandomGenerator
    Modifier and Type | Method and Description
    --- | ---
    `static RandomGenerator` | GeneticAlgorithm.`getRandomGenerator()` Returns the (static) random generator.

    Methods in org.apache.commons.math3.genetics with parameters of type RandomGenerator
    Modifier and Type | Method and Description
    --- | ---
    `static void` | GeneticAlgorithm.`setRandomGenerator(RandomGenerator random)` Set the (static) random generator.

  - ### Uses of RandomGenerator in org.apache.commons.math3.ml.clustering

    Methods in org.apache.commons.math3.ml.clustering that return RandomGenerator
    Modifier and Type | Method and Description
    --- | ---
    `RandomGenerator` | KMeansPlusPlusClusterer.`getRandomGenerator()` Returns the random generator this instance will use.
    `RandomGenerator` | FuzzyKMeansClusterer.`getRandomGenerator()` Returns the random generator this instance will use.

    Constructors in org.apache.commons.math3.ml.clustering with parameters of type RandomGenerator
    Constructor and Description
    `FuzzyKMeansClusterer(int k, double fuzziness, int maxIterations, DistanceMeasure measure, double epsilon, RandomGenerator random)` Creates a new instance of a FuzzyKMeansClusterer.
    `KMeansPlusPlusClusterer(int k, int maxIterations, DistanceMeasure measure, RandomGenerator random)` Build a clusterer.
    `KMeansPlusPlusClusterer(int k, int maxIterations, DistanceMeasure measure, RandomGenerator random, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)` Build a clusterer.

  - ### Uses of RandomGenerator in org.apache.commons.math3.ml.neuralnet

    Methods in org.apache.commons.math3.ml.neuralnet with parameters of type RandomGenerator
    Modifier and Type | Method and Description
    --- | ---
    `static FeatureInitializer` | FeatureInitializerFactory.`uniform(RandomGenerator rng, double min, double max)` Uniform sampling of the given range.

  - ### Uses of RandomGenerator in org.apache.commons.math3.optim.nonlinear.scalar.noderiv

    Constructors in org.apache.commons.math3.optim.nonlinear.scalar.noderiv with parameters of type RandomGenerator
    Constructor and Description
    `CMAESOptimizer(int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics, ConvergenceChecker<PointValuePair> checker)`

  - ### Uses of RandomGenerator in org.apache.commons.math3.optim.univariate

    Constructors in org.apache.commons.math3.optim.univariate with parameters of type RandomGenerator
    Constructor and Description
    `MultiStartUnivariateOptimizer(UnivariateOptimizer optimizer, int starts, RandomGenerator generator)` Create a multi-start optimizer from a single-start optimizer.

  - ### Uses of RandomGenerator in org.apache.commons.math3.optimization.direct

    Fields in org.apache.commons.math3.optimization.direct declared as RandomGenerator
    Modifier and Type | Field and Description
    --- | ---
    `static RandomGenerator` | CMAESOptimizer.`DEFAULT_RANDOMGENERATOR` Deprecated. Default value for `CMAESOptimizer.random`.

    Constructors in org.apache.commons.math3.optimization.direct with parameters of type RandomGenerator
    Constructor and Description
    `CMAESOptimizer(int lambda, double[] inputSigma, int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics)` Deprecated. See `SimpleValueChecker.SimpleValueChecker()`
    `CMAESOptimizer(int lambda, double[] inputSigma, int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics, ConvergenceChecker<PointValuePair> checker)` Deprecated. As of version 3.1: Parameters `lambda` and `inputSigma` must be passed with the call to `optimize`.
    `CMAESOptimizer(int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics, ConvergenceChecker<PointValuePair> checker)` Deprecated.

  - ### Uses of RandomGenerator in org.apache.commons.math3.optimization.univariate

    Constructors in org.apache.commons.math3.optimization.univariate with parameters of type RandomGenerator
    Constructor and Description
    `UnivariateMultiStartOptimizer(BaseUnivariateOptimizer<FUNC> optimizer, int starts, RandomGenerator generator)` Deprecated. Create a multi-start optimizer from a single-start optimizer.

  - ### Uses of RandomGenerator in org.apache.commons.math3.random

    Classes in org.apache.commons.math3.random that implement RandomGenerator
    Modifier and Type | Class and Description
    --- | ---
    `class` | `AbstractRandomGenerator` Abstract class implementing the `RandomGenerator` interface.
    `class` | `AbstractWell` This abstract class implements the WELL class of pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
    `class` | `BitsStreamGenerator` Base class for random number generators that generates bits streams.
    `class` | `ISAACRandom` ISAAC: a fast cryptographic pseudo-random number generator ISAAC (Indirection, Shift, Accumulate, Add, and Count) generates 32-bit random numbers.
    `class` | `JDKRandomGenerator` Extension of `java.util.Random` to implement `RandomGenerator`.
    `class` | `MersenneTwister` This class implements a powerful pseudo-random number generator developed by Makoto Matsumoto and Takuji Nishimura during 1996-1997.
    `class` | `RandomAdaptor` Extension of `java.util.Random` wrapping a `RandomGenerator`.
    `class` | `SynchronizedRandomGenerator` Any `RandomGenerator` implementation can be thread-safe if it is used through an instance of this class.
    `class` | `Well1024a` This class implements the WELL1024a pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
    `class` | `Well19937a` This class implements the WELL19937a pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
    `class` | `Well19937c` This class implements the WELL19937c pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
    `class` | `Well44497a` This class implements the WELL44497a pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
    `class` | `Well44497b` This class implements the WELL44497b pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.
    `class` | `Well512a` This class implements the WELL512a pseudo-random number generator from François Panneton, Pierre L'Ecuyer and Makoto Matsumoto.

    Methods in org.apache.commons.math3.random that return RandomGenerator
    Modifier and Type | Method and Description
    --- | ---
    `static RandomGenerator` | RandomGeneratorFactory.`createRandomGenerator(Random rng)` Creates a `RandomDataGenerator` instance that wraps a `Random` instance.
    `RandomGenerator` | RandomDataGenerator.`getRandomGenerator()` Returns the RandomGenerator used to generate non-secure random data.

    Methods in org.apache.commons.math3.random with parameters of type RandomGenerator
    Modifier and Type | Method and Description
    --- | ---
    `static Random` | RandomAdaptor.`createAdaptor(RandomGenerator randomGenerator)` Factory method to create a `Random` using the supplied `RandomGenerator`.

    Constructors in org.apache.commons.math3.random with parameters of type RandomGenerator
    Constructor and Description
    `EmpiricalDistribution(int binCount, RandomGenerator generator)` Creates a new EmpiricalDistribution with the specified bin count using the provided `RandomGenerator` as the source of random data.
    `EmpiricalDistribution(RandomGenerator generator)` Creates a new EmpiricalDistribution with default bin count using the provided `RandomGenerator` as the source of random data.
    `GaussianRandomGenerator(RandomGenerator generator)` Create a new generator.
    `RandomAdaptor(RandomGenerator randomGenerator)` Construct a RandomAdaptor wrapping the supplied RandomGenerator.
    `RandomDataGenerator(RandomGenerator rand)` Construct a RandomDataGenerator using the supplied `RandomGenerator` as the source of (non-secure) random data.
    `RandomDataImpl(RandomGenerator rand)` Deprecated. Construct a RandomDataImpl using the supplied `RandomGenerator` as the source of (non-secure) random data.
    `StableRandomGenerator(RandomGenerator generator, double alpha, double beta)` Create a new generator.
    `SynchronizedRandomGenerator(RandomGenerator rng)` Creates a synchronized wrapper for the given `RandomGenerator` instance.
    `UniformRandomGenerator(RandomGenerator generator)` Create a new generator.
    `UnitSphereRandomVectorGenerator(int dimension, RandomGenerator rand)`
    `ValueServer(RandomGenerator generator)` Construct a ValueServer instance using a RandomGenerator as its source of random data.

  - ### Uses of RandomGenerator in org.apache.commons.math3.stat.inference

    Constructors in org.apache.commons.math3.stat.inference with parameters of type RandomGenerator
    Constructor and Description
    `KolmogorovSmirnovTest(RandomGenerator rng)` Construct a KolmogorovSmirnovTest with the provided random data generator.

  - ### Uses of RandomGenerator in org.apache.commons.math3.stat.ranking

    Constructors in org.apache.commons.math3.stat.ranking with parameters of type RandomGenerator
    Constructor and Description
    `NaturalRanking(NaNStrategy nanStrategy, RandomGenerator randomGenerator)` Create a NaturalRanking with the given NaNStrategy, TiesStrategy.RANDOM and the given source of random data.
    `NaturalRanking(RandomGenerator randomGenerator)` Create a NaturalRanking with TiesStrategy.RANDOM and the given RandomGenerator as the source of random data.

  - ### Uses of RandomGenerator in org.apache.commons.math3.util

    Methods in org.apache.commons.math3.util with parameters of type RandomGenerator
    Modifier and Type | Method and Description
    --- | ---
    `static void` | MathArrays.`shuffle(int[] list, int start, MathArrays.Position pos, RandomGenerator rng)` Shuffle the entries of the given array, using the Fisher–Yates algorithm.
    `static void` | MathArrays.`shuffle(int[] list, RandomGenerator rng)` Shuffle the entries of the given array.

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