Uses of Interface
org.apache.commons.rng.sampling.distribution.SharedStateDiscreteSampler
Packages that use SharedStateDiscreteSampler
Package
Description
This package provides sampling utilities.
This package contains classes for sampling from statistical distributions.
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Uses of SharedStateDiscreteSampler in org.apache.commons.rng.sampling
Methods in org.apache.commons.rng.sampling that return types with arguments of type SharedStateDiscreteSamplerModifier and TypeMethodDescriptionCompositeSamplers.newSharedStateDiscreteSamplerBuilder()Create a new builder for a compositeSharedStateDiscreteSampler. -
Uses of SharedStateDiscreteSampler in org.apache.commons.rng.sampling.distribution
Classes in org.apache.commons.rng.sampling.distribution that implement SharedStateDiscreteSamplerModifier and TypeClassDescriptionclassDistribution sampler that uses the Alias method.classDiscrete uniform distribution sampler.classDistribution sampler that uses the Fast Loaded Dice Roller (FLDR).final classCompute a sample fromnvalues each with an associated probability.classDistribution sampler that uses the inversion method.final classSampler for the Poisson distribution.classSampler for the Poisson distribution.classSampler for the Poisson distribution.classImplementation of the Zipf distribution.classSampler for the Poisson distribution.Subinterfaces with type arguments of type SharedStateDiscreteSampler in org.apache.commons.rng.sampling.distributionModifier and TypeInterfaceDescriptioninterfaceSampler that generates values of typeintand can create new instances to sample from the same state with a given source of randomness.Methods in org.apache.commons.rng.sampling.distribution that return SharedStateDiscreteSamplerModifier and TypeMethodDescriptionPoissonSamplerCache.createSharedStateSampler(UniformRandomProvider rng, double mean) Creates a new Poisson sampler.static SharedStateDiscreteSamplerAliasMethodDiscreteSampler.of(UniformRandomProvider rng, double[] probabilities) Creates a sampler.static SharedStateDiscreteSamplerAliasMethodDiscreteSampler.of(UniformRandomProvider rng, double[] probabilities, int alpha) Creates a sampler.static SharedStateDiscreteSamplerDiscreteUniformSampler.of(UniformRandomProvider rng, int lower, int upper) Creates a new discrete uniform distribution sampler.static SharedStateDiscreteSamplerGeometricSampler.of(UniformRandomProvider rng, double probabilityOfSuccess) Creates a new geometric distribution sampler.static SharedStateDiscreteSamplerGuideTableDiscreteSampler.of(UniformRandomProvider rng, double[] probabilities) Create a new sampler for an enumerated distribution using the givenprobabilities.static SharedStateDiscreteSamplerGuideTableDiscreteSampler.of(UniformRandomProvider rng, double[] probabilities, double alpha) Create a new sampler for an enumerated distribution using the givenprobabilities.static SharedStateDiscreteSamplerInverseTransformDiscreteSampler.of(UniformRandomProvider rng, DiscreteInverseCumulativeProbabilityFunction function) Create a new inverse-transform discrete sampler.static SharedStateDiscreteSamplerKempSmallMeanPoissonSampler.of(UniformRandomProvider rng, double mean) Creates a new sampler for the Poisson distribution.static SharedStateDiscreteSamplerLargeMeanPoissonSampler.of(UniformRandomProvider rng, double mean) Creates a new Poisson distribution sampler.static SharedStateDiscreteSamplerMarsagliaTsangWangDiscreteSampler.Binomial.of(UniformRandomProvider rng, int trials, double probabilityOfSuccess) Creates a sampler for the Binomial distribution.static SharedStateDiscreteSamplerMarsagliaTsangWangDiscreteSampler.Enumerated.of(UniformRandomProvider rng, double[] probabilities) Creates a sampler for an enumerated distribution ofnvalues each with an associated probability.static SharedStateDiscreteSamplerMarsagliaTsangWangDiscreteSampler.Poisson.of(UniformRandomProvider rng, double mean) Creates a sampler for the Poisson distribution.static SharedStateDiscreteSamplerPoissonSampler.of(UniformRandomProvider rng, double mean) Creates a new Poisson distribution sampler.static SharedStateDiscreteSamplerRejectionInversionZipfSampler.of(UniformRandomProvider rng, int numberOfElements, double exponent) Creates a new Zipf distribution sampler.static SharedStateDiscreteSamplerSmallMeanPoissonSampler.of(UniformRandomProvider rng, double mean) Creates a new sampler for the Poisson distribution.AliasMethodDiscreteSampler.withUniformRandomProvider(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.DiscreteUniformSampler.withUniformRandomProvider(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.GuideTableDiscreteSampler.withUniformRandomProvider(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.InverseTransformDiscreteSampler.withUniformRandomProvider(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.KempSmallMeanPoissonSampler.withUniformRandomProvider(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.LargeMeanPoissonSampler.withUniformRandomProvider(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.PoissonSampler.withUniformRandomProvider(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.RejectionInversionZipfSampler.withUniformRandomProvider(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.SmallMeanPoissonSampler.withUniformRandomProvider(UniformRandomProvider rng) Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.