Interface RandomNumberGenerator
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- All Superinterfaces:
Seedable
- All Known Subinterfaces:
LinearCongruentialGenerator,RandomBetaGenerator,RandomExpGenerator,RandomGammaGenerator,RandomLongGenerator,RandomStandardNormalGenerator
- All Known Implementing Classes:
ARIMASim,BaumWelch,BernoulliTrial,BinomialRNG,BoxMuller,BurnInRNG,Cheng1978,CompositeLinearCongruentialGenerator,ConcurrentCachedRLG,ConcurrentCachedRNG,ConcurrentStandardNormalRNG,ContextRNG,DiscreteHMM,ExtremeValueMC,GARCHSim,HiddenMarkovModel,HMMRNG,InverseTransformSampling,InverseTransformSamplingEVDRNG,InverseTransformSamplingExpRNG,InverseTransformSamplingGammaRNG,InverseTransformSamplingTruncatedNormalRNG,KnightSatchellTran1995,Knuth1969,KunduGupta2007,LEcuyer,Lehmer,LogNormalRNG,MARMASim,MarsagliaBray1964,MarsagliaTsang2000,MersenneTwister,MixtureHMM,MixtureHMMEM,MRG,MWC8222,NormalRNG,OUSim,RandomProcess,RandomWalk,RayleighRNG,SHR0,SHR3,SimpleMC,StandardNormalRNG,ThinRNG,ThreadIDRLG,ThreadIDRNG,UniformRNG,VanDerWaerden1969,WeibullRNG,XiTanLiu2010a,XiTanLiu2010b,Ziggurat2000,Ziggurat2000Exp,Zignor2005
public interface RandomNumberGenerator extends Seedable
A (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern. However, it is very important to know that the sequence is not random at all and that it is completely determined by a relatively small set of initial values. Knowing the generation algorithm and the states can predict the next value, as the values are generated in a deterministic way. By default, an implementation ofRandomNumberGeneratoris not thread-safe, and thus should not be shared among multiple threads. If aRandomNumberGeneratorinstance is used in a multi-threaded program, for example, useRandomNumberGenerator rng = RandomNumberGenerators.synchronizedRNG(new Gaussian());
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description doublenextDouble()Get the next randomdouble.
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