Class SmallMeanPoissonSampler
- java.lang.Object
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- org.apache.commons.rng.sampling.distribution.SmallMeanPoissonSampler
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- All Implemented Interfaces:
DiscreteSampler
,SharedStateDiscreteSampler
,SharedStateSampler<SharedStateDiscreteSampler>
public class SmallMeanPoissonSampler extends java.lang.Object implements SharedStateDiscreteSampler
Sampler for the Poisson distribution.-
For small means, a Poisson process is simulated using uniform deviates, as described in
Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Chapter 3.4.1.F.3 Important integer-valued distributions: The Poisson distribution. Addison Wesley.
The Poisson process (and hence, the returned value) is bounded by1000 * mean
.
This sampler is suitable for
mean < 40
. For large means,LargeMeanPoissonSampler
should be used instead.Sampling uses
UniformRandomProvider.nextDouble()
and requires on averagemean + 1
deviates per sample.- Since:
- 1.1
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Field Summary
Fields Modifier and Type Field Description private int
limit
Pre-compute1000 * mean
as the upper limit of the sample.private double
p0
Pre-computeMath.exp(-mean)
.private UniformRandomProvider
rng
Underlying source of randomness.
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Constructor Summary
Constructors Modifier Constructor Description SmallMeanPoissonSampler(UniformRandomProvider rng, double mean)
private
SmallMeanPoissonSampler(UniformRandomProvider rng, SmallMeanPoissonSampler source)
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static SharedStateDiscreteSampler
of(UniformRandomProvider rng, double mean)
Creates a new sampler for the Poisson distribution.int
sample()
Creates anint
sample.java.lang.String
toString()
SharedStateDiscreteSampler
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.-
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
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Methods inherited from interface org.apache.commons.rng.sampling.distribution.DiscreteSampler
samples, samples
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Field Detail
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p0
private final double p0
Pre-computeMath.exp(-mean)
. Note: This is the probability of the Poisson sampleP(n=0)
.
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limit
private final int limit
Pre-compute1000 * mean
as the upper limit of the sample.
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rng
private final UniformRandomProvider rng
Underlying source of randomness.
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Constructor Detail
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SmallMeanPoissonSampler
public SmallMeanPoissonSampler(UniformRandomProvider rng, double mean)
- Parameters:
rng
- Generator of uniformly distributed random numbers.mean
- Mean.- Throws:
java.lang.IllegalArgumentException
- ifmean <= 0
orMath.exp(-mean) == 0
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SmallMeanPoissonSampler
private SmallMeanPoissonSampler(UniformRandomProvider rng, SmallMeanPoissonSampler source)
- Parameters:
rng
- Generator of uniformly distributed random numbers.source
- Source to copy.
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Method Detail
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sample
public int sample()
Creates anint
sample.- Specified by:
sample
in interfaceDiscreteSampler
- Returns:
- a sample.
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toString
public java.lang.String toString()
- Overrides:
toString
in classjava.lang.Object
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withUniformRandomProvider
public SharedStateDiscreteSampler 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.- Specified by:
withUniformRandomProvider
in interfaceSharedStateSampler<SharedStateDiscreteSampler>
- Parameters:
rng
- Generator of uniformly distributed random numbers.- Returns:
- the sampler
- Since:
- 1.3
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of
public static SharedStateDiscreteSampler of(UniformRandomProvider rng, double mean)
Creates a new sampler for the Poisson distribution.- Parameters:
rng
- Generator of uniformly distributed random numbers.mean
- Mean of the distribution.- Returns:
- the sampler
- Throws:
java.lang.IllegalArgumentException
- ifmean <= 0
orMath.exp(-mean) == 0
.- Since:
- 1.3
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