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https://issues.apache.org/jira/browse/MATH-310?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12842514#action_12842514
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Gilles commented on MATH-310:
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>From what I've read in the ML thread, people that want to have "nextSample()"
>don't really care about configuring the RNG, so you don't have to expose it to
>provide that syntactic sugar.
Those who would like more flexibility (like configuring the RNG) should use
"RandomData" explicitely.
And the concern that all implementations dealing with sampling stay in package
"random" is fulfilled.
> Supply nextSample for all distributions with inverse cdf using inverse
> transform sampling approach
> --------------------------------------------------------------------------------------------------
>
> Key: MATH-310
> URL: https://issues.apache.org/jira/browse/MATH-310
> Project: Commons Math
> Issue Type: Improvement
> Affects Versions: 2.0
> Reporter: Mikkel Meyer Andersen
> Priority: Minor
> Attachments: patch_proposal
>
> Original Estimate: 3h
> Remaining Estimate: 3h
>
> To be able to generate samples from the supported probability distributions,
> a generic function nextSample is implemented in
> AbstractContinuousDistribution and AbstractIntegerDistribution. This also
> gives the possibility to override the method if better algorithms are
> available for specific distributions as shown in the small example with the
> exponential distribution.
> Because the nextExponential is used several places: in nextPoisson it can be
> replaces by an instance if the ExponentialDistribution and in ValueServer it
> can as well, although maybe not in as natural maner as the other.
> This problem with the Exponential is a special problem. In general the
> nextSample-approaches immediately gives the possibility the sample from all
> the distributions with inverse cdf instead just only a couple.
> Only AbstractContinuousDistribution and AbstractIntegerDistribution extends
> AbstractDistribution, and both AbstractIntegerDistribution and
> AbstractContinuousDistribution has an inverseCumulativeProbability-function.
> But in AbstractContinuousDistribution the inverse cdf returns a double, and
> at AbstractIntegerDistribution it - naturally - returns an integer. Therefor
> the nextSample is not put on AbstractDistribution, but on each extension with
> different return types.
> RandomGenerator as parameter instead of getting a RNG inside the nextSample,
> because one typically wants to use the same RNG because often several random
> samples are wanted. Another option is to have a RNG as a field in the class,
> but that would be more ugly and also result in several RNGs at runtime.
> The nextPoisson etc. ought to be moved as well, if the enhancement is
> accepted, but it should be a quick fix.
> Tests has to be written for this change as well.
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