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https://issues.apache.org/jira/browse/MATH-310?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12843204#action_12843204
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Phil Steitz commented on MATH-310:
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I guess I am OK with this, as long as RandomDataImpl retains its strategy of
lazy initialization of SecureRandom and Random instances - so what is "dragged
along" is not a big memory footprint or initialization overhead. This creates
a dependency shared by all distributions, but if we are willing to manage it,
then OK.
> 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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