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From "Dennis Hendriks (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MATH-764) New sample() API should accept RandomGenerator as parameter
Date Tue, 29 May 2012 07:14:23 GMT

    [ https://issues.apache.org/jira/browse/MATH-764?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13284641#comment-13284641
] 

Dennis Hendriks commented on MATH-764:
--------------------------------------

bq [...] I would suggest, as Gilles mentions above, just using RandomDataImpl directly, which
provides direct support for sampling with configurable RandomGenerator. The setup in the patch
looks like a long way around the barn to just get back to what is there already in RandomDataImpl.

In my opinion, RandomDataImpl should just provide random data, and should not implement all
the different distributions, as that is what the distributions (classes) themselves are for.
It seems we currently use a mixed approach: some distributions are implemented as methods
on the RandomDataImpl class (nextPoisson, nextExponential, nextUniform, nextBeta, ...), while
other distributions (TriangularDistribution, ...) don't have a corresponding method in the
RandomDataImpl class, and exist solely as distribution class. The methods in RandomDataImpl
mostly seem to just use 'return nextInversionDeviate(new SomeDistributionClass(param1, param2,
..., paramn));' as method implementation. As such, we get a tight coupling between the distributions
and the RandomDataImpl class. Would it not be better to separate the RandomData(Impl) from
the distributions? Also, using nextInversionDeviate is probably not what we want, as most
distribution classes have a sample method that provides a direct implementation instead of
the generic nextInversionDeviate...

                
> New sample() API should accept RandomGenerator as parameter
> -----------------------------------------------------------
>
>                 Key: MATH-764
>                 URL: https://issues.apache.org/jira/browse/MATH-764
>             Project: Commons Math
>          Issue Type: Improvement
>    Affects Versions: 3.0
>            Reporter: Alex Bertram
>         Attachments: sampler-refactor.diff
>
>   Original Estimate: 48h
>  Remaining Estimate: 48h
>
> This may come to late as I know the 3.0 release is nearing completion, but I had some
concerns about the new sample() method on the math3 RealDistribution interface. 
> Specifically, there doesn't seem to be a way to supply a random generator to the sampler.
Perhaps it would be better to have a factory method on the RealDistribution interface that
accepted a RandomGenerator and returns an instance of some new interface, Sampler, which contains
the sample() methods. 
> That is:
> interface RealDistribution {
>     Sampler createSampler(RandomGenerator generator);
>     Sample createSampler(); // uses default RandomGenerator
> }
> interface Sampler {
>     double sample();
>     double[] sample(int sampleSize);
> }

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