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https://issues.apache.org/jira/browse/RNG-68?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16753998#comment-16753998
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Alex D Herbert commented on RNG-68:
-----------------------------------
Here is the PR:
[Pull Request 17|https://github.com/apache/commons-rng/pull/17]
> AhrensDieterMarsagliaTsangGammaSampler constructor can be optimised for the
> theta parameter
> -------------------------------------------------------------------------------------------
>
> Key: RNG-68
> URL: https://issues.apache.org/jira/browse/RNG-68
> Project: Commons RNG
> Issue Type: Improvement
> Components: sampling
> Affects Versions: 1.2
> Reporter: Alex D Herbert
> Priority: Trivial
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> The AhrensDieterExponentialSampler has two algorithms based on the {{theta}}
> parameter. The constructor precomputes factors for both algorithms even
> though only one algorithm will even be called.
> I suggest optimising the constructor using:
> {code:java}
> public AhrensDieterMarsagliaTsangGammaSampler(UniformRandomProvider rng,
> double alpha,
> double theta) {
> super(null);
> this.rng = rng;
> this.alpha = alpha;
> this.theta = theta;
> gaussian = new ZigguratNormalizedGaussianSampler(rng);
> if (theta < 1) {
> oneOverTheta = 1 / theta;
> bGSOptim = 1 + theta / Math.E;
> // Unused
> dOptim = cOptim = 0;
> } else {
> dOptim = theta - ONE_THIRD;
> cOptim = ONE_THIRD / Math.sqrt(dOptim);
> // Unused
> oneOverTheta = bGSOptim = 0;
> }
> }
> {code}
> An alternative is to split the two algorithms into two classes as was done
> for the {{PoissonSampler}} for small and large mean.
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