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https://issues.apache.org/jira/browse/MATH-385?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12975150#action_12975150
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Mikkel Meyer Andersen commented on MATH-385:
--------------------------------------------
I could convert pow(x, 2) to x*x, but the what about the following, then:
{{...$ grep -iHR "pow(.*, 2)" .
./WeibullDistributionImpl.java: return FastMath.pow(scale, 2) *
./WeibullDistributionImpl.java: FastMath.pow(mean, 2);
./ExponentialDistributionImpl.java: return FastMath.pow(getMean(), 2);
./NormalDistributionImpl.java: return
FastMath.pow(getStandardDeviation(), 2);
./GammaDistributionImpl.java: return getAlpha() *
FastMath.pow(getBeta(), 2);
./ZipfDistributionImpl.java: return (Hs2 / Hs) - (FastMath.pow(Hs1, 2) /
FastMath.pow(Hs, 2));
./FDistributionImpl.java: return ( 2 * FastMath.pow(denominatorDF,
2) * (numeratorDF + denominatorDF - 2) )
./FDistributionImpl.java: / ( (numeratorDF *
Math.pow(denominatorDF - 2, 2) * (denominatorDF - 4)) );
./BetaDistributionImpl.java: return (alpha * beta) /
(FastMath.pow(alphabetasum, 2) * (alphabetasum + 1));}}
> Characteristic (support, mean, variance, ...) on Distributions
> --------------------------------------------------------------
>
> Key: MATH-385
> URL: https://issues.apache.org/jira/browse/MATH-385
> Project: Commons Math
> Issue Type: New Feature
> Reporter: Mikkel Meyer Andersen
> Assignee: Mikkel Meyer Andersen
> Fix For: 2.2
>
> Attachments: MATH385-PATCH1, MATH385-PATCH2
>
> Original Estimate: 5h
> Remaining Estimate: 5h
>
> I wish that the Distributions could contain some characteristics. For example
> support, mean, and variance.
> Support:
> AbstractContinuousDistribution and AbstractIntegerDistribution should have
> double getSupport{Lower, Upper}Bound() and int getSupport{Lower,
> Upper}Bound(), respectively. Also methods a la boolean isSupport{Lower,
> Upper}BoundInclusive() on AbstractContinuousDistribution should reflect if
> the support is open of closed. In practise the implemented distributions are
> easy since the support for all continuous distributions are real intervals
> (connected sets), and the support for all the discrete distributions are
> connected integer sets. This means that the lower and upper bound (together
> with isSupport{Lower, Upper}BoundInclusive() on
> AbstractContinuousDistribution because it is not needed on the discrete
> distributions because of their nature) are sufficient for determine the
> support.
> Mean and variance:
> double get{Mean, Variance}() should be on AbstractDistribution.
> With such characteristic an invalidateParameters-method might come in handy
> because they often depend on the parameters. The characteristics should not
> be calculated before the first time they are get'ted, and when calculated,
> they should be saved for later use. When parameters change, an
> invalidateParameters-method should be called to force the characteristics to
> be recalculated.
> Values such as Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, and
> Double.NaN should be used where appropriate.
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