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From "Lance Norskog (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SOLR-3673) Random variate functions
Date Wed, 25 Jul 2012 07:20:34 GMT

    [ https://issues.apache.org/jira/browse/SOLR-3673?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13422053#comment-13422053
] 

Lance Norskog commented on SOLR-3673:
-------------------------------------

You might want to ask the Mahout people about good random numbers and packaging them. They
went to a lot of work to get this stuff right for packaged software. Determinism is really
important for writing unit tests, and Mahout has a weird hack to make this easy.

[SecureRandom|http://docs.oracle.com/javase/6/docs/api/java/security/SecureRandom.html] might
be better than dragging in uncommons.

(Java.util.Random's double generator always gives something less than 20% away from the previous
value.)
                
> Random variate functions
> ------------------------
>
>                 Key: SOLR-3673
>                 URL: https://issues.apache.org/jira/browse/SOLR-3673
>             Project: Solr
>          Issue Type: Improvement
>    Affects Versions: 4.0, 5.0
>            Reporter: Greg Bowyer
>            Assignee: Greg Bowyer
>         Attachments: SOLR-3673.patch
>
>
> Hi all
> At my $DAYJOB I have been asked to build a few random variate functions that return random
numbers bound to a distribution.
> I think these can be added to solr.
> I have a hesitation in that the code as written uses / needs uncommons math (because
we want a far better RNG than java's and because I am lazy and did not want to write distributions)
> uncommons math is apache license so we are good on that front
> anyone have any thoughts on this ?
> For reference the functions are:
> rgaussian(mean, stddev) -> Random value aligned to gaussian distribution
> rpoisson(mean) -> Random value aligned to poisson distribution
> rbinomial(n, prob) -> Random value aligned to binomial distribtion
> rcontinous(min ,max) -> random continuous value between min and max
> rdiscrete(min, max) -> Random discrete value between min and max
> rexponential(rate) -> Random value from the exponential distribution

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