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From "Greg Bowyer (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (SOLR-3673) Random variate functions
Date Wed, 25 Jul 2012 18:06:35 GMT

    [ https://issues.apache.org/jira/browse/SOLR-3673?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13422451#comment-13422451
] 

Greg Bowyer edited comment on SOLR-3673 at 7/25/12 6:05 PM:
------------------------------------------------------------

Good idea, although interestingly I have noticed (from some brief source diving) that mahout
actual embeds uncommons math :S

As for secure random, its aimed at crypto and so reads from high quality entropy like /dev/random
(which blocks), whilst this can be changed around it gets more complex than it needs to be.
                
      was (Author: gbowyer@fastmail.co.uk):
    Good idea, although interestingly I have noticed (from some brief source diving) that
mahout actual embeds uncommons math :S

As for secure random, its aimed at crypto and so reads from high quality entropy like /dev/random,
whilst this can be changed around it gets more complex than it needs to be.
                  
> 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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