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From "Chris Douglas (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HADOOP-4437) Use qMC sequence to improve the accuracy of PiEstimator
Date Tue, 04 Nov 2008 00:09:44 GMT

    [ https://issues.apache.org/jira/browse/HADOOP-4437?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12644857#action_12644857

Chris Douglas commented on HADOOP-4437:

Since it's in the examples, a few brief notes on the initialization of HaltonSequence and
on nextPoint would help orient readers. It doesn't need to be a course in statistics- the
existing code isn't, either- but even a sentence or two on why PiEstimator is using it and
perhaps a couple comments identifying the variables would be helpful.

+1 on the patch, though; documentation is just a suggestion.

> Use qMC sequence to improve the accuracy of PiEstimator
> -------------------------------------------------------
>                 Key: HADOOP-4437
>                 URL: https://issues.apache.org/jira/browse/HADOOP-4437
>             Project: Hadoop Core
>          Issue Type: Improvement
>          Components: examples
>            Reporter: Tsz Wo (Nicholas), SZE
>            Priority: Minor
>         Attachments: 4437_20081019.patch
> Currently, PiEstimator uses java.util.Random to generate random 2d-points for estimating
pi. The numbers generated by java.util.Random are uniformly distributed.  The 2d-points generated
tense to have clump and gap. So the accuracy of the estimated pi is low.  The accuracy can
be improved by using a quasi-Monte Carlo (qMC) sequence.

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