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From Antonio Gomes Rodrigues <ra0...@gmail.com>
Subject Re: [GitHub] jmeter issue #296: Bug 61078 - Percentile calculation error
Date Wed, 31 May 2017 11:27:45 GMT
Hi,

I don't have time to read the posted links yet

But I am OK to have the same way to calculate percentiles and documented it

Antonio

2017-05-28 11:51 GMT+02:00 Philippe Mouawad <philippe.mouawad@gmail.com>:

> Hello,
> After reading further on this topic and also reading the different
> comments, my position would be:
> - switch everywhere to R1 (also in commons-math)
> - use the PR from contributor for the median and jorphan computations
> - document the change and algo somewhere
>
> From my understanding, tests having large results should not be affected by
> change.
>
> This would at least make computations uniform until we decide what library
> to use.
>
> I need your go before going further.
>
> If we decide for statusquo then please comment on respective bugs to
> explain to reported and contributor why we won't change anything.
>
> Regards
>
> On Tuesday, May 9, 2017, Felix Schumacher <felix.schumacher@
> internetallee.de>
> wrote:
>
> > Am 09.05.2017 09:11, schrieb pmouawad:
> >
> >> Github user pmouawad commented on the issue:
> >>
> >>     https://github.com/apache/jmeter/pull/296
> >>
> >>     Hello @abalanonline ,
> >>     Thanks for your replies and explanations !
> >>
> >>     I am not a math expert as you seem to be, so I have few questions
> >> you may be able to help on:
> >>
> >>     1. Thanks to your comment, I see default method is LEGACY, and the
> >> one you have created is R_1. Do you have some insights on the
> >> different method and their limits / use cases ?
> >>
> >>     2. Why does the "bug" you report affect all libraries I checked
> >> (HdrHistogram, https://github.com/tdunning/t-digest/ and JOrphan ) ?
> >> Can't it be due to a different method estimation algorithm ?
> >>
> >>     Note I share your thoughts on using a dedicated library but
> >> commons-math may be overkill in terms of performance compared to
> >> HdrHistogram or t-digest.
> >>
> >
> > I have tried to do a bit of research on percentiles, quantiles and
> median.
> >
> > It looks to me, that those "points" are more like ranges, and there is no
> > exact value.
> >
> > R and numpy will interpolate the median and the percentiles/quantiles.
> The
> > statistics module
> > of python 3 has three different median implementations called median,
> > median_high and median_low,
> > that interpolate, give the highest possible median and the lowest.
> >
> > Wikipedia (the german one), gives a definition of an "Empirisches
> > Quantile" (empiric quantile),
> > where it settles on the lower border of the quantiles (and therefore the
> > median).
> >
> > I wonder if we should change our implementation at all.
> >
> > Felix
> >
> >
> >>     Thanks
> >>
> >>
> >> ---
> >> If your project is set up for it, you can reply to this email and have
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> >> reply appear on GitHub as well. If your project does not have this
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> >> enabled and wishes so, or if the feature is enabled but not working,
> >> please
> >> contact infrastructure at infrastructure@apache.org or file a JIRA
> ticket
> >> with INFRA.
> >> ---
> >>
> >
>
> --
> Cordialement.
> Philippe Mouawad.
>

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