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https://issues.apache.org/jira/browse/MATH-984?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13671773#comment-13671773
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Phil Steitz commented on MATH-984:
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That should work. Looks like you have hit a new bug, which should be opened as
a separate issue if you don't mind doing that. What I suspect is going on is
that your data has singleton bins, which results in zero variance within bin.
The getKernel method tries to create a NormalDistribution instance using the
bin stats. This throws NotStrictlyPositiveException if the standard deviation
parameter is not strictly positive. This is part of the reason that the
singleton check is there in getNextValue. I forgot to account for this case in
inverseCumulativeProbability (added in 3.2). A unit test demonstrating the bug
would be most appreciated.
I think it would probably be a little more efficient though to keep the direct
implementation of getNextValue as it is now, but just fix the bug. Arguably,
the bug is in getKernel, which should return a distribution object with support
equal to the bin. On the other hand, that makes it a little harder for those
wanting to supply a custom kernel.
> Incorrect (bugged) generating function getNextValue() in
> .random.EmpiricalDistribution
> --------------------------------------------------------------------------------------
>
> Key: MATH-984
> URL: https://issues.apache.org/jira/browse/MATH-984
> Project: Commons Math
> Issue Type: Bug
> Affects Versions: 3.2, 3.1.1
> Environment: all
> Reporter: Radoslav Tsvetkov
>
> The generating function getNextValue() in
> org.apache.commons.math3.random.EmpiricalDistribution
> will generate wrong values for all Distributions that are single tailed or
> limited. For example Data which are resembling Exponential or Lognormal
> distributions.
> The problem could be easily seen in code and tested.
> In last version code
> ...
> 490 return getKernel(stats).sample();
> ...
> it samples from Gaussian distribution to "smooth" in_the_bin. Obviously
> Gaussian Distribution is not limited and sometimes it does generates numbers
> outside the bin. In the case when it is the last bin it will generate wrong
> numbers.
> For example for empirical non-negative data it will generate negative rubbish.
> Additionally the proposed algorithm boldly returns only the mean value of
> the bin in case of one value! This last makes the generating function
> unusable for heavy tailed distributions with small number of values. (for
> example computer network traffic)
> On the last place usage of Gaussian soothing in the bin will change greatly
> some empirical distribution properties.
> The proposed method should be reworked to be applicable for real data which
> have often limited ranges. (either non-negative or both sides limited)
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