Re: [ojAlgo-user] apparent bug in Gamma distribution

[Anders P. <an...@op...>]

Thanks for the contribution!

I've included your additions to RandomNumberTest.

And then...

1) There is now a RandomUtils.gamma(x) function - with junit tests
2) Weibull mean and variance are now calculated analytically, and tested that it matches Exponential if shape=1.0
3) Your testWeibull method still fails. The resulting values are very large. I believe that's part of the problem

Could you have a look at that to see what needs to be fixed; the Weibull class or the test case?

/Anders


On 19 jun 2012, at 20:05, Chris Lucas wrote:

> Thanks for the new snapshot. The gamma distribution looks good.
> 
> I've extended RandomNumberTest a bit:
> 
> * Added a template for checking the mean and variance of a sample
> against the analytic values for two-parameter distributions, using a
> tiny inner-class wrapper.
> * Applied said template to Gamma, Normal, and Weibull dists.
> * Added couple of miscellaneous tests. I noticed that the Weibull mean
> and variance are not computed analytically, which could be an issue
> for users of that distribution.
> 
> The attached file contains those changes. Note that the package name
> is what I used in my own testing, and will need to be changed if you
> replace the current RandomNumberTest.java with it.
> 
> Chris
> 
> On Tue, Jun 5, 2012 at 6:35 PM, Chris Lucas <cg...@gm...> wrote:
>> Hi --
>> 
>> I've noticed what appears to be a bug in org.ojalgo.random.Gamma. I
>> noticed that a Gamma(1,2) getDoubleValue() was returning negative
>> values. After looking at the documentation (thinking it was just a
>> non-standard parameterization at work) and confirming that the
>> empirical means don't align with the value of getExpected(), I looked
>> at the source (via
>> http://ojalgo.cvs.sourceforge.net/viewvc/ojalgo/DevProj/src/org/ojalgo/random/Gamma.java?revision=1.14&view=markup)
>> and found the following definition for generate() [which is called by
>> getDoubleValue() in RandomNumber()]:
>> 
>> 60    @Override
>> 61      protected double generate() {
>> 62
>> 63      double tmpVal = ZERO;
>> 64
>> 65      for (int i = 0; i < myCount; i++) {
>> 66      tmpVal += this.random().nextDouble();
>> 67      }
>> 68
>> 69      return -tmpVal / myLambda;
>> 70      }
>> 
>> The function this.random() is inherited from RandomNumber, and appears
>> to be a uniform distribution. Am I missing something? If not, this
>> appears to be a fairly serious bug for anyone who wants to use the
>> random number libraries.
>> 
>> More generally, it appears that the test coverage in the random number
>> unit tests  (at
>> http://ojalgo.cvs.sourceforge.net/viewvc/ojalgo/TestProj/src/org/ojalgo/random/RandomNumberTest.java?revision=1.11&view=markup)
>> is fairly limited. Perhaps some tests could be added to establish that
>> large-sample means are within certain easy-to-satisfy tolerances of
>> the intended expected values?
>> 
>> Chris
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