[SciPy-Dev] Subversion scipy.stats irregular problem with source code example
Charles Moliere
charles.moliere@gmail....
Thu Dec 9 15:59:48 CST 2010
2010/12/9 Charles Moliere <charles.moliere@gmail.com>
>
>
> 2010/12/9 <josef.pktd@gmail.com>
>
> On Thu, Dec 9, 2010 at 3:59 PM, Skipper Seabold <jsseabold@gmail.com>
>> wrote:
>> > On Thu, Dec 9, 2010 at 3:53 PM, <josef.pktd@gmail.com> wrote:
>> >> On Thu, Dec 9, 2010 at 3:12 PM, Skipper Seabold <jsseabold@gmail.com>
>> wrote:
>> >>> On Thu, Dec 9, 2010 at 2:34 PM, Charles <charles.moliere@gmail.com>
>> wrote:
>> >>>>
>> >>>> Hi,
>> >>>> I'm very sorry for entering the thread like this, but after a long
>> search over
>> >>>> the web, this thread is the more relevant to my problem which I'm
>> stuck with.
>> >>>> I'm actually trying to fit a gamma distribution on a set of
>> experimental
>> >>>> values with gamma.fit() in scipy 0.8.0. Here is the very simple code
>> I'm using
>> >>>> with a sample of my data:
>> >>>>
>> >>>> ##########################
>> >>>> import scipy as sp
>> >>>> import scipy.stats as ss
>> >>>>
>> >>>> exp_data
>> =[25.6,35.8,100.2,115.2,125.2,140.1,160.6,210.1,250.5,4500.3]
>> >>>> data = sp.array(exp_data)
>> >>>>
>> >>>> fit_alpha, fit_loc, fit_beta = ss.gamma.fit(data)
>> >>>> print(fit_alpha,fit_loc,fit_beta)
>> >>>> #########################
>> >>>>
>> >>>> I then receive many messages on the command line:
>> >>>> Warning: invalid value encountered in subtract
>> >>>>
>> >>>> Which ends with no fitting of the parameters:
>> >>>> (1.0, 0.0, 1.0)
>> >>>>
>> >>>> With earlier version of scipy (0.7.2), the error message are absent
>> but still
>> >>>> no fitting is done. Apparently, it is the extrem value of "4500.3"
>> that is
>> >>>> causing problem with the fitting in this case.
>> >>>>
>> >>>> I know you metionned earlier that the fitting code should be
>> considered as
>> >>>> experimental, however I was wondering if this should be considered as
>> a bug,
>> >>>> or if I'm making a mistake. In either case, is there a fix for the
>> fit
>> >>>> method to work with a gamma distribution?
>> >>>>
>> >>>
>> >>> It looks like Josef's recent changes have got this working. Using the
>> >>> most recent trunk, so you might want to upgrade or see the changeset
>> >>
>> >> I don't remember any changes, but in this case choosing the right
>> >> starting values will be important, and the default ones might work
>> >> with one scipy version but with an other.
>> >>
>> >
>> > Ah, well I had an older trunk installed on this machine and got the
>> > same as the OP. I updated and then it worked (ie., returned
>> > something)...
>>
>> with the version I have:
>>
>> >>>
>> stats.gamma._fitstart([25.6,35.8,100.2,115.2,125.2,140.1,160.6,210.1,250.5,4500.3])
>> (0.56777181998920023, -422.95393359547484, 1742.449869411083)
>>
>> >>>
>> stats.gamma.fit([25.6,35.8,100.2,115.2,125.2,140.1,160.6,210.1,250.5,4500.3])
>> (0.37079853616614639, 25.599999999999998, 2459.7325273317128)
>>
>> Charles,
>> you could try starting parameters like (0.5, 0.0, 2000) and see if this
>> works.
>>
>> I still haven't looked at all the changes in the fit
>>
>> Josef
>>
>> >
>> > Skipper
>> > _______________________________________________
>> > SciPy-Dev mailing list
>> > SciPy-Dev@scipy.org
>> > http://mail.scipy.org/mailman/listinfo/scipy-dev
>> >
>> _______________________________________________
>> SciPy-Dev mailing list
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>>
>
>
> Thanks a lot for your help to you both.
>
> I haven't found any documentation about _fitstart(). Is it a new fitting
> that uses the data to choose the starting parameters (as mentioned in
> ticket#808 http://projects.scipy.org/scipy/ticket/808)? In this case, is
> it available in the new trunk or in 0.9.0?
>
> I also noticed you are both using 64bits, perhaps it could have an effect
> on the convergence of the fitting?
>
> I apologize for all the questions.
>
Got my answer:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.fit.html
It is in 0.9.x. Then I'll have to wait for the release as i'm not familiar
with compiling.
Many thanks again!
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