[SciPy-User] R: Re: R: Re: R: Re: Epanechnikov kernel

francescoboccacci@libe... francescoboccacci@libe...
Sat Jan 19 09:18:01 CST 2013


Thanks Josef, i will investigate on it.
I'm using scipy version '0.9.0' so i need to update it.
If i have some problems i will ask you again :).
Thanks for your time

Francesco

>----Messaggio originale----
>Da: josef.pktd@gmail.com
>Data: 19/01/2013 16.06
>A: "francescoboccacci@libero.it"<francescoboccacci@libero.it>, "SciPy Users 
List"<scipy-user@scipy.org>
>Ogg: Re: [SciPy-User] R: Re: R: Re: Epanechnikov kernel
>
>On Sat, Jan 19, 2013 at 9:57 AM, francescoboccacci@libero.it
><francescoboccacci@libero.it> wrote:
>> Hi,
>> i would like to use a Epanechnikov kernel because i would  like replicate 
an R
>> function that use Epanechnikov kernel.
>> Reading in depth a documentation below documentation:
>>
>>
>> http://rgm3.lab.nig.ac.jp/RGM/r_function?p=adehabitatHR&f=kernelUD
>>
>> i found that i can use normal kernel (i think guaussion kernel).
>> Below i write a pieces of my code:
>>
>>
>>                   xmin = min(xPoints)
>>                   xmax = max(xPoints)
>>                   ymin = min(yPoints)
>>                   ymax = max(yPoints)
>>                   X,Y = np.mgrid[xmin:xmax:40j, ymin:ymax:40j]
>>                   positions = np.vstack([X.ravel(), Y.ravel()])
>>                   values = np.vstack([xPoints,yPoints])
>>                   # scipy.stats.kde.gaussian_kde --
>>                   # Representation of a kernel-density estimate using 
Gaussian
>> kernels.
>>                   kernel = stats.kde.gaussian_kde(values)
>>
>>                   Z = np.reshape(kernel(positions).T, X.T.shape)
>>
>> If i understood in right way the missing part that i have to implement is 
the
>> smoothing paramter h:
>>
>> h = Sigma*n^(-1/6)
>>
>> where
>>
>> Sigma = 0.5*(sd(x)+sd(y))
>>
>>
>> My new question is:
>>
>> How can set smooting parameter in stats.kde.gaussian_kde function? is it
>> possible?
>
>In a recent scipy (since 0.10 IIRC) you can directly set the bandwidth
>without subclassing
>
>http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.
html#scipy.stats.gaussian_kde
>http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html#kernel-density-
estimation
>
>Josef
>
>>
>> Thanks
>>
>> Francesco
>>
>>
>>>----Messaggio originale----
>>>Da: jsseabold@gmail.com
>>>Data: 19/01/2013 15.21
>>>A: "francescoboccacci@libero.it"<francescoboccacci@libero.it>, "SciPy Users
>> List"<scipy-user@scipy.org>
>>>Ogg: Re: [SciPy-User] R: Re: Epanechnikov kernel
>>>
>>>On Sat, Jan 19, 2013 at 8:48 AM, francescoboccacci@libero.it
>>><francescoboccacci@libero.it> wrote:
>>>> Hi,
>>>> is there a possibility to multivariate  KDE using Epanechnikov kernel? my
>>>> variables are X Y (point position)
>>>>
>>>
>>>As Josef mentioned there is no way for the user to choose the kernel
>>>at present. The functionality is there, but it needs to be hooked in
>>>with a suitable API. I didn't keep up with these discussions, so I
>>>don't know the current status. If it's something you're interested in
>>>trying to help with, I'm sure people would be appreciative and you can
>>>ping the statsmodels mailing list.
>>>
>>>Practically though, the reason this hasn't been done yet is that the
>>>choice of the kernel is not all that important. Bandwidth selection is
>>>the most important variable and other kernels perform similarly given
>>>a good bandwidth. Is there any particular reason you want Epanechnikov
>>>kernel in particular?
>>>
>>>Skipper
>>>
>>>> Thanks
>>>>
>>>> Francesco
>>>>
>>>>>----Messaggio originale----
>>>>>Da: jsseabold@gmail.com
>>>>>Data: 19/01/2013 14.32
>>>>>A: "SciPy Users List"<scipy-user@scipy.org>
>>>>>Ogg: Re: [SciPy-User] Epanechnikov kernel
>>>>>
>>>>>On Sat, Jan 19, 2013 at 7:49 AM,  <josef.pktd@gmail.com> wrote:
>>>>>> On Sat, Jan 19, 2013 at 6:34 AM, francescoboccacci@libero.it
>>>>>> <francescoboccacci@libero.it> wrote:
>>>>>>> Hi all,
>>>>>>>
>>>>>>> I have a question for you. Is it possible in scipy using a 
Epanechnikov
>>>>>>> kernel function?
>>>>>>>
>>>>>>> I checked on scipy documentation but i found that the only way to
>>>> calculate
>>>>>>> kernel-density estimate is possible only with using Gaussian kernels?
>>>>>>>
>>>>>>> Is it true?
>>>>>>
>>>>>> Yes, kde in scipy.stats only has gaussian_kde
>>>>>>
>>>>>> Also in statsmodels currently only gaussian is supported for
>>>>>> continuous data
>>>>>> http://statsmodels.sourceforge.net/devel/nonparametric.html
>>>>>> (It was removed because in the references only the bandwidth selection
>>>>>> made much difference in the estimation, but not the shape of the
>>>>>> kernel. Other kernels for continuous variables will come back
>>>>>> eventually.
>>>>>
>>>>>If you're interested in univariate KDE, then we do have the Epanechnikov
>>>> kernel.
>>>>>
>>>>>http://statsmodels.sourceforge.net/devel/generated/statsmodels.
>> nonparametric.
>>>> kde.KDEUnivariate.fit.html#statsmodels.nonparametric.kde.KDEUnivariate.
fit
>>>>>
>>>>>Skipper
>>>>>_______________________________________________
>>>>>SciPy-User mailing list
>>>>>SciPy-User@scipy.org
>>>>>http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>>
>>>>
>>>>
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>>
>>
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