[SciPy-User] Epanechnikov kernel
Sat Jan 19 07:18:03 CST 2013
for a pdf function that can use most of the standard kernels see
"y, x = pdf(data, weight = None, h = None, kernel = 'Epanechnikov', n = 100)\n"
"y = pdf(data, x, weight = None, h = None, kernel = 'Epanechnikov')\n"
"This function estimates the probability density function from the random\n"
"numbers in the array data, using the bandwidth h and the specified kernel\n"
"o) The keyword argument 'kernel' specifies the kernel function:\n"
" -'E' or 'Epanechnikov' : Epanechnikov kernel (default)\n"
" -'U' or 'Uniform' : Uniform kernel\n"
" -'T' or 'Triangle' : Triangle kernel\n"
" -'G' or 'Gaussian' : Gaussian kernel\n"
" -'B' or 'Biweight' : Quartic/biweight kernel\n"
" -'3' or 'Triweight' : Triweight kernel\n"
" -'C' or 'Cosine' : Cosine kernel\n"
On Jan 19, 2013, at 7:49 AM, firstname.lastname@example.org wrote:
> On Sat, Jan 19, 2013 at 6:34 AM, email@example.com
> <firstname.lastname@example.org> 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
> (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
> There is still some old code in the sandbox for generic kernels
> No idea about the status.
> On the other hand we do have automatic bandwidth selection, and
> kernels for categorical and ordered variables.)
> astroML http://astroml.github.com/modules/generated/astroML.density_estimation.KDE.html#astroML.density_estimation.KDE
> has some other kernels, but not Epanechnikov.
>> Can you help me?
>> SciPy-User mailing list
> SciPy-User mailing list
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