Fri Aug 27 13:47:37 CDT 2010
Well perhaps I should start with a module that does other kernels &
bandwidth estimation then? Then everybody who uses them can use a standard
implementation. Is that an appropriate addition to SciPy core?
On Fri, Aug 27, 2010 at 2:38 PM, <firstname.lastname@example.org> wrote:
> On Fri, Aug 27, 2010 at 2:17 PM, Sam Birch <email@example.com> wrote:
> > Hi all,
> > I was thinking of renovating the kernel density estimation package
> > no promises; I'm leaving for college tomorrow morning!). I was wondering:
> > a) whether anyone had started code in that direction
> Mike Crowe wrote code for kernel regression and Skipper started a 1D
> kernel density estimator in scikits.statsmodels, which cover a larger
> number of kernels
> I don't think I have seen any higher dimensional kernel density
> estimation in python besides scipy.stats.kde. The Gaussian kde in
> scipy.stats is targeted to the underlying Fortran code for
> multivariate normal cdf.
> It's not clear to me what other n-dimensional kdes would require or
> whether they would fit well with the current code.
> One extension that Robert also mentioned in the past that it would be
> nice to have adaptive kernels, which I also haven't seen in python
> > b) what people want in it
> > I was thinking (as an ideal, not necessarily goal):
> > - Support for more than Gaussian kernels (e.g. custom,
> > uniform, Epanechnikov, triangular, quartic, cosine, etc.)
> > - More options for bandwidth selection (custom bandwidth matrices, AMISE
> > optimization, cross-validation, etc.)
> definitely yes, I don't think they are even available for 1D yet.
> > - Assorted conveniences: automatically generate the mesh, limit the
> > support for speed
> Using scipy.spatial to limit the number of neighbors in a bounded
> support kernel might be a good idea.
> (just some thought on the topic)
> > So, thoughts anyone? I figure it's better to over-specify and then
> > under-produce, so don't hold back.
> > Thanks,
> > Sam
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