[SciPy-user] Fast Gauss Transform

David Huard david.huard at gmail.com
Tue Mar 21 08:52:02 CST 2006

That was my first impulse, however, I came across an article comparing
different fast kde algorithms and Yang's performance seemed overated (i.e.
not faster than the naive implementation). The conclusion was that the
kd-tree method (A.G Gray) was the most efficient for N-D problems by orders
of magnitude. I have no clue yet what this is about but I'll gladly share
the code once it is functional.



2006/3/20, Robert Kern <robert.kern at gmail.com>:
> David Huard wrote:
> > Hi,
> >
> > Is anyone aware of a piece of code for N-D Fast Gauss Transform (in
> > python, C or fortran) ? The only codes I could find were for one
> > dimensional cases (Strain's), or in C++ (Yang) but relied on the matlab
> > mex library. I used stats.gaussian_kde but it proves too slow for large
> > arrays (4 x 100 000)
> I don't know of any, but if you find some suitably licensed code or write
> one
> yourself, I would like to put it into scipy to speed up gaussian_kde.
> By the way, Yang's code doesn't seem to require Matlab. It just has a
> Matlab
> wrapper around it. It should be relatively easy to wrap it for Python.
> --
> Robert Kern
> robert.kern at gmail.com
> "I have come to believe that the whole world is an enigma, a harmless
> enigma
> that is made terrible by our own mad attempt to interpret it as though it
> had
> an underlying truth."
>   -- Umberto Eco
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