[Numpy-discussion] A faster median (Wirth's method)

[John Salvatier]

I am very interested in this result. I have wanted to know how to do an
apply_along_axis function for a while now.

On Tue, Nov 30, 2010 at 11:21 AM, Keith Goodman <kwgoodman at gmail.com> wrote:

> On Tue, Sep 1, 2009 at 2:37 PM, Sturla Molden <sturla at molden.no> wrote:
> > Dag Sverre Seljebotn skrev:
> >>
> >> Nitpick: This will fail on large arrays. I guess numpy.npy_intp is the
> >> right type to use in this case?
> >>
> > By the way, here is a more polished version, does it look ok?
> >
> >
> http://projects.scipy.org/numpy/attachment/ticket/1213/generate_qselect.py
> > http://projects.scipy.org/numpy/attachment/ticket/1213/quickselect.pyx
>
> This is my favorite numpy/scipy ticket. So I am happy that I can
> contribute in a small way by pointing out a bug. The search for the
> k-th smallest element is only done over the first k elements (that's
> the bug) instead of over the entire array. Specifically "while l < k"
> should be "while l < r".
>
> I added a median function to the Bottleneck package:
> https://github.com/kwgoodman/bottleneck
>
> Timings:
>
> >> import bottleneck as bn
> >> arr = np.random.rand(100, 100)
> >> timeit np.median(arr)
> 1000 loops, best of 3: 762 us per loop
> >> timeit bn.median(arr)
> 10000 loops, best of 3: 198 us per loop
>
> What other functions could be built from a selection algorithm?
>
> nanmedian
> scoreatpercentile
> quantile
> knn
> select
> others?
>
> But before I add more functions to the package I need to figure out
> how to make a cython apply_along_axis function. For the first release
> I am hand coding the 1d, 2d, and 3d cases. Boring to write, hard to
> maintain, and doesn't solve the nd case.
>
> Does anyone have a cython apply_along_axis that takes a cython
> reducing function as input? The ticket has an example but I couldn't
> get it to run. If no one has one (the horror!) I'll begin to work on
> one sometime after the first release.
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