[Numpy-discussion] Optimized sum of squares
Charles R Harris
Sun Oct 18 13:19:32 CDT 2009
On Sun, Oct 18, 2009 at 11:37 AM, <email@example.com> wrote:
> On Sun, Oct 18, 2009 at 12:06 PM, Skipper Seabold <firstname.lastname@example.org>
> > On Sun, Oct 18, 2009 at 8:09 AM, Gael Varoquaux
> > <email@example.com> wrote:
> >> On Sun, Oct 18, 2009 at 09:06:15PM +1100, Gary Ruben wrote:
> >>> Hi Gaël,
> >>> If you've got a 1D array/vector called "a", I think the normal idiom is
> >>> np.dot(a,a)
> >>> For the more general case, I think
> >>> np.tensordot(a, a, axes=something_else)
> >>> should do it, where you should be able to figure out something_else for
> >>> your particular case.
> >> Ha, yes. Good point about the tensordot trick.
> >> Thank you
> >> Gaël
> > I'm curious about this as I use ss, which is just np.sum(a*a, axis),
> > in statsmodels and didn't much think about it.
> > There is
> > import numpy as np
> > from scipy.stats import ss
> > a = np.ones(5000)
> > but
> > timeit ss(a)
> > 10000 loops, best of 3: 21.5 µs per loop
> > timeit np.add.reduce(a*a)
> > 100000 loops, best of 3: 15 µs per loop
> > timeit np.dot(a,a)
> > 100000 loops, best of 3: 5.38 µs per loop
> > Do the number of loops matter in the timings and is dot always faster
> > even without the blas dot?
> David's reply once was that it depends on ATLAS and the version of
> I usually switched to using dot for 1d. Using tensordot looks to
> complicated for me, to figure out the axes when I quickly want a sum of
> I never tried the timing of tensordot for 2d arrays, especially for
> axis=0 for a
> c ordered array. If it's faster, this could be useful to rewrite stats.ss.
> I don't remember that np.add.reduce is much faster than np.sum. This might
> the additional call overhead from using another function in between.
If you are using numpy from svn, it might be due to te recent optimizations
that Luca Citi did for some of the ufuncs. Now we just need a multiply and
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