[Numpy-discussion] Bringing order to higher dimensional operations

Sebastian Berg sebastian at sipsolutions.net
Fri Jul 19 12:10:34 EDT 2013

On Fri, 2013-07-19 at 16:14 +0100, Nathaniel Smith wrote:
> On Thu, Jul 18, 2013 at 2:23 PM, Sebastian Berg
> <sebastian at sipsolutions.net> wrote:
> > On Thu, 2013-07-18 at 13:52 +0100, Nathaniel Smith wrote:
> >> Hi all,
> >>
> What I mean is: Suppose we wrote a gufunc for 'sum', where the
> intrinsic operation took a vector and returned a scalar. (E.g. we want
> to implement one of the specialized algorithms for vector summation,
> like Kahan summation, which can be more accurate than applying scalar
> addition repeatedly.)
> Then we'd have:
> np.sum(ones((2, 3))).shape == ()
> np.add.reduce(ones((2, 3))).shape == (3,)
> gufunc_sum(ones((2, 3))).shape == (2,)

Ah, indeed! So we have a different default behaviour for ufunc.reduce
and all other reduce-like functions, didn't realize that. Changing that
would be one huge thing...
As to implementing such thing as a Kahan summation, it is true, I also
can't see how it fits into the machinery. Maybe it shouldn't even be a
gufunc, but we rather need a way to specialize the reduction, or tag on
more information into the ufunc itself?

- Sebastian

> These are three names for exactly the same underlying function... but
> they all have different defaults for how they vectorize.
> -n
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