To repeat and (hopefully) clarify/summarize the other answers:

It's been left out on purpose so far.

Why was it left out? A few reasons:

- Usually in-place operations like "a += b" are preferred over the
out-of-place equivalents like "a[...] = a + b" because they avoid some
copies and potentially large temporary arrays. But for @= this is
impossible -- you have to make a temporary copy of the whole matrix,
because otherwise you find yourself writing output elements on top of input
elements that you're still using. So it's probably better style to write
this as "a[...] = a @ b": this makes it more clear to the reader that a
potentially large temporary array is being allocated.

- The one place where this doesn't apply, and where "a @= b" really could
be a performance win, is when working with higher dimensional stacks of
matrices. In this case we still have to make a temporary copy of each
matrix, but only of one matrix at a time, not the whole stack together.

- But, not that many people are using matrix stacks yet, and in any case "a
@= b" is limited to cases where both matrices are square. And making it
efficient in the stacked case may require some non-trivial surgery on the
internals. So there hasn't been much urgency to fix this.

My guess is that eventually it will be supported because the stacked matrix
use case is somewhat compelling, but it will take a bit until someone
(maybe you!) decides they care enough and have the time/energy to fix it.

-n
On Jun 21, 2016 17:39, "Hans Larsen" <jo...@mail.dk> wrote:

> I have Python 3-5-1 and NumPy 1-11! windows 64bits!
> When will by side 'M=M@P' be supported with 'M@=P'???:-(
>
> --
> Hans Larsen Galgebakken Sønder 4-11A 2620 Albertslund Danmark/Danio
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Reply via email to