On Thu, 2013-05-23 at 15:42 +0100, Nathaniel Smith wrote:
> On Thu, May 23, 2013 at 3:19 PM, Matthieu Brucher
> wrote:
> > Hi,
> >
> > It's to be expected. You are overwritten one of your input vector while it
> > is still being used.
> > So not a numpy bug ;)
>
> Sure, that's clearly what's goin
On Thu, May 23, 2013 at 7:19 AM, Matthieu Brucher
wrote:
> It's to be expected. You are overwritten one of your input vector while it
> is still being used.
> So not a numpy bug ;)
It's a doc bug, at least.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/
On Thu, May 23, 2013 at 11:14 AM, Nathaniel Smith wrote:
> On Thu, May 23, 2013 at 3:57 PM, Matthieu Brucher
> wrote:
>> In my point of view, you should never use an output argument equal to an
>> input argument. It can impede a lot of optimizations.
>
> This is a fine philosophy in some cases, b
> Can you file a bug in the bug tracker so this won't get lost?
Done.
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On Thu, May 23, 2013 at 3:57 PM, Matthieu Brucher
wrote:
> In my point of view, you should never use an output argument equal to an
> input argument. It can impede a lot of optimizations.
This is a fine philosophy in some cases, but a non-starter in others.
Python doesn't have optimizations in th
In my point of view, you should never use an output argument equal to an
input argument. It can impede a lot of optimizations.
Matthieu
2013/5/23 Nicolas Rougier
>
> >
> > Sure, that's clearly what's going on, but numpy shouldn't let you
> > silently shoot yourself in the foot like that. Re-us
>
> Sure, that's clearly what's going on, but numpy shouldn't let you
> silently shoot yourself in the foot like that. Re-using input as
> output is a very common operation, and usually supported fine.
> Probably we should silently make a copy of any input(s) that overlap
> with the output? For h
On Thu, May 23, 2013 at 3:19 PM, Matthieu Brucher
wrote:
> Hi,
>
> It's to be expected. You are overwritten one of your input vector while it
> is still being used.
> So not a numpy bug ;)
Sure, that's clearly what's going on, but numpy shouldn't let you
silently shoot yourself in the foot like t
Hi,
It's to be expected. You are overwritten one of your input vector while it
is still being used.
So not a numpy bug ;)
Matthieu
2013/5/23 Pierre Haessig
> Hi Nicolas,
>
> Le 23/05/2013 15:45, Nicolas Rougier a écrit :
> > if I use either a or b as output, results are wrong (and nothing in
Hi Nicolas,
Le 23/05/2013 15:45, Nicolas Rougier a écrit :
> if I use either a or b as output, results are wrong (and nothing in the dot
> documentation prevents me from doing this):
>
> a = np.array([[1, 2], [3, 4]])
> b = np.array([[1, 2], [3, 4]])
> np.dot(a,b,out=a)
>
> -> array([[ 6, 20],
>
Hi,
>From the dot documentation, I tried something simple:
a = np.array([[1, 2], [3, 4]])
b = np.array([[1, 2], [3, 4]])
np.dot(a, b)
-> array([[ 7, 10],
[15, 22]])
And I got expected result but if I use either a or b as output, results are
wrong (and nothing in the dot documentatio
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