that discussion.
-Stefan
Gesendet: Montag, 09. Februar 2015 um 09:51 Uhr
Von: "Nathaniel Smith"
An: "Discussion of Numerical Python"
Betreff: Re: [Numpy-discussion] Silent Broadcasting considered harmful
On 8 Feb 2015 23:34, "Stefan Reiterer" <dom...@gmx.n
That sounds like a good idea! I didn't see any real good examples of usage
after some googling. Giving more examples of effective usage could also clear
more things up regarding design decisions. Additionally I'm always interested
in learning some new tricks :)
Cheers,
Stefan
Gesendet:
endet: Sonntag, 08. Februar 2015 um 23:52 Uhr
Von: "Nathaniel Smith"
An: "Discussion of Numerical Python"
Betreff: Re: [Numpy-discussion] Silent Broadcasting considered harmful
On 8 Feb 2015 13:04, "Stefan Reiterer" <dom...@gmx.net> wrote:
>
> So I sugges
bruar 2015 um 22:56 Uhr
Von: "Matthew Brett"
An: "Discussion of Numerical Python"
Betreff: Re: [Numpy-discussion] Silent Broadcasting considered harmful
Hi,
On Sun, Feb 8, 2015 at 1:39 PM, Simon Wood wrote:
>
>
> On Sun, Feb 8, 2015 at 4:24 PM, Stefan Reiterer wrote:
merits.
Gesendet: Sonntag, 08. Februar 2015 um 22:17 Uhr
Von: "Charles R Harris"
An: "Discussion of Numerical Python"
Betreff: Re: [Numpy-discussion] Silent Broadcasting considered harmful
On Sun, Feb 8, 2015 at 2:14 PM, Stefan Reiterer <dom...@gmx.net> wrote:
Yeah I'm aware of that, that's the reason why I suggested a warning level as an alternative.
Setting no warnings as default would avoid breaking existing code.
Gesendet: Sonntag, 08. Februar 2015 um 22:08 Uhr
Von: "Eelco Hoogendoorn"
An: "Discussion of Numerical Python"
Betreff: Re: [Numpy-di
Hi!
As shortly discussed on github:
https://github.com/numpy/numpy/issues/5541
I personally think that silent Broadcasting is not a good thing. I had recently a lot
of trouble with row and column vectors which got bradcastet toghether altough it was
more annoying than useful, especially
ays
> A Monday 17 January 2011 17:02:43 Stefan Reiterer escrigué:
> > Hi all!
> >
> > I made some "performance" tests with numpy to compare numpy on one
> > cpu with mpi on 4 processesors, and something appears quite strange
> > to me:
> >
&
Hi all!
I made some "performance" tests with numpy to compare numpy on one cpu with mpi
on 4 processesors, and something appears quite strange to me:
I have the following code:
N = 2**10*4
K = 16000
x = numpy.random.randn(N).astype(numpy.float32)
x *= 10**10
print "x:", x
t1 = time.time()
#do
