On 22 Sep 2013 23:04, "David Goldsmith" wrote:
>
> Is np.random.randint(2, size=N) the fastest way to do this? Thanks!
Are you concerned about speed or memory use? The operation you show should
already be quite fast. A more memory efficient approach would be to
generate integers and use their bi
Is np.random.randint(2, size=N) the fastest way to do this? Thanks!
DG
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Sun, 2013-09-22 at 10:54 -0400, David Reed wrote:
> Sebastian,
>
>
> I apologize, that did work. I forgot to autoreload. Can you explain
> why the original code did work at the debug prompt?
>
Probably the debug prompt clears the errors or such. There was a bug (it
actually is already fixed
Sebastian,
I apologize, that did work. I forgot to autoreload. Can you explain why
the original code did work at the debug prompt?
-Dave
On Sun, Sep 22, 2013 at 10:45 AM, David Reed wrote:
> Thanks Sebastian, but still have same error. Also doesn't explain why it
> works when I run the same
Thanks Sebastian, but still have same error. Also doesn't explain why it
works when I run the same code at the debug prompt. Scratching my head on
this one.
Dave
On Sun, Sep 22, 2013 at 10:42 AM, Sebastian Berg wrote:
> On Sun, 2013-09-22 at 10:21 -0400, David Reed wrote:
> > Hi,
> >
> >
> >
On Sun, 2013-09-22 at 10:21 -0400, David Reed wrote:
> Hi,
>
>
> I am getting a strange error when finding the minimum of a matrix.
> The weird thing is I get this while running within iPython shell, and
> if I do %debug and go to the line where this fails and run the command
> `a = np.min(D,
Hi,
I am getting a strange error when finding the minimum of a matrix. The
weird thing is I get this while running within iPython shell, and if I do
%debug and go to the line where this fails and run the command `a =
np.min(D, axis=0)`, I get no error.
Here is the trace:
draw_lines/main.pyc in
On Sun, Sep 22, 2013 at 1:24 PM, wrote:
> On Sat, Sep 21, 2013 at 1:55 PM, Jeremy Hetzel wrote:
> > I've added a trapezoidal distribution to numpy.random for consideration,
> > pull request 3770:
> > https://github.com/numpy/numpy/pull/3770
> >
> > Similar to the triangular distribution, the tra
On Sat, Sep 21, 2013 at 1:55 PM, Jeremy Hetzel wrote:
> I've added a trapezoidal distribution to numpy.random for consideration,
> pull request 3770:
> https://github.com/numpy/numpy/pull/3770
>
> Similar to the triangular distribution, the trapezoidal distribution may be
> used where the underlyi
