Dear Colleagues,
the SIAM CSE13 conference will be held next year in Boston, and this is a
conference that is well suited for much of the type of work that goes on in
the open source scientific Python development community (and Julia). The
conference is co-chaired by Hans-Petter Langtangen, well
On Thu, Aug 2, 2012 at 1:01 PM, Vlad Niculae wrote:
> Either way, is there a reason that I'm missing, why np.array([0]) should be
> both C- and F-contiguous, but np.array([[0]]) can only be one of them at a
> time?
Contiguous 1-d arrays are always both C and Fortran contiguous,
because, well, t
Either way, is there a reason that I'm missing, why np.array([0]) should be
both C- and F-contiguous, but np.array([[0]]) can only be one of them at a time?
On Aug 2, 2012, at 17:26 , Olivier Grisel wrote:
> 2012/8/2 Skipper Seabold :
>> On Thu, Aug 2, 2012 at 11:05 AM, Olivier Grisel
>> wrote
2012/8/2 Skipper Seabold :
> On Thu, Aug 2, 2012 at 11:05 AM, Olivier Grisel
> wrote:
>> 2012/8/2 Olivier Grisel :
>>> 2012/8/2 Jim Vickroy :
On 8/2/2012 8:27 AM, Brian Holt wrote:
> Thanks Jim,
>
> Could you try it again with
>
> X = np.array([[0]])
>
> Note the d
On Thu, Aug 2, 2012 at 11:05 AM, Olivier Grisel
wrote:
> 2012/8/2 Olivier Grisel :
>> 2012/8/2 Jim Vickroy :
>>> On 8/2/2012 8:27 AM, Brian Holt wrote:
Thanks Jim,
Could you try it again with
X = np.array([[0]])
Note the double "[" bracket - this is what causes t
2012/8/2 Olivier Grisel :
> 2012/8/2 Jim Vickroy :
>> On 8/2/2012 8:27 AM, Brian Holt wrote:
>>> Thanks Jim,
>>>
>>> Could you try it again with
>>>
>>> X = np.array([[0]])
>>>
>>> Note the double "[" bracket - this is what causes the problem for me.
>
> I can reproduce it too. Sounds like a numpy
2012/8/2 Jim Vickroy :
> On 8/2/2012 8:27 AM, Brian Holt wrote:
>> Thanks Jim,
>>
>> Could you try it again with
>>
>> X = np.array([[0]])
>>
>> Note the double "[" bracket - this is what causes the problem for me.
I can reproduce it too. Sounds like a numpy bug to me. We can have a
helper in scik
Thanks Jim,
I'm on numpy 1.3.0, which might be the problem.
Its not a show stopper for me, I think I've found a way not to end up
with this case.
Regards
Brian
On 2 August 2012 15:54, Jim Vickroy wrote:
> On 8/2/2012 8:27 AM, Brian Holt wrote:
>> Thanks Jim,
>>
>> Could you try it again with
>
On 8/2/2012 8:27 AM, Brian Holt wrote:
> Thanks Jim,
>
> Could you try it again with
>
> X = np.array([[0]])
>
> Note the double "[" bracket - this is what causes the problem for me.
>
> Cheers
> Brian
Sorry about that.
>>> x = numpy.array([[0]])
>>> a = numpy.asfortranarray(x)
>>> a.flags
Thanks Jim,
Could you try it again with
X = np.array([[0]])
Note the double "[" bracket - this is what causes the problem for me.
Cheers
Brian
On 2 August 2012 15:23, Jim Vickroy wrote:
> On 8/2/2012 6:05 AM, Brian Holt wrote:
>> Hi list,
>>
>> I'm refactoring the tree module to introduce laz
On 8/2/2012 6:05 AM, Brian Holt wrote:
> Hi list,
>
> I'm refactoring the tree module to introduce lazy argsorting and my
> unit tests are failing with:
>
> Exception ValueError: ValueError(u'ndarray is not Fortran
> contiguous',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
>
> I
Hi list,
I'm refactoring the tree module to introduce lazy argsorting and my
unit tests are failing with:
Exception ValueError: ValueError(u'ndarray is not Fortran
contiguous',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
I think I've pinned down the problem to this minimal samp
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