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 <[email protected]> wrote:
> 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
>    C_CONTIGUOUS : False
>    F_CONTIGUOUS : True
>    OWNDATA : True
>    WRITEABLE : True
>    ALIGNED : True
>    UPDATEIFCOPY : False
>  >>>
>
>
>>
>> On 2 August 2012 15:23, Jim Vickroy<[email protected]>  wrote:
>>> 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 think I've pinned down the problem to this minimal sample:
>>>>
>>>>>>> import numpy as np
>>>>>>> X = np.array([[0]])
>>>>>>> a = np.asfortranarray(X)
>>>>>>> a.flags
>>>>     C_CONTIGUOUS : True
>>>>     F_CONTIGUOUS : False
>>>>     OWNDATA : True
>>>>     WRITEABLE : True
>>>>     ALIGNED : True
>>>>     UPDATEIFCOPY : False
>>>>
>>>> Is this a bug in numpy? And if so, is there any workaround?
>>>>
>>>> Regards
>>>> Brian
>>> Hi Brian,
>>>
>>> Here are the results for my setup:
>>>
>>>   >>>  sys.version
>>> '2.7.3 (default, Apr 10 2012, 23:31:26) [MSC v.1500 32 bit (Intel)]'
>>>   >>>  import numpy
>>>   >>>  numpy.__version__
>>> '1.6.2'
>>>   >>>  x = numpy.array([0])
>>>   >>>  a = numpy.asfortranarray(x)
>>>   >>>  a.flags
>>>     C_CONTIGUOUS : True
>>>     F_CONTIGUOUS : True
>>>     OWNDATA : True
>>>     WRITEABLE : True
>>>     ALIGNED : True
>>>     UPDATEIFCOPY : False
>>>
>>>
>>>
>>>
>>>
>>>
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