Hi,
While reviewing the Theano op that wrap numpy.fill_diagonal, we found
an unexpected behavior of it:
# as expected for square matrix
>>> a=numpy.zeros((5,5))
>>> numpy.fill_diagonal(a, 10)
>>> print a
# as expected long rectangular matrix
>>> a=numpy.zeros((3,5))
>>> numpy.fill_diagonal(a, 10
On Fri, Jun 8, 2012 at 11:31 AM, Bob Cowdery wrote:
> Hi all,
>
> I am reading a datagram which contains within it a type. The type
> dictates the structure of the datagram. I want to put this into a numpy
> structure, one of which is:
> np.zeros(1,dtype=('2uint8,uint8,uint8,uint32,8uint8,504uint8
> On 08/06/12 14:14, Neal Becker wrote:
>> The fact that this proposed numpy behavior would not match python list
>> behavior
>> holds little weight for me. I would still favor this change, unless it added
>> significant overhead. My opinion, of course.
As a "Joe User", I think using the [-2:2]
On 08/06/12 14:14, Neal Becker wrote:
> The fact that this proposed numpy behavior would not match python list
> behavior
> holds little weight for me. I would still favor this change, unless it added
> significant overhead. My opinion, of course.
It holds enormous weight for me. My opinion i
On 06/07/2012 12:55 PM, Neal Becker wrote:
> In [3]: u = np.arange(10)
>
> In [4]: u
> Out[4]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>
> In [5]: u[-2:]
> Out[5]: array([8, 9])
>
> In [6]: u[-2:2]
> Out[6]: array([], dtype=int64)
>
> I would argue for consistency it would be desirable for this to re
On 6/8/2012 9:14 AM, Neal Becker wrote:
> The fact that this proposed numpy behavior would not match python list
> behavior
> holds little weight for me.
It is not just Python behavior for lists.
It is the semantics for all sequence types.
Breaking this would be appalling.
Alan Isaac
Robert Kern wrote:
> On Thu, Jun 7, 2012 at 7:55 PM, Neal Becker wrote:
>> In [3]: u = np.arange(10)
>>
>> In [4]: u
>> Out[4]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>
>> In [5]: u[-2:]
>> Out[5]: array([8, 9])
>>
>> In [6]: u[-2:2]
>> Out[6]: array([], dtype=int64)
>>
>> I would argue for consi
Hi all,
I am reading a datagram which contains within it a type. The type
dictates the structure of the datagram. I want to put this into a numpy
structure, one of which is:
np.zeros(1,dtype=('2uint8,uint8,uint8,uint32,8uint8,504uint8,8uint8,504uint8'))
As I don't know what I'm getting until I've