[Numpy-discussion] matrices in 1.1
Alan G Isaac
aisaac@american....
Sat Mar 22 20:02:19 CDT 2008
> On Sat, Mar 22, 2008 at 5:49 PM, Alan G Isaac
> <aisaac@american.edu> wrote:
>> Are you trying to suggest that in most matrix programming
>> languages if you extract a row you will then need to use two
>> indices to extract an element of that row? This does not
>> match my experience. I would ask you to justify that by
>> listing the languages you have in mind.
On Sat, 22 Mar 2008, Stéfan van der Walt apparently wrote:
> No, I agree with you that that is unintuitive -- but it can be solved
> by introducing Row and ColumnVectors, which are still 2-dimensional.
To me, this seems to be adding a needless level of
complexity. I am not necessarily opposing it;
I just do not see a commensurate payoff.
In contrast, I see great payoff to keeping as much
ndarray behavior as possible.
> One important result you don't want is:
> In [9]: x = np.array([[1,2,3],[4,5,6],[7,8,9]])
> In [10]: x[:,0]
> Out[10]: array([1, 4, 7])
Agreed. I would hope it has been clear from earlier
discussion that the proposal retains that any use
of multiple indexes will produce a 2d submatrix.
That offers a simple way to say how matrix indexing
will differ from ndarray indexing.
> Do I understand correctly that you want M[0,:] and M[0] to
> behave differently?
Yes. Again, I think that I have been consistent on this point.
Any use of multiple indexes such as M[0,:] will produce a 2d submatrix.
Any use of scalar indexes such as M[0] behave as with an ndarray.
> Would you like M[0] to return the first element of the
> matrix as in Octave?
No!
Deviations from ndarray behavior should be minimized.
They should be:
1. Multiplication is redefined to matrix multiplication.
2. Powers are redefined accordingly.
3. The ``A`` and ``I`` attributes.
4. Any use of multiple indexes will produce a 2d submatrix.
I think that is it.
> If I'm the only one who is not completely satisfied, then
> please, submit a patch and have it applied.
Always a reasonable request, but with respect to NumPy, I'm
a user not a developer. That said, it looks to be simple:
perhaps no more than adding to __getitem__ after the
existing lines::
if not isinstance(out, N.ndarray):
return out
two new lines::
if isscalar(index):
return out
(Not that I like multiple points of return from a function.)
Cheers,
Alan Isaac
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