[Numpy-discussion] untenable matrix behavior in SVN
Travis E. Oliphant
oliphant@enthought....
Tue Apr 29 10:49:50 CDT 2008
Charles R Harris wrote:
>
>
> On Tue, Apr 29, 2008 at 7:24 AM, Stéfan van der Walt <stefan@sun.ac.za
> <mailto:stefan@sun.ac.za>> wrote:
>
> Hi Charles
>
> 2008/4/29 Charles R Harris <charlesr.harris@gmail.com
> <mailto:charlesr.harris@gmail.com>>:
> > May I add that if I edit defmatrix.py to act like an array for
> scalar
> > indexing, then the following works.
> >
> > In [1]: a = matrix(eye(2))
> >
> > In [2]: array([a,a])
> > Out[2]:
> > array([[[ 1., 0.],
> > [ 0., 1.]],
> >
> > [[ 1., 0.],
> > [ 0., 1.]]])
> >
> > This generates an error with the current version of matrix and,
> frankly, I
> > am not going to be bothered going all through the numpy c
> sources to special
> > case matrices to fix that. Someone else can do it if they wish.
> There are
> > recursive routines that expect the dimensions to decrease on
> each call.
>
> I'd also like to see matrices become proper hierarchical containers --
> the question is just how to do that. Thus far, I'm most convinced by
> the arguments for RowVectors/Columns, which leaves us with a sane
> model for doing linear algebra, while providing the enhancements you
> mentioned here and in comments to another ticket.
>
> We were thinking of raising a warning on scalar indexing for 1.1, but
> given the above, would that be sensical?
>
>
> Hi Stefan,
>
> The numpy c routines call PySequence_GetItem(s,i) as well as ask for
> the length (first index), so I think these should be left as they are
> for arrays in order to guarantee that matrices are compatible with all
> the normal array operations. This means either returning special row
> objects that index as expected
> or returning 1D arrays. I don't think the '*' operator has these
> problems, but in any case that is a well documented feature of matrices.
Thanks for looking in to this Chuck,
I'm quite persuaded now that a[i] should return a 1-d object for
arrays. In addition to the places Chuck identified, there are at
least 2 other places where special code was written to work-around the
expectation that item selection returns an object with reduced
dimensionality (a special-cased .tolist for matrices and a special-cased
getitem in the fancy indexing code).
As the number of special-case work-arounds grows the more I'm convinced
the conceptualization is wrong. So, I now believe we should change the
a[i] for matrices to return a 1-d array.
The only down-side I see is that a[i] != a[i,:] for matrices.
However, matrix(a[i]) == a[i,:], and so I'm not sure there is really a
problem, there. I also don't think that whatever problem may actually
be buried in the fact that type(a[i]) != type(a[i,:]) is worse than the
problem that several pieces of NumPy code actually expect hierarchical
container behavior of multi-dimensional sequences.
I don't think making the small change to have a[i] return 1-d arrays
precludes us from that 1-d array being a bit more formalized in 1.2 as a
RowVector should we choose that direction. It does, however, fix
several real bugs right now.
So, I'm
+1 on making the following change:
def __getitem__(self, index):
+ if isscalar(index):
+ return self.__array__()[index]
-Travis
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