[Numpy-discussion] determinant of a scalar not handled
Mon Jul 26 19:18:22 CDT 2010
On Mon, Jul 26, 2010 at 7:38 PM, Charles R Harris
> On Mon, Jul 26, 2010 at 5:05 PM, Skipper Seabold <email@example.com>
>> On Mon, Jul 26, 2010 at 5:48 PM, Alan G Isaac <firstname.lastname@example.org> wrote:
>> > On 7/26/2010 12:45 PM, Skipper Seabold wrote:
>> >> Right now np.linalg.det does not handle scalars or 1d (scalar) arrays.
>> > I don't have a real opinion on changing this, but I am curious
>> > to know the use case, as the current behavior seems
>> Use case is just so that I can have less atleast_2d's in my code,
>> since checks are done in linalg.det anyway.
>> > a) correct and b) to provide an error check.
>> Isn't the determinant defined for a scalar b such that det(b) ==
>> det([b]) == det([[b]])?
> Well, no ;) Matrices have determinants, scalars don't. Where are you
> running into a problem? Is something returning a scalar where a square array
> would be more appropriate?
No, linalg.det always returns a scalar, and I, of course, could be
more careful and always ensure that whatever the user supplies it
becomes a 2d array, but I don't like putting atleast_2d everywhere if
I don't need to. I thought that the determinant of a scalar was by
definition a scalar (e.g, google "determinant of a scalar is"), hence
which should either fail or if not, then I think np.linalg.det should
handle scalars and scalars as 1d arrays.
So instead of me having to do
b = np.array()
b = np.atleast_2d(b)
I could just do
b = np.array()
Regardless, doing asarray, checking if something is 2d, and then
checking if its square seems redundant and could be replaced by an
atleast_2d in linalg.slogdet which 1) takes a view as an array, 2)
ensures that the we have a 2d array, and 3) handles the scalar case.
Then we check if it's square. It doesn't really change much except
keeping me from having to put atleast_2d's in my code.
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