[Numpy-discussion] determinant of a scalar not handled
Skipper Seabold
jsseabold@gmail....
Mon Jul 26 19:18:22 CDT 2010
On Mon, Jul 26, 2010 at 7:38 PM, Charles R Harris
<charlesr.harris@gmail.com> wrote:
>
>
> On Mon, Jul 26, 2010 at 5:05 PM, Skipper Seabold <jsseabold@gmail.com>
> wrote:
>>
>> On Mon, Jul 26, 2010 at 5:48 PM, Alan G Isaac <aisaac@american.edu> 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
np.linalg.det(np.array([[2]]))
#2.0
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([2])
b = np.atleast_2d(b)
np.linalg.det(b)
#2.0
I could just do
b = np.array([2])
np.linalg.det(b)
#2.0
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.
Skipper
More information about the NumPy-Discussion
mailing list