[Numpy-discussion] Detect subclass of ndarray

Travis Oliphant oliphant@ee.byu....
Sat Mar 24 15:31:14 CDT 2007


Charles R Harris wrote:
>
>
> On 3/24/07, *Travis Oliphant* <oliphant@ee.byu.edu 
> <mailto:oliphant@ee.byu.edu>> wrote:
>
>     Alan G Isaac wrote:
>     > On Sat, 24 Mar 2007, Charles R Harris apparently wrote:
>     >
>     >> Yes, that is what I am thinking. Given that there are only the two
>     >> possibilities, row or column, choose the only one that is
>     compatible with
>     >> the multiplying matrix. The result will not always be a column
>     vector, for
>     >> instance, mat([[1]])*ones(3) will be a 1x3 row vector.
>     >>
>     >
>     >
>     >
>     > Ack!  The simple rule `post multiply means its a column vector`
>     > would be horrible enough: A*ones(n)*B becomes utterly obscure.
>     > Now even that simple rule is to be violated??
>     >
>     > Down this path lies madness.
>     > Please, just raise an exception.
>     >
>
>     My opinion is that a 1-d array in matrix-multiplication should
>     always be
>     interpreted as a row vector.  Is this not what is currently done?   If
>     not, then it is a bug in my mind.
>
>
> Fair enough. But in that case mat(eye(2))*ones(2) should raise an 
> error instead of returning a row vector.
>
> In [1]: mat(eye(2))*ones(2)
> Out[1]: matrix([[ 1.,  1.]])
>
> The problem is that the return object is created as a row matrix and 
> then filled as if the rhs was a column vector in 
> PyArray_MatrixProduct. So this should be made a bug?
>
Yes, as you describe, dot allows 2x2 times a 1-d array (and interprets 
the 1-d array as a column vector).   This has always been the behavior 
of dot and won't change.

But, that doesn't mean we have to allow this behavior for matrices.  In 
other words we should probably check against it for matrix-multiplication.

The dot operator allows mixing n-d arrays, but I'm persuaded that the 
matrix multiplication should only work for 2-d arrays and if it is given 
a 1-d array, it seems that it should have a specific interpretation of 
that 1-d array as a 2-d array.

-Travis





> Chuck
>
>
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