> Why do these belong in NumPy? What is the broad field of application of these functions? And,
> does a more general concept underpin them?
Multiplication of a matrix with a vector is about as common as matrix
with matrix or vector with vector, and not currently easy to do for
stacks of vectors, so I think the case for matvec is similarly strong as
that for matmul and vecdot.
Arguably, vecmat is slightly less common, though completes the quad.
-- Marten
Could you please offer some code or math notation to help communicate this?
I am forced to guess at the need.
The words "matrix" and "vector" are ambiguous.
After all, matrices (of given shape) are a type of vector (i.e., can be added and scaled.)
So if by "matrix" you mean "2d array" and by "stack of vectors" you effectively mean "2d array",
this sounds like a use for np.dot (possibly after a transpose).
However I am going to guess that here by "vector" you actually mean a matrix
(i.e., a 2d array) with only one row or only one column, so a "stack" of them
is actually 3d. Perhaps the needless dimension is then the real problem
and can either not be produced or can be squeezed away..
Thanks, Alan Isaac