Currently there are lots of ways to compute dot products (dot, vdot, inner, tensordot, einsum...), but none of them are really convenient for the case of arrays of vectors, where one dimension (usually the last or the first) is the vector dimension. The simplest way to do this currently is `np.sum(a * b, axis=axis)`, but this makes vector algebra less readable without a wrapper function, and it's probably not optimized as much as matrix products. Another way to do it is by adding appropriate dimensions and using matmul, but that's arguably less readable and not obvious to do generically for arbitrary axes. I think either np.dot or np.vdot could easily be extended with an `axis` parameter that would convert it into a bulk vector operation, with the same semantics as `np.sum(a * b, axis=axis)`. It should also maybe have a `keep_dims` parameter, which is useful for preserving broadcasting.
I submitted a corresponding issue at https://github.com/numpy/numpy/issues/21915 _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
