On Thu, Jul 18, 2013 at 2:23 PM, Sebastian Berg <sebast...@sipsolutions.net> wrote: > It is so difficult because of the fact that dot is basically a > combination of many functions: > o vector * vector -> vector > o vector * matrix -> matrix (add dimensions to vector on right) > o matrix * vector -> matrix (add dimensions to vector on left) > o matrix * matrix -> matrix > plus scalar cases.
Though, just throwing this out there for the archives since I was thinking about it... I think we *could* consolidate all dot's functionality into a single gufunc, with a few small changes: 1) Deprecate and get rid of the scalar special cases. (For those following along: right now, np.dot(10, array) does scalar multiplication, but this doesn't make much sense conceptually, it's not documented, and I don't think anyone uses it. Except maybe np.matrix.__mul__, but that could be fixed.) 2) Deprecate the strange "broadcasting" behaviour for high-dimensional inputs, in favor of the gufunc version suggested in the previous email. That leaves the vector * vector, vector * matrix, matrix * vector, matrix * matrix cases. To handle these: 3) Extend the gufunc machinery to understand the idea that some core dimensions are allowed to take on a special "nonexistent" size. So the signature for dot would be: (m*,k) x (k, n*) -> (m*, n*) where '*' denotes dimensions who are allowed to take on the "nonexistent" size if necessary. So dot(ones((2, 3)), ones((3, 4))) would have m = 2 k = 3 n = 4 and produce an output with shape (m, n) = (2, 4). But dot(ones((2, 3)), ones((3,))) would have m = 2 k = 3 n = <nothing> and produce an output with shape (m, n) = (2, <nothing>) = (2,). And dot(ones((3,)), ones((3,))) would have m = <nothing> k = 3 n = <nothing> and produce an output with shape (m, n) = (<nothing>, <nothing>) = (), i.e., dot(vector, vector) would return a scalar. I'm not sure if there are any other cases where this would be useful, but even if it were just for 'dot', that's still a pretty important case that might justify the mechanism all on its own. -n _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
