On Fri, Jul 18, 2014 at 9:03 AM, Sebastian Berg <sebast...@sipsolutions.net> wrote: > On Do, 2014-07-17 at 09:48 -0400, Robert Lupton the Good wrote: >> Having just re-read the PEP I'm concerned that this proposal leaves at least >> one major (?) trap for naive users, namely >> x = np.array([1, 10]) >> print X.T@x >> which will print 101, not [[1, 10], [10, 100]] >> >> Yes, I know why this is happening but it's still a problem -- the user said, >> "I'm thinking matrices" when they wrote @ but the x.T had done the "wrong" >> thing before the @ kicked in. And yes, a savvy user would have written x = >> np.ones([[1, 10]]) (but then np.dot(x, x.T) isn't a scalar). >> >> This is the way things are at present, but with the new @ syntax coming in I >> think we should consider fixing it. >> >> I can think of three possibilities: >> 1. Leave this as a trap for the unwary, and a reason for people to >> stick to np.matrix (np.matrix([1, 10]) behaves "correctly") >> 2. Make x.T a syntax error for 1-D arrays. It's a no-op and IMHO a >> trap. >> 3. Make x.T promote the shape == (2,) array to (1, 2) and return a (2, >> 1) array. This may be too magic, but it's my preferred solution. > > Making it a warning may be another option. Changing `.T` to promote to > 2-d (also maybe to actually only transpose the last two axes for higher > D arrays), could be nice, but getting there might take quite a long > FutureWarning or even Error -> new feature cycle...
Hmm, just the other day I wrote some code that relies on the current behavior. I was writing a function that could work both on 3-vectors and arrays of 3-vectors. To unpack the input into the separate components, I did: x, y, z = vector.T Which works correctly whether `vector` is shaped (3,) or (N, 3). -- Robert Kern _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
