On Fri, Jul 18, 2014 at 9:03 AM, Sebastian Berg <sebastian@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