On Tue, May 29, 2018 at 5:40 AM, Stephan Hoyer <sho...@gmail.com> wrote: > But given that idiomatic NumPy code uses 1D arrays in favor of explicit > row/column vectors with shapes (1,n) and (n,1), I do think it does make > sense for matrix transpose on 1D arrays to be the identity, because matrix > transpose should convert back and forth between row and column vectors > representations. > > Certainly, matrix transpose should error on 0d arrays, because it doesn't > make sense to transpose a scalar.
Apologies for the probably academic nitpick, but if idiomatic code uses 1d arrays as vectors then shouldn't scalars be compatible with matrices with dimension (in the mathematical sense) of 1? Since the matrix product of shapes (1,n) and (n,1) is (1,1) but the same for shapes (n,) and (n,) is (), it might make sense after all for the matrix transpose to be identity for scalars. I'm aware that this is tangential to the primary discussion, but I'm also wondering if I'm being confused about the subject (wouldn't be the first time that I got confused about numpy scalars). András _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion