On 29 May 2018 at 05:40, 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. >
When doing algebra on paper, I like the braket notation. It makes abundantly clear the shape of the outputs, without having to remember on which side the transpose goes: <u|v> is a scalar, |u><v| is a matrix. I don't have a good way of translating this to numpy, but maybe someone else has an idea. Certainly, matrix transpose should error on 0d arrays, because it doesn't > make sense to transpose a scalar. > Unless the scalar is 8, in which case the transpose is np.inf... Right now, np.int(8).T throws an error, but np.transpose(np.int(8)) gives a 0-d array. On one hand, it is nice to be able to use the same code for scalars as for vectors, but on the other, you may be making a mistake. /David.
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