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Of course the method with length-1 axes will look good if it's the only way to do broadcasting. I've hardly touched it (and never saw the NumPy code mentioned i

Of course the method with length-1 axes will look good if it's the only way to do broadcasting. I've hardly touched it (and never saw the NumPy code mentioned in the quote), but some people in the APL community have so I'm reporting on their thoughts. Although length-1 axes already being in the data sounds worrying: you have an array that doesn't depend on an axis, but there's an axis anyway to show where it would go if it did? Named axis systems like einops are much better regarded. I've seen some implementations of similar functionality in APL, and various discussions of how it could be built into an array language (I have my own design where that's a core idea, but not using APL syntax).

imurray 3y ago
> Although length-1 axes already being in the data sounds worrying: you have an array that doesn't depend on an axis, but there's an axis anyway to show where it would go if it did? Example: A / A.sum(axis=3, keepdims=True) Make all of the vectors along axis=3 sum up to one. There are other ways of doing it, but this way seems fairly clear to me. The shape of the denominator is the same as the numerator, except for a 1 in position 3. Unfortunately we have to specify `keepdims`, because the default of `False` removes the dimension being summed over, which doesn't work in general. `keepdims=True` is the behavior in Matlab/Octave, so the example becomes A ./ sum(A, 4) with 4=3+1 because Matlab is 1-based like Fortran.