It's because in Python 1-dimensional arrays are actually a thing, unlike in Matlab. That line of code is a non-example; it is easier to make it work in Python than in Matlab.
To make `Z` a column vector, we would need something like `Z = (Y @ X)[:,np.newaxis]`.
Although, I'm not sure why the author is using `concatenate` when the more idiomatic function would be stack, so the change you suggest works and is pretty clean:
Z = Y @ X
np.stack([Z, Z], axis=1)
# array([[14, 14],
# [32, 32],
# [50, 50]])
I know what all of these do, but I just can’t shake the feeling that I’m constantly fighting with an actual python. Very aptly named.
I also think it’s more to do with the libraries than with the language, which I actually like the syntax of. But numpy tries to be this highly unopinionated tool that can do everything, and ends up annoying to use anywhere. Matplotlib is even worse, possibly the worst API I’ve ever used.
Doesn't just (Y @ X)[None] work? None adding an extra dimension works in practice but I don't know if you're "supposed" to do that
(Y @ X)[None]
# array([[14, 32, 50]])
(Y @ X)[None].T
# array([[14],
# [32],
# [50]])
This seems to work,
Z = Y @ X[:,np.newaxis]
Or just X.T, the shorthand alias for that
> # or rely on broadcasting rules carefully.
> Z = Y @ X.reshape(3, 1)
Why not use X.transpose()?