On Wed, Apr 6, 2016 at 10:43 AM, Todd <toddr...@gmail.com> wrote: > On Tue, Apr 5, 2016 at 11:14 PM, Nathaniel Smith <n...@pobox.com> wrote: >> >> On Tue, Apr 5, 2016 at 7:11 PM, Todd <toddr...@gmail.com> wrote: >> > When you try to transpose a 1D array, it does nothing. This is the >> > correct >> > behavior, since it transposing a 1D array is meaningless. However, this >> > can >> > often lead to unexpected errors since this is rarely what you want. You >> > can >> > convert the array to 2D, using `np.atleast_2d` or `arr[None]`, but this >> > makes simple linear algebra computations more difficult. >> > >> > I propose adding an argument to transpose, perhaps called `expand` or >> > `expanddim`, which if `True` (it is `False` by default) will force the >> > array >> > to be at least 2D. A shortcut property, `ndarray.T2`, would be the same >> > as >> > `ndarray.transpose(True)`. >> >> An alternative that was mentioned in the bug tracker >> (https://github.com/numpy/numpy/issues/7495), possibly by me, would be >> to have arr.T2 act as a stacked-transpose operator, i.e. treat an arr >> with shape (..., n, m) as being a (...)-shaped stack of (n, m) >> matrices, and transpose each of those matrices, so the output shape is >> (..., m, n). And since this operation intrinsically acts on arrays >> with shape (..., n, m) then trying to apply it to a 0d or 1d array >> would be an error. >> > > My intention was to make linear algebra operations easier in numpy. With > the @ operator available, it is now very easy to do basic linear algebra on > arrays without needing the matrix class. But getting an array into a state > where you can use the @ operator effectively is currently pretty verbose and > confusing. I was trying to find a way to make the @ operator more useful.
Can you elaborate on what you're doing that you find verbose and confusing, maybe paste an example? I've never had any trouble like this doing linear algebra with @ or dot (which have similar semantics for 1d arrays), which is probably just because I've had different use cases, but it's much easier to talk about these things with a concrete example in front of us to put everyone on the same page. -n -- Nathaniel J. Smith -- https://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion