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.
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