On Tue, Apr 5, 2016 at 9:14 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. > > -n > > -- > Nathaniel J. Smith -- https://vorpus.org > I agree that we could really use a shorter syntax for a broadcasting transpose. Swapaxes is far too verbose for something that should be so common now that we've introduced the new matmul operator. That said, the fact that 1-D vectors are conceptually so similar to row vectors makes transposing a 1-D array a potential pitfall for a lot of people. When broadcasting along the leading dimension, a (n) shaped array and a (1, n) shaped array are already treated as equivalent. Treating a 1-D array like a row vector for transposes seems like a reasonable way to make things more intuitive for users. Rather than raising an error for arrays with fewer than two dimensions, the new syntax could be made equivalent to np.swapaxes(np.atleast2d(arr), -1, -2). From the standpoint of broadcasting semantics, using atleast2d can be viewed as allowing broadcasting along the inner dimensions. Though that's not a common thing, at least there's a precedent. The only downside I can see with allowing T2 to call atleast2d is that it would make things like A @ b and A @ b.T2 equivalent when B is one-dimensional. That's already the case with our current syntax though. There's some inherent design tension between the fact that broadcasting usually prepends ones to fill in missing dimensions and the fact that our current linear algebra semantics often treat rows as columns, but making 1-D arrays into rows makes a lot of sense as far as user experience goes. Great ideas everyone! Best, -Ian Henriksen
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