On Sat, Nov 7, 2009 at 11:59 PM, Anne Archibald <peridot.face...@gmail.com>wrote:
> 2009/11/7 David Goldsmith <d.l.goldsm...@gmail.com>: > > So in essence, at least as it presently functions, the shape of 'a' > > *defines* what the individual choices are within 'choices`, and if > 'choices' > > can't be parsed into an integer number of such individual choices, that's > > when an exception is raised? > > Um, I don't think so. > > Think of it this way: you provide np.choose with a selector array, a, > and a list (not array!) [c0, c1, ..., cM] of choices. You construct an > output array, say r, the same shape as a (no matter how many > dimensions it has). Except that I haven't yet seen a working example with 'a' greater than 1-D, Josef's last two examples notwithstanding; or is that what you're saying is the bug. The (i0, i1, ..., iN) element of the output array > is obtained by looking at the (i0, i1, ..., iN) element of a, which > should be an integer no larger than M; say j. Then r[i0, i1, ..., iN] > = cj[i0, i1, ..., iN]. That is, each element of the selector array > determines which of the choice arrays to pull the corresponding > element from. > That's pretty clear (thanks for doing my work for me). ;-), Yet, see above. For example, suppose that you are processing an array C, and have > constructed a selector array A the same shape as C in which a value is > 0, 1, or 2 depending on whether the C value is too small, okay, or too > big respectively. Then you might do something like: > > C = np.choose(A, [-inf, C, inf]) > > This is something you might want to do no matter what shape A and C > have. It's important not to require that the choices be an array of > choices, because they often have quite different shapes (here, two are > scalars) and it would be wasteful to broadcast them up to the same > shape as C, just to stack them. > OK, that's a pretty generic use-case, thanks; let me see if I understand it correctly: A is some how created independently with a 0 everywhere C is too small, a 1 everywhere C is OK, and a 2 everywhere C is too big; then np.choose(A, [-inf, C, inf]) creates an array that is -inf everywhere C is too small, inf everywhere C is too large, and C otherwise (and since -inf and inf are scalars, this implies broadcasting of these is taking place). This is what you're asserting *should* be the behavior. So, unless there is disagreement about this (you yourself said the opposite viewpoint might rationally be held) np.choose definitely presently has a bug, namely, the index array can't be of arbitrary shape. DG > > Anne > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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