[Numpy-discussion] Use-case for np.choose
Sun Nov 8 02:33:16 CST 2009
On Sat, Nov 7, 2009 at 11:59 PM, Anne Archibald
> 2009/11/7 David Goldsmith <email@example.com>:
> > So in essence, at least as it presently functions, the shape of 'a'
> > *defines* what the individual choices are within 'choices`, and if
> > 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 (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.
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