I've had a pretty similar idea for a new indexing function
'split_classes' which would help in your case, which essentially does
def split_classes(c, v):
return [v[c == u] for u in unique(c)]
Your example could be coded as
>>> [sum(c) for c in split_classes(label, data)]
[9, 12, 15]
I feel I've come across the need for such a function often enough that
it might be generally useful to people as part of numpy. The
implementation of split_classes above has pretty poor performance
because it creates many temporary boolean arrays, so my plan for a PR
was to have a speedy version of it that uses a single pass through v.
(I often wanted to use this function on large datasets).
If anyone has any comments on the idea (good idea. bad idea?) I'd love
to hear.
I have some further notes and examples here:
https://gist.github.com/ahaldane/1e673d2fe6ffe0be4f21
Allan
On 02/12/2016 09:40 AM, Sérgio wrote:
Hello,
This is my first e-mail, I will try to make the idea simple.
Similar to masked array it would be interesting to use a label array to
guide operations.
Ex.:
>>> x
labelled_array(data =
[[0 1 2]
[3 4 5]
[6 7 8]],
label =
[[0 1 2]
[0 1 2]
[0 1 2]])
>>> sum(x)
array([9, 12, 15])
The operations would create a new axis for label indexing.
You could think of it as a collection of masks, one for each label.
I don't know a way to make something like this efficiently without a
loop. Just wondering...
Sérgio.
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