Thank you for the suggestion, but it looks like that has the same behavior too:

In [43]: x = zeros(5)

In [44]: idx = array([1,1,1,3,4])

In [45]: put(x,idx, [2,4,8,10,30])

In [46]: x
Out[46]: array([  0.,   8.,   0.,  10.,  30.])

On Wed, Jun 6, 2012 at 6:07 AM, Frédéric Bastien wrote:
Hi,

I get across the numpy.put[1] function. I'm not sure, but maybe it do
this in the doc of this function.

Fred

[1] http://docs.scipy.org/doc/numpy/reference/generated/numpy.put.html

On Wed, Jun 6, 2012 at 4:48 AM, John Salvatier
<jsalvati@u.washington.edu> wrote:
> Hello,
>
> I've noticed that If you try to increment elements of an array with advanced
> indexing, repeated indexes don't get repeatedly incremented. For example:
>
> In [30]: x = zeros(5)
>
> In [31]: idx = array([1,1,1,3,4])
>
> In [32]: x[idx] += [2,4,8,10,30]
>
> In [33]: x
> Out[33]: array([  0.,   8.,   0.,  10.,  30.])
>
> I would intuitively expect the output to be array([0,14, 0,10,30]) since
> index 1 is incremented by 2+4+8=14, but instead it seems to only increment
> by 8. What is numpy actually doing here?
>
> The authors of Theano noticed this behavior a while ago so they python loop
> through the values in idx (this kind of calculation is necessary for
> calculating gradients), but this is a bit slow for my purposes, so I'd like
> to figure out how to get the behavior I expected, but faster.
>
> I'm also not sure how to navigate the numpy codebase, where would I look for
> the code responsible for this behavior?
>
> _______________________________________________
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>
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