On Wed, Jun 6, 2012 at 4:30 PM, Robert Cimrman <cimrman3@ntc.zcu.cz> wrote:

On 06/06/2012 05:06 PM, Nathaniel Smith wrote:

On Wed, Jun 6, 2012 at 9:48 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?

Strictly speaking, it isn't actually in the numpy codebase at all -- what's happening is that the Python interpreter sees this code:

x[idx] += vals

and then it translates it into this code before running it:

tmp = x.__getitem__(idx) tmp = tmp.__iadd__(vals) x.__setitem__(idx, tmp)

So you can find the implementations of the ndarray methods __getitem__, __iadd__, __setitem__ (they're called array_subscript_nice, array_inplace_add, and array_ass_sub in the C code), but there's no way to fix them so that this works the way you want it to, because there's no way for __iadd__ to know that the temporary values that it's working with are really duplicate copies of "the same" value in the original array.

It would be nice if numpy had some sort of standard API for doing what you want, but not sure what a good API would look like, and someone would have to implement it.

This operation is also heavily used for the finite element assembling, and a similar question has been raised already several times (e.g. http://old.nabble.com/How-to-assemble-large-sparse-matrices-effectively-td33...). So why not adding a function np.assemble()?

I read that message, but I don't see what it has to do with this discussion? It seemed to be about fast ways to assign dense matrices into sparse matrices, not fast ways of applying in-place arithmetic to specific spots in a dense matrix. -n