On Sat, Sep 27, 2008 at 10:01 PM, Nathan Bell wrote:

On Sun, Sep 28, 2008 at 12:34 AM, Geoffrey Irving wrote:

...

Is there an efficient way to implement a nonuniform gather operation in numpy? Specifically, I want to do something like

n,m = 100,1000 X = random.uniform(size=n) K = random.randint(n, size=m) Y = random.uniform(size=m)

for k,y in zip(K,Y): X[k] += y

but I want it to be fast. The naive attempt "X[K] += Y" does not work, since the slice assumes the indices don't repeat.

I don't know of numpy solution, but in scipy you could use a sparse matrix to perform the operation. I think the following does what you want.

from scipy.sparse import coo_matrix X += coo_matrix( (Y, (K,zeros(m,dtype=int)), shape=(n,1)).sum(axis=1)

This reduces to a simple C++ loop, so speed should be good: http://projects.scipy.org/scipy/scipy/browser/trunk/scipy/sparse/sparsetools...

Thanks. That works great. A slightly cleaner version is X += coo_matrix((Y, (K, zeros_like(K)))).sum(axis=1) The next question is: is there a similar way that generalizes to the case where X is n by 3 and Y is m by 3 (besides the obvious loop over range(3), that is)? Geoffrey