Re: [Numpy-discussion] nonuniform scatter operations
On Sun, Sep 28, 2008 at 4:15 PM, Geoffrey Irving [EMAIL PROTECTED] wrote: 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)? You could flatten the arrays and make a single matrix that implemented the operation. I'd stick with the loop over range(3) though, it's more readable and likely to be as fast or faster than flattening the arrays yourself. -- Nathan Bell [EMAIL PROTECTED] http://graphics.cs.uiuc.edu/~wnbell/ ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] nonuniform scatter operations
2008/9/28 Geoffrey Irving [EMAIL PROTECTED]: 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 believe histogram can be persuaded to do this. Anne ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] nonuniform scatter operations
On Sat, Sep 27, 2008 at 10:01 PM, Nathan Bell [EMAIL PROTECTED] wrote: On Sun, Sep 28, 2008 at 12:34 AM, Geoffrey Irving [EMAIL PROTECTED] 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/coo.h#L139 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 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] nonuniform scatter operations
Hello, 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. Thanks, Geoffrey ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] nonuniform scatter operations
On Sun, Sep 28, 2008 at 12:34 AM, Geoffrey Irving [EMAIL PROTECTED] 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/coo.h#L139 -- Nathan Bell [EMAIL PROTECTED] http://graphics.cs.uiuc.edu/~wnbell/ ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
