[SciPy-user] fastest way to populate sparse matrix?
Wed Dec 10 17:56:38 CST 2008
Hmmm, surprisingly the vectorized version seems to take longer:
data assignment done, filling matrix 1.84783697128
total time to fill coo_matrix: 1.85190200806
data assignment done, filling matrix 3.22157812119
total time to fill coo_matrix: 3.2216091156
On Wed, Dec 10, 2008 at 4:46 PM, Nathan Bell <firstname.lastname@example.org> wrote:
> On Wed, Dec 10, 2008 at 4:18 PM, Peter Skomoroch
> <email@example.com> wrote:
>> Thanks for the pointer, I had missed that wiki page.
> It's fairly recent, so don't feel bad :)
>> The bottleneck now seems to be this for-loop, which takes the majority
>> of the remaining time (1.82258105278 seconds):
>> for index, (i,j) in enumerate(nonzero_indices):
>> data[index] = dot(W[i,:],H[:,j])
>> Is there a better approach for this assignment block?
> You could vectorize the loop:
> W = random([n,r]).astype(float32)
> H = random([m,r]).astype(float32) # note, shape is (m,r)
> I,J = V.nonzero()
> X = (W[I,:] * H[J,:]).sum(axis=1)
> V_approx = sparse.coo_matrix((X,(I,J)), shape=(n,m))
> If memory usage of the above is too costly, you could use the same
> approach, but on fixed-sized chunks of the arrays.
> Nathan Bell firstname.lastname@example.org
> SciPy-user mailing list
Peter N. Skomoroch
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