On Wed, Apr 11, 2012 at 11:38 PM, santhu kumar <mesan...@gmail.com> wrote:
> Hello all, > > I am trying to optimise a code and want your suggestions. > A : - NX3 matrix (coordinates of N points) > > After performing pairwise distance computations(called pdist) between > these points, depending upon a condition that the distance is in, I would > perform further computations. > Most of the computations require schur products (element by element) of > NXN matrices with each other and then computing either the coloumn sum or > row sum. > > As N goes to be large, these computations are taking some time (0.7 secs) > which is not much generally but since this is being called many times, it > acts as a bottleneck. > I want to leverage on the fact that many of the NXN computations are not > going to be used, or would be set to zero (if the pdist is greater than > some minimum distance). > > How do i achieve it ?? Is masked array the elegant solution? Would it save > me time? > > Thanks > Santhosh > > > You might want to consider using scipy.spatial's KDTree as a way to efficiently find all points that are within a specified distance from each other. Then, using those pairs, load up a sparse array with only the relevant pairs. It should save in computation and memory as well. Cheers! Ben Root
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