On Wed, May 2, 2012 at 7:25 PM, Aronne Merrelli <aronne.merre...@gmail.com>wrote:
> In general this is a good suggestion - I was going to mention it > earlier - but I think for this particular problem it is not better > than the "brute force" and argmin() NumPy approach. On my laptop, the > KDTree query is about a factor of 7 slower (ignoring the time cost to > create the KDTree) > > The cKDTree implementation is more than 4 times faster than the brute-force approach: T = scipy.spatial.cKDTree(targets) In [11]: %timeit foo1(element, targets) # Brute force 1000 loops, best of 3: 385 us per loop In [12]: %timeit foo2(element, T) # cKDTree 10000 loops, best of 3: 83.5 us per loop In [13]: 385/83.5 Out[13]: 4.610778443113772 A FLANN implementation should be even faster--perhaps by as much as another factor of two. -Kevin
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