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Alex Rivera
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I have a collection of n dimensional points and I want to find which 2 are the closest. The best I could come up for 2 dimensions is: from numpy import * myArr = array( [[1, 2], [3, 4], [5, 6], [7, 8]] ) n = myArr.shape[0] cross = [[sum( ( myArr[i] - myArr[j] ) ** 2 ), i, j] for i in xrange( n ) for j in xrange( n ) if i != j ] print min( cross ) which gives [8, 0, 1] But this is too slow for large arrays. What kind of optimisation can I apply to it? RELATED: Euclidean distance between points in two different Numpy arrays, not within
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