def distance_metric(seed, base):
    num = 0.0
    den = 0.0
    num = sum(numpy.minimum(seed,base))
    den = sum(numpy.maximum(seed,base))
    dist = round(1.0 - 1.0*num/den,4)
    return dist

The metric is used to gauge similarity in the context of locality sensitive hashing.

Items within a bucket are kept if their distance is < 0.16.


1 Answer 1


This is the weighted Jaccard Index.


This is different from the regular Jaccard Index (Similarity).


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