jaccard(u, v, w=None)
The Jaccard-Needham dissimilarity between 1-D boolean arrays u and v, is defined as
where $c_{ij}$ is the number of occurrences of $\mathtt{u[k]} = i$ and $\mathtt{v[k]} = j$ for $k < n$ .
When both u and v lead to a :None:None:`0/0` division i.e. there is no overlap between the items in the vectors the returned distance is 0. See the Wikipedia page on the Jaccard index , and this paper .
Previously, when :None:None:`u` and :None:None:`v` lead to a :None:None:`0/0` division, the function would return NaN. This was changed to return 0 instead.
Input array.
Input array.
The weights for each value in u and v. Default is None, which gives each value a weight of 1.0
Compute the Jaccard-Needham dissimilarity between two boolean 1-D arrays.
>>> from scipy.spatial import distance
... distance.jaccard([1, 0, 0], [0, 1, 0]) 1.0
>>> distance.jaccard([1, 0, 0], [1, 1, 0]) 0.5
>>> distance.jaccard([1, 0, 0], [1, 2, 0]) 0.5
>>> distance.jaccard([1, 0, 0], [1, 1, 1]) 0.66666666666666663See :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.spatial.distance.jaccard
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