# scipy.spatial.distance.jaccard¶

scipy.spatial.distance.jaccard(u, v, w=None)[source]

Compute the Jaccard-Needham dissimilarity between two boolean 1-D arrays.

The Jaccard-Needham dissimilarity between 1-D boolean arrays u and v, is defined as

$\frac{c_{TF} + c_{FT}} {c_{TT} + c_{FT} + c_{TF}}$

where $$c_{ij}$$ is the number of occurrences of $$\mathtt{u[k]} = i$$ and $$\mathtt{v[k]} = j$$ for $$k < n$$.

Parameters: u : (N,) array_like, bool Input array. v : (N,) array_like, bool Input array. w : (N,) array_like, optional The weights for each value in u and v. Default is None, which gives each value a weight of 1.0 jaccard : double The Jaccard distance between vectors u and v.

Examples

>>> 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])
0.66666666666666663


#### Previous topic

scipy.spatial.distance.hamming

#### Next topic

scipy.spatial.distance.kulsinski