# scipy.spatial.distance.sokalmichener¶

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

Compute the Sokal-Michener dissimilarity between two boolean 1-D arrays.

The Sokal-Michener dissimilarity between boolean 1-D arrays u and v, is defined as

$\frac{R} {S + R}$

where $$c_{ij}$$ is the number of occurrences of $$\mathtt{u[k]} = i$$ and $$\mathtt{v[k]} = j$$ for $$k < n$$, $$R = 2 * (c_{TF} + c_{FT})$$ and $$S = c_{FF} + c_{TT}$$.

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 sokalmichener : double The Sokal-Michener dissimilarity between vectors u and v.

Examples

>>> from scipy.spatial import distance
>>> distance.sokalmichener([1, 0, 0], [0, 1, 0])
0.8
>>> distance.sokalmichener([1, 0, 0], [1, 1, 0])
0.5
>>> distance.sokalmichener([1, 0, 0], [2, 0, 0])
-1.0


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