# scipy.spatial.distance.sokalsneath¶

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

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

The Sokal-Sneath dissimilarity between u and v,

$\frac{R} {c_{TT} + R}$

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

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

Returns
sokalsneathdouble

The Sokal-Sneath dissimilarity between vectors u and v.

Examples

>>> from scipy.spatial import distance
>>> distance.sokalsneath([1, 0, 0], [0, 1, 0])
1.0
>>> distance.sokalsneath([1, 0, 0], [1, 1, 0])
0.66666666666666663
>>> distance.sokalsneath([1, 0, 0], [2, 1, 0])
0.0
>>> distance.sokalsneath([1, 0, 0], [3, 1, 0])
-2.0


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