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