scipy.spatial.distance.sokalsneath¶
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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