scipy.spatial.distance.sokalmichener¶
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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
- Returns
- sokalmichenerdouble
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