scipy.spatial.distance.kulsinski¶
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scipy.spatial.distance.kulsinski(u, v, w=None)[source]¶ Compute the Kulsinski dissimilarity between two boolean 1-D arrays.
The Kulsinski dissimilarity between two boolean 1-D arrays u and v, is defined as
\[\frac{c_{TF} + c_{FT} - c_{TT} + n} {c_{FT} + c_{TF} + n}\]where \(c_{ij}\) is the number of occurrences of \(\mathtt{u[k]} = i\) and \(\mathtt{v[k]} = j\) for \(k < n\).
- 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
- kulsinskidouble
The Kulsinski distance between vectors u and v.
Examples
>>> from scipy.spatial import distance >>> distance.kulsinski([1, 0, 0], [0, 1, 0]) 1.0 >>> distance.kulsinski([1, 0, 0], [1, 1, 0]) 0.75 >>> distance.kulsinski([1, 0, 0], [2, 1, 0]) 0.33333333333333331 >>> distance.kulsinski([1, 0, 0], [3, 1, 0]) -0.5