scipy.spatial.distance.kulczynski1#
- scipy.spatial.distance.kulczynski1(u, v, *, w=None)[source]#
Compute the Kulczynski 1 dissimilarity between two boolean 1-D arrays.
The Kulczynski 1 dissimilarity between two boolean 1-D arrays u and v of length
n
, is defined as\[\frac{c_{11}} {c_{01} + c_{10}}\]where \(c_{ij}\) is the number of occurrences of \(\mathtt{u[k]} = i\) and \(\mathtt{v[k]} = j\) for \(k \in {0, 1, ..., n-1}\).
- 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
- kulczynski1float
The Kulczynski 1 distance between vectors u and v.
Notes
This measure has a minimum value of 0 and no upper limit. It is un-defined when there are no non-matches.
New in version 1.8.0.
References
- 1
Kulczynski S. et al. Bulletin International de l’Academie Polonaise des Sciences et des Lettres, Classe des Sciences Mathematiques et Naturelles, Serie B (Sciences Naturelles). 1927; Supplement II: 57-203.
Examples
>>> from scipy.spatial import distance >>> distance.kulczynski1([1, 0, 0], [0, 1, 0]) 0.0 >>> distance.kulczynski1([True, False, False], [True, True, False]) 1.0 >>> distance.kulczynski1([True, False, False], [True]) 0.5 >>> distance.kulczynski1([1, 0, 0], [3, 1, 0]) -3.0