kulczynski1#
- scipy.spatial.distance.kulczynski1(u, v, *, w=None)[source]#
- Compute the Kulczynski 1 dissimilarity between two boolean 1-D arrays. - Deprecated since version 1.15.0: This function is deprecated and will be removed in SciPy 1.17.0. Replace usage of - kulczynski1(u, v)with- 1/jaccard(u, v) - 1.- 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. - Added 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