Document

scipy 1.8.0 Pypi GitHub Homepage

kulczynski1(u, v, *, w=None)

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 $k \in {0, 1, ..., n-1}$ .

Notes

This measure has a minimum value of 0 and no upper limit. It is un-defined when there are no non-matches.

versionadded

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

kulczynski1 : float: The Kulczynski 1 distance between vectors u and v.

Compute the Kulczynski 1 dissimilarity between two boolean 1-D arrays.

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.spatial.distance.kulczynski1

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them