hamming — SciPy v1.15.3 Manual (original) (raw)

scipy.spatial.distance.

scipy.spatial.distance.hamming(u, v, w=None)[source]#

Compute the Hamming distance between two 1-D arrays.

The Hamming distance between 1-D arrays u and v, is simply the proportion of disagreeing components in u and v. If u and v are boolean vectors, the Hamming distance is

\[\frac{c_{01} + c_{10}}{n}\]

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

Input array.

v(N,) array_like

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:

hammingdouble

The Hamming distance between vectors u and v.

Examples

from scipy.spatial import distance distance.hamming([1, 0, 0], [0, 1, 0]) 0.66666666666666663 distance.hamming([1, 0, 0], [1, 1, 0]) 0.33333333333333331 distance.hamming([1, 0, 0], [2, 0, 0]) 0.33333333333333331 distance.hamming([1, 0, 0], [3, 0, 0]) 0.33333333333333331