scipy.spatial.distance.hamming¶
-
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