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