# scipy.spatial.distance.hamming

scipy.spatial.distance.hamming(u, v, w=None)

  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
    hamming double

      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

scipy.spatial.distance.dice scipy.spatial.distance.jaccard