# scipy.spatial.distance.kulsinski

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

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

  The Kulsinski dissimilarity between two boolean 1-D arrays u and v, is defined as

  \[\frac{c_{TF} + c_{FT} - c_{TT} + n} {c_{FT} + c_{TF} + n}\]

  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, 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
    kulsinski double

      The Kulsinski distance between vectors u and v.

  Examples

      >>> from scipy.spatial import distance
      >>> distance.kulsinski([1, 0, 0], [0, 1, 0])
      1.0
      >>> distance.kulsinski([1, 0, 0], [1, 1, 0])
      0.75
      >>> distance.kulsinski([1, 0, 0], [2, 1, 0])
      0.33333333333333331
      >>> distance.kulsinski([1, 0, 0], [3, 1, 0])
      -0.5

scipy.spatial.distance.jaccard scipy.spatial.distance.rogerstanimoto