code: def 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
  
      .. math::
  
           \\frac{c_{TF} + c_{FT} - c_{TT} + n}
                {c_{FT} + c_{TF} + n}
  
      where :math:`c_{ij}` is the number of occurrences of
      :math:`\\mathtt{u[k]} = i` and :math:`\\mathtt{v[k]} = j` for
      :math:`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
  
      """
      u = _validate_vector(u)
      v = _validate_vector(v)
      if w is None:
          n = float(len(u))
      else:
          w = _validate_weights(w)
          n = w.sum()
      (nff, nft, ntf, ntt) = _nbool_correspond_all(u, v, w=w)
  
      return (ntf + nft - ntt + n) / (ntf + nft + n)
  
imports: from __future__ import division, print_function, absolute_import
  import warnings
  import numpy
  from functools import partial
  from collections import namedtuple
  from scipy._lib.six import callable, string_types
  from scipy._lib.six import xrange
  from scipy._lib._util import _asarray_validated
  from .None import _distance_wrap
  from .None import _hausdorff
  from ..linalg import norm
  from ..special import rel_entr
label_desc: Use image node_id 26 for calling a global function with example usage: kulsinski(u, v, w) and returns: unknown