code: def rogerstanimoto(u, v, w=None):
      """
      Compute the Rogers-Tanimoto dissimilarity between two boolean 1-D arrays.
  
      The Rogers-Tanimoto dissimilarity between two boolean 1-D arrays
      `u` and `v`, is defined as
  
      .. math::
         \\frac{R}
              {c_{TT} + c_{FF} + R}
  
      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` and :math:`R = 2(c_{TF} + c_{FT})`.
  
      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
      -------
      rogerstanimoto : double
          The Rogers-Tanimoto dissimilarity between vectors
          `u` and `v`.
  
      Examples
      --------
      >>> from scipy.spatial import distance
      >>> distance.rogerstanimoto([1, 0, 0], [0, 1, 0])
      0.8
      >>> distance.rogerstanimoto([1, 0, 0], [1, 1, 0])
      0.5
      >>> distance.rogerstanimoto([1, 0, 0], [2, 0, 0])
      -1.0
  
      """
      u = _validate_vector(u)
      v = _validate_vector(v)
      if w is not None:
          w = _validate_weights(w)
      (nff, nft, ntf, ntt) = _nbool_correspond_all(u, v, w=w)
      return float(2.0 * (ntf + nft)) / float(
          ntt + nff + (2.0 * (ntf + nft))
      )
  
imports: import math
  import warnings
  import numpy
  import dataclasses
  from typing import Optional, Callable
  from functools import partial
  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
  from .None import _distance_pybind
label_desc: Use image node_id 34 for calling a global function with example usage: rogerstanimoto(u, v, w) and returns: unknown