Module « scipy.spatial.distance »

Fonction kulsinski - module scipy.spatial.distance

Signature de la fonction kulsinski

def kulsinski(u, v, w=None) 

Description

kulsinski.__doc__

    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