Module « scipy.spatial.distance »

Fonction sokalmichener - module scipy.spatial.distance

Signature de la fonction sokalmichener

def sokalmichener(u, v, w=None) 

Description

sokalmichener.__doc__

    Compute the Sokal-Michener dissimilarity between two boolean 1-D arrays.

    The Sokal-Michener dissimilarity between boolean 1-D arrays `u` and `v`,
    is defined as

    .. math::

       \frac{R}
            {S + 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`, :math:`R = 2 * (c_{TF} + c_{FT})` and
    :math:`S = c_{FF} + c_{TT}`.

    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
    -------
    sokalmichener : double
        The Sokal-Michener dissimilarity between vectors `u` and `v`.

    Examples
    --------
    >>> from scipy.spatial import distance
    >>> distance.sokalmichener([1, 0, 0], [0, 1, 0])
    0.8
    >>> distance.sokalmichener([1, 0, 0], [1, 1, 0])
    0.5
    >>> distance.sokalmichener([1, 0, 0], [2, 0, 0])
    -1.0