Module « scipy.spatial.distance »

Fonction jensenshannon - module scipy.spatial.distance

Signature de la fonction jensenshannon

def jensenshannon(p, q, base=None, *, axis=0, keepdims=False) 

Description

jensenshannon.__doc__

    Compute the Jensen-Shannon distance (metric) between
    two probability arrays. This is the square root
    of the Jensen-Shannon divergence.

    The Jensen-Shannon distance between two probability
    vectors `p` and `q` is defined as,

    .. math::

       \sqrt{\frac{D(p \parallel m) + D(q \parallel m)}{2}}

    where :math:`m` is the pointwise mean of :math:`p` and :math:`q`
    and :math:`D` is the Kullback-Leibler divergence.

    This routine will normalize `p` and `q` if they don't sum to 1.0.

    Parameters
    ----------
    p : (N,) array_like
        left probability vector
    q : (N,) array_like
        right probability vector
    base : double, optional
        the base of the logarithm used to compute the output
        if not given, then the routine uses the default base of
        scipy.stats.entropy.
    axis : int, optional
        Axis along which the Jensen-Shannon distances are computed. The default
        is 0.

        .. versionadded:: 1.7.0
    keepdims : bool, optional
        If this is set to `True`, the reduced axes are left in the
        result as dimensions with size one. With this option,
        the result will broadcast correctly against the input array.
        Default is False.

        .. versionadded:: 1.7.0

    Returns
    -------
    js : double or ndarray
        The Jensen-Shannon distances between `p` and `q` along the `axis`.

    Notes
    -----

    .. versionadded:: 1.2.0

    Examples
    --------
    >>> from scipy.spatial import distance
    >>> distance.jensenshannon([1.0, 0.0, 0.0], [0.0, 1.0, 0.0], 2.0)
    1.0
    >>> distance.jensenshannon([1.0, 0.0], [0.5, 0.5])
    0.46450140402245893
    >>> distance.jensenshannon([1.0, 0.0, 0.0], [1.0, 0.0, 0.0])
    0.0
    >>> a = np.array([[1, 2, 3, 4],
    ...               [5, 6, 7, 8],
    ...               [9, 10, 11, 12]])
    >>> b = np.array([[13, 14, 15, 16],
    ...               [17, 18, 19, 20],
    ...               [21, 22, 23, 24]])
    >>> distance.jensenshannon(a, b, axis=0)
    array([0.1954288, 0.1447697, 0.1138377, 0.0927636])
    >>> distance.jensenshannon(a, b, axis=1)
    array([0.1402339, 0.0399106, 0.0201815])