Vous êtes un professionnel et vous avez besoin d'une formation ? Coder avec une
Intelligence Artificielle Voir le programme détaillé

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

help(scipy.spatial.distance.jensenshannon)

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
>>> import numpy as np
>>> 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])

Vous êtes un professionnel et vous avez besoin d'une formation ? Deep Learning avec Python
et Keras et Tensorflow Voir le programme détaillé