Method kl_div

ot/backend.py:984–998

r""" Computes the (Generalized) Kullback-Leibler divergence. This function follows the api from :any:`scipy.stats.entropy`. Parameter eps is used to avoid numerical errors and is added in the log. .. math:: KL(p,q) = \langle \mathbf{p}, log(\mathbf{p}

(self, p, q, mass=False, eps=1e-16)

        raise NotImplementedError()

    def kl_div(self, p, q, mass=False, eps=1e-16):
        r"""
        Computes the (Generalized) Kullback-Leibler divergence.

        This function follows the api from :any:`scipy.stats.entropy`.

        Parameter eps is used to avoid numerical errors and is added in the log.

        .. math::
             KL(p,q) = \langle \mathbf{p}, log(\mathbf{p} / \mathbf{q} + eps \rangle
             + \mathbb{1}_{mass=True} \langle \mathbf{q} - \mathbf{p}, \mathbf{1} \rangle

        See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html
        """
        raise NotImplementedError()

    def isfinite(self, a):
        r"""