evolution_strategy.py 文件源码

def mahalanobis_norm(self, dx):
        """return Mahalanobis norm based on the current sample
        distribution.

        The norm is based on Covariance matrix ``C`` times ``sigma**2``,
        and includes ``sigma_vec``. The expected Mahalanobis distance to
        the sample mean is about ``sqrt(dimension)``.

        Argument
        --------
        A *genotype* difference `dx`.

        Example
        -------
        >>> import cma, numpy
        >>> es = cma.CMAEvolutionStrategy(numpy.ones(10), 1)  #doctest: +ELLIPSIS
        (5_w,...
        >>> xx = numpy.random.randn(2, 10)
        >>> d = es.mahalanobis_norm(es.gp.geno(xx[0]-xx[1]))

        `d` is the distance "in" the true sample distribution,
        sampled points have a typical distance of ``sqrt(2*es.N)``,
        where ``es.N`` is the dimension, and an expected distance of
        close to ``sqrt(N)`` to the sample mean. In the example,
        `d` is the Euclidean distance, because C = I and sigma = 1.

        """
        return self.sm.norm(np.asarray(dx) / self.sigma_vec.scaling) / self.sigma