(self, dx)
return sqrt(sum((self.D**-1 * np.dot(self.B.T, dx))**2)) / self.sigma
compute the Mahalanobis norm that is induced by the adapted covariance
        matrix C times sigma**2.

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

        Example
        -------
        >>> import cma, numpy
        >>> es = cma.CMAEvolutionStrategy(numpy.ones(10), 1)
        >>> xx = numpy.random.randn(2, 10)
        >>> d = es.mahalanobisNorm(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 `N` is the dimension. In the example, `d` is the
        Euclidean distance, because C = I and sigma = 1.
compute the Mahalanobis norm that is induced by the adapted covariance
        matrix C times sigma**2.
compute the Mahalanobis norm that is induced by the adapted covariance matrix C times sigma ** 2 .
def mahalanobisNorm(self, dx):
        """
        compute the Mahalanobis norm that is induced by the adapted covariance
        matrix C times sigma**2.

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

        Example
        -------
        >>> import cma, numpy
        >>> es = cma.CMAEvolutionStrategy(numpy.ones(10), 1)
        >>> xx = numpy.random.randn(2, 10)
        >>> d = es.mahalanobisNorm(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 `N` is the dimension. In the example, `d` is the
        Euclidean distance, because C = I and sigma = 1.

        """
        return sqrt(sum((self.D**-1 * np.dot(self.B.T, dx))**2)) / self.sigma
def mahalanobisNorm ( self , dx ) : return sqrt ( sum ( ( self . D ** - 1 * np . dot ( self . B . T , dx ) ) ** 2 ) ) / self . sigma