'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.'

def mahalanobisNorm(self, dx):

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