def emp_quantiles(self, X, quantiles=arange(0.1, 1, 0.1)):
        # need inverse chi2 cdf with self.dimension degrees of freedom
        chi2_instance = chi2(self.dimension)
        cutoffs = chi2_instance.isf(1 - quantiles)
        # whitening
        D, U = eig(self.L.dot(self.L.T))
        D = D ** (-0.5)
        W = (diag(D).dot(U.T).dot((X - self.mu).T)).T
        norms_squared = array([norm(w) ** 2 for w in W])
        results = zeros([len(quantiles)])
        for jj in range(0, len(quantiles)):
            results[jj] = mean(norms_squared < cutoffs[jj])
        return results
dataset/ETHPy150Open karlnapf/kameleon-mcmc/kameleon_mcmc/distribution/Gaussian.py/Gaussian.emp_quantiles/original