4c08f6a92d547a3308164499ec92d35254ed1da5,scipy/stats/tests/test_multivariate.py,TestUnitaryGroup,test_haar,#TestUnitaryGroup#,1436

Before Change

        eigs = np.vstack(scipy.linalg.eigvals(x) for x in xs)
        x = np.arctan(eigs.imag/eigs.real)

        h = np.histogram(x.flatten())[0]

        // The angles "x" of the eigenvalues should be uniformly distributed
        // We are not checking that the range is correct; maybe we should?
        // Overall this seems to be a necessary but weak test of the distribution.

After Change

        // Overall this seems to be a necessary but weak test of the distribution.
        eigs = np.vstack(scipy.linalg.eigvals(x) for x in xs)
        x = np.arctan2(eigs.imag, eigs.real)
        res = kstest(x.ravel(),
                     lambda x: uniform(0, 2*np.pi).cdf(x + np.pi))
        // Note that this should be uniform(-pi, pi), but uniform"s
        // range can"t seem to include 0 as an interior point

Instances