def METHOD_NAME():
    """Test non-square infomax."""
    rng = np.random.RandomState(0)
    n_samples = 200
    # Generate two sources:
    t = np.linspace(0, 100, n_samples)
    s1 = np.sin(t)
    s2 = np.ceil(np.sin(np.pi * t))
    s = np.c_[s1, s2].T
    center_and_norm(s)
    s1, s2 = s
    # Mixing matrix
    n_observed = 6
    mixing = rng.randn(n_observed, 2)
    for add_noise in (False, True):
        m = np.dot(mixing, s)
        if add_noise:
            m += 0.1 * rng.randn(n_observed, n_samples)
        center_and_norm(m)
        m = m.T
        m = _get_pca(rng).fit_transform(m)
        # we need extended since input signals are sub-gaussian
        unmixing_ = infomax(m, random_state=rng, extended=True)
        s_ = np.dot(unmixing_, m.T)
        # Check that the mixing model described in the docstring holds:
        mixing_ = linalg.pinv(unmixing_.T)
        assert_almost_equal(m, s_.T.dot(mixing_))
        center_and_norm(s_)
        s1_, s2_ = s_
        # Check to see if the sources have been estimated
        # in the wrong order
        if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
            s2_, s1_ = s_
        s1_ *= np.sign(np.dot(s1_, s1))
        s2_ *= np.sign(np.dot(s2_, s2))
        # Check that we have estimated the original sources
        if not add_noise:
            assert_almost_equal(np.dot(s1_, s1) / n_samples, 1, decimal=2)
            assert_almost_equal(np.dot(s2_, s2) / n_samples, 1, decimal=2)