def test_opt_soft_shape_error_1(self):
A = np.zeros((2, 3), dtype=np.float64)
scvecs = np.zeros((3, 4))
scvecs[:, 0] = 1.0
with pytest.raises(ValueError,
match="Rotation matrix isn't quadratic."):
_opt_soft(scvecs, A)
def test_opt_soft_shape_error_3(self):
A = np.zeros((1, 1), dtype=np.float64)
scvecs = np.zeros((1, 1))
scvecs[:, 0] = 1.0
with pytest.raises(
ValueError,
match=
r"Expected the rotation matrix to be at least of shape \(2, 2\)",
):
_opt_soft(scvecs, A)
def test_opt_soft_shape_error_2(self):
A = np.zeros((3, 3), dtype=np.float64)
scvecs = np.zeros((2, 4))
scvecs[:, 0] = 1.0
with pytest.raises(
ValueError,
match=
"The dimensions of the rotation matrix don't match with the number of Schur vectors.",
):
_opt_soft(scvecs, A)
def test_opt_soft_nelder_mead_mu1000(self, svecs_mu1000: np.ndarray,
A_mu1000: np.ndarray):
A, chi, fopt = _opt_soft(svecs_mu1000, A_mu1000)
crispness = np.true_divide(5 - fopt, 5)
assert_allclose(crispness, 0.804, atol=0.0025)
def test_opt_soft_nelder_mead_mu0(self, svecs_mu0: np.ndarray,
A_mu0: np.ndarray):
A, chi, fopt = _opt_soft(svecs_mu0, A_mu0)
crispness = np.true_divide(3 - fopt, 3)
assert_allclose(crispness, 0.973, atol=1e-3)
def test_opt_soft_nelder_mead_more(self):
kmin, kmax = 2, 8
kopt = []
ks = np.arange(kmin, kmax)
for mu_ in [10, 50, 100, 200, 500, 1000]:
mu_ = mu(mu_)
P, sd = get_known_input(mu_)
X, _, _ = _do_schur(P, eta=sd, m=kmax)
crisp = [-np.inf] * (kmax - kmin)
for j, k in enumerate(range(kmin, kmax)):
svecs = X[:, :k]
A = _initialize_rot_matrix(svecs)
_, _, fopt = _opt_soft(svecs, A)
crisp[j] = (k - fopt) / k
kopt.append(ks[np.argmax(crisp)])
np.testing.assert_array_equal(kopt, [3, 3, 3, 2, 2, 7])