def test_eigvalsh_grad():
rng = np.random.default_rng(utt.fetch_seed())
a = rng.standard_normal((5, 5))
a = a + a.T
b = 10 * np.eye(5, 5) + rng.standard_normal((5, 5))
utt.verify_grad(lambda a, b: eigvalsh(a, b).dot([1, 2, 3, 4, 5]), [a, b],
rng=np.random)
def test_eigvalsh_grad():
pytest.importorskip("scipy")
rng = np.random.RandomState(utt.fetch_seed())
a = rng.randn(5, 5)
a = a + a.T
b = 10 * np.eye(5, 5) + rng.randn(5, 5)
utt.verify_grad(
lambda a, b: eigvalsh(a, b).dot([1, 2, 3, 4, 5]), [a, b], rng=np.random
)
def test_eigvalsh():
A = dmatrix("a")
B = dmatrix("b")
f = function([A, B], eigvalsh(A, B))
rng = np.random.default_rng(utt.fetch_seed())
a = rng.standard_normal((5, 5))
a = a + a.T
for b in [10 * np.eye(5, 5) + rng.standard_normal((5, 5))]:
w = f(a, b)
refw = scipy.linalg.eigvalsh(a, b)
np.testing.assert_array_almost_equal(w, refw)
# We need to test None separately, as otherwise DebugMode will
# complain, as this isn't a valid ndarray.
b = None
B = aet.NoneConst
f = function([A], eigvalsh(A, B))
w = f(a)
refw = scipy.linalg.eigvalsh(a, b)
np.testing.assert_array_almost_equal(w, refw)
def test_eigvalsh():
scipy = pytest.importorskip("scipy")
A = dmatrix("a")
B = dmatrix("b")
f = function([A, B], eigvalsh(A, B))
rng = np.random.RandomState(utt.fetch_seed())
a = rng.randn(5, 5)
a = a + a.T
for b in [10 * np.eye(5, 5) + rng.randn(5, 5)]:
w = f(a, b)
refw = scipy.linalg.eigvalsh(a, b)
np.testing.assert_array_almost_equal(w, refw)
# We need to test None separatly, as otherwise DebugMode will
# complain, as this isn't a valid ndarray.
b = None
B = aet.NoneConst
f = function([A], eigvalsh(A, B))
w = f(a)
refw = scipy.linalg.eigvalsh(a, b)
np.testing.assert_array_almost_equal(w, refw)