def test_operators_adjointness(size=4):
"""The same as test_identity_adjointness, but with generic design matrix"""
rng = check_random_state(42)
# A mask full of ones
mask = np.ones((size, size, size), dtype=np.bool)
# But with some zeros
mask[0:3, 0:3, 0:3] = 0
adjoint_mask = np.array([mask for _ in range(mask.ndim)])
n_samples = 200
X = rng.rand(n_samples, np.sum(mask))
l1_ratio = 0.5
for _ in range(10):
x = rng.rand(np.sum(mask))
y = rng.rand(n_samples + np.sum(mask) * mask.ndim)
Axdoty = np.dot(_graph_net_data_function(X, x, mask, l1_ratio), y)
xdotAty = np.dot(_graph_net_adjoint_data_function(
X, y, adjoint_mask, l1_ratio), x)
np.testing.assert_almost_equal(Axdoty, xdotAty)
def test_operators_adjointness(size=4):
"""The same as test_identity_adjointness, but with generic design matrix"""
rng = check_random_state(42)
# A mask full of ones
mask = np.ones((size, size, size), dtype=np.bool)
# But with some zeros
mask[0:3, 0:3, 0:3] = 0
adjoint_mask = np.array([mask for _ in range(mask.ndim)])
n_samples = 200
X = rng.rand(n_samples, np.sum(mask))
l1_ratio = 0.5
for _ in range(10):
x = rng.rand(np.sum(mask))
y = rng.rand(n_samples + np.sum(mask) * mask.ndim)
Axdoty = np.dot(_graph_net_data_function(X, x, mask, l1_ratio), y)
xdotAty = np.dot(
_graph_net_adjoint_data_function(X, y, adjoint_mask, l1_ratio), x)
np.testing.assert_almost_equal(Axdoty, xdotAty)
def test_identity_adjointness(size=4):
"""Tests adjointess between _graph_net_data_function and
_graph_net_adjoint_data_function, with identity design matrix"""
rng = check_random_state(42)
# A mask full of ones
mask = np.ones((size, size, size), dtype=np.bool)
# But with some zeros
mask[0:3, 0:3, 0:3] = 0
adjoint_mask = np.array([mask for _ in range(mask.ndim)])
n_samples = np.sum(mask)
X = np.eye(n_samples)
l1_ratio = 0.5
for _ in range(10):
x = rng.rand(np.sum(mask))
y = rng.rand(n_samples + np.sum(mask) * mask.ndim)
Axdoty = np.dot(_graph_net_data_function(X, x, mask, l1_ratio), y)
xdotAty = np.dot(_graph_net_adjoint_data_function(
X, y, adjoint_mask, l1_ratio), x)
assert_almost_equal(Axdoty, xdotAty)
def test_identity_adjointness(size=4):
"""Tests adjointess between _graph_net_data_function and
_graph_net_adjoint_data_function, with identity design matrix"""
rng = check_random_state(42)
# A mask full of ones
mask = np.ones((size, size, size), dtype=np.bool)
# But with some zeros
mask[0:3, 0:3, 0:3] = 0
adjoint_mask = np.array([mask for _ in range(mask.ndim)])
n_samples = np.sum(mask)
X = np.eye(n_samples)
l1_ratio = 0.5
for _ in range(10):
x = rng.rand(np.sum(mask))
y = rng.rand(n_samples + np.sum(mask) * mask.ndim)
Axdoty = np.dot(_graph_net_data_function(X, x, mask, l1_ratio), y)
xdotAty = np.dot(
_graph_net_adjoint_data_function(X, y, adjoint_mask, l1_ratio), x)
assert_almost_equal(Axdoty, xdotAty)
