def test_adjointness(size=4):
"""Tests for adjointness between gradient and div operators"""
rng = check_random_state(42)
for _ in range(3):
image_1 = rng.rand(size, size, size)
image_2 = rng.rand(3, size, size, size)
Axdoty = np.dot((_gradient(image_1).ravel()), image_2.ravel())
xdotAty = np.dot((_div(image_2).ravel()), image_1.ravel())
assert_almost_equal(Axdoty, - xdotAty)
def test_adjointness(size=4):
"""Tests for adjointness between gradient and div operators"""
rng = check_random_state(42)
for _ in range(3):
image_1 = rng.rand(size, size, size)
image_2 = rng.rand(3, size, size, size)
Axdoty = np.dot((_gradient(image_1).ravel()), image_2.ravel())
xdotAty = np.dot((_div(image_2).ravel()), image_1.ravel())
assert_almost_equal(Axdoty, -xdotAty)
def test_grad_matrix():
"""Test for matricial form of gradient"""
rng = check_random_state(42)
G = get_gradient_matrix(w.size, mask)
image_buffer = np.zeros(mask.shape)
grad_mask = np.array([mask for _ in range(mask.ndim)])
for _ in range(10):
v = rng.rand(w.size) * rng.randint(1000)
image_buffer[mask] = v
assert_almost_equal(_gradient(image_buffer)[grad_mask], np.dot(G, v))
def test_grad_matrix():
"""Test for matricial form of gradient"""
rng = check_random_state(42)
G = get_gradient_matrix(w.size, mask)
image_buffer = np.zeros(mask.shape)
grad_mask = np.array([mask for _ in range(mask.ndim)])
for _ in range(10):
v = rng.rand(w.size) * rng.randint(1000)
image_buffer[mask] = v
assert_almost_equal(_gradient(image_buffer)[grad_mask], np.dot(G, v))
def get_gradient_matrix(w_size, mask):
"""
Given a number of features and a mask (which has the property
mask[mask==True].size == w_size) computes a matrix G such that for
a w vector we have np.dot(G, w) == gradient(w_masked)[mask]
"""
grad_matrix = np.zeros((mask.ndim * w_size, w_size))
grad_mask = np.array([mask for _ in range(mask.ndim)])
image_buffer = np.zeros(mask.shape)
for i in range(w_size):
base_vector = np.zeros(w_size)
base_vector[i] = 1
image_buffer[mask] = base_vector
gradient_column = _gradient(image_buffer)[grad_mask]
grad_matrix[:, i] = gradient_column
return grad_matrix
def get_gradient_matrix(w_size, mask):
"""
Given a number of features and a mask (which has the property
mask[mask==True].size == w_size) computes a matrix G such that for
a w vector we have np.dot(G, w) == gradient(w_masked)[mask]
"""
grad_matrix = np.zeros((mask.ndim * w_size, w_size))
grad_mask = np.array([mask for _ in range(mask.ndim)])
image_buffer = np.zeros(mask.shape)
for i in range(w_size):
base_vector = np.zeros(w_size)
base_vector[i] = 1
image_buffer[mask] = base_vector
gradient_column = _gradient(image_buffer)[grad_mask]
grad_matrix[:, i] = gradient_column
return grad_matrix
