def check_smw_solver(p, q, r, s):
# Helper to check that _smw_solver results do in fact solve the desired
# SMW equation
d = q - r
A = np.random.normal(size=(p, q))
AtA = np.dot(A.T, A)
B = np.zeros((q, q))
B[0:r, 0:r] = np.random.normal(size=(r, r))
di = np.random.uniform(size=d)
B[r:q, r:q] = np.diag(1 / di)
Qi = np.linalg.inv(B[0:r, 0:r])
s = 0.5
x = np.random.normal(size=p)
y2 = np.linalg.solve(s * np.eye(p, p) + np.dot(A, np.dot(B, A.T)), x)
f = _smw_solver(s, A, AtA, Qi, di)
y1 = f(x)
assert_allclose(y1, y2)
f = _smw_solver(s, sparse.csr_matrix(A), sparse.csr_matrix(AtA), Qi,
di)
y1 = f(x)
assert_allclose(y1, y2)
def tester(p, q, r, s):
d = q - r
A = np.random.normal(size=(p, q))
AtA = np.dot(A.T, A)
B = np.zeros((q, q))
B[0:r, 0:r] = np.random.normal(size=(r, r))
di = np.random.uniform(size=d)
B[r:q, r:q] = np.diag(1 / di)
Qi = np.linalg.inv(B[0:r, 0:r])
s = 0.5
x = np.random.normal(size=p)
y2 = np.linalg.solve(s * np.eye(p, p) + np.dot(A, np.dot(B, A.T)), x)
f = _smw_solver(s, A, AtA, Qi, di)
y1 = f(x)
assert_allclose(y1, y2)
f = _smw_solver(s, sparse.csr_matrix(A), sparse.csr_matrix(AtA), Qi,
di)
y1 = f(x)
assert_allclose(y1, y2)
def check_smw_solver(p, q, r, s):
# Helper to check that _smw_solver results do in fact solve the desired
# SMW equation
d = q - r
A = np.random.normal(size=(p, q))
AtA = np.dot(A.T, A)
B = np.zeros((q, q))
B[0:r, 0:r] = np.random.normal(size=(r, r))
di = np.random.uniform(size=d)
B[r:q, r:q] = np.diag(1 / di)
Qi = np.linalg.inv(B[0:r, 0:r])
s = 0.5
x = np.random.normal(size=p)
y2 = np.linalg.solve(s * np.eye(p, p) + np.dot(A, np.dot(B, A.T)), x)
f = _smw_solver(s, A, AtA, Qi, di)
y1 = f(x)
assert_allclose(y1, y2)
f = _smw_solver(s, sparse.csr_matrix(A), sparse.csr_matrix(AtA), Qi, di)
y1 = f(x)
assert_allclose(y1, y2)
def test_smw_solver():
np.random.seed(23)
p = 5
q = 4
r = 2
s = 2
A = np.random.normal(size=(p, q))
AtA = np.dot(A.T, A)
B = np.zeros((q, q))
B[0:r, 0:r] = np.random.normal(size=(r, r))
di = np.random.uniform(size=s)
B[r:q, r:q] = np.diag(1/di)
Qi = np.linalg.inv(B[0:r, 0:r])
s = 0.5
x = np.random.normal(size=p)
y2 = np.linalg.solve(s*np.eye(p, p) + np.dot(A, np.dot(B, A.T)), x)
f = _smw_solver(s, A, AtA, Qi, di)
y1 = f(x)
assert_allclose(y1, y2)
f = _smw_solver(s, sparse.csr_matrix(A), sparse.csr_matrix(AtA), Qi, di)
y1 = f(x)
assert_allclose(y1, y2)
