def test_approx_log_det():
D = 100
rs = RandomState(0)
mat = np.eye(D) - 0.1 * np.diag(rs.rand(D))
mvp = lambda v: np.dot(mat, v)
N_trials = 10000
approx = 0
for i in xrange(N_trials):
approx += approx_log_det(mvp, D, rs)
approx = approx / N_trials
exact = exact_log_det(mvp, D)
assert exact > approx > (exact - 0.1 * np.abs(exact))
print exact, approx
def test_approx_log_det():
D = 100
rs = RandomState(0)
mat = np.eye(D) - 0.1 * np.diag(rs.rand(D))
mvp = lambda v : np.dot(mat, v)
N_trials = 10000
approx = 0
for i in xrange(N_trials):
approx += approx_log_det(mvp, D, rs)
approx = approx / N_trials
exact = exact_log_det(mvp, D)
assert exact > approx > (exact - 0.1 * np.abs(exact))
print exact, approx
def test_exact_log_det():
D = 100
rs = RandomState(1)
mat = np.eye(D) - 0.1 * np.diag(rs.rand(D))
mvp = lambda v: np.dot(mat, v)
assert exact_log_det(mvp, D) == np.log(np.linalg.det(mat))
def test_exact_log_det():
D = 100
rs = RandomState(1)
mat = np.eye(D) - 0.1 * np.diag(rs.rand(D))
mvp = lambda v : np.dot(mat, v)
assert exact_log_det(mvp, D) == np.log(np.linalg.det(mat))