def test_Z3(): """ Test of implementation 2. """ traj = simple_traj10() theta = np.array([500.0, -100.0]) weights = [[0.1, 0.9], [0.9, 0.1]] elts = LearningElementsSecure(traj, theta, weights=weights) elts.computeLogZ()
def test_opt0_traj10_1():
""" test_opt0_traj10_1 """
traj = simple_traj10()
start_theta = np.array([-1.0, 2.0])
choices = [0, 0]
traj_choices = [(traj, choices)]
obj_fun_2 = trajs_obj_fun_2(traj_choices)
obj_fun_1 = trajs_obj_fun_1(traj_choices)
obj_fun_0bis = trajs_obj_fun_0bis(traj_choices)
max_iters = 5
(theta_0bis, ys_0bis) = \
optimize_function(obj_fun_0bis, start_theta, max_iters, regularizer=1e-4)
(theta_1, ys_1) = \
optimize_function(obj_fun_1, start_theta, max_iters, regularizer=1e-4)
(theta_2, ys_2) = \
optimize_function(obj_fun_2, start_theta, max_iters, regularizer=1e-4)
assert(np.abs(theta_1 - theta_0bis).max() < 1e-3), (theta_1, theta_0bis)
assert(np.abs(theta_2 - theta_0bis).max() < 1e-3), (theta_2, theta_0bis)
assert within(ys_1[0], ys_0bis[0], 1e-3), (theta_0bis, ys_0bis, theta_1, ys_1)
assert within(ys_2[0], ys_0bis[0], 1e-3), (theta_0bis, ys_0bis, theta_2, ys_2)
def test_opt0_traj10_1():
""" test_opt0_traj10_1 """
traj = simple_traj10()
start_theta = np.array([-1.0, 2.0])
choices = [0, 0]
traj_choices = [(traj, choices)]
obj_fun_2 = trajs_obj_fun_2(traj_choices)
obj_fun_1 = trajs_obj_fun_1(traj_choices)
obj_fun_0bis = trajs_obj_fun_0bis(traj_choices)
max_iters = 5
(theta_0bis, ys_0bis) = \
optimize_function(obj_fun_0bis, start_theta, max_iters, regularizer=1e-4)
(theta_1, ys_1) = \
optimize_function(obj_fun_1, start_theta, max_iters, regularizer=1e-4)
(theta_2, ys_2) = \
optimize_function(obj_fun_2, start_theta, max_iters, regularizer=1e-4)
assert (np.abs(theta_1 - theta_0bis).max() < 1e-3), (theta_1, theta_0bis)
assert (np.abs(theta_2 - theta_0bis).max() < 1e-3), (theta_2, theta_0bis)
assert within(ys_1[0], ys_0bis[0],
1e-3), (theta_0bis, ys_0bis, theta_1, ys_1)
assert within(ys_2[0], ys_0bis[0],
1e-3), (theta_0bis, ys_0bis, theta_2, ys_2)