def test_smoother_ref_traj3_1():
""" test_smoother_ref_traj3_1 .
Just check if it can be computed and does not trigger underflow warnings. """
traj = simple_traj3()
theta = np.array([-1.0, 1.0])
smoother_1 = TrajectorySmoother1(traj, theta)
smoother_1.computeProbs()
def test_smoother_ref_traj3_1(): """ test_smoother_ref_traj3_1 . Just check if it can be computed and does not trigger underflow warnings. """ traj = simple_traj3() theta = np.array([-1.0, 1.0]) smoother_1 = TrajectorySmoother1(traj, theta) smoother_1.computeProbs()
def test_em_1():
""" Very simple test: we pick some trajectories and verify that
the LL increases with EM.
"""
trajs = [simple_traj1(), simple_traj4(), simple_traj3()]
theta_start = 0.1 * np.ones(2)
history = learn_em(trajs_estim_obj_fun_1, trajs, theta_start)
# (ll_end, theta_end) = history[-1]
# Very simple check here: we verify the progression goes upward
# the likelihood:
for t in range(len(history)-1):
(ll_1, _) = history[t]
(ll_2, _) = history[t+1]
assert ll_1 <= ll_2
def test_em_1():
""" Very simple test: we pick some trajectories and verify that
the LL increases with EM.
"""
trajs = [simple_traj1(), simple_traj4(), simple_traj3()]
theta_start = 0.1 * np.ones(2)
history = learn_em(trajs_estim_obj_fun_1, trajs, theta_start)
# (ll_end, theta_end) = history[-1]
# Very simple check here: we verify the progression goes upward
# the likelihood:
for t in range(len(history) - 1):
(ll_1, _) = history[t]
(ll_2, _) = history[t + 1]
assert ll_1 <= ll_2