def test_opt_traj5_1ref(): """ test_opt_traj5_1 """ traj = simple_traj5() start_theta = np.array([-1.0]) choices = [1, 0, 2] traj_choices = [(traj, choices)] obj_fun = trajs_obj_fun_ref(traj_choices) (theta, ys) = optimize_function(obj_fun, start_theta, max_iters=20) assert ys[0] <= ys[-1], (theta, ys, len(ys))
def test_opt_traj5_1ref():
""" test_opt_traj5_1 """
traj = simple_traj5()
start_theta = np.array([-1.0])
choices = [1, 0, 2]
traj_choices = [(traj, choices)]
obj_fun = trajs_obj_fun_ref(traj_choices)
(theta, ys) = optimize_function(obj_fun, start_theta, max_iters=20)
assert ys[0] <= ys[-1], (theta, ys, len(ys))
def test_smoother_ref_traj5_1():
""" test_smoother_ref_traj5_1 """
traj = simple_traj5()
theta = np.array([-1.0])
smoother_ref = TrajectorySmootherRef(traj, theta)
smoother_ref.computeProbs()
smoother_1 = TrajectorySmoother1(traj, theta)
smoother_1.computeProbs()
check_probs(smoother_1, smoother_ref)
def test_smoother_ref_traj5_1(): """ test_smoother_ref_traj5_1 """ traj = simple_traj5() theta = np.array([-1.0]) smoother_ref = TrajectorySmootherRef(traj, theta) smoother_ref.computeProbs() smoother_1 = TrajectorySmoother1(traj, theta) smoother_1.computeProbs() check_probs(smoother_1, smoother_ref)
def test_gradient_ref_traj5_1(): """ test_gradient_ref_traj5_1. """ traj = simple_traj5() choices = [1, 0, 2] fun = get_fun_ref(traj, choices) grad_fun = get_grad_ref(traj, choices) # Do not forget to define the vector as floating point numbers!!! theta = np.array([1.0]) g_emp = compute_grad(fun, theta) g = grad_fun(theta) assert(np.abs(g - g_emp).max() < 1e-3), (g, g_emp)
def test_traj_5_2():
""" test_traj_5_2 """
traj = simple_traj5()
theta = np.array([-1.0])
choices = [1, 0, 2]
elts = LearningElementsSecure(traj, theta, choices)
elts.computeLogZ()
elts_ref = LearningElementsRef(traj, theta, choices)
elts_ref.computeLogZ()
assert(within(elts.Z, elts_ref.Z, 1e-5)), (elts.Z, elts_ref.Z, 1e-5)
assert(within(elts.logZ, elts_ref.logZ, 1e-5)), \
(elts.logZ, elts_ref.logZ, 1e-5)
def test_grad_traj5_2(): """ test_grad_traj5_2 Test of implementation 1 of gradient. """ traj = simple_traj5() theta = np.array([-1.0]) choices = [1, 0, 2] elts = LearningElementsSecure(traj, theta, choices) elts.computeGradientLogZ() elts_ref = LearningElementsRef(traj, theta, choices) elts_ref.computeGradientLogZ() g = elts.grad_logZ g_ref = elts_ref.grad_logZ assert(np.abs(g - g_ref).max() < 1e-3), (g, g_ref)
def test_hess_traj5_2():
""" test_hess_traj5_2 """
traj = simple_traj5()
theta = np.array([-1.0])
choices = [1, 0, 2]
elts = LearningElementsSecure(traj, theta, choices)
elts.computeHessianLogZ()
elts_ref = LearningElementsRef(traj, theta, choices)
elts_ref.computeHessianLogZ()
h = elts.hess_logZ
h_ref = elts_ref.hess_logZ
assert(np.abs(h - h_ref).max() < 1e-3), \
(h, h_ref, elts.hess_Z, elts_ref.hess_Z, \
elts.grad_Z, elts_ref.grad_Z, elts.Z, elts_ref.Z,)
def test_opt0_traj5_1(): """ test_opt0_traj5_1 """ traj = simple_traj5() start_theta = np.array([-1.0]) choices = [1, 0, 2] traj_choices = [(traj, choices)] obj_fun_1 = trajs_obj_fun_1(traj_choices) obj_fun_0 = trajs_obj_fun_0(traj_choices) obj_fun_ref = trajs_obj_fun_ref(traj_choices) max_iters = 5 (theta_0, ys_0) = optimize_function(obj_fun_0, start_theta, max_iters) (theta_1, ys_1) = optimize_function(obj_fun_1, start_theta, max_iters) (theta_ref, ys_ref) = optimize_function(obj_fun_ref, start_theta, max_iters) assert(np.abs(theta_0 - theta_ref).max() < 1e-3), (theta_0, theta_ref) assert(np.abs(theta_1 - theta_ref).max() < 1e-3), (theta_1, theta_ref) assert within(ys_0[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_0, ys_0) assert within(ys_1[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_1, ys_1)
def test_opt0_traj5_1():
""" test_opt0_traj5_1 """
traj = simple_traj5()
start_theta = np.array([-1.0])
choices = [1, 0, 2]
traj_choices = [(traj, choices)]
obj_fun_1 = trajs_obj_fun_1(traj_choices)
obj_fun_0 = trajs_obj_fun_0(traj_choices)
obj_fun_ref = trajs_obj_fun_ref(traj_choices)
max_iters = 5
(theta_0, ys_0) = optimize_function(obj_fun_0, start_theta, max_iters)
(theta_1, ys_1) = optimize_function(obj_fun_1, start_theta, max_iters)
(theta_ref, ys_ref) = optimize_function(obj_fun_ref, start_theta,
max_iters)
assert (np.abs(theta_0 - theta_ref).max() < 1e-3), (theta_0, theta_ref)
assert (np.abs(theta_1 - theta_ref).max() < 1e-3), (theta_1, theta_ref)
assert within(ys_0[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_0, ys_0)
assert within(ys_1[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_1, ys_1)
def test_hessian_ref_traj5_1(): """ test_hessian_ref_traj5_1 Simple test to check that the reference implementation of the hessian is equal to a empirical definition based on logZ. """ traj = simple_traj5() choices = [1, 0, 2] # Do not forget to define the vector as floating point numbers!!! theta = np.array([1.0]) # Z fun = get_fun_ref0(traj, choices) hess_fun = get_hess_ref0(traj, choices) h_emp = compute_hess(fun, theta) h = hess_fun(theta) assert(np.abs(h - h_emp).max() < 1e-2), (h, h_emp) # LogZ fun = get_fun_ref(traj, choices) hess_fun = get_hess_ref(traj, choices) h_emp = compute_hess(fun, theta) h = hess_fun(theta) assert(np.abs(h - h_emp).max() < 1e-3), (h, h_emp)
