def test_opt0_traj4_1ref(): """ test_opt0_traj4_1 """ traj = simple_traj4() start_theta = np.array([0.0, 0.0]) choices = [1, 0, 2] estims = [np.array([0.0, 1.0]), np.array([1.0]), np.array([0.0, 0.0, 1.0])] traj_choices = [(traj, choices)] traj_estims = [(traj, estims)] obj_fun_2 = trajs_obj_fun_2(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) obj_fun_estim_ref = trajs_estim_obj_fun_ref(traj_estims) 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_2, ys_2) = optimize_function(obj_fun_2, start_theta, max_iters) (theta_ref, ys_ref) = optimize_function(obj_fun_ref, start_theta, max_iters) (theta_estim_ref, ys_estim_ref) = optimize_function(obj_fun_estim_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(np.abs(theta_2 - theta_ref).max() < 1e-3), (theta_2, theta_ref) assert(np.abs(theta_estim_ref - theta_ref).max() < 1e-3), (theta_estim_ref, \ 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) assert within(ys_2[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_2, ys_2) assert within(ys_estim_ref[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, \ theta_estim_ref, \ ys_estim_ref)
def test_opt0_traj9_1(): """ test_opt0_traj9_1 """ traj = simple_traj9() start_theta = np.array([-1.0]) 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_0 = trajs_obj_fun_0(traj_choices) obj_fun_0bis = trajs_obj_fun_0bis(traj_choices) obj_fun_ref = trajs_obj_fun_ref(traj_choices) max_iters = 5 (theta_ref, ys_ref) = \ optimize_function(obj_fun_ref, start_theta, max_iters, regularizer=1e-4) print ys_ref, theta_ref (theta_0bis, ys_0bis) = \ optimize_function(obj_fun_0bis, start_theta, max_iters, regularizer=1e-4) (theta_0, ys_0) = \ optimize_function(obj_fun_0, 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_0bis - theta_ref).max() < 1e-3), (theta_0bis, theta_ref) 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(np.abs(theta_2 - theta_ref).max() < 1e-3), (theta_2, theta_ref) assert within(ys_0bis[0], ys_ref[0], 1e-3), \ (theta_ref, ys_ref, theta_0bis, ys_0bis) 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) assert within(ys_2[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_2, ys_2)
def test_opt0_traj4_1ref(): """ test_opt0_traj4_1 """ traj = simple_traj4() start_theta = np.array([0.0, 0.0]) choices = [1, 0, 2] estims = [np.array([0.0, 1.0]), np.array([1.0]), np.array([0.0, 0.0, 1.0])] traj_choices = [(traj, choices)] traj_estims = [(traj, estims)] obj_fun_2 = trajs_obj_fun_2(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) obj_fun_estim_ref = trajs_estim_obj_fun_ref(traj_estims) 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_2, ys_2) = optimize_function(obj_fun_2, start_theta, max_iters) (theta_ref, ys_ref) = optimize_function(obj_fun_ref, start_theta, max_iters) (theta_estim_ref, ys_estim_ref) = optimize_function(obj_fun_estim_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 (np.abs(theta_2 - theta_ref).max() < 1e-3), (theta_2, theta_ref) assert(np.abs(theta_estim_ref - theta_ref).max() < 1e-3), (theta_estim_ref, \ 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) assert within(ys_2[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_2, ys_2) assert within(ys_estim_ref[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, \ theta_estim_ref, \ ys_estim_ref)
def test_opt0_traj9_1(): """ test_opt0_traj9_1 """ traj = simple_traj9() start_theta = np.array([-1.0]) 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_0 = trajs_obj_fun_0(traj_choices) obj_fun_0bis = trajs_obj_fun_0bis(traj_choices) obj_fun_ref = trajs_obj_fun_ref(traj_choices) max_iters = 5 (theta_ref, ys_ref) = \ optimize_function(obj_fun_ref, start_theta, max_iters, regularizer=1e-4) print ys_ref, theta_ref (theta_0bis, ys_0bis) = \ optimize_function(obj_fun_0bis, start_theta, max_iters, regularizer=1e-4) (theta_0, ys_0) = \ optimize_function(obj_fun_0, 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_0bis - theta_ref).max() < 1e-3), (theta_0bis, theta_ref) 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 (np.abs(theta_2 - theta_ref).max() < 1e-3), (theta_2, theta_ref) assert within(ys_0bis[0], ys_ref[0], 1e-3), \ (theta_ref, ys_ref, theta_0bis, ys_0bis) 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) assert within(ys_2[0], ys_ref[0], 1e-3), (theta_ref, ys_ref, theta_2, ys_2)
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)