def test_dmp_save_and_load_bug_83():
random_state = np.random.RandomState(0)
dmp = DMPBehavior(execution_time=1.0, dt=0.001)
dmp.init(6, 6)
x0 = np.zeros(2)
g = np.ones(2)
dmp.set_meta_parameters(["x0", "g"], [x0, g])
w = dmp.get_params()
w = random_state.randn(*w.shape) * 1000.0
dmp.set_params(w)
Y, Yd, Ydd = dmp.trajectory()
try:
dmp.save("tmp_dmp_model2.yaml")
dmp.save_config("tmp_dmp_config2.yaml")
dmp = DMPBehavior(configuration_file="tmp_dmp_model2.yaml")
dmp.init(6, 6)
dmp.load_config("tmp_dmp_config2.yaml")
finally:
if os.path.exists("tmp_dmp_model2.yaml"):
os.remove("tmp_dmp_model2.yaml")
if os.path.exists("tmp_dmp_config2.yaml"):
os.remove("tmp_dmp_config2.yaml")
Y2, Yd2, Ydd2 = dmp.trajectory()
assert_array_almost_equal(Y, Y2)
assert_array_almost_equal(Yd, Yd2)
assert_array_almost_equal(Ydd, Ydd2)
def test_dmp_save_and_load():
beh_original = DMPBehavior(execution_time=0.853, dt=0.001, n_features=10)
beh_original.init(3 * n_task_dims, 3 * n_task_dims)
x0 = np.ones(n_task_dims) * 1.29
g = np.ones(n_task_dims) * 2.13
beh_original.set_meta_parameters(["x0", "g"], [x0, g])
xva = np.zeros(3 * n_task_dims)
xva[:n_task_dims] = x0
beh_original.reset()
t = 0
while beh_original.can_step():
eval_loop(beh_original, xva)
if t == 0:
assert_array_almost_equal(xva[:n_task_dims], x0)
t += 1
assert_array_almost_equal(xva[:n_task_dims], g, decimal=3)
assert_equal(t, 854)
assert_equal(beh_original.get_n_params(), n_task_dims * 10)
try:
beh_original.save("tmp_dmp_model.yaml")
beh_original.save_config("tmp_dmp_config.yaml")
beh_loaded = DMPBehavior(configuration_file="tmp_dmp_model.yaml")
beh_loaded.init(3 * n_task_dims, 3 * n_task_dims)
beh_loaded.load_config("tmp_dmp_config.yaml")
finally:
if os.path.exists("tmp_dmp_model.yaml"):
os.remove("tmp_dmp_model.yaml")
if os.path.exists("tmp_dmp_config.yaml"):
os.remove("tmp_dmp_config.yaml")
xva = np.zeros(3 * n_task_dims)
xva[:n_task_dims] = x0
beh_loaded.reset()
t = 0
while beh_loaded.can_step():
eval_loop(beh_loaded, xva)
if t == 0:
assert_array_almost_equal(xva[:n_task_dims], x0)
t += 1
assert_array_almost_equal(xva[:n_task_dims], g, decimal=3)
assert_equal(t, 854)
assert_equal(beh_loaded.get_n_params(), n_task_dims * 10)
def test_dmp_save_and_load2():
import pickle
with open("reload_test_trajectory_and_dt.pickle", "r") as f:
recorded_trajectory, dt = pickle.load(f)
artificial_trajectory = np.zeros((500, 1))
artificial_trajectory[:, 0] = np.sin(np.linspace(0, 500, 1))
trajectories = [artificial_trajectory, recorded_trajectory]
for trajectory in trajectories:
n_steps = trajectory.shape[0]
n_task_dims = trajectory.shape[1]
execution_time = dt * (n_steps - 1)
beh_original = DMPBehavior(execution_time=execution_time,
dt=dt,
n_features=20)
beh_original.init(3 * n_task_dims, 3 * n_task_dims)
x0 = trajectory[0, :]
g = trajectory[-1, :]
beh_original.set_meta_parameters(["x0", "g"], [x0, g])
X = np.zeros((n_task_dims, n_steps, 1))
X[:, :, 0] = np.swapaxes(trajectory, axis1=1, axis2=0)
beh_original.imitate(X, alpha=0.01)
xva = np.zeros(3 * n_task_dims)
xva[:n_task_dims] = x0
beh_original.reset()
imitated_trajectory = beh_original.trajectory()
try:
beh_original.save("tmp_dmp_model.yaml")
beh_original.save_config("tmp_dmp_config.yaml")
beh_loaded = DMPBehavior(configuration_file="tmp_dmp_model.yaml")
beh_loaded.init(3 * n_task_dims, 3 * n_task_dims)
beh_loaded.load_config("tmp_dmp_config.yaml")
finally:
if os.path.exists("tmp_dmp_model.yaml"):
os.remove("tmp_dmp_model.yaml")
if os.path.exists("tmp_dmp_config.yaml"):
os.remove("tmp_dmp_config.yaml")
beh_loaded.reset()
reimitated_trajectory = beh_loaded.trajectory()
assert_array_almost_equal(imitated_trajectory,
reimitated_trajectory,
decimal=4)