def test_make_subgoals():
execution_times = [0.2, 0.3, 0.5]
n_features = [5, 5, 5]
dmp_seq = DMPSequence(3, execution_times, 0.01, n_features, None,
learn_goal_velocities=True)
dmp_seq.init(3, 3)
assert_equal(len(dmp_seq.subgoals), 4)
def test_make_n_features():
execution_times = [0.2, 0.3, 0.5]
subgoals = [[0], [0.5], [1.0], [2]]
dmp_seq = DMPSequence(3, execution_times, 0.01, None, subgoals,
learn_goal_velocities=True)
dmp_seq.init(3, 3)
assert_array_almost_equal(dmp_seq.n_features, [50, 50, 50])
def test_make_execution_times():
n_features = [5, 5, 5]
subgoals = [[0], [0.5], [1.0], [2]]
dmp_seq = DMPSequence(3, None, 0.01, n_features, subgoals,
learn_goal_velocities=True)
dmp_seq.init(3, 3)
assert_array_almost_equal(dmp_seq.execution_times, [1, 1, 1])
def test_initial_weights():
execution_times = [0.2, 0.3, 0.5]
n_features = [5, 5, 5]
subgoals = [[0], [0.5], [1.0], [2]]
initial_weights = [np.ones(5), np.ones(5), np.ones(5)]
dmp_seq = DMPSequence(3, execution_times, 0.01, n_features, subgoals,
learn_goal_velocities=True,
initial_weights=initial_weights)
dmp_seq.init(3, 3)
assert_equal(len(dmp_seq.weights), 3)
def test_dimensions_mismatch():
execution_times = [0.2, 0.3, 0.5]
n_features = [5, 5, 5]
subgoals = [[0], [0.5], [1.0], [2]]
dmp_seq = DMPSequence(3, execution_times, 0.01, n_features, subgoals,
learn_goal_velocities=True)
assert_raises_regexp(ValueError, "Input and output dimensions",
dmp_seq.init, 3, 4)
def test_empty_metaparameters():
execution_times = [0.2, 0.3, 0.5]
n_features = [5, 5, 5]
subgoals = [[0], [0.5], [1.0], [2]]
dmp_seq = DMPSequence(3, execution_times, 0.01, n_features, subgoals,
learn_goal_velocities=True)
dmp_seq.init(3, 3)
dmp_seq.set_meta_parameters([], [])
def create_dmp_seq(n_task_dims=1):
execution_times = [0.2, 0.3, 0.5]
n_features = [5, 5, 5]
subgoals = [[0] * n_task_dims, [0.5] * n_task_dims,
[1.0] * n_task_dims, [2] * n_task_dims]
dmp_seq = DMPSequence(3, execution_times, 0.01, n_features, subgoals,
learn_goal_velocities=True)
dmp_seq.init(3 * n_task_dims, 3 * n_task_dims)
dmp_seq.set_meta_parameters(["x0", "g"], [np.zeros(n_task_dims),
2 * np.ones(n_task_dims)])
return dmp_seq, subgoals
def test_n_params():
execution_times = [0.2, 0.3, 0.5]
n_features = [5, 5, 5]
subgoals = [[0], [0.5], [1.0], [2]]
initial_weights = [np.ones(5), np.ones(5), np.ones(5)]
dmp_seq = DMPSequence(3, execution_times, 0.01, n_features, subgoals,
learn_goal_velocities=True,
initial_weights=initial_weights)
dmp_seq.init(3, 3)
assert_equal(len(dmp_seq.get_params()), 3 * 5 + 2 * 1 + 4 * 1)
assert_equal(dmp_seq.get_n_params(), 3 * 5 + 2 * 1 + 4 * 1)
dmp_seq.set_params(dmp_seq.get_params())
dmp_seq = DMPSequence(3, execution_times, 0.01, n_features, subgoals,
learn_goal_velocities=False,
initial_weights=initial_weights)
dmp_seq.init(3, 3)
assert_equal(len(dmp_seq.get_params()), 3 * 5 + 2)
assert_equal(dmp_seq.get_n_params(), 3 * 5 + 2)
dmp_seq.set_params(dmp_seq.get_params())
def test_no_dimensions():
dmp_seq = DMPSequence()
dmp_seq.init(0, 0)
dmp_seq.set_meta_parameters([], [])
io = np.empty(0)
dmp_seq.set_inputs(io)
dmp_seq.step()
dmp_seq.get_outputs(io)
