steps = X[:, :, 0].mean(axis=0)
expected_mean = X[:, :, 1].mean(axis=0)
expected_std = X[:, :, 1].std(axis=0)
n_demonstrations, n_steps, n_task_dims = X.shape
X_train = np.empty((n_demonstrations, n_steps, n_task_dims + 1))
X_train[:, :, 1:] = X
t = np.linspace(0, 1, n_steps)
X_train[:, :, 0] = t
X_train = X_train.reshape(n_demonstrations * n_steps, n_task_dims + 1)
random_state = check_random_state(0)
n_components = 4
initial_means = kmeansplusplus_initialization(X_train, n_components,
random_state)
initial_covs = covariance_initialization(X_train, n_components)
bgmm = BayesianGaussianMixture(n_components=n_components,
max_iter=100).fit(X_train)
gmm = GMM(n_components=n_components,
priors=bgmm.weights_,
means=bgmm.means_,
covariances=bgmm.covariances_,
random_state=random_state)
plt.figure(figsize=(10, 5))
plt.subplot(121)
plt.title("Confidence Interval from GMM")
plt.plot(X[:, :, 0].T, X[:, :, 1].T, c="k", alpha=0.1)
means_over_time = []
def test_initialize_two_covariances():
cov = covariance_initialization(np.array([[0], [1], [2]]), 2)
assert_equal(len(cov), 2)
assert_array_almost_equal(cov, np.array([[[2.0 / 3.0]], [[2.0 / 3.0]]])**2)
def test_initialize_2d_covariance():
cov = covariance_initialization(np.array([[0, 0], [3, 4]]), 1)
assert_equal(len(cov), 1)
assert_array_almost_equal(cov, np.array([[[9.0, 0.0], [0.0, 16.0]]]))
def test_initialize_one_covariance():
cov = covariance_initialization(np.array([[0], [1]]), 1)
assert_equal(len(cov), 1)
assert_array_almost_equal(cov, np.array([[[1.0]]]))