def test_learn_atoms():
"""Test learning of atoms."""
X, ds, z = simulate_data(n_trials, n_times, n_times_atom, n_atoms)
d_hat, _ = update_d(X, z, n_times_atom)
assert np.allclose(ds, d_hat)
X_hat = construct_X(z, d_hat)
assert np.allclose(X, X_hat, rtol=1e-05, atol=1e-12)
def test_update_d():
"""Test vanilla d update."""
rng = check_random_state(42)
X, ds, z = simulate_data(n_trials, n_times, n_times_atom, n_atoms)
ds_init = rng.randn(n_atoms, n_times_atom)
# This number of iteration is 1 in the general case, but needs to be
# increased to compare with update_d
n_iter_d_block = 5
# All solvers should give the same results
d_hat_0, _ = update_d(X, z, n_times_atom, lambd0=None, ds_init=ds_init)
d_hat_1, _ = update_d_block(X, z, n_times_atom, lambd0=None,
ds_init=ds_init, n_iter=n_iter_d_block)
assert np.allclose(d_hat_0, d_hat_1, rtol=1e-5)