def test_learn_codes():
"""Test learning of codes."""
thresh = 0.25
X, ds, z = simulate_data(n_trials, n_times, n_times_atom, n_atoms)
for solver in ('l-bfgs', 'ista', 'fista'):
z_hat = update_z(X,
ds,
reg,
solver=solver,
solver_kwargs=dict(factr=1e11, max_iter=50))
X_hat = construct_X(z_hat, ds)
assert np.corrcoef(X.ravel(), X_hat.ravel())[1, 1] > 0.99
assert np.max(X - X_hat) < 0.1
# Find position of non-zero entries
idx = np.ravel_multi_index(z[0].nonzero(), z[0].shape)
loc_x, loc_y = np.where(z_hat[0] > thresh)
# shift position by half the length of atom
idx_hat = np.ravel_multi_index((loc_x, loc_y), z_hat[0].shape)
# make sure that the positions are a subset of the positions
# in the original z
mask = np.in1d(idx_hat, idx)
assert np.sum(mask) == len(mask)
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)