def test_update_z_sample_weights():
"""Test z update with weights."""
rng = check_random_state(42)
X, ds, z = simulate_data(n_trials, n_times, n_times_atom, n_atoms)
b_hat_0 = rng.randn(n_atoms * (n_times - n_times_atom + 1))
# Having sample_weights all identical is equivalent to having
# sample_weights=None and a scaled regularization
factor = 1.6
sample_weights = np.ones_like(X) * factor
for solver in ('l_bfgs', 'ista', 'fista'):
z_0 = update_z(X,
ds,
reg * factor,
n_times_atom,
solver=solver,
solver_kwargs=dict(factr=1e7, max_iter=50),
b_hat_0=b_hat_0.copy(),
sample_weights=sample_weights)
z_1 = update_z(X,
ds,
reg,
n_times_atom,
solver=solver,
solver_kwargs=dict(factr=1e7, max_iter=50),
b_hat_0=b_hat_0.copy(),
sample_weights=None)
assert_allclose(z_0, z_1, rtol=1e-4)
# All solvers should give the same results
sample_weights = np.abs(rng.randn(*X.shape))
sample_weights /= sample_weights.mean()
z_list = []
for solver in ('l_bfgs', 'ista', 'fista'):
z_hat = update_z(X,
ds,
reg,
n_times_atom,
solver=solver,
solver_kwargs=dict(factr=1e7, max_iter=2000),
b_hat_0=b_hat_0.copy(),
sample_weights=sample_weights)
z_list.append(z_hat)
assert_allclose(z_list[0][z != 0], z_list[1][z != 0], rtol=1e-3)
assert_allclose(z_list[0][z != 0], z_list[2][z != 0], rtol=1e-3)
# And using no sample weights should give different results
z_hat = update_z(X,
ds,
reg,
n_times_atom,
solver=solver,
solver_kwargs=dict(factr=1e7, max_iter=2000),
b_hat_0=b_hat_0.copy(),
sample_weights=None)
assert_raises(AssertionError, assert_allclose, z_list[0][z != 0],
z_hat[z != 0], 1e-3)
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_z0_read_only():
# If n_atoms == 1, the reshape in update_z does not copy the data (cf #26)
n_atoms = 1
X, ds, z = simulate_data(n_trials, n_times, n_times_atom, n_atoms)
z.flags.writeable = False
update_z(X, ds, 0.1, z0=z, solver='ista')