def test_get_cost(solver, X, D_hat, requires_dicodile):
"""Test for valid values."""
with get_z_encoder_for(solver=solver,
X=X,
D_hat=D_hat,
n_atoms=N_ATOMS,
n_times_atom=N_TIMES_ATOM,
n_jobs=2) as z_encoder:
initial_cost = z_encoder.get_cost()
z_encoder.compute_z()
z_hat = z_encoder.get_z_hat()
final_cost = z_encoder.get_cost()
assert final_cost < initial_cost
X_hat = construct_X_multi(z_hat, D_hat, n_channels=N_CHANNELS)
cost = compute_objective(X=X,
X_hat=X_hat,
z_hat=z_hat,
reg=0.1,
D=D_hat)
assert np.isclose(cost, final_cost)
def test_fast_cost():
"""Test that _shifted_objective_uv compute the right thing"""
# Generate synchronous D
n_times_atom, n_times = 10, 40
n_channels = 3
n_atoms = 2
n_trials = 4
rng = np.random.RandomState()
X = rng.normal(size=(n_trials, n_channels, n_times))
z = rng.normal(size=(n_trials, n_atoms, n_times - n_times_atom + 1))
constants = _get_d_update_constants(X, z)
def objective(uv):
X_hat = construct_X_multi(z, D=uv, n_channels=n_channels)
res = X - X_hat
return .5 * np.sum(res * res)
for _ in range(5):
uv = rng.normal(size=(n_atoms, n_channels + n_times_atom))
cost_fast = compute_objective(D=uv, constants=constants)
cost_full = objective(uv)
assert np.isclose(cost_full, cost_fast)
def func(d0):
D0 = d0.reshape(n_atoms, n_channels, n_times_atom)
X_hat = construct_X_multi(z, D=D0)
return compute_objective(X, X_hat, loss=loss, loss_params=loss_params)
def objective(uv):
X_hat = construct_X_multi(z, D=uv, n_channels=n_channels)
return compute_objective(X, X_hat, loss='l2')
def func(uv0):
uv0 = uv0.reshape(n_atoms, n_channels + n_times_atom)
X_hat = construct_X_multi(z, D=uv0, n_channels=n_channels)
return compute_objective(X, X_hat, loss=loss, loss_params=loss_params)
