def test_mtl():
rho_noise = 0.3
SNR = 1
n_epochs, n_channels, n_sources, n_times = 5, 20, 10, 30
pb_name = "MTL"
tol = 1e-7
X, all_epochs, B_star, S_star = get_data_me(
dictionary_type="Gaussian", noise_type="Gaussian_iid",
n_epochs=n_epochs, n_channels=n_channels, n_times=n_times,
n_sources=n_sources, n_active=3, rho_noise=rho_noise,
SNR=SNR)
Y = np.mean(all_epochs, axis=0)
sigma_min = get_sigma_min(Y)
alpha_max = get_alpha_max(X, Y, sigma_min, pb_name=pb_name)
alpha_div = 5
alpha = alpha_max / alpha_div
B_mtl, S_inv, E, gaps = solver(
X, Y, alpha, alpha_max, sigma_min, B0=None,
tol=tol, pb_name=pb_name, n_iter=10000)
gap = gaps[-1]
np.testing.assert_array_less(gap, tol)
_, _, E, gaps = solver(
X, Y, alpha, alpha_max, sigma_min, B0=B_mtl,
tol=tol, pb_name=pb_name, n_iter=10000)
np.testing.assert_equal(len(E), 2)
gap = gaps[-1]
np.testing.assert_array_less(gap, tol * E[0])
def test_mtl_me():
rho_noise = 0.3
SNR = 1
n_epochs, n_channels, n_sources, n_times = 5, 20, 10, 30
pb_name = "MTLME"
tol = 1e-7
X, all_epochs, _, _ = get_data_me(dictionary_type="Gaussian",
noise_type="Gaussian_iid",
n_epochs=n_epochs,
n_channels=n_channels,
n_times=n_times,
n_sources=n_sources,
n_active=3,
rho_noise=rho_noise,
SNR=SNR)
Y = np.mean(all_epochs, axis=0)
sigma_min = get_sigma_min(Y)
alpha_max = get_alpha_max(X, all_epochs, sigma_min, pb_name=pb_name)
alpha_div = 1.1
alpha = alpha_max / alpha_div
gap = solver(X,
all_epochs,
alpha,
sigma_min,
B0=None,
tol=tol,
pb_name=pb_name,
n_iter=10000)[-1]
np.testing.assert_array_less(gap, tol)
def test_update_sigma_glasso():
rho_noise = 0.8
SNR = 1
n_epochs, n_channels, n_sources, n_times = 5, 20, 10, 30
X, all_epochs, B_star, (_, S_star) = get_data_me(
dictionary_type="Gaussian",
noise_type="Gaussian_multivariate",
n_epochs=n_epochs,
n_channels=n_channels,
n_times=n_times,
n_sources=n_sources,
n_active=3,
rho_noise=rho_noise,
SNR=SNR)
emp_cov = np.zeros((n_channels, n_channels))
for i in range(n_epochs):
emp_cov += all_epochs[i, :, :] @ all_epochs[i, :, :].T
emp_cov /= (n_times * n_epochs)
alpha_prec = 0.001
covariance, precision = update_sigma_glasso(emp_cov,
alpha_prec,
cov_init=None,
mode='cd',
tol=1e-4,
enet_tol=1e-4,
max_iter=100,
verbose=False,
return_costs=False,
eps=np.finfo(np.float64).eps,
return_n_iter=False)
def tests_sgcl(dictionary_type="Gaussian",
noise_type="Gaussian_iid",
rho_noise=0.3,
SNR=0.5,
n_channels=20,
n_times=30,
n_sources=10,
n_active=3,
gap_freq=100,
active_set_freq=1,
S_freq=10,
n_iter=10**6,
p_alpha_max=0.9,
tol=10**-4):
X, all_epochs, _, _ = get_data_me(dictionary_type="Gaussian",
noise_type=noise_type,
n_channels=n_channels,
n_times=n_times,
n_sources=n_sources,
n_active=n_active,
rho_noise=rho_noise,
SNR=SNR,
n_epochs=50)
Y = all_epochs.mean(axis=0)
sigma_min = get_sigma_min(Y)
alpha_max = get_alpha_max(X, Y, sigma_min, "SGCL")
alpha = alpha_max * p_alpha_max
print("alpha = %.2e" % alpha)
print("sigma_min = %.2e" % sigma_min)
all_epochs = np.zeros((1, *Y.shape))
all_epochs[0] = Y
B_sgcl, S_inv_sgcl, E, (gaps, gaps_accel) = solver(
X,
Y,
alpha,
alpha_max,
sigma_min,
B0=None,
n_iter=n_iter,
gap_freq=gap_freq,
active_set_freq=active_set_freq,
S_freq=S_freq,
pb_name="SGCL",
use_accel=True,
tol=tol,
verbose=True)
log_gap = np.log10(gaps[-1])
log_gap_accel = np.log10(gaps_accel[-1])
assert log_gap_accel < np.log10(tol) * E[0] or \
log_gap < np.log10(tol) * E[0]
def test_clar(noise_type="Gaussian_iid",
rho_noise=0.3,
SNR=0.5,
n_channels=20,
n_times=30,
n_sources=10,
n_epochs=50,
n_active=3,
gap_freq=50,
active_set_freq=1,
S_freq=10,
n_iter=10**4,
alpha_under_alpha_max=0.2,
tol=1e-7):
X, all_epochs, _, _ = get_data_me(dictionary_type="Gaussian",
noise_type=noise_type,
n_epochs=n_epochs,
n_channels=n_channels,
n_times=n_times,
n_sources=n_sources,
n_active=n_active,
rho_noise=rho_noise,
SNR=SNR)
Y = np.mean(all_epochs, axis=0)
sigma_min = get_sigma_min(Y)
alpha_max = get_alpha_max(X, all_epochs, sigma_min, pb_name="CLAR")
alpha = alpha_max * \
alpha_under_alpha_max
print("alpha = %.2e" % alpha)
print("alpha = %.2e" % alpha)
print("sigma_min = %.2e" % sigma_min)
gaps_me = solver(X,
all_epochs,
alpha,
sigma_min,
B0=None,
n_iter=n_iter,
gap_freq=gap_freq,
active_set_freq=active_set_freq,
S_freq=S_freq,
tol=tol,
pb_name="CLAR")[-1]
gap_me = gaps_me[-1]
assert gap_me < tol
def test_mrce():
rho_noise = 0.6
SNR = 1
n_epochs, n_channels, n_sources, n_times = 5, 20, 10, 30
pb_name = "mrce"
tol = 1e-4
X, all_epochs, B_star, (_, S_star) = get_data_me(
dictionary_type="Gaussian",
noise_type="Gaussian_multivariate",
n_epochs=n_epochs,
n_channels=n_channels,
n_times=n_times,
n_sources=n_sources,
n_active=3,
rho_noise=rho_noise,
SNR=SNR)
alpha_Sigma_inv = 0.01
Y = np.mean(all_epochs, axis=0)
sigma_min = get_sigma_min(Y)
alpha_max = get_alpha_max(X,
all_epochs,
sigma_min,
pb_name=pb_name,
alpha_Sigma_inv=alpha_Sigma_inv)
alpha = alpha_max * 0.9
B_mrce, (Sigma,
Sigma_inv), E, gaps = solver(X,
all_epochs,
alpha,
alpha_max,
sigma_min,
B0=None,
tol=tol,
pb_name=pb_name,
n_iter=1000,
alpha_Sigma_inv=alpha_Sigma_inv)
def test_update_sigma_glasso():
rho_noise = 0.8
SNR = 1
n_epochs, n_channels, n_sources, n_times = 5, 20, 10, 30
all_epochs = get_data_me(dictionary_type="Gaussian",
noise_type="Gaussian_multivariate",
n_epochs=n_epochs,
n_channels=n_channels,
n_times=n_times,
n_sources=n_sources,
n_active=3,
rho_noise=rho_noise,
SNR=SNR)[1]
emp_cov = np.zeros((n_channels, n_channels))
for i in range(n_epochs):
emp_cov += all_epochs[i, :, :] @ all_epochs[i, :, :].T
emp_cov /= (n_times * n_epochs)
alpha_prec = 0.001
update_sigma_glasso(emp_cov, alpha_prec, enet_tol=1e-4, max_iter=100)
SNR = 0.5
n_channels = 20
n_times = 30
n_sources = 10
n_epochs = 50
n_active = 3
gap_freq = 50
update_S_freq = 10
n_iter = 10**4
tol = 1e-7
X, all_epochs, B_star, S_star = get_data_me(dictionary_type="Gaussian",
noise_type="Gaussian_multivariate",
n_epochs=n_epochs,
n_channels=n_channels,
n_times=n_times,
n_sources=n_sources,
n_active=n_active,
rho_noise=rho_noise,
SNR=SNR)
S_star = S_star[-1]
Y = np.mean(all_epochs, axis=0)
sigma_min = get_sigma_min(Y)
alpha_max = get_alpha_max_me(X, all_epochs, sigma_min)
alpha = alpha_max / 5
print("alpha = %.2e" % alpha)
print("sigma_min = %.2e" % sigma_min)
B_clar, S_inv, E, gaps_me = solver(X,
