def ppm_experiment_1d(seed, sigma, L, b, Ncopy, use_signal_prior):
np.random.seed(seed)
n_iter = 1000
tol = 1e-7
# Generate MRA measurements
y, s, n, x = make_data_1d(L, Ncopy, sigma, b)
if use_signal_prior:
b_prior = b
else:
b_prior = None
t = time()
y_s, s = synchronize_1d(y, method='ppm')
x_init = np.expand_dims(np.mean(y_s, axis=1), axis=-1)
t_ppm = time() - t
err_ppm = relative_error_1d(x_init, x)
results = pd.DataFrame()
results = results.append(
{
'use_signal_prior': use_signal_prior,
'L': L,
'b': b,
'sigma': sigma,
'seed': seed,
'N': Ncopy,
'err': err_ppm,
'num_iter': 0,
't': t_ppm
},
ignore_index=True)
return results
def ppm_synch_em_experiment(seed, sigma, L, b, Ncopy, use_signal_prior):
np.random.seed(seed)
n_iter = 1000
tol = 1e-7
# Generate MRA measurements
y, s, n, x = make_data_1d(L, Ncopy, sigma, b)
if use_signal_prior:
b_prior = b
else:
b_prior = None
y_s, s_est = synchronize_1d(y, method='ppm')
x_init = np.expand_dims(np.mean(y_s, axis=1), axis=-1)
print('start em processing')
t = time()
x_est_em, rho_est_em, num_iter_em_uniform = em_1d(y_s,
sigma,
n_iter,
tol,
x_init,
b=b_prior,
rho_prior=None,
uniform=False)
t_em_uniform = time() - t
err_em_uniform = relative_error_1d(x_est_em, x)
h, bin_edges = np.histogram((s_est - s) % L, bins=np.arange(-0.5, L + 0.5))
measured_rho = h / np.sum(h)
results = pd.DataFrame()
results = results.append(
{
'use_signal_prior': use_signal_prior,
'L': L,
'b': b,
'sigma': sigma,
'seed': seed,
'N': Ncopy,
'err': err_em_uniform,
'num_iter': num_iter_em_uniform,
't': t_em_uniform
},
ignore_index=True)
return results
def synchronize_and_match_em_experiment(seed, sigma, L, b, Ncopy,
use_signal_prior, P):
np.random.seed(seed)
n_iter = 1000
tol = 1e-7
# Generate MRA measurements
y, s, n, x = make_data_1d(L, Ncopy, sigma, b)
if use_signal_prior:
b_prior = b
else:
b_prior = None
y_s, s, _, _ = synchronize_and_match_1d(y, P)
x_init = np.expand_dims(np.mean(y_s, axis=1), axis=-1)
print('start em processing')
t = time()
x_est_em, rho_est_em, num_iter_em_uniform = em_1d(y_s,
sigma,
n_iter,
tol,
x_init,
b=b_prior,
rho_prior=None,
uniform=False)
t_em_uniform = time() - t
err_em_uniform = relative_error_1d(x_est_em, x)
results = pd.DataFrame()
results = results.append(
{
'use_signal_prior': use_signal_prior,
'L': L,
'b': b,
'sigma': sigma,
'seed': seed,
'N': Ncopy,
'err': err_em_uniform,
'num_iter': num_iter_em_uniform,
't': t_em_uniform
},
ignore_index=True)
return results
def synch_em_1d_experiment(seed, sigma, L, b, Ncopy, use_signal_prior, P,
gamma):
np.random.seed(seed)
n_iter = 1000
tol = 1e-7
# Generate MRA measurements
y, s, n, x = make_data_1d(L, Ncopy, sigma, b)
if use_signal_prior:
b_prior = b
else:
b_prior = None
t = time()
y_s, s_est, _, _ = synchronize_and_match_1d(y, P)
#y_s, s_est = synchronize_1d(y,method='ppm')
t_synch = time() - t
x_init = np.expand_dims(np.mean(y_s, axis=1), axis=-1)
h, bin_edges = np.histogram((s_est - s) % L, bins=np.arange(-0.5, L + 0.5))
measured_rho = h / np.sum(h)
print('start synch em 1d processing')
t = time()
x_est_em, rho_est_em, num_iter_em_uniform = em_1d(y_s,
sigma,
n_iter,
tol,
x_init,
b=b_prior,
rho_prior=(measured_rho,
gamma),
uniform=False)
t_em_uniform = time() - t
err_em_uniform = relative_error_1d(x_est_em, x)
plt.plot(measured_rho)
plt.xlabel('shift')
plt.ylabel('Probability')
plt.savefig(
'est_dist/rho_est_L_%d_sigma_%.2f_N_%d_b_%d_seed_%d_SNR_%.2f.png' %
(L, sigma, Ncopy, b, seed, 1 / sigma**2))
plt.clf()
results = pd.DataFrame()
results = results.append(
{
'use_signal_prior': use_signal_prior,
'L': L,
'b': b,
'sigma': sigma,
'seed': seed,
'N': Ncopy,
'err': err_em_uniform,
'num_iter': num_iter_em_uniform,
't': t_synch + t_em_uniform
},
ignore_index=True)
return results