functions_name = ['BETA', 'NM', 'SMLA', 'SMLB']
if __name__ == '__main__':
for s in size:
for i, fun in enumerate(functions):
for j, tau in enumerate(taus):
generator = LSW(pdf=fun, sample_size=s, seed=123)
results = {'selected_param': [], 'oracle_param': [], 'oracle_loss': [], 'loss': [], 'solution': [],
'oracle_solution': []}
for _ in range(replications):
try:
spectrum = LordWillisSpektor(transformed_measure=True)
obs = generator.generate()
tsvd = TSVD(kernel=kernel, singular_values=spectrum.singular_values,
left_singular_functions=spectrum.left_functions,
right_singular_functions=spectrum.right_functions,
observations=obs, sample_size=s, transformed_measure=True,
max_size=max_size, njobs=-1, tau=tau)
tsvd.estimate()
tsvd.oracle(fun)
solution = list(tsvd.solution(np.linspace(0, 1, 10000)))
results['selected_param'].append(tsvd.regularization_param)
results['oracle_param'].append(tsvd.oracle_param)
results['oracle_loss'].append(tsvd.oracle_loss)
results['loss'].append(tsvd.residual)
results['solution'].append(solution)
results['oracle_solution'].append(list(tsvd.oracle_solution))
tsvd.client.close()
except:
pass
pd.DataFrame(results).to_csv('TSVD_{}_{}.csv'.format(functions_name[i], taus_name[j]))
spectrum = LordWillisSpektor(transformed_measure=True)
obs = generator.generate()
landweber = TSVD(
kernel=kernel_transformed,
singular_values=spectrum.singular_values,
left_singular_functions=spectrum.left_functions,
right_singular_functions=spectrum.right_functions,
observations=obs,
sample_size=s,
max_size=max_size,
tau=tau,
transformed_measure=True,
njobs=-1)
landweber.estimate()
landweber.oracle(fun, patience=10)
solution = list(
landweber.solution(np.linspace(0, 1, 10000)))
results['selected_param'].append(
landweber.regularization_param)
results['oracle_param'].append(landweber.oracle_param)
results['oracle_loss'].append(landweber.oracle_loss)
results['loss'].append(landweber.residual)
results['solution'].append(solution)
results['oracle_solution'].append(
list(landweber.oracle_solution))
landweber.client.close()
except:
pass
pd.DataFrame(results).to_csv('TSVD_{}_{}_tau_{}.csv'.format(
functions_name[i], s, taus_name[j]))