def fit_amplitudes_and_variability(erp_model, model_type, n_gen, seed,
mean_data, cov_data, with_locations=False,
max_fun_eval=100000):
parameter_list = prepare_variability_parameter_list(erp_model, model_type,
n_gen)
parameter_list.insert(0,'amplitudes')
if with_locations is True:
parameter_list.insert(0,'locations and orientations')
description = 'Fit of generator locations, amplitudes and ' +\
'variability to mean and covariance'
else:
description = 'Fit of generator amplitudes and variability to ' +\
'mean and covariance'
print(description + '\n' + '-' * len(description))
start_time = time.time()
output = fit_variability_model(erp_model, parameter_list,
fit_to='mean and covariance',
fit_data=[mean_data, cov_data],
method='tnc', bounds= True,
max_fun_eval=max_fun_eval, disp=True)
elapsed_time = time.time() - start_time
print('* Elapsed time: ' + str(elapsed_time) + 's\n')
return fit_info(erp_model, description, output)
def fit_amplitudes(erp_model, n_gen, seed, mean_data, with_locations=False,
max_fun_eval=100000):
parameter_list = ['amplitudes']
if with_locations is True:
parameter_list.insert(0,'locations and orientations')
description = 'Fit of generator locations and amplitudes to mean'
else:
description = 'Fit of generator amplitudes to mean'
print(description + '\n' + '-' * len(description))
start_time = time.time()
output = fit_variability_model(erp_model,
parameter_list,
fit_to='mean', fit_data=mean_data,
method='tnc', bounds= True,
max_fun_eval=max_fun_eval,
disp=True)
elapsed_time = time.time() - start_time
print('* Elapsed time: ' + str(elapsed_time) + 's\n')
return fit_info(erp_model, description, output)
