def fit_collapsed_mixture_model(self):
'''
Fit the new Collapsed Mixture Model, using data created
just above in generate_data_subject_split.
One fit per subject, obtain parametric estimates of kappa.
'''
self.dataset['collapsed_em_fits_subjects'] = dict()
self.dataset['collapsed_em_fits'] = dict()
for subject, subject_data_dict in self.dataset['data_subject_split']['data_subject'].iteritems():
print 'Fitting Collapsed Mixture model for subject %d' % subject
# Bug here, fit is not using the good dimensionality for the number of Nontarget angles...
params_fit = em_circmixtmodel_parametric.fit(
self.dataset['data_subject_split']['nitems_space'],
subject_data_dict['responses'],
subject_data_dict['targets'],
subject_data_dict['nontargets'],
debug=False
)
self.dataset['collapsed_em_fits_subjects'][subject] = params_fit
## Now compute mean/std collapsed_em_fits
self.dataset['collapsed_em_fits']['mean'] = dict()
self.dataset['collapsed_em_fits']['std'] = dict()
self.dataset['collapsed_em_fits']['sem'] = dict()
self.dataset['collapsed_em_fits']['values'] = dict()
# Need to extract the values for a subject/nitems pair, for all keys of em_fits. Annoying dictionary indexing needed
emfits_keys = params_fit.keys()
for key in emfits_keys:
values_allsubjects = [self.dataset['collapsed_em_fits_subjects'][subject][key] for subject in self.dataset['data_subject_split']['subjects_space']]
self.dataset['collapsed_em_fits']['mean'][key] = np.mean(values_allsubjects, axis=0)
self.dataset['collapsed_em_fits']['std'][key] = np.std(values_allsubjects, axis=0)
self.dataset['collapsed_em_fits']['sem'][key] = self.dataset['collapsed_em_fits']['std'][key]/np.sqrt(self.dataset['data_subject_split']['subjects_space'].size)
self.dataset['collapsed_em_fits']['values'][key] = values_allsubjects
def fit_collapsed_mixture_model(self):
'''
Fit the new Collapsed Mixture Model, using data created
just above in generate_data_subject_split.
Do:
* One fit per subject/nitems, using trecall as T_space
* One fit per subject/trecall, using nitems as T_space
* One fit per subject, using the double-powerlaw on nitems/trecall
'''
Tmax = self.dataset['data_subject_split']['nitems_space'].max()
Tnum = self.dataset['data_subject_split']['nitems_space'].size
self.dataset['collapsed_em_fits_subjects_nitems'] = dict()
self.dataset['collapsed_em_fits_nitems'] = dict()
self.dataset['collapsed_em_fits_subjects_trecall'] = dict()
self.dataset['collapsed_em_fits_trecall'] = dict()
self.dataset['collapsed_em_fits_doublepowerlaw_subjects'] = dict()
self.dataset['collapsed_em_fits_doublepowerlaw'] = dict()
self.dataset['collapsed_em_fits_doublepowerlaw_array'] = np.nan*np.empty((Tnum, Tnum, 4))
for subject, subject_data_dict in self.dataset['data_subject_split']['data_subject'].iteritems():
print 'Fitting Collapsed Mixture model for subject %d' % subject
if True:
# Use trecall as T_space, bit weird
for n_items_i, n_items in enumerate(self.dataset['data_subject_split']['nitems_space']):
print '%d nitems, using trecall as T_space' % n_items
params_fit = em_circularmixture_parametrickappa.fit(np.arange(1, n_items+1), subject_data_dict['responses'][n_items_i, :(n_items)], subject_data_dict['targets'][n_items_i, :(n_items)], subject_data_dict['nontargets'][n_items_i, :(n_items), :, :(n_items - 1)], debug=False)
self.dataset['collapsed_em_fits_subjects_nitems'].setdefault(subject, dict())[n_items] = params_fit
# Use nitems as T_space, as a function of trecall (be careful)
for trecall_i, trecall in enumerate(self.dataset['data_subject_split']['nitems_space']):
print 'trecall %d, using n_items as T_space' % trecall
params_fit = em_circularmixture_parametrickappa.fit(np.arange(trecall, Tmax+1), subject_data_dict['responses'][trecall_i:, trecall_i], subject_data_dict['targets'][trecall_i:, trecall_i], subject_data_dict['nontargets'][trecall_i:, trecall_i], debug=False)
self.dataset['collapsed_em_fits_subjects_trecall'].setdefault(subject, dict())[trecall] = params_fit
# Now do the correct fit, with double powerlaw on nitems+trecall
print 'Double powerlaw fit'
params_fit_double = em_circularmixture_parametrickappa_doublepowerlaw.fit(self.dataset['data_subject_split']['nitems_space'], subject_data_dict['responses'], subject_data_dict['targets'], subject_data_dict['nontargets'], debug=False)
self.dataset['collapsed_em_fits_doublepowerlaw_subjects'][subject] = params_fit_double
if True:
## Now compute mean/std collapsed_em_fits_nitems
self.dataset['collapsed_em_fits_nitems']['mean'] = dict()
self.dataset['collapsed_em_fits_nitems']['std'] = dict()
self.dataset['collapsed_em_fits_nitems']['sem'] = dict()
self.dataset['collapsed_em_fits_nitems']['values'] = dict()
# Need to extract the values for a subject/nitems pair, for all keys of em_fits. Annoying dictionary indexing needed
emfits_keys = params_fit.keys()
for n_items_i, n_items in enumerate(self.dataset['data_subject_split']['nitems_space']):
for key in emfits_keys:
values_allsubjects = [self.dataset['collapsed_em_fits_subjects_nitems'][subject][n_items][key] for subject in self.dataset['data_subject_split']['subjects_space']]
self.dataset['collapsed_em_fits_nitems']['mean'].setdefault(n_items, dict())[key] = np.mean(values_allsubjects, axis=0)
self.dataset['collapsed_em_fits_nitems']['std'].setdefault(n_items, dict())[key] = np.std(values_allsubjects, axis=0)
self.dataset['collapsed_em_fits_nitems']['sem'].setdefault(n_items, dict())[key] = self.dataset['collapsed_em_fits_nitems']['std'][n_items][key]/np.sqrt(self.dataset['data_subject_split']['subjects_space'].size)
self.dataset['collapsed_em_fits_nitems']['values'].setdefault(n_items, dict())[key] = values_allsubjects
## Same for the other ones
self.dataset['collapsed_em_fits_trecall']['mean'] = dict()
self.dataset['collapsed_em_fits_trecall']['std'] = dict()
self.dataset['collapsed_em_fits_trecall']['sem'] = dict()
self.dataset['collapsed_em_fits_trecall']['values'] = dict()
# Need to extract the values for a subject/nitems pair, for all keys of em_fits. Annoying dictionary indexing needed
emfits_keys = params_fit.keys()
for trecall_i, trecall in enumerate(self.dataset['data_subject_split']['nitems_space']):
for key in emfits_keys:
values_allsubjects = [self.dataset['collapsed_em_fits_subjects_trecall'][subject][trecall][key] for subject in self.dataset['data_subject_split']['subjects_space']]
self.dataset['collapsed_em_fits_trecall']['mean'].setdefault(trecall, dict())[key] = np.mean(values_allsubjects, axis=0)
self.dataset['collapsed_em_fits_trecall']['std'].setdefault(trecall, dict())[key] = np.std(values_allsubjects, axis=0)
self.dataset['collapsed_em_fits_trecall']['sem'].setdefault(trecall, dict())[key] = self.dataset['collapsed_em_fits_trecall']['std'][trecall][key]/np.sqrt(self.dataset['data_subject_split']['subjects_space'].size)
self.dataset['collapsed_em_fits_trecall']['values'].setdefault(trecall, dict())[key] = values_allsubjects
# Collapsed full double powerlaw model across subjects
self.dataset['collapsed_em_fits_doublepowerlaw']['mean'] = dict()
self.dataset['collapsed_em_fits_doublepowerlaw']['std'] = dict()
self.dataset['collapsed_em_fits_doublepowerlaw']['sem'] = dict()
self.dataset['collapsed_em_fits_doublepowerlaw']['values'] = dict()
# Need to extract the values for a subject/nitems pair, for all keys of em_fits. Annoying dictionary indexing needed
emfits_keys = params_fit_double.keys()
for key in emfits_keys:
values_allsubjects = [self.dataset['collapsed_em_fits_doublepowerlaw_subjects'][subject][key] for subject in self.dataset['data_subject_split']['subjects_space']]
self.dataset['collapsed_em_fits_doublepowerlaw']['mean'][key] = np.mean(values_allsubjects, axis=0)
self.dataset['collapsed_em_fits_doublepowerlaw']['std'][key] = np.std(values_allsubjects, axis=0)
self.dataset['collapsed_em_fits_doublepowerlaw']['sem'][key] = self.dataset['collapsed_em_fits_doublepowerlaw']['std'][key]/np.sqrt(self.dataset['data_subject_split']['subjects_space'].size)
self.dataset['collapsed_em_fits_doublepowerlaw']['values'][key] = values_allsubjects
# Construct some easy arrays to compare the fit to the dataset
self.dataset['collapsed_em_fits_doublepowerlaw_array'][..., 0] = self.dataset['collapsed_em_fits_doublepowerlaw']['mean']['kappa']
self.dataset['collapsed_em_fits_doublepowerlaw_array'][..., 1] = self.dataset['collapsed_em_fits_doublepowerlaw']['mean']['mixt_target_tr']
self.dataset['collapsed_em_fits_doublepowerlaw_array'][..., 2] = self.dataset['collapsed_em_fits_doublepowerlaw']['mean']['mixt_nontargets_tr']
self.dataset['collapsed_em_fits_doublepowerlaw_array'][..., 3] = self.dataset['collapsed_em_fits_doublepowerlaw']['mean']['mixt_random_tr']