def merge(stats):
"""
.. todo :: implement merge function for bootstrap data that calculates EIC etc.
"""
result = StatCollector()
for s in stats:
pass
pass
if "eval_bootstrap_repetitions" in parameters:
eval_bootstrap_repetitions = parameters["eval_bootstrap_repetitions"]
if "eval_trials" in parameters:
eval_trials = parameters["eval_trials"]
identifier = str(trial_number)+"_"+str(condition)
all_data = ni.data.monkey.Data(trial_number).condition(condition)
data = all_data.trial(range(int(all_data.nr_trials/2)))
test_data = all_data.trial(range(int(all_data.nr_trials/2),all_data.nr_trials))
use_cells = range(all_data.nr_cells)
model_cells = [0]#range(all_data.nr_cells)
prototypes = StatCollector()
stats = StatCollector()
path = _current_session.path
job_path = _current_job.path
def to_path(l):
return "/".join([str(c) for c in sorted(l)])
results_titles = {
'llf_test_model':'Loglikelihood on test data',
'EIC':'negative EIC',
'AIC':'negative AIC',
'llf_boot':'Loglikelihood on bootstrap data',
'llf_train':'Bootmodel loglikelihood on training data',
'llf_test':'Bootmodel loglikelihood on test data',
'llf_train_model':'Model loglikelihood on training data',
'EICE_bias':'EICE_bias',
condition = parameters["condition"]
if "eval_bootstrap_repetitions" in parameters:
eval_bootstrap_repetitions = parameters["eval_bootstrap_repetitions"]
if "eval_trials" in parameters:
eval_trials = parameters["eval_trials"]
identifier = str(trial_number)+"_"+str(condition)
all_data = ni.data.monkey.Data(trial_number).condition(condition)
data = all_data.trial(range(int(all_data.nr_trials/2)))
test_data = all_data.trial(range(int(all_data.nr_trials/2),all_data.nr_trials))
use_cells = range(all_data.nr_cells)
model_cells = range(all_data.nr_cells)
prototypes = StatCollector()
stats = StatCollector()
path = _current_session.path
job_path = _current_job.path
def to_path(l):
return "/".join([str(c) for c in sorted(l)])
results_titles = {
'llf_test_model':'Loglikelihood on test data',
'EIC':'negative EIC',
'AIC':'negative AIC',
'llf_boot':'Loglikelihood on bootstrap data',
'llf_train':'Bootmodel loglikelihood on training data',
'llf_test':'Bootmodel loglikelihood on test data',
'llf_train_model':'Model loglikelihood on training data',
