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',