def gmrf(): global gmrf_ if not gmrf_: if not os.path.exists(gmrf_fname): tic("creating empty gmrf", experiment_name) gmrf_ = emptyValues(tt_graph()) else: tic("reading gmrf from %s"%gmrf_fname, experiment_name) gmrf_ = pickle.load(open(gmrf_fname,'r')) return gmrf_
if graph_type == 'simple': hmm_graph = model.createHMMGraphFromNetwork(net, mode_counts=traj_conv.modeCounts()) hmm_graph_one_mode = model.createHMMGraphFromNetwork(net, mode_counts=traj_conv_one_mode.modeCounts()) # hmm_graph = model.createHMMGraphFromNetwork(net, mode_counts=mode_counts) else: # Complex model not implemented assert False tt_graph = createTravelTimeGraph(hmm_graph, radius=2e-4) tt_graph.checkInvariants() tt_graph_one_mode = createTravelTimeGraph(hmm_graph_one_mode, radius=2e-4) tt_graph_one_mode.checkInvariants() gmrf = emptyValues(tt_graph) gmrf_one_mode = emptyValues(tt_graph) # Checkpoint: save the structures save_ttg_structure(tt_graph, experiment_name=experiment_name) # The TTG structure is required when loading the GMRF (and the GMRF estimators) # Make sure they are saved in all the directories save_ttg_structure(tt_graph_one_mode, experiment_name='{0}_one_mode'.format(experiment_name)) save_ttg_structure(tt_graph_one_mode, experiment_name='{0}_one_mode_indep'.format(experiment_name)) save_ttg_structure(tt_graph, experiment_name='{0}_indep'.format(experiment_name)) # Loading the learning data data_source = experiment_design['data_source']