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']