def run_dsm(input_option, args):
######## preprocessing traces & trace sampling ###########
input_sampler.select_traces(input_option.raw_input_trace_file,
input_option.cluster_trace_file)
######## train RNNLM model ########
if not os.path.isdir(input_option.save_dir):
os.makedirs(input_option.save_dir)
p = multiprocessing.Process(target=RNNLM_training.train, args=(args, ))
p.start()
p.join()
# RNNLM_training.train(input_option.args)
######## feature extraction ########
feature_extractor.feature_engineering(input_option)
######## clustering ########
clustering_processing.clustering_step(input_option)
######## model selection #######
final_file = model_selection.selecting_model(input_option)
print("Done! Final FSM is stored in", final_file)
if not os.path.isdir(input_option.args.save_dir) or (
input_option.args.additional_trace is not None
and input_option.args.init_from is not None):
if not os.path.isdir(input_option.args.save_dir):
os.makedirs(input_option.args.save_dir)
p = multiprocessing.Process(target=RNNLM_training.train,
args=(input_option.args, ))
p.start()
p.join()
#RNNLM_training.train(input_option.args)s
######## feature extraction ########
feature_extractor.feature_engineering(input_option)
######## clustering ########
clustering_processing.clustering_step(input_option)
######## model selection #######
final_file = model_selection.selecting_model(input_option)
print("Done! Final FSM is stored in", final_file)
######## merge two automata ######
if input_option.update_mode:
model_updater.update(input_option)