def run_all_subclone(args):
time_start = time.time()
ll_lst = []
subclone_num_lst = []
for subclone_num in range(1, 6):
# run the joint model
output_filename_base_k = args.output_filename_base + '_subclone_num_' + \
str(subclone_num)
joint_model = JointProbabilisticModel(args.max_copynumber,
subclone_num,
args.baseline_thred)
joint_model.read_data(args.input_filename_base)
joint_model.preprocess()
joint_model.run(args.max_iters, args.stop_value)
joint_model.write_results(output_filename_base_k)
ll_lst.append(joint_model.trainer.ll)
subclone_num_lst.append(subclone_num)
time_end = time.time()
run_time = time_end - time_start
get_summary(ll_lst, subclone_num_lst, run_time, args.output_filename_base)
def run_all_subclone(args):
time_start = time.time()
ll_lst = []
subclone_num_lst = []
for subclone_num in range(1, 6):
# run the joint model
output_filename_base_k = args.output_filename_base + '_subclone_num_' + \
str(subclone_num)
joint_model = JointProbabilisticModel(args.max_copynumber, subclone_num,
args.baseline_thred)
joint_model.read_data(args.input_filename_base)
joint_model.preprocess()
joint_model.run(args.max_iters, args.stop_value)
joint_model.write_results(output_filename_base_k)
ll_lst.append(joint_model.trainer.ll)
subclone_num_lst.append(subclone_num)
time_end = time.time()
run_time = time_end - time_start
get_summary(ll_lst, subclone_num_lst, run_time, args.output_filename_base)
def run_one_subclone(args):
time_start = time.time()
# run the joint model
joint_model = JointProbabilisticModel(args.max_copynumber, args.subclone_num, \
args.baseline_thred)
joint_model.read_data(args.input_filename_base)
joint_model.preprocess()
joint_model.run(args.max_iters, args.stop_value)
joint_model.write_results(args.output_filename_base)
time_end = time.time()
print "*" * 100
print "* Finish."
print "* Run time : {0:.2f} seconds".format(time_end - time_start)
print "* Optimum log-likelihood : ", joint_model.trainer.ll
print "*" * 100
sys.stdout.flush()
def run_one_subclone(args):
time_start = time.time()
# run the joint model
joint_model = JointProbabilisticModel(args.max_copynumber, args.subclone_num, \
args.baseline_thred)
joint_model.read_data(args.input_filename_base)
joint_model.preprocess()
joint_model.run(args.max_iters, args.stop_value)
joint_model.write_results(args.output_filename_base)
time_end = time.time()
print "*" * 100
print "* Finish."
print "* Run time : {0:.2f} seconds".format(time_end - time_start)
print "* Optimum log-likelihood : ", joint_model.trainer.ll
print "*" * 100
sys.stdout.flush()
