Пример #1
0
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)
Пример #2
0
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)
Пример #3
0
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()
Пример #4
0
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()