dls_funct.py 文件源码

def timing_experiments(TCC_dist, TCC_dls_dist, num_cells, distribution_flname, distance_flname):
    num_processes=1
    distance_time=[]
    distance_time_dls=[]
    for num in num_cells:  
        TCC_dist_short= TCC_dist[0:num, :] 
        TCC_dls_dist_short= TCC_dls_dist[0:num, :]
        dist_flname='timing_exp/'+ distribution_flname + str(num) +'.dat'
        dist_dls_flname= 'timing_exp/'+ distribution_flname + '_dls_' + str(num) +'.dat'
        distan_flname='timing_exp/'+ distance_flname + str(num) +'.dat'
        distan_dls_flname= 'timing_exp/'+ distance_flname + '_dls_' + str(num) +'.dat'
        with open(dist_flname , 'wb') as outfile:
            pickle.dump(scipy.sparse.csr_matrix(TCC_dist_short.todense()), outfile, pickle.HIGHEST_PROTOCOL)
        with open(dist_dls_flname , 'wb') as outfile:
            pickle.dump(scipy.sparse.csr_matrix(TCC_dls_dist_short.todense()), outfile, pickle.HIGHEST_PROTOCOL)
        t=time()
        os.system('python get_pairwise_distances.py '+dist_flname +' '+distan_flname+' '+str(num_processes))
        distance_time.append( time() - t )
        t=time()
        os.system('python get_pairwise_distances.py '+dist_dls_flname +' '+distan_dls_flname+' '+str(num_processes))
        distance_time_dls.append( time() - t)
    return(distance_time, distance_time_dls)