PE.table_CRR(train_features, train_classes, test_features, test_classes) PE.performance_evaluation(train_features, train_classes, test_features, test_classes) #thresholds_2=[0.74,0.76,0.78] # this part is for bootsrap starttime = datetime.datetime.now() thresholds_3 = np.arange(0.6, 0.9, 0.02) times = 100 #running 100 times takes about 1 to 2 hours total_fmrs, total_fnmrs, crr_mean, crr_u, crr_l = IM.IrisMatchingBootstrap( train_features, train_classes, test_features, test_classes, times, thresholds_3) fmrs_mean, fmrs_l, fmrs_u, fnmrs_mean, fnmrs_l, fnmrs_u = IM.calcROCBootstrap( total_fmrs, total_fnmrs) endtime = datetime.datetime.now() print('Bootsrap takes' + str((endtime - starttime).seconds) + 'seconds') fmrs_mean *= 100 #use for percent(%) fmrs_l *= 100 fmrs_u *= 100 fnmrs_mean *= 100 fnmrs_l *= 100 fnmrs_u *= 100 PE.FM_FNM_table(fmrs_mean, fmrs_l, fmrs_u, fnmrs_mean, fnmrs_l, fnmrs_u, thresholds_3) PE.FMR_conf(fmrs_mean, fmrs_l, fmrs_u, fnmrs_mean, fnmrs_l, fnmrs_u) PE.FNMR_conf(fmrs_mean, fmrs_l, fmrs_u, fnmrs_mean, fnmrs_l, fnmrs_u)