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I$ HIPHHރH~H9HHAH@AH@A H@ AH@AH@vAH@vAH@A H @ bA$H @$DA(H @(A,H @,A0H @0;A4H@4sA8@8rf.IctH9b|HWHLHHރL9IOH)HWHHIHHH~1HH9|H9LJH~I)II@HL9}D)HuLI95H<@MI)L_L$9JJ4I9HH9@@KIAHIxHރH)H^H9HHH<HZJD9HD8HZ0D9HD8HZD9 HD8 HZD9HD8HZD9HD8HZD9HD8HZD9HD8HZD9 H D8 HZ t~D9$H D8$HZ thD9(H D8(HZ tRD9,H D8,HZ t<D90H D80HZ t&D94HD84HZtD98HZD88IH@M9wLHHL9tfDHH9|HHHEbu(~HP HL`~)@"H@"HsHSH{ )P)ehHE1H`HPHMHuboEHDž(?H0HX)HhHUHpHH ZH@Ht HxbHHHt HxbHHHf \IIB11'1 I@ fHwHHt HxaHj`HH@HtJHxwaHHHtHxkaHHHt(HzwNawfrfwHHwf.LT$HArUHAWAVAUATAARSMLHABMbI:Mz IB(H(HdH%(HE1WLH]LH]H(IcIHHWLHT]HHE]Lc HIHM AHHIH9J HIHH1E11H;}HL1HHH)HVHL0HuEELL}HEHEHcƅ0HDž8HDž@HDžHHDžPHDžXHEHDž`HDžhHDžpEHHEHHEacMHHH`L E1LXLPH(KHH fLcHc@A9L)IE1L$HN4L@KcMāz H HILHH9}>H(H)LI41IHLH9HquIM9uH LH H;2]HP]HXHt _H`Ht_HQHHS@H9HP@H9HHHHHOHHIHWHHNHqH9HHCH@CH@C H@ |CH@|CH@|CH@^CH@mC H @ mC$H @$OC(H @(cC,H @,EC0H @0'C4H@4RC8@8I)H1ML H4I1b|H( Hb|HH@I9wLHHL9t!HHH9|HHH9~QTH{ HMLMHULE0SHHLEHUHLMHFI<$HN HHMHHEffDHx HMHULERHtRHHMHUHLELn IEI|$yI|$IDHPUiOUUHIv HHu wL|YHxw0Pf.@AWAVIAUATIUSHHHOHHI)H)HHH4IH9LH$LH]HMMH$III)Mt zI9t LHLHL$L$jVHL$L$IODM)L9tLLLsIIMHtHKLmLuL}H[]A\A]A^A_DL<FfDH?H9-HuME1E1>fHH7HWOtAHtHH)H)H9HOHI9}hH L)1IMHI>HHHD/HHH9HHH*H?HHH)HRHIHD$(dH3%(7H8[]A\A]A^A_DHH?H>HH)H)H KI.LHI9IMH9HLH/~IH%I$I$IHHHH?HII)II9HHL$1H$IIIHD$LHHH)H;<$HmN 1IH|$H9ILNIM9LHIHtLH H)HHHHI)L+f.H=d!VL%+d!hH1I$ID$ID$HH߁GenuAuthjcAMDuentizfD$E11E1A.fDA-A#DDHHAA AuAAA AAAAEA΃xAHHH*HHHH?HH)HRHH9HOIWI9GMN$I>LHK41HHH9HNH HH*H?HHH)H RHHHHHt&HHt@H)HHHHHH)H I@IH1IDHFHD$AH$AAXHIHJMYMI)LLHHI1HI)LNMHH$IHD$MeI.Lf.AMDi4ter!sbet1HHω HHD  AAHcMcHHNMI<$H=[a!LNMɸ M|$LNML$<@HL 1HIIH$IAHD$DLN1HMMH$IHD$Lc $?E11E1ntel$|ineIpADAtADDHHAA AuAAA AAAAEDAɃuA $H fIHu HH@@LHL $+EgHHމt$L$Ht$ T$H HT$D$E1E11E1@ ׉PT}I.wB;A|3.wJ.ʋVwA9=GWF@BO GJAWGQF;A0WGVfDFHBSHH?HHH9}I?.EIwERE9~Ef.(LIIH9{EHI}AMHK IILX{ MII{.vEJfDHDuHII?LHH9IIIHPAII?IIH9?d@.wRAPA9~IHHPIAHH?HLHL9}*IMII{.vAP@HL{AH[DHTHHIIIEH{DHHBHH/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/cmdparser.cppUsage (same as LibSVM): thundersvm [options] training_set_file [model_file] options: -s svm_type: set type of SVM (default 0) 0 -- C-SVC (multi-class classification) 1 -- nu-SVC (multi-class classification) 2 -- one-class SVM 3 -- epsilon-SVR (regression) 4 -- nu-SVR (regression) -t kernel_type: set type of kernel function (default 2) 0 -- linear: u'*v 1 -- polynomial: (gamma*u'*v + coef0)^degree 2 -- radial basis function: exp(-gamma*|u-v|^2) 3 -- sigmoid: tanh(gamma*u'*v + coef0) -d degree: set degree in kernel function (default 3) -g gamma: set gamma in kernel function (default 1/num_features) -r coef0: set coef0 in kernel function (default 0) -c cost: set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu: set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon: set the epsilon in loss function of epsilon-SVR (default 0.1) -m memory size: constrain the maximum memory size (MB) that thundersvm uses (default 8192) -e epsilon: set tolerance of termination criterion (default 0.001) -b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0) -wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1) -v n: n-fold cross validation mode -u n: specify which gpu to use (default 0) -o n: set the number of cpu cores to use, -1 for maximum(default -1) -q: quiet modeUsage: svm-predict [options] test_file model_file output_file options: -m memory size: constrain the maximum memory size (MB) that thundersvm uses -u n: specify which gpu to use (default 0) degree of polynomial kernel < 0probability != 0 and probability != 1one-class SVM and SVR probability output not supported yetbasic_string::_M_construct null not valid%s: __pos (which is %zu) > this->size() (which is %zu)n-fold cross validation: n must >= 2the number of cpu cores must be positive or -1n-fold cross validation: n must >= 2 defaultunknown svm typeunknown kernel typegamma < 0epsilon <= 0C <= 0nu <= 0 or nu > 1p < 0max memory size <= 0/basic_string::substrthundersvm-trainthundersvm-train.exethundersvm-predictshrinking is not supportedUnknown option: basic_string::append.modelthundersvm-predict.exeUnknown option: -%c """"#p ####`h8X6866575X4777743843h3H3(33221bool CMDParser::check_parameter()void CMDParser::parse_python(int, char**)void CMDParser::parse_command_line(int, char**)void HelpInfo_svmpredict()void HelpInfo_svmtrain()N2el4base6NoCopyEN2el4base6WriterE?vector::_M_range_insert%d:%fCheck failed: [] #instances = , #features = loading dataset from file " not found/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/dataset.cppsscanf(tuple.c_str(), "%d:%f", &i, &v) == 2read error, using [index]:[value] formatsscanf(tuple.c_str(), "%d:%f", &ind, &v) == 2void DataSet::load_from_dense(int, int, float*, float*)void DataSet::load_from_sparse(int, float*, int*, int*, float*)void DataSet::load_from_python(float*, char**, int)void DataSet::load_from_file(std::__cxx11::string)< void KernelMatrix::dns_dns_mul(const SyncArray&, int, const SyncArray&, SyncArray&) constvoid KernelMatrix::dns_csr_mul(const SyncArray&, int, SyncArray&) constvoid KernelMatrix::get_rows(const node2d&, SyncArray&) constvoid KernelMatrix::get_rows(const SyncArray&, SyncArray&) const /home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/kernelmatrix.cppdense_mat.size() == n_rows * n_features_dense matrix features doesn't matchkernel_rows.size() >= idx.size() * n_instances_kernel_rows memory is too smallkernel_rows.size() >= instances.size() * n_instances_N2el8LoggableE9SyncArrayIfE9SyncArrayIiE?/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/model/nusvc.cpprho = #sv = virtual void NuSVC::train_binary(const DataSet&, int, int, SyncArray&, float_type&)    5NuSVC?5NuSVR/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/model/oneclass_svc.cppvirtual void OneClassSVC::train(const DataSet&, SvmParam)????????11OneClassSVCExceeds max_iter in multiclass_probLine search fails in two-class probability estimatesReaching maximal iterations in two-class probability estimates/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/model/svc.cpporiginal_index.size() == alpha[k].size()#classes = weighted label #total unique sv = performing probability trainvector::_M_default_appendpredict with probabilitystd::vector SVC::predict_label(const SyncArray&, int)virtual void SVC::train_binary(const DataSet&, int, int, SyncArray&, float_type&)virtual void SVC::train(const DataSet&, SvmParam)virtual void SVC::model_setup(const DataSet&, SvmParam&)???????? @@@@@@@@$$$$$$$$    9SyncArrayIdE3SVC{Gzt?-q=@h㈵>-C6?|=MbP?z>P?Hz>???[[[[P[int SvmModel::get_working_set_size(int, int)virtual void SvmModel::load_from_file(std::__cxx11::string)virtual void SvmModel::save_to_file(std::__cxx11::string)virtual std::vector SvmModel::cross_validation(DataSet, SvmParam, int)/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/model/svmmodel.cppsscanf(tuple.c_str(), "%d:%f", &sv.back().back().index, &sv.back().back().value) == 2y_predict.size() == test_idx.size()working set size = fs_model.is_open()create file failedsvm_type kernel_type degree gamma nr_class coef0 total_sv rho nr_sv SV probA probB :svm_typekernel_typedegreenr_classcoef0gammatotal_svrholabelnr_svprobAprobBSVerror when loading model file fold cross-validation (c_svcnu_svcone_classepsilon_svrnu_svrNULLlinearpolynomialrbfsigmoidprecomputed8SvmModelC/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/model/svr.cppvoid SVR::save_svr_coef(const SyncArray&, const node2d&)3SVR/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/solver/csmosolver.cpptraining startglobal iter = , total local iter = , diff = training finishedobj = void CSMOSolver::solve(const KernelMatrix&, const SyncArray&, SyncArray&, float_type&, SyncArray&, float_type, float_type, float_type, int, int) const 10CSMOSolverMbP?11NuSMOSolverfeature == "svm_type"Nonespecified nu is infeasiblein auto-w/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/svm_R_interface.cppn_cores must be positive or -1class weight wrong input format!8 x void predict_R(char**, char**, char**)int* train_R(char**, int*, int*, int*, char**, double*, double*, double*, double*, double*, int*, char**, int*, int*, int*, int*, int*, char**)/home/runner/work/ml-builds/ml-builds/thundersvm/dataset/evaluating training score/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/svm_interface_api.cppx2833H+0void thundersvm_predict_sub(DataSet&, CMDParser&, char*, char*)void thundersvm_train_sub(DataSet&, CMDParser&, char*),]/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/syncarray.cppdestination and source count doesn't matchsize() == source.size()void SyncArray::copy_from(const SyncArray&) [with T = double]void SyncArray::copy_from(const SyncArray&) [with T = float]void SyncArray::copy_from(const SyncArray&) [with T = int]/home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/syncmem.cppCannot use GPU when compiling without GPU((data)) != nullptrvoid thunder::SyncMem::set_device_data(void*)void thunder::SyncMem::set_host_data(void*)void thunder::SyncMem::to_device()void thunder::SyncMem::to_host()void* thunder::SyncMem::device_data()cdxexb8cvoid dense_model_scikit(int, int, float*, float*, int, int, int, float, float, float, float, float, float, int, int, int*, float*, int, int, int, int, int, int*, int*, int*, SvmModel*)void sparse_model_scikit(int, float*, int*, int*, float*, int, int, int, float, float, float, float, float, float, int, int, int*, float*, int, int, int, int, int, int*, int*, int*, SvmModel*) /home/runner/work/ml-builds/ml-builds/thundersvm/src/thundersvm/thundersvm-scikit.cppn_jobs must be positive or -1?St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN2el10LogBuilderESt15_Sp_counted_ptrIPN2el4base17DefaultLogBuilderELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN2el4base7StorageELN9__gnu_cxx12_Lock_policyE2EEbasic_string::at: __n (which is %zu) >= this->size() (which is %zu)abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-._Configuration value not a valid integer [ASSERTION FAILURE FROM EASYLOGGING++ (LINE: Configuration error - No ending quote found in [Empty configuration value for [) [(quotesStart + 1 != quotesEnd)) [*currLevel != Level::Unknown) [currConfig != ConfigurationType::UnknownUnable to open configuration file [Unable to parse configuration line: ) [(assertionPassed = base::utils::File::pathExists(configurationFile.c_str(), true)) == true%datetime %level [%logger] %msg%datetime %level [%logger] [%user@%host] [%func] [%loc] %msg%datetime %level-%vlevel [%logger] %msg%datetime %level [%logger] [%func] [%loc] %msgvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)]. Not registering this logger.Aborting application. Reason: Fatal log at [ If you wish to disable 'abort on fatal log' please use el::Helpers::addFlag(el::LoggingFlag::DisableApplicationAbortOnFatalLog)Unable to open global configuration file [%Y-%M-%d %H:%m:%s,%gbasic_string::eraseGLOBALDEBUGINFOWARNINGERRORFATALTRACEUNKNOWNENABLEDFILENAMEFORMATTO_FILETO_STANDARD_OUTPUTSUBSECOND_PRECISIONPERFORMANCE_TRACKINGLOG_FLUSH_THRESHOLDMAX_LOG_FILE_SIZE##TRUEtrue*basic_string::replace%level%levshortIWF%user%hostwhoamiUSERhostnameunknown-hostHOSTNAMETERMxtermxterm-colorxterm-256colorscreenlinuxcygwinscreen-256colordaysPMAM%app%logger%thread%file%fbase%line%loc%func%vlevel%datetimemap::at) [valid] WITH MESSAGE "%Y-%M-%d_%H-%m , 9.95.002-08-2017 2312hrsenabledtoFiletoStandardOutputperformanceTracking.c.cpp.cc.cxx.-inl.h.hxx.hpp.hh--verbose--VERBOSE--v--V-vmodule-VMODULE) [(quotesStart < quotesEnd)Unrecognized severity level [Unrecognized configuration [] for parsing.) 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