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HelpInfo_svmtrain()Usage (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 modevoid HelpInfo_svmpredict()Usage: 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) thundersvm-trainthundersvm-train.exevoid CMDParser::parse_command_line(int, char **)shrinking is not supportedfalsen-fold cross validation: n must >= 2Unknown option: the number of cpu cores must be positive or -1.modelthundersvm-predictthundersvm-predict.exeUnknown option: -%c void CMDParser::parse_python(int, char **)n-fold cross validation: n must >= 2 bool CMDParser::check_parameter()unknown svm typeunknown kernel typegamma < 0degree of polynomial kernel < 0epsilon <= 0C <= 0nu <= 0 or nu > 1p < 0max memory size <= 0probability != 0 and probability != 1one-class SVM and SVR probability output not supported yetdefault /Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/dataset.cppvoid DataSet::load_from_file(std::__1::string)loading dataset from file ""file not found%d:%fCheck failed: [sscanf(tuple.c_str(), "%d:%f", &i, &v) == 2] read error, using [index]:[value] format;unknown;unknown;0;0;;#instances = , #features = void DataSet::load_from_python(float *, char **, int)sscanf(tuple.c_str(), "%d:%f", &ind, &v) == 2void DataSet::load_from_sparse(int, float *, int *, int *, float *)void DataSet::load_from_dense(int, int, float *, float *)allocator::allocate(size_t n) 'n' exceeds maximum supported sizerb/Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/kernelmatrix.cppvoid KernelMatrix::get_rows(const SyncArray &, SyncArray &) constkernel_rows.size() >= idx.size() * n_instances_kernel_rows memory is too smallvoid KernelMatrix::get_rows(const DataSet::node2d &, SyncArray &) constkernel_rows.size() >= instances.size() * n_instances_void KernelMatrix::dns_csr_mul(const SyncArray &, int, SyncArray &) constdense_mat.size() == n_rows * n_features_dense matrix features doesn't matchvoid KernelMatrix::dns_dns_mul(const SyncArray &, int, const SyncArray &, SyncArray &) const/Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/model/nusvc.cppvirtual void NuSVC::train_binary(const DataSet &, int, int, SyncArray &, float_type &)rho = #sv = /Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/model/oneclass_svc.cppvirtual void OneClassSVC::train(const DataSet &, SvmParam)/Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/model/svc.cppvirtual void SVC::model_setup(const DataSet &, SvmParam &)#classes = weighted label virtual void SVC::train(const DataSet &, SvmParam)original_index.size() == alpha[k].size()#total unique sv = performing probability trainvirtual void SVC::train_binary(const DataSet &, int, int, SyncArray &, float_type &)vector SVC::predict_label(const SyncArray &, int)predict with probabilityExceeds max_iter in multiclass_probReaching maximal iterations in two-class probability estimatesLine search fails in two-class probability estimates/Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/model/svmmodel.cppvirtual vector SvmModel::cross_validation(DataSet, SvmParam, int) fold cross-validation (/)y_predict.size() == test_idx.size()virtual void SvmModel::save_to_file(std::__1::string)fs_model.is_open()create file failedsvm_type kernel_type degree gamma coef0 nr_class total_sv rho label nr_sv probA probB SV :virtual void SvmModel::load_from_file(std::__1::string)svm_typekernel_typedegreenr_classcoef0gammatotal_svrholabelnr_svprobAprobBSVsscanf(tuple.c_str(), "%d:%f", &sv.back().back().index, &sv.back().back().value) == 2error when loading model fileint SvmModel::get_working_set_size(int, int)working set size = linearpolynomialrbfsigmoidprecomputedNULLc_svcnu_svcone_classepsilon_svrnu_svrwr/Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/model/svr.cppvoid SVR::save_svr_coef(const SyncArray &, const DataSet::node2d &)/Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/solver/csmosolver.cppvoid CSMOSolver::solve(const KernelMatrix &, const SyncArray &, SyncArray &, float_type &, SyncArray &, float_type, float_type, float_type, int, int) consttraining startglobal iter = , total local iter = , diff = training finishedobj = /Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/svm_R_interface.cppint *train_R(char **, int *, int *, int *, char **, double *, double *, double *, double *, double *, int *, char **, int *, int *, int *, int *, int *, char **)n_cores must be positive or -1../R/Noneauto-wclass weight wrong input format! = void predict_R(char **, char **, char **)feature == "svm_type"in autospecified nu is infeasible/Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/svm_interface_api.cppvoid thundersvm_train_sub(DataSet &, CMDParser &, char *)evaluating training scorevoid thundersvm_predict_sub(DataSet &, CMDParser &, char *, char *)/Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/syncarray.cppvoid SyncArray::copy_from(const SyncArray &) [T = int]size() == source.size()destination and source count doesn't match[,]void SyncArray::copy_from(const SyncArray &) [T = float]void SyncArray::copy_from(const SyncArray &) [T = double]/Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/syncmem.cppvoid *thunder::SyncMem::device_data()Cannot use GPU when compiling without GPUvoid thunder::SyncMem::to_host()void thunder::SyncMem::to_device()void thunder::SyncMem::set_host_data(void *)((data)) != nullptrvoid thunder::SyncMem::set_device_data(void *)/Users/runner/runners/2.164.0/work/ml-builds/ml-builds/thundersvm/src/thundersvm/thundersvm-scikit.cppvoid 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 *)n_jobs must be positive or -1void 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 *)GLOBALDEBUGINFOWARNINGERRORFATALVERBOSETRACEUNKNOWNENABLEDFILENAMEFORMATTO_FILETO_STANDARD_OUTPUTSUBSECOND_PRECISIONPERFORMANCE_TRACKINGMAX_LOG_FILE_SIZELOG_FLUSH_THRESHOLDConfiguration file [] does not exist!ASSERTION FAILURE FROM EASYLOGGING++ (LINE: ) [(assertionPassed = base::utils::File::pathExists(configurationFile.c_str(), true)) == true] WITH MESSAGE "true%datetime %level [%logger] %msg%datetime %level [%logger] [%user@%host] [%func] [%loc] %msg%datetime %level-%vlevel [%logger] %msg%datetime %level [%logger] [%func] [%loc] %msgUnable to open configuration file [] for parsing.) [fileStream_.is_open()Unable to parse configuration line: ) [parsedSuccessfullyConfiguration error - No ending quote found in [) [(quotesStart < quotesEnd)Empty configuration value for [) [(quotesStart + 1 != quotesEnd)Unrecognized severity level [) [*currLevel != Level::UnknownUnrecognized configuration [) [currConfig != ConfigurationType::Unknown..USERHOSTNAMETERMxtermxterm-colorxterm-256colorscreenlinuxcygwinscreen-256colorenabledfilenameConfiguration value not a valid integer [) [validInvalid logger ID []. Not registering this logger.) [validId-v--verbose-V--VERBOSE--v--V-vmodule-VMODULEperformance%datetime %level %msgDefaultLogDispatchCallback , Logger [] is not registered yet!Aborting application. Reason: Fatal log at [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 [) [gcfStream.is_open()9.95.002-08-2017 2312hrsglobaldebuginfowarningerrorfatalverbosetraceto_fileto_standard_outputformatsubsecond_precisionmilliseconds_widthperformance_trackingmax_log_file_sizelog_flush_thresholdlogs/myeasylog.log##userunknown-hostusmssecondsminuteshoursdaysSunMonTueWedThuFriSatSundayMondayTuesdayWednesdayThursdayFridaySaturdayJanFebMarAprMayJunJulAugSepOctNovDecJanuaryFebruaryMarchApriJuneJulyAugustSeptemberOctoberNovemberDecemberPMAM%app%level%levshort%logger%thread%file%fbase%line%loc%func%user%host%msg%vlevel%datetime%Y-%M-%d %H:%m:%s,%gTRUE1%Y-%M-%d_%H-%m.h.c.cpp.cc.cxx.-inl.h.hxx.hpp.hh--default-log-filenullptramap::at: key not foundMean Squared Error HLXQXa !QaQ! 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