diff options
Diffstat (limited to 'train.cpp')
| -rw-r--r-- | train.cpp | 27 |
1 files changed, 18 insertions, 9 deletions
@@ -27,7 +27,9 @@ int train(DataProvider &dp) { LOG(INFO)<<"Training started"; dp.getAllDataSet(D); LOG(INFO)<<"Read "<<D.getSize()<<" entries with "<< D.getfSize()<<" features"; - LOG(INFO)<<"C: "<<C; + LOG(INFO)<<"C: "<<C<<" ,iter: "<<maxiter<<" ,prec: "<<prec; + LOG(INFO)<<"cg_maxiter: "<<cg_maxiter<<" ,cg_prec:"<<cg_prec<<" ,ls_maxiter: "<<ls_maxiter<<" ,ls_prec: "<<ls_prec; + rsvm->train(D); LOG(INFO)<<"Training finished,saving model"; @@ -109,7 +111,13 @@ int main(int argc, char **argv) { ("model,m", po::value<string>(), "set input model file") ("output,o", po::value<string>(), "set output model/prediction file") ("feature,i", po::value<string>(), "set input feature file") - ("c,c",po::value<double>(),"trades margin size against training error"); + ("c,c",po::value<double>(),"trades margin size against training error") + ("iter",po::value<int>(),"iter main") + ("prec",po::value<double>(),"prec main") + ("cg_iter",po::value<int>(),"iter conjugate gradient") + ("cg_prec",po::value<double>(),"prec conjugate gradient") + ("ls_iter",po::value<int>(),"iter line search") + ("ls_prec",po::value<double>(),"prec line search"); // Parsing program options po::store(po::parse_command_line(argc, argv, desc), vm); @@ -127,14 +135,15 @@ int main(int argc, char **argv) { el::Loggers::reconfigureLogger("default", defaultConf); mainFunc mainf; - if (vm.count("single")) - RidList::single=true; - else - RidList::single=false; + RidList::single=vm.count("single")>0; if (vm.count("train")) { - if (vm.count("c")) { - C=vm["c"].as<double>(); - } + if (vm.count("c")) { C=vm["c"].as<double>(); } + if (vm.count("iter")) { maxiter=vm["iter"].as<int>(); } + if (vm.count("prec")) { prec=vm["prec"].as<double>(); } + if (vm.count("cg_iter")) { cg_maxiter=vm["cg_iter"].as<int>(); } + if (vm.count("cg_prec")) { cg_prec=vm["cg_prec"].as<double>(); } + if (vm.count("ls_iter")) { ls_maxiter=vm["ls_iter"].as<int>(); } + if (vm.count("ls_prec")) { ls_prec=vm["ls_prec"].as<double>(); } mainf = &train; } else if (vm.count("validate")||vm.count("predict")) { |