variable input

4 files changed, 26 insertions, 17 deletions

diff --git a/model/ranksvm.cpp b/model/ranksvm.cpp
index 2f366d6..368e16d 100644
--- a/model/ranksvm.cpp
+++ b/model/ranksvm.cpp
@@ -15,11 +15,11 @@ int maxiter = 1; // max iteration count
 double prec=1e-10; // precision
 // conjugate gradient
 double cg_prec=1e-10; // precision
-int cg_maxiter = 2000;
+int cg_maxiter = 100;
 int ls_maxiter = 20;
 // line search
-double line_prec=1e-10; // precision
-double line_turb=1e-15; // purturbation
+double ls_prec=1e-10; // precision
+double ls_turb=1e-15; // purturbation
 
 
 int RSVM::saveModel(const string fname){
diff --git a/model/ranksvm.h b/model/ranksvm.h
index 41de0f4..a17e3c9 100644
--- a/model/ranksvm.h
+++ b/model/ranksvm.h
@@ -46,7 +46,7 @@ extern double cg_prec; // precision
 extern int cg_maxiter; // not worth having a large number
 extern int ls_maxiter;
 // line search
-extern double line_prec; // precision
-extern double line_turb; // purturbation
+extern double ls_prec; // precision
+extern double ls_turb; // perturbation
 
 #endif
\ No newline at end of file
diff --git a/model/ranksvmtn.cpp b/model/ranksvmtn.cpp
index 9beed65..01fcb83 100644
--- a/model/ranksvmtn.cpp
+++ b/model/ranksvmtn.cpp
@@ -192,12 +192,12 @@ int line_search(const VectorXd &w,RidList &D,const VectorXd &corr,const VectorXd
         g = grad.dot(step);
         cal_Hs(D,rank,corr,alpha,step,Hs);
         h = Hs.dot(step);
-        g=g+line_turb;
-        h = h+line_turb;
+        g=g+ls_turb;
+        h = h+ls_turb;
         t=t-g/h;
         ++iter;
         LOG(INFO) << "line search iter "<<iter<<", prec:"<<g*g/h;
-        if (g*g/h<line_prec)
+        if (g*g/h<ls_prec)
             break;
         if (iter >= ls_maxiter)
         {
diff --git a/train.cpp b/train.cpp
index 4b8439a..07f9edc 100644
--- a/train.cpp
+++ b/train.cpp
@@ -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")) {