c

4 files changed, 10 insertions, 2 deletions

diff --git a/model/ranksvm.cpp b/model/ranksvm.cpp
index 7ee72ac..bb50126 100644
--- a/model/ranksvm.cpp
+++ b/model/ranksvm.cpp
@@ -8,6 +8,8 @@
 using namespace Eigen;
 using namespace std;
 
+double C=1e-4;// Compensating & scaling
+
 int RSVM::saveModel(const string fname){
 
     std::ofstream fout(fname.c_str());
diff --git a/model/ranksvm.h b/model/ranksvm.h
index 58cd7f0..9051343 100644
--- a/model/ranksvm.h
+++ b/model/ranksvm.h
@@ -37,4 +37,6 @@ public:
     int setModel(const SVMModel &model);
 };
 
+extern double C;// Compensating & scaling
+
 #endif
\ No newline at end of file
diff --git a/model/ranksvmtn.cpp b/model/ranksvmtn.cpp
index 3136426..3d0db5f 100644
--- a/model/ranksvmtn.cpp
+++ b/model/ranksvmtn.cpp
@@ -6,7 +6,6 @@
 using namespace std;
 using namespace Eigen;
 
-const double C=1e-4; // Compensating & scaling
 // Main terminating criteria
 const int maxiter = 60; // max iteration count
 const double prec=1e-10; // precision
diff --git a/train.cpp b/train.cpp
index 05787c9..0d84e24 100644
--- a/train.cpp
+++ b/train.cpp
@@ -27,6 +27,7 @@ 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;
     rsvm->train(D);
 
     LOG(INFO)<<"Training finished,saving model";
@@ -109,7 +110,8 @@ int main(int argc, char **argv) {
             ("debug,d", "show debug messages")
             ("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");
+            ("feature,i", po::value<string>(), "set input feature file")
+            ("c,c",po::value<double>(),"trades margin size against training error");
 
     // Parsing program options
     po::store(po::parse_command_line(argc, argv, desc), vm);
@@ -128,6 +130,9 @@ int main(int argc, char **argv) {
 
     mainFunc mainf;
     if (vm.count("train")) {
+        if (vm.count("c")) {
+            C=vm["c"].as<double>();
+        }
         mainf = &train;
     }
     else if (vm.count("validate")||vm.count("predict")) {