misc

1 files changed, 15 insertions, 9 deletions

diff --git a/model/ranksvmtn.cpp b/model/ranksvmtn.cpp
index 4572484..15a5e10 100644
--- a/model/ranksvmtn.cpp
+++ b/model/ranksvmtn.cpp
@@ -6,6 +6,8 @@
 using namespace std;
 using namespace Eigen;
 
+double RC=0;
+
 void cal_Dw(RidList &D,const VectorXd &w, VectorXd &Dw)
 {
     int n = D.getSize();
@@ -63,7 +65,7 @@ int cal_Hs(RidList &D,const vector<int> &rank,const VectorXd &corr,const VectorX
         tmp(i) = alpha(i)*Ds(i)-gamma(i);
     VectorXd res = VectorXd::Zero(D.getfSize());
     cal_Dtw(D,tmp,res);
-    Hs = s + C*res;
+    Hs = s + RC*res;
     return 0;
 }
 
@@ -188,7 +190,7 @@ int line_search(const VectorXd &w,RidList &D,const VectorXd &corr,const VectorXd
         VectorXd tmp = alpha.cwiseProduct(yt)-beta;
         VectorXd res = VectorXd::Zero(D.getfSize());
         cal_Dtw(D,tmp,res);
-        grad = grad + C*res;
+        grad = grad + RC*res;
         g = grad.dot(step);
         cal_Hs(D,rank,corr,alpha,step,Hs);
         h = Hs.dot(step);
@@ -216,7 +218,7 @@ int train_orig(int fsize, RidList &Data,const VectorXd &corr,VectorXd &weight){
     VectorXd grad(fsize);
     VectorXd step(fsize);
     vector<int> rank(n);
-    double obj,t;
+    double obj,t,l;
 
     VectorXd dw(n);
     VectorXd yt;
@@ -227,11 +229,12 @@ int train_orig(int fsize, RidList &Data,const VectorXd &corr,VectorXd &weight){
         cal_Dw(Data,weight,dw);
         cal_alpha_beta(dw,corr,Data,rank,yt,alpha,beta);
         // Generate support vector matrix sv & gradient
-        obj = (weight.dot(weight) + C*(alpha.dot(yt.cwiseProduct(yt))-beta.dot(yt)))/2;
+        l=alpha.dot(yt.cwiseProduct(yt))-beta.dot(yt);
+        obj = (weight.dot(weight) + RC*l)/2;
         VectorXd tmp = alpha.cwiseProduct(yt)-beta;
         VectorXd res = VectorXd::Zero(fsize);
         cal_Dtw(Data,tmp,res);
-        grad = weight + C*res;
+        grad = weight + RC*res;
         // Solve
         cg_solve(Data,rank,corr,alpha,grad,step);
         // do line search
@@ -240,7 +243,7 @@ int train_orig(int fsize, RidList &Data,const VectorXd &corr,VectorXd &weight){
         // When dec is small enough
         double nprec = step.dot(grad)/obj;
         ++iter;
-        LOG(INFO)<<"Iter: "<<iter<<" Obj: " <<obj << " Ndec/Obj:"<<nprec << " linesearch: "<< -t ;
+        LOG(INFO)<<"Iter: "<<iter<<" Obj: " <<obj<< " l: "<< l << " Ndec/Obj:"<<nprec << " linesearch: "<< -t ;
         if (iter>= maxiter)
         {
             LOG(INFO)<< "Maxiter reached";
@@ -254,11 +257,11 @@ int train_orig(int fsize, RidList &Data,const VectorXd &corr,VectorXd &weight){
 
 int RSVMTN::train(RidList &D){
     VectorXd corr(D.getSize());
-    vector<int> A1,A2;
-    int i,j;
+    int i;
     LOG(INFO)<<"Processing input";
     for (i=0;i<D.getSize();++i)
         corr(i)=D.getL(i)>0?0.5:-0.5;
+    RC=2.0*C;
     train_orig(fsize,D,corr,model.weight);
     return 0;
 };
@@ -267,6 +270,9 @@ int RSVMTN::predict(RidList &D, vector<double> &res){
     res.clear();
     int n = D.getSize();
     for (int i=0;i<n;++i)
-        res.push_back(D.getVecDot(i,model.weight));
+    {
+        double r=D.getVecDot(i,model.weight);
+        res.push_back(r);
+    }
     return 0;
 };
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