input processing complete

1 files changed, 55 insertions, 10 deletions

diff --git a/model/ranksvmtn.cpp b/model/ranksvmtn.cpp
index 821d231..776d4db 100644
--- a/model/ranksvmtn.cpp
+++ b/model/ranksvmtn.cpp
@@ -79,13 +79,10 @@ int line_search(const VectorXd &w,const MatrixXd &D,const MatrixXd &A,const Vect
     return 0;
 }
 
-int RSVMTN::train(DataList &D){
-    /*int iter = 0;
+int train_orig(int fsize, MatrixXd &D,MatrixXd &A,VectorXd &weight){
+    int iter = 0;
     double C=1;
 
-    MatrixXd A;
-    // TODO Undefined
-
     long n=D.rows();
     LOG(INFO) << "training with feature size:" << fsize << " Data size:" << n;
     MatrixXd sv=MatrixXd::Identity(n, n);
@@ -94,7 +91,7 @@ int RSVMTN::train(DataList &D){
     VectorXd pred(n);
     double obj,t;
 
-    pred=VectorXd::Ones(n) - (A*(D*model.weight));
+    pred=VectorXd::Ones(n) - (A*(D*weight));
 
     while (true)
     {
@@ -106,7 +103,7 @@ int RSVMTN::train(DataList &D){
         }
 
         // Generate support vector matrix sv & gradient
-        objfunc_linear(model.weight,A,C,pred,grad,obj,sv);
+        objfunc_linear(weight,A,C,pred,grad,obj,sv);
         step = grad*0;
         MatrixXd H = MatrixXd::Identity(grad.cols(),grad.cols());
 
@@ -117,12 +114,60 @@ int RSVMTN::train(DataList &D){
         // Solve
         cg_solve(H,grad,step);
         // do line search
-        line_search(model.weight,D,A,step,pred,t);
-        model.weight=model.weight+step*t;
+        line_search(weight,D,A,step,pred,t);
+        weight=weight+step*t;
         // When dec is small enough
         if (-step.dot(grad) < prec * obj)
             break;
-    }*/
+    }
+    return 0;
+}
+
+int RSVMTN::train(DataList &D){
+    MatrixXd Data(D.getSize(),D.getfSize()),A;
+    int i=0,j=0;
+    list<DataEntry*>::iterator iter,st,nx;
+    for (iter= D.getData().begin();i<D.getSize();++i,++iter)
+        for (j=0;j<D.getfSize();++j)
+            Data(i,j)=(*iter)->feature(j);
+    nx=st=iter= D.getData().begin();
+    ++nx;
+    int cnt=0;
+    while (iter!=D.getData().end())
+    {
+        if ((nx == D.getData().end())||(*iter)->qid!=(*nx)->qid)
+        {
+            list<DataEntry*>::iterator high,low=iter;
+            for (high=st;((*high)->rank)>0;++high)
+                for (low=iter;((*low)->rank)<0;--low)
+                    ++cnt;
+            st = nx;
+        }
+        ++iter;
+    }
+    A.resize(cnt,D.getSize());
+    nx=st=iter= D.getData().begin();
+    ++nx;
+    cnt=i=j=0;
+    while (iter!=D.getData().end())
+    {
+        if ((nx == D.getData().end())||(*iter)->qid!=(*nx)->qid)
+        {
+            int v1=j,v2;
+            list<DataEntry*>::iterator high,low=iter;
+            for (high=st;((*high)->rank)>0;++high,++v1)
+                for (low=iter,v2=i;((*low)->rank)<0;--low,--v2) {
+                    A(cnt,v1) = 1;
+                    A(cnt,v2) = -1;
+                    ++cnt;
+                }
+            st = nx;
+            j=i+1;
+        }
+        ++i;
+        ++iter;
+    }
+    train_orig(fsize,Data,A,model.weight);
     return 0;
 };