svm main construction complete

1 files changed, 12 insertions, 4 deletions

diff --git a/model/ranksvmtn.cpp b/model/ranksvmtn.cpp
index 193a7d4..559723c 100644
--- a/model/ranksvmtn.cpp
+++ b/model/ranksvmtn.cpp
@@ -13,7 +13,7 @@ int cg_solve(const MatrixXd &A, const VectorXd &b, VectorXd &x)
     double alpha,beta,r_1,r_2;
     int step=0;
     VectorXd q;
-    VectorXd res = b - A*x;
+    VectorXd res = b - A*x; // TODO Hessian product
     VectorXd p = res;
     while (1)
     {
@@ -28,7 +28,7 @@ int cg_solve(const MatrixXd &A, const VectorXd &b, VectorXd &x)
             p = res + p * beta;
         }
 
-        q = A*p;
+        q = A*p; // TODO Hessian product
         alpha = r_1/p.dot(q);
         x=x+p*alpha;
         res=res-q*alpha;
@@ -80,11 +80,12 @@ int line_search(const VectorXd &w,const MatrixXd &D,const MatrixXd &A,const Vect
 
 int RSVMTN::train(DataSet &D, Labels &label){
     int iter = 0;
+    double C=1;
 
     MatrixXd A;
     // TODO Undefined
 
-    int n=D.rows();
+    long n=D.rows();
     LOG(INFO) << "training with feature size:" << fsize << " Data size:" << n;
     MatrixXd sv=MatrixXd::Identity(n, n);
     VectorXd grad(fsize);
@@ -104,9 +105,16 @@ int RSVMTN::train(DataSet &D, Labels &label){
         }
 
         // Generate support vector matrix sv & gradient
-        objfunc_linear(model.weight,A,1,pred,grad,obj,sv);
+        objfunc_linear(model.weight,A,C,pred,grad,obj,sv);
+        step = grad*0;
+        MatrixXd H = MatrixXd::Identity(grad.cols(),grad.cols());
 
+        // Compute Hessian directly
+        for (int i=0;i<n;++i)
+            if (sv(i,i)>0)
+            H = H + 2*C*A.row(i).transpose()*A.row(i);
         // Solve
+        cg_solve(H,grad,step);
         // do line search
         line_search(model.weight,D,A,step,pred,t);
         model.weight=model.weight+step*t;