1 files changed, 29 insertions, 13 deletions

diff --git a/model/ranksvmtn.cpp b/model/ranksvmtn.cpp
index 539ab5e..fe29468 100644
--- a/model/ranksvmtn.cpp
+++ b/model/ranksvmtn.cpp
@@ -1,4 +1,6 @@
 #include "ranksvmtn.h"
+#include<iostream>
+#include"../tools/matrixIO.h"
 
 using namespace std;
 using namespace Eigen;
@@ -6,31 +8,44 @@ using namespace Eigen;
 const int maxiter = 10;
 const double prec=1e-3;
 
-int cg_solve(const MatrixXd &A, const VectorXd &b, const VectorXd &x)
+int cg_solve(const MatrixXd &A, const VectorXd &b, VectorXd &x)
 {
     double alpha,beta,r_1,r_2;
-    VectorXd p = x;
+    int step=0;
     VectorXd q;
-    VectorXd res;
+    VectorXd res = b - A*x;
+    VectorXd p = res;
     while (1)
     {
-        beta = r_1/r_2;
-        p = res + beta*p;
+        // Non preconditioned version
+        r_1 = res.dot(res);
+        cout<<step<<":"<<r_1<<endl;
+        write_stream(cout,res);
+        if (r_1<1e-5) // Terminate condition
+            break;
+        if (step){
+            beta = r_1 / r_2;
+            p = res + p * beta;
+        }
+
         q = A*p;
         alpha = r_1/p.dot(q);
-        // Non preconditioned version
-        alpha = p.dot(p)/(p.dot(q));
-        res=res-alpha*q;
-        break;
+        x=x+p*alpha;
+        res=res-q*alpha;
+        write_stream(cout,p);
+        write_stream(cout,q);
+        cin.get();
+        ++step;
+        r_2=r_1;
     }
     return 0;
 }
 
 // Calculate objfunc gradient & support vectors
-int objfunc_linear(const VectorXd &w,const double C,const VectorXd &pred,const VectorXd &grad, double &obj,MatrixXd &sv)
+int objfunc_linear(const VectorXd &w,const MatrixXd &A,const double C,VectorXd &pred,VectorXd &grad, double &obj,MatrixXd &sv)
 {
-    pred = pred.cwiseMax(Matrix::Zero(pred.rows(),pred.cols()));
-    obj = (pred.cwiseProduct(pred)*(C/2)) + w.transpose()*w/2;
+    pred = pred.cwiseMax(MatrixXd::Zero(pred.rows(),pred.cols()));
+//    obj = (pred.cwiseProduct(pred)*(C/2)) + w.transpose()*w/2;
     grad = w - (((pred*C).transpose()*A)*w).transpose();
     for (int i=0;i<pred.cols();++i)
         if (pred(i)>0)
@@ -50,6 +65,7 @@ int RSVMTN::train(DataSet &D, Labels &label){
     int iter = 0;
 
     MatrixXd A;
+    // TODO Undefined
 
     int n=D.rows();
     LOG(INFO) << "training with feature size:" << fsize << " Data size:" << n;
@@ -71,7 +87,7 @@ int RSVMTN::train(DataSet &D, Labels &label){
         }
 
         // Generate support vector matrix sv & gradient
-        objfunc_linear(D,1,pred,grad,obj,sv);
+        objfunc_linear(D,A,1,pred,grad,obj,sv);
         model.weight=model.weight+step*t;
         // When dec is small enough
         if (-step.dot(grad) < prec * obj)