structuring input

3 files changed, 12 insertions, 10 deletions

diff --git a/model/ranksvm.h b/model/ranksvm.h
index b4ec7ce..e82b6be 100644
--- a/model/ranksvm.h
+++ b/model/ranksvm.h
@@ -3,6 +3,7 @@
 
 #include<Eigen/Dense>
 #include<string>
+#include<list>
 #include"../tools/dataProvider.h"
 #include "../tools/easylogging++.h"
 
@@ -24,8 +25,8 @@ protected:
     SVMModel model;
     int fsize;
 public:
-    virtual int train(DataSet &D, Labels &label)=0;
-    virtual int predict(DataSet &D, Labels &res)=0;
+    virtual int train(DataList &D)=0;
+    virtual int predict(DataList &D,std::list<double> &res)=0;
     // TODO Not sure how to construct this
     //  Possible solution: generate a nxn matrix each row contains the sorted list of ranker result.
     int saveModel(const std::string fname);
diff --git a/model/ranksvmtn.cpp b/model/ranksvmtn.cpp
index 3c9808b..821d231 100644
--- a/model/ranksvmtn.cpp
+++ b/model/ranksvmtn.cpp
@@ -1,5 +1,6 @@
 #include "ranksvmtn.h"
 #include<iostream>
+#include<list>
 #include"../tools/matrixIO.h"
 
 using namespace std;
@@ -78,8 +79,8 @@ int line_search(const VectorXd &w,const MatrixXd &D,const MatrixXd &A,const Vect
     return 0;
 }
 
-int RSVMTN::train(DataSet &D, Labels &label){
-    int iter = 0;
+int RSVMTN::train(DataList &D){
+    /*int iter = 0;
     double C=1;
 
     MatrixXd A;
@@ -121,13 +122,13 @@ int RSVMTN::train(DataSet &D, Labels &label){
         // When dec is small enough
         if (-step.dot(grad) < prec * obj)
             break;
-    }
+    }*/
     return 0;
 };
 
-int RSVMTN::predict(DataSet &D, Labels &res){
+int RSVMTN::predict(DataList &D, list<double> &res){
     //TODO define A
-    MatrixXd A;
-    res = A*(D * model.weight);
+    for (list<DataEntry*>::iterator i=D.getData().begin(), end=D.getData().end();i!=end;++i)
+        res.push_back(((*i)->feature).dot(model.weight));
     return 0;
 };
\ No newline at end of file
diff --git a/model/ranksvmtn.h b/model/ranksvmtn.h
index 703fee4..6ed6ad7 100644
--- a/model/ranksvmtn.h
+++ b/model/ranksvmtn.h
@@ -12,8 +12,8 @@ public:
     {
         return "TN";
     };
-    virtual int train(DataSet &D, Labels &label);
-    virtual int predict(DataSet &D, Labels &res);
+    virtual int train(DataList &D);
+    virtual int predict(DataList &D,std::list<double> &res);
 };
 
 int cg_solve(const Eigen::MatrixXd &A, const Eigen::VectorXd &b, Eigen::VectorXd &x);