From 68b7998f12e7e3c9b257a9d2a4c14e8d32a048b9 Mon Sep 17 00:00:00 2001
From: Joe Zhao <ztuowen@gmail.com>
Date: Thu, 12 Mar 2015 18:21:48 +0800
Subject: modeling

---
 model/ranksvm.h     |  7 +++++++
 model/ranksvmtn.cpp | 48 ++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 55 insertions(+)

diff --git a/model/ranksvm.h b/model/ranksvm.h
index edb80f6..b4ec7ce 100644
--- a/model/ranksvm.h
+++ b/model/ranksvm.h
@@ -6,6 +6,13 @@
 #include"../tools/dataProvider.h"
 #include "../tools/easylogging++.h"
 
+// Model File:
+// Model type -> String
+// Number of features (fsize) -> int
+// Weight matrix size, (fsize,1) ->  (int,int)
+// Weight vector
+// beta
+
 typedef struct SVMModel{
     Eigen::VectorXd weight;
     double beta;
diff --git a/model/ranksvmtn.cpp b/model/ranksvmtn.cpp
index 746e967..59980b4 100644
--- a/model/ranksvmtn.cpp
+++ b/model/ranksvmtn.cpp
@@ -3,10 +3,58 @@
 using namespace std;
 using namespace Eigen;
 
+const int maxiter = 10;
+const double prec=1e-3;
+
+// Calculate objfunc gradient & support vectors
+int objfunc_linear(const VectorXd &w,const double C,const VectorXd &pred, double &obj,MatrixXd &sv)
+{
+
+}
+
+// line search
+int line_search(const VectorXd &w,const double C,const VectorXd &step,VectorXd &pred,double &t)
+{
+
+}
+
 int RSVMTN::train(DataSet &D, Labels &label){
+    int iter = 0;
+
+    MatrixXd A;
+
+    int n=D.rows();
+    LOG(INFO) << "training with feature size:" << fsize << " Data size:" << n;
+    MatrixXd sv=MatrixXd::Identity(n, n);
+    VectorXd grad(fsize);
+    VectorXd step(fsize);
+    VectorXd pred(n);
+    double obj,t;
+
+    pred=VectorXd::Ones(n) - (A*(D*model.weight));
+
+    while (true)
+    {
+        iter+=1;
+        if (iter> maxiter)
+        {
+            LOG(INFO)<< "Maxiter :"<<maxiter<<" reached";
+            break;
+        }
+
+        // Generate support vector matrix sv & gradient
+        model.weight=model.weight+step*t;
+        // When dec is small enough
+        if (-step.dot(grad) < prec * obj)
+            break;
+    }
+
     return 0;
 };
 
 int RSVMTN::predict(DataSet &D, Labels &res){
+    res = model.weight * D;
+    for (int i=0;i<res.cols();++i)
+        res[i] = (res[i] + model.beta);
     return 0;
 };
\ No newline at end of file
-- 
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