diff --git a/booster/tree/xgboost_tree.hpp b/booster/tree/xgboost_tree.hpp
new file mode 100644
index 000000000..76ec073ce
--- /dev/null
+++ b/booster/tree/xgboost_tree.hpp
@@ -0,0 +1,146 @@
+#ifndef _XGBOOST_TREE_HPP_
+#define _XGBOOST_TREE_HPP_
+/*!
+ * \file xgboost_tree.hpp
+ * \brief implementation of regression tree
+ * \author Tianqi Chen: tianqi.tchen@gmail.com 
+ */
+#include "xgboost_tree_model.h"
+
+namespace xgboost{
+    namespace booster{
+        const bool rt_debug = false;
+        // whether to check bugs
+        const bool check_bug = false;
+    
+        const float rt_eps = 1e-5f;
+        const float rt_2eps = rt_eps * 2.0f;
+        
+        inline double sqr( double a ){
+            return a * a;
+        }
+    };
+};
+
+#include "xgboost_svdf_tree.hpp"
+
+namespace xgboost{
+    namespace booster{
+        // regression tree, construction algorithm is seperated from this class
+        // see RegTreeUpdater
+        class RegTreeTrainer : public IBooster{
+        public:
+            RegTreeTrainer( void ){ silent = 0; }
+            virtual ~RegTreeTrainer( void ){}
+        public:
+            virtual void SetParam( const char *name, const char *val ){
+                if( !strcmp( name, "silent") )  silent = atoi( val );
+                param.SetParam( name, val );
+                tree.param.SetParam( name, val );
+            }
+            virtual void LoadModel( utils::IStream &fi ){
+                tree.LoadModel( fi );
+            }
+            virtual void SaveModel( utils::IStream &fo ) const{
+                tree.SaveModel( fo );
+            }
+            virtual void InitModel( void ){
+                tree.InitModel();
+            }
+        public:
+            virtual void DoBoost( std::vector<float> &grad, 
+                                  std::vector<float> &hess,
+                                  const FMatrixS &smat,
+                                  const std::vector<unsigned> &group_id ){
+                this->DoBoost_( grad, hess, smat, group_id );
+            }
+            
+            virtual int GetLeafIndex( const std::vector<float> &feat,
+                                      const std::vector<bool>  &funknown,
+                                  unsigned gid = 0 ){
+                // start from groups that belongs to current data
+                int pid = (int)gid;
+                // tranverse tree
+                while( !tree[ pid ].is_leaf() ){
+                    unsigned split_index = tree[ pid ].split_index();
+                    pid = this->GetNext( pid, feat[ split_index ], funknown[ split_index ] );
+                }
+                return pid;
+            }
+            virtual float Predict( const FMatrixS::Line &feat, unsigned gid = 0 ){
+                this->InitTmp();
+                for( unsigned i = 0; i < feat.len; i ++ ){
+                    utils::Assert( feat[i].findex < (unsigned)tmp_funknown.size() , "input feature execeed bound" );
+                    tmp_funknown[ feat[i].findex ] = false;
+                    tmp_feat[ feat[i].findex ] = feat[i].fvalue;
+                } 
+                int pid = this->GetLeafIndex( tmp_feat, tmp_funknown, gid );
+                // set back
+                for( unsigned i = 0; i < feat.len; i ++ ){
+                    tmp_funknown[ feat[i].findex ] = true;
+                }            
+                return tree[ pid ].leaf_value();
+            }
+            virtual float Predict( const std::vector<float> &feat, 
+                                   const std::vector<bool>  &funknown,
+                                   unsigned gid = 0 ){
+                utils::Assert( feat.size() >= (size_t)tree.param.num_feature,
+                               "input data smaller than num feature" );
+                int pid = this->GetLeafIndex( feat, funknown, gid );
+                return tree[ pid ].leaf_value();
+            }
+            
+            virtual void DumpModel( FILE *fo ){
+                tree.DumpModel( fo );
+            }
+        private:
+            template<typename FMatrix>
+            inline void DoBoost_( std::vector<float> &grad, 
+                                  std::vector<float> &hess,
+                                  const FMatrix &smat,
+                                  const std::vector<unsigned> &group_id ){
+                utils::Assert( grad.size() < UINT_MAX, "number of instance exceed what we can handle" );
+                if( !silent ){
+                    printf( "\nbuild GBRT with %u instances\n", (unsigned)grad.size() );
+                }
+                // start with a id set
+                RTreeUpdater<FMatrix> updater( param, tree, grad, hess, smat, group_id );
+                int num_pruned;
+                tree.param.max_depth = updater.do_boost( num_pruned );
+                
+                if( !silent ){
+                    printf( "tree train end, %d roots, %d extra nodes, %d pruned nodes ,max_depth=%d\n", 
+                            tree.param.num_roots, tree.num_extra_nodes(), num_pruned, tree.param.max_depth );
+                }
+            }
+        private:
+            int silent;
+            RegTree tree;
+            TreeParamTrain param;
+        private:
+            std::vector<float> tmp_feat;
+            std::vector<bool>  tmp_funknown;
+            inline void InitTmp( void ){
+                if( tmp_feat.size() != (size_t)tree.param.num_feature ){
+                    tmp_feat.resize( tree.param.num_feature );
+                    tmp_funknown.resize( tree.param.num_feature );
+                    std::fill( tmp_funknown.begin(), tmp_funknown.end(), true );
+                }
+            }
+
+            inline int GetNext( int pid, float fvalue, bool is_unknown ){
+                float split_value = tree[ pid ].split_cond();
+                if( is_unknown ){
+                    if( tree[ pid ].default_left() ) return tree[ pid ].cleft();
+                else return tree[ pid ].cright();
+                }else{
+                    if( fvalue < split_value ) return tree[ pid ].cleft();
+                    else return tree[ pid ].cright();
+                }
+            }
+        };
+    };
+};
+
+#endif
+