diff --git a/fastlib/u/gmravi/allknn/allknn.h b/fastlib/u/gmravi/allknn/allknn.h
index eb3523c417..9b04e96fa7 100644
--- a/fastlib/u/gmravi/allknn/allknn.h
+++ b/fastlib/u/gmravi/allknn/allknn.h
@@ -1,500 +1,794 @@
-#include <time.h>
-#include <math.h>
-#include <string.h>
-#include "batree.h"
-#include "amgr.h"
-#include "ongr.h"
-#include "dsut.h"
-#include "hrect.h"
-#include "distutils.h"
-#include "sheap.h"
-#include "ballutils.h"
-#include "batree.h"
-#include "allnn.h"
-#include "dymutil.h"
-#include "timing_dyvs.h"
-#include "my_time.h"
-#include "bruteforce.h"
-#include "stats.h"
+// This is the new header file that is being formed as a result of code improvement and code displacement
 
-/* TIMING is defined in batree.h, because it has to be used in this file
- * and allnn. -crotella
- */
+//Lets do it step-by-step
+#include "fastlib/fastlib_int.h"
+#ifndef ALLKNN_H
+#define ALLKNN_H
+#define LEAF_SIZE 3
+#define MAX_DOUBLE 32768.0
+//Class definition for AllNNNaive..............................................
 
-#define NUM_ITERRATIONS 10
-
-extern int Num_ball_prunes;
-extern int Num_hrect_prunes;
-extern double Verbosity;
-
-#define DEBUG 0
-#ifndef MBW
-#define MBW 1e-35
-#endif
-
-#ifndef FLT_MAX
-#define FLT_MAX 1e30
-#endif
-
-dyv_array *mk_dyv_array_from_dym(dym *d) {
-  int i;
-  dyv_array *ret = mk_empty_dyv_array();
-  for(i=0; i<dym_rows(d); i++) {
-    dyv *row = mk_dyv_from_dym_row(d, i);
-    add_to_dyv_array(ret, row);
-    free_dyv(row);
-  }
-  return ret;
-}
-
-extern dym *mk_dym_from_filename_improved(char *filename, int argc, 
-					  char *argv[]);
-
-void batree_allnearest_neighbor2(dyv_array *sps, dyv_array *sps2, banode *Q, 
-				 banode *R, double mindist, ivec *nn_rows, 
-				 dyv* nn_dists)
+class AllNNNaive
 {
-  int num_queries = Q->num_points, num_data = R->num_points;
+  
+ public:
+  ArrayList <index_t> results;
+  AllNNNaive(){};
 
-  if(mindist > Q->mindist_sofar) {
-    Num_ball_prunes++; return;  //Num_hrect_prunes++; return;
-  }
-  else {
-    if(banode_is_leaf(Q) && banode_is_leaf(R)) {
-      int i; 
-      double max_of_node = 0;
-      for(i=0; i< num_queries; i++) {
-        int row_i = ivec_ref(Q->rows,i), j; double d;
-        dyv *point_i = dyv_array_ref(sps,row_i);
 
-        for(j=0; j<num_data; j++) {
-          int row_j = ivec_ref(R->rows,j);
-          dyv *point_j = dyv_array_ref(sps2,row_j);
-
-          if (row_j != row_i) {
-            double dist = dyv_distance(point_i,point_j);
-            if(dist < dyv_ref(nn_dists,row_i)) { 
-	      dyv_set(nn_dists,row_i,dist); 
-	      ivec_set(nn_rows,row_i,row_j); 
-	    }
-          }
-        } 
-        d = dyv_ref(nn_dists,row_i); 
-	if(d > max_of_node) 
-	  max_of_node = d;
-      }
-      Q->mindist_sofar = max_of_node;
-    }
-    else if(banode_is_leaf(Q) && !banode_is_leaf(R)) {
-      //double dist1 = hrect_hrect_min_dsqd(Q->hr,R->child1->hr);
-      double dist1 = (dyv_distance(Q->pivot,R->child1->pivot) - Q->radius 
-		      - R->child1->radius);
-      double dist2 = (dyv_distance(Q->pivot,R->child2->pivot) - Q->radius 
-		      - R->child2->radius);
-      
-      banode *first = (dist1 < dist2) ? R->child1 : R->child2;
-      double first_dist = real_min(dist1,dist2);
-      banode *second = (dist1 < dist2) ? R->child2 : R->child1;
-      double second_dist = real_max(dist1,dist2);
-      
-      batree_allnearest_neighbor2(sps,sps2, Q,first,first_dist,
-                                 nn_rows,nn_dists);
-      batree_allnearest_neighbor2(sps,sps2, Q,second,second_dist,
-                                 nn_rows,nn_dists);
-    }
-    else if(!banode_is_leaf(Q) && banode_is_leaf(R))
+  //Interesting functions.......
+  void Compute(Matrix *q_matrix, Matrix *r_matrix)
     {
-      double dist1 = (dyv_distance(R->pivot,Q->child1->pivot) - R->radius 
-		      - Q->child1->radius);
-      double dist2 = (dyv_distance(R->pivot,Q->child2->pivot) - R->radius 
-		      - Q->child2->radius);
+      //for each column vector of q_matrix find the nearest neighbour in r_matrix
       
-      banode *first = (dist1 < dist2) ? Q->child1 : Q->child2;
-      double first_dist = real_min(dist1,dist2);
-      banode *second = (dist1 < dist2) ? Q->child2 : Q->child1;
-      double second_dist = real_max(dist1,dist2);
-      
-      batree_allnearest_neighbor2(sps,sps2, first,R,first_dist,
-                                 nn_rows,nn_dists);
-      batree_allnearest_neighbor2(sps,sps2, second,R,second_dist,
-                                 nn_rows,nn_dists);
-      
-      Q->mindist_sofar = real_min(Q->mindist_sofar,
-				  real_max(Q->child1->mindist_sofar, 
-					   Q->child2->mindist_sofar));
-    }
-    else if(!banode_is_leaf(Q) && !banode_is_leaf(R)) {
-      {
-	double dist1 = (dyv_distance(Q->child1->pivot,R->child1->pivot) 
-			- Q->child1->radius - R->child1->radius);
-	double dist2 = (dyv_distance(Q->child1->pivot,R->child2->pivot) 
-			- Q->child1->radius - R->child2->radius);
-	
-	banode *first = (dist1 < dist2) ? R->child1 : R->child2;
-	double first_dist = real_min(dist1,dist2);
-	banode *second = (dist1 < dist2) ? R->child2 : R->child1;
-	double second_dist = real_max(dist1,dist2);
-	
-	batree_allnearest_neighbor2(sps,sps2, Q->child1,first,first_dist,
-				   nn_rows,nn_dists);
-	batree_allnearest_neighbor2(sps,sps2, Q->child1,second,second_dist,
-				   nn_rows,nn_dists);
-      }
-      {
-	double dist1 = (dyv_distance(Q->child2->pivot,R->child1->pivot) 
-			- Q->child2->radius - R->child1->radius);
-	double dist2 = (dyv_distance(Q->child2->pivot,R->child2->pivot) 
-			- Q->child2->radius - R->child2->radius);
-	
-	banode *first = (dist1 < dist2) ? R->child1 : R->child2;
-	double first_dist = real_min(dist1,dist2);
-	banode *second = (dist1 < dist2) ? R->child2 : R->child1;
-	double second_dist = real_max(dist1,dist2);
-	
-	batree_allnearest_neighbor2(sps,sps2, Q->child2,first,first_dist,
-				   nn_rows,nn_dists);
-	batree_allnearest_neighbor2(sps,sps2, Q->child2,second,second_dist,
-				    nn_rows,nn_dists);
-      }
-      Q->mindist_sofar = real_min(Q->mindist_sofar,
-				  real_max(Q->child1->mindist_sofar, Q->child2->mindist_sofar));
-    }
-  }
-}
-
-void batree_allnearest_neighbor(dyv_array *sps, banode *Q, banode *R,
-                                double mindist, ivec *nn_rows, dyv* nn_dists)
-{
-  int num_queries = Q->num_points, num_data = R->num_points;
-
-  if(mindist > Q->mindist_sofar) {
-    Num_ball_prunes++; return;  //Num_hrect_prunes++; return;
-  }
-  else {
-    if(banode_is_leaf(Q) && banode_is_leaf(R)) {
-      int i; 
-      double max_of_node = 0;
-      for(i=0; i< num_queries; i++) {
-        int row_i = ivec_ref(Q->rows,i), j; double d;
-        dyv *point_i = dyv_array_ref(sps,row_i);
-
-        for(j=0; j<num_data; j++) {
-          int row_j = ivec_ref(R->rows,j);
-          dyv *point_j = dyv_array_ref(sps,row_j);
-
-          if (row_j != row_i) {
-            double dist = dyv_distance(point_i,point_j);
-            if(dist < dyv_ref(nn_dists,row_i)) { 
-	      dyv_set(nn_dists,row_i,dist); 
-	      ivec_set(nn_rows,row_i,row_j); 
-	    }
-          }
-        } 
-        d = dyv_ref(nn_dists,row_i); 
-	if(d > max_of_node) 
-	  max_of_node = d;
-      }
-      Q->mindist_sofar = max_of_node;
-    }
-    else if(banode_is_leaf(Q) && !banode_is_leaf(R)) {
-      //double dist1 = hrect_hrect_min_dsqd(Q->hr,R->child1->hr);
-      double dist1 = (dyv_distance(Q->pivot,R->child1->pivot) - Q->radius 
-		      - R->child1->radius);
-      double dist2 = (dyv_distance(Q->pivot,R->child2->pivot) - Q->radius 
-		      - R->child2->radius);
-      
-      banode *first = (dist1 < dist2) ? R->child1 : R->child2;
-      double first_dist = real_min(dist1,dist2);
-      banode *second = (dist1 < dist2) ? R->child2 : R->child1;
-      double second_dist = real_max(dist1,dist2);
-      
-      batree_allnearest_neighbor(sps,Q,first,first_dist,
-                                 nn_rows,nn_dists);
-      batree_allnearest_neighbor(sps,Q,second,second_dist,
-                                 nn_rows,nn_dists);
-    }
-    else if(!banode_is_leaf(Q) && banode_is_leaf(R))
-    {
-      double dist1 = (dyv_distance(R->pivot,Q->child1->pivot) - R->radius 
-		      - Q->child1->radius);
-      double dist2 = (dyv_distance(R->pivot,Q->child2->pivot) - R->radius 
-		      - Q->child2->radius);
-      
-      banode *first = (dist1 < dist2) ? Q->child1 : Q->child2;
-      double first_dist = real_min(dist1,dist2);
-      banode *second = (dist1 < dist2) ? Q->child2 : Q->child1;
-      double second_dist = real_max(dist1,dist2);
-      
-      batree_allnearest_neighbor(sps,first,R,first_dist,
-                                 nn_rows,nn_dists);
-      batree_allnearest_neighbor(sps,second,R,second_dist,
-                                 nn_rows,nn_dists);
-      
-      Q->mindist_sofar = real_min(Q->mindist_sofar,
-				  real_max(Q->child1->mindist_sofar, 
-					   Q->child2->mindist_sofar));
-    }
-    else if(!banode_is_leaf(Q) && !banode_is_leaf(R)) {
-      {
-	double dist1 = (dyv_distance(Q->child1->pivot,R->child1->pivot) 
-			- Q->child1->radius - R->child1->radius);
-	double dist2 = (dyv_distance(Q->child1->pivot,R->child2->pivot) 
-			- Q->child1->radius - R->child2->radius);
-	
-	banode *first = (dist1 < dist2) ? R->child1 : R->child2;
-	double first_dist = real_min(dist1,dist2);
-	banode *second = (dist1 < dist2) ? R->child2 : R->child1;
-	double second_dist = real_max(dist1,dist2);
-	
-	batree_allnearest_neighbor(sps,Q->child1,first,first_dist,
-				   nn_rows,nn_dists);
-	batree_allnearest_neighbor(sps,Q->child1,second,second_dist,
-				   nn_rows,nn_dists);
-      }
-      {
-	double dist1 = (dyv_distance(Q->child2->pivot,R->child1->pivot) 
-			- Q->child2->radius - R->child1->radius);
-	double dist2 = (dyv_distance(Q->child2->pivot,R->child2->pivot) 
-			- Q->child2->radius - R->child2->radius);
-	
-	banode *first = (dist1 < dist2) ? R->child1 : R->child2;
-	double first_dist = real_min(dist1,dist2);
-	banode *second = (dist1 < dist2) ? R->child2 : R->child1;
-	double second_dist = real_max(dist1,dist2);
-	
-	batree_allnearest_neighbor(sps,Q->child2,first,first_dist,
-				   nn_rows,nn_dists);
-	batree_allnearest_neighbor(sps,Q->child2,second,second_dist,
-				   nn_rows,nn_dists);
-      }
-      Q->mindist_sofar = real_min(Q->mindist_sofar,
-				  real_max(Q->child1->mindist_sofar, Q->child2->mindist_sofar));
-    }
-  }
-}
-
-void allnnbatch_main(int argc, char **argv)
-{
-  char *filename = string_from_args("in",argc,argv,"default.fds");
-  dym *data = mk_dym_from_filename_improved(filename, argc, argv);
-  dyv_array *sps = mk_dyv_array_from_dym(data);
-  int rmin = int_from_args("rmin",argc,argv,5);
-  double mbw = double_from_args("mbw",argc,argv,MBW);
-  int num_rows = sps->size;
-  int iter, iter2;
-  alloc_timing_dyvs();
-
-  for(iter = 0; iter < 5; iter++) {
-    for(iter2 = 0; iter2 < 2; iter2++) {
-
-      dym *queries;
-      dym *sub_data;
-      //int local_queries = getnThPartOfnParts(data, 2, iter2, 
-      //				     &queries, &sub_data);
-      break_dym_into_train_and_test(data, num_rows / 2, &queries, &sub_data);
-
-
-      dyv_array *queries_dyv_array = mk_dyv_array_from_dym(queries);
-      dyv_array *sub_data_dyv_array = mk_dyv_array_from_dym(sub_data);
-      
-      free_dym(queries);
-      free_dym(sub_data);
-      
-      batree *query_tree = mk_batree_from_dyv_array(queries_dyv_array,rmin,mbw,
-						    TRUE);
-      batree *sub_data_tree = mk_batree_from_dyv_array(sub_data_dyv_array, 
-						       rmin, mbw, FALSE);
-      
-      int num_queries = dyv_array_size(queries_dyv_array);
-      dyv *num_visited = mk_dyv(num_queries);
-      
-      /* hacky, buy necessary to avoid worse hacks later on... */
-      
-      ivec *true_nn_rows[1];
-      ivec *guess_nn_rows[1];
-      dyv *guess_nn_dists[1];
-      
-      true_nn_rows[0] = mk_ivec(num_queries);
-      guess_nn_rows[0] = mk_ivec(num_queries);
-      guess_nn_dists[0] = mk_constant_dyv(num_queries, FLT_MAX);
-      
-      double time1 = get_time();
-      
-      Num_pt_dists = 0; Num_hr_dists = 0; Num_ball_prunes = 0; 
-      Num_hrect_prunes = 0;
-      
-      batree_allnearest_neighbor2(queries_dyv_array, sub_data_dyv_array,
-				  query_tree->root,
-				  sub_data_tree->root,FLT_MAX,
-				  guess_nn_rows[0], guess_nn_dists[0]);
-      
-      double time2 = get_time();
-      
-      double query_time = (time2-time1);
-      
-      add_to_dyv(query_times, query_time);
-      
-      free_ivec(true_nn_rows[0]); 
-      free_ivec(guess_nn_rows[0]);
-      free_dyv(guess_nn_dists[0]); 
-      free_dyv(num_visited); 
-      
-      
-      free_dyv_array(sub_data_dyv_array);
-      free_dyv_array(queries_dyv_array);
-      free_batree(query_tree);
-      free_batree(sub_data_tree);
-    }
-  }
-  dyv *total = mk_dyv(dyv_size(build_times));
-  for(iter = 0; iter < dyv_size(build_times); iter++)
-    dyv_set(total, iter, dyv_ref(build_times, iter) + 
-	    dyv_ref(query_times, iter)+ dyv_ref(query_build_times, iter));
-
-  print_timing_report(TRUE);
-  printf("Total Elapsed time:\n");
-  printf("  STD: %f\n", dyv_sdev(total));
-  printf("  MIN: %f\n", dyv_min(total));
-  printf("  MAX: %f\n", dyv_max(total));
-  printf("  AVE: %f\n", dyv_mean(total));
-	 
-  free_timing_dyvs();
-  free_dym(data);
-  free_dyv(total);
-  free_dyv_array(sps);
-}
-
-void allnnsingle_main(int argc, char **argv)
-{
-  char *filename = string_from_args("in",argc,argv,"default.fds");
-  dym *data = mk_dym_from_filename_improved(filename, argc, argv);
-  dyv_array *sps = mk_dyv_array_from_dym(data);
-  int rmin = int_from_args("rmin",argc,argv,5);
-  double mbw = double_from_args("mbw",argc,argv,MBW);
-  int num_rows = sps->size;
-  double time1 = get_time();
-  alloc_timing_dyvs();
-  batree *data_tree = mk_batree_from_dyv_array(sps, rmin, mbw, FALSE);
-
-  int r;
-  for(r = 0; r < num_rows; r++) {
-    dyv *query = dyv_array_ref(sps, r);
-    ivec *visited;
-    int row = batree_nearest_neighbor(sps, data_tree, query, r, &visited);
-    free_ivec(visited);
-  }
-  double time2 = get_time();
-  printf("Build + Query time: %f\n", time2 - time1);
-  free_dyv_array(sps);
-  free_dym(data);
-  free_batree(data_tree);
-  print_timing_report(FALSE);
-  free_timing_dyvs();
-}
-
-void allnn_main_bruteforce(int argc, char **argv) {
-  char *filename = string_from_args("in",argc,argv,"default.fds");
-  dym *data = mk_dym_from_filename_improved(filename, argc, argv);
-  dyv_array *sps = mk_dyv_array_from_dym(data);
-  //free_dym(data);
-
-
-
-  int i, j;
-  int num_row = dym_rows(data);
-  double time1 = get_time();
-  for(i = 0; i < num_row; i++) {
-    double best_dist = 1e90;
-    int best_nn = -1;
-    dyv *row = dyv_array_ref(sps, i);
-    for(j = 0; j < num_row; j++) {
+      // ArrayList <double> arr1;
+      //ArrayList <double> arr2;
+      index_t cols_q;
+      index_t cols_r;
+      double min_dist;
       double dist;
-      if(i == j)
-	continue;
-      dyv *cmp = dyv_array_ref(sps, j);
-      dist = dyv_dsqd(row, cmp);
-      if(dist < best_dist) {
-	best_dist = dist;
-	best_nn = j;
-      }
+      //arr1.Init(q_matrix->n_rows()+1);
+      //arr2.Init(q_matrix->n_rows()+1);
+      
+      results.Init(q_matrix->n_cols());
+      
+      //printf("cols_q is %d\n",q_matrix->n_cols());
+      //printf("cols_r is %d\n",r_matrix->n_cols());
+      //printf("entered nearest neighbour computation\n");
+      
+      for(cols_q=0; cols_q < q_matrix->n_cols(); cols_q++)
+	{
+	  min_dist=32768.0;
+	  dist=0.0;
+	  for(cols_r=0; cols_r < r_matrix->n_cols(); cols_r++)
+	    {
+	      using namespace la;
+	      dist=la::DistanceSqEuclidean(q_matrix->n_rows(),q_matrix->GetColumnPtr(cols_q),q_matrix->GetColumnPtr(cols_r));
+	      //printf("The distance is %f\n",dist);
+	      if(dist<min_dist&&dist!=0.0)
+		{
+		  results[cols_q]=cols_r;
+		  min_dist=dist;
+		}
+	    }
+	  
+	}
+    }
+  void PrintResults(Matrix *q_matrix, Matrix *r_matrix)
+    {
+      FILE *fp;
+      fp=fopen("naive_nearest_neighbour.txt","w");
+      printf("The nearest neighbours are as follows\n");
+      // printf("The number of results are\n");
+      //printf("%d\n",results.size());
+      for(index_t i=0;i< results.size();i++)
+	{
+	  fprintf(fp,"Point:%d\tDistance:%f\n", i,la::DistanceSqEuclidean(r_matrix->n_rows(),r_matrix->GetColumnPtr(results[i]),q_matrix->GetColumnPtr(i)));
+	  //fprintf(fp,"\n");
+	  
+	}
+    }
+};
+
+//End of class definition of AllNNNaive........................................
+
+
+//Definition of class Stat.....................................................
+ class Stat 
+{
+  //This is not used in case of single tree operations. used only in case of dual tree operations
+  
+ private:
+  double distance_max; //This gives the max distance within which all neighbours should be found for all points
+ public:
+  void set_maximum_distance(double distance)
+    {
+      this->distance_max=distance;
+    }
+  double get_maximum_distance()
+    {
+      return distance_max;
+    }
+  void Init() {set_maximum_distance(32768.0);}
+  
+  void Init(const Matrix& dataset, index_t start, index_t count) 
+    {
+      Init();
+    }
+  
+  void Init(const Matrix& dataset, index_t start, index_t count,
+	    const Stat& left_stat, const Stat& right_stat) 
+    {
+      Init();
+    }
+};
+
+//End of definition of class Stat..............................................
+
+
+
+
+//Definition of class SingleTreeResults begins................................
+
+class SingleTreeResults
+{
+ private: 
+  double distance_;
+  index_t index_;
+  
+ public:
+
+  //setter function
+  void set_result(double distance, index_t index)
+    {
+      //Here index is the index in r_matrix which is the potential nn for a given query point
+      this->distance_=distance;
+      this->index_=index;
+    }
+
+  //getter function
+
+  double get_distance()
+    {
+      return distance_;
+    }
+
+  index_t get_index()
+    {
+      return index_;
+    }
+
+ 
+  SingleTreeResults()
+    {
+      this->distance_=MAX_DOUBLE;
+      this->index_=-1;
+    }
+};
+
+//definition of class SingleTreeResults ends...................................
+
+
+
+typedef BinarySpaceTree<DHrectBound<2>, Matrix, Stat> Tree;
+
+
+//Definition of AllNNSingleTree begins........................................
+
+class AllNNSingleTree
+{
+ public:
+  ArrayList <SingleTreeResults> str;
+ 
+  AllNNSingleTree()
+    {
+      //Initialize the results list
+      str.Init();
+    }
+  
+
+  //Interesting functions....
+
+  //This is a friend function and can be invoked by other classes too. This takes in two points and sees if they are the same or not
+  friend bool check_if_equal(double *arr1, double *arr2,index_t size)
+    {
+      index_t i=0;
+      for(i=0;i<size;i++)
+	{
+	  if(arr1[i]!=arr2[i])
+	    return false;
+	}
+      return true;
+    }
+
+  //This is the function which will perform the actual single tree algorithm and spit the results 
+  
+  void ComputeAllNNSingleTree(Matrix *q_matrix, Matrix *r_matrix)
+    {
+
+      // expand the size of results
+      str.GrowTo(q_matrix->n_cols()); 
+      
+      // Build the kdtree form the reference matrix.
+      using namespace tree;
+      Tree *root=tree::MakeKdTreeMidpoint<Tree>(*r_matrix,LEAF_SIZE,NULL,NULL);
+      
+      
+      double max_dist_sq=MAX_DOUBLE;  
+      
+      //For each point find out the nearest neighbour
+     
+      for(index_t i=0;i<q_matrix->n_cols();i++)
+	{
+	  SingleTreeResults *nearest_nn=new SingleTreeResults();
+	  str[i]=*FindNearestNeighbour(root,q_matrix->GetColumnPtr(i),r_matrix,nearest_nn);
+	  printf("Distance to the nearest neighbour is %f\n",str[i].get_distance());
+	  nearest_nn->set_result(MAX_DOUBLE,-1);  //This function flushes the values stored in nearest_nn, for the next iteration
+	} 
+    }
+
+  SingleTreeResults *FindNearestNeighbour(Tree *root,double *point, Matrix *r_matrix, SingleTreeResults *nearest_nn)
+    {
+      //check if it is the leaf 
+      if(root->is_leaf())
+	{
+	  //find out the minimum distance by querying all points
+
+	  double min_dist_for_this_node=MAX_DOUBLE;
+	  SingleTreeResults *potential_nn;
+	  potential_nn=new SingleTreeResults();
+	  potential_nn->set_result(32768.0,-1);
+	  
+	  for(index_t i=root->begin();i<root->end();i++)
+	    {
+	      //print(r_matrix->GetColumnPtr(i),r_matrix->n_rows());
+	      //print(point,r_matrix->n_rows());
+	      double temp_dist=la::DistanceSqEuclidean(r_matrix->n_rows(),r_matrix->GetColumnPtr(i),point);
+
+	     
+	      if(check_if_equal(r_matrix->GetColumnPtr(i),point,r_matrix->n_rows())==false && temp_dist < min_dist_for_this_node) //check_if_equal function is being called to avoid comparison between the same points
+		{
+		 
+		  potential_nn->set_result(temp_dist,i);
+		  min_dist_for_this_node=temp_dist;
+		}
+	    }
+	  return potential_nn;
+	}
+      
+      else
+	{
+	  //This is not a leaf
+	  //so find the nearer node and the farther node
+	  //WE CAN FIND OUT THE MINIMUM DISTANCE OF THE POINT TO THE BOUNDING BOX AND EXPLORE THE BOUNDING BOX WHICH IS THE CLOSEST
+	  
+	  float min_distance_to_left_child=root->left()->bound().MinDistanceSq(point);
+	  float min_distance_to_right_child=root->right()->bound().MinDistanceSq(point);
+
+	 
+	  Tree *farther;
+	  SingleTreeResults *potential_nn;
+	  if(min_distance_to_left_child < min_distance_to_right_child)
+	    {
+	      //recursively explore the left half
+	      farther=root->right();
+	      potential_nn=FindNearestNeighbour(root->left(),point,r_matrix,nearest_nn);
+	    }
+	  else
+	    {
+	      //recursively explore the right half
+	      
+	      farther=root->left();
+	      potential_nn=FindNearestNeighbour(root->right(),point,r_matrix,nearest_nn);	
+	    }
+	  
+	  if(potential_nn->get_distance()< nearest_nn->get_distance())
+	    {
+	      nearest_nn->set_result(potential_nn->get_distance(),potential_nn->get_index());
+	    }
+	  
+	  if(potential_nn->get_distance()>farther->bound().MinDistanceSq(point))
+	    {
+	      SingleTreeResults *potential_nn=FindNearestNeighbour(farther,point,r_matrix,nearest_nn);
+	      if(potential_nn->get_distance()<nearest_nn->get_distance()) 
+		{
+		  nearest_nn->set_result(potential_nn->get_distance(),potential_nn->get_index());
+		  return nearest_nn;
+		}
+	    }
+	  else
+	    {
+	      //the farther node need not be explored . Simply return nearest_nn
+	      return nearest_nn;
+	    }
+	  return nearest_nn;
+	}
+    }
+
+  };
+
+
+//Definition of AllNNSingleTree ends.........................................
+
+
+//Definition of AllKNNSingleTreeResults begins....................................
+class AllKNNSingleTreeResults
+{
+ public:
+  ArrayList<ArrayList<SingleTreeResults> > astr; // u see it is basically a 2-D array of SingleTreeResults
+
+  AllKNNSingleTreeResults()
+    {
+     
+    }
+};
+
+//Definition of AllKNNSignleTreeResults ends here.................................................
+
+//definition of AllKnnSingleTree begines....................................................
+class AllKNNSingleTree
+{
+ public:
+
+  friend bool same_points(double *point1,double *point2, int len);//already defined 
+  AllKNNSingleTreeResults results_matrix;
+  
+  AllKNNSingleTree() 
+    {
+      //Initialize  results_matrix. This will later be reinitialized to the number of columns in the function ComputeAllKNNSingleTree
+      // results_matrix.astr.Init();
+     
+    }
+
+
+  //This is the function which will perform the actual single tree algorithm and spit the results 
+  
+   void ComputeAllKNNSingleTree(Matrix *q_matrix,Matrix *r_matrix,int k)
+     {
+       //Initialize results_matrix
+       
+       results_matrix.astr.Init(r_matrix->n_cols());
+       for(int i=0;i<r_matrix->n_cols();i++)
+	 results_matrix.astr[i].Init(k);
+       
+       //printf("results_matrix initialized\n");
+       // Build the kdtree form the reference matrix.
+       using namespace tree;
+       
+       Tree *root=tree::MakeKdTreeMidpoint<Tree>(*r_matrix,3,NULL,NULL); //here the leaf size is set to 3
+        printf("Tree built\n");
+       
+       //Now for each query point find the k nearest neighbours
+       
+      
+       
+       for(int i=0;i<r_matrix->n_cols();i++)
+	 {   
+	   ArrayList<SingleTreeResults> str;
+	   str.Init(k);
+	   int length=0;
+	   
+	   //Length=k if there are more than k number of reference points.By clever programming the length parameter which is now being sent explicitly, can be totally avoided
+	   length=FindKNearestNeighbours(root,q_matrix->GetColumnPtr(i),r_matrix,str,length,k);
+	   
+	   printf("The nearest neighbour distances are\n");
+	   
+	   for(int l=0;l<length;l++)
+	     printf("%f\n",str[l].get_distance());
+	   
+	   
+	   for(int j=0;j<length;j++)
+	     {
+	       //printf("Will add distance=%f\n",str[j].get_distance());
+	       results_matrix.astr[i][j].set_result(str[j].get_distance(),str[j].get_index());
+	     }
+	   
+	   
+	   
+	   printf("The origianl point is\n");
+	   
+	   for(int j=0;j<r_matrix->n_rows();j++)
+	     printf("%f\n",q_matrix->GetColumnPtr(i)[j]);
+	   
+	 }
+     }
+   
+   //A 1-D matrix of the k nearest neighbours is returned
+
+   int FindKNearestNeighbours(Tree *root, double *point,Matrix *r_matrix,ArrayList<SingleTreeResults> &str,int length,int k)
+     {
+       //Base case is that the node is a leaf 
+       
+          if(root->is_leaf())
+	 {
+	   // printf("Will explore leaf\n");
+	   int position;
+	   //check for number of elements
+	   double dist;
+	   int end;
+	   if(length==0)
+	     { 
+	       end=-1;  //end points to the position where the array ends. That is it is the index of the last element 
+	     }
+	   else
+	     {
+	       end=length-1;
+	     }
+
+	   for(int i=root->begin();i<root->end();i++)
+	     {
+	       //one very important check is to see that query point is not the same point as any other point beingcompared
+	       if(!check_if_equal(point,r_matrix->GetColumnPtr(i),r_matrix->n_rows()))
+		 { 
+		   dist=la::DistanceSqEuclidean(r_matrix->n_rows(),point,r_matrix->GetColumnPtr(i)); 
+		  //find where the new element should be pushed
+		   int start=0;
+		   position=find_index(str,dist,start,end); 
+		  
+		   length+=push_into_array(str,position,dist,length,i,k);
+		   end=length-1; 
+		  
+		   //this is just like a test. One can safely remove it
+		   if(length>k)
+		     {
+		       printf("SOMEWHERE ERROR HAS OCCURED\n");
+		       exit(0);
+		     }
+		   
+		 }
+	       //printf("The value of length to be returned from this leaf node is %d\n",length);
+	     }
+	   return length;
+	 }
+
+       else
+	 {
+	   //this is not a root node. Hence find the distance to the bounding boxes
+	   
+	   double min_distance_to_left_child=root->left()->bound().MinDistanceSq(point);
+	   double min_distance_to_right_child=root->right()->bound().MinDistanceSq(point);
+	   
+	   Tree *farther;
+	   
+	   if(min_distance_to_left_child < min_distance_to_right_child)
+	     {
+	       //Recursively explore the left child
+	       farther=root->right();
+	       length=FindKNearestNeighbours(root->left(),point,r_matrix,str,length,k);
+	     }
+	   else
+	     {
+	       //Recursively explore the right child
+	       farther=root->left();
+	       length=FindKNearestNeighbours(root->right(),point,r_matrix,str,length,k);
+	     
+	       
+	     }
+	   
+	   //If number of neighbours found are less than k then go ahead and explore the other half too
+	   
+	   if(length<k)
+	     {
+	       //recursively explore the farther child
+	      
+	       length=FindKNearestNeighbours(farther,point,r_matrix,str,length,k);
+	      
+	     }
+	   else
+	     {
+	       //check the other half only if the kth nn distance is greater than the distance of the point form the farther bounding box
+	      
+	       if(str[k-1].get_distance()> farther->bound().MinDistanceSq(point))
+		 {
+		   length=FindKNearestNeighbours(farther,point,r_matrix,str,length,k);
+		 }
+	     }
+	   return length;
+	 }
+     }
+
+   //The function sto follow are friend functions. they are also used by the dual tree algorithms
+
+friend int find_index(ArrayList<SingleTreeResults> str,double dist,int start,int end)
+{
+ 
+  //this means that there are no elements in the array
+  if(start>end)
+    return 0;
+
+  //this means there is exactly 1 element in the array
+  if(start==end)
+    {
+     
+      if(dist>str[start].get_distance())
+	{
+	  // printf("will return %d\n",end+1);
+	  return end+1; //return end if the element to be added is at the end of the list
+	}
+      else return start;
+    }
+
+  //find where the element will be in the sorted array. This is just the binary search
+
+  if(dist==str[start+end/2].get_distance()) 
+    return start+end/2;
+
+  else
+    {
+     
+      if(dist<str[(end+start)/2].get_distance())
+	{
+	  //go left
+	  return find_index(str,dist,start,(start+end)/2);
+	}
+      else
+	{
+	      //go right
+	  return find_index(str,dist,(start+end)/2+1,end);
+	}
     }
-  }
-  double time2 = get_time();
-  printf("Query time: %f\n", time2 - time1);
-  free_dym(data);
-  free_dyv_array(sps);
 }
 
-void allnn_main_single_tree(int argc, char **argv) {
+friend int push_into_array(ArrayList<SingleTreeResults> &str,int position, double dist,int length, int index,int k)
+{
+  // printf("Length is %d\n",length);
+  //printf("Index to be inserted is %d\n",index);
+  //printf("Distance to be inserted is %f\n",dist);
+  //printf("The position is %d\n",position);
+  if(position==length) //that means add the element to the end of list
+    {
+      if(length==k)
+	{
+	  //cannot push into array
+	  return 0;
+	}
+      else
+	{
 
+	  //add it to the end of the array
+	  str[length].set_result(dist,index);
+	  return 1;
+	}
+    }
 
-  char *filename = string_from_args("in",argc,argv,"default.fds");
-  dym *data = mk_dym_from_filename_improved(filename, argc, argv);
-  dyv_array *sps = mk_dyv_array_from_dym(data);
-  //free_dym(data);
-
-  /* Mine: till here data has been read and an array of points created. sps is a pointer to a dyv_array which is a structure which holds a double dimensional array of points */
-
-
-  alloc_timing_dyvs();
-
-  int rmin = int_from_args("rmin",argc,argv,5);
-  double mbw = double_from_args("mbw",argc,argv,MBW);
-
-  double time1 = get_time();
-  batree *bat = mk_batree_from_dyv_array(sps,rmin,mbw, FALSE);
-
-  /* Mine: A batree has been created by using the set of points stored in dyv_array */
-
+  ArrayList <SingleTreeResults> temp;
+  temp.Init(k);
+   
+  //the element will be added in the middle of the array
  
-  int num_data = dyv_array_size(sps);
-  int num_queries = num_data;
-  dyv *num_visited = mk_dyv(num_queries);
 
-  /* num_data and num_queries give the number of query and data points. Both atre set to the same value  */
-  /* hacky, buy necessary to avoid worse hacks later on... */
+  for(int j=0;j<position;j++)
+    temp[j].set_result(str[j].get_distance(),str[j].get_index());
 
-  ivec *true_nn_rows[1];
-  ivec *guess_nn_rows[1];
-  dyv *guess_nn_dists[1];
+  
+  temp[position].set_result(dist,index);
+  
 
-  true_nn_rows[0] = mk_ivec(num_queries);
-  guess_nn_rows[0] = mk_ivec(num_queries);
-  guess_nn_dists[0] = mk_constant_dyv(num_queries, FLT_MAX);
+  if(length==k)
+    {
+      for(int t=length-1;t>position;t--)
+	temp[t].set_result(str[t-1].get_distance(),str[t-1].get_index());
 
-  Num_pt_dists = 0; Num_hr_dists = 0; Num_ball_prunes = 0; 
-  Num_hrect_prunes = 0;
-
- 
+      for(int j=0;j<length;j++)
+	str[j].set_result(temp[j].get_distance(),temp[j].get_index());
+      // delete(temp);
+	return 0;
+    }
+  else
+    {
+     
     
-  batree_allnearest_neighbor(sps,bat->root,bat->root,FLT_MAX,
-			     guess_nn_rows[0], guess_nn_dists[0]);
+      for(int t=length;t>position;t-=1)
+	{
+	  temp[t].set_result(str[t-1].get_distance(),str[t-1].get_index());
+	}
+      
+    
+      for(int j=0;j<length+1;j++)
+	{
+	  str[j].set_result(temp[j].get_distance(),temp[j].get_index());
+	}
+      return 1;
+    }
+}
+};
+
+//Definition of AllKNNSingleTree ends................................................................................
+
+
+
+//Definition of AllKNNDualTreeResults begins................................................................................
+
+class AllKNNDualTreeResults //This is just the same as AllKNNSingleTreeResults
+{
+ public:
+   ArrayList<ArrayList<SingleTreeResults> > astr; 
+};
+
+class AllKNNDualTree
+{
+ public:
+  AllKNNDualTreeResults results_matrix; 
+  AllKNNDualTree() 
+    {
+    
+    }
+  
+  //Friend Functions............................
+
+  bool check_if_equal(double *arr1, double *arr2, int length);
+
   
 
-  double time2 = get_time();
+  //This is the function which will perform the actual dual tree algorithm and spit the results 
 
-  double tot_time = (time2-time1);
+  void ComputeAllKNNDualTree(Matrix *q_matrix,Matrix *r_matrix,int k) 
+    {
+        
+      // Build the kdtree form the reference matrix and the query matrix
+      using namespace tree;
+      
+      Tree *r_tree=tree::MakeKdTreeMidpoint<Tree>(*r_matrix,3,NULL,NULL); //here the leaf size is set to 3
+      printf("ref tree built\n"); 
+      
+      Tree *q_tree=tree::MakeKdTreeMidpoint<Tree>(*r_matrix,3,NULL,NULL); //here the leaf size is set to 3
+      printf("query tree built\n");
+      
+     
+      //Inialize the 2-D array
 
-  printf("Build + Query time: %f\n",tot_time); 
+      results_matrix.astr.Init(r_matrix->n_cols()); //Initialize the array list to initially contain the number of elements passed on as the parameter
+     
+      for(int i=0;i<r_matrix->n_cols();i++) //initalize each column of the arraylist
+	results_matrix.astr[i].Init();
 
+      FindKNearestNeighboursDualTree(q_tree,r_tree,q_matrix,r_matrix,k);
+     
+      /* for(int l=0;l<r_matrix->n_cols();l++)
+	{
+	  for(int t=0;t<k;t++)
+	    {
+	      printf("distance is %f ",results_matrix.astr[l][t].get_distance());
+	    }
+	  printf("\n");
+	  }*/
+      //****************************************************************
+    }
   
-  print_timing_report(FALSE);
-  
-  free_ivec(true_nn_rows[0]);
-  free_ivec(guess_nn_rows[0]);
-  free_dyv(guess_nn_dists[0]);
-  free_dyv(num_visited); 
-  free_batree(bat); free_dyv_array(sps);
-  free_timing_dyvs();
 
-#ifdef TIMING
-  printf("Timing enabled on this run.\n");
+
+double  FindKNearestNeighboursDualTree(Tree *q_tree,Tree *r_tree,Matrix *q_matrix,Matrix *r_matrix,int k) //A 1-D matrix of the k nearest neighbours is returned
+ {
+
+ //if distance between the two boxes is larger than the max _distance then return
+  double distance_between_boxes=q_tree->bound().MinDistanceSq (r_tree->bound()); //base case
+  if(q_tree->is_leaf()&& r_tree->is_leaf())
+    {
+      int start,end;
+
+      //check if pruneable
+      if(q_tree->stat().get_maximum_distance() < distance_between_boxes)
+	{
+	  //then there is no need to go further and hence we can return
+	  return MAX_DOUBLE;
+	}
+      else
+	{
+	  //not purneable. therefore carry out exhaustive point-to-point computations
+
+	  double max_dist=0.0;
+	  double distance;
+	  int position;
+	  int count=0;
+
+	  for(int i=q_tree->begin();i<q_tree->end();i++)
+	    {
+	     
+	      count=0;
+	      for(int j=r_tree->begin();j<r_tree->end();j++)
+		{
+		  if(!check_if_equal(q_matrix->GetColumnPtr(i),r_matrix->GetColumnPtr(j),q_matrix->n_rows())) //to make sure that we are not comparing the same set of points
+		    {
+		      
+		      distance=la::DistanceSqEuclidean (r_matrix->n_rows(),q_matrix->GetColumnPtr(i),r_matrix->GetColumnPtr(j));
+		      //we would like to find index of this point into str. this will enter into str[i]. This function takes in as argument an array list of single tree results            
+		     
+
+		      if(results_matrix.astr[i].size()==0)  
+			{
+			  //initialize start and end
+			 
+			  start=0;
+			  end=-1;
+			}
+
+		      int length=results_matrix.astr[i].size(); //this calculates the old length		     
+		      
+		      position=find_index(results_matrix.astr[i],distance,start,results_matrix.astr[i].size()-1);
+		      if(length<k)
+			{
+			 
+			  //increase the length of the arraylist, only if the number of elements as of now are lesser than k
+			  results_matrix.astr[i].AddBack(1); 
+			 
+			}
+		      //Note the length is still the old length, the one that has been claulated in the step above. So it is 1 less than the actual length
+		      push_into_array(results_matrix.astr[i],position,distance,length,j,k);
+		      count++;
+		     
+		    }
+		
+		}
+
+	      if(results_matrix.astr[i].size()<k)
+		
+		{
+		  max_dist=MAX_DOUBLE;
+		}
+	      else 
+		{
+		  //all k nn have been found
+		  max_dist=max_dist > results_matrix.astr[i][k-1].get_distance()?max_dist:results_matrix.astr[i][k-1].get_distance(); 
+	
+		}
+
+	      for(int z=0;z<results_matrix.astr[i].size();z++)
+		{
+		  printf("distance=%f and index=%d\n",results_matrix.astr[i][z].get_distance(),results_matrix.astr[i][z].get_index());
+		}
+	 
+
+	      return max_dist;
+	    }
+	}
+    }
+  
+  else
+    {
+      //Not base case. Check if one can Prune
+      if(q_tree->stat().get_maximum_distance() < distance_between_boxes)
+	{
+	  //then there is no need to go further and henraghavce we can return
+	  return MAX_DOUBLE;
+	}
+
+      //NOT PRUNEABLE
+      //both are not leafs
+      if(!q_tree->is_leaf() && !r_tree->is_leaf())
+	{
+	 
+	  double max_dist_q_left_r_left=FindKNearestNeighboursDualTree(q_tree->left(),r_tree->left(),q_matrix,r_matrix,k);
+	  
+	 
+	  
+	  double max_dist_q_left_r_right=FindKNearestNeighboursDualTree(q_tree->left(),r_tree->right(),q_matrix,r_matrix,k);
+
+	  double max_dist_q_left= max_dist_q_left_r_left< max_dist_q_left_r_right? max_dist_q_left_r_left: max_dist_q_left_r_right;//take minimum
+
+	 
+	  double max_dist_q_right_r_left=FindKNearestNeighboursDualTree(q_tree->right(),r_tree->left(),q_matrix,r_matrix,k);
+	 
+	  double max_dist_q_right_r_right=FindKNearestNeighboursDualTree(q_tree->right(),r_tree->right(),q_matrix,r_matrix,k);
+
+	  double max_dist_q_right= max_dist_q_right_r_left< max_dist_q_right_r_right? max_dist_q_right_r_left: max_dist_q_right_r_right;
+	 
+	  double max_dist_q=max_dist_q_left>max_dist_q_right?max_dist_q_left:max_dist_q_right;
+	  q_tree->stat().set_maximum_distance(max_dist_q);
+	  return q_tree->stat().get_maximum_distance();
+	}
+
+      else
+	{
+	 
+	  //q_tree is leaf and r_tree is not
+	  if(q_tree->is_leaf()&&!r_tree->is_leaf())
+	    {
+	 
+	      double max_dist_q_r_left=FindKNearestNeighboursDualTree(q_tree,r_tree->left(),q_matrix,r_matrix,k);
+	      double max_dist_q_r_right=FindKNearestNeighboursDualTree(q_tree,r_tree->right(),q_matrix,r_matrix,k);
+	      double max_dist_q = max_dist_q_r_left > max_dist_q_r_right?max_dist_q_r_right:max_dist_q_r_left; //take minimum
+	      q_tree->stat().set_maximum_distance(max_dist_q);
+	      return q_tree->stat().get_maximum_distance();
+	    }
+
+	  else
+	    {
+	      
+	      //q_tree is not a leaf and r_tree is
+
+	      if(!q_tree->is_leaf()&&r_tree->is_leaf())
+		{
+		  
+		  double max_dist_q_left_r=FindKNearestNeighboursDualTree(q_tree->left(),r_tree,q_matrix,r_matrix,k);
+		  double max_dist_q_right_r=FindKNearestNeighboursDualTree(q_tree->right(),r_tree,q_matrix,r_matrix,k);
+		  double max_dist_q= max_dist_q_left_r > max_dist_q_right_r? max_dist_q_left_r: max_dist_q_right_r;
+		  q_tree->stat().set_maximum_distance(max_dist_q);
+		  return max_dist_q;
+		}
+	    }
+	}
+    }
+ }
+};
+
 #endif
-
-  free_dym(data);
-}
-
-void allnn_main(int argc,char *argv[]) {
-  Verbosity = 0;
-  allnn_main_single_tree(argc, argv);
-}
diff --git a/fastlib/u/gmravi/allknn/allnn.h b/fastlib/u/gmravi/allknn/allnn.h
index 6817da9181..7e64ba8fab 100644
--- a/fastlib/u/gmravi/allknn/allnn.h
+++ b/fastlib/u/gmravi/allknn/allnn.h
@@ -12,10 +12,20 @@ class AllNNNaive
   
  class Stat 
 {
+  //This is not used in case of single tree operations. used only in case of dual tree operations
 
-  //This is justa dummy class
+ private:
+  double distance_max; //This gives the max distance within which all neighbours should be found for all points
  public:
-  void Init() {}
+  void set_maximum_distance(double distance)
+    {
+      this->distance_max=distance;
+    }
+  double get_maximum_distance()
+    {
+      return distance_max;
+    }
+  void Init() {set_maximum_distance(32768.0);}
     
 void Init(const Matrix& dataset, index_t start, index_t count) 
 {
@@ -86,7 +96,7 @@ class AllKNNSingleTreeResults
 
   AllKNNSingleTreeResults()
     {
-      
+     
     }
 };
 
@@ -102,41 +112,35 @@ class AllKNNSingleTree
       // results_matrix.astr.Init();
       printf("Initialized properly\n");
     }
-  
+  friend bool same_points(double *point1,double *point2, int len);
   //This is the function which will perform the actual single tree algorithm and spew the results 
   
   void ComputeAllKNNSingleTree(Matrix*,Matrix*,int);  // A 2-D matrix is returned
-  int  FindKNearestNeighbours(Tree*,double*,Matrix*,SingleTreeResults*,int,int); //A 1-D matrix of the k nearest neighbours is returned
+  int  FindKNearestNeighbours(Tree*,double*,Matrix*,ArrayList<SingleTreeResults>&,int,int); //A 1-D matrix of the k nearest neighbours is returned
+
 
 };
 
-
-
 class AllKNNDualTreeResults //This is just the same as AllKNNSingleTreeResults
 {
  public:
    ArrayList<ArrayList<SingleTreeResults> > astr; 
-
-   
 };
 
-classAllKNNDualTree
+class AllKNNDualTree
 {
-public:
-  AllKNNDualTreeResults results_matrix;
-  //constructor
-  //cosntructor definition
+ public:
+  AllKNNDualTreeResults results_matrix; 
   AllKNNDualTree() 
     {
-      //Initialize  results_matrix_ This will later be reinitialized to the number of columns in the function ComputeAllKNNSingleTree
-      // results_matrix.astr.Init();
-      printf("Initialized properly\n");
+    
     }
-  
-  //This is the function which will perform the actual single tree algorithm and spew the results 
-  
-  void ComputeAllKNNSingleTree(Matrix*,Matrix*,int);  // A 2-D matrix is returned
-  int  FindKNearestNeighbours(Tree*,double*,Matrix*,SingleTreeResults*,int,int); //A 1-D matrix of the k nearest neighbours is returned
-}
+  friend bool same_points(double*, double*);
+  //This is the function which will perform the actual dual tree algorithm and spew the results 
+
+  void ComputeAllKNNDualTree(Matrix*,Matrix*,int);  // A 2-D matrix is returned
+  double  FindKNearestNeighboursDualTree(Tree *q_tree,Tree *r_tree,Matrix*,Matrix*,int k); //A 1-D matrix of the k nearest neighbours is returned
+};
+
 
 
diff --git a/fastlib/u/gmravi/allknn/build.py b/fastlib/u/gmravi/allknn/build.py
index e664b5aa47..6396f1f151 100644
--- a/fastlib/u/gmravi/allknn/build.py
+++ b/fastlib/u/gmravi/allknn/build.py
@@ -1,16 +1,16 @@
 
 librule(
-    name = "allknn_mine",                    # this line can be safely omitted
-    sources = [],                          # files that must be compiled
-    headers = ["allnn.h"],             # include files part of the 'lib'
+    name = "allknn",                    # this line can be safely omitted
+    sources = [""],                          # files that must be compiled
+    headers = ["allknn.h"],             # include files part of the 'lib'
     deplibs = ["fastlib:fastlib_int"]        # depends on fastlib core
     )
 
 binrule(
-    name = "main",                  # the executable name
-    sources = ["main.cc"],          # compile main.cc
-    headers = [],                   # no extra headers
-    deplibs = [":allknn_mine"]           #
+    name = "allknn_main",                  # the executable name
+    sources = ["allknn_main.cc"],          # compile main.cc
+    headers = ["allknn.h"],                   # no extra headers
+    deplibs = ["fastlib:fastlib_int"]           
     )
 
 # to build:
diff --git a/fastlib/u/gmravi/allknn/main.cc b/fastlib/u/gmravi/allknn/main.cc
index cc66e2461b..728bd477e1 100644
--- a/fastlib/u/gmravi/allknn/main.cc
+++ b/fastlib/u/gmravi/allknn/main.cc
@@ -7,10 +7,10 @@ bool check_if_equal(ArrayList <double> arr1, ArrayList <double> arr2)
   for(i=0;i<arr1.size();i++)
     {
       if(arr1[i]!=arr2[i])
-	return 0;
+	return false;
     }
-  return 1;
-}
+  return true;
+} 
 void AllNNNaive::Compute(Matrix *q_matrix, Matrix *r_matrix)
 {
   //for each column vector of q_matrix find the nearest neighbour in r_matrix
@@ -175,18 +175,24 @@ void AllNNSingleTree::ComputeAllNNSingleTree(Matrix* q_matrix, Matrix  *r_matrix
   //return str_;
 }
 
-int find_index(SingleTreeResults *str,double dist,int start,int end)
+int find_index(ArrayList<SingleTreeResults> str,double dist,int start,int end)
 {
   printf("Start is %d and end is %d\n",start,end);
+  printf("Searching for distance %f\n",dist);
+  printf("In function find index str is...\n");
+  for(int j=start;j<=end;j++)
+    {
+      printf("distance is %f\n",str[j].get_distance());
+    }
   if(start>end)
     return 0;
   if(start==end)
     {
-      //printf("str[start].get_distance() is %f",str[start].get_distance());
-      //printf("dist is %f\n",dist);
+      printf("str[start].get_distance() is %f",str[start].get_distance());
+      printf("dist is %f\n",dist);
       if(dist>str[start].get_distance())
 	{
-	  //  printf("will return %d\n",end+1);
+	    printf("will return %d\n",end+1);
 	  return end+1; //returned if the element to be added is at the end of the list
 	}
       else return start;
@@ -200,30 +206,32 @@ int find_index(SingleTreeResults *str,double dist,int start,int end)
       if(dist<str[(end+start)/2].get_distance())
 	{
 	  //go left
-	  //printf("left\n");
+	  printf("left\n");
 	  return find_index(str,dist,start,(start+end)/2);
 	}
       else
 	{
 	      //go right
-	  // printf("right\n");
-	  //printf("New start is %d and new end is %d\n",(start+end)/2+1,end);
+	  printf("right\n");
+	  printf("New start is %d and new end is %d\n",(start+end)/2+1,end);
 	  return find_index(str,dist,(start+end)/2+1,end);
 	}
     }
 }
 
-int push_into_array(SingleTreeResults *str,int position, double dist,int length, int index,int k)
+int push_into_array(ArrayList<SingleTreeResults> &str,int position, double dist,int length, int index,int k)
 {
-  //printf("Length is %d\n",length);
-  //printf("Index to be inserted is %d\n",index);
-  //printf("Distance to be inserted is %f\n",dist);
-  //printf("The position is %d\n",position);
+  printf("Length is %d\n",length);
+  printf("Index to be inserted is %d\n",index);
+  printf("Distance to be inserted is %f\n",dist);
+  printf("The position is %d\n",position);
   if(position==length) //that means add the element to the end of list
     {
       if(length==k)
 	{
 	  //cannot push into array
+	  for(int l=0;l<length;l++)
+	printf("distance:%f and index:%d\n",str[l].get_distance(),str[l].get_index());
 	  return 0;
 	}
       else
@@ -231,21 +239,29 @@ int push_into_array(SingleTreeResults *str,int position, double dist,int length,
 
 	  //add it to the end of the array
 	  str[length].set_result(dist,index);
+	  for(int l=0;l<length+1;l++)
+	    printf("distance:%f and index:%d\n",str[l].get_distance(),str[l].get_index());
+
+
+ 
 	  return 1;
 	}
     }
 
   ArrayList <SingleTreeResults> temp;
   temp.Init(k);
-  // printf("temp created\n");
+   printf("temp created\n");
   //the element will be added in the middle of the array
-  //printf("The element will be added to the middle of the array\n");
+  printf("The element will be added to the middle of the array\n");
 
   for(int j=0;j<position;j++)
     temp[j].set_result(str[j].get_distance(),str[j].get_index());
 
+  printf("Distance is %f\n",dist);
+  printf("index is %d\n",index);
   temp[position].set_result(dist,index);
-  //printf("Length is %d and k=%d\n",length,k);
+  printf("Length is %d and k=%d\n",length,k);
+  printf("temp at position is set up\n");
 
   if(length==k)
     {
@@ -255,22 +271,57 @@ int push_into_array(SingleTreeResults *str,int position, double dist,int length,
       for(int j=0;j<length;j++)
 	str[j].set_result(temp[j].get_distance(),temp[j].get_index());
       // delete(temp);
+      printf("Before returning i have str as\n");
+      for(int l=0;l<length;l++)
+	printf("distance:%f and index:%d\n",str[l].get_distance(),str[l].get_index());
+     
 	return 0;
     }
   else
     {
-      for(int t=length;t>position;t--)
-	temp[t].set_result(str[t-1].get_distance(),str[t-1].get_index());
-      //  printf("adjusted.....");
+      printf("since length is not equal to k\n");
+
+    
+      for(int t=length;t>position;t-=1)
+	{
+	  
+	  printf("came here\n");
+	  printf("Length is %d\n",length);
+	  printf("position is %d\n",position);
+	  printf("will copy to temp\n");
+	  printf("t is %d\n",t);
+	  printf("distance is %f\n",str[t-1].get_distance());
+	  printf("index is %d\n",str[t-1].get_index());
+	  temp[t].set_result(str[t-1].get_distance(),str[t-1].get_index());
+	  printf("Set up..\n");
+	  printf("t is %d\n",t);
+	  printf("wi.ll loop...\n");
+	}
+      
+      printf("adjusrwrewerwerwerwerwe.....");
       for(int j=0;j<length+1;j++)
-	str[j].set_result(temp[j].get_distance(),temp[j].get_index());
+	{
+	  printf("copying element by element...\n");
+	  printf("woll copy distance %f\n",temp[j].get_distance());
+	  printf("Will copy index =%d\n",temp[j].get_index());
+	  printf("j is %d\n",j);
+	 
+	  str[j].set_result(temp[j].get_distance(),temp[j].get_index());
+	  printf("copied....\n");
+	}
       printf("copied\n");
       // delete(temp);
       // printf("deleted temp");
+      printf("str before leaving is..\n");
+      for(int l=0;l<length+1;l++)
+	printf("distance:%f and index:%d\n",str[l].get_distance(),str[l].get_index());
+
+
       return 1;
     }
 }
-bool same_points(double *point1,double *point2, int len)
+
+bool same_points(double *point1,double *point2,int len)
 {
   for(int i=0;i<len;i++)
     {
@@ -286,7 +337,7 @@ bool same_points(double *point1,double *point2, int len)
   return true;
 }
 
-int AllKNNSingleTree::FindKNearestNeighbours(Tree *root, double *point,Matrix *r_matrix,SingleTreeResults *str,int length,int k)
+int AllKNNSingleTree::FindKNearestNeighbours(Tree *root, double *point,Matrix *r_matrix,ArrayList<SingleTreeResults> &str,int length,int k)
 {
   //Base case is that the node is a leaf 
 
@@ -308,18 +359,21 @@ int AllKNNSingleTree::FindKNearestNeighbours(Tree *root, double *point,Matrix *r
 	  if(!same_points(point,r_matrix->GetColumnPtr(i),r_matrix->n_rows()))
 	    { 
 	      dist=la::DistanceSqEuclidean(r_matrix->n_rows(),point,r_matrix->GetColumnPtr(i));
-	      //printf("Distance is %f\n",dist);
+	      printf("Distance is %f\n",dist);
 	      //find where the new element should be pushed
 	      int start=0;
+	      printf("Before going to funcction find index ..\n");
+	      for(int l=0;l<length;l++)
+		printf("distance is %f\n",str[l].get_distance());
 	      position=find_index(str,dist,start,end); 
-	      //printf("The position where this willbe inserted is \n");
-	      //printf("%d\n",position);
+	      printf("The position where this willbe inserted is \n");
+	      printf("%d\n",position);
 	      length+=push_into_array(str,position,dist,length,i,k);
 	      end=length-1;
-	      /*printf("After push...\n");
+	      printf("After pushAFTWER PUSH AFTER PUSH...\n");
 
 	      for(int count=0;count<length;count++)
-		printf("distance:%f\n",str[count].get_distance());*/
+		printf("distance:%f\n",str[count].get_distance());
 
 	      //printf("length is now %d\n",length);
 	      if(length>k)
@@ -404,7 +458,8 @@ int AllKNNSingleTree::FindKNearestNeighbours(Tree *root, double *point,Matrix *r
 
   //Now for each query point find the k nearest neighbours
 
-  SingleTreeResults *str=new SingleTreeResults[k];
+  ArrayList<SingleTreeResults> str;
+  str.Init(k);
 
   //printf("Will start knn calculations\n"); 
  
@@ -413,11 +468,8 @@ int AllKNNSingleTree::FindKNearestNeighbours(Tree *root, double *point,Matrix *r
   for(int i=0;i<r_matrix->n_cols();i++)
     {   
       int length=0;
-      // printf("came here...\n");
-      //printf("root=%X\n",root);
-      //printf("the number of points covered are %d\n",i);
-      //printf("Total number of points left to be covered are %d\n",r_matrix->n_cols()-i);
-      length=FindKNearestNeighbours(root,q_matrix->GetColumnPtr(i),r_matrix,str,length,k);
+      
+      length+=FindKNearestNeighbours(root,q_matrix->GetColumnPtr(i),r_matrix,str,length,k);
  
       printf("The nearest neighbour distances are\n");
 
@@ -431,12 +483,7 @@ int AllKNNSingleTree::FindKNearestNeighbours(Tree *root, double *point,Matrix *r
 	  results_matrix.astr[i][j].set_result(str[j].get_distance(),str[j].get_index());
 	}
 
-      /*  printf("knn for this point have been added...........\n");
     
-      for(int j=0;j<length;j++)
-	{
-          printf("Index:%d distance: %f\n",str[j].get_index(),str[j].get_distance());
-	  }*/
 
        printf("The origianl point is\n");
 
@@ -446,30 +493,215 @@ int AllKNNSingleTree::FindKNearestNeighbours(Tree *root, double *point,Matrix *r
     
       printf("Will clear str now........\n");
       printf("Length is %d\n",length);
-      /* for(int l=0;l<length;l++)
-	 str[l].set_result(32768.0,-1);*/
-    }
-  printf("The knn distances are\n");
-  printf("The number of points are %d\n",r_matrix->n_cols());
-  char fname[40];
-  strcpy(fname,"allknn");
-  char query_file[40];
-  strcat(fname,"_colors50k.ds");
-  strcat(fname,query_file);
-  printf("The name of query_file is %s\n",query_file);
-  FILE *fp=fopen(fname,"w");
-  for(int t=0;t<r_matrix->n_cols();t++)
-    {
-      for(int l=0;l<k;l++)
-	fprintf(fp,"%f ",results_matrix.astr[t][l].get_distance());
-      fprintf(fp,"\n");
+     
     }
 }
 
-
-void test_module()
+double AllKNNDualTree::FindKNearestNeighboursDualTree(Tree *q_tree,Tree *r_tree,Matrix *q_matrix,Matrix *r_matrix,int k)
 {
+  //if distance between the two boxes is larger than the max _distance then return
+  double distance_between_boxes=q_tree->bound().MinDistanceSq (r_tree->bound()); //base case
+  if(q_tree->is_leaf()&& r_tree->is_leaf())
+    {
+      int start,end;
+      //check if pruneable
+      if(q_tree->stat().get_maximum_distance() < distance_between_boxes)
+	{
+	  //then there is no need to go further and hence we can return
+	  return 32768.0;
+	}
+      else
+	{
+	  //not purneable. therefore carry out exhaustive point-to-point computations
+	  double max_dist=0.0;
+	  double distance;
+	  int position;
+	  int count=0;
+	  for(int i=q_tree->begin();i<q_tree->end();i++)
+	    {
+	      printf("for this point count is %d\n",count);
+	      printf("TAKING NEW I=%d............................................................\n",i);
+	     
+	      count=0;
+	      for(int j=r_tree->begin();j<r_tree->end();j++)
+		{
+		  if(!same_points(q_matrix->GetColumnPtr(i),r_matrix->GetColumnPtr(j),q_matrix->n_rows()))
+		    {
+		      
+		      distance=la::DistanceSqEuclidean (r_matrix->n_rows(),q_matrix->GetColumnPtr(i),r_matrix->GetColumnPtr(j));
+		      //we would like to find index of this point into str. this will enter into str[i]. This function takes in as argument an array list of single tree results            
+		      printf("distance is %f\n",distance);
+		      printf("size is %d\n",results_matrix.astr[i].size());
+		      if(results_matrix.astr[i].size()==0)  
+			{
+			  //initialize start and end
+			  printf("Initialized start and end\n");
+			  start=0;
+			  end=-1;
+			}
+
+		      int length=results_matrix.astr[i].size(); //this calculates the old length
+		      printf("at the moment length is %d\n",length);
+		      
+		      position=find_index(results_matrix.astr[i],distance,start,results_matrix.astr[i].size()-1);
+		      if(length<k)
+			{
+			  printf("since the size of array is still lesser than k will increase the size\n");
+			  printf("Before expanding length is %d \n",length);
+			  results_matrix.astr[i].AddBack(1); //increase the length of the arraylist, only if the number of elements as of now are lesser than k
+			  printf("new length after expanding is %d\n",results_matrix.astr[i].size());
+			}
+
+		      push_into_array(results_matrix.astr[i],position,distance,length,j,k);
+		      printf("pushed into array..\n");
+		      count++;
+		     
+		    }
+		
+		}
+
+	      if(results_matrix.astr[i].size()<k)
+		max_dist=32768.0;
+	      else //all k nn have been found
+		max_dist=max_dist > results_matrix.astr[i][k-1].get_distance()?max_dist:results_matrix.astr[i][k-1].get_distance(); //this is an update after every point IN THE QUERY NODE
+	      
+	      for(int z=0;z<results_matrix.astr[i].size();z++)
+		printf("distance=%f and index=%d\n",results_matrix.astr[i][z].get_distance(),results_matrix.astr[i][z].get_index());
+	    }
+	  printf("Will return from this node a value of %f%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",max_dist);
+
+	  return max_dist;
+	}
+    }
+
+  else
+    {
+      //Not base case. PRUNING CASE
+      if(q_tree->stat().get_maximum_distance() < distance_between_boxes)
+	{
+	  //then there is no need to go further and henraghavce we can return
+	  return 32768.0;
+	}
+
+      //NOT PRUNEABLE
+      //both are not leafs
+      if(!q_tree->is_leaf() && !r_tree->is_leaf())
+	{
+	  printf("both are not leafs......\n");
+	  double max_dist_q_left_r_left=FindKNearestNeighboursDualTree(q_tree->left(),r_tree->left(),q_matrix,r_matrix,k);
+	  
+	  /*  if(max_dist < q_tree->stat().get_maximum_distance())
+	    {
+	      q_tree->stat().set_maximum_distance(max_dist);
+	      }*/
+	  
+	  double max_dist_q_left_r_right=FindKNearestNeighboursDualTree(q_tree->left(),r_tree->right(),q_matrix,r_matrix,k);
+
+	  double max_dist_q_left= max_dist_q_left_r_left< max_dist_q_left_r_right? max_dist_q_left_r_left: max_dist_q_left_r_right;//take minimum
+
+	  /* if(max_dist_q_left< q_tree->stat().get_maximum_distance())
+	    {
+	      q_tree->stat().set_maximum_distance(max_dist);
+	      }*/
+
+	  double max_dist_q_right_r_left=FindKNearestNeighboursDualTree(q_tree->right(),r_tree->left(),q_matrix,r_matrix,k);
+	  /*if(max_dist<q_tree->stat().get_maximum_distance())
+	    {
+	      q_tree->stat().set_maximum_distance(max_dist);
+	      }*/
+
+	  double max_dist_q_right_r_right=FindKNearestNeighboursDualTree(q_tree->right(),r_tree->right(),q_matrix,r_matrix,k);
+
+	  double max_dist_q_right= max_dist_q_right_r_left< max_dist_q_right_r_right? max_dist_q_right_r_left: max_dist_q_right_r_right;
+	 
+	  double max_dist_q=max_dist_q_left>max_dist_q_right?max_dist_q_left:max_dist_q_right;
+	  q_tree->stat().set_maximum_distance(max_dist_q);
+	  return q_tree->stat().get_maximum_distance();
+	}
+      else
+	{
+	  printf("q is leaf and r is not...\n");
+	  //q_tree is leaf and r_tree is not
+	  if(q_tree->is_leaf()&&!r_tree->is_leaf())
+	    {
+	 
+	      double max_dist_q_r_left=FindKNearestNeighboursDualTree(q_tree,r_tree->left(),q_matrix,r_matrix,k);
+	      
+	      /*if(max_dist < q_tree->stat().get_maximum_distance())
+		{
+		  q_tree->stat().set_maximum_distance(max_dist);
+		  }*/
+	      
+	      double max_dist_q_r_right=FindKNearestNeighboursDualTree(q_tree,r_tree->right(),q_matrix,r_matrix,k);
+	      
+	      /*if(max_dist<q_tree->stat().get_maximum_distance())
+		{
+		   q_tree->stat().set_maximum_distance(max_dist);
+		  }*/
+	      
+	      double max_dist_q = max_dist_q_r_left > max_dist_q_r_right?max_dist_q_r_right:max_dist_q_r_left; //take minimum
+	      q_tree->stat().set_maximum_distance(max_dist_q);
+	      return q_tree->stat().get_maximum_distance();
+	    }
+
+	  else
+	    {
+	      printf("q is not a leaf and r is \n");
+	      //q_tree is not a leaf and r_tree is
+
+	      if(!q_tree->is_leaf()&&r_tree->is_leaf())
+		{
+		  
+		  double max_dist_q_left_r=FindKNearestNeighboursDualTree(q_tree->left(),r_tree,q_matrix,r_matrix,k);
+		  //printf("After getting back to the function the value is %f\n",max_dist);
+		  printf("will compare this vlaue with %f\n",q_tree->stat().get_maximum_distance());
+		  /* if(max_dist < q_tree->stat().get_maximum_distance())
+		    {
+		      q_tree->stat().set_maximum_distance(max_dist);
+		      }*/
+		  
+		  double max_dist_q_right_r=FindKNearestNeighboursDualTree(q_tree->right(),r_tree,q_matrix,r_matrix,k);
+		  
+		  double max_dist_q= max_dist_q_left_r > max_dist_q_right_r? max_dist_q_left_r: max_dist_q_right_r;
+		  q_tree->stat().set_maximum_distance(max_dist_q);
+		  return max_dist_q;
+		}
+	    }
+	}
+    }
 }
+  
+void AllKNNDualTree::ComputeAllKNNDualTree(Matrix *q_matrix,Matrix *r_matrix,int k)
+{
+
+ //printf("results_matrix initialized\n");
+ // Build the kdtree form the reference matrix and the query matrix
+  using namespace tree;
+ 
+ Tree *r_tree=tree::MakeKdTreeMidpoint<Tree>(*r_matrix,3,NULL,NULL); //here the leaf size is set to 3
+ printf("ref tree built\n"); 
+ 
+ Tree *q_tree=tree::MakeKdTreeMidpoint<Tree>(*r_matrix,3,NULL,NULL); //here the leaf size is set to 3
+ printf("query tree built\n");
+
+ printf("Trees built\n");
+ //****************************************************************
+ results_matrix.astr.Init(r_matrix->n_cols()); //the arraylist ahs been completely initialized
+ printf("size has been set to %d\n",r_matrix->n_cols());
+  for(int i=0;i<r_matrix->n_cols();i++)
+    results_matrix.astr[i].Init();
+  FindKNearestNeighboursDualTree(q_tree,r_tree,q_matrix,r_matrix,k);
+  printf("completed the algo. Printing results\n");
+  printf("the k nearest neighbours are............\n");
+  for(int l=0;l<r_matrix->n_cols();l++)
+    {
+      for(int t=0;t<k;t++)
+       {printf("distance is %f ",results_matrix.astr[l][t].get_distance());}
+      printf("\n");
+    }
+  //****************************************************************
+}
+
 
 int main(int argc, char *argv[])
 { 
@@ -490,8 +722,8 @@ int main(int argc, char *argv[])
   //strcpy(ref_file,fx_param_str_req(NULL,"ref_file"));
   //strcpy(method,fx_param_str_req(NULL,"method"));
 
-  strcpy(query_file,"colors50k.ds");
-  strcpy(ref_file,"colors50k.ds");
+  strcpy(query_file,"dummy.ds");
+  strcpy(ref_file,"dummy.ds");
 
 
   int k=3;
@@ -507,7 +739,7 @@ int main(int argc, char *argv[])
 
   //if method is allnnnaive. AllNNNaive computations follow
   //printf("String compare with allknnsingletree is %d\n",strcmp(method,"allknnsingletree"));
-  if(strcmp(method,"allnnnaive")==0)
+   if(strcmp(method,"allnnnaive")==0)
   {
     printf("In allnaive");
     AllNNNaive naive;
@@ -541,15 +773,14 @@ int main(int argc, char *argv[])
 	 akst->ComputeAllKNNSingleTree(q_matrix,r_matrix,k);
     }
 
-     //the code that follows is just a test module
-     if(strcmp(method,"test"))
+  /*
+     if(strcmp(method,"allknndualtree")==0)
        {
-	 printf("came here");
-	 test_module();
-       }
-     if(strcmp(method,"allnndual")==0)
-       {
-
-       }
+	 printf("in allknndualtree\n");
+	 //AllKNNSDual tree computations
+	 //int k=fx_param_int_req(NULL,"k");
+	 AllKNNDualTree *akdt= new AllKNNDualTree();
+	 akdt->ComputeAllKNNDualTree(q_matrix,r_matrix,k);
+	 }*/
      fx_done();     
 }
diff --git a/fastlib/u/gmravi/regression/Makefile b/fastlib/u/gmravi/regression/Makefile
new file mode 100644
index 0000000000..827924539e
--- /dev/null
+++ b/fastlib/u/gmravi/regression/Makefile
@@ -0,0 +1,203 @@
+/net/hc295/gmravi/home/fastlib/fastlib/u/gmravi/regression/main: /net/hc295/gmravi/home/fastlib/fastlib/u/gmravi/regression/Makefile /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/u/gmravi/regression/main
+	@echo '... Making /net/hc295/gmravi/home/fastlib/fastlib/u/gmravi/regression/main'
+	@rm -f /net/hc295/gmravi/home/fastlib/fastlib/u/gmravi/regression/main
+	@ln -s -f /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/u/gmravi/regression/main /net/hc295/gmravi/home/fastlib/fastlib/u/gmravi/regression/main
+	@echo '*** Created 1 symlinks in /net/hc295/gmravi/home/fastlib/fastlib/u/gmravi/regression/.'
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/u/gmravi/regression/main: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libu_gmravi_regression_main__auto.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libpar_par.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libmath_math.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/u/gmravi/regression/main'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/u/gmravi/regression
+	@g++ -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/u/gmravi/regression/main -g3 -DDEBUG -O0  /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libu_gmravi_regression_main__auto.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libpar_par.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libmath_math.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a -lm -lpthread -lg2c
+pseudo_4: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libu_gmravi_regression_main__auto.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libpar_par.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libmath_math.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libu_gmravi_regression_main__auto.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libpar_par.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libmath_math.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a'
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libu_gmravi_regression_main__auto.a: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/u_gmravi_regression_main.o
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/libu_gmravi_regression_main__auto.a'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_
+	@ar r /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libu_gmravi_regression_main__auto.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/u_gmravi_regression_main.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/u_gmravi_regression_main.o: /net/hc295/gmravi/home/fastlib/fastlib/u/gmravi/regression/main.cc /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfastlib_fastlib_int.h /net/hc295/gmravi/home/fastlib/fastlib/u/gmravi/regression/regression.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/u_gmravi_regression_main.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/u/gmravi/regression && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c main.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/u_gmravi_regression_main.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfastlib_fastlib_int.h: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libdata_data.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libtree_tree.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfx_fx.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libmath_math.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file_int.h /net/hc295/gmravi/home/fastlib/fastlib/fastlib/fastlib.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libpar_par.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libla_la.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libfastlib_fastlib_int.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfastlib_fastlib_int.h
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libpar_par.h: /net/hc295/gmravi/home/fastlib/fastlib/par/grain.h /net/hc295/gmravi/home/fastlib/fastlib/par/task.h /net/hc295/gmravi/home/fastlib/fastlib/par/thread.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libpar_par.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libpar_par.h
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libtree_tree.h: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libdata_data.h /net/hc295/gmravi/home/fastlib/fastlib/tree/kdtree_impl.h /net/hc295/gmravi/home/fastlib/fastlib/tree/spacetree.h /net/hc295/gmravi/home/fastlib/fastlib/tree/kdtree.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfx_fx.h /net/hc295/gmravi/home/fastlib/fastlib/tree/bounds.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file_int.h /net/hc295/gmravi/home/fastlib/fastlib/tree/statistic.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libla_la.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libtree_tree.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libtree_tree.h
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libmath_math.h: /net/hc295/gmravi/home/fastlib/fastlib/math/math.h /net/hc295/gmravi/home/fastlib/fastlib/math/discrete.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/math/kernel.h /net/hc295/gmravi/home/fastlib/fastlib/math/geometry.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libmath_math.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libmath_math.h
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libdata_data.h: /net/hc295/gmravi/home/fastlib/fastlib/data/dataset.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libla_la.h /net/hc295/gmravi/home/fastlib/fastlib/data/crossvalidation.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libdata_data.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libdata_data.h
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file_int.h: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file_int.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file_int.h
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfx_fx.h: /net/hc295/gmravi/home/fastlib/fastlib/fx/fx.h /net/hc295/gmravi/home/fastlib/fastlib/fx/timer.h /net/hc295/gmravi/home/fastlib/fastlib/fx/datastore.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libfx_fx.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfx_fx.h
+pseudo_22: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libpar_par.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libmath_math.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libpar_par.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libmath_math.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a'
+pseudo_24: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libpar_par.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libpar_par.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a'
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libpar_par.a: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/par_thread.o
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/libpar_par.a'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_
+	@ar r /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libpar_par.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/par_thread.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/par_thread.o: /net/hc295/gmravi/home/fastlib/fastlib/par/task.h /net/hc295/gmravi/home/fastlib/fastlib/par/thread.h /net/hc295/gmravi/home/fastlib/fastlib/par/thread.cc /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/par/grain.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/par_thread.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/par && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c thread.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/par_thread.o
+pseudo_31: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a'
+pseudo_33: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libmath_math.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libmath_math.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a'
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libmath_math.a: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/math_geometry.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/math_discrete.o
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/libmath_math.a'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_
+	@ar r /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libmath_math.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/math_discrete.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/math_geometry.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/math_geometry.o: /net/hc295/gmravi/home/fastlib/fastlib/math/math.h /net/hc295/gmravi/home/fastlib/fastlib/math/discrete.h /net/hc295/gmravi/home/fastlib/fastlib/math/geometry.cc /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/math/kernel.h /net/hc295/gmravi/home/fastlib/fastlib/math/geometry.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/math_geometry.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/math && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c geometry.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/math_geometry.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/math_discrete.o: /net/hc295/gmravi/home/fastlib/fastlib/math/math.h /net/hc295/gmravi/home/fastlib/fastlib/math/discrete.h /net/hc295/gmravi/home/fastlib/fastlib/math/discrete.cc /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/math/kernel.h /net/hc295/gmravi/home/fastlib/fastlib/math/geometry.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/math_discrete.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/math && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c discrete.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/math_discrete.o
+pseudo_43: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a'
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/data_dataset.o
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/libdata_data.a'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_
+	@ar r /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libdata_data.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/data_dataset.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/data_dataset.o: /net/hc295/gmravi/home/fastlib/fastlib/data/dataset.h /net/hc295/gmravi/home/fastlib/fastlib/data/crossvalidation.h /net/hc295/gmravi/home/fastlib/fastlib/data/dataset.cc /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libla_la.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/data_dataset.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/data && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c dataset.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/data_dataset.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libla_la.h: /net/hc295/gmravi/home/fastlib/fastlib/la/blas.h /net/hc295/gmravi/home/fastlib/fastlib/la/matrix.h /net/hc295/gmravi/home/fastlib/fastlib/la/uselapack.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/la/clapack.h /net/hc295/gmravi/home/fastlib/fastlib/la/la.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libla_la.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libla_la.h
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file.h: /net/hc295/gmravi/home/fastlib/fastlib/file/textfile.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libfile_file.h
+pseudo_51: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a'
+pseudo_53: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a'
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/file_textfile.o
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/libfile_file.a'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_
+	@ar r /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfile_file.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/file_textfile.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/file_textfile.o: /net/hc295/gmravi/home/fastlib/fastlib/file/textfile.cc /net/hc295/gmravi/home/fastlib/fastlib/file/textfile.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/file_textfile.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/file && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c textfile.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/file_textfile.o
+pseudo_58: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a'
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_fx.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_timer.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_datastore.o
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/libfx_fx.a'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_
+	@ar r /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libfx_fx.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_datastore.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_timer.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_fx.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_fx.o: /net/hc295/gmravi/home/fastlib/fastlib/fx/fx.h /net/hc295/gmravi/home/fastlib/fastlib/fx/fx.c /net/hc295/gmravi/home/fastlib/fastlib/fx/timer.h /net/hc295/gmravi/home/fastlib/fastlib/fx/datastore.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/fx_fx.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/fx && gcc -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c fx.c -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_fx.o -Wall
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_timer.o: /net/hc295/gmravi/home/fastlib/fastlib/fx/fx.h /net/hc295/gmravi/home/fastlib/fastlib/fx/timer.h /net/hc295/gmravi/home/fastlib/fastlib/fx/datastore.h /net/hc295/gmravi/home/fastlib/fastlib/fx/timer.c /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/fx_timer.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/fx && gcc -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c timer.c -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_timer.o -Wall
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_datastore.o: /net/hc295/gmravi/home/fastlib/fastlib/fx/fx.h /net/hc295/gmravi/home/fastlib/fastlib/fx/timer.h /net/hc295/gmravi/home/fastlib/fastlib/fx/datastore.h /net/hc295/gmravi/home/fastlib/fastlib/fx/datastore.c /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/fx_datastore.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/fx && gcc -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c datastore.c -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/fx_datastore.o -Wall
+pseudo_69: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a'
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/la_uselapack.o
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/libla_la.a'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_
+	@ar r /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libla_la.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/la_uselapack.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/la_uselapack.o: /net/hc295/gmravi/home/fastlib/fastlib/la/blas.h /net/hc295/gmravi/home/fastlib/fastlib/la/matrix.h /net/hc295/gmravi/home/fastlib/fastlib/la/uselapack.cc /net/hc295/gmravi/home/fastlib/fastlib/la/uselapack.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/la/clapack.h /net/hc295/gmravi/home/fastlib/fastlib/la/la.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/la_uselapack.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/la && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c uselapack.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/la_uselapack.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h: /net/hc295/gmravi/home/fastlib/fastlib/col/fastalloc.h /net/hc295/gmravi/home/fastlib/fastlib/col/queue.h /net/hc295/gmravi/home/fastlib/fastlib/col/heap.h /net/hc295/gmravi/home/fastlib/fastlib/col/intmap.h /net/hc295/gmravi/home/fastlib/fastlib/col/rangeset.h /net/hc295/gmravi/home/fastlib/fastlib/col/arraylist.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/col/string.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libcol_col.h
+/net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a: /net/hc295/gmravi/home/fastlib/fastlib/la/blaspack.tgz
+	@echo '... Making /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON
+	@echo '... Extracting FORTRAN files...'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack_workspace
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack_workspace && tar -xzf /net/hc295/gmravi/home/fastlib/fastlib/la/blaspack.tgz
+	@echo '*** Compiling LAPACK with BLAS reference implementation.'
+	@echo '!!! LAPACK WARNING: For better performance, install ATLAS or Intel MKL.'
+	@echo '... Our compilation differs slightly from regular LAPACK/BLAS:'
+	@echo '... NOTE 2: We omit complex-number routines (halves compile time).'
+	@echo '... NOTE 3: We require case sensitivity for LAPACK/BLAS string parameters.'
+	@echo '... This may take several minutes (about 800 FORTRAN files).'
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack_workspace && g77 -O2 -c src/*.f
+	@echo '... Almost done with LAPACK/BLAS...'
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack_workspace && g77 -O0 -c src/dlamch.f src/slamch.f
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack_workspace && ar r /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack.a *.o
+	@echo '... Created archive, cleaning up.'
+	@rm -rf /net/hc295/gmravi/home/fastlib/fastlib/bin_keep/x86_64_Linux_COMMON_gcc_COMMON/libblaspack_workspace
+	@echo '*** Done with LAPACK and BLAS!'
+/net/hc295/gmravi/home/fastlib/fastlib/la/blaspack.tgz: 
+	@echo '... Making /net/hc295/gmravi/home/fastlib/fastlib/la/blaspack.tgz'
+	@echo 'Downloading the file using curl...'
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/la && curl -L -o blaspack.tgz "http://www.cc.gatech.edu/~garryb/fastlib/blaspack.tgz"
+pseudo_81: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a'
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/col_col.o
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/libcol_col.a'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_
+	@ar r /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libcol_col.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/col_col.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/col_col.o: /net/hc295/gmravi/home/fastlib/fastlib/col/fastalloc.h /net/hc295/gmravi/home/fastlib/fastlib/col/queue.h /net/hc295/gmravi/home/fastlib/fastlib/col/heap.h /net/hc295/gmravi/home/fastlib/fastlib/col/intmap.h /net/hc295/gmravi/home/fastlib/fastlib/col/rangeset.h /net/hc295/gmravi/home/fastlib/fastlib/col/arraylist.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h /net/hc295/gmravi/home/fastlib/fastlib/col/col.cc /net/hc295/gmravi/home/fastlib/fastlib/col/string.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/col_col.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/col && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c col.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/col_col.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h: /net/hc295/gmravi/home/fastlib/fastlib/base/ccmem.h /net/hc295/gmravi/home/fastlib/fastlib/base/scale.h /net/hc295/gmravi/home/fastlib/fastlib/base/cc.h /net/hc295/gmravi/home/fastlib/fastlib/base/common.h /net/hc295/gmravi/home/fastlib/fastlib/base/otrav_impl.h /net/hc295/gmravi/home/fastlib/fastlib/base/test.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/base/basic_types.h /net/hc295/gmravi/home/fastlib/fastlib/base/compiler_impl.h /net/hc295/gmravi/home/fastlib/fastlib/base/compiler.h /net/hc295/gmravi/home/fastlib/fastlib/base/fortran.h /net/hc295/gmravi/home/fastlib/fastlib/base/debug.h /net/hc295/gmravi/home/fastlib/fastlib/base/otrav.h
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON
+	@touch /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/libbase_base.h
+pseudo_93: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a
+	@echo '*** Done with /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a'
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a: /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_common.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_cc.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_otrav.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_ccmem.o
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/libbase_base.a'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_
+	@ar r /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/libbase_base.a /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_common.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_cc.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_ccmem.o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_otrav.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_otrav.o: /net/hc295/gmravi/home/fastlib/fastlib/base/ccmem.h /net/hc295/gmravi/home/fastlib/fastlib/base/otrav.cc /net/hc295/gmravi/home/fastlib/fastlib/base/scale.h /net/hc295/gmravi/home/fastlib/fastlib/base/cc.h /net/hc295/gmravi/home/fastlib/fastlib/base/common.h /net/hc295/gmravi/home/fastlib/fastlib/base/otrav_impl.h /net/hc295/gmravi/home/fastlib/fastlib/base/test.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/base/basic_types.h /net/hc295/gmravi/home/fastlib/fastlib/base/compiler_impl.h /net/hc295/gmravi/home/fastlib/fastlib/base/compiler.h /net/hc295/gmravi/home/fastlib/fastlib/base/fortran.h /net/hc295/gmravi/home/fastlib/fastlib/base/debug.h /net/hc295/gmravi/home/fastlib/fastlib/base/otrav.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/base_otrav.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/base && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c otrav.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_otrav.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_ccmem.o: /net/hc295/gmravi/home/fastlib/fastlib/base/ccmem.h /net/hc295/gmravi/home/fastlib/fastlib/base/scale.h /net/hc295/gmravi/home/fastlib/fastlib/base/cc.h /net/hc295/gmravi/home/fastlib/fastlib/base/common.h /net/hc295/gmravi/home/fastlib/fastlib/base/otrav_impl.h /net/hc295/gmravi/home/fastlib/fastlib/base/test.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/base/basic_types.h /net/hc295/gmravi/home/fastlib/fastlib/base/compiler_impl.h /net/hc295/gmravi/home/fastlib/fastlib/base/compiler.h /net/hc295/gmravi/home/fastlib/fastlib/base/fortran.h /net/hc295/gmravi/home/fastlib/fastlib/base/debug.h /net/hc295/gmravi/home/fastlib/fastlib/base/ccmem.cc /net/hc295/gmravi/home/fastlib/fastlib/base/otrav.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/base_ccmem.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/base && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c ccmem.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_ccmem.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_cc.o: /net/hc295/gmravi/home/fastlib/fastlib/base/ccmem.h /net/hc295/gmravi/home/fastlib/fastlib/base/scale.h /net/hc295/gmravi/home/fastlib/fastlib/base/cc.h /net/hc295/gmravi/home/fastlib/fastlib/base/common.h /net/hc295/gmravi/home/fastlib/fastlib/base/otrav_impl.h /net/hc295/gmravi/home/fastlib/fastlib/base/test.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/base/basic_types.h /net/hc295/gmravi/home/fastlib/fastlib/base/cc.cc /net/hc295/gmravi/home/fastlib/fastlib/base/compiler_impl.h /net/hc295/gmravi/home/fastlib/fastlib/base/compiler.h /net/hc295/gmravi/home/fastlib/fastlib/base/fortran.h /net/hc295/gmravi/home/fastlib/fastlib/base/debug.h /net/hc295/gmravi/home/fastlib/fastlib/base/otrav.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/base_cc.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/base && g++ -Wall -Woverloaded-virtual -fno-exceptions -Wparentheses -fno-exceptions -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c cc.cc -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_cc.o
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_common.o: /net/hc295/gmravi/home/fastlib/fastlib/base/ccmem.h /net/hc295/gmravi/home/fastlib/fastlib/base/scale.h /net/hc295/gmravi/home/fastlib/fastlib/base/cc.h /net/hc295/gmravi/home/fastlib/fastlib/base/common.h /net/hc295/gmravi/home/fastlib/fastlib/base/otrav_impl.h /net/hc295/gmravi/home/fastlib/fastlib/base/test.h /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/base/basic_types.h /net/hc295/gmravi/home/fastlib/fastlib/base/common.c /net/hc295/gmravi/home/fastlib/fastlib/base/compiler_impl.h /net/hc295/gmravi/home/fastlib/fastlib/base/compiler.h /net/hc295/gmravi/home/fastlib/fastlib/base/fortran.h /net/hc295/gmravi/home/fastlib/fastlib/base/debug.h /net/hc295/gmravi/home/fastlib/fastlib/base/otrav.h
+	@echo '... Making bin/x86_64_Linux_debug_gcc_/obj/base_common.o'
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj
+	@cd /net/hc295/gmravi/home/fastlib/fastlib/base && gcc -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON -I/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_ -I/net/hc295/gmravi/home/fastlib/fastlib -g3 -DDEBUG -O0   -c common.c -o /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/obj/base_common.o -Wall
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/base/basic_types.h: /net/hc295/gmravi/home/fastlib/fastlib/script/config.py /net/hc295/gmravi/home/fastlib/fastlib/base/config/template_types.c
+	@echo '... Making bin/x86_64_Linux_COMMON_gcc_COMMON/base/basic_types.h'
+	@/net/hc295/gmravi/home/fastlib/fastlib/script/config.py --genfiles_dir=/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON --source_dir=/net/hc295/gmravi/home/fastlib/fastlib
+	@mkdir -p /net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_COMMON_gcc_COMMON/base
+clean:
+	@echo 'Removing the bin/ directory.'
+	rm -rf /net/hc295/gmravi/home/fastlib/fastlib/bin/*
diff --git a/fastlib/u/gmravi/regression/build.py b/fastlib/u/gmravi/regression/build.py
new file mode 100644
index 0000000000..f6f405513f
--- /dev/null
+++ b/fastlib/u/gmravi/regression/build.py
@@ -0,0 +1,18 @@
+binrule(
+    name = "main",                        # the executable name
+    sources = ["main.cc"],                   #
+    headers = ["regression.h"],                            # no extra headers
+    deplibs = ["fastlib:fastlib_int"]
+    )
+
+
+# to build:
+# 1. make sure have environment variables set up:
+#    $ source /full/path/to/fastlib/script/fl-env /full/path/to/fastlib
+#    (you might want to put this in bashrc)
+# 2. fl-build main
+#    - this automatically will assume --mode=check, the default
+#    - type fl-build --help for help
+# 3. ./main
+#    - to build same target again, type: make
+#    - to force recompilation, type: make clean
diff --git a/fastlib/u/gmravi/regression/cmdline_params b/fastlib/u/gmravi/regression/cmdline_params
new file mode 100644
index 0000000000..fb79553f5d
--- /dev/null
+++ b/fastlib/u/gmravi/regression/cmdline_params
@@ -0,0 +1,18 @@
+These are the command line parameters which the code regression.h uses
+
+
+
+do_naive=true/false
+
+method=fast
+
+fast_kde_output: file name to which the output of fast kde should be written to
+
+
+naive_kde_output:file name to which the navie kde should outptut its results
+
+kernel: the kernel u want to use
+
+data: the file name from which the data is read
+
+query: the file name from which the query is read
diff --git a/fastlib/u/gmravi/regression/cmdline_params.txt b/fastlib/u/gmravi/regression/cmdline_params.txt
new file mode 100644
index 0000000000..7efe664e0d
--- /dev/null
+++ b/fastlib/u/gmravi/regression/cmdline_params.txt
@@ -0,0 +1,31 @@
+These are the command line parameters which the code regression.h uses
+
+
+
+do_naive=true/false
+
+method=fast
+
+fast_kde_output: file name to which the output of fast kde should be written to
+
+
+naive_kde_output:file name to which the navie kde should outptut its results
+
+kernel: the kernel u want to use
+
+data: the file name from which the data is read
+
+query: the file name from which the query is read
+
+bandwidth: The bandwidth of the density estimation
+weights: this file is the weights assigned to the dofferent points
+
+
+the  command should be:
+
+
+./main --method=fast --do_naive --fast_kde_output=fast_kde_output.txt --naive_kde_output=naive_kde_output.txt --kernel=gaussian --data=query_file.csv --query=query_file.csv --scaling=none --bandwidth=0.1
+
+
+
+./main --method=fast --do_naive --fast_kde_output=fast_kde_output.txt --naive_kde_output=naive_kde_output.txt --kernel=gaussian --data=colors50k.ds --query=colors50k.ds --scaling=none --bandwidth=0.1
diff --git a/fastlib/u/gmravi/regression/colors50k.ds b/fastlib/u/gmravi/regression/colors50k.ds
new file mode 100644
index 0000000000..b516373a1d
--- /dev/null
+++ b/fastlib/u/gmravi/regression/colors50k.ds
@@ -0,0 +1,50002 @@
+g_r,r_i
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diff --git a/fastlib/u/gmravi/regression/fast_kde_output.txt b/fastlib/u/gmravi/regression/fast_kde_output.txt
new file mode 100644
index 0000000000..448b6d8289
--- /dev/null
+++ b/fastlib/u/gmravi/regression/fast_kde_output.txt
@@ -0,0 +1,20 @@
+0.012931
+0.004784
+0.008621
+0.017845
+0.012751
+0.018197
+0.021199
+0.034751
+0.012699
+0.031747
+0.033863
+0.051949
+0.012699
+-0.004535
+-0.008466
+-0.069266
+0.012983
+0.013763
+0.008707
+0.029319
diff --git a/fastlib/u/gmravi/regression/fft_kde.h b/fastlib/u/gmravi/regression/fft_kde.h
new file mode 100644
index 0000000000..eee0084aaf
--- /dev/null
+++ b/fastlib/u/gmravi/regression/fft_kde.h
@@ -0,0 +1,883 @@
+#ifndef FFT_KDE_H
+#define FFT_KDE_H
+
+#include <math.h>
+#include <values.h>
+
+/** 
+ * computing kernel estimate using Fast Fourier Transform: I have
+ * used multidimensional fast fourier transform called ffteasy
+ */
+class FFTKde {
+  
+ private:
+
+  /** constant TAU */
+  static const double TAU = 4.0;
+
+  /** query dataset */
+  Matrix qset_;
+
+  /** reference dataset */
+  Matrix rset_;
+
+  /** kernel */
+  GaussianKernel kernel_;
+
+  /** computed densities */
+  Vector densities_;
+
+  /** number of grid points along each dimension */
+  int m_;
+
+  /** number of points along each dimension in the zero padded */
+  ArrayList<int> size_;
+  
+  /** minimum coordinate along each dimension */
+  Vector mincoords_;
+
+  ArrayList<int> minindices_;
+
+  /** maximum coordinate along each dimension */
+  Vector maxcoords_;
+  
+  /** difference between min and max along each dimension */
+  Vector diffcoords_;
+
+  /** size of grid along each dimension */
+  Vector gridsizes_;
+
+  /** kernel weights along each dimension */
+  ArrayList<int>  kernelweights_dims_;
+
+  /** total number of grid points */
+  int numgridpts_;
+  
+  /** grid box volume */
+  double gridbinvolume_;
+
+  /** discretized dataset storing the assigned kernel weights */
+  Vector discretized_;
+
+  int nyquistnum_;
+
+  Vector d_fnyquist_;
+  
+  Vector k_fnyquist_;
+  
+  Vector kernelweights_;
+
+  // preprocessing: scaling the dataset; this has to be moved to the dataset
+  // module
+  /* scales each attribute to 0-1 using the min/max values */
+  void scale_data_by_minmax() {
+
+    int num_dims = rset_.n_rows();
+    DHrectBound<2> qset_bound;
+    DHrectBound<2> rset_bound;
+    qset_bound.Init(qset_.n_rows());
+    rset_bound.Init(qset_.n_rows());
+
+    // go through each query/reference point to find out the bounds
+    for(index_t r = 0; r < rset_.n_cols(); r++) {
+      Vector ref_vector;
+      rset_.MakeColumnVector(r, &ref_vector);
+      rset_bound |= ref_vector;
+    }
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      Vector query_vector;
+      qset_.MakeColumnVector(q, &query_vector);
+      qset_bound |= query_vector;
+    }
+
+    for(index_t i = 0; i < num_dims; i++) {
+      DRange qset_range = qset_bound.get(i);
+      DRange rset_range = rset_bound.get(i);
+      double min_coord = min(qset_range.lo, rset_range.lo);
+      double max_coord = max(qset_range.hi, rset_range.hi);
+      double width = max_coord - min_coord;
+
+      for(index_t j = 0; j < rset_.n_cols(); j++) {
+	rset_.set(i, j, (rset_.get(i, j) - min_coord) / width);
+      }
+      if(fx_param_str(NULL, "query", NULL) != NULL) {
+	for(index_t j = 0; j < qset_.n_cols(); j++) {
+	  qset_.set(i, j, (qset_.get(i, j) - min_coord) / width);
+	}
+      }
+    }
+  }
+  
+  /**
+   * Do a Fourier transform of an array of N complex numbers separated by
+   * steps of (complex) size skip.  The array f should be of length 2N*skip
+   * and N must be a power of 2.  Forward determines whether to do a
+   * forward transform (1) or an inverse one (-1)
+   */
+  void fftc1(double *f, int N, int skip, int forward) {
+
+    int b, index1, index2, trans_size, trans;
+    double pi2 = 4. * asin(1.);
+    
+    // used in recursive formula for Re(W^b) and Im(W^b)
+    double pi2n, cospi2n, sinpi2n;
+    
+    // wk = W^k = e^(2 pi i b/N) in the Danielson-Lanczos formula for a
+    // transform of length N
+    struct complex wb;
+    
+    // buffers for implementing recursive formulas
+    struct complex temp1, temp2;
+
+    // treat f as an array of N complex numbers
+    struct complex *c = (struct complex *)f;
+
+    // Place the elements of the array c in bit-reversed order
+    for(index1 = 1, index2 = 0; index1 < N; index1++) {
+      
+      // to find the next bit reversed array index subtract leading 1's from
+      // index2
+      for(b = N / 2; index2 >= b; b /= 2) {
+	index2 -= b;
+      }
+      
+      // Next replace the first 0 in index2 with a 1 and this gives the 
+      // correct next value
+      index2 += b;
+      
+      // swap each pair only the first time it is found
+      if(index2 > index1) {
+	temp1 = c[index2 * skip];
+	c[index2 * skip] = c[index1 * skip];
+	c[index1 * skip] = temp1;
+      }
+    }
+
+    // Next perform successive transforms of length 2,4,...,N using the
+    // Danielson-Lanczos formula
+
+    // trans_size = size of transform being computed
+    for(trans_size = 2; trans_size <= N; trans_size *= 2) {
+      
+      // +- 2 pi/trans_size
+      pi2n = forward * pi2 / (double)trans_size;
+      
+      // Used to calculate W^k in D-L formula
+      cospi2n = cos(pi2n);
+      sinpi2n = sin(pi2n);
+      
+      // Initialize W^b for b=0
+      wb.real = 1.;
+      wb.imag = 0.;
+      
+      // Step over half of the elements in the transform
+      for(b = 0; b < trans_size / 2; b++) {
+	
+	// Iterate over all transforms of size trans_size to be computed
+	for(trans = 0; trans < N / trans_size; trans++) {
+
+	  // Index of element in first half of transform being computed
+	  index1 = (trans * trans_size + b) * skip;
+
+	  // Index of element in second half of transform being computed
+	  index2 = index1 + trans_size / 2 * skip;
+	  temp1 = c[index1];
+	  temp2 = c[index2];
+
+	  // implement D-L formula
+	  c[index1].real = temp1.real + wb.real * temp2.real - 
+	    wb.imag * temp2.imag;
+	  c[index1].imag = temp1.imag + wb.real * temp2.imag + 
+	    wb.imag * temp2.real;
+	  c[index2].real = temp1.real - wb.real * temp2.real + 
+	    wb.imag * temp2.imag;
+	  c[index2].imag = temp1.imag - wb.real * temp2.imag - 
+	    wb.imag * temp2.real;
+	}
+	temp1 = wb;
+
+	// Real part of e^(2 pi i b/trans_size) used in D-L formula
+	wb.real = cospi2n * temp1.real - sinpi2n * temp1.imag;
+	
+	// Imaginary part of e^(2 pi i b/trans_size) used in D-L formula
+	wb.imag = cospi2n*temp1.imag + sinpi2n*temp1.real;
+      }
+    }
+    
+    // For an inverse transform divide by the number of grid points
+    if(forward<0) {
+      for(index1 = 0; index1 < skip * N; index1 += skip) {
+	c[index1].real /= N;
+	c[index1].imag /= N;
+      }
+    }
+  }
+
+  /**
+   * Do a Fourier transform of an ndims dimensional array of complex numbers
+   * Array dimensions are given by size[0],...,size[ndims-1]. Note that these 
+   * are sizes of complex arrays. The array f should be of length 
+   * 2*size[0]*...*size[ndims-1] and all sizes must be powers of 2.
+   * Forward determines whether to do a forward transform (1) or an inverse 
+   * one(-1)
+   */
+  void fftcn(double *f, int ndims, int *size, int forward) {
+
+    // These determine where to begin successive transforms and the skip 
+    // between their elements (see below)
+    int planesize = 1, skip = 1;
+
+    // Total size of the ndims dimensional array
+    int totalsize = 1;
+    
+    // determine total size of array
+    for(index_t dim = 0; dim < ndims; dim++) {
+      totalsize *= size[dim];
+    }
+
+    // loop over dimensions
+    for(index_t dim = ndims - 1; dim >= 0; dim--) {
+      
+      // planesize = Product of all sizes up to and including size[dim] 
+      planesize *= size[dim];
+
+      // Take big steps to begin loops of transforms 
+      for(index_t i = 0; i < totalsize; i += planesize) {
+	
+	// Skip sets the number of transforms in between big steps as well as 
+	// the skip between elements
+	for(index_t j = 0; j < skip; j++) {
+	  
+	  // 1-D Fourier transform. (Factor of two converts complex index to 
+	  // double index.)
+	  fftc1(f + 2 * (i + j), size[dim], skip, forward);
+	}
+      }
+      // Skip = Product of all sizes up to (but not including) size[dim]
+      skip *= size[dim];
+    }
+  }
+
+  /**
+   * Do a Fourier transform of an array of N real numbers
+   * N must be a power of 2
+   * Forward determines whether to do a forward transform (>=0) or an inverse 
+   * one(<0)
+   */
+  void fftr1(double *f, int N, int forward) {
+
+    int b;
+    
+    // pi2n = 2 Pi/N
+    double pi2n = 4. * asin(1.) / N, cospi2n = cos(pi2n), sinpi2n = sin(pi2n);
+    
+    // wb = W^b = e^(2 pi i b/N) in the Danielson-Lanczos formula for a 
+    // transform of length N
+    struct complex wb;
+    
+    // Buffers for implementing recursive formulas
+    struct complex temp1, temp2;
+    
+    // Treat f as an array of N/2 complex numbers
+    struct complex *c = (struct complex *)f;
+    
+    // Do a transform of f as if it were N/2 complex points
+    if(forward == 1) {
+      fftc1(f, N / 2, 1, 1);
+    }
+
+    // initialize W^b for b = 0
+    wb.real = 1.;
+    wb.imag = 0.;
+
+    // Loop over elements of transform. See documentation for these formulas 
+    for(b = 1; b < N / 4; b++) {
+
+      temp1 = wb;
+
+      // Real part of e^(2 pi i b/N) used in D-L formula 
+      wb.real = cospi2n * temp1.real - sinpi2n * temp1.imag;
+      
+      // Imaginary part of e^(2 pi i b/N) used in D-L formula
+      wb.imag = cospi2n * temp1.imag + sinpi2n * temp1.real;
+      temp1 = c[b];
+      temp2 = c[N / 2 - b];
+      c[b].real = .5 * (temp1.real + temp2.real + forward * wb.real * 
+			(temp1.imag + temp2.imag) + wb.imag * 
+			(temp1.real - temp2.real));
+      c[b].imag = .5 * (temp1.imag-temp2.imag - forward * wb.real * 
+			(temp1.real - temp2.real) + wb.imag * 
+			(temp1.imag + temp2.imag));
+      c[N/2-b].real = .5 * (temp1.real + temp2.real - forward * wb.real * 
+			    (temp1.imag + temp2.imag) - wb.imag * 
+			    (temp1.real - temp2.real));
+      c[N/2-b].imag = .5 * (-temp1.imag + temp2.imag - forward * wb.real * 
+			    (temp1.real - temp2.real) + wb.imag * 
+			    (temp1.imag + temp2.imag));
+    }
+
+    temp1 = c[0];
+
+    // set b = 0 term in transform
+    c[0].real = temp1.real+temp1.imag;
+    
+    // put b = N / 2 term in imaginary part of first term
+    c[0].imag = temp1.real-temp1.imag;
+    
+    if(forward == -1) {
+      c[0].real *= .5;
+      c[0].imag *= .5;
+      fftc1(f, N / 2, 1, -1);
+    }
+  }
+
+  /**
+   * Do a Fourier transform of an ndims dimensional array of real numbers
+   * Array dimensions are given by size[0],...,size[ndims-1]. All sizes must 
+   * be powers of 2. The (complex) nyquist frequency components are stored in 
+   * fnyquist[size[0]][size[1]]...[2*size[ndims-2]]
+   * Forward determines whether to do a forward transform (1) or an inverse 
+   * one (-1)
+   */
+  void fftrn(double *f, double *fnyquist, int ndims, int *size, int forward) {
+
+    int i, j, b;
+
+    // Positions in the 1-d arrays of points labeled by indices 
+    // (i0,i1,...,i(ndims-1)); indexneg gives the position in the array of 
+    // the corresponding negative frequency
+    int index,indexneg = 0;
+    int stepsize; // Used in calculating indexneg
+
+    // The size of the last dimension is used often enough to merit its own 
+    // name.
+    int N = size[ndims - 1];
+    
+    // pi2n = 2Pi / N
+    double pi2n = 4. * asin(1.) / N, cospi2n = cos(pi2n), sinpi2n = sin(pi2n);
+
+    // wb = W^b = e^(2 pi i b/N) in the Danielson-Lanczos formula for a 
+    // transform of length N
+    struct complex wb; 
+
+    // Buffers for implementing recursive formulas
+    struct complex temp1, temp2;
+
+    // Treat f and fnyquist as arrays of complex numbers
+    struct complex *c = (struct complex *)f, 
+      *cnyquist = (struct complex *)fnyquist;
+
+    // Total number of complex points in array
+    int totalsize = 1;
+
+    // Indices for looping through array
+    ArrayList<int> indices;
+    indices.Init(ndims);
+    
+    // Set size[] to be the sizes of f viewed as a complex array
+    size[ndims - 1] /= 2;
+
+    for(i = 0; i < ndims; i++) {
+      totalsize *= size[i];
+      indices[i] = 0;
+    }
+    
+    // forward transform
+    if(forward == 1) {
+
+      // Do a transform of f as if it were N/2 complex points 
+      fftcn(f, ndims, size, 1);
+
+      // Copy b=0 data into cnyquist so the recursion formulas below for b=0 
+      // and cnyquist don't overwrite data they later need
+      for(i = 0; i < totalsize / size[ndims - 1]; i++) {
+	
+	// Only copy points where last array index for c is 0
+	cnyquist[i] = c[i * size[ndims - 1]];
+      }
+    }
+    
+    // Loop over all but last array index
+    for(index = 0; index < totalsize; index += size[ndims-1]) {
+
+      wb.real = 1.; /* Initialize W^b for b=0 */
+      wb.imag = 0.;
+
+      // Loop over elements of transform. See documentation for these formulas
+      for(b = 1; b < N / 4; b++) {
+
+	temp1 = wb;
+
+	// Real part of e^(2 pi i b/N_real) used in D-L formula
+	wb.real = cospi2n*temp1.real - sinpi2n*temp1.imag;
+
+	// Imaginary part of e^(2 pi i b/N_real) used in D-L formula
+	wb.imag = cospi2n*temp1.imag + sinpi2n*temp1.real;
+
+	temp1 = c[index + b];
+
+	// Note that N-b is NOT the negative frequency for b. Only 
+	// nonnegative b momenta are stored.
+	temp2 = c[indexneg + N / 2 - b];
+
+	c[index + b].real = .5 * (temp1.real + temp2.real + forward * wb.real * 
+				  (temp1.imag + temp2.imag) + wb.imag * 
+				  (temp1.real - temp2.real));
+	c[index + b].imag = .5 * (temp1.imag - temp2.imag - forward * wb.real * 
+				  (temp1.real - temp2.real) + wb.imag * 
+				  (temp1.imag + temp2.imag));
+	c[indexneg + N / 2 - b].real = .5 * (temp1.real + temp2.real - 
+					     forward * 
+					     wb.real * 
+					     (temp1.imag + temp2.imag) - 
+					     wb.imag * 
+					     (temp1.real - temp2.real));
+	c[indexneg + N / 2 - b].imag = .5 * (-temp1.imag + temp2.imag - 
+					     forward * wb.real * 
+					     (temp1.real - temp2.real) + 
+					     wb.imag * 
+					     (temp1.imag + temp2.imag));
+      }
+      temp1 = c[index];
+
+      // Index is smaller for cnyquist because it doesn't have the last 
+      // dimension
+      temp2 = cnyquist[indexneg / size[ndims - 1]];
+
+      // Set b=0 term in transform
+      c[index].real = .5 * (temp1.real + temp2.real + forward * 
+			    (temp1.imag + temp2.imag));
+      c[index].imag = .5 * (temp1.imag - temp2.imag - forward * 
+			    (temp1.real - temp2.real));
+
+      // Set b=N/2 transform.
+      cnyquist[indexneg / size[ndims - 1]].real = 
+	.5 * (temp1.real + temp2.real - forward * (temp1.imag + temp2.imag));
+      cnyquist[indexneg / size[ndims - 1]].imag = 
+	.5 * (-temp1.imag + temp2.imag - forward * (temp1.real - temp2.real));
+      
+      // Find indices for positive and single index for negative frequency. 
+      // In each dimension indexneg[j]=0 if index[j]=0, 
+      // indexneg[j]=size[j]-index[j] otherwise.
+
+      // amount to increment indexneg by as each individual index is 
+      // incremented
+      stepsize = size[ndims - 1];
+
+      // If the rightmost indices are maximal reset them to 0. Indexneg goes 
+      // from 1 to 0 in these dimensions
+      for(j = ndims - 2; j >= 0 && indices[j] == size[j] - 1; j--) {
+	indices[j] = 0;
+	indexneg -= stepsize;
+	stepsize *= size[j];
+      }
+      
+      // If index[j] goes from 0 to 1 indexneg[j] goes from 0 to size[j]-1
+      if(j >= 0 && indices[j] == 0) {
+	indexneg += stepsize * (size[j] - 1);
+      }
+      // Otherwise increasing index[j] decreases indexneg by one unit.
+      else {
+	indexneg -= stepsize;
+      }
+
+      // This avoids writing outside the array bounds on the last pass 
+      // through the array loop
+      if(j >= 0) {
+	indices[j]++;
+      }
+    } // End of i loop (over total array)
+    
+    // inverse transform
+    if(forward == -1) {
+      fftcn(f, ndims, size, -1);
+    }
+    
+    // Give the user back the array size[] in its original condition
+    size[ndims - 1] *= 2;
+
+  }
+  
+  void assign_weights(int reference_pt_num, int level, double volume, int pos,
+		      int skip) {
+    if(level == -1) {
+      discretized_[pos] += volume;
+    }
+    else {
+      
+      // Recurse in the right direction
+      double coord = rset_.get(level, reference_pt_num);
+      double leftgridcoord = mincoords_[level] + minindices_[level] *
+	gridsizes_[level];
+      double rightgridcoord = leftgridcoord + gridsizes_[level];
+      double leftvolume = volume * (rightgridcoord - coord);
+      double rightvolume = volume * (coord - leftgridcoord);
+      int nextskip = size_[level] * skip;
+      int nextleftpos = pos + skip * minindices_[level];
+      
+      if(leftvolume > 0.0) {
+	assign_weights(reference_pt_num, level - 1, leftvolume, nextleftpos, 
+		       nextskip);
+      }
+
+      if(rightvolume > 0.0) {
+	assign_weights(reference_pt_num, level - 1, rightvolume, 
+		       nextleftpos + skip, nextskip);
+      }
+    }
+  }
+
+  void retrieve_weights(int query_pt_num, double volume, int level, int pos, 
+			int skip, double divfactor) {
+
+    if(level == -1) {
+      densities_[query_pt_num] += discretized_[pos] * volume / divfactor;
+    }
+    else {
+      
+      // Recurse in the right direction
+      double coord = qset_.get(level, query_pt_num);
+      double leftgridcoord = mincoords_[level] + minindices_[level] *
+	gridsizes_[level];
+      double rightgridcoord = leftgridcoord + gridsizes_[level];
+      double leftvolume = volume * (rightgridcoord - coord);
+      double rightvolume = volume * (coord - leftgridcoord);
+      int nextskip = size_[level] * skip;
+      int nextleftpos = pos + skip * minindices_[level];
+      
+      if(leftvolume > 0.0) {
+	retrieve_weights(query_pt_num, leftvolume, level - 1, nextleftpos, 
+			 nextskip, divfactor);
+      }
+      if(rightvolume > 0.0) {
+	retrieve_weights(query_pt_num, rightvolume, level - 1, 
+			 nextleftpos + skip, nextskip, divfactor);
+      }
+    }
+  }
+
+  /**
+   * Query the normalized density for each query point.
+   */
+  void RetrieveDensities() {
+
+    double normc = 
+      (kernel_.CalcNormConstant(rset_.n_rows()) * rset_.n_cols());
+
+    for(index_t r = 0; r < qset_.n_cols(); r++) {
+      densities_[r] = 0.0;
+      
+      for(index_t d = 0; d < qset_.n_rows(); d++) {
+	minindices_[d] = (int) floor((qset_.get(d, r) - mincoords_[d])/
+				     gridsizes_[d]);
+      }
+      retrieve_weights(r, 1.0, qset_.n_rows() - 1, 0, 1, 
+		       gridbinvolume_ * normc);
+    }
+  }
+
+  void discretize_dataset() {
+
+    // Temporary used to count the number of elements in the enlarged 
+    // matrices for the kernel weights and bin counts. Also calculate the 
+    // volume of each grid bin.
+    numgridpts_ = 1;
+    gridbinvolume_ = 1.0;
+        
+    double min, max;
+    
+    // Find the min/max in each coordinate direction, and calculate the grid
+    // size in each dimension.
+    for(index_t d = 0; d < qset_.n_rows(); d++) {
+      int possiblesample;
+      min = MAXDOUBLE;
+      max = -MAXDOUBLE;
+      
+      for(index_t r = 0; r < rset_.n_cols(); r++) {
+	double coord = rset_.get(d, r);
+	if(coord > max)
+	  max = coord;
+	if(coord < min)
+	  min = coord;
+      }
+      
+      // Following Silverman's advice here
+      mincoords_[d] = min;
+      maxcoords_[d] = max;
+      diffcoords_[d] = maxcoords_[d] - mincoords_[d];
+      gridsizes_[d] = diffcoords_[d] / ((double) m_ - 1);
+      gridbinvolume_ *= gridsizes_[d];
+
+      // Determine how many kernel weight calculation to do for this 
+      // dimension.
+      kernelweights_dims_[d] = m_ - 1;
+      possiblesample = (int) floor(TAU * sqrt(kernel_.bandwidth_sq()) / 
+				   gridsizes_[d]);
+      
+      if(kernelweights_dims_[d] > possiblesample) {
+	if(possiblesample == 0) {
+	  possiblesample = 1;
+	}
+	kernelweights_dims_[d] = possiblesample;
+      }
+
+      // Wand p440: Need to calculate the actual dimension of the matrix
+      // after the necessary 0 padding of the kernel weight matrix and the
+      // bin count matrix.
+      size_[d] = (int) ceil(log(m_ + kernelweights_dims_[d]) / log(2));
+      size_[d] = 1 << size_[d];
+
+      numgridpts_ *= size_[d];
+    }
+
+    // Allocate the memory for discretized grid count matrix and initialize 
+    // it.
+    discretized_.Init(numgridpts_);
+    discretized_.SetZero();
+
+    double inv_gvolume = 1.0 / gridbinvolume_;
+    
+    // Now loop over each data and calculate the weights at each grid point.
+    for(index_t r = 0; r < rset_.n_cols(); r++) {
+
+      // First locate the bin the data point falls into and identify it by
+      // the lower grid coordinates.
+      for(index_t d = 0; d < rset_.n_rows(); d++) {
+	minindices_[d] = (int) floor((rset_.get(d, r) - mincoords_[d])/
+				     gridsizes_[d]);
+      }
+
+      // Assign the weights around the neighboring grid points due to this
+      // data point. This results in 2^num_dims number of recursion per data
+      // point.
+      assign_weights(r, qset_.n_rows() - 1, inv_gvolume, 0, 1);
+    }
+
+  }
+  
+  void gaussify(double acc, double precalc, int level, int pos, int skip) {
+
+    if(level == -1) {
+      kernelweights_[pos] = exp(precalc * acc);
+    }
+    else {
+      int half = kernelweights_dims_[level];
+      int g;
+      for(g = 0; g <= half; g++) {
+	double addThis = g * gridsizes_[level];
+	double newacc = acc + addThis * addThis;
+	int newskip = skip * size_[level];
+	
+	gaussify(newacc, precalc, level - 1, pos + skip * g, newskip);
+	
+	// If this is not the 0th frequency, then do the mirror image thingie.
+	if(g != 0) {
+	  gaussify(newacc, precalc, level - 1,
+		   pos + skip * (size_[level] - g), newskip);
+	}
+      }
+    }
+  }
+  
+  
+ public:
+  
+  struct complex {
+    double real;
+    double imag;
+  };
+
+  FFTKde() {}
+  
+  ~FFTKde() {}
+  
+  // getters and setters
+  
+  /** get the reference dataset */
+  Matrix &get_reference_dataset() { return rset_; }
+
+  /** get the query dataset */
+  Matrix &get_query_dataset() { return qset_; }
+
+  /** get the density estimate */
+  const Vector &get_density_estimates() { return densities_; }
+
+  void Init(Matrix &qset, Matrix &rset) {
+    
+    printf("Initializing FFT KDE...\n");
+    fx_timer_start(NULL, "fft_kde_init");
+
+    // initialize the kernel and read in the number of grid points
+    kernel_.Init(fx_param_double_req(NULL, "bandwidth"));
+    m_ = fx_param_int(NULL, "num_grid_pts_per_dim", 128);
+
+    // set aliases to the query and reference datasets and initialize
+    // query density sets
+    qset_.Alias(qset);
+    densities_.Init(qset_.n_cols());
+    rset_.Alias(rset);
+
+    // scale dataset if the user wants to
+    if(!strcmp(fx_param_str(NULL, "scaling", NULL), "range")) {
+      scale_data_by_minmax();
+    }
+
+    // initialize member variables.
+    size_.Init(qset_.n_rows());
+    minindices_.Init(rset_.n_rows());
+    mincoords_.Init(qset_.n_rows());
+    maxcoords_.Init(qset_.n_rows());
+    diffcoords_.Init(qset_.n_rows());
+    gridsizes_.Init(qset_.n_rows());
+    kernelweights_dims_.Init(qset_.n_rows());
+
+    // set up the discretized grid for the reference dataset
+    discretize_dataset();
+
+    nyquistnum_ = 2 * numgridpts_ / size_[rset_.n_rows() - 1];
+
+    d_fnyquist_.Init(nyquistnum_);
+    k_fnyquist_.Init(nyquistnum_);
+    kernelweights_.Init(numgridpts_);
+
+    fx_timer_stop(NULL, "fft_kde_init");
+    printf("FFT KDE initialization completed...\n");
+  }
+
+  void Init() {
+
+    const char *rfname = fx_param_str_req(NULL, "data");
+    const char *qfname = fx_param_str(NULL, "query", rfname);
+
+    // initialize the kernel and read in the number of grid points
+    kernel_.Init(fx_param_double_req(NULL, "bandwidth"));
+    m_ = fx_param_int(NULL, "num_grid_pts_per_dim", 128);
+
+    // read reference dataset
+    Dataset ref_dataset;
+    ref_dataset.InitFromFile(rfname);
+    rset_.Own(&(ref_dataset.matrix()));
+
+    // read query dataset if different
+    if(!strcmp(qfname, rfname)) {
+      qset_.Alias(rset_);
+    }
+    else {
+      Dataset query_dataset;
+      query_dataset.InitFromFile(qfname);
+      qset_.Own(&(query_dataset.matrix()));
+    }
+
+    // scale dataset if the user wants to
+    if(!strcmp(fx_param_str(NULL, "scaling", NULL), "range")) {
+      scale_data_by_minmax();
+    }
+
+    printf("Initializing FFT KDE...\n");
+    fx_timer_start(NULL, "fft_kde_init");
+
+    // initialize member variables.
+    size_.Init(qset_.n_rows());
+    densities_.Init(qset_.n_cols());
+    minindices_.Init(rset_.n_rows());
+    mincoords_.Init(qset_.n_rows());
+    maxcoords_.Init(qset_.n_rows());
+    diffcoords_.Init(qset_.n_rows());
+    gridsizes_.Init(qset_.n_rows());
+    kernelweights_dims_.Init(qset_.n_rows());
+
+    // set up the discretized grid for the reference dataset
+    discretize_dataset();
+
+    nyquistnum_ = 2 * numgridpts_ / size_[rset_.n_rows() - 1];
+
+    d_fnyquist_.Init(nyquistnum_);
+    k_fnyquist_.Init(nyquistnum_);
+    kernelweights_.Init(numgridpts_);
+    fx_timer_stop(NULL, "fft_kde_init");
+    printf("FFT KDE initialization completed...\n");
+
+  }
+
+  void Compute() {
+
+    printf("Computing FFT KDE...\n");
+    fx_timer_start(NULL, "fft_kde");
+
+    // FFT the discretized bin count matrix.
+    d_fnyquist_.SetZero();
+    k_fnyquist_.SetZero();
+    kernelweights_.SetZero();
+    fftrn(discretized_.ptr(), d_fnyquist_.ptr(), rset_.n_rows(), 
+	  size_.begin(), 1);
+
+    // Calculate the required kernel weights at each grid point. This matrix
+    // will be convolved with fourier transformed data set.
+    double precalc = -0.5 / kernel_.bandwidth_sq();
+    gaussify(0.0, precalc, rset_.n_rows() - 1, 0, 1);
+
+    // FFT the kernel weight matrix.
+    fftrn(kernelweights_.ptr(), k_fnyquist_.ptr(), 
+	  rset_.n_rows(), size_.begin(), 1);
+
+    // We need to invoke the convolution theorem for FFT here. Take each
+    // corresponding complex number in kernelweights and discretized and do
+    // an element-wise multiplication. Later, pass it to inverse fft function,
+    // and we have our answer!
+    for(index_t d = 0; d < numgridpts_; d += 2) {
+      double real1 = discretized_[d];
+      double complex1 = discretized_[d + 1];
+      double real2 = kernelweights_[d];
+      double complex2 = kernelweights_[d + 1];
+      discretized_[d] = real1 * real2 - complex1 * complex2;
+      discretized_[d + 1] = real1 * complex2 + complex1 * real2;
+    }
+
+    for(index_t d = 0; d < nyquistnum_; d += 2) {
+      double real1 = d_fnyquist_[d];
+      double complex1 = d_fnyquist_[d + 1];
+      double real2 = k_fnyquist_[d];
+      double complex2 = k_fnyquist_[d + 1];
+      d_fnyquist_[d] = real1 * real2 - complex1 * complex2;
+      d_fnyquist_[d + 1] = real1 * complex2 + complex1 * real2;
+    }
+
+    // Inverse FFT the elementwise multiplied matrix.
+    fftrn(discretized_.ptr(), d_fnyquist_.ptr(), 
+	  rset_.n_rows(), size_.begin(), -1);
+
+    // Retrieve the densities of each data point.
+    RetrieveDensities();
+
+    fx_timer_stop(NULL, "fft_kde");
+    printf("FFT KDE completed...\n");
+  }
+
+  void NormalizeDensities() {
+    double norm_const = kernel_.CalcNormConstant(qset_.n_rows()) *
+      rset_.n_cols();
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      densities_[q] /= norm_const;
+    }
+  }
+
+  void PrintDebug() {
+
+    FILE *stream = stdout;
+    const char *fname = NULL;
+
+    if((fname = fx_param_str(NULL, "fft_kde_output", NULL)) != NULL) {
+      stream = fopen(fname, "w+");
+    }
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      fprintf(stream, "%g\n", densities_[q]);
+    }
+    
+    if(stream != stdout) {
+      fclose(stream);
+    }
+  }
+
+};
+
+#endif
diff --git a/fastlib/u/gmravi/regression/fgt_kde.h b/fastlib/u/gmravi/regression/fgt_kde.h
new file mode 100644
index 0000000000..8d52f6fe50
--- /dev/null
+++ b/fastlib/u/gmravi/regression/fgt_kde.h
@@ -0,0 +1,1040 @@
+#ifndef FGT_KDE_H
+#define FGT_KDE_H
+
+#include <math.h>
+#include <values.h>
+
+#include "u/dongryel/series_expansion/mult_series_expansion_aux.h"
+
+/** 
+ * Computing kernel estimate using Fast Gauss Transform by Lelie Greengard
+ * and John Strain
+ */
+class FGTKde {
+  
+ private:
+
+  /** query dataset */
+  Matrix qset_;
+
+  /** reference dataset */
+  Matrix rset_;
+
+  /** kernel */
+  GaussianKernel kernel_;
+
+  /** computed densities */
+  Vector densities_;
+  
+  /** accuracy parameter */
+  double tau_;
+
+  /** precomputed constants */
+  MultSeriesExpansionAux msea_;
+
+  /* returns the index in a single-dim array, for the given coords in a
+   * d-dim array, with n[i] elements in the ith dimension
+   */
+  int multi_dim_index_in_single_array(ArrayList<int> &coords, 
+				      ArrayList<int> &n) {
+    
+    int sum = 0;
+    
+    for(index_t i = 0; i < coords.size(); i++) {
+      int prod = 1;
+      for(index_t j = 0; j < i; j++) {
+	prod *= n[j];
+      }
+      sum += coords[i] * prod;
+    }
+    return sum;
+  }
+
+  /** Returns an ivec containing index translated into coordinates in
+   * {n[0],  ..., n[d-1]} space.
+   */
+  void single_dim_index_in_multi_array(ArrayList<int> &n, int index, 
+				       ArrayList<int> &coords) {
+
+    for(index_t i = 0; i < coords.size(); i++) {
+      int this_coord = index % n[i];
+      index /= n[i];
+      coords[i] = this_coord;
+    }
+  }
+
+  int is_ongrid(ArrayList<int> &old_coords, int delta, 
+		ArrayList<int> &new_coords, ArrayList<int> &nsides) {
+    
+    int dim = old_coords.size();
+    for(index_t d = 0; d < dim; d++) {
+      int new_val = old_coords[d] + delta;
+      
+      if(new_val < 0 || new_val >= nsides[d]) {
+	return 0;
+      }
+      
+      new_coords[d] = new_val;
+    }
+    return 1;
+  }
+
+  void mknbor(int ibox, ArrayList<int> &nsides, int kdis, 
+	      ArrayList<int> &ret) {
+
+    // Compute actual vector position of a given box.
+    ArrayList<int> coords;
+    coords.Init(nsides.size());
+    single_dim_index_in_multi_array(nsides, ibox, coords);
+
+    ArrayList<int> dummy_n;
+    dummy_n.Init(coords.size());
+    ArrayList<int> all_ones;
+    all_ones.Init(coords.size()); 
+    for(index_t i = 0; i < coords.size(); i++) {
+      dummy_n[i] = 2 * kdis + 1;
+      all_ones[i] = kdis;
+    }
+
+    int num_neighbors = (2 * kdis + 1);
+    int i;
+
+    // number of neighbors in $D$ dimension is num_neighbors^D
+    num_neighbors = (int) pow(num_neighbors, coords.size());
+
+    // We need to generate every combination of [-1, 0, 1] (except [0, 0, 0]).
+    // This is slightly hacky but also pretty crafty. We can easily enumerate 
+    // these combinations by viewing the space as a grid in d dimensions with 
+    // 3 cells in each. Therefore, we get coordinates in [0, 1, 2]. We achieve 
+    // our goal by simply subtracting 1. We filter out values that aren't on 
+    // the grid.
+    ArrayList<int> delta, new_coords;
+    delta.Init(coords.size());
+    new_coords.Init(coords.size());
+
+    for(i = 0; i < num_neighbors; i++) {
+      single_dim_index_in_multi_array(dummy_n, i, delta);
+
+      for(index_t j = 0; j < coords.size(); j++) {
+	new_coords[j] = coords[j] + delta[j];
+      }
+      int ongrid = is_ongrid(new_coords, -kdis, new_coords, nsides);
+      
+      if(ongrid) {
+	*(ret.AddBack()) = 
+	  multi_dim_index_in_single_array(new_coords, nsides);
+      }
+      
+    }
+  }
+
+  void assign(int nallbx, ArrayList<int> &nsides, Vector &sidelengths, 
+	      Vector &mincoords, Matrix &center, 
+	      ArrayList<ArrayList<int> > &queries_assigned, 
+	      ArrayList<ArrayList<int> > &references_assigned) {
+
+    int r;
+    int num_query_rows = qset_.n_cols();
+    int num_ref_rows = rset_.n_cols();
+    int dim = qset_.n_rows();
+    int di;
+  
+    // Assign the reference points.
+    for(r = 0; r < num_ref_rows; r++) {
+
+      int boxnum = 0;
+
+      for(di = dim - 1; di >= 0; di--) {
+	int nside = nsides[di];
+	double h = sidelengths[di];
+	int binnum = (int) floor((rset_.get(di, r) - mincoords[di]) / h);
+      
+	binnum = max(0, min(binnum, nside - 1));
+	boxnum = boxnum * nside + binnum;
+      }
+      *(references_assigned[boxnum].AddBack()) = r;
+    }
+
+    // Assign the query points
+    for(r = 0; r < num_query_rows; r++) {
+    
+      int boxnum = 0;
+
+      for(di = dim - 1; di >= 0; di--) {
+	int nside = nsides[di];
+	double h = sidelengths[di];
+	int binnum = (int) floor((qset_.get(di, r) - mincoords[di]) / h);
+      
+	binnum = max(0, min(binnum, nside - 1));
+	boxnum = boxnum * nside + binnum;
+      }
+      *(queries_assigned[boxnum].AddBack()) = r;
+    }
+  
+    // Create centers for all boxes.
+    for(r = 0; r < nallbx; r++) {
+      int sf = nallbx;
+      int ind = r, rem;
+      Vector box_center;    
+      center.MakeColumnVector(r, &box_center);
+
+      for(di = dim - 1; di >= 0; di--) {
+	int nside = nsides[di];
+	double h = sidelengths[di];
+	sf /= nside;
+	rem = ind % sf;
+	ind = ind / sf;
+
+	box_center[di] = mincoords[di] + (ind + 0.5) * h;
+
+	ind = rem;
+      }
+    }
+  }
+
+  void TranslateMultipoleToLocal(int ref_box_num, int query_box_num,
+				 Matrix &mcoeffsb, Matrix &lcoeffsb,
+				 int p_alpha, int totalnumcoeffs,
+				 double bwsqd_2, const double *hrcentroid,
+				 const double *dest_hrcentroid) {
+
+    double bandwidth = sqrt(bwsqd_2);
+    int j, k, l, d, step;
+
+    int dim = qset_.n_rows();
+
+    Vector lcoeffs;
+    lcoeffsb.MakeColumnVector(query_box_num, &lcoeffs);
+    Vector mcoeffs;
+    mcoeffsb.MakeColumnVector(ref_box_num, &mcoeffs);
+  
+    {
+      Vector dest_minus_parent;
+      dest_minus_parent.Init(dim);
+      
+      la::SubOverwrite(dim, dest_hrcentroid, hrcentroid, 
+		       dest_minus_parent.ptr());
+
+      int limit = 2 * p_alpha - 2;
+      Matrix hermite_map;
+      hermite_map.Init(dim, limit + 1);
+      Matrix arrtmp;
+      arrtmp.Init(dim, totalnumcoeffs);
+
+      Vector C_k_neg;
+      C_k_neg.Alias(msea_.get_neg_inv_multiindex_factorials());
+    
+      for(j = 0; j < dim; j++) {
+	double coord_div_band = dest_minus_parent[j] / bandwidth;
+	double d2 = 2 * coord_div_band;
+	double facj = exp(-coord_div_band * coord_div_band);
+      
+	hermite_map.set(j, 0, facj);
+      
+	if(p_alpha > 1) {
+	  hermite_map.set(j, 1, d2 * facj);
+	
+	  for(k = 1; k < limit; k++) {
+	    int k2 = k * 2;
+	    hermite_map.set(j, k + 1,
+			    d2 * hermite_map.get(j, k) - k2 * 
+			    hermite_map.get(j, k - 1));
+	  }
+	}
+	
+	for(l = 0; l < totalnumcoeffs; l++) {
+	  arrtmp.set(j, l, 0.0);
+	}
+      }
+    
+      step = totalnumcoeffs / p_alpha;
+      d = 0;
+    
+      for(j = 0; j < totalnumcoeffs; j++) {
+	const ArrayList<int> &mapping = msea_.get_multiindex(j);
+	
+	for(k = 0, l = j % step; k < p_alpha; k++, l += step) {
+	  arrtmp.set(d, j, arrtmp.get(d, j) + mcoeffs[l] * 
+		     hermite_map.get(d, mapping[d] + k));
+	}
+      }
+    
+    
+      if(p_alpha > 1) {
+	int boundary, boundary2;
+
+	for(boundary = totalnumcoeffs / p_alpha, step = step / p_alpha, d = 1; 
+	    step >= 1; step /= p_alpha, d++, boundary /= p_alpha) {
+	
+	  boundary2 = 0;
+	
+	  for(j = 0; j < totalnumcoeffs; j++) {
+	    const ArrayList<int> &mapping = msea_.get_multiindex(j);
+
+	    if(j % boundary == 0) {
+	      boundary2 += boundary;
+	    }
+	  
+	    for(k = 0; k < p_alpha; k++) {
+	      
+	      int jump = (j + step * k) % boundary2;
+	    
+	      if(jump < boundary2 - boundary) {
+		jump += boundary2 - boundary;
+	      }
+	    
+	      const ArrayList<int> &mapping2 = msea_.get_multiindex(jump);
+
+	      arrtmp.set(d, j,
+			 arrtmp.get(d, j) +
+			 arrtmp.get(d - 1, jump) * 
+			 hermite_map.get(d, mapping2[d] + mapping[d]));
+	    }
+	  }
+	}
+      }
+    
+      d = dim - 1;
+      
+      for(j = 0; j < totalnumcoeffs; j++) {
+	lcoeffs[j] = lcoeffs[j] + C_k_neg[j] * arrtmp.get(d, j);
+      }
+    }
+  }
+
+  void ComputeMultipoleCoeffs(Matrix &mcoeffs, int dim,
+			      int p_alpha, int totalnumcoeffs, 
+			      int ref_box_num, double bwsqd_two, 
+			      ArrayList<int> &rows, const double *x_R) {
+    
+    Vector A_k;
+    mcoeffs.MakeColumnVector(ref_box_num, &A_k);
+    double bw_times_sqrt_two = sqrt(bwsqd_two);
+
+    // If the thing has been computed already, return. Otherwise, compute it
+    // and store it as a cached sufficient statistics.
+    if(A_k[0] != 0) {
+      return;
+    }
+
+    Vector C_k;
+    C_k.Alias(msea_.get_inv_multiindex_factorials());
+  
+    Vector tmp;
+    tmp.Init(totalnumcoeffs);
+    Vector x_r;
+    x_r.Init(dim);
+    int num_rows = rows.size();
+    int step, boundary;
+    int r, i, j;
+  
+    A_k[0] = num_rows;
+    for(i = 1; i < totalnumcoeffs; i++) {
+      A_k[i] = 0.0;
+    }
+  
+    if(p_alpha > 1) {
+    
+      for(r = 0; r < num_rows; r++) {
+      
+	int row_num = rows[r];
+      
+	for(i = 0; i < dim; i++) {
+	  x_r[i] = (rset_.get(i, row_num) - x_R[i]) / bw_times_sqrt_two;
+	}
+      
+	tmp[0] = 1.0;
+      
+	for(boundary = totalnumcoeffs, step = totalnumcoeffs / p_alpha,
+	      j = 0;
+	    step >= 1; step /= p_alpha, boundary /= p_alpha, j++) {
+	  for(i = 0; i < totalnumcoeffs; ) {
+	    int limit = i + boundary;
+	  
+	    i += step;
+	  
+	    for( ; i < limit; i += step) {
+	      tmp[i] = tmp[i - step] * x_r[j];
+	    }
+	  }
+	}
+      
+      
+	for(i = 1; i < totalnumcoeffs; i++) {
+	  A_k[i] = A_k[i] + tmp[i];
+	}
+      }
+    }
+  
+    for(r = 1; r < totalnumcoeffs; r++) {
+      A_k[r] = A_k[r] * C_k[r];
+    }
+
+    return;
+  }
+
+
+  void DirectLocalAccumulation(ArrayList<int> &rows, int query_box_num,
+			       Matrix &locexps, double delta,
+			       const double *dest_hrcentroid, int p_alpha, 
+			       int totalnumcoeffs) {
+
+    int num_rows = rows.size();
+    int r, d, boundary, step, i, j, k;
+
+    // Retrieve the centroid
+    int dim = rset_.n_rows();
+    int limit2 = p_alpha - 1;
+    Matrix hermite_map;
+    hermite_map.Init(dim, p_alpha);
+    Vector arrtmp;
+    arrtmp.Init(totalnumcoeffs);
+    Vector x_r_minus_x_Q;
+    x_r_minus_x_Q.Init(dim);
+    double bandwidth = sqrt(delta);
+    Vector neg_inv_multiindex_factorials;
+    neg_inv_multiindex_factorials.Alias
+      (msea_.get_neg_inv_multiindex_factorials());
+
+    Vector arr;
+    locexps.MakeColumnVector(query_box_num, &arr);
+
+    /**
+     * For each data point,
+     */
+    for(r = 0; r < num_rows; r++) {
+
+      int row_num = rows[r];
+    
+      /**
+       * Calculate (x_r - x_Q)
+       */
+      for(d = 0; d < dim; d++) {
+	x_r_minus_x_Q[d] = dest_hrcentroid[d] - rset_.get(d, row_num);
+      }
+    
+      /**
+       * Compute the necessary Hermite precomputed map based on the coordinate
+       * difference.
+       */
+      for(d = 0; d < dim; d++) {
+
+	double coord_div_band = x_r_minus_x_Q[d] / bandwidth;
+	double d2 = 2 * coord_div_band;
+	double facj = exp(-coord_div_band * coord_div_band);
+      
+	hermite_map.set(d, 0, facj);
+      
+	if(p_alpha > 1) {
+	  hermite_map.set(d, 1, d2 * facj);
+	
+	  for(k = 1; k < limit2; k++) {
+	    int k2 = k * 2;
+	    hermite_map.set(d, k + 1,
+			    d2 * hermite_map.get(d, k) - k2 * 
+			    hermite_map.get(d, k - 1));
+	  }
+	}
+      }
+    
+      /**
+       * Seed to start out the coefficients.
+       */
+      arrtmp[0] = 1.0;
+    
+      if(p_alpha > 1) {
+      
+	/**
+	 * Compute the Taylor coefficients directly...
+	 */
+	for(boundary = totalnumcoeffs, step = totalnumcoeffs / p_alpha,
+	      d = 0;
+	    step >= 1; step /= p_alpha, boundary /= p_alpha, d++) {
+	  for(i = 0; i < totalnumcoeffs; ) {
+	    int limit = i + boundary;
+	  
+	    /**
+	     * Skip the first one.
+	     */
+	    int first = i;
+	    i += step;
+	  
+	    for(j = 1; i < limit; i += step, j++) {
+	      arrtmp[i] = arrtmp[first] * hermite_map.get(d, j);
+	    }
+	  
+	    arrtmp[first] *= hermite_map.get(d, 0);
+	  }
+	}
+      }
+      else {
+	for(d = 0; d < dim; d++) {
+	  arrtmp[0] *= hermite_map.get(d, 0);
+	}
+      }
+    
+      for(j = 0; j < totalnumcoeffs; j++) {
+	arr[j] = arr[j] + neg_inv_multiindex_factorials[j] * arrtmp[j];
+      }    
+    }
+  }
+
+  void EvaluateMultipoleExpansion(ArrayList<int> &rows, int p_alpha,
+				  int totalnumcoeffs, Matrix &mcoeffsb,
+				  int ref_box_num, double bwsqd_times_2, 
+				  const double *ref_hrcentroid) {
+    
+    double bandwidth = sqrt(bwsqd_times_2);
+    int num_query_rows = rows.size();
+    int r, d, boundary, step, i, j, k;
+
+    // Retrieve the reference centroid.
+    int dim = qset_.n_rows();
+    Vector x_q_minus_x_R;
+    x_q_minus_x_R.Init(dim);
+    Matrix hermite_map;
+    hermite_map.Init(dim, p_alpha);
+    Vector arrtmp;
+    arrtmp.Init(totalnumcoeffs);
+    int limit2 = p_alpha - 1;
+    Vector mcoeffs;
+    mcoeffsb.MakeColumnVector(ref_box_num, &mcoeffs);
+  
+    // For each data point,
+    for(r = 0; r < num_query_rows; r++) {
+
+      double multipolesum = 0.0;
+      int row_num = rows[r];
+
+      // Calculate (x_q - x_R)
+      for(d = 0; d < dim; d++) {
+	x_q_minus_x_R[d] = qset_.get(d, row_num) - ref_hrcentroid[d];
+      }
+      
+      // Compute the necessary Hermite precomputed map based on the coordinate
+      // difference.
+      for(d = 0; d < dim; d++) {
+	double coord_div_band = x_q_minus_x_R[d] / bandwidth;
+	double d2 = 2 * coord_div_band;
+	double facj = exp(-coord_div_band * coord_div_band);
+      
+	hermite_map.set(d, 0, facj);
+      
+	if(p_alpha > 1) {
+	  hermite_map.set(d, 1, d2 * facj);
+	
+	  for(k = 1; k < limit2; k++) {
+	    int k2 = k * 2;
+	    hermite_map.set(d, k + 1,
+			    d2 * hermite_map.get(d, k) - k2 * 
+			    hermite_map.get(d, k - 1));
+	  }
+	}
+      }
+    
+      // Seed to start out the coefficients.
+      arrtmp[0] = 1.0;
+    
+      if(p_alpha > 1) {
+      
+	for(boundary = totalnumcoeffs, step = totalnumcoeffs / p_alpha,
+	      d = 0;
+	    step >= 1; step /= p_alpha, boundary /= p_alpha, d++) {
+	  for(i = 0; i < totalnumcoeffs; ) {
+	    int limit = i + boundary;
+	  
+	    // Skip the first one.
+	    int first = i;
+	    i += step;
+	    
+	    for(j = 1; i < limit; i += step, j++) {
+	      arrtmp[i] = arrtmp[first] * hermite_map.get(d, j);
+	    }
+	  
+	    arrtmp[first] *= hermite_map.get(d, 0);
+	  }
+	}
+      }
+      else {
+	for(d = 0; d < dim; d++) {
+	  arrtmp[0] *= hermite_map.get(d, 0);
+	}
+      }
+    
+      for(j = 0; j < totalnumcoeffs; j++) {
+	multipolesum += mcoeffs[j] * arrtmp[j];
+      }
+
+      densities_[row_num] += multipolesum;
+    }
+  }
+
+  double compute_v_alpha(int row_q, Vector &x_Q, double h, 
+			 int query_box_num, Matrix &lcoeffsb, 
+			 int totalnumcoeffs, int p_alpha) {
+    
+    int dim = qset_.n_rows();
+    Vector x_Q_to_x_q;
+    x_Q_to_x_q.Init(dim);
+    int i, j, boundary, step;
+    double multipolesum = 0;
+
+    /**
+     * First calculate (x_q - x_Q) / (sqrt(2) * h)
+     */
+    for(i = 0; i < dim; i++) {
+      double x_Q_val = x_Q[i];
+      double x_q_val = qset_.get(i, row_q);
+      x_Q_to_x_q[i] = (x_q_val - x_Q_val) / h;
+    }
+
+    {
+      Vector lcoeffs;
+      lcoeffsb.MakeColumnVector(query_box_num, &lcoeffs);
+
+      Vector tmp;
+      tmp.Init(totalnumcoeffs);
+      tmp[0] = 1.0;
+    
+      if(totalnumcoeffs > 1) {
+	
+	for(boundary = totalnumcoeffs, step = totalnumcoeffs / p_alpha,
+	      j = 0;
+	    step >= 1; step /= p_alpha, boundary /= p_alpha, j++) {
+	  for(i = 0; i < totalnumcoeffs; ) {
+	    int limit = i + boundary;
+	  
+	    i += step;
+	  
+	    for( ; i < limit; i += step) {
+	      tmp[i] = tmp[i - step] * x_Q_to_x_q[j];
+	    }
+	  }
+	}    
+      }
+    
+      for(i = 0; i < totalnumcoeffs; i++) {
+	multipolesum += lcoeffs[i] * tmp[i];
+      }
+    }
+    return multipolesum;
+  }
+
+  void gaeval(double delta, int nterms, int nallbx,
+	      ArrayList<int> &nsides, Matrix &locexp, int nlmax,
+	      ArrayList<ArrayList<int> > &queries_assigned, 
+	      Matrix &center, int totalnumcoeffs) {
+
+    int i, j;
+    
+    // GO through all query boxes.
+    for(i = 0; i < nallbx; i++) {
+      ArrayList<int> &query_rows = queries_assigned[i];
+      int ninbox = query_rows.size();
+      
+      if(ninbox <= nlmax) {
+	continue;
+      }
+      else {
+	
+	for(j = 0; j < ninbox; j++) {
+	  int row_q = query_rows[j];
+	  Vector x_Q;
+	  center.MakeColumnVector(i, &x_Q);
+	  
+	  double result = compute_v_alpha(row_q, x_Q, sqrt(delta), 
+					  i, locexp, totalnumcoeffs, nterms);
+	  
+	  densities_[row_q] += result;
+	}
+      }
+    }
+  }
+
+  void gafexp(double delta, int nterms,
+	      int nallbx, ArrayList<int> &nsides, Vector &sidelengths, 
+	      Vector &mincoords, Matrix &locexp, int nfmax, int nlmax, 
+	      int kdis, Matrix &center, 
+	      ArrayList<ArrayList<int> > &queries_assigned,
+	      ArrayList<ArrayList<int> > &references_assigned, 
+	      Matrix &mcoeffs) {
+
+    int dim = qset_.n_rows();
+
+    int totalnumcoeffs = (int) pow(nterms, dim);
+
+    // Step 1: Assign query points and reference points to boxes.
+    assign(nallbx, nsides, sidelengths, mincoords, center, 
+	   queries_assigned, references_assigned);
+
+    // Initialize local expansions to zero.
+    int i, j, k, l;
+
+    // Process all reference boxes
+    for(i = 0; i < nallbx; i++) {
+
+      ArrayList<int> &reference_rows = references_assigned[i];
+      int ninbox = reference_rows.size();
+
+      // If the box contains no reference points, skip it.
+      if(ninbox <= 0) {
+	continue;
+      }
+
+      // In this case, no far-field expansion is created
+      else if(ninbox <= nfmax) {
+
+	// Get the query boxes that are in the interaction range.
+	ArrayList <int> nbors;
+	nbors.Init();
+	mknbor(i, nsides, kdis, nbors);
+	int nnbors = nbors.size();
+
+	for(j = 0; j < nnbors; j++) {
+
+	  // Number of query points in this neighboring box.
+	  int query_box_num = nbors[j];
+	  ArrayList<int> &query_rows = queries_assigned[query_box_num];
+	  int ninnbr = query_rows.size();
+	
+	  if(ninnbr <= nlmax) {
+
+	    // Direct Interaction
+	    for(k = 0; k < ninnbr; k++) {
+	    
+	      int query_row = query_rows[k];
+	      const double *query = qset_.GetColumnPtr(query_row);
+
+	      for(l = 0; l < ninbox; l++) {
+		int reference_row = reference_rows[l];
+		const double *reference = rset_.GetColumnPtr(reference_row);
+
+		double dsqd = 
+		  la::DistanceSqEuclidean(qset_.n_rows(), query, reference);
+
+		double pot = exp(-dsqd / delta);
+
+		// Here, I hard-coded to do only one bandwidth.
+		densities_[query_row] += pot;
+	      }
+	    }
+	  
+	  }
+
+	  // In this case, take each reference point and convert into the 
+	  // Taylor series.
+	  else {
+	  
+	    DirectLocalAccumulation(reference_rows, query_box_num,
+				    locexp, delta,
+				    center.GetColumnPtr(query_box_num),
+				    nterms, totalnumcoeffs);
+	  }
+	
+	}
+      }
+
+      // In this case, create a far field expansion.
+      else {
+
+	ComputeMultipoleCoeffs(mcoeffs ,dim, nterms,
+			       totalnumcoeffs, i, delta, reference_rows,
+			       center.GetColumnPtr(i));
+
+	// Get the query boxes that are in the interaction range.
+	ArrayList<int> nbors;
+	nbors.Init();
+	mknbor(i, nsides, kdis, nbors);
+	int nnbors = nbors.size();
+
+	for(j = 0; j < nnbors; j++) {
+	  int query_box_num = nbors[j];
+	  ArrayList<int> &query_rows = queries_assigned[query_box_num];
+	  int ninnbr = query_rows.size();
+
+	  // If this is true, evaluate far field expansion at each query point.
+	  if(ninnbr <= nlmax) {
+
+	    EvaluateMultipoleExpansion(query_rows, nterms,
+				       totalnumcoeffs, mcoeffs,
+				       i, delta, center.GetColumnPtr(i));
+	  }
+
+	  // In this case do multipole to local translation.
+	  else {
+	    
+	    TranslateMultipoleToLocal(i, query_box_num,
+				      mcoeffs, locexp,
+				      nterms, totalnumcoeffs, delta,
+				      center.GetColumnPtr(i),
+				      center.GetColumnPtr(query_box_num));
+	  
+	  }
+	}
+      }
+    }
+    gaeval(delta, nterms, nallbx, nsides, locexp, nlmax,
+	   queries_assigned, center, totalnumcoeffs);
+  }
+
+  void gauss_t(double delta, int nterms, int nallbx, ArrayList<int> &nsides, 
+	       Vector &sidelengths, Vector &mincoords,
+	       Matrix &locexp, Matrix &center,
+	       ArrayList<ArrayList<int> > &queries_assigned, 
+	       ArrayList<ArrayList<int> > &references_assigned,
+	       Matrix &mcoeffs) {
+
+    int dim = qset_.n_rows();
+    int kdis = (int) (sqrt(log(tau_) * -2.0) + 1);
+
+    // This is a slight modification of Strain's cutoff since he never
+    // implemented this above 2 dimensions.
+    int nfmax = (int) pow(nterms, dim - 1) + 2;
+    int nlmax = nfmax;
+
+    // Call gafexp to create all expansions on grid, evaluate all appropriate
+    // far-field expansions and evaluate all appropriate direct interactions.
+    gafexp(delta, nterms, nallbx, nsides, sidelengths, mincoords,
+	   locexp, nfmax, nlmax, kdis, center, queries_assigned,
+	   references_assigned, mcoeffs);
+  }
+
+ public:
+
+  FGTKde() {}
+  
+  ~FGTKde() {}
+  
+  // getters and setters
+  
+  /** get the reference dataset */
+  Matrix &get_reference_dataset() { return rset_; }
+
+  /** get the query dataset */
+  Matrix &get_query_dataset() { return qset_; }
+
+  /** get the density estimate */
+  const Vector &get_density_estimates() { return densities_; }
+
+  void Init(Matrix &qset, Matrix &rset) {
+
+    // initialize the kernel and read in the number of grid points
+    kernel_.Init(fx_param_double_req(NULL, "bandwidth"));
+
+    // set aliases to the query and reference datasets and initialize
+    // query density sets
+    qset_.Alias(qset);
+    densities_.Init(qset_.n_cols());
+    rset_.Alias(rset);
+
+    // set accuracy
+    tau_ = fx_param_double(NULL, "tau", 0.1);
+  }
+
+  void Init() {
+
+    const char *rfname = fx_param_str_req(NULL, "data");
+    const char *qfname = fx_param_str(NULL, "query", rfname);
+
+    // initialize the kernel and read in the number of grid points
+    kernel_.Init(fx_param_double_req(NULL, "bandwidth"));
+
+    // read reference dataset
+    Dataset ref_dataset;
+    ref_dataset.InitFromFile(rfname);
+    rset_.Own(&(ref_dataset.matrix()));
+
+    // read query dataset if different
+    if(!strcmp(qfname, rfname)) {
+      qset_.Alias(rset_);
+    }
+    else {
+      Dataset query_dataset;
+      query_dataset.InitFromFile(qfname);
+      qset_.Own(&(query_dataset.matrix()));
+    }
+    
+    // allocate densities
+    densities_.Init(qset_.n_cols());
+
+    // set accuracy
+    tau_ = fx_param_double(NULL, "tau", 0.1);
+  }
+
+  void FastGaussTransformPreprocess(double *interaction_radius, 
+				    ArrayList<int> &nsides, 
+				    Vector &sidelengths, Vector &mincoords, 
+				    int *nboxes, int *nterms) {
+  
+    // Compute the interaction radius.
+    double bandwidth = sqrt(kernel_.bandwidth_sq());
+    *interaction_radius = sqrt(-2.0 * kernel_.bandwidth_sq() * log(tau_));
+
+    int di, n, num_rows = rset_.n_cols();
+    int dim = rset_.n_rows();
+
+    /**
+     * Discretize the grid space into boxes.
+     */
+    Vector maxcoords;
+    maxcoords.Init(dim);
+    maxcoords.SetAll(MINDOUBLE);
+    double boxside = -1.0;
+    *nboxes = 1;
+
+    for(di = 0; di < dim; di++) {
+      mincoords[di] = MAXDOUBLE;
+    }
+    
+    for(n = 0; n < num_rows; n++) {
+      for(di = 0; di < dim; di++) {
+	if(mincoords[di] > rset_.get(di, n)) {
+	  mincoords[di] = rset_.get(di, n);
+	}
+	if(maxcoords[di] < rset_.get(di, n)) {
+	  maxcoords[di] = rset_.get(di, n);
+	}
+      }
+    }
+
+    /**
+     * Figure out how many boxes lie along each direction.
+     */
+    for(di = 0; di < dim; di++) {
+      nsides[di] = (int) 
+	((maxcoords[di] - mincoords[di]) / bandwidth + 1);
+      (*nboxes) = (*nboxes) * nsides[di];
+      double tmp = (maxcoords[di] - mincoords[di]) /
+	(nsides[di] * 2 * bandwidth);
+    
+      if(tmp > boxside) {
+	boxside = tmp;
+      }
+
+      sidelengths[di] = (maxcoords[di] - mincoords[di]) / 
+	((double) nsides[di]);
+      
+    }
+    
+    int ip = 0;
+    double two_r = 2.0 * boxside;
+    double one_minus_two_r = 1.0 - two_r;
+    double ret = 1.0 / pow(one_minus_two_r * one_minus_two_r, dim);
+    double factorialvalue = 1.0;
+    double r_raised_to_p_alpha = 1.0;
+    double first_factor, second_factor;
+    double ret2;
+                                                                       
+    do {
+      ip++;
+      factorialvalue *= ip;
+    
+      r_raised_to_p_alpha *= two_r;
+      first_factor = 1.0 - r_raised_to_p_alpha;
+      first_factor *= first_factor;
+      second_factor = r_raised_to_p_alpha * (2.0 - r_raised_to_p_alpha)
+	/ sqrt(factorialvalue);
+    
+      ret2 = ret * (pow((first_factor + second_factor), dim) -
+		    pow(first_factor, dim));
+    
+    } while(ret2 > tau_);
+
+    *nterms = ip;
+  }
+
+  void Compute() {
+
+    double interaction_radius;
+    ArrayList<int> nsides;
+    Vector sidelengths;
+    Vector mincoords;
+    int nboxes, nterms;
+    int dim = rset_.n_rows();
+    
+    nsides.Init(rset_.n_rows());
+    sidelengths.Init(rset_.n_rows());
+    mincoords.Init(rset_.n_rows());
+
+    printf("Computing FGT KDE...\n");
+    
+
+    // initialize densities to zero
+    densities_.SetZero();
+
+    fx_timer_start(NULL, "fgt_kde_init");
+    FastGaussTransformPreprocess(&interaction_radius, nsides, sidelengths,
+				 mincoords, &nboxes, &nterms);
+    fx_timer_stop(NULL, "fgt_kde_init");
+
+    // precompute factorials
+    msea_.Init(nterms - 1, qset_.n_rows());
+
+    // stores the coordinate of each grid box
+    Matrix center;
+    center.Init(dim, nboxes);
+
+    // stores the local expansion of each grid box
+    Matrix locexp;
+    locexp.Init((int) pow(nterms, dim), nboxes);
+
+    // stores the ids of query points assigned to each grid box
+    ArrayList<ArrayList<int> > queries_assigned;
+    queries_assigned.Init(nboxes);
+    
+    for(index_t i = 0; i < nboxes; i++) {
+      queries_assigned[i].Init();
+    }
+
+    // stores the ids of reference points assigned to each grid box
+    ArrayList<ArrayList<int> > references_assigned;
+    references_assigned.Init(nboxes);
+
+    for(index_t i = 0; i < nboxes; i++) {
+      references_assigned[i].Init();
+    }
+
+    // stores the multipole moments of the reference points in each grid box
+    Matrix mcoeffs;
+    mcoeffs.Init((int)pow(nterms, dim), nboxes);
+    
+    double delta = 2 * kernel_.bandwidth_sq();
+
+    fx_timer_start(NULL, "fgt_kde");
+    gauss_t(delta, nterms, nboxes, nsides, sidelengths, mincoords,
+	    locexp, center, queries_assigned, references_assigned, mcoeffs);
+
+    // normalize the sum
+    NormalizeDensities();
+    fx_timer_stop(NULL, "fgt_kde");
+    printf("FGT KDE completed...\n");
+  }
+
+  void NormalizeDensities() {
+    double norm_const = kernel_.CalcNormConstant(qset_.n_rows()) *
+      rset_.n_cols();
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      densities_[q] /= norm_const;
+    }
+  }
+
+  void PrintDebug() {
+
+    FILE *stream = stdout;
+    const char *fname = NULL;
+
+    if((fname = fx_param_str(NULL, "fgt_kde_output", NULL)) != NULL) {
+      stream = fopen(fname, "w+");
+    }
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      fprintf(stream, "%g\n", densities_[q]);
+    }
+    
+    if(stream != stdout) {
+      fclose(stream);
+    }
+  }
+
+};
+
+#endif
diff --git a/fastlib/u/gmravi/regression/ifgt_kde.cc b/fastlib/u/gmravi/regression/ifgt_kde.cc
new file mode 100644
index 0000000000..94c71736de
--- /dev/null
+++ b/fastlib/u/gmravi/regression/ifgt_kde.cc
@@ -0,0 +1,346 @@
+//-------------------------------------------------------------------
+// The code was written by Vikas Raykar and Changjiang Yang 
+// and is copyrighted under the Lesser GPL: 
+//
+// Copyright (C) 2006 Vikas Raykar and Changjiang Yang 
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU Lesser General Public License as
+// published by the Free Software Foundation; version 2.1 or later.
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 
+// See the GNU Lesser General Public License for more details. 
+// You should have received a copy of the GNU Lesser General Public
+// License along with this program; if not, write to the Free Software
+// Foundation, Inc., 59 Temple Place - Suite 330, Boston, 
+// MA 02111-1307, USA.  
+//
+// The author may be contacted via email at:
+// vikas(at)umiacs(.)umd(.)edu, cyang(at)sarnoff(.)com
+//-------------------------------------------------------------------
+
+
+//-------------------------------------------------------------------
+// File    : ImprovedFastGaussTransform.cpp
+// Purpose : Implementation for the Improved Fast Gauss Transform 
+// Author  : Vikas C. Raykar (vikas@cs.umd.edu)
+// Date    : July 15 2005
+//-------------------------------------------------------------------
+
+#include "ifgt_kde.h"
+#include "fastlib/fastlib_int.h"
+#include <math.h>
+#include <values.h>
+
+//-------------------------------------------------------------------
+// Constructor 
+//
+// PURPOSE                                                    
+// -------   
+// Initialize the class. 
+// Read the parameters.
+// Allocate memory.
+//-------------------------------------------------------------------
+
+ImprovedFastGaussTransform::ImprovedFastGaussTransform(int Dim,
+						       int NSources,
+						       int MTargets,
+						       double *pSources,
+						       double Bandwidth,
+						       double *pWeights,
+						       double *pTargets,
+						       int MaxTruncNumber,
+						       int NumClusters,
+						       int *pClusterIndex, 
+						       double *pClusterCenter,
+						       double *pClusterRadii,
+						       double CutoffRadius,
+						       double epsilon,
+						       double *pGaussTransform,
+						       int *pTruncNumber
+						       )		
+{	
+
+  //Read the parameters
+
+  d=Dim;
+  N=NSources;
+  M=MTargets;
+  px=pSources;
+  h=Bandwidth * sqrt(2);
+  pq=pWeights;
+  py=pTargets;
+  p_max=MaxTruncNumber;
+  K=NumClusters;
+  pci=pClusterIndex;
+  pcc=pClusterCenter;
+  pcr=pClusterRadii;
+  r=CutoffRadius;
+  pG=pGaussTransform;
+  pT=pTruncNumber;
+  eps=epsilon;
+
+  //Memory allocation
+
+  p_max_total=nchoosek(p_max-1+d,d);
+  constant_series=new double[p_max_total];
+  source_center_monomials = new double[p_max_total];
+  target_center_monomials = new double[p_max_total];
+  dx = new double[d];
+  dy = new double[d];
+  heads = new int[d];
+  C=new double[K*p_max_total];
+	
+  h_square=h*h;
+
+  ry=new double[K];
+  ry_square=new double[K];
+  for(int i=0; i<K; i++)
+    {
+      ry[i]=r+pcr[i];
+      ry_square[i]=ry[i]*ry[i];
+    }
+		
+}
+
+//-------------------------------------------------------------------
+// Destructor
+//-------------------------------------------------------------------
+
+ImprovedFastGaussTransform::~ImprovedFastGaussTransform()
+{
+}
+
+
+//-------------------------------------------------------------------
+// Compute the combinatorial number nchoosek.
+//-------------------------------------------------------------------
+
+int
+ImprovedFastGaussTransform::nchoosek(int n, int k){
+  int n_k = n - k;
+	
+  if (k < n_k)
+    {
+      k = n_k;
+      n_k = n - k;
+    }
+
+  int  nchsk = 1; 
+  for ( int i = 1; i <= n_k; i++)
+    {
+      nchsk *= (++k);
+      nchsk /= i;
+    }
+
+  return nchsk;
+}
+
+//-------------------------------------------------------------------
+//Computes p_i such error(a,p_i,h) <= q_i epsilon.
+//-------------------------------------------------------------------
+
+int
+ImprovedFastGaussTransform::return_p(double a_square, int cluster_index)
+{
+  double a=sqrt(a_square);
+  double b,c;
+  double error=1;
+  double temp=1;
+  int p=1;
+
+  while((error > eps) & (p <= p_max))
+    {
+      b=min(((a+sqrt((a_square)+(2*p*h_square)))/2),ry[cluster_index]);
+      c=a-b;
+      temp=temp*(((2*a*b)/h_square)/p);
+      error=temp*(exp(-(c*c)/h_square));
+      p++;
+    }
+
+  return p-1;
+	
+}
+
+
+
+//-------------------------------------------------------------------
+// This function computes the constants  2^alpha/alpha!.
+//-------------------------------------------------------------------
+void
+ImprovedFastGaussTransform::compute_constant_series(){
+	
+  int *heads = new int[d+1];
+  int *cinds = new int[p_max_total];
+	
+  for (int i = 0; i < d; i++)
+    heads[i] = 0;
+  heads[d] = MAXINT;
+	
+  cinds[0] = 0;
+  constant_series[0] = 1.0;
+  for (int k=1, t=1, tail=1; k < p_max; k++, tail=t)
+    {
+      for (int i = 0; i < d; i++)
+	{
+	  int head = heads[i];
+	  heads[i] = t;
+	  for ( int j = head; j < tail; j++, t++)
+	    {
+	      cinds[t] = (j < heads[i+1])? cinds[j] + 1 : 1;
+	      constant_series[t] = 2.0 * constant_series[j];
+	      constant_series[t] /= (double) cinds[t];
+	    }
+	}
+    }
+	
+  delete []cinds;
+  delete []heads;
+	
+}
+
+//-------------------------------------------------------------------
+// This function computes the monomials [(x_i-c_k)/h]^{alpha}
+// and norm([(x_i-c_k)/h])^2
+//-------------------------------------------------------------------
+void
+ImprovedFastGaussTransform::compute_source_center_monomials(int p)
+{		
+
+  for (int i = 0; i < d; i++){
+    dx[i]=dx[i]/h;
+    heads[i] = 0;
+  }
+		
+  source_center_monomials[0] = 1.0;
+  for (int k=1, t=1, tail=1; k < p; k++, tail=t){
+    for (int i = 0; i < d; i++){
+      int head = heads[i];
+      heads[i] = t;
+      for ( int j = head; j < tail; j++, t++)
+	source_center_monomials[t] = dx[i] * source_center_monomials[j];
+    }						
+  }					
+
+}
+
+//-------------------------------------------------------------------
+// This function computes the monomials [(y_j-c_k)/h]^{alpha}
+//-------------------------------------------------------------------
+void
+ImprovedFastGaussTransform::compute_target_center_monomials()
+{		
+
+  for (int i = 0; i < d; i++){
+    dy[i]=dy[i]/h;
+    heads[i] = 0;
+  }
+		
+  target_center_monomials[0] = 1.0;
+  for (int k=1, t=1, tail=1; k < p_max; k++, tail=t){
+    for (int i = 0; i < d; i++){
+      int head = heads[i];
+      heads[i] = t;
+      for ( int j = head; j < tail; j++, t++)
+	target_center_monomials[t] = dy[i] * target_center_monomials[j];
+    }						
+  }					
+
+}
+
+//-------------------------------------------------------------------
+// This function computes the coeffeicients C_k for all clusters.
+//-------------------------------------------------------------------
+void
+ImprovedFastGaussTransform::compute_C()
+{
+
+  for (int i = 0; i < K*p_max_total; i++){
+    C[i]=0.0;
+  }
+
+  p_max_actual=-1;
+
+  for(int i=0; i<N; i++){
+    int k=pci[i];
+
+    int source_base=i*d;
+    int center_base=k*d;
+
+    source_center_distance_square=0.0;
+
+    for (int j = 0; j < d; j++){
+      dx[j]=(px[source_base+j]-pcc[center_base+j]);
+      source_center_distance_square += (dx[j]*dx[j]);
+    }
+
+    pT[i]=return_p(source_center_distance_square,k);
+
+    if (pT[i]>p_max_actual){
+      p_max_actual=pT[i];
+    }
+	
+    compute_source_center_monomials(pT[i]);		
+		
+    double f=pq[i]*exp(-source_center_distance_square/h_square);
+
+    for(int alpha=0; alpha<nchoosek(pT[i]-1+d,d); alpha++){
+      C[k*p_max_total+alpha]+=(f*source_center_monomials[alpha]);
+    }
+		
+  }
+
+  p_max_actual_total=nchoosek(p_max_actual-1+d,d);
+
+  compute_constant_series();
+	
+  for(int k=0; k<K; k++){
+    for(int alpha=0; alpha<p_max_total; alpha++){
+      C[k*p_max_total+alpha]*=constant_series[alpha];
+    }
+  }
+
+
+}
+
+//-------------------------------------------------------------------
+// Actual function to evaluate the Gauss Transform.
+//-------------------------------------------------------------------
+
+void
+ImprovedFastGaussTransform::Evaluate()
+{
+	
+  compute_C();	
+
+  for(int j=0; j < M; j++)
+    {
+      pG[j]=0.0;	
+
+      int target_base=j*d;	    	
+		
+      for(int k=0; k<K; k++){
+
+	int center_base=k*d;
+
+	double  target_center_distance_square=0.0;
+	for(int i=0; i<d; i++){
+	  dy[i]=py[target_base+i]-pcc[center_base+i];
+	  target_center_distance_square += dy[i]*dy[i];
+	  if (target_center_distance_square > ry_square[k]) break;
+	}
+
+	if (target_center_distance_square <= ry_square[k]){
+	  compute_target_center_monomials();
+	  double g=exp(-target_center_distance_square/h_square);
+	  for(int alpha=0; alpha<p_max_actual_total; alpha++){
+	    pG[j]+=(C[k*p_max_total+alpha]*g*target_center_monomials[alpha]);
+	  }											
+	}
+      }
+    }
+	
+
+}
+
diff --git a/fastlib/u/gmravi/regression/ifgt_kde.h b/fastlib/u/gmravi/regression/ifgt_kde.h
new file mode 100644
index 0000000000..d09779d440
--- /dev/null
+++ b/fastlib/u/gmravi/regression/ifgt_kde.h
@@ -0,0 +1,139 @@
+//-------------------------------------------------------------------
+// The code was written by Vikas Raykar and Changjiang Yang 
+// and is copyrighted under the Lesser GPL: 
+//
+// Copyright (C) 2006 Vikas Raykar and Changjiang Yang 
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU Lesser General Public License as
+// published by the Free Software Foundation; version 2.1 or later.
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 
+// See the GNU Lesser General Public License for more details. 
+// You should have received a copy of the GNU Lesser General Public
+// License along with this program; if not, write to the Free Software
+// Foundation, Inc., 59 Temple Place - Suite 330, Boston, 
+// MA 02111-1307, USA.  
+//
+// The author may be contacted via email at:
+// vikas(at)umiacs(.)umd(.)edu, cyang(at)sarnoff(.)com
+//-------------------------------------------------------------------
+
+
+//-------------------------------------------------------------
+// File    : ImprovedFastGaussTransform.h
+// Purpose : Interface for 
+//			 Data Adaptive Improved Fast Gauss Transform.
+// Author  : Vikas C. Raykar (vikas@cs.umd.edu)
+// Date    : July 15 2005
+//-------------------------------------------------------------
+// Data Adaptive Improved Fast Gauss Transform (IFGT).
+// All Sources have the same scales 'h'.
+//
+// 
+// A new version of the IFGT where the parameters are chosen
+// based on the acutal distribution of the source points.
+// The truncation number for each source point is chosen based
+// on its distance to the cluster center.
+//
+// Advantages:
+// -----------
+// 1. Better Speedup.  
+// 2. Choice of parameters is fully automatic taking into
+//    consideration the actual distribtion of the data points.
+// 3. Uses more tight pointwise error bounds in choosing the
+//    parameters.
+//
+// Implementation based on:
+//
+// Fast computation of sums of Gaussians in high dimensions. 
+// Vikas C. Raykar, C. Yang, R. Duraiswami, and N. Gumerov,
+// CS-TR-4767, Department of computer science,
+// University of Maryland, Collegepark.
+// ------------------------------------------------------------
+
+
+#ifndef IMPROVED_FAST_GAUSS_TRANSFORM_H
+#define IMPROVED_FAST_GAUSS_TRANSFORM_H
+
+class ImprovedFastGaussTransform{
+ public:
+  //constructor 
+  ImprovedFastGaussTransform(int Dim,
+			     int NSources,
+			     int MTargets,
+			     double *pSources,
+			     double Bandwidth,
+			     double *pWeights,
+			     double *pTargets,
+			     int MaxTruncNumber,
+			     int NumClusters,
+			     int *pClusterIndex, 
+			     double *pClusterCenter,
+			     double *pClusterRadii,
+			     double CutoffRadius,
+			     double epsilon,
+			     double *pGaussTransform,
+			     int *pTruncNumber
+			     );		
+
+  //destructor
+  ~ImprovedFastGaussTransform();				
+
+  //function to evaluate the Gauss Transform.
+  void Evaluate();
+
+ private:
+  //Parameters
+
+  int d;				
+  int N;				
+  int M;				
+  double *px;			
+  double  h;			
+  double *pq;			
+  double *py;	
+  int p_max;	
+  int K;	
+  int *pci; 
+  double *pcc;  
+  double *pcr;
+  double r;
+  double eps;
+
+
+  double *pG;         
+  int *pT;			       		      
+
+  //
+
+  int     p_max_total;
+  int     p_max_actual;
+  int     p_max_actual_total;
+  double *constant_series;
+  double *source_center_monomials;
+  double  source_center_distance_square;
+  double *target_center_monomials;
+  double  target_center_distance_square;
+  double *dx;
+  double *dy;
+  int	   *heads;		
+  double *C;
+  double h_square;
+  double *ry;
+  double *ry_square;
+
+  //Functions
+
+  int  nchoosek(int n, int k);
+  int  return_p(double a_square, int cluster_index);
+  void compute_constant_series();
+  void compute_source_center_monomials(int p);
+  void compute_target_center_monomials();
+  void compute_C();
+    
+};
+
+
+#endif
diff --git a/fastlib/u/gmravi/regression/ifgt_main.cc b/fastlib/u/gmravi/regression/ifgt_main.cc
new file mode 100644
index 0000000000..f757fdb6dd
--- /dev/null
+++ b/fastlib/u/gmravi/regression/ifgt_main.cc
@@ -0,0 +1,193 @@
+#include "fastlib/fastlib_int.h"
+#include "ifgt_kde.h"
+#include "ifgt_choose_parameters.h"
+#include "kde.h"
+#include "ifgt_choose_truncation_number.h"
+#include "kcenter_clustering.h"
+
+void concatenate_vectors(Matrix &source, Vector &dest) {
+
+  for(index_t i = 0; i < source.n_cols(); i++) {
+    for(index_t j = 0; j < source.n_rows(); j++) {
+      dest[i * source.n_rows() + j] = source.get(j, i);
+    }
+  }
+}
+
+// preprocessing: scaling the dataset; this has to be moved to the dataset
+// module
+/* scales each attribute to 0-1 using the min/max values */
+void scale_data_by_minmax(Matrix &qset_, Matrix &rset_) {
+
+  int num_dims = rset_.n_rows();
+  DHrectBound<2> qset_bound;
+  DHrectBound<2> rset_bound;
+  qset_bound.Init(qset_.n_rows());
+  rset_bound.Init(qset_.n_rows());
+
+  // go through each query/reference point to find out the bounds
+  for(index_t r = 0; r < rset_.n_cols(); r++) {
+    Vector ref_vector;
+    rset_.MakeColumnVector(r, &ref_vector);
+    rset_bound |= ref_vector;
+  }
+  for(index_t q = 0; q < qset_.n_cols(); q++) {
+    Vector query_vector;
+    qset_.MakeColumnVector(q, &query_vector);
+    qset_bound |= query_vector;
+  }
+
+  for(index_t i = 0; i < num_dims; i++) {
+    DRange qset_range = qset_bound.get(i);
+    DRange rset_range = rset_bound.get(i);
+    double min_coord = min(qset_range.lo, rset_range.lo);
+    double max_coord = max(qset_range.hi, rset_range.hi);
+    double width = max_coord - min_coord;
+
+    for(index_t j = 0; j < rset_.n_cols(); j++) {
+      rset_.set(i, j, (rset_.get(i, j) - min_coord) / width);
+    }
+    if(fx_param_str(NULL, "query", NULL) != NULL) {
+      for(index_t j = 0; j < qset_.n_cols(); j++) {
+	qset_.set(i, j, (qset_.get(i, j) - min_coord) / width);
+      }
+    }
+  }
+}
+
+int main(int argc, char *argv[]) {
+
+  fx_init(argc, argv);
+
+  // read the datasets and do k-center clustering
+  Dataset ref_dataset;
+  Matrix qset_;
+  Matrix rset_;
+  Vector pWeights;
+
+  // read the datasets
+  const char *rfname = fx_param_str_req(NULL, "data");
+  const char *qfname = fx_param_str(NULL, "query", rfname);
+
+  // read reference dataset
+  ref_dataset.InitFromFile(rfname);
+  rset_.Own(&(ref_dataset.matrix()));
+
+  // read the reference weights
+  char *rwfname = NULL;
+  if(fx_param_exists(NULL, "dwgts")) {
+    rwfname = (char *)fx_param_str(NULL, "dwgts", NULL);
+  }
+
+  if(rwfname != NULL) {
+    Dataset ref_weights;
+    ref_weights.InitFromFile(rwfname);
+    pWeights.Copy(ref_weights.matrix().GetColumnPtr(0),
+		  ref_weights.matrix().n_rows());
+  }
+  else {
+    pWeights.Init(rset_.n_cols());
+    pWeights.SetAll(1);
+  }
+
+  if(!strcmp(qfname, rfname)) {
+    qset_.Alias(rset_);
+  }
+  else {
+    Dataset query_dataset;
+    query_dataset.InitFromFile(qfname);
+    qset_.Own(&(query_dataset.matrix()));
+  }
+  
+  // scale dataset if the user wants to
+  if(!strcmp(fx_param_str(NULL, "scaling", NULL), "range")) {
+    scale_data_by_minmax(qset_, rset_);
+  }
+
+  Vector pSources;
+  pSources.Init(rset_.n_rows() * rset_.n_cols());
+  concatenate_vectors(rset_, pSources);
+  Vector pTargets;
+  pTargets.Init(qset_.n_rows() * qset_.n_cols());
+  concatenate_vectors(qset_, pTargets);
+
+  double Bandwidth = fx_param_double_req(NULL, "bandwidth");
+  Vector pGaussTransform;
+  pGaussTransform.Init(qset_.n_cols());
+  double epsilon = fx_param_double(NULL, "tau", 0.1);
+
+
+  // choose parameters
+  fx_timer_start(NULL, "ifgt_kde_compute");
+  ImprovedFastGaussTransformChooseParameters cp(rset_.n_rows(), Bandwidth,
+						epsilon, 
+						(int) ceil(0.2 * 100 / 
+							   sqrt
+							   (2 * Bandwidth * 
+							    Bandwidth)));
+  
+  printf("Number of clusters chosen: %d\n", cp.K);
+  printf("Maximum truncation number: %d\n", cp.p_max);
+  printf("Maximum cutoff radius: %g\n", cp.r);
+  
+  // run k-center clustering
+  ArrayList<int> pClusterIndex;
+  pClusterIndex.Init(rset_.n_cols());
+  KCenterClustering kc(rset_.n_rows(), rset_.n_cols(), pSources.ptr(),
+		       pClusterIndex.begin(), cp.K);
+  kc.Cluster();
+
+  ArrayList<int> pNumPoints;
+  pNumPoints.Init(cp.K);
+  Vector pClusterCenter;
+  pClusterCenter.Init(rset_.n_rows() * cp.K);
+  Vector pClusterRadii;
+  pClusterRadii.Init(cp.K);
+  kc.ComputeClusterCenters(cp.K, pClusterCenter.ptr(), pNumPoints.begin(),
+			   pClusterRadii.ptr());
+
+  // update truncation number
+  ImprovedFastGaussTransformChooseTruncationNumber ct(rset_.n_rows(), 
+						      Bandwidth, epsilon,
+						      kc.MaxClusterRadius);
+
+  // initialize IFGT instance
+  ArrayList<int> pTruncNumber;
+  pTruncNumber.Init(rset_.n_cols());
+  for(index_t i = 0; i < rset_.n_cols(); i++) {
+    pTruncNumber[i] = 0;
+  }
+  ImprovedFastGaussTransform* pIFGT = new 
+    ImprovedFastGaussTransform(rset_.n_rows(), rset_.n_cols(), qset_.n_cols(), 
+			       pSources.ptr(), Bandwidth, pWeights.ptr(),
+			       pTargets.ptr(), ct.p_max,
+			       cp.K, pClusterIndex.begin(),
+			       pClusterCenter.ptr(), pClusterRadii.ptr(),
+			       cp.r, epsilon, pGaussTransform.ptr(),
+			       pTruncNumber.begin());
+
+  // run IFGT
+  pIFGT->Evaluate();
+  
+  GaussianKernel kernel;
+  kernel.Init(Bandwidth);
+  double norm_const = kernel.CalcNormConstant(qset_.n_rows()) *
+    rset_.n_cols();
+
+  // normalize density estimates
+  for(index_t q = 0; q < qset_.n_cols(); q++) {
+    pGaussTransform[q] /= norm_const;
+  }
+  fx_timer_stop(NULL, "ifgt_kde_compute");
+
+  // check answer with naive
+  NaiveKde<GaussianKernel> naive_kde;
+  naive_kde.Init(qset_, rset_);
+  naive_kde.Compute();
+  naive_kde.ComputeMaximumRelativeError(pGaussTransform);
+
+  delete pIFGT;
+
+  fx_done();
+  return 0;
+}
diff --git a/fastlib/u/gmravi/regression/kcenter_clustering.cc b/fastlib/u/gmravi/regression/kcenter_clustering.cc
new file mode 100644
index 0000000000..042de9f15b
--- /dev/null
+++ b/fastlib/u/gmravi/regression/kcenter_clustering.cc
@@ -0,0 +1,317 @@
+//-------------------------------------------------------------------
+// The code was written by Changjiang Yang and Vikas Raykar
+// and is copyrighted under the Lesser GPL: 
+//
+// Copyright (C) 2006  Changjiang Yang and Vikas Raykar
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU Lesser General Public License as
+// published by the Free Software Foundation; version 2.1 or later.
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 
+// See the GNU Lesser General Public License for more details. 
+// You should have received a copy of the GNU Lesser General Public
+// License along with this program; if not, write to the Free Software
+// Foundation, Inc., 59 Temple Place - Suite 330, Boston, 
+// MA 02111-1307, USA.  
+//
+// The author may be contacted via email at:cyang(at)sarnoff(.)com
+// vikas(at)umiacs(.)umd(.)edu
+//-------------------------------------------------------------------
+
+//-------------------------------------------------------------------
+// File    : KCenterClustering.cpp
+// Purpose : Implementation for the k-center clustering algorithm.
+// Author  : Vikas C. Raykar (vikas@cs.umd.edu)
+// Date    : April 25 2005, June 10 2005, August 23, 2005
+//-------------------------------------------------------------------
+
+#include "kcenter_clustering.h"
+#include <math.h>
+#include <time.h>
+#include <stdlib.h>
+#include "fastlib/fastlib_int.h"
+
+//-------------------------------------------------------------------
+// Constructor 
+//
+// PURPOSE                                                    
+// -------   
+// Initialize the class. 
+// Read the parameters.
+//
+// INPUT                                                      
+// ----------
+// Dim			   --> dimension of the points.
+// NSources		   --> number of sources.
+// pSources		   --> pointer to sources, (d*N).
+// pClusterIndex   --> pointer to a vector of length N where the 
+//                     i th element is the cluster number to 
+//					   which the i th point belongs.
+//	
+//-------------------------------------------------------------------
+
+KCenterClustering::KCenterClustering(int Dim,
+				     int NSources,
+				     double *pSources,
+				     int *pClusterIndex,
+				     int NumClusters
+				     )
+{	
+
+  //Read the parameters
+
+  d=Dim;
+  N=NSources;
+  px=pSources;
+  pci=pClusterIndex;
+  K=NumClusters;
+  dist_C = new double[N]; //distances to the center.
+  r=new double[K];
+
+
+}
+
+//-------------------------------------------------------------------
+// Destructor
+//-------------------------------------------------------------------
+
+KCenterClustering::~KCenterClustering()
+{	
+  delete [] dist_C;
+  delete [] r;
+}
+
+
+
+//-------------------------------------------------------------------
+// ddist is the square of the distance of two vectors(double)
+//-------------------------------------------------------------------
+
+
+double
+KCenterClustering::ddist(const int d, const double *x, const double *y)
+{
+  double t, s = 0.0;
+  for (int i = d; i != 0; i--)
+    {
+      t = *x++ - *y++;
+      s += t * t;
+    }
+  return s;
+}
+
+
+
+//-------------------------------------------------------------------
+// Find the largest element from a vector
+//-------------------------------------------------------------------
+
+int
+KCenterClustering::idmax(int n, double *x)
+{
+  int k = 0;
+  double t = -1.0;
+  for (int i = 0; i < n; i++, x++)
+    if( t < *x )
+      {
+	t = *x;
+	k = i;
+      }
+  return k;
+
+}
+
+
+//-------------------------------------------------------------------
+// k-center Clustering.
+//-------------------------------------------------------------------
+//
+// Gonzalez's farthest-point clustering algorithm.
+//
+// OUTPUT
+// ----------------
+//
+// MaxClusterRadius	--> maximum radius of the clusters, (rx).
+// pci              --> vector of length N where the i th element is the
+//                    cluster number to which the i th point belongs.
+//                    pci[i] varies between 0 to K-1. 
+//-------------------------------------------------------------------
+
+
+void 
+KCenterClustering::Cluster()
+{
+	
+	
+  int *pCenters = new int[K]; //indices of the centers.
+
+  int    *cprev = new int[N];	    // index to the previous node
+  int    *cnext = new int[N];	    // index to the next node
+  int    *far2c = new int[K];     // farthest node to the center
+	
+  // randomly pick one node as the first center.
+  srand( (unsigned)time( NULL ) );
+  int nc = rand() % N;	// new center
+	
+  // add the ind-th node to the first center.
+  pCenters[0] = nc;
+
+  // compute the distances from each node to the first center.
+  // initialize the circular linked list, the center is the
+  // sentinel node.
+  const double *x_nc, *x_j;
+  x_nc = px + nc*d;
+  x_j = px;
+  for (int j = 0; j < N; x_j += d, j++)
+    {
+      dist_C[j] = (j==nc)? 0.0:ddist(d, x_j, x_nc);
+      cnext[j] = j+1;
+      cprev[j] = j-1;
+      
+      // my fix (by Dongryeol Lee)
+      pci[j] = 0;
+    }
+  cnext[N-1] = 0; // link the tail to the head.
+  cprev[0] = N-1; // link the head to the tail.
+
+  // compute the radius of the first cluster and the farthest 
+  // node to the center.
+  nc = idmax(N,dist_C);
+  far2c[0] = nc;
+  r[0] = dist_C[nc];
+
+  for(int i = 1; i < K; i++)
+    {	 
+      //find the maximum of vector dist_C, i.e., find the node
+      //that is farthest away from C. It is a new center.
+      nc = idmax(i,r);
+      nc = far2c[nc];
+      pCenters[i] = nc; //add the ind-th node to the current center.
+      r[i] = dist_C[nc] = 0.0;pci[nc]=i;
+      far2c[i] = nc;
+      cnext[cprev[nc]] = cnext[nc]; // delete nc
+      cprev[cnext[nc]] = cprev[nc];
+      cnext[nc] = cprev[nc] = nc; //self-loop
+
+      //update the distances from each point to the current center.
+      x_nc = px + nc*d;
+      for (int j = 0; j < i; j++)
+	{
+	  int ct_j = pCenters[j];
+	  x_j = px + ct_j*d;
+	  double dc2cq = ddist(d, x_j, x_nc) / 4;
+	  if (dc2cq < r[j]) // neighbor cluster
+	    {
+	      r[j] = 0.0;
+	      far2c[j] = ct_j;
+	      int k = cnext[ct_j];
+	      while (k != ct_j) // visit the circular linked list
+		{
+		  int nextk = cnext[k];
+		  //compare the distances from new center 
+		  //and from current center.	
+		  double dist2c_k = dist_C[k];
+		  if ( dc2cq < dist2c_k )
+		    {
+						
+		      x_j = px + k*d;
+		      double dd = ddist(d, x_j, x_nc);
+		      if ( dd < dist2c_k )
+			{
+			  dist_C[k] = dd; // update distances to center
+			  pci[k]=i;
+			  if (r[i] < dd)  // find max r
+			    {
+			      r[i] = dd;
+			      far2c[i] = k;
+								
+			    }
+			  cnext[cprev[k]] = nextk; // delete nextk from ct_j
+			  cprev[nextk] = cprev[k];
+			  cnext[k] = cnext[nc]; // insert nextk to nc
+			  cprev[cnext[nc]] = k;
+			  cnext[nc] = k;
+			  cprev[k] = nc;
+							
+							
+			}
+		      else if ( r[j] < dist2c_k )
+			{
+			  r[j] = dist2c_k;
+			  far2c[j] = k;
+							
+			}
+		    }
+		  else if ( r[j] < dist2c_k )
+		    {
+		      r[j] = dist2c_k;
+		      far2c[j] = k;
+		    } // if d < 2 r_k
+		  k = nextk;
+		} // while k
+	    } // if d < 2 r
+	} // for j
+    } // for i
+
+  nc = idmax(K,r);
+  MaxClusterRadius=sqrt(r[nc]);
+}
+
+//------------------------------------------------------------------------
+// Computes
+// [1] the cluster centers by taking the mean of all the points
+// belonging to a cluster.
+// [2] the number of points in each cluster.
+// [3] the radius of each cluster.
+//------------------------------------------------------------------------
+// NumClusters     --> number of clusters
+// pClusterCenters --> pointer to the cluster centers, (d*K), 
+// pNumPoints      --> pointer to the num of points in each cluster, (K). 
+// pClusterRadii   --> pointer to the radius of each cluster, (K).
+//------------------------------------------------------------------------
+
+void 
+KCenterClustering::ComputeClusterCenters(
+					 int NumClusters,
+					 double *pClusterCenters,
+					 int *pNumPoints,
+					 double *pClusterRadii
+					 )
+{
+  int K=NumClusters;
+	
+  for(int k=0; k<K; k++)
+    {
+      pNumPoints[k]=0;
+      pClusterRadii[k]=sqrt(r[k]);
+      for(int dim=0; dim<d; dim++)
+	{
+	  pClusterCenters[(k*d)+dim]=0.0;
+	}
+    }
+
+  for(int i=0; i<N; i++)
+    {
+		
+      pNumPoints[pci[i]] += 1;
+
+      for(int dim=0; dim<d; dim++)
+	{
+	  pClusterCenters[(pci[i]*d)+dim] += px[(i*d)+dim];
+	}
+    }
+
+  for(int k=0; k<K; k++)
+    {		
+      for(int dim=0; dim<d; dim++)
+	{
+	  pClusterCenters[(k*d)+dim]=pClusterCenters[(k*d)+dim]/pNumPoints[k];
+	}
+    }
+
+	
+
+}
+	
diff --git a/fastlib/u/gmravi/regression/kcenter_clustering.h b/fastlib/u/gmravi/regression/kcenter_clustering.h
new file mode 100644
index 0000000000..3991a1a69e
--- /dev/null
+++ b/fastlib/u/gmravi/regression/kcenter_clustering.h
@@ -0,0 +1,118 @@
+//-------------------------------------------------------------------
+// The code was written by Changjiang Yang and Vikas Raykar
+// and is copyrighted under the Lesser GPL: 
+//
+// Copyright (C) 2006  Changjiang Yang and Vikas Raykar
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU Lesser General Public License as
+// published by the Free Software Foundation; version 2.1 or later.
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 
+// See the GNU Lesser General Public License for more details. 
+// You should have received a copy of the GNU Lesser General Public
+// License along with this program; if not, write to the Free Software
+// Foundation, Inc., 59 Temple Place - Suite 330, Boston, 
+// MA 02111-1307, USA.  
+//
+// The author may be contacted via email at:cyang(at)sarnoff(.)com
+// vikas(at)umiacs(.)umd(.)edu
+//-------------------------------------------------------------------
+
+//----------------------------------------------------------------------------
+// File    : KCenterClustering.h
+// Purpose : Interface for the k-center clustering algorithm.
+// Author  : Vikas C. Raykar (vikas@cs.umd.edu)
+// Date    : April 25 2005, June 10 2005, August 23, 2005
+//
+//----------------------------------------------------------------------------
+// Gonzalez's farthest-point clustering algorithm.
+//
+// June 10, 2005: 
+// This version now returns the number points and the radius of each cluster.
+//
+// August 23, 2005:
+// Speed up using the doubly circular list.
+// The clusters far away are trimmed. The nodes inside the neighboring
+// clusters which are within half sphere are trimmed.
+// The computational complexity is reduced to O(n log k).
+//
+//----------------------------------------------------------------------------
+//
+// INPUT 
+// ----------------
+//
+// Dim			    --> dimension of the points.
+// NSources  	    --> number of sources.
+// pSources	        --> pointer to sources, (d*N).
+// NumClusters	    --> number of clusters.
+//
+// OUTPUT
+// ----------------
+//
+// MaxClusterRadius	--> maximum radius of the clusters, (rx).
+// pClusterIndex    --> vector of length N where the i th element is the
+//                     cluster number to which the i th point belongs.
+//                     pClusterIndex[i] varies between 0 to K-1. 
+// pClusterCenters --> pointer to the cluster centers, (d*K). 
+// pNumPoints      --> pointer to the number of points in each cluster, (K).
+// pClusterRadii   --> pointer to the radius of each cluster, (K). 
+//----------------------------------------------------------------------------
+
+#ifndef K_CENTER_CLUSTERING_H
+#define K_CENTER_CLUSTERING_H
+
+class KCenterClustering{
+ public:		
+
+  //Output parameters
+
+  double MaxClusterRadius;	//maximum cluster radius
+	
+  //Functions
+
+  //constructor 
+  KCenterClustering(int Dim,
+		    int NSources,
+		    double *pSources,
+		    int *pClusterIndex,
+		    int NumClusters
+		    );
+
+  //destructor
+  ~KCenterClustering();
+		
+  //k-center clustering
+  void Cluster();
+
+  //Compute cluster centers and the number of points in each cluster
+  //and the radius of each cluster.
+
+  void ComputeClusterCenters(int NumClusters,
+			     double *pClusterCenters,
+			     int *pNumPoints,
+			     double *pClusterRadii);				  
+		
+ private:
+  //Input Parameters
+
+  int d;				//dimension of the points.
+  int N;				//number of sources.
+  double *px;			//pointer to sources, (d*N).
+  int K;				//number of clusters
+  int *pci;		    //pointer to a vector of length N where the i th element is the 
+  //cluster number to which the i th point belongs.
+  double *dist_C;		//distances to the center.
+  double *r;
+
+  //Functions
+
+  double ddist(const int d, const double *x, const double *y);
+  int idmax(int n, double *x);
+		
+    
+};
+
+
+#endif
diff --git a/fastlib/u/gmravi/regression/kde.h b/fastlib/u/gmravi/regression/kde.h
new file mode 100644
index 0000000000..89b3e8d94e
--- /dev/null
+++ b/fastlib/u/gmravi/regression/kde.h
@@ -0,0 +1,973 @@
+#ifndef KDE_H
+#define KDE_H
+
+#include "fastlib/fastlib_int.h"
+#include "u/dongryel/series_expansion/farfield_expansion.h"
+#include "u/dongryel/series_expansion/local_expansion.h"
+#include "u/dongryel/series_expansion/mult_farfield_expansion.h"
+#include "u/dongryel/series_expansion/mult_local_expansion.h"
+#include "u/dongryel/series_expansion/kernel_aux.h"
+
+template<typename TKernel>
+class NaiveKde {
+
+ private:
+  
+  /** query dataset */
+  Matrix qset_;
+
+  /** reference dataset */
+  Matrix rset_;
+
+  /** kernel */
+  TKernel kernel_;
+
+  /** computed densities */
+  Vector densities_;
+  
+ public:
+  
+  void Compute() {
+
+    printf("\nStarting naive KDE...\n");
+    fx_timer_start(NULL, "naive_kde_compute");
+
+    // compute unnormalized sum
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      
+      const double *q_col = qset_.GetColumnPtr(q);
+      for(index_t r = 0; r < rset_.n_cols(); r++) {
+	const double *r_col = rset_.GetColumnPtr(r);
+	double dsqd = la::DistanceSqEuclidean(qset_.n_rows(), q_col, r_col);
+	
+	densities_[q] += kernel_.EvalUnnormOnSq(dsqd);
+      }
+    }
+    
+    // then normalize it
+    double norm_const = kernel_.CalcNormConstant(qset_.n_rows()) * 
+      rset_.n_cols();
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      densities_[q] /= norm_const;
+    }
+    fx_timer_stop(NULL, "naive_kde_compute");
+    printf("\nNaive KDE completed...\n");
+  }
+
+  void Init() {
+    densities_.SetZero();
+  }
+
+  void Init(Matrix &qset, Matrix &rset) {
+
+    // get datasets
+    qset_.Alias(qset);
+    rset_.Alias(rset);
+
+    // get bandwidth
+    kernel_.Init(fx_param_double_req(NULL, "bandwidth"));
+    
+    // allocate density storage
+    densities_.Init(qset.n_cols());
+    densities_.SetZero();
+  }
+
+  void PrintDebug() {
+
+    FILE *stream = stdout;
+    const char *fname = NULL;
+
+    if(fx_param_exists(NULL, "naive_kde_output")) {
+      fname = fx_param_str(NULL, "naive_kde_output", NULL);
+      stream = fopen(fname, "w+");
+    }
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      fprintf(stream, "%g\n", densities_[q]);
+    }
+    
+    if(stream != stdout) {
+      fclose(stream);
+    }    
+  }
+
+  void ComputeMaximumRelativeError(const Vector &density_estimate) {
+    
+    double max_rel_err = 0;
+    for(index_t q = 0; q < densities_.length(); q++) {
+      double rel_err = fabs(density_estimate[q] - densities_[q]) / 
+	densities_[q];
+      
+      if(rel_err > max_rel_err) {
+	max_rel_err = rel_err;
+      }
+    }
+    
+    fx_format_result(NULL, "maxium_relative_error_for_fast_KDE", "%g", 
+		     max_rel_err);
+  }
+
+};
+
+
+template<typename TKernel, typename TKernelAux>
+class FastKde {
+
+  public:
+
+  // forward declaration of KdeStat class
+  class KdeStat;
+
+  // our tree type using the KdeStat
+  typedef BinarySpaceTree<DHrectBound<2>, Matrix, KdeStat > Tree;
+  
+  class KdeStat {
+  public:
+    
+    /** lower bound on the densities for the query points owned by this node 
+     */
+    double mass_l_;
+    
+    /**
+     * additional offset for the lower bound on the densities for the query
+     * points owned by this node (for leaf nodes only).
+     */
+    double more_l_;
+    
+    /**
+     * lower bound offset passed from above
+     */
+    double owed_l_;
+    
+    /** stores the portion pruned by finite difference
+     */
+    double mass_e_;
+
+    /** upper bound on the densities for the query points owned by this node 
+     */
+    double mass_u_;
+    
+    /**
+     * additional offset for the upper bound on the densities for the query
+     * points owned by this node (for leaf nodes only)
+     */
+    double more_u_;
+    
+    /**
+     * upper bound offset passed from above
+     */
+    double owed_u_;
+    
+    /** extra error that can be used for the query points in this node */
+    double mass_t_;
+    
+    /**
+     * Far field expansion created by the reference points in this node.
+     */
+    typename TKernelAux::TFarFieldExpansion farfield_expansion_;
+    
+    /**
+     * Local expansion stored in this node.
+     */
+    typename TKernelAux::TLocalExpansion local_expansion_;
+    
+    /** Initialize the statistics */
+    void Init() {
+      mass_l_ = 0;
+      more_l_ = 0;
+      owed_l_ = 0;
+      mass_e_ = 0;
+      mass_u_ = 0;
+      more_u_ = 0;
+      owed_u_ = 0;
+      mass_t_ = 0;    
+    }
+    
+    void Init(double bandwidth, 
+	      typename TKernelAux::TSeriesExpansionAux *sea) {
+
+      farfield_expansion_.Init(bandwidth, sea);
+      local_expansion_.Init(bandwidth, sea);
+    }
+    
+    void Init(const Matrix& dataset, index_t &start, index_t &count) {
+      Init();
+    }
+    
+    void Init(const Matrix& dataset, index_t &start, index_t &count,
+	      const KdeStat& left_stat,
+	      const KdeStat& right_stat) {
+      Init();
+    }
+    
+    void Init(double bandwidth, const Vector& center,
+	      typename TKernelAux::TSeriesExpansionAux *sea) {
+      
+      farfield_expansion_.Init(bandwidth, center, sea);
+      local_expansion_.Init(bandwidth, center, sea);
+      Init();
+    }
+
+    void MergeChildBounds(KdeStat &left_stat, KdeStat &right_stat) {
+
+      // steal left and right children's tokens
+      double min_mass_t = min(left_stat.mass_t_, right_stat.mass_t_);
+
+      // improve lower and upper bound
+      mass_l_ = max(mass_l_, min(left_stat.mass_l_, right_stat.mass_l_));
+      mass_u_ = min(mass_u_, max(left_stat.mass_u_, right_stat.mass_u_));
+      mass_t_ += min_mass_t;
+      left_stat.mass_t_ -= min_mass_t;
+      right_stat.mass_t_ -= min_mass_t;
+    }
+
+    void PushDownTokens
+      (KdeStat &left_stat, KdeStat &right_stat, double *de,
+       typename TKernelAux::TLocalExpansion *local_expansion, double *dt) {
+    
+      if(de != NULL) {
+	double de_ref = *de;
+	left_stat.mass_e_ += de_ref;
+	right_stat.mass_e_ += de_ref;
+	*de = 0;
+      }
+
+      if(local_expansion != NULL) {
+	local_expansion->TranslateToLocal(left_stat.local_expansion_);
+	local_expansion->TranslateToLocal(right_stat.local_expansion_);
+      }
+      if(dt != NULL) {
+	double dt_ref = *dt;
+	left_stat.mass_t_ += dt_ref;
+	right_stat.mass_t_ += dt_ref;
+	*dt = 0;
+      }
+    }
+
+    KdeStat() { }
+    
+    ~KdeStat() {}
+    
+  };
+  
+  private:
+
+  /** series expansion auxililary object */
+  typename TKernelAux::TSeriesExpansionAux sea_;
+
+  /** query dataset */
+  Matrix qset_;
+
+  /** query tree */
+  Tree *qroot_;
+
+  /** reference dataset */
+  Matrix rset_;
+  
+  /** reference tree */
+  Tree *rroot_;
+  
+  /** reference weights */
+  Vector rset_weights_;
+
+  /** list of kernels to evaluate */
+  TKernel kernel_;
+
+  /** lower bound on the densities */
+  Vector densities_l_;
+
+  /** densities computed */
+  Vector densities_e_;
+
+  /** upper bound on the densities */
+  Vector densities_u_;
+
+  /** accuracy parameter */
+  double tau_;
+
+  int num_farfield_to_local_prunes_;
+
+  int num_farfield_prunes_;
+  
+  int num_local_prunes_;
+  
+  int num_finite_difference_prunes_;
+
+  // preprocessing: scaling the dataset; this has to be moved to the dataset
+  // module
+  /* scales each attribute to 0-1 using the min/max values */
+  void scale_data_by_minmax() {
+
+    int num_dims = rset_.n_rows();
+    DHrectBound<2> qset_bound;
+    DHrectBound<2> rset_bound;
+    qset_bound.Init(qset_.n_rows());
+    rset_bound.Init(qset_.n_rows());
+
+    // go through each query/reference point to find out the bounds
+    for(index_t r = 0; r < rset_.n_cols(); r++) {
+      Vector ref_vector;
+      rset_.MakeColumnVector(r, &ref_vector);
+      rset_bound |= ref_vector;
+    }
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      Vector query_vector;
+      qset_.MakeColumnVector(q, &query_vector);
+      qset_bound |= query_vector;
+    }
+
+    for(index_t i = 0; i < num_dims; i++) {
+      DRange qset_range = qset_bound.get(i);
+      DRange rset_range = rset_bound.get(i);
+      double min_coord = min(qset_range.lo, rset_range.lo);
+      double max_coord = max(qset_range.hi, rset_range.hi);
+      double width = max_coord - min_coord;
+
+      printf("Dimension %d range: [%g, %g]\n", i, min_coord, max_coord);
+
+      for(index_t j = 0; j < rset_.n_cols(); j++) {
+	rset_.set(i, j, (rset_.get(i, j) - min_coord) / width);
+      }
+      
+      if(strcmp(fx_param_str(NULL, "query", NULL), 
+		fx_param_str_req(NULL, "data"))) {
+	for(index_t j = 0; j < qset_.n_cols(); j++) {
+	  qset_.set(i, j, (qset_.get(i, j) - min_coord) / width);
+	}
+      }
+    }
+  }
+
+  // member functions
+  void UpdateBounds(Tree *qnode, Tree *rnode, 
+		    double *dl, double *de, double *du, double *dt,
+		    int *order_farfield_to_local, int *order_farfield,
+		    int *order_local) {
+    
+    // query self statistics
+    KdeStat &qstat = qnode->stat();
+
+    // reference node statistics
+    KdeStat &rstat = rnode->stat();
+
+    // incorporate into the self
+    double dl_ref = *dl;
+    double du_ref = *du;
+    qstat.mass_l_ += dl_ref;
+    qstat.mass_u_ += du_ref;
+
+    // incorporate finite difference pruning if available
+    if(de != NULL) {
+      qstat.mass_e_ += (*de);
+    }
+
+    // incorporate token change
+    if(dt != NULL) {
+      qstat.mass_t_ += (*dt);
+    }
+    
+    // incorporate series approximation into the self
+
+    // far field to local translation
+    if(order_farfield_to_local != NULL && *order_farfield_to_local >= 0) {
+      rstat.farfield_expansion_.TranslateToLocal(qstat.local_expansion_,
+						 *order_farfield_to_local);
+    }
+    // far field pruning
+    else if(order_farfield != NULL && *order_farfield >= 0) {
+      for(index_t q = qnode->begin(); q < qnode->end(); q++) {
+	densities_e_[q] += 
+	  rstat.farfield_expansion_.EvaluateField(&qset_, q, NULL, 
+						  *order_farfield);
+      }
+    }
+    // local accumulation pruning
+    else if(order_local != NULL && *order_local >= 0) {
+      qstat.local_expansion_.AccumulateCoeffs(rset_, rset_weights_,
+					      rnode->begin(), rnode->end(),
+					      *order_local);
+    }
+    
+    // for a leaf node, incorporate the lower and upper bound changes into
+    // its additional offset
+    if(qnode->is_leaf()) 
+      {
+	qstat.more_l_ += dl_ref;
+	qstat.more_u_ += du_ref;
+      } 
+    
+    // otherwise, incorporate the bound changes into the owed slots of
+    // the immediate descendants
+    else {      
+      qnode->left()->stat().owed_l_ += dl_ref; //transmission of the owed valuesto the children
+      qnode->left()->stat().owed_u_ += du_ref;
+      qnode->right()->stat().owed_l_ += dl_ref;
+      qnode->right()->stat().owed_u_ += du_ref;
+    }
+  }
+
+  /** exhaustive base KDE case */
+  void FKdeBase(Tree *qnode, Tree *rnode) {
+
+    // compute unnormalized sum
+    for(index_t q = qnode->begin(); q < qnode->end(); q++) {
+      
+      // get query point
+      const double *q_col = qset_.GetColumnPtr(q);
+      for(index_t r = rnode->begin(); r < rnode->end(); r++) {
+
+	// get reference point
+	const double *r_col = rset_.GetColumnPtr(r);
+
+	// pairwise distance and kernel value
+	double dsqd = la::DistanceSqEuclidean(qset_.n_rows(), q_col, r_col);
+	double ker_value = kernel_.EvalUnnormOnSq(dsqd);
+
+	densities_l_[q] += ker_value;
+	densities_e_[q] += ker_value;
+	densities_u_[q] += ker_value;
+      }
+    }
+    
+    // tally up the unused error components due to exhaustive computation
+    qnode->stat().mass_t_ += rnode->count();
+
+    // get a tighter lower and upper bound by looping over each query point
+    // in the current query leaf node
+    double min_l = MAXDOUBLE;
+    double max_u = -MAXDOUBLE;
+    for(index_t q = qnode->begin(); q < qnode->end(); q++) {
+      if(densities_l_[q] < min_l) {
+	min_l = densities_l_[q];
+      }
+      if(densities_u_[q] > max_u) {
+	max_u = densities_u_[q];
+      }
+    }
+    
+    // subtract the contribution accounted by the exhaustive computation
+    qnode->stat().more_u_ -= rnode->count();
+
+    // tighten lower and upper bound
+    qnode->stat().mass_l_ = min_l + qnode->stat().more_l_;
+    qnode->stat().mass_u_ = max_u + qnode->stat().more_u_;
+  }
+
+  /** 
+   * checking for prunability of the query and the reference pair using
+   * four types of pruning methods
+   */
+  int PrunableEnhanced(Tree *qnode, Tree *rnode, DRange &dsqd_range,
+		       DRange &kernel_value_range, double &dl, double &du, 
+		       double &dt, int &order_farfield_to_local,
+		       int &order_farfield, int &order_local) {
+
+    int dim = rset_.n_rows();
+
+    // actual amount of error incurred per each query/ref pair
+    double actual_err_farfield_to_local = 0;
+    double actual_err_farfield = 0;
+    double actual_err_local = 0;
+
+    // estimated computational cost
+    int cost_farfield_to_local = MAXINT;
+    int cost_farfield = MAXINT;
+    int cost_local = MAXINT;
+    int cost_exhaustive = (qnode->count()) * (rnode->count()) * dim;
+    int min_cost = 0;
+
+    // query node and reference node statistics
+    KdeStat &qstat = qnode->stat();
+    KdeStat &rstat = rnode->stat();
+    
+    // expansion objects
+    typename TKernelAux::TFarFieldExpansion &farfield_expansion = 
+      rstat.farfield_expansion_;
+    typename TKernelAux::TLocalExpansion &local_expansion = 
+      qstat.local_expansion_;
+
+    // number of reference points
+    int num_references = rnode->count();
+
+    // try pruning after bound refinement:
+    // the new lower bound after incorporating new info
+    dl = kernel_value_range.lo * num_references;
+    du = -kernel_value_range.hi * num_references;
+
+    // refine the lower bound using the new lower bound info
+    double new_mass_l = qstat.mass_l_ + dl;    
+    double allowed_err = tau_ * new_mass_l *
+      ((double)(num_references + qstat.mass_t_)) / 
+      ((double) rroot_->count() * num_references);
+    
+    // get the order of approximations
+    order_farfield_to_local = 
+      farfield_expansion.OrderForConvertingToLocal
+      (rnode->bound(), qnode->bound(), dsqd_range.lo, dsqd_range.hi, 
+       allowed_err, &actual_err_farfield_to_local);
+    order_farfield = 
+      farfield_expansion.OrderForEvaluating(rnode->bound(), qnode->bound(),
+					    dsqd_range.lo, dsqd_range.hi,
+					    allowed_err, &actual_err_farfield);
+    order_local = 
+      local_expansion.OrderForEvaluating(rnode->bound(), qnode->bound(), 
+					 dsqd_range.lo, dsqd_range.hi,
+					 allowed_err, &actual_err_local);
+
+    // update computational cost and compute the minimum
+    if(order_farfield_to_local >= 0) {
+      cost_farfield_to_local = (int) pow(order_farfield_to_local + 1, 
+					 2 * dim);
+    }
+    if(order_farfield >= 0) {
+      cost_farfield = (int) pow(order_farfield + 1, dim) * (qnode->count());
+    }
+    if(order_local >= 0) {
+      cost_local = (int) pow(order_local + 1, dim) * (rnode->count());
+    }
+
+    min_cost = min(cost_farfield_to_local, 
+		   min(cost_farfield, min(cost_local, cost_exhaustive)));
+
+    if(cost_farfield_to_local == min_cost) {
+      dt = num_references * 
+	(1.0 - (rroot_->count()) * actual_err_farfield_to_local / 
+	 (new_mass_l * tau_));
+      order_farfield = order_local = -1;
+      num_farfield_to_local_prunes_++;
+      return 1;
+    }
+
+    if(cost_farfield == min_cost) {
+      dt = num_references * 
+	(1.0 - (rroot_->count()) * actual_err_farfield / (new_mass_l * tau_));
+      order_farfield_to_local = order_local = -1;
+      num_farfield_prunes_++;
+      return 1;
+    }
+
+    if(cost_local == min_cost) {
+      dt = num_references * 
+	(1.0 - (rroot_->count()) * actual_err_local / (new_mass_l * tau_));
+      order_farfield_to_local = order_farfield = -1;
+      num_local_prunes_++;
+      return 1;
+    }
+
+    order_farfield_to_local = order_farfield = order_local = -1;
+    dl = du = dt = 0;
+    return 0;
+  }
+
+  /** checking for prunability of the query and the reference pair */
+  int Prunable(Tree *qnode, Tree *rnode, DRange &dsqd_range,
+	       DRange &kernel_value_range, double &dl, double &de,
+	       double &du, double &dt) {
+
+    // query node stat
+    KdeStat &stat = qnode->stat();
+    
+    // number of reference points
+    int num_references = rnode->count();
+
+    // try pruning after bound refinement: first compute distance/kernel
+    // value bounds
+    dsqd_range.lo = qnode->bound().MinDistanceSq(rnode->bound());
+    dsqd_range.hi = qnode->bound().MaxDistanceSq(rnode->bound());
+    kernel_value_range = kernel_.RangeUnnormOnSq(dsqd_range);
+
+    // the new lower bound after incorporating new info
+    dl = kernel_value_range.lo * num_references;
+    de = 0.5 * num_references * 
+      (kernel_value_range.lo + kernel_value_range.hi);
+    du = -kernel_value_range.hi * num_references;
+
+    // refine the lower bound using the new lower bound info
+    double new_mass_l = stat.mass_l_ + dl;    
+    double allowed_err = tau_ * new_mass_l *
+      ((double)(num_references + stat.mass_t_)) / ((double) rroot_->count());
+
+    // this is error per each query/reference pair for a fixed query
+    double m = 0.5 * (kernel_value_range.hi - kernel_value_range.lo);
+
+    // this is total error for each query point
+    double error = m * num_references;
+
+    // check pruning condition
+    if(error <= allowed_err) {
+      dt = num_references * 
+	(1.0 - (rroot_->count()) * m / (new_mass_l * tau_));
+      num_finite_difference_prunes_++;
+      return 1;
+    }
+    else {
+      dl = de = du = dt = 0;
+      return 0;
+    }
+  }
+
+  /** determine which of the node to expand first */
+  void BestNodePartners(Tree *nd, Tree *nd1, Tree *nd2, Tree **partner1,
+			Tree **partner2) {
+    
+    double d1 = nd->bound().MinDistanceSq(nd1->bound());
+    double d2 = nd->bound().MinDistanceSq(nd2->bound());
+
+    if(d1 <= d2) {
+      *partner1 = nd1;
+      *partner2 = nd2;
+    }
+    else {
+      *partner1 = nd2;
+      *partner2 = nd1;
+    }
+  }
+
+  /** canonical fast KDE case */
+  void FKde(Tree *qnode, Tree *rnode) {
+
+    /** temporary variable for storing lower bound change */
+    double dl = 0, de = 0, du = 0, dt = 0;
+    int order_farfield_to_local = -1, order_farfield = -1, order_local = -1;
+
+    // temporary variable for holding distance/kernel value bounds
+    DRange dsqd_range;
+    DRange kernel_value_range;
+
+    // query node statistics
+    KdeStat &stat = qnode->stat();
+
+    // left child and right child of query node statistics
+    KdeStat *left_stat = NULL;
+    KdeStat *right_stat = NULL;
+
+    // process density bound changes sent from the ancestor query nodes,
+    UpdateBounds(qnode, rnode, &stat.owed_l_, NULL, &stat.owed_u_, NULL,
+		 NULL, NULL, NULL);
+
+    // for non-leaf query node, tighten lower/upper bounds and the 
+    // reclaim tokens unused by the children.
+    if(!qnode->is_leaf()) {
+      left_stat = &(qnode->left()->stat());
+      right_stat = &(qnode->right()->stat());
+      stat.MergeChildBounds(*left_stat, *right_stat);
+    }
+
+    // try finite difference pruning first
+    if(Prunable(qnode, rnode, dsqd_range, kernel_value_range, 
+		dl, de, du, dt)) {
+      UpdateBounds(qnode, rnode, &dl, &de, &du, &dt, NULL, NULL, NULL);
+      return;
+    }
+    
+    // try series-expansion pruning
+    else if(PrunableEnhanced(qnode, rnode, dsqd_range, kernel_value_range, 
+			     dl, du, dt, order_farfield_to_local, 
+			     order_farfield, order_local)) {
+
+      UpdateBounds(qnode, rnode, &dl, NULL, &du, &dt,
+		   &order_farfield_to_local, &order_farfield,
+		   &order_local);
+      return;
+    }
+
+    // for leaf query node
+    if(qnode->is_leaf()) {
+      
+      // for leaf pairs, go exhaustive
+      if(rnode->is_leaf()) {
+	FKdeBase(qnode, rnode);
+	return;
+      }
+      
+      // for non-leaf reference, expand reference node
+      else {
+	Tree *rnode_first = NULL, *rnode_second = NULL;
+	BestNodePartners(qnode, rnode->left(), rnode->right(), &rnode_first,
+			 &rnode_second);
+	FKde(qnode, rnode_first);
+	FKde(qnode, rnode_second);
+	return;
+      }
+    }
+    
+    // for non-leaf query node
+    else {
+      
+      // for a leaf reference node, expand query node
+      if(rnode->is_leaf()) {
+	Tree *qnode_first = NULL, *qnode_second = NULL;
+
+	stat.PushDownTokens(*left_stat, *right_stat, NULL, NULL, 
+			    &stat.mass_t_);
+	BestNodePartners(rnode, qnode->left(), qnode->right(), &qnode_first,
+			 &qnode_second);
+	FKde(qnode_first, rnode);
+	FKde(qnode_second, rnode);
+	return;
+      }
+
+      // for non-leaf reference node, expand both query and reference nodes
+      else {
+	Tree *rnode_first = NULL, *rnode_second = NULL;
+	stat.PushDownTokens(*left_stat, *right_stat, NULL, NULL, 
+			    &stat.mass_t_);
+	
+	BestNodePartners(qnode->left(), rnode->left(), rnode->right(),
+			 &rnode_first, &rnode_second);
+	FKde(qnode->left(), rnode_first);
+	FKde(qnode->left(), rnode_second);
+
+	BestNodePartners(qnode->right(), rnode->left(), rnode->right(),
+			 &rnode_first, &rnode_second);
+	FKde(qnode->right(), rnode_first);
+	FKde(qnode->right(), rnode_second);
+	return;
+      }
+    }
+  }
+
+  /** 
+   * pre-processing step - this wouldn't be necessary if the core
+   * fastlib supported a Init function for Stat objects that take
+   * more arguments.
+   */
+  void PreProcess(Tree *node) {
+
+    // initialize the center of expansions and bandwidth for
+    // series expansion
+    node->stat().Init(sqrt(kernel_.bandwidth_sq()), &sea_);
+    node->bound().CalculateMidpoint
+      (node->stat().farfield_expansion_.get_center());
+    node->bound().CalculateMidpoint
+      (node->stat().local_expansion_.get_center());
+    
+    // initialize lower bound to 0
+    node->stat().mass_l_ = 0;
+    
+    // set the finite difference approximated amounts to 0
+    node->stat().mass_e_ = 0;
+
+    // set the upper bound to the number of reference points
+    node->stat().mass_u_ = rset_.n_cols();
+
+    // set the number of tokens to 0
+    node->stat().mass_t_ = 0;
+
+    // for non-leaf node, recurse
+    if(!node->is_leaf()) {
+      node->stat().owed_l_ = node->stat().owed_u_ = 0;
+      PreProcess(node->left());
+      PreProcess(node->right());
+
+      // translate multipole moments
+      node->stat().farfield_expansion_.TranslateFromFarField
+	(node->left()->stat().farfield_expansion_);
+      node->stat().farfield_expansion_.TranslateFromFarField
+	(node->right()->stat().farfield_expansion_);
+    }
+    else {
+      node->stat().more_l_ = node->stat().more_u_ = 0;
+      
+      // exhaustively compute multipole moments
+      node->stat().farfield_expansion_.RefineCoeffs(rset_, rset_weights_,
+						    node->begin(), node->end(),
+						    sea_.get_max_order());
+    }
+  }
+
+  /** post processing step */
+  void PostProcess(Tree *qnode) {
+    
+    KdeStat &stat = qnode->stat();
+
+    // for leaf query node
+    if(qnode->is_leaf()) {
+      for(index_t q = qnode->begin(); q < qnode->end(); q++) {
+	densities_e_[q] +=
+	  stat.local_expansion_.EvaluateField(&qset_, q, NULL) +
+	  stat.mass_e_;
+      }
+    }
+    else {
+
+      // push down approximations
+      stat.PushDownTokens(qnode->left()->stat(), qnode->right()->stat(),
+			  &stat.mass_e_, &stat.local_expansion_, NULL);
+      PostProcess(qnode->left());
+      PostProcess(qnode->right());
+    }
+  }
+
+  void NormalizeDensities() {
+    double norm_const = kernel_.CalcNormConstant(qset_.n_rows()) *
+      rset_.n_cols();
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      densities_l_[q] /= norm_const;
+      densities_e_[q] /= norm_const;
+      densities_u_[q] /= norm_const;
+    }
+  }
+
+  public:
+
+  // constructor/destructor
+  FastKde() {}
+
+  ~FastKde() { 
+    
+    if(qroot_ != rroot_ ) {
+      delete qroot_; 
+      delete rroot_; 
+    } 
+    else {
+      delete rroot_;
+    }
+
+  }
+
+  // getters and setters
+
+  /** get the reference dataset */
+  Matrix &get_reference_dataset() { return rset_; }
+
+  /** get the query dataset */
+  Matrix &get_query_dataset() { return qset_; }
+
+  /** get the density estimate */
+  const Vector &get_density_estimates() { return densities_e_; }
+
+  // interesting functions...
+
+  void Compute(double tau) {
+
+    // set accuracy parameter
+    tau_ = tau;
+    
+    // initialize the lower and upper bound densities
+    densities_l_.SetZero();
+    densities_e_.SetZero();
+    densities_u_.SetAll(rset_.n_cols());
+
+    num_finite_difference_prunes_ = num_farfield_to_local_prunes_ =
+      num_farfield_prunes_ = num_local_prunes_ = 0;
+
+    printf("\nStarting fast KDE...\n");
+    fx_timer_start(NULL, "fast_kde_compute");
+
+    // preprocessing step for initializing series expansion objects
+    PreProcess(rroot_);
+    if(qroot_ != rroot_) {
+      PreProcess(qroot_);
+    }
+    
+    // call main routine
+    FKde(qroot_, rroot_);
+
+    // postprocessing step for finalizing the sums
+    PostProcess(qroot_);
+
+    // normalize densities
+    NormalizeDensities();
+    fx_timer_stop(NULL, "fast_kde_compute");
+    printf("\nFast KDE completed...\n");
+    printf("Finite difference prunes: %d\n", num_finite_difference_prunes_);
+    printf("F2L prunes: %d\n", num_farfield_to_local_prunes_);
+    printf("F prunes: %d\n", num_farfield_prunes_);
+    printf("L prunes: %d\n", num_local_prunes_);
+  }
+
+  void Init() {
+    
+    Dataset ref_dataset;
+
+    // read in the number of points owned by a leaf
+    int leaflen = fx_param_int(NULL, "leaflen", 20);
+
+    // read the datasets
+    const char *rfname = fx_param_str_req(NULL, "data");
+    const char *qfname = fx_param_str(NULL, "query", rfname);
+
+    // read reference dataset
+    ref_dataset.InitFromFile(rfname);
+    rset_.Own(&(ref_dataset.matrix()));
+
+    // read the reference weights
+    char *rwfname = NULL;
+    if(fx_param_exists(NULL, "dwgts")) {
+      rwfname = (char *)fx_param_str(NULL, "dwgts", NULL);
+    }
+
+    if(rwfname != NULL) {
+      Dataset ref_weights;
+      ref_weights.InitFromFile(rwfname);
+      rset_weights_.Copy(ref_weights.matrix().GetColumnPtr(0),
+			 ref_weights.matrix().n_rows());
+    }
+    else {
+      rset_weights_.Init(rset_.n_cols());
+      rset_weights_.SetAll(1);
+    }
+
+    if(!strcmp(qfname, rfname)) {
+      qset_.Alias(rset_);
+    }
+    else {
+      Dataset query_dataset;
+      query_dataset.InitFromFile(qfname);
+      qset_.Own(&(query_dataset.matrix()));
+    }
+
+    // scale dataset if the user wants to
+    if(!strcmp(fx_param_str(NULL, "scaling", NULL), "range")) {
+      scale_data_by_minmax();
+    }
+
+    // construct query and reference trees
+    fx_timer_start(NULL, "tree_d");
+    rroot_ = tree::MakeKdTreeMidpoint<Tree>(rset_, leaflen);
+
+    if(!strcmp(qfname, rfname)) {
+      qroot_ = rroot_;
+    }
+    else {
+      qroot_ = tree::MakeKdTreeMidpoint<Tree>(qset_, leaflen);
+    }
+    fx_timer_stop(NULL, "tree_d");
+    
+    // initialize the density lists
+    densities_l_.Init(qset_.n_cols());
+    densities_e_.Init(qset_.n_cols());
+    densities_u_.Init(qset_.n_cols());
+
+    // initialize the kernel
+    kernel_.Init(fx_param_double_req(NULL, "bandwidth"));
+
+    // initialize the series expansion object
+    if(qset_.n_rows() <= 2) {
+      sea_.Init(fx_param_int(NULL, "order", 5), qset_.n_rows());
+    }
+    else {
+      sea_.Init(fx_param_int(NULL, "order", 0), qset_.n_rows());
+    }
+  }
+
+  void PrintDebug() {
+
+    FILE *stream = stdout;
+    const char *fname = NULL;
+
+    if((fname = fx_param_str(NULL, "fast_kde_output", NULL)) != NULL) {
+      stream = fopen(fname, "w+");
+    }
+    for(index_t q = 0; q < qset_.n_cols(); q++) {
+      fprintf(stream, "%g\n", densities_e_[q]);
+    }
+    
+    if(stream != stdout) {
+      fclose(stream);
+    }
+  }
+
+};
+
+#endif
diff --git a/fastlib/u/gmravi/regression/kde_test_to_dongryeol.nb b/fastlib/u/gmravi/regression/kde_test_to_dongryeol.nb
new file mode 100644
index 0000000000..1ccb529787
--- /dev/null
+++ b/fastlib/u/gmravi/regression/kde_test_to_dongryeol.nb
@@ -0,0 +1,17881 @@
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+
+This notebook can be used with any Mathematica-compatible
+application, such as Mathematica, MathReader or Publicon. The data
+for the notebook starts with the line containing stars above.
+
+To get the notebook into a Mathematica-compatible application, do
+one of the following:
+
+* Save the data starting with the line of stars above into a file
+  with a name ending in .nb, then open the file inside the
+  application;
+
+* Copy the data starting with the line of stars above to the
+  clipboard, then use the Paste menu command inside the application.
+
+Data for notebooks contains only printable 7-bit ASCII and can be
+sent directly in email or through ftp in text mode.  Newlines can be
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+the word CacheID, otherwise Mathematica-compatible applications may
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+
+For more information on notebooks and Mathematica-compatible 
+applications, contact Wolfram Research:
+  web: http://www.wolfram.com
+  email: info@wolfram.com
+  phone: +1-217-398-0700 (U.S.)
+
+Notebook reader applications are available free of charge from 
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+*******************************************************************)
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+(*WindowFrame->Normal*)
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+To Dongryeol: You need to change the directory names, filenames in \
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+                          x \\^ 2 \\/ 
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+                                    2)\)\\)\\) \\/ \\(4\\\ \\@5\\)]\\\ 
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+    \(\\!\\(\\(\(-2\)\\)\\\ 
+            If[Abs[x] < 1, 1 \\/ 2, 
+              0] + \[Integral]\(\(\\_\)\(\\(\(-\[Infinity]\)\\) \\% \
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+                n}, {j, n}]; \[IndentingNewLine]m1 = 
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+            2  n^\((\(-1\))\) h^\((\(-1\))\) 
+              kernel[0]; \[IndentingNewLine]m1starpartiNEQj = 
+          m1starpart - 
+            n^\((\(-2\))\)\ h^\((\(-1\))\) starkernel[0] 
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+Cell[CellGroupData[{
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+Cell[BoxData[
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+Cell[BoxData[
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+Cell[594041, 15095, 86, 1, 27, "Input"],
+Cell[594130, 15098, 42278, 1182, 186, 33516, 1069, "GraphicsData", \
+"PostScript", "Graphics"],
+Cell[636411, 16282, 130, 3, 27, "Output"]
+}, Open  ]],
+
+Cell[CellGroupData[{
+Cell[636578, 16290, 72, 1, 27, "Input"],
+Cell[636653, 16293, 14553, 393, 186, 3688, 254, "GraphicsData", "PostScript", \
+"Graphics"],
+Cell[651209, 16688, 130, 3, 27, "Output"]
+}, Open  ]],
+
+Cell[CellGroupData[{
+Cell[651376, 16696, 92, 1, 27, "Input"],
+Cell[651471, 16699, 13252, 399, 186, 4485, 286, "GraphicsData", "PostScript", \
+"Graphics"],
+Cell[664726, 17100, 130, 3, 27, "Output"]
+}, Open  ]],
+
+Cell[CellGroupData[{
+Cell[664893, 17108, 55, 1, 27, "Input"],
+Cell[664951, 17111, 675, 10, 62, "Output"]
+}, Open  ]]
+}, Closed]]
+}, Open  ]]
+}
+]
+*)
+
+
+
+(*******************************************************************
+End of Mathematica Notebook file.
+*******************************************************************)
+
diff --git a/fastlib/u/gmravi/regression/kg.m b/fastlib/u/gmravi/regression/kg.m
new file mode 100644
index 0000000000..59554ec696
--- /dev/null
+++ b/fastlib/u/gmravi/regression/kg.m
@@ -0,0 +1,3 @@
+function k=kg(x)
+k=exp(-x .* x ./2)/sqrt(2*pi);
+end
diff --git a/fastlib/u/gmravi/regression/lscvscore.m b/fastlib/u/gmravi/regression/lscvscore.m
new file mode 100644
index 0000000000..9e250da622
--- /dev/null
+++ b/fastlib/u/gmravi/regression/lscvscore.m
@@ -0,0 +1,16 @@
+function moh=lscvscore(x,h)
+% x is the input data
+% h is the bandwidth
+n=length(x);
+sqterm=0;
+xterm=0;
+for i=1:n
+    sqterm=sqterm+sum(kg((x-x(i))/(sqrt(2)*h)))/sqrt(2);   
+    % The sqrt(2) factors are for the K(2) term which is
+    % equivalent to a kernel with variance of 2 that is the convolution of 2
+    % gaussian kernels.  
+    xterm=xterm+sum(kg((x-x(i))/h));
+end;
+sqterm=sqterm/(n*n*h);
+xterm=2*(xterm/(n*n)-kg(0)/n)/h;
+moh=sqterm-xterm;
diff --git a/fastlib/u/gmravi/regression/main b/fastlib/u/gmravi/regression/main
new file mode 120000
index 0000000000..f68a8e2e47
--- /dev/null
+++ b/fastlib/u/gmravi/regression/main
@@ -0,0 +1 @@
+/net/hc295/gmravi/home/fastlib/fastlib/bin/x86_64_Linux_debug_gcc_/u/gmravi/regression/main
\ No newline at end of file
diff --git a/fastlib/u/gmravi/regression/main.cc b/fastlib/u/gmravi/regression/main.cc
new file mode 100644
index 0000000000..8945ac981b
--- /dev/null
+++ b/fastlib/u/gmravi/regression/main.cc
@@ -0,0 +1,103 @@
+#include "fastlib/fastlib_int.h"
+#include "regression.h"
+#define  MAXDOUBLE 32768.0
+
+int
+main (int argc, char *argv[])
+{
+  fx_init (argc, argv);
+
+  const char *algorithm = fx_param_str_req (NULL, "method");
+  bool do_naive = fx_param_exists (NULL, "do_naive");
+
+  Matrix query_dataset;
+  Matrix reference_dataset;
+  Vector rset_weights;
+  ArrayList<Vector> fast_kde_results;
+  FastKde <GaussianKernel> fast_kde;
+  if (!strcmp (algorithm, "fast"))
+    {
+      if (!strcmp (fx_param_str (NULL, "kernel", "gaussian"), "gaussian"))
+	{
+	  //  printf("will do  fast kde with gaussian kernel....\n");
+	  ArrayList<Vector> fast_kde_results;
+	
+	  printf ("O(p^D) expansion KDE\n");
+	 
+	  fast_kde.Init ();
+	  fast_kde.Compute (fx_param_double (NULL, "tau", 0.1));	
+	  //default value is 0.1
+
+	  printf("all computations done......");
+	  if (fx_param_exists (NULL, "fast_kde_output")) {
+	    fast_kde.PrintDebug ();
+	  }
+	  printf("Size of reference datset is %d\n",fast_kde.get_reference_dataset().n_cols());
+	  fast_kde_results.Init(fast_kde.get_query_dataset().n_cols());
+
+	  for(int i=0;i<fast_kde.get_query_dataset().n_cols();i++)
+	    fast_kde_results[i].Alias(fast_kde.get_density_estimates(i));
+	  
+	  
+	  query_dataset.Copy (fast_kde.get_query_dataset());
+	  reference_dataset.Copy (fast_kde.get_reference_dataset());
+	  rset_weights.Copy(fast_kde.get_reference_weights());
+	  printf("all values copied for naive kde...\n");
+	}
+
+
+      if (do_naive)
+	{
+	  //Vector fast_kde_results;
+	  
+	  NaiveKde <GaussianKernel> naive_kde;
+	  naive_kde.Init (query_dataset, reference_dataset);
+
+	  naive_kde.Compute ();
+ 
+	  if (fx_param_exists (NULL, "naive_kde_output"))
+	    {
+	      naive_kde.PrintDebug ();
+	    }
+	  printf("Naive KDE done...\n");
+	  printf("Fast kde results length is %d\n",fast_kde_results.size());
+	  naive_kde.ComputeMaximumRelativeError(fast_kde_results);
+	}
+    }
+  //Do fastkde wih epanechnikov kernel
+  else
+    {
+      /* if (!strcmp (fx_param_str (NULL, "kernel", "epan"), "epan"))
+	 {
+	 FastKde < EpanKernel > fast_kde;
+	 fast_kde.Init ();
+	 fast_kde.Compute (fx_param_double (NULL, "tau", 0.1));
+
+	 if (fx_param_exists (NULL, "fast_kde_output"))
+	 {
+	 fast_kde.PrintDebug ();
+	 }
+
+	 Vector fast_kde_results;
+	 fast_kde_results.Alias (fast_kde.get_density_estimates ());
+
+	 if (do_naive)
+	 {
+	 NaiveKde < EpanKernel > naive_kde;
+	 naive_kde.Init (fast_kde.get_query_dataset (),
+	 fast_kde.get_reference_dataset ());
+	 naive_kde.Compute ();
+
+	 if (fx_param_exists (NULL, "naive_kde_output"))
+	 {
+	 naive_kde.PrintDebug ();
+	 }
+	 naive_kde.ComputeMaximumRelativeError (fast_kde_results);
+	 }
+	 }*/
+      printf("do nothing..............\n");
+
+      fx_done ();
+      return 0;
+    }
+}
diff --git a/fastlib/u/gmravi/regression/naive_kde_output.txt b/fastlib/u/gmravi/regression/naive_kde_output.txt
new file mode 100644
index 0000000000..e629709e2e
--- /dev/null
+++ b/fastlib/u/gmravi/regression/naive_kde_output.txt
@@ -0,0 +1,20 @@
+0.002586
+0.002679
+0.002586
+0.002617
+0.002550
+0.002548
+0.002544
+0.002548
+0.002540
+0.002540
+0.002540
+0.002540
+0.002540
+0.002540
+0.002540
+0.002540
+0.002597
+0.002569
+0.002612
+0.002580
diff --git a/fastlib/u/gmravi/regression/query_file.csv b/fastlib/u/gmravi/regression/query_file.csv
new file mode 100644
index 0000000000..ebd746b3d7
--- /dev/null
+++ b/fastlib/u/gmravi/regression/query_file.csv
@@ -0,0 +1,5 @@
+1.0, 2.0, 3.0
+4.0, 5.0, 6.0
+7.0, 8.0, 9.0
+-1.0,-2.0,-12.0
+3.0,2.0,5.0
diff --git a/fastlib/u/gmravi/regression/regression.h b/fastlib/u/gmravi/regression/regression.h
new file mode 100644
index 0000000000..c7e96f697d
--- /dev/null
+++ b/fastlib/u/gmravi/regression/regression.h
@@ -0,0 +1,1191 @@
+#ifndef KDE_H
+#define KDE_H
+#define MAXDOUBLE 32768.0
+#include "fastlib/fastlib_int.h"
+
+
+template < typename TKernel > class NaiveKde
+{
+
+private:
+
+  /** query dataset */
+  Matrix qset_;
+
+  /** reference dataset */
+  Matrix rset_;
+
+  /** kernel */
+  TKernel kernel_;
+
+  /** computed densities */
+  ArrayList < Vector > densities_;
+
+  /**Reference weights*/
+  Vector rset_weights_;
+
+public:
+  void Compute ()
+  {
+
+    printf ("\nStarting naive KDE computations...\n");
+    fx_timer_start (NULL, "naive_kde_compute");
+
+    for (int i = 0; i < rset_.n_cols (); i++)
+      {
+	printf ("weight is %f\n", rset_weights_[i]);
+      }
+
+    // compute unnormalized sum
+    for (index_t q = 0; q < qset_.n_cols (); q++)	//for each query point
+      {
+	const double *q_col = qset_.GetColumnPtr (q);
+	for (index_t d = 0; d < rset_.n_rows () + 1; d++)	//along each direction
+	  {
+
+	    for (index_t r = 0; r < rset_.n_cols (); r++)	//for each reference point
+	      {
+
+		if (d == 0)
+		  {
+
+		    const double *r_col = rset_.GetColumnPtr (r);
+		    double dsqd =
+		      la::DistanceSqEuclidean (qset_.n_rows (), q_col, r_col);
+		    double val = kernel_.EvalUnnormOnSq (dsqd);
+		    val *= rset_weights_[r];
+		    printf ("weight is %f\n", rset_weights_[r]);
+		    printf ("initial densities q,d is %f\n",
+			    densities_[q][d]);
+		    printf ("val to be added is %f\n", val);
+		    densities_[q][d] += val;
+		  }
+		else
+		  {
+
+		    const double *r_col = rset_.GetColumnPtr (r);
+		    double dsqd =
+		      la::DistanceSqEuclidean (qset_.n_rows (), q_col, r_col);
+		    double val = kernel_.EvalUnnormOnSq (dsqd);
+		    val *= rset_weights_[r];
+		    val *= rset_.get (d - 1, r);
+		    /*printf("weight is %f\n",rset_weights_[r]);
+		       printf("val to be added is %f\n",val);
+		       printf("Initial densities q,d is %f\n",densities_[q][d]); */
+		    densities_[q][d] += val;
+		  }
+	      }
+	    printf ("denisty q ,d is %f\n", densities_[q][d]);
+	  }
+      }
+
+    // then normalize it
+
+    double norm_const = kernel_.CalcNormConstant (qset_.n_rows ()) *
+      rset_.n_cols ();
+    for (index_t q = 0; q < qset_.n_cols (); q++)	//for each query point
+      {
+	for (index_t d = 0; d < rset_.n_rows () + 1; d++)	//along each dimension
+	  {
+	    float weight_of_dimension = 0;
+	    for (index_t r = 0; r < rset_.n_cols (); r++)
+	      {
+		if (d != 0)
+		  {
+		    weight_of_dimension +=
+		      rset_.get (d - 1, q) * rset_weights_[r];
+		  }
+		else
+		  {
+		    weight_of_dimension += rset_weights_[r];
+		  }
+	      }
+	    densities_[q][d] /= (norm_const * weight_of_dimension);
+	  }
+      }
+  }
+
+  void Init ()
+  {
+    printf ("In the init function of Naivekde.....\n");
+    densities_.Init (qset_.n_cols ());
+    for (index_t i = 0; i < qset_.n_cols (); i++)
+      {
+	densities_[i].Init (rset_.n_rows () + 1);
+	densities_[i].SetAll (0);
+      }
+  }
+
+  void Init (Matrix & qset, Matrix & rset)
+  {
+    printf ("In the init function which takes arguments of naive KDE..\n");
+    // get datasets
+    qset_.Alias (qset);
+    rset_.Alias (rset);
+    printf ("qset and rsets  have been aliased....\n");
+    // get bandwidth
+    kernel_.Init (fx_param_double_req (NULL, "bandwidth"));
+    printf ("Kernel initializesd......\n");
+
+    // allocate density storage
+    printf ("Number of coulmns are ....\n");
+    printf ("%d\n", qset_.n_cols ());
+    densities_.Init (qset_.n_cols ());
+    printf ("density initialized as a 1-d array..\n");
+    for (index_t i = 0; i < qset_.n_cols (); i++)
+      {
+	densities_[i].Init (rset_.n_rows () + 1);
+	densities_[i].SetZero ();
+      }
+
+    printf ("densitites initialized...\n");
+
+    // read the reference weights.
+    char *rwfname = NULL;
+
+    if (fx_param_exists (NULL, "dwgts"))
+      {
+	//rwfname is the filename having the refernece weights
+	rwfname = (char *) fx_param_str (NULL, "dwgts", NULL);
+      }
+
+    if (rwfname != NULL)
+      {
+	Dataset ref_weights;
+	ref_weights.InitFromFile (rwfname);
+	rset_weights_.Copy (ref_weights.matrix ().GetColumnPtr (0), ref_weights.matrix ().n_rows ());	//Note rset_weights_ is a vector of weights
+      }
+
+    else
+      {
+	rset_weights_.Init (rset_.n_cols ());
+	rset_weights_.SetAll (1);
+
+      }
+
+  }
+
+  void PrintDebug ()
+  {
+
+    FILE *fp;
+    const char *fname;
+
+    if (fx_param_exists (NULL, "naive_kde_output"))
+      {
+	fname = fx_param_str (NULL, "naive_kde_output", NULL);
+	fp = fopen (fname, "w+");
+      }
+    for (index_t q = 0; q < qset_.n_cols (); q++)
+      {
+	for (index_t d = 0; d < rset_.n_rows () + 1; d++)
+	  {
+	    printf ("density q,d is %f\n", densities_[q][d]);
+	    fprintf (fp, "%f\n", densities_[q][d]);
+	  }
+      }
+  }
+
+  void ComputeMaximumRelativeError (ArrayList < Vector > density_estimate)
+  {
+    printf ("Came to compute maximum relative error...\n");
+    double max_rel_err = 0;
+    for (index_t q = 0; q < qset_.n_cols (); q++)
+      {
+	for (index_t d = 0; d < rset_.n_rows () + 1; d++)
+	  {
+	    printf ("The values being compared are ...\n");
+	    printf ("density estimate is %f and density is %f\n",
+		    density_estimate[q][d], densities_[q][d]);
+	    double rel_err =
+	      fabs (density_estimate[q][d] -
+		    densities_[q][d]) / densities_[q][d];
+
+	    if (rel_err > max_rel_err)
+	      {
+		max_rel_err = rel_err;
+	      }
+	  }
+      }
+
+    fx_format_result (NULL, "maxium_relative_error_for_fast_KDE", "%g",
+		      max_rel_err);
+  }
+
+};				//Class NaiveKDE ends........
+
+
+template < typename TKernel > class FastKde
+{
+public:
+
+
+  // forward declaration of KdeStat class
+  class KdeStat;
+
+  // our tree type using the KdeStat
+  typedef BinarySpaceTree < DHrectBound < 2 >, Matrix, KdeStat > Tree;
+
+  class KdeStat
+  {
+
+  public:
+
+      /** This is the weights dimension wise. $\sum (x_{ij}y_{i})$. It is a D+1 dimensional array */
+
+    Vector weight_of_dimension;
+
+      /** lower bound on the densities for the query points owned by this node . Realise that now it is a 1-D vector
+       */
+    Vector mass_l;
+
+      /**
+       * additional offset for the lower bound on the densities for the query
+       * points owned by this node (for leaf nodes only). Realise that now it is a 1-D vector
+       */
+    Vector more_l;
+
+      /**
+       * lower bound offset passed from above. Realise that now it is a 1-D vector
+       */
+    Vector owed_l;
+
+      /** stores the portion pruned by finite difference. Realise that now it is a 1-D vector
+       */
+    Vector mass_e;
+
+      /** upper bound on the densities for the query points owned by this node. Realise that now it is a 1-D vector
+       */
+    Vector mass_u;
+
+      /**
+       * additional offset for the upper bound on the densities for the query
+       * points owned by this node (for leaf nodes only). Realise that now it is a 1-D vector
+       */
+    Vector more_u;
+
+      /**
+       * upper bound offset passed from above. Realise that now it is a 1-D vector
+       */
+    Vector owed_u;
+
+
+    //These are the Init functions of KdeStat
+    void Init ()
+    {
+
+    }
+
+    void Init (const Matrix & dataset, index_t & start, index_t & count)
+    {
+
+      Init ();
+    }
+
+    void Init (const Matrix & dataset, index_t & start, index_t & count,
+	       const KdeStat & left_stat, const KdeStat & right_stat)
+    {
+      Init ();
+    }
+
+    void MergeChildBounds (KdeStat & left_stat, KdeStat & right_stat,
+			   index_t dim)
+    {
+      // improve lower and upper bound
+      for (index_t i = 0; i < dim; i++)
+	{
+	  mass_l[i] =
+	    max (mass_l[i], min (left_stat.mass_l[i], right_stat.mass_l[i]));
+	  mass_u[i] =
+	    min (mass_u[i], max (left_stat.mass_u[i], right_stat.mass_u[i]));
+	}
+    }
+
+    KdeStat ()
+    {
+    }
+
+    ~KdeStat ()
+    {
+    }
+
+  };				//WITH THIS CLASS KDESTAT COMES TO AN END
+
+  //Private member of FastKde
+private:
+
+  //total sum of weights
+  double total_sum_of_weights_;
+
+  /** query dataset */
+  Matrix qset_;
+
+  /**reference dataset */
+  Matrix rset_;
+
+  /** query tree */
+  Tree *qroot_;
+
+
+  /** reference tree */
+  Tree *rroot_;
+
+  /** reference weights */
+  Vector rset_weights_;
+
+  /** list of kernels to evaluate */
+  TKernel kernel_;
+
+  /** lower bound on the densities.This will now be a 2-D vector*/
+  ArrayList < Vector > densities_l_;
+
+  /** densities computed .This will now be a 2-D vector*/
+  ArrayList < Vector > densities_e_;
+
+  /** upper bound on the densities.This will now be a 2-D vector */
+  ArrayList < Vector > densities_u_;
+
+  /** accuracy parameter */
+  double tau_;
+
+
+  //Get a particular element from the reference set
+  /* double get_element_from_reference_dataset (int row, int column)
+     {
+     return rset_.get (row, column);
+     } */
+
+  // preprocessing: scaling the dataset; this has to be moved to the dataset
+  // module
+  /* scales each attribute to 0-1 using the min/max values */
+  void scale_data_by_minmax ()
+  {
+    int num_dims = rset_.n_rows ();
+    DHrectBound < 2 > qset_bound;
+    DHrectBound < 2 > rset_bound;
+    qset_bound.Init (qset_.n_rows ());
+    rset_bound.Init (qset_.n_rows ());
+
+    // go through each query/reference point to find out the bounds
+    for (index_t r = 0; r < rset_.n_cols (); r++)
+      {
+	Vector ref_vector;
+	rset_.MakeColumnVector (r, &ref_vector);
+	rset_bound |= ref_vector;
+      }
+    for (index_t q = 0; q < qset_.n_cols (); q++)
+      {
+	Vector query_vector;
+	qset_.MakeColumnVector (q, &query_vector);
+	qset_bound |= query_vector;
+      }
+
+    for (index_t i = 0; i < num_dims; i++)
+      {
+	DRange qset_range = qset_bound.get (i);
+	DRange rset_range = rset_bound.get (i);
+	double min_coord = min (qset_range.lo, rset_range.lo);
+	double max_coord = max (qset_range.hi, rset_range.hi);
+	double width = max_coord - min_coord;
+
+	printf ("Dimension %d range: [%g, %g]\n", i, min_coord, max_coord);
+
+	for (index_t j = 0; j < rset_.n_cols (); j++)
+	  {
+	    rset_.set (i, j, (rset_.get (i, j) - min_coord) / width);
+	  }
+
+	if (strcmp (fx_param_str (NULL, "query", NULL),
+		    fx_param_str_req (NULL, "data")))
+	  {
+	    for (index_t j = 0; j < qset_.n_cols (); j++)
+	      {
+		qset_.set (i, j, (qset_.get (i, j) - min_coord) / width);
+	      }
+	  }
+      }
+  }
+
+  // member functions
+
+  /** Initialize the statistics */
+
+  void PreProcess (Tree * node)
+  {
+
+    //more_l,more_u, owed_l,owed_u are all vectors now
+    node->stat ().mass_l.Init (rset_.n_rows () + 1);
+
+
+    node->stat ().owed_l.Init (rset_.n_rows () + 1);
+    node->stat ().owed_u.Init (rset_.n_rows () + 1);
+    node->stat ().weight_of_dimension.Init (rset_.n_rows () + 1);
+
+
+    // initialize lower bound to 0
+
+    for (int i = 0; i < rset_.n_rows () + 1; i++)
+      {
+	node->stat ().mass_l[i] = 0;
+	node->stat ().owed_l[i] = 0;
+	node->stat ().owed_u[i] = 0;
+      }
+    //BAse CAse.....
+
+    if (node->is_leaf ())
+      {
+
+	//This is a leaf node.......................
+	node->stat ().more_l.Init (rset_.n_rows () + 1);
+	node->stat ().more_u.Init (rset_.n_rows () + 1);
+	node->stat ().more_l.SetAll (0);
+	node->stat ().more_u.SetAll (0);
+
+	node->stat ().weight_of_dimension.Init (rset_.n_rows () + 1);
+	node->stat ().weight_of_dimension.SetZero ();
+
+	for (int i = 0; i < rset_.n_rows () + 1; i++)
+	  {
+	    for (int j = node->begin (); j < node->end (); j++)	//loping over all the points
+	      {
+		if (i != 0)
+		  {
+		    node->stat ().weight_of_dimension[i] +=
+		      rset_.get (i - 1, j) * rset_weights_[j];
+
+		  }
+		else
+		  {
+		    node->stat ().weight_of_dimension[i] += rset_weights_[j];
+
+		  }
+	      }
+	  }
+	printf ("THE WEIGHT VECTOR I WILL BE RETURNING IS ...\n");
+	for (int i = 0; i < rset_.n_rows () + 1; i++)
+	  printf ("weight is %f\n", node->stat ().weight_of_dimension[i]);
+
+
+      }
+
+    // for non-leaf node, recurse
+    else
+      {
+
+
+	printf ("This is not a leaf node...\n");
+
+	PreProcess (node->left ());
+	PreProcess (node->right ());
+	printf ("weight of dimension received is...\n");
+	for (int i = 0; i < node->stat ().weight_of_dimension.length (); i++)
+	  {
+	    printf ("weight of dimension left is %f\n",
+		    node->left ()->stat ().weight_of_dimension[i]);
+	    printf ("weight of dimension right is %f\n",
+		    node->right ()->stat ().weight_of_dimension[i]);
+	  }
+
+	for (int count = 0; count < rset_.n_rows () + 1; count++)
+	  {
+
+	    node->stat ().weight_of_dimension[count] =
+	      node->left ()->stat ().weight_of_dimension[count] +
+	      node->right ()->stat ().weight_of_dimension[count];
+	  }
+
+      }
+
+
+  }
+
+  void UpdateBounds (Tree * qnode, Vector dl, Vector du)
+  {
+
+
+    // query self statistics
+    KdeStat & qstat = qnode->stat ();
+
+    // Changes the upper and lower bounds.
+
+    for (int i = 0; i < rset_.n_rows () + 1; i++)
+      {
+	//printf("Will add dl=%f and du=%f\n",dl[i],du[i]);
+	qstat.mass_l[i] += dl[i];
+	qstat.mass_u[i] += du[i];
+      }
+
+    // for a leaf node, incorporate the lower and upper bound changes into
+    // its additional offset
+
+    if (qnode->is_leaf ())
+      {
+
+	for (int t = 0; t < rset_.n_rows () + 1; t++)
+	  {
+	    qstat.more_l[t] += dl[t];	//Why are we doing this. Why dowe maintain a field called qstat.more_l_ for a leaf node
+	    qstat.more_u[t] += du[t];
+	  }
+      }
+
+    // otherwise, incorporate the bound changes into the owed slots of
+    // the immediate descendants
+    else
+      {
+
+	for (int i = 0; i < rset_.n_rows () + 1; i++)
+	  {
+	    //transmission of the owed values to the children
+	    qnode->left ()->stat ().owed_l[i] += dl[i];
+	    qnode->left ()->stat ().owed_u[i] += du[i];
+
+	    qnode->right ()->stat ().owed_l[i] += dl[i];
+	    qnode->right ()->stat ().owed_u[i] += du[i];
+	  }
+
+      }
+    //Since owed_l and owed_u have already been incorporated into 
+    for (int i = 0; i < qstat.owed_l.length (); i++)
+      qstat.owed_l[i] = qstat.owed_u[i] = 0;
+  }
+
+  /** exhaustive base KDE case */
+  void FKdeBase (Tree * qnode, Tree * rnode)
+  {
+    printf ("In FKdeBas ...\n");
+
+
+    // compute unnormalized sum
+    for (index_t q = qnode->begin (); q < qnode->end (); q++)
+      {
+
+	// get query point
+	const double *q_col = qset_.GetColumnPtr (q);
+
+	for (index_t i = 0; i < rset_.n_rows () + 1; i++)	//Along all dimensions
+	  {
+	    for (index_t r = rnode->begin (); r < rnode->end (); r++)
+	      {
+		// get reference point
+		const double *r_col = rset_.GetColumnPtr (r);
+
+		// pairwise distance and kernel value
+		double dsqd =
+		  la::DistanceSqEuclidean (qset_.n_rows (), q_col, r_col);
+		double ker_value = kernel_.EvalUnnormOnSq (dsqd);
+		if (i != 0)
+		  {
+		    densities_l_[q][i] +=
+		      ker_value * rset_weights_[r] * rset_.get (i - 1, r);
+		    densities_u_[q][i] +=
+		      ker_value * rset_weights_[r] * rset_.get (i - 1, r);
+		  }
+		else
+		  {
+		    densities_l_[q][i] += ker_value * rset_weights_[r];
+		    densities_u_[q][i] += ker_value * rset_weights_[r];
+
+		  }
+	      }
+	  }
+      }
+
+
+    // get a tighter lower and upper bound for every dimension by looping over each query point
+    // in the current query leaf node
+
+    Vector min_l;
+    min_l.Init (rset_.n_rows () + 1);
+
+    Vector max_u;
+    max_u.Init (rset_.n_rows () + 1);
+    min_l.SetAll (MAXDOUBLE);
+    max_u.SetAll (-MAXDOUBLE);
+
+    for (index_t t = 0; t < rset_.n_rows () + 1; t++)
+      {
+	for (index_t q = qnode->begin (); q < qnode->end (); q++)
+	  {
+	    if (densities_l_[q][t] < min_l[t])
+	      {
+		min_l[t] = densities_l_[q][t];
+	      }
+	    if (densities_u_[q][t] > max_u[t])
+	      {
+		max_u[t] = densities_u_[q][t];
+	      }
+	  }
+      }
+
+    // subtract the contribution accounted by the exhaustive computation
+    for (index_t i = 0; i < rset_.n_rows () + 1; i++)
+      {
+	qnode->stat ().more_u[i] -= rroot_->stat ().weight_of_dimension[i];
+	printf ("WILL BE SUBTRCTING FROM MORE_U A VALUE OF %f\n",
+		rroot_->stat ().weight_of_dimension[i]);
+      }
+
+    // tighten lower and upper bound
+    for (index_t i = 0; i < rset_.n_rows () + 1; i++)
+      {
+	qnode->stat ().mass_l[i] = min_l[i] + qnode->stat ().more_l[i];
+	qnode->stat ().mass_u[i] = max_u[i] + qnode->stat ().more_u[i];
+      }
+  }
+
+
+  /** 
+   * checking for prunability of the query and the reference pair using
+   * four types of pruning methods
+   */
+
+  /** checking for prunability of the query and the reference pair */
+  int Prunable (Tree * qnode, Tree * rnode, DRange & dsqd_range,
+		DRange & kernel_value_range, Vector dl, Vector du)
+  {
+
+    // query node stat
+    KdeStat & stat = qnode->stat ();
+
+
+
+    // try pruning after bound refinement: first compute distance/kernel
+    // value bounds
+    dsqd_range.lo = qnode->bound ().MinDistanceSq (rnode->bound ());
+    dsqd_range.hi = qnode->bound ().MaxDistanceSq (rnode->bound ());
+    kernel_value_range = kernel_.RangeUnnormOnSq (dsqd_range);
+
+    // the new lower bound after incorporating new info for each dimension
+    for (int i = 0; i < rset_.n_rows () + 1; i++)
+      {
+	dl[i] = rnode->stat ().weight_of_dimension[i] * kernel_value_range.lo;
+	du[i] =
+	  -1 * rnode->stat ().weight_of_dimension[i] * (1 -
+							kernel_value_range.
+							hi);
+      }
+
+    // refine the lower bound using the new lower bound info
+
+    Vector new_mass_l;
+    new_mass_l.Init (rset_.n_rows () + 1);
+    new_mass_l.SetAll (0);
+
+    for (int i = 0; i < rset_.n_rows () + 1; i++)
+      {
+	new_mass_l[i] = stat.mass_l[i] + dl[i];
+	double allowed_err = tau_ * new_mass_l[i] *
+	  ((double) (rnode->stat ().weight_of_dimension[i]) /
+	   ((double) rroot_->stat ().weight_of_dimension[i]));
+
+	// this is error per each query/reference pair for a fixed query
+	double m = 0.5 * (kernel_value_range.hi - kernel_value_range.lo);
+
+	// this is total maximumn error for each query point
+	double error = m * rnode->stat ().weight_of_dimension[i];
+	// check pruning condition
+	if (error > allowed_err)
+	  {
+	    dl.SetAll (0);
+	    du.SetAll (0);
+	    return 0;
+	  }
+      }
+    return 1;
+  }
+
+  /** determine which of the node to expand first */
+  void BestNodePartners (Tree * nd, Tree * nd1, Tree * nd2, Tree ** partner1,
+			 Tree ** partner2)
+  {
+
+    double d1 = nd->bound ().MinDistanceSq (nd1->bound ());
+    double d2 = nd->bound ().MinDistanceSq (nd2->bound ());
+
+    if (d1 <= d2)
+      {
+	*partner1 = nd1;
+	*partner2 = nd2;
+      }
+    else
+      {
+	*partner1 = nd2;
+	*partner2 = nd1;
+      }
+  }
+
+  /** canonical fast KDE case */
+  void FKde (Tree * qnode, Tree * rnode)
+  {
+    printf ("start FKde code.......\n");
+    /** temporary variable for storing lower bound change */
+    Vector dl;
+    dl.Init (rset_.n_rows () + 1);
+    Vector du;
+    du.Init (rset_.n_rows () + 1);
+    dl.SetAll (0);
+    du.SetAll (0);
+
+    // temporary variable for holding distance/kernel value bounds
+    DRange dsqd_range;
+    DRange kernel_value_range;
+
+    // query node statistics
+    KdeStat & stat = qnode->stat ();
+
+    // left child and right child of query node statistics
+    KdeStat *left_stat = NULL;
+    KdeStat *right_stat = NULL;
+    printf ("will call update bounds\n");
+    // process density bound changes sent from the ancestor query nodes,
+    if (qnode == qroot_ && rnode == rroot_)
+      {
+	printf ("Yeah the root\n");
+      }
+    /*printf("debug stmts\n");
+       printf("Length is %d \n",qnode->stat().owed_l.length());
+       printf("Length is %d \n",qnode->stat().owed_u.length());
+
+       for(int i=0;i<qnode->stat().owed_l.length();i++)
+       {
+       printf("%f\n",stat.owed_l[i]);
+       printf("%f\n",stat.owed_u[i]);
+       } */
+
+    UpdateBounds (qnode, qnode->stat ().owed_l, qnode->stat ().owed_u);
+
+    if (!qnode->is_leaf ())
+      {
+	left_stat = &(qnode->left ()->stat ());
+	right_stat = &(qnode->right ()->stat ());
+	stat.MergeChildBounds (*left_stat, *right_stat, rset_.n_rows () + 1);
+      }
+
+    // try finite difference pruning first
+    if (Prunable (qnode, rnode, dsqd_range, kernel_value_range, dl, du))
+      {
+	UpdateBounds (qnode, dl, du);
+	return;
+      }
+
+    // for leaf query node
+    if (qnode->is_leaf ())
+      {
+
+	// for leaf pairs, go exhaustive
+	if (rnode->is_leaf ())
+	  {
+	    FKdeBase (qnode, rnode);
+	    return;
+	  }
+
+	// for non-leaf reference, expand reference node
+	else
+	  {
+	    Tree *rnode_first = NULL, *rnode_second = NULL;
+	    BestNodePartners (qnode, rnode->left (), rnode->right (),
+			      &rnode_first, &rnode_second);
+	    FKde (qnode, rnode_first);
+	    FKde (qnode, rnode_second);
+	    return;
+	  }
+      }
+
+    // for non-leaf query node
+    else
+      {
+
+	// for a leaf reference node, expand query node
+	if (rnode->is_leaf ())
+	  {
+	    Tree *qnode_first = NULL, *qnode_second = NULL;
+
+	    //stat.PushDownTokens(*left_stat, *right_stat, NULL, NULL,&stat.mass_t_);
+	    BestNodePartners (rnode, qnode->left (), qnode->right (),
+			      &qnode_first, &qnode_second);
+	    FKde (qnode_first, rnode);
+	    FKde (qnode_second, rnode);
+	    return;
+	  }
+
+	// for non-leaf reference node, expand both query and reference nodes
+	else
+	  {
+	    Tree *rnode_first = NULL, *rnode_second = NULL;
+	    //stat.PushDownTokens(*left_stat, *right_stat, NULL, NULL,&stat.mass_t_);
+
+	    BestNodePartners (qnode->left (), rnode->left (), rnode->right (),
+			      &rnode_first, &rnode_second);
+	    FKde (qnode->left (), rnode_first);
+	    FKde (qnode->left (), rnode_second);
+
+	    BestNodePartners (qnode->right (), rnode->left (),
+			      rnode->right (), &rnode_first, &rnode_second);
+	    FKde (qnode->right (), rnode_first);
+	    FKde (qnode->right (), rnode_second);
+	    return;
+	  }
+      }
+  }
+
+  /** post processing step */
+  void PostProcess (Tree * qnode)
+  {
+
+    KdeStat & stat = qnode->stat ();
+
+    // for leaf query node
+    if (qnode->is_leaf ())
+      {
+	UpdateBounds (qnode, qnode->stat ().owed_l, qnode->stat ().owed_u);
+	for (index_t q = qnode->begin (); q < qnode->end (); q++)
+	  {
+	    for (int i = 0; i < rset_.n_rows () + 1; i++)	//Along each dimension
+	      {
+		/*printf("densities are as follows..\n");
+		   printf("density lower estimate is %f..\n",densities_l_[q][i]);
+		   printf("density upper estimate is %f..\n",densities_u_[q][i]);
+		   printf("more l  estimate is %f..\n",qnode->stat().more_l[i]);
+		   printf("more u  estimate is %f..\n",qnode->stat().more_u[i]); */
+
+
+		densities_e_[q][i] =
+		  (densities_l_[q][i] + qnode->stat ().more_l[i] +
+		   densities_u_[q][i] + qnode->stat ().more_u[i]) / 2.0;
+	      }
+	  }
+      }
+    else
+      {
+	//It is a non-leaf node
+	UpdateBounds (qnode, stat.owed_l, stat.owed_u);
+	PostProcess (qnode->left ());
+	PostProcess (qnode->right ());
+      }
+  }
+
+  void NormalizeDensities (Tree * rnode)
+  {
+    printf ("In normalize densities...\n");
+
+    for (int i = 0; i < rnode->stat ().weight_of_dimension.length (); i++)
+      printf ("weight of dimension[%d]=%f\n", i,
+	      rnode->stat ().weight_of_dimension[i]);
+
+    for (index_t q = 0; q < qset_.n_cols (); q++)
+      {
+	for (index_t i = 0; i < rset_.n_rows () + 1; i++)	//this is the dimension
+	  {
+	    double norm_const =
+	      kernel_.CalcNormConstant (qset_.n_rows ()) *
+	      rnode->stat ().weight_of_dimension[i];
+
+	    densities_l_[q][i] /= norm_const;
+	    densities_e_[q][i] /= norm_const;
+	    densities_u_[q][i] /= norm_const;
+	  }
+      }
+  }
+
+public:
+
+  // constructor/destructor
+  FastKde ()
+  {
+  }
+
+  ~FastKde ()
+  {
+    printf ("Have come to destructor of fastkde...\n");
+    /* if (qroot_ != rroot_)
+       {
+       delete qroot_;
+       delete rroot_;
+       }
+       else
+       {
+       delete rroot_;
+       } */
+
+  }
+
+  // getters and setters
+
+  void set_total_sum_of_weights (Vector rset_weights_)
+  {
+    double total_sum_of_weights_ = 0;
+    for (int i = 0; i < rset_weights_.length (); i++)
+      {
+	total_sum_of_weights_ += rset_weights_[i];
+      }
+  }
+
+  void set_total_sum_of_weights (index_t value)
+  {
+    total_sum_of_weights_ = value;
+  }
+  /**get the reference weights*/
+
+  Vector get_reference_weights ()
+  {
+    return rset_weights_;
+  }
+
+  /** get the reference dataset */
+  Matrix & get_reference_dataset ()
+  {
+    return rset_;
+  }
+
+  /** get the query dataset */
+  Matrix & get_query_dataset ()
+  {
+    return qset_;
+  }
+
+  /** get the density estimate */
+  const Vector & get_density_estimates (int i)
+  {
+    return densities_e_[i];
+  }
+
+  // interesting functions.......
+
+
+  void SetUpperBoundOfDensity (Tree * qnode)
+  {
+    if (qnode->is_leaf ())
+      {
+	printf ("will set up the upper bounds of mass_u..\n");
+	qnode->stat ().mass_u.Init (rset_.n_rows () + 1);
+	//Initialize the value
+	for (int i = 0; i < rset_.n_rows () + 1; i++)
+	  {
+	    qnode->stat ().mass_u[i] = qnode->stat ().weight_of_dimension[i];
+	  }
+	return;
+      }
+    //initialize the size
+    printf ("will set up the upper bounds of mass_u..\n");
+    qnode->stat ().mass_u.Init (rset_.n_rows () + 1);
+    //Initialize the value
+    for (int i = 0; i < rset_.n_rows () + 1; i++)
+      {
+	qnode->stat ().mass_u[i] = qnode->stat ().weight_of_dimension[i];
+      }
+    SetUpperBoundOfDensity (qnode->left ());
+    SetUpperBoundOfDensity (qnode->right ());
+  }
+
+
+  void Compute (double tau)
+  {
+    printf ("Came to compute function...\n");
+    // set accuracy parameter
+    tau_ = tau;
+
+    //num_finite_difference_prunes_ = num_farfield_to_local_prunes_ =num_farfield_prunes_ = num_local_prunes_ = 0;
+
+    // fx_timer_start(NULL, "fast_kde_compute");
+
+    printf ("Will  call FKde....\n");
+    printf ("Will preprocess now....\n");
+    if (qroot_ == rroot_)
+      {
+	printf ("Both are thee same trees..\n");
+	exit (0);
+      }
+    else
+      {
+	printf
+	  ("tested if the pointer are the same or not and found they are not...\n");
+      }
+    //PreProcess first.....
+    PreProcess (qroot_);
+    printf ("done with preprocessing of the query tree.....\n");
+    //Here some kind of optimization can be done. this is being called because the ref tree wil have the weight_of_dimension
+
+    if (qroot_ == rroot_)
+      {
+	printf ("Both are the same trees..\n");
+      }
+
+    PreProcess (rroot_);
+    if (qroot_ == rroot_)
+      {
+	printf ("Both are thee same trees..\n");
+      }
+    printf ("Preprocessing complete....\n");
+
+    //Initialize all the densities properly
+
+    densities_l_.Init (qset_.n_cols ());
+    densities_u_.Init (qset_.n_cols ());
+    densities_e_.Init (qset_.n_cols ());
+
+    for (int i = 0; i < qset_.n_cols (); i++)
+      {
+	densities_l_[i].Init (rset_.n_rows () + 1);	//size is number of dimensions+1
+	densities_l_[i].SetAll (0);
+
+	densities_e_[i].Init (rset_.n_rows () + 1);
+	densities_e_[i].SetAll (0);
+      }
+    printf ("lower and estimate bounds of densities set...\n");
+
+    for (int i = 0; i < qset_.n_cols (); i++)	//for every point
+      {
+	densities_u_[i].Init (rset_.n_rows () + 1);
+	for (int j = 0; j < rset_.n_rows () + 1; j++)	//for every dimension
+	  {
+	    densities_u_[i][j] = rroot_->stat ().weight_of_dimension[j];
+	  }
+      }
+    printf ("estimates are also initalized....\n");
+    printf ("will now have to initialize mass_u...\n");
+    SetUpperBoundOfDensity (qroot_);
+    printf ("Upper bounds completely set.....\n");
+
+
+    printf ("will print upper bounds on densities at different levels..\n");
+    for (int i = 0; i < rset_.n_rows () + 1; i++)
+      {
+
+	printf ("mass_u is %f\n", qroot_->stat ().mass_u[i]);
+	printf ("The weight is %f\n", rroot_->stat ().weight_of_dimension[i]);
+      }
+
+    // call main routine
+    FKde (qroot_, rroot_);
+    printf ("will now postprocess..\n");
+
+    // postprocessing step for finalizing the sums
+    PostProcess (qroot_);
+
+    /**normalize densities*/
+
+    NormalizeDensities (rroot_);
+    printf ("\nFast KDE completed...hogaya bey hogaya\n");
+  }
+
+  void Init ()			//This is the Init function of FastKde
+  {
+
+    Dataset ref_dataset;
+
+    // read in the number of points owned by a leaf
+    int leaflen = fx_param_int (NULL, "leaflen", 10);
+
+    // read the datasets
+    const char *rfname = fx_param_str_req (NULL, "data");
+    const char *qfname = fx_param_str (NULL, "query", rfname);
+
+    printf ("the reference file is %s....\n", rfname);
+    printf ("the query file is %s...\n", qfname);
+
+    // read reference dataset
+    ref_dataset.InitFromFile (rfname);
+    rset_.Own (&(ref_dataset.matrix ()));
+
+    // read the reference weights.
+    char *rwfname = NULL;
+
+    if (fx_param_exists (NULL, "dwgts"))
+      {
+	//rwfname is the filename having the refernece weights
+	rwfname = (char *) fx_param_str (NULL, "dwgts", NULL);
+      }
+
+    if (rwfname != NULL)
+      {
+	Dataset ref_weights;
+	ref_weights.InitFromFile (rwfname);
+	rset_weights_.Copy (ref_weights.matrix ().GetColumnPtr (0), ref_weights.matrix ().n_rows ());	//Note rset_weights_ is a vector of weights
+	set_total_sum_of_weights (rset_weights_);
+      }
+
+    else
+      {
+	rset_weights_.Init (rset_.n_cols ());
+	rset_weights_.SetAll (1);
+	set_total_sum_of_weights (rset_.n_cols ());
+      }
+    printf ("Printing all weights....\n");
+
+    for (int i = 0; i < rset_weights_.length (); i++)
+      printf ("%f\n", rset_weights_[i]);
+
+    if (!strcmp (qfname, rfname))
+      {
+	qset_.Alias (rset_);
+      }
+    else
+      {
+	Dataset query_dataset;
+	query_dataset.InitFromFile (qfname);
+	qset_.Own (&(query_dataset.matrix ()));
+      }
+
+    printf ("will get scaling parameter................................");
+    // scale dataset if the user wants to. NOTE: THIS HAS TO BE VERIFIED.....
+
+    if (!strcmp (fx_param_str (NULL, "scaling", NULL), "range"))
+      {
+	scale_data_by_minmax ();
+      }
+
+    // construct query and reference trees. This also fills up the statistics in the reference tree
+    fx_timer_start (NULL, "tree_d");
+    rroot_ = tree::MakeKdTreeMidpoint < Tree > (rset_, leaflen);
+    if (!strcmp (qfname, rfname))
+      {
+	qroot_ = rroot_;
+      }
+    else
+      {
+	qroot_ = tree::MakeKdTreeMidpoint < Tree > (qset_, leaflen);
+      }
+    qroot_ = tree::MakeKdTreeMidpoint < Tree > (qset_, leaflen);
+
+
+    fx_timer_stop (NULL, "tree_d");
+
+    // initialize the kernel
+    kernel_.Init (fx_param_double_req (NULL, "bandwidth"));
+    Print (rset_weights_);
+  }
+
+  void Print (Vector rset_weights_)
+  {
+    /* printf("the weights are....\n");
+       for(int i=0;i<rset_weights_.length();i++)
+       {
+       printf("%f ",rset_weights_[i]);
+       } */
+  }
+  void PrintDebug ()
+  {
+
+    FILE *stream = stdout;
+    const char *fname = NULL;
+    FILE *fp;
+
+    if ((fname = fx_param_str (NULL, "fast_kde_output", NULL)) != NULL)
+      {
+
+	fp = fopen (fname, "w+");
+      }
+    for (index_t q = 0; q < qset_.n_cols (); q++)
+      {
+	for (index_t d = 0; d < rset_.n_rows () + 1; d++)
+	  {
+	    fprintf (fp, "%f\n", densities_e_[q][d]);
+	  }
+      }
+
+    fclose (fp);
+  }
+
+};
+
+#endif