#ifndef _KDTREE_H_#define _KDTREE_H_#pragma pack(1)struct _Node {  float key;  int pntidx;	int leftIdx;	int rightIdx;};#pragma pack()typedef float *Range;typedef int IRange[2];class KDTree {public:  KDTree(float *setpoints, int N, int setndim);  KDTree();   virtual ~ KDTree();  int create(float *setpoints, int setnpoints, int setndim,						 bool setCopy = false, 						 struct _Node *setNodeMem = (struct _Node *)0);	int create(float *setpoints,int setnpoints,int setndim,						 void *mem);  int ndim;  int npoints;  float *points;  // Search for the nearest neighbor to pnt and   // return its index  int closest_point(float *pnt, int &idx, bool approx=false);	int get_points_in_range(Range *range);	int nPntsInRange;	int *pntsInRange;	  // Return distance squared between points  // between points  inline float distsq(float *pnt1, float *pnt2);	// The following functions allow all the information in the class  // to be serialized and unserialized.  This is convenient, for example,  // for writing the tree to a disk or to a MATLAB variable	static int get_serialize_length(int npoints,int ndim);						 						   static KDTree *unserialize(void *mem);		int set_verbosity(int v){verbosity=v;return 0;}	protected:  // Do we copy the points or not  // if we do, this is where they go   bool copyPoints;  int check_border_distance(int nodeIdx, int dim,			    float *pnt, float &dist, int &idx);	int range_search(int nodeIdx, Range *range, int dim);  int heapsort(int dim, int *idx, int len);  int build_kdtree(int **sortidx, int dim, int *pidx, int len);    int *workArr;	struct _Node *nodeMem;	int nodeMemCnt;	bool nodeMemAlloc;	struct _Node *node_alloc();	int *intArrMem;	int  intArrMemCnt;	int *int_alloc(int len);  	static int (*logmsg)(const char *,...);	int verbosity;  };#endif