/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
* Copyright 2011-2013  Jose Luis Blanco (joseluisblancoc@gmail.com).
*   All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
*    notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
*    notice, this list of conditions and the following disclaimer in the
*    documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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*************************************************************************/
 
#ifndef  NANOFLANN_HPP_
#define  NANOFLANN_HPP_
  
#include <vector>
#include <cassert>
#include <algorithm>
#include <stdexcept>
#include <cstdio>  // for fwrite()
#include <cmath>   // for fabs(),...
#include <limits>

// Avoid conflicting declaration of min/max macros in windows headers
#if !defined(NOMINMAX) && (defined(_WIN32) || defined(_WIN32_)  || defined(WIN32) || defined(_WIN64))
# define NOMINMAX
# ifdef max
#  undef   max
#  undef   min
# endif
#endif


template <typename T>
struct PointCloud2
{
	struct Point
	{
		T  x, y, z;
	};

	std::vector<Point>  pts;
	
	// Must return the number of data points
	inline size_t kdtree_get_point_count() const { return pts.size(); }

	// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
	inline T kdtree_distance(const T *p1, const size_t idx_p2, size_t size) const
	{
		const T d0 = p1[0] - pts[idx_p2].x;
		const T d1 = p1[1] - pts[idx_p2].y;
		const T d2 = p1[2] - pts[idx_p2].z;
		return d0*d0 + d1*d1 + d2*d2;
	}

	// Returns the dim'th component of the idx'th point in the class:
	// Since this is inlined and the "dim" argument is typically an immediate value, the
	//  "if/else's" are actually solved at compile time.
	inline T kdtree_get_pt(const size_t idx, int dim) const
	{
		if (dim == 0) return pts[idx].x;
		else if (dim == 1) return pts[idx].y;
		else return pts[idx].z;
	}

	// Optional bounding-box computation: return false to default to a standard bbox computation loop.
	//   Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
	//   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
	template <class BBOX>
	bool kdtree_get_bbox(BBOX &bb) const { return false; }

};

namespace nanoflann
{
	/** @addtogroup nanoflann_grp nanoflann C++ library for ANN
	*  @{ */

	/** Library version: 0xMmP (M=Major,m=minor,P=path) */
#define NANOFLANN_VERSION 0x116

	/** @addtogroup result_sets_grp Result set classes
	*  @{ */
	template <typename DistanceType, typename IndexType = size_t, typename CountType = size_t>
	class KNNResultSet
	{
		IndexType * indices;
		DistanceType* dists;
		CountType capacity;
		CountType count;

	public:
		inline KNNResultSet(CountType capacity_) : capacity(capacity_), count(0)
		{
		}

		inline void init(IndexType* indices_, DistanceType* dists_)
		{
			indices = indices_;
			dists = dists_;
			count = 0;
			dists[capacity - 1] = (std::numeric_limits<DistanceType>::max)();
		}

		inline CountType size() const
		{
			return count;
		}

		inline bool full() const
		{
			return count == capacity;
		}


		inline void addPoint(DistanceType dist, IndexType index)
		{
			CountType i;
			for (i = count; i>0; --i) {
#ifdef NANOFLANN_FIRST_MATCH   // If defined and two poins have the same distance, the one with the lowest-index will be returned first.
				if ((dists[i - 1]>dist) || ((dist == dists[i - 1]) && (indices[i - 1]>index))) {
#else
				if (dists[i - 1]>dist) {
#endif
					if (i<capacity) {
						dists[i] = dists[i - 1];
						indices[i] = indices[i - 1];
					}
				}
				else break;
				}
			if (i<capacity) {
				dists[i] = dist;
				indices[i] = index;
			}
			if (count<capacity) count++;
			}

		inline DistanceType worstDist() const
		{
			return dists[capacity - 1];
		}
		};


	/**
	* A result-set class used when performing a radius based search.
	*/
	template <typename DistanceType, typename IndexType = size_t>
	class RadiusResultSet
	{
	public:
		const DistanceType radius;

		std::vector<std::pair<IndexType, DistanceType> >& m_indices_dists;

		inline RadiusResultSet(DistanceType radius_, std::vector<std::pair<IndexType, DistanceType> >& indices_dists) : radius(radius_), m_indices_dists(indices_dists)
		{
			init();
		}

		inline ~RadiusResultSet() { }

		inline void init() { clear(); }
		inline void clear() { m_indices_dists.clear(); }

		inline size_t size() const { return m_indices_dists.size(); }

		inline bool full() const { return true; }

		inline void addPoint(DistanceType dist, IndexType index)
		{
			if (dist<radius)
				m_indices_dists.push_back(std::pair<IndexType, DistanceType>(index, dist));
		}

		inline DistanceType worstDist() const { return radius; }

		/** Clears the result set and adjusts the search radius. */
		inline void set_radius_and_clear(const DistanceType r)
		{
			radius = r;
			clear();
		}

		/**
		* Find the worst result (furtherest neighbor) without copying or sorting
		* Pre-conditions: size() > 0
		*/
		std::pair<IndexType, DistanceType> worst_item() const
		{
			if (m_indices_dists.empty()) throw std::runtime_error("Cannot invoke RadiusResultSet::worst_item() on an empty list of results.");
			typedef typename std::vector<std::pair<IndexType, DistanceType> >::const_iterator DistIt;
			DistIt it = std::max_element(m_indices_dists.begin(), m_indices_dists.end());
			return *it;
		}
	};

	/** operator "<" for std::sort() */
	struct IndexDist_Sorter
	{
		/** PairType will be typically: std::pair<IndexType,DistanceType> */
		template <typename PairType>
		inline bool operator()(const PairType &p1, const PairType &p2) const {
			return p1.second < p2.second;
		}
	};

	/** @} */


	/** @addtogroup loadsave_grp Load/save auxiliary functions
	* @{ */
	template<typename T>
	void save_value(FILE* stream, const T& value, size_t count = 1)
	{
		fwrite(&value, sizeof(value), count, stream);
	}

	template<typename T>
	void save_value(FILE* stream, const std::vector<T>& value)
	{
		size_t size = value.size();
		fwrite(&size, sizeof(size_t), 1, stream);
		fwrite(&value[0], sizeof(T), size, stream);
	}

	template<typename T>
	void load_value(FILE* stream, T& value, size_t count = 1)
	{
		size_t read_cnt = fread(&value, sizeof(value), count, stream);
		if (read_cnt != count) {
			throw std::runtime_error("Cannot read from file");
		}
	}


	template<typename T>
	void load_value(FILE* stream, std::vector<T>& value)
	{
		size_t size;
		size_t read_cnt = fread(&size, sizeof(size_t), 1, stream);
		if (read_cnt != 1) {
			throw std::runtime_error("Cannot read from file");
		}
		value.resize(size);
		read_cnt = fread(&value[0], sizeof(T), size, stream);
		if (read_cnt != size) {
			throw std::runtime_error("Cannot read from file");
		}
	}
	/** @} */


	/** @addtogroup metric_grp Metric (distance) classes
	* @{ */

	template<typename T> inline T abs(T x) { return (x<0) ? -x : x; }
	template<> inline int abs<int>(int x) { return ::abs(x); }
	template<> inline float abs<float>(float x) { return fabsf(x); }
	template<> inline double abs<double>(double x) { return fabs(x); }
	template<> inline long double abs<long double>(long double x) { return fabsl(x); }

	/** Manhattan distance functor (generic version, optimized for high-dimensionality data sets).
	*  Corresponding distance traits: nanoflann::metric_L1
	* \tparam T Type of the elements (e.g. double, float, uint8_t)
	* \tparam DistanceType Type of distance variables (must be signed) (e.g. float, double, int64_t)
	*/
	template<class T, class DataSource, typename _DistanceType = T>
	struct L1_Adaptor
	{
		typedef T ElementType;
		typedef _DistanceType DistanceType;

		const DataSource &data_source;

		L1_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }

		inline DistanceType operator()(const T* a, const size_t b_idx, size_t size, DistanceType worst_dist = -1) const
		{
			DistanceType result = DistanceType();
			const T* last = a + size;
			const T* lastgroup = last - 3;
			size_t d = 0;

			/* Process 4 items with each loop for efficiency. */
			while (a < lastgroup) {
				const DistanceType diff0 = nanoflann::abs(a[0] - data_source.kdtree_get_pt(b_idx, d++));
				const DistanceType diff1 = nanoflann::abs(a[1] - data_source.kdtree_get_pt(b_idx, d++));
				const DistanceType diff2 = nanoflann::abs(a[2] - data_source.kdtree_get_pt(b_idx, d++));
				const DistanceType diff3 = nanoflann::abs(a[3] - data_source.kdtree_get_pt(b_idx, d++));
				result += diff0 + diff1 + diff2 + diff3;
				a += 4;
				if ((worst_dist>0) && (result>worst_dist)) {
					return result;
				}
			}
			/* Process last 0-3 components.  Not needed for standard vector lengths. */
			while (a < last) {
				result += nanoflann::abs(*a++ - data_source.kdtree_get_pt(b_idx, d++));
			}
			return result;
		}

		template <typename U, typename V>
		inline DistanceType accum_dist(const U a, const V b, int) const
		{
			return nanoflann::abs(a - b);
		}
	};

	/** Squared Euclidean distance functor (generic version, optimized for high-dimensionality data sets).
	*  Corresponding distance traits: nanoflann::metric_L2
	* \tparam T Type of the elements (e.g. double, float, uint8_t)
	* \tparam DistanceType Type of distance variables (must be signed) (e.g. float, double, int64_t)
	*/
	template<class T, class DataSource, typename _DistanceType = T>
	struct L2_Adaptor
	{
		typedef T ElementType;
		typedef _DistanceType DistanceType;

		const DataSource &data_source;

		L2_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }

		inline DistanceType operator()(const T* a, const size_t b_idx, size_t size, DistanceType worst_dist = -1) const
		{
			DistanceType result = DistanceType();
			const T* last = a + size;
			const T* lastgroup = last - 3;
			size_t d = 0;

			/* Process 4 items with each loop for efficiency. */
			while (a < lastgroup) {
				const DistanceType diff0 = a[0] - data_source.kdtree_get_pt(b_idx, d++);
				const DistanceType diff1 = a[1] - data_source.kdtree_get_pt(b_idx, d++);
				const DistanceType diff2 = a[2] - data_source.kdtree_get_pt(b_idx, d++);
				const DistanceType diff3 = a[3] - data_source.kdtree_get_pt(b_idx, d++);
				result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
				a += 4;
				if ((worst_dist>0) && (result>worst_dist)) {
					return result;
				}
			}
			/* Process last 0-3 components.  Not needed for standard vector lengths. */
			while (a < last) {
				const DistanceType diff0 = *a++ - data_source.kdtree_get_pt(b_idx, d++);
				result += diff0 * diff0;
			}
			return result;
		}

		template <typename U, typename V>
		inline DistanceType accum_dist(const U a, const V b, int) const
		{
			return (a - b)*(a - b);
		}
	};

	/** Squared Euclidean distance functor (suitable for low-dimensionality datasets, like 2D or 3D point clouds)
	*  Corresponding distance traits: nanoflann::metric_L2_Simple
	* \tparam T Type of the elements (e.g. double, float, uint8_t)
	* \tparam DistanceType Type of distance variables (must be signed) (e.g. float, double, int64_t)
	*/
	template<class T, class DataSource, typename _DistanceType = T>
	struct L2_Simple_Adaptor
	{
		typedef T ElementType;
		typedef _DistanceType DistanceType;

		const DataSource &data_source;

		L2_Simple_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }

		inline DistanceType operator()(const T* a, const size_t b_idx, size_t size) const {
			return data_source.kdtree_distance(a, b_idx, size);
		}

		template <typename U, typename V>
		inline DistanceType accum_dist(const U a, const V b, int) const
		{
			return (a - b)*(a - b);
		}
	};

	/** Metaprogramming helper traits class for the L1 (Manhattan) metric */
	struct metric_L1 {
		template<class T, class DataSource>
		struct traits {
			typedef L1_Adaptor<T, DataSource> distance_t;
		};
	};
	/** Metaprogramming helper traits class for the L2 (Euclidean) metric */
	struct metric_L2 {
		template<class T, class DataSource>
		struct traits {
			typedef L2_Adaptor<T, DataSource> distance_t;
		};
	};
	/** Metaprogramming helper traits class for the L2_simple (Euclidean) metric */
	struct metric_L2_Simple {
		template<class T, class DataSource>
		struct traits {
			typedef L2_Simple_Adaptor<T, DataSource> distance_t;
		};
	};

	/** @} */



	/** @addtogroup param_grp Parameter structs
	* @{ */

	/**  Parameters (see http://code.google.com/p/nanoflann/ for help choosing the parameters)
	*/
	struct KDTreeSingleIndexAdaptorParams
	{
		KDTreeSingleIndexAdaptorParams(size_t _leaf_max_size = 10, int dim_ = -1) :
			leaf_max_size(_leaf_max_size), dim(dim_)
		{}

		size_t leaf_max_size;
		int dim;
	};

	/** Search options for KDTreeSingleIndexAdaptor::findNeighbors() */
	struct SearchParams
	{
		/** Note: The first argument (checks_IGNORED_) is ignored, but kept for compatibility with the FLANN interface */
		SearchParams(int checks_IGNORED_ = 32, float eps_ = 0, bool sorted_ = true) :
			checks(checks_IGNORED_), eps(eps_), sorted(sorted_) {}

		int   checks;  //!< Ignored parameter (Kept for compatibility with the FLANN interface).
		float eps;  //!< search for eps-approximate neighbours (default: 0)
		bool sorted; //!< only for radius search, require neighbours sorted by distance (default: true)
	};
	/** @} */


	/** @addtogroup memalloc_grp Memory allocation
	* @{ */

	/**
	* Allocates (using C's malloc) a generic type T.
	*
	* Params:
	*     count = number of instances to allocate.
	* Returns: pointer (of type T*) to memory buffer
	*/
	template <typename T>
	inline T* allocate(size_t count = 1)
	{
		T* mem = (T*) ::malloc(sizeof(T)*count);
		return mem;
	}


	/**
	* Pooled storage allocator
	*
	* The following routines allow for the efficient allocation of storage in
	* small chunks from a specified pool.  Rather than allowing each structure
	* to be freed individually, an entire pool of storage is freed at once.
	* This method has two advantages over just using malloc() and free().  First,
	* it is far more efficient for allocating small objects, as there is
	* no overhead for remembering all the information needed to free each
	* object or consolidating fragmented memory.  Second, the decision about
	* how long to keep an object is made at the time of allocation, and there
	* is no need to track down all the objects to free them.
	*
	*/

	const size_t     WORDSIZE = 16;
	const size_t     BLOCKSIZE = 8192;

	class PooledAllocator
	{
		/* We maintain memory alignment to word boundaries by requiring that all
		allocations be in multiples of the machine wordsize.  */
		/* Size of machine word in bytes.  Must be power of 2. */
		/* Minimum number of bytes requested at a time from	the system.  Must be multiple of WORDSIZE. */


		size_t  remaining;  /* Number of bytes left in current block of storage. */
		void*   base;     /* Pointer to base of current block of storage. */
		void*   loc;      /* Current location in block to next allocate memory. */
		size_t  blocksize;

		void internal_init()
		{
			remaining = 0;
			base = NULL;
			usedMemory = 0;
			wastedMemory = 0;
		}

	public:
		size_t  usedMemory;
		size_t  wastedMemory;

		/**
		Default constructor. Initializes a new pool.
		*/
		PooledAllocator(const size_t blocksize_ = BLOCKSIZE) : blocksize(blocksize_) {
			internal_init();
		}

		/**
		* Destructor. Frees all the memory allocated in this pool.
		*/
		~PooledAllocator() {
			free_all();
		}

		/** Frees all allocated memory chunks */
		void free_all()
		{
			while (base != NULL) {
				void *prev = *((void**)base); /* Get pointer to prev block. */
				::free(base);
				base = prev;
			}
			internal_init();
		}

		/**
		* Returns a pointer to a piece of new memory of the given size in bytes
		* allocated from the pool.
		*/
		void* malloc(const size_t req_size)
		{
			/* Round size up to a multiple of wordsize.  The following expression
			only works for WORDSIZE that is a power of 2, by masking last bits of
			incremented size to zero.
			*/
			const size_t size = (req_size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);

			/* Check whether a new block must be allocated.  Note that the first word
			of a block is reserved for a pointer to the previous block.
			*/
			if (size > remaining) {

				wastedMemory += remaining;

				/* Allocate new storage. */
				const size_t blocksize = (size + sizeof(void*) + (WORDSIZE - 1) > BLOCKSIZE) ?
					size + sizeof(void*) + (WORDSIZE - 1) : BLOCKSIZE;

				// use the standard C malloc to allocate memory
				void* m = ::malloc(blocksize);
				if (!m) {
					fprintf(stderr, "Failed to allocate memory.\n");
					return NULL;
				}

				/* Fill first word of new block with pointer to previous block. */
				((void**)m)[0] = base;
				base = m;

				size_t shift = 0;
				//int size_t = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1);

				remaining = blocksize - sizeof(void*) - shift;
				loc = ((char*)m + sizeof(void*) + shift);
			}
			void* rloc = loc;
			loc = (char*)loc + size;
			remaining -= size;

			usedMemory += size;

			return rloc;
		}

		/**
		* Allocates (using this pool) a generic type T.
		*
		* Params:
		*     count = number of instances to allocate.
		* Returns: pointer (of type T*) to memory buffer
		*/
		template <typename T>
		T* allocate(const size_t count = 1)
		{
			T* mem = (T*) this->malloc(sizeof(T)*count);
			return mem;
		}

	};
	/** @} */


	/** @addtogroup kdtrees_grp KD-tree classes and adaptors
	* @{ */

	/** kd-tree index
	*
	* Contains the k-d trees and other information for indexing a set of points
	* for nearest-neighbor matching.
	*
	*  The class "DatasetAdaptor" must provide the following interface (can be non-virtual, inlined methods):
	*
	*  \code
	*   // Must return the number of data points
	*   inline size_t kdtree_get_point_count() const { ... }
	*
	*   // Must return the Euclidean (L2) distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
	*   inline DistanceType kdtree_distance(const T *p1, const size_t idx_p2,size_t size) const { ... }
	*
	*   // Must return the dim'th component of the idx'th point in the class:
	*   inline T kdtree_get_pt(const size_t idx, int dim) const { ... }
	*
	*   // Optional bounding-box computation: return false to default to a standard bbox computation loop.
	*   //   Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
	*   //   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
	*   template <class BBOX>
	*   bool kdtree_get_bbox(BBOX &bb) const
	*   {
	*      bb[0].low = ...; bb[0].high = ...;  // 0th dimension limits
	*      bb[1].low = ...; bb[1].high = ...;  // 1st dimension limits
	*      ...
	*      return true;
	*   }
	*
	*  \endcode
	*
	* \tparam IndexType Will be typically size_t or int
	*/
	template <typename Distance, class DatasetAdaptor, int DIM = -1, typename IndexType = size_t>
	class KDTreeSingleIndexAdaptor
	{
	public:
		typedef typename Distance::ElementType  ElementType;
		typedef typename Distance::DistanceType DistanceType;
	protected:

		/**
		*  Array of indices to vectors in the dataset.
		*/
		std::vector<IndexType> vind;

		size_t m_leaf_max_size;


		/**
		* The dataset used by this index
		*/
		const DatasetAdaptor &dataset; //!< The source of our data

		const KDTreeSingleIndexAdaptorParams index_params;

		size_t m_size;
		int dim;  //!< Dimensionality of each data point


				  /*--------------------- Internal Data Structures --------------------------*/
		struct Node
		{
			union {
				struct
				{
					/**
					* Indices of points in leaf node
					*/
					IndexType left, right;
				} lr;
				struct
				{
					/**
					* Dimension used for subdivision.
					*/
					int divfeat;
					/**
					* The values used for subdivision.
					*/
					DistanceType divlow, divhigh;
				} sub;
			};
			/**
			* The child nodes.
			*/
			Node* child1, *child2;
		};
		typedef Node* NodePtr;


		struct Interval
		{
			ElementType low, high;
		};

		typedef std::vector<Interval> BoundingBox;


		/** This record represents a branch point when finding neighbors in
		the tree.  It contains a record of the minimum distance to the query
		point, as well as the node at which the search resumes.
		*/
		template <typename T, typename DistanceType>
		struct BranchStruct
		{
			T node;           /* Tree node at which search resumes */
			DistanceType mindist;     /* Minimum distance to query for all nodes below. */

			BranchStruct() {}
			BranchStruct(const T& aNode, DistanceType dist) : node(aNode), mindist(dist) {}

			inline bool operator<(const BranchStruct<T, DistanceType>& rhs) const
			{
				return mindist<rhs.mindist;
			}
		};

		/**
		* Array of k-d trees used to find neighbours.
		*/
		NodePtr root_node;
		typedef BranchStruct<NodePtr, DistanceType> BranchSt;
		typedef BranchSt* Branch;

		BoundingBox root_bbox;

		/**
		* Pooled memory allocator.
		*
		* Using a pooled memory allocator is more efficient
		* than allocating memory directly when there is a large
		* number small of memory allocations.
		*/
		PooledAllocator pool;

	public:

		Distance distance;

		/**
		* KDTree constructor
		*
		* Params:
		*          inputData = dataset with the input features
		*          params = parameters passed to the kdtree algorithm (see http://code.google.com/p/nanoflann/ for help choosing the parameters)
		*/
		KDTreeSingleIndexAdaptor(const int dimensionality, const DatasetAdaptor& inputData, const KDTreeSingleIndexAdaptorParams& params = KDTreeSingleIndexAdaptorParams()) :
			dataset(inputData), index_params(params), root_node(NULL), distance(inputData)
		{
			m_size = dataset.kdtree_get_point_count();
			dim = dimensionality;
			if (DIM>0) dim = DIM;
			else {
				if (params.dim>0) dim = params.dim;
			}
			m_leaf_max_size = params.leaf_max_size;

			// Create a permutable array of indices to the input vectors.
			init_vind();
		}

		/**
		* Standard destructor
		*/
		~KDTreeSingleIndexAdaptor()
		{
		}

		/** Frees the previously-built index. Automatically called within buildIndex(). */
		void freeIndex()
		{
			pool.free_all();
			root_node = NULL;
		}

		/**
		* Builds the index
		*/
		void buildIndex()
		{
			init_vind();
			computeBoundingBox(root_bbox);
			freeIndex();
			root_node = divideTree(0, m_size, root_bbox);   // construct the tree
		}

		/**
		*  Returns size of index.
		*/
		size_t size() const
		{
			return m_size;
		}

		/**
		* Returns the length of an index feature.
		*/
		size_t veclen() const
		{
			return static_cast<size_t>(DIM>0 ? DIM : dim);
		}

		/**
		* Computes the inde memory usage
		* Returns: memory used by the index
		*/
		size_t usedMemory() const
		{
			return pool.usedMemory + pool.wastedMemory + dataset.kdtree_get_point_count() * sizeof(IndexType);  // pool memory and vind array memory
		}

		/** \name Query methods
		* @{ */

		/**
		* Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored inside
		* the result object.
		*
		* Params:
		*     result = the result object in which the indices of the nearest-neighbors are stored
		*     vec = the vector for which to search the nearest neighbors
		*
		* \tparam RESULTSET Should be any ResultSet<DistanceType>
		* \sa knnSearch, radiusSearch
		*/
		template <typename RESULTSET>
		void findNeighbors(RESULTSET& result, const ElementType* vec, const SearchParams& searchParams) const
		{
			assert(vec);
			if (!root_node) throw std::runtime_error("[nanoflann] findNeighbors() called before building the index.");
			float epsError = 1 + searchParams.eps;

			std::vector<DistanceType> dists((DIM>0 ? DIM : dim), 0);
			DistanceType distsq = computeInitialDistances(vec, dists);
			searchLevel(result, vec, root_node, distsq, dists, epsError);  // "count_leaf" parameter removed since was neither used nor returned to the user.
		}

		/**
		* Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1]. Their indices are stored inside
		* the result object.
		*  \sa radiusSearch, findNeighbors
		* \note nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.
		*/
		inline void knnSearch(const ElementType *query_point, const size_t num_closest, IndexType *out_indices, DistanceType *out_distances_sq, const int nChecks_IGNORED = 10) const
		{
			nanoflann::KNNResultSet<DistanceType, IndexType> resultSet(num_closest);
			resultSet.init(out_indices, out_distances_sq);
			this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
		}

		/**
		* Find all the neighbors to \a query_point[0:dim-1] within a maximum radius.
		*  The output is given as a vector of pairs, of which the first element is a point index and the second the corresponding distance.
		*  Previous contents of \a IndicesDists are cleared.
		*
		*  If searchParams.sorted==true, the output list is sorted by ascending distances.
		*
		*  For a better performance, it is advisable to do a .reserve() on the vector if you have any wild guess about the number of expected matches.
		*
		*  \sa knnSearch, findNeighbors
		* \return The number of points within the given radius (i.e. indices.size() or dists.size() )
		*/
		size_t radiusSearch(const ElementType *query_point, const DistanceType radius, std::vector<std::pair<IndexType, DistanceType> >& IndicesDists, const SearchParams& searchParams) const
		{
			RadiusResultSet<DistanceType, IndexType> resultSet(radius, IndicesDists);
			this->findNeighbors(resultSet, query_point, searchParams);

			if (searchParams.sorted)
				std::sort(IndicesDists.begin(), IndicesDists.end(), IndexDist_Sorter());

			return resultSet.size();
		}

		/** @} */

	private:
		/** Make sure the auxiliary list \a vind has the same size than the current dataset, and re-generate if size has changed. */
		void init_vind()
		{
			// Create a permutable array of indices to the input vectors.
			m_size = dataset.kdtree_get_point_count();
			if (vind.size() != m_size) vind.resize(m_size);
			for (size_t i = 0; i < m_size; i++) vind[i] = i;
		}

		/// Helper accessor to the dataset points:
		inline ElementType dataset_get(size_t idx, int component) const {
			return dataset.kdtree_get_pt(idx, component);
		}


		void save_tree(FILE* stream, NodePtr tree)
		{
			save_value(stream, *tree);
			if (tree->child1 != NULL) {
				save_tree(stream, tree->child1);
			}
			if (tree->child2 != NULL) {
				save_tree(stream, tree->child2);
			}
		}


		void load_tree(FILE* stream, NodePtr& tree)
		{
			tree = pool.allocate<Node>();
			load_value(stream, *tree);
			if (tree->child1 != NULL) {
				load_tree(stream, tree->child1);
			}
			if (tree->child2 != NULL) {
				load_tree(stream, tree->child2);
			}
		}


		void computeBoundingBox(BoundingBox& bbox)
		{
			bbox.resize((DIM>0 ? DIM : dim));
			if (dataset.kdtree_get_bbox(bbox))
			{
				// Done! It was implemented in derived class
			}
			else
			{
				for (int i = 0; i<(DIM>0 ? DIM : dim); ++i) {
					bbox[i].low =
						bbox[i].high = dataset_get(0, i);
				}
				const size_t N = dataset.kdtree_get_point_count();
				for (size_t k = 1; k<N; ++k) {
					for (int i = 0; i<(DIM>0 ? DIM : dim); ++i) {
						if (dataset_get(k, i)<bbox[i].low) bbox[i].low = dataset_get(k, i);
						if (dataset_get(k, i)>bbox[i].high) bbox[i].high = dataset_get(k, i);
					}
				}
			}
		}


		/**
		* Create a tree node that subdivides the list of vecs from vind[first]
		* to vind[last].  The routine is called recursively on each sublist.
		* Place a pointer to this new tree node in the location pTree.
		*
		* Params: pTree = the new node to create
		*                  first = index of the first vector
		*                  last = index of the last vector
		*/
		NodePtr divideTree(const IndexType left, const IndexType right, BoundingBox& bbox)
		{
			NodePtr node = pool.allocate<Node>(); // allocate memory

												  /* If too few exemplars remain, then make this a leaf node. */
			if ((right - left) <= m_leaf_max_size) {
				node->child1 = node->child2 = NULL;    /* Mark as leaf node. */
				node->lr.left = left;
				node->lr.right = right;

				// compute bounding-box of leaf points
				for (int i = 0; i<(DIM>0 ? DIM : dim); ++i) {
					bbox[i].low = dataset_get(vind[left], i);
					bbox[i].high = dataset_get(vind[left], i);
				}
				for (IndexType k = left + 1; k<right; ++k) {
					for (int i = 0; i<(DIM>0 ? DIM : dim); ++i) {
						if (bbox[i].low>dataset_get(vind[k], i)) bbox[i].low = dataset_get(vind[k], i);
						if (bbox[i].high<dataset_get(vind[k], i)) bbox[i].high = dataset_get(vind[k], i);
					}
				}
			}
			else {
				IndexType idx;
				int cutfeat;
				DistanceType cutval;
				middleSplit_(&vind[0] + left, right - left, idx, cutfeat, cutval, bbox);

				node->sub.divfeat = cutfeat;

				BoundingBox left_bbox(bbox);
				left_bbox[cutfeat].high = cutval;
				node->child1 = divideTree(left, left + idx, left_bbox);

				BoundingBox right_bbox(bbox);
				right_bbox[cutfeat].low = cutval;
				node->child2 = divideTree(left + idx, right, right_bbox);

				node->sub.divlow = left_bbox[cutfeat].high;
				node->sub.divhigh = right_bbox[cutfeat].low;

				for (int i = 0; i<(DIM>0 ? DIM : dim); ++i)
				{
					bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
					bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
				}
			}

			return node;
		}

		void computeMinMax(IndexType* ind, IndexType count, int element, ElementType& min_elem, ElementType& max_elem)
		{
			min_elem = dataset_get(ind[0], element);
			max_elem = dataset_get(ind[0], element);
			for (IndexType i = 1; i<count; ++i) {
				ElementType val = dataset_get(ind[i], element);
				if (val<min_elem) min_elem = val;
				if (val>max_elem) max_elem = val;
			}
		}

		void middleSplit(IndexType* ind, IndexType count, IndexType& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
		{
			// find the largest span from the approximate bounding box
			ElementType max_span = bbox[0].high - bbox[0].low;
			cutfeat = 0;
			cutval = (bbox[0].high + bbox[0].low) / 2;
			for (int i = 1; i<(DIM>0 ? DIM : dim); ++i) {
				ElementType span = bbox[i].low - bbox[i].low;
				if (span>max_span) {
					max_span = span;
					cutfeat = i;
					cutval = (bbox[i].high + bbox[i].low) / 2;
				}
			}

			// compute exact span on the found dimension
			ElementType min_elem, max_elem;
			computeMinMax(ind, count, cutfeat, min_elem, max_elem);
			cutval = (min_elem + max_elem) / 2;
			max_span = max_elem - min_elem;

			// check if a dimension of a largest span exists
			size_t k = cutfeat;
			for (size_t i = 0; i<(DIM>0 ? DIM : dim); ++i) {
				if (i == k) continue;
				ElementType span = bbox[i].high - bbox[i].low;
				if (span>max_span) {
					computeMinMax(ind, count, i, min_elem, max_elem);
					span = max_elem - min_elem;
					if (span>max_span) {
						max_span = span;
						cutfeat = i;
						cutval = (min_elem + max_elem) / 2;
					}
				}
			}
			IndexType lim1, lim2;
			planeSplit(ind, count, cutfeat, cutval, lim1, lim2);

			if (lim1>count / 2) index = lim1;
			else if (lim2<count / 2) index = lim2;
			else index = count / 2;
		}


		void middleSplit_(IndexType* ind, IndexType count, IndexType& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
		{
			const DistanceType EPS = static_cast<DistanceType>(0.00001);
			ElementType max_span = bbox[0].high - bbox[0].low;
			for (int i = 1; i<(DIM>0 ? DIM : dim); ++i) {
				ElementType span = bbox[i].high - bbox[i].low;
				if (span>max_span) {
					max_span = span;
				}
			}
			ElementType max_spread = -1;
			cutfeat = 0;
			for (int i = 0; i<(DIM>0 ? DIM : dim); ++i) {
				ElementType span = bbox[i].high - bbox[i].low;
				if (span>(1 - EPS)*max_span) {
					ElementType min_elem, max_elem;
					computeMinMax(ind, count, cutfeat, min_elem, max_elem);
					ElementType spread = max_elem - min_elem;;
					if (spread>max_spread) {
						cutfeat = i;
						max_spread = spread;
					}
				}
			}
			// split in the middle
			DistanceType split_val = (bbox[cutfeat].low + bbox[cutfeat].high) / 2;
			ElementType min_elem, max_elem;
			computeMinMax(ind, count, cutfeat, min_elem, max_elem);

			if (split_val<min_elem) cutval = min_elem;
			else if (split_val>max_elem) cutval = max_elem;
			else cutval = split_val;

			IndexType lim1, lim2;
			planeSplit(ind, count, cutfeat, cutval, lim1, lim2);

			if (lim1>count / 2) index = lim1;
			else if (lim2<count / 2) index = lim2;
			else index = count / 2;
		}


		/**
		*  Subdivide the list of points by a plane perpendicular on axe corresponding
		*  to the 'cutfeat' dimension at 'cutval' position.
		*
		*  On return:
		*  dataset[ind[0..lim1-1]][cutfeat]<cutval
		*  dataset[ind[lim1..lim2-1]][cutfeat]==cutval
		*  dataset[ind[lim2..count]][cutfeat]>cutval
		*/
		void planeSplit(IndexType* ind, const IndexType count, int cutfeat, DistanceType cutval, IndexType& lim1, IndexType& lim2)
		{
			/* Move vector indices for left subtree to front of list. */
			IndexType left = 0;
			IndexType right = count - 1;
			for (;;) {
				while (left <= right && dataset_get(ind[left], cutfeat)<cutval) ++left;
				while (right && left <= right && dataset_get(ind[right], cutfeat) >= cutval) --right;
				if (left>right || !right) break;  // "!right" was added to support unsigned Index types
				std::swap(ind[left], ind[right]);
				++left;
				--right;
			}
			/* If either list is empty, it means that all remaining features
			* are identical. Split in the middle to maintain a balanced tree.
			*/
			lim1 = left;
			right = count - 1;
			for (;;) {
				while (left <= right && dataset_get(ind[left], cutfeat) <= cutval) ++left;
				while (right && left <= right && dataset_get(ind[right], cutfeat)>cutval) --right;
				if (left>right || !right) break;  // "!right" was added to support unsigned Index types
				std::swap(ind[left], ind[right]);
				++left;
				--right;
			}
			lim2 = left;
		}

		DistanceType computeInitialDistances(const ElementType* vec, std::vector<DistanceType>& dists) const
		{
			assert(vec);
			DistanceType distsq = 0.0;

			for (int i = 0; i < (DIM>0 ? DIM : dim); ++i) {
				if (vec[i] < root_bbox[i].low) {
					dists[i] = distance.accum_dist(vec[i], root_bbox[i].low, i);
					distsq += dists[i];
				}
				if (vec[i] > root_bbox[i].high) {
					dists[i] = distance.accum_dist(vec[i], root_bbox[i].high, i);
					distsq += dists[i];
				}
			}

			return distsq;
		}

		/**
		* Performs an exact search in the tree starting from a node.
		* \tparam RESULTSET Should be any ResultSet<DistanceType>
		*/
		template <class RESULTSET>
		void searchLevel(RESULTSET& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq,
			std::vector<DistanceType>& dists, const float epsError) const
		{
			/* If this is a leaf node, then do check and return. */
			if ((node->child1 == NULL) && (node->child2 == NULL)) {
				//count_leaf += (node->lr.right-node->lr.left);  // Removed since was neither used nor returned to the user.
				DistanceType worst_dist = result_set.worstDist();
				for (IndexType i = node->lr.left; i<node->lr.right; ++i) {
					const IndexType index = vind[i];// reorder... : i;
					DistanceType dist = distance(vec, index, (DIM>0 ? DIM : dim));
					if (dist<worst_dist) {
						result_set.addPoint(dist, vind[i]);
					}
				}
				return;
			}

			/* Which child branch should be taken first? */
			int idx = node->sub.divfeat;
			ElementType val = vec[idx];
			DistanceType diff1 = val - node->sub.divlow;
			DistanceType diff2 = val - node->sub.divhigh;

			NodePtr bestChild;
			NodePtr otherChild;
			DistanceType cut_dist;
			if ((diff1 + diff2)<0) {
				bestChild = node->child1;
				otherChild = node->child2;
				cut_dist = distance.accum_dist(val, node->sub.divhigh, idx);
			}
			else {
				bestChild = node->child2;
				otherChild = node->child1;
				cut_dist = distance.accum_dist(val, node->sub.divlow, idx);
			}

			/* Call recursively to search next level down. */
			searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError);

			DistanceType dst = dists[idx];
			mindistsq = mindistsq + cut_dist - dst;
			dists[idx] = cut_dist;
			if (mindistsq*epsError <= result_set.worstDist()) {
				searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError);
			}
			dists[idx] = dst;
		}

	public:
		/**  Stores the index in a binary file.
		*   IMPORTANT NOTE: The set of data points is NOT stored in the file, so when loading the index object it must be constructed associated to the same source of data points used while building it.
		* See the example: examples/saveload_example.cpp
		* \sa loadIndex  */
		void saveIndex(FILE* stream)
		{
			save_value(stream, m_size);
			save_value(stream, dim);
			save_value(stream, root_bbox);
			save_value(stream, m_leaf_max_size);
			save_value(stream, vind);
			save_tree(stream, root_node);
		}

		/**  Loads a previous index from a binary file.
		*   IMPORTANT NOTE: The set of data points is NOT stored in the file, so the index object must be constructed associated to the same source of data points used while building the index.
		* See the example: examples/saveload_example.cpp
		* \sa loadIndex  */
		void loadIndex(FILE* stream)
		{
			load_value(stream, m_size);
			load_value(stream, dim);
			load_value(stream, root_bbox);
			load_value(stream, m_leaf_max_size);
			load_value(stream, vind);
			load_tree(stream, root_node);
		}

	};   // class KDTree


		 /** A simple KD-tree adaptor for working with data directly stored in an Eigen Matrix, without duplicating the data storage.
		 *  Each row in the matrix represents a point in the state space.
		 *
		 *  Example of usage:
		 * \code
		 * 	Eigen::Matrix<num_t,Dynamic,Dynamic>  mat;
		 * 	// Fill out "mat"...
		 *
		 * 	typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic> >  my_kd_tree_t;
		 * 	const int max_leaf = 10;
		 * 	my_kd_tree_t   mat_index(dimdim, mat, max_leaf );
		 * 	mat_index.index->buildIndex();
		 * 	mat_index.index->...
		 * \endcode
		 *
		 *  \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality for the points in the data set, allowing more compiler optimizations.
		 *  \tparam Distance The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
		 *  \tparam IndexType The type for indices in the KD-tree index (typically, size_t of int)
		 */
	template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>
	struct KDTreeEigenMatrixAdaptor
	{
		typedef KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance, IndexType> self_t;
		typedef typename MatrixType::Scalar              num_t;
		typedef typename Distance::template traits<num_t, self_t>::distance_t metric_t;
		typedef KDTreeSingleIndexAdaptor< metric_t, self_t, DIM, IndexType>  index_t;

		index_t* index; //! The kd-tree index for the user to call its methods as usual with any other FLANN index.

						/// Constructor: takes a const ref to the matrix object with the data points
		KDTreeEigenMatrixAdaptor(const int dimensionality, const MatrixType &mat, const int leaf_max_size = 10) : m_data_matrix(mat)
		{
			const size_t dims = mat.cols();
			if (DIM>0 && static_cast<int>(dims) != DIM)
				throw std::runtime_error("Data set dimensionality does not match the 'DIM' template argument");
			index = new index_t(dims, *this /* adaptor */, nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size, dims));
			index->buildIndex();
		}

		~KDTreeEigenMatrixAdaptor() {
			delete index;
		}

		const MatrixType &m_data_matrix;

		/** Query for the \a num_closest closest points to a given point (entered as query_point[0:dim-1]).
		*  Note that this is a short-cut method for index->findNeighbors().
		*  The user can also call index->... methods as desired.
		* \note nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.
		*/
		inline void query(const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq, const int nChecks_IGNORED = 10) const
		{
			nanoflann::KNNResultSet<typename MatrixType::Scalar, IndexType> resultSet(num_closest);
			resultSet.init(out_indices, out_distances_sq);
			index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
		}

		/** @name Interface expected by KDTreeSingleIndexAdaptor
		* @{ */

		const self_t & derived() const {
			return *this;
		}
		self_t & derived() {
			return *this;
		}

		// Must return the number of data points
		inline size_t kdtree_get_point_count() const {
			return m_data_matrix.rows();
		}

		// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
		inline num_t kdtree_distance(const num_t *p1, const size_t idx_p2, size_t size) const
		{
			num_t s = 0;
			for (size_t i = 0; i<size; i++) {
				const num_t d = p1[i] - m_data_matrix.coeff(idx_p2, i);
				s += d*d;
			}
			return s;
		}

		// Returns the dim'th component of the idx'th point in the class:
		inline num_t kdtree_get_pt(const size_t idx, int dim) const {
			return m_data_matrix.coeff(idx, dim);
		}

		// Optional bounding-box computation: return false to default to a standard bbox computation loop.
		//   Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
		//   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
		template <class BBOX>
		bool kdtree_get_bbox(BBOX &bb) const {
			return false;
		}

		/** @} */

	}; // end of KDTreeEigenMatrixAdaptor
	   /** @} */

	   /** @} */ // end of grouping
	} // end of NS


#endif /* NANOFLANN_HPP_ */

/*----------------------------------------------------------------------
//---------------------nanoflann class usage-------------------------------

typedef KDTreeSingleIndexAdaptor<L2_Simple_Adaptor<double, PointCloud<double> > ,PointCloud<double>,3> my_kd_tree_t;

typedef KDTreeSingleIndexAdaptor<L2_Simple_Adaptor<double, PointCloud<double> > ,PointCloud<double>,3> my_kd_tree_t;
my_kd_tree_t index(3,m_cloud2,KDTreeSingleIndexAdaptorParams(10));

index.buildIndex();

for (int i=0; i<source1->m_recordsNumber;++i)
{
	double query_point[3] = {0.0,0.0,0.0,};
	query_point[0] = source1->m_xyzPoint[i].x;
	query_point[1] = source1->m_xyzPoint[i].y;
	query_point[2] = source1->m_xyzPoint[i].z;

	const size_t results_num = 2;//numer of closest points;
	vector<size_t> results_index(results_num);//for storage of searched points index;
	vector<double> out_dist_sqr(results_num);//for storage of distances from query points to target points;

	index.knnSearch(&query_point[0],results_num,&results_index[0],&out_dist_sqr[0]);//run the Knnsearch;

	double average_distance = 0;
	for (size_t j=0; j<results_num; ++j)
	{
		average_distance += out_dist_sqr[j];
	}
	average_distance = average_distance/results_num;

	source1->m_returnIntensity[i] = average_distance;
}




//*----------------------------------------------------------------------*/

