nanoflann.hpp

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/***********************************************************************
 * 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 2011-2016  Jose Luis Blanco (joseluisblancoc@gmail.com).
 *   All rights reserved.
 *
 * THE BSD LICENSE
 *
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 * modification, are permitted provided that the following conditions
 * are met:
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 * 1. Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer.
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#ifndef NANOFLANN_HPP_
#define NANOFLANN_HPP_

#include <algorithm>
#include <cassert>
#include <cmath>    // for abs()
#include <cstdio>   // for fwrite()
#include <cstdlib>  // for abs()
#include <limits>
#include <stdexcept>
#include <vector>

// 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

namespace nanoflann
{
#define NANOFLANN_VERSION 0x123

  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_)
      : indices(0)
      , dists(0)
      , capacity(capacity_)
      , count(0)
    {
    }

    inline void init(IndexType* indices_, DistanceType* dists_)
    {
      indices = indices_;
      dists = dists_;
      count = 0;
      if (capacity)
        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 points 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];
    }
  };  // namespace nanoflann

  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::make_pair(index, dist));
    }

    inline DistanceType worstDist() const { return radius; }

    inline void set_radius_and_clear(const DistanceType r)
    {
      radius = r;
      clear();
    }

    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;
    }
  };

  struct IndexDist_Sorter
  {
    template <typename PairType>
    inline bool operator()(const PairType& p1, const PairType& p2) const
    {
      return p1.second < p2.second;
    }
  };

  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");
    }
  }
  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 = std::abs(a[0] - data_source.kdtree_get_pt(b_idx, d++));
        const DistanceType diff1 = std::abs(a[1] - data_source.kdtree_get_pt(b_idx, d++));
        const DistanceType diff2 = std::abs(a[2] - data_source.kdtree_get_pt(b_idx, d++));
        const DistanceType diff3 = std::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 += std::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 std::abs(a - b);
    }
  };

  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);
    }
  };

  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);
    }
  };

  struct metric_L1
  {
    template <class T, class DataSource>
    struct traits
    {
      typedef L1_Adaptor<T, DataSource> distance_t;
    };
  };
  struct metric_L2
  {
    template <class T, class DataSource>
    struct traits
    {
      typedef L2_Adaptor<T, DataSource> distance_t;
    };
  };
  struct metric_L2_Simple
  {
    template <class T, class DataSource>
    struct traits
    {
      typedef L2_Simple_Adaptor<T, DataSource> distance_t;
    };
  };

  struct KDTreeSingleIndexAdaptorParams
  {
    KDTreeSingleIndexAdaptorParams(size_t _leaf_max_size = 10)
      : leaf_max_size(_leaf_max_size)
    {
    }

    size_t leaf_max_size;
  };

  struct SearchParams
  {
    SearchParams(int checks_IGNORED_ = 32, float eps_ = 0, bool sorted_ = true)
      : checks(checks_IGNORED_)
      , eps(eps_)
      , sorted(sorted_)
    {
    }

    int checks;   
    float eps;    
    bool sorted;  
  };
  template <typename T>
  inline T* allocate(size_t count = 1)
  {
    T* mem = static_cast<T*>(::malloc(sizeof(T) * count));
    return mem;
  }

  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. */

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

   public:
    size_t usedMemory;
    size_t wastedMemory;

// cppcheck-suppress uninitMemberVar
    PooledAllocator()
    {
      internal_init();
    }

    ~PooledAllocator()
    {
      free_all();
    }

    void free_all()
    {
      while (base != NULL)
      {
        void* prev = *(static_cast<void**>(base)); /* Get pointer to prev block. */
        ::free(base);
        base = prev;
      }
      internal_init();
    }

    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. */
        static_cast<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 = (static_cast<char*>(m) + sizeof(void*) + shift);
      }
      void* rloc = loc;
      loc = static_cast<char*>(loc) + size;
      remaining -= size;

      usedMemory += size;

      return rloc;
    }

    template <typename T>
    T* allocate(const size_t count = 1)
    {
      T* mem = static_cast<T*>(this->malloc(sizeof(T) * count));
      return mem;
    }
  };
  // ----------------  CArray -------------------------
  template <typename T, std::size_t N>
  class CArray
  {
   public:
    T elems[N];  // fixed-size array of elements of type T

   public:
    // type definitions
    typedef T value_type;
    typedef T* iterator;
    typedef const T* const_iterator;
    typedef T& reference;
    typedef const T& const_reference;
    typedef std::size_t size_type;
    typedef std::ptrdiff_t difference_type;

    // iterator support
    inline iterator begin() { return elems; }
    inline const_iterator begin() const { return elems; }
    inline iterator end() { return elems + N; }
    inline const_iterator end() const { return elems + N; }

    // reverse iterator support
#if !defined(BOOST_NO_TEMPLATE_PARTIAL_SPECIALIZATION) && !defined(BOOST_MSVC_STD_ITERATOR) && !defined(BOOST_NO_STD_ITERATOR_TRAITS)
    typedef std::reverse_iterator<iterator> reverse_iterator;
    typedef std::reverse_iterator<const_iterator> const_reverse_iterator;
#elif defined(_MSC_VER) && (_MSC_VER == 1300) && defined(BOOST_DINKUMWARE_STDLIB) && (BOOST_DINKUMWARE_STDLIB == 310)
    // workaround for broken reverse_iterator in VC7
    typedef std::reverse_iterator<std::_Ptrit<value_type, difference_type, iterator,
                                              reference, iterator, reference> >
        reverse_iterator;
    typedef std::reverse_iterator<std::_Ptrit<value_type, difference_type, const_iterator,
                                              const_reference, iterator, reference> >
        const_reverse_iterator;
#else
  // workaround for broken reverse_iterator implementations
  typedef std::reverse_iterator<iterator, T> reverse_iterator;
  typedef std::reverse_iterator<const_iterator, T> const_reverse_iterator;
#endif

    reverse_iterator rbegin()
    {
      return reverse_iterator(end());
    }
    const_reverse_iterator rbegin() const { return const_reverse_iterator(end()); }
    reverse_iterator rend() { return reverse_iterator(begin()); }
    const_reverse_iterator rend() const { return const_reverse_iterator(begin()); }
    // operator[]
    inline reference operator[](size_type i) { return elems[i]; }
    inline const_reference operator[](size_type i) const { return elems[i]; }
    // at() with range check
    reference at(size_type i)
    {
      rangecheck(i);
      return elems[i];
    }
    const_reference at(size_type i) const
    {
      rangecheck(i);
      return elems[i];
    }
    // front() and back()
    reference front() { return elems[0]; }
    const_reference front() const { return elems[0]; }
    reference back() { return elems[N - 1]; }
    const_reference back() const { return elems[N - 1]; }
    // size is constant
    static inline size_type size() { return N; }
    static bool empty() { return false; }
    static size_type max_size() { return N; }
    enum
    {
      static_size = N
    };
    inline void resize(const size_t nElements)
    {
      if (nElements != N) throw std::logic_error("Try to change the size of a CArray.");
    }
    // swap (note: linear complexity in N, constant for given instantiation)
    void swap(CArray<T, N>& y) { std::swap_ranges(begin(), end(), y.begin()); }
    // direct access to data (read-only)
    const T* data() const { return elems; }
    // use array as C array (direct read/write access to data)
    T* data() { return elems; }
    // assignment with type conversion
    template <typename T2>
// cppcheck-suppress *
    CArray<T, N>& operator=(const CArray<T2, N>& rhs)
    {
      std::copy(rhs.begin(), rhs.end(), begin());
      return *this;
    }
    // assign one value to all elements
    inline void assign(const T& value)
    {
      for (size_t i = 0; i < N; i++) elems[i] = value;
    }
    // assign (compatible with std::vector's one) (by JLBC for MRPT)
    void assign(const size_t n, const T& value)
    {
      assert(N == n);
      for (size_t i = 0; i < N; i++) elems[i] = value;
    }

   private:
    // check range (may be private because it is static)
    static void rangecheck(size_type i)
    {
      if (i >= size())
      {
        throw std::out_of_range("CArray<>: index out of range");
      }
    }
  };  // end of CArray

  template <int DIM, typename T>
  struct array_or_vector_selector
  {
    typedef CArray<T, DIM> container_t;
  };
  template <typename T>
  struct array_or_vector_selector<-1, T>
  {
    typedef std::vector<T> container_t;
  };
  template <typename Distance, class DatasetAdaptor, int DIM = -1, typename IndexType = size_t>
  class KDTreeSingleIndexAdaptor
  {
   private:
    KDTreeSingleIndexAdaptor(const KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>&);

   public:
    typedef typename Distance::ElementType ElementType;
    typedef typename Distance::DistanceType DistanceType;

   protected:
    std::vector<IndexType> vind;

    size_t m_leaf_max_size;

    const DatasetAdaptor& dataset;  

    const KDTreeSingleIndexAdaptorParams index_params;

    size_t m_size;                 
    size_t m_size_at_index_build;  
    int dim;                       

    /*--------------------- Internal Data Structures --------------------------*/
    struct Node
    {
      union {
        struct leaf
        {
          IndexType left, right;  
        } lr;
        struct nonleaf
        {
          int divfeat;                   
          DistanceType divlow, divhigh;  
        } sub;
      } node_type;
      Node *child1, *child2;  
    };
    typedef Node* NodePtr;

    struct Interval
    {
      ElementType low, high;
    };

    typedef typename array_or_vector_selector<DIM, Interval>::container_t BoundingBox;

    typedef typename array_or_vector_selector<DIM, DistanceType>::container_t distance_vector_t;

    NodePtr root_node;
    BoundingBox root_bbox;

    PooledAllocator pool;

   public:
    Distance distance;

    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();
      m_size_at_index_build = m_size;
      dim = dimensionality;
      if (DIM > 0) dim = DIM;
      m_leaf_max_size = params.leaf_max_size;

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

    ~KDTreeSingleIndexAdaptor() {}

    void freeIndex()
    {
      pool.free_all();
      root_node = NULL;
      m_size_at_index_build = 0;
    }

    void buildIndex()
    {
      init_vind();
      freeIndex();
      m_size_at_index_build = m_size;
      if (m_size == 0) return;
      computeBoundingBox(root_bbox);
      root_node = divideTree(0, m_size, root_bbox);  // construct the tree
    }

    size_t size() const { return m_size; }

    size_t veclen() const
    {
      return static_cast<size_t>(DIM > 0 ? DIM : dim);
    }

    size_t usedMemory() const
    {
      return pool.usedMemory + pool.wastedMemory + dataset.kdtree_get_point_count() * sizeof(IndexType);  // pool memory and vind array memory
    }

    template <typename RESULTSET>
    bool findNeighbors(RESULTSET& result, const ElementType* vec, const SearchParams& searchParams) const
    {
      assert(vec);
      if (size() == 0)
        return false;
      if (!root_node)
        throw std::runtime_error("[nanoflann] findNeighbors() called before building the index.");
      float epsError = 1 + searchParams.eps;

      distance_vector_t dists;                 // fixed or variable-sized container (depending on DIM)
      dists.assign((DIM > 0 ? DIM : dim), 0);  // Fill it with zeros.
      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.
      return result.full();
    }

    size_t 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());
      return resultSet.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);
      const size_t nFound = radiusSearchCustomCallback(query_point, resultSet, searchParams);
      if (searchParams.sorted)
        std::sort(IndicesDists.begin(), IndicesDists.end(), IndexDist_Sorter());
      return nFound;
    }

    template <class SEARCH_CALLBACK>
    size_t radiusSearchCustomCallback(const ElementType* query_point, SEARCH_CALLBACK& resultSet, const SearchParams& searchParams = SearchParams()) const
    {
      this->findNeighbors(resultSet, query_point, searchParams);
      return resultSet.size();
    }

   private:
    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;
    }

    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
      {
        const size_t N = dataset.kdtree_get_point_count();
        if (!N) throw std::runtime_error("[nanoflann] computeBoundingBox() called but no data points found.");
        for (int i = 0; i < (DIM > 0 ? DIM : dim); ++i)
        {
          bbox[i].low =
              bbox[i].high = dataset_get(0, i);
        }
        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);
          }
        }
      }
    }

    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) <= static_cast<IndexType>(m_leaf_max_size))
      {
        node->child1 = node->child2 = NULL; /* Mark as leaf node. */
        node->node_type.lr.left = left;
        node->node_type.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->node_type.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->node_type.sub.divlow = left_bbox[cutfeat].high;
        node->node_type.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)
    {
      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, i, 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;
    }

    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, distance_vector_t& dists) const
    {
      assert(vec);
      DistanceType distsq = DistanceType();

      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;
    }

    template <class RESULTSET>
    void searchLevel(RESULTSET& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq,
                     distance_vector_t& 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->node_type.lr.left; i < node->node_type.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->node_type.sub.divfeat;
      ElementType val = vec[idx];
      DistanceType diff1 = val - node->node_type.sub.divlow;
      DistanceType diff2 = val - node->node_type.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->node_type.sub.divhigh, idx);
      }
      else
      {
        bestChild = node->child2;
        otherChild = node->child1;
        cut_dist = distance.accum_dist(val, node->node_type.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:
    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);
    }

    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

  template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2>
  struct KDTreeEigenMatrixAdaptor
  {
    typedef KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance> self_t;
    typedef typename MatrixType::Scalar num_t;
    typedef typename MatrixType::Index IndexType;
    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;  

    KDTreeEigenMatrixAdaptor(const int dimensionality, const MatrixType& mat, const int leaf_max_size = 10)
      : m_data_matrix(mat)
    {
      const IndexType dims = mat.cols();
      if (dims != dimensionality) throw std::runtime_error("Error: 'dimensionality' must match column count in data matrix");
      if (DIM > 0 && static_cast<int>(dims) != DIM)
        throw std::runtime_error("Data set dimensionality does not match the 'DIM' template argument");
      // cppcheck-suppress noOperatorEq
      index = new index_t(dims, *this /* adaptor */, nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size));
      index->buildIndex();
    }

   private:
    KDTreeEigenMatrixAdaptor(const self_t&);

   public:
    ~KDTreeEigenMatrixAdaptor()
    {
      delete index;
    }

    const MatrixType& m_data_matrix;

    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<num_t, IndexType> resultSet(num_closest);
      resultSet.init(out_indices, out_distances_sq);
      index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
    }

    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 L2 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 IndexType idx_p2, IndexType size) const
    {
      num_t s = 0;
      for (IndexType 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 IndexType idx, int dim) const
    {
      return m_data_matrix.coeff(idx, IndexType(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
}  // namespace nanoflann

#endif /* NANOFLANN_HPP_ */