
/***********************************************************************
 * 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 2011-2021  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
 * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
 * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
 * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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 * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *************************************************************************/

/** \mainpage nanoflann C++ APi documentation
 *  nanoflann is a C++ header-only library for building KD-Trees, mostly
 *  optimized for 2D or 3D point clouds.
 *
 *  nanoflann does not require compiling or installing, just an
 *  #include <nanoflann.hpp> in your code.
 *
 *  See:
 *   - <a href="modules.html" >C++ APi organized by modules</a>
 *   - <a href="https://github.com/jlblancoc/nanoflann" >Online README</a>
 *   - <a href="http://jlblancoc.github.io/nanoflann/" >Doxygen
 * documentation</a>
 */

#ifndef NANOFLANN_HPP_
#define NANOFLANN_HPP_

#include <algorithm>
#include <array>
#include <cassert>
#include <cmath>   // for abs()
#include <cstdlib> // for abs()
#include <functional>
#include <istream>
#include <limits> // std::reference_wrapper
#include <ostream>
#include <stdexcept>
#include <vector>
#include <Rcpp.h>

/** Library version: 0xMmP (M=Major,m=minor,P=patch) */
#define NANOFLANN_VERSION 0x132

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

/** the Pi constant (required to avoid MSVC missing symbols) */
template <typename T> T pi_const() {
  return static_cast<T>(3.14159265358979323846);
}

/**
 * Traits if object is resizable and assignable (typically has a resize | assign
 * method)
 */
template <typename T, typename = int> struct has_resize : std::false_type {};

template <typename T>
struct has_resize<T, decltype((void)std::declval<T>().resize(1), 0)>
    : std::true_type {};

template <typename T, typename = int> struct has_assign : std::false_type {};

template <typename T>
struct has_assign<T, decltype((void)std::declval<T>().assign(1, 0), 0)>
    : std::true_type {};

/**
 * Free function to resize a resizable object
 */
template <typename Container>
inline typename std::enable_if<has_resize<Container>::value, void>::type
resize(Container &c, const size_t nElements) {
  c.resize(nElements);
}

/**
 * Free function that has no effects on non resizable containers (e.g.
 * std::array) It raises an exception if the expected size does not match
 */
template <typename Container>
inline typename std::enable_if<!has_resize<Container>::value, void>::type
resize(Container &c, const size_t nElements) {
  if (nElements != c.size())
    throw std::logic_error("Try to change the size of a std::array.");
}

/**
 * Free function to assign to a container
 */
template <typename Container, typename T>
inline typename std::enable_if<has_assign<Container>::value, void>::type
assign(Container &c, const size_t nElements, const T &value) {
  c.assign(nElements, value);
}

/**
 * Free function to assign to a std::array
 */
template <typename Container, typename T>
inline typename std::enable_if<!has_assign<Container>::value, void>::type
assign(Container &c, const size_t nElements, const T &value) {
  for (size_t i = 0; i < nElements; i++)
    c[i] = value;
}

/** @addtogroup result_sets_grp Result set classes
 *  @{ */
template <typename _DistanceType, typename _IndexType = size_t,
          typename _CountType = size_t>
class KNNResultSet {
public:
  typedef _DistanceType DistanceType;
  typedef _IndexType IndexType;
  typedef _CountType CountType;

private:
  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; }

  /**
   * Called during search to add an element matching the criteria.
   * @return true if the search should be continued, false if the results are
   * sufficient
   */
  inline bool 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++;

    // tell caller that the search shall continue
    return true;
  }

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

/** 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;
  }
};

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

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 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; }

  /**
   * Called during search to add an element matching the criteria.
   * @return true if the search should be continued, false if the results are
   * sufficient
   */
  inline bool addPoint(DistanceType dist, IndexType index) {
    if (dist < radius)
      m_indices_dists.push_back(std::make_pair(index, dist));
    return true;
  }

  inline DistanceType worstDist() const { return radius; }

  /**
   * 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(),
                                 IndexDist_Sorter());
    return *it;
  }
};

/** @} */

/** @addtogroup loadsave_grp Load/save auxiliary functions
 * @{ */
template <typename T>
void save_value(std::ostream &stream, const T &value) {
  stream.write(reinterpret_cast<const char*>(&value), sizeof(T));
}

template <typename T>
void save_value(std::ostream& stream, const std::vector<T> &value) {
  size_t size = value.size();
  stream.write(reinterpret_cast<const char*>(&size), sizeof(size_t));
  stream.write(reinterpret_cast<const char*>(value.data()), sizeof(T) * size);
}

template <typename T>
void load_value(std::istream& stream, T &value) {
  stream.read(reinterpret_cast<char*>(&value), sizeof(T));
}

template <typename T>
void load_value(std::istream &stream, std::vector<T> &value) {
  size_t size;
  stream.read(reinterpret_cast<char*>(&size), sizeof(size_t));
  value.resize(size);
  stream.read(reinterpret_cast<char*>(value.data()), sizeof(T) * size);
}
/** @} */

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

struct Metric {};

/** 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 DataSource Source of the data, i.e. where the vectors are stored
 * \tparam _DistanceType Type of distance variables (must be signed)
 * \tparam AccessorType Type of the arguments with which the data can be accessed
 * (e.g. float, double, int64_t, T*)
 */
template <class T, class DataSource, typename _DistanceType = T,
          typename AccessorType = uint32_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 evalMetric(const T *a, const AccessorType 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, const size_t) const {
    return std::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 DataSource Source of the data, i.e. where the vectors are stored
 * \tparam _DistanceType Type of distance variables (must be signed)
 * \tparam AccessorType Type of the arguments with which the data can be accessed
 * (e.g. float, double, int64_t, T*)
 */
template <class T, class DataSource, typename _DistanceType = T,
          typename AccessorType = uint32_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 evalMetric(const T *a, const AccessorType 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, const size_t) const {
    return (a - b) * (a - b);
  }
};

/** Squared Euclidean (L2) 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 DataSource Source of the data, i.e. where the vectors are stored
 * \tparam _DistanceType Type of distance variables (must be signed)
 * \tparam AccessorType Type of the arguments with which the data can be accessed
 * (e.g. float, double, int64_t, T*)
 */
template <class T, class DataSource, typename _DistanceType = T,
          typename AccessorType = uint32_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 evalMetric(const T *a, const AccessorType b_idx,
                                 size_t size) const {
    DistanceType result = DistanceType();
    for (size_t i = 0; i < size; ++i) {
      const DistanceType diff = a[i] - data_source.kdtree_get_pt(b_idx, i);
      result += diff * diff;
    }
    return result;
  }

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

/** SO2 distance functor
 *  Corresponding distance traits: nanoflann::metric_SO2
 *
 * \tparam T Type of the elements (e.g. double, float, uint8_t)
 * \tparam DataSource Source of the data, i.e. where the vectors are stored
 * \tparam _DistanceType Type of distance variables (must be signed) (e.g.
 * float, double) orientation is constrained to be in [-pi, pi]
 * \tparam AccessorType Type of the arguments with which the data can be accessed
 * (e.g. float, double, int64_t, T*)
 */
template <class T, class DataSource, typename _DistanceType = T,
          typename AccessorType = uint32_t>
struct SO2_Adaptor {
  typedef T ElementType;
  typedef _DistanceType DistanceType;

  const DataSource &data_source;

  SO2_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}

  inline DistanceType evalMetric(const T *a, const AccessorType b_idx,
                                 size_t size) const {
    return accum_dist(a[size - 1], data_source.kdtree_get_pt(b_idx, size - 1),
                      size - 1);
  }

  /** Note: this assumes that input angles are already in the range [-pi,pi] */
  template <typename U, typename V>
  inline DistanceType accum_dist(const U a, const V b, const size_t) const {
    DistanceType result = DistanceType();
    DistanceType Pi = pi_const<DistanceType>();
    result = b - a;
    if (result > Pi)
      result -= 2 * Pi;
    else if (result < -Pi)
      result += 2 * Pi;
    return result;
  }
};

/** SO3 distance functor (Uses L2_Simple)
 *  Corresponding distance traits: nanoflann::metric_SO3
 *
 * \tparam T Type of the elements (e.g. double, float, uint8_t)
 * \tparam DataSource Source of the data, i.e. where the vectors are stored
 * \tparam _DistanceType Type of distance variables (must be signed) (e.g.
 * float, double)
 * \tparam AccessorType Type of the arguments with which the data can be accessed
 * (e.g. float, double, int64_t, T*)
 */
template <class T, class DataSource, typename _DistanceType = T,
          typename AccessorType = uint32_t>
struct SO3_Adaptor {
  typedef T ElementType;
  typedef _DistanceType DistanceType;

  L2_Simple_Adaptor<T, DataSource, DistanceType, AccessorType> distance_L2_Simple;

  SO3_Adaptor(const DataSource &_data_source)
      : distance_L2_Simple(_data_source) {}

  inline DistanceType evalMetric(const T *a, const AccessorType b_idx,
                                 size_t size) const {
    return distance_L2_Simple.evalMetric(a, b_idx, size);
  }

  template <typename U, typename V>
  inline DistanceType accum_dist(const U a, const V b, const size_t idx) const {
    return distance_L2_Simple.accum_dist(a, b, idx);
  }
};

/** Metaprogramming helper traits class for the L1 (Manhattan) metric */
struct metric_L1 : public Metric {
  template <class T, class DataSource, typename AccessorType = uint32_t> struct traits {
    typedef L1_Adaptor<T, DataSource, T, AccessorType> distance_t;
  };
};
/** Metaprogramming helper traits class for the L2 (Euclidean) metric */
struct metric_L2 : public Metric {
  template <class T, class DataSource, typename AccessorType = uint32_t> struct traits {
    typedef L2_Adaptor<T, DataSource, T, AccessorType> distance_t;
  };
};
/** Metaprogramming helper traits class for the L2_simple (Euclidean) metric */
struct metric_L2_Simple : public Metric {
  template <class T, class DataSource, typename AccessorType = uint32_t> struct traits {
    typedef L2_Simple_Adaptor<T, DataSource, T, AccessorType> distance_t;
  };
};
/** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */
struct metric_SO2 : public Metric {
  template <class T, class DataSource, typename AccessorType = uint32_t> struct traits {
    typedef SO2_Adaptor<T, DataSource, T, AccessorType> distance_t;
  };
};
/** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */
struct metric_SO3 : public Metric {
  template <class T, class DataSource, typename AccessorType = uint32_t> struct traits {
    typedef SO3_Adaptor<T, DataSource, T, AccessorType> distance_t;
  };
};

/** @} */

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

/**  Parameters (see README.md) */
struct KDTreeSingleIndexAdaptorParams {
  KDTreeSingleIndexAdaptorParams(size_t _leaf_max_size = 10)
      : leaf_max_size(_leaf_max_size) {}

  size_t leaf_max_size;
};

/** 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 = static_cast<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. */

  using Offset    = uint32_t;
  using Size      = uint32_t;
  using Dimension = int32_t;

  Size 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 = nullptr;
    usedMemory = 0;
    wastedMemory = 0;
  }

public:
  Size usedMemory;
  Size wastedMemory;

  /**
      Default constructor. Initializes a new pool.
   */
  PooledAllocator() { internal_init(); }

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

  /** Frees all allocated memory chunks */
  void free_all() {
    while (base != nullptr) {
      void *prev =
          *(static_cast<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 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 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) {
        Rcpp::Rcerr << "Failed to allocate memory.\n";
        // fprintf(stderr, "Failed to allocate memory.\n");
        throw std::bad_alloc();
      }

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

      Size 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;
  }

  /**
   * 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 = static_cast<T *>(this->malloc(sizeof(T) * count));
    return mem;
  }
};
/** @} */

/** @addtogroup nanoflann_metaprog_grp Auxiliary metaprogramming stuff
 * @{ */

/** Used to declare fixed-size arrays when DIM>0, dynamically-allocated vectors
 * when DIM=-1. Fixed size version for a generic DIM:
 */
template <int32_t DIM, typename T> struct array_or_vector_selector {
  typedef std::array<T, DIM> container_t;
};
/** Dynamic size version */
template <typename T> struct array_or_vector_selector<-1, T> {
  typedef std::vector<T> container_t;
};

/** @} */

/** kd-tree base-class
 *
 * Contains the member functions common to the classes KDTreeSingleIndexAdaptor
 * and KDTreeSingleIndexDynamicAdaptor_.
 *
 * \tparam Derived The name of the class which inherits this class.
 * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
 * \tparam Distance The distance metric to use, these are all classes derived
 * from nanoflann::Metric \tparam DIM Dimensionality of data points (e.g. 3 for
 * 3D points) \tparam AccessorType Will be typically size_t or int
 */

template <class Derived, typename Distance, class DatasetAdaptor, int32_t DIM = -1,
          typename AccessorType = uint32_t>
class KDTreeBaseClass {

public:
  /** Frees the previously-built index. Automatically called within
   * buildIndex(). */
  void freeIndex(Derived &obj) {
    obj.pool.free_all();
    obj.root_node = NULL;
    obj.m_size_at_index_build = 0;
  }

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

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

  using Offset    = typename decltype(vAcc)::size_type;
  using Size      = typename decltype(vAcc)::size_type;
  using Dimension = int32_t;

  /*--------------------- Internal Data Structures --------------------------*/
  struct Node {
    /** Union used because a node can be either a LEAF node or a non-leaf node,
     * so both data fields are never used simultaneously */
    union {
      struct leaf {
        Offset left, right;            //!< Indices of points in leaf node
      } lr;
      struct nonleaf {
        Dimension divfeat;             //!< Dimension used for subdivision.
        DistanceType divlow, divhigh;  //!< The values used for subdivision.
      } sub;
    } node_type;
    Node *child1, *child2; //!< Child nodes (both=NULL mean its a leaf node)
  };

  typedef Node *NodePtr;

  struct Interval {
    ElementType low, high;
  };

  NodePtr root_node;

  Size m_leaf_max_size;

  Size m_size;                  //!< Number of current points in the dataset
  Size m_size_at_index_build;   //!< Number of points in the dataset when the
                                //!< index was built
  Dimension dim;                //!< Dimensionality of each data point

  /** Define "BoundingBox" as a fixed-size or variable-size container depending
   * on "DIM" */
  typedef
      typename array_or_vector_selector<DIM, Interval>::container_t BoundingBox;

  /** Define "distance_vector_t" as a fixed-size or variable-size container
   * depending on "DIM" */
  typedef typename array_or_vector_selector<DIM, DistanceType>::container_t
      distance_vector_t;

  /** The KD-tree used to find neighbours */

  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;

  /** Returns number of points in dataset  */
  Size size(const Derived &obj) const { return obj.m_size; }

  /** Returns the length of each point in the dataset */
  Size veclen(const Derived &obj) {
    return DIM > 0 ? DIM : obj.dim;
  }

  /// Helper accessor to the dataset points:
  inline ElementType dataset_get(const Derived &obj, AccessorType element,
                                 Dimension component) const {
    return obj.dataset.kdtree_get_pt(element, component);
  }

  /**
   * Computes the inde memory usage
   * Returns: memory used by the index
   */
  Size usedMemory(Derived &obj) {
    return obj.pool.usedMemory + obj.pool.wastedMemory +
           obj.dataset.kdtree_get_point_count() *
               sizeof(AccessorType); // pool memory and vind array memory
  }

  void computeMinMax(const Derived &obj, Offset ind, Size count,
                     Dimension element, ElementType &min_elem,
                     ElementType &max_elem) {
    min_elem = dataset_get(obj, vAcc[ind], element);
    max_elem = min_elem;
    for (Offset i = 1; i < count; ++i) {
      ElementType val = dataset_get(obj, vAcc[ind + i], element);
      if (val < min_elem)
        min_elem = val;
      if (val > max_elem)
        max_elem = val;
    }
  }

  /**
   * Create a tree node that subdivides the list of vecs from vind[first]
   * to vind[last].  The routine is called recursively on each sublist.
   *
   * @param left index of the first vector
   * @param right index of the last vector
   */
  NodePtr divideTree(Derived &obj, const Offset left, const Offset right,
                     BoundingBox &bbox) {
    NodePtr node = obj.pool.template allocate<Node>(); // allocate memory

    /* If too few exemplars remain, then make this a leaf node. */
    if ((right - left) <= static_cast<Offset>(obj.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 (Dimension i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
        bbox[i].low = dataset_get(obj, obj.vAcc[left], i);
        bbox[i].high = dataset_get(obj, obj.vAcc[left], i);
      }
      for (Offset k = left + 1; k < right; ++k) {
        for (Dimension i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
          if (bbox[i].low > dataset_get(obj, obj.vAcc[k], i))
            bbox[i].low = dataset_get(obj, obj.vAcc[k], i);
          if (bbox[i].high < dataset_get(obj, obj.vAcc[k], i))
            bbox[i].high = dataset_get(obj, obj.vAcc[k], i);
        }
      }
    } else {
      Offset idx;
      Dimension cutfeat;
      DistanceType cutval;
      middleSplit_(obj, 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(obj, left, left + idx, left_bbox);

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

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

      for (Dimension i = 0; i < (DIM > 0 ? DIM : obj.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 middleSplit_(Derived &obj, Offset ind, Size count, Offset &index,
                    Dimension &cutfeat, DistanceType &cutval,
                    const BoundingBox &bbox) {
    const auto EPS = static_cast<DistanceType>(0.00001);
    ElementType max_span = bbox[0].high - bbox[0].low;
    for (Dimension i = 1; i < (DIM > 0 ? DIM : obj.dim); ++i) {
      ElementType span = bbox[i].high - bbox[i].low;
      if (span > max_span) {
        max_span = span;
      }
    }
    ElementType max_spread = -1;
    cutfeat = 0;
    for (Dimension i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
      ElementType span = bbox[i].high - bbox[i].low;
      if (span > (1 - EPS) * max_span) {
        ElementType min_elem, max_elem;
        computeMinMax(obj, 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(obj, 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;

    Offset lim1, lim2;
    planeSplit(obj, 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(Derived &obj, Offset ind, const Size count, Dimension cutfeat,
                  DistanceType &cutval, Offset &lim1, Offset &lim2) {
    /* Move vector indices for left subtree to front of list. */
    Offset left = 0;
    Offset right = count - 1;
    for (;;) {
      while (left <= right &&
             dataset_get(obj, vAcc[ind + left], cutfeat) < cutval)
        ++left;
      while (right && left <= right &&
             dataset_get(obj, vAcc[ind + right], cutfeat) >= cutval)
        --right;
      if (left > right || !right)
        break; // "!right" was added to support unsigned Index types
      std::swap(vAcc[ind + left], vAcc[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(obj, vAcc[ind + left], cutfeat) <= cutval)
        ++left;
      while (right && left <= right &&
             dataset_get(obj, vAcc[ind + right], cutfeat) > cutval)
        --right;
      if (left > right || !right)
        break; // "!right" was added to support unsigned Index types
      std::swap(vAcc[ind + left], vAcc[ind + right]);
      ++left;
      --right;
    }
    lim2 = left;
  }

  DistanceType computeInitialDistances(const Derived &obj,
                                       const ElementType *vec,
                                       distance_vector_t &dists) const {
    assert(vec);
    DistanceType distsq = DistanceType();

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

  void save_tree(Derived &obj, std::ostream &stream, NodePtr tree) {
    save_value(stream, *tree);
    if (tree->child1 != NULL) {
      save_tree(obj, stream, tree->child1);
    }
    if (tree->child2 != NULL) {
      save_tree(obj, stream, tree->child2);
    }
  }

  void load_tree(Derived &obj, std::istream &stream, NodePtr &tree) {
    tree = obj.pool.template allocate<Node>();
    load_value(stream, *tree);
    if (tree->child1 != NULL) {
      load_tree(obj, stream, tree->child1);
    }
    if (tree->child2 != NULL) {
      load_tree(obj, stream, tree->child2);
    }
  }

  /**  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_(Derived &obj, std::ostream &stream) {
    save_value(stream, obj.m_size);
    save_value(stream, obj.dim);
    save_value(stream, obj.root_bbox);
    save_value(stream, obj.m_leaf_max_size);
    save_value(stream, obj.vAcc);
    save_tree(obj, stream, obj.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_(Derived &obj, std::istream &stream) {
    load_value(stream, obj.m_size);
    load_value(stream, obj.dim);
    load_value(stream, obj.root_bbox);
    load_value(stream, obj.m_leaf_max_size);
    load_value(stream, obj.vAcc);
    load_tree(obj, stream, obj.root_node);
  }
};

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

/** kd-tree static 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 poins
 *   inline size_t kdtree_get_point_count() const { ... }
 *
 *
 *   // Must return the dim'th component of the idx'th point in the class:
 *   inline T kdtree_get_pt(const size_t idx, const size_t 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 DatasetAdaptor The user-provided adaptor (see comments above).
 * \tparam Distance The distance metric to use: nanoflann::metric_L1,
 * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
 * Dimensionality of data points (e.g. 3 for 3D points) \tparam IndexType Will
 * be typically size_t or int
 */
template <typename Distance, class DatasetAdaptor, int32_t DIM = -1,
          typename AccessorType = uint32_t>
class KDTreeSingleIndexAdaptor
    : public KDTreeBaseClass<
          KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, AccessorType>,
          Distance, DatasetAdaptor, DIM, AccessorType> {
public:
  /** Deleted copy constructor*/
  KDTreeSingleIndexAdaptor(
      const KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM,
                                     AccessorType> &) = delete;

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

  const KDTreeSingleIndexAdaptorParams index_params;

  Distance distance;

  typedef typename nanoflann::KDTreeBaseClass<
      nanoflann::KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM,
                                          AccessorType>,
      Distance, DatasetAdaptor, DIM, AccessorType>
      BaseClassRef;

  using Offset    = typename BaseClassRef::Offset;
  using Size      = typename BaseClassRef::Size;
  using Dimension = typename BaseClassRef::Dimension;

  typedef typename BaseClassRef::ElementType ElementType;
  typedef typename BaseClassRef::DistanceType DistanceType;

  typedef typename BaseClassRef::Node Node;
  typedef Node *NodePtr;

  typedef typename BaseClassRef::Interval Interval;
  /** Define "BoundingBox" as a fixed-size or variable-size container depending
   * on "DIM" */
  typedef typename BaseClassRef::BoundingBox BoundingBox;

  /** Define "distance_vector_t" as a fixed-size or variable-size container
   * depending on "DIM" */
  typedef typename BaseClassRef::distance_vector_t distance_vector_t;

  /**
   * KDTree constructor
   *
   * Refer to docs in README.md or online in
   * https://github.com/jlblancoc/nanoflann
   *
   * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
   * for 3D points) is determined by means of:
   *  - The \a DIM template parameter if >0 (highest priority)
   *  - Otherwise, the \a dimensionality parameter of this constructor.
   *
   * @param inputData Dataset with the input features
   * @param params Basically, the maximum leaf node size
   */
  KDTreeSingleIndexAdaptor(const Dimension dimensionality,
                           const DatasetAdaptor &inputData,
                           const KDTreeSingleIndexAdaptorParams &params =
                               KDTreeSingleIndexAdaptorParams())
      : dataset(inputData), index_params(params), distance(inputData) {
    BaseClassRef::root_node = NULL;
    BaseClassRef::m_size = dataset.kdtree_get_point_count();
    BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
    BaseClassRef::dim = dimensionality;
    if (DIM > 0)
      BaseClassRef::dim = DIM;
    BaseClassRef::m_leaf_max_size = params.leaf_max_size;

    buildIndex();
  }

  /**
   * Builds the index
   */
  void buildIndex() {
    BaseClassRef::m_size = dataset.kdtree_get_point_count();
    BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
    init_vind();
    this->freeIndex(*this);
    BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
    if (BaseClassRef::m_size == 0)
      return;
    computeBoundingBox(BaseClassRef::root_bbox);
    BaseClassRef::root_node =
        this->divideTree(*this, 0, BaseClassRef::m_size,
                         BaseClassRef::root_bbox); // construct the tree
  }

  /** \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>
   * \return  True if the requested neighbors could be found.
   * \sa knnSearch, radiusSearch
   */
  template <typename RESULTSET>
  bool findNeighbors(RESULTSET &result, const ElementType *vec,
                     const SearchParams &searchParams) const {
    assert(vec);
    if (this->size(*this) == 0)
      return false;
    if (!BaseClassRef::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)
    auto zero = static_cast<decltype(result.worstDist())>(0);
    assign(dists, (DIM > 0 ? DIM : BaseClassRef::dim),
           zero); // Fill it with zeros.
    DistanceType distsq = this->computeInitialDistances(*this, vec, dists);
    searchLevel(result, vec, BaseClassRef::root_node, distsq, dists,
                epsError); // "count_leaf" parameter removed since was neither
                           // used nor returned to the user.
    return result.full();
  }

  /**
   * 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. \return Number `N` of valid points in
   * the result set. Only the first `N` entries in `out_indices` and
   * `out_distances_sq` will be valid. Return may be less than `num_closest`
   * only if the number of elements in the tree is less than `num_closest`.
   */
  Size knnSearch(const ElementType *query_point, const Size num_closest,
                   AccessorType *out_indices, DistanceType *out_distances_sq,
                   const int /* nChecks_IGNORED */ = 10) const {
    nanoflann::KNNResultSet<DistanceType, AccessorType> resultSet(num_closest);
    resultSet.init(out_indices, out_distances_sq);
    this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
    return resultSet.size();
  }

  /**
   * 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, radiusSearchCustomCallback
   * \return The number of points within the given radius (i.e. indices.size()
   * or dists.size() )
   */
  Size
  radiusSearch(const ElementType *query_point, const DistanceType &radius,
               std::vector<std::pair<AccessorType, DistanceType>> &IndicesDists,
               const SearchParams &searchParams) const {
    RadiusResultSet<DistanceType, AccessorType> resultSet(radius, IndicesDists);
    const Size nFound =
        radiusSearchCustomCallback(query_point, resultSet, searchParams);
    if (searchParams.sorted)
      std::sort(IndicesDists.begin(), IndicesDists.end(), IndexDist_Sorter());
    return nFound;
  }

  /**
   * Just like radiusSearch() but with a custom callback class for each point
   * found in the radius of the query. See the source of RadiusResultSet<> as a
   * start point for your own classes. \sa radiusSearch
   */
  template <class SEARCH_CALLBACK>
  Size radiusSearchCustomCallback(
      const ElementType *query_point, SEARCH_CALLBACK &resultSet,
      const SearchParams &searchParams = SearchParams()) const {
    this->findNeighbors(resultSet, query_point, searchParams);
    return resultSet.size();
  }

  /** @} */

public:
  /** 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.
    BaseClassRef::m_size = dataset.kdtree_get_point_count();
    if (BaseClassRef::vAcc.size() != BaseClassRef::m_size)
      BaseClassRef::vAcc.resize(BaseClassRef::m_size);
    for (Size i = 0; i < BaseClassRef::m_size; i++)
      BaseClassRef::vAcc[i] = i;
  }

  void computeBoundingBox(BoundingBox &bbox) {
    resize(bbox, (DIM > 0 ? DIM : BaseClassRef::dim));
    if (dataset.kdtree_get_bbox(bbox)) {
      // Done! It was implemented in derived class
    } else {
      const Size N = dataset.kdtree_get_point_count();
      if (!N)
        throw std::runtime_error("[nanoflann] computeBoundingBox() called but "
                                 "no data points found.");
      for (Dimension i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
        bbox[i].low = bbox[i].high =
            this->dataset_get(*this, BaseClassRef::vAcc[0], i);
      }
      for (Offset k = 1; k < N; ++k) {
        for (Dimension i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
          if (this->dataset_get(*this, BaseClassRef::vAcc[k], i) < bbox[i].low)
            bbox[i].low = this->dataset_get(*this, BaseClassRef::vAcc[k], i);
          if (this->dataset_get(*this, BaseClassRef::vAcc[k], i) > bbox[i].high)
            bbox[i].high = this->dataset_get(*this, BaseClassRef::vAcc[k], i);
        }
      }
    }
  }

  /**
   * Performs an exact search in the tree starting from a node.
   * \tparam RESULTSET Should be any ResultSet<DistanceType>
   * \return true if the search should be continued, false if the results are
   * sufficient
   */
  template <class RESULTSET>
  bool 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 (Offset i = node->node_type.lr.left; i < node->node_type.lr.right; ++i) {
        const AccessorType accessor = BaseClassRef::vAcc[i]; // reorder... : i;
        DistanceType dist = distance.evalMetric(
            vec, accessor, (DIM > 0 ? DIM : BaseClassRef::dim));
        if (dist < worst_dist) {
          if (!result_set.addPoint(dist, BaseClassRef::vAcc[i])) {
            // the resultset doesn't want to receive any more points, we're done
            // searching!
            return false;
          }
        }
      }
      return true;
    }

    /* Which child branch should be taken first? */
    Dimension 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. */
    if (!searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError)) {
      // the resultset doesn't want to receive any more points, we're done
      // searching!
      return false;
    }

    DistanceType dst = dists[idx];
    mindistsq = mindistsq + cut_dist - dst;
    dists[idx] = cut_dist;
    if (mindistsq * epsError <= result_set.worstDist()) {
      if (!searchLevel(result_set, vec, otherChild, mindistsq, dists,
                       epsError)) {
        // the resultset doesn't want to receive any more points, we're done
        // searching!
        return false;
      }
    }
    dists[idx] = dst;
    return true;
  }

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(std::ostream &stream) { this->saveIndex_(*this, stream); }

  /**  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(std::istream &stream) { this->loadIndex_(*this, stream); }

}; // class KDTree

/** kd-tree dynamic 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 poins
 *   inline size_t kdtree_get_point_count() const { ... }
 *
 *   // Must return the dim'th component of the idx'th point in the class:
 *   inline T kdtree_get_pt(const size_t idx, const size_t 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 DatasetAdaptor The user-provided adaptor (see comments above).
 * \tparam Distance The distance metric to use: nanoflann::metric_L1,
 * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
 * Dimensionality of data points (e.g. 3 for 3D points) \tparam AccessorType
 * Will be typically size_t or int
 */
template <typename Distance, class DatasetAdaptor, int32_t DIM = -1,
          typename AccessorType = uint32_t>
class KDTreeSingleIndexDynamicAdaptor_
    : public KDTreeBaseClass<KDTreeSingleIndexDynamicAdaptor_<
                                 Distance, DatasetAdaptor, DIM, AccessorType>,
                             Distance, DatasetAdaptor, DIM, AccessorType> {
public:
  /**
   * The dataset used by this index
   */
  const DatasetAdaptor &dataset; //!< The source of our data

  KDTreeSingleIndexAdaptorParams index_params;

  std::vector<int> &treeIndex;

  Distance distance;

  typedef typename nanoflann::KDTreeBaseClass<
      nanoflann::KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM,
                                                  AccessorType>,
      Distance, DatasetAdaptor, DIM, AccessorType>
      BaseClassRef;

  typedef typename BaseClassRef::ElementType ElementType;
  typedef typename BaseClassRef::DistanceType DistanceType;

  using Offset    = typename BaseClassRef::Offset;
  using Size      = typename BaseClassRef::Size;
  using Dimension = typename BaseClassRef::Dimension;

  typedef typename BaseClassRef::Node Node;
  typedef Node *NodePtr;

  typedef typename BaseClassRef::Interval Interval;
  /** Define "BoundingBox" as a fixed-size or variable-size container depending
   * on "DIM" */
  typedef typename BaseClassRef::BoundingBox BoundingBox;

  /** Define "distance_vector_t" as a fixed-size or variable-size container
   * depending on "DIM" */
  typedef typename BaseClassRef::distance_vector_t distance_vector_t;

  /**
   * KDTree constructor
   *
   * Refer to docs in README.md or online in
   * https://github.com/jlblancoc/nanoflann
   *
   * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
   * for 3D points) is determined by means of:
   *  - The \a DIM template parameter if >0 (highest priority)
   *  - Otherwise, the \a dimensionality parameter of this constructor.
   *
   * @param inputData Dataset with the input features
   * @param params Basically, the maximum leaf node size
   */
  KDTreeSingleIndexDynamicAdaptor_(
      const Dimension dimensionality, const DatasetAdaptor &inputData,
      std::vector<int> &treeIndex_,
      const KDTreeSingleIndexAdaptorParams &params =
          KDTreeSingleIndexAdaptorParams())
      : dataset(inputData), index_params(params), treeIndex(treeIndex_),
        distance(inputData) {
    BaseClassRef::root_node = NULL;
    BaseClassRef::m_size = 0;
    BaseClassRef::m_size_at_index_build = 0;
    BaseClassRef::dim = dimensionality;
    if (DIM > 0)
      BaseClassRef::dim = DIM;
    BaseClassRef::m_leaf_max_size = params.leaf_max_size;
  }

  /** Assignment operator definiton */
  KDTreeSingleIndexDynamicAdaptor_
  operator=(const KDTreeSingleIndexDynamicAdaptor_ &rhs) {
    KDTreeSingleIndexDynamicAdaptor_ tmp(rhs);
    std::swap(BaseClassRef::vAcc, tmp.BaseClassRef::vAcc);
    std::swap(BaseClassRef::m_leaf_max_size, tmp.BaseClassRef::m_leaf_max_size);
    std::swap(index_params, tmp.index_params);
    std::swap(treeIndex, tmp.treeIndex);
    std::swap(BaseClassRef::m_size, tmp.BaseClassRef::m_size);
    std::swap(BaseClassRef::m_size_at_index_build,
              tmp.BaseClassRef::m_size_at_index_build);
    std::swap(BaseClassRef::root_node, tmp.BaseClassRef::root_node);
    std::swap(BaseClassRef::root_bbox, tmp.BaseClassRef::root_bbox);
    std::swap(BaseClassRef::pool, tmp.BaseClassRef::pool);
    return *this;
  }

  /**
   * Builds the index
   */
  void buildIndex() {
    BaseClassRef::m_size = BaseClassRef::vAcc.size();
    this->freeIndex(*this);
    BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
    if (BaseClassRef::m_size == 0)
      return;
    computeBoundingBox(BaseClassRef::root_bbox);
    BaseClassRef::root_node =
        this->divideTree(*this, 0, BaseClassRef::m_size,
                         BaseClassRef::root_bbox); // construct the tree
  }

  /** \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>
   * \return  True if the requested neighbors could be found.
   * \sa knnSearch, radiusSearch
   */
  template <typename RESULTSET>
  bool findNeighbors(RESULTSET &result, const ElementType *vec,
                     const SearchParams &searchParams) const {
    assert(vec);
    if (this->size(*this) == 0)
      return false;
    if (!BaseClassRef::root_node)
      return false;
    float epsError = 1 + searchParams.eps;

    // fixed or variable-sized container (depending on DIM)
    distance_vector_t dists;
    // Fill it with zeros.
    assign(dists, (DIM > 0 ? DIM : BaseClassRef::dim),
           static_cast<typename distance_vector_t::value_type>(0));
    DistanceType distsq = this->computeInitialDistances(*this, vec, dists);
    searchLevel(result, vec, BaseClassRef::root_node, distsq, dists,
                epsError); // "count_leaf" parameter removed since was neither
                           // used nor returned to the user.
    return result.full();
  }

  /**
   * 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. \return Number `N` of valid points in
   * the result set. Only the first `N` entries in `out_indices` and
   * `out_distances_sq` will be valid. Return may be less than `num_closest`
   * only if the number of elements in the tree is less than `num_closest`.
   */
  Size knnSearch(const ElementType *query_point, const Size num_closest,
                   AccessorType *out_indices, DistanceType *out_distances_sq,
                   const int /* nChecks_IGNORED */ = 10) const {
    nanoflann::KNNResultSet<DistanceType, AccessorType> resultSet(num_closest);
    resultSet.init(out_indices, out_distances_sq);
    this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
    return resultSet.size();
  }

  /**
   * 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, radiusSearchCustomCallback
   * \return The number of points within the given radius (i.e. indices.size()
   * or dists.size() )
   */
  Size
  radiusSearch(const ElementType *query_point, const DistanceType &radius,
               std::vector<std::pair<AccessorType, DistanceType>> &IndicesDists,
               const SearchParams &searchParams) const {
    RadiusResultSet<DistanceType, AccessorType> 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;
  }

  /**
   * Just like radiusSearch() but with a custom callback class for each point
   * found in the radius of the query. See the source of RadiusResultSet<> as a
   * start point for your own classes. \sa radiusSearch
   */
  template <class SEARCH_CALLBACK>
  Size radiusSearchCustomCallback(
      const ElementType *query_point, SEARCH_CALLBACK &resultSet,
      const SearchParams &searchParams = SearchParams()) const {
    this->findNeighbors(resultSet, query_point, searchParams);
    return resultSet.size();
  }

  /** @} */

public:
  void computeBoundingBox(BoundingBox &bbox) {
    resize(bbox, (DIM > 0 ? DIM : BaseClassRef::dim));

    if (dataset.kdtree_get_bbox(bbox)) {
      // Done! It was implemented in derived class
    } else {
      const Size N = BaseClassRef::m_size;
      if (!N)
        throw std::runtime_error("[nanoflann] computeBoundingBox() called but "
                                 "no data points found.");
      for (Dimension i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
        bbox[i].low = bbox[i].high =
            this->dataset_get(*this, BaseClassRef::vAcc[0], i);
      }
      for (Offset k = 1; k < N; ++k) {
        for (Dimension i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
          if (this->dataset_get(*this, BaseClassRef::vAcc[k], i) < bbox[i].low)
            bbox[i].low = this->dataset_get(*this, BaseClassRef::vAcc[k], i);
          if (this->dataset_get(*this, BaseClassRef::vAcc[k], i) > bbox[i].high)
            bbox[i].high = this->dataset_get(*this, BaseClassRef::vAcc[k], i);
        }
      }
    }
  }

  /**
   * 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,
                   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 (Offset i = node->node_type.lr.left;
           i < node->node_type.lr.right; ++i) {
        const AccessorType index = BaseClassRef::vAcc[i]; // reorder... : i;
        if (treeIndex[index] == -1)
          continue;
        DistanceType dist = distance.evalMetric(
            vec, index, (DIM > 0 ? DIM : BaseClassRef::dim));
        if (dist < worst_dist) {
          if (!result_set.addPoint(
                  static_cast<typename RESULTSET::DistanceType>(dist),
                  static_cast<typename RESULTSET::IndexType>(
                      BaseClassRef::vAcc[i]))) {
            // the resultset doesn't want to receive any more points, we're done
            // searching!
            return; // false;
          }
        }
      }
      return;
    }

    /* Which child branch should be taken first? */
    Dimension 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:
  /**  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(std::ostream &stream) { this->saveIndex_(*this, stream); }

  /**  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(std::istream &stream) { this->loadIndex_(*this, stream); }
};

/** kd-tree dynaimic index
 *
 * class to create multiple static index and merge their results to behave as
 * single dynamic index as proposed in Logarithmic Approach.
 *
 *  Example of usage:
 *  examples/dynamic_pointcloud_example.cpp
 *
 * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
 * \tparam Distance The distance metric to use: nanoflann::metric_L1,
 * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
 * Dimensionality of data points (e.g. 3 for 3D points) \tparam AccessorType Will
 * be typically size_t or int
 */
template <typename Distance, class DatasetAdaptor, int32_t DIM = -1,
          typename AccessorType = uint32_t>
class KDTreeSingleIndexDynamicAdaptor {
public:
  typedef typename Distance::ElementType ElementType;
  typedef typename Distance::DistanceType DistanceType;

  using Offset    = typename KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>::Offset;
  using Size      = typename KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>::Size;
  using Dimension = typename KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>::Dimension;


protected:
  Size m_leaf_max_size;
  Size treeCount;
  Size pointCount;

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

  std::vector<int> treeIndex; //!< treeIndex[idx] is the index of tree in which
                              //!< point at idx is stored. treeIndex[idx]=-1
                              //!< means that point has been removed.

  KDTreeSingleIndexAdaptorParams index_params;

  Dimension dim; //!< Dimensionality of each data point

  typedef KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>
      index_container_t;
  std::vector<index_container_t> index;

public:
  /** Get a const ref to the internal list of indices; the number of indices is
   * adapted dynamically as the dataset grows in size. */
  const std::vector<index_container_t> &getAllIndices() const { return index; }

private:
  /** finds position of least significant unset bit */
  int First0Bit(AccessorType num) {
    int pos = 0;
    while (num & 1) {
      num = num >> 1;
      pos++;
    }
    return pos;
  }

  /** Creates multiple empty trees to handle dynamic support */
  void init() {
    typedef KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>
        my_kd_tree_t;
    std::vector<my_kd_tree_t> index_(
        treeCount, my_kd_tree_t(dim /*dim*/, dataset, treeIndex, index_params));
    index = index_;
  }

public:
  Distance distance;

  /**
   * KDTree constructor
   *
   * Refer to docs in README.md or online in
   * https://github.com/jlblancoc/nanoflann
   *
   * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
   * for 3D points) is determined by means of:
   *  - The \a DIM template parameter if >0 (highest priority)
   *  - Otherwise, the \a dimensionality parameter of this constructor.
   *
   * @param inputData Dataset with the input features
   * @param params Basically, the maximum leaf node size
   */
  KDTreeSingleIndexDynamicAdaptor(const int dimensionality,
                                  const DatasetAdaptor &inputData,
                                  const KDTreeSingleIndexAdaptorParams &params =
                                      KDTreeSingleIndexAdaptorParams(),
                                  const size_t maximumPointCount = 1000000000U)
      : dataset(inputData), index_params(params), distance(inputData) {
    treeCount = static_cast<size_t>(std::log2(maximumPointCount));
    pointCount = 0U;
    dim = dimensionality;
    treeIndex.clear();
    if (DIM > 0)
      dim = DIM;
    m_leaf_max_size = params.leaf_max_size;
    init();
    const size_t num_initial_points = dataset.kdtree_get_point_count();
    if (num_initial_points > 0) {
      addPoints(0, num_initial_points - 1);
    }
  }

  /** Deleted copy constructor*/
  KDTreeSingleIndexDynamicAdaptor(
      const KDTreeSingleIndexDynamicAdaptor<Distance, DatasetAdaptor, DIM,
                                            AccessorType> &) = delete;

  /** Add points to the set, Inserts all points from [start, end] */
  void addPoints(AccessorType start, AccessorType end) {
    Size count = end - start + 1;
    treeIndex.resize(treeIndex.size() + count);
    for (AccessorType idx = start; idx <= end; idx++) {
      int pos = First0Bit(pointCount);
      index[pos].vAcc.clear();
      treeIndex[pointCount] = pos;
      for (int i = 0; i < pos; i++) {
        for (int j = 0; j < static_cast<int>(index[i].vAcc.size()); j++) {
          index[pos].vAcc.push_back(index[i].vAcc[j]);
          if (treeIndex[index[i].vAcc[j]] != -1)
            treeIndex[index[i].vAcc[j]] = pos;
        }
        index[i].vAcc.clear();
        index[i].freeIndex(index[i]);
      }
      index[pos].vAcc.push_back(idx);
      index[pos].buildIndex();
      pointCount++;
    }
  }

  /** Remove a point from the set (Lazy Deletion) */
  void removePoint(size_t idx) {
    if (idx >= pointCount)
      return;
    treeIndex[idx] = -1;
  }

  /**
   * 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>
   * \return  True if the requested neighbors could be found.
   * \sa knnSearch, radiusSearch
   */
  template <typename RESULTSET>
  bool findNeighbors(RESULTSET &result, const ElementType *vec,
                     const SearchParams &searchParams) const {
    for (size_t i = 0; i < treeCount; i++) {
      index[i].findNeighbors(result, &vec[0], searchParams);
    }
    return result.full();
  }
};

/** An L2-metric KD-tree adaptor for working with data directly stored in an
 * Eigen Matrix, without duplicating the data storage. You can select whether a
 * row or column 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(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 row_major
 * If set to true the rows of the matrix are used as the points, if set to false
 * the columns of the matrix are used as the points.
 */
template <class MatrixType, int32_t DIM = -1, class Distance = nanoflann::metric_L2,
          bool row_major = true>
struct KDTreeEigenMatrixAdaptor {
  typedef KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance, row_major> self_t;
  typedef typename MatrixType::Scalar num_t;
  typedef typename MatrixType::Index IndexType;
  typedef
      typename Distance::template traits<num_t, self_t, IndexType>::distance_t
          metric_t;

  typedef KDTreeSingleIndexAdaptor<metric_t, self_t,
                                   row_major ? MatrixType::ColsAtCompileTime
                                             : MatrixType::RowsAtCompileTime,
                                   IndexType>
      index_t;

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

  using Offset    = typename index_t::Offset;
  using Size      = typename index_t::Size;
  using Dimension = typename index_t::Dimension;

  /// Constructor: takes a const ref to the matrix object with the data points
  KDTreeEigenMatrixAdaptor(const Dimension dimensionality,
                           const std::reference_wrapper<const MatrixType> &mat,
                           const int leaf_max_size = 10)
      : m_data_matrix(mat) {
    const auto dims = row_major ? mat.get().cols() : mat.get().rows();
    if (static_cast<Dimension>(dims) != dimensionality)
      throw std::runtime_error(
          "Error: 'dimensionality' must match column count in data matrix");
    if (DIM > 0 && static_cast<int32_t>(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));
    index->buildIndex();
  }

public:
  /** Deleted copy constructor */
  KDTreeEigenMatrixAdaptor(const self_t &) = delete;

  ~KDTreeEigenMatrixAdaptor() { delete index; }

  const std::reference_wrapper<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 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());
  }

  /** @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 kdtree_get_point_count() const {
    if (row_major)
      return m_data_matrix.get().rows();
    else
      return m_data_matrix.get().cols();
  }

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

  // 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_ */
