nanoflann.hpp

/***********************************************************************
 * 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 2011-2026  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,
 * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
 * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
 * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *************************************************************************/

 
#pragma once
 
#include <algorithm>
#include <array>
#include <atomic>
#include <cassert>
#include <chrono>  // std::chrono (async incremental index polling)
#include <cmath>  // for abs()
#include <cstdint>
#include <cstdio>  // snprintf
#include <cstdlib>  // for abs()
#include <functional>  // std::reference_wrapper
#include <future>
#include <istream>
#include <limits>  // std::numeric_limits
#include <memory>  // std::unique_ptr (async incremental index)
#include <new>  // placement new (incremental index node pool)
#include <ostream>
#include <stack>
#include <stdexcept>
#include <type_traits>  // std::is_trivially_destructible
#include <unordered_map>
#include <vector>

#define NANOFLANN_VERSION_STRING "1.10.1"
#define NANOFLANN_VERSION 0x010A01
 
// 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
// Avoid conflicts with X11 headers
#ifdef None
#undef None
#endif
 
// Handle restricted pointers
#if defined(__GNUC__) || defined(__clang__)
#define NANOFLANN_RESTRICT __restrict__
#elif defined(_MSC_VER)
#define NANOFLANN_RESTRICT __restrict
#else
#define NANOFLANN_RESTRICT
#endif
 
// [[nodiscard]] support
#if defined(__has_cpp_attribute) && __has_cpp_attribute(nodiscard)
#define NANOFLANN_NODISCARD [[nodiscard]]
#else
#define NANOFLANN_NODISCARD
#endif
 
// [[fallthrough]] support for intentional switch fall-throughs
#if defined(__has_cpp_attribute) && __has_cpp_attribute(fallthrough)
#define NANOFLANN_FALLTHROUGH [[fallthrough]]
#else
#define NANOFLANN_FALLTHROUGH
#endif
 
// Memory alignment of KD-tree nodes:
#ifndef NANOFLANN_NODE_ALIGNMENT
#define NANOFLANN_NODE_ALIGNMENT 16
#endif
 
namespace nanoflann
{

template <typename T>
constexpr T pi_const()
{
    return static_cast<T>(3.14159265358979323846);
}

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
{
};

template <typename Container>
inline typename std::enable_if<has_resize<Container>::value, void>::type resize(
    Container& c, const size_t nElements)
{
    c.resize(nElements);
}

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("Attempt to resize a fixed size 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);
}

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;
}

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

template <typename IndexType = size_t, typename DistanceType = double>
struct ResultItem
{
    ResultItem() = default;
    ResultItem(const IndexType index, const DistanceType distance) : first(index), second(distance)
    {
    }
 
    IndexType    first;  
    DistanceType second;  
};
 
namespace detail
{
template <typename DistanceType, typename IndexType, typename CountType>
bool addPointToSortedResultSet(
    DistanceType* dists, IndexType* indices, CountType& count, CountType capacity,
    DistanceType dist, IndexType index)
{
    CountType i;
    for (i = count; i > 0; --i)
    {
#ifdef NANOFLANN_FIRST_MATCH
        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++;
    return true;
}
}  // namespace detail


template <typename _DistanceType, typename _IndexType = size_t, typename _CountType = size_t>
class KNNResultSet
{
   public:
    using DistanceType = _DistanceType;
    using IndexType    = _IndexType;
    using CountType    = _CountType;
 
   private:
    IndexType*    indices;
    DistanceType* dists;
    CountType     capacity;
    CountType     count;
 
   public:
    explicit KNNResultSet(CountType capacity_)
        : indices(nullptr), dists(nullptr), capacity(capacity_), count(0)
    {
    }
 
    void init(IndexType* indices_, DistanceType* dists_)
    {
        indices = indices_;
        dists   = dists_;
        count   = 0;
    }
 
    NANOFLANN_NODISCARD CountType size() const noexcept { return count; }
    NANOFLANN_NODISCARD bool      empty() const noexcept { return count == 0; }
    NANOFLANN_NODISCARD bool      full() const noexcept { return count == capacity; }

    bool addPoint(DistanceType dist, IndexType index)
    {
        return detail::addPointToSortedResultSet(dists, indices, count, capacity, dist, index);
    }

    NANOFLANN_NODISCARD DistanceType worstDist() const noexcept
    {
        return (count < capacity || !count) ? std::numeric_limits<DistanceType>::max()
                                            : dists[count - 1];
    }
 
    void sort()
    {
        // already sorted
    }
};

template <typename _DistanceType, typename _IndexType = size_t, typename _CountType = size_t>
class RKNNResultSet
{
   public:
    using DistanceType = _DistanceType;
    using IndexType    = _IndexType;
    using CountType    = _CountType;
 
   private:
    IndexType*    indices;
    DistanceType* dists;
    CountType     capacity;
    CountType     count;
    DistanceType  maximumSearchDistanceSquared;
 
   public:
    explicit RKNNResultSet(CountType capacity_, DistanceType maximumSearchDistanceSquared_)
        : indices(nullptr),
          dists(nullptr),
          capacity(capacity_),
          count(0),
          maximumSearchDistanceSquared(maximumSearchDistanceSquared_)
    {
    }
 
    void init(IndexType* indices_, DistanceType* dists_)
    {
        indices = indices_;
        dists   = dists_;
        count   = 0;
        if (capacity) dists[capacity - 1] = maximumSearchDistanceSquared;
    }
 
    NANOFLANN_NODISCARD CountType size() const noexcept { return count; }
    NANOFLANN_NODISCARD bool      empty() const noexcept { return count == 0; }
    NANOFLANN_NODISCARD bool      full() const noexcept { return count == capacity; }

    bool addPoint(DistanceType dist, IndexType index)
    {
        return detail::addPointToSortedResultSet(dists, indices, count, capacity, dist, index);
    }

    NANOFLANN_NODISCARD DistanceType worstDist() const noexcept
    {
        return (count < capacity || !count) ? maximumSearchDistanceSquared : dists[count - 1];
    }
 
    void sort()
    {
        // already sorted
    }
};

template <typename _DistanceType, typename _IndexType = size_t>
class RadiusResultSet
{
   public:
    using DistanceType = _DistanceType;
    using IndexType    = _IndexType;
 
   public:
    const DistanceType radius;
 
    std::vector<ResultItem<IndexType, DistanceType>>& m_indices_dists;
 
    explicit RadiusResultSet(
        DistanceType radius_, std::vector<ResultItem<IndexType, DistanceType>>& indices_dists)
        : radius(radius_), m_indices_dists(indices_dists)
    {
        init();
    }
 
    void init() { clear(); }
    void clear() { m_indices_dists.clear(); }
 
    NANOFLANN_NODISCARD size_t size() const noexcept { return m_indices_dists.size(); }
    NANOFLANN_NODISCARD bool   empty() const noexcept { return m_indices_dists.empty(); }
    NANOFLANN_NODISCARD bool   full() const noexcept { return true; }

    bool addPoint(DistanceType dist, IndexType index)
    {
        if (dist < radius) m_indices_dists.emplace_back(index, dist);
        return true;
    }
 
    NANOFLANN_NODISCARD DistanceType worstDist() const noexcept { return radius; }

    ResultItem<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.");
        auto it =
            std::max_element(m_indices_dists.begin(), m_indices_dists.end(), IndexDist_Sorter());
        return *it;
    }
 
    void sort() { std::sort(m_indices_dists.begin(), m_indices_dists.end(), IndexDist_Sorter()); }
};

template <typename _IndexType = size_t>
class BoxResultSet
{
   public:
    using IndexType = _IndexType;
 
    std::vector<IndexType>& m_indices;
 
    explicit BoxResultSet(std::vector<IndexType>& indices) : m_indices(indices)
    {
        m_indices.clear();
    }
 
    NANOFLANN_NODISCARD size_t size() const noexcept { return m_indices.size(); }
    NANOFLANN_NODISCARD bool   empty() const noexcept { return m_indices.empty(); }
    NANOFLANN_NODISCARD bool   full() const noexcept { return true; }

    template <typename DistanceType>
    bool addPoint(DistanceType /*dist*/, IndexType index)
    {
        m_indices.push_back(index);
        return true;
    }
 
    void sort() { std::sort(m_indices.begin(), m_indices.end()); }
};


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);
}

 
struct Metric
{
};

template <class T, class DataSource, typename _DistanceType = T, typename IndexType = size_t>
struct L1_Adaptor
{
    using ElementType  = T;
    using DistanceType = _DistanceType;
 
    const DataSource& data_source;
 
    L1_Adaptor(const DataSource& _data_source) : data_source(_data_source) {}
 
    inline DistanceType evalMetric(
        const T* NANOFLANN_RESTRICT a, const IndexType b_idx, size_t size) const
    {
        DistanceType result  = DistanceType();
        const size_t multof4 = (size >> 2) << 2;  // largest multiple of 4
        size_t       d;
 
        for (d = 0; d < multof4; d += 4)
        {
            const DistanceType diff0 = std::abs(a[d + 0] - data_source.kdtree_get_pt(b_idx, d + 0));
            const DistanceType diff1 = std::abs(a[d + 1] - data_source.kdtree_get_pt(b_idx, d + 1));
            const DistanceType diff2 = std::abs(a[d + 2] - data_source.kdtree_get_pt(b_idx, d + 2));
            const DistanceType diff3 = std::abs(a[d + 3] - data_source.kdtree_get_pt(b_idx, d + 3));
            /* Parentheses break dependency chain: */
            result += (diff0 + diff1) + (diff2 + diff3);
        }
        /* Process last 0-3 components. Unrolled loop with fall-through switch.
         */
        switch (size - multof4)
        {
            case 3:
                result += std::abs(a[d + 2] - data_source.kdtree_get_pt(b_idx, d + 2));
                NANOFLANN_FALLTHROUGH;
            case 2:
                result += std::abs(a[d + 1] - data_source.kdtree_get_pt(b_idx, d + 1));
                NANOFLANN_FALLTHROUGH;
            case 1:
                result += std::abs(a[d + 0] - data_source.kdtree_get_pt(b_idx, d + 0));
                NANOFLANN_FALLTHROUGH;
            case 0:
                break;
        }
        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);
    }
};

template <class T, class DataSource, typename _DistanceType = T, typename IndexType = size_t>
struct L2_Adaptor
{
    using ElementType  = T;
    using DistanceType = _DistanceType;
 
    const DataSource& data_source;
 
    L2_Adaptor(const DataSource& _data_source) : data_source(_data_source) {}
 
    inline DistanceType evalMetric(
        const T* NANOFLANN_RESTRICT a, const IndexType b_idx, size_t size) const
    {
        DistanceType result  = DistanceType();
        const size_t multof4 = (size >> 2) << 2;  // largest multiple of 4
        size_t       d;
 
        for (d = 0; d < multof4; d += 4)
        {
            const DistanceType diff0 = a[d + 0] - data_source.kdtree_get_pt(b_idx, d + 0);
            const DistanceType diff1 = a[d + 1] - data_source.kdtree_get_pt(b_idx, d + 1);
            const DistanceType diff2 = a[d + 2] - data_source.kdtree_get_pt(b_idx, d + 2);
            const DistanceType diff3 = a[d + 3] - data_source.kdtree_get_pt(b_idx, d + 3);
            /* Parentheses break dependency chain: */
            result += (diff0 * diff0 + diff1 * diff1) + (diff2 * diff2 + diff3 * diff3);
        }
        /* Process last 0-3 components. Unrolled loop with fall-through switch.
         */
        DistanceType diff;
        switch (size - multof4)
        {
            case 3:
                diff = a[d + 2] - data_source.kdtree_get_pt(b_idx, d + 2);
                result += diff * diff;
                NANOFLANN_FALLTHROUGH;
            case 2:
                diff = a[d + 1] - data_source.kdtree_get_pt(b_idx, d + 1);
                result += diff * diff;
                NANOFLANN_FALLTHROUGH;
            case 1:
                diff = a[d + 0] - data_source.kdtree_get_pt(b_idx, d + 0);
                result += diff * diff;
                NANOFLANN_FALLTHROUGH;
            case 0:
                break;
        }
        return result;
    }
 
    template <typename U, typename V>
    inline DistanceType accum_dist(const U a, const V b, const size_t) const
    {
        auto diff = a - b;
        return diff * diff;
    }
};

template <class T, class DataSource, typename _DistanceType = T, typename IndexType = size_t>
struct L2_Simple_Adaptor
{
    using ElementType  = T;
    using DistanceType = _DistanceType;
 
    const DataSource& data_source;
 
    L2_Simple_Adaptor(const DataSource& _data_source) : data_source(_data_source) {}
 
    inline DistanceType evalMetric(const T* a, const IndexType 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
    {
        auto diff = a - b;
        return diff * diff;
    }
};

template <class T, class DataSource, typename _DistanceType = T, typename IndexType = size_t>
struct SO2_Adaptor
{
    using ElementType  = T;
    using DistanceType = _DistanceType;
 
    const DataSource& data_source;
 
    SO2_Adaptor(const DataSource& _data_source) : data_source(_data_source) {}
 
    inline DistanceType evalMetric(const T* a, const IndexType b_idx, size_t size) const
    {
        return accum_dist(a[size - 1], data_source.kdtree_get_pt(b_idx, size - 1), size - 1);
    }

    template <typename U, typename V>
    inline DistanceType accum_dist(const U a, const V b, const size_t) const
    {
        DistanceType       diff = static_cast<DistanceType>(b) - static_cast<DistanceType>(a);
        const DistanceType PI   = pi_const<DistanceType>();
        if (diff > PI)
            diff -= 2 * PI;
        else if (diff < -PI)
            diff += 2 * PI;
        return diff < DistanceType(0) ? -diff : diff;  // abs without <cmath> dependency
    }
};

template <class T, class DataSource, typename _DistanceType = T, typename IndexType = size_t>
struct SO3_Adaptor
{
    using ElementType  = T;
    using DistanceType = _DistanceType;
 
    L2_Simple_Adaptor<T, DataSource, DistanceType, IndexType> distance_L2_Simple;
 
    SO3_Adaptor(const DataSource& _data_source) : distance_L2_Simple(_data_source) {}
 
    inline DistanceType evalMetric(const T* a, const IndexType 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);
    }
};

struct metric_L1 : public Metric
{
    template <class T, class DataSource, typename IndexType = size_t>
    struct traits
    {
        using distance_t = L1_Adaptor<T, DataSource, T, IndexType>;
    };
};

struct metric_L2 : public Metric
{
    template <class T, class DataSource, typename IndexType = size_t>
    struct traits
    {
        using distance_t = L2_Adaptor<T, DataSource, T, IndexType>;
    };
};

struct metric_L2_Simple : public Metric
{
    template <class T, class DataSource, typename IndexType = size_t>
    struct traits
    {
        using distance_t = L2_Simple_Adaptor<T, DataSource, T, IndexType>;
    };
};

struct metric_SO2 : public Metric
{
    template <class T, class DataSource, typename IndexType = size_t>
    struct traits
    {
        using distance_t = SO2_Adaptor<T, DataSource, T, IndexType>;
    };
};

struct metric_SO3 : public Metric
{
    template <class T, class DataSource, typename IndexType = size_t>
    struct traits
    {
        using distance_t = SO3_Adaptor<T, DataSource, T, IndexType>;
    };
};


 
enum class KDTreeSingleIndexAdaptorFlags
{
    None                  = 0,
    SkipInitialBuildIndex = 1
};
 
inline std::underlying_type<KDTreeSingleIndexAdaptorFlags>::type operator&(
    KDTreeSingleIndexAdaptorFlags lhs, KDTreeSingleIndexAdaptorFlags rhs)
{
    using underlying = typename std::underlying_type<KDTreeSingleIndexAdaptorFlags>::type;
    return static_cast<underlying>(lhs) & static_cast<underlying>(rhs);
}

inline bool has_flag(KDTreeSingleIndexAdaptorFlags f, KDTreeSingleIndexAdaptorFlags flag)
{
    return (f & flag) != 0;
}

struct KDTreeSingleIndexAdaptorParams
{
    KDTreeSingleIndexAdaptorParams(
        size_t                        _leaf_max_size  = 10,
        KDTreeSingleIndexAdaptorFlags _flags          = KDTreeSingleIndexAdaptorFlags::None,
        unsigned int                  _n_thread_build = 1)
        : leaf_max_size(_leaf_max_size), flags(_flags), n_thread_build(_n_thread_build)
    {
    }
 
    size_t                        leaf_max_size;
    KDTreeSingleIndexAdaptorFlags flags;
    unsigned int                  n_thread_build;
};

struct SearchParameters
{
    SearchParameters(float eps_ = 0, bool sorted_ = true) : eps(eps_), sorted(sorted_) {}
 
    float eps;  
    bool  sorted;  
};



class PooledAllocator
{
    static constexpr size_t WORDSIZE  = 16;  // WORDSIZE must >= 8
    static constexpr size_t BLOCKSIZE = 8192;
 
    /* 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 Size = size_t;
 
    Size  remaining_ = 0;  
    void* base_      = nullptr;  
    void* loc_       = nullptr;  
 
    void internal_init()
    {
        remaining_   = 0;
        base_        = nullptr;
        usedMemory   = 0;
        wastedMemory = 0;
    }
 
   public:
    Size usedMemory   = 0;
    Size wastedMemory = 0;

    PooledAllocator() { internal_init(); }

    ~PooledAllocator() { free_all(); }

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

    void* allocateBytes(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 > BLOCKSIZE ? size + WORDSIZE : BLOCKSIZE + WORDSIZE;
 
            // use the standard C malloc to allocate memory
            void* m = ::malloc(blocksize);
            if (!m)
            {
                throw std::bad_alloc();
            }
 
            /* Fill first word of new block with pointer to previous block. */
            static_cast<void**>(m)[0] = base_;
            base_                     = m;
 
            remaining_ = blocksize - WORDSIZE;
            loc_       = static_cast<char*>(m) + WORDSIZE;
        }
        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->allocateBytes(sizeof(T) * count));
        return mem;
    }
};



template <int32_t DIM, typename T>
struct array_or_vector
{
    using type = std::array<T, DIM>;
};

template <typename T>
struct array_or_vector<-1, T>
{
    using type = std::vector<T>;
};


template <
    class Derived, typename Distance, class DatasetAdaptor, int32_t DIM = -1,
    typename index_t = uint32_t>
class KDTreeBaseClass
{
   public:
    void freeIndex(Derived& obj)
    {
        obj.pool_.free_all();
        obj.root_node_           = nullptr;
        obj.size_at_index_build_ = 0;
    }
 
    using ElementType  = typename Distance::ElementType;
    using DistanceType = typename Distance::DistanceType;
    using IndexType    = index_t;

    std::vector<IndexType> vAcc_;
 
    using Offset    = typename decltype(vAcc_)::size_type;
    using Size      = typename decltype(vAcc_)::size_type;
    using Dimension = int32_t;
 
    /*-------------------------------------------------------------------
     * Internal Data Structures
     *
     * "Node" below can be declared with alignas(N) to improve
     * cache friendliness and SIMD load/store performance.
     *
     * The optimal N depends on the underlying hardware:
     *  + Intel x86-64: 16 for SSE, 32 for AVX/AVX2 and 64 for AVX-512
     *  + NVIDIA Jetson: 16 for ARM + NEON and CUDA float4/
     *  To avoid unnecessary padding, the smallest alignment
     *  compatible with a platform's vector width should be chosen.
     * ------------------------------------------------------------------*/
    struct alignas(NANOFLANN_NODE_ALIGNMENT) Node
    {
        union
        {
            struct leaf
            {
                Offset left, right;  
            } lr;
            struct nonleaf
            {
                Dimension divfeat;  
                DistanceType divlow, divhigh;
            } sub;
        } node_type;

        Node *child1 = nullptr, *child2 = nullptr;
    };
 
    using NodePtr      = Node*;
    using NodeConstPtr = const Node*;
 
    struct Interval
    {
        ElementType low, high;
    };
 
    NodePtr root_node_ = nullptr;
 
    Size leaf_max_size_ = 0;

    Size n_thread_build_ = 1;
    Size size_ = 0;
    Size      size_at_index_build_ = 0;
    Dimension dim_                 = 0;  

    using BoundingBox = typename array_or_vector<DIM, Interval>::type;

    using distance_vector_t = typename array_or_vector<DIM, DistanceType>::type;

    BoundingBox root_bbox_;

    PooledAllocator pool_;

    NANOFLANN_NODISCARD Size size(const Derived& obj) const noexcept { return obj.size_; }

    NANOFLANN_NODISCARD Size veclen(const Derived& obj) const noexcept
    {
#if defined(__cpp_if_constexpr) && __cpp_if_constexpr >= 201606L
        if constexpr (DIM > 0)
        {
            return DIM;
        }
        else
        {
            return obj.dim_;
        }
#else
        return DIM > 0 ? DIM : obj.dim_;
#endif
    }

    ElementType dataset_get(const Derived& obj, IndexType element, Dimension component) const
    {
        return obj.dataset_.kdtree_get_pt(element, component);
    }

    NANOFLANN_NODISCARD Size usedMemory(const Derived& obj) const
    {
        return obj.pool_.usedMemory + obj.pool_.wastedMemory +
               obj.dataset_.kdtree_get_point_count() *
                   sizeof(IndexType);  // pool memory and vind array memory
    }

    void computeMinMax(
        const Derived& obj, Offset ind, Size count, Dimension element, ElementType& min_elem,
        ElementType& max_elem) const
    {
        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;
        }
    }

    NANOFLANN_NODISCARD bool isActive(IndexType /*idx*/) const { return true; }

    void computeBoundingBox(BoundingBox& bbox)
    {
        Derived&        obj  = static_cast<Derived&>(*this);
        const Dimension dims = static_cast<Dimension>(veclen(obj));
        resize(bbox, dims);
        if (obj.dataset_.kdtree_get_bbox(bbox)) return;
        if (!size_)
            throw std::runtime_error(
                "[nanoflann] computeBoundingBox() called but "
                "no data points found.");
        for (Dimension i = 0; i < dims; ++i)
            bbox[i].low = bbox[i].high = dataset_get(obj, vAcc_[0], i);
        for (Offset k = 1; k < size_; ++k)
            for (Dimension i = 0; i < dims; ++i)
            {
                const auto val = dataset_get(obj, vAcc_[k], i);
                if (val < bbox[i].low) bbox[i].low = val;
                if (val > bbox[i].high) bbox[i].high = val;
            }
    }

    template <class RESULTSET>
    bool searchLevel(
        RESULTSET& result_set, const ElementType* vec, const NodePtr node, DistanceType mindist,
        distance_vector_t& dists, const DistanceType epsError) const
    {
        const Derived& obj = static_cast<const Derived&>(*this);
        // If this is a leaf node, then do check and return.
        if (!node->child1)  // (if one node is nullptr, both are)
        {
            // Hoist the point length out of the per-point loop. For a
            // fixed-size tree (DIM > 0) this is a compile-time constant; for a
            // runtime dimension it avoids re-reading obj.dim_ on every point.
            const Size dim = veclen(obj);
            for (Offset i = node->node_type.lr.left; i < node->node_type.lr.right; ++i)
            {
                const IndexType accessor = vAcc_[i];
                if (!obj.isActive(accessor)) continue;
                DistanceType dist = obj.distance_.evalMetric(vec, accessor, dim);
                if (dist < result_set.worstDist())
                {
                    if (!result_set.addPoint(
                            static_cast<typename RESULTSET::DistanceType>(dist),
                            static_cast<typename RESULTSET::IndexType>(accessor)))
                        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   = obj.distance_.accum_dist(val, node->node_type.sub.divhigh, idx);
        }
        else
        {
            bestChild  = node->child2;
            otherChild = node->child1;
            cut_dist   = obj.distance_.accum_dist(val, node->node_type.sub.divlow, idx);
        }
 
        /* Call recursively to search next level down. */
        if (!searchLevel(result_set, vec, bestChild, mindist, dists, epsError)) return false;
 
        DistanceType dst = dists[idx];
        mindist          = mindist + cut_dist - dst;
        dists[idx]       = cut_dist;
        if (mindist * epsError <= result_set.worstDist())
        {
            if (!searchLevel(result_set, vec, otherChild, mindist, dists, epsError)) return false;
        }
        dists[idx] = dst;
        return true;
    }

    bool makeNode(
        Derived& obj, NodePtr node, const Offset left, const Offset right, BoundingBox& bbox,
        Offset& idx, Dimension& cutfeat, DistanceType& cutval)
    {
        const Dimension dims = static_cast<Dimension>(veclen(obj));
 
        /* If too few exemplars remain, then make this a leaf node. */
        if ((right - left) <= static_cast<Offset>(obj.leaf_max_size_))
        {
            node->child1 = node->child2 = nullptr; /* 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 < dims; ++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 < dims; ++i)
                {
                    const auto val = dataset_get(obj, obj.vAcc_[k], i);
                    if (bbox[i].low > val) bbox[i].low = val;
                    if (bbox[i].high < val) bbox[i].high = val;
                }
            }
            return true;
        }
 
        /* Determine the index, dimension and value for split plane */
        middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox);
        node->node_type.sub.divfeat = cutfeat;
        return false;
    }

    void finalizeSplitNode(
        Derived& obj, NodePtr node, const Dimension cutfeat, const BoundingBox& left_bbox,
        const BoundingBox& right_bbox, BoundingBox& bbox)
    {
        node->node_type.sub.divlow  = left_bbox[cutfeat].high;
        node->node_type.sub.divhigh = right_bbox[cutfeat].low;
 
        const Dimension dims = static_cast<Dimension>(veclen(obj));
        for (Dimension i = 0; i < dims; ++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);
        }
    }
 
    NodePtr divideTree(Derived& obj, const Offset left, const Offset right, BoundingBox& bbox)
    {
        assert(static_cast<Size>(obj.vAcc_.at(left)) < obj.dataset_.kdtree_get_point_count());
 
        NodePtr      node = obj.pool_.template allocate<Node>();  // allocate memory
        Offset       idx;
        Dimension    cutfeat;
        DistanceType cutval;
        if (makeNode(obj, node, left, right, bbox, idx, cutfeat, cutval)) return node;
 
        /* Recurse on left */
        BoundingBox left_bbox(bbox);
        left_bbox[cutfeat].high = cutval;
        node->child1            = this->divideTree(obj, left, left + idx, left_bbox);
 
        /* Recurse on right */
        BoundingBox right_bbox(bbox);
        right_bbox[cutfeat].low = cutval;
        node->child2            = this->divideTree(obj, left + idx, right, right_bbox);
 
        finalizeSplitNode(obj, node, cutfeat, left_bbox, right_bbox, bbox);
 
        return node;
    }

    NodePtr divideTreeConcurrent(
        Derived& obj, const Offset left, const Offset right, BoundingBox& bbox,
        std::atomic<unsigned int>& thread_count, std::mutex& mutex)
    {
        std::unique_lock<std::mutex> lock(mutex);
        NodePtr                      node = obj.pool_.template allocate<Node>();  // allocate memory
        lock.unlock();
 
        Offset       idx;
        Dimension    cutfeat;
        DistanceType cutval;
        if (makeNode(obj, node, left, right, bbox, idx, cutfeat, cutval)) return node;
 
        std::future<NodePtr> right_future;
 
        /* Recurse on right concurrently, if possible */
 
        BoundingBox right_bbox(bbox);
        right_bbox[cutfeat].low = cutval;
        if (++thread_count < n_thread_build_)
        {
            /* Concurrent thread for right recursion */
 
            right_future = std::async(
                std::launch::async, &KDTreeBaseClass::divideTreeConcurrent, this, std::ref(obj),
                left + idx, right, std::ref(right_bbox), std::ref(thread_count), std::ref(mutex));
        }
        else
        {
            --thread_count;
        }
 
        /* Recurse on left in this thread */
 
        BoundingBox left_bbox(bbox);
        left_bbox[cutfeat].high = cutval;
        node->child1 =
            this->divideTreeConcurrent(obj, left, left + idx, left_bbox, thread_count, mutex);
 
        if (right_future.valid())
        {
            /* Block and wait for concurrent right from above */
 
            node->child2 = right_future.get();
            --thread_count;
        }
        else
        {
            /* Otherwise, recurse on right in this thread */
 
            node->child2 =
                this->divideTreeConcurrent(obj, left + idx, right, right_bbox, thread_count, mutex);
        }
 
        finalizeSplitNode(obj, node, cutfeat, left_bbox, right_bbox, bbox);
 
        return node;
    }
 
    void middleSplit_(
        const Derived& obj, const Offset ind, const Size count, Offset& index, Dimension& cutfeat,
        DistanceType& cutval, const BoundingBox& bbox)
    {
        const Dimension dims = static_cast<Dimension>(veclen(obj));
        const auto      EPS  = static_cast<DistanceType>(0.00001);
 
        // Pre-compute max_span once
        ElementType max_span = bbox[0].high - bbox[0].low;
        for (Dimension i = 1; i < dims; ++i)
        {
            ElementType span = bbox[i].high - bbox[i].low;
            if (span > max_span) max_span = span;
        }
 
        // Two-pass: first find max_span (done above), then scan candidate dims
        // inline — no heap allocation for a candidates vector.
        cutfeat                      = 0;
        ElementType       max_spread = -1;
        ElementType       min_elem = 0, max_elem = 0;
        const ElementType threshold = (1 - EPS) * max_span;
 
        for (Dimension dim = 0; dim < dims; ++dim)
        {
            if (bbox[dim].high - bbox[dim].low < threshold) continue;
 
            ElementType local_min = dataset_get(obj, vAcc_[ind], dim);
            ElementType local_max = local_min;
 
            // Unrolled loop for better performance
            constexpr size_t UNROLL = 4;
            Offset           k      = 1;
            for (; k + UNROLL <= count; k += UNROLL)
            {
                ElementType v0 = dataset_get(obj, vAcc_[ind + k], dim);
                ElementType v1 = dataset_get(obj, vAcc_[ind + k + 1], dim);
                ElementType v2 = dataset_get(obj, vAcc_[ind + k + 2], dim);
                ElementType v3 = dataset_get(obj, vAcc_[ind + k + 3], dim);
 
                local_min = std::min({local_min, v0, v1, v2, v3});
                local_max = std::max({local_max, v0, v1, v2, v3});
            }
 
            // Handle remainder
            for (; k < count; ++k)
            {
                ElementType val = dataset_get(obj, vAcc_[ind + k], dim);
                local_min       = std::min(local_min, val);
                local_max       = std::max(local_max, val);
            }
 
            ElementType spread = local_max - local_min;
            if (spread > max_spread)
            {
                cutfeat    = dim;
                max_spread = spread;
                min_elem   = local_min;
                max_elem   = local_max;
            }
        }
 
        // Median-of-three for better balance
        DistanceType split_val = (bbox[cutfeat].low + bbox[cutfeat].high) / 2;
        if (split_val < min_elem) split_val = min_elem;
        if (split_val > max_elem) split_val = max_elem;
 
        cutval = split_val;
 
        // Optimized partitioning
        Offset lim1, lim2;
        planeSplit(obj, ind, count, cutfeat, cutval, lim1, lim2);
 
        index = (lim1 > count / 2) ? lim1 : (lim2 < count / 2) ? lim2 : count / 2;
    }

    void planeSplit(
        const Derived& obj, const Offset ind, const Size count, const Dimension cutfeat,
        const DistanceType& cutval, Offset& lim1, Offset& lim2)
    {
        // Dutch National Flag algorithm for three-way partitioning
        Offset left  = 0;
        Offset mid   = 0;
        Offset right = count - 1;
 
        while (mid <= right)
        {
            ElementType val = dataset_get(obj, vAcc_[ind + mid], cutfeat);
 
            if (val < cutval)
            {
                std::swap(vAcc_[ind + left], vAcc_[ind + mid]);
                left++;
                mid++;
            }
            else if (val > cutval)
            {
                std::swap(vAcc_[ind + mid], vAcc_[ind + right]);
                right--;
            }
            else
            {
                mid++;
            }
        }
 
        lim1 = left;
        lim2 = mid;
    }
 
    DistanceType computeInitialDistances(
        const Derived& obj, const ElementType* vec, distance_vector_t& dists) const
    {
        assert(vec);
        DistanceType dist = DistanceType();
 
        const Dimension dims = static_cast<Dimension>(veclen(obj));
        for (Dimension i = 0; i < dims; ++i)
        {
            if (vec[i] < obj.root_bbox_[i].low)
            {
                dists[i] = obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].low, i);
                dist += dists[i];
            }
            else if (vec[i] > obj.root_bbox_[i].high)
            {
                dists[i] = obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].high, i);
                dist += dists[i];
            }
        }
        return dist;
    }
 
    static void save_tree(const Derived& obj, std::ostream& stream, const NodeConstPtr tree)
    {
        save_value(stream, *tree);
        if (tree->child1 != nullptr)
        {
            save_tree(obj, stream, tree->child1);
        }
        if (tree->child2 != nullptr)
        {
            save_tree(obj, stream, tree->child2);
        }
    }
 
    static void load_tree(Derived& obj, std::istream& stream, NodePtr& tree)
    {
        tree = obj.pool_.template allocate<Node>();
        load_value(stream, *tree);
        if (tree->child1 != nullptr)
        {
            load_tree(obj, stream, tree->child1);
        }
        if (tree->child2 != nullptr)
        {
            load_tree(obj, stream, tree->child2);
        }
    }

    static constexpr uint32_t SAVE_MAGIC = 0x4E464C4E;

    void saveIndex(const Derived& obj, std::ostream& stream) const
    {
        // 10-byte header: magic | version | sizeof_size_t | sizeof_IndexType
        //                 | sizeof_ElementType | sizeof_DistanceType
        // Use local copies: passing a static constexpr by const-ref ODR-uses it
        // in C++11/14, which requires an out-of-class definition we cannot provide
        // in a header-only library.
        const uint32_t hdr_magic   = SAVE_MAGIC;
        const uint32_t hdr_version = static_cast<uint32_t>(NANOFLANN_VERSION);
        const uint8_t  hdr_sz_st   = static_cast<uint8_t>(sizeof(size_t));
        const uint8_t  hdr_sz_idx  = static_cast<uint8_t>(sizeof(IndexType));
        const uint8_t  hdr_sz_elem = static_cast<uint8_t>(sizeof(ElementType));
        const uint8_t  hdr_sz_dist = static_cast<uint8_t>(sizeof(DistanceType));
        save_value(stream, hdr_magic);
        save_value(stream, hdr_version);
        save_value(stream, hdr_sz_st);
        save_value(stream, hdr_sz_idx);
        save_value(stream, hdr_sz_elem);
        save_value(stream, hdr_sz_dist);
 
        save_value(stream, obj.size_);
        save_value(stream, obj.dim_);
        save_value(stream, obj.root_bbox_);
        save_value(stream, obj.leaf_max_size_);
        save_value(stream, obj.vAcc_);
        if (obj.root_node_)
        {
            save_tree(obj, stream, obj.root_node_);
        }
    }

    void loadIndex(Derived& obj, std::istream& stream)
    {
        // Validate header
        uint32_t magic = 0;
        load_value(stream, magic);
        if (stream.fail() || magic != SAVE_MAGIC)
        {
            throw std::runtime_error(
                "nanoflann loadIndex: invalid file (wrong magic number). "
                "The stream was not written by nanoflann saveIndex().");
        }
 
        uint32_t file_version = 0;
        load_value(stream, file_version);
        if (file_version != static_cast<uint32_t>(NANOFLANN_VERSION))
        {
            char msg[200];
            snprintf(
                msg, sizeof(msg),
                "nanoflann loadIndex: version mismatch "
                "(file=0x%03X, library=0x%03X). Rebuild the index.",
                file_version, static_cast<unsigned>(NANOFLANN_VERSION));
            throw std::runtime_error(msg);
        }
 
        uint8_t sz_size_t = 0;
        uint8_t sz_idx    = 0;
        uint8_t sz_elem   = 0;
        uint8_t sz_dist   = 0;
        load_value(stream, sz_size_t);
        load_value(stream, sz_idx);
        load_value(stream, sz_elem);
        load_value(stream, sz_dist);
        if (sz_size_t != static_cast<uint8_t>(sizeof(size_t)) ||
            sz_idx != static_cast<uint8_t>(sizeof(IndexType)) ||
            sz_elem != static_cast<uint8_t>(sizeof(ElementType)) ||
            sz_dist != static_cast<uint8_t>(sizeof(DistanceType)))
        {
            throw std::runtime_error(
                "nanoflann loadIndex: type-size mismatch between saved index and "
                "current template instantiation (sizeof size_t / IndexType / "
                "ElementType / DistanceType differ). Rebuild the index.");
        }
 
        load_value(stream, obj.size_);
        load_value(stream, obj.dim_);
        load_value(stream, obj.root_bbox_);
        load_value(stream, obj.leaf_max_size_);
        load_value(stream, obj.vAcc_);
 
        if (obj.size_ > 0)
        {
            load_tree(obj, stream, obj.root_node_);
        }
 
        if (stream.fail())
        {
            throw std::runtime_error(
                "nanoflann loadIndex: unexpected end of stream or read error.");
        }
    }
};


template <typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename index_t = uint32_t>
class KDTreeSingleIndexAdaptor
    : public KDTreeBaseClass<
          KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, index_t>, Distance,
          DatasetAdaptor, DIM, index_t>
{
   public:
    explicit KDTreeSingleIndexAdaptor(
        const KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, index_t>&) = delete;

    const DatasetAdaptor& dataset_;
 
    const KDTreeSingleIndexAdaptorParams indexParams;
 
    Distance distance_;
 
    using Base = typename nanoflann::KDTreeBaseClass<
        nanoflann::KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, index_t>, Distance,
        DatasetAdaptor, DIM, index_t>;
 
    using Offset    = typename Base::Offset;
    using Size      = typename Base::Size;
    using Dimension = typename Base::Dimension;
 
    using ElementType  = typename Base::ElementType;
    using DistanceType = typename Base::DistanceType;
    using IndexType    = typename Base::IndexType;
 
    using Node    = typename Base::Node;
    using NodePtr = Node*;
 
    using Interval = typename Base::Interval;

    using BoundingBox = typename Base::BoundingBox;

    using distance_vector_t = typename Base::distance_vector_t;

    template <class... Args>
    explicit KDTreeSingleIndexAdaptor(
        const Dimension dimensionality, const DatasetAdaptor& inputData,
        const KDTreeSingleIndexAdaptorParams& params, Args&&... args)
        : dataset_(inputData),
          indexParams(params),
          distance_(inputData, std::forward<Args>(args)...)
    {
        init(dimensionality, params);
    }
 
    explicit KDTreeSingleIndexAdaptor(
        const Dimension dimensionality, const DatasetAdaptor& inputData,
        const KDTreeSingleIndexAdaptorParams& params = {})
        : dataset_(inputData), indexParams(params), distance_(inputData)
    {
        init(dimensionality, params);
    }
 
   private:
    void init(const Dimension dimensionality, const KDTreeSingleIndexAdaptorParams& params)
    {
        Base::size_                = dataset_.kdtree_get_point_count();
        Base::size_at_index_build_ = Base::size_;
        Base::dim_                 = dimensionality;
        if (DIM > 0) Base::dim_ = DIM;
        Base::leaf_max_size_ = params.leaf_max_size;
        if (params.n_thread_build > 0)
        {
            Base::n_thread_build_ = params.n_thread_build;
        }
        else
        {
            Base::n_thread_build_ = std::max(std::thread::hardware_concurrency(), 1u);
        }
 
        if (!has_flag(params.flags, KDTreeSingleIndexAdaptorFlags::SkipInitialBuildIndex))
        {
            // Build KD-tree:
            buildIndex();
        }
    }
 
   public:
    void buildIndex()
    {
        Base::size_                = dataset_.kdtree_get_point_count();
        Base::size_at_index_build_ = Base::size_;
        init_vind();
        this->freeIndex(*this);
        Base::size_at_index_build_ = Base::size_;
        if (Base::size_ == 0) return;
        this->computeBoundingBox(Base::root_bbox_);
        // construct the tree
        if (Base::n_thread_build_ == 1)
        {
            Base::root_node_ = this->divideTree(*this, 0, Base::size_, Base::root_bbox_);
        }
        else
        {
#ifndef NANOFLANN_NO_THREADS
            std::atomic<unsigned int> thread_count(0u);
            std::mutex                mutex;
            Base::root_node_ = this->divideTreeConcurrent(
                *this, 0, Base::size_, Base::root_bbox_, thread_count, mutex);
#else /* NANOFLANN_NO_THREADS */
            throw std::runtime_error("Multithreading is disabled");
#endif /* NANOFLANN_NO_THREADS */
        }
    }


    template <typename RESULTSET>
    bool findNeighbors(
        RESULTSET& result, const ElementType* vec, const SearchParameters& searchParams = {}) const
    {
        assert(vec);
        if (this->size(*this) == 0) return false;
        if (!Base::root_node_)
            throw std::runtime_error(
                "[nanoflann] findNeighbors() called before building the "
                "index.");
        DistanceType epsError = 1 + static_cast<DistanceType>(searchParams.eps);
 
        // fixed or variable-sized container (depending on DIM)
        distance_vector_t dists;
        // Fill it with zeros.
        auto zero = static_cast<typename RESULTSET::DistanceType>(0);
        assign(dists, this->veclen(*this), zero);
        DistanceType dist = this->computeInitialDistances(*this, vec, dists);
        this->searchLevel(result, vec, Base::root_node_, dist, dists, epsError);
 
        if (searchParams.sorted) result.sort();
 
        return result.full();
    }

    template <typename RESULTSET>
    NANOFLANN_NODISCARD Size findWithinBox(RESULTSET& result, const BoundingBox& bbox) const
    {
        if (this->size(*this) == 0) return 0;
        if (!Base::root_node_)
            throw std::runtime_error(
                "[nanoflann] findWithinBox() called before building the "
                "index.");
 
        std::stack<NodePtr> stack;
        stack.push(Base::root_node_);
 
        while (!stack.empty())
        {
            const NodePtr node = stack.top();
            stack.pop();
 
            // If this is a leaf node, then do check and return.
            if (!node->child1)  // (if one node is nullptr, both are)
            {
                for (Offset i = node->node_type.lr.left; i < node->node_type.lr.right; ++i)
                {
                    if (contains(bbox, Base::vAcc_[i]))
                    {
                        if (!result.addPoint(0, Base::vAcc_[i]))
                        {
                            // the resultset doesn't want to receive any more
                            // points, we're done searching!
                            return result.size();
                        }
                    }
                }
            }
            else
            {
                const Dimension idx        = node->node_type.sub.divfeat;
                const auto      low_bound  = node->node_type.sub.divlow;
                const auto      high_bound = node->node_type.sub.divhigh;
 
                if (bbox[idx].low <= low_bound) stack.push(node->child1);
                if (bbox[idx].high >= high_bound) stack.push(node->child2);
            }
        }
 
        return result.size();
    }

    NANOFLANN_NODISCARD Size knnSearch(
        const ElementType* query_point, const Size num_closest, IndexType* out_indices,
        DistanceType* out_distances) const
    {
        nanoflann::KNNResultSet<DistanceType, IndexType> resultSet(num_closest);
        resultSet.init(out_indices, out_distances);
        findNeighbors(resultSet, query_point);
        return resultSet.size();
    }

    NANOFLANN_NODISCARD Size radiusSearch(
        const ElementType* query_point, const DistanceType& radius,
        std::vector<ResultItem<IndexType, DistanceType>>& IndicesDists,
        const SearchParameters&                           searchParams = {}) const
    {
        RadiusResultSet<DistanceType, IndexType> resultSet(radius, IndicesDists);
        const Size nFound = radiusSearchCustomCallback(query_point, resultSet, searchParams);
        return nFound;
    }

    template <class SEARCH_CALLBACK>
    NANOFLANN_NODISCARD Size radiusSearchCustomCallback(
        const ElementType* query_point, SEARCH_CALLBACK& resultSet,
        const SearchParameters& searchParams = {}) const
    {
        findNeighbors(resultSet, query_point, searchParams);
        return resultSet.size();
    }

    NANOFLANN_NODISCARD Size rknnSearch(
        const ElementType* query_point, const Size num_closest, IndexType* out_indices,
        DistanceType* out_distances, const DistanceType& radius) const
    {
        nanoflann::RKNNResultSet<DistanceType, IndexType> resultSet(num_closest, radius);
        resultSet.init(out_indices, out_distances);
        findNeighbors(resultSet, query_point);
        return resultSet.size();
    }

 
   public:
    void init_vind()
    {
        // Create a permutable array of indices to the input vectors.
        Base::size_ = dataset_.kdtree_get_point_count();
        if (Base::vAcc_.size() != Base::size_) Base::vAcc_.resize(Base::size_);
        for (IndexType i = 0; i < static_cast<IndexType>(Base::size_); i++) Base::vAcc_[i] = i;
    }
 
    bool contains(const BoundingBox& bbox, IndexType idx) const
    {
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        for (Dimension i = 0; i < dims; ++i)
        {
            const auto point = this->dataset_.kdtree_get_pt(idx, i);
            if (point < bbox[i].low || point > bbox[i].high) return false;
        }
        return true;
    }
 
   public:
    void saveIndex(std::ostream& stream) const { Base::saveIndex(*this, stream); }

    void loadIndex(std::istream& stream) { Base::loadIndex(*this, stream); }
 
};  // class KDTree

template <typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename IndexType = uint32_t>
class KDTreeSingleIndexDynamicAdaptor_
    : public KDTreeBaseClass<
          KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM, IndexType>, Distance,
          DatasetAdaptor, DIM, IndexType>
{
   public:
    const DatasetAdaptor& dataset_;  
 
    KDTreeSingleIndexAdaptorParams index_params_;
 
    std::vector<int>& treeIndex_;
 
    Distance distance_;
 
    using Base = typename nanoflann::KDTreeBaseClass<
        nanoflann::KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM, IndexType>,
        Distance, DatasetAdaptor, DIM, IndexType>;
 
    using ElementType  = typename Base::ElementType;
    using DistanceType = typename Base::DistanceType;
 
    using Offset    = typename Base::Offset;
    using Size      = typename Base::Size;
    using Dimension = typename Base::Dimension;
 
    using Node    = typename Base::Node;
    using NodePtr = Node*;
 
    using Interval = typename Base::Interval;
    using BoundingBox = typename Base::BoundingBox;

    using distance_vector_t = typename Base::distance_vector_t;

    NANOFLANN_NODISCARD bool isActive(IndexType idx) const { return treeIndex_[idx] != -1; }

    KDTreeSingleIndexDynamicAdaptor_(
        const Dimension dimensionality, const DatasetAdaptor& inputData,
        std::vector<int>&                     treeIndex,
        const KDTreeSingleIndexAdaptorParams& params = KDTreeSingleIndexAdaptorParams())
        : dataset_(inputData), index_params_(params), treeIndex_(treeIndex), distance_(inputData)
    {
        Base::size_                = 0;
        Base::size_at_index_build_ = 0;
        for (auto& v : Base::root_bbox_) v = {};
        Base::dim_ = dimensionality;
        if (DIM > 0) Base::dim_ = DIM;
        Base::leaf_max_size_ = params.leaf_max_size;
        if (params.n_thread_build > 0)
        {
            Base::n_thread_build_ = params.n_thread_build;
        }
        else
        {
            Base::n_thread_build_ = std::max(std::thread::hardware_concurrency(), 1u);
        }
    }

    KDTreeSingleIndexDynamicAdaptor_(const KDTreeSingleIndexDynamicAdaptor_& rhs) = default;

    KDTreeSingleIndexDynamicAdaptor_& operator=(const KDTreeSingleIndexDynamicAdaptor_& rhs)
    {
        if (this == &rhs) return *this;
        KDTreeSingleIndexDynamicAdaptor_ tmp(rhs);
        std::swap(Base::vAcc_, tmp.Base::vAcc_);
        std::swap(Base::leaf_max_size_, tmp.Base::leaf_max_size_);
        std::swap(index_params_, tmp.index_params_);
        // treeIndex_ is a reference member and cannot be rebound; do not swap.
        std::swap(Base::size_, tmp.Base::size_);
        std::swap(Base::size_at_index_build_, tmp.Base::size_at_index_build_);
        std::swap(Base::root_node_, tmp.Base::root_node_);
        std::swap(Base::root_bbox_, tmp.Base::root_bbox_);
        std::swap(Base::pool_, tmp.Base::pool_);
        return *this;
    }

    void buildIndex()
    {
        Base::size_ = Base::vAcc_.size();
        this->freeIndex(*this);
        Base::size_at_index_build_ = Base::size_;
        if (Base::size_ == 0) return;
        this->computeBoundingBox(Base::root_bbox_);
        // construct the tree
        if (Base::n_thread_build_ == 1)
        {
            Base::root_node_ = this->divideTree(*this, 0, Base::size_, Base::root_bbox_);
        }
        else
        {
#ifndef NANOFLANN_NO_THREADS
            std::atomic<unsigned int> thread_count(0u);
            std::mutex                mutex;
            Base::root_node_ = this->divideTreeConcurrent(
                *this, 0, Base::size_, Base::root_bbox_, thread_count, mutex);
#else /* NANOFLANN_NO_THREADS */
            throw std::runtime_error("Multithreading is disabled");
#endif /* NANOFLANN_NO_THREADS */
        }
    }


    template <typename RESULTSET>
    bool findNeighbors(
        RESULTSET& result, const ElementType* vec, const SearchParameters& searchParams = {}) const
    {
        assert(vec);
        if (this->size(*this) == 0) return false;
        if (!Base::root_node_) return false;
        DistanceType epsError = 1 + static_cast<DistanceType>(searchParams.eps);
 
        // fixed or variable-sized container (depending on DIM)
        distance_vector_t dists;
        // Fill it with zeros.
        assign(dists, this->veclen(*this), static_cast<typename distance_vector_t::value_type>(0));
        DistanceType dist = this->computeInitialDistances(*this, vec, dists);
        this->searchLevel(result, vec, Base::root_node_, dist, dists, epsError);
 
        if (searchParams.sorted) result.sort();
 
        return result.full();
    }

    NANOFLANN_NODISCARD Size knnSearch(
        const ElementType* query_point, const Size num_closest, IndexType* out_indices,
        DistanceType* out_distances, const SearchParameters& searchParams = {}) const
    {
        nanoflann::KNNResultSet<DistanceType, IndexType> resultSet(num_closest);
        resultSet.init(out_indices, out_distances);
        findNeighbors(resultSet, query_point, searchParams);
        return resultSet.size();
    }

    NANOFLANN_NODISCARD Size radiusSearch(
        const ElementType* query_point, const DistanceType& radius,
        std::vector<ResultItem<IndexType, DistanceType>>& IndicesDists,
        const SearchParameters&                           searchParams = {}) const
    {
        RadiusResultSet<DistanceType, IndexType> resultSet(radius, IndicesDists);
        const Size nFound = radiusSearchCustomCallback(query_point, resultSet, searchParams);
        return nFound;
    }

    template <class SEARCH_CALLBACK>
    NANOFLANN_NODISCARD Size radiusSearchCustomCallback(
        const ElementType* query_point, SEARCH_CALLBACK& resultSet,
        const SearchParameters& searchParams = {}) const
    {
        findNeighbors(resultSet, query_point, searchParams);
        return resultSet.size();
    }

 
   public:
   public:
    void saveIndex(std::ostream& stream) { Base::saveIndex(*this, stream); }

    void loadIndex(std::istream& stream) { Base::loadIndex(*this, stream); }
};

template <typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename IndexType = uint32_t>
class KDTreeSingleIndexDynamicAdaptor
{
   public:
    using ElementType  = typename Distance::ElementType;
    using DistanceType = typename Distance::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 leaf_max_size_;
    Size treeCount_;
    Size pointCount_;

    const DatasetAdaptor& dataset_;  

    std::vector<int> treeIndex_;
    std::unordered_map<IndexType, int> removedPoints_;
 
    KDTreeSingleIndexAdaptorParams index_params_;
 
    Dimension dim_;  
 
    using index_container_t =
        KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM, IndexType>;
    std::vector<index_container_t> index_;
 
   public:
    const std::vector<index_container_t>& getAllIndices() const { return index_; }
 
   private:
    int First0Bit(Size num)
    {
        int pos = 0;
        while (num & 1)
        {
            num = num >> 1;
            pos++;
        }
        return pos;
    }

    void init()
    {
        using my_kd_tree_t =
            KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM, IndexType>;
        std::vector<my_kd_tree_t> index(
            treeCount_, my_kd_tree_t(dim_ /*dim*/, dataset_, treeIndex_, index_params_));
        index_ = index;
    }
 
   public:
    Distance distance_;

    explicit 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)) + 1;
        pointCount_ = 0U;
        dim_        = dimensionality;
        treeIndex_.clear();
        if (DIM > 0) dim_ = DIM;
        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, static_cast<IndexType>(num_initial_points - 1));
        }
    }

    explicit KDTreeSingleIndexDynamicAdaptor(
        const KDTreeSingleIndexDynamicAdaptor<Distance, DatasetAdaptor, DIM, IndexType>&) = delete;

    void addPoints(IndexType start, IndexType end)
    {
        int maxIndex = 0;
        for (IndexType idx = start; idx <= end; idx++)
        {
            // If this index was previously removed, its point is still
            // physically present in its sub-tree (removal is lazy and never
            // deletes from vAcc_). Just clear the "removed" mark and restore its
            // tree index. Re-inserting it would create a duplicate entry that
            // grows the trees without bound and yields duplicate search results.
            const auto it = removedPoints_.find(idx);
            if (it != removedPoints_.end())
            {
                treeIndex_[idx] = it->second;
                removedPoints_.erase(it);
                continue;
            }
 
            const int pos = First0Bit(pointCount_);
            maxIndex      = std::max(pos, maxIndex);
            if (treeIndex_.size() <= static_cast<size_t>(pointCount_))
                treeIndex_.resize(static_cast<size_t>(pointCount_) + 1);
            treeIndex_[pointCount_] = pos;
 
            for (int i = 0; i < pos; i++)
            {
                for (size_t j = 0; j < index_[i].vAcc_.size(); j++)
                {
                    const IndexType e = index_[i].vAcc_[j];
                    index_[pos].vAcc_.push_back(e);
                    if (treeIndex_[e] != -1)
                        treeIndex_[e] = pos;
                    else
                        removedPoints_[e] = pos;  // keep tombstone's tree index current
                }
                index_[i].vAcc_.clear();
            }
            index_[pos].vAcc_.push_back(idx);
            pointCount_++;
        }
 
        for (int i = 0; i <= maxIndex; ++i)
        {
            index_[i].freeIndex(index_[i]);
            if (!index_[i].vAcc_.empty()) index_[i].buildIndex();
        }
    }

    void removePoint(size_t idx)
    {
        if (idx >= pointCount_) return;
        if (treeIndex_[idx] == -1) return;  // already removed
        // Remember which sub-tree still physically holds this point, so it can
        // be reactivated in place if re-added later (see addPoints).
        removedPoints_[static_cast<IndexType>(idx)] = treeIndex_[idx];
        treeIndex_[idx]                             = -1;
    }

    template <typename RESULTSET>
    bool findNeighbors(
        RESULTSET& result, const ElementType* vec, const SearchParameters& searchParams = {}) const
    {
        for (size_t i = 0; i < treeCount_; i++)
        {
            index_[i].findNeighbors(result, &vec[0], searchParams);
        }
        return result.full();
    }
};

struct KDTreeIncrementalIndexParams
{
    KDTreeIncrementalIndexParams(float alpha_balance_ = 0.75f, float alpha_deleted_ = 0.5f)
        : alpha_balance(alpha_balance_), alpha_deleted(alpha_deleted_)
    {
    }
 
    float alpha_balance;
    float alpha_deleted;
};

template <typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename IndexType = uint32_t>
class KDTreeSingleIndexIncrementalAdaptor
    : public KDTreeBaseClass<
          KDTreeSingleIndexIncrementalAdaptor<Distance, DatasetAdaptor, DIM, IndexType>, Distance,
          DatasetAdaptor, DIM, IndexType>
{
   public:
    using Base = typename nanoflann::KDTreeBaseClass<
        KDTreeSingleIndexIncrementalAdaptor<Distance, DatasetAdaptor, DIM, IndexType>, Distance,
        DatasetAdaptor, DIM, IndexType>;
 
    using ElementType  = typename Base::ElementType;
    using DistanceType = typename Base::DistanceType;
 
    using Offset    = typename Base::Offset;
    using Size      = typename Base::Size;
    using Dimension = typename Base::Dimension;
 
    using Interval          = typename Base::Interval;
    using BoundingBox       = typename Base::BoundingBox;
    using distance_vector_t = typename Base::distance_vector_t;

    const DatasetAdaptor& dataset_;
 
    Distance distance_;

    struct INode
    {
        IndexType   ptIdx         = 0;  
        Dimension   divfeat       = 0;  
        bool        deleted       = false;  
        bool        treeDeleted   = false;  
        INode*      child1        = nullptr;  
        INode*      child2        = nullptr;  
        INode*      parent        = nullptr;  
        Size        subtree_size  = 0;  
        Size        invalid_count = 0;  
        BoundingBox box;  
        typename array_or_vector<DIM, ElementType>::type pcoord;
    };

#if defined(NANOFLANN_INCREMENTAL_NO_COORD_CACHE)
    static constexpr bool kCacheCoords = false;
#else
    static constexpr bool kCacheCoords = (DIM > 0);
#endif
 
   private:
    INode* iroot_    = nullptr;  
    INode* freeList_ = nullptr;  
 
    Size liveCount_  = 0;  
    Size totalCount_ = 0;  
 
    float alphaBal_ = 0.75f;
    float alphaDel_ = 0.5f;
    static constexpr Size kMinBalanceRebuild = 4;
    static constexpr double kBulkInsertFraction = 0.5;

    INode* pendingRebuild_ = nullptr;

    bool inlineRebuild_ = true;

    std::vector<INode*> nodeOfPoint_;

    std::vector<IndexType> buildBuf_;

    bool                   collectRemoved_ = false;
    std::vector<IndexType> removedSink_;
 
   public:
    explicit KDTreeSingleIndexIncrementalAdaptor(
        const Dimension dimensionality, const DatasetAdaptor& inputData,
        const KDTreeIncrementalIndexParams& params = {})
        : dataset_(inputData), distance_(inputData)
    {
        Base::dim_ = dimensionality;
        if (DIM > 0) Base::dim_ = DIM;
        alphaBal_ = params.alpha_balance;
        alphaDel_ = params.alpha_deleted;
        resize(Base::root_bbox_, static_cast<Dimension>(this->veclen(*this)));
    }

    KDTreeSingleIndexIncrementalAdaptor(const KDTreeSingleIndexIncrementalAdaptor&) = delete;
    KDTreeSingleIndexIncrementalAdaptor& operator=(const KDTreeSingleIndexIncrementalAdaptor&) =
        delete;
 
    ~KDTreeSingleIndexIncrementalAdaptor() { destroyNodeObjects(); }


    void addPoint(IndexType idx)
    {
        ensureNodeMap(idx);
        insertOne(idx);
        syncRootBox();
    }

    void addPoints(IndexType start, IndexType end)
    {
        if (end < start) return;
        ensureNodeMap(end);
        const Size batch = static_cast<Size>(end - start) + 1;
        if (!iroot_ ||
            static_cast<double>(batch) >= kBulkInsertFraction * static_cast<double>(liveCount_))
        {
            buildBuf_.clear();
            if (iroot_) collectLiveAndFree(iroot_, buildBuf_);  // keep existing live points
            for (IndexType idx = start; idx <= end; ++idx) buildBuf_.push_back(idx);
            iroot_      = buildBalanced(buildBuf_, 0, buildBuf_.size(), 0, nullptr);
            liveCount_  = buildBuf_.size();
            totalCount_ = liveCount_;
        }
        else
        {
            for (IndexType idx = start; idx <= end; ++idx) insertOne(idx);
        }
        syncRootBox();
    }

    void removePoint(IndexType idx)
    {
        if (idx >= nodeOfPoint_.size()) return;
        INode* n = nodeOfPoint_[idx];
        if (!n || n->deleted) return;
        // A point can also be logically dead via a treeDeleted ancestor (a
        // lazily-killed box region). Detect that and treat it as already gone.
        for (INode* p = n->parent; p; p = p->parent)
            if (p->treeDeleted) return;
 
        n->deleted = true;
        for (INode* p = n; p; p = p->parent) ++p->invalid_count;
        --liveCount_;
        maybeRebuildForDeletion();
        syncRootBox();
    }

    void removeBox(const BoundingBox& box)
    {
        if (iroot_) removeBoxRec(iroot_, box);
        maybeRebuildForDeletion();
        syncRootBox();
    }

    void removeOutsideBox(const BoundingBox& keep)
    {
        if (iroot_) removeOutsideBoxRec(iroot_, keep);
        maybeRebuildForDeletion();
        syncRootBox();
    }

    void setCollectRemovedPoints(bool enable)
    {
        collectRemoved_ = enable;
        if (!enable) std::vector<IndexType>().swap(removedSink_);
    }

    std::vector<IndexType> acquireRemovedPoints()
    {
        std::vector<IndexType> out;
        out.swap(removedSink_);
        return out;
    }

    void setInlineRebuild(bool enable) { inlineRebuild_ = enable; }

    void snapshotLiveIndices(std::vector<IndexType>& out) const { snapshotRec(iroot_, out); }

    void collectPhysicalIndices(std::vector<IndexType>& out) const { collectAllRec(iroot_, out); }

    NANOFLANN_NODISCARD bool referencesIndex(IndexType idx) const
    {
        return idx < nodeOfPoint_.size() && nodeOfPoint_[idx] != nullptr;
    }

    void buildFromIndices(const std::vector<IndexType>& idxs)
    {
        if (iroot_)
        {
            buildBuf_.clear();
            collectLiveAndFree(iroot_, buildBuf_);  // recycle existing nodes
            iroot_ = nullptr;
        }
        IndexType maxIdx = 0;
        for (IndexType v : idxs) maxIdx = std::max(maxIdx, v);
        if (!idxs.empty()) ensureNodeMap(maxIdx);
        buildBuf_.assign(idxs.begin(), idxs.end());
        iroot_      = buildBalanced(buildBuf_, 0, buildBuf_.size(), 0, nullptr);
        liveCount_  = buildBuf_.size();
        totalCount_ = liveCount_;
        syncRootBox();
    }



    NANOFLANN_NODISCARD Size size() const noexcept { return liveCount_; }
    NANOFLANN_NODISCARD bool empty() const noexcept { return liveCount_ == 0; }

    NANOFLANN_NODISCARD Size physicalSize() const noexcept { return totalCount_; }

    NANOFLANN_NODISCARD Size usedMemory() const
    {
        return Base::pool_.usedMemory + Base::pool_.wastedMemory +
               nodeOfPoint_.capacity() * sizeof(INode*);
    }

    NANOFLANN_NODISCARD BoundingBox boundingBox() const { return Base::root_bbox_; }

    void reserve(Size n)
    {
        nodeOfPoint_.reserve(n);
        buildBuf_.reserve(n);
    }



    template <typename RESULTSET>
    bool findNeighbors(
        RESULTSET& result, const ElementType* vec, const SearchParameters& searchParams = {}) const
    {
        assert(vec);
        if (!iroot_ || liveCount_ == 0) return false;
        const DistanceType epsError = 1 + static_cast<DistanceType>(searchParams.eps);
 
        distance_vector_t dists;
        assign(dists, this->veclen(*this), static_cast<typename distance_vector_t::value_type>(0));
        const DistanceType dist = this->computeInitialDistances(*this, vec, dists);
        searchLevelInc(result, vec, iroot_, dist, dists, epsError, this->veclen(*this));
        if (searchParams.sorted) result.sort();
        return result.full();
    }

    NANOFLANN_NODISCARD Size knnSearch(
        const ElementType* query_point, const Size num_closest, IndexType* out_indices,
        DistanceType* out_distances, const SearchParameters& searchParams = {}) const
    {
        nanoflann::KNNResultSet<DistanceType, IndexType> resultSet(num_closest);
        resultSet.init(out_indices, out_distances);
        findNeighbors(resultSet, query_point, searchParams);
        return resultSet.size();
    }

    NANOFLANN_NODISCARD Size radiusSearch(
        const ElementType* query_point, const DistanceType& radius,
        std::vector<ResultItem<IndexType, DistanceType>>& IndicesDists,
        const SearchParameters&                           searchParams = {}) const
    {
        RadiusResultSet<DistanceType, IndexType> resultSet(radius, IndicesDists);
        findNeighbors(resultSet, query_point, searchParams);
        return resultSet.size();
    }

    template <class SEARCH_CALLBACK>
    NANOFLANN_NODISCARD Size radiusSearchCustomCallback(
        const ElementType* query_point, SEARCH_CALLBACK& resultSet,
        const SearchParameters& searchParams = {}) const
    {
        findNeighbors(resultSet, query_point, searchParams);
        return resultSet.size();
    }

    NANOFLANN_NODISCARD Size rknnSearch(
        const ElementType* query_point, const Size num_closest, IndexType* out_indices,
        DistanceType* out_distances, const DistanceType& radius) const
    {
        nanoflann::RKNNResultSet<DistanceType, IndexType> resultSet(num_closest, radius);
        resultSet.init(out_indices, out_distances);
        findNeighbors(resultSet, query_point);
        return resultSet.size();
    }

    template <typename RESULTSET>
    NANOFLANN_NODISCARD Size findWithinBox(RESULTSET& result, const BoundingBox& bbox) const
    {
        if (iroot_) findWithinBoxRec(result, iroot_, bbox);
        return result.size();
    }

 
   private:
    // --------------------------------------------------------------------
    //  Node allocation (bump-allocate from the pool, recycle via free-list)
    // --------------------------------------------------------------------
    INode* allocNode()
    {
        if (freeList_)
        {
            INode* n  = freeList_;
            freeList_ = n->child1;
            return n;  // already constructed; box storage reused
        }
        INode* n = Base::pool_.template allocate<INode>();
        // Placement-new so that, for DIM=-1, the std::vector box is constructed.
        ::new (static_cast<void*>(n)) INode();
        resize(n->box, static_cast<Dimension>(this->veclen(*this)));
        if (kCacheCoords) resize(n->pcoord, static_cast<Dimension>(this->veclen(*this)));
        return n;
    }

    void cacheCoords(INode* n)
    {
        if (!kCacheCoords) return;
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        for (Dimension i = 0; i < dims; ++i) n->pcoord[i] = pt(n->ptIdx, i);
    }

    ElementType nodeCoord(const INode* n, Dimension d) const
    {
        return kCacheCoords ? n->pcoord[d] : pt(n->ptIdx, d);
    }

    bool nodeInBox(const INode* n, const BoundingBox& b) const
    {
        if (!kCacheCoords) return pointInBox(n->ptIdx, b);
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        for (Dimension i = 0; i < dims; ++i)
            if (n->pcoord[i] < b[i].low || n->pcoord[i] > b[i].high) return false;
        return true;
    }
 
    void recycleNode(INode* n)
    {
        n->child1 = freeList_;
        freeList_ = n;
    }

    void destroyNodeObjects()
    {
        if (std::is_trivially_destructible<INode>::value) return;
        destroySubtree(iroot_);
        iroot_ = nullptr;
        while (freeList_)
        {
            INode* n  = freeList_;
            freeList_ = n->child1;
            n->~INode();
        }
    }
 
    void destroySubtree(INode* n)
    {
        if (!n) return;
        destroySubtree(n->child1);
        destroySubtree(n->child2);
        n->~INode();
    }
 
    // --------------------------------------------------------------------
    //  Helpers
    // --------------------------------------------------------------------
    ElementType pt(IndexType idx, Dimension d) const { return dataset_.kdtree_get_pt(idx, d); }
 
    void ensureNodeMap(IndexType idx)
    {
        if (idx >= nodeOfPoint_.size()) nodeOfPoint_.resize(static_cast<size_t>(idx) + 1, nullptr);
    }
 
    void syncRootBox()
    {
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        if (iroot_)
            for (Dimension i = 0; i < dims; ++i) Base::root_bbox_[i] = iroot_->box[i];
        else
            for (Dimension i = 0; i < dims; ++i) Base::root_bbox_[i] = Interval{0, 0};
    }
 
    void initBoxToPoint(INode* n)
    {
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        for (Dimension i = 0; i < dims; ++i)
        {
            const ElementType v = pt(n->ptIdx, i);
            n->box[i].low = n->box[i].high = v;
        }
    }
 
    void expandBoxToPoint(INode* n, IndexType idx)
    {
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        for (Dimension i = 0; i < dims; ++i)
        {
            const ElementType v = pt(idx, i);
            if (v < n->box[i].low) n->box[i].low = v;
            if (v > n->box[i].high) n->box[i].high = v;
        }
    }
 
    void unionBox(INode* n, const INode* c)
    {
        if (!c) return;
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        for (Dimension i = 0; i < dims; ++i)
        {
            if (c->box[i].low < n->box[i].low) n->box[i].low = c->box[i].low;
            if (c->box[i].high > n->box[i].high) n->box[i].high = c->box[i].high;
        }
    }
 
    bool pointInBox(IndexType idx, const BoundingBox& b) const
    {
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        for (Dimension i = 0; i < dims; ++i)
        {
            const ElementType v = pt(idx, i);
            if (v < b[i].low || v > b[i].high) return false;
        }
        return true;
    }
 
    bool boxFullyInside(const BoundingBox& inner, const BoundingBox& outer) const
    {
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        for (Dimension i = 0; i < dims; ++i)
            if (inner[i].low < outer[i].low || inner[i].high > outer[i].high) return false;
        return true;
    }
 
    bool boxDisjoint(const BoundingBox& a, const BoundingBox& b) const
    {
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        for (Dimension i = 0; i < dims; ++i)
            if (a[i].high < b[i].low || a[i].low > b[i].high) return true;
        return false;
    }
 
    // --------------------------------------------------------------------
    //  Insertion
    // --------------------------------------------------------------------
    INode* makeLeaf(IndexType idx, Dimension depth, INode* parent)
    {
        INode*          n    = allocNode();
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
        n->ptIdx             = idx;
        n->divfeat           = static_cast<Dimension>(depth % dims);
        n->deleted           = false;
        n->treeDeleted       = false;
        n->child1 = n->child2 = nullptr;
        n->parent             = parent;
        n->subtree_size       = 1;
        n->invalid_count      = 0;
        initBoxToPoint(n);
        cacheCoords(n);
        nodeOfPoint_[idx] = n;
        return n;
    }

    void insertOne(IndexType idx)
    {
        pendingRebuild_ = nullptr;
        iroot_          = insertRec(iroot_, idx, 0, nullptr);
        ++liveCount_;
        ++totalCount_;
        if (pendingRebuild_ && inlineRebuild_) rebuildAt(pendingRebuild_);
    }
 
    INode* insertRec(INode* node, IndexType idx, Dimension depth, INode* parent)
    {
        if (!node) return makeLeaf(idx, depth, parent);
        if (node->treeDeleted) pushDownDelete(node);
 
        ++node->subtree_size;
        expandBoxToPoint(node, idx);
 
        const Dimension axis = node->divfeat;
        if (pt(idx, axis) < nodeCoord(node, axis))
            node->child1 = insertRec(node->child1, idx, static_cast<Dimension>(depth + 1), node);
        else
            node->child2 = insertRec(node->child2, idx, static_cast<Dimension>(depth + 1), node);
 
        // On the unwind, remember the *highest* unbalanced node seen on the
        // path (ancestors are visited after descendants, so the last write
        // wins). insertOne() rebuilds it once, avoiding a second descent.
        if (isBalanceScapegoat(node)) pendingRebuild_ = node;
        return node;
    }

    void pushDownDelete(INode* node)
    {
        node->deleted = true;
        if (node->child1)
        {
            node->child1->treeDeleted   = true;
            node->child1->invalid_count = node->child1->subtree_size;
        }
        if (node->child2)
        {
            node->child2->treeDeleted   = true;
            node->child2->invalid_count = node->child2->subtree_size;
        }
        node->treeDeleted = false;  // invalid_count already == subtree_size
    }
 
    Size maxChildSize(const INode* node) const
    {
        const Size l = node->child1 ? node->child1->subtree_size : 0;
        const Size r = node->child2 ? node->child2->subtree_size : 0;
        return l > r ? l : r;
    }
 
    bool isBalanceScapegoat(const INode* node) const
    {
        if (node->subtree_size < kMinBalanceRebuild) return false;
        return static_cast<float>(maxChildSize(node)) >
               alphaBal_ * static_cast<float>(node->subtree_size);
    }
 
    // --------------------------------------------------------------------
    //  Deletion (lazy) + box-region deletion
    // --------------------------------------------------------------------
    void killSubtree(INode* node)
    {
        node->treeDeleted   = true;
        node->invalid_count = node->subtree_size;
    }

    Size removeOutsideBoxRec(INode* node, const BoundingBox& keep)
    {
        if (!node) return 0;
        if (node->invalid_count == node->subtree_size) return 0;  // already all dead
        if (boxFullyInside(node->box, keep)) return 0;  // keep entire subtree
        if (boxDisjoint(node->box, keep))
        {
            const Size newly = node->subtree_size - node->invalid_count;
            killSubtree(node);
            liveCount_ -= newly;
            return newly;
        }
        Size newly = 0;
        if (!node->deleted && !nodeInBox(node, keep))
        {
            node->deleted = true;
            ++newly;
            --liveCount_;
        }
        newly += removeOutsideBoxRec(node->child1, keep);
        newly += removeOutsideBoxRec(node->child2, keep);
        node->invalid_count += newly;
        return newly;
    }

    Size removeBoxRec(INode* node, const BoundingBox& box)
    {
        if (!node) return 0;
        if (node->invalid_count == node->subtree_size) return 0;
        if (boxDisjoint(node->box, box)) return 0;  // nothing inside
        if (boxFullyInside(node->box, box))
        {
            const Size newly = node->subtree_size - node->invalid_count;
            killSubtree(node);
            liveCount_ -= newly;
            return newly;
        }
        Size newly = 0;
        if (!node->deleted && nodeInBox(node, box))
        {
            node->deleted = true;
            ++newly;
            --liveCount_;
        }
        newly += removeBoxRec(node->child1, box);
        newly += removeBoxRec(node->child2, box);
        node->invalid_count += newly;
        return newly;
    }
 
    bool isDeletionScapegoat(const INode* node) const
    {
        if (node->subtree_size == 0) return false;
        return static_cast<float>(node->invalid_count) >
               alphaDel_ * static_cast<float>(node->subtree_size);
    }
 
    INode* findDeletionScapegoat(INode* node) const
    {
        if (!node) return nullptr;
        if (isDeletionScapegoat(node)) return node;  // highest wins
        if (INode* l = findDeletionScapegoat(node->child1)) return l;
        return findDeletionScapegoat(node->child2);
    }
 
    void maybeRebuildForDeletion()
    {
        if (!iroot_ || !inlineRebuild_) return;
        if (INode* sg = findDeletionScapegoat(iroot_)) rebuildAt(sg);
    }
 
    // --------------------------------------------------------------------
    //  Partial rebuild (scapegoat): flatten live points, rebuild balanced
    // --------------------------------------------------------------------
    void rebuildAt(INode* node)
    {
        INode*  par  = node->parent;
        INode** link = par ? (par->child1 == node ? &par->child1 : &par->child2) : &iroot_;
 
        const Size oldSize    = node->subtree_size;
        const Size oldInvalid = node->invalid_count;
 
        buildBuf_.clear();
        collectLiveAndFree(node, buildBuf_);
 
        INode* nb = buildBalanced(buildBuf_, 0, buildBuf_.size(), 0, par);
        *link     = nb;
 
        const Size newSize = nb ? nb->subtree_size : 0;  // == number of live pts
        // Propagate the change in (size, invalid) up to the ancestors.
        for (INode* p = par; p; p = p->parent)
        {
            p->subtree_size  = p->subtree_size - oldSize + newSize;
            p->invalid_count = p->invalid_count - oldInvalid;
        }
        totalCount_ = totalCount_ - oldSize + newSize;
    }

    void collectLiveAndFree(INode* node, std::vector<IndexType>& out)
    {
        if (!node) return;
        if (node->treeDeleted)
        {
            freeDeadSubtree(node);
            return;
        }
        if (!node->deleted)
            out.push_back(node->ptIdx);
        else
            dropDeadPoint(node->ptIdx);
        collectLiveAndFree(node->child1, out);
        collectLiveAndFree(node->child2, out);
        recycleNode(node);
    }
 
    void freeDeadSubtree(INode* node)
    {
        if (!node) return;
        dropDeadPoint(node->ptIdx);
        freeDeadSubtree(node->child1);
        freeDeadSubtree(node->child2);
        recycleNode(node);
    }
 
    void dropDeadPoint(IndexType idx)
    {
        if (idx < nodeOfPoint_.size()) nodeOfPoint_[idx] = nullptr;
        if (collectRemoved_) removedSink_.push_back(idx);
    }

    void snapshotRec(const INode* node, std::vector<IndexType>& out) const
    {
        if (!node) return;
        if (node->invalid_count == node->subtree_size) return;  // whole subtree dead
        if (!node->deleted) out.push_back(node->ptIdx);
        snapshotRec(node->child1, out);
        snapshotRec(node->child2, out);
    }

    void collectAllRec(const INode* node, std::vector<IndexType>& out) const
    {
        if (!node) return;
        out.push_back(node->ptIdx);
        collectAllRec(node->child1, out);
        collectAllRec(node->child2, out);
    }

    INode* buildBalanced(
        std::vector<IndexType>& buf, size_t lo, size_t hi, Dimension depth, INode* parent)
    {
        if (lo >= hi) return nullptr;
        const Dimension dims = static_cast<Dimension>(this->veclen(*this));
 
        // Widest-spread axis over buf[lo,hi).
        Dimension   axis     = static_cast<Dimension>(depth % dims);
        ElementType bestSpan = -1;
        for (Dimension d = 0; d < dims; ++d)
        {
            ElementType mn = pt(buf[lo], d), mx = mn;
            for (size_t k = lo + 1; k < hi; ++k)
            {
                const ElementType v = pt(buf[k], d);
                if (v < mn) mn = v;
                if (v > mx) mx = v;
            }
            const ElementType span = mx - mn;
            if (span > bestSpan)
            {
                bestSpan = span;
                axis     = d;
            }
        }
 
        const size_t mid = lo + (hi - lo) / 2;
        std::nth_element(
            buf.begin() + lo, buf.begin() + mid, buf.begin() + hi,
            [this, axis](IndexType a, IndexType b) { return pt(a, axis) < pt(b, axis); });
 
        INode* node       = allocNode();
        node->ptIdx       = buf[mid];
        node->divfeat     = axis;
        node->deleted     = false;
        node->treeDeleted = false;
        node->parent      = parent;
        cacheCoords(node);
        nodeOfPoint_[buf[mid]] = node;
 
        node->child1 = buildBalanced(buf, lo, mid, static_cast<Dimension>(depth + 1), node);
        node->child2 = buildBalanced(buf, mid + 1, hi, static_cast<Dimension>(depth + 1), node);
 
        node->subtree_size  = hi - lo;
        node->invalid_count = 0;
        initBoxToPoint(node);
        unionBox(node, node->child1);
        unionBox(node, node->child2);
        return node;
    }
 
    // --------------------------------------------------------------------
    //  Search
    // --------------------------------------------------------------------
    template <class RESULTSET>
    void searchLevelInc(
        RESULTSET& rs, const ElementType* vec, const INode* node, DistanceType mindist,
        distance_vector_t& dists, const DistanceType epsError, const Size dim) const
    {
        if (!node) return;
        if (node->invalid_count == node->subtree_size) return;  // whole subtree dead
 
        if (!node->deleted)
        {
#if defined(NANOFLANN_INCREMENTAL_INNODE_DISTANCE)
            // Opt-in: compute the node distance from the in-node coordinate cache
            // as a sum of per-axis accum_dist contributions. This avoids the
            // dataset_get() indirection and is ~12% faster on KNN, but is only
            // valid for *additive* (axis-decomposable) metrics — L1, L2,
            // L2_Simple. Do NOT enable it for SO2/SO3.
            DistanceType d = DistanceType();
            if (kCacheCoords)
                for (Size i = 0; i < dim; ++i)
                    d += distance_.accum_dist(
                        vec[i], node->pcoord[static_cast<Dimension>(i)], static_cast<Dimension>(i));
            else
                d = distance_.evalMetric(vec, node->ptIdx, dim);
#else
            const DistanceType d = distance_.evalMetric(vec, node->ptIdx, dim);
#endif
            if (d < rs.worstDist())
                rs.addPoint(
                    static_cast<typename RESULTSET::DistanceType>(d),
                    static_cast<typename RESULTSET::IndexType>(node->ptIdx));
        }
 
        const Dimension    axis     = node->divfeat;
        const ElementType  splitval = nodeCoord(node, axis);
        const ElementType  val      = vec[axis];
        const DistanceType cut      = distance_.accum_dist(val, splitval, axis);
 
        const INode* nearChild;
        const INode* farChild;
        if (val < splitval)
        {
            nearChild = node->child1;
            farChild  = node->child2;
        }
        else
        {
            nearChild = node->child2;
            farChild  = node->child1;
        }
 
        searchLevelInc(rs, vec, nearChild, mindist, dists, epsError, dim);
 
        const DistanceType dst    = dists[axis];
        const DistanceType newmin = mindist + cut - dst;
        dists[axis]               = cut;
        if (newmin * epsError <= rs.worstDist())
            searchLevelInc(rs, vec, farChild, newmin, dists, epsError, dim);
        dists[axis] = dst;
    }
 
    template <typename RESULTSET>
    void findWithinBoxRec(RESULTSET& result, const INode* node, const BoundingBox& bbox) const
    {
        if (!node) return;
        if (node->invalid_count == node->subtree_size) return;
        if (boxDisjoint(node->box, bbox)) return;
        if (!node->deleted && nodeInBox(node, bbox)) result.addPoint(0, node->ptIdx);
        findWithinBoxRec(result, node->child1, bbox);
        findWithinBoxRec(result, node->child2, bbox);
    }
};
 
#ifndef NANOFLANN_NO_THREADS
template <typename Distance, class DatasetAdaptor, int32_t DIM = -1, typename IndexType = uint32_t>
class KDTreeSingleIndexIncrementalAdaptorMT
{
   public:
    using Inner = KDTreeSingleIndexIncrementalAdaptor<Distance, DatasetAdaptor, DIM, IndexType>;
    using ElementType  = typename Inner::ElementType;
    using DistanceType = typename Inner::DistanceType;
    using Size         = typename Inner::Size;
    using Dimension    = typename Inner::Dimension;
    using BoundingBox  = typename Inner::BoundingBox;

    explicit KDTreeSingleIndexIncrementalAdaptorMT(
        const Dimension dimensionality, const DatasetAdaptor& inputData,
        const KDTreeIncrementalIndexParams& params = {}, double rebuild_growth = 1.3,
        Size min_rebuild_size = 10000)
        : dataset_(inputData),
          dim_(dimensionality),
          params_(params),
          rebuildGrowth_(rebuild_growth),
          minRebuildSize_(min_rebuild_size)
    {
        active_.reset(new Inner(dimensionality, inputData, params));
        active_->setInlineRebuild(false);
    }
 
    KDTreeSingleIndexIncrementalAdaptorMT(const KDTreeSingleIndexIncrementalAdaptorMT&) = delete;
    KDTreeSingleIndexIncrementalAdaptorMT& operator=(const KDTreeSingleIndexIncrementalAdaptorMT&) =
        delete;
 
    ~KDTreeSingleIndexIncrementalAdaptorMT()
    {
        if (fut_.valid()) fut_.wait();  // let the worker finish before teardown
    }

    void addPoints(IndexType start, IndexType end)
    {
        integrateIfReady();
        active_->addPoints(start, end);
        if (building_) log_.push_back({OpKind::Add, start, end, {}});
        maybeTriggerRebuild();
    }
    void addPoint(IndexType idx) { addPoints(idx, idx); }
 
    void removePoint(IndexType idx)
    {
        integrateIfReady();
        active_->removePoint(idx);
        if (building_) log_.push_back({OpKind::Remove, idx, idx, {}});
        maybeTriggerRebuild();
    }
    void removeBox(const BoundingBox& box)
    {
        integrateIfReady();
        active_->removeBox(box);
        if (building_) log_.push_back({OpKind::RemoveBox, 0, 0, box});
        maybeTriggerRebuild();
    }
    void removeOutsideBox(const BoundingBox& keep)
    {
        integrateIfReady();
        active_->removeOutsideBox(keep);
        if (building_) log_.push_back({OpKind::RemoveOutsideBox, 0, 0, keep});
        maybeTriggerRebuild();
    }

    template <typename RESULTSET>
    bool findNeighbors(
        RESULTSET& result, const ElementType* vec, const SearchParameters& sp = {}) const
    {
        return active_->findNeighbors(result, vec, sp);
    }
    Size knnSearch(
        const ElementType* query_point, const Size num_closest, IndexType* out_indices,
        DistanceType* out_distances, const SearchParameters& sp = {}) const
    {
        return active_->knnSearch(query_point, num_closest, out_indices, out_distances, sp);
    }
    Size radiusSearch(
        const ElementType* query_point, const DistanceType& radius,
        std::vector<ResultItem<IndexType, DistanceType>>& IndicesDists,
        const SearchParameters&                           sp = {}) const
    {
        return active_->radiusSearch(query_point, radius, IndicesDists, sp);
    }
    Size rknnSearch(
        const ElementType* query_point, const Size num_closest, IndexType* out_indices,
        DistanceType* out_distances, const DistanceType& radius) const
    {
        return active_->rknnSearch(query_point, num_closest, out_indices, out_distances, radius);
    }
    template <typename RESULTSET>
    Size findWithinBox(RESULTSET& result, const BoundingBox& bbox) const
    {
        return active_->findWithinBox(result, bbox);
    }

    Size size() const noexcept { return active_->size(); }
    bool empty() const noexcept { return active_->empty(); }
    Size physicalSize() const noexcept { return active_->physicalSize(); }
    bool isRebuilding() const noexcept { return building_; }

    NANOFLANN_NODISCARD BoundingBox boundingBox() const { return active_->boundingBox(); }

    void snapshotLiveIndices(std::vector<IndexType>& out) const
    {
        active_->snapshotLiveIndices(out);
    }

    void reserve(Size n) { active_->reserve(n); }

    void sync()
    {
        if (building_ && fut_.valid()) fut_.wait();
        integrateIfReady();
    }

    const Inner& activeIndex() const { return *active_; }

    void setRebuildCallback(std::function<void(Inner&)> cb) { rebuildCallback_ = std::move(cb); }


    void setCollectRemovedPoints(bool enable)
    {
        collectRemoved_ = enable;
        if (!enable) std::vector<IndexType>().swap(removedSink_);
    }

    std::vector<IndexType> acquireRemovedPoints()
    {
        std::vector<IndexType> out;
        out.swap(removedSink_);
        return out;
    }

 
   private:
    enum class OpKind
    {
        Add,
        Remove,
        RemoveBox,
        RemoveOutsideBox
    };
    struct LoggedOp
    {
        OpKind      kind;
        IndexType   a, b;
        BoundingBox box;
    };
 
    void maybeTriggerRebuild()
    {
        if (building_) return;
        const Size phys = active_->physicalSize();
        if (phys < minRebuildSize_) return;
        const Size base = lastBuildLive_ ? lastBuildLive_ : Size(1);
        if (static_cast<double>(phys) < rebuildGrowth_ * static_cast<double>(base)) return;
 
        // Snapshot the live indices on the foreground thread, then build a fresh
        // balanced tree from them on a background thread.
        auto snapshot = std::make_shared<std::vector<IndexType>>();
        active_->snapshotLiveIndices(*snapshot);
 
        const DatasetAdaptor&        ds = dataset_;
        const Dimension              d  = dim_;
        KDTreeIncrementalIndexParams p  = params_;
        std::function<void(Inner&)>  cb = rebuildCallback_;
        fut_                            = std::async(
                                       std::launch::async,
                                       [snapshot, &ds, d, p, cb]() -> std::unique_ptr<Inner>
                                       {
                std::unique_ptr<Inner> t(new Inner(d, ds, p));
                t->setInlineRebuild(false);
                t->buildFromIndices(*snapshot);
                if (cb) cb(*t);  // background post-rebuild hook (e.g. recompute covariances)
                return t;
            });
        building_ = true;
        log_.clear();
    }
 
    void integrateIfReady()
    {
        if (!building_ || !fut_.valid()) return;
        if (fut_.wait_for(std::chrono::seconds(0)) != std::future_status::ready) return;
 
        std::unique_ptr<Inner> fresh = fut_.get();
        fresh->setInlineRebuild(false);
        // Replay the operations buffered while the background build was running.
        for (const auto& op : log_)
        {
            switch (op.kind)
            {
                case OpKind::Add:
                    fresh->addPoints(op.a, op.b);
                    break;
                case OpKind::Remove:
                    fresh->removePoint(op.a);
                    break;
                case OpKind::RemoveBox:
                    fresh->removeBox(op.box);
                    break;
                case OpKind::RemoveOutsideBox:
                    fresh->removeOutsideBox(op.box);
                    break;
            }
        }
        log_.clear();
        // Dataset slots referenced by the OLD tree but not by the fresh one are
        // now free for the caller to recycle (no node references them anymore).
        if (collectRemoved_)
        {
            std::vector<IndexType> oldPhysical;
            active_->collectPhysicalIndices(oldPhysical);
            for (IndexType idx : oldPhysical)
                if (!fresh->referencesIndex(idx)) removedSink_.push_back(idx);
        }
        active_        = std::move(fresh);
        lastBuildLive_ = active_->size();
        building_      = false;
    }
 
    const DatasetAdaptor&        dataset_;
    Dimension                    dim_;
    KDTreeIncrementalIndexParams params_;
    double                       rebuildGrowth_;
    Size                         minRebuildSize_;
 
    std::unique_ptr<Inner>              active_;
    std::future<std::unique_ptr<Inner>> fut_;
    bool                                building_      = false;
    Size                                lastBuildLive_ = 0;
    std::vector<LoggedOp>               log_;
 
    bool                        collectRemoved_ = false;
    std::vector<IndexType>      removedSink_;
    std::function<void(Inner&)> rebuildCallback_;
};
#endif  // NANOFLANN_NO_THREADS

template <
    class MatrixType, int32_t DIM = -1, class Distance = nanoflann::metric_L2,
    bool row_major = true>
struct KDTreeEigenMatrixAdaptor
{
    using self_t    = KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance, row_major>;
    using num_t     = typename MatrixType::Scalar;
    using IndexType = typename MatrixType::Index;
    using metric_t  = typename Distance::template traits<num_t, self_t, IndexType>::distance_t;
 
    using index_t = KDTreeSingleIndexAdaptor<
        metric_t, self_t, row_major ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
        IndexType>;
 
    index_t* index_;  
 
    using Offset    = typename index_t::Offset;
    using Size      = typename index_t::Size;
    using Dimension = typename index_t::Dimension;

    explicit KDTreeEigenMatrixAdaptor(
        const Dimension dimensionality, const std::reference_wrapper<const MatrixType>& mat,
        const int leaf_max_size = 10, const unsigned int n_thread_build = 1)
        : 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(
            static_cast<Dimension>(dims), *this /* adaptor */,
            nanoflann::KDTreeSingleIndexAdaptorParams(
                leaf_max_size, nanoflann::KDTreeSingleIndexAdaptorFlags::None, n_thread_build));
    }
 
   public:
    KDTreeEigenMatrixAdaptor(const self_t&) = delete;
    self_t& operator=(const self_t&)        = delete;

    KDTreeEigenMatrixAdaptor(self_t&&) = delete;
    self_t& operator=(self_t&&)        = delete;
 
    ~KDTreeEigenMatrixAdaptor() { delete index_; }
 
    const std::reference_wrapper<const MatrixType> m_data_matrix;

    void query(
        const num_t* query_point, const Size num_closest, IndexType* out_indices,
        num_t* out_distances) const
    {
        nanoflann::KNNResultSet<num_t, IndexType> resultSet(num_closest);
        resultSet.init(out_indices, out_distances);
        index_->findNeighbors(resultSet, query_point);
    }

 
    inline const self_t& derived() const noexcept { return *this; }
    inline self_t&       derived() noexcept { 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>
    inline bool kdtree_get_bbox(BBOX& /*bb*/) const
    {
        return false;
    }

 
};  // end of KDTreeEigenMatrixAdaptor

  // end of grouping
}  // namespace nanoflann
 
#undef NANOFLANN_RESTRICT