nanoflann.hpp

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 *
 * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
 * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
 * Copyright 2011-2016  Jose Luis Blanco (joseluisblancoc@gmail.com).
 *   All rights reserved.
 *
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#ifndef  NANOFLANN_HPP_
#define  NANOFLANN_HPP_
 
#include <vector>
#include <cassert>
#include <algorithm>
#include <stdexcept>
#include <cstdio>  // for fwrite()
#include <cmath>   // for abs()
#include <cstdlib> // for abs()
#include <limits>
 
// Avoid conflicting declaration of min/max macros in windows headers
#if !defined(NOMINMAX) && (defined(_WIN32) || defined(_WIN32_)  || defined(WIN32) || defined(_WIN64))
# define NOMINMAX
# ifdef max
#  undef   max
#  undef   min
# endif
#endif
 
namespace nanoflann
{
    #define NANOFLANN_VERSION 0x123
 
    template <typename DistanceType, typename IndexType = size_t, typename CountType = size_t>
    class KNNResultSet
    {
        IndexType * indices;
        DistanceType* dists;
        CountType capacity;
        CountType count;
 
    public:
        inline KNNResultSet(CountType capacity_) : indices(0), dists(0), capacity(capacity_), count(0)
        {
        }
 
        inline void init(IndexType* indices_, DistanceType* dists_)
        {
            indices = indices_;
            dists = dists_;
            count = 0;
            if (capacity)
                dists[capacity-1] = (std::numeric_limits<DistanceType>::max)();
        }
 
        inline CountType size() const
        {
            return count;
        }
 
        inline bool full() const
        {
            return count == capacity;
        }
 
 
                inline bool addPoint(DistanceType dist, IndexType index)
        {
            CountType i;
            for (i=count; i>0; --i) {
#ifdef NANOFLANN_FIRST_MATCH   // If defined and two points have the same distance, the one with the lowest-index will be returned first.
                if ( (dists[i-1]>dist) || ((dist==dists[i-1])&&(indices[i-1]>index)) ) {
#else
                if (dists[i-1]>dist) {
#endif
                    if (i<capacity) {
                        dists[i] = dists[i-1];
                        indices[i] = indices[i-1];
                    }
                }
                else break;
            }
            if (i<capacity) {
                dists[i] = dist;
                indices[i] = index;
            }
            if (count<capacity) count++;
 
                        // tell caller that the search shall continue
                        return true;
        }
 
        inline DistanceType worstDist() const
        {
            return dists[capacity-1];
        }
    };
 
    struct IndexDist_Sorter
    {
        template <typename PairType>
        inline bool operator()(const PairType &p1, const PairType &p2) const {
            return p1.second < p2.second;
        }
    };
 
    template <typename DistanceType, typename IndexType = size_t>
    class RadiusResultSet
    {
    public:
        const DistanceType radius;
 
        std::vector<std::pair<IndexType,DistanceType> >& m_indices_dists;
 
        inline RadiusResultSet(DistanceType radius_, std::vector<std::pair<IndexType,DistanceType> >& indices_dists) : radius(radius_), m_indices_dists(indices_dists)
        {
            init();
        }
 
        inline ~RadiusResultSet() { }
 
        inline void init() { clear(); }
        inline void clear() { m_indices_dists.clear(); }
 
        inline size_t size() const { return m_indices_dists.size(); }
 
        inline bool full() const { return true; }
 
                inline bool addPoint(DistanceType dist, IndexType index)
        {
            if (dist<radius)
                m_indices_dists.push_back(std::make_pair(index,dist));
                        return true;
        }
 
        inline DistanceType worstDist() const { return radius; }
 
        std::pair<IndexType,DistanceType> worst_item() const
        {
           if (m_indices_dists.empty()) throw std::runtime_error("Cannot invoke RadiusResultSet::worst_item() on an empty list of results.");
           typedef typename std::vector<std::pair<IndexType,DistanceType> >::const_iterator DistIt;
           DistIt it = std::max_element(m_indices_dists.begin(), m_indices_dists.end(), IndexDist_Sorter());
           return *it;
        }
    };
 
 
    template<typename T>
    void save_value(FILE* stream, const T& value, size_t count = 1)
    {
        fwrite(&value, sizeof(value),count, stream);
    }
 
    template<typename T>
    void save_value(FILE* stream, const std::vector<T>& value)
    {
        size_t size = value.size();
        fwrite(&size, sizeof(size_t), 1, stream);
        fwrite(&value[0], sizeof(T), size, stream);
    }
 
    template<typename T>
    void load_value(FILE* stream, T& value, size_t count = 1)
    {
        size_t read_cnt = fread(&value, sizeof(value), count, stream);
        if (read_cnt != count) {
            throw std::runtime_error("Cannot read from file");
        }
    }
 
 
    template<typename T>
    void load_value(FILE* stream, std::vector<T>& value)
    {
        size_t size;
        size_t read_cnt = fread(&size, sizeof(size_t), 1, stream);
        if (read_cnt!=1) {
            throw std::runtime_error("Cannot read from file");
        }
        value.resize(size);
        read_cnt = fread(&value[0], sizeof(T), size, stream);
        if (read_cnt!=size) {
            throw std::runtime_error("Cannot read from file");
        }
    }
    template<class T, class DataSource, typename _DistanceType = T>
    struct L1_Adaptor
    {
        typedef T ElementType;
        typedef _DistanceType DistanceType;
 
        const DataSource &data_source;
 
        L1_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
 
        inline DistanceType operator()(const T* a, const size_t b_idx, size_t size, DistanceType worst_dist = -1) const
        {
            DistanceType result = DistanceType();
            const T* last = a + size;
            const T* lastgroup = last - 3;
            size_t d = 0;
 
            /* Process 4 items with each loop for efficiency. */
            while (a < lastgroup) {
                const DistanceType diff0 = std::abs(a[0] - data_source.kdtree_get_pt(b_idx,d++));
                const DistanceType diff1 = std::abs(a[1] - data_source.kdtree_get_pt(b_idx,d++));
                const DistanceType diff2 = std::abs(a[2] - data_source.kdtree_get_pt(b_idx,d++));
                const DistanceType diff3 = std::abs(a[3] - data_source.kdtree_get_pt(b_idx,d++));
                result += diff0 + diff1 + diff2 + diff3;
                a += 4;
                if ((worst_dist>0)&&(result>worst_dist)) {
                    return result;
                }
            }
            /* Process last 0-3 components.  Not needed for standard vector lengths. */
            while (a < last) {
                result += std::abs( *a++ - data_source.kdtree_get_pt(b_idx,d++) );
            }
            return result;
        }
 
        template <typename U, typename V>
        inline DistanceType accum_dist(const U a, const V b, int ) const
        {
            return std::abs(a-b);
        }
    };
 
    template<class T, class DataSource, typename _DistanceType = T>
    struct L2_Adaptor
    {
        typedef T ElementType;
        typedef _DistanceType DistanceType;
 
        const DataSource &data_source;
 
        L2_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
 
        inline DistanceType operator()(const T* a, const size_t b_idx, size_t size, DistanceType worst_dist = -1) const
        {
            DistanceType result = DistanceType();
            const T* last = a + size;
            const T* lastgroup = last - 3;
            size_t d = 0;
 
            /* Process 4 items with each loop for efficiency. */
            while (a < lastgroup) {
                const DistanceType diff0 = a[0] - data_source.kdtree_get_pt(b_idx,d++);
                const DistanceType diff1 = a[1] - data_source.kdtree_get_pt(b_idx,d++);
                const DistanceType diff2 = a[2] - data_source.kdtree_get_pt(b_idx,d++);
                const DistanceType diff3 = a[3] - data_source.kdtree_get_pt(b_idx,d++);
                result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
                a += 4;
                if ((worst_dist>0)&&(result>worst_dist)) {
                    return result;
                }
            }
            /* Process last 0-3 components.  Not needed for standard vector lengths. */
            while (a < last) {
                const DistanceType diff0 = *a++ - data_source.kdtree_get_pt(b_idx,d++);
                result += diff0 * diff0;
            }
            return result;
        }
 
        template <typename U, typename V>
        inline DistanceType accum_dist(const U a, const V b, int ) const
        {
            return (a-b)*(a-b);
        }
    };
 
    template<class T, class DataSource, typename _DistanceType = T>
    struct L2_Simple_Adaptor
    {
        typedef T ElementType;
        typedef _DistanceType DistanceType;
 
        const DataSource &data_source;
 
        L2_Simple_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
 
        inline DistanceType operator()(const T* a, const size_t b_idx, size_t size) const {
            return data_source.kdtree_distance(a,b_idx,size);
        }
 
        template <typename U, typename V>
        inline DistanceType accum_dist(const U a, const V b, int ) const
        {
            return (a-b)*(a-b);
        }
    };
 
    struct metric_L1 {
        template<class T, class DataSource>
        struct traits {
            typedef L1_Adaptor<T,DataSource> distance_t;
        };
    };
    struct metric_L2 {
        template<class T, class DataSource>
        struct traits {
            typedef L2_Adaptor<T,DataSource> distance_t;
        };
    };
    struct metric_L2_Simple {
        template<class T, class DataSource>
        struct traits {
            typedef L2_Simple_Adaptor<T,DataSource> distance_t;
        };
    };
 
    struct KDTreeSingleIndexAdaptorParams
    {
        KDTreeSingleIndexAdaptorParams(size_t _leaf_max_size = 10) :
            leaf_max_size(_leaf_max_size)
        {}
 
        size_t leaf_max_size;
    };
 
    struct SearchParams
    {
        SearchParams(int checks_IGNORED_ = 32, float eps_ = 0, bool sorted_ = true ) :
            checks(checks_IGNORED_), eps(eps_), sorted(sorted_) {}
 
        int   checks;  
        float eps;  
        bool sorted; 
    };
    template <typename T>
    inline T* allocate(size_t count = 1)
    {
        T* mem = static_cast<T*>( ::malloc(sizeof(T)*count));
        return mem;
    }
 
 
    const size_t     WORDSIZE=16;
    const size_t     BLOCKSIZE=8192;
 
    class PooledAllocator
    {
        /* We maintain memory alignment to word boundaries by requiring that all
            allocations be in multiples of the machine wordsize.  */
        /* Size of machine word in bytes.  Must be power of 2. */
        /* Minimum number of bytes requested at a time from the system.  Must be multiple of WORDSIZE. */
 
 
        size_t  remaining;  /* Number of bytes left in current block of storage. */
        void*   base;     /* Pointer to base of current block of storage. */
        void*   loc;      /* Current location in block to next allocate memory. */
 
        void internal_init()
        {
            remaining = 0;
            base = NULL;
            usedMemory = 0;
            wastedMemory = 0;
        }
 
    public:
        size_t  usedMemory;
        size_t  wastedMemory;
 
        PooledAllocator() {
            internal_init();
        }
 
        ~PooledAllocator() {
            free_all();
        }
 
        void free_all()
        {
            while (base != NULL) {
                void *prev = *(static_cast<void**>( base)); /* Get pointer to prev block. */
                ::free(base);
                base = prev;
            }
            internal_init();
        }
 
        void* malloc(const size_t req_size)
        {
            /* Round size up to a multiple of wordsize.  The following expression
                only works for WORDSIZE that is a power of 2, by masking last bits of
                incremented size to zero.
             */
            const size_t size = (req_size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
 
            /* Check whether a new block must be allocated.  Note that the first word
                of a block is reserved for a pointer to the previous block.
             */
            if (size > remaining) {
 
                wastedMemory += remaining;
 
                /* Allocate new storage. */
                const size_t blocksize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
                            size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE;
 
                // use the standard C malloc to allocate memory
                void* m = ::malloc(blocksize);
                if (!m) {
                    fprintf(stderr,"Failed to allocate memory.\n");
                    return NULL;
                }
 
                /* Fill first word of new block with pointer to previous block. */
                static_cast<void**>(m)[0] = base;
                base = m;
 
                size_t shift = 0;
                //int size_t = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1);
 
                remaining = blocksize - sizeof(void*) - shift;
                loc = (static_cast<char*>(m) + sizeof(void*) + shift);
            }
            void* rloc = loc;
            loc = static_cast<char*>(loc) + size;
            remaining -= size;
 
            usedMemory += size;
 
            return rloc;
        }
 
        template <typename T>
        T* allocate(const size_t count = 1)
        {
            T* mem = static_cast<T*>(this->malloc(sizeof(T)*count));
            return mem;
        }
 
    };
    // ----------------  CArray -------------------------
    template <typename T, std::size_t N>
    class CArray {
      public:
        T elems[N];    // fixed-size array of elements of type T
 
      public:
        // type definitions
        typedef T              value_type;
        typedef T*             iterator;
        typedef const T*       const_iterator;
        typedef T&             reference;
        typedef const T&       const_reference;
        typedef std::size_t    size_type;
        typedef std::ptrdiff_t difference_type;
 
        // iterator support
        inline iterator begin() { return elems; }
        inline const_iterator begin() const { return elems; }
        inline iterator end() { return elems+N; }
        inline const_iterator end() const { return elems+N; }
 
        // reverse iterator support
#if !defined(BOOST_NO_TEMPLATE_PARTIAL_SPECIALIZATION) && !defined(BOOST_MSVC_STD_ITERATOR) && !defined(BOOST_NO_STD_ITERATOR_TRAITS)
        typedef std::reverse_iterator<iterator> reverse_iterator;
        typedef std::reverse_iterator<const_iterator> const_reverse_iterator;
#elif defined(_MSC_VER) && (_MSC_VER == 1300) && defined(BOOST_DINKUMWARE_STDLIB) && (BOOST_DINKUMWARE_STDLIB == 310)
        // workaround for broken reverse_iterator in VC7
        typedef std::reverse_iterator<std::_Ptrit<value_type, difference_type, iterator,
                                      reference, iterator, reference> > reverse_iterator;
        typedef std::reverse_iterator<std::_Ptrit<value_type, difference_type, const_iterator,
                                      const_reference, iterator, reference> > const_reverse_iterator;
#else
        // workaround for broken reverse_iterator implementations
        typedef std::reverse_iterator<iterator,T> reverse_iterator;
        typedef std::reverse_iterator<const_iterator,T> const_reverse_iterator;
#endif
 
        reverse_iterator rbegin() { return reverse_iterator(end()); }
        const_reverse_iterator rbegin() const { return const_reverse_iterator(end()); }
        reverse_iterator rend() { return reverse_iterator(begin()); }
        const_reverse_iterator rend() const { return const_reverse_iterator(begin()); }
        // operator[]
        inline reference operator[](size_type i) { return elems[i]; }
        inline const_reference operator[](size_type i) const { return elems[i]; }
        // at() with range check
        reference at(size_type i) { rangecheck(i); return elems[i]; }
        const_reference at(size_type i) const { rangecheck(i); return elems[i]; }
        // front() and back()
        reference front() { return elems[0]; }
        const_reference front() const { return elems[0]; }
        reference back() { return elems[N-1]; }
        const_reference back() const { return elems[N-1]; }
        // size is constant
        static inline size_type size() { return N; }
        static bool empty() { return false; }
        static size_type max_size() { return N; }
        enum { static_size = N };
        inline void resize(const size_t nElements) { if (nElements!=N) throw std::logic_error("Try to change the size of a CArray."); }
        // swap (note: linear complexity in N, constant for given instantiation)
        void swap (CArray<T,N>& y) { std::swap_ranges(begin(),end(),y.begin()); }
        // direct access to data (read-only)
        const T* data() const { return elems; }
        // use array as C array (direct read/write access to data)
        T* data() { return elems; }
        // assignment with type conversion
        template <typename T2> CArray<T,N>& operator= (const CArray<T2,N>& rhs) {
            std::copy(rhs.begin(),rhs.end(), begin());
            return *this;
        }
        // assign one value to all elements
        inline void assign (const T& value) { for (size_t i=0;i<N;i++) elems[i]=value; }
        // assign (compatible with std::vector's one) (by JLBC for MRPT)
        void assign (const size_t n, const T& value) { assert(N==n); for (size_t i=0;i<N;i++) elems[i]=value; }
      private:
        // check range (may be private because it is static)
        static void rangecheck (size_type i) { if (i >= size()) { throw std::out_of_range("CArray<>: index out of range"); } }
    }; // end of CArray
 
    template <int DIM, typename T>
    struct array_or_vector_selector
    {
        typedef CArray<T,DIM> container_t;
    };
    template <typename T>
    struct array_or_vector_selector<-1,T> {
        typedef std::vector<T> container_t;
    };
    template <typename Distance, class DatasetAdaptor,int DIM = -1, typename IndexType = size_t>
    class KDTreeSingleIndexAdaptor
    {
    private:
        KDTreeSingleIndexAdaptor(const KDTreeSingleIndexAdaptor<Distance,DatasetAdaptor,DIM,IndexType>&);
    public:
        typedef typename Distance::ElementType  ElementType;
        typedef typename Distance::DistanceType DistanceType;
    protected:
 
        std::vector<IndexType> vind;
 
        size_t m_leaf_max_size;
 
 
        const DatasetAdaptor &dataset; 
 
        const KDTreeSingleIndexAdaptorParams index_params;
 
        size_t m_size; 
        size_t m_size_at_index_build; 
        int dim;  
 
 
        /*--------------------- Internal Data Structures --------------------------*/
        struct Node
        {
            union {
                struct leaf
                                {
                    IndexType    left, right;  
                } lr;
                struct nonleaf
                                {
                    int          divfeat; 
                    DistanceType divlow, divhigh; 
                } sub;
            } node_type;
            Node* child1, * child2;  
        };
        typedef Node* NodePtr;
 
 
        struct Interval
        {
            ElementType low, high;
        };
 
        typedef typename array_or_vector_selector<DIM,Interval>::container_t BoundingBox;
 
        typedef typename array_or_vector_selector<DIM,DistanceType>::container_t distance_vector_t;
 
        NodePtr root_node;
        BoundingBox root_bbox;
 
        PooledAllocator pool;
 
    public:
 
        Distance distance;
 
        KDTreeSingleIndexAdaptor(const int dimensionality, const DatasetAdaptor& inputData, const KDTreeSingleIndexAdaptorParams& params = KDTreeSingleIndexAdaptorParams() ) :
            dataset(inputData), index_params(params), root_node(NULL), distance(inputData)
        {
            m_size = dataset.kdtree_get_point_count();
            m_size_at_index_build = m_size;
            dim = dimensionality;
            if (DIM>0) dim=DIM;
            m_leaf_max_size = params.leaf_max_size;
 
            // Create a permutable array of indices to the input vectors.
            init_vind();
        }
 
        ~KDTreeSingleIndexAdaptor() { }
 
        void freeIndex()
        {
            pool.free_all();
            root_node=NULL;
            m_size_at_index_build = 0;
        }
 
        void buildIndex()
        {
            init_vind();
            freeIndex();
            m_size_at_index_build = m_size;
            if(m_size == 0) return;
            computeBoundingBox(root_bbox);
            root_node = divideTree(0, m_size, root_bbox );   // construct the tree
        }
 
        size_t size() const { return m_size; }
 
        size_t veclen() const {
            return static_cast<size_t>(DIM>0 ? DIM : dim);
        }
 
        size_t usedMemory() const
        {
            return pool.usedMemory+pool.wastedMemory+dataset.kdtree_get_point_count()*sizeof(IndexType);  // pool memory and vind array memory
        }
 
        template <typename RESULTSET>
        bool findNeighbors(RESULTSET& result, const ElementType* vec, const SearchParams& searchParams) const
        {
            assert(vec);
            if (size() == 0)
                return false;
            if (!root_node)
                throw std::runtime_error("[nanoflann] findNeighbors() called before building the index.");
            float epsError = 1+searchParams.eps;
 
            distance_vector_t dists; // fixed or variable-sized container (depending on DIM)
            dists.assign((DIM>0 ? DIM : dim) ,0); // Fill it with zeros.
            DistanceType distsq = computeInitialDistances(vec, dists);
            searchLevel(result, vec, root_node, distsq, dists, epsError);  // "count_leaf" parameter removed since was neither used nor returned to the user.
            return result.full();
        }
 
        size_t knnSearch(const ElementType *query_point, const size_t num_closest, IndexType *out_indices, DistanceType *out_distances_sq, const int /* nChecks_IGNORED */ = 10) const
        {
            nanoflann::KNNResultSet<DistanceType,IndexType> resultSet(num_closest);
            resultSet.init(out_indices, out_distances_sq);
            this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
            return resultSet.size();
        }
 
        size_t radiusSearch(const ElementType *query_point,const DistanceType &radius, std::vector<std::pair<IndexType,DistanceType> >& IndicesDists, const SearchParams& searchParams) const
        {
            RadiusResultSet<DistanceType,IndexType> resultSet(radius,IndicesDists);
            const size_t nFound = radiusSearchCustomCallback(query_point,resultSet,searchParams);
            if (searchParams.sorted)
                std::sort(IndicesDists.begin(),IndicesDists.end(), IndexDist_Sorter() );
            return nFound;
        }
 
        template <class SEARCH_CALLBACK>
        size_t radiusSearchCustomCallback(const ElementType *query_point,SEARCH_CALLBACK &resultSet, const SearchParams& searchParams = SearchParams() ) const
        {
            this->findNeighbors(resultSet, query_point, searchParams);
            return resultSet.size();
        }
 
    private:
        void init_vind()
        {
            // Create a permutable array of indices to the input vectors.
            m_size = dataset.kdtree_get_point_count();
            if (vind.size()!=m_size) vind.resize(m_size);
            for (size_t i = 0; i < m_size; i++) vind[i] = i;
        }
 
        inline ElementType dataset_get(size_t idx, int component) const {
            return dataset.kdtree_get_pt(idx,component);
        }
 
 
        void save_tree(FILE* stream, NodePtr tree)
        {
            save_value(stream, *tree);
            if (tree->child1!=NULL) {
                save_tree(stream, tree->child1);
            }
            if (tree->child2!=NULL) {
                save_tree(stream, tree->child2);
            }
        }
 
 
        void load_tree(FILE* stream, NodePtr& tree)
        {
            tree = pool.allocate<Node>();
            load_value(stream, *tree);
            if (tree->child1!=NULL) {
                load_tree(stream, tree->child1);
            }
            if (tree->child2!=NULL) {
                load_tree(stream, tree->child2);
            }
        }
 
 
        void computeBoundingBox(BoundingBox& bbox)
        {
            bbox.resize((DIM>0 ? DIM : dim));
            if (dataset.kdtree_get_bbox(bbox))
            {
                // Done! It was implemented in derived class
            }
            else
            {
                const size_t N = dataset.kdtree_get_point_count();
                if (!N) throw std::runtime_error("[nanoflann] computeBoundingBox() called but no data points found.");
                for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
                    bbox[i].low =
                    bbox[i].high = dataset_get(0,i);
                }
                for (size_t k=1; k<N; ++k) {
                    for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
                        if (dataset_get(k,i)<bbox[i].low) bbox[i].low = dataset_get(k,i);
                        if (dataset_get(k,i)>bbox[i].high) bbox[i].high = dataset_get(k,i);
                    }
                }
            }
        }
 
 
        NodePtr divideTree(const IndexType left, const IndexType right, BoundingBox& bbox)
        {
            NodePtr node = pool.allocate<Node>(); // allocate memory
 
            /* If too few exemplars remain, then make this a leaf node. */
            if ( (right-left) <= static_cast<IndexType>(m_leaf_max_size) ) {
                node->child1 = node->child2 = NULL;    /* Mark as leaf node. */
                node->node_type.lr.left = left;
                node->node_type.lr.right = right;
 
                // compute bounding-box of leaf points
                for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
                    bbox[i].low = dataset_get(vind[left],i);
                    bbox[i].high = dataset_get(vind[left],i);
                }
                for (IndexType k=left+1; k<right; ++k) {
                    for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
                        if (bbox[i].low>dataset_get(vind[k],i)) bbox[i].low=dataset_get(vind[k],i);
                        if (bbox[i].high<dataset_get(vind[k],i)) bbox[i].high=dataset_get(vind[k],i);
                    }
                }
            }
            else {
                IndexType idx;
                int cutfeat;
                DistanceType cutval;
                middleSplit_(&vind[0]+left, right-left, idx, cutfeat, cutval, bbox);
 
                node->node_type.sub.divfeat = cutfeat;
 
                BoundingBox left_bbox(bbox);
                left_bbox[cutfeat].high = cutval;
                node->child1 = divideTree(left, left+idx, left_bbox);
 
                BoundingBox right_bbox(bbox);
                right_bbox[cutfeat].low = cutval;
                node->child2 = divideTree(left+idx, right, right_bbox);
 
                node->node_type.sub.divlow = left_bbox[cutfeat].high;
                node->node_type.sub.divhigh = right_bbox[cutfeat].low;
 
                for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
                    bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
                    bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
                }
            }
 
            return node;
        }
 
 
        void computeMinMax(IndexType* ind, IndexType count, int element, ElementType& min_elem, ElementType& max_elem)
        {
            min_elem = dataset_get(ind[0],element);
            max_elem = dataset_get(ind[0],element);
            for (IndexType i=1; i<count; ++i) {
                ElementType val = dataset_get(ind[i],element);
                if (val<min_elem) min_elem = val;
                if (val>max_elem) max_elem = val;
            }
        }
 
        void middleSplit_(IndexType* ind, IndexType count, IndexType& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
        {
            const DistanceType EPS=static_cast<DistanceType>(0.00001);
            ElementType max_span = bbox[0].high-bbox[0].low;
            for (int i=1; i<(DIM>0 ? DIM : dim); ++i) {
                ElementType span = bbox[i].high-bbox[i].low;
                if (span>max_span) {
                    max_span = span;
                }
            }
            ElementType max_spread = -1;
            cutfeat = 0;
            for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
                ElementType span = bbox[i].high-bbox[i].low;
                if (span>(1-EPS)*max_span) {
                    ElementType min_elem, max_elem;
                    computeMinMax(ind, count, i, min_elem, max_elem);
                    ElementType spread = max_elem-min_elem;;
                    if (spread>max_spread) {
                        cutfeat = i;
                        max_spread = spread;
                    }
                }
            }
            // split in the middle
            DistanceType split_val = (bbox[cutfeat].low+bbox[cutfeat].high)/2;
            ElementType min_elem, max_elem;
            computeMinMax(ind, count, cutfeat, min_elem, max_elem);
 
            if (split_val<min_elem) cutval = min_elem;
            else if (split_val>max_elem) cutval = max_elem;
            else cutval = split_val;
 
            IndexType lim1, lim2;
            planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
 
            if (lim1>count/2) index = lim1;
            else if (lim2<count/2) index = lim2;
            else index = count/2;
        }
 
 
        void planeSplit(IndexType* ind, const IndexType count, int cutfeat, DistanceType &cutval, IndexType& lim1, IndexType& lim2)
        {
            /* Move vector indices for left subtree to front of list. */
            IndexType left = 0;
            IndexType right = count-1;
            for (;; ) {
                while (left<=right && dataset_get(ind[left],cutfeat)<cutval) ++left;
                while (right && left<=right && dataset_get(ind[right],cutfeat)>=cutval) --right;
                if (left>right || !right) break;  // "!right" was added to support unsigned Index types
                std::swap(ind[left], ind[right]);
                ++left;
                --right;
            }
            /* If either list is empty, it means that all remaining features
             * are identical. Split in the middle to maintain a balanced tree.
             */
            lim1 = left;
            right = count-1;
            for (;; ) {
                while (left<=right && dataset_get(ind[left],cutfeat)<=cutval) ++left;
                while (right && left<=right && dataset_get(ind[right],cutfeat)>cutval) --right;
                if (left>right || !right) break;  // "!right" was added to support unsigned Index types
                std::swap(ind[left], ind[right]);
                ++left;
                --right;
            }
            lim2 = left;
        }
 
        DistanceType computeInitialDistances(const ElementType* vec, distance_vector_t& dists) const
        {
            assert(vec);
            DistanceType distsq = DistanceType();
 
            for (int i = 0; i < (DIM>0 ? DIM : dim); ++i) {
                if (vec[i] < root_bbox[i].low) {
                    dists[i] = distance.accum_dist(vec[i], root_bbox[i].low, i);
                    distsq += dists[i];
                }
                if (vec[i] > root_bbox[i].high) {
                    dists[i] = distance.accum_dist(vec[i], root_bbox[i].high, i);
                    distsq += dists[i];
                }
            }
 
            return distsq;
        }
 
        template <class RESULTSET>
                bool searchLevel(RESULTSET& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq,
                         distance_vector_t& dists, const float epsError) const
        {
            /* If this is a leaf node, then do check and return. */
            if ((node->child1 == NULL)&&(node->child2 == NULL)) {
                //count_leaf += (node->lr.right-node->lr.left);  // Removed since was neither used nor returned to the user.
                DistanceType worst_dist = result_set.worstDist();
                for (IndexType i=node->node_type.lr.left; i<node->node_type.lr.right; ++i) {
                    const IndexType index = vind[i];// reorder... : i;
                    DistanceType dist = distance(vec, index, (DIM>0 ? DIM : dim));
                    if (dist<worst_dist) {
                                                if(!result_set.addPoint(dist,vind[i])) {
                                                    // the resultset doesn't want to receive any more points, we're done searching!
                                                    return false;
                                                }
                    }
                }
                                return true;
            }
 
            /* Which child branch should be taken first? */
            int idx = node->node_type.sub.divfeat;
            ElementType val = vec[idx];
            DistanceType diff1 = val - node->node_type.sub.divlow;
            DistanceType diff2 = val - node->node_type.sub.divhigh;
 
            NodePtr bestChild;
            NodePtr otherChild;
            DistanceType cut_dist;
            if ((diff1+diff2)<0) {
                bestChild = node->child1;
                otherChild = node->child2;
                cut_dist = distance.accum_dist(val, node->node_type.sub.divhigh, idx);
            }
            else {
                bestChild = node->child2;
                otherChild = node->child1;
                cut_dist = distance.accum_dist( val, node->node_type.sub.divlow, idx);
            }
 
            /* Call recursively to search next level down. */
                        if(!searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError)) {
                            // the resultset doesn't want to receive any more points, we're done searching!
                            return false;
                        }
 
            DistanceType dst = dists[idx];
            mindistsq = mindistsq + cut_dist - dst;
            dists[idx] = cut_dist;
            if (mindistsq*epsError<=result_set.worstDist()) {
                            if(!searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError)) {
                                // the resultset doesn't want to receive any more points, we're done searching!
                                return false;
                            }
            }
            dists[idx] = dst;
                        return true;
        }
 
    public:
        void saveIndex(FILE* stream)
        {
            save_value(stream, m_size);
            save_value(stream, dim);
            save_value(stream, root_bbox);
            save_value(stream, m_leaf_max_size);
            save_value(stream, vind);
            save_tree(stream, root_node);
        }
 
        void loadIndex(FILE* stream)
        {
            load_value(stream, m_size);
            load_value(stream, dim);
            load_value(stream, root_bbox);
            load_value(stream, m_leaf_max_size);
            load_value(stream, vind);
            load_tree(stream, root_node);
        }
 
    };   // class KDTree
 
 
    template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2>
    struct KDTreeEigenMatrixAdaptor
    {
        typedef KDTreeEigenMatrixAdaptor<MatrixType,DIM,Distance> self_t;
        typedef typename MatrixType::Scalar              num_t;
        typedef typename MatrixType::Index IndexType;
        typedef typename Distance::template traits<num_t,self_t>::distance_t metric_t;
        typedef KDTreeSingleIndexAdaptor< metric_t,self_t,DIM,IndexType>  index_t;
 
        index_t* index; 
 
        KDTreeEigenMatrixAdaptor(const int dimensionality, const MatrixType &mat, const int leaf_max_size = 10) : m_data_matrix(mat)
        {
            const IndexType dims = mat.cols();
            if (dims!=dimensionality) throw std::runtime_error("Error: 'dimensionality' must match column count in data matrix");
            if (DIM>0 && static_cast<int>(dims)!=DIM)
                throw std::runtime_error("Data set dimensionality does not match the 'DIM' template argument");
            index = new index_t( dims, *this /* adaptor */, nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size ) );
            index->buildIndex();
        }
    private:
        KDTreeEigenMatrixAdaptor(const self_t&);
    public:
 
        ~KDTreeEigenMatrixAdaptor() {
            delete index;
        }
 
        const MatrixType &m_data_matrix;
 
        inline void query(const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq, const int /* nChecks_IGNORED */ = 10) const
        {
            nanoflann::KNNResultSet<num_t,IndexType> resultSet(num_closest);
            resultSet.init(out_indices, out_distances_sq);
            index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
        }
 
        const self_t & derived() const {
            return *this;
        }
        self_t & derived()       {
            return *this;
        }
 
        // Must return the number of data points
        inline size_t kdtree_get_point_count() const {
            return m_data_matrix.rows();
        }
 
        // Returns the L2 distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
        inline num_t kdtree_distance(const num_t *p1, const IndexType idx_p2,IndexType size) const
        {
            num_t s=0;
            for (IndexType i=0; i<size; i++) {
                const num_t d= p1[i]-m_data_matrix.coeff(idx_p2,i);
                s+=d*d;
            }
            return s;
        }
 
        // Returns the dim'th component of the idx'th point in the class:
        inline num_t kdtree_get_pt(const IndexType idx, int dim) const {
            return m_data_matrix.coeff(idx,IndexType(dim));
        }
 
        // Optional bounding-box computation: return false to default to a standard bbox computation loop.
        //   Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
        //   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
        template <class BBOX>
        bool kdtree_get_bbox(BBOX& /*bb*/) const {
            return false;
        }
 
    }; // end of KDTreeEigenMatrixAdaptor // end of grouping
} // end of NS
 
 
#endif /* NANOFLANN_HPP_ */