hierarchical_clustering_index.h

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 * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
 * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
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#ifndef RTABMAP_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_
#define RTABMAP_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_
 
#include <algorithm>
#include <string>
#include <map>
#include <cassert>
#include <limits>
#include <cmath>
 
#ifndef SIZE_MAX
#define SIZE_MAX ((size_t) -1)
#endif
 
#include "rtflann/general.h"
#include "rtflann/algorithms/nn_index.h"
#include "rtflann/algorithms/dist.h"
#include "rtflann/util/matrix.h"
#include "rtflann/util/result_set.h"
#include "rtflann/util/heap.h"
#include "rtflann/util/allocator.h"
#include "rtflann/util/random.h"
#include "rtflann/util/saving.h"
#include "rtflann/util/serialization.h"
 
namespace rtflann
{
 
struct HierarchicalClusteringIndexParams : public IndexParams
{
    HierarchicalClusteringIndexParams(int branching = 32,
                                      flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM,
                                      int trees = 4, int leaf_max_size = 100)
    {
        (*this)["algorithm"] = FLANN_INDEX_HIERARCHICAL;
        // The branching factor used in the hierarchical clustering
        (*this)["branching"] = branching;
        // Algorithm used for picking the initial cluster centers
        (*this)["centers_init"] = centers_init;
        // number of parallel trees to build
        (*this)["trees"] = trees;
        // maximum leaf size
        (*this)["leaf_max_size"] = leaf_max_size;
    }
};
 
 
 
template <typename Distance>
class HierarchicalClusteringIndex : public NNIndex<Distance>
{
public:
    typedef typename Distance::ElementType ElementType;
    typedef typename Distance::ResultType DistanceType;
 
    typedef NNIndex<Distance> BaseClass;
 
    HierarchicalClusteringIndex(const IndexParams& index_params = HierarchicalClusteringIndexParams(), Distance d = Distance())
        : BaseClass(index_params, d)
    {
        memoryCounter_ = 0;
 
        branching_ = get_param(index_params_,"branching",32);
        centers_init_ = get_param(index_params_,"centers_init", FLANN_CENTERS_RANDOM);
        trees_ = get_param(index_params_,"trees",4);
        leaf_max_size_ = get_param(index_params_,"leaf_max_size",100);
 
        initCenterChooser();
    }
 
 
    HierarchicalClusteringIndex(const Matrix<ElementType>& inputData, const IndexParams& index_params = HierarchicalClusteringIndexParams(),
                                Distance d = Distance())
        : BaseClass(index_params, d)
    {
        memoryCounter_ = 0;
 
        branching_ = get_param(index_params_,"branching",32);
        centers_init_ = get_param(index_params_,"centers_init", FLANN_CENTERS_RANDOM);
        trees_ = get_param(index_params_,"trees",4);
        leaf_max_size_ = get_param(index_params_,"leaf_max_size",100);
 
        initCenterChooser();
        
        setDataset(inputData);
 
        chooseCenters_->setDataSize(veclen_);
    }
 
 
    HierarchicalClusteringIndex(const HierarchicalClusteringIndex& other) : BaseClass(other),
                memoryCounter_(other.memoryCounter_),
                branching_(other.branching_),
                trees_(other.trees_),
                centers_init_(other.centers_init_),
                leaf_max_size_(other.leaf_max_size_)
 
    {
        initCenterChooser();
        tree_roots_.resize(other.tree_roots_.size());
        for (size_t i=0;i<tree_roots_.size();++i) {
                copyTree(tree_roots_[i], other.tree_roots_[i]);
        }
    }
 
    HierarchicalClusteringIndex& operator=(HierarchicalClusteringIndex other)
    {
        this->swap(other);
        return *this;
    }
 
 
    void initCenterChooser()
    {
        switch(centers_init_) {
        case FLANN_CENTERS_RANDOM:
                chooseCenters_ = new RandomCenterChooser<Distance>(distance_, points_);
                break;
        case FLANN_CENTERS_GONZALES:
                chooseCenters_ = new GonzalesCenterChooser<Distance>(distance_, points_);
                break;
        case FLANN_CENTERS_KMEANSPP:
            chooseCenters_ = new KMeansppCenterChooser<Distance>(distance_, points_);
                break;
        case FLANN_CENTERS_GROUPWISE:
            chooseCenters_ = new GroupWiseCenterChooser<Distance>(distance_, points_);
            break;
        default:
            throw FLANNException("Unknown algorithm for choosing initial centers.");
        }
    }
 
    virtual ~HierarchicalClusteringIndex()
    {
        delete chooseCenters_;
        freeIndex();
    }
 
    BaseClass* clone() const
    {
        return new HierarchicalClusteringIndex(*this);
    }
 
    int usedMemory() const
    {
        return pool_.usedMemory+pool_.wastedMemory+memoryCounter_;
    }
    
    using BaseClass::buildIndex;
 
    void addPoints(const Matrix<ElementType>& points, float rebuild_threshold = 2)
    {
        assert(points.cols==veclen_);
        size_t old_size = size_;
 
        extendDataset(points);
        
        if (rebuild_threshold>1 && size_at_build_*rebuild_threshold<size_) {
            buildIndex();
        }
        else {
            for (size_t i=0;i<points.rows;++i) {
                for (int j = 0; j < trees_; j++) {
                    addPointToTree(tree_roots_[j], old_size + i);
                }
            }            
        }
    }
 
 
    flann_algorithm_t getType() const
    {
        return FLANN_INDEX_HIERARCHICAL;
    }
 
 
    template<typename Archive>
    void serialize(Archive& ar)
    {
        ar.setObject(this);
 
        ar & *static_cast<NNIndex<Distance>*>(this);
 
        ar & branching_;
        ar & trees_;
        ar & centers_init_;
        ar & leaf_max_size_;
 
        if (Archive::is_loading::value) {
                tree_roots_.resize(trees_);
        }
        for (size_t i=0;i<tree_roots_.size();++i) {
                if (Archive::is_loading::value) {
                        tree_roots_[i] = new(pool_) Node();
                }
                ar & *tree_roots_[i];
        }
 
        if (Archive::is_loading::value) {
            index_params_["algorithm"] = getType();
            index_params_["branching"] = branching_;
            index_params_["trees"] = trees_;
            index_params_["centers_init"] = centers_init_;
            index_params_["leaf_size"] = leaf_max_size_;
        }
    }
 
    void saveIndex(FILE* stream)
    {
        serialization::SaveArchive sa(stream);
        sa & *this;
    }
 
 
    void loadIndex(FILE* stream)
    {
        serialization::LoadArchive la(stream);
        la & *this;
    }
 
 
    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) const
    {
        if (removed_) {
                findNeighborsWithRemoved<true>(result, vec, searchParams);
        }
        else {
                findNeighborsWithRemoved<false>(result, vec, searchParams);
        }
    }
 
protected:
 
    void buildIndexImpl()
    {
        chooseCenters_->setDataSize(veclen_);
 
        if (branching_<2) {
            throw FLANNException("Branching factor must be at least 2");
        }
        tree_roots_.resize(trees_);
        std::vector<int> indices(size_);
        for (int i=0; i<trees_; ++i) {
            for (size_t j=0; j<size_; ++j) {
                indices[j] = j;
            }
            tree_roots_[i] = new(pool_) Node();
            computeClustering(tree_roots_[i], &indices[0], size_);
        }
    }
 
private:
 
    struct PointInfo
    {
        size_t index;
        ElementType* point;
 
    private:
        template<typename Archive>
        void serialize(Archive& ar)
        {
                typedef HierarchicalClusteringIndex<Distance> Index;
                Index* obj = static_cast<Index*>(ar.getObject());
 
                ar & index;
//              ar & point;
 
                        if (Archive::is_loading::value) {
                                point = obj->points_[index];
                        }
        }
        friend struct serialization::access;
    };
 
    struct Node
    {
        ElementType* pivot;
        size_t pivot_index;
        std::vector<Node*> childs;
        std::vector<PointInfo> points;
 
                Node(){
                        pivot = NULL;
                        pivot_index = SIZE_MAX;
                }
        ~Node()
        {
                for(size_t i=0; i<childs.size(); i++){
                        childs[i]->~Node();
                                pivot = NULL;
                                pivot_index = -1;
                }
        };
 
    private:
        template<typename Archive>
        void serialize(Archive& ar)
        {
                typedef HierarchicalClusteringIndex<Distance> Index;
                Index* obj = static_cast<Index*>(ar.getObject());
                ar & pivot_index;
                if (Archive::is_loading::value) {
                                if (pivot_index != SIZE_MAX)
                                        pivot = obj->points_[pivot_index];
                                else
                                        pivot = NULL;
                }
                size_t childs_size;
                if (Archive::is_saving::value) {
                        childs_size = childs.size();
                }
                ar & childs_size;
 
                if (childs_size==0) {
                        ar & points;
                }
                else {
                        if (Archive::is_loading::value) {
                                childs.resize(childs_size);
                        }
                        for (size_t i=0;i<childs_size;++i) {
                                if (Archive::is_loading::value) {
                                        childs[i] = new(obj->pool_) Node();
                                }
                                ar & *childs[i];
                        }
                }
 
        }
        friend struct serialization::access;
    };
    typedef Node* NodePtr;
 
 
 
    typedef BranchStruct<NodePtr, DistanceType> BranchSt;
 
 
    void freeIndex(){
        for (size_t i=0; i<tree_roots_.size(); ++i) {
                tree_roots_[i]->~Node();
        }
        pool_.free();
    }
 
    void copyTree(NodePtr& dst, const NodePtr& src)
    {
        dst = new(pool_) Node();
        dst->pivot_index = src->pivot_index;
        dst->pivot = points_[dst->pivot_index];
 
        if (src->childs.size()==0) {
                dst->points = src->points;
        }
        else {
                dst->childs.resize(src->childs.size());
                for (size_t i=0;i<src->childs.size();++i) {
                        copyTree(dst->childs[i], src->childs[i]);
                }
        }
    }
 
 
 
    void computeLabels(int* indices, int indices_length,  int* centers, int centers_length, int* labels, DistanceType& cost)
    {
        cost = 0;
        for (int i=0; i<indices_length; ++i) {
            ElementType* point = points_[indices[i]];
            DistanceType dist = distance_(point, points_[centers[0]], veclen_);
            labels[i] = 0;
            for (int j=1; j<centers_length; ++j) {
                DistanceType new_dist = distance_(point, points_[centers[j]], veclen_);
                if (dist>new_dist) {
                    labels[i] = j;
                    dist = new_dist;
                }
            }
            cost += dist;
        }
    }
 
    void computeClustering(NodePtr node, int* indices, int indices_length)
    {
        if (indices_length < leaf_max_size_) { // leaf node
            node->points.resize(indices_length);
            for (int i=0;i<indices_length;++i) {
                node->points[i].index = indices[i];
                node->points[i].point = points_[indices[i]];
            }
            node->childs.clear();
            return;
        }
 
        std::vector<int> centers(branching_);
        std::vector<int> labels(indices_length);
 
        int centers_length;
        (*chooseCenters_)(branching_, indices, indices_length, &centers[0], centers_length);
 
        if (centers_length<branching_) {
            node->points.resize(indices_length);
            for (int i=0;i<indices_length;++i) {
                node->points[i].index = indices[i];
                node->points[i].point = points_[indices[i]];
            }
            node->childs.clear();
            return;
        }
 
 
        //  assign points to clusters
        DistanceType cost;
        computeLabels(indices, indices_length, &centers[0], centers_length, &labels[0], cost);
 
        node->childs.resize(branching_);
        int start = 0;
        int end = start;
        for (int i=0; i<branching_; ++i) {
            for (int j=0; j<indices_length; ++j) {
                if (labels[j]==i) {
                    std::swap(indices[j],indices[end]);
                    std::swap(labels[j],labels[end]);
                    end++;
                }
            }
 
            node->childs[i] = new(pool_) Node();
            node->childs[i]->pivot_index = centers[i];
            node->childs[i]->pivot = points_[centers[i]];
            node->childs[i]->points.clear();
            computeClustering(node->childs[i],indices+start, end-start);
            start=end;
        }
    }
 
 
    template<bool with_removed>
    void findNeighborsWithRemoved(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) const
    {
        int maxChecks = searchParams.checks;
 
        // Priority queue storing intermediate branches in the best-bin-first search
        Heap<BranchSt>* heap = new Heap<BranchSt>(size_);
 
        DynamicBitset checked(size_);
        int checks = 0;
        for (int i=0; i<trees_; ++i) {
            findNN<with_removed>(tree_roots_[i], result, vec, checks, maxChecks, heap, checked);
        }
 
        BranchSt branch;
        while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
            NodePtr node = branch.node;
            findNN<with_removed>(node, result, vec, checks, maxChecks, heap, checked);
        }
 
        delete heap;
    }
 
 
    template<bool with_removed>
    void findNN(NodePtr node, ResultSet<DistanceType>& result, const ElementType* vec, int& checks, int maxChecks,
                Heap<BranchSt>* heap,  DynamicBitset& checked) const
    {
        if (node->childs.empty()) {
            if (checks>=maxChecks) {
                if (result.full()) return;
            }
 
            for (size_t i=0; i<node->points.size(); ++i) {
                PointInfo& pointInfo = node->points[i];
                if (with_removed) {
                        if (removed_points_.test(pointInfo.index)) continue;
                }
                if (checked.test(pointInfo.index)) continue;
                DistanceType dist = distance_(pointInfo.point, vec, veclen_);
                result.addPoint(dist, pointInfo.index);
                checked.set(pointInfo.index);
                ++checks;
            }
        }
        else {
            DistanceType* domain_distances = new DistanceType[branching_];
            int best_index = 0;
            domain_distances[best_index] = distance_(vec, node->childs[best_index]->pivot, veclen_);
            for (int i=1; i<branching_; ++i) {
                domain_distances[i] = distance_(vec, node->childs[i]->pivot, veclen_);
                if (domain_distances[i]<domain_distances[best_index]) {
                    best_index = i;
                }
            }
            for (int i=0; i<branching_; ++i) {
                if (i!=best_index) {
                    heap->insert(BranchSt(node->childs[i],domain_distances[i]));
                }
            }
            delete[] domain_distances;
            findNN<with_removed>(node->childs[best_index],result,vec, checks, maxChecks, heap, checked);
        }
    }
    
    void addPointToTree(NodePtr node, size_t index)
    {
        ElementType* point = points_[index];
        
        if (node->childs.empty()) { // leaf node
                PointInfo pointInfo;
                pointInfo.point = point;
                pointInfo.index = index;
            node->points.push_back(pointInfo);
 
            if (node->points.size()>=size_t(branching_)) {
                std::vector<int> indices(node->points.size());
 
                for (size_t i=0;i<node->points.size();++i) {
                        indices[i] = node->points[i].index;
                }
                computeClustering(node, &indices[0], indices.size());
            }
        }
        else {            
            // find the closest child
            int closest = 0;
            ElementType* center = node->childs[closest]->pivot;
            DistanceType dist = distance_(center, point, veclen_);
            for (size_t i=1;i<size_t(branching_);++i) {
                center = node->childs[i]->pivot;
                DistanceType crt_dist = distance_(center, point, veclen_);
                if (crt_dist<dist) {
                    dist = crt_dist;
                    closest = i;
                }
            }
            addPointToTree(node->childs[closest], index);
        }                
    }
 
    void swap(HierarchicalClusteringIndex& other)
    {
        BaseClass::swap(other);
 
        std::swap(tree_roots_, other.tree_roots_);
        std::swap(pool_, other.pool_);
        std::swap(memoryCounter_, other.memoryCounter_);
        std::swap(branching_, other.branching_);
        std::swap(trees_, other.trees_);
        std::swap(centers_init_, other.centers_init_);
        std::swap(leaf_max_size_, other.leaf_max_size_);
        std::swap(chooseCenters_, other.chooseCenters_);
    }
 
private:
 
    std::vector<Node*> tree_roots_;
 
    PooledAllocator pool_;
 
    int memoryCounter_;
 
    int branching_;
    
    int trees_;
    
    flann_centers_init_t centers_init_;
    
    int leaf_max_size_;
    
    CenterChooser<Distance>* chooseCenters_;
 
    USING_BASECLASS_SYMBOLS
};
 
}
 
#endif /* FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ */