Classes | Public Types | Public Member Functions | Protected Member Functions | Private Types | Private Member Functions | Private Attributes | List of all members
rtflann::KMeansIndex< Distance > Class Template Reference

#include <kmeans_index.h>

Inheritance diagram for rtflann::KMeansIndex< Distance >:
Inheritance graph
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Classes

struct  Node
 
struct  PointInfo
 

Public Types

typedef NNIndex< Distance > BaseClass
 
typedef Distance::ResultType DistanceType
 
typedef Distance::ElementType ElementType
 
typedef bool needs_vector_space_distance
 
- Public Types inherited from rtflann::NNIndex< Distance >
typedef Distance::ResultType DistanceType
 
typedef Distance::ElementType ElementType
 

Public Member Functions

void addPoints (const Matrix< ElementType > &points, float rebuild_threshold=2)
 Incrementally add points to the index. More...
 
BaseClassclone () const
 
void findNeighbors (ResultSet< DistanceType > &result, const ElementType *vec, const SearchParams &searchParams) const
 
int getClusterCenters (Matrix< DistanceType > &centers)
 
flann_algorithm_t getType () const
 
void initCenterChooser ()
 
 KMeansIndex (const Matrix< ElementType > &inputData, const IndexParams &params=KMeansIndexParams(), Distance d=Distance())
 
 KMeansIndex (const IndexParams &params=KMeansIndexParams(), Distance d=Distance())
 
 KMeansIndex (const KMeansIndex &other)
 
void loadIndex (FILE *stream)
 
KMeansIndexoperator= (KMeansIndex other)
 
void saveIndex (FILE *stream)
 
template<typename Archive >
void serialize (Archive &ar)
 
void set_cb_index (float index)
 
int usedMemory () const
 
virtual ~KMeansIndex ()
 
- Public Member Functions inherited from rtflann::NNIndex< Distance >
virtual void buildIndex ()
 
virtual void buildIndex (const Matrix< ElementType > &dataset)
 
IndexParams getParameters () const
 
virtual ElementTypegetPoint (size_t id)
 
virtual int knnSearch (const Matrix< ElementType > &queries, Matrix< size_t > &indices, Matrix< DistanceType > &dists, size_t knn, const SearchParams &params) const
 Perform k-nearest neighbor search. More...
 
virtual int knnSearch (const Matrix< ElementType > &queries, std::vector< std::vector< size_t > > &indices, std::vector< std::vector< DistanceType > > &dists, size_t knn, const SearchParams &params) const
 Perform k-nearest neighbor search. More...
 
int knnSearch (const Matrix< ElementType > &queries, std::vector< std::vector< int > > &indices, std::vector< std::vector< DistanceType > > &dists, size_t knn, const SearchParams &params) const
 
 NNIndex (Distance d)
 
 NNIndex (const IndexParams &params, Distance d)
 
 NNIndex (const NNIndex &other)
 
virtual int radiusSearch (const Matrix< ElementType > &queries, Matrix< size_t > &indices, Matrix< DistanceType > &dists, float radius, const SearchParams &params) const
 Perform radius search. More...
 
int radiusSearch (const Matrix< ElementType > &queries, Matrix< int > &indices, Matrix< DistanceType > &dists, float radius, const SearchParams &params) const
 
virtual int radiusSearch (const Matrix< ElementType > &queries, std::vector< std::vector< size_t > > &indices, std::vector< std::vector< DistanceType > > &dists, float radius, const SearchParams &params) const
 Perform radius search. More...
 
int radiusSearch (const Matrix< ElementType > &queries, std::vector< std::vector< int > > &indices, std::vector< std::vector< DistanceType > > &dists, float radius, const SearchParams &params) const
 
size_t removedCount () const
 
virtual void removePoint (size_t id)
 
template<typename Archive >
void serialize (Archive &ar)
 
size_t size () const
 
size_t sizeAtBuild () const
 
size_t veclen () const
 
virtual ~NNIndex ()
 
- Public Member Functions inherited from rtflann::IndexBase
virtual ~IndexBase ()
 

Protected Member Functions

void buildIndexImpl ()
 
- Protected Member Functions inherited from rtflann::NNIndex< Distance >
void cleanRemovedPoints ()
 
void extendDataset (const Matrix< ElementType > &new_points)
 
size_t id_to_index (size_t id)
 
void indices_to_ids (const size_t *in, size_t *out, size_t size) const
 
void setDataset (const Matrix< ElementType > &dataset)
 
void swap (NNIndex &other)
 

Private Types

typedef BranchStruct< NodePtr, DistanceTypeBranchSt
 
typedef NodeNodePtr
 

Private Member Functions

void addPointToTree (NodePtr node, size_t index, DistanceType dist_to_pivot)
 
void computeClustering (NodePtr node, int *indices, int indices_length, int branching)
 
void computeNodeStatistics (NodePtr node, const std::vector< int > &indices)
 
void copyTree (NodePtr &dst, const NodePtr &src)
 
int exploreNodeBranches (NodePtr node, const ElementType *q, Heap< BranchSt > *heap) const
 
template<bool with_removed>
void findExactNN (NodePtr node, ResultSet< DistanceType > &result, const ElementType *vec) const
 
template<bool with_removed>
void findNeighborsWithRemoved (ResultSet< DistanceType > &result, const ElementType *vec, const SearchParams &searchParams) const
 
template<bool with_removed>
void findNN (NodePtr node, ResultSet< DistanceType > &result, const ElementType *vec, int &checks, int maxChecks, Heap< BranchSt > *heap) const
 
void freeIndex ()
 
void getCenterOrdering (NodePtr node, const ElementType *q, std::vector< int > &sort_indices) const
 
DistanceType getDistanceToBorder (DistanceType *p, DistanceType *c, DistanceType *q) const
 
int getMinVarianceClusters (NodePtr root, std::vector< NodePtr > &clusters, int clusters_length, DistanceType &varianceValue) const
 
void swap (KMeansIndex &other)
 

Private Attributes

int branching_
 
float cb_index_
 
flann_centers_init_t centers_init_
 
CenterChooser< Distance > * chooseCenters_
 
int iterations_
 
int memoryCounter_
 
PooledAllocator pool_
 
NodePtr root_
 

Additional Inherited Members

- Protected Attributes inherited from rtflann::NNIndex< Distance >
ElementTypedata_ptr_
 
Distance distance_
 
std::vector< size_t > ids_
 
IndexParams index_params_
 
size_t last_id_
 
std::vector< ElementType * > points_
 
bool removed_
 
size_t removed_count_
 
DynamicBitset removed_points_
 
size_t size_
 
size_t size_at_build_
 
size_t veclen_
 

Detailed Description

template<typename Distance>
class rtflann::KMeansIndex< Distance >

Hierarchical kmeans index

Contains a tree constructed through a hierarchical kmeans clustering and other information for indexing a set of points for nearest-neighbour matching.

Definition at line 83 of file kmeans_index.h.

Member Typedef Documentation

◆ BaseClass

template<typename Distance>
typedef NNIndex<Distance> rtflann::KMeansIndex< Distance >::BaseClass

Definition at line 89 of file kmeans_index.h.

◆ BranchSt

template<typename Distance>
typedef BranchStruct<NodePtr, DistanceType> rtflann::KMeansIndex< Distance >::BranchSt
private

Alias definition for a nicer syntax.

Definition at line 454 of file kmeans_index.h.

◆ DistanceType

template<typename Distance>
typedef Distance::ResultType rtflann::KMeansIndex< Distance >::DistanceType

Definition at line 87 of file kmeans_index.h.

◆ ElementType

template<typename Distance>
typedef Distance::ElementType rtflann::KMeansIndex< Distance >::ElementType

Definition at line 86 of file kmeans_index.h.

◆ needs_vector_space_distance

template<typename Distance>
typedef bool rtflann::KMeansIndex< Distance >::needs_vector_space_distance

Definition at line 91 of file kmeans_index.h.

◆ NodePtr

template<typename Distance>
typedef Node* rtflann::KMeansIndex< Distance >::NodePtr
private

Definition at line 449 of file kmeans_index.h.

Constructor & Destructor Documentation

◆ KMeansIndex() [1/3]

template<typename Distance>
rtflann::KMeansIndex< Distance >::KMeansIndex ( const Matrix< ElementType > &  inputData,
const IndexParams params = KMeansIndexParams(),
Distance  d = Distance() 
)
inline

Index constructor

Params: inputData = dataset with the input features params = parameters passed to the hierarchical k-means algorithm

Definition at line 107 of file kmeans_index.h.

◆ KMeansIndex() [2/3]

template<typename Distance>
rtflann::KMeansIndex< Distance >::KMeansIndex ( const IndexParams params = KMeansIndexParams(),
Distance  d = Distance() 
)
inline

Index constructor

Params: inputData = dataset with the input features params = parameters passed to the hierarchical k-means algorithm

Definition at line 131 of file kmeans_index.h.

◆ KMeansIndex() [3/3]

template<typename Distance>
rtflann::KMeansIndex< Distance >::KMeansIndex ( const KMeansIndex< Distance > &  other)
inline

Definition at line 146 of file kmeans_index.h.

◆ ~KMeansIndex()

template<typename Distance>
virtual rtflann::KMeansIndex< Distance >::~KMeansIndex ( )
inlinevirtual

Index destructor.

Release the memory used by the index.

Definition at line 187 of file kmeans_index.h.

Member Function Documentation

◆ addPoints()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::addPoints ( const Matrix< ElementType > &  points,
float  rebuild_threshold = 2 
)
inlinevirtual

Incrementally add points to the index.

Parameters
pointsMatrix with points to be added
rebuild_threshold

Reimplemented from rtflann::NNIndex< Distance >.

Definition at line 215 of file kmeans_index.h.

◆ addPointToTree()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::addPointToTree ( NodePtr  node,
size_t  index,
DistanceType  dist_to_pivot 
)
inlineprivate

Definition at line 969 of file kmeans_index.h.

◆ buildIndexImpl()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::buildIndexImpl ( )
inlineprotectedvirtual

Builds the index

Implements rtflann::NNIndex< Distance >.

Definition at line 329 of file kmeans_index.h.

◆ clone()

template<typename Distance>
BaseClass* rtflann::KMeansIndex< Distance >::clone ( ) const
inlinevirtual

Implements rtflann::NNIndex< Distance >.

Definition at line 193 of file kmeans_index.h.

◆ computeClustering()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::computeClustering ( NodePtr  node,
int *  indices,
int  indices_length,
int  branching 
)
inlineprivate

The method responsible with actually doing the recursive hierarchical clustering

Params: node = the node to cluster indices = indices of the points belonging to the current node branching = the branching factor to use in the clustering

TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)

Definition at line 542 of file kmeans_index.h.

◆ computeNodeStatistics()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::computeNodeStatistics ( NodePtr  node,
const std::vector< int > &  indices 
)
inlineprivate

Computes the statistics of a node (mean, radius, variance).

Params: node = the node to use indices = the indices of the points belonging to the node

Definition at line 495 of file kmeans_index.h.

◆ copyTree()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::copyTree ( NodePtr dst,
const NodePtr src 
)
inlineprivate

Definition at line 467 of file kmeans_index.h.

◆ exploreNodeBranches()

template<typename Distance>
int rtflann::KMeansIndex< Distance >::exploreNodeBranches ( NodePtr  node,
const ElementType q,
Heap< BranchSt > *  heap 
) const
inlineprivate

Helper function that computes the nearest childs of a node to a given query point. Params: node = the node q = the query point distances = array with the distances to each child node. Returns:

Definition at line 802 of file kmeans_index.h.

◆ findExactNN()

template<typename Distance>
template<bool with_removed>
void rtflann::KMeansIndex< Distance >::findExactNN ( NodePtr  node,
ResultSet< DistanceType > &  result,
const ElementType vec 
) const
inlineprivate

Function the performs exact nearest neighbor search by traversing the entire tree.

Definition at line 835 of file kmeans_index.h.

◆ findNeighbors()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::findNeighbors ( ResultSet< DistanceType > &  result,
const ElementType vec,
const SearchParams searchParams 
) const
inlinevirtual

Find set of nearest neighbors to vec. Their indices are stored inside the result object.

Params: result = the result object in which the indices of the nearest-neighbors are stored vec = the vector for which to search the nearest neighbors searchParams = parameters that influence the search algorithm (checks, cb_index)

Implements rtflann::NNIndex< Distance >.

Definition at line 283 of file kmeans_index.h.

◆ findNeighborsWithRemoved()

template<typename Distance>
template<bool with_removed>
void rtflann::KMeansIndex< Distance >::findNeighborsWithRemoved ( ResultSet< DistanceType > &  result,
const ElementType vec,
const SearchParams searchParams 
) const
inlineprivate

Definition at line 715 of file kmeans_index.h.

◆ findNN()

template<typename Distance>
template<bool with_removed>
void rtflann::KMeansIndex< Distance >::findNN ( NodePtr  node,
ResultSet< DistanceType > &  result,
const ElementType vec,
int &  checks,
int  maxChecks,
Heap< BranchSt > *  heap 
) const
inlineprivate

Performs one descent in the hierarchical k-means tree. The branches not visited are stored in a priority queue.

Params: node = node to explore result = container for the k-nearest neighbors found vec = query points checks = how many points in the dataset have been checked so far maxChecks = maximum dataset points to checks

Definition at line 755 of file kmeans_index.h.

◆ freeIndex()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::freeIndex ( )
inlineprivatevirtual

Helper function

Implements rtflann::NNIndex< Distance >.

Definition at line 460 of file kmeans_index.h.

◆ getCenterOrdering()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::getCenterOrdering ( NodePtr  node,
const ElementType q,
std::vector< int > &  sort_indices 
) const
inlineprivate

Helper function.

I computes the order in which to traverse the child nodes of a particular node.

Definition at line 880 of file kmeans_index.h.

◆ getClusterCenters()

template<typename Distance>
int rtflann::KMeansIndex< Distance >::getClusterCenters ( Matrix< DistanceType > &  centers)
inline

Clustering function that takes a cut in the hierarchical k-means tree and return the clusters centers of that clustering. Params: numClusters = number of clusters to have in the clustering computed Returns: number of cluster centers

Definition at line 301 of file kmeans_index.h.

◆ getDistanceToBorder()

template<typename Distance>
DistanceType rtflann::KMeansIndex< Distance >::getDistanceToBorder ( DistanceType p,
DistanceType c,
DistanceType q 
) const
inlineprivate

Method that computes the squared distance from the query point q from inside region with center c to the border between this region and the region with center p

Definition at line 902 of file kmeans_index.h.

◆ getMinVarianceClusters()

template<typename Distance>
int rtflann::KMeansIndex< Distance >::getMinVarianceClusters ( NodePtr  root,
std::vector< NodePtr > &  clusters,
int  clusters_length,
DistanceType varianceValue 
) const
inlineprivate

Helper function the descends in the hierarchical k-means tree by spliting those clusters that minimize the overall variance of the clustering. Params: root = root node clusters = array with clusters centers (return value) varianceValue = variance of the clustering (return value) Returns:

Definition at line 926 of file kmeans_index.h.

◆ getType()

template<typename Distance>
flann_algorithm_t rtflann::KMeansIndex< Distance >::getType ( ) const
inlinevirtual

Implements rtflann::IndexBase.

Definition at line 95 of file kmeans_index.h.

◆ initCenterChooser()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::initCenterChooser ( )
inline

Definition at line 165 of file kmeans_index.h.

◆ loadIndex()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::loadIndex ( FILE *  stream)
inlinevirtual

Implements rtflann::IndexBase.

Definition at line 266 of file kmeans_index.h.

◆ operator=()

template<typename Distance>
KMeansIndex& rtflann::KMeansIndex< Distance >::operator= ( KMeansIndex< Distance >  other)
inline

Definition at line 158 of file kmeans_index.h.

◆ saveIndex()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::saveIndex ( FILE *  stream)
inlinevirtual

Implements rtflann::IndexBase.

Definition at line 260 of file kmeans_index.h.

◆ serialize()

template<typename Distance>
template<typename Archive >
void rtflann::KMeansIndex< Distance >::serialize ( Archive &  ar)
inline

Definition at line 234 of file kmeans_index.h.

◆ set_cb_index()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::set_cb_index ( float  index)
inline

Definition at line 199 of file kmeans_index.h.

◆ swap()

template<typename Distance>
void rtflann::KMeansIndex< Distance >::swap ( KMeansIndex< Distance > &  other)
inlineprivate

Definition at line 1010 of file kmeans_index.h.

◆ usedMemory()

template<typename Distance>
int rtflann::KMeansIndex< Distance >::usedMemory ( ) const
inlinevirtual

Computes the inde memory usage Returns: memory used by the index

Implements rtflann::IndexBase.

Definition at line 208 of file kmeans_index.h.

Member Data Documentation

◆ branching_

template<typename Distance>
int rtflann::KMeansIndex< Distance >::branching_
private

The branching factor used in the hierarchical k-means clustering

Definition at line 1025 of file kmeans_index.h.

◆ cb_index_

template<typename Distance>
float rtflann::KMeansIndex< Distance >::cb_index_
private

Cluster border index. This is used in the tree search phase when determining the closest cluster to explore next. A zero value takes into account only the cluster centres, a value greater then zero also take into account the size of the cluster.

Definition at line 1039 of file kmeans_index.h.

◆ centers_init_

template<typename Distance>
flann_centers_init_t rtflann::KMeansIndex< Distance >::centers_init_
private

Algorithm for choosing the cluster centers

Definition at line 1031 of file kmeans_index.h.

◆ chooseCenters_

template<typename Distance>
CenterChooser<Distance>* rtflann::KMeansIndex< Distance >::chooseCenters_
private

Algorithm used to choose initial centers

Definition at line 1059 of file kmeans_index.h.

◆ iterations_

template<typename Distance>
int rtflann::KMeansIndex< Distance >::iterations_
private

Maximum number of iterations to use when performing k-means clustering

Definition at line 1028 of file kmeans_index.h.

◆ memoryCounter_

template<typename Distance>
int rtflann::KMeansIndex< Distance >::memoryCounter_
private

Memory occupied by the index.

Definition at line 1054 of file kmeans_index.h.

◆ pool_

template<typename Distance>
PooledAllocator rtflann::KMeansIndex< Distance >::pool_
private

Pooled memory allocator.

Definition at line 1049 of file kmeans_index.h.

◆ root_

template<typename Distance>
NodePtr rtflann::KMeansIndex< Distance >::root_
private

The root node in the tree.

Definition at line 1044 of file kmeans_index.h.


The documentation for this class was generated from the following file:


rtabmap
Author(s): Mathieu Labbe
autogenerated on Mon Jan 23 2023 03:39:00