Detailed Description

template<typename Distance, class DatasetAdaptor, int DIM = -1, typename IndexType = size_t>
class nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >

kd-tree index

Contains the k-d trees and other information for indexing a set of points for nearest-neighbor matching.

The class "DatasetAdaptor" must provide the following interface (can be non-virtual, inlined methods):

// Must return the number of data poins
inline size_t kdtree_get_point_count() const { ... }
// [Only if using the metric_L2_Simple type] Must return the Euclidean (L2) distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
inline DistanceType kdtree_distance(const T *p1, const size_t idx_p2,size_t size) const { ... }
// Must return the dim'th component of the idx'th point in the class:
inline T kdtree_get_pt(const size_t idx, int dim) const { ... }
// Optional bounding-box computation: return false to default to a standard bbox computation loop.
//   Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
//   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
template <class BBOX>
bool kdtree_get_bbox(BBOX &bb) const
{
   bb[0].low = ...; bb[0].high = ...;  // 0th dimension limits
   bb[1].low = ...; bb[1].high = ...;  // 1st dimension limits
   ...
   return true;
}

Template Parameters

DatasetAdaptor	The user-provided adaptor (see comments above).
Distance	The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
DIM	Dimensionality of data points (e.g. 3 for 3D points)
IndexType	Will be typically size_t or int

Definition at line 740 of file nanoflann.hpp.

#include <nanoflann.hpp>

Classes
struct	Interval

struct	Node

Public Types
typedef Distance::ElementType	ElementType

typedef Distance::DistanceType	DistanceType

Public Member Functions
	KDTreeSingleIndexAdaptor (const int dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams &params=KDTreeSingleIndexAdaptorParams())
	KDTree constructor. More...

	~KDTreeSingleIndexAdaptor ()=default
	Standard destructor. More...

void	freeIndex ()
	Frees the previously-built index. More...

void	buildIndex ()
	Builds the index. More...

size_t	size () const
	Returns number of points in dataset. More...

size_t	veclen () const
	Returns the length of each point in the dataset. More...

size_t	usedMemory () const
	Computes the inde memory usage Returns: memory used by the index. More...

void	saveIndex (FILE *stream)
	Stores the index in a binary file. More...

void	loadIndex (FILE *stream)
	Loads a previous index from a binary file. More...

Query methods
template<typename RESULTSET >
bool	findNeighbors (RESULTSET &result, const ElementType *vec, const SearchParams &searchParams) const
	Find set of nearest neighbors to vec[0:dim-1]. More...

size_t	knnSearch (const ElementType query_point, const size_t num_closest, IndexType out_indices, DistanceType *out_distances_sq, const int=10) const
	Find the "num_closest" nearest neighbors to the query_point[0:dim-1]. More...

size_t	radiusSearch (const ElementType *query_point, const DistanceType &radius, std::vector< std::pair< IndexType, DistanceType > > &IndicesDists, const SearchParams &searchParams) const
	Find all the neighbors to query_point[0:dim-1] within a maximum radius. More...

template<class SEARCH_CALLBACK >
size_t	radiusSearchCustomCallback (const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParams &searchParams=SearchParams()) const
	Just like radiusSearch() but with a custom callback class for each point found in the radius of the query. More...

Public Attributes
Distance	distance

Protected Types
typedef Node *	NodePtr

typedef array_or_vector_selector< DIM, Interval >::container_t	BoundingBox
	Define "BoundingBox" as a fixed-size or variable-size container depending on "DIM". More...

typedef array_or_vector_selector< DIM, DistanceType >::container_t	distance_vector_t
	Define "distance_vector_t" as a fixed-size or variable-size container depending on "DIM". More...

Protected Attributes
std::vector< IndexType >	vind
	Array of indices to vectors in the dataset. More...

size_t	m_leaf_max_size

const DatasetAdaptor &	dataset
	The dataset used by this index. More...

const KDTreeSingleIndexAdaptorParams	index_params

size_t	m_size
	Number of current poins in the dataset. More...

size_t	m_size_at_index_build
	Number of points in the dataset when the index was built. More...

int	dim
	Dimensionality of each data point. More...

NodePtr	root_node
	The KD-tree used to find neighbours. More...

BoundingBox	root_bbox

PooledAllocator	pool
	Pooled memory allocator. More...

Private Member Functions
	KDTreeSingleIndexAdaptor (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &)=delete
	Hidden copy constructor, to disallow copying indices (Not implemented) More...

void	init_vind ()
	Make sure the auxiliary list vind has the same size than the current dataset, and re-generate if size has changed. More...

ElementType	dataset_get (size_t idx, int component) const
	Helper accessor to the dataset points: More...

void	save_tree (FILE *stream, NodePtr tree)

void	load_tree (FILE *stream, NodePtr &tree)

void	computeBoundingBox (BoundingBox &bbox)

NodePtr	divideTree (const IndexType left, const IndexType right, BoundingBox &bbox)
	Create a tree node that subdivides the list of vecs from vind[first] to vind[last]. More...

void	computeMinMax (IndexType *ind, IndexType count, int element, ElementType &min_elem, ElementType &max_elem)

void	middleSplit_ (IndexType *ind, IndexType count, IndexType &index, int &cutfeat, DistanceType &cutval, const BoundingBox &bbox)

void	planeSplit (IndexType *ind, const IndexType count, int cutfeat, DistanceType &cutval, IndexType &lim1, IndexType &lim2)
	Subdivide the list of points by a plane perpendicular on axe corresponding to the 'cutfeat' dimension at 'cutval' position. More...

DistanceType	computeInitialDistances (const ElementType *vec, distance_vector_t &dists) const

template<class RESULTSET >
void	searchLevel (RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindistsq, distance_vector_t &dists, const float epsError) const
	Performs an exact search in the tree starting from a node. More...

Member Typedef Documentation

◆ BoundingBox

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef array_or_vector_selector<DIM,Interval>::container_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::BoundingBox

protected

Define "BoundingBox" as a fixed-size or variable-size container depending on "DIM".

Definition at line 796 of file nanoflann.hpp.

◆ distance_vector_t

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef array_or_vector_selector<DIM,DistanceType>::container_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::distance_vector_t

protected

Define "distance_vector_t" as a fixed-size or variable-size container depending on "DIM".

Definition at line 799 of file nanoflann.hpp.

◆ DistanceType

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef Distance::DistanceType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::DistanceType

Definition at line 747 of file nanoflann.hpp.

◆ ElementType

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef Distance::ElementType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::ElementType

Definition at line 746 of file nanoflann.hpp.

◆ NodePtr

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef Node* nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::NodePtr

protected

Definition at line 787 of file nanoflann.hpp.

Constructor & Destructor Documentation

◆ KDTreeSingleIndexAdaptor() [1/2]

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::KDTreeSingleIndexAdaptor ( const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > & )

privatedelete

Hidden copy constructor, to disallow copying indices (Not implemented)

◆ KDTreeSingleIndexAdaptor() [2/2]

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::KDTreeSingleIndexAdaptor	(	const int	dimensionality,
		const DatasetAdaptor &	inputData,
		const KDTreeSingleIndexAdaptorParams &	params = `KDTreeSingleIndexAdaptorParams()`
	)

inline

KDTree constructor.

Refer to docs in README.md or online in https://github.com/jlblancoc/nanoflann

The KD-Tree point dimension (the length of each point in the datase, e.g. 3 for 3D points) is determined by means of:

The DIM template parameter if >0 (highest priority)
Otherwise, the dimensionality parameter of this constructor.

Parameters

inputData	Dataset with the input features
params	Basically, the maximum leaf node size

Definition at line 831 of file nanoflann.hpp.

References mrpt::containers::m_size, and params.

◆ ~KDTreeSingleIndexAdaptor()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::~KDTreeSingleIndexAdaptor ( )

default

Standard destructor.

Member Function Documentation

◆ buildIndex()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::buildIndex ( )

inline

Builds the index.

Definition at line 858 of file nanoflann.hpp.

References mrpt::containers::m_size.

Referenced by nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance >::KDTreeEigenMatrixAdaptor().

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◆ computeBoundingBox()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::computeBoundingBox ( BoundingBox & bbox )

inlineprivate

Definition at line 1009 of file nanoflann.hpp.

◆ computeInitialDistances()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

DistanceType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::computeInitialDistances	(	const ElementType *	vec,
		distance_vector_t &	dists
	)		const

inlineprivate

Definition at line 1183 of file nanoflann.hpp.

References mrpt::math::distance(), and nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::dists.

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◆ computeMinMax()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::computeMinMax	(	IndexType *	ind,
		IndexType	count,
		int	element,
		ElementType &	min_elem,
		ElementType &	max_elem
	)

inlineprivate

Definition at line 1092 of file nanoflann.hpp.

References nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::count, and val.

◆ dataset_get()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

ElementType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::dataset_get	(	size_t	idx,
		int	component
	)		const

inlineprivate

Helper accessor to the dataset points:

Definition at line 979 of file nanoflann.hpp.

◆ divideTree()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

NodePtr nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::divideTree	(	const IndexType	left,
		const IndexType	right,
		BoundingBox &	bbox
	)

inlineprivate

Create a tree node that subdivides the list of vecs from vind[first] to vind[last].

The routine is called recursively on each sublist.

Parameters

left	index of the first vector
right	index of the last vector

Definition at line 1041 of file nanoflann.hpp.

References nanoflann::PooledAllocator::allocate(), nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child1, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child2, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::lr, and nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::node_type.

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◆ findNeighbors()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

template<typename RESULTSET >

bool nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::findNeighbors	(	RESULTSET &	result,
		const ElementType *	vec,
		const SearchParams &	searchParams
	)		const

inline

Find set of nearest neighbors to vec[0:dim-1].

Their indices are stored inside the result object.

Params: result = the result object in which the indices of the nearest-neighbors are stored vec = the vector for which to search the nearest neighbors

Template Parameters

RESULTSET Should be any ResultSet<DistanceType>

Returns: True if the requested neighbors could be found.

See also: knnSearch, radiusSearch

Definition at line 901 of file nanoflann.hpp.

References nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::dists, nanoflann::SearchParams::eps, and nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::size().

Referenced by nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance >::query().

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◆ freeIndex()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::freeIndex ( )

inline

Frees the previously-built index.

Automatically called within buildIndex().

Definition at line 848 of file nanoflann.hpp.

References nanoflann::PooledAllocator::free_all().

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◆ init_vind()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::init_vind ( )

inlineprivate

Make sure the auxiliary list vind has the same size than the current dataset, and re-generate if size has changed.

Definition at line 970 of file nanoflann.hpp.

References mrpt::containers::m_size.

◆ knnSearch()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::knnSearch	(	const ElementType *	query_point,
		const size_t	num_closest,
		IndexType *	out_indices,
		DistanceType *	out_distances_sq,
		const int	= `10`
	)		const

inline

Find the "num_closest" nearest neighbors to the query_point[0:dim-1].

Their indices are stored inside the result object.

See also: radiusSearch, findNeighbors

Note: nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.

Returns: Number N of valid points in the result set. Only the first N entries in out_indices and out_distances_sq will be valid. Return may be less than num_closest only if the number of elements in the tree is less than num_closest.

Definition at line 925 of file nanoflann.hpp.

References nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::init(), and nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::size().

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◆ load_tree()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::load_tree	(	FILE *	stream,
		NodePtr &	tree
	)

inlineprivate

Definition at line 996 of file nanoflann.hpp.

References nanoflann::PooledAllocator::allocate(), nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child1, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child2, and nanoflann::load_value().

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◆ loadIndex()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::loadIndex ( FILE * stream )

inline

Loads a previous index from a binary file.

IMPORTANT NOTE: The set of data points is NOT stored in the file, so the index object must be constructed associated to the same source of data points used while building the index. See the example: examples/saveload_example.cpp

See also: loadIndex

Definition at line 1275 of file nanoflann.hpp.

References nanoflann::load_value(), and mrpt::containers::m_size.

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◆ middleSplit_()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::middleSplit_	(	IndexType *	ind,
		IndexType	count,
		IndexType &	index,
		int &	cutfeat,
		DistanceType &	cutval,
		const BoundingBox &	bbox
	)

inlineprivate

Definition at line 1103 of file nanoflann.hpp.

References nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::count.

◆ planeSplit()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::planeSplit	(	IndexType *	ind,
		const IndexType	count,
		int	cutfeat,
		DistanceType &	cutval,
		IndexType &	lim1,
		IndexType &	lim2
	)

inlineprivate

Subdivide the list of points by a plane perpendicular on axe corresponding to the 'cutfeat' dimension at 'cutval' position.

On return: dataset[ind[0..lim1-1]][cutfeat]<cutval dataset[ind[lim1..lim2-1]][cutfeat]==cutval dataset[ind[lim2..count]][cutfeat]>cutval

Definition at line 1154 of file nanoflann.hpp.

References nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::count.

◆ radiusSearch()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::radiusSearch	(	const ElementType *	query_point,
		const DistanceType &	radius,
		std::vector< std::pair< IndexType, DistanceType > > &	IndicesDists,
		const SearchParams &	searchParams
	)		const

inline

Find all the neighbors to query_point[0:dim-1] within a maximum radius.

The output is given as a vector of pairs, of which the first element is a point index and the second the corresponding distance. Previous contents of IndicesDists are cleared.

If searchParams.sorted==true, the output list is sorted by ascending distances.

For a better performance, it is advisable to do a .reserve() on the vector if you have any wild guess about the number of expected matches.

See also: knnSearch, findNeighbors, radiusSearchCustomCallback

Returns: The number of points within the given radius (i.e. indices.size() or dists.size() )

Definition at line 945 of file nanoflann.hpp.

References nanoflann::SearchParams::sorted.

◆ radiusSearchCustomCallback()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

template<class SEARCH_CALLBACK >

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::radiusSearchCustomCallback	(	const ElementType *	query_point,
		SEARCH_CALLBACK &	resultSet,
		const SearchParams &	searchParams = `SearchParams()`
	)		const

inline

Just like radiusSearch() but with a custom callback class for each point found in the radius of the query.

See the source of RadiusResultSet<> as a start point for your own classes.

See also: radiusSearch

Definition at line 960 of file nanoflann.hpp.

◆ save_tree()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::save_tree	(	FILE *	stream,
		NodePtr	tree
	)

inlineprivate

Definition at line 984 of file nanoflann.hpp.

References nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child1, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child2, and nanoflann::save_value().

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◆ saveIndex()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::saveIndex ( FILE * stream )

inline

Stores the index in a binary file.

IMPORTANT NOTE: The set of data points is NOT stored in the file, so when loading the index object it must be constructed associated to the same source of data points used while building it. See the example: examples/saveload_example.cpp

See also: loadIndex

Definition at line 1261 of file nanoflann.hpp.

References mrpt::containers::m_size, and nanoflann::save_value().

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◆ searchLevel()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

template<class RESULTSET >

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::searchLevel	(	RESULTSET &	result_set,
		const ElementType *	vec,
		const NodePtr	node,
		DistanceType	mindistsq,
		distance_vector_t &	dists,
		const float	epsError
	)		const

inlineprivate

Performs an exact search in the tree starting from a node.

Template Parameters

RESULTSET Should be any ResultSet<DistanceType>

Definition at line 1207 of file nanoflann.hpp.

References nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child1, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child2, mrpt::math::distance(), nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::dists, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::lr, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::node_type, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::sub, and val.

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◆ size()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::size ( ) const

inline

Returns number of points in dataset.

Definition at line 869 of file nanoflann.hpp.

References mrpt::containers::m_size.

◆ usedMemory()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::usedMemory ( ) const

inline

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

Definition at line 880 of file nanoflann.hpp.

References nanoflann::PooledAllocator::usedMemory, and nanoflann::PooledAllocator::wastedMemory.

◆ veclen()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::veclen ( ) const

inline

Returns the length of each point in the dataset.

Definition at line 872 of file nanoflann.hpp.

Member Data Documentation

◆ dataset

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

const DatasetAdaptor& nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::dataset

protected

The dataset used by this index.

The source of our data

Definition at line 761 of file nanoflann.hpp.

◆ dim

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

int nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::dim

protected

Dimensionality of each data point.

Definition at line 767 of file nanoflann.hpp.

◆ distance

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

Distance nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::distance

Definition at line 816 of file nanoflann.hpp.

◆ index_params

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

const KDTreeSingleIndexAdaptorParams nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::index_params

protected

Definition at line 763 of file nanoflann.hpp.

◆ m_leaf_max_size

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::m_leaf_max_size

protected

Definition at line 755 of file nanoflann.hpp.

◆ m_size

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::m_size

protected

Number of current poins in the dataset.

Definition at line 765 of file nanoflann.hpp.

◆ m_size_at_index_build

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::m_size_at_index_build

protected

Number of points in the dataset when the index was built.

Definition at line 766 of file nanoflann.hpp.

◆ pool

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

PooledAllocator nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::pool

protected

Pooled memory allocator.

Using a pooled memory allocator is more efficient than allocating memory directly when there is a large number small of memory allocations.

Definition at line 812 of file nanoflann.hpp.

◆ root_bbox

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

BoundingBox nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::root_bbox

protected

Definition at line 803 of file nanoflann.hpp.

◆ root_node

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

NodePtr nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::root_node

protected

The KD-tree used to find neighbours.

Definition at line 802 of file nanoflann.hpp.

◆ vind

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

std::vector<IndexType> nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::vind

protected

Array of indices to vectors in the dataset.

Definition at line 753 of file nanoflann.hpp.

Detailed Description

template<typename Distance, class DatasetAdaptor, int DIM = -1, typename IndexType = size_t> class nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >

Classes

Public Types

Public Member Functions

Public Attributes

Protected Types

Protected Attributes

Private Member Functions

Member Typedef Documentation

◆ BoundingBox

◆ distance_vector_t

◆ DistanceType

◆ ElementType

◆ NodePtr

Constructor & Destructor Documentation

◆ KDTreeSingleIndexAdaptor() [1/2]

◆ KDTreeSingleIndexAdaptor() [2/2]

◆ ~KDTreeSingleIndexAdaptor()

Member Function Documentation

◆ buildIndex()

◆ computeBoundingBox()

◆ computeInitialDistances()

◆ computeMinMax()

◆ dataset_get()

◆ divideTree()

◆ findNeighbors()

◆ freeIndex()

◆ init_vind()

◆ knnSearch()

◆ load_tree()

◆ loadIndex()

◆ middleSplit_()

◆ planeSplit()

◆ radiusSearch()

◆ radiusSearchCustomCallback()

◆ save_tree()

◆ saveIndex()

◆ searchLevel()

◆ size()

◆ usedMemory()

◆ veclen()

Member Data Documentation

◆ dataset

◆ dim

◆ distance

◆ index_params

◆ m_leaf_max_size

◆ m_size

◆ m_size_at_index_build

◆ pool

◆ root_bbox

◆ root_node

◆ vind

template<typename Distance, class DatasetAdaptor, int DIM = -1, typename IndexType = size_t>
class nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >