#include <coresoftware/blob/master/offline/packages/trackreco/nanoflann.hpp>

Collaboration diagram for nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >:

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())

	~KDTreeSingleIndexAdaptor ()

void	freeIndex ()

void	buildIndex ()

size_t	size () const

size_t	veclen () const

size_t	usedMemory () const

void	saveIndex (FILE *stream)

void	loadIndex (FILE *stream)

Query methods
template<typename RESULTSET >
bool	findNeighbors (RESULTSET &result, const ElementType *vec, const SearchParams &searchParams) const

size_t	knnSearch (const ElementType query_point, const size_t num_closest, IndexType out_indices, DistanceType *out_distances_sq, const int=10) const

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

template<class SEARCH_CALLBACK >
size_t	radiusSearchCustomCallback (const ElementType *query_point, SEARCH_CALLBACK &resultSet, const SearchParams &searchParams=SearchParams()) const

Public Attributes
Distance	distance

Protected Types
typedef Node *	NodePtr

typedef array_or_vector_selector< DIM, Interval >::container_t	BoundingBox

typedef array_or_vector_selector< DIM, DistanceType >::container_t	distance_vector_t

Protected Attributes
std::vector< IndexType >	vind

size_t	m_leaf_max_size

const DatasetAdaptor &	dataset
	The source of our data.

const KDTreeSingleIndexAdaptorParams	index_params

size_t	m_size
	Number of current poins in the dataset.

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

int	dim
	Dimensionality of each data point.

NodePtr	root_node

BoundingBox	root_bbox

PooledAllocator	pool

Private Member Functions
	KDTreeSingleIndexAdaptor (const KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > &)

void	init_vind ()

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

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)

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)

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

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):

  @code 
   // 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;
   }

  \endcode

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

Definition at line 816 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 816 of file nanoflann.hpp

Member Typedef Documentation

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 870 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 870 of file nanoflann.hpp

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 873 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 873 of file nanoflann.hpp

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 824 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 824 of file nanoflann.hpp

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 823 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 823 of file nanoflann.hpp

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 862 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 862 of file nanoflann.hpp

Constructor & Destructor Documentation

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 > & )

private

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

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 904 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 904 of file nanoflann.hpp

References TauVsDIS_MachineLearning_Differentiation::dataset, and Acts::Test::dim.

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

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

inline

Standard destructor

Definition at line 921 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 921 of file nanoflann.hpp

Member Function Documentation

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 934 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 934 of file nanoflann.hpp

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 1088 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1088 of file nanoflann.hpp

References TauVsDIS_MachineLearning_Differentiation::dataset, Acts::Test::dim, i, k, and N.

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 1283 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1283 of file nanoflann.hpp

References assert, Acts::Test::dim, distance(), and i.

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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 1178 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1178 of file nanoflann.hpp

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

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 1056 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1056 of file nanoflann.hpp

References TauVsDIS_MachineLearning_Differentiation::dataset.

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 1122 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1122 of file nanoflann.hpp

References nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child1, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child2, Acts::Test::dim, i, ambiguity_solver_full_chain::idx, k, left(), nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::lr, Acts::UnitConstants::min, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::node_type, and nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::sub.

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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

    \tparam RESULTSET Should be any ResultSet<DistanceType>

Returns: True if the requested neighbors could be found.

See Also: knnSearch, radiusSearch

Definition at line 978 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 978 of file nanoflann.hpp

References assert, nanoflann::CArray< T, N >::assign(), Acts::Test::dim, nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::dists, nanoflann::SearchParams::eps, and nanoflann::KNNResultSet< DistanceType, IndexType, CountType >::size().

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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 924 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 924 of file nanoflann.hpp

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 1047 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1047 of file nanoflann.hpp

References TauVsDIS_MachineLearning_Differentiation::dataset, and i.

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 1002 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1002 of file nanoflann.hpp

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

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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 1074 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1074 of file nanoflann.hpp

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

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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 1384 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1384 of file nanoflann.hpp

References Acts::Test::dim, load_tree(), and nanoflann::load_value().

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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 1190 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1190 of file nanoflann.hpp

References Acts::Test::dim, and i.

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 1252 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1252 of file nanoflann.hpp

References left(), and swap().

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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 1022 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1022 of file nanoflann.hpp

References Acts::Experimental::detail::BlueprintHelper::sort(), and nanoflann::SearchParams::sorted.

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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 1037 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1037 of file nanoflann.hpp

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 1061 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1061 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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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 1370 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1370 of file nanoflann.hpp

References Acts::Test::dim, and nanoflann::save_value().

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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 1310 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 1310 of file nanoflann.hpp

References nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child1, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::child2, Acts::Test::dim, dist(), distance(), i, ambiguity_solver_full_chain::idx, index, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::lr, nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::node_type, and nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Node::sub.

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

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

inline

Returns number of points in dataset

Definition at line 945 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 945 of file nanoflann.hpp

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 957 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 957 of file nanoflann.hpp

References TauVsDIS_MachineLearning_Differentiation::dataset.

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 948 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 948 of file nanoflann.hpp

References Acts::Test::dim.

Member Data Documentation

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

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

protected

The source of our data.

The dataset used by this index

Definition at line 837 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 837 of file nanoflann.hpp

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 843 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 843 of file nanoflann.hpp

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

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

Definition at line 889 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 889 of file nanoflann.hpp

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 839 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 839 of file nanoflann.hpp

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 832 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 832 of file nanoflann.hpp

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 841 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 841 of file nanoflann.hpp

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 842 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 842 of file nanoflann.hpp

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 886 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 886 of file nanoflann.hpp

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 877 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 877 of file nanoflann.hpp

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 876 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 876 of file nanoflann.hpp

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 830 of file nanoflann.hpp.

View newest version in sPHENIX GitHub at line 830 of file nanoflann.hpp

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

coresoftware/blob/master/offline/packages/trackreco/nanoflann.hpp

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Detailed Description

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

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