ECCE @ EIC Software
Reference for
ECCE @ EIC
simulation and reconstruction software on GitHub
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#include <coresoftware/blob/master/offline/packages/PHTpcTracker/externals/nanoflann.hpp>
Classes | |
struct | Interval |
struct | Node |
Public Types | |
typedef Distance::ElementType | ElementType |
typedef Distance::DistanceType | DistanceType |
typedef Distance::ElementType | ElementType |
typedef Distance::DistanceType | DistanceType |
Public Member Functions | |
KDTreeSingleIndexAdaptor (const int dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams ¶ms=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) |
KDTreeSingleIndexAdaptor (const int dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams ¶ms=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 |
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 |
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 |
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 |
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):
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 816 of file nanoflann.hpp.
View newest version in sPHENIX GitHub at line 816 of file nanoflann.hpp
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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
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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
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Define "distance_vector_t" as a fixed-size or variable-size container depending on "DIM"
Definition at line 873 of file nanoflann.hpp.
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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
typedef Distance::DistanceType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::DistanceType |
Definition at line 824 of file nanoflann.hpp.
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typedef Distance::DistanceType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::DistanceType |
Definition at line 824 of file nanoflann.hpp.
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typedef Distance::ElementType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::ElementType |
Definition at line 823 of file nanoflann.hpp.
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typedef Distance::ElementType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::ElementType |
Definition at line 823 of file nanoflann.hpp.
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Definition at line 862 of file nanoflann.hpp.
View newest version in sPHENIX GitHub at line 862 of file nanoflann.hpp
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Definition at line 862 of file nanoflann.hpp.
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Hidden copy constructor, to disallow copying indices (Not implemented)
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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:
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
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Standard destructor
Definition at line 921 of file nanoflann.hpp.
View newest version in sPHENIX GitHub at line 921 of file nanoflann.hpp
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Hidden copy constructor, to disallow copying indices (Not implemented)
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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:
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
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Standard destructor
Definition at line 921 of file nanoflann.hpp.
View newest version in sPHENIX GitHub at line 921 of file nanoflann.hpp
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Builds the index
Definition at line 934 of file nanoflann.hpp.
View newest version in sPHENIX GitHub at line 934 of file nanoflann.hpp
Referenced by kdfinder::find_tracks(), and PHTpcLookup::init().
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Builds the index
Definition at line 934 of file nanoflann.hpp.
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Definition at line 1088 of file nanoflann.hpp.
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Definition at line 1088 of file nanoflann.hpp.
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Definition at line 1283 of file nanoflann.hpp.
View newest version in sPHENIX GitHub at line 1283 of file nanoflann.hpp
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Definition at line 1283 of file nanoflann.hpp.
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Definition at line 1178 of file nanoflann.hpp.
View newest version in sPHENIX GitHub at line 1178 of file nanoflann.hpp
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Definition at line 1178 of file nanoflann.hpp.
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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
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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
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Create a tree node that subdivides the list of vecs from vind[first] to vind[last]. The routine is called recursively on each sublist.
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
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Create a tree node that subdivides the list of vecs from vind[first] to vind[last]. The routine is called recursively on each sublist.
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
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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
RESULTSET | Should be any ResultSet<DistanceType> |
Definition at line 978 of file nanoflann.hpp.
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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
RESULTSET | Should be any ResultSet<DistanceType> |
Definition at line 978 of file nanoflann.hpp.
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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
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Frees the previously-built index. Automatically called within buildIndex().
Definition at line 924 of file nanoflann.hpp.
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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.
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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.
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Find the "num_closest" nearest neighbors to the query_point[0:dim-1]. Their indices are stored inside the result object.
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
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Find the "num_closest" nearest neighbors to the query_point[0:dim-1]. Their indices are stored inside the result object.
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
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Definition at line 1074 of file nanoflann.hpp.
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Definition at line 1074 of file nanoflann.hpp.
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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
Definition at line 1384 of file nanoflann.hpp.
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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
Definition at line 1384 of file nanoflann.hpp.
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Definition at line 1190 of file nanoflann.hpp.
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Definition at line 1190 of file nanoflann.hpp.
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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.
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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.
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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.
Definition at line 1022 of file nanoflann.hpp.
View newest version in sPHENIX GitHub at line 1022 of file nanoflann.hpp
Referenced by PHTpcLookup::find().
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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.
Definition at line 1022 of file nanoflann.hpp.
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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.
Definition at line 1037 of file nanoflann.hpp.
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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.
Definition at line 1037 of file nanoflann.hpp.
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Definition at line 1061 of file nanoflann.hpp.
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Definition at line 1061 of file nanoflann.hpp.
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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
Definition at line 1370 of file nanoflann.hpp.
View newest version in sPHENIX GitHub at line 1370 of file nanoflann.hpp
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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
Definition at line 1370 of file nanoflann.hpp.
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Performs an exact search in the tree starting from a node.
RESULTSET | Should be any ResultSet<DistanceType> |
Definition at line 1310 of file nanoflann.hpp.
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Performs an exact search in the tree starting from a node.
RESULTSET | Should be any ResultSet<DistanceType> |
Definition at line 1310 of file nanoflann.hpp.
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Returns number of points in dataset
Definition at line 945 of file nanoflann.hpp.
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Returns number of points in dataset
Definition at line 945 of file nanoflann.hpp.
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Computes the inde memory usage Returns: memory used by the index
Definition at line 957 of file nanoflann.hpp.
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Computes the inde memory usage Returns: memory used by the index
Definition at line 957 of file nanoflann.hpp.
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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
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Returns the length of each point in the dataset
Definition at line 948 of file nanoflann.hpp.
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The source of our data.
The dataset used by this index
Definition at line 837 of file nanoflann.hpp.
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Dimensionality of each data point.
Definition at line 843 of file nanoflann.hpp.
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Distance nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::distance |
Definition at line 889 of file nanoflann.hpp.
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Definition at line 839 of file nanoflann.hpp.
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Definition at line 832 of file nanoflann.hpp.
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Number of current poins in the dataset.
Definition at line 841 of file nanoflann.hpp.
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Number of points in the dataset when the index was built.
Definition at line 842 of file nanoflann.hpp.
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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.
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Definition at line 877 of file nanoflann.hpp.
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The KD-tree used to find neighbours
Definition at line 876 of file nanoflann.hpp.
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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