nanoflann
C++ header-only ANN library
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nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, IndexType > Struct Template Reference

#include <nanoflann.hpp>

Public Types

typedef
KDTreeEigenMatrixAdaptor
< MatrixType, DIM, Distance,
IndexType > 
self_t
 
typedef MatrixType::Scalar num_t
 
typedef Distance::template
traits< num_t, self_t >
::distance_t 
metric_t
 
typedef
KDTreeSingleIndexAdaptor
< metric_t, self_t, DIM,
IndexType > 
index_t
 

Public Member Functions

 KDTreeEigenMatrixAdaptor (const int dimensionality, const MatrixType &mat, const int leaf_max_size=10)
 The kd-tree index for the user to call its methods as usual with any other FLANN index.
 
void query (const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq, const int nChecks_IGNORED=10) const
 
Interface expected by KDTreeSingleIndexAdaptor
const self_tderived () const
 
self_tderived ()
 
size_t kdtree_get_point_count () const
 
num_t kdtree_distance (const num_t *p1, const size_t idx_p2, size_t size) const
 
num_t kdtree_get_pt (const size_t idx, int dim) const
 
template<class BBOX >
bool kdtree_get_bbox (BBOX &bb) const
 

Public Attributes

index_tindex
 
const MatrixType & m_data_matrix
 

Detailed Description

template<class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>
struct nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, IndexType >

A simple KD-tree adaptor for working with data directly stored in an Eigen Matrix, without duplicating the data storage. Each row in the matrix represents a point in the state space.

Example of usage:

Eigen::Matrix<num_t,Dynamic,Dynamic> mat;
// Fill out "mat"...
typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic> > my_kd_tree_t;
const int max_leaf = 10;
my_kd_tree_t mat_index(dimdim, mat, max_leaf );
mat_index.index->buildIndex();
mat_index.index->...
Template Parameters
DIMIf set to >0, it specifies a compile-time fixed dimensionality for the points in the data set, allowing more compiler optimizations.
DistanceThe distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
IndexTypeThe type for indices in the KD-tree index (typically, size_t of int)

Constructor & Destructor Documentation

template<class MatrixType , int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>
nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, IndexType >::KDTreeEigenMatrixAdaptor ( const int  dimensionality,
const MatrixType &  mat,
const int  leaf_max_size = 10 
)
inline

The kd-tree index for the user to call its methods as usual with any other FLANN index.

Constructor: takes a const ref to the matrix object with the data points

Member Function Documentation

template<class MatrixType , int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>
void nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, IndexType >::query ( const num_t *  query_point,
const size_t  num_closest,
IndexType *  out_indices,
num_t *  out_distances_sq,
const int  nChecks_IGNORED = 10 
) const
inline

Query for the num_closest closest points to a given point (entered as query_point[0:dim-1]). Note that this is a short-cut method for index->findNeighbors(). The user can also call index->... methods as desired.

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

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