nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major > Struct Template Reference

#include <nanoflann.hpp>

Public Types
using	self_t = KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance, row_major>
using	num_t = typename MatrixType::Scalar
using	IndexType = typename MatrixType::Index
using	metric_t = typename Distance::template traits<num_t, self_t, IndexType>::distance_t
using	index_t
using	Offset = typename index_t::Offset
	The kd-tree index for the user to call its methods as usual with any other FLANN index.
using	Size = typename index_t::Size
using	Dimension = typename index_t::Dimension

Public Member Functions
	KDTreeEigenMatrixAdaptor (const Dimension dimensionality, const std::reference_wrapper< const MatrixType > &mat, const int leaf_max_size=10, const unsigned int n_thread_build=1)
	Constructor: takes a const ref to the matrix object with the data points.
	KDTreeEigenMatrixAdaptor (const self_t &)=delete
void	query (const num_t *query_point, const Size num_closest, IndexType *out_indices, num_t *out_distances) const
Interface expected by KDTreeSingleIndexAdaptor
const self_t &	derived () const noexcept
self_t &	derived () noexcept
Size	kdtree_get_point_count () const
num_t	kdtree_get_pt (const IndexType idx, size_t dim) const
template<class BBOX>
bool	kdtree_get_bbox (BBOX &) const

Public Attributes
index_t *	index_
const std::reference_wrapper< const MatrixType >	m_data_matrix

Detailed Description

template<class MatrixType, int32_t DIM = -1, class Distance = nanoflann::metric_L2, bool row_major = true>
struct nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major >

An L2-metric KD-tree adaptor for working with data directly stored in an Eigen Matrix, without duplicating the data storage. You can select whether a row or column in the matrix represents a point in the state space.

Example of usage:

Eigen::Matrix<num_t,Eigen::Dynamic,Eigen::Dynamic>  mat;
 
// Fill out "mat"...
using my_kd_tree_t = nanoflann::KDTreeEigenMatrixAdaptor<
  Eigen::Matrix<num_t,Dynamic,Dynamic>>;
 
const int max_leaf = 10;
my_kd_tree_t mat_index(mat, max_leaf);
mat_index.index->...

Template Parameters

DIM	If set to >0, it specifies a compile-time fixed dimensionality for the points in the data set, allowing more compiler optimizations.
Distance	The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
row_major	If set to true the rows of the matrix are used as the points, if set to false the columns of the matrix are used as the points.

Member Typedef Documentation

index_t

template<class MatrixType, int32_t DIM = -1, class Distance = nanoflann::metric_L2, bool row_major = true>

using nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major >::index_t

Initial value:

 KDTreeSingleIndexAdaptor<
        metric_t, self_t, row_major ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
        IndexType>

Constructor & Destructor Documentation

KDTreeEigenMatrixAdaptor()

template<class MatrixType, int32_t DIM = -1, class Distance = nanoflann::metric_L2, bool row_major = true>

nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major >::KDTreeEigenMatrixAdaptor ( const self_t & )

delete

Deleted copy constructor

Member Function Documentation

query()

template<class MatrixType, int32_t DIM = -1, class Distance = nanoflann::metric_L2, bool row_major = true>

void nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major >::query ( const num_t * query_point, const Size num_closest, IndexType * out_indices, num_t * out_distances ) 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

If L2 norms are used, all returned distances are actually squared distances.

---

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

• include/nanoflann.hpp