#include <nanoflann.hpp>

Public Types
typedef KDTreeEigenMatrixAdaptor < MatrixType, DIM, Distance >	self_t

typedef MatrixType::Scalar	num_t

typedef MatrixType::Index	IndexType

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. More...

void	query (const num_t query_point, const size_t num_closest, IndexType out_indices, num_t *out_distances_sq, const int=10) const

Interface expected by KDTreeSingleIndexAdaptor
const self_t &	derived () const

self_t &	derived ()

size_t	kdtree_get_point_count () const

num_t	kdtree_distance (const num_t *p1, const IndexType idx_p2, IndexType size) const

num_t	kdtree_get_pt (const IndexType idx, int dim) const

template<class BBOX >
bool	kdtree_get_bbox (BBOX &) const

Public Attributes
index_t *	index

const MatrixType &	m_data_matrix

Detailed Description

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

An L2-metric 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

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.

Constructor & Destructor Documentation

template<class MatrixType , int DIM = -1, class Distance = nanoflann::metric_L2>

nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance >::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>

void nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance >::query	(	const num_t *	query_point,
		const size_t	num_closest,
		IndexType *	out_indices,
		num_t *	out_distances_sq,
		const int	= `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:

include/nanoflann.hpp

Public Types

Public Member Functions

Public Attributes

Detailed Description

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

Constructor & Destructor Documentation

Member Function Documentation

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