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
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)
	Constructor: takes a const ref to the matrix object with the data points.
	KDTreeEigenMatrixAdaptor (const self_t &)=delete
	~KDTreeEigenMatrixAdaptor ()
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
self_t &	derived ()
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

Dimension

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

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

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>

IndexType

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

using nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major >::IndexType = typename MatrixType::Index

metric_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 >::metric_t = typename Distance::template traits<num_t, self_t, IndexType>::distance_t

num_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 >::num_t = typename MatrixType::Scalar

Offset

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

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

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

self_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 >::self_t = KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance, row_major>

Size

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

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

Constructor & Destructor Documentation

KDTreeEigenMatrixAdaptor() [1/2]

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 Dimension dimensionality, const std::reference_wrapper< const MatrixType > & mat, const int leaf_max_size = 10 )

inlineexplicit

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

KDTreeEigenMatrixAdaptor() [2/2]

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

~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 ( )

inline

Member Function Documentation

derived() [1/2]

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

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

inline

derived() [2/2]

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

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

inline

kdtree_get_bbox()

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

template<class BBOX >

bool nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major >::kdtree_get_bbox ( BBOX & ) const

inline

kdtree_get_point_count()

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

Size nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major >::kdtree_get_point_count ( ) const

inline

kdtree_get_pt()

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

num_t nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major >::kdtree_get_pt ( const IndexType idx, size_t dim ) const

inline

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.

Member Data Documentation

index_

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

index_t* nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major >::index_

m_data_matrix

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

const std::reference_wrapper<const MatrixType> nanoflann::KDTreeEigenMatrixAdaptor< MatrixType, DIM, Distance, row_major >::m_data_matrix

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

• krotos_ml/kd_tree/nanoflann/nanoflann.hpp