KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType > Struct Template Reference

#include <KDTreeVectorOfVectorsAdaptor.h>

Public Types
using	self_t = KDTreeVectorOfVectorsAdaptor<VectorOfVectorsType, num_t, DIM, Distance>
using	metric_t = typename Distance::template traits<num_t, self_t>::distance_t
using	index_t

Public Member Functions
	KDTreeVectorOfVectorsAdaptor (const size_t, const VectorOfVectorsType &mat, const int leaf_max_size=10)
	~KDTreeVectorOfVectorsAdaptor ()
void	query (const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq) const
Interface expected by KDTreeSingleIndexAdaptor
const self_t &	derived () const
self_t &	derived ()
size_t	kdtree_get_point_count () const
num_t	kdtree_get_pt (const size_t idx, const size_t dim) const
template<class BBOX >
bool	kdtree_get_bbox (BBOX &) const

Public Attributes
index_t *	index = nullptr
const VectorOfVectorsType &	m_data

Detailed Description

template<class VectorOfVectorsType, typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>
struct KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >

A simple vector-of-vectors adaptor for nanoflann, without duplicating the storage. The i'th vector represents a point in the state space.

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.
num_t	The type of the point coordinates (typ. double or float).
Distance	The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
IndexType	The type for indices in the KD-tree index (typically, size_t of int)

Member Typedef Documentation

index_t

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

using KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::index_t

Initial value:

 nanoflann::KDTreeSingleIndexAdaptor<metric_t, self_t, DIM,
                                                        IndexType>

metric_t

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

using KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::metric_t = typename Distance::template traits<num_t, self_t>::distance_t

self_t

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

using KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::self_t = KDTreeVectorOfVectorsAdaptor<VectorOfVectorsType, num_t, DIM, Distance>

Constructor & Destructor Documentation

KDTreeVectorOfVectorsAdaptor()

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::KDTreeVectorOfVectorsAdaptor ( const size_t , const VectorOfVectorsType & mat, const int leaf_max_size = 10 )

inline

Constructor: takes a const ref to the vector of vectors object with the data points

~KDTreeVectorOfVectorsAdaptor()

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::~KDTreeVectorOfVectorsAdaptor ( )

inline

Member Function Documentation

derived() [1/2]

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

self_t & KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::derived ( )

inline

derived() [2/2]

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

const self_t & KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::derived ( ) const

inline

kdtree_get_bbox()

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

template<class BBOX >

bool KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::kdtree_get_bbox ( BBOX & ) const

inline

kdtree_get_point_count()

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

size_t KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::kdtree_get_point_count ( ) const

inline

kdtree_get_pt()

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

num_t KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::kdtree_get_pt ( const size_t idx, const size_t dim ) const

inline

query()

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

void KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::query ( const num_t * query_point, const size_t num_closest, IndexType * out_indices, num_t * out_distances_sq ) 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.

Member Data Documentation

index

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

index_t* KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::index = nullptr

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

m_data

template<class VectorOfVectorsType , typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>

const VectorOfVectorsType& KDTreeVectorOfVectorsAdaptor< VectorOfVectorsType, num_t, DIM, Distance, IndexType >::m_data

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

• krotos_ml/kd_tree/nanoflann/KDTreeVectorOfVectorsAdaptor.h