ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits > Class Template Reference

#include <KdTree.hpp>

List of all members.

Public Member Functions
template<typename Container , typename DistSq = EuclideanDistSq, typename = typename std::enable_if<ContainerTags::has_randomAccessIterator<Container>::value>::type>
void	filter (Container &points, const AABB< Dim > &aabb, Container &output, bool invert=false)
std::pair< PREC, PREC >	getMoments ()
std::vector< PREC > &	getNearestDists ()
std::tuple< std::size_t, PREC, std::size_t >	getSettings ()
	NearestNeighbourFilter (std::size_t kNeighboursMean=20, PREC stdDevMult=1.0, std::size_t allowSplitAboveNPoints=10)
void	setSettings (std::size_t kNeighboursMean, PREC stdDevMult, std::size_t allowSplitAboveNPoints)
Static Public Attributes
static const unsigned int	Dim = PointDataTraits::Dimension
Private Attributes
std::size_t	m_allowSplitAboveNPoints
std::size_t	m_kNeighboursMean
PREC	m_mean = 0
NearestDistancesType	m_nearestDists
PREC	m_stdDev = 0
PREC	m_stdDevMult

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Detailed Description

template<typename TTraits = KdTree::DefaultPointDataTraits<>>
class ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >

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Definition at line 2419 of file KdTree.hpp.

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Constructor & Destructor Documentation

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::NearestNeighbourFilter	(	std::size_t	kNeighboursMean = 20,
		PREC	stdDevMult = 1.0,
		std::size_t	allowSplitAboveNPoints = 10
	)		[inline]

CTor

Definition at line 2436 of file KdTree.hpp.

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Member Function Documentation

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

template<typename Container , typename DistSq = EuclideanDistSq, typename = typename std::enable_if<ContainerTags::has_randomAccessIterator<Container>::value>::type>

void ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::filter	(	Container &	points,
		const AABB< Dim > &	aabb,
		Container &	output,
		bool	invert = false
	)		[inline]

This filter function computes the nearest distance distribution (mean, standart deviation) of the points points and classifies all points which have mean nearest distance < mean + stdDevMult * standart deviation as outliers. The AABB aabb is for building the kd-Tree. The function modifies the container points (shuffling, sorting etc.) and saves the remaining points in output. If invert is on the outliers are saved in output.

Definition at line 2472 of file KdTree.hpp.

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

std::pair<PREC,PREC> ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::getMoments

(

)

[inline]

Get the computed mean/standart deviation of the nearest distance (the mean of the returned array of getNearestDists())

Definition at line 2594 of file KdTree.hpp.

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

std::vector<PREC>& ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::getNearestDists

(

)

[inline]

Definition at line 2587 of file KdTree.hpp.

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

std::tuple<std::size_t,PREC,std::size_t> ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::getSettings

(

)

[inline]

Definition at line 2446 of file KdTree.hpp.

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

void ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::setSettings	(	std::size_t	kNeighboursMean,
		PREC	stdDevMult,
		std::size_t	allowSplitAboveNPoints
	)		[inline]

Definition at line 2451 of file KdTree.hpp.

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Member Data Documentation

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

const unsigned int ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::Dim = PointDataTraits::Dimension [static]

Definition at line 2423 of file KdTree.hpp.

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

std::size_t ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::m_allowSplitAboveNPoints [private]

Definition at line 2613 of file KdTree.hpp.

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

std::size_t ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::m_kNeighboursMean [private]

How many neighbours points are search for one point to classify to build the mean neighbour distance

Definition at line 2606 of file KdTree.hpp.

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

PREC ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::m_mean = 0 [private]

Definition at line 2602 of file KdTree.hpp.

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

NearestDistancesType ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::m_nearestDists [private]

Definition at line 2601 of file KdTree.hpp.

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

PREC ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::m_stdDev = 0 [private]

Definition at line 2603 of file KdTree.hpp.

template<typename TTraits = KdTree::DefaultPointDataTraits<>>

PREC ApproxMVBB::KdTree::NearestNeighbourFilter< TTraits >::m_stdDevMult [private]

The multiplier for the standart deviation, if the distance of the point to classify is > stdDevMult * stdDevDist + meanDist, then the point is classfied as an outlier.

Definition at line 2611 of file KdTree.hpp.

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

• KdTree.hpp