npstat::KDEFilterND< MaxDeg > Class Template Reference

#include <KDEFilterND.hh>

Inheritance diagram for npstat::KDEFilterND< MaxDeg >:

Public Types
enum	{ deg_size = MaxDeg }

Public Member Functions
template<class Array >
	KDEFilterND (const double *taper, unsigned maxDegree, const Array &weight, ConvolutionEngineND &engine, unsigned long id, ArrayND< double > &workBuffer, bool mirrorData=true, bool mirrorWeight=true, bool manageSlot=true)
unsigned	dim () const
unsigned	maxDegree () const
ArrayShape	dataShape () const
double	taper (unsigned degree) const
bool	mirrorsData () const
unsigned long	engineSlotId () const
double	filterAtTheCenter () const
double	selfContribution (const unsigned *index, unsigned indexLen) const
double	linearSelfContribution (unsigned long index) const
bool	selfContributionVaries () const
template<class Array >
bool	isCompatible (const Array &in) const
template<class ArrIn , class ArrOut >
void	filter (const ArrIn &in, ArrOut *out) const
template<class ArrIn , class ArrOut >
void	convolve (const ArrIn &in, ArrOut *out) const

Static Public Member Functions
static unsigned	classMaxDegree ()

Detailed Description

template<unsigned MaxDeg>
class npstat::KDEFilterND< MaxDeg >

This class performs kernel density estimation on a regularly spaced multivariate grid with arbitrary, tabulated symmetric kernels

Constructor & Destructor Documentation

KDEFilterND()

template<unsigned MaxDeg>

template<class Array >

npstat::KDEFilterND< MaxDeg >::KDEFilterND	(	const double *	taper,
		unsigned	maxDegree,
		const Array &	weight,
		ConvolutionEngineND &	engine,
		unsigned long	id,
		ArrayND< double > &	workBuffer,
		bool	mirrorData = true,
		bool	mirrorWeight = true,
		bool	manageSlot = true
	)

Main constructor. The arguments are as follows:

taper – Damping factors for each polynomial degree (starting with the 0th order term). This can be NULL in which case it is assumed that all factors are 1.

maxDegree – Maximum degree of the polynomials. The value must not exceed the "MaxDeg" template parameter. The length of the "taper" array (if not NULL) must be equal to maxDegree + 1. Note that the same filter will be produced using an even degree N and an odd degree N+1.

weight – The array of weights. By default, this is just one hyperoctant corresponding to positive directions in all dimensions. The remaining part of the kernel is generated by reflections. If the full weight is provided instead, change the "mirrorWeight" default.

engine – The engine to use for convolutions. This object will not make its own copy of the engine. It is the responsibility of the user to ensure that the lifetime of the engine exceeds the lifetime of this object. It is assumed that the size of the arrays to filter will be two times smaller in each dimension than what is expected by the engine. Note that dimensionalities of weight array must not exceed data dimensionalities.

id – Filter slot number in the engine. Each filter used with this particular engine must be provided with a unique slot id.

workBuffer – Buffer array for various calculations. Must have the same shape as the convolution engine. Will not be copied internally. Must exist while this object operates.

mirrorData – If true, the data will be mirrored at the boundaries to reduce the edge effects.

mirrorWeight – If true, the weight will be mirrored. If false, the weight is considered to be complete. In the latter case the array size in each dimension must be odd and the weight should be centered in the array.

manageSlot – If true, the Fourier transform of this filter will be removed from the engine filter collection in the destructor of this object.

Member Function Documentation

convolve()

template<unsigned MaxDeg>

template<class ArrIn , class ArrOut >

void npstat::KDEFilterND< MaxDeg >::convolve ( const ArrIn &  in, ArrOut *  out  ) const

inline

This method is included for compatibility with LocalPolyFilterND. For KDE, there is no difference between "convolve" and "filter".

dataShape()

template<unsigned MaxDeg>

ArrayShape npstat::KDEFilterND< MaxDeg >::dataShape ( ) const

inlinevirtual

Required shape of the data array

Implements npstat::AbsPolyFilterND.

dim()

template<unsigned MaxDeg>

unsigned npstat::KDEFilterND< MaxDeg >::dim ( ) const

inlinevirtual

Inspect object properties

Implements npstat::AbsPolyFilterND.

filter()

template<unsigned MaxDeg>

template<class ArrIn , class ArrOut >

void npstat::KDEFilterND< MaxDeg >::filter	(	const ArrIn &	in,
		ArrOut *	out
	)		const

The method which performs the filtering

isCompatible()

template<unsigned MaxDeg>

template<class Array >

bool npstat::KDEFilterND< MaxDeg >::isCompatible ( const Array &  in ) const

inline

Check the compatibility of an array with the filter

linearSelfContribution()

template<unsigned MaxDeg>

double npstat::KDEFilterND< MaxDeg >::linearSelfContribution ( unsigned long  index ) const

virtual

Contribution of a single point into the density estimate using the linear index of the point

Implements npstat::AbsPolyFilterND.

selfContribution()

template<unsigned MaxDeg>

double npstat::KDEFilterND< MaxDeg >::selfContribution ( const unsigned *  index, unsigned  indexLen  ) const

virtual

Contribution of a single point into the density estimate at that point (not normalized). This is needed for various leaving-one-out cross validation procedures.

Implements npstat::AbsPolyFilterND.

selfContributionVaries()

template<unsigned MaxDeg>

bool npstat::KDEFilterND< MaxDeg >::selfContributionVaries ( ) const

inline

Check if the self contribution varies from point to point

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

• stat/KDEFilterND.hh