npstat::ConvolutionDensity1D< Kernel > Class Template Reference

Inheritance diagram for npstat::ConvolutionDensity1D< Kernel >:

Public Types
typedef Kernel	kernel_type
Public Types inherited from npstat::Functor1< long double, long double >
typedef long double	result_type
typedef long double	first_argument_type

Public Member Functions
template<class Functor1D >
	ConvolutionDensity1D (const Functor1D &fcn, const Kernel &kernel, unsigned nIntegPointsX, double xmin, double xmax, unsigned nIntegPointsY=0, double ymin=0., double ymax=0.)
template<typename Numeric >
	ConvolutionDensity1D (const Kernel &kernel, const Numeric *coords, unsigned lenCoords, unsigned nIntegPointsY=0, double ymin=0., double ymax=0.)
template<typename Numeric1 , typename Numeric2 >
	ConvolutionDensity1D (const Kernel &kernel, const Numeric1 *coords, unsigned lenCoords, const Numeric2 *weights, unsigned lenWeights, unsigned nIntegPointsY=0, double ymin=0., double ymax=0.)
template<typename Numeric1 , typename Numeric2 >
	ConvolutionDensity1D (const Kernel &kernel, const std::vector< std::pair< Numeric1, Numeric2 > > &points, unsigned nIntegPointsY=0, double ymin=0., double ymax=0.)
long double	operator() (const long double &y) const
long double	variance (long double y, long double sampleSize) const
long double	covariance (long double y, long double z, long double sampleSize) const

Constructor & Destructor Documentation

ConvolutionDensity1D() [1/4]

template<class Kernel >

template<class Functor1D >

npstat::ConvolutionDensity1D< Kernel >::ConvolutionDensity1D	(	const Functor1D &	fcn,
		const Kernel &	kernel,
		unsigned	nIntegPointsX,
		double	xmin,
		double	xmax,
		unsigned	nIntegPointsY = 0,
		double	ymin = 0.,
		double	ymax = 0.
	)

In the following constructor, the input functor "fcn" must represent a smooth 1-d density. If it is not normalized, it will be normalized internally. xmin and xmax represent the interval on which the integration is performed used Gauss-Legendre quadrature with nIntegPointsX points. Provide arguments nIntegPointsY, ymin, and ymax if you want automatic kernel normalization in the y direction (also with a Gauss-Legendre quadrature).

ConvolutionDensity1D() [2/4]

template<class Kernel >

template<typename Numeric >

npstat::ConvolutionDensity1D< Kernel >::ConvolutionDensity1D	(	const Kernel &	kernel,
		const Numeric *	coords,
		unsigned	lenCoords,
		unsigned	nIntegPointsY = 0,
		double	ymin = 0.,
		double	ymax = 0.
	)

KDE. Provide arguments nIntegPointsY, ymin, and ymax if you want automatic kernel normalization in the y direction (that is, if you want to create a baloon estimator).

ConvolutionDensity1D() [3/4]

template<class Kernel >

template<typename Numeric1 , typename Numeric2 >

npstat::ConvolutionDensity1D< Kernel >::ConvolutionDensity1D	(	const Kernel &	kernel,
		const Numeric1 *	coords,
		unsigned	lenCoords,
		const Numeric2 *	weights,
		unsigned	lenWeights,
		unsigned	nIntegPointsY = 0,
		double	ymin = 0.,
		double	ymax = 0.
	)

KDE with weighted points. Provide arguments nIntegPointsY, ymin, and ymax if you want automatic kernel normalization in the y direction (that is, if you want to create a baloon estimator).

ConvolutionDensity1D() [4/4]

template<class Kernel >

template<typename Numeric1 , typename Numeric2 >

npstat::ConvolutionDensity1D< Kernel >::ConvolutionDensity1D	(	const Kernel &	kernel,
		const std::vector< std::pair< Numeric1, Numeric2 > > &	points,
		unsigned	nIntegPointsY = 0,
		double	ymin = 0.,
		double	ymax = 0.
	)

KDE with weighted points. The first argument of the pair is the coordinate and the second is the weight. Provide arguments nIntegPointsY, ymin, and ymax if you want automatic kernel normalization in the y direction (that is, if you want to create a baloon estimator).

Member Function Documentation

covariance()

template<class Kernel >

long double npstat::ConvolutionDensity1D< Kernel >::covariance	(	long double	y,
		long double	z,
		long double	sampleSize
	)		const

Expected density covariance at (y, z) for the unweighted sample of given size

operator()()

template<class Kernel >

long double npstat::ConvolutionDensity1D< Kernel >::operator() ( const long double &  y ) const

virtual

Convolution density at y

Implements npstat::Functor1< long double, long double >.

variance()

template<class Kernel >

long double npstat::ConvolutionDensity1D< Kernel >::variance	(	long double	y,
		long double	sampleSize
	)		const

Expected density variance at y for the unweighted sample of given size

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

• nm/ConvolutionDensity1D.hh