npstat::GaussianMixture1D Class Reference

#include <GaussianMixture1D.hh>

Inheritance diagram for npstat::GaussianMixture1D:

Public Member Functions
	GaussianMixture1D (double location, double scale, const GaussianMixtureEntry *entries, unsigned nEntries)
	GaussianMixture1D (double location, double scale, const std::vector< GaussianMixtureEntry > &entries)
	GaussianMixture1D (const Gauss1D &gauss)
virtual GaussianMixture1D *	clone () const
unsigned	nentries () const
const GaussianMixtureEntry &	entry (const unsigned n) const
const std::vector< GaussianMixtureEntry > &	entries () const
virtual unsigned	random (AbsRandomGenerator &g, double *r) const
double	mean () const
double	stdev () const
double	gaussianMISE (unsigned maxPolyDegree, double bandwidth, unsigned long npoints) const
double	miseOptimalBw (unsigned maxPolyDegree, unsigned long npoints, double *mise=0) const
virtual gs::ClassId	classId () const
virtual bool	write (std::ostream &os) const
Public Member Functions inherited from npstat::AbsScalableDistribution1D
	AbsScalableDistribution1D (const double location, const double scale)
double	location () const
double	scale () const
void	setLocation (const double v)
void	setScale (const double v)
double	density (const double x) const
double	cdf (const double x) const
double	exceedance (const double x) const
double	quantile (const double x) const
Public Member Functions inherited from npstat::AbsDistribution1D
bool	operator== (const AbsDistribution1D &r) const
bool	operator!= (const AbsDistribution1D &r) const
long double	empiricalMoment (long double center, unsigned order, unsigned nIntegrationPoints=1024U, bool useFejerQuadrature=false) const
template<class Functor >
double	expectation (const Functor &fcn, unsigned nIntegrationPoints=1024U, bool useFejerQuadrature=false) const

Static Public Member Functions
static const char *	classname ()
static unsigned	version ()
static GaussianMixture1D *	read (const gs::ClassId &id, std::istream &in)
Static Public Member Functions inherited from npstat::AbsScalableDistribution1D
static bool	read (std::istream &is, double *location, double *scale)
Static Public Member Functions inherited from npstat::AbsDistribution1D
static const char *	classname ()
static unsigned	version ()
static AbsDistribution1D *	read (const gs::ClassId &id, std::istream &)

Protected Member Functions
virtual bool	isEqual (const AbsDistribution1D &r) const

Friends
class	ScalableDistribution1DFactory< GaussianMixture1D >
class	GaussianMISEFunctor

Detailed Description

One-dimensional Gaussian mixture distribution

Constructor & Destructor Documentation

GaussianMixture1D() [1/2]

npstat::GaussianMixture1D::GaussianMixture1D	(	double	location,
		double	scale,
		const GaussianMixtureEntry *	entries,
		unsigned	nEntries
	)

This constructor takes an array of GaussianMixtureEntry objects together with an overall location and scale parameters which are used to shift and scale the whole distribution. There must be at least one component with positive weight in the array of entries. The sum of weights will be normalized to 1 internally.

GaussianMixture1D() [2/2]

npstat::GaussianMixture1D::GaussianMixture1D ( const Gauss1D &  gauss )

explicit

Constructor from a single Gaussian distribution

Member Function Documentation

classId()

virtual gs::ClassId npstat::GaussianMixture1D::classId ( ) const

inlinevirtual

Methods needed for "geners" I/O

Implements npstat::AbsScalableDistribution1D.

clone()

virtual GaussianMixture1D* npstat::GaussianMixture1D::clone ( ) const

inlinevirtual

"Virtual copy constructor"

Implements npstat::AbsScalableDistribution1D.

entries()

const std::vector<GaussianMixtureEntry>& npstat::GaussianMixture1D::entries ( ) const

inline

Get all mixture components

entry()

const GaussianMixtureEntry& npstat::GaussianMixture1D::entry ( const unsigned  n ) const

inline

Get the mixture component with the given number

gaussianMISE()

double npstat::GaussianMixture1D::gaussianMISE	(	unsigned	maxPolyDegree,
		double	bandwidth,
		unsigned long	npoints
	)		const

Mean Integrated Squared Error expected for KDE of a sample from this mixture using Hermite polynomial kernel. The formula is from the article "Exact Mean Integrated Squared Error" by J.S. Marron and M.P. Wand, The Annals Of Statistics, Vol 20, pp. 712-736 (1992). Kernel order is maxPolyDegree + 2 if maxPolyDegree is even, and maxPolyDegree + 1 if maxPolyDegree is odd.

Due to accumulation of round-off errors, the calculation becomes unreliable for maxPolyDegree above 16 or so.

isEqual()

virtual bool npstat::GaussianMixture1D::isEqual ( const AbsDistribution1D &  other ) const

protectedvirtual

Derived classes should override the following method as long as they have at least one additional data member. Don't forget to call "isEqual" of the base class inside the derived classes.

Reimplemented from npstat::AbsScalableDistribution1D.

mean()

double npstat::GaussianMixture1D::mean

(

)

const

Mean of the whole mixture

miseOptimalBw()

double npstat::GaussianMixture1D::miseOptimalBw	(	unsigned	maxPolyDegree,
		unsigned long	npoints,
		double *	mise = 0
	)		const

Bandwidth which minimizes the KDE MISE

nentries()

unsigned npstat::GaussianMixture1D::nentries ( ) const

inline

Number of mixture components

random()

virtual unsigned npstat::GaussianMixture1D::random ( AbsRandomGenerator &  g, double *  r  ) const

virtual

Generate random numbers asscording to this distribution

Reimplemented from npstat::AbsScalableDistribution1D.

stdev()

double npstat::GaussianMixture1D::stdev

(

)

const

Standard deviation of the whole mixture

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

• stat/GaussianMixture1D.hh