Univariate Gaussian mixture. More...

#include <CRNUnivariateGaussianMixture.h>

Inheritance diagram for crn::UnivariateGaussianMixture:

Collaboration diagram for crn::UnivariateGaussianMixture:

Public Member Functions
	UnivariateGaussianMixture ()
	Default constructor. More...

	UnivariateGaussianMixture (const UnivariateGaussianMixture &m)=default

	UnivariateGaussianMixture (UnivariateGaussianMixture &&)=default

template<typename ITER >
	UnivariateGaussianMixture (ITER it_begin, ITER it_end, size_t nb_seeds=2)

virtual	~UnivariateGaussianMixture () override
	Destructor. More...

UnivariateGaussianMixture &	operator= (const UnivariateGaussianMixture &)=delete

UnivariateGaussianMixture &	operator= (UnivariateGaussianMixture &&)=default

size_t	GetNbMembers () const noexcept
	Returns the number of density functions. More...

double	GetWeight (size_t k) const
	Returns the weight of a given density function. More...

UnivariateGaussianPDF	GetMember (size_t k) const
	Returns a given density function. More...

double	GetMean (size_t k) const
	Returns the mean of a given density function. More...

double	GetVariance (size_t k) const
	Returns the variance of a given density function. More...

void	AddMember (UnivariateGaussianPDF pdf, double Weight)
	Adds a density function. More...

void	SetMember (UnivariateGaussianPDF pdf, double w, size_t k)
	Replaces a given density function and its weight. More...

void	SortMembersByMeans (bool reverse=false)
	Sort members by means. More...

void	SortMembersByVariances (bool reverse=false)
	Sort members by means. More...

void	SortMembersByWeights (bool reverse=false)
	Sort members by weights. More...

double	ValueAt (const double x) const
	Evaluates a pattern. More...

double	ValueAt (const double x, size_t k, bool weight_flag=false) const
	Evaluates a pattern for a given density function. More...

unsigned int	EM (const MatrixDouble &patterns, size_t nbSeeds=2, double epsilon=std::numeric_limits< double >::epsilon(), size_t maximalIterations=100)
	Expectation Maximization. More...

unsigned int	EM (const std::vector< double > &patterns, size_t nbSeeds=2, double epsilon=std::numeric_limits< double >::epsilon(), size_t maximalIterations=100)
	Expectation Maximization. More...

template<typename ITER >
unsigned int	EM (ITER it_begin, ITER it_end, size_t nbSeeds=2, double epsilon=std::numeric_limits< double >::epsilon(), size_t maximalIterations=100)
	Expectation Maximization. More...

String	ToString () const
	Dumps a summary of the mixture to a string. More...

String	ToString (size_t k) const
	Dumps a summary of one element of the mixture to a string. More...

double	MLLE (const MatrixDouble &Data) const
	Maximum log-likelihood estimation. More...

double	MLLE (const std::vector< double > &Data) const
	Maximum log-likelihood estimation. More...

template<typename ITER >
double	MLLE (ITER it_begin, ITER it_end) const
	Maximum log-likelihood estimation. More...

double	BIC (double l, size_t n) const
	Bayes Information Criterion estimation. More...

double	BIC (const std::vector< double > &Data) const
	Bayes Information Criterion estimation. More...

std::vector< double >	MakeRandomSample (size_t n=1, size_t m=100, bool reseed=true) const
	Creates a data sample following the mixture's probability law. More...

void	Deserialize (xml::Element &el)

xml::Element	Serialize (xml::Element &parent) const

Public Member Functions inherited from crn::Object
virtual	~Object ()=default

Detailed Description

Univariate Gaussian mixture.

Model for univariate gaussian mixture

Author: Jean DUONG

Date: August 2008

Version: 0.1

Definition at line 45 of file CRNUnivariateGaussianMixture.h.

Constructor & Destructor Documentation

crn::UnivariateGaussianMixture::UnivariateGaussianMixture ( )

inline

Default constructor.

Definition at line 49 of file CRNUnivariateGaussianMixture.h.

crn::UnivariateGaussianMixture::UnivariateGaussianMixture ( const UnivariateGaussianMixture & m )

default

crn::UnivariateGaussianMixture::UnivariateGaussianMixture ( UnivariateGaussianMixture && )

default

template<typename ITER >

crn::UnivariateGaussianMixture::UnivariateGaussianMixture	(	ITER	it_begin,
		ITER	it_end,
		size_t	nb_seeds = `2`
	)

inline

Create a gaussian mixture to model a given set of data

Parameters

[in]	it_begin	iterator pointing to first pair of input data
[in]	it_end	ending iterator
[in]	nbSeeds	the number of density functions

Returns: the inferred gaussian mixture model

Definition at line 61 of file CRNUnivariateGaussianMixture.h.

UnivariateGaussianMixture::~UnivariateGaussianMixture ( )

overridevirtual

Destructor.

Destructor

Definition at line 38 of file CRNUnivariateGaussianMixture.cpp.

Member Function Documentation

void UnivariateGaussianMixture::AddMember	(	UnivariateGaussianPDF	pdf,
		double	w
	)

Adds a density function.

Add a member in mixture

Parameters

[in]	PDF	pointer to gaussian PDF to add as new member
[in]	w	Weight of the new member

Definition at line 128 of file CRNUnivariateGaussianMixture.cpp.

double crn::UnivariateGaussianMixture::BIC	(	double	l,
		size_t	n
	)		const

inline

Bayes Information Criterion estimation.

Definition at line 127 of file CRNUnivariateGaussianMixture.h.

double crn::UnivariateGaussianMixture::BIC ( const std::vector< double > & Data ) const

inline

Bayes Information Criterion estimation.

Definition at line 129 of file CRNUnivariateGaussianMixture.h.

void UnivariateGaussianMixture::Deserialize ( xml::Element & el )

Unsafe load from XML node

Author: Jean DUONG

Exceptions

ExceptionInvalidArgument	not a UnivariateGaussianMixture
ExceptionNotFound	cannot find attribute
ExceptionDomain	wrong attribute

Parameters

[in] el the element to load

Definition at line 739 of file CRNUnivariateGaussianMixture.cpp.

unsigned int UnivariateGaussianMixture::EM	(	const MatrixDouble &	patterns,
		size_t	nbSeeds = `2`,
		double	epsilon = `std::numeric_limits<double>::epsilon()`,
		size_t	maximalIterations = `100`
	)

Expectation Maximization.

Expectation-Maximization algorithm for Gaussian mixture estimation optimization

Parameters

[in]	patterns	set of patterns stored as rows of a data matrix
[in]	nbSeeds	integer
[in]	epsilon	double
[in]	maximalIterations	integer

Returns: the number of iterations done to optimize the mixture

Definition at line 312 of file CRNUnivariateGaussianMixture.cpp.

unsigned int UnivariateGaussianMixture::EM	(	const std::vector< double > &	patterns,
		size_t	nbSeeds = `2`,
		double	epsilon = `std::numeric_limits<double>::epsilon()`,
		size_t	maximalIterations = `100`
	)

Expectation Maximization.

Expectation-Maximization algorithm for Gaussian mixture estimation optimization

Parameters

[in]	patterns	set of patterns stored as standard vector
[in]	nbSeeds	number of seeds
[in]	epsilon	precision
[in]	maximalIterations	maximal number of iterations

Returns: the number of iterations done to optimize the mixture

Definition at line 458 of file CRNUnivariateGaussianMixture.cpp.

template<typename ITER >

unsigned int crn::UnivariateGaussianMixture::EM	(	ITER	it_begin,
		ITER	it_end,
		size_t	nbSeeds = `2`,
		double	epsilon = `std::numeric_limits<double>::epsilon()`,
		size_t	maximalIterations = `100`
	)

Expectation Maximization.

Expectation-Maximization algorithm for Gaussian mixture estimation optimization

Parameters

[in]	it_begin	iterator pointing to first double-size_t pair of input data
[in]	it_end	ending iterator
[in]	nbSeeds	number of seeds
[in]	epsilon	precision
[in]	maximalIterations	maximal number of iterations

Returns: the number of iterations done to optimize the mixture

Definition at line 180 of file CRNUnivariateGaussianMixture.h.

double UnivariateGaussianMixture::GetMean ( size_t k ) const

Returns the mean of a given density function.

Get mean for a member

Exceptions

ExceptionDomain index out of bounds

Parameters

[in] k integer

Returns: the mean

Definition at line 91 of file CRNUnivariateGaussianMixture.cpp.

UnivariateGaussianPDF UnivariateGaussianMixture::GetMember ( size_t k ) const

Returns a given density function.

Get a member

Exceptions

ExceptionDomain index out of bounds

Parameters

[in] k integer

Returns: the member

Definition at line 51 of file CRNUnivariateGaussianMixture.cpp.

size_t crn::UnivariateGaussianMixture::GetNbMembers ( ) const

inlinenoexcept

Returns the number of density functions.

Definition at line 80 of file CRNUnivariateGaussianMixture.h.

double UnivariateGaussianMixture::GetVariance ( size_t k ) const

Returns the variance of a given density function.

Get variance for a member

Exceptions

ExceptionDomain index out of bounds

Parameters

[in] k integer

Returns: the variance

Definition at line 111 of file CRNUnivariateGaussianMixture.cpp.

double UnivariateGaussianMixture::GetWeight ( size_t k ) const

Returns the weight of a given density function.

Get weight for a member

Exceptions

ExceptionDomain index out of bounds

Parameters

[in] k integer

Returns: the weight

Definition at line 71 of file CRNUnivariateGaussianMixture.cpp.

std::vector< double > UnivariateGaussianMixture::MakeRandomSample	(	size_t	n = `1`,
		size_t	m = `100`,
		bool	reseed = `true`
	)		const

Creates a data sample following the mixture's probability law.

Simulate a random sample following the mixture's low

Parameters

[in]	n	size of sample
[in]	m	temporary sample size
[in]	reseed	shall the random seed be reinitialized?

Returns: a set of patterns generated by the mixture's low

Definition at line 603 of file CRNUnivariateGaussianMixture.cpp.

double UnivariateGaussianMixture::MLLE ( const MatrixDouble & data ) const

Maximum log-likelihood estimation.

Maximum log-likelyhood estimator

Parameters

[in] data data stored as a column matrix

Returns: the maximum log-likelyhood of a set of patterns

Definition at line 273 of file CRNUnivariateGaussianMixture.cpp.

double UnivariateGaussianMixture::MLLE ( const std::vector< double > & data ) const

Maximum log-likelihood estimation.

Maximum log-likelyhood estimator

Parameters

[in] data set of patterns stored as rows of a data matrix

Returns: the maximum log-likelyhood of a set of patterns

Definition at line 291 of file CRNUnivariateGaussianMixture.cpp.

template<typename ITER >

double crn::UnivariateGaussianMixture::MLLE	(	ITER	it_begin,
		ITER	it_end
	)		const

Maximum log-likelihood estimation.

Maximum log-likelihood estimator

Parameters

[in]	it_begin	iterator pointing to first double-size_t pair of input data
[in]	it_end	ending iterator

Returns: the maximum log-likelihood of a set of patterns

Definition at line 159 of file CRNUnivariateGaussianMixture.h.

UnivariateGaussianMixture& crn::UnivariateGaussianMixture::operator= ( const UnivariateGaussianMixture & )

delete

UnivariateGaussianMixture& crn::UnivariateGaussianMixture::operator= ( UnivariateGaussianMixture && )

default

xml::Element UnivariateGaussianMixture::Serialize ( xml::Element & parent ) const

Unsafe save

Author: Jean DUONG

Parameters

[in] parent the parent element to which we will add the new element

Returns: The newly created element

Definition at line 770 of file CRNUnivariateGaussianMixture.cpp.

void UnivariateGaussianMixture::SetMember	(	UnivariateGaussianPDF	pdf,
		double	w,
		size_t	k
	)

Replaces a given density function and its weight.

Set a member in mixture

Exceptions

ExceptionDomain index out of bounds

Parameters

[in]	PDF	pointer to gaussian PDF to add as new member
[in]	w	Weight of the new member
[in]	k	index

Definition at line 142 of file CRNUnivariateGaussianMixture.cpp.

void UnivariateGaussianMixture::SortMembersByMeans ( bool reverse = false )

Sort members by means.

Sort members in mixture in increasing mean order

Definition at line 204 of file CRNUnivariateGaussianMixture.cpp.

void UnivariateGaussianMixture::SortMembersByVariances ( bool reverse = false )

Sort members by means.

Sort members in mixture in increasing mean order

Definition at line 212 of file CRNUnivariateGaussianMixture.cpp.

void UnivariateGaussianMixture::SortMembersByWeights ( bool reverse = false )

Sort members by weights.

Sort members in mixture in increasing weight

Definition at line 220 of file CRNUnivariateGaussianMixture.cpp.

String UnivariateGaussianMixture::ToString ( ) const

Dumps a summary of the mixture to a string.

Dump the mixture parameters to a string

Returns: the string

Definition at line 688 of file CRNUnivariateGaussianMixture.cpp.

String UnivariateGaussianMixture::ToString ( size_t k ) const

Dumps a summary of one element of the mixture to a string.

Dump the parameters for one member in the mixture to a string

Parameters

[in] k integer

Returns: the string

Definition at line 707 of file CRNUnivariateGaussianMixture.cpp.

double UnivariateGaussianMixture::ValueAt ( const double x ) const

Evaluates a pattern.

Evaluates an element

Parameters

[in] x data argument to evaluate

Returns: the probability of the element

Definition at line 232 of file CRNUnivariateGaussianMixture.cpp.

double UnivariateGaussianMixture::ValueAt	(	const double	x,
		size_t	k,
		bool	weight_flag = `false`
	)		const

Evaluates a pattern for a given density function.

Evaluate only for one member of the mixture

Exceptions

ExceptionDomain index out of bounds

Parameters

[in]	x	data argument to evaluate
[in]	k	member index

Returns: the probability of the member

Definition at line 252 of file CRNUnivariateGaussianMixture.cpp.

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

/home/travis/build/Liris-Pleiad/libcrn/src/libcrn/CRNMath/CRNUnivariateGaussianMixture.h
/home/travis/build/Liris-Pleiad/libcrn/src/libcrn/CRNMath/CRNUnivariateGaussianMixture.cpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation