STK::GaussianModel< Array > Class Template Reference

Compute the maximum likelihood estimates of a complete Gaussian statistical model. More...

#include <STK_GaussianModel.h>

Inheritance diagram for STK::GaussianModel< Array >:

Public Types
typedef IGaussianModel< Array >	Base
typedef hidden::Traits< Array >::Col	ColVector
typedef hidden::Traits< Array >::Row	RowVector
Public Types inherited from STK::IStatModel< IGaussianModel< Array > >
typedef hidden::ModelTraits< IGaussianModel< Array > >::Data	Data
typedef hidden::ModelTraits< IGaussianModel< Array > >::ParamHandler	ParamHandler
typedef Data::Type	Type
	Type of the data contained in the container.
typedef hidden::Traits< Data >::Row	Row
	Type of the row of the data container (a sample)

Public Member Functions
	GaussianModel (Array const *p_data)
	constructor.
	GaussianModel (Array const &data)
	constructor.
	~GaussianModel ()
	destructor.
MultiLaw::Normal< RowVector > *const &	p_law () const
	Accessor to the normal law.
bool	run ()
	implementation of the Gaussian statistical model
bool	run (ColVector const &weights)
	implementation of the weighted Gaussian statistical model
ArraySquareX const &	covariance () const
	get the empirical covariance
Public Member Functions inherited from STK::IGaussianModel< Array >
virtual	~IGaussianModel ()
	destructor.
RowVector const &	mean () const
Public Member Functions inherited from STK::IStatModel< IGaussianModel< Array > >
	~IStatModel ()
	destructor
Data const *const	p_dataij () const
void	setData (Data const &data)
	Set the data set of the model.
void	setData (Data const *p_data)
	Set the data set of the model.
Public Member Functions inherited from STK::IStatModelBase
int	nbSample () const
Real	lnNbSample () const
int	nbVariable () const
Real	lnLikelihood () const
Real	likelihood () const
int	nbFreeParameter () const
Real	computeBIC () const
Real	computeAIC () const
Real	computeML () const

Public Attributes
RowVector	mean_
	Vector of the empirical means.

Protected Member Functions
void	compCovariance ()
	compute the empirical covariance matrix.
void	compWeightedCovariance (ColVector const &weights)
	compute the empirical weighted covariance matrix.
Protected Member Functions inherited from STK::IGaussianModel< Array >
	IGaussianModel (Array const *p_data)
	constructor.
	IGaussianModel (Array const &data)
	constructor.
void	compMean ()
	compute the empirical mean
void	compWeightedMean (ColVector const &weights)
	compute the empirical weighted mean
Protected Member Functions inherited from STK::IStatModel< IGaussianModel< Array > >
	IStatModel (Data const &data)
	Constructor with data set.
	IStatModel (Data const *p_data)
	Constructor with a ptr on the data set.
Protected Member Functions inherited from STK::IStatModelBase
	IStatModelBase ()
	Default constructor.
	IStatModelBase (int nbSample)
	Constructor with specified dimension.
	IStatModelBase (int nbSample, int nbVariable)
	Constructor with specified dimension.
	IStatModelBase (IStatModelBase const &model)
	Copy constructor.
	~IStatModelBase ()
	destructor
void	setNbFreeParameter (int const &nbFreeParameter)
	set the number of free parameters of the model
void	setNbSample (int const &nbSample)
	set the number of samples of the model
void	setNbVariable (int const &nbVariable)
	set the number of variables of the model
void	setLnLikelihood (Real const &lnLikelihood)
	set the log-likelihood of the model
void	initialize (int nbSample, int nbVariable)
	set the dimensions of the parameters of the model

Protected Attributes
ArraySquareX	cov_
	ArrayXX of the empirical covaiance.
MultiLaw::Normal< RowVector > *	p_law_
	pointer on the normal law
Protected Attributes inherited from STK::IGaussianModel< Array >
RowVector	mean_
	Vector of the empirical means.
Protected Attributes inherited from STK::IStatModel< IGaussianModel< Array > >
Data const *	p_dataij_
	A pointer on the original data set.

Detailed Description

template<class Array>
class STK::GaussianModel< Array >

Compute the maximum likelihood estimates of a complete Gaussian statistical model.

Definition at line 52 of file STK_GaussianModel.h.

Member Typedef Documentation

Base

template<class Array >

typedef IGaussianModel<Array> STK::GaussianModel< Array >::Base

Definition at line 55 of file STK_GaussianModel.h.

ColVector

template<class Array >

typedef hidden::Traits<Array>::Col STK::GaussianModel< Array >::ColVector

Definition at line 60 of file STK_GaussianModel.h.

RowVector

template<class Array >

typedef hidden::Traits<Array>::Row STK::GaussianModel< Array >::RowVector

Definition at line 61 of file STK_GaussianModel.h.

Constructor & Destructor Documentation

GaussianModel() [1/2]

template<class Array >

STK::GaussianModel< Array >::GaussianModel

(

Array const *

p_data

)

constructor.

Parameters

p_data

pointer on the data set

Definition at line 103 of file STK_GaussianModel.h.

                                 : Base(p_dataij)
                                  , cov_(p_dataij_->cols())
                                  , p_law_(0)
{
  this->setNbFreeParameter(p_dataij_->sizeCols() + (p_dataij_->sizeCols()* (p_dataij_->sizeCols()-1))/2);
}

References STK::IStatModel< IGaussianModel< Array > >::p_dataij_, and STK::IStatModelBase::setNbFreeParameter().

GaussianModel() [2/2]

template<class Array >

STK::GaussianModel< Array >::GaussianModel

(

Array const &

data

)

constructor.

Parameters

data	reference on the data set

Definition at line 113 of file STK_GaussianModel.h.

                                  : Base(data)
                                   , cov_(data.cols())
                                   , p_law_(0)
{ setNbFreeParameter(p_dataij_->sizeCols() + (p_dataij_->sizeCols()* (p_dataij_->sizeCols()-1))/2);}

References STK::IStatModel< IGaussianModel< Array > >::p_dataij_, and STK::IStatModelBase::setNbFreeParameter().

~GaussianModel()

template<class Array >

STK::GaussianModel< Array >::~GaussianModel

(

)

destructor.

Definition at line 121 of file STK_GaussianModel.h.

{ if (p_law_) delete p_law_;}

Member Function Documentation

compCovariance()

template<class Array >

void STK::GaussianModel< Array >::compCovariance ( )

protectedvirtual

compute the empirical covariance matrix.

Implements STK::IGaussianModel< Array >.

Definition at line 167 of file STK_GaussianModel.h.

{ cov_ = Stat::covariance(*p_dataij_);}

References STK::Stat::covariance().

compWeightedCovariance()

template<class Array >

void STK::GaussianModel< Array >::compWeightedCovariance ( ColVector const &  weights )

protectedvirtual

compute the empirical weighted covariance matrix.

Parameters

weights

the weights of the samples

Implements STK::IGaussianModel< Array >.

Definition at line 173 of file STK_GaussianModel.h.

{ cov_ = Stat::covariance(*p_dataij_, weights);}

References STK::Stat::covariance().

covariance()

template<class Array >

ArraySquareX const & STK::GaussianModel< Array >::covariance ( ) const

inline

get the empirical covariance

Returns

the empirical covariance

Definition at line 86 of file STK_GaussianModel.h.

{ return cov_;}

References STK::GaussianModel< Array >::cov_.

p_law()

template<class Array >

MultiLaw::Normal< RowVector > *const & STK::GaussianModel< Array >::p_law ( ) const

inline

Accessor to the normal law.

Definition at line 73 of file STK_GaussianModel.h.

{ return p_law_;}

References STK::GaussianModel< Array >::p_law_.

run() [1/2]

template<class Array >

bool STK::GaussianModel< Array >::run

(

)

implementation of the Gaussian statistical model

Returns

true if no error occur and false otherwise.

Definition at line 128 of file STK_GaussianModel.h.

{
  // compute the mean
  this->compMean();
  // compute the covariance matrix
  compCovariance();
  // create p_law_ (will be deleted in base class)
  // update gaussian law (will be deleted in base class)
  if (!p_law_) p_law_ = new MultiLaw::Normal<RowVector>(mean_, cov_);
  else p_law_->setParameters(mean_, cov_);
  // compute log likelihood of the gaussian law
  this->setLnLikelihood(p_law_->lnLikelihood(*p_dataij_ ));
  // everything ok
  return true;
}

run() [2/2]

template<class Array >

bool STK::GaussianModel< Array >::run

(

ColVector const &

weights

)

implementation of the weighted Gaussian statistical model

Parameters

weights

the weights of the samples

Returns

true if no error occur and false otherwise.

Definition at line 149 of file STK_GaussianModel.h.

{
  // compute the mean
  this->compWeightedMean(weights);
  // compute the covariance matrix
  compWeightedCovariance(weights);
  // create p_law_ (will be deleted in base class)
  // update gaussian law (will be deleted in base class)
  if (!p_law_) p_law_ = new MultiLaw::Normal<RowVector>(mean_, cov_);
  else p_law_->setParameters(mean_, cov_);
  // compute log likelihood of the gaussian law
  this->setLnLikelihood(p_law_->lnLikelihood(*p_dataij_ ));
  // everything ok
  return true;
}

Member Data Documentation

cov_

template<class Array >

ArraySquareX STK::GaussianModel< Array >::cov_

protected

ArrayXX of the empirical covaiance.

Definition at line 90 of file STK_GaussianModel.h.

Referenced by STK::GaussianModel< Array >::covariance().

mean_

template<class Array >

RowVector STK::IGaussianModel< Array >::mean_

Vector of the empirical means.

Definition at line 176 of file STK_IGaussianModel.h.

p_law_

template<class Array >

MultiLaw::Normal<RowVector>* STK::GaussianModel< Array >::p_law_

protected

pointer on the normal law

Definition at line 98 of file STK_GaussianModel.h.

Referenced by STK::GaussianModel< Array >::p_law().

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

• STK_GaussianModel.h