A joint Gaussian model is a statistical model of the form: following form. More...

#include <STK_JointGaussianModel.h>

Inheritance diagram for STK::JointGaussianModel< Array, WColVector >:

Public Types
typedef Array::Type	Type
	Type of the data contained in the container.

typedef hidden::Traits< Array >::Row	RowVector
	Type of the row vector of the container.

typedef hidden::Traits< Array >::Col	ColVector
	Type of the column vector of the container.

typedef IMultiStatModel< Array, WColVector, JointGaussianParameters >	Base
	Base class.

Public Types inherited from STK::IMultiStatModel< Derived >
typedef hidden::StatModelTraits< Derived >::Data	Data
	Type of the container with the data.

typedef hidden::StatModelTraits< Derived >::Type	Type
	Type of the data in the container.

typedef hidden::StatModelTraits< Derived >::WColVector	WColVector
	Type of the vector with the weights.

typedef hidden::StatModelTraits< Derived >::Parameters	Parameters
	Type of the parameters of the Model.

Public Member Functions
	JointGaussianModel ()
	default constructor.

	JointGaussianModel (Array const &data)
	Constructor with data set.

	JointGaussianModel (Array const *p_data)
	Constructor with a ptr on the data set.

	JointGaussianModel (JointGaussianModel const &model)
	Copy constructor.

virtual	~JointGaussianModel ()
	destructor

JointGaussianModel *	clone () const
	clone pattern.

virtual int	computeNbFreeParameters () const
	compute the number of free parameters

virtual Real	computeLnLikelihood (RowVector const &rowData) const
	compute the log Likelihood of an observation.

Data const *const	p_data () const

Public Member Functions inherited from STK::IMultiStatModel< Derived >
Data const *const	p_data () const

Parameters const &	param () const

String const &	error () const

void	setData (Data const *p_data)
	Set the data set.

bool	run ()
	Estimate the parameters of the model and update the model.

bool	run (WColVector const &weights)
	compute the weighted empirical probability of success based on the observed variables.

void	writeParameters (ostream &os)

void	writeParametersImpl (ostream &os)
	default implementation of the writeParameters method.

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 Member Functions inherited from STK::IRecursiveTemplate< Derived >
Derived &	asDerived ()
	static cast : return a reference of this with a cast to the derived class.

Derived const &	asDerived () const
	static cast : return a const reference of this with a cast to the derived class.

Derived *	asPtrDerived ()
	static cast : return a ptr on a `Derived` of this with a cast to the derived class.

Derived const *	asPtrDerived () const
	static cast : return a ptr on a constant `Derived` of this with a cast to the derived class.

Derived *	clone () const
	create a leaf using the copy constructor of the Derived class.

Derived *	clone (bool isRef) const
	create a leaf using the copy constructor of the Derived class and a flag determining if the clone is a reference or not.

Protected Member Functions
virtual void	computeParameters ()
	compute the parameters

virtual void	computeParameters (WColVector const &weights)
	compute the weighted parameters

Protected Member Functions inherited from STK::IMultiStatModel< Derived >
	IMultiStatModel ()
	default constructor.

	IMultiStatModel (Data const &data)
	Constructor with data set.

	IMultiStatModel (Data const *p_data)
	Constructor with a ptr on the data set.

	IMultiStatModel (IMultiStatModel const &model)
	Copy constructor.

	~IMultiStatModel ()
	destructor

Parameters &	param ()

Real	computeLnLikelihood () const
	compute the log Likelihood of the statistical model.

void	update ()
	update the model if a new data set is 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 Member Functions inherited from STK::IRecursiveTemplate< Derived >
	IRecursiveTemplate ()
	constructor.

	~IRecursiveTemplate ()
	destructor.

Additional Inherited Members
Protected Attributes inherited from STK::IMultiStatModel< Derived >
Data const *	p_data_
	Pointer on the parameters of the model.

Parameters	param_
	Pointer on the parameters of the model.

String	msg_error_
	String with the last error message.

Detailed Description

template<class Array, class WColVector = CVectorX>
class STK::JointGaussianModel< Array, WColVector >

A joint Gaussian model is a statistical model of the form: following form.

$f(\mathbf{x}_i|\theta) = \prod_{j=1}^p \frac{1}{\sigma_j\sqrt{2\pi}} \exp\left(-\frac{\left(x_i-\mu_j\right)^2}{2\sigma_j^2} \right) \quad j=1,\ldots,p, \quad i=1,\ldots,n.$

Definition at line 102 of file STK_JointGaussianModel.h.

Member Typedef Documentation

◆ Base

template<class Array , class WColVector = CVectorX>

typedef IMultiStatModel<Array, WColVector, JointGaussianParameters > STK::JointGaussianModel< Array, WColVector >::Base

Base class.

Definition at line 112 of file STK_JointGaussianModel.h.

◆ ColVector

template<class Array , class WColVector = CVectorX>

typedef hidden::Traits<Array>::Col STK::JointGaussianModel< Array, WColVector >::ColVector

Type of the column vector of the container.

Definition at line 110 of file STK_JointGaussianModel.h.

◆ RowVector

template<class Array , class WColVector = CVectorX>

typedef hidden::Traits<Array>::Row STK::JointGaussianModel< Array, WColVector >::RowVector

Type of the row vector of the container.

Definition at line 108 of file STK_JointGaussianModel.h.

◆ Type

template<class Array , class WColVector = CVectorX>

typedef Array::Type STK::JointGaussianModel< Array, WColVector >::Type

Type of the data contained in the container.

Definition at line 106 of file STK_JointGaussianModel.h.

Constructor & Destructor Documentation

◆ JointGaussianModel() [1/4]

template<class Array , class WColVector = CVectorX>

STK::JointGaussianModel< Array, WColVector >::JointGaussianModel ( )

inline

default constructor.

Definition at line 116 of file STK_JointGaussianModel.h.

116: Base() {}

STK::JointGaussianModel::Base

IMultiStatModel< Array, WColVector, JointGaussianParameters > Base

Base class.

Definition STK_JointGaussianModel.h:112

Referenced by STK::JointGaussianModel< Array, WColVector >::clone().

◆ JointGaussianModel() [2/4]

template<class Array , class WColVector = CVectorX>

STK::JointGaussianModel< Array, WColVector >::JointGaussianModel ( Array const & data )

inline

Constructor with data set.

Definition at line 118 of file STK_JointGaussianModel.h.

118: Base(data) {}

◆ JointGaussianModel() [3/4]

template<class Array , class WColVector = CVectorX>

STK::JointGaussianModel< Array, WColVector >::JointGaussianModel ( Array const * p_data )

inline

Constructor with a ptr on the data set.

Definition at line 120 of file STK_JointGaussianModel.h.

120: Base(p_data) {}

STK::JointGaussianModel::p_data

Data const *const p_data() const

Definition STK_IMultiStatModel.h:152

◆ JointGaussianModel() [4/4]

template<class Array , class WColVector = CVectorX>

STK::JointGaussianModel< Array, WColVector >::JointGaussianModel ( JointGaussianModel< Array, WColVector > const & model )

inline

Copy constructor.

Definition at line 122 of file STK_JointGaussianModel.h.

122: Base(model) {}

◆ ~JointGaussianModel()

template<class Array , class WColVector = CVectorX>

virtual STK::JointGaussianModel< Array, WColVector >::~JointGaussianModel ( )

inlinevirtual

destructor

Definition at line 124 of file STK_JointGaussianModel.h.

124{}

Member Function Documentation

◆ clone()

template<class Array , class WColVector = CVectorX>

JointGaussianModel * STK::JointGaussianModel< Array, WColVector >::clone ( ) const

inline

clone pattern.

Returns: a clone of this.

Definition at line 126 of file STK_JointGaussianModel.h.

126{ return new JointGaussianModel(*this);}

STK::JointGaussianModel::JointGaussianModel

JointGaussianModel()

default constructor.

Definition STK_JointGaussianModel.h:116

References STK::JointGaussianModel< Array, WColVector >::JointGaussianModel().

◆ computeLnLikelihood()

template<class Array , class WColVector = CVectorX>

virtual Real STK::JointGaussianModel< Array, WColVector >::computeLnLikelihood ( RowVector const & rowData ) const

inlinevirtual

compute the log Likelihood of an observation.

Definition at line 132 of file STK_JointGaussianModel.h.

    {
      Real sum =0.;
      for (Integer j= rowData.begin(); j < rowData.end(); ++j)
      { sum += Law::Normal::lpdf(rowData[j], p_param()->mu(j), p_param()->sigma(j));}
      return sum;
    }

References STK::Law::Normal::lpdf(), and STK::sum().

◆ computeNbFreeParameters()

template<class Array , class WColVector = CVectorX>

virtual int STK::JointGaussianModel< Array, WColVector >::computeNbFreeParameters ( ) const

inlinevirtual

compute the number of free parameters

Definition at line 129 of file STK_JointGaussianModel.h.

130 { return 2*p_data()->sizeCols();}

References STK::JointGaussianModel< Array, WColVector >::p_data().

◆ computeParameters() [1/2]

template<class Array , class WColVector = CVectorX>

virtual void STK::JointGaussianModel< Array, WColVector >::computeParameters ( )

inlineprotectedvirtual

compute the parameters

Definition at line 141 of file STK_JointGaussianModel.h.

    {
      for (int j=p_data()->beginCols(); j < p_data()->endCols(); ++j)
      {
        p_param()->setMu(j, p_data()->col(j).safe().mean());
        p_param()->setSigma(j,std::sqrt(Stat::varianceWithFixedMean(p_data()->col(j), p_param()->mu(j), true)));
      }
    }

References STK::mean(), STK::JointGaussianModel< Array, WColVector >::p_data(), and STK::Stat::varianceWithFixedMean().

◆ computeParameters() [2/2]

template<class Array , class WColVector = CVectorX>

virtual void STK::JointGaussianModel< Array, WColVector >::computeParameters ( WColVector const & weights )

inlineprotectedvirtual

compute the weighted parameters

Definition at line 150 of file STK_JointGaussianModel.h.

    {
      for (int j=p_data()->beginCols(); j < p_data()->endCols(); ++j)
      {
        p_param()->setMu(j, p_data()->col(j).safe().wmean(weights));
        p_param()->setSigma(j,std::sqrt(Stat::varianceWithFixedMean(p_data()->col(j), weights, p_param()->mu(j), true)));
      }
    }

References STK::JointGaussianModel< Array, WColVector >::p_data(), and STK::Stat::varianceWithFixedMean().

◆ p_data()

template<class Array , class WColVector = CVectorX>

Data const *const STK::IMultiStatModel< Derived >::p_data ( ) const

inline

Returns: the pointer on the parameters

Definition at line 152 of file STK_IMultiStatModel.h.

152{ return (p_data_);}

STK::IMultiStatModel::p_data_

Data const * p_data_

Pointer on the parameters of the model.

Definition STK_IMultiStatModel.h:226

Referenced by STK::JointGaussianModel< Array, WColVector >::computeNbFreeParameters(), STK::JointGaussianModel< Array, WColVector >::computeParameters(), and STK::JointGaussianModel< Array, WColVector >::computeParameters().

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

STK_JointGaussianModel.h

Public Types

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Base

◆ ColVector

◆ RowVector

◆ Type

Constructor & Destructor Documentation

◆ JointGaussianModel() [1/4]

◆ JointGaussianModel() [2/4]

◆ JointGaussianModel() [3/4]

◆ JointGaussianModel() [4/4]

◆ ~JointGaussianModel()

Member Function Documentation

◆ clone()

◆ computeLnLikelihood()

◆ computeNbFreeParameters()

◆ computeParameters() [1/2]

◆ computeParameters() [2/2]

◆ p_data()