The diagonal Gaussian mixture model DiagGaussian_sj have a density function of the form. More...

#include <STK_DiagGaussian_sj.h>

Inheritance diagram for STK::DiagGaussian_sj< Array >:

Public Types
typedef DiagGaussianBase< DiagGaussian_sj< Array > >	Base

Public Types inherited from STK::DiagGaussianBase< DiagGaussian_sj< Array > >
typedef IMixtureDensity< DiagGaussian_sj< Array > >	Base

Public Types inherited from STK::IMixtureDensity< Derived >
typedef hidden::MixtureTraits< Derived >::Array	Array

typedef hidden::MixtureTraits< Derived >::Parameters	Parameters

typedef hidden::Traits< Array >::Type	Type

Public Member Functions
	DiagGaussian_sj (int nbCluster)
	default constructor

	DiagGaussian_sj (DiagGaussian_sj const &model)
	copy constructor

	~DiagGaussian_sj ()
	destructor

void	randomInit (CArrayXX const const &p_tik, CPointX const const &p_tk)
	Initialize randomly the parameters of the Gaussian mixture.

bool	run (CArrayXX const const &p_tik, CPointX const const &p_tk)
	Compute the weighted mean and the common standard deviation.

int	computeNbFreeParameters () const

Public Member Functions inherited from STK::DiagGaussianBase< DiagGaussian_sj< Array > >
Real const &	mean (int k, int j) const

Real const &	sigma (int k, int j) const

void	initializeModelImpl ()
	Initialize the parameters of the model.

Real	lnComponentProbability (int i, int k) const

Real	impute (int i, int j, Weights const &pk) const

Real	rand (int i, int j, int k) const

void	getParameters (Array &params) const
	This function is used in order to get the current values of the means and standard deviations.

void	writeParameters (CArrayXX const *p_tik, ostream &os) const
	This function can be used to write summary of parameters to the output stream.

int	nbCluster () const

int	nbSample () const

Array const *const &	p_data () const

Public Member Functions inherited from STK::IMixtureDensity< Derived >
	~IMixtureDensity ()
	destructor

int	nbCluster () const

int	nbSample () const

Real	lnNbSample () const

Array const *const &	p_data () const

Parameters const &	param () const

void	setData (Array const &data)
	Set the data set.

void	setData (Array const &data, int nbRow, int nbCol, bool byRow=true)
	Set the data set and give dimensions.

bool	initializeStep ()
	This function will be called at the beginning of the estimation process once the model is created and data is set.

void	setParametersStep ()
	set the parameters obtained with the intermediate results and release the intermediate results.

void	finalizeStep ()
	This function will be called once the model is estimated.

template<class Weights >
Type	sample (int i, int j, Weights const &tk) 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.

Additional Inherited Members
Public Attributes inherited from STK::DiagGaussianBase< DiagGaussian_sj< Array > >
Parameters	param_
	parameters of the derived mixture model.

Protected Member Functions inherited from STK::DiagGaussianBase< DiagGaussian_sj< Array > >
	DiagGaussianBase (int nbCluster)
	default constructor

	DiagGaussianBase (DiagGaussianBase const &model)
	copy constructor

	~DiagGaussianBase ()
	destructor

void	randomMean (CArrayXX const *p_tik)
	sample randomly the mean of each component by sampling randomly a row of the data set.

bool	updateMean (CArrayXX const *p_tik)
	compute the weighted mean of a Gaussian mixture.

Protected Member Functions inherited from STK::IMixtureDensity< Derived >
	IMixtureDensity (int nbCluster)
	Default constructor.

	IMixtureDensity (IMixtureDensity const &model)
	copy constructor.

void	initializeModel ()
	Initialize the model before its first use.

bool	initializeStepImpl ()
	default implementation of initializeStepImpl (do nothing and return true)

void	finalizeStepImpl ()
	default implementation of finalizeStepImpl (do nothing)

void	setNbSample (int nbSample)
	Set the number of sample of the model (needed by kernel models)

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

	~IRecursiveTemplate ()
	destructor.

Protected Attributes inherited from STK::IMixtureDensity< Derived >
Parameters	param_
	parameters of the derived mixture model.

Detailed Description

template<class Array>
class STK::DiagGaussian_sj< Array >

The diagonal Gaussian mixture model DiagGaussian_sj have a density function of the form.

$f(\mathbf{x}|\theta) = \sum_{k=1}^K p_k \prod_{j=1}^d \frac{1}{\sqrt{2\pi}\sigma_j} \exp\left\{-\frac{(x^j-\mu^j_k)^2}{2\sigma_j^2}\right\}.$

Definition at line 69 of file STK_DiagGaussian_sj.h.

Member Typedef Documentation

◆ Base

template<class Array >

typedef DiagGaussianBase<DiagGaussian_sj<Array> > STK::DiagGaussian_sj< Array >::Base

Definition at line 72 of file STK_DiagGaussian_sj.h.

Constructor & Destructor Documentation

◆ DiagGaussian_sj() [1/2]

template<class Array >

STK::DiagGaussian_sj< Array >::DiagGaussian_sj ( int nbCluster )

inline

default constructor

Parameters

nbCluster number of cluster in the model

Definition at line 79 of file STK_DiagGaussian_sj.h.

79: Base(nbCluster) {}

STK::DiagGaussianBase< DiagGaussian_sj< Array > >::nbCluster

int nbCluster() const

Definition STK_IMixtureDensity.h:124

STK::DiagGaussian_sj::Base

DiagGaussianBase< DiagGaussian_sj< Array > > Base

Definition STK_DiagGaussian_sj.h:72

◆ DiagGaussian_sj() [2/2]

template<class Array >

STK::DiagGaussian_sj< Array >::DiagGaussian_sj ( DiagGaussian_sj< Array > const & model )

inline

copy constructor

Parameters

model The model to copy

Definition at line 83 of file STK_DiagGaussian_sj.h.

84 : Base(model)

85 {}

◆ ~DiagGaussian_sj()

template<class Array >

STK::DiagGaussian_sj< Array >::~DiagGaussian_sj ( )

inline

destructor

Definition at line 87 of file STK_DiagGaussian_sj.h.

87{}

Member Function Documentation

◆ computeNbFreeParameters()

template<class Array >

int STK::DiagGaussian_sj< Array >::computeNbFreeParameters ( ) const

inline

Returns: the number of free parameters of the model

Definition at line 96 of file STK_DiagGaussian_sj.h.

97 { return this->nbCluster()*p_data()->sizeCols()+p_data()->sizeCols();}

STK::DiagGaussianBase< DiagGaussian_sj< Array > >::p_data

Array const *const & p_data() const

Definition STK_IMixtureDensity.h:131

References STK::DiagGaussianBase< DiagGaussian_sj< Array > >::nbCluster(), and STK::DiagGaussianBase< DiagGaussian_sj< Array > >::p_data().

◆ randomInit()

template<class Array >

void STK::DiagGaussian_sj< Array >::randomInit	(	CArrayXX const *const &	p_tik,
		CPointX const *const &	p_tk
	)

Initialize randomly the parameters of the Gaussian mixture.

The centers will be selected randomly among the data set and the standard-deviation will be set to 1.

Definition at line 105 of file STK_DiagGaussian_sj.h.

{
  // compute the initial mean
  this->randomMean(p_tik);
  // compute the standard deviation
  Array2DPoint<Real> variance(p_data()->cols(), 0.);
  for (int k= p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    variance += p_tik->col(k).transpose()
               *(*p_data() - (Const::Vector<Real>(this->nbSample()) * param_.mean_[k])
                ).square()
                ;
  }
  // store the standard deviation
  param_.sigma_ = (variance /= this->nbSample()).sqrt();
#ifdef STK_MIXTURE_VERY_VERBOSE
  stk_cout << _T("DiagGaussian_sj<Array>::randomInit( CArrayXX const* const& p_tik, CPointX const* const& p_tk)  done\n");
#endif
}

References _T, STK::ICArray< Derived >::col(), and stk_cout.

◆ run()

template<class Array >

bool STK::DiagGaussian_sj< Array >::run	(	CArrayXX const *const &	p_tik,
		CPointX const *const &	p_tk
	)

Compute the weighted mean and the common standard deviation.

Definition at line 127 of file STK_DiagGaussian_sj.h.

{
  // compute the means
  if (!this->updateMean(p_tik)) return false;
  // compute the standard deviation
  Array2DPoint<Real> variance(p_data()->cols(), 0.);
  for (int k= p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    variance += p_tik->col(k).transpose()
               *(*p_data() - (Const::Vector<Real>(this->nbSample()) * param_.mean_[k])
                ).square()
                ;
  }
//  if (variance.nbAvailableValues() != p_data()->sizeCols()) return false;
//  if ((variance > 0.).template cast<int>().sum() != p_data()->sizeCols()) return false;
  // compute the standard deviation
  param_.sigma_ = (variance /= this->nbSample()).sqrt();
  return true;
}

References STK::ICArray< Derived >::col().

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

STK_DiagGaussian_sj.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Base

Constructor & Destructor Documentation

◆ DiagGaussian_sj() [1/2]

◆ DiagGaussian_sj() [2/2]

◆ ~DiagGaussian_sj()

Member Function Documentation

◆ computeNbFreeParameters()

◆ randomInit()

◆ run()