The diagonal Gaussian mixture model HDGaussian_AjkBkQkD is the most general HD Gaussian model and have a density function of the form. More...

#include <STK_HDGaussian_AjkBkQkD.h>

Inheritance diagram for STK::HDGaussian_AjkBkQkD< Array >:

Public Types
typedef DiagGaussianBase< HDGaussian_AjkBkQkDk< Array > >	Base

Public Types inherited from STK::HDGaussianBase< HDGaussian_AjkBkQkD< Array > >
typedef IMixtureDensity< HDGaussian_AjkBkQkD< 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
	HDGaussian_AjkBkQkD (int nbCluster)
	default constructor

	HDGaussian_AjkBkQkD (HDGaussian_AjkBkQkD const &model)
	copy constructor

	~HDGaussian_AjkBkQkD ()
	destructor

Real	lnComponentProbability (int i, int k) const

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

Array const *const &	p_data () const

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

int const &	d (int k) const

Real const &	b (int k) const

Real const &	q (int k) const

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

void	initializeModelImpl ()
	Initialize the parameters of the model.

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

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.

Public Attributes
Parameters	param_
	parameters of the derived mixture model.

Public Attributes inherited from STK::HDGaussianBase< HDGaussian_AjkBkQkD< Array > >
Parameters	param_
	parameters of the derived mixture model.

Additional Inherited Members
Protected Member Functions inherited from STK::HDGaussianBase< HDGaussian_AjkBkQkD< Array > >
	HDGaussianBase (int nbCluster)
	default constructor

	HDGaussianBase (HDGaussianBase const &model)
	copy constructor

	~HDGaussianBase ()
	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::HDGaussian_AjkBkQkD< Array >

The diagonal Gaussian mixture model HDGaussian_AjkBkQkD is the most general HD Gaussian model and have a density function of the form.

Definition at line 102 of file STK_HDGaussian_AjkBkQkD.h.

Member Typedef Documentation

◆ Base

template<class Array >

typedef DiagGaussianBase<HDGaussian_AjkBkQkDk<Array> > STK::HDGaussian_AjkBkQkD< Array >::Base

Definition at line 105 of file STK_HDGaussian_AjkBkQkD.h.

Constructor & Destructor Documentation

◆ HDGaussian_AjkBkQkD() [1/2]

template<class Array >

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

inline

default constructor

Parameters

nbCluster number of cluster in the model

Definition at line 112 of file STK_HDGaussian_AjkBkQkD.h.

112: Base(nbCluster) {}

STK::HDGaussianBase< HDGaussian_AjkBkQkD< Array > >::nbCluster

int nbCluster() const

Definition STK_IMixtureDensity.h:124

STK::HDGaussian_AjkBkQkD::Base

DiagGaussianBase< HDGaussian_AjkBkQkDk< Array > > Base

Definition STK_HDGaussian_AjkBkQkD.h:105

◆ HDGaussian_AjkBkQkD() [2/2]

template<class Array >

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

inline

copy constructor

Parameters

model The model to copy

Definition at line 116 of file STK_HDGaussian_AjkBkQkD.h.

116: Base(model) {}

◆ ~HDGaussian_AjkBkQkD()

template<class Array >

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

inline

destructor

Definition at line 118 of file STK_HDGaussian_AjkBkQkD.h.

118{}

Member Function Documentation

◆ computeNbFreeParameters()

template<class Array >

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

inline

Returns: the number of free parameters of the model

Definition at line 140 of file STK_HDGaussian_AjkBkQkD.h.

141 { return 2*this->nbCluster()*p_data()->sizeCols();}

STK::HDGaussian_AjkBkQkD::p_data

Array const *const & p_data() const

Definition STK_IMixtureDensity.h:131

References STK::HDGaussianBase< HDGaussian_AjkBkQkD< Array > >::nbCluster(), and STK::HDGaussian_AjkBkQkD< Array >::p_data().

◆ lnComponentProbability()

template<class Array >

Real STK::HDGaussian_AjkBkQkD< Array >::lnComponentProbability	(	int	i,
		int	k
	)		const

inline

Returns: the value of the probability of the i-th sample in the k-th component.

Parameters

i,k	indexes of the sample and of the component

Definition at line 122 of file STK_HDGaussian_AjkBkQkD.h.

    {
      Real sum =0.;
      for (int j=p_data()->beginCols(); j<p_data()->endCols(); ++j)
      {
        if (param_.sigma_[k][j])
        { sum += Law::Normal::lpdf(p_data()->elt(i,j), param_.mean_[k][j], param_.sigma_[k][j]);}
      }
      return sum;
    }

References STK::Law::Normal::lpdf(), STK::HDGaussian_AjkBkQkD< Array >::p_data(), STK::HDGaussian_AjkBkQkD< Array >::param_, and STK::sum().

◆ p_data()

template<class Array >

Array const *const & STK::IMixtureDensity< Derived >::p_data ( ) const

inline

Returns: a pointer on the current data set

Definition at line 60 of file STK_IMixtureDensity.h.

131{ return p_dataij_;}

STK::IMixtureDensity::p_dataij_

Array const * p_dataij_

pointer on the data set

Definition STK_IMixtureDensity.h:211

Referenced by STK::HDGaussian_AjkBkQkD< Array >::computeNbFreeParameters(), and STK::HDGaussian_AjkBkQkD< Array >::lnComponentProbability().

◆ randomInit()

template<class Array >

void STK::HDGaussian_AjkBkQkD< 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 149 of file STK_HDGaussian_AjkBkQkD.h.

{
  this->randomMean(p_tik);
  // compute the standard deviation
  for (int k= p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    param_.sigma_[k] = Stat::varianceWithFixedMean(*p_data(), p_tik->col(k), param_.mean_[k], false).sqrt();
  }
#ifdef STK_MIXTURE_VERY_VERBOSE
  stk_cout << _T("MixtureHDGaussian_ajk_bk_qk_d<Array>::randomInit( CArrayXX const* const& p_tik, CPointX const* const& p_tk)  done\n");
#endif
}

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

◆ run()

template<class Array >

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

Compute the weighted mean and the common standard deviation.

Definition at line 164 of file STK_HDGaussian_AjkBkQkD.h.

{
  // compute the means
  if (!this->updateMean(p_tik)) return false;
  // compute the standard deviation
  for (int k= p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    param_.sigma_[k] = Stat::varianceWithFixedMean(*p_data(), p_tik->col(k), param_.mean_[k], false).sqrt();
#ifdef STK_MIXTURE_DEBUG
    if( (param_.sigma_[k] <= 0).any()  )
    {
      stk_cout << _T("MixtureHDGaussian_ajk_bk_qk_d::run() failed\n");
      stk_cout << _T("p_tik->col(") << k << _T(") =\n") << p_tik->col(k).transpose() << _T("\n");
      stk_cout << _T("param_.mean_[") << k << _T("]  =") << param_.mean_[k];
      stk_cout << _T("param_.sigma_[") << k << _T("] =") << param_.sigma_[k];
    }
#endif
  }
  return true;
}