Main class for learning mixture models. More...

#include <STK_MixtureLearner.h>

Inheritance diagram for STK::MixtureLearner:

Public Types
typedef std::vector< IMixture * >::const_iterator	ConstMixtIterator

typedef std::vector< IMixture * >::iterator	MixtIterator

Public Types inherited from STK::IMixtureStatModel
typedef std::vector< IMixture * >::const_iterator	ConstMixtIterator

typedef std::vector< IMixture * >::iterator	MixtIterator

Public Member Functions
	MixtureLearner (int nbSample, int nbCluster)
	Constructor.

	MixtureLearner (MixtureLearner const &composer)
	copy constructor.

virtual	~MixtureLearner ()
	The registered mixtures will be deleted there.

virtual MixtureLearner *	create () const
	Create a composer, but reinitialize the mixtures parameters.

virtual MixtureLearner *	clone () const
	Create a clone of the current model, with mixtures parameters preserved.

virtual Real	lnComponentProbability (int i, int k) const

virtual void	writeParameters (ostream &os) const
	write the parameters of the model in the stream os.

virtual void	randomInit ()
	initialize randomly the parameters of the components of the model

virtual void	paramUpdateStep ()
	Compute the model parameters given the current imputed/simulated missing values.

virtual void	imputationStep ()
	Impute the data missing values.

virtual void	samplingStep ()
	Simulation of all the data missing values.

virtual void	storeIntermediateResults (int iteration)
	This step can be used to signal to the mixtures that they must store results.

virtual void	releaseIntermediateResults ()
	This step can be used to signal to the mixtures that they must release the stored results.

virtual void	setParametersStep ()
	Utility method allowing to signal to a mixture to set its parameters.

virtual void	finalizeStep ()
	This step can be used by developer to finalize any thing.

Public Member Functions inherited from STK::IMixtureLearner
virtual	~IMixtureLearner ()
	destructor

CVectorXi const &	ziPred () const

Clust::modelState	state () const

void	setState (Clust::modelState state)
	set the state of the model : should be used by any strategy

virtual void	mapStep ()
	Compute ziPred using the Map estimate.

template<class ColVector >
void	setClassLabels (ColVector const &zi)
	set the mixture parameters using the given class labels.

template<class ColVector , class RowVector >
void	setClassLabels (ColVector const &zi, RowVector const &pk)
	set the mixture parameters using the class labels and giving the proportions.

Public Member Functions inherited from STK::IMixtureStatModel
virtual	~IMixtureStatModel ()
	destructor

int	nbCluster () const

CPointX const &	pk () const

CArrayXX const &	tik () const

CPointX const &	tk () const

CVectorXi const &	zi () const

std::vector< IMixture * > const &	v_mixtures () const

Real	computeLnLikelihood (int i) const

Real	computeLikelihood (int i) const

Real	computeLnLikelihood () const

Real	computeICL () const

IMixture *	getMixture (String const &idData) const
	Utility lookup function allowing to find a Mixture from its idData.

void	registerMixture (IMixture *p_mixture)
	register a mixture to the composer.

void	releaseMixture (String const &idData)
	release a mixture from the composer.

int	computeNbFreeParameters () const
	compute the number of free parameters of the model.

int	computeNbMissingValues () const
	compute the missing values of the model.

virtual void	initializeStep ()
	Initialize the model before at its first use.

template<class Array >
void	setMixtureParameters (Array const &tik)
	set the mixture parameters using an array of posterior probabilities.

template<class Array , class RowVector >
void	setMixtureParameters (Array const &tik, RowVector const &pk)
	set the mixture parameters giving the posterior probabilities and the proportions.

template<class RowVector >
void	setProportions (RowVector const &pk)
	Set proportions of each classes.

template<class Manager , class Parameters >
void	setParameters (IMixtureManager< Manager > const &manager, String const &idData, Parameters const &param)
	Utility method allowing to set the parameters to a specific mixture.

template<class Manager >
void	createMixture (IMixtureManager< Manager > &manager)
	Utility method allowing to create all the mixtures registered in the data handler of a mixture manager and to register them.

template<class Manager >
IMixture *	createMixture (IMixtureManager< Manager > &manager, String const &idData)
	Utility method allowing to create a mixture with a given data set and register it.

template<class Manager >
void	removeMixture (IMixtureManager< Manager > &manager, String const &idData)
	Utility method allowing to release completely a mixture with its data set.

template<class Manager , class Parameters >
void	getParameters (IMixtureManager< Manager > const &manager, String const &idData, Parameters &param) const
	Utility method allowing to get the parameters of a specific mixture.

template<class Manager , class MissingValues >
void	getMissingValues (IMixtureManager< Manager > const &manager, String const &idData, MissingValues &missing) const
	Utility method allowing to get the missing values of a specific mixture.

template<class DataHandler >
void	createMixture (IMixtureManager< DataHandler > &manager)

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

Protected Member Functions
void	createLearner (std::vector< IMixture * > const &v_mixtures_)
	Create the composer using existing data handler and mixtures.

Protected Member Functions inherited from STK::IMixtureLearner
	IMixtureLearner (int nbSample, int nbCluster)
	Constructor.

	IMixtureLearner (IMixtureLearner const &model)
	copy constructor.

Protected Member Functions inherited from STK::IMixtureStatModel
	IMixtureStatModel (int nbSample, int nbCluster)
	Constructor.

	IMixtureStatModel (IMixtureStatModel const &model)
	copy constructor.

void	setNbCluster (int nbCluster)
	set the number of cluster of the model

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

Private Attributes
Real	meanlnLikelihood_
	averaged lnLikelihood values.

Additional Inherited Members
Protected Attributes inherited from STK::IMixtureStatModel
int	nbCluster_
	number of cluster.

CPointX	pk_
	The proportions of each mixtures.

CArrayXX	tik_
	The tik probabilities.

CPointX	tk_
	The sum of the columns of tik_.

CVectorXi	zi_
	The zi class label.

std::vector< IMixture * >	v_mixtures_
	vector of pointers to the mixtures components

Detailed Description

Main class for learning mixture models.

A composed mixture model on some composed space $\mathbb{X} = \subset \mathcal{X}^1\times \ldots \times \mathcal{X}^L$ is a density of the form

$f(\mathbf{x}|\boldsymbol{\theta}) = \sum_{k=1}^K p_k \prod_{l=1}^L f^l(\mathbf{x}^l;\boldsymbol{\lambda}^l_k,\boldsymbol{\alpha}^l) \quad \mathbf{x} \in \mathbb{X}.$

The $ p_k > 0$ with $\sum_{k=1}^K p_k =1$ are the mixing proportions. The density f is called the component of the model. The parameters $\boldsymbol{\lambda}^l_k, \, k=1,\ldots K$ are the cluster specific parameters and the parameters $\boldsymbol{\alpha}^l$ are the shared parameters.

The MixtureLearner class is a final class implementing the features requested by the interface class IMixtureLearner.

It uses injection dependency in order to create/release mixture and get/set parameters of a mixture. The class responsible of the injection is derived from an STK::IMixtureManager.

See also: IMixtureLearner, IMixtureManager

Definition at line 70 of file STK_MixtureLearner.h.

Member Typedef Documentation

◆ ConstMixtIterator

typedef std::vector<IMixture*>::const_iterator STK::MixtureLearner::ConstMixtIterator

Definition at line 73 of file STK_MixtureLearner.h.

◆ MixtIterator

typedef std::vector<IMixture*>::iterator STK::MixtureLearner::MixtIterator

Definition at line 74 of file STK_MixtureLearner.h.

Constructor & Destructor Documentation

◆ MixtureLearner() [1/2]

STK::MixtureLearner::MixtureLearner	(	int	nbSample,
		int	nbCluster
	)

Constructor.

Parameters

nbCluster,nbSample number of clusters and samples.

Definition at line 46 of file STK_MixtureLearner.cpp.

                              : IMixtureLearner( nbSample, nbCluster)
                              , meanlnLikelihood_(0.)
{ setNbFreeParameter(nbCluster-1);}

References STK::IMixtureStatModel::nbCluster(), and STK::IStatModelBase::setNbFreeParameter().

◆ MixtureLearner() [2/2]

STK::MixtureLearner::MixtureLearner ( MixtureLearner const & composer )

copy constructor.

Parameters

composer the composer to copy

Definition at line 54 of file STK_MixtureLearner.cpp.

                              : IMixtureLearner(learner)
                              , meanlnLikelihood_(learner.meanlnLikelihood_)
{}

◆ ~MixtureLearner()

STK::MixtureLearner::~MixtureLearner ( )

virtual

The registered mixtures will be deleted there.

Definition at line 59 of file STK_MixtureLearner.cpp.

59{}

Member Function Documentation

◆ clone()

MixtureLearner * STK::MixtureLearner::clone ( ) const

virtual

Create a clone of the current model, with mixtures parameters preserved.

Implements STK::IMixtureStatModel.

Definition at line 62 of file STK_MixtureLearner.cpp.

63{ return new MixtureLearner(*this);}

STK::MixtureLearner::MixtureLearner

MixtureLearner(int nbSample, int nbCluster)

Constructor.

Definition STK_MixtureLearner.cpp:46

◆ create()

MixtureLearner * STK::MixtureLearner::create ( ) const

virtual

Create a composer, but reinitialize the mixtures parameters.

Implements STK::IMixtureStatModel.

Definition at line 65 of file STK_MixtureLearner.cpp.

{
  // set dimensions
  MixtureLearner* p_composer = new MixtureLearner(nbSample(), nbCluster());
  p_composer->createLearner( v_mixtures_);
  return p_composer;
}

References createLearner(), STK::IMixtureStatModel::nbCluster(), STK::IStatModelBase::nbSample(), and STK::IMixtureStatModel::v_mixtures_.

◆ createLearner()

void STK::MixtureLearner::createLearner ( std::vector< IMixture * > const & v_mixtures_ )

protected

Create the composer using existing data handler and mixtures.

This method is essentially used by the create() method and can be reused in derived classes.

See also: MixtureLearnerFixedProp

Definition at line 167 of file STK_MixtureLearner.cpp.

{
  initialize(nbSample(), 0);
  v_mixtures_.resize( v_mixtures.size());
  for (size_t l = 0; l < v_mixtures_.size(); ++l)
  {
    v_mixtures_[l] = v_mixtures[l]->create();
    v_mixtures_[l]->setMixtureModel(this);
    v_mixtures_[l]->initializeStep();
  }
  // compute number of free parameters
  setNbFreeParameter(computeNbFreeParameters());
}

References STK::IMixtureStatModel::computeNbFreeParameters(), STK::IStatModelBase::initialize(), STK::IRegression< YArray, XArray, Weights >::initializeStep(), STK::IStatModelBase::nbSample(), STK::IStatModelBase::setNbFreeParameter(), STK::IMixtureStatModel::v_mixtures(), and STK::IMixtureStatModel::v_mixtures_.

Referenced by create().

◆ finalizeStep()

void STK::MixtureLearner::finalizeStep ( )

virtual

This step can be used by developer to finalize any thing.

It will be called only once after we finish running the estimation algorithm.

Reimplemented from STK::IMixtureStatModel.

Definition at line 158 of file STK_MixtureLearner.cpp.

{
  for (MixtIterator it = v_mixtures_.begin(); it != v_mixtures_.end(); ++it)
  { (*it)->finalizeStep();}
}

References STK::IRegression< YArray, XArray, Weights >::finalizeStep(), and STK::IMixtureStatModel::v_mixtures_.

◆ imputationStep()

void STK::MixtureLearner::imputationStep ( )

virtual

Impute the data missing values.

Reimplemented from STK::IMixtureStatModel.

Definition at line 118 of file STK_MixtureLearner.cpp.

{
  for (MixtIterator it = v_mixtures_.begin(); it != v_mixtures_.end(); ++it)
  { (*it)->imputationStep();}
}

References STK::IMixtureStatModel::v_mixtures_.

◆ lnComponentProbability()

Real STK::MixtureLearner::lnComponentProbability	(	int	i,
		int	k
	)		const

virtual

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 class

Implements STK::IMixtureStatModel.

Definition at line 73 of file STK_MixtureLearner.cpp.

{
  Real sum=0.0;
  for (ConstMixtIterator it = v_mixtures_.begin() ; it != v_mixtures_.end(); ++it)
  { sum += (*it)->lnComponentProbability(i,k);}
  return sum;
}

References STK::sum(), and STK::IMixtureStatModel::v_mixtures_.

◆ paramUpdateStep()

void STK::MixtureLearner::paramUpdateStep ( )

virtual

Compute the model parameters given the current imputed/simulated missing values.

This method updates then the lnLikelihood_ of the model.

Implements STK::IMixtureLearner.

Definition at line 81 of file STK_MixtureLearner.cpp.

{
  for (MixtIterator it = v_mixtures_.begin() ; it != v_mixtures_.end(); ++it)
  { (*it)->paramUpdateStep();}
  setLnLikelihood(computeLnLikelihood());
}

References STK::IMixtureStatModel::computeLnLikelihood(), STK::IStatModelBase::setLnLikelihood(), and STK::IMixtureStatModel::v_mixtures_.

◆ randomInit()

void STK::MixtureLearner::randomInit ( )

virtual

initialize randomly the parameters of the components of the model

Implements STK::IMixtureStatModel.

Definition at line 103 of file STK_MixtureLearner.cpp.

{
#ifdef STK_MIXTURE_VERBOSE
  stk_cout << _T("Entering MixtureLearner::RandomInit()\n");
#endif
  if (state() < 2) { initializeStep();}
  for (MixtIterator it = v_mixtures_.begin(); it != v_mixtures_.end(); ++it)
  { (*it)->randomInit();}
  setState(Clust::modelParamInitialized_);
#ifdef STK_MIXTURE_VERBOSE
  stk_cout << _T("MixtureLearner::RandomInit() done\n");
#endif
}

References _T, STK::IMixtureStatModel::initializeStep(), STK::Clust::modelParamInitialized_, STK::IMixtureLearner::setState(), STK::IMixtureLearner::state(), stk_cout, and STK::IMixtureStatModel::v_mixtures_.

◆ releaseIntermediateResults()

void STK::MixtureLearner::releaseIntermediateResults ( )

virtual

This step can be used to signal to the mixtures that they must release the stored results.

This is usually called if the estimation process failed.

Reimplemented from STK::IMixtureStatModel.

Definition at line 141 of file STK_MixtureLearner.cpp.

{
  for (MixtIterator it = v_mixtures_.begin(); it != v_mixtures_.end(); ++it)
  { (*it)->releaseIntermediateResults();}
  meanlnLikelihood_ = 0.;
}

References meanlnLikelihood_, and STK::IMixtureStatModel::v_mixtures_.

◆ samplingStep()

void STK::MixtureLearner::samplingStep ( )

virtual

Simulation of all the data missing values.

Reimplemented from STK::IMixtureStatModel.

Definition at line 127 of file STK_MixtureLearner.cpp.

{
  for (MixtIterator it = v_mixtures_.begin(); it != v_mixtures_.end(); ++it)
  { (*it)->samplingStep();}
}

References STK::IMixtureStatModel::v_mixtures_.

◆ setParametersStep()

void STK::MixtureLearner::setParametersStep ( )

virtual

Utility method allowing to signal to a mixture to set its parameters.

It will be called once enough intermediate results have been stored.

Reimplemented from STK::IMixtureStatModel.

Definition at line 149 of file STK_MixtureLearner.cpp.

{
  for (MixtIterator it = v_mixtures_.begin(); it != v_mixtures_.end(); ++it)
  { (*it)->setParametersStep();}
  setLnLikelihood(meanlnLikelihood_);
  meanlnLikelihood_ = 0.;
}

References meanlnLikelihood_, STK::IStatModelBase::setLnLikelihood(), and STK::IMixtureStatModel::v_mixtures_.

◆ storeIntermediateResults()

void STK::MixtureLearner::storeIntermediateResults ( int iteration )

virtual

This step can be used to signal to the mixtures that they must store results.

This is usually called after a burn-in phase. The composer store the current value of the log-Likelihood.

Reimplemented from STK::IMixtureStatModel.

Definition at line 134 of file STK_MixtureLearner.cpp.

{
  for (MixtIterator it = v_mixtures_.begin(); it != v_mixtures_.end(); ++it)
  { (*it)->storeIntermediateResults(iteration);}
  meanlnLikelihood_ += (lnLikelihood() - meanlnLikelihood_)/iteration;
}

References STK::IStatModelBase::lnLikelihood(), meanlnLikelihood_, and STK::IMixtureStatModel::v_mixtures_.

◆ writeParameters()

void STK::MixtureLearner::writeParameters ( ostream & os ) const

virtual

write the parameters of the model in the stream os.

Reimplemented from STK::IMixtureStatModel.

Definition at line 88 of file STK_MixtureLearner.cpp.

{
  os << _T("Learner nbSample = ") << nbSample() << std::endl;
  os << _T("Learner nbCluster = ") << nbCluster() << std::endl;
  os << _T("Learner nbFreeParameter = ") << nbFreeParameter() << std::endl;
  os << _T("Learner lnLikelihood = ") << lnLikelihood() << std::endl;
  os << _T("Learner proportions = ") << pk();
 
  for (ConstMixtIterator it = v_mixtures_.begin(); it != v_mixtures_.end(); ++it)
  {
    os << _T("\nParameters of the mixture: ") << (*it)->idData() << _T("\n");
    (*it)->writeParameters(os);
  }
}

References _T, STK::IStatModelBase::lnLikelihood(), STK::IMixtureStatModel::nbCluster(), STK::IStatModelBase::nbFreeParameter(), STK::IStatModelBase::nbSample(), STK::IMixtureStatModel::pk(), and STK::IMixtureStatModel::v_mixtures_.

Member Data Documentation

◆ meanlnLikelihood_

Real STK::MixtureLearner::meanlnLikelihood_

private

averaged lnLikelihood values.

Will be used by the storeIntermediateResults method.

Definition at line 137 of file STK_MixtureLearner.h.

Referenced by releaseIntermediateResults(), setParametersStep(), and storeIntermediateResults().

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

Public Types

Public Member Functions

Protected Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ ConstMixtIterator

◆ MixtIterator

Constructor & Destructor Documentation

◆ MixtureLearner() [1/2]

◆ MixtureLearner() [2/2]

◆ ~MixtureLearner()

Member Function Documentation

◆ clone()

◆ create()

◆ createLearner()

◆ finalizeStep()

◆ imputationStep()

◆ lnComponentProbability()

◆ paramUpdateStep()

◆ randomInit()

◆ releaseIntermediateResults()

◆ samplingStep()

◆ setParametersStep()

◆ storeIntermediateResults()

◆ writeParameters()

Member Data Documentation

◆ meanlnLikelihood_