STK::IMixtureLearner Class Reference

Base class for Learner of a Mixture mixed model. More...

#include <STK_IMixtureLearner.h>

Inheritance diagram for STK::IMixtureLearner:

Public Member Functions
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.
virtual void	paramUpdateStep ()=0
	Compute the model parameters given the current mixture parameters and imputation/simulation of the missing values.
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 IMixtureStatModel *	create () const =0
	create pattern
virtual IMixtureStatModel *	clone () const =0
	clone pattern
virtual void	randomInit ()=0
	initialize randomly the parameters of the components of the model
virtual Real	lnComponentProbability (int i, int k) const =0
virtual void	initializeStep ()
	Initialize the model before at its first use.
virtual void	imputationStep ()
	Impute the missing values.
virtual void	samplingStep ()
	Simulation of all the latent variables and/or missing data excluding class labels.
virtual void	setParametersStep ()
	Utility method allowing to signal to a mixture to set its parameters.
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	finalizeStep ()
	Finalize the estimation of the model.
virtual void	writeParameters (ostream &os) const
	write the parameters of the model in the stream os.
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
	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
CVectorXi	ziPred_
	The predicted class label.
Clust::modelState	state_
	state of the model

Additional Inherited Members
Public Types inherited from STK::IMixtureStatModel
typedef std::vector< IMixture * >::const_iterator	ConstMixtIterator
typedef std::vector< IMixture * >::iterator	MixtIterator
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

Base class for Learner of a Mixture mixed model.

In this interface we assume there is an underline generative model that will be estimated using a MCMC algorithm.

The pure virtual function to implement in derived class are

virtual void paramUpdateStep() = 0;

See also

IMixtureComposer

Definition at line 56 of file STK_IMixtureLearner.h.

Constructor & Destructor Documentation

IMixtureLearner() [1/2]

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

protected

Constructor.

Parameters

nbCluster,nbSample

number of clusters and samples

Definition at line 43 of file STK_IMixtureLearner.cpp.

                                : IMixtureStatModel(nbSample, nbCluster)
                                , ziPred_(tik_.rows())
                                , state_(Clust::modelCreated_)
{}

IMixtureLearner() [2/2]

STK::IMixtureLearner::IMixtureLearner ( IMixtureLearner const &  model )

protected

copy constructor.

Parameters

model

the model to clone

Definition at line 51 of file STK_IMixtureLearner.cpp.

                                : IMixtureStatModel(model), state_(model.state_)
{}

~IMixtureLearner()

STK::IMixtureLearner::~IMixtureLearner ( )

virtual

destructor

Definition at line 55 of file STK_IMixtureLearner.cpp.

{}

Member Function Documentation

mapStep()

void STK::IMixtureLearner::mapStep ( )

virtual

Compute ziPred using the Map estimate.

Definition at line 58 of file STK_IMixtureLearner.cpp.

{
  CPointX lnComp_(tik_.cols());
  int kmax;
  Real max;
  for (int i=tik_.beginRows(); i < tik_.endRows(); i++)
  {
    // get maximal element of ln(x_i,\theta_k) + ln(p_k)
    for (int k=tik_.beginCols(); k< tik_.endCols(); k++)
    { lnComp_[k] = std::log(pk_[k])+lnComponentProbability(i,k);}
    if (lnComp_.isInfinite().any()) { throw(Clust::mapStepFail_);}
    max = lnComp_.maxElt(kmax);
    // set ziPred_
    ziPred_[i] = kmax;
    // return  max + sum_k p_k exp{lnCom_k - lnComp_kmax}
    Real sum =  (tik_.row(i) = (lnComp_ - max).exp()).sum();
    tik_.row(i) /= sum;
  }
}

References STK::ExprBase< Derived >::isInfinite(), STK::IMixtureStatModel::lnComponentProbability(), STK::Clust::mapStepFail_, STK::max(), STK::ExprBase< Derived >::maxElt(), STK::IMixtureStatModel::pk_, STK::ICArray< Derived >::row(), STK::sum(), STK::IMixtureStatModel::tik_, and ziPred_.

Referenced by STK::ImputeAlgo::run(), and STK::SimulAlgo::run().

paramUpdateStep()

virtual void STK::IMixtureLearner::paramUpdateStep ( )

pure virtual

Compute the model parameters given the current mixture parameters and imputation/simulation of the missing values.

Implemented in STK::MixtureLearner.

Referenced by STK::ImputeAlgo::run(), and STK::SimulAlgo::run().

setClassLabels() [1/2]

template<class ColVector >

void STK::IMixtureLearner::setClassLabels

(

ColVector const &

zi

)

set the mixture parameters using the given class labels.

Posterior probabilities, numbers in each class and proportions are computed using the class labels.

Parameters

zi	class labels

Definition at line 113 of file STK_IMixtureLearner.h.

{
  zi_  = zi;
  tik_ = 0.;
  for (int i=tik_.beginRows(); i < tik_.endRows(); i++)
  { tik_(i, zi_[i]) = 1.;}
  // count the number of individuals in each class
  tk_ = Stat::sumByCol(tik_);
  pk_ = tk_/nbSample();
}

References STK::IStatModelBase::nbSample(), STK::IMixtureStatModel::pk_, STK::Stat::sumByCol(), STK::IMixtureStatModel::tik_, STK::IMixtureStatModel::tk_, STK::IMixtureStatModel::zi(), and STK::IMixtureStatModel::zi_.

setClassLabels() [2/2]

template<class ColVector , class RowVector >

void STK::IMixtureLearner::setClassLabels	(	ColVector const &	zi,
		RowVector const &	pk
	)

set the mixture parameters using the class labels and giving the proportions.

Parameters

zi	class labels
pk	proportions of each class

Definition at line 129 of file STK_IMixtureLearner.h.

{
  pk_  = pk;
  zi_  = zi;
  tik_ = 0.;
  for (int i=tik_.beginRows(); i < tik_.endRows(); i++)
  { tik_(i, zi_[i]) = 1.;}
  // count the number of individuals in each class
  tk_ = Stat::sumByCol(tik_);
}

References STK::IMixtureStatModel::pk(), STK::IMixtureStatModel::pk_, STK::Stat::sumByCol(), STK::IMixtureStatModel::tik_, STK::IMixtureStatModel::tk_, STK::IMixtureStatModel::zi(), and STK::IMixtureStatModel::zi_.

setState()

void STK::IMixtureLearner::setState ( Clust::modelState  state )

inline

set the state of the model : should be used by any strategy

Definition at line 77 of file STK_IMixtureLearner.h.

{ state_ = state;}

References state(), and state_.

Referenced by STK::MixtureLearner::randomInit().

state()

Clust::modelState STK::IMixtureLearner::state ( ) const

inline

Returns

the state of the model

Definition at line 75 of file STK_IMixtureLearner.h.

{ return state_;}

References state_.

Referenced by STK::MixtureLearner::randomInit(), and setState().

ziPred()

CVectorXi const & STK::IMixtureLearner::ziPred ( ) const

inline

Returns

the predicted class label

Definition at line 73 of file STK_IMixtureLearner.h.

{ return ziPred_;};

References ziPred_.

Member Data Documentation

state_

Clust::modelState STK::IMixtureLearner::state_

private

state of the model

Definition at line 105 of file STK_IMixtureLearner.h.

Referenced by setState(), and state().

ziPred_

CVectorXi STK::IMixtureLearner::ziPred_

private

The predicted class label.

Definition at line 103 of file STK_IMixtureLearner.h.

Referenced by mapStep(), and ziPred().

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

• STK_IMixtureLearner.h
• STK_IMixtureLearner.cpp