STK::ImputeAlgo Class Reference

Implementation of the ImputeAlgo learning algorithm. More...

#include <STK_MixtureAlgoLearn.h>

Inheritance diagram for STK::ImputeAlgo:

Public Member Functions
	ImputeAlgo ()
	default constructor
	ImputeAlgo (ImputeAlgo const &algo)
	Copy constructor.
virtual	~ImputeAlgo ()
	destructor
virtual ImputeAlgo *	clone () const
	clone pattern
virtual bool	run ()
	run the algorithm on the model calling the eStep and mStep of the model until the maximal number of iteration is reached or the variation of the lnLikelihood is less than epsilon.
Public Member Functions inherited from STK::IMixtureAlgoLearn
virtual	~IMixtureAlgoLearn ()
	destructor
int	nbIterMax () const
int	epsilon () const
void	setModel (IMixtureLearner *p_model)
	set model
void	setNbIterMax (int nbIterMax)
	set maximal number of iterations
void	setEpsilon (Real epsilon)
	set tolerance value
Public Member Functions inherited from STK::IRunnerBase
String const &	error () const
	get the last error message.

Additional Inherited Members
Protected Member Functions inherited from STK::IMixtureAlgoLearn
	IMixtureAlgoLearn ()
	default constructor
	IMixtureAlgoLearn (IMixtureAlgoLearn const &algo)
	Copy constructor.
Protected Member Functions inherited from STK::IRunnerBase
	IRunnerBase ()
	default constructor
	IRunnerBase (IRunnerBase const &runner)
	copy constructor
virtual	~IRunnerBase ()
	destructor
virtual void	update ()
	update the runner.
Protected Attributes inherited from STK::IMixtureAlgoLearn
IMixtureLearner *	p_model_
	pointer on the mixture model
int	nbIterMax_
	maximal number of iterations of the algorithm
Real	epsilon_
	tolerance of the algorithm.
Protected Attributes inherited from STK::IRunnerBase
String	msg_error_
	String with the last error message.
bool	hasRun_
	true if run has been used, false otherwise

Detailed Description

Implementation of the ImputeAlgo learning algorithm.

The Impute algorithm call alternatively the steps:

• imputeStep()
• paramUpdateStep() until the maximum number of iterations is reached or the variation of the log-likelihood is less than the tolerance.

Definition at line 51 of file STK_MixtureAlgoLearn.h.

Constructor & Destructor Documentation

ImputeAlgo() [1/2]

STK::ImputeAlgo::ImputeAlgo ( )

inline

default constructor

Definition at line 55 of file STK_MixtureAlgoLearn.h.

: IMixtureAlgoLearn() {}

Referenced by clone().

ImputeAlgo() [2/2]

STK::ImputeAlgo::ImputeAlgo ( ImputeAlgo const &  algo )

inline

Copy constructor.

Parameters

algo	the algorithm to copy

Definition at line 58 of file STK_MixtureAlgoLearn.h.

: IMixtureAlgoLearn(algo) {}

~ImputeAlgo()

virtual STK::ImputeAlgo::~ImputeAlgo ( )

inlinevirtual

destructor

Definition at line 60 of file STK_MixtureAlgoLearn.h.

{}

Member Function Documentation

clone()

virtual ImputeAlgo * STK::ImputeAlgo::clone ( ) const

inlinevirtual

clone pattern

Definition at line 62 of file STK_MixtureAlgoLearn.h.

{ return new ImputeAlgo(*this);}

References ImputeAlgo().

run()

bool STK::ImputeAlgo::run ( )

virtual

run the algorithm on the model calling the eStep and mStep of the model until the maximal number of iteration is reached or the variation of the lnLikelihood is less than epsilon.

Returns

true if no error occur, false otherwise

Implements STK::IRunnerBase.

Definition at line 44 of file STK_MixtureAlgoLearn.cpp.

{
#ifdef STK_MIXTURE_VERY_VERBOSE
  stk_cout << _T("--------------------------------\n");
  stk_cout << _T("Entering ImputeAlgo::run() with:\n")
           << _T("nbIterMax_ = ") << nbIterMax_ << _T("\n")
           << _T("epsilon_ = ") << epsilon_ << _T("\n");
#endif
 
  try
  {
    Real currentLnLikelihood = p_model_->lnLikelihood();
    int iter;
    for (iter = 0; iter < nbIterMax_; iter++)
    {
      p_model_->imputationStep();
      p_model_->paramUpdateStep();
      Real lnLikelihood = p_model_->lnLikelihood();
      // no abs as the likelihood should increase
      if ( (lnLikelihood - currentLnLikelihood) < epsilon_)
      {
#ifdef STK_MIXTURE_VERY_VERBOSE
        stk_cout << _T("Terminating ImputeAlgo::run() with:\n")
                 << _T("iter = ") << iter << _T("\n")
                 << _T("delta = ") << lnLikelihood - currentLnLikelihood << _T("\n");
#endif
        break;
      }
      currentLnLikelihood = lnLikelihood;
    }
    // finalize and compute posterior probabilities and predicted values
    p_model_->finalizeStep();
    p_model_->mapStep();
#ifdef STK_MIXTURE_VERBOSE
    stk_cout << _T("In ImputeAlgo::run() iteration ") << iter << _T("terminated.\n")
             << _T("p_model_->lnLikelihood = ") << p_model_->lnLikelihood() << _T("\n");
#endif
  }
  catch (Clust::exceptions const& error)
  {
    msg_error_ = Clust::exceptionToString(error);
#ifdef STK_MIXTURE_VERBOSE
  stk_cout << _T("An error occur in ImputeAlgo::run():\n") << msg_error_ << _T("\n");
#endif
    return false;
  }
  return true;
}

References _T, STK::IMixtureAlgoLearn::epsilon_, STK::IRunnerBase::error(), STK::Clust::exceptionToString(), STK::IMixtureStatModel::finalizeStep(), STK::IMixtureStatModel::imputationStep(), STK::IStatModelBase::lnLikelihood(), STK::IMixtureLearner::mapStep(), STK::IRunnerBase::msg_error_, STK::IMixtureAlgoLearn::nbIterMax_, STK::IMixtureAlgoLearn::p_model_, STK::IMixtureLearner::paramUpdateStep(), and stk_cout.

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

• STK_MixtureAlgoLearn.h
• STK_MixtureAlgoLearn.cpp