MMSD_Model Class Reference

This class is a general MMSD Model. More...

#include <MMSD_Model.h>

Inheritance diagram for MMSD_Model:

Collaboration diagram for MMSD_Model:

Public Member Functions
void	setIsEmptyClusterDeleted (const tBoolean &v)
	if true the empty clusters are deleted during esperaceEvaluation() defalut value: false. see MMSD_Model:::esperanceEvaluation() More...
void	setDataSet (SP::MMSD_DataSet data)
	set the data set More...
const MMSD_DataSet *	getDataSet () const
	get the data set More...
MMSD_DataSet *	getDataSet ()
	get the data set More...
void	setEMMaximumIterationsNumber (const int &n)
	set the maximum iterations number for EM algorithm More...
int	getEMMaximumIterationsNumber () const
	get the maximum iterations number for EM algorithm More...
int	getEMIterationsNumber () const
	get the number of iterations done More...
tVectorIndex	getClustersNumber () const
	get clusters number More...
const MMSD_Cluster *	getCluster (const tVectorIndex &index) const
	get the cluster at index More...
const MMSD_DoubleFullMatrix &	getSampleClusterProbabilities () const
	get the probability of samples to be in cluster matrix of size NxK More...
const MMSD_IntegerVector &	getSampleClusterIndices () const
	get the cluster of samples vector of size N More...
void	setMinEigenValue (const double &v)
	set min eigen value for eigen value decomposition of property covariance matrix law More...
double	getMinEigenValue () const
	get min eigen value for eigen value decomposition of property covariance matrix law More...
virtual SP::MMSD_Cluster	NewClusterInstance () const =0
	create a cluster More...
virtual SP::MMSD_Law	NewLawInstance () const =0
	create a cluster More...
void	setWeightInitializationType (const tString &type, const double &scale, const double &rate)
	set the weight initialization type & parameters More...
tBoolean	parametersOptimizationByEMMethod (const int &nClusters, const MMSD_IntegerVector &sampleClusters, const tString &backupPath, const tString &backupPrefix, const int &nDigits, const int &backupSteps)
	optimise the parameters of each laws by EM algorithm More...
tBoolean	parametersOptimizationByEMMethod (const int &nClusters, const MMSD_IntegerVector &sampleClusters)
	optimise the parameters of each laws by EM algorithm More...
tBoolean	parametersOptimizationByEMMethod ()
	optimise the parameters of each laws by EM algorithm More...
tBoolean	parametersOptimizationByEMMethod (const tString &backupPath, const tString &backupPrefix, const int &nDigits, const int &backupSteps)
	optimise the parameters of each laws by EM algorithm More...
tBoolean	restore (SP::CORE_ClassFactory factory, const tString &backupPath, const tString &backupPrefix, const int &nDigits, const int &step)
	restore the model from a file More...
tBoolean	restore (SP::CORE_ClassFactory factory, const tString &file)
	restore the model from a backup file More...
void	computeSampleClusterProbabilities (MMSD_DoubleFullMatrix &P, MMSD_DoubleVector &work) const
	compute clusters probability for each sample More...
void	computeSampleClusterProbabilities ()
	compute clusters probability for each sample More...
void	computeSampleClusterIndices (MMSD_DoubleVector &work, MMSD_IntegerVector &indices) const
	compute the index of cluster for each sample More...
tBoolean	esperanceEvaluation ()
	compute Esperance Evaluation step More...
tBoolean	esperanceMaximization ()
	compute Esperance Maximization step More...
tBoolean	hasConverged () const
	test of convergence More...
tBoolean	computeLogLikelihood (double &logL)
	compute logLikelihood More...
const MMSD_DoubleVector &	getLogL () const
	get logL for each iteration of the optimization process More...
virtual void	saveToUIClass (UI_Class &mclass) const
	how to save the object from a mate model class More...
virtual void	loadFromUIClass (const UI_Class &mclass)
	how to load the object from a Meta Model class More...
virtual tString	toString () const
	turn the class into string More...
void	setHasBeenLoaded (const tBoolean &v)
	set the if the object has completely been loaded More...
tBoolean	hasBeenLoaded () const
	return true if the object has completely been loaded More...
void	getSharedPointer (SP::CORE_Object &p)
	get the shared pointer of this class into p More...
void	getSharedPointer (SPC::CORE_Object &p) const
	get the shared pointer of this class into p More...
tString	getClassName () const
	return the class name of the object More...
tString	getIdentityString () const
	return the identity string of the object of the form className_at_address More...
tString	getPointerAddress () const
	return the identity string of the object More...
template<class T >
tBoolean	isInstanceOf () const
	return true if the object is an instance of T More...
virtual void	print ()
	print the class More...
virtual ostream &	print (ostream &out) const
	print the class More...
virtual void	print (const tString &message)
	print the class More...
virtual void	print (const tInteger &str)
	print More...
virtual void	print (const tRelativeInteger &str)
	print More...
virtual void	print (const tReal &str)
	print More...
virtual void	print (const int &str)
	print More...

Static Public Member Functions
static void	initSeed (const long int &v)
	init the seed with v value More...
static void	initSeed ()
	init the seed More...
static tString	getClassName (const tString &identityString)
	return the class name of the object using only the identity string More...
template<class T >
static tString	getTypeName ()
	get type name More...
static tBoolean	is64Architecture ()
	return true if the machine is a 64 bits machine More...
static tBoolean	is32Architecture ()
	return true if the machine is a 32 bits machine More...
static tString	pointer2String (const void *obj)
	return the string represantation of a pointer More...
static void	setOutput (ostream &out)
	set output More...
static ostream &	getOutput ()
	get output More...
static void	printObjectsInMemory ()
	print object in memory More...
static ostream &	print (ostream &out, const tString &message)
	print the class More...
static void	outputPrint (const tString &message)

Static Public Attributes
static tBoolean	mIsMemoryTesting =false
	indicator to store all classes created and deleted only for debuging version More...

Protected Member Functions
	MMSD_Model (void)
	create an object More...
virtual	~MMSD_Model (void)
	destroy an object. More...
virtual void	restore (const MMSD_DoubleFullMatrix &properties)
	restore the unstored values after a backup More...
virtual void	initialize (const MMSD_IntegerVector &clusters, const MMSD_DoubleFullMatrix &properties, const int &nClusters)
	initialization INPUT parameters: More...
void	setThis (SP::CORE_Object p)
	set this weak shared pointer called toDoAfterThis setting method More...
virtual void	setType (tString type)
	set the type of the object More...
virtual void	toDoAfterThisSetting ()
	method called after setThis() method this method can oly be called once. More...

Detailed Description

This class is a general MMSD Model.

A model is a class which makes clustering of samples.

It needs:

• to define the model cluster MMSD_Model::NewClusterInstance() method
• to define the model law MMSD_Model::NewLawInstance() method

a MMSD_Model class has:

• a list of clusters MMSD_Cluster of size K
• a sample probability to be in cluster k in [0,K[ (matrix of size NxK) see (MMSD_Model::getSampleClusterProbabilities())

The main method of the class is MMSD_Model::parametersOptimizationByEMMethod().

The number of iterations of this EM Method is set by the method MMSD_Model::setEMMaximumIterationsNumber(). This method calls successivly MMSD_Model::esperanceEvaluation() && MMSD_Model::esperanceMaximization(). The value of the optimized function is computed in MMSD_Model::computeLogLikelihood() method.

The computing of sample probabilities to be in cluster is done by MMSD_Model::computeSampleClusterProbabilities() method which use the method MMSD_Cluster::computeMultivariateDensity()

The sample properties described by MMSD_DataSet are set by MMSD_Model::setDataSet()

Author

Stephane Despreaux

Version

1.0

Constructor & Destructor Documentation

MMSD_Model::MMSD_Model ( void  )

protected

create an object

References CORE_Object::setType().

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MMSD_Model::~MMSD_Model ( void  )

protectedvirtual

destroy an object.

Member Function Documentation

tBoolean MMSD_Model::computeLogLikelihood ( double &  logL )

inline

compute logLikelihood

Parameters

logL	OUTPUT value of log-likelihood function

Returns

true if the computing succeeds

Referenced by parametersOptimizationByEMMethod().

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void MMSD_Model::computeSampleClusterIndices	(	MMSD_DoubleVector &	work,
		MMSD_IntegerVector &	indices
	)		const

compute the index of cluster for each sample

Parameters

work	is an array of working, used for memory optimization
indices	: output integer vector of storing the index of cluster for each sample.

indice[i]=index of cluster for with the probability Pk[i] is max

References LAP_DoubleFullGeneralMatrix::getColumnByReference(), LAP_DoubleFullGeneralMatrix::getRowsNumber(), and LAP_Vector< T >::setSize().

Referenced by parametersOptimizationByEMMethod().

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void MMSD_Model::computeSampleClusterProbabilities	(	MMSD_DoubleFullMatrix &	P,
		MMSD_DoubleVector &	work
	)		const

compute clusters probability for each sample

Parameters

P	OUTPUT parameter the cluster probability of each sample
work	array of size nThreads*nSamples

work array is used for temporary computing.

N is the number of samples, K is the number of clusters For each cluster k in [0,K[

• compute the multivariateDensity of the cluster Pk of size N (see MMSD_Cluster::computeMultivariateDensity())
• to each value of Pk , add log of cluster rate (see MMSD_Cluster::getRate())
• compute the max value of all Pk : Pmax

For each cluster k in [0,K[

• Pk:=exp(Pk-Pmax)
• compute the sum of the Pk: S[i]+=Pk[i] i in [0,N[;

For each cluster k in [0,K[

• Pk[i]/=S[i] i in [0,N[;

References MMSD_Cluster::computeMultivariateDensity(), LAP_DoubleFullGeneralMatrix::getColumnByReference(), MMSD_Cluster::getRate(), LAP_DoubleFullGeneralMatrix::getRowsNumber(), LAP_Vector< T >::getSize(), LAP_DoubleVector::maxValue(), LAP_Vector< T >::setSize(), and tLVectorIndex.

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void MMSD_Model::computeSampleClusterProbabilities ( )

inline

compute clusters probability for each sample

Referenced by esperanceMaximization(), initialize(), and restore().

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tBoolean MMSD_Model::esperanceEvaluation

(

)

compute Esperance Evaluation step

for each cluster k in [0,K[

• get the probability of all samples to be in cluster k called Pk
• update the rate of the cluster k (see MMSD_Cluster::updateRate() )
• if the cluster is empy :
  • if empty clusters are not deleted, raises an error (see MMSD_Model::setIsEmptyClusterDeleted() ) mIsEmp
  • if not, erase it.

update the weights of cluster k (see MMSD_Cluster::updateWeights())

References LAP_DoubleFullGeneralMatrix::getColumnByReference(), LAP_Vector< T >::getValues(), MMSD_Cluster::isEmpty(), LAP_DoubleFullGeneralMatrix::removeColumn(), LAP_Vector< T >::setSize(), CORE_Integer::toString(), MMSD_Cluster::updateRate(), and MMSD_Cluster::updateWeights().

Referenced by parametersOptimizationByEMMethod().

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tBoolean MMSD_Model::esperanceMaximization

(

)

compute Esperance Maximization step

• for each cluster in [0,K[
  • get the probability of all samples to be in cluster k called Pk
  • update the law of the cluster (see MMSD_Cluster::updateLaw()) with properties data set & Pk
  • update the freedom degrees of the cluster (see MMSD_Cluster::updateFreedomDegrees()) with Pk
  • sort the cluster parameters see MMSD_Cluster::sort()
• recompute the probability of all samples to be in cluster k See MMSD_Model::computeSampleClusterProbabilities()

References computeSampleClusterProbabilities(), LAP_DoubleFullGeneralMatrix::getColumnByReference(), MMSD_Cluster::sort(), MMSD_Cluster::updateFreedomDegrees(), and MMSD_Cluster::updateLaw().

Referenced by parametersOptimizationByEMMethod().

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static tString CORE_Object::getClassName ( const tString &  identityString )

inlinestaticinherited

return the class name of the object using only the identity string

Referenced by UI_Class::createVector2D(), LAP_DoublePackedUpperMatrix::matrixProduct(), LAP_DoubleBandedUpperMatrix::matrixProduct(), LAP_DoubleFullUpperMatrix::matrixProduct(), and CORE_Object::printObjectsInMemory().

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tString CORE_Object::getClassName ( ) const

inherited

return the class name of the object

Returns

the class name of the object

References tString.

Referenced by CORE_Object::getIdentityString().

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const MMSD_Cluster* MMSD_Model::getCluster ( const tVectorIndex &  index ) const

inline

get the cluster at index

References null.

Referenced by parametersOptimizationByEMMethod().

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tVectorIndex MMSD_Model::getClustersNumber ( ) const

inline

get clusters number

const MMSD_DataSet* MMSD_Model::getDataSet ( ) const

inline

get the data set

Referenced by parametersOptimizationByEMMethod().

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MMSD_DataSet* MMSD_Model::getDataSet ( )

inline

get the data set

int MMSD_Model::getEMIterationsNumber ( ) const

inline

get the number of iterations done

see MMSD_Model::parametersOptimizationByEMMethod().

int MMSD_Model::getEMMaximumIterationsNumber ( ) const

inline

get the maximum iterations number for EM algorithm

see MMSD_Model::parametersOptimizationByEMMethod().

tString CORE_Object::getIdentityString ( ) const

inlineinherited

return the identity string of the object of the form className_at_address

Returns

the identity string of the object

References CORE_Object::getClassName(), CORE_Object::pointer2String(), and tString.

Referenced by UI_Class::createPrimitiveMap(), UI_Class::saveAssociation(), UI_Class::saveClass(), UI_ClassFactory::saveIntoClass(), MATH_StiefelFunction::toString(), LAP_2DView::toString(), CORE_Object::toString(), MATH_StiefelOptimizer::toString(), and MM_Class::toString().

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const MMSD_DoubleVector& MMSD_Model::getLogL ( ) const

inline

get logL for each iteration of the optimization process

double MMSD_Model::getMinEigenValue ( ) const

inline

get min eigen value for eigen value decomposition of property covariance matrix law

Referenced by MMSD_GaussianModel::NewLawInstance().

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static ostream& CORE_Object::getOutput ( )

inlinestaticinherited

get output

tString CORE_Object::getPointerAddress ( ) const

inlineinherited

return the identity string of the object

Returns

the identity string of the object

References CORE_Object::pointer2String().

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const MMSD_IntegerVector& MMSD_Model::getSampleClusterIndices ( ) const

inline

get the cluster of samples vector of size N

const MMSD_DoubleFullMatrix& MMSD_Model::getSampleClusterProbabilities ( ) const

inline

get the probability of samples to be in cluster matrix of size NxK

void CORE_Object::getSharedPointer ( SP::CORE_Object &  p )

inlineinherited

get the shared pointer of this class into p

Referenced by CORE_Map< Key, Value >::getSharedPointer(), CORE_Array< tFlag >::getSharedPointer(), LAP_ConstVector< lapack_real >::getSharedPointer(), STAT_Combinatorial< T >::getSharedPointer(), LAP_Vector< lapack_int >::getSharedPointer(), CORE_Vector< T >::getSharedPointer(), CORE_Matrix< T >::getSharedPointer(), and CORE_Object::printObjectsInMemory().

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void CORE_Object::getSharedPointer ( SPC::CORE_Object &  p ) const

inlineinherited

get the shared pointer of this class into p

template<class T >

static tString CORE_Object::getTypeName ( )

inlinestaticinherited

get type name

References tString.

tBoolean UI_Object::hasBeenLoaded ( ) const

inlineinherited

return true if the object has completely been loaded

Referenced by UI_Class::loadAssociation().

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tBoolean MMSD_Model::hasConverged

(

)

const

test of convergence

Referenced by parametersOptimizationByEMMethod().

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void MMSD_Model::initialize ( const MMSD_IntegerVector &  clusters, const MMSD_DoubleFullMatrix &  properties, const int &  nClusters  )

protectedvirtual

initialization INPUT parameters:

Parameters

clusters	: index of cluster for each sample
properties	properties matrix
nClusters	number of clusters

• Build the clusters list:
  • creates a cluster MMSD_Model::NewClusterInstance()
  • creates a law of the cluster MMSD_Model::NewClusterLaw()
  • attachs the law to the cluster MMSD_Cluster::setLaw()
  • initializes the cluster weights MMSD_Cluster::setWeightInitializationType set by MMSD_Model::setWeightInitializationType()
  • initializes the cluster MMSD_Cluster::initialize()
  • if the cluster is not empty, adds it in the list
  • verifies the consistency of the cluster:
    • the added cluster must not be empty (raises an error if not)
    • the parameters of the cluster law must not be out of bounds (write a warning if not)
• copy the initial cluster indices in the mSampleClusterIndices int vector.
• compute the cluster probablity for each sample. (see MMSD_Model::computeSampleClusterProbabilities())

References computeSampleClusterProbabilities(), NewClusterInstance(), NewLawInstance(), LAP_DoubleFullGeneralMatrix::setSize(), tBoolean, and tString.

Referenced by parametersOptimizationByEMMethod().

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void MMSD_Object::initSeed ( const long int &  v )

staticinherited

init the seed with v value

References STAT_Distribution::initSeed().

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void MMSD_Object::initSeed ( )

staticinherited

init the seed

References CORE_Time::getTime().

Referenced by MMSD_Object::MMSD_Object(), testGaussianLaw11N(), testGaussianLaw1PN(), and testGaussianLaw2PN().

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static tBoolean CORE_Object::is32Architecture ( )

inlinestaticinherited

return true if the machine is a 32 bits machine

References CORE_Object::is64Architecture().

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tBoolean CORE_Object::is64Architecture ( )

staticinherited

return true if the machine is a 64 bits machine

Referenced by CORE_Object::is32Architecture().

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template<class T >

tBoolean CORE_Object::isInstanceOf ( ) const

inlineinherited

return true if the object is an instance of T

References null.

void MMSD_Model::loadFromUIClass ( const UI_Class &  mclass )

virtual

how to load the object from a Meta Model class

Reimplemented from UI_Object.

References UI_Class::getInterfaceType(), UI_Class::loadAssociation(), UI_Class::loadField(), LAP_DoubleFullGeneralMatrix::setValues(), LAP_Vector< T >::setValues(), and tBoolean.

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virtual SP::MMSD_Cluster MMSD_Model::NewClusterInstance ( ) const

pure virtual

create a cluster

Implemented in MMSD_GaussianModel, and MMSD_ConstraintGaussianModel.

Referenced by initialize().

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virtual SP::MMSD_Law MMSD_Model::NewLawInstance ( ) const

pure virtual

create a cluster

Implemented in MMSD_GaussianModel.

Referenced by initialize().

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void CORE_Object::outputPrint ( const tString &  message )

staticinherited

print on output

References null, and CORE_Object::print().

Referenced by CORE_Exception::CORE_Exception().

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tBoolean MMSD_Model::parametersOptimizationByEMMethod	(	const int &	nClusters,
		const MMSD_IntegerVector &	sampleClusters,
		const tString &	backupPath,
		const tString &	backupPrefix,
		const int &	nDigits,
		const int &	backupSteps
	)

optimise the parameters of each laws by EM algorithm

Parameters

nClusters	the number of clusters
sampleClusters	the cluster index of each sample
backupPath	: the path to store the results
backupPrefix	: the prefix of all saved files
nDigits	the number of print digits for real values
backupSteps	the step size for saving file

Returns

true if the method succeeds.

A data set MMSD_DataSet must be attached to the model. (MMSD_Model::setDataSet). If no data set is provided an exception is raised.

The algorithm is as follow:

• initialization step: if the number of clusters is null, it means that the initialization of the process has to be restored from a preceding computations. (see MMSD_Model::restore() ). Otherwise, the initialize method is invoked (MMSD_Model::initialize())
• while EM iterations number less than max EM iterations number (see MMSD_Model::setEMMaximumIterationsNumber() ) do
  • compute esperance (see MMSD_Model::esperanceEvaluation() )
  • maximize esperance (see MMSD_Model::esperancemaximization() )
  • verify the consistency of the law parameters (see MMSD_Law::isOutOfBounds() )
  • compute the log-likelihood & store it (see MMSD_Model::getLogL() )
  • verify the log-likelihood is not a real number, in than case raises a nan exception
  • save the data for future restore if needed
• compute the sample cluster index (see MMSD_Model::computeSampleClusterIndices())

References computeLogLikelihood(), computeSampleClusterIndices(), esperanceEvaluation(), esperanceMaximization(), getCluster(), getDataSet(), MMSD_DataSet::getProperties(), hasConverged(), initialize(), MM_ClassFactory::New(), null, CORE_File::PATH_SEPARATOR, restore(), LAP_Vector< T >::setSize(), tBoolean, CORE_Integer::toString(), CORE_String::toString(), and tString.

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tBoolean MMSD_Model::parametersOptimizationByEMMethod ( const int &  nClusters, const MMSD_IntegerVector &  sampleClusters  )

inline

optimise the parameters of each laws by EM algorithm

Parameters

nClusters	the number of clusters
sampleClusters	the cluster index of each sample

Returns

true if the method succeeds.

References parametersOptimizationByEMMethod().

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tBoolean MMSD_Model::parametersOptimizationByEMMethod ( )

inline

optimise the parameters of each laws by EM algorithm

Returns

true if the method succeeds.

Referenced by parametersOptimizationByEMMethod().

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tBoolean MMSD_Model::parametersOptimizationByEMMethod ( const tString &  backupPath, const tString &  backupPrefix, const int &  nDigits, const int &  backupSteps  )

inline

optimise the parameters of each laws by EM algorithm

Parameters

backupPath	: the path to store the results
backupPrefix	: the prefix of all saved files
nDigits	the number of print digits for real values
backupSteps	the step size for saving file

Returns

true if the method succeeds.

References parametersOptimizationByEMMethod().

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tString CORE_Object::pointer2String ( const void *  obj )

staticinherited

return the string represantation of a pointer

References tString.

Referenced by CORE_Object::CORE_Object(), CORE_Object::getIdentityString(), CORE_Object::getPointerAddress(), and CORE_Object::~CORE_Object().

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virtual void CORE_Object::print ( )

inlinevirtualinherited

print the class

References CORE_Object::toString().

Referenced by CORE_Object::outputPrint(), CORE_Out::print(), CORE_Object::print(), CORE_Out::printInt(), CORE_Out::println(), CORE_Out::printReal(), CORE_Out::printString(), and CORE_Out::setAction().

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virtual ostream& CORE_Object::print ( ostream &  out ) const

inlinevirtualinherited

print the class

References CORE_Object::print(), and CORE_Object::toString().

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void CORE_Object::print ( const tString &  message )

virtualinherited

print the class

Reimplemented in CORE_Out.

References null, and CORE_Object::print().

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void CORE_Object::print ( const tInteger &  str )

virtualinherited

print

References null.

void CORE_Object::print ( const tRelativeInteger &  str )

virtualinherited

print

References null.

void CORE_Object::print ( const tReal &  str )

virtualinherited

print

References null.

void CORE_Object::print ( const int &  str )

virtualinherited

print

References null.

static ostream& CORE_Object::print ( ostream &  out, const tString &  message  )

inlinestaticinherited

print the class

void CORE_Object::printObjectsInMemory ( )

staticinherited

print object in memory

References CORE_Object::getClassName(), CORE_Object::getSharedPointer(), and CORE_Object::mIsMemoryTesting.

Referenced by main().

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tBoolean MMSD_Model::restore ( SP::CORE_ClassFactory  factory, const tString &  backupPath, const tString &  backupPrefix, const int &  nDigits, const int &  step  )

inline

restore the model from a file

Parameters

backupPath	: the backup path
backupPrefix	: the backup prefix
nDigits	the number of print digits for EM iteration
step	the backup step

Returns

true if the restoring succeeds

References CORE_File::PATH_SEPARATOR, CORE_Integer::toString(), and tString.

Referenced by parametersOptimizationByEMMethod().

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tBoolean MMSD_Model::restore	(	SP::CORE_ClassFactory	factory,
		const tString &	file
	)

restore the model from a backup file

Parameters

factory	: class factory to generate instances of class
file	backup file

Returns

true if the restoring succeeds

References MM_ClassFactory::New().

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void MMSD_Model::restore ( const MMSD_DoubleFullMatrix &  properties )

protectedvirtual

restore the unstored values after a backup

Parameters

properties

the data set properties

• restore all the cluster computing ( see MMSD_Cluster::restore())
• recompute the clusters probablity for each sample. (see MMSD_Model::computeSampleClusterProbabilities())
• update the weight of each cluster thanks to the clusters probability for each sample (see MMSD_Cluster::updateWeights())

References computeSampleClusterProbabilities(), LAP_DoubleFullGeneralMatrix::getColumnByReference(), LAP_DoubleFullGeneralMatrix::getRowsNumber(), and LAP_DoubleFullGeneralMatrix::setSize().

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void MMSD_Model::saveToUIClass ( UI_Class &  mclass ) const

virtual

how to save the object from a mate model class

Reimplemented from UI_Object.

References UI_Class::getInterfaceType(), LAP_Vector< T >::getSize(), LAP_DoubleMatrix::getStorage(), LAP_DoubleMatrixStorage::getValues(), LAP_Vector< T >::getValues(), LAP_DoubleMatrix::getValuesNumber(), UI_Class::saveAssociation(), and UI_Class::saveField().

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void MMSD_Model::setDataSet ( SP::MMSD_DataSet  data )

inline

set the data set

void MMSD_Model::setEMMaximumIterationsNumber ( const int &  n )

inline

set the maximum iterations number for EM algorithm

see MMSD_Model::

void UI_Object::setHasBeenLoaded ( const tBoolean &  v )

inlineinherited

set the if the object has completely been loaded

Referenced by UI_Class::loadAssociation().

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void MMSD_Model::setIsEmptyClusterDeleted ( const tBoolean &  v )

inline

if true the empty clusters are deleted during esperaceEvaluation() defalut value: false. see MMSD_Model:::esperanceEvaluation()

void MMSD_Model::setMinEigenValue ( const double &  v )

inline

set min eigen value for eigen value decomposition of property covariance matrix law

static void CORE_Object::setOutput ( ostream &  out )

inlinestaticinherited

set output

void CORE_Object::setThis ( SP::CORE_Object  p )

inlineprotectedinherited

set this weak shared pointer called toDoAfterThis setting method

References CORE_Object::toDoAfterThisSetting().

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virtual void CORE_Object::setType ( tString  type )

inlineprotectedvirtualinherited

set the type of the object

Referenced by LAP_IntegerVector::LAP_IntegerVector(), MATH_BetaFunction::MATH_BetaFunction(), MATH_C1Function::MATH_C1Function(), MATH_DigammaFunction::MATH_DigammaFunction(), MATH_EigenFunction::MATH_EigenFunction(), MATH_Equation::MATH_Equation(), MATH_Function::MATH_Function(), MATH_GammaFunction::MATH_GammaFunction(), MATH_KeplerFunction::MATH_KeplerFunction(), MATH_LogGammaFunction::MATH_LogGammaFunction(), MATH_NewtonEquation::MATH_NewtonEquation(), MATH_StiefelFunction::MATH_StiefelFunction(), MATH_StiefelOptimizer::MATH_StiefelOptimizer(), MATH_TranscendentEquation::MATH_TranscendentEquation(), MATH_TrigammaFunction::MATH_TrigammaFunction(), MATH_ZeroEquation::MATH_ZeroEquation(), MATH_ZKeplerFunction::MATH_ZKeplerFunction(), MM_Attribute::MM_Attribute(), MM_Class::MM_Class(), MM_ClassFactory::MM_ClassFactory(), MM_Data::MM_Data(), MM_Structure::MM_Structure(), MMSD_ClassFactory::MMSD_ClassFactory(), MMSD_Cluster::MMSD_Cluster(), MMSD_ConstDoubleVector::MMSD_ConstDoubleVector(), MMSD_ConstraintGaussianCluster::MMSD_ConstraintGaussianCluster(), MMSD_ConstraintGaussianModel::MMSD_ConstraintGaussianModel(), MMSD_DataSet::MMSD_DataSet(), MMSD_DataSetClassFactory::MMSD_DataSetClassFactory(), MMSD_DoubleFullMatrix::MMSD_DoubleFullMatrix(), MMSD_DoubleSymmetricMatrix::MMSD_DoubleSymmetricMatrix(), MMSD_DoubleVector::MMSD_DoubleVector(), MMSD_FluryGautschiGaussianLaw::MMSD_FluryGautschiGaussianLaw(), MMSD_GaussianCluster::MMSD_GaussianCluster(), MMSD_GaussianFDFunction::MMSD_GaussianFDFunction(), MMSD_GaussianLaw::MMSD_GaussianLaw(), MMSD_GaussianModel::MMSD_GaussianModel(), MMSD_Law::MMSD_Law(), MMSD_Model(), MMSD_ModelClassFactory::MMSD_ModelClassFactory(), MMSD_Object::MMSD_Object(), MMSD_StiefelFunction::MMSD_StiefelFunction(), MMSD_StiefelGaussianLaw::MMSD_StiefelGaussianLaw(), STAT_BernoulliDistribution::STAT_BernoulliDistribution(), STAT_BinomialDistribution::STAT_BinomialDistribution(), STAT_Combinatorial< T >::STAT_Combinatorial(), STAT_DiracDistribution::STAT_DiracDistribution(), STAT_Distribution::STAT_Distribution(), STAT_ExponentialDistribution::STAT_ExponentialDistribution(), STAT_GammaDistribution::STAT_GammaDistribution(), STAT_GeometricDistribution::STAT_GeometricDistribution(), STAT_InverseNormalDistribution::STAT_InverseNormalDistribution(), STAT_NormalDistribution::STAT_NormalDistribution(), STAT_Object::STAT_Object(), STAT_PoissonDistribution::STAT_PoissonDistribution(), STAT_UniformDistribution::STAT_UniformDistribution(), and STAT_UniformLaplaceTransform::STAT_UniformLaplaceTransform().

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void MMSD_Model::setWeightInitializationType ( const tString &  type, const double &  scale, const double &  rate  )

inline

set the weight initialization type & parameters

Parameters

type	of weight initialization ["gamma" or "constant"]
shape	: parameter for statistic function weight initialization
rate	: parameter for statistic function weight initialization

see MMSD_Cluster::setWeightInitializationType()

virtual void CORE_Object::toDoAfterThisSetting ( )

inlineprotectedvirtualinherited

method called after setThis() method this method can oly be called once.

Reimplemented in STAT_UniformLaplaceTransform.

Referenced by CORE_Object::setThis(), and STAT_UniformLaplaceTransform::toDoAfterThisSetting().

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virtual tString MMSD_Model::toString ( ) const

inlinevirtual

turn the class into string

Reimplemented from CORE_Object.

References CORE_Object::toString(), and tString.

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Member Data Documentation

tBoolean CORE_Object::mIsMemoryTesting =false

staticinherited

indicator to store all classes created and deleted only for debuging version

Referenced by CORE_Object::CORE_Object(), main(), CORE_Object::printObjectsInMemory(), and CORE_Object::~CORE_Object().

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

• MMSD_Model.h
• MMSD_Model.cpp