C++ main module for mmsd Package  1.0
Public Member Functions | Static Public Member Functions | Static Public Attributes | Protected Member Functions | List of all members
MMSD_ConstraintGaussianModel Class Reference

This class describes a constraint gaussian model inherits from MMSD_GaussainModel. More...

#include <MMSD_ConstraintGaussianModel.h>

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Public Member Functions

virtual SP::MMSD_Cluster NewClusterInstance () const
 create a cluster instance More...
 
void setGaussianLaw (const tFlag &f)
 set the method More...
 
int getGaussianLaw () const
 get the method More...
 
void setGaussianLawMaximumIterationsNumber (const int &n)
 set the maximum iterations number More...
 
int getGaussianLawMaximumIterationsNumber () const
 get the number of iterations done More...
 
virtual SP::MMSD_Law NewLawInstance () const
 create a law instance More...
 
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_DataSetgetDataSet () const
 get the data set More...
 
MMSD_DataSetgetDataSet ()
 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_ClustergetCluster (const tVectorIndex &index) const
 get the cluster at index More...
 
const MMSD_DoubleFullMatrixgetSampleClusterProbabilities () const
 get the probability of samples to be in cluster matrix of size NxK More...
 
const MMSD_IntegerVectorgetSampleClusterIndices () 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...
 
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_DoubleVectorgetLogL () 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
SP::MMSD_ConstraintGaussianModel 
New ()
 create a Gaussain Model More...
 
static
SP::MMSD_ConstraintGaussianModel 
New (const tFlag &gaussianLaw, const int &nIterations)
 create a Constraint Gaussain Model with gaussian law (see MMSD_GaussianModel::FLURY_GAUTSCHI_LAW, MMSD_GaussianModel::STIEFEL_LAW More...
 
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 const tFlag FLURY_GAUTSCHI_LAW =0
 
static const tFlag STIEFEL_LAW =1
 
static tBoolean mIsMemoryTesting =false
 indicator to store all classes created and deleted only for debuging version More...
 

Protected Member Functions

 MMSD_ConstraintGaussianModel (void)
 create an object More...
 
virtual ~MMSD_ConstraintGaussianModel (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 describes a constraint gaussian model inherits from MMSD_GaussainModel.

it uses a MMSD_ConstraintGaussianCluster cluster

Author
Stephane Despreaux
Version
1.0

Constructor & Destructor Documentation

MMSD_ConstraintGaussianModel::MMSD_ConstraintGaussianModel ( void  )
protected

create an object

References CORE_Object::setType().

Referenced by New().

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MMSD_ConstraintGaussianModel::~MMSD_ConstraintGaussianModel ( void  )
protectedvirtual

destroy an object.

Member Function Documentation

tBoolean MMSD_Model::computeLogLikelihood ( double &  logL)
inlineinherited

compute logLikelihood

Parameters
logLOUTPUT value of log-likelihood function
Returns
true if the computing succeeds

Referenced by MMSD_Model::parametersOptimizationByEMMethod().

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

compute the index of cluster for each sample

Parameters
workis 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 MMSD_Model::parametersOptimizationByEMMethod().

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

compute clusters probability for each sample

Parameters
POUTPUT parameter the cluster probability of each sample
workarray 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[

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 ( )
inlineinherited

compute clusters probability for each sample

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

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tBoolean MMSD_Model::esperanceEvaluation ( )
inherited

compute Esperance Evaluation step

for each cluster k in [0,K[

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 MMSD_Model::parametersOptimizationByEMMethod().

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tBoolean MMSD_Model::esperanceMaximization ( )
inherited

compute Esperance Maximization step

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

Referenced by MMSD_Model::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
inlineinherited

get the cluster at index

References null.

Referenced by MMSD_Model::parametersOptimizationByEMMethod().

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tVectorIndex MMSD_Model::getClustersNumber ( ) const
inlineinherited

get clusters number

const MMSD_DataSet* MMSD_Model::getDataSet ( ) const
inlineinherited

get the data set

Referenced by MMSD_Model::parametersOptimizationByEMMethod().

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MMSD_DataSet* MMSD_Model::getDataSet ( )
inlineinherited

get the data set

int MMSD_Model::getEMIterationsNumber ( ) const
inlineinherited

get the number of iterations done

see MMSD_Model::parametersOptimizationByEMMethod().

int MMSD_Model::getEMMaximumIterationsNumber ( ) const
inlineinherited

get the maximum iterations number for EM algorithm

see MMSD_Model::parametersOptimizationByEMMethod().

int MMSD_GaussianModel::getGaussianLaw ( ) const
inlineinherited

get the method

Referenced by MMSD_GaussianModel::NewLawInstance().

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int MMSD_GaussianModel::getGaussianLawMaximumIterationsNumber ( ) const
inlineinherited

get the number of iterations done

Referenced by MMSD_GaussianModel::NewLawInstance().

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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
inlineinherited

get logL for each iteration of the optimization process

double MMSD_Model::getMinEigenValue ( ) const
inlineinherited

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
inlineinherited

get the cluster of samples vector of size N

const MMSD_DoubleFullMatrix& MMSD_Model::getSampleClusterProbabilities ( ) const
inlineinherited

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

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

test of convergence

Referenced by MMSD_Model::parametersOptimizationByEMMethod().

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

initialization INPUT parameters:

Parameters
clusters: index of cluster for each sample
propertiesproperties matrix
nClustersnumber of clusters

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

Referenced by MMSD_Model::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)
virtualinherited

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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static SP::MMSD_ConstraintGaussianModel MMSD_ConstraintGaussianModel::New ( )
inlinestatic

create a Gaussain Model

References MMSD_GaussianModel::FLURY_GAUTSCHI_LAW.

static SP::MMSD_ConstraintGaussianModel MMSD_ConstraintGaussianModel::New ( const tFlag gaussianLaw,
const int &  nIterations 
)
inlinestatic

create a Constraint Gaussain Model with gaussian law (see MMSD_GaussianModel::FLURY_GAUTSCHI_LAW, MMSD_GaussianModel::STIEFEL_LAW

References MMSD_ConstraintGaussianModel().

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virtual SP::MMSD_Cluster MMSD_ConstraintGaussianModel::NewClusterInstance ( ) const
inlinevirtual

create a cluster instance

Reimplemented from MMSD_GaussianModel.

References MMSD_ConstraintGaussianCluster::New().

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virtual SP::MMSD_Law MMSD_GaussianModel::NewLawInstance ( ) const
inlinevirtualinherited
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 
)
inherited

optimise the parameters of each laws by EM algorithm

Parameters
nClustersthe number of clusters
sampleClustersthe cluster index of each sample
backupPath: the path to store the results
backupPrefix: the prefix of all saved files
nDigitsthe number of print digits for real values
backupStepsthe 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:

References MMSD_Model::computeLogLikelihood(), MMSD_Model::computeSampleClusterIndices(), MMSD_Model::esperanceEvaluation(), MMSD_Model::esperanceMaximization(), MMSD_Model::getCluster(), MMSD_Model::getDataSet(), MMSD_DataSet::getProperties(), MMSD_Model::hasConverged(), MMSD_Model::initialize(), MM_ClassFactory::New(), null, CORE_File::PATH_SEPARATOR, MMSD_Model::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 
)
inlineinherited

optimise the parameters of each laws by EM algorithm

Parameters
nClustersthe number of clusters
sampleClustersthe cluster index of each sample
Returns
true if the method succeeds.

References MMSD_Model::parametersOptimizationByEMMethod().

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tBoolean MMSD_Model::parametersOptimizationByEMMethod ( )
inlineinherited

optimise the parameters of each laws by EM algorithm

Returns
true if the method succeeds.

Referenced by MMSD_Model::parametersOptimizationByEMMethod().

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

optimise the parameters of each laws by EM algorithm

Parameters
backupPath: the path to store the results
backupPrefix: the prefix of all saved files
nDigitsthe number of print digits for real values
backupStepsthe step size for saving file
Returns
true if the method succeeds.

References MMSD_Model::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 
)
inlineinherited

restore the model from a file

Parameters
backupPath: the backup path
backupPrefix: the backup prefix
nDigitsthe number of print digits for EM iteration
stepthe backup step
Returns
true if the restoring succeeds

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

Referenced by MMSD_Model::parametersOptimizationByEMMethod().

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

restore the model from a backup file

Parameters
factory: class factory to generate instances of class
filebackup file
Returns
true if the restoring succeeds

References MM_ClassFactory::New().

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void MMSD_Model::restore ( const MMSD_DoubleFullMatrix properties)
protectedvirtualinherited

restore the unstored values after a backup

Parameters
propertiesthe data set properties

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

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void MMSD_Model::saveToUIClass ( UI_Class mclass) const
virtualinherited
void MMSD_Model::setDataSet ( SP::MMSD_DataSet  data)
inlineinherited

set the data set

void MMSD_Model::setEMMaximumIterationsNumber ( const int &  n)
inlineinherited

set the maximum iterations number for EM algorithm

see MMSD_Model::

void MMSD_GaussianModel::setGaussianLaw ( const tFlag f)
inlineinherited

set the method

void MMSD_GaussianModel::setGaussianLawMaximumIterationsNumber ( const int &  n)
inlineinherited

set the maximum iterations number

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)
inlineinherited

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

void MMSD_Model::setMinEigenValue ( const double &  v)
inlineinherited

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_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_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 
)
inlineinherited

set the weight initialization type & parameters

Parameters
typeof 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
inlinevirtualinherited

turn the class into string

Reimplemented from CORE_Object.

References CORE_Object::toString(), and tString.

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Member Data Documentation

const tFlag MMSD_GaussianModel::FLURY_GAUTSCHI_LAW =0
staticinherited
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().

const tFlag MMSD_GaussianModel::STIEFEL_LAW =1
staticinherited

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