mixmod  3.2.0
Mixture models for clustering and classification
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XEM::Model Class Reference
Inheritance diagram for XEM::Model:

Public Member Functions

 Model ()
 Default constructor.
 
virtual Modelclone ()
 
 Model (Model *iModel)
 Constructor.
 
 Model (ModelType *modelType, int64_t nbCluster, Data *&data, Partition *knownPartition)
 Constructor.
 
virtual ~Model ()
 Destructor.
 
void updateForCV (Model *originalModel, CVBlock &CVBlock)
 
ParametergetParameter ()
 Selector. More...
 
GaussianParametergetGaussianParameter ()
 
BinaryParametergetBinaryParameter ()
 
int64_t getNbCluster ()
 Selector. More...
 
DatagetData ()
 Selector. More...
 
GaussianDatagetGaussianData ()
 Return Gaussian data. More...
 
BinaryDatagetBinaryData ()
 Return Binary data. More...
 
ExceptiongetErrorType () const
 Selector. More...
 
ModelType *const & getModelType () const
 Selector. More...
 
int64_t getNbSample ()
 Selector. More...
 
double ** getTabFik ()
 Selector. More...
 
double * getTabSumF ()
 return _tabSumF
 
double ** getTabTik ()
 Selector. More...
 
int64_t ** getTabZikKnown ()
 Selector. More...
 
double ** getTabCik ()
 
bool * getTabZiKnown ()
 getTabZikKnown
 
double * getTabNk ()
 Selector. More...
 
bool getDeleteData ()
 
void computeFik ()
 compute _fik
 
void computeNk ()
 Compute the number of points in each class.
 
double getLogLikelihood (bool fikMustBeComputed)
 Compute the log-likelihood. More...
 
double getLogLikelihoodOne ()
 Compute the log-likelihood with one cluster. More...
 
double getEntropy ()
 Compute the entropy. More...
 
double getCompletedLogLikelihood ()
 Compute the completed log-likelihood. More...
 
double getCompletedLogLikelihoodOrLogLikelihood ()
 
int64_t getFreeParameter ()
 return the number of free parameters
 
double getLogN ()
 Selector. More...
 
void getLabelAndPartitionByMAPOrKnownPartition (int64_t *label, int64_t **partition)
 
int64_t getLabelByMAPOrKnownPartition (int64_t i)
 
int64_t getKnownLabel (int64_t i)
 
double ** getPostProba ()
 getPostProba
 
int64_t computeLabel (int64_t i0)
 Compute the label of the i0-th point of the sample. More...
 
int64_t computeLabel (Sample *x)
 Compute the label of new point x. More...
 
void MAPstep ()
 Maximum a posteriori step method.
 
void Estep ()
 Expectation step method.
 
void Mstep ()
 Maximization step method.
 
void Sstep ()
 Stochastic classification step method.
 
void Cstep ()
 Classification step method.
 
void initRANDOM (int64_t nbTry)
 Random center initialization of the parameters of the model.
 
void randomForInitRANDOMorUSER_PARTITION (bool *tabIndividualCanBeUsedForInitRandom, bool *tabClusterToInitialize)
 random step for init RANDOM or USER_PARTITION
 
void initUSER (Parameter *initParameter)
 User initialization of the parameters of the model.
 
void initUSER_PARTITION (Partition *initPartition, int64_t nbTryInInit=defaultNbTryInInit)
 User partition initialization of the parameters of the model.
 
void setParameter (Parameter *parameter)
 
void setAlgoName (AlgoName algoName)
 
AlgoName getAlgoName ()
 
void setError (Exception &errorType)
 
void FixKnownPartition (Partition *&y)
 Fix label Known.
 
void editDebugInformation ()
 
void editFik ()
 
void editCik ()
 
void editTik ()
 
void editNk ()
 

Member Function Documentation

int64_t XEM::Model::computeLabel ( int64_t  i0)

Compute the label of the i0-th point of the sample.

Returns
The label of i0 (i0=0 -> _nBSample -1)
int64_t XEM::Model::computeLabel ( Sample x)

Compute the label of new point x.

Returns
The label of x
BinaryData * XEM::Model::getBinaryData ( )
inline

Return Binary data.

Returns

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double XEM::Model::getCompletedLogLikelihood ( )

Compute the completed log-likelihood.

Returns
The completed log-likelihood

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double XEM::Model::getCompletedLogLikelihoodOrLogLikelihood ( )

get completed LL (if CEM) or LL (elseif)

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Data * XEM::Model::getData ( )
inline

Selector.

Returns
The current data

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double XEM::Model::getEntropy ( )

Compute the entropy.

Returns
The entropy

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Exception & XEM::Model::getErrorType ( ) const
inline

Selector.

Returns
The type of Error

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GaussianData * XEM::Model::getGaussianData ( )
inline

Return Gaussian data.

Returns

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int64_t XEM::Model::getKnownLabel ( int64_t  i)

get knownLabel of the ith individual (i=0 .... nbSample-1) return value in [0 nbCluster-1] throw an error if the label is unknown

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void XEM::Model::getLabelAndPartitionByMAPOrKnownPartition ( int64_t *  label,
int64_t **  partition 
)

getLabel and partition label=1...nbSample

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int64_t XEM::Model::getLabelByMAPOrKnownPartition ( int64_t  i)

get label of the ith individual (i=0 .... nbSample-1) by MAP (or known label) return value in [0 nbCluster-1]

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double XEM::Model::getLogLikelihood ( bool  fikMustBeComputed)

Compute the log-likelihood.

Returns
The log-likelihood

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double XEM::Model::getLogLikelihoodOne ( )

Compute the log-likelihood with one cluster.

Returns
The log-likelihood
double XEM::Model::getLogN ( )

Selector.

Returns
Log of the weight total
ModelType *const & XEM::Model::getModelType ( ) const
inline

Selector.

Returns
The type of the model

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int64_t XEM::Model::getNbCluster ( )
inline

Selector.

Returns
The current number for cluster

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int64_t XEM::Model::getNbSample ( )
inline

Selector.

Returns
The number of samples

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Parameter * XEM::Model::getParameter ( )
inline

Selector.

Returns
The parameters

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double ** XEM::Model::getTabFik ( )
inline

Selector.

Returns
Table of Fik of each cluster : probabilitites: _fik = pk * f(xi,muk,Sk)
double * XEM::Model::getTabNk ( )
inline

Selector.

Returns
Table of number of elements in each cluster
double ** XEM::Model::getTabTik ( )
inline

Selector.

Returns
Table of Tik of each cluster : conditional probabilities that xi arises from the k-th mixture component, 0 <= tik[i]k0] <= 1
int64_t ** XEM::Model::getTabZikKnown ( )
inline

Selector.

Returns
Table of Zik zik[i][k0] = 1 if xi arises from the k0-th mixture component, 0 else

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