The Poisson mixture model Poisson_ljlk is a Poisson model with a probability function of the form. More...

#include <STK_Poisson_ljlk.h>

Inheritance diagram for STK::Poisson_ljlk< Array >:

Public Types
typedef PoissonBase< Poisson_ljlk< Array > >	Base

Public Types inherited from STK::PoissonBase< Poisson_ljlk< Array > >
typedef IMixtureDensity< Poisson_ljlk< Array > >	Base

Public Types inherited from STK::IMixtureDensity< Derived >
typedef hidden::MixtureTraits< Derived >::Array	Array

typedef hidden::MixtureTraits< Derived >::Parameters	Parameters

typedef hidden::Traits< Array >::Type	Type

Public Member Functions
	Poisson_ljlk (int nbCluster)
	default constructor

	Poisson_ljlk (Poisson_ljlk const &model)
	copy constructor

	~Poisson_ljlk ()
	destructor

void	randomInit (CArrayXX const const &p_tik, CPointX const const &p_tk)
	Initialize randomly the parameters of the Poisson mixture.

bool	run (CArrayXX const const &p_tik, CPointX const const &p_tk)
	Compute the weighted probabilities.

int	computeNbFreeParameters () const

Public Member Functions inherited from STK::PoissonBase< Poisson_ljlk< Array > >
Real	lambda (int k, int j) const

void	initializeModelImpl ()
	Initialize the parameters of the model.

Real	lnComponentProbability (int i, int k) const

int	impute (int i, int j, Weights const &pk) const

Real	impute (int i, int j, CArrayXX const *p_tik) const

int	rand (int i, int j, int k) const

void	getParameters (Array &params) const
	This function is used in order to get the current values of the lambdas.

void	writeParameters (CArrayXX const *p_tik, ostream &os) const
	This function can be used to write summary of parameters to the output stream.

Array const *const &	p_data () const

int	nbCluster () const

Public Member Functions inherited from STK::IMixtureDensity< Derived >
	~IMixtureDensity ()
	destructor

int	nbCluster () const

int	nbSample () const

Real	lnNbSample () const

Array const *const &	p_data () const

Parameters const &	param () const

void	setData (Array const &data)
	Set the data set.

void	setData (Array const &data, int nbRow, int nbCol, bool byRow=true)
	Set the data set and give dimensions.

bool	initializeStep ()
	This function will be called at the beginning of the estimation process once the model is created and data is set.

void	setParametersStep ()
	set the parameters obtained with the intermediate results and release the intermediate results.

void	finalizeStep ()
	This function will be called once the model is estimated.

template<class Weights >
Type	sample (int i, int j, Weights const &tk) const

Public Member Functions inherited from STK::IRecursiveTemplate< Derived >
Derived &	asDerived ()
	static cast : return a reference of this with a cast to the derived class.

Derived const &	asDerived () const
	static cast : return a const reference of this with a cast to the derived class.

Derived *	asPtrDerived ()
	static cast : return a ptr on a `Derived` of this with a cast to the derived class.

Derived const *	asPtrDerived () const
	static cast : return a ptr on a constant `Derived` of this with a cast to the derived class.

Derived *	clone () const
	create a leaf using the copy constructor of the Derived class.

Derived *	clone (bool isRef) const
	create a leaf using the copy constructor of the Derived class and a flag determining if the clone is a reference or not.

Additional Inherited Members
Public Attributes inherited from STK::PoissonBase< Poisson_ljlk< Array > >
Parameters	param_
	parameters of the derived mixture model.

Protected Member Functions inherited from STK::PoissonBase< Poisson_ljlk< Array > >
	PoissonBase (int nbCluster)
	default constructor

	PoissonBase (PoissonBase const &model)
	copy constructor

	~PoissonBase ()
	destructor

Protected Member Functions inherited from STK::IMixtureDensity< Derived >
	IMixtureDensity (int nbCluster)
	Default constructor.

	IMixtureDensity (IMixtureDensity const &model)
	copy constructor.

void	initializeModel ()
	Initialize the model before its first use.

bool	initializeStepImpl ()
	default implementation of initializeStepImpl (do nothing and return true)

void	finalizeStepImpl ()
	default implementation of finalizeStepImpl (do nothing)

void	setNbSample (int nbSample)
	Set the number of sample of the model (needed by kernel models)

Protected Member Functions inherited from STK::IRecursiveTemplate< Derived >
	IRecursiveTemplate ()
	constructor.

	~IRecursiveTemplate ()
	destructor.

Protected Attributes inherited from STK::IMixtureDensity< Derived >
Parameters	param_
	parameters of the derived mixture model.

Detailed Description

template<class Array>
class STK::Poisson_ljlk< Array >

The Poisson mixture model Poisson_ljlk is a Poisson model with a probability function of the form.

$P(\mathbf{x}=(n_1,\ldots,n_d)|\theta) = \sum_{k=1}^K p_k \prod_{j=1}^d e^{-\lambda_{j}\lambda_{k}} \frac{(\lambda_{j}\lambda_{k})^{n_j}}{n_j!}.$

Definition at line 70 of file STK_Poisson_ljlk.h.

Member Typedef Documentation

◆ Base

template<class Array >

typedef PoissonBase<Poisson_ljlk<Array> > STK::Poisson_ljlk< Array >::Base

Definition at line 73 of file STK_Poisson_ljlk.h.

Constructor & Destructor Documentation

◆ Poisson_ljlk() [1/2]

template<class Array >

STK::Poisson_ljlk< Array >::Poisson_ljlk ( int nbCluster )

inline

default constructor

Parameters

nbCluster number of cluster in the model

Definition at line 80 of file STK_Poisson_ljlk.h.

80: Base(nbCluster) {}

STK::PoissonBase< Poisson_ljlk< Array > >::nbCluster

int nbCluster() const

Definition STK_IMixtureDensity.h:124

STK::Poisson_ljlk::Base

PoissonBase< Poisson_ljlk< Array > > Base

Definition STK_Poisson_ljlk.h:73

◆ Poisson_ljlk() [2/2]

template<class Array >

STK::Poisson_ljlk< Array >::Poisson_ljlk ( Poisson_ljlk< Array > const & model )

inline

copy constructor

Parameters

model The model to copy

Definition at line 84 of file STK_Poisson_ljlk.h.

84: Base(model) {}

◆ ~Poisson_ljlk()

template<class Array >

STK::Poisson_ljlk< Array >::~Poisson_ljlk ( )

inline

destructor

Definition at line 86 of file STK_Poisson_ljlk.h.

86{}

Member Function Documentation

◆ computeNbFreeParameters()

template<class Array >

int STK::Poisson_ljlk< Array >::computeNbFreeParameters ( ) const

inline

Returns: the number of free parameters of the model

Definition at line 92 of file STK_Poisson_ljlk.h.

93 { return this->nbCluster()+p_data()->sizeCols();}

STK::PoissonBase< Poisson_ljlk< Array > >::p_data

Array const *const & p_data() const

Definition STK_IMixtureDensity.h:131

References STK::PoissonBase< Poisson_ljlk< Array > >::nbCluster(), and STK::PoissonBase< Poisson_ljlk< Array > >::p_data().

◆ randomInit()

template<class Array >

void STK::Poisson_ljlk< Array >::randomInit	(	CArrayXX const *const &	p_tik,
		CPointX const *const &	p_tk
	)

Initialize randomly the parameters of the Poisson mixture.

Definition at line 98 of file STK_Poisson_ljlk.h.

{
  for (int j=p_data()->beginCols(); j< p_data()->endCols(); ++j)
  {
    Real m = p_data()->col(j).template cast<Real>().mean();
    for (int k= p_tik->beginCols(); k < p_tik->endCols(); ++k)
    {
      param_.lambdak_[k] = Law::Exponential::rand(m)/param_.lambdaj_[j];
    }
  }
}

References STK::Law::Exponential::rand().

◆ run()

template<class Array >

bool STK::Poisson_ljlk< Array >::run	(	CArrayXX const *const &	p_tik,
		CPointX const *const &	p_tk
	)

Compute the weighted probabilities.

Definition at line 113 of file STK_Poisson_ljlk.h.

{
  param_.lambdaj_ =   (Stat::sumByRow(*p_tik).transpose() * (*p_data()))
                     /(Stat::sumByRow(*p_tik) * Stat::sumByRow(*p_data())).sum();
  param_.lambdak_ = Stat::sumByRow(*p_data()).transpose() * (*p_tik)/(*p_tk);
  return true;
}

References STK::sum(), and STK::Stat::sumByRow().

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

STK_Poisson_ljlk.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Base

Constructor & Destructor Documentation

◆ Poisson_ljlk() [1/2]

◆ Poisson_ljlk() [2/2]

◆ ~Poisson_ljlk()

Member Function Documentation

◆ computeNbFreeParameters()

◆ randomInit()

◆ run()