STK::HDGaussianBase< Derived > Class Template Reference

Base class for the diagonal Gaussian models. More...

#include <STK_HDGaussianBase.h>

Inheritance diagram for STK::HDGaussianBase< Derived >:

Public Types
typedef IMixtureDensity< Derived >	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
Real const &	mean (int k, int j) const
int const &	d (int k) const
Real const &	b (int k) const
Real const &	q (int k) const
Real const &	a (int j, int k) const
void	initializeModelImpl ()
	Initialize the parameters of the model.
template<class Weights >
Real	impute (int i, int j, Weights const &pk) const
Real	rand (int i, int j, int k) const
template<class Array >
void	getParameters (Array &params) const
	This function is used in order to get the current values of the means and standard deviations.
void	writeParameters (CArrayXX const *p_tik, ostream &os) const
	This function can be used to write summary of parameters to the output stream.
int	nbCluster () const
Array const *const &	p_data () 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.

Public Attributes
Parameters	param_
	parameters of the derived mixture model.

Protected Member Functions
	HDGaussianBase (int nbCluster)
	default constructor
	HDGaussianBase (HDGaussianBase const &model)
	copy constructor
	~HDGaussianBase ()
	destructor
void	randomMean (CArrayXX const *p_tik)
	sample randomly the mean of each component by sampling randomly a row of the data set.
bool	updateMean (CArrayXX const *p_tik)
	compute the weighted mean of a Gaussian mixture.
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.

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

Detailed Description

template<class Derived>
class STK::HDGaussianBase< Derived >

Base class for the diagonal Gaussian models.

Definition at line 55 of file STK_HDGaussianBase.h.

Member Typedef Documentation

Base

template<class Derived >

typedef IMixtureDensity<Derived > STK::HDGaussianBase< Derived >::Base

Definition at line 58 of file STK_HDGaussianBase.h.

Constructor & Destructor Documentation

HDGaussianBase() [1/2]

template<class Derived >

STK::HDGaussianBase< Derived >::HDGaussianBase ( int  nbCluster )

inlineprotected

default constructor

Parameters

nbCluster

number of cluster in the model

Definition at line 67 of file STK_HDGaussianBase.h.

: Base(nbCluster) {}

HDGaussianBase() [2/2]

template<class Derived >

STK::HDGaussianBase< Derived >::HDGaussianBase ( HDGaussianBase< Derived > const &  model )

inlineprotected

copy constructor

Parameters

model

The model to copy

Definition at line 71 of file STK_HDGaussianBase.h.

: Base(model) {}

~HDGaussianBase()

template<class Derived >

STK::HDGaussianBase< Derived >::~HDGaussianBase ( )

inlineprotected

destructor

Definition at line 73 of file STK_HDGaussianBase.h.

{}

Member Function Documentation

a()

template<class Derived >

Real const & STK::HDGaussianBase< Derived >::a ( int  j, int  k  ) const

inline

Returns

rotation matrix of the kth cluster

Definition at line 85 of file STK_HDGaussianBase.h.

{ return param_.a(k, j);}

References STK::HDGaussianBase< Derived >::param_.

b()

template<class Derived >

Real const & STK::HDGaussianBase< Derived >::b ( int  k ) const

inline

Returns

noise variance of the kth cluster

Definition at line 81 of file STK_HDGaussianBase.h.

{ return param_.b(k);}

References STK::HDGaussianBase< Derived >::param_.

d()

template<class Derived >

int const & STK::HDGaussianBase< Derived >::d ( int  k ) const

inline

Returns

dimension of low sub-subspace of the kth cluster

Definition at line 79 of file STK_HDGaussianBase.h.

{ return param_.d(k);}

References STK::HDGaussianBase< Derived >::param_.

getParameters()

template<class Derived >

template<class Array >

void STK::HDGaussianBase< Derived >::getParameters

(

Array &

params

)

const

This function is used in order to get the current values of the means and standard deviations.

Parameters

[out]

params

the array with the parameters of the mixture.

Definition at line 169 of file STK_HDGaussianBase.h.

{
  int nbClust = nbCluster();
  params.resize(2*nbClust, p_data()->cols());
  for (int k= 0; k < nbClust; ++k)
  {
    for (int j= params.beginCols();  j< params.endCols(); ++j)
    {
      params(baseIdx+2*k  , j) = mean(baseIdx + k, j);
      params(baseIdx+2*k+1, j) = sigma(baseIdx + k, j);
    }
  }
}

References STK::baseIdx, and STK::mean().

impute()

template<class Derived >

template<class Weights >

Real STK::HDGaussianBase< Derived >::impute	(	int	i,
		int	j,
		Weights const &	pk
	)		const

Returns

an imputation value for the jth variable of the ith sample

Parameters

i,j	indexes of the data to impute
pk	the probabilities of each class for the ith individual

Definition at line 123 of file STK_HDGaussianBase.h.

{
  Real sum = 0.;
  for (int k= pk.begin(); k < pk.end(); ++k)
  { sum += pk[k] * mean(k,j);}
  return sum;
}

References STK::mean(), and STK::sum().

initializeModelImpl()

template<class Derived >

void STK::HDGaussianBase< Derived >::initializeModelImpl ( )

inline

Initialize the parameters of the model.

Definition at line 88 of file STK_HDGaussianBase.h.

{ param_.resize(p_data()->cols());}

References STK::HDGaussianBase< Derived >::p_data(), and STK::HDGaussianBase< Derived >::param_.

mean()

template<class Derived >

Real const & STK::HDGaussianBase< Derived >::mean ( int  k, int  j  ) const

inline

Returns

mean of the kth cluster and jth variable

Definition at line 77 of file STK_HDGaussianBase.h.

{ return param_.mean(k,j);}

References STK::HDGaussianBase< Derived >::param_.

Referenced by STK::HDGaussianBase< Derived >::rand().

nbCluster()

template<class Derived >

int STK::IMixtureDensity< Derived >::nbCluster ( ) const

inline

Returns

the number of cluster

Definition at line 124 of file STK_IMixtureDensity.h.

{ return nbCluster_;}

p_data()

template<class Derived >

Array const *const & STK::IMixtureDensity< Derived >::p_data ( ) const

inline

Returns

a pointer on the current data set

Definition at line 131 of file STK_IMixtureDensity.h.

{ return p_dataij_;}

Referenced by STK::HDGaussianBase< Derived >::initializeModelImpl().

q()

template<class Derived >

Real const & STK::HDGaussianBase< Derived >::q ( int  k ) const

inline

Returns

rotation matrix of the kth cluster

Definition at line 83 of file STK_HDGaussianBase.h.

{ return param_.q(k);}

References STK::HDGaussianBase< Derived >::param_.

rand()

template<class Derived >

Real STK::HDGaussianBase< Derived >::rand ( int  i, int  j, int  k  ) const

inline

Returns

a simulated value for the jth variable of the ith sample in the kth cluster

Parameters

i,j,k

indexes of the data to simulate

Definition at line 98 of file STK_HDGaussianBase.h.

    { return Law::Normal::rand(mean(k, j), sigma(k,j));}

References STK::HDGaussianBase< Derived >::mean(), and STK::Law::Normal::rand().

randomMean()

template<class Derived >

void STK::HDGaussianBase< Derived >::randomMean ( CArrayXX const *  p_tik )

protected

sample randomly the mean of each component by sampling randomly a row of the data set.

Definition at line 132 of file STK_HDGaussianBase.h.

{
  // indexes array
  VectorXi indexes(this->nbSample());
  for(int i=p_data()->beginRows(); i< p_data()->endRows(); ++i) { indexes[i] = i;}
  Range rind = this->nbSample();
  // sample without repetition
  for (int k= p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    // random number in [0, end-k[
    int i = Law::UniformDiscrete::rand(rind.begin(), rind.end()-1);
    // get ith individuals
    param_.mean_[k] = p_data()->row(indexes[i]);
    // exchange it with nth
    indexes.swap(i, rind.lastIdx());
    // decrease
    rind.decLast(1);
  }
}

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

updateMean()

template<class Derived >

bool STK::HDGaussianBase< Derived >::updateMean ( CArrayXX const *  p_tik )

protected

compute the weighted mean of a Gaussian mixture.

Definition at line 153 of file STK_HDGaussianBase.h.

{
  for (int k= p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    for (int j=p_data()->beginCols(); j< p_data()->endCols(); ++j)
    { param_.mean_[k][j] = p_data()->col(j).wmean(p_tik->col(k));}
  }
  return true;
}

References STK::ICArray< Derived >::col().

writeParameters()

template<class Derived >

void STK::HDGaussianBase< Derived >::writeParameters	(	CArrayXX const *	p_tik,
		ostream &	os
	)		const

This function can be used to write summary of parameters to the output stream.

Parameters

p_tik	a constant pointer on the posterior probabilities
os	Stream where you want to write the summary of parameters.

Definition at line 189 of file STK_HDGaussianBase.h.

{
  CPointX m(p_data()->cols()), s(p_data()->cols());
  for (int k= p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    // store sigma values in an array for a nice output
    for (int j= s.begin();  j < s.end(); ++j)
    { m[j] = mean(k,j); s[j] = sigma(k,j);}
    os << _T("---> Component ") << k << _T("\n");
    os << _T("mean = ") << m;
    os << _T("sigma = ")<< s;
  }
}

References _T, and STK::mean().

Member Data Documentation

param_

template<class Derived >

Parameters STK::IMixtureDensity< Derived >::param_

parameters of the derived mixture model.

Should be an instance of the STK::ModelParameters struct.

See also

STK::ModelParameters

Definition at line 180 of file STK_IMixtureDensity.h.

Referenced by STK::HDGaussianBase< Derived >::a(), STK::HDGaussianBase< Derived >::b(), STK::HDGaussianBase< Derived >::d(), STK::HDGaussianBase< Derived >::initializeModelImpl(), STK::HDGaussianBase< Derived >::mean(), and STK::HDGaussianBase< Derived >::q().

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

• STK_HDGaussianBase.h