Abstract Class representing all Conditional gaussians. More...

#include <conditionalgaussian.h>

Inheritance diagram for ConditionalGaussian:

Public Member Functions
	ConditionalGaussian (int dim=0, int num_conditional_arguments=0)
	Constructor. More...

virtual	~ConditionalGaussian ()
	Destructor.

virtual ConditionalGaussian *	Clone () const
	Clone function.

virtual Probability	ProbabilityGet (const MatrixWrapper::ColumnVector &input) const
	Get the probability of a certain argument. More...

virtual bool	SampleFrom (Sample< MatrixWrapper::ColumnVector > &sample, int method=DEFAULT, void *args=NULL) const

virtual bool	SampleFrom (std::vector< Sample< MatrixWrapper::ColumnVector > > &samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const

unsigned int	NumConditionalArgumentsGet () const
	Get the Number of conditional arguments. More...

virtual void	NumConditionalArgumentsSet (unsigned int numconditionalarguments)
	Set the Number of conditional arguments. More...

const std::vector < MatrixWrapper::ColumnVector > &	ConditionalArgumentsGet () const
	Get the whole list of conditional arguments. More...

virtual void	ConditionalArgumentsSet (std::vector< MatrixWrapper::ColumnVector > ConditionalArguments)
	Set the whole list of conditional arguments. More...

const MatrixWrapper::ColumnVector &	ConditionalArgumentGet (unsigned int n_argument) const
	Get the n-th argument of the list. More...

virtual void	ConditionalArgumentSet (unsigned int n_argument, const MatrixWrapper::ColumnVector &argument)
	Set the n-th argument of the list. More...

virtual bool	SampleFrom (vector< Sample< MatrixWrapper::ColumnVector > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const
	Draw multiple samples from the Pdf (overloaded) More...

virtual bool	SampleFrom (Sample< MatrixWrapper::ColumnVector > &one_sample, int method=DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf: More...

unsigned int	DimensionGet () const
	Get the dimension of the argument. More...

virtual void	DimensionSet (unsigned int dim)
	Set the dimension of the argument. More...

virtual MatrixWrapper::ColumnVector	ExpectedValueGet () const
	Get the expected value E[x] of the pdf. More...

virtual MatrixWrapper::SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. More...

Protected Attributes
ColumnVector	_diff

ColumnVector	_Mu

Matrix	_Low_triangle

ColumnVector	_samples

ColumnVector	_SampleValue

Detailed Description

Abstract Class representing all Conditional gaussians.

This class inherits only from ConditionalPdf<ColumnVector, ColumnVector>.

So this class represents all Pdf's of the type

$P ( A | B, C, D, ... )$

where

$\mu_A = f(B,C,D, ...)$

and

$\Sigma_A = g(B,C,D, ...)$

and

$A = N(\mu_A, \Sigma_A)$

f and g are not necessarily analytical functions

Definition at line 40 of file conditionalgaussian.h.

Constructor & Destructor Documentation

ConditionalGaussian	(	int	dim = `0`,
		int	num_conditional_arguments = `0`
	)

Constructor.

Parameters

dim	Dimension of state
num_conditional_arguments	The number of conditional arguments.

Member Function Documentation

const MatrixWrapper::ColumnVector & ConditionalArgumentGet ( unsigned int n_argument ) const

inherited

Get the n-th argument of the list.

Returns: The current value of the n-th conditional argument (starting from 0!)

virtual void ConditionalArgumentSet	(	unsigned int	n_argument,
		const MatrixWrapper::ColumnVector &	argument
	)

virtualinherited

Set the n-th argument of the list.

Parameters

n_argument	which one of the conditional arguments
argument	value of the n-th argument

const std::vector<MatrixWrapper::ColumnVector >& ConditionalArgumentsGet ( ) const

inherited

Get the whole list of conditional arguments.

Returns: an STL-vector containing all the current values of the conditional arguments

virtual void ConditionalArgumentsSet ( std::vector< MatrixWrapper::ColumnVector > ConditionalArguments )

virtualinherited

Set the whole list of conditional arguments.

Parameters

ConditionalArguments

an STL-vector of type

containing the condtional arguments

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const

virtualinherited

Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.

Get first order statistic (Covariance) of this AnalyticPdf

Returns: The Covariance of the Pdf (a SymmetricMatrix of dim DIMENSION)

Todo:: extend this more general to n-th order statistic

Bug:: Discrete pdfs should not be able to use this!

Reimplemented in NonLinearAnalyticConditionalGaussian_Ginac, Gaussian, ConditionalGaussianAdditiveNoise, AnalyticConditionalGaussianAdditiveNoise, FilterProposalDensity, and OptimalImportanceDensity.

unsigned int DimensionGet ( ) const

inherited

Get the dimension of the argument.

Returns: the dimension of the argument

virtual void DimensionSet ( unsigned int dim )

virtualinherited

Set the dimension of the argument.

Parameters

dim	the dimension

Reimplemented in Gaussian.

virtual MatrixWrapper::ColumnVector ExpectedValueGet ( ) const

virtualinherited

Get the expected value E[x] of the pdf.

Get low order statistic (Expected Value) of this AnalyticPdf

Returns: The Expected Value of the Pdf (a ColumnVector with DIMENSION rows)

Note: No set functions here! This can be useful for analytic functions, but not for sample based representations!; For certain discrete Pdfs, this function has no meaning, what is the average between yes and no?

Reimplemented in NonLinearAnalyticConditionalGaussian_Ginac, Gaussian, LinearAnalyticConditionalGaussian, FilterProposalDensity, and OptimalImportanceDensity.

unsigned int NumConditionalArgumentsGet ( ) const

inherited

Get the Number of conditional arguments.

Returns: the number of conditional arguments

virtual void NumConditionalArgumentsSet ( unsigned int numconditionalarguments )

virtualinherited

Set the Number of conditional arguments.

Parameters

numconditionalarguments the number of conditionalarguments

Bug:: will probably give rise to memory allocation problems if you herit from this class and do not redefine this method.

Reimplemented in LinearAnalyticConditionalGaussian.

virtual Probability ProbabilityGet ( const MatrixWrapper::ColumnVector & input ) const

virtual

Get the probability of a certain argument.

Parameters

input T argument of the Pdf

Returns: the probability value of the argument

Reimplemented from Pdf< MatrixWrapper::ColumnVector >.

virtual bool SampleFrom	(	vector< Sample< MatrixWrapper::ColumnVector > > &	list_samples,
		const unsigned int	num_samples,
		int	method = `DEFAULT`,
		void *	args = `NULL`
	)		const

virtualinherited

Draw multiple samples from the Pdf (overloaded)

Parameters

list_samples	list of samples that will contain result of sampling
num_samples	Number of Samples to be drawn (iid)
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer to a struct representing extra sample arguments. "Sample Arguments" can be anything (the number of steps a gibbs-iterator should take, the interval width in MCMC, ... (or nothing), so it is hard to give a meaning to what exactly Sample Arguments should represent...

Todo:: replace the C-call "void * args" by a more object-oriented structure: Perhaps something like virtual Sample * Sample (const int num_samples,class Sampler)

Bug:: Sometimes the compiler doesn't know which method to choose!

virtual bool SampleFrom	(	Sample< MatrixWrapper::ColumnVector > &	one_sample,
		int	method = `DEFAULT`,
		void *	args = `NULL`
	)		const

virtualinherited

Draw 1 sample from the Pdf:

There's no need to create a list for only 1 sample!

Parameters

one_sample	sample that will contain result of sampling
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer to a struct representing extra sample arguments

See Also: SampleFrom()

Bug:: Sometimes the compiler doesn't know which method to choose!

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

conditionalgaussian.h

Public Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation