Abstract Class representing all FULL Analytical Conditional gaussians. More...

#include <analyticconditionalgaussian.h>

Inheritance diagram for AnalyticConditionalGaussian:

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

virtual	~AnalyticConditionalGaussian ()
	Destructor.

virtual MatrixWrapper::Matrix	dfGet (unsigned int i) const
	returns derivative from function to n-th conditional variable

virtual ConditionalGaussian *	Clone () const
	Clone function.

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

virtual Probability	ProbabilityGet (const T &input) const
	Get the probability of a certain argument.

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

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

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

virtual bool	SampleFrom (Sample< T > &one_sample, const SampleMthd method=SampleMthd::DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf:

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

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

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

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

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

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

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

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

virtual T	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.

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

Protected Attributes
ColumnVector	_diff

ColumnVector	_Mu

Matrix	_Low_triangle

ColumnVector	_samples

ColumnVector	_SampleValue

Detailed Description

Abstract Class representing all FULL Analytical Conditional gaussians.

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

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

Definition at line 36 of file analyticconditionalgaussian.h.

Constructor & Destructor Documentation

◆ AnalyticConditionalGaussian()

AnalyticConditionalGaussian	(	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

◆ Clone()

virtual ConditionalGaussian * Clone ( ) const

virtualinherited

Clone function.

Reimplemented from ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >.

Reimplemented in LinearAnalyticConditionalGaussian.

◆ ConditionalArgumentGet()

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!)

Definition at line 97 of file conditionalpdf.h.

◆ ConditionalArgumentSet()

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

Definition at line 104 of file conditionalpdf.h.

◆ ConditionalArgumentsGet()

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

Definition at line 85 of file conditionalpdf.h.

◆ ConditionalArgumentsSet()

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

Definition at line 91 of file conditionalpdf.h.

◆ CovarianceGet()

template<typename T >

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 AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

Definition at line 222 of file pdf.h.

◆ dfGet()

virtual MatrixWrapper::Matrix dfGet ( unsigned int i ) const

virtual

returns derivative from function to n-th conditional variable

Parameters

i	Number of the conditional variable to use for partial derivation

Returns: Partial derivative with respect to conditional variable i

Reimplemented in FilterProposalDensity, LinearAnalyticConditionalGaussian, and NonLinearAnalyticConditionalGaussian_Ginac.

◆ DimensionGet()

template<typename T >

unsigned int DimensionGet ( ) const

inlineinherited

Get the dimension of the argument.

Returns: the dimension of the argument

Definition at line 166 of file pdf.h.

◆ DimensionSet()

template<typename T >

void DimensionSet ( unsigned int dim )

virtualinherited

Set the dimension of the argument.

Parameters

dim	the dimension

Reimplemented in Gaussian.

Definition at line 172 of file pdf.h.

◆ ExpectedValueGet()

template<typename T >

T 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 FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

Definition at line 212 of file pdf.h.

◆ NumConditionalArgumentsGet()

unsigned int NumConditionalArgumentsGet ( ) const

inlineinherited

Get the Number of conditional arguments.

Returns: the number of conditional arguments

Definition at line 71 of file conditionalpdf.h.

◆ NumConditionalArgumentsSet()

void NumConditionalArgumentsSet ( unsigned int numconditionalarguments )

inlinevirtualinherited

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.

Definition at line 79 of file conditionalpdf.h.

◆ ProbabilityGet()

template<typename T >

Probability ProbabilityGet ( const T & input ) const

virtualinherited

Get the probability of a certain argument.

Parameters

input T argument of the Pdf

Returns: the probability value of the argument

Reimplemented in DiscretePdf, Gaussian, Uniform, and Mixture< T >.

Definition at line 204 of file pdf.h.

◆ SampleFrom() [1/2]

template<typename T >

bool SampleFrom	(	Sample< T > &	one_sample,
		const SampleMthd	method = `SampleMthd::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 an enum, eg. SampleMthd::BOXMULLER
args	Pointer to a struct representing extra sample arguments

See also: SampleFrom()

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

Reimplemented in DiscretePdf, Gaussian, Uniform, MCPdf< T >, and Mixture< T >.

Definition at line 194 of file pdf.h.

◆ SampleFrom() [2/2]

template<typename T >

bool SampleFrom	(	vector< Sample< T > > &	list_samples,
		const unsigned int	num_samples,
		const SampleMthd	method = `SampleMthd::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 an enum eg. SampleMthd::BOXMULLER
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!

Reimplemented in DiscretePdf, Gaussian, Uniform, MCPdf< T >, and Mixture< T >.

Definition at line 179 of file pdf.h.

Referenced by MCPdf< T >::SampleFrom(), and Mixture< T >::SampleFrom().

Member Data Documentation

◆ _diff

ColumnVector _diff

mutableprotectedinherited

Definition at line 67 of file conditionalgaussian.h.

◆ _Low_triangle

Matrix _Low_triangle

mutableprotectedinherited

Definition at line 69 of file conditionalgaussian.h.

◆ _Mu

ColumnVector _Mu

mutableprotectedinherited

Definition at line 68 of file conditionalgaussian.h.

◆ _samples

ColumnVector _samples

mutableprotectedinherited

Definition at line 70 of file conditionalgaussian.h.

◆ _SampleValue

ColumnVector _SampleValue

mutableprotectedinherited

Definition at line 71 of file conditionalgaussian.h.

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

analyticconditionalgaussian.h

Public Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ AnalyticConditionalGaussian()

Member Function Documentation

◆ Clone()

◆ ConditionalArgumentGet()

◆ ConditionalArgumentSet()

◆ ConditionalArgumentsGet()

◆ ConditionalArgumentsSet()

◆ CovarianceGet()

◆ dfGet()

◆ DimensionGet()

◆ DimensionSet()

◆ ExpectedValueGet()

◆ NumConditionalArgumentsGet()

◆ NumConditionalArgumentsSet()

◆ ProbabilityGet()

◆ SampleFrom() [1/2]

◆ SampleFrom() [2/2]

Member Data Documentation

◆ _diff

◆ _Low_triangle

◆ _Mu

◆ _samples

◆ _SampleValue