Proposal Density for non-linear systems with additive Gaussian Noise (using a (analytic) Filter) More...

#include <filterproposaldensity.h>

Inheritance diagram for FilterProposalDensity:

Public Member Functions
	FilterProposalDensity (AnalyticSystemModelGaussianUncertainty SysModel, AnalyticMeasurementModelGaussianUncertainty MeasModel)
	Constructor. More...

	FilterProposalDensity (const FilterProposalDensity &fpd)
	Copy constructor. More...

virtual	~FilterProposalDensity ()
	Destructor.

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...

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

void	SystemModelSet (AnalyticSystemModelGaussianUncertainty *SysModel)
	Set SystemModel. More...

void	MeasurementModelSet (AnalyticMeasurementModelGaussianUncertainty *MeasModel)
	Set Measurementmodel. More...

void	SampleCovSet (MatrixWrapper::SymmetricMatrix &cov)
	Set SampleCov. More...

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

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	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...

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

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

Protected Member Functions
virtual void	FilterStep () const
	internal method

Protected Attributes
Gaussian *	_TmpPrior

Filter < MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector > *	_filter

AnalyticSystemModelGaussianUncertainty *	_sysmodel

AnalyticMeasurementModelGaussianUncertainty *	_measmodel

MatrixWrapper::SymmetricMatrix	_sample_cov

ColumnVector	_diff

ColumnVector	_Mu

Matrix	_Low_triangle

ColumnVector	_samples

ColumnVector	_SampleValue

Detailed Description

Proposal Density for non-linear systems with additive Gaussian Noise (using a (analytic) Filter)

Calculates an importance density for all systems of the form

$x_k = f(x_{k-1}[,u_k]) + v_k, \quad v_k \sim N(0, \Sigma_v)$

$z_k = h(x_k[,s_k]) + w_k, \quad w_k \sim N(0, \Sigma_w)$

This means all systems with a system equation and measurement equation that use a AnalyticConditionalGaussian Class. It uses a Filter to generate a proposal

Definition at line 40 of file filterproposaldensity.h.

Constructor & Destructor Documentation

FilterProposalDensity	(	AnalyticSystemModelGaussianUncertainty *	SysModel,
		AnalyticMeasurementModelGaussianUncertainty *	MeasModel
	)

Constructor.

Parameters

SysModel
MeasModel

FilterProposalDensity ( const FilterProposalDensity & fpd )

Copy constructor.

Parameters

fpd

Bug:: Not implemented yet

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

virtual

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 from Pdf< MatrixWrapper::ColumnVector >.

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 from AnalyticConditionalGaussian.

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

virtual

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 from Pdf< MatrixWrapper::ColumnVector >.

void MeasurementModelSet ( AnalyticMeasurementModelGaussianUncertainty * MeasModel )

Set Measurementmodel.

Parameters

MeasModel

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

virtualinherited

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 >.

void SampleCovSet ( MatrixWrapper::SymmetricMatrix & cov )

Set SampleCov.

Parameters

cov

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!

void SystemModelSet ( AnalyticSystemModelGaussianUncertainty * SysModel )

Set SystemModel.

Parameters

SysModel

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

filterproposaldensity.h

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation