NonLinearAnalyticConditionalGaussian_Ginac Class Reference

Conditional Gaussian for an analytic nonlinear system using Ginac: More...

#include <nonlinearanalyticconditionalgaussian_ginac.h>

Inheritance diagram for NonLinearAnalyticConditionalGaussian_Ginac:

List of all members.

Public Member Functions
	NonLinearAnalyticConditionalGaussian_Ginac (const GiNaC::matrix &func, const vector< GiNaC::symbol > &u, const vector< GiNaC::symbol > &x, const Gaussian &additiveNoise, const vector< GiNaC::symbol > &cond)
	constructor
	NonLinearAnalyticConditionalGaussian_Ginac (const GiNaC::matrix &func, const vector< GiNaC::symbol > &u, const vector< GiNaC::symbol > &x, const Gaussian &additiveNoise)
	constructor
	NonLinearAnalyticConditionalGaussian_Ginac (const NonLinearAnalyticConditionalGaussian_Ginac &g)
	copy constructor
virtual	~NonLinearAnalyticConditionalGaussian_Ginac ()
	Destructor.
GiNaC::matrix	FunctionGet ()
	return function
vector< GiNaC::symbol >	InputGet ()
	return substitution symbols
vector< GiNaC::symbol >	StateGet ()
	return state symbols
vector< GiNaC::symbol >	ConditionalGet ()
	Get conditional arguments.
virtual MatrixWrapper::ColumnVector	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.
virtual MatrixWrapper::Matrix	dfGet (unsigned int i) const
const MatrixWrapper::ColumnVector &	AdditiveNoiseMuGet () const
	Get the mean Value of the Additive Gaussian uncertainty.
const MatrixWrapper::SymmetricMatrix &	AdditiveNoiseSigmaGet () const
	Get the covariance matrix of the Additive Gaussian uncertainty.
void	AdditiveNoiseMuSet (const MatrixWrapper::ColumnVector &mu)
	Set the mean Value of the Additive Gaussian uncertainty.
void	AdditiveNoiseSigmaSet (const MatrixWrapper::SymmetricMatrix &sigma)
	Set the covariance of the Additive Gaussian uncertainty.
virtual ConditionalGaussian *	Clone () const
	Clone function.
virtual Probability	ProbabilityGet (const MatrixWrapper::ColumnVector &input) const
	Get the probability of a certain argument.
virtual bool	SampleFrom (Sample< MatrixWrapper::ColumnVector > &sample, int method=DEFAULT, void *args=NULL) const
virtual bool	SampleFrom (std::vector< Sample< MatrixWrapper::ColumnVector > > &samples, const 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)
virtual bool	SampleFrom (Sample< MatrixWrapper::ColumnVector > &one_sample, int method=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.
unsigned int	DimensionGet () const
	Get the dimension of the argument.
unsigned int	DimensionGet () const
	Get the dimension of the argument.
unsigned int	DimensionGet () const
	Get the dimension of the argument.
virtual void	DimensionSet (unsigned int dim)
	Set the dimension of the argument.
virtual void	DimensionSet (unsigned int dim)
	Set the dimension of the argument.
virtual void	DimensionSet (unsigned int dim)
	Set the dimension of the argument.
virtual void	DimensionSet (unsigned int dim)
	Set the dimension of the argument.
Protected Attributes
MatrixWrapper::ColumnVector	_additiveNoise_Mu
	additive noise expected value
MatrixWrapper::SymmetricMatrix	_additiveNoise_Sigma
	additive noise covariance
ColumnVector	_diff
ColumnVector	_Mu
Matrix	_Low_triangle
ColumnVector	_samples
ColumnVector	_SampleValue
Friends
std::ostream &	operator<< (std::ostream &os, NonLinearAnalyticConditionalGaussian_Ginac &p)
	output stream for measurement model

---

Detailed Description

Conditional Gaussian for an analytic nonlinear system using Ginac:

Describes classes of the type

with

or

Constructor for the first type:

Constructor for the second type:

When the second type is used, the additive noise on c will be converted to additive noise on f, by locally linearising the function.

Bug:

: This class is higly biased towards filtering applications.

Definition at line 48 of file nonlinearanalyticconditionalgaussian_ginac.h.

---

Constructor & Destructor Documentation

NonLinearAnalyticConditionalGaussian_Ginac	(	const GiNaC::matrix &	func,
		const vector< GiNaC::symbol > &	u,
		const vector< GiNaC::symbol > &	x,
		const Gaussian &	additiveNoise,
		const vector< GiNaC::symbol > &	cond
	)

constructor

Parameters:

func	function to be evaluated for expected value
u	symbols to be substituted (by numeric values) for evaluation. These can be system inputs or sensor parameters
x	symbols representing state
additiveNoise	Gaussian representing additive noise
cond	parameters where additive noise applies to

NonLinearAnalyticConditionalGaussian_Ginac	(	const GiNaC::matrix &	func,
		const vector< GiNaC::symbol > &	u,
		const vector< GiNaC::symbol > &	x,
		const Gaussian &	additiveNoise
	)

constructor

Parameters:

func	function to be evaluated for expected value
u	symbols to be substituted (by numeric values) for evaluation. These can be system inputs or sensor parameters
x	symbols representing state
additiveNoise	Gaussian representing additive noise on function output

---

Member Function Documentation

const MatrixWrapper::ColumnVector& AdditiveNoiseMuGet

(

)

const [inherited]

Get the mean Value of the Additive Gaussian uncertainty.

Returns:

the mean Value of the Additive Gaussian uncertainty

void AdditiveNoiseMuSet

(

const MatrixWrapper::ColumnVector &

mu

)

[inherited]

Set the mean Value of the Additive Gaussian uncertainty.

Parameters:

mu	the mean Value of the Additive Gaussian uncertainty

const MatrixWrapper::SymmetricMatrix& AdditiveNoiseSigmaGet

(

)

const [inherited]

Get the covariance matrix of the Additive Gaussian uncertainty.

Returns:

the mean Value of the Additive Gaussian uncertainty

void AdditiveNoiseSigmaSet

(

const MatrixWrapper::SymmetricMatrix &

sigma

)

[inherited]

Set the covariance of the Additive Gaussian uncertainty.

Parameters:

sigma

the covariance matrix of the Additive Gaussian uncertainty

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
	)		[virtual, inherited]

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

)

[virtual, inherited]

Set the whole list of conditional arguments.

Parameters:

ConditionalArguments

an STL-vector of type T 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 AnalyticConditionalGaussianAdditiveNoise.

virtual MatrixWrapper::Matrix dfGet

(

unsigned int

i

)

const [virtual]

Bug:

only implemented for i = 0 for now (so in a filter context, only the derivative with respect to x is implemented

Reimplemented from AnalyticConditionalGaussian.

unsigned int DimensionGet

(

)

const [inherited]

Get the dimension of the argument.

Returns:

the dimension of the argument

unsigned int DimensionGet

(

)

const [inherited]

Get the dimension of the argument.

Returns:

the dimension of the argument

unsigned int DimensionGet

(

)

const [inherited]

Get the dimension of the argument.

Returns:

the dimension of the argument

unsigned int DimensionGet

(

)

const [inherited]

Get the dimension of the argument.

Returns:

the dimension of the argument

virtual void DimensionSet

(

unsigned int

dim

)

[virtual, inherited]

Set the dimension of the argument.

Parameters:

dim	the dimension

virtual void DimensionSet

(

unsigned int

dim

)

[virtual, inherited]

Set the dimension of the argument.

Parameters:

dim	the dimension

virtual void DimensionSet

(

unsigned int

dim

)

[virtual, inherited]

Set the dimension of the argument.

Parameters:

dim	the dimension

virtual void DimensionSet

(

unsigned int

dim

)

[virtual, inherited]

Set the dimension of the argument.

Parameters:

dim	the dimension

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 >.

unsigned int NumConditionalArgumentsGet

(

)

const [inherited]

Get the Number of conditional arguments.

Returns:

the number of conditional arguments

virtual void NumConditionalArgumentsSet

(

unsigned int

numconditionalarguments

)

[virtual, inherited]

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, inherited]

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 [virtual, inherited]

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 [virtual, inherited]

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:

• nonlinearanalyticconditionalgaussian_ginac.h