#include <linearanalyticconditionalgaussian.h>

Inheritance diagram for BFL::LinearAnalyticConditionalGaussian:

[legend]

Public Member Functions
virtual LinearAnalyticConditionalGaussian *	Clone () const
	Clone function. More...

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

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

	LinearAnalyticConditionalGaussian (const vector< MatrixWrapper::Matrix > &ratio, const Gaussian &additiveNoise)
	Constructor. More...

	LinearAnalyticConditionalGaussian (const MatrixWrapper::Matrix &a, const Gaussian &additiveNoise)
	Constructor (overloaded) More...

const MatrixWrapper::Matrix &	MatrixGet (unsigned int i) const
	Get the i-th matrix of the system. More...

void	MatrixSet (unsigned int i, const MatrixWrapper::Matrix &m)
	Set the i-th Matrix for calculation of $\mu$ . More...

virtual void	NumConditionalArgumentsSet (unsigned int numconditionalarguments)
	Be careful: you don't want to use this one: Redefined. More...

virtual	~LinearAnalyticConditionalGaussian ()
	Destructor. More...

Public Member Functions inherited from BFL::AnalyticConditionalGaussianAdditiveNoise
const MatrixWrapper::ColumnVector &	AdditiveNoiseMuGet () const
	Get the mean Value of the Additive Gaussian uncertainty. More...

void	AdditiveNoiseMuSet (const MatrixWrapper::ColumnVector &mu)
	Set the mean Value of the Additive Gaussian uncertainty. More...

const MatrixWrapper::SymmetricMatrix &	AdditiveNoiseSigmaGet () const
	Get the covariance matrix of the Additive Gaussian uncertainty. More...

void	AdditiveNoiseSigmaSet (const MatrixWrapper::SymmetricMatrix &sigma)
	Set the covariance of the Additive Gaussian uncertainty. More...

	AnalyticConditionalGaussianAdditiveNoise (const Gaussian &gaus, int num_conditional_arguments=1)
	Constructor. More...

	AnalyticConditionalGaussianAdditiveNoise (int dim=0, int num_conditional_arguments=0)
	Constructor 2, Gaussian not yet known. More...

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

virtual	~AnalyticConditionalGaussianAdditiveNoise ()
	Destructor. More...

Public Member Functions inherited from BFL::AnalyticConditionalGaussian
	AnalyticConditionalGaussian (int dim=0, int num_conditional_arguments=0)
	Constructor. More...

virtual	~AnalyticConditionalGaussian ()
	Destructor. More...

Public Member Functions inherited from BFL::ConditionalGaussian
	ConditionalGaussian (int dim=0, int num_conditional_arguments=0)
	Constructor. More...

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 int num_samples, int method=DEFAULT, void *args=NULL) const

virtual	~ConditionalGaussian ()
	Destructor. More...

Public Member Functions inherited from BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >
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...

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...

	ConditionalPdf (int dimension=0, unsigned int num_conditional_arguments=0)
	Constructor. More...

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

virtual	~ConditionalPdf ()
	Destructor. More...

Public Member Functions inherited from BFL::BFL::Pdf< MatrixWrapper::ColumnVector >
unsigned int	DimensionGet () const
	Get the dimension of the argument. More...

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

	Pdf (unsigned int dimension=0)
	Constructor. 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...

virtual	~Pdf ()
	Destructor. More...

Private Attributes
MatrixWrapper::ColumnVector	_arg

MatrixWrapper::ColumnVector	_mean_temp

vector< MatrixWrapper::Matrix >	_ratio

Additional Inherited Members
Protected Attributes inherited from BFL::AnalyticConditionalGaussianAdditiveNoise
MatrixWrapper::ColumnVector	_additiveNoise_Mu
	additive noise expected value More...

MatrixWrapper::SymmetricMatrix	_additiveNoise_Sigma
	additive noise covariance More...

Protected Attributes inherited from BFL::ConditionalGaussian
ColumnVector	_diff

Matrix	_Low_triangle

ColumnVector	_Mu

ColumnVector	_samples

ColumnVector	_SampleValue

Detailed Description

Linear Conditional Gaussian.

$\mu = Matrix[1] . ConditionalArguments[0] + Matrix[2]. ConditionalArguments[1] + ... + Noise.\mu$
Covariance is independent of the ConditionalArguments, and is the covariance of the Noise pdf

Definition at line 35 of file linearanalyticconditionalgaussian.h.

Constructor & Destructor Documentation

BFL::LinearAnalyticConditionalGaussian::LinearAnalyticConditionalGaussian	(	const vector< MatrixWrapper::Matrix > &	ratio,
		const Gaussian &	additiveNoise
	)

Constructor.

Precondition: : Every Matrix should have the same amount of rows! This is currently not checked. The same goes for the number of columns, which should be equal to the number of rows of the corresponding conditional argument!

Parameters

ratio	vector containing the different matrices of the linear relationship between the conditional arguments and $\mu$
additiveNoise	Pdf representing the additive Gaussian uncertainty

BFL::LinearAnalyticConditionalGaussian::LinearAnalyticConditionalGaussian	(	const MatrixWrapper::Matrix &	a,
		const Gaussian &	additiveNoise
	)

Constructor (overloaded)

Precondition: There is only 1 conditional argument.

Parameters

a	Matrix for calculation of $\mu$ : $\mu = a . ConditionalArguments[0] + Noise.\mu$
additiveNoise	Pdf representing the additive Gaussian uncertainty

BFL::LinearAnalyticConditionalGaussian::~LinearAnalyticConditionalGaussian ( )

virtual

Destructor.

Definition at line 60 of file linearanalyticconditionalgaussian.cpp.

Member Function Documentation

LinearAnalyticConditionalGaussian * BFL::LinearAnalyticConditionalGaussian::Clone ( ) const

virtual

Clone function.

Reimplemented from BFL::ConditionalGaussian.

Definition at line 63 of file linearanalyticconditionalgaussian.cpp.

Matrix BFL::LinearAnalyticConditionalGaussian::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 BFL::AnalyticConditionalGaussian.

Definition at line 82 of file linearanalyticconditionalgaussian.cpp.

ColumnVector BFL::LinearAnalyticConditionalGaussian::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 BFL::BFL::Pdf< MatrixWrapper::ColumnVector >.

Definition at line 69 of file linearanalyticconditionalgaussian.cpp.

const Matrix & BFL::LinearAnalyticConditionalGaussian::MatrixGet ( unsigned int i ) const

Get the i-th matrix of the system.

Parameters

i	index determining which conditional Arg. multiplier matrix will returned

Returns: the n-th Matrix of the system-equation

Definition at line 103 of file linearanalyticconditionalgaussian.cpp.

void BFL::LinearAnalyticConditionalGaussian::MatrixSet	(	unsigned int	i,
		const MatrixWrapper::Matrix &	m
	)

Set the i-th Matrix for calculation of $\mu$ .

Set the i-th Matrix of the $\mu$ calculation in the conditonal gaussian class

Precondition: i < Numconditionalarg

Parameters

i	index determining which conditional Arg. will be multiplied with the given matrix
m	Matrix for calculation of $\mu$ : $\mu = ... m . ConditionalArguments[i] + ...$

Definition at line 96 of file linearanalyticconditionalgaussian.cpp.

void BFL::LinearAnalyticConditionalGaussian::NumConditionalArgumentsSet ( unsigned int numconditionalarguments )

virtual

Be careful: you don't want to use this one: Redefined.

Bug:: This method is not implemented, we can ReSize the std::vector<BFL::Matrix>, but we don't know the dimensions of the matrices self. So this will most certainly result in a segfault. Anyway, why would you need this?

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

Definition at line 89 of file linearanalyticconditionalgaussian.cpp.