Bayesian Filtering Library Generated from SVN r
Public Member Functions | Protected Attributes
AnalyticConditionalGaussian Class Reference

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

#include <analyticconditionalgaussian.h>

Inheritance diagram for AnalyticConditionalGaussian:
ConditionalGaussian ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector > Pdf< MatrixWrapper::ColumnVector > AnalyticConditionalGaussianAdditiveNoise FilterProposalDensity OptimalImportanceDensity LinearAnalyticConditionalGaussian NonLinearAnalyticConditionalGaussian_Ginac EKFProposalDensity

List of all members.

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 ConditionalGaussianClone () 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.
virtual MatrixWrapper::ColumnVector ExpectedValueGet () const
 Get the expected value E[x] of the pdf.
virtual MatrixWrapper::ColumnVector ExpectedValueGet () const
 Get the expected value E[x] of the pdf.
virtual MatrixWrapper::ColumnVector ExpectedValueGet () const
 Get the expected value E[x] of the pdf.
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::SymmetricMatrix 
CovarianceGet () const
 Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
virtual
MatrixWrapper::SymmetricMatrix 
CovarianceGet () const
 Get the Covariance Matrix E[(x - E[x])^2] of the Analytic 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 ( int  dim = 0,
int  num_conditional_arguments = 0 
)

Constructor.

Parameters:
dimDimension of state
num_conditional_argumentsThe 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 
) [virtual, inherited]

Set the n-th argument of the list.

Parameters:
n_argumentwhich one of the conditional arguments
argumentvalue 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:
ConditionalArgumentsan STL-vector of type
T
containing the condtional arguments
virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

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

returns derivative from function to n-th conditional variable

Parameters:
iNumber 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.

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:
dimthe dimension
virtual void DimensionSet ( unsigned int  dim) [virtual, inherited]

Set the dimension of the argument.

Parameters:
dimthe dimension
virtual void DimensionSet ( unsigned int  dim) [virtual, inherited]

Set the dimension of the argument.

Parameters:
dimthe dimension
virtual void DimensionSet ( unsigned int  dim) [virtual, inherited]

Set the dimension of the argument.

Parameters:
dimthe dimension
virtual MatrixWrapper::ColumnVector ExpectedValueGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::ColumnVector ExpectedValueGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::ColumnVector ExpectedValueGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::ColumnVector ExpectedValueGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, 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) [virtual, inherited]

Set the Number of conditional arguments.

Parameters:
numconditionalargumentsthe 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:
inputT argument of the Pdf
Returns:
the probability value of the argument

Reimplemented from Pdf< MatrixWrapper::ColumnVector >.

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_samplesample that will contain result of sampling
methodSampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
argsPointer to a struct representing extra sample arguments
See also:
SampleFrom()
Bug:
Sometimes the compiler doesn't know which method to choose!
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_sampleslist of samples that will contain result of sampling
num_samplesNumber of Samples to be drawn (iid)
methodSampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
argsPointer 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!

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