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

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

#include <nonlinearanalyticconditionalgaussian_ginac.h>

Inheritance diagram for NonLinearAnalyticConditionalGaussian_Ginac:
AnalyticConditionalGaussianAdditiveNoise AnalyticConditionalGaussian ConditionalGaussian ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector > Pdf< MatrixWrapper::ColumnVector >

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 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.

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

\[ P(z | subs) \]

with

\[ z=f(subs) + N(\mu,\Sigma) \]

or

\[ z=f(subs,c+N(\mu, \Sigma)) \]

Constructor for the first type:

\[ NonLinearAnalyticConditionalGaussian_Ginac(f(subs), subs, N(\mu, \Sigma) ) \]

Constructor for the second type:

\[ NonLinearAnalyticConditionalGaussian_Ginac(f(subs,z), subs, N(\mu, \Sigma) ,c) \]

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:
funcfunction to be evaluated for expected value
usymbols to be substituted (by numeric values) for evaluation. These can be system inputs or sensor parameters
xsymbols representing state
additiveNoiseGaussian representing additive noise
condparameters 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:
funcfunction to be evaluated for expected value
usymbols to be substituted (by numeric values) for evaluation. These can be system inputs or sensor parameters
xsymbols representing state
additiveNoiseGaussian 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:
muthe 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:
sigmathe 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_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]

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:
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]

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:
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 ( 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!
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!

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