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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

Public Member Functions

 AnalyticConditionalGaussian (int dim=0, int num_conditional_arguments=0)
 Constructor. More...
 
virtual ~AnalyticConditionalGaussian ()
 Destructor.
 
virtual MatrixWrapper::Matrix dfGet (unsigned int i) const
 returns derivative from function to n-th conditional variable More...
 
virtual ConditionalGaussianClone () 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::ColumnVectorConditionalArgumentGet (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...
 
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...
 

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()

AnalyticConditionalGaussian ( int  dim = 0,
int  num_conditional_arguments = 0 
)

Constructor.

Parameters
dimDimension of state
num_conditional_argumentsThe number of conditional arguments.

Member Function Documentation

◆ ConditionalArgumentGet()

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!)

Definition at line 165 of file conditionalpdf.h.

◆ ConditionalArgumentSet()

void ConditionalArgumentSet ( unsigned int  n_argument,
const MatrixWrapper::ColumnVector argument 
)
virtualinherited

Set the n-th argument of the list.

Parameters
n_argumentwhich one of the conditional arguments
argumentvalue of the n-th argument

Definition at line 173 of file conditionalpdf.h.

◆ ConditionalArgumentsGet()

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

Definition at line 152 of file conditionalpdf.h.

◆ ConditionalArgumentsSet()

void ConditionalArgumentsSet ( std::vector< MatrixWrapper::ColumnVector ConditionalArguments)
virtualinherited

Set the whole list of conditional arguments.

Parameters
ConditionalArgumentsan STL-vector of type
T
containing the condtional arguments

Definition at line 158 of file conditionalpdf.h.

◆ CovarianceGet()

MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const
virtualinherited

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 NonLinearAnalyticConditionalGaussian_Ginac, Gaussian, ConditionalGaussianAdditiveNoise, AnalyticConditionalGaussianAdditiveNoise, FilterProposalDensity, and OptimalImportanceDensity.

Definition at line 225 of file mixtureParticleFilter.h.

◆ dfGet()

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 NonLinearAnalyticConditionalGaussian_Ginac, LinearAnalyticConditionalGaussian, and FilterProposalDensity.

◆ DimensionGet()

unsigned int DimensionGet ( ) const
inlineinherited

Get the dimension of the argument.

Returns
the dimension of the argument

Definition at line 169 of file mixtureParticleFilter.h.

◆ DimensionSet()

void DimensionSet ( unsigned int  dim)
virtualinherited

Set the dimension of the argument.

Parameters
dimthe dimension

Reimplemented in Gaussian.

Definition at line 175 of file mixtureParticleFilter.h.

◆ ExpectedValueGet()

MatrixWrapper::ColumnVector ExpectedValueGet ( ) const
virtualinherited

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 NonLinearAnalyticConditionalGaussian_Ginac, Gaussian, LinearAnalyticConditionalGaussian, FilterProposalDensity, and OptimalImportanceDensity.

Definition at line 215 of file mixtureParticleFilter.h.

◆ NumConditionalArgumentsGet()

unsigned int NumConditionalArgumentsGet ( ) const
inlineinherited

Get the Number of conditional arguments.

Returns
the number of conditional arguments

Definition at line 135 of file conditionalpdf.h.

◆ NumConditionalArgumentsSet()

void NumConditionalArgumentsSet ( unsigned int  numconditionalarguments)
inlinevirtualinherited

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.

Definition at line 141 of file conditionalpdf.h.

◆ ProbabilityGet()

virtual Probability ProbabilityGet ( const MatrixWrapper::ColumnVector input) const
virtualinherited

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 >.

◆ SampleFrom() [1/2]

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_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!

Definition at line 182 of file mixtureParticleFilter.h.

◆ SampleFrom() [2/2]

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_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!

Definition at line 197 of file mixtureParticleFilter.h.


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