Public Member Functions | Private Attributes | List of all members
BFL::OptimalImportanceDensity Class Reference

Optimal importance density for Nonlinear Gaussian SS Models. More...

#include <optimal_importance_density.h>

Inheritance diagram for BFL::OptimalImportanceDensity:
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Public Member Functions

virtual SymmetricMatrix CovarianceGet () const
 Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. More...
 
virtual Matrix dfGet (int i) const
 
virtual ColumnVector ExpectedValueGet () const
 Get the expected value E[x] of the pdf. More...
 
 OptimalImportanceDensity (AnalyticConditionalGaussian *SystemPdf, LinearAnalyticConditionalGaussian *MeasPdf)
 Constructor. More...
 
virtual ~OptimalImportanceDensity ()
 Destructor. More...
 
- Public Member Functions inherited from BFL::AnalyticConditionalGaussian
 AnalyticConditionalGaussian (int dim=0, int num_conditional_arguments=0)
 Constructor. More...
 
virtual MatrixWrapper::Matrix dfGet (unsigned int i) const
 returns derivative from function to n-th conditional variable More...
 
virtual ~AnalyticConditionalGaussian ()
 Destructor. More...
 
- Public Member Functions inherited from BFL::ConditionalGaussian
virtual ConditionalGaussianClone () const
 Clone function. More...
 
 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 void NumConditionalArgumentsSet (unsigned int numconditionalarguments)
 Set 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

LinearAnalyticConditionalGaussian_MeasPdf
 
AnalyticConditionalGaussian_SystemPdf
 

Additional Inherited Members

- Protected Attributes inherited from BFL::ConditionalGaussian
ColumnVector _diff
 
Matrix _Low_triangle
 
ColumnVector _Mu
 
ColumnVector _samples
 
ColumnVector _SampleValue
 

Detailed Description

Optimal importance density for Nonlinear Gaussian SS Models.

Describes the optimal importance density for all systems of the form

\[ x_k = f(x_{k-1}) + v_k, \quad v_k \sim N(0, \Sigma_v) \]

\[ z_k = H x_k + w_k, \quad w_k \sim N(0, \Sigma_w) \]

This means all systems with a system equation that uses a AnalyticConditionalGaussian Class and a measurement equation that uses a LinearAnalyticConditionalGaussian class

Definition at line 37 of file optimal_importance_density.h.

Constructor & Destructor Documentation

◆ OptimalImportanceDensity()

BFL::OptimalImportanceDensity::OptimalImportanceDensity ( AnalyticConditionalGaussian SystemPdf,
LinearAnalyticConditionalGaussian MeasPdf 
)

Constructor.

Parameters
SystemPdf
MeasPdf

◆ ~OptimalImportanceDensity()

virtual BFL::OptimalImportanceDensity::~OptimalImportanceDensity ( )
virtual

Destructor.

Member Function Documentation

◆ CovarianceGet()

virtual SymmetricMatrix BFL::OptimalImportanceDensity::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 BFL::BFL::Pdf< MatrixWrapper::ColumnVector >.

◆ dfGet()

virtual Matrix BFL::OptimalImportanceDensity::dfGet ( int  i) const
virtual

◆ ExpectedValueGet()

virtual ColumnVector BFL::OptimalImportanceDensity::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 >.

Member Data Documentation

◆ _MeasPdf

LinearAnalyticConditionalGaussian* BFL::OptimalImportanceDensity::_MeasPdf
private

Definition at line 59 of file optimal_importance_density.h.

◆ _SystemPdf

AnalyticConditionalGaussian* BFL::OptimalImportanceDensity::_SystemPdf
private

Definition at line 58 of file optimal_importance_density.h.


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


bfl
Author(s): Klaas Gadeyne, Wim Meeussen, Tinne Delaet and many others. See web page for a full contributor list. ROS package maintained by Wim Meeussen.
autogenerated on Mon Feb 28 2022 21:56:34