Namespaces | Classes | Functions | Variables
BFL Namespace Reference

Namespaces

namespace  BFL
namespace  MatrixWrapper

Classes

class  AnalyticConditionalGaussian
 Abstract Class representing all _FULL_ Analytical Conditional gaussians. More...
class  AnalyticConditionalGaussianAdditiveNoise
 Abstract Class representing all full Analytical Conditional gaussians with Additive Gaussian Noise. More...
class  AnalyticMeasurementModelGaussianUncertainty
class  AnalyticSystemModelGaussianUncertainty
 Class for analytic system models with additive Gauss. uncertainty. More...
class  ASIRFilter
 ASIR: Auxiliary Particle Filter. More...
class  BackwardFilter
 Virtual Baseclass representing all bayesian backward filters. More...
class  bflToolkitPlugin
class  BootstrapFilter
 Particular particle filter : Proposal PDF = SystemPDF. More...
class  ConditionalGaussian
 Abstract Class representing all Conditional gaussians. More...
class  ConditionalGaussianAdditiveNoise
 Abstract Class representing all Conditional Gaussians with additive gaussian noise. More...
class  ConditionalPdf
 Abstract Class representing conditional Pdfs P(x | ...) More...
class  ConditionalUniformMeasPdf1d
class  DiscreteConditionalPdf
 Abstract Class representing all _FULLY_ Discrete Conditional PDF's. More...
class  DiscretePdf
 Class representing a PDF on a discrete variable. More...
class  DiscreteSystemModel
 Class for discrete System Models. More...
class  EKFProposalDensity
 Proposal Density for non-linear systems with additive Gaussian Noise (using a EKF Filter) More...
class  EKFTest
class  EKParticleFilter
 Particle filter using EKF for proposal step. More...
class  ExtendedKalmanFilter
class  Filter
 Abstract class representing an interface for Bayesian Filters. More...
class  FilterProposalDensity
 Proposal Density for non-linear systems with additive Gaussian Noise (using a (analytic) Filter) More...
class  Gaussian
 Class representing Gaussian (or normal density) More...
struct  get_size
class  HistogramFilter
 Class representing the histogram filter. More...
class  InnovationCheck
 Class implementing an innovationCheck used in IEKF. More...
class  IteratedExtendedKalmanFilter
class  KalmanFilter
 Class representing the family of all Kalman Filters (EKF, IEKF, ...) More...
class  LinearAnalyticConditionalGaussian
 Linear Conditional Gaussian. More...
class  LinearAnalyticMeasurementModelGaussianUncertainty
 Class for linear analytic measurementmodels with additive gaussian noise. More...
class  LinearAnalyticMeasurementModelGaussianUncertainty_Implicit
 Class for linear analytic measurementmodels with additive gaussian noise. More...
class  LinearAnalyticSystemModelGaussianUncertainty
 Class for linear analytic systemmodels with additive gaussian noise. More...
struct  matrix_i_j_constructor
struct  matrix_index
struct  MatrixAssignChecker
struct  MatrixIndexChecker
struct  MatrixTypeInfo
class  MCPdf
 Monte Carlo Pdf: Sample based implementation of Pdf. More...
class  MeasurementModel
class  Mixture
 Class representing a mixture of PDFs, the mixture can contain different. More...
class  MixtureBootstrapFilter
 Particular mixture particle filter : Proposal PDF = SystemPDF. More...
class  MixtureParticleFilter
 Virtual Class representing all Mixture particle filters. More...
class  MobileRobot
 This is a class simulating a mobile robot. More...
class  NonLinearAnalyticConditionalGaussian_Ginac
 Conditional Gaussian for an analytic nonlinear system using Ginac: More...
class  NonLinearAnalyticConditionalGaussianMobile
 Non Linear Conditional Gaussian. More...
class  NonLinearAnalyticMeasurementModelGaussianUncertainty_Ginac
 Class for nonlinear analytic measurementmodels with additive gaussian noise. More...
class  NonLinearAnalyticSystemModelGaussianUncertainty_Ginac
 Class for nonlinear analytic systemmodels with additive gaussian noise. More...
class  NonlinearMeasurementPdf
 Non Linear Conditional Gaussian. More...
class  NonlinearSystemPdf
 Non Linear Conditional Gaussian. More...
class  NonminimalKalmanFilter
class  OptimalImportanceDensity
 Optimal importance density for Nonlinear Gaussian SS Models. More...
class  Optimalimportancefilter
 Particular particle filter: Proposal PDF = Optimal Importance function. More...
class  ParticleFilter
 Virtual Class representing all particle filters. More...
class  ParticleSmoother
 Class representing a particle backward filter. More...
class  Pdf
 Class PDF: Virtual Base class representing Probability Density Functions. More...
class  Probability
 Class representing a probability (a double between 0 and 1) More...
struct  Probability_ctor
struct  ProbabilityTypeInfo
class  RauchTungStriebel
 Class representing all Rauch-Tung-Striebel backward filters. More...
struct  rget_size
struct  rvector_index
struct  rvector_index_constructor
struct  RVectorTypeInfo
class  Sample
struct  Sample_ctor
struct  SampleTypeInfo
class  SRIteratedExtendedKalmanFilter
struct  symmetricMatrix_index_constructor
struct  SymmetricMatrixTypeInfo
class  SystemModel
class  Uniform
 Class representing uniform density. More...
struct  vector_index
struct  vector_index_constructor
struct  VectorAssignChecker
struct  VectorTypeInfo
class  WeightedSample
struct  WeightedSample_ctor
struct  WeightedSampleTypeInfo

Functions

template<class T >
bool composeProperty (const PropertyBag &bag, Sample< T > &sample)
template<class T >
bool composeProperty (const PropertyBag &bag, WeightedSample< T > &weightedSample)
template<class T >
void decomposeProperty (const Sample< T > &sample, PropertyBag &targetbag)
template<class T >
void decomposeProperty (const WeightedSample< T > &weightedSample, PropertyBag &targetbag)
std::ostream & operator<< (std::ostream &os, const Uniform &u)
ostream & operator<< (ostream &stream, Probability &prob)
std::ostream & operator<< (std::ostream &os, const Gaussian &g)
std::ostream & operator<< (std::ostream &os, NonLinearAnalyticConditionalGaussian_Ginac &p)
template<typename S >
ostream & operator<< (ostream &stream, WeightedSample< S > &mws)
template<typename S >
ostream & operator<< (ostream &stream, Sample< S > &my_sample)
istream & operator>> (istream &stream, Probability &prob)
template<typename S >
istream & operator>> (istream &stream, Sample< S > &my_sample)
double rnorm (const double &mu, const double &sigma)
double runif ()
double runif (const double &min, const double &max)

Variables

bflToolkitPlugin bflToolkit

Function Documentation

template<class T >
bool BFL::composeProperty ( const PropertyBag &  bag,
Sample< T > &  sample 
)

A composeProperty method for composing a property of a vector<T> The dimension of the vector must be less than 100.

Definition at line 95 of file SampleComposition.hpp.

template<class T >
bool BFL::composeProperty ( const PropertyBag &  bag,
WeightedSample< T > &  weightedSample 
)

A composeProperty method for composing a property of a vector<T> The dimension of the vector must be less than 100.

Definition at line 224 of file SampleComposition.hpp.

template<class T >
void BFL::decomposeProperty ( const Sample< T > &  sample,
PropertyBag &  targetbag 
)

A decomposePropertyBag method for decomposing a sample<T> into a PropertyBag with Property<T>'s.

Definition at line 62 of file SampleComposition.hpp.

template<class T >
void BFL::decomposeProperty ( const WeightedSample< T > &  weightedSample,
PropertyBag &  targetbag 
)

A decomposePropertyBag method for decomposing a sample<T> into a PropertyBag with Property<T>'s.

Definition at line 188 of file SampleComposition.hpp.

std::ostream& BFL::operator<< ( std::ostream &  os,
const Uniform &  u 
)

Definition at line 54 of file uniform.cpp.

ostream& BFL::operator<< ( ostream &  stream,
Probability &  prob 
)

Definition at line 62 of file bfl_toolkit.cpp.

std::ostream& BFL::operator<< ( std::ostream &  os,
const Gaussian &  g 
)

Definition at line 62 of file gaussian.cpp.

std::ostream& BFL::operator<< ( std::ostream &  os,
NonLinearAnalyticConditionalGaussian_Ginac &  p 
)
template<typename S >
ostream& BFL::operator<< ( ostream &  stream,
WeightedSample< S > &  mws 
)
Parameters:
streamthe stream to be returned
mwsthe weighted sample to be printed
Returns:
the stream :-)

Definition at line 116 of file weightedsample.h.

template<typename S >
ostream& BFL::operator<< ( ostream &  stream,
Sample< S > &  my_sample 
)
Parameters:
streamthe stream to be returned
my_samplethe sample to be printed
Returns:
the stream :-)

Definition at line 155 of file sample.h.

istream& BFL::operator>> ( istream &  stream,
Probability &  prob 
)

Definition at line 68 of file bfl_toolkit.cpp.

template<typename S >
istream& BFL::operator>> ( istream &  stream,
Sample< S > &  my_sample 
)

Definition at line 161 of file sample.h.

double BFL::rnorm ( const double &  mu,
const double &  sigma 
)
double BFL::runif ( )
double BFL::runif ( const double &  min,
const double &  max 
)

Variable Documentation

Definition at line 85 of file bfl_toolkit.cpp.



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 Sun Oct 5 2014 22:29:53