Bayesian Filtering Library Generated from SVN r
Public Member Functions
Pdf< T > Class Template Reference

Class PDF: Virtual Base class representing Probability Density Functions. More...

#include <mixtureParticleFilter.h>

Inheritance diagram for Pdf< T >:
MCPdf< T > MCPdf< T > Mixture< T > MCPdf< T > Mixture< T >

List of all members.

Public Member Functions

 Pdf (unsigned int dimension=0)
 Constructor.
virtual ~Pdf ()
 Destructor.
virtual Pdf< T > * Clone () const =0
 Pure virtual clone function.
virtual bool SampleFrom (vector< Sample< T > > &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< T > &one_sample, int method=DEFAULT, void *args=NULL) const
 Draw 1 sample from the Pdf:
virtual Probability ProbabilityGet (const T &input) const
 Get the probability of a certain argument.
unsigned int DimensionGet () const
 Get the dimension of the argument.
virtual void DimensionSet (unsigned int dim)
 Set the dimension of the argument.
virtual T 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.
 Pdf (unsigned int dimension=0)
 Constructor.
virtual ~Pdf ()
 Destructor.
virtual Pdf< T > * Clone () const =0
 Pure virtual clone function.
virtual bool SampleFrom (vector< Sample< T > > &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< T > &one_sample, int method=DEFAULT, void *args=NULL) const
 Draw 1 sample from the Pdf:
virtual Probability ProbabilityGet (const T &input) const
 Get the probability of a certain argument.
unsigned int DimensionGet () const
 Get the dimension of the argument.
virtual void DimensionSet (unsigned int dim)
 Set the dimension of the argument.
virtual T 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.
 Pdf (unsigned int dimension=0)
 Constructor.
virtual ~Pdf ()
 Destructor.
virtual Pdf< T > * Clone () const =0
 Pure virtual clone function.
virtual bool SampleFrom (vector< Sample< T > > &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< T > &one_sample, int method=DEFAULT, void *args=NULL) const
 Draw 1 sample from the Pdf:
virtual Probability ProbabilityGet (const T &input) const
 Get the probability of a certain argument.
unsigned int DimensionGet () const
 Get the dimension of the argument.
virtual void DimensionSet (unsigned int dim)
 Set the dimension of the argument.
virtual T 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.
 Pdf (unsigned int dimension=0)
 Constructor.
virtual ~Pdf ()
 Destructor.
virtual Pdf< T > * Clone () const =0
 Pure virtual clone function.
virtual bool SampleFrom (vector< Sample< T > > &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< T > &one_sample, int method=DEFAULT, void *args=NULL) const
 Draw 1 sample from the Pdf:
virtual Probability ProbabilityGet (const T &input) const
 Get the probability of a certain argument.
unsigned int DimensionGet () const
 Get the dimension of the argument.
virtual void DimensionSet (unsigned int dim)
 Set the dimension of the argument.
virtual T 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.

Detailed Description

template<typename T>
class BFL::BFL::Pdf< T >

Class PDF: Virtual Base class representing Probability Density Functions.

Definition at line 53 of file mixtureParticleFilter.h.


Constructor & Destructor Documentation

Pdf ( unsigned int  dimension = 0)

Constructor.

Parameters:
dimensionint representing the number of rows of the state

Definition at line 150 of file mixtureParticleFilter.h.

Pdf ( unsigned int  dimension = 0)

Constructor.

Parameters:
dimensionint representing the number of rows of the state
Pdf ( unsigned int  dimension = 0)

Constructor.

Parameters:
dimensionint representing the number of rows of the state
Pdf ( unsigned int  dimension = 0)

Constructor.

Parameters:
dimensionint representing the number of rows of the state

Member Function Documentation

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 in Mixture< T >, MCPdf< T >, AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, OptimalImportanceDensity, MCPdf< T >, MCPdf< T >, MCPdf< T >, MCPdf< T >, and MCPdf< T >.

Definition at line 225 of file mixtureParticleFilter.h.

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 in Mixture< T >, MCPdf< T >, AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, OptimalImportanceDensity, MCPdf< T >, MCPdf< T >, MCPdf< T >, MCPdf< T >, and MCPdf< T >.

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 in Mixture< T >, MCPdf< T >, AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, OptimalImportanceDensity, MCPdf< T >, MCPdf< T >, MCPdf< T >, MCPdf< T >, and MCPdf< T >.

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 in Mixture< T >, MCPdf< T >, AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, OptimalImportanceDensity, MCPdf< T >, MCPdf< T >, MCPdf< T >, MCPdf< T >, and MCPdf< T >.

unsigned int DimensionGet ( ) const [inline]

Get the dimension of the argument.

Returns:
the dimension of the argument

Definition at line 169 of file mixtureParticleFilter.h.

unsigned int DimensionGet ( ) const

Get the dimension of the argument.

Returns:
the dimension of the argument
unsigned int DimensionGet ( ) const

Get the dimension of the argument.

Returns:
the dimension of the argument
unsigned int DimensionGet ( ) const

Get the dimension of the argument.

Returns:
the dimension of the argument
virtual void DimensionSet ( unsigned int  dim) [virtual]

Set the dimension of the argument.

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

Set the dimension of the argument.

Parameters:
dimthe dimension

Definition at line 175 of file mixtureParticleFilter.h.

virtual void DimensionSet ( unsigned int  dim) [virtual]

Set the dimension of the argument.

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

Set the dimension of the argument.

Parameters:
dimthe dimension
T 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 in Mixture< T >, MCPdf< T >, FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, OptimalImportanceDensity, MCPdf< T >, Mixture< T >, Mixture< T >, Mixture< T >, Mixture< T >, MCPdf< T >, MCPdf< T >, MCPdf< T >, and MCPdf< T >.

Definition at line 215 of file mixtureParticleFilter.h.

virtual T 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 in Mixture< T >, MCPdf< T >, FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, OptimalImportanceDensity, MCPdf< T >, Mixture< T >, Mixture< T >, Mixture< T >, Mixture< T >, MCPdf< T >, MCPdf< T >, MCPdf< T >, and MCPdf< T >.

virtual T 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 in Mixture< T >, MCPdf< T >, FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, OptimalImportanceDensity, MCPdf< T >, Mixture< T >, Mixture< T >, Mixture< T >, Mixture< T >, MCPdf< T >, MCPdf< T >, MCPdf< T >, and MCPdf< T >.

virtual T 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 in Mixture< T >, MCPdf< T >, FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, OptimalImportanceDensity, MCPdf< T >, Mixture< T >, Mixture< T >, Mixture< T >, Mixture< T >, MCPdf< T >, MCPdf< T >, MCPdf< T >, and MCPdf< T >.

virtual Probability ProbabilityGet ( const T &  input) const [virtual]

Get the probability of a certain argument.

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

Reimplemented in DiscretePdf, Mixture< T >, ConditionalGaussian, DiscreteConditionalPdf, DiscretePdf, Gaussian, Mixture< T >, and Uniform.

virtual Probability ProbabilityGet ( const T &  input) const [virtual]

Get the probability of a certain argument.

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

Reimplemented in DiscretePdf, Mixture< T >, ConditionalGaussian, DiscreteConditionalPdf, DiscretePdf, Gaussian, Mixture< T >, and Uniform.

Probability ProbabilityGet ( const T &  input) const [virtual]

Get the probability of a certain argument.

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

Reimplemented in DiscretePdf, Mixture< T >, ConditionalGaussian, DiscreteConditionalPdf, DiscretePdf, Gaussian, Mixture< T >, and Uniform.

Definition at line 207 of file mixtureParticleFilter.h.

virtual Probability ProbabilityGet ( const T &  input) const [virtual]

Get the probability of a certain argument.

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

Reimplemented in DiscretePdf, Mixture< T >, ConditionalGaussian, DiscreteConditionalPdf, DiscretePdf, Gaussian, Mixture< T >, and Uniform.

virtual bool SampleFrom ( vector< Sample< T > > &  list_samples,
const unsigned int  num_samples,
int  method = DEFAULT,
void *  args = NULL 
) const [virtual]

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!

Reimplemented in DiscretePdf, Mixture< T >, MCPdf< T >, and MCPdf< T >.

virtual bool SampleFrom ( Sample< T > &  one_sample,
int  method = DEFAULT,
void *  args = NULL 
) const [virtual]

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!

Reimplemented in DiscretePdf, Mixture< T >, MCPdf< T >, and MCPdf< T >.

virtual bool SampleFrom ( Sample< T > &  one_sample,
int  method = DEFAULT,
void *  args = NULL 
) const [virtual]

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!

Reimplemented in DiscretePdf, Mixture< T >, MCPdf< T >, and MCPdf< T >.

virtual bool SampleFrom ( vector< Sample< T > > &  list_samples,
const unsigned int  num_samples,
int  method = DEFAULT,
void *  args = NULL 
) const [virtual]

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!

Reimplemented in DiscretePdf, Mixture< T >, MCPdf< T >, and MCPdf< T >.

virtual bool SampleFrom ( Sample< T > &  one_sample,
int  method = DEFAULT,
void *  args = NULL 
) const [virtual]

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!

Reimplemented in DiscretePdf, Mixture< T >, MCPdf< T >, and MCPdf< T >.

virtual bool SampleFrom ( vector< Sample< T > > &  list_samples,
const unsigned int  num_samples,
int  method = DEFAULT,
void *  args = NULL 
) const [virtual]

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!

Reimplemented in DiscretePdf, Mixture< T >, MCPdf< T >, and MCPdf< T >.

bool SampleFrom ( vector< Sample< T > > &  list_samples,
const unsigned int  num_samples,
int  method = DEFAULT,
void *  args = NULL 
) const [virtual]

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!

Reimplemented in DiscretePdf, Mixture< T >, MCPdf< T >, and MCPdf< T >.

Definition at line 182 of file mixtureParticleFilter.h.

bool SampleFrom ( Sample< T > &  one_sample,
int  method = DEFAULT,
void *  args = NULL 
) const [virtual]

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!

Reimplemented in DiscretePdf, Mixture< T >, MCPdf< T >, and MCPdf< T >.

Definition at line 197 of file mixtureParticleFilter.h.


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