BFL::BFL::Pdf< T > Class Template Reference

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

#include <mixtureParticleFilter.h>

Inheritance diagram for BFL::BFL::Pdf< T >:

List of all members.

Public Member Functions
virtual Pdf< T > *	Clone () const =0
	Pure virtual clone function.
virtual Pdf< T > *	Clone () const =0
	Pure virtual clone function.
virtual Pdf< T > *	Clone () const =0
	Pure virtual clone function.
virtual Pdf< T > *	Clone () const =0
	Pure virtual clone function.
virtual MatrixWrapper::SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
virtual MatrixWrapper::SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
virtual MatrixWrapper::SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
virtual MatrixWrapper::SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
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.
virtual T	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
virtual T	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
virtual T	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
virtual T	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
	Pdf (unsigned int dimension=0)
	Constructor.
	Pdf (unsigned int dimension=0)
	Constructor.
	Pdf (unsigned int dimension=0)
	Constructor.
	Pdf (unsigned int dimension=0)
	Constructor.
virtual Probability	ProbabilityGet (const T &input) const
	Get the probability of a certain argument.
virtual Probability	ProbabilityGet (const T &input) const
	Get the probability of a certain argument.
virtual Probability	ProbabilityGet (const T &input) const
	Get the probability of a certain argument.
virtual Probability	ProbabilityGet (const T &input) const
	Get the probability of a certain argument.
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 (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 (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 (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 bool	SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf:
virtual bool	SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf:
virtual bool	SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf:
virtual	~Pdf ()
	Destructor.
virtual	~Pdf ()
	Destructor.
virtual	~Pdf ()
	Destructor.
virtual	~Pdf ()
	Destructor.
Private Attributes
unsigned int	_dimension
	Dimension of the argument x of P(x | ...).

---

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

template<typename T >

BFL::BFL::Pdf< T >::Pdf

(

unsigned int

dimension = 0

)

Constructor.

Parameters:

dimension

int representing the number of rows of the state

Definition at line 150 of file mixtureParticleFilter.h.

template<typename T >

BFL::BFL::Pdf< T >::~Pdf

(

)

[virtual]

Destructor.

Definition at line 161 of file mixtureParticleFilter.h.

template<typename T>

BFL::BFL::Pdf< T >::Pdf

(

unsigned int

dimension = 0

)

Constructor.

Parameters:

dimension

int representing the number of rows of the state

template<typename T>

virtual BFL::BFL::Pdf< T >::~Pdf

(

)

[virtual]

Destructor.

template<typename T>

BFL::BFL::Pdf< T >::Pdf

(

unsigned int

dimension = 0

)

Constructor.

Parameters:

dimension

int representing the number of rows of the state

template<typename T>

virtual BFL::BFL::Pdf< T >::~Pdf

(

)

[virtual]

Destructor.

template<typename T>

BFL::BFL::Pdf< T >::Pdf

(

unsigned int

dimension = 0

)

Constructor.

Parameters:

dimension

int representing the number of rows of the state

template<typename T>

virtual BFL::BFL::Pdf< T >::~Pdf

(

)

[virtual]

Destructor.

---

Member Function Documentation

template<typename T>

virtual Pdf<T>* BFL::BFL::Pdf< T >::Clone

(

)

const [pure virtual]

Pure virtual clone function.

Implemented in BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::ConditionalPdf< Var, CondArg >, BFL::ConditionalPdf< int, int >, BFL::ConditionalPdf< MeasVar, StateVar >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, int >, BFL::ConditionalPdf< T, T >, BFL::ConditionalPdf< StateVar, StateVar >, BFL::ConditionalPdf< ColumnVector, ColumnVector >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::LinearAnalyticConditionalGaussian, BFL::Gaussian, BFL::Uniform, and BFL::ConditionalGaussian.

template<typename T>

virtual Pdf<T>* BFL::BFL::Pdf< T >::Clone

(

)

const [pure virtual]

Pure virtual clone function.

Implemented in BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::ConditionalPdf< Var, CondArg >, BFL::ConditionalPdf< int, int >, BFL::ConditionalPdf< MeasVar, StateVar >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, int >, BFL::ConditionalPdf< T, T >, BFL::ConditionalPdf< StateVar, StateVar >, BFL::ConditionalPdf< ColumnVector, ColumnVector >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::LinearAnalyticConditionalGaussian, BFL::Gaussian, BFL::Uniform, and BFL::ConditionalGaussian.

template<typename T>

virtual Pdf<T>* BFL::BFL::Pdf< T >::Clone

(

)

const [pure virtual]

Pure virtual clone function.

Implemented in BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::ConditionalPdf< Var, CondArg >, BFL::ConditionalPdf< int, int >, BFL::ConditionalPdf< MeasVar, StateVar >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, int >, BFL::ConditionalPdf< T, T >, BFL::ConditionalPdf< StateVar, StateVar >, BFL::ConditionalPdf< ColumnVector, ColumnVector >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::LinearAnalyticConditionalGaussian, BFL::Gaussian, BFL::Uniform, and BFL::ConditionalGaussian.

template<typename T>

virtual Pdf<T>* BFL::BFL::Pdf< T >::Clone

(

)

const [pure virtual]

Pure virtual clone function.

Implemented in BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::ConditionalPdf< Var, CondArg >, BFL::ConditionalPdf< int, int >, BFL::ConditionalPdf< MeasVar, StateVar >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, int >, BFL::ConditionalPdf< T, T >, BFL::ConditionalPdf< StateVar, StateVar >, BFL::ConditionalPdf< ColumnVector, ColumnVector >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::LinearAnalyticConditionalGaussian, BFL::Gaussian, BFL::Uniform, and BFL::ConditionalGaussian.

template<typename T >

MatrixWrapper::SymmetricMatrix BFL::BFL::Pdf< T >::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 BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::ConditionalGaussianAdditiveNoise, BFL::AnalyticConditionalGaussianAdditiveNoise, BFL::FilterProposalDensity, and BFL::OptimalImportanceDensity.

Definition at line 225 of file mixtureParticleFilter.h.

template<typename T>

virtual MatrixWrapper::SymmetricMatrix BFL::BFL::Pdf< T >::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 BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::ConditionalGaussianAdditiveNoise, BFL::AnalyticConditionalGaussianAdditiveNoise, BFL::FilterProposalDensity, and BFL::OptimalImportanceDensity.

template<typename T>

virtual MatrixWrapper::SymmetricMatrix BFL::BFL::Pdf< T >::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 BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::ConditionalGaussianAdditiveNoise, BFL::AnalyticConditionalGaussianAdditiveNoise, BFL::FilterProposalDensity, and BFL::OptimalImportanceDensity.

template<typename T>

virtual MatrixWrapper::SymmetricMatrix BFL::BFL::Pdf< T >::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 BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::ConditionalGaussianAdditiveNoise, BFL::AnalyticConditionalGaussianAdditiveNoise, BFL::FilterProposalDensity, and BFL::OptimalImportanceDensity.

template<typename T >

unsigned int BFL::BFL::Pdf< T >::DimensionGet

(

)

const [inline]

Get the dimension of the argument.

Returns:

the dimension of the argument

Definition at line 169 of file mixtureParticleFilter.h.

template<typename T>

unsigned int BFL::BFL::Pdf< T >::DimensionGet

(

)

const

Get the dimension of the argument.

Returns:

the dimension of the argument

template<typename T>

unsigned int BFL::BFL::Pdf< T >::DimensionGet

(

)

const

Get the dimension of the argument.

Returns:

the dimension of the argument

template<typename T>

unsigned int BFL::BFL::Pdf< T >::DimensionGet

(

)

const

Get the dimension of the argument.

Returns:

the dimension of the argument

template<typename T>

virtual void BFL::BFL::Pdf< T >::DimensionSet

(

unsigned int

dim

)

[virtual]

Set the dimension of the argument.

Parameters:

dim	the dimension

Reimplemented in BFL::Gaussian.

template<typename T >

void BFL::BFL::Pdf< T >::DimensionSet

(

unsigned int

dim

)

[virtual]

Set the dimension of the argument.

Parameters:

dim	the dimension

Reimplemented in BFL::Gaussian.

Definition at line 175 of file mixtureParticleFilter.h.

template<typename T>

virtual void BFL::BFL::Pdf< T >::DimensionSet

(

unsigned int

dim

)

[virtual]

Set the dimension of the argument.

Parameters:

dim	the dimension

Reimplemented in BFL::Gaussian.

template<typename T>

virtual void BFL::BFL::Pdf< T >::DimensionSet

(

unsigned int

dim

)

[virtual]

Set the dimension of the argument.

Parameters:

dim	the dimension

Reimplemented in BFL::Gaussian.

template<typename T >

T BFL::BFL::Pdf< 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 BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::LinearAnalyticConditionalGaussian, BFL::FilterProposalDensity, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::OptimalImportanceDensity, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::Mixture< T >, and BFL::Mixture< T >.

Definition at line 215 of file mixtureParticleFilter.h.

template<typename T>

virtual T BFL::BFL::Pdf< 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 BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::LinearAnalyticConditionalGaussian, BFL::FilterProposalDensity, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::OptimalImportanceDensity, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::Mixture< T >, and BFL::Mixture< T >.

template<typename T>

virtual T BFL::BFL::Pdf< 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 BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::LinearAnalyticConditionalGaussian, BFL::FilterProposalDensity, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::OptimalImportanceDensity, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::Mixture< T >, and BFL::Mixture< T >.

template<typename T>

virtual T BFL::BFL::Pdf< 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 BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::LinearAnalyticConditionalGaussian, BFL::FilterProposalDensity, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::OptimalImportanceDensity, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::Mixture< T >, and BFL::Mixture< T >.

template<typename T>

virtual Probability BFL::BFL::Pdf< T >::ProbabilityGet

(

const T &

input

)

const [virtual]

Get the probability of a certain argument.

Parameters:

input

T argument of the Pdf

Returns:

the probability value of the argument

Reimplemented in BFL::DiscreteConditionalPdf, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::Gaussian, BFL::Uniform, BFL::ConditionalGaussian, BFL::ConditionalUniformMeasPdf1d, and BFL::NonlinearMeasurementPdf.

template<typename T>

virtual Probability BFL::BFL::Pdf< T >::ProbabilityGet

(

const T &

input

)

const [virtual]

Get the probability of a certain argument.

Parameters:

input

T argument of the Pdf

Returns:

the probability value of the argument

Reimplemented in BFL::DiscreteConditionalPdf, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::Gaussian, BFL::Uniform, BFL::ConditionalGaussian, BFL::ConditionalUniformMeasPdf1d, and BFL::NonlinearMeasurementPdf.

template<typename T>

Probability BFL::BFL::Pdf< T >::ProbabilityGet

(

const T &

input

)

const [virtual]

Get the probability of a certain argument.

Parameters:

input

T argument of the Pdf

Returns:

the probability value of the argument

Reimplemented in BFL::DiscreteConditionalPdf, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::Gaussian, BFL::Uniform, BFL::ConditionalGaussian, BFL::ConditionalUniformMeasPdf1d, and BFL::NonlinearMeasurementPdf.

Definition at line 207 of file mixtureParticleFilter.h.

template<typename T>

virtual Probability BFL::BFL::Pdf< T >::ProbabilityGet

(

const T &

input

)

const [virtual]

Get the probability of a certain argument.

Parameters:

input

T argument of the Pdf

Returns:

the probability value of the argument

Reimplemented in BFL::DiscreteConditionalPdf, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::Gaussian, BFL::Uniform, BFL::ConditionalGaussian, BFL::ConditionalUniformMeasPdf1d, and BFL::NonlinearMeasurementPdf.

template<typename T>

bool BFL::BFL::Pdf< T >::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_samples	list of samples that will contain result of sampling
num_samples	Number of Samples to be drawn (iid)
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer 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 BFL::BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, and BFL::BFL::DiscretePdf.

Definition at line 182 of file mixtureParticleFilter.h.

template<typename T>

virtual bool BFL::BFL::Pdf< T >::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_samples	list of samples that will contain result of sampling
num_samples	Number of Samples to be drawn (iid)
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer 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 BFL::BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, and BFL::BFL::DiscretePdf.

template<typename T>

virtual bool BFL::BFL::Pdf< T >::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_samples	list of samples that will contain result of sampling
num_samples	Number of Samples to be drawn (iid)
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer 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 BFL::BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, and BFL::BFL::DiscretePdf.

template<typename T>

virtual bool BFL::BFL::Pdf< T >::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_samples	list of samples that will contain result of sampling
num_samples	Number of Samples to be drawn (iid)
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer 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 BFL::BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, and BFL::BFL::DiscretePdf.

template<typename T>

virtual bool BFL::BFL::Pdf< T >::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_sample	sample that will contain result of sampling
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer to a struct representing extra sample arguments

See also:

SampleFrom()

Bug:

Sometimes the compiler doesn't know which method to choose!

Reimplemented in BFL::BFL::Mixture< T >, BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::DiscretePdf, and BFL::DiscretePdf.

template<typename T>

virtual bool BFL::BFL::Pdf< T >::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_sample	sample that will contain result of sampling
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer to a struct representing extra sample arguments

See also:

SampleFrom()

Bug:

Sometimes the compiler doesn't know which method to choose!

Reimplemented in BFL::BFL::Mixture< T >, BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::DiscretePdf, and BFL::DiscretePdf.

template<typename T>

virtual bool BFL::BFL::Pdf< T >::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_sample	sample that will contain result of sampling
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer to a struct representing extra sample arguments

See also:

SampleFrom()

Bug:

Sometimes the compiler doesn't know which method to choose!

Reimplemented in BFL::BFL::Mixture< T >, BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::DiscretePdf, and BFL::DiscretePdf.

template<typename T>

bool BFL::BFL::Pdf< T >::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_sample	sample that will contain result of sampling
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer to a struct representing extra sample arguments

See also:

SampleFrom()

Bug:

Sometimes the compiler doesn't know which method to choose!

Reimplemented in BFL::BFL::Mixture< T >, BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::DiscretePdf, and BFL::DiscretePdf.

Definition at line 197 of file mixtureParticleFilter.h.

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Member Data Documentation

template<typename T>

unsigned int BFL::BFL::Pdf< T >::_dimension [private]

Dimension of the argument x of P(x | ...).

Definition at line 145 of file mixtureParticleFilter.h.

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

• pdf.h