Monte Carlo Pdf: Sample based implementation of Pdf. More...

#include <particlefilter.h>

Inheritance diagram for MCPdf< T >:

Public Member Functions
	MCPdf (unsigned int num_samples=0, unsigned int dimension=0)
	Constructor. More...

virtual	~MCPdf ()
	destructor

	MCPdf (const MCPdf< T > &)
	copy constructor

virtual MCPdf< T > *	Clone () const
	Clone function.

bool	SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf: More...

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) More...

T	ExpectedValueGet () const
	Get the expected value E[x] of the pdf. More...

MatrixWrapper::SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. More...

void	NumSamplesSet (unsigned int num_samples)
	Set number of samples. More...

unsigned int	NumSamplesGet () const
	Get number of samples. More...

const WeightedSample< T > &	SampleGet (unsigned int i) const
	Get one sample. More...

bool	ListOfSamplesSet (const vector< WeightedSample< T > > &list_of_samples)
	Set the list of weighted samples. More...

bool	ListOfSamplesSet (const vector< Sample< T > > &list_of_samples)
	Overloading: Set the list of Samples (uniform weights) More...

const vector< WeightedSample < T > > &	ListOfSamplesGet () const
	Get the list of weighted samples. More...

bool	ListOfSamplesUpdate (const vector< WeightedSample< T > > &list_of_samples)
	Update the list of samples (overloaded) More...

bool	ListOfSamplesUpdate (const vector< Sample< T > > &list_of_samples)
	Update the list of samples (overloaded) More...

vector< double > &	CumulativePDFGet ()
	Add a sample to the list. More...

template<>
	MCPdf (unsigned int num_samples, unsigned int dimension)

template<>
	MCPdf (const MCPdf &pdf)

template<>
ColumnVector	ExpectedValueGet () const
	Get the expected value E[x] of the pdf. More...

template<>
SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. More...

template<>
unsigned int	ExpectedValueGet () const
	Get the expected value E[x] of the pdf. More...

template<>
SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. More...

virtual Probability	ProbabilityGet (const T &input) const
	Get the probability of a certain argument. 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...

Protected Member Functions
bool	SumWeightsUpdate ()
	STL-iterator for cumulative PDF list. More...

bool	NormalizeWeights ()
	Normalizing the weights.

void	CumPDFUpdate ()
	After updating weights, we have to update the cumPDF.

Protected Attributes
double	_SumWeights
	Sum of all weights: used for normalising purposes.

vector< WeightedSample< T > >	_listOfSamples
	STL-list containing the list of samples.

vector< double >	_CumPDF
	STL-iterator. More...

Detailed Description

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

Monte Carlo Pdf: Sample based implementation of Pdf.

Class Monte Carlo Pdf: This is a sample based representation of a Pdf P(x), which can both be continu or discrete

Todo:: This class can and should be made far more efficient!!!

Definition at line 49 of file particlefilter.h.

Constructor & Destructor Documentation

MCPdf	(	unsigned int	num_samples = `0`,
		unsigned int	dimension = `0`
	)

Constructor.

Parameters

num_samples	the number of samples this pdf has
dimension	the dimension of these samples. You can use this parameter to avoid runtime memory allocation and

Definition at line 171 of file particlefilter.h.

Member Function Documentation

SymmetricMatrix CovarianceGet ( ) const

inlinevirtual

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 Pdf< T >.

Definition at line 89 of file particlefilter.h.

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 from Pdf< T >.

Definition at line 541 of file particlefilter.h.

SymmetricMatrix CovarianceGet ( ) const

inlinevirtual

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 Pdf< T >.

Definition at line 131 of file particlefilter.h.

vector< double > & CumulativePDFGet ( )

Add a sample to the list.

Parameters

sample the sample to be added

Todo:: what's the best way to remove some samples?

Get the Cumulative Pdf

Returns: a vector of doubles representing the CumulativePDF

Definition at line 554 of file particlefilter.h.

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.

void DimensionSet ( unsigned int dim )

virtualinherited

Set the dimension of the argument.

Parameters

dim	the dimension

Reimplemented in Gaussian.

Definition at line 175 of file mixtureParticleFilter.h.

ColumnVector ExpectedValueGet ( ) const

inlinevirtual

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 Pdf< T >.

Definition at line 78 of file particlefilter.h.

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 from Pdf< T >.

Definition at line 529 of file particlefilter.h.

unsigned int ExpectedValueGet ( ) const

inlinevirtual

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 Pdf< T >.

Definition at line 112 of file particlefilter.h.

const vector< WeightedSample< T > > & ListOfSamplesGet ( ) const

Get the list of weighted samples.

Returns: an STL-list with the list of weighted samples

Definition at line 422 of file particlefilter.h.

bool ListOfSamplesSet ( const vector< WeightedSample< T > > & list_of_samples )

Set the list of weighted samples.

Parameters

list_of_samples an STL-list containing the list of all weighted samples

Definition at line 382 of file particlefilter.h.

bool ListOfSamplesSet ( const vector< Sample< T > > & list_of_samples )

Overloading: Set the list of Samples (uniform weights)

Parameters

list_of_samples an STL-list containing the list of all samples

Definition at line 395 of file particlefilter.h.

bool ListOfSamplesUpdate ( const vector< WeightedSample< T > > & list_of_samples )

Update the list of samples (overloaded)

Parameters

list_of_samples the list of weighted samples

Precondition: list_of_samples must contain exactly as many elements as this->NumSamplesGet() returns

Definition at line 429 of file particlefilter.h.

bool ListOfSamplesUpdate ( const vector< Sample< T > > & list_of_samples )

Update the list of samples (overloaded)

Parameters

list_of_samples the list of samples

Precondition: list_of_samples must contain exactly as many elements as this->NumSamplesGet() returns

Definition at line 441 of file particlefilter.h.

unsigned int NumSamplesGet ( ) const

Get number of samples.

Returns: the number of samples

Definition at line 330 of file particlefilter.h.

void NumSamplesSet ( unsigned int num_samples )

Set number of samples.

Parameters

num_samples the number of samples offcourse :-)

See Also: sample, weightedsample

Definition at line 344 of file particlefilter.h.

Probability ProbabilityGet ( const T & input ) const

virtualinherited

Get the probability of a certain argument.

Parameters

input T argument of the Pdf

Returns: the probability value of the argument

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

Definition at line 207 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_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 from Pdf< T >.

Definition at line 295 of file particlefilter.h.

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_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 from Pdf< T >.

Definition at line 228 of file particlefilter.h.

const WeightedSample< T > & SampleGet ( unsigned int i ) const

Get one sample.

Returns: sample

Parameters

i	the ith sample

Definition at line 336 of file particlefilter.h.

bool SumWeightsUpdate ( )

protected

STL-iterator for cumulative PDF list.

After updating weights, we have to recalculate the sum of weights

Definition at line 466 of file particlefilter.h.

Member Data Documentation

vector<double> _CumPDF

protected

STL-iterator.

STL-list containing the Cumulative PDF (for efficient sampling)

Definition at line 60 of file particlefilter.h.

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

mcpdf.h

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

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

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

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