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

#include <particlefilter.h>

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

Public Member Functions
virtual MCPdf< T > *	Clone () const
	Clone function.
virtual MCPdf< T > *	Clone () const
	Clone function.
template<>
SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
template<>
SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
MatrixWrapper::SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
MatrixWrapper::SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
template<>
SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
template<>
SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
vector< double > &	CumulativePDFGet ()
	Add a sample to the list.
vector< double > &	CumulativePDFGet ()
	Add a sample to the list.
template<>
ColumnVector	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
template<>
ColumnVector	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
T	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
T	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
template<>
unsigned int	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
template<>
unsigned int	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
const vector< WeightedSample < T > > &	ListOfSamplesGet () const
	Get the list of weighted samples.
const vector< WeightedSample < T > > &	ListOfSamplesGet () const
	Get the list of weighted samples.
bool	ListOfSamplesSet (const vector< WeightedSample< T > > &list_of_samples)
	Set the list of weighted samples.
bool	ListOfSamplesSet (const vector< WeightedSample< T > > &list_of_samples)
	Set the list of weighted samples.
bool	ListOfSamplesSet (const vector< Sample< T > > &list_of_samples)
	Overloading: Set the list of Samples (uniform weights)
bool	ListOfSamplesSet (const vector< Sample< T > > &list_of_samples)
	Overloading: Set the list of Samples (uniform weights)
bool	ListOfSamplesUpdate (const vector< WeightedSample< T > > &list_of_samples)
	Update the list of samples (overloaded)
bool	ListOfSamplesUpdate (const vector< WeightedSample< T > > &list_of_samples)
	Update the list of samples (overloaded)
bool	ListOfSamplesUpdate (const vector< Sample< T > > &list_of_samples)
	Update the list of samples (overloaded)
bool	ListOfSamplesUpdate (const vector< Sample< T > > &list_of_samples)
	Update the list of samples (overloaded)
template<>
	MCPdf (unsigned int num_samples, unsigned int dimension)
template<>
	MCPdf (unsigned int num_samples, unsigned int dimension)
template<>
	MCPdf (const MCPdf &pdf)
template<>
	MCPdf (const MCPdf &pdf)
	MCPdf (unsigned int num_samples=0, unsigned int dimension=0)
	Constructor.
	MCPdf (unsigned int num_samples=0, unsigned int dimension=0)
	Constructor.
	MCPdf (const MCPdf< T > &)
	copy constructor
	MCPdf (const MCPdf< T > &)
	copy constructor
unsigned int	NumSamplesGet () const
	Get number of samples.
unsigned int	NumSamplesGet () const
	Get number of samples.
void	NumSamplesSet (unsigned int num_samples)
	Set number of samples.
void	NumSamplesSet (unsigned int num_samples)
	Set number of samples.
bool	SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf:
bool	SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf:
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)
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)
const WeightedSample< T > &	SampleGet (unsigned int i) const
	Get one sample.
const WeightedSample< T > &	SampleGet (unsigned int i) const
	Get one sample.
virtual	~MCPdf ()
	destructor
virtual	~MCPdf ()
	destructor
Protected Member Functions
void	CumPDFUpdate ()
	After updating weights, we have to update the cumPDF.
void	CumPDFUpdate ()
	After updating weights, we have to update the cumPDF.
bool	NormalizeWeights ()
	Normalizing the weights.
bool	NormalizeWeights ()
	Normalizing the weights.
bool	SumWeightsUpdate ()
	STL-iterator for cumulative PDF list.
bool	SumWeightsUpdate ()
	STL-iterator for cumulative PDF list.
Protected Attributes
vector< double >	_CumPDF
	STL-iterator.
vector< WeightedSample< T > >	_listOfSamples
	STL-list containing the list of samples.
double	_SumWeights
	Sum of all weights: used for normalising purposes.
Private Attributes
SymmetricMatrix	_covariance
T	_CumSum
T	_diff
Matrix	_diffsum
vector< WeightedSample< T > >::iterator	_it_los
vector< WeightedSample< T > >	_los
T	_mean

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!!!

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

template<typename T >

BFL::BFL::MCPdf< T >::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.

template<typename T >

BFL::BFL::MCPdf< T >::~MCPdf ( ) [virtual]

destructor

Definition at line 195 of file particlefilter.h.

template<typename T >

BFL::BFL::MCPdf< T >::MCPdf ( const MCPdf< T > & pdf )

copy constructor

Definition at line 204 of file particlefilter.h.

template<typename T>

BFL::BFL::MCPdf< T >::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

template<typename T>

virtual BFL::BFL::MCPdf< T >::~MCPdf ( ) [virtual]

destructor

template<typename T>

BFL::BFL::MCPdf< T >::MCPdf ( const MCPdf< T > & )

copy constructor

template<>

BFL::BFL::MCPdf< ColumnVector >::MCPdf	(	unsigned int	num_samples,
		unsigned int	dimension
	)		`[inline]`

Definition at line 32 of file particlefilter.h.

template<>

BFL::BFL::MCPdf< ColumnVector >::MCPdf ( const MCPdf< T > & pdf ) [inline]

Definition at line 58 of file particlefilter.h.

template<>

BFL::BFL::MCPdf< ColumnVector >::MCPdf	(	unsigned int	num_samples,
		unsigned int	dimension
	)		`[inline]`

Definition at line 32 of file particlesmoother.h.

template<>

BFL::BFL::MCPdf< ColumnVector >::MCPdf ( const MCPdf< T > & pdf ) [inline]

Definition at line 58 of file particlesmoother.h.

Member Function Documentation

template<typename T >

MCPdf< T > * BFL::BFL::MCPdf< T >::Clone ( ) const [virtual]

Clone function.

Implements BFL::BFL::Pdf< T >.

Definition at line 222 of file particlefilter.h.

template<typename T>

virtual MCPdf<T>* BFL::BFL::MCPdf< T >::Clone ( ) const [virtual]

Clone function.

Implements BFL::BFL::Pdf< T >.

template<>

SymmetricMatrix BFL::BFL::MCPdf< ColumnVector >::CovarianceGet ( ) const [inline, 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< T >.

Definition at line 89 of file particlefilter.h.

template<>

SymmetricMatrix BFL::BFL::MCPdf< ColumnVector >::CovarianceGet ( ) const [inline, 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< T >.

Definition at line 89 of file particlesmoother.h.

template<typename T >

MatrixWrapper::SymmetricMatrix BFL::BFL::MCPdf< 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 from BFL::BFL::Pdf< T >.

Definition at line 541 of file particlefilter.h.

template<typename T>

MatrixWrapper::SymmetricMatrix BFL::BFL::MCPdf< 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 from BFL::BFL::Pdf< T >.

template<>

SymmetricMatrix BFL::BFL::MCPdf< unsigned int >::CovarianceGet ( ) const [inline, 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< T >.

Definition at line 131 of file particlefilter.h.

template<>

SymmetricMatrix BFL::BFL::MCPdf< unsigned int >::CovarianceGet ( ) const [inline, 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< T >.

Definition at line 131 of file particlesmoother.h.

template<typename T >

void BFL::BFL::MCPdf< T >::CumPDFUpdate ( ) [protected]

After updating weights, we have to update the cumPDF.

Definition at line 510 of file particlefilter.h.

template<typename T>

void BFL::BFL::MCPdf< T >::CumPDFUpdate ( ) [protected]

After updating weights, we have to update the cumPDF.

template<typename T >

vector< double > & BFL::BFL::MCPdf< T >::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.

template<typename T>

vector<double>& BFL::BFL::MCPdf< T >::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

template<>

ColumnVector BFL::BFL::MCPdf< ColumnVector >::ExpectedValueGet ( ) const [inline, 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< T >.

Definition at line 78 of file particlefilter.h.

template<>

ColumnVector BFL::BFL::MCPdf< ColumnVector >::ExpectedValueGet ( ) const [inline, 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< T >.

Definition at line 78 of file particlesmoother.h.

template<typename T >

T BFL::BFL::MCPdf< 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 BFL::BFL::Pdf< T >.

Definition at line 529 of file particlefilter.h.

template<typename T>

T BFL::BFL::MCPdf< 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 BFL::BFL::Pdf< T >.

template<>

unsigned int BFL::BFL::MCPdf< unsigned int >::ExpectedValueGet ( ) const [inline, 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< T >.

Definition at line 112 of file particlefilter.h.

template<>

unsigned int BFL::BFL::MCPdf< unsigned int >::ExpectedValueGet ( ) const [inline, 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< T >.

Definition at line 112 of file particlesmoother.h.

template<typename T >

const vector< WeightedSample< T > > & BFL::BFL::MCPdf< 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.

template<typename T>

const vector<WeightedSample<T> >& BFL::BFL::MCPdf< T >::ListOfSamplesGet ( ) const

Get the list of weighted samples.

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

template<typename T >

bool BFL::BFL::MCPdf< T >::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.

template<typename T>

bool BFL::BFL::MCPdf< T >::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

template<typename T>

bool BFL::BFL::MCPdf< T >::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

template<typename T >

bool BFL::BFL::MCPdf< T >::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.

template<typename T>

bool BFL::BFL::MCPdf< T >::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

template<typename T >

bool BFL::BFL::MCPdf< T >::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.

template<typename T>

bool BFL::BFL::MCPdf< T >::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

template<typename T >

bool BFL::BFL::MCPdf< T >::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.

template<typename T>

bool BFL::BFL::MCPdf< T >::NormalizeWeights ( ) [protected]

Normalizing the weights.

template<typename T >

bool BFL::BFL::MCPdf< T >::NormalizeWeights ( ) [protected]

Normalizing the weights.

Definition at line 492 of file particlefilter.h.

template<typename T >

unsigned int BFL::BFL::MCPdf< T >::NumSamplesGet ( ) const

Get number of samples.

Returns:: the number of samples

Definition at line 330 of file particlefilter.h.

template<typename T>

unsigned int BFL::BFL::MCPdf< T >::NumSamplesGet ( ) const

Get number of samples.

Returns:: the number of samples

template<typename T>

void BFL::BFL::MCPdf< T >::NumSamplesSet ( unsigned int num_samples )

Set number of samples.

Parameters:

num_samples the number of samples offcourse :-)

See also:: sample, weightedsample

template<typename T >

void BFL::BFL::MCPdf< T >::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.

template<typename T>

bool BFL::BFL::MCPdf< 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 from BFL::BFL::Pdf< T >.

template<typename T >

bool BFL::BFL::MCPdf< 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 from BFL::BFL::Pdf< T >.

Definition at line 295 of file particlefilter.h.

template<typename T >

bool BFL::BFL::MCPdf< 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 from BFL::BFL::Pdf< T >.

Definition at line 228 of file particlefilter.h.

template<typename T>

bool BFL::BFL::MCPdf< 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 from BFL::BFL::Pdf< T >.

template<typename T>

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

Get one sample.

Returns:: sample

Parameters:

i	the ith sample

template<typename T >

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

Get one sample.

Returns:: sample

Parameters:

i	the ith sample

Definition at line 336 of file particlefilter.h.

template<typename T >

bool BFL::BFL::MCPdf< T >::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.

template<typename T>

bool BFL::BFL::MCPdf< T >::SumWeightsUpdate ( ) [protected]

STL-iterator for cumulative PDF list.

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

Member Data Documentation

template<typename T>

SymmetricMatrix BFL::BFL::MCPdf< T >::_covariance [mutable, private]

Definition at line 79 of file particlefilter.h.

template<typename T>

vector< double > BFL::BFL::MCPdf< T >::_CumPDF [protected]

STL-iterator.

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

Definition at line 60 of file particlefilter.h.

template<typename T>

T BFL::BFL::MCPdf< T >::_CumSum [mutable, private]

Definition at line 75 of file particlefilter.h.

template<typename T>

T BFL::BFL::MCPdf< T >::_diff [mutable, private]

Definition at line 78 of file particlefilter.h.

template<typename T>

Matrix BFL::BFL::MCPdf< T >::_diffsum [mutable, private]

Definition at line 80 of file particlefilter.h.

template<typename T>

vector< WeightedSample< T > >::iterator BFL::BFL::MCPdf< T >::_it_los [mutable, private]

Definition at line 81 of file particlefilter.h.

template<typename T>

vector< WeightedSample< T > > BFL::BFL::MCPdf< T >::_listOfSamples [protected]

STL-list containing the list of samples.

Definition at line 56 of file particlefilter.h.

template<typename T>

vector< WeightedSample< T > > BFL::BFL::MCPdf< T >::_los [mutable, private]

Definition at line 76 of file particlefilter.h.

template<typename T>

T BFL::BFL::MCPdf< T >::_mean [mutable, private]

Definition at line 77 of file particlefilter.h.

template<typename T>

double BFL::BFL::MCPdf< T >::_SumWeights [protected]

Sum of all weights: used for normalising purposes.

Definition at line 54 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

Private 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 >