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

#include <mcpdf.h>

Inheritance diagram for BFL::MCPdf< T >:

Public Member Functions
virtual MCPdf< T > *	Clone () const
	Clone function. More...

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

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

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

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

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

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

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

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

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...

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

template<>
	MCPdf (const MCPdf &pdf)

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

	MCPdf (const MCPdf< T > &)
	copy constructor More...

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

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

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

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

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

virtual	~MCPdf ()
	destructor More...

Public Member Functions inherited from BFL::BFL::Pdf< T >
unsigned int	DimensionGet () const
	Get the dimension of the argument. More...

virtual void	DimensionSet (unsigned int dim)
	Set the dimension of the argument. More...

	Pdf (unsigned int dimension=0)
	Constructor. More...

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

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) More...

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

virtual	~Pdf ()
	Destructor. More...

Protected Member Functions
void	CumPDFUpdate ()
	After updating weights, we have to update the cumPDF. More...

bool	NormalizeWeights ()
	Normalizing the weights. More...

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

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

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

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

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::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 mcpdf.h.

Constructor & Destructor Documentation

◆ MCPdf() [1/4]

template<typename T>

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

◆ ~MCPdf()

template<typename T>

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

virtual

destructor

◆ MCPdf() [2/4]

template<typename T>

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

copy constructor

◆ MCPdf() [3/4]

template<>

BFL::MCPdf< ColumnVector >::MCPdf	(	unsigned int	num_samples,
		unsigned int	dimension
	)

inline

Definition at line 31 of file mcpdf.cpp.

◆ MCPdf() [4/4]

template<>

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

inline

Definition at line 57 of file mcpdf.cpp.

Member Function Documentation

◆ Clone()

template<typename T>

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

virtual

Clone function.

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

◆ CovarianceGet() [1/3]

template<>

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

Definition at line 88 of file mcpdf.cpp.

◆ CovarianceGet() [2/3]

template<typename T>

MatrixWrapper::SymmetricMatrix 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 >.

◆ CovarianceGet() [3/3]

template<>

SymmetricMatrix BFL::MCPdf< unsigned int >::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 BFL::BFL::Pdf< T >.

Definition at line 130 of file mcpdf.cpp.

◆ CumPDFUpdate()

template<typename T>

void BFL::MCPdf< T >::CumPDFUpdate ( )

protected

After updating weights, we have to update the cumPDF.

◆ CumulativePDFGet()

template<typename T>

vector<double>& 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

◆ ExpectedValueGet() [1/3]

template<>

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

Definition at line 77 of file mcpdf.cpp.

◆ ExpectedValueGet() [2/3]

template<typename T>

T 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 >.

◆ ExpectedValueGet() [3/3]

template<>

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

Definition at line 111 of file mcpdf.cpp.

◆ ListOfSamplesGet()

template<typename T>

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

Get the list of weighted samples.

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

◆ ListOfSamplesSet() [1/2]

template<typename T>

bool 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

◆ ListOfSamplesSet() [2/2]

template<typename T>

bool 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

◆ ListOfSamplesUpdate() [1/2]

template<typename T>

bool 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

◆ ListOfSamplesUpdate() [2/2]

template<typename T>

bool 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

◆ NormalizeWeights()

template<typename T>

bool BFL::MCPdf< T >::NormalizeWeights ( )

protected

Normalizing the weights.

◆ NumSamplesGet()

template<typename T>

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

Get number of samples.

Returns: the number of samples

◆ NumSamplesSet()

template<typename T>

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

Set number of samples.

Parameters

num_samples the number of samples offcourse :-)

See also: sample, weightedsample

◆ SampleFrom() [1/2]

template<typename T>

bool BFL::MCPdf< T >::SampleFrom	(	Sample< T > &	one_sample,
		int	method = `DEFAULT`,
		void *	args = `NULL`
	)		const

◆ SampleFrom() [2/2]

template<typename T>

bool BFL::MCPdf< T >::SampleFrom	(	vector< Sample< T > > &	list_samples,
		const unsigned int	num_samples,
		int	method = `DEFAULT`,
		void *	args = `NULL`
	)		const

◆ SampleGet()

template<typename T>

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

Get one sample.

Returns: sample

Parameters

i	the ith sample

◆ SumWeightsUpdate()

template<typename T>

bool 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

◆ _covariance

template<typename T>

SymmetricMatrix BFL::MCPdf< T >::_covariance

mutableprivate

Definition at line 79 of file mcpdf.h.

◆ _CumPDF

template<typename T>

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

protected

STL-iterator.

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

Definition at line 60 of file mcpdf.h.

◆ _CumSum

template<typename T>

T BFL::MCPdf< T >::_CumSum

mutableprivate

Definition at line 75 of file mcpdf.h.

◆ _diff

template<typename T>

T BFL::MCPdf< T >::_diff

mutableprivate

Definition at line 78 of file mcpdf.h.

◆ _diffsum

template<typename T>

Matrix BFL::MCPdf< T >::_diffsum

mutableprivate

Definition at line 80 of file mcpdf.h.

◆ _it_los

template<typename T>

vector<WeightedSample<T> >::iterator BFL::MCPdf< T >::_it_los

mutableprivate

Definition at line 81 of file mcpdf.h.

◆ _listOfSamples

template<typename T>

vector<WeightedSample<T> > BFL::MCPdf< T >::_listOfSamples

protected

STL-list containing the list of samples.

Definition at line 56 of file mcpdf.h.

◆ _los

template<typename T>

vector<WeightedSample<T> > BFL::MCPdf< T >::_los

mutableprivate

Definition at line 76 of file mcpdf.h.

◆ _mean

template<typename T>

T BFL::MCPdf< T >::_mean

mutableprivate

Definition at line 77 of file mcpdf.h.

◆ _SumWeights

template<typename T>

double BFL::MCPdf< T >::_SumWeights

protected

Sum of all weights: used for normalising purposes.

Definition at line 54 of file mcpdf.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::MCPdf< T >

Constructor & Destructor Documentation

◆ MCPdf() [1/4]

◆ ~MCPdf()

◆ MCPdf() [2/4]

◆ MCPdf() [3/4]

◆ MCPdf() [4/4]

Member Function Documentation

◆ Clone()

◆ CovarianceGet() [1/3]

◆ CovarianceGet() [2/3]

◆ CovarianceGet() [3/3]

◆ CumPDFUpdate()

◆ CumulativePDFGet()

◆ ExpectedValueGet() [1/3]

◆ ExpectedValueGet() [2/3]

◆ ExpectedValueGet() [3/3]

◆ ListOfSamplesGet()

◆ ListOfSamplesSet() [1/2]

◆ ListOfSamplesSet() [2/2]

◆ ListOfSamplesUpdate() [1/2]

◆ ListOfSamplesUpdate() [2/2]

◆ NormalizeWeights()

◆ NumSamplesGet()

◆ NumSamplesSet()

◆ SampleFrom() [1/2]

◆ SampleFrom() [2/2]

◆ SampleGet()

◆ SumWeightsUpdate()

Member Data Documentation

◆ _covariance

◆ _CumPDF

◆ _CumSum

◆ _diff

◆ _diffsum

◆ _it_los

◆ _listOfSamples

◆ _los

◆ _mean

◆ _SumWeights

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