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.
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.
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.
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.
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.
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	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)
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.
	MCPdf (const MCPdf< T > &)
	copy constructor
unsigned int	NumSamplesGet () const
	Get 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
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.
virtual	~MCPdf ()
	destructor
Protected Member Functions
void	CumPDFUpdate ()
	After updating weights, we have to update the cumPDF.
bool	NormalizeWeights ()
	Normalizing the weights.
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::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

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

template<typename T>

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

destructor

template<typename T>

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

copy constructor

template<>

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

Definition at line 31 of file mcpdf.cpp.

template<>

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

Definition at line 57 of file mcpdf.cpp.

Member Function Documentation

template<typename T>

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

Clone function.

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

template<>

SymmetricMatrix 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 88 of file mcpdf.cpp.

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 >.

template<>

SymmetricMatrix 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 130 of file mcpdf.cpp.

template<typename T>

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

After updating weights, we have to update the cumPDF.

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

template<>

ColumnVector 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 77 of file mcpdf.cpp.

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 >.

template<>

unsigned int 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 111 of file mcpdf.cpp.

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

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

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

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

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

template<typename T>

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

Normalizing the weights.

template<typename T>

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

Get number of samples.

Returns:: the number of samples

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

template<typename T>

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

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

template<typename T>

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

Get one sample.

Returns:: sample

Parameters:

i	the ith sample

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

template<typename T>

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

Definition at line 79 of file mcpdf.h.

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.

template<typename T>

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

Definition at line 75 of file mcpdf.h.

template<typename T>

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

Definition at line 78 of file mcpdf.h.

template<typename T>

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

Definition at line 80 of file mcpdf.h.

template<typename T>

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

Definition at line 81 of file mcpdf.h.

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.

template<typename T>

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

Definition at line 76 of file mcpdf.h.

template<typename T>

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

Definition at line 77 of file mcpdf.h.

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

Member Function Documentation

Member Data Documentation

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