Interface class for generic sampling of a distribution, i.e.

generating random numbers according to arbitrary distributions

Definition at line 57 of file DistSampler.h.

Public Member Functions
	DistSampler ()
	default constructor

virtual	~DistSampler ()
	virtual destructor

virtual bool	Generate (unsigned int nevt, const int *nbins, ROOT::Fit::BinData &data, bool extend=true)
	generate a bin data set .

bool	Generate (unsigned int nevt, int nbins, double xmin, double xmax, ROOT::Fit::BinData &data, bool extend=true)
	same as before but passing the range in case of 1 dim data

virtual bool	Generate (unsigned int nevt, ROOT::Fit::UnBinData &data)
	generate a un-binned data sets (fill the given data set) if dataset has already data append to it

virtual TRandom *	GetRandom ()
	Get the random engine used by the sampler To be implemented by the derived classes who needs it Returns zero by default.

virtual bool	Init (const char *="")
	initialize the generators with the given algorithm Implemented by derived classes who needs it (like UnuranSampler) If nothing is specified use default algorithm from DistSamplerOptions::SetDefaultAlgorithm

virtual bool	Init (const DistSamplerOptions &opt)
	initialize the generators with the given option which my include the algorithm but also more if the method is re-impelmented by derived class The default implementation calls the above method passing just the algorithm name

unsigned int	NDim () const
	return the dimension of the parent distribution (and the data)

const ROOT::Math::IMultiGenFunction &	ParentPdf () const
	get the parent distribution function (must be called after setting the function)

const double *	Sample ()
	sample one event and rerturning array x with coordinates

virtual bool	Sample (double *x)=0
	sample one event in multi-dimension by filling the given array return false if sampling failed

virtual double	Sample1D ()
	sample one event in one dimension better implementation could be provided by the derived classes

virtual bool	SampleBin (double prob, double &value, double *error=0)
	sample one bin given an estimated of the pdf in the bin (this can be function value at the center or its integral in the bin divided by the bin width) By default do not do random sample, just return the function values Typically Poisson statistics will be used

virtual bool	SampleBins (unsigned int n, const double prob, double values, double *errors=0)
	sample a set of bins given a vector of probabilities Typically multinomial statistics will be used and the sum of the probabilities will be equal to the total number of events to be generated For sampling the bins indipendently, SampleBin should be used

virtual void	SetArea (double)
	set the normalization area of distribution implemented by derived classes if needed

virtual void	SetFunction (const ROOT::Math::IGenFunction &func)
	set the parent function distribution to use for random sampling (one dim case)

virtual void	SetFunction (const ROOT::Math::IMultiGenFunction &func)
	set the parent function distribution to use for random sampling (multi-dim case)

template<class Function >
void	SetFunction (Function &func, unsigned int dim)
	set the parent function distribution to use for sampling (generic case)

virtual void	SetMode (double)
	set the mode of the distribution (could be useful to some methods) implemented by derived classes if needed

virtual void	SetRandom (TRandom *)
	Set the random engine to be used To be implemented by the derived classes who provides random sampling.

void	SetRange (const double xmin, const double xmax)
	set range for all dimensions

void	SetRange (const ROOT::Fit::DataRange &range)
	set range using DataRange class

void	SetRange (double xmin, double xmax, int icoord=0)
	set range in a given dimension

virtual void	SetSeed (unsigned int)
	Set the random seed for the TRandom instances used by the sampler classes To be implemented by the derived classes who provides random sampling.

Protected Member Functions
virtual void	DoSetFunction (const ROOT::Math::IMultiGenFunction &func, bool copy)

bool	IsInitialized ()

const ROOT::Fit::DataRange &	PdfRange () const
	return the data range of the Pdf . Must be called after setting the function

Private Attributes
std::vector< double >	fData

const ROOT::Math::IMultiGenFunction *	fFunc

bool	fOwnFunc

ROOT::Fit::DataRange *	fRange

#include <Math/DistSampler.h>

Inheritance diagram for ROOT::Math::DistSampler:

[legend]

Constructor & Destructor Documentation

◆ DistSampler()

ROOT::Math::DistSampler::DistSampler ( )

inline

default constructor

Definition at line 62 of file DistSampler.h.

◆ ~DistSampler()

ROOT::Math::DistSampler::~DistSampler ( )

virtual

virtual destructor

Definition at line 29 of file DistSampler.cxx.

Member Function Documentation

◆ DoSetFunction()

void ROOT::Math::DistSampler::DoSetFunction	(	const ROOT::Math::IMultiGenFunction &	func,
		bool	copy
	)

protectedvirtual

Definition at line 63 of file DistSampler.cxx.

◆ Generate() [1/3]

bool ROOT::Math::DistSampler::Generate	(	unsigned int	nevt,
		const int *	nbins,
		ROOT::Fit::BinData &	data,
		bool	extend = `true`
	)

virtual

generate a bin data set .

A range must have been set before (otherwise inf is returned) and the bins are equidinstant in the previously defined range bin center values must be present in given data set If the sampler is implemented by a random one, the entries will be binned according to the Poisson distribution It is assumed the distribution is normalized, otherwise the nevt must be scaled accordingly. The expected value/bin nexp = f(x_i) * binArea/ nevt Extend control if use a fixed (i.e. multinomial statistics) or floating total number of events

Definition at line 112 of file DistSampler.cxx.

◆ Generate() [2/3]

bool ROOT::Math::DistSampler::Generate	(	unsigned int	nevt,
		int	nbins,
		double	xmin,
		double	xmax,
		ROOT::Fit::BinData &	data,
		bool	extend = `true`
	)

inline

same as before but passing the range in case of 1 dim data

Definition at line 227 of file DistSampler.h.

◆ Generate() [3/3]

bool ROOT::Math::DistSampler::Generate	(	unsigned int	nevt,
		ROOT::Fit::UnBinData &	data
	)

virtual

generate a un-binned data sets (fill the given data set) if dataset has already data append to it

Definition at line 95 of file DistSampler.cxx.

◆ GetRandom()

virtual TRandom * ROOT::Math::DistSampler::GetRandom ( )

inlinevirtual

Get the random engine used by the sampler To be implemented by the derived classes who needs it Returns zero by default.

Reimplemented in TFoamSampler, and TUnuranSampler.

Definition at line 132 of file DistSampler.h.

◆ Init() [1/2]

virtual bool ROOT::Math::DistSampler::Init ( const char * = "" )

inlinevirtual

initialize the generators with the given algorithm Implemented by derived classes who needs it (like UnuranSampler) If nothing is specified use default algorithm from DistSamplerOptions::SetDefaultAlgorithm

Reimplemented in TFoamSampler, and TUnuranSampler.

Definition at line 101 of file DistSampler.h.

◆ Init() [2/2]

bool ROOT::Math::DistSampler::Init ( const DistSamplerOptions & opt )

virtual

initialize the generators with the given option which my include the algorithm but also more if the method is re-impelmented by derived class The default implementation calls the above method passing just the algorithm name

Reimplemented in TFoamSampler, and TUnuranSampler.

Definition at line 35 of file DistSampler.cxx.

◆ IsInitialized()

bool ROOT::Math::DistSampler::IsInitialized ( )

protected

Definition at line 84 of file DistSampler.cxx.

◆ NDim()

unsigned int ROOT::Math::DistSampler::NDim ( ) const

inline

return the dimension of the parent distribution (and the data)

Definition at line 91 of file DistSampler.h.

◆ ParentPdf()

const ROOT::Math::IMultiGenFunction & ROOT::Math::DistSampler::ParentPdf ( ) const

inline

get the parent distribution function (must be called after setting the function)

Definition at line 152 of file DistSampler.h.

◆ PdfRange()

const ROOT::Fit::DataRange & ROOT::Math::DistSampler::PdfRange ( ) const

inlineprotected

return the data range of the Pdf . Must be called after setting the function

Definition at line 241 of file DistSampler.h.

◆ Sample() [1/2]

const double * ROOT::Math::DistSampler::Sample ( )

inline

sample one event and rerturning array x with coordinates

Definition at line 169 of file DistSampler.h.

◆ Sample() [2/2]

virtual bool ROOT::Math::DistSampler::Sample ( double * x )

pure virtual

sample one event in multi-dimension by filling the given array return false if sampling failed

Implemented in TFoamSampler, and TUnuranSampler.

◆ Sample1D()

virtual double ROOT::Math::DistSampler::Sample1D ( )

inlinevirtual

sample one event in one dimension better implementation could be provided by the derived classes

Reimplemented in TUnuranSampler.

Definition at line 161 of file DistSampler.h.

◆ SampleBin()

virtual bool ROOT::Math::DistSampler::SampleBin	(	double	prob,
		double &	value,
		double *	error = `0`
	)

inlinevirtual

sample one bin given an estimated of the pdf in the bin (this can be function value at the center or its integral in the bin divided by the bin width) By default do not do random sample, just return the function values Typically Poisson statistics will be used

Reimplemented in TFoamSampler, and TUnuranSampler.

Definition at line 187 of file DistSampler.h.

◆ SampleBins()

virtual bool ROOT::Math::DistSampler::SampleBins	(	unsigned int	n,
		const double *	prob,
		double *	values,
		double *	errors = `0`
	)

inlinevirtual

sample a set of bins given a vector of probabilities Typically multinomial statistics will be used and the sum of the probabilities will be equal to the total number of events to be generated For sampling the bins indipendently, SampleBin should be used

Definition at line 198 of file DistSampler.h.

◆ SetArea()

virtual void ROOT::Math::DistSampler::SetArea ( double )

inlinevirtual

set the normalization area of distribution implemented by derived classes if needed

Reimplemented in TUnuranSampler.

Definition at line 149 of file DistSampler.h.

◆ SetFunction() [1/3]

virtual void ROOT::Math::DistSampler::SetFunction ( const ROOT::Math::IGenFunction & func )

inlinevirtual

set the parent function distribution to use for random sampling (one dim case)

Reimplemented in TFoamSampler, and TUnuranSampler.

Definition at line 80 of file DistSampler.h.

◆ SetFunction() [2/3]

virtual void ROOT::Math::DistSampler::SetFunction ( const ROOT::Math::IMultiGenFunction & func )

inlinevirtual

set the parent function distribution to use for random sampling (multi-dim case)

Definition at line 86 of file DistSampler.h.

◆ SetFunction() [3/3]

template<class Function >

void ROOT::Math::DistSampler::SetFunction	(	Function &	func,
		unsigned int	dim
	)

inline

set the parent function distribution to use for sampling (generic case)

Definition at line 72 of file DistSampler.h.

◆ SetMode()

virtual void ROOT::Math::DistSampler::SetMode ( double )

inlinevirtual

set the mode of the distribution (could be useful to some methods) implemented by derived classes if needed

Reimplemented in TUnuranSampler.

Definition at line 145 of file DistSampler.h.

◆ SetRandom()

virtual void ROOT::Math::DistSampler::SetRandom ( TRandom * )

inlinevirtual

Set the random engine to be used To be implemented by the derived classes who provides random sampling.

Reimplemented in TFoamSampler, and TUnuranSampler.

Definition at line 118 of file DistSampler.h.

◆ SetRange() [1/3]

void ROOT::Math::DistSampler::SetRange	(	const double *	xmin,
		const double *	xmax
	)

set range for all dimensions

Definition at line 48 of file DistSampler.cxx.

◆ SetRange() [2/3]

void ROOT::Math::DistSampler::SetRange ( const ROOT::Fit::DataRange & range )

set range using DataRange class

Definition at line 58 of file DistSampler.cxx.

◆ SetRange() [3/3]

void ROOT::Math::DistSampler::SetRange	(	double	xmin,
		double	xmax,
		int	icoord = `0`
	)

set range in a given dimension

Definition at line 40 of file DistSampler.cxx.

◆ SetSeed()

virtual void ROOT::Math::DistSampler::SetSeed ( unsigned int )

inlinevirtual

Set the random seed for the TRandom instances used by the sampler classes To be implemented by the derived classes who provides random sampling.

Reimplemented in TFoamSampler, and TUnuranSampler.

Definition at line 125 of file DistSampler.h.

Member Data Documentation

◆ fData

std::vector<double> ROOT::Math::DistSampler::fData

mutableprivate

Definition at line 254 of file DistSampler.h.

◆ fFunc

const ROOT::Math::IMultiGenFunction* ROOT::Math::DistSampler::fFunc

private

Definition at line 256 of file DistSampler.h.

◆ fOwnFunc

bool ROOT::Math::DistSampler::fOwnFunc

private

Definition at line 253 of file DistSampler.h.

◆ fRange

ROOT::Fit::DataRange* ROOT::Math::DistSampler::fRange

private

Definition at line 255 of file DistSampler.h.

Libraries for ROOT::Math::DistSampler:

[legend]

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

math/mathcore/inc/Math/DistSampler.h
math/mathcore/src/DistSampler.cxx

Public Member Functions

Protected Member Functions

Private Attributes

Constructor & Destructor Documentation

◆ DistSampler()

◆ ~DistSampler()

Member Function Documentation

◆ DoSetFunction()

◆ Generate() [1/3]

◆ Generate() [2/3]

◆ Generate() [3/3]

◆ GetRandom()

◆ Init() [1/2]

◆ Init() [2/2]

◆ IsInitialized()

◆ NDim()

◆ ParentPdf()

◆ PdfRange()

◆ Sample() [1/2]

◆ Sample() [2/2]

◆ Sample1D()

◆ SampleBin()

◆ SampleBins()

◆ SetArea()

◆ SetFunction() [1/3]

◆ SetFunction() [2/3]

◆ SetFunction() [3/3]

◆ SetMode()

◆ SetRandom()

◆ SetRange() [1/3]

◆ SetRange() [2/3]

◆ SetRange() [3/3]

◆ SetSeed()

Member Data Documentation

◆ fData

◆ fFunc

◆ fOwnFunc

◆ fRange