npstat::SmoothedEMUnfoldND Class Reference

Inheritance diagram for npstat::SmoothedEMUnfoldND:

Public Types
typedef AbsUnfoldND	Base
Public Types inherited from npstat::AbsUnfoldND
typedef ResponseMatrix	response_matrix_type
typedef Matrix< double >	input_covariance_type
typedef Matrix< double >	output_covariance_type
typedef AbsUnfoldingFilterND	filter_type

Public Member Functions
	SmoothedEMUnfoldND (const ResponseMatrix &responseMatrix, const AbsUnfoldingFilterND &filter, bool useConvolutions, bool useMultinomialCovariance=false, bool smoothLastIter=true, double convergenceEpsilon=1.0e-10, unsigned maxIterations=100000U)
void	setMaxIterations (const unsigned u)
void	useMultinomialCovariance (const bool b)
void	smoothLastIteration (const bool b)
void	setConvergenceEpsilon (double eps)
double	convergenceEpsilon () const
unsigned	maxIterations () const
bool	usingMultinomialCovariance () const
bool	smoothingLastIteration () const
unsigned	lastNIterations () const
unsigned	lastEPIterations () const
double	lastSmoothingNormfactor () const
virtual bool	unfold (const ArrayND< double > &observed, const Matrix< double > *observationCovarianceMatrix, ArrayND< double > *unfolded, Matrix< double > *unfoldedCovarianceMatrix)
Public Member Functions inherited from npstat::AbsUnfoldND
	AbsUnfoldND (const ResponseMatrix &responseMatrix)
const ResponseMatrix &	responseMatrix () const
const ArrayND< double > &	efficiency () const
virtual void	setInitialApproximation (const ArrayND< double > &approx)
virtual void	clearInitialApproximation ()
virtual const ArrayND< double > &	getInitialApproximation () const
virtual void	setFilter (const AbsUnfoldingFilterND *f)
virtual const AbsUnfoldingFilterND *	getFilter (bool throwIfNull=false) const
virtual void	useConvolutions (const bool b)
bool	usingConvolutions () const
ArrayShape	getObservedShape () const
ArrayShape	getUnfoldedShape () const
void	validateUnfoldedShape (const ArrayND< double > &unfolded) const
void	validateUnfoldedShape (const ArrayShape &unfoldedShape) const
void	validateObservedShape (const ArrayND< double > &observed) const
void	validateObservedShape (const ArrayShape &observedShape) const

Protected Member Functions
void	update (const ArrayND< double > &observed, const ArrayND< double > *prev, ArrayND< double > *next, bool performSmoothing) const
Protected Member Functions inherited from npstat::AbsUnfoldND
void	buildUniformInitialApproximation (const ArrayND< double > &observed, ArrayND< double > *result) const

Additional Inherited Members
Static Public Member Functions inherited from npstat::AbsUnfoldND
static double	probDelta (const ArrayND< double > &prev, const ArrayND< double > &next)

Constructor & Destructor Documentation

SmoothedEMUnfoldND()

npstat::SmoothedEMUnfoldND::SmoothedEMUnfoldND	(	const ResponseMatrix &	responseMatrix,
		const AbsUnfoldingFilterND &	filter,
		bool	useConvolutions,
		bool	useMultinomialCovariance = false,
		bool	smoothLastIter = true,
		double	convergenceEpsilon = 1.0e-10,
		unsigned	maxIterations = 100000U
	)

The constructor arguments are:

responseMatrix – Naturally, the problem response matrix.

filter – The filter to use for smoothing the unfolded values. This object will not make a copy of the filter. It is the responsibility of the caller to ensure that the argument filter exists while this object is in use.

useConvolutions – If "true", the code will call the "convolve" method of the filter rather than its "filter" method.

useMultinomialCovariance – Specifies whether we should use multinomial distribution to estimate covariance of fitted observations (otherwise Poisson assumption is used).

smoothLastIter – If "false", smoothing will not be applied after the last iteration. Setting this parameter to "false" is not recommended for production results because it is unclear how to compare such results with models.

convergenceEpsilon – Convergence criterion parameter for various iterations.

maxIterations – Maximum number of iterations allowed (both for the expectation-maximization iterations and for the code estimating the error propagation matrix).

Member Function Documentation

convergenceEpsilon()

double npstat::SmoothedEMUnfoldND::convergenceEpsilon ( ) const

inline

Simple inspector of object properties

lastEPIterations()

unsigned npstat::SmoothedEMUnfoldND::lastEPIterations ( ) const

inline

The last number of iterations used to calculate the error propagation matrix

lastNIterations()

unsigned npstat::SmoothedEMUnfoldND::lastNIterations ( ) const

inline

Returns the last number of iterations used to calculate the unfolded results. This number will be filled after each "unfold" call.

lastSmoothingNormfactor()

double npstat::SmoothedEMUnfoldND::lastSmoothingNormfactor ( ) const

inline

The normalization factor applied during the last smoothing step

setConvergenceEpsilon()

void npstat::SmoothedEMUnfoldND::setConvergenceEpsilon

(

double

eps

)

Change the convergence criterion

setMaxIterations()

void npstat::SmoothedEMUnfoldND::setMaxIterations ( const unsigned  u )

inline

Change maximum number of allowed iterations

smoothLastIteration()

void npstat::SmoothedEMUnfoldND::smoothLastIteration ( const bool  b )

inline

Switch between smoothing/not smoothing the last iteration

unfold()

virtual bool npstat::SmoothedEMUnfoldND::unfold ( const ArrayND< double > &  observed, const Matrix< double > *  observationCovarianceMatrix, ArrayND< double > *  unfolded, Matrix< double > *  unfoldedCovarianceMatrix  )

virtual

The main unfolding method

Implements npstat::AbsUnfoldND.

update()

void npstat::SmoothedEMUnfoldND::update ( const ArrayND< double > &  observed, const ArrayND< double > *  prev, ArrayND< double > *  next, bool  performSmoothing  ) const

protected

Single expectation-maximization (a.k.a. D'Agostini) iteration

useMultinomialCovariance()

void npstat::SmoothedEMUnfoldND::useMultinomialCovariance ( const bool  b )

inline

Switch between multinomial/Poisson covariance for observed space

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

• stat/SmoothedEMUnfoldND.hh