emsunfold::SmoothedEMSparseUnfoldND< Matrix > Class Template Reference

Inheritance diagram for emsunfold::SmoothedEMSparseUnfoldND< Matrix >:

Public Types
typedef AbsSparseUnfoldND< Matrix >	Base
typedef Base::response_matrix_type	response_matrix_type
typedef Base::input_covariance_type	input_covariance_type
typedef Base::output_covariance_type	output_covariance_type
typedef Base::filter_type	filter_type
Public Types inherited from emsunfold::AbsSparseUnfoldND< Matrix >
typedef Matrix	response_matrix_type
typedef Eigen::DiagonalMatrix< double, Eigen::Dynamic >	input_covariance_type
typedef Matrix	output_covariance_type
typedef AbsSparseUnfoldingFilterND	filter_type

Public Member Functions
	SmoothedEMSparseUnfoldND (const response_matrix_type &responseMatrix, const filter_type &filter, const npstat::ArrayShape &observedShape, bool useConvolutions, bool smoothLastIter=true, double convergenceEpsilon=1.0e-10, unsigned maxIterations=100000U)
void	setMaxIterations (const unsigned u)
void	useMultinomialCovariance (bool)
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 npstat::ArrayND< double > &observed, const input_covariance_type *observationCovarianceMatrix, npstat::ArrayND< double > *unfolded, output_covariance_type *unfoldedCovarianceMatrix)
Public Member Functions inherited from emsunfold::AbsSparseUnfoldND< Matrix >
	AbsSparseUnfoldND (const response_matrix_type &responseMatrix, const npstat::ArrayShape &unfoldedShape, const npstat::ArrayShape &observedShape)
	AbsSparseUnfoldND (const response_matrix_type &responseMatrix, const unsigned *unfoldedShape, unsigned unfoldedDim, const unsigned *observedShape, unsigned observedDim)
const response_matrix_type &	responseMatrix () const
const npstat::ArrayND< double > &	efficiency () const
virtual void	setInitialApproximation (const npstat::ArrayND< double > &a)
virtual void	clearInitialApproximation ()
virtual const npstat::ArrayND< double > &	getInitialApproximation () const
virtual void	setFilter (const filter_type *f)
virtual const filter_type *	getFilter (bool throwIfNull=false) const
virtual void	useConvolutions (const bool b)
bool	usingConvolutions () const
npstat::ArrayShape	getObservedShape () const
npstat::ArrayShape	getUnfoldedShape () const
void	validateUnfoldedShape (const npstat::ArrayND< double > &u) const
void	validateUnfoldedShape (const npstat::ArrayShape &uShape) const
void	validateObservedShape (const npstat::ArrayND< double > &o) const
void	validateObservedShape (const npstat::ArrayShape &oShape) const

Protected Member Functions
void	update (const npstat::ArrayND< double > &observed, const npstat::ArrayND< double > *prev, npstat::ArrayND< double > *next, bool performSmoothing) const
Protected Member Functions inherited from emsunfold::AbsSparseUnfoldND< Matrix >
void	buildUniformInitialApproximation (const npstat::ArrayND< double > &o, npstat::ArrayND< double > *r) const

Constructor & Destructor Documentation

SmoothedEMSparseUnfoldND()

template<class Matrix >

emsunfold::SmoothedEMSparseUnfoldND< Matrix >::SmoothedEMSparseUnfoldND	(	const response_matrix_type &	responseMatrix,
		const filter_type &	filter,
		const npstat::ArrayShape &	observedShape,
		bool	useConvolutions,
		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 a responsibility of the caller to ensure that the argument filter exists while this object is in use.

observedShape – Expected shape of the observed data. The array of observations provided in all subsequent calls of the "unfold" method must have this shape.

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

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()

template<class Matrix >

double emsunfold::SmoothedEMSparseUnfoldND< Matrix >::convergenceEpsilon ( ) const

inline

Simple inspector of object properties

lastEPIterations()

template<class Matrix >

unsigned emsunfold::SmoothedEMSparseUnfoldND< Matrix >::lastEPIterations ( ) const

inline

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

lastNIterations()

template<class Matrix >

unsigned emsunfold::SmoothedEMSparseUnfoldND< Matrix >::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()

template<class Matrix >

double emsunfold::SmoothedEMSparseUnfoldND< Matrix >::lastSmoothingNormfactor ( ) const

inline

The normalization factor applied during the last smoothing step

setConvergenceEpsilon()

template<class Matrix >

void emsunfold::SmoothedEMSparseUnfoldND< Matrix >::setConvergenceEpsilon

(

double

eps

)

Change the convergence criterion

setMaxIterations()

template<class Matrix >

void emsunfold::SmoothedEMSparseUnfoldND< Matrix >::setMaxIterations ( const unsigned  u )

inline

Change maximum number of allowed iterations

smoothLastIteration()

template<class Matrix >

void emsunfold::SmoothedEMSparseUnfoldND< Matrix >::smoothLastIteration ( const bool  b )

inline

Switch between smoothing/not smoothing the last iteration

unfold()

template<class Matrix >

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

virtual

The main unfolding method

Implements emsunfold::AbsSparseUnfoldND< Matrix >.

update()

template<class Matrix >

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

protected

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

useMultinomialCovariance()

template<class Matrix >

void emsunfold::SmoothedEMSparseUnfoldND< Matrix >::useMultinomialCovariance ( bool  )

inline

This method is included for compatibility with the npstat::SmoothedEMUnfoldND class. The call will be ignored.

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

• emsunfold/SmoothedEMSparseUnfoldND.hh