emsunfold::AbsSparseUnfoldND< Matrix > Class Template Reference

Inheritance diagram for emsunfold::AbsSparseUnfoldND< Matrix >:

Public Types
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
	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
virtual bool	unfold (const npstat::ArrayND< double > &observed, const input_covariance_type *observationCovarianceMatrix, npstat::ArrayND< double > *unfolded, output_covariance_type *unfoldedCovarianceMatrix)=0
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	buildUniformInitialApproximation (const npstat::ArrayND< double > &o, npstat::ArrayND< double > *r) const

Member Function Documentation

clearInitialApproximation()

template<class Matrix >

virtual void emsunfold::AbsSparseUnfoldND< Matrix >::clearInitialApproximation ( )

virtual

Clear the initial approximation to the unfolded solution

getFilter()

template<class Matrix >

virtual const filter_type* emsunfold::AbsSparseUnfoldND< Matrix >::getFilter ( bool  throwIfNull = false ) const

virtual

Retrieve the smoothing filter used

getInitialApproximation()

template<class Matrix >

virtual const npstat::ArrayND<double>& emsunfold::AbsSparseUnfoldND< Matrix >::getInitialApproximation ( ) const

virtual

Return the initial approximation to the unfolded solution

getObservedShape()

template<class Matrix >

npstat::ArrayShape emsunfold::AbsSparseUnfoldND< Matrix >::getObservedShape ( ) const

inline

Shape of the expected observed input

getUnfoldedShape()

template<class Matrix >

npstat::ArrayShape emsunfold::AbsSparseUnfoldND< Matrix >::getUnfoldedShape ( ) const

inline

Shape of the expected unfolded output

setFilter()

template<class Matrix >

virtual void emsunfold::AbsSparseUnfoldND< Matrix >::setFilter ( const filter_type *  f )

virtual

Set the smoothing filter used. The filter will not be copied. The user must ensure that the filter exists while this object is in use.

setInitialApproximation()

template<class Matrix >

virtual void emsunfold::AbsSparseUnfoldND< Matrix >::setInitialApproximation ( const npstat::ArrayND< double > &  a )

virtual

Set the initial approximation to the unfolded solution

unfold()

template<class Matrix >

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

pure virtual

Method to be implemented by derived classes. The covariance matrix of observations should assume linear ordering of the observed data, per ordering by the "ArrayND" class. If the "observationCovarianceMatrix" pointer is NULL, the matrix should be constructed internally, assuming Poisson statistics. The "unfoldedCovarianceMatrix" pointer can be NULL as well in which case the corresponding matrix should not be calculated. If it is not NULL, the resulting matrix should be pruned.

This function should return "true" on success, "false" on failure.

Implemented in emsunfold::SmoothedEMSparseUnfoldND< Matrix >.

useConvolutions()

template<class Matrix >

virtual void emsunfold::AbsSparseUnfoldND< Matrix >::useConvolutions ( const bool  b )

inlinevirtual

Switch between using filtering or convolution

usingConvolutions()

template<class Matrix >

bool emsunfold::AbsSparseUnfoldND< Matrix >::usingConvolutions ( ) const

inline

Check if the filter should use "filter" or "convolve" method

validateUnfoldedShape()

template<class Matrix >

void emsunfold::AbsSparseUnfoldND< Matrix >::validateUnfoldedShape

(

const npstat::ArrayND< double > &

u

)

const

This function will throw the "std::invalid_argument" exception if the dimensions are incompatible with those of the response matrix

---

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

• emsunfold/AbsSparseUnfoldND.hh