npstat::SmoothedEMUnfold1D Class Reference

Inheritance diagram for npstat::SmoothedEMUnfold1D:

Public Types
typedef AbsUnfold1D	Base

Public Member Functions
	SmoothedEMUnfold1D (const Matrix< double > &responseMatrix, const LocalPolyFilter1D &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 double *observed, unsigned lenObserved, const Matrix< double > *observationCovarianceMatrix, double *unfolded, unsigned lenUnfolded, Matrix< double > *unfoldedCovarianceMatrix)
Public Member Functions inherited from npstat::AbsUnfold1D
	AbsUnfold1D (const Matrix< double > &responseMatrix)
const Matrix< double > &	responseMatrix () const
const Matrix< double > &	transposedResponseMatrix () const
const std::vector< double > &	efficiency () const
std::pair< double, double >	smoothingNDoF () const
std::pair< double, double >	responseNDoF () const
std::pair< double, double >	smoothedResponseNDoF () const
std::pair< double, double >	modelNDoF ()
virtual void	setInitialApproximation (const double *approx, unsigned lenApprox)
virtual void	clearInitialApproximation ()
virtual const std::vector< double > &	getInitialApproximation () const
virtual void	setFilter (const LocalPolyFilter1D *f)
virtual const LocalPolyFilter1D *	getFilter (bool throwIfNull=false) const
virtual void	useConvolutions (const bool b)
bool	usingConvolutions () const

Protected Member Functions
void	update (const double *observed, unsigned lenObserved, const double *prev, double *next, unsigned lenUnfolded, bool performSmoothing) const
Matrix< double >	unfoldingMatrix0 (const double *unfolded, const double *yhat) const
Matrix< double >	errorPropagationMatrix (const double *observed, unsigned lenObserved, const double *unfolded, unsigned lenUnfolded, const double *yHat, double norm, bool smoothLast, unsigned maxiter, double convergenceEps, unsigned *itersMade, bool *converged) const
Protected Member Functions inherited from npstat::AbsUnfold1D
void	buildUniformInitialApproximation (const double *observed, unsigned lenObserved, std::vector< double > *result) const
void	validateDimensions (unsigned lenObserved, unsigned lenUnfold) const

Additional Inherited Members
Static Public Member Functions inherited from npstat::AbsUnfold1D
static double	probDelta (const double *prev, const double *next, const unsigned len)
static Matrix< double >	observationCovariance (const double *yhat, unsigned lenObserved, bool isMultinomial)
Static Protected Member Functions inherited from npstat::AbsUnfold1D
static void	validateDensity (const double *observ, unsigned lenObserved)

Constructor & Destructor Documentation

SmoothedEMUnfold1D()

npstat::SmoothedEMUnfold1D::SmoothedEMUnfold1D	(	const Matrix< double > &	responseMatrix,
		const LocalPolyFilter1D &	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::SmoothedEMUnfold1D::convergenceEpsilon ( ) const

inline

Simple inspector of object properties

lastEPIterations()

unsigned npstat::SmoothedEMUnfold1D::lastEPIterations ( ) const

inline

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

lastNIterations()

unsigned npstat::SmoothedEMUnfold1D::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::SmoothedEMUnfold1D::lastSmoothingNormfactor ( ) const

inline

The normalization factor applied during the last smoothing step

setConvergenceEpsilon()

void npstat::SmoothedEMUnfold1D::setConvergenceEpsilon

(

double

eps

)

Change the convergence criterion

setMaxIterations()

void npstat::SmoothedEMUnfold1D::setMaxIterations ( const unsigned  u )

inline

Change maximum number of allowed iterations

smoothLastIteration()

void npstat::SmoothedEMUnfold1D::smoothLastIteration ( const bool  b )

inline

Switch between smoothing/not smoothing the last iteration

unfold()

virtual bool npstat::SmoothedEMUnfold1D::unfold ( const double *  observed, unsigned  lenObserved, const Matrix< double > *  observationCovarianceMatrix, double *  unfolded, unsigned  lenUnfolded, Matrix< double > *  unfoldedCovarianceMatrix  )

virtual

The main unfolding method. The structure of the covariance matrix of observations (if provided) will be ignored for optimizing the unfolded values – it is only used to evaluate their covariance. The "unfoldedCovarianceMatrix" parameter can be NULL in which case the corresponding calculation is not performed (which saves some CPU time).

Implements npstat::AbsUnfold1D.

update()

void npstat::SmoothedEMUnfold1D::update ( const double *  observed, unsigned  lenObserved, const double *  prev, double *  next, unsigned  lenUnfolded, bool  performSmoothing  ) const

protected

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

useMultinomialCovariance()

void npstat::SmoothedEMUnfold1D::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/SmoothedEMUnfold1D.hh