#include <MultiscaleEMUnfold1D.hh>

Inheritance diagram for npstat::MultiscaleEMUnfold1D:

Public Member Functions
	MultiscaleEMUnfold1D (const Matrix< double > &responseMatrix, const LocalPolyFilter1D &filter, int symbetaPower, double maxDegree, double xMinUnfolded, double xMaxUnfolded, const BoundaryHandling &filterBoundaryMethod, double minBandwidth, double maxBandwidth, unsigned nFilters, unsigned itersPerFilter, bool useConvolutions, bool useMultinomialCovariance=false, bool smoothLastIter=true, double convergenceEpsilon=1.0e-10, unsigned maxIterations=100000U)

Public Member Functions inherited from 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)

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

Additional Inherited Members
Public Types inherited from npstat::SmoothedEMUnfold1D
typedef AbsUnfold1D	Base

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)

Protected Member Functions inherited from npstat::SmoothedEMUnfold1D
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

Static Protected Member Functions inherited from npstat::AbsUnfold1D
static void	validateDensity (const double *observ, unsigned lenObserved)

Detailed Description

In the situations with narrow response matrix, this class might be able to speed up convergence of the expectation-maximization iterations (in comparison with SmoothedEMUnfold1D). This is done by performing initial iterations with filters that have wider bandwidth. The maximum bandwidth of such filters should be comparable with the size of typical structures in the unfolded distribution, while the minimum bandwidth should be just slightly above the bandwidth of the final filter. The bandwidth is progressively reduced in the iteration process.

Note that construction of additional filters is itself rather CPU-intensive. Once constructed, the filters are memoized. It makes sense to use this code instead of SmoothedEMUnfold1D only if you plan to unfold multiple distributions.

Constructor & Destructor Documentation

◆ MultiscaleEMUnfold1D()

npstat::MultiscaleEMUnfold1D::MultiscaleEMUnfold1D	(	const Matrix< double > &	responseMatrix,
		const LocalPolyFilter1D &	filter,
		int	symbetaPower,
		double	maxDegree,
		double	xMinUnfolded,
		double	xMaxUnfolded,
		const BoundaryHandling &	filterBoundaryMethod,
		double	minBandwidth,
		double	maxBandwidth,
		unsigned	nFilters,
		unsigned	itersPerFilter,
		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 a responsibility of the caller to ensure that the argument filter exists while this object is in use.

symbetaPower – Before running the standard expectation maxDegree maximization iterations with the filter xMinUnfolded provided by the previous argument, the xMaxUnfolded code will run iterations with a family filterBoundaryMethod of filters created by the "symbetaLOrPEFilter1D" function. These parameters will be passed to the "symbetaLOrPEFilter1D" call.

minBandwidth – Minimum bandwidth for the extra filters.

maxBandwidth – Maximum bandwidth for the extra filters.

nFilters – Number of extra filters. Logarithms of the bandwidth values of these filters will be equidistant.

itersPerFilter – Number of expectation-maximization iterations to perform per extra filter. These additional filters will be used sequentially, in the order of decreasing bandwidth.

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).

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

stat/MultiscaleEMUnfold1D.hh

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ MultiscaleEMUnfold1D()