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AbsUnfoldND.hh
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#ifndef NPSTAT_ABSUNFOLDND_HH_
#define NPSTAT_ABSUNFOLDND_HH_
/*!
// \file AbsUnfoldND.hh
//
// \brief Interface definition for multivariate unfolding algorithms
//
// Author: I. Volobouev
//
// June 2014
*/
#include "npstat/stat/ResponseMatrix.hh"
#include "npstat/stat/AbsUnfoldingFilterND.hh"
namespace npstat {
class AbsUnfoldND
{
public:
typedef ResponseMatrix response_matrix_type;
typedef Matrix<double> input_covariance_type;
typedef Matrix<double> output_covariance_type;
typedef AbsUnfoldingFilterND filter_type;
explicit AbsUnfoldND(const ResponseMatrix& responseMatrix);
inline virtual ~AbsUnfoldND() {}
inline const ResponseMatrix& responseMatrix() const
{return responseMatrix_;}
inline const ArrayND<double>& efficiency() const
{return efficiency_;}
/** Set the initial approximation to the unfolded solution */
virtual void setInitialApproximation(const ArrayND<double>& approx);
/** Clear the initial approximation to the unfolded solution */
virtual void clearInitialApproximation();
/** Return the initial approximation to the unfolded solution */
virtual const ArrayND<double>& getInitialApproximation() const;
/**
// 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.
*/
virtual void setFilter(const AbsUnfoldingFilterND* f);
/** Retrieve the smoothing filter used */
virtual const AbsUnfoldingFilterND* getFilter(
bool throwIfNull = false) const;
/** Switch between using filtering or convolution */
inline virtual void useConvolutions(const bool b) {useConvolutions_=b;}
/** Check if the filter should use "filter" or "convolve" method */
inline bool usingConvolutions() const {return useConvolutions_;}
/** Shape of the expected observed input */
inline ArrayShape getObservedShape() const
{return responseMatrix_.observedShape();}
/** Shape of the expected unfolded output */
inline ArrayShape getUnfoldedShape() const
{return responseMatrix_.shape();}
/**
// 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 or multinomial
// statistics. The "unfoldedCovarianceMatrix" pointer can be NULL
// as well in which case the corresponding matrix should not be
// calculated. This function should return "true" on success,
// "false" on failure.
*/
virtual bool unfold(const ArrayND<double>& observed,
const Matrix<double>* observationCovarianceMatrix,
ArrayND<double>* unfolded,
Matrix<double>* unfoldedCovarianceMatrix) = 0;
//@{
/**
// This function will throw the "std::invalid_argument" exception if
// the dimensions are incompatible with those of the response matrix
*/
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;
//@}
/** L1 distance between two unnormalized distributions */
static double probDelta(const ArrayND<double>& prev,
const ArrayND<double>& next);
protected:
// Build uniform initial approximation to the unfolded solution.
// This is useful if the initial approximation was not set explicitly.
void buildUniformInitialApproximation(const ArrayND<double>& observed,
ArrayND<double>* result) const;
private:
// Disable copy and assignment because using the same
// filter by two different objects of this type is not
// necessarily cool
AbsUnfoldND();
AbsUnfoldND(const AbsUnfoldND&);
AbsUnfoldND& operator=(const AbsUnfoldND&);
const ResponseMatrix responseMatrix_;
ArrayND<double> efficiency_;
ArrayND<double> initialApproximation_;
const AbsUnfoldingFilterND* filt_;
bool useConvolutions_;
};
}
#endif // NPSTAT_ABSUNFOLDND_HH_

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