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#ifndef NPSTAT_ABSBANDWIDTHCV_HH_
#define NPSTAT_ABSBANDWIDTHCV_HH_
/*!
// \file AbsBandwidthCV.hh
//
// \brief Interface definitions for KDE or LOrPE cross-validation calculations
//
// Implementations of these interfaces should calculate the quantity
// which is to be maximized (pseudo log likelihood, -AMISE, etc.)
//
// Author: I. Volobouev
//
// September 2010
*/
#include "npstat/stat/HistoND.hh"
namespace npstat {
// Forward declarations
class AbsPolyFilter1D;
class AbsPolyFilterND;
/**
// Cross-validation for univariate densities.
// "Numeric" template parameter is the type of histogram bins.
// "Num2" is the type produced by the density estimation code
// (usually just double).
*/
template<typename Numeric, typename Num2>
struct AbsBandwidthCV1D
{
inline virtual ~AbsBandwidthCV1D() {}
/**
// It should be assumed that the "nFillsInRange" method of the
// argument histogram returns the actual number of fills (that is,
// the histogram represents an actual collection of points, has
// possible bin values of 0, 1, 2, ..., and it is not scaled).
//
// "densityEstimate" is allowed to be an estimate without truncation
// (even if it includes negative values). Dependence of the
// optimized quantity on the bandwidth should be smoother without
// truncation. Naturally, the order of values in "densityEstimate"
// is the same as the order of bins in "histo".
//
// The "filterUsed" parameter is needed in order to be able to
// do "leave-one-out" type of cross-validation. In such
// calculations one has to know how much of the density estimate
// at coordinate x is contributed by the source point at x. When
// density estimates are done on the grid, the filters differ for
// the center of the grid and on the border. Because of this, we
// will need to have an access to the complete filter collection.
*/
virtual double operator()(
const HistoND<Numeric>& histo,
const Num2* densityEstimate,
unsigned lenEstimate,
const AbsPolyFilter1D& filterUsed) const = 0;
};
/**
// Cross-validation for multivariate densities.
// "Numeric" template parameter is the type of histogram bins.
// "Array" template parameter should be one of ArrayND types.
*/
template<typename Numeric, class Array>
struct AbsBandwidthCVND
{
inline virtual ~AbsBandwidthCVND() {}
/**
// It should be assumed that the "nFillsInRange" method of the
// argument histogram returns the actual number of fills.
//
// "densityEstimate" is allowed to be an estimate without truncation
// (even if it includes negative values).
*/
virtual double operator()(
const HistoND<Numeric>& histo,
const Array& densityEstimate,
const AbsPolyFilterND& filterUsed) const = 0;
};
}
#endif // NPSTAT_ABSBANDWIDTHCV_HH_

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