cdKernelSensitivityMatrix.hh

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#ifndef NPSTAT_CDKERNELSENSITIVITYMATRIX_HH_
#define NPSTAT_CDKERNELSENSITIVITYMATRIX_HH_
 
/*!
// \file cdKernelSensitivityMatrix.hh
//
// \brief Calculate kernel sensitivity matrix for KDE-like density estimation
//        using the definition based on comparison density
//
// Author: I. Volobouev
//
// December 2022
*/
 
#include "npstat/nm/Matrix.hh"
#include "npstat/nm/AbsIntervalQuadrature1D.hh"
#include "npstat/nm/ContOrthoPoly1D.hh"
 
namespace npstat {
    /**
    // Template class Fcn2D should provide a method
    // "Real operator()(Real x, Real y) const", where "Real" is one of
    // floating point types (long double works best).
    //
    // It is assumed that the interface of class "InPoly" is similar to that of
    // npstat classes AbsClassicalOrthoPoly1D or ScalableClassicalOrthoPoly1D.
    //
    // In the returned matrix, row numbers correspond to the "output"
    // polynomial degrees (y space) and column numbers to the "input"
    // polynomial degrees (x space).
    //
    // "chebyshevDegree" is the degree to use for modeling the convolved
    // density with Chebyshev polynomials (this is needed for fast
    // calculation of its cumulative distribution function).
    //
    // Set "normalizeKernel" parameter to "false" if the kernel is already known
    // to be normalized, that is, Int_ymin^ymax K(x, y) dy = 1 for every x.
    */
    template <class Fcn2D, class InPoly>
    Matrix<double> cdKernelSensitivityMatrix(
        const Fcn2D& kernel, const InPoly& inputPoly,
        unsigned maxdegInputPoly, unsigned maxdegOutPoly,
        long double ymin, long double ymax,
        const AbsIntervalQuadrature1D& xIntegrator,
        const AbsIntervalQuadrature1D& yIntegrator,
        unsigned chebyshevDegree, bool normalizeKernel=true);
 
    /**
    // Template class Fcn2D should provide a method
    // "Real operator()(Real x, Real y) const", where "Real" is one of
    // floating point types (long double works best).
    //
    // In the returned matrix, row numbers correspond to the "output"
    // polynomial degrees (y space) and column numbers to the "input"
    // polynomial degrees (x space).
    //
    // "chebyshevDegree" is the degree to use for modeling the convolved
    // density with Chebyshev polynomials (this is needed for fast
    // calculation of its cumulative distribution function).
    //
    // Set "normalizeKernel" parameter to "false" if the kernel is already known
    // to be normalized, that is, Int_ymin^ymax K(x, y) dy = 1 for every x.
    */
    template <class Fcn2D>
    Matrix<double> cdKernelSensitivityMatrix(
        const Fcn2D& kernel, const ContOrthoPoly1D& inputPoly,
        unsigned maxdegInputPoly, unsigned maxdegOutPoly,
        long double ymin, long double ymax,
        const AbsIntervalQuadrature1D& yIntegrator,
        unsigned chebyshevDegree, bool normalizeKernel=true);
}
 
#include "npstat/nm/cdKernelSensitivityMatrix.icc"
 
#endif // NPSTAT_CDKERNELSENSITIVITYMATRIX_HH_