/usr/include/openturns/KernelSmoothing.hxx is in libopenturns-dev 1.9-5.
This file is owned by root:root, with mode 0o644.
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/**
* @brief This class acts like a (possibly truncated) KernelMixture factory, implementing density estimation
* using the kernel smoothing method. It uses Silverman's rule for product kernel.
*
* Copyright 2005-2017 Airbus-EDF-IMACS-Phimeca
*
* This library is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* along with this library. If not, see <http://www.gnu.org/licenses/>.
*
*/
#ifndef OPENTURNS_KERNELSMOOTHING_HXX
#define OPENTURNS_KERNELSMOOTHING_HXX
#include "openturns/DistributionFactoryImplementation.hxx"
#include "openturns/Distribution.hxx"
#include "openturns/Sample.hxx"
BEGIN_NAMESPACE_OPENTURNS
/**
* @class KernelSmoothing
*
* The class describes the probabilistic concept of KernelSmoothing.
*/
class OT_API KernelSmoothing
: public DistributionFactoryImplementation
{
CLASSNAME;
public:
/** Default constructor */
KernelSmoothing();
/** Default constructor */
explicit KernelSmoothing(const Distribution & kernel,
const Bool bined = true,
const UnsignedInteger binNumber = ResourceMap::GetAsUnsignedInteger("KernelSmoothing-BinNumber"),
const Bool boundaryCorrection = false);
/** Virtual constructor */
virtual KernelSmoothing * clone() const;
/** Build a Normal kernel mixture based on the given sample. If no bandwith has already been set, Silverman's rule is used */
using DistributionFactoryImplementation::build;
virtual Implementation build(const Sample & sample) const;
/** Build a (possibly truncated) kernel mixture based on the given sample and bandwidth */
virtual Implementation build(const Sample & sample,
const Point & bandwidth) const;
/** Bandwidth accessor */
Point getBandwidth() const;
/** Kernel accessor */
Distribution getKernel() const;
void setBoundaryCorrection(const Bool boundaryCorrection);
/** Compute the bandwidth according to Silverman's rule */
Point computeSilvermanBandwidth(const Sample & sample) const;
/** Compute the bandwidth according to the plugin rule. Warning!
* it can take a lot of time for large samples, as the cost is
* quadratic with the sample size
*/
Point computePluginBandwidth(const Sample & sample) const;
/** Compute the bandwidth according to a mixed rule:
* simply use the plugin rule for small sample, and
* estimate the ratio between the plugin rule and
* the Silverman rule on a small sample, then
* scale the Silverman bandwidth computed on the full
* sample with this ratio
*/
Point computeMixedBandwidth(const Sample & sample) const;
/** Method save() stores the object through the StorageManager */
virtual void save(Advocate & adv) const;
/** Method load() reloads the object from the StorageManager */
virtual void load(Advocate & adv);
private:
void setBandwidth(const Point & bandwidth) const;
// Bandwith of the smoothing
mutable Point bandwidth_;
// 1D kernel for kernel product
Distribution kernel_;
// Flag to tell if we compute a bined version of the estimator
Bool bined_;
// Number of bins
UnsignedInteger binNumber_;
Bool boundaryCorrection_;
}; /* class KernelSmoothing */
END_NAMESPACE_OPENTURNS
#endif /* OPENTURNS_KERNELSMOOTHING_HXX */
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