/usr/include/openturns/KernelSmoothing.hxx is in libopenturns-dev 1.2-2.
This file is owned by root:root, with mode 0o644.
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/**
* @file KernelSmoothing.hxx
* @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 (C) 2005-2013 EDF-EADS-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/>.
*
* @author schueller
* @date 2012-04-18 17:56:46 +0200 (Wed, 18 Apr 2012)
*/
#ifndef OPENTURNS_KERNELSMOOTHING_HXX
#define OPENTURNS_KERNELSMOOTHING_HXX
#include "PersistentObject.hxx"
#include "Distribution.hxx"
#include "NumericalSample.hxx"
BEGIN_NAMESPACE_OPENTURNS
/**
* @class KernelSmoothing
*
* The class describes the probabilistic concept of KernelSmoothing.
*/
class KernelSmoothing : public PersistentObject
{
CLASSNAME;
public:
/** Default constructor */
explicit KernelSmoothing(const String & name = "KernelSmoothing");
/** Default constructor */
explicit KernelSmoothing(const Distribution & kernel,
const Bool & bined = true,
const UnsignedLong binNumber = ResourceMap::GetAsUnsignedLong( "KernelSmoothing-BinNumber" ),
const String & name = "KernelSmoothing");
/** 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 */
virtual Distribution build(const NumericalSample & sample,
const Bool boundaryCorrection = false);
/** Build a (possibly truncated) kernel mixture based on the given sample and bandwidth */
virtual Distribution build(const NumericalSample & sample,
const NumericalPoint & bandwidth,
const Bool boundaryCorrection = false);
/** Bandwidth accessor */
NumericalPoint getBandwidth() const;
/** Kernel accessor */
Distribution getKernel() const;
/** Compute the bandwidth according to Silverman's rule */
NumericalPoint computeSilvermanBandwidth(const NumericalSample & sample);
/** 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
*/
NumericalPoint computePluginBandwidth(const NumericalSample & sample);
/** 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
*/
NumericalPoint computeMixedBandwidth(const NumericalSample & sample);
/** 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 NumericalPoint & bandwidth);
// Bandwith of the smoothing
NumericalPoint 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
UnsignedLong binNumber_;
}; /* class KernelSmoothing */
END_NAMESPACE_OPENTURNS
#endif /* OPENTURNS_KERNELSMOOTHING_HXX */
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