/usr/include/openturns/KrigingResult.hxx is in libopenturns-dev 1.9-5.
This file is owned by root:root, with mode 0o644.
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/**
* @brief The result of a kriging estimation
*
* 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_KRIGINGRESULT_HXX
#define OPENTURNS_KRIGINGRESULT_HXX
#include "openturns/MetaModelResult.hxx"
#include "openturns/CovarianceModel.hxx"
#include "openturns/Sample.hxx"
#include "openturns/Collection.hxx"
#include "openturns/Basis.hxx"
#include "openturns/PersistentCollection.hxx"
#include "openturns/Function.hxx"
#include "openturns/Normal.hxx"
#include "openturns/HMatrix.hxx"
#include "openturns/Basis.hxx"
BEGIN_NAMESPACE_OPENTURNS
/**
* @class KrigingResult
*
* The result of a chaos expansion
*/
class OT_API KrigingResult
: public MetaModelResult
{
CLASSNAME;
public:
// friend class Factory<KrigingResult>;
typedef Collection<Point> PointCollection;
typedef PersistentCollection<Point> PointPersistentCollection;
typedef Collection<Basis> BasisCollection;
typedef PersistentCollection<Basis> BasisPersistentCollection;
/** Default constructor */
KrigingResult();
/** Parameter constructor without any cholesky factor*/
KrigingResult(const Sample & inputSample,
const Sample & outputSample,
const Function & metaModel,
const Point & residuals,
const Point & relativeErrors,
const BasisCollection & basis,
const PointCollection & trendCoefficients,
const CovarianceModel & covarianceModel,
const Sample & covarianceCoefficients);
/** Parameter constructor with Cholesky factor (Lapack)*/
KrigingResult(const Sample & inputSample,
const Sample & outputSample,
const Function & metaModel,
const Point & residuals,
const Point & relativeErrors,
const BasisCollection & basis,
const PointCollection & trendCoefficients,
const CovarianceModel & covarianceModel,
const Sample & covarianceCoefficients,
const TriangularMatrix & covarianceCholeskyFactor,
const HMatrix & covarianceHMatrix);
/** Virtual constructor */
virtual KrigingResult * clone() const;
/** String converter */
virtual String __repr__() const;
virtual String __str__(const String & offset = "") const;
/** Design accessors */
virtual Sample getInputSample() const;
virtual Sample getOutputSample() const;
/** Trend basis accessor */
virtual BasisCollection getBasisCollection() const;
/** Trend coefficients accessor */
virtual PointCollection getTrendCoefficients() const;
/** Conditional covariance models accessor */
virtual CovarianceModel getCovarianceModel() const;
/** Process coefficients accessor */
virtual Sample getCovarianceCoefficients() const;
/** Transformation accessor */
virtual Function getTransformation() const;
virtual void setTransformation(const Function & transformation);
/** Compute mean of new points conditionnaly to observations */
virtual Point getConditionalMean(const Sample & xi) const;
/** Compute mean of new points conditionnaly to observations */
virtual Point getConditionalMean(const Point & xi) const;
/** Compute covariance matrix conditionnaly to observations*/
virtual CovarianceMatrix getConditionalCovariance(const Sample & xi) const ;
/** Compute covariance matrix conditionnaly to observations*/
virtual CovarianceMatrix getConditionalCovariance(const Point & xi) const;
/** Compute joint normal distribution conditionnaly to observations*/
virtual Normal operator()(const Sample & xi) const;
/** Compute joint normal distribution conditionnaly to observations*/
virtual Normal operator()(const Point & xi) 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);
protected:
/** Compute cross matrix method ==> not necessary square matrix */
Matrix getCrossMatrix(const Sample & x) const;
void computeF() const;
private:
// Structure for evaluation of crossCovariance
friend struct KrigingResultCrossCovarianceFunctor;
/** inputData should be keeped*/
Sample inputSample_;
/** input transformed data: store data*/
Sample inputTransformedSample_;
Sample outputSample_;
/** inputTransformation ==> iso-probabilistic transformation */
Function inputTransformation_;
/** Boolean transformation */
Bool hasTransformation_;
/** The trend basis */
BasisPersistentCollection basis_;
/** The trend coefficients */
PointPersistentCollection trendCoefficients_;
/** The covariance model */
CovarianceModel covarianceModel_;
/** The covariance coefficients */
Sample covarianceCoefficients_;
/** Boolean for cholesky. The factor is not mandatory (see KrigingAlgorithm) */
Bool hasCholeskyFactor_;
/** Cholesky factor */
mutable TriangularMatrix covarianceCholeskyFactor_;
/** Cholesky factor when using hmat-oss */
mutable HMatrix covarianceHMatrix_;
/** Matrix F : the regression matrix */
mutable Matrix F_;
/** Matrix phi = L^{-1}F ==> phiT is the transposed matrix */
mutable Matrix phiT_;
/** Matrix F^{t}R^{-1}F writes phi_ = L^{-1}F ==> QR decomposition */
// G_ is the triangular matrix ==> Gt the transposed
mutable Matrix Gt_;
} ; /* class KrigingResult */
END_NAMESPACE_OPENTURNS
#endif /* OPENTURNS_KRIGINGRESULT_HXX */
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