/usr/include/openturns/TensorApproximationAlgorithm.hxx is in libopenturns-dev 1.9-5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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/**
* @brief Tensor approximation algorithm
*
* 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_TENSORAPPROXIMATIONALGORITHM_HXX
#define OPENTURNS_TENSORAPPROXIMATIONALGORITHM_HXX
#include "openturns/MetaModelAlgorithm.hxx"
#include "openturns/CanonicalTensorEvaluation.hxx"
#include "openturns/TensorApproximationResult.hxx"
#include "openturns/OrthogonalProductFunctionFactory.hxx"
#include "openturns/ApproximationAlgorithmImplementationFactory.hxx"
BEGIN_NAMESPACE_OPENTURNS
/**
* @class TensorApproximationAlgorithm
*
* Tensor approximation algorithm
*/
class OT_API TensorApproximationAlgorithm
: public MetaModelAlgorithm
{
CLASSNAME;
public:
/** Constructor */
TensorApproximationAlgorithm(const Sample & inputSample,
const Sample & outputSample,
const Distribution & distribution,
const OrthogonalProductFunctionFactory & functionFactory,
const Indices & nk,
const UnsignedInteger maxRank = 1);
/** Virtual constructor */
virtual TensorApproximationAlgorithm * clone() const;
/** String converter */
virtual String __repr__() const;
/** Computes the functional chaos */
void run();
/** Result accessor */
TensorApproximationResult getResult() const;
/** Sample accessors */
Sample getInputSample() const;
Sample getOutputSample() 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);
/** Max ALS iteration accessor */
void setMaximumAlternatingLeastSquaresIteration(const UnsignedInteger maximumAlternatingLeastSquaresIteration);
UnsignedInteger getMaximumAlternatingLeastSquaresIteration() const;
/** Radius error accessor */
void setMaximumRadiusError(const Scalar maximumRadiusError);
Scalar getMaximumRadiusError() const;
/** Residual error accessor */
void setMaximumResidualError(const Scalar maximumResidualError);
Scalar getMaximumResidualError() const;
protected:
friend class Factory<TensorApproximationAlgorithm>;
/** Default constructor */
TensorApproximationAlgorithm();
private:
/** Marginal computation */
void runMarginal(const UnsignedInteger marginalIndex,
Scalar & marginalResidual,
Scalar & marginalRelativeError);
/** Greedy rank-1 algorithm */
void greedyRankOne(const Sample & x,
const Sample & y,
CanonicalTensorEvaluation & tensor,
Scalar & marginalResidual,
Scalar & marginalRelativeError);
/** Alternating least-squares algorithm to estimate a rank-1 tensor */
void rankOne(const Sample & x,
const Sample & y,
CanonicalTensorEvaluation & tensor,
const UnsignedInteger k,
Scalar & marginalResidual,
Scalar & marginalRelativeError);
/** Rank-M algorithm */
void rankM (const Sample & x,
const Sample & y,
CanonicalTensorEvaluation & tensor,
Scalar & marginalResidual,
Scalar & marginalRelativeError);
void rankMComponent (const Sample & x,
const Sample & y,
CanonicalTensorEvaluation & tensor,
const UnsignedInteger j);
/** The isoprobabilistic transformation maps the distribution into the orthogonal measure */
Function transformation_;
/** The inverse isoprobabilistic transformation */
Function inverseTransformation_;
/** The composed model */
Function composedModel_;
// samples
Sample inputSample_;
Sample outputSample_;
UnsignedInteger maxRank_;
Sample transformedInputSample_;
// tensorized basis
OrthogonalProductFunctionFactory basisFactory_;
// tensor format
Collection<CanonicalTensorEvaluation> tensor_;
// maximum rank-1 iterations
UnsignedInteger maximumAlternatingLeastSquaresIteration_;
// error on the radius for rank-1
Scalar maximumRadiusError_;
// error on the residual for rank-1
Scalar maximumResidualError_;
mutable Collection<DesignProxy> proxy_;
/** Result of the projection */
TensorApproximationResult result_;
} ; /* class TensorApproximationAlgorithm */
END_NAMESPACE_OPENTURNS
#endif /* OPENTURNS_TENSORAPPROXIMATIONALGORITHM_HXX */
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