diff --git a/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/ProjectionStrategy.cxx b/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/ProjectionStrategy.cxx index 994905963e0..32747fca1a9 100644 --- a/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/ProjectionStrategy.cxx +++ b/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/ProjectionStrategy.cxx @@ -130,6 +130,11 @@ Point ProjectionStrategy::getCoefficients() const return getImplementation()->getCoefficients(); } +/* Design proxy accessor */ +DesignProxy ProjectionStrategy::getDesignProxy() const +{ + return getImplementation()->getDesignProxy(); +} /* Compute the components alpha_k_p_ by projecting the model on the partial L2 basis */ void ProjectionStrategy::computeCoefficients(const Function & function, @@ -153,9 +158,9 @@ String ProjectionStrategy::__repr__() const /* String converter */ -String ProjectionStrategy::__str__(const String & ) const +String ProjectionStrategy::__str__(const String & offset) const { - return __repr__(); + return OSS() << getImplementation()->__str__(offset); } diff --git a/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/ProjectionStrategyImplementation.cxx b/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/ProjectionStrategyImplementation.cxx index d1148c0bfba..9f9b8a05fa8 100644 --- a/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/ProjectionStrategyImplementation.cxx +++ b/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/ProjectionStrategyImplementation.cxx @@ -25,6 +25,7 @@ #include "openturns/FixedExperiment.hxx" #include "openturns/UserDefined.hxx" #include "openturns/Exception.hxx" +#include "openturns/Os.hxx" BEGIN_NAMESPACE_OPENTURNS @@ -130,10 +131,42 @@ ProjectionStrategyImplementation * ProjectionStrategyImplementation::clone() con /* String converter */ String ProjectionStrategyImplementation::__repr__() const { - return OSS() << "class=" << GetClassName() - << " measure=" << measure_; + OSS oss(false); + oss << "class=" << GetClassName() + << " alpha_k_p=" << alpha_k_p_ + << " residual=" << residual_p_ + << " relativeError=" << relativeError_p_ + << " measure=" << measure_ + << " weightedExperiment=" << weightedExperiment_ + << " inputSample_=" << inputSample_ + << " outputSample=" << outputSample_ + << " weights_=" << weights_ + << " proxy=" << proxy_; + return oss; } +/* String converter */ +String ProjectionStrategyImplementation::__str__(const String &) const +{ + return __repr_markdown__(); +} + +/* String converter */ +String ProjectionStrategyImplementation::__repr_markdown__() const +{ + OSS oss(false); + oss << GetClassName() << Os::GetEndOfLine() + << "- coefficients: " << alpha_k_p_.getDimension() << Os::GetEndOfLine() + << "- residual: " << residual_p_ << Os::GetEndOfLine() + << "- relative error: " << relativeError_p_ << Os::GetEndOfLine() + << "- measure: " << measure_.getClassName() << Os::GetEndOfLine() + << "- weighted experiment: " << weightedExperiment_.getClassName() << Os::GetEndOfLine() + << "- input sample: size= " << inputSample_.getSize() <<" x dimension= " << inputSample_.getDimension() << Os::GetEndOfLine() + << "- output sample: size= " << outputSample_.getSize() <<" x dimension= " << outputSample_.getDimension() << Os::GetEndOfLine() + << "- weights: dimension= " << weights_.getDimension() << Os::GetEndOfLine() + << "- design: size= " << proxy_.getSampleSize() << Os::GetEndOfLine(); + return oss; +} /* Measure accessor */ void ProjectionStrategyImplementation::setMeasure(const Distribution & measure) @@ -219,6 +252,12 @@ Point ProjectionStrategyImplementation::getCoefficients() const return alpha_k_p_; } +/* Design proxy accessor */ +DesignProxy ProjectionStrategyImplementation::getDesignProxy() const +{ + return proxy_; +} + /* Compute the components alpha_k_p_ by projecting the model on the partial L2 basis */ void ProjectionStrategyImplementation::computeCoefficients(const Function &, const FunctionCollection &, diff --git a/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/openturns/ProjectionStrategy.hxx b/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/openturns/ProjectionStrategy.hxx index 99d96de2587..7dee661fce8 100644 --- a/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/openturns/ProjectionStrategy.hxx +++ b/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/openturns/ProjectionStrategy.hxx @@ -84,6 +84,9 @@ public: virtual void setExperiment(const WeightedExperiment & weightedExperiment); virtual WeightedExperiment getExperiment() const; + /** Design proxy accessor */ + virtual DesignProxy getDesignProxy() const; + /** String converter */ String __repr__() const override; diff --git a/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/openturns/ProjectionStrategyImplementation.hxx b/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/openturns/ProjectionStrategyImplementation.hxx index e796450e1c7..4cd62c060ae 100644 --- a/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/openturns/ProjectionStrategyImplementation.hxx +++ b/lib/src/Uncertainty/Algorithm/MetaModel/FunctionalChaos/openturns/ProjectionStrategyImplementation.hxx @@ -79,6 +79,8 @@ public: /** String converter */ String __repr__() const override; + String __str__(const String & offset = "") const override; + String __repr_markdown__() const; /** Measure accessor */ virtual void setMeasure(const Distribution & measure); @@ -102,13 +104,15 @@ public: virtual Scalar getRelativeError() const; /** Relative error accessor */ -// virtual void setCoefficients(const Point & alpha_k); virtual Point getCoefficients() const; /** Experiment accessors */ virtual void setExperiment(const WeightedExperiment & weightedExperiment); virtual WeightedExperiment getExperiment() const; + /** Design proxy accessor */ + virtual DesignProxy getDesignProxy() const; + /** Method save() stores the object through the StorageManager */ void save(Advocate & adv) const override; diff --git a/lib/test/CMakeLists.txt b/lib/test/CMakeLists.txt index 01c98f00c98..c28902f3fb6 100644 --- a/lib/test/CMakeLists.txt +++ b/lib/test/CMakeLists.txt @@ -642,6 +642,7 @@ ot_check_test (FunctionalChaos_gsobol_sparse) ot_check_test (FunctionalChaos_ishigami_sparse) ot_check_test (FunctionalChaos_ishigami_database) ot_check_test (FunctionalChaos_nd) +ot_check_test (FunctionalChaos_2d IGNOREOUT) ot_check_test (LeastSquaresExpansion_std IGNOREOUT) ot_check_test (IntegrationExpansion_std IGNOREOUT) ot_check_test (KrigingAlgorithm_std) diff --git a/lib/test/t_FunctionalChaos_2d.cxx b/lib/test/t_FunctionalChaos_2d.cxx new file mode 100644 index 00000000000..68e27810591 --- /dev/null +++ b/lib/test/t_FunctionalChaos_2d.cxx @@ -0,0 +1,94 @@ +// -*- C++ -*- +/** + * @brief Test of a FunctionalChaosAlgorithm with 2 outputs + * + * Copyright 2005-2023 Airbus-EDF-IMACS-ONERA-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 License + * along with this library. If not, see . + * + */ +#include "openturns/OT.hxx" +#include "openturns/OTtestcode.hxx" + +using namespace OT; +using namespace OT::Test; + +int main(int, char *[]) +{ + TESTPREAMBLE; + OStream fullprint(std::cout); + setRandomGenerator(); + + try + { + + // Problem parameters + const UnsignedInteger inputDimension = 2; + const UnsignedInteger outputDimension = 2; + + // Create the function + Description inputVariables(inputDimension); + inputVariables[0] = "x1"; + inputVariables[1] = "x2"; + Description formula(outputDimension); + formula[0] = "cos(x1 + x2)"; + formula[1] = "(x2 + 1) * exp(x1 - 2 * x2)"; + const SymbolicFunction model(inputVariables, formula); + + // Create the input distribution + Collection marginals(inputDimension); + for (UnsignedInteger i = 0; i < inputDimension; ++i) marginals[i] = Normal(0.0, 1.0); + const ComposedDistribution distribution(marginals); + + // Create the orthogonal basis + Collection polynomialCollection(inputDimension); + for (UnsignedInteger i = 0; i < inputDimension; ++i) + { + polynomialCollection[i] = HermiteFactory(); + } + const LinearEnumerateFunction enumerateFunction(inputDimension); + const OrthogonalProductPolynomialFactory productBasis(polynomialCollection, enumerateFunction); + + const UnsignedInteger degree = 4; + const UnsignedInteger basisSize = enumerateFunction.getBasisSizeFromTotalDegree(degree); + const AdaptiveStrategy adaptiveStrategy(FixedStrategy(productBasis, basisSize)); + + + // Create the projection strategy + UnsignedInteger samplingSize = 100; + const MonteCarloExperiment experiment(distribution, samplingSize); + RandomGenerator::SetSeed(0); + const Sample X(experiment.generate()); + const Sample Y(model(X)); + FunctionalChaosAlgorithm algo(X, Y, distribution, adaptiveStrategy, LeastSquaresStrategy()); + algo.run(); + + // Examine the results + const FunctionalChaosResult result(algo.getResult()); + const ProjectionStrategy projectionStrategy(algo.getProjectionStrategy()); + fullprint << "ProjectionStrategy (repr)= " << std::endl; + fullprint << projectionStrategy << std::endl; + std::cout << "ProjectionStrategy (str)= " << std::endl; + std::cout << projectionStrategy << std::endl; + + } // try + catch (TestFailed & ex) + { + std::cerr << ex << std::endl; + return ExitCode::Error; + } + + + return ExitCode::Success; +} diff --git a/python/src/IntegrationStrategy.i b/python/src/IntegrationStrategy.i index 24135f81668..4ec0dbe720d 100644 --- a/python/src/IntegrationStrategy.i +++ b/python/src/IntegrationStrategy.i @@ -8,3 +8,12 @@ %include openturns/IntegrationStrategy.hxx namespace OT{ %extend IntegrationStrategy { IntegrationStrategy(const IntegrationStrategy & other) { return new OT::IntegrationStrategy(other); } } } + +%pythoncode %{ +def __IntegrationStrategy_repr_html(self): + """Get HTML representation.""" + html = openturns.ProjectionStrategy(self)._repr_html_() + return html + +IntegrationStrategy._repr_html_ = __IntegrationStrategy_repr_html +%} diff --git a/python/src/LeastSquaresStrategy.i b/python/src/LeastSquaresStrategy.i index 1bf7271808b..fcfa0008ca7 100644 --- a/python/src/LeastSquaresStrategy.i +++ b/python/src/LeastSquaresStrategy.i @@ -8,3 +8,13 @@ %include openturns/LeastSquaresStrategy.hxx namespace OT{ %extend LeastSquaresStrategy { LeastSquaresStrategy(const LeastSquaresStrategy & other) { return new OT::LeastSquaresStrategy(other); } } } + +%pythoncode %{ +def __LeastSquaresStrategy_repr_html(self): + """Get HTML representation.""" + html = openturns.ProjectionStrategy(self)._repr_html_() + return html + +LeastSquaresStrategy._repr_html_ = __LeastSquaresStrategy_repr_html +%} + diff --git a/python/src/ProjectionStrategy.i b/python/src/ProjectionStrategy.i index bb50b513f0b..5c0ab26fade 100644 --- a/python/src/ProjectionStrategy.i +++ b/python/src/ProjectionStrategy.i @@ -10,3 +10,25 @@ OTTypedInterfaceObjectHelper(ProjectionStrategy) %include openturns/ProjectionStrategy.hxx namespace OT{ %extend ProjectionStrategy { ProjectionStrategy(const ProjectionStrategy & other) { return new OT::ProjectionStrategy(other); } } } + +%pythoncode %{ +def __ProjectionStrategy_repr_html(self): + """Get HTML representation.""" + html = "" + html += "
    \n" + html += f"
  • coefficients: dimension= {self.getCoefficients().getDimension()}
    • \n" + html += f"
  • residual: {self.getResidual()}
    • \n" + html += ( + f"
  • relative error: {self.getRelativeError()}
    • \n" + ) + html += f"
  • measure: {self.getMeasure().getImplementation().getClassName()}
    • \n" + html += f"
  • experiment: {self.getExperiment().getClassName()}
    • \n" + html += f"
  • input sample: size= {self.getInputSample().getSize()} x dimension= {self.getInputSample().getDimension()}
    • \n" + html += f"
  • output sample: size= {self.getOutputSample().getSize()} x dimension= {self.getOutputSample().getDimension()}
    • \n" + html += f"
  • weights: dimension= {self.getWeights().getDimension()}
    • \n" + html += f"
  • design: size= {self.getDesignProxy().getSampleSize()}
    • \n" + html += "
\n" + return html + +ProjectionStrategy._repr_html_ = __ProjectionStrategy_repr_html +%} diff --git a/python/src/ProjectionStrategyImplementation_doc.i.in b/python/src/ProjectionStrategyImplementation_doc.i.in index f1d4a485851..18179461f3b 100644 --- a/python/src/ProjectionStrategyImplementation_doc.i.in +++ b/python/src/ProjectionStrategyImplementation_doc.i.in @@ -249,3 +249,16 @@ m : Distribution %enddef %feature("docstring") OT::ProjectionStrategyImplementation::setMeasure OT_ProjectionStrategy_setMeasure_doc + +// --------------------------------------------------------------------- + +%define OT_ProjectionStrategy_getDesignProxy_doc +"Accessor to the design proxy. + +Parameters +---------- +designProxy : DesignProxy + The design matrix." +%enddef +%feature("docstring") OT::ProjectionStrategyImplementation::getDesignProxy +OT_ProjectionStrategy_getDesignProxy_doc diff --git a/python/src/ProjectionStrategy_doc.i.in b/python/src/ProjectionStrategy_doc.i.in index aedd21793e5..420e25e0e11 100644 --- a/python/src/ProjectionStrategy_doc.i.in +++ b/python/src/ProjectionStrategy_doc.i.in @@ -28,3 +28,5 @@ OT_ProjectionStrategy_getWeights_doc OT_ProjectionStrategy_setExperiment_doc %feature("docstring") OT::ProjectionStrategy::setMeasure OT_ProjectionStrategy_setMeasure_doc +%feature("docstring") OT::ProjectionStrategy::getDesignProxy +OT_ProjectionStrategy_getDesignProxy_doc diff --git a/python/test/CMakeLists.txt b/python/test/CMakeLists.txt index d92f032a9fe..10cd9a460ad 100644 --- a/python/test/CMakeLists.txt +++ b/python/test/CMakeLists.txt @@ -734,6 +734,7 @@ ot_pyinstallcheck_test (LinearTaylor_std) ot_pyinstallcheck_test (LinearLeastSquares_std) ot_pyinstallcheck_test (QuadraticLeastSquares_std) ot_pyinstallcheck_test (QuadraticTaylor_std) +ot_pyinstallcheck_test (ProjectionStrategy_std IGNOREOUT) ot_pyinstallcheck_test (FunctionalChaos_ishigami) ot_pyinstallcheck_test (FunctionalChaos_ishigami_sparse) ot_pyinstallcheck_test (FunctionalChaos_ishigami_database) diff --git a/python/test/t_FunctionalChaos_gsobol.expout b/python/test/t_FunctionalChaos_gsobol.expout index 5e026a72489..85061cb5d11 100644 --- a/python/test/t_FunctionalChaos_gsobol.expout +++ b/python/test/t_FunctionalChaos_gsobol.expout @@ -1,6 +1,16 @@ ################################### class=AdaptiveStrategy implementation=class=CleaningStrategy maximum size=10 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=35 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=ComposedDistribution implementation=class=SampleImplementation name=ComposedDistribution size=250 dimension=3 description=[X0,X1,X2] data=[[0.629877,0.43381,0.253795],[0.882805,0.940772,0.429314],[0.135276,0.615122,0.0204337],[0.0325028,0.295983,0.51169],[0.347057,0.87299,0.481518],[0.969423,0.187737,0.978697],[0.92068,0.895326,0.725105],[0.50304,0.679968,0.550516],[0.0632061,0.934031,0.76771],[0.292757,0.358425,0.569658],[0.714382,0.197267,0.628995],[0.383362,0.987889,0.711662],[0.373767,0.707594,0.972339],[0.737268,0.542022,0.38338],[0.883503,0.360548,0.716526],[0.294994,0.901532,0.883387],[0.92851,0.872797,0.909294],[0.820811,0.320412,0.0511235],[0.684575,0.996377,0.721716],[0.828027,0.311808,0.877846],[0.359802,0.220652,0.834947],[0.954746,0.349851,0.549311],[0.588615,0.949285,0.796874],[0.182039,0.0477304,0.185003],[0.085785,0.0791328,0.0994375],[0.660727,0.0606909,0.907442],[0.210442,0.0475671,0.249664],[0.386229,0.395072,0.214906],[0.0245595,0.871399,0.354399],[0.418919,0.00775935,0.105242],[0.981841,0.963321,0.856746],[0.916132,0.150419,0.518905],[0.955603,0.768282,0.0399128],[0.47295,0.52002,0.211035],[0.259861,0.33744,0.719794],[0.66134,0.819759,0.164955],[0.489775,0.408766,0.998465],[0.468524,0.0121888,0.58276],[0.111078,0.503649,0.459105],[0.345319,0.294143,0.282338],[0.655977,0.229737,0.165129],[0.675088,0.842879,0.175967],[0.97898,0.626339,0.401304],[0.646334,0.117037,0.95512],[0.0642123,0.00424093,0.464518],[0.77302,0.0959054,0.91348],[0.994199,0.414321,0.573855],[0.0830734,0.241681,0.759522],[0.224867,0.698317,0.685527],[0.302619,0.793294,0.599481],[0.217211,0.315328,0.11762],[0.629665,0.501471,0.490815],[0.580153,0.933127,0.498501],[0.467088,0.971199,0.693914],[0.695994,0.215199,0.723598],[0.7411,0.364325,0.150939],[0.936819,0.713948,0.489266],[0.871448,0.638032,0.38804],[0.39318,0.866042,0.515168],[0.629248,0.482234,0.368962],[0.913529,0.0693131,0.333008],[0.913401,0.492862,0.699854],[0.159386,0.0881152,0.167483],[0.544438,0.530443,0.0519198],[0.203653,0.908716,0.272503],[0.855795,0.411431,0.916083],[0.435078,0.154144,0.0314856],[0.361664,0.448536,0.469154],[0.00210762,0.199529,0.783467],[0.239434,0.527627,0.00444693],[0.255156,0.493307,0.175808],[0.591534,0.291282,0.960051],[0.169217,0.360299,0.319286],[0.977334,0.6069,0.509083],[0.482992,0.555687,0.01879],[0.709308,0.552027,0.58184],[0.957944,0.600321,0.929952],[0.703018,0.903757,0.350229],[0.820178,0.218234,0.753129],[0.481881,0.534703,0.792789],[0.206568,0.242476,0.759641],[0.751194,0.423206,0.633719],[0.925531,0.139994,0.798922],[0.635923,0.836659,0.882795],[0.602162,0.955023,0.260345],[0.701505,0.798566,0.653773],[0.0794149,0.757314,0.861233],[0.252333,0.616354,0.439678],[0.676976,0.960683,0.888738],[0.358453,0.0493002,0.297716],[0.420129,0.81209,0.621804],[0.321355,0.442067,0.0308063],[0.7986,0.533371,0.948673],[0.575562,0.122134,0.250718],[0.328719,0.726119,0.256264],[0.971683,0.938274,0.383321],[0.517628,0.101407,0.986556],[0.10782,0.89066,0.859676],[0.907789,0.536602,0.318308],[0.749745,0.293536,0.22163],[0.501307,0.864078,0.663711],[0.514402,0.627852,0.533507],[0.91172,0.196993,0.781865],[0.720115,0.207499,0.665661],[0.587001,0.996742,0.223279],[0.824617,0.00143427,0.747211],[0.712151,0.313233,0.00291806],[0.148702,0.0199802,0.412696],[0.824057,0.0515033,0.0158012],[0.559861,0.20001,0.859267],[0.653406,0.0286926,0.147885],[0.0199465,0.562026,0.34912],[0.722557,0.670163,0.168197],[0.744502,0.559764,0.290976],[0.285938,0.592898,0.823184],[0.857752,0.611045,0.445103],[0.524686,0.463699,0.515978],[0.955851,0.367699,0.238667],[0.246347,0.893983,0.84527],[0.226161,0.913126,0.871445],[0.956461,0.121696,0.34421],[0.603466,0.100329,0.706781],[0.490258,0.160161,0.533343],[0.439102,0.743017,0.936858],[0.130402,0.147739,0.84254],[0.927336,0.674196,0.348014],[0.101504,0.240217,0.71065],[0.325771,0.561498,0.491741],[0.400443,0.492979,0.0877009],[0.405661,0.618953,0.909474],[0.398973,0.588282,0.175653],[0.894128,0.756584,0.643057],[0.116207,0.436101,0.944443],[0.215626,0.180434,0.308764],[0.122525,0.465164,0.198332],[0.147325,0.189075,0.131614],[0.34841,0.667181,0.7564],[0.524555,0.68124,0.370018],[0.142395,0.00751686,0.939574],[0.292465,0.199793,0.339416],[0.530312,0.855826,0.286888],[0.716967,0.49455,0.241089],[0.245577,0.153684,0.295472],[0.57791,0.279509,0.644726],[0.884829,0.00110479,0.491913],[0.294274,0.295463,0.331054],[0.830291,0.449075,0.617105],[0.68879,0.922485,0.172896],[0.060546,0.603463,0.0750455],[0.802415,0.975901,0.511945],[0.351195,0.00510658,0.216676],[0.790619,0.117853,0.396025],[0.493325,0.64898,0.026715],[0.314479,0.232903,0.0187214],[0.725769,0.225701,0.73757],[0.00930063,0.369216,0.205996],[0.557543,0.0871069,0.377617],[0.922267,0.595619,0.774303],[0.345749,0.226544,0.599221],[0.765081,0.295896,0.207031],[0.616143,0.32545,0.71844],[0.179054,0.81107,0.445441],[0.378697,0.387509,0.0577331],[0.0790875,0.336025,0.0490961],[0.564674,0.758478,0.242542],[0.237977,0.316421,0.256608],[0.671188,0.520973,0.259121],[0.658043,0.988946,0.00477589],[0.442878,0.770525,0.484872],[0.744773,0.644731,0.815318],[0.853223,0.451343,0.724005],[0.241203,0.73164,0.580271],[0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+ProjectionStrategyImplementation +- coefficients: 11 +- residual: 0.00906333 +- relative error: 0.0344988 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.00906333] relative errors= [0.0344988] mean=1.01345049 absolute error=0.01345049 @@ -14,7 +24,17 @@ Sobol index [1,2] =0.01799886 absolute error=0.00075114 Sobol index [0,1,2] =0.00056580 absolute error=0.00568420 ################################### class=AdaptiveStrategy implementation=class=CleaningStrategy maximum size=10 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=35 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=250 dimension=3 description=[X0,X1,X2] 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+ProjectionStrategyImplementation +- coefficients: 11 +- residual: 0.00959568 +- relative error: 0.0356757 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.00959568] relative errors= [0.0356757] mean=0.99047885 absolute error=0.00952115 @@ -28,7 +48,17 @@ Sobol index [1,2] =0.01986253 absolute error=0.00111253 Sobol index [0,1,2] =0.00000000 absolute error=0.00625000 ################################### class=AdaptiveStrategy implementation=class=CleaningStrategy maximum size=10 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=35 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=250 dimension=3 description=[y0,y1,y2] 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+ProjectionStrategyImplementation +- coefficients: 11 +- residual: 0.00971889 +- relative error: 0.0390684 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.00971889] relative errors= [0.0390684] mean=0.99807210 absolute error=0.00192790 @@ -42,7 +72,17 @@ Sobol index [1,2] =0.01826201 absolute error=0.00048799 Sobol index [0,1,2] =0.00000000 absolute error=0.00625000 ################################### class=AdaptiveStrategy implementation=class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=35 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=ComposedDistribution implementation=class=SampleImplementation name=ComposedDistribution size=250 dimension=3 description=[X0,X1,X2] 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+ProjectionStrategyImplementation +- coefficients: 35 +- residual: 0.00851458 +- relative error: 0.0304478 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.00851458] relative errors= [0.0304478] mean=1.01296639 absolute error=0.01296639 @@ -56,7 +96,17 @@ Sobol index [1,2] =0.01842773 absolute error=0.00032227 Sobol index [0,1,2] =0.00175343 absolute error=0.00449657 ################################### class=AdaptiveStrategy implementation=class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=35 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=250 dimension=3 description=[X0,X1,X2] 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+ProjectionStrategyImplementation +- coefficients: 35 +- residual: 0.00881056 +- relative error: 0.0300766 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.00881056] relative errors= [0.0300766] mean=0.99116011 absolute error=0.00883989 @@ -70,7 +120,17 @@ Sobol index [1,2] =0.02108508 absolute error=0.00233508 Sobol index [0,1,2] =0.00053997 absolute error=0.00571003 ################################### class=AdaptiveStrategy implementation=class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=35 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=250 dimension=3 description=[y0,y1,y2] 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weights=class=Point name=Unnamed dimension=250 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+ProjectionStrategyImplementation +- coefficients: 35 +- residual: 0.00936606 +- relative error: 0.0362832 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.00936606] relative errors= [0.0362832] mean=0.99781283 absolute error=0.00218717 diff --git a/python/test/t_FunctionalChaos_ishigami.expout b/python/test/t_FunctionalChaos_ishigami.expout index 81121d5cdcb..87a3682704d 100644 --- a/python/test/t_FunctionalChaos_ishigami.expout +++ b/python/test/t_FunctionalChaos_ishigami.expout @@ -1,6 +1,16 @@ ################################### class=AdaptiveStrategy implementation=class=CleaningStrategy maximum size=20 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=ComposedDistribution implementation=class=SampleImplementation name=ComposedDistribution size=250 dimension=3 description=[X0,X1,X2] 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+ProjectionStrategyImplementation +- coefficients: 21 +- residual: 0.0313673 +- relative error: 0.0178023 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.0313673] relativeErrors= [0.0178023] mean=3.46342005 absolute error=0.0365799482 @@ -61,7 +71,17 @@ FunctionalChaosSobolIndices ################################### class=AdaptiveStrategy implementation=class=CleaningStrategy maximum size=20 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=250 dimension=3 description=[X0,X1,X2] 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+ProjectionStrategyImplementation +- coefficients: 21 +- residual: 0.032434 +- relative error: 0.0196503 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.032434] relativeErrors= [0.0196503] mean=3.54042934 absolute error=0.0404293402 @@ -123,7 +143,17 @@ FunctionalChaosSobolIndices ################################### class=AdaptiveStrategy implementation=class=CleaningStrategy maximum size=20 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=250 dimension=3 description=[y0,y1,y2] 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+ProjectionStrategyImplementation +- coefficients: 21 +- residual: 0.0290505 +- relative error: 0.0154051 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.0290505] relativeErrors= [0.0154051] mean=3.52113827 absolute error=0.0211382679 @@ -185,7 +215,17 @@ FunctionalChaosSobolIndices ################################### class=AdaptiveStrategy implementation=class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=ComposedDistribution implementation=class=SampleImplementation name=ComposedDistribution size=250 dimension=3 description=[X0,X1,X2] 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+ProjectionStrategyImplementation +- coefficients: 84 +- residual: 0.0241084 +- relative error: 0.0105163 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.0241084] relativeErrors= [0.0105163] mean=3.45801565 absolute error=0.0419843459 @@ -254,7 +294,17 @@ FunctionalChaosSobolIndices ################################### class=AdaptiveStrategy implementation=class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=250 dimension=3 description=[X0,X1,X2] 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+ProjectionStrategyImplementation +- coefficients: 84 +- residual: 0.0248146 +- relative error: 0.0115022 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.0248146] relativeErrors= [0.0115022] mean=3.48612447 absolute error=0.0138755275 @@ -323,7 +373,17 @@ FunctionalChaosSobolIndices ################################### class=AdaptiveStrategy implementation=class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=250 dimension=3 description=[y0,y1,y2] 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+ProjectionStrategyImplementation +- coefficients: 84 +- residual: 0.024718 +- relative error: 0.0111528 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.024718] relativeErrors= [0.0111528] mean=3.52947075 absolute error=0.0294707509 diff --git a/python/test/t_FunctionalChaos_ishigami_database.expout b/python/test/t_FunctionalChaos_ishigami_database.expout index a9265bbd9b4..1642702cde9 100644 --- a/python/test/t_FunctionalChaos_ishigami_database.expout +++ b/python/test/t_FunctionalChaos_ishigami_database.expout @@ -1,6 +1,16 @@ ################################### -class=AdaptiveStrategy implementation=class=CleaningStrategy maximum size=20 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=MonteCarloExperiment name=Unnamed distribution=class=Uniform name=Uniform dimension=1 a=-1 b=1 size=100 +class=CleaningStrategy maximum size=20 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=84 +ProjectionStrategyImplementation +- coefficients: 21 +- residual: 0.0290505 +- relative error: 0.0154051 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.0290505] relativeErrors= [0.0154051] mean=3.52113827 absolute error=0.0211382679 @@ -20,8 +30,18 @@ Sobol total index [0,2] =0.24415868 absolute error=0.0004750191 Sobol total index [1,2] =0.00043642 absolute error=0.0004364176 Sobol total index [0,1,2] =0.00171843 absolute error=0.0017184293 ################################### -class=AdaptiveStrategy implementation=class=CleaningStrategy maximum size=20 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=MonteCarloExperiment name=Unnamed distribution=class=Uniform name=Uniform dimension=1 a=-1 b=1 size=100 +class=CleaningStrategy maximum size=20 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=84 +ProjectionStrategyImplementation +- coefficients: 10 +- residual: 0.031185 +- relative error: 8.89613e-05 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.031185] relativeErrors= [8.89613e-05] mean=3.51972123 absolute error=0.0197212272 @@ -41,8 +61,18 @@ Sobol total index [0,2] =0.24436796 absolute error=0.0006842969 Sobol total index [1,2] =0.00000000 absolute error=0.0000000000 Sobol total index [0,1,2] =0.00000000 absolute error=0.0000000000 ################################### -class=AdaptiveStrategy implementation=class=CleaningStrategy maximum size=20 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=IntegrationStrategy experiment=class=MonteCarloExperiment name=Unnamed distribution=class=Uniform name=Uniform dimension=1 a=-1 b=1 size=100 +class=CleaningStrategy maximum size=20 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=84 +ProjectionStrategyImplementation +- coefficients: 21 +- residual: 0.0782409 +- relative error: 0 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.0782409] relativeErrors= [0] mean=3.57690572 absolute error=0.0769057245 @@ -62,8 +92,18 @@ Sobol total index [0,2] =0.26319037 absolute error=0.0195067022 Sobol total index [1,2] =0.01460970 absolute error=0.0146096968 Sobol total index [0,1,2] =0.02263050 absolute error=0.0226305044 ################################### -class=AdaptiveStrategy implementation=class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=MonteCarloExperiment name=Unnamed distribution=class=Uniform name=Uniform dimension=1 a=-1 b=1 size=100 +class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=84 +ProjectionStrategyImplementation +- coefficients: 84 +- residual: 0.024718 +- relative error: 0.0111528 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.024718] relativeErrors= [0.0111528] mean=3.52947075 absolute error=0.0294707509 @@ -83,8 +123,18 @@ Sobol total index [0,2] =0.25121369 absolute error=0.0075300230 Sobol total index [1,2] =0.00251830 absolute error=0.0025182974 Sobol total index [0,1,2] =0.00548925 absolute error=0.0054892488 ################################### -class=AdaptiveStrategy implementation=class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=MonteCarloExperiment name=Unnamed distribution=class=Uniform name=Uniform dimension=1 a=-1 b=1 size=100 +class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=84 +ProjectionStrategyImplementation +- coefficients: 84 +- residual: 0.0295638 +- relative error: 8.25367e-05 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.0295638] relativeErrors= [8.25367e-05] mean=3.52361492 absolute error=0.0236149151 @@ -104,8 +154,18 @@ Sobol total index [0,2] =0.24688054 absolute error=0.0031968719 Sobol total index [1,2] =0.00000000 absolute error=0.0000000000 Sobol total index [0,1,2] =0.00205430 absolute error=0.0020543003 ################################### -class=AdaptiveStrategy implementation=class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=IntegrationStrategy experiment=class=MonteCarloExperiment name=Unnamed distribution=class=Uniform name=Uniform dimension=1 a=-1 b=1 size=100 +class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=84 +ProjectionStrategyImplementation +- coefficients: 84 +- residual: 0.0865757 +- relative error: 0 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 1 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.0865757] relativeErrors= [0] mean=3.57690572 absolute error=0.0769057245 diff --git a/python/test/t_FunctionalChaos_ishigami_database.py b/python/test/t_FunctionalChaos_ishigami_database.py index f3e6403bcb3..aa8ad1b4dc2 100755 --- a/python/test/t_FunctionalChaos_ishigami_database.py +++ b/python/test/t_FunctionalChaos_ishigami_database.py @@ -98,11 +98,10 @@ algo.run() # Examine the results - result = ot.FunctionalChaosResult(algo.getResult()) + result = algo.getResult() print("###################################") - print(ot.AdaptiveStrategy(adaptiveStrategy)) - print(ot.ProjectionStrategy(projectionStrategy)) - # print "coefficients=", result.getCoefficients() + print(adaptiveStrategy) + print(algo.getProjectionStrategy()) residuals = result.getResiduals() print("residuals=", residuals) relativeErrors = result.getRelativeErrors() diff --git a/python/test/t_FunctionalChaos_nd.expout b/python/test/t_FunctionalChaos_nd.expout index afaec17f649..06968f54663 100644 --- a/python/test/t_FunctionalChaos_nd.expout +++ b/python/test/t_FunctionalChaos_nd.expout @@ -1,6 +1,16 @@ ################################### class=AdaptiveStrategy implementation=class=CleaningStrategy maximum size=20 significance factor=1e-06 derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=250 dimension=3 description=[X0,X1,X2] 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+ProjectionStrategyImplementation +- coefficients: 21 +- residual: 0.032434 +- relative error: 0.0196503 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 2 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.00527094,0.032434] relative errors= [0.0107646,0.0196503] output= 0 @@ -45,7 +55,17 @@ Sobol total index [1,2] =0.00145 absolute error=1.44634e-03 Sobol total index [0,1,2] =0.00014 absolute error=1.42405e-04 ################################### class=AdaptiveStrategy implementation=class=FixedStrategy derived from class=AdaptiveStrategyImplementation maximumDimension=84 -class=ProjectionStrategy implementation=class=LeastSquaresStrategy experiment=class=FixedExperiment name=Unnamed sample=class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=250 dimension=3 description=[X0,X1,X2] 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+ProjectionStrategyImplementation +- coefficients: 84 +- residual: 0.0248146 +- relative error: 0.0115022 +- measure: Distribution +- weighted experiment: WeightedExperiment +- input sample: size= 250 x dimension= 3 +- output sample: size= 250 x dimension= 2 +- weights: dimension= 250 +- design: size= 250 + residuals= [0.0042755,0.0248146] relative errors= [0.00708265,0.0115022] output= 0 diff --git a/python/test/t_ProjectionStrategy_std.py b/python/test/t_ProjectionStrategy_std.py new file mode 100644 index 00000000000..e5ee900286f --- /dev/null +++ b/python/test/t_ProjectionStrategy_std.py @@ -0,0 +1,112 @@ +#! /usr/bin/env python + +import openturns as ot +from openturns.usecases import flood_model + +ot.TESTPREAMBLE() + +ot.Log.Show(ot.Log.NONE) + +sampleSize = 500 +totalDegree = 7 + +# +print("+ Compute flood model by regression") +fm = flood_model.FloodModel() +inputDescription = fm.model.getInputDescription() +marginals = [fm.distribution.getMarginal(i) for i in range(fm.dim)] +basis = ot.OrthogonalProductPolynomialFactory(marginals) +inputSample = fm.distribution.getSample(sampleSize) +outputSample = fm.model(inputSample) +selectionAlgorithm = ot.LeastSquaresMetaModelSelectionFactory() +leastSquaresStrategy = ot.LeastSquaresStrategy(selectionAlgorithm) +print("leastSquaresStrategy") +print(leastSquaresStrategy) +print("leastSquaresStrategy (repr)") +print(leastSquaresStrategy.__repr__()) +print("leastSquaresStrategy (html)") +print(leastSquaresStrategy._repr_html_()) + +enumerateFunction = basis.getEnumerateFunction() +basisSize = enumerateFunction.getBasisSizeFromTotalDegree(totalDegree) +adaptiveStrategy = ot.FixedStrategy(basis, basisSize) +chaosAlgo = ot.FunctionalChaosAlgorithm( + inputSample, outputSample, fm.distribution, adaptiveStrategy, leastSquaresStrategy +) +projectionStrategy = chaosAlgo.getProjectionStrategy() +print("projectionStrategy") +print(projectionStrategy) +print("projectionStrategy (repr)") +print(projectionStrategy.__repr__()) +print("projectionStrategy (html)") +print(projectionStrategy._repr_html_()) + + +# +print("+ Compute flood model by integration") +integrationStrategy = ot.IntegrationStrategy() +print("integrationStrategy") +print(integrationStrategy) +print("integrationStrategy (repr)") +print(integrationStrategy.__repr__()) +print("integrationStrategy (html)") +print(integrationStrategy._repr_html_()) +chaosAlgo = ot.FunctionalChaosAlgorithm( + inputSample, outputSample, fm.distribution, adaptiveStrategy, integrationStrategy +) +projectionStrategy = chaosAlgo.getProjectionStrategy() +print("projectionStrategy") +print(projectionStrategy) +print("projectionStrategy (repr)") +print(projectionStrategy.__repr__()) +print("projectionStrategy (html)") +print(projectionStrategy._repr_html_()) + + +class RepeatedFloodOutputDimensionFunction(ot.OpenTURNSPythonFunction): + def __init__(self, outputDimension): + super().__init__(4, outputDimension) + self.fm = flood_model.FloodModel() + self.outputDimension = outputDimension + + def _exec(self, x): + y = ot.Point(self.outputDimension) + y_base = fm.model(x) + for i in range(self.outputDimension): + y[i] = i + y_base[0] + return y + + +# +print("+ Compute flood model with large output dimension") +outputDimension = 20 +model = ot.Function(RepeatedFloodOutputDimensionFunction(outputDimension)) +sampleSize = 500 +totalDegree = 7 +fm = flood_model.FloodModel() +inputDescription = fm.model.getInputDescription() +marginals = [fm.distribution.getMarginal(i) for i in range(fm.dim)] +basis = ot.OrthogonalProductPolynomialFactory(marginals) +inputSample = fm.distribution.getSample(sampleSize) +outputSample = model(inputSample) +selectionAlgorithm = ot.LeastSquaresMetaModelSelectionFactory() +leastSquaresStrategy = ot.LeastSquaresStrategy(selectionAlgorithm) +print("leastSquaresStrategy") +print(leastSquaresStrategy) +print("leastSquaresStrategy (repr)") +print(leastSquaresStrategy.__repr__()) +print(leastSquaresStrategy._repr_html_()) + +enumerateFunction = basis.getEnumerateFunction() +basisSize = enumerateFunction.getBasisSizeFromTotalDegree(totalDegree) +adaptiveStrategy = ot.FixedStrategy(basis, basisSize) +chaosAlgo = ot.FunctionalChaosAlgorithm( + inputSample, outputSample, fm.distribution, adaptiveStrategy, leastSquaresStrategy +) +projectionStrategy = chaosAlgo.getProjectionStrategy() +print("projectionStrategy") +print(projectionStrategy) +print("projectionStrategy (repr)") +print(projectionStrategy.__repr__()) +print("projectionStrategy (html)") +print(projectionStrategy._repr_html_())