SG++-Doxygen-Documentation
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This class provides the covariance matrix a sparse grid function. More...
#include <OperationCovariance.hpp>
Public Member Functions | |
virtual void | doQuadrature (base::DataVector &alpha, base::DataMatrix &cov, base::DataMatrix *bounds=nullptr) |
Integrate the sparse grid function. More... | |
OperationCovariance (base::Grid &grid) | |
Constructor. More... | |
virtual | ~OperationCovariance () |
Destructor. More... | |
Protected Attributes | |
base::Grid & | grid |
This class provides the covariance matrix a sparse grid function.
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inlineexplicit |
Constructor.
grid | grid |
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inlinevirtual |
Destructor.
References alpha, doQuadrature(), and grid.
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virtual |
Integrate the sparse grid function.
alpha | the function's values in the nodal basis |
cov | where the covariance matrix will be stored |
bounds | describes the boundaries of the hypercube of the original function |
References sgpp::op_factory::createOperationDensityMargTo1D(), sgpp::base::DataMatrix::getNcols(), sgpp::base::DataMatrix::getNrows(), python.utils.pca_normalize_dataset::means, sgpp::base::DataMatrix::resize(), sgpp::base::DataMatrix::set(), and sgpp::base::DataMatrix::setAll().
Referenced by ~OperationCovariance().
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protected |
Referenced by python.uq.learner.Interpolant.Interpolant::doLearningIteration(), python.learner.Classifier.Classifier::evalError(), python.uq.learner.Interpolant.Interpolant::evalError(), python.uq.learner.SimulationLearner.SimulationLearner::getCollocationNodes(), python.uq.learner.SimulationLearner.SimulationLearner::getGrid(), python.uq.learner.SimulationLearner.SimulationLearner::getLearner(), python.uq.learner.Regressor.Regressor::learnData(), python.uq.learner.Regressor.Regressor::learnDataWithFolding(), python.uq.learner.Regressor.Regressor::learnDataWithTest(), python.learner.Classifier.Classifier::refineGrid(), python.learner.Regressor.Regressor::refineGrid(), python.uq.learner.Regressor.Regressor::refineGrid(), python.uq.learner.SimulationLearner.SimulationLearner::refineGrid(), python.learner.Classifier.Classifier::updateResults(), python.learner.Regressor.Regressor::updateResults(), python.uq.learner.Regressor.Regressor::updateResults(), and ~OperationCovariance().