sgpp::optimization::ASInterpolantScalarFunction Class Reference

Sparse grid interpolant of a scalar-valued function. More...

#include <ASInterpolantScalarFunction.hpp>

Inheritance diagram for sgpp::optimization::ASInterpolantScalarFunction:

Public Member Functions
	ASInterpolantScalarFunction (base::Grid &grid, const base::DataVector &alpha)
	Constructor.
void	clone (std::unique_ptr< base::ScalarFunction > &clone) const override
double	eval (const base::DataVector &x) override
	Evaluation of the function.
const base::DataVector &	getAlpha () const
size_t	getSize ()
void	setAlpha (const base::DataVector &alpha)
	~ASInterpolantScalarFunction () override
	Destructor.
Public Member Functions inherited from sgpp::base::ScalarFunction
virtual void	eval (const DataMatrix &x, DataVector &value)
	Convenience method for calculating \(f(\vec{x})\) for multiple \(\vec{x}\).
size_t	getNumberOfParameters () const
	ScalarFunction (size_t d)
	Constructor.
virtual	~ScalarFunction ()
	Destructor.

Protected Attributes
base::DataVector	alpha
	coefficient vector
base::Grid &	grid
	sparse grid
std::unique_ptr< base::OperationEval >	opEval
	pointer to evaluation operation
Protected Attributes inherited from sgpp::base::ScalarFunction
size_t	d
	dimension of the domain

Detailed Description

Sparse grid interpolant of a scalar-valued function.

Special case for active subspace response surfaces which is defined on mathbb{R}^d and not only [0,1]^d

More generally, the function can be any linear combination $f colon mathbb{R}^d to mathbb{R}$, $f(vec{x}) = sum_{k=1}^N alpha_k varphi_k(vec{x})$ of the basis functions $varphi_k = varphi_{vec{ell}_k,vec{i}_k}$ of a sparse grid with grid points $vec{x}_k = vec{x}_{vec{ell}_k,vec{i}_k}$. But most often, the function (e.g., its coefficients) is constructed as an interpolant at the grid points for some function values.

Constructor & Destructor Documentation

ASInterpolantScalarFunction()

sgpp::optimization::ASInterpolantScalarFunction::ASInterpolantScalarFunction ( base::Grid &  grid, const base::DataVector &  alpha  )

inline

Constructor.

Do not destruct the grid before the ASInterpolantScalarFunction object!

Parameters

grid	sparse grid
alpha	coefficient vector

~ASInterpolantScalarFunction()

sgpp::optimization::ASInterpolantScalarFunction::~ASInterpolantScalarFunction ( )

inlineoverride

Destructor.

Member Function Documentation

clone()

void sgpp::optimization::ASInterpolantScalarFunction::clone ( std::unique_ptr< base::ScalarFunction > &  clone ) const

inlineoverridevirtual

Parameters

[out]

clone

pointer to cloned object

Implements sgpp::base::ScalarFunction.

References alpha, clone(), and grid.

Referenced by clone().

eval()

double sgpp::optimization::ASInterpolantScalarFunction::eval ( const base::DataVector &  x )

inlineoverridevirtual

Evaluation of the function.

Parameters

x	evaluation point $vec{x} in mathbb{R}^d$

Returns

$f(vec{x})$

Implements sgpp::base::ScalarFunction.

References alpha, and opEval.

Referenced by python.uq.analysis.asgc.ASGCAnalysis.ASGCAnalysis::estimateDensity().

getAlpha()

const base::DataVector & sgpp::optimization::ASInterpolantScalarFunction::getAlpha ( ) const

inline

Returns

coefficient vector

References alpha.

Referenced by python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge::getSparseGridFunction().

getSize()

size_t sgpp::optimization::ASInterpolantScalarFunction::getSize ( )

inline

Returns

number of grid points

References sgpp::base::Grid::getSize(), and grid.

Referenced by python.uq.uq_setting.UQSetting.UQSetting::convert(), python.uq.uq_setting.UQSetting.UQSetting::getTimeDependentResults(), python.uq.uq_setting.UQSetting.UQSetting::runSamples(), and python.uq.uq_setting.UQSetting.UQSetting::runSamples_withoutDistribution().

setAlpha()

void sgpp::optimization::ASInterpolantScalarFunction::setAlpha ( const base::DataVector &  alpha )

inline

Parameters

alpha

coefficient vector

References alpha.

Member Data Documentation

alpha

base::DataVector sgpp::optimization::ASInterpolantScalarFunction::alpha

protected

coefficient vector

Referenced by clone(), python.learner.Learner.Learner::doLearningIteration(), eval(), getAlpha(), python.learner.Learner.Learner::learnData(), python.learner.Learner.Learner::learnDataWithTest(), python.uq.learner.Interpolant.Interpolant::learnDataWithTest(), python.uq.dists.SGDEdist.SGDEdist::pdf(), setAlpha(), and python.uq.dists.SGDEdist.SGDEdist::toJson().

grid

base::Grid& sgpp::optimization::ASInterpolantScalarFunction::grid

protected

sparse grid

Referenced by python.uq.dists.SGDEdist.SGDEdist::__str__(), python.learner.Learner.Learner::applyData(), python.tools.Matrix::ApplyMatrix(), python.uq.dists.SGDEdist.SGDEdist::cdf(), clone(), python.uq.learner.Interpolant.Interpolant::doLearningIteration(), python.learner.Learner.Learner::doLearningIteration(), python.learner.Classifier.Classifier::evalError(), python.uq.learner.Interpolant.Interpolant::evalError(), python.tools.Matrix::generateb(), python.controller.CheckpointController.CheckpointController::generateFoldValidationJob(), python.uq.learner.SimulationLearner.SimulationLearner::getCollocationNodes(), python.uq.learner.SimulationLearner.SimulationLearner::getGrid(), python.uq.learner.SimulationLearner.SimulationLearner::getLearner(), getSize(), python.learner.Learner.Learner::learnData(), python.learner.Learner.Learner::learnDataWithFolding(), python.uq.learner.Regressor.Regressor::learnDataWithFolding(), python.learner.Learner.Learner::learnDataWithTest(), python.uq.learner.Regressor.Regressor::learnDataWithTest(), python.controller.CheckpointController.CheckpointController::loadAll(), python.uq.operations.forcePositivity.operationMakePositive.OperationMakePositive::makePositive(), python.uq.operations.forcePositivity.operationMakePositiveFast.OperationMakePositiveFast::makePositive(), python.uq.dists.SGDEdist.SGDEdist::mean(), python.uq.dists.SGDEdist.SGDEdist::pdf(), python.uq.dists.SGDEdist.SGDEdist::ppf(), python.learner.Classifier.Classifier::refineGrid(), python.learner.Regressor.Regressor::refineGrid(), python.uq.learner.Regressor.Regressor::refineGrid(), python.uq.learner.SimulationLearner.SimulationLearner::refineGrid(), python.controller.CheckpointController.CheckpointController::saveGrid(), python.controller.CheckpointController.CheckpointController::setGrid(), python.learner.Learner.Learner::setGrid(), python.uq.learner.Learner.Learner::setGrid(), python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGrid::split(), python.uq.dists.SGDEdist.SGDEdist::toJson(), and python.uq.dists.SGDEdist.SGDEdist::var().

opEval

std::unique_ptr<base::OperationEval> sgpp::optimization::ASInterpolantScalarFunction::opEval

protected

pointer to evaluation operation

Referenced by eval().

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The documentation for this class was generated from the following file:

• optimization/src/sgpp/optimization/function/scalar/ASInterpolantScalarFunction.hpp