sgpp::datadriven::OperationDensityMarginalize Class Reference

Marginalize Probability Density Function. More...

#include <OperationDensityMarginalize.hpp>

Inheritance diagram for sgpp::datadriven::OperationDensityMarginalize:

Public Member Functions
virtual void	doMarginalize (base::DataVector &alpha, base::Grid *&mg, base::DataVector &malpha, unsigned int mdim)
	Marginalizes (Density) Functions. More...
	OperationDensityMarginalize (base::Grid *grid)
virtual	~OperationDensityMarginalize ()

Protected Attributes
base::Grid *	grid

Detailed Description

Marginalize Probability Density Function.

Constructor & Destructor Documentation

OperationDensityMarginalize()

sgpp::datadriven::OperationDensityMarginalize::OperationDensityMarginalize ( base::Grid *  grid )

inlineexplicit

~OperationDensityMarginalize()

virtual sgpp::datadriven::OperationDensityMarginalize::~OperationDensityMarginalize ( )

inlinevirtual

References alpha, and doMarginalize().

Member Function Documentation

doMarginalize()

void sgpp::datadriven::OperationDensityMarginalize::doMarginalize ( base::DataVector &  alpha, base::Grid *&  mg, base::DataVector &  malpha, unsigned int  mdim  )

virtual

Marginalizes (Density) Functions.

Parameters

alpha	Coefficient vector for current grid
mg	Referenz of grid pointer
malpha	Coefficient vector for new grid (mg). Will be resized.
mdim	Marginalize in dimension mdim

Note: Because of adaptively refined sparse grids, we cannot simply generate a regular grid. Thus, we need to add point after point to the new grid mg

Compute coefficients for marginalized density Each coefficient has to be weighted with the integral of the basis functions in direction mdim

Attention: The integral of one basis functions changes for if another type of basis is used!

Reimplemented in sgpp::datadriven::OperationDensityMarginalizeLinear.

References sgpp::base::Grid::createGridOfEquivalentType(), sgpp::base::Grid::getBasis(), sgpp::base::HashGridStorage::getDimension(), sgpp::base::HashGridPoint::getIndex(), sgpp::base::HashGridPoint::getLevel(), sgpp::base::HashGridStorage::getPoint(), sgpp::base::HashGridStorage::getSequenceNumber(), sgpp::base::HashGridStorage::getSize(), sgpp::base::Grid::getStorage(), grid, python.statsfileInfo::i, sgpp::base::HashGridStorage::insert(), sgpp::base::HashGridStorage::isContaining(), sgpp::base::HashGridStorage::recalcLeafProperty(), sgpp::base::HashGridPoint::set(), and sgpp::base::DataVector::setAll().

Referenced by sgpp::datadriven::OperationDensityMargTo1D::marg_next_dim(), and ~OperationDensityMarginalize().

Member Data Documentation

grid

base::Grid* sgpp::datadriven::OperationDensityMarginalize::grid

protected

Referenced by python.uq.learner.Interpolant.Interpolant::doLearningIteration(), doMarginalize(), sgpp::datadriven::OperationDensityMarginalizeLinear::doMarginalize(), 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(), and python.uq.learner.Regressor.Regressor::updateResults().

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

• datadriven/src/sgpp/datadriven/operation/hash/simple/OperationDensityMarginalize.hpp
• datadriven/src/sgpp/datadriven/operation/hash/simple/OperationDensityMarginalize.cpp