Abstract class for providing functionality for accomplishment of learning with cross-validation by generating a set of training data/validation data pairs. More...

Inheritance diagram for python.learner.folding.FoldingPolicy.FoldingPolicy:

Public Member Functions
def	__init__ (self, dataset, level=1)
	Constructor. More...

def	__iter__ (self)
	Implementation of iterator method iter() iterates through subsets. More...

def	__next__ (self)
	Implementation of iterator method next() More...

def	createFoldsets (self, dataContainer, validationIndeces)
	Create fold new data set Brings points given by validationIndeces together as test subset and the rest of points as train subset. More...

Public Attributes
	dataFold
	List of partitioned data sets. More...

	dataset
	Dataset. More...

	level
	Folding level. More...

	seq
	Sequence of indices of points from data set. More...

	size
	Size of dataset. More...

	window
	Number of points in one subset. More...

Detailed Description

Abstract class for providing functionality for accomplishment of learning with cross-validation by generating a set of training data/validation data pairs.

Constructor & Destructor Documentation

◆ init()

def python.learner.folding.FoldingPolicy.FoldingPolicy.__init__	(	self,
		dataset,
		level = `1`
	)

Constructor.

Parameters

dataset	DataContainer with data set
level	Integer folding level, default value: 1

Member Function Documentation

◆ iter()

def python.learner.folding.FoldingPolicy.FoldingPolicy.__iter__ ( self )

Implementation of iterator method iter() iterates through subsets.

◆ next()

def python.learner.folding.FoldingPolicy.FoldingPolicy.__next__ ( self )

Implementation of iterator method next()

Returns: : the next subset

References python.learner.folding.FoldingPolicy.FoldingPolicy.dataFold, sgpp::combigrid::TensorGrid.level, sgpp::combigrid::ExponentialChebyshevPermutationIterator.level, sgpp::combigrid::ExponentialNoBoundaryPermutationIterator.level, sgpp::combigrid::ExponentialLevelorderPermutationIterator.level, python.learner.folding.FoldingPolicy.FoldingPolicy.level, sgpp::combigrid::AbstractEvaluator< V >.level, sgpp::combigrid::QueueEntry.level, python.learner.folding.FilesFoldingPolicy.FilesFoldingPolicy.level, and sgpp::base::HashGridPoint.level.

◆ createFoldsets()

def python.learner.folding.FoldingPolicy.FoldingPolicy.createFoldsets	(	self,
		dataContainer,
		validationIndeces
	)

Create fold new data set Brings points given by validationIndeces together as test subset and the rest of points as train subset.

Parameters

dataContainer	DataContainer with points
validationIndeces	list of indices for validation subset

Returns: : DataContainer partitioned data set

References python.learner.folding.FoldingPolicy.FoldingPolicy.seq, and sgpp::base::AbstractRefinement_refinement_key.seq.

Member Data Documentation

◆ dataFold

python.learner.folding.FoldingPolicy.FoldingPolicy.dataFold

List of partitioned data sets.

Referenced by python.learner.folding.FoldingPolicy.FoldingPolicy.__next__().

◆ dataset

python.learner.folding.FoldingPolicy.FoldingPolicy.dataset

Dataset.

◆ level

python.learner.folding.FoldingPolicy.FoldingPolicy.level

Folding level.

Referenced by python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGrid.__eq__(), python.learner.folding.FoldingPolicy.FoldingPolicy.__next__(), python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGrid.contains(), python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGrid.containsDimx(), python.uq.learner.builder.GridDescriptor.GridDescriptor.createGrid(), python.uq.estimators.MarginalIntegralStrategy.MarginalIntegralStrategy.estimate(), python.uq.learner.builder.GridDescriptor.GridDescriptor.fromGrid(), python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGrid.getLevelIndex(), python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGrid.getMaxLevel(), python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGrid.overlap(), python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGrid.overlapDimx(), python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGrid.transformToReferenceGrid(), python.uq.manager.ASGCStatistics.ASGCStatistics.updateResults(), python.uq.learner.SimulationLearner.SimulationLearner.updateResults(), python.uq.learner.builder.GridDescriptor.GridDescriptor.withLevel(), and python.uq.learner.builder.RegressorSpecificationDescriptor.FoldingDescriptor.withLevel().

◆ seq

python.learner.folding.FoldingPolicy.FoldingPolicy.seq

Sequence of indices of points from data set.

Referenced by python.learner.folding.FoldingPolicy.FoldingPolicy.createFoldsets().

◆ size

python.learner.folding.FoldingPolicy.FoldingPolicy.size

Size of dataset.

◆ window

python.learner.folding.FoldingPolicy.FoldingPolicy.window

Number of points in one subset.

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

datadriven/python/learner/folding/FoldingPolicy.py

Public Member Functions

Public Attributes