Inheritance diagram for python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy:

Public Member Functions
	__init__ (self, samples=None, ixs=None, n=5000, npaths=100, isPositive=False, percentile=1)

	mean (self, grid, alpha, U, T)

	var (self, grid, alpha, U, T, mean)

Public Attributes
	samples

	verbose

Constructor & Destructor Documentation

◆ init()

python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__init__	(	self,
		samples = `None`,
		ixs = `None`,
		n = `5000`,
		npaths = `100`,
		isPositive = `False`,
		percentile = `1`
	)

Constructor
@param samples: ndarray containing monte carlo samples
@param ixs: list of indices for which there is data available
@param n: number of samples per path
@param npaths: number of paths
@param epsilon: maximal error with respect to the central limit theorem
@param beta: confidence level for central limit theorem
@param isPositive: forces the function to be positive

References python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__init__().

Referenced by python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__init__().

Member Function Documentation

◆ mean()

python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.mean	(	self,
		grid,
		alpha,
		U,
		T
	)

Estimate the expectation value using Monte-Carlo.

\frac{1}{N}\sum\limits_{i = 1}^N f_N(x_i)

where x_i \in \Gamma
@return: (mean, error of bootstrapping)

References python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__getSamples(), python.uq.estimators.MCEstimator.MCEstimator.__npaths, python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__npaths, python.uq.estimators.MCEstimator.MCEstimator.__percentile, python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__percentile, and python.uq.estimators.SparseGridEstimationStrategy.SparseGridEstimationStrategy._extractPDFforMomentEstimation().

Referenced by python.uq.analysis.asgc.ASGCAnalysis.ASGCAnalysis.computeMoments(), python.uq.analysis.mc.MCAnalysis.MCAnalysis.computeMoments(), python.uq.dists.LibAGFDist.LibAGFDist.var(), and python.uq.dists.SGDEdist.SGDEdist.var().

◆ var()

python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.var	(	self,
		grid,
		alpha,
		U,
		T,
		mean
	)

Estimate the expectation value using Monte-Carlo.

\frac{1}{N}\sum\limits_{i = 1}^N (f_N(x_i) - E(f))^2

where x_i \in \Gamma
@return: (variance, error of bootstrapping)

References python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__getSamples(), python.uq.estimators.MCEstimator.MCEstimator.__npaths, python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__npaths, python.uq.estimators.MCEstimator.MCEstimator.__percentile, python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__percentile, and python.uq.estimators.SparseGridEstimationStrategy.SparseGridEstimationStrategy._extractPDFforMomentEstimation().

Referenced by python.uq.analysis.asgc.ASGCAnalysis.ASGCAnalysis.computeMoments(), python.uq.analysis.mc.MCAnalysis.MCAnalysis.computeMoments(), python.uq.dists.Dist.Dist.cov(), python.uq.dists.DataDist.DataDist.std(), python.uq.dists.Dist.Dist.std(), and python.uq.dists.J.J.std().

Member Data Documentation

◆ samples

python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.samples

Referenced by python.uq.sampler.Sample.SamplesIterator.__next__(), python.uq.dists.DataDist.DataDist.cdf(), python.uq.dists.DataDist.DataDist.mean(), python.uq.dists.DataDist.DataDist.rvs(), python.uq.dists.LibAGFDist.LibAGFDist.rvs(), python.uq.dists.DataDist.DataDist.toJson(), and python.uq.dists.DataDist.DataDist.var().

◆ verbose

python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.verbose

Referenced by python.uq.operations.forcePositivity.operationMakePositiveFast.OperationMakePositiveFast.addFullGridPoints(), python.uq.refinement.RefinementManager.RefinementManager.candidates(), python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGridCandidates.computeCandidates(), python.uq.operations.forcePositivity.findIntersections.IntersectionCandidates.findIntersections(), python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGridCandidates.getLocalMaxLevel(), python.uq.manager.ASGCUQManager.ASGCUQManager.learnDataWithoutTest(), python.uq.manager.ASGCUQManager.ASGCUQManager.learnDataWithTest(), python.uq.operations.forcePositivity.operationMakePositive.OperationMakePositive.makeCurrentNodalValuesPositive(), python.uq.operations.forcePositivity.operationMakePositive.OperationMakePositive.makePositive(), python.uq.operations.forcePositivity.operationMakePositiveFast.OperationMakePositiveFast.makePositive(), python.uq.manager.ASGCUQManager.ASGCUQManager.recomputeStats(), python.uq.refinement.RefinementManager.RefinementManager.refineGrid(), and python.uq.operations.forcePositivity.localFullGridSearch.LocalFullGridCandidates.splitFullGrids().

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

datadriven/python/uq/estimators/MonteCarloStrategy.py

Public Member Functions

Public Attributes