python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge Class Reference

Inheritance diagram for python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge:

Public Member Functions
	__init__ (self)
	__str__ (self)
	clearAlphas (self)
	createMemento (self)
	fromJson (cls, jsonObject)
	getAlpha (self, qoi='_', t=0, dtype=KnowledgeTypes.SIMPLE, iteration=None)
	getAlphas (self)
	getAlphasByQoI (self, qoi='_', dtype=KnowledgeTypes.SIMPLE, iteration=None)
	getAvailableIterations (self)
	getAvailableKnowledgeTypes (self)
	getAvailableQoI (self)
	getAvailableTimeSteps (self)
	getGrid (self, qoi='_', iteration=None)
	getGrids (self)
	getIteration (self)
	getSparseGridFunction (self, qoi='_', t=0, dtype=KnowledgeTypes.SIMPLE, iteration=None)
	hasAlpha (self, iteration, qoi, t, dtype)
	hasGrid (self, iteration, qoi)
	initWithStandardValues (cls, grid, alpha)
	setAlphas (self, alphas)
	setGrids (self, grids)
	setIteration (self, iteration)
	setMemento (self, memento)
	toJson (self)
	update (self, grid, alpha, qoi, t, dtype, iteration)
	writeToFile (self, filename)

Detailed Description

The ASGC knowledge class

Constructor & Destructor Documentation

__init__()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__init__

(

self

)

Constructor

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__grids, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__iteration, and python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__iteration.

Member Function Documentation

__str__()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__str__

(

self

)

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__grids, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__iteration, and python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__iteration.

clearAlphas()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.clearAlphas

(

self

)

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas.

createMemento()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.createMemento

(

self

)

Creates a new memento to hold the current state

References python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.toJson(), python.uq.dists.Beta.Beta.toJson(), python.uq.dists.Corr.Corr.toJson(), python.uq.dists.CorrBeta.CorrBeta.toJson(), python.uq.dists.DataDist.DataDist.toJson(), python.uq.dists.J.J.toJson(), python.uq.dists.KDEDist.KDEDist.toJson(), python.uq.dists.Lognormal.Lognormal.toJson(), python.uq.dists.MultivariateNormal.MultivariateNormal.toJson(), python.uq.dists.NatafDist.NatafDist.toJson(), python.uq.dists.Normal.Normal.toJson(), python.uq.dists.SGDEdist.SGDEdist.toJson(), python.uq.dists.TLognormal.TLognormal.toJson(), python.uq.dists.TNormal.TNormal.toJson(), python.uq.dists.Uniform.Uniform.toJson(), python.uq.estimators.SparseGridEstimationStrategy.SparseGridEstimationStrategy.toJson(), python.uq.parameters.Parameter.Parameter.toJson(), python.uq.parameters.ParameterSet.ParameterSet.toJson(), python.uq.sampler.asgc.ASGCSampler.ASGCSampler.toJson(), python.uq.sampler.Sample.Sample.toJson(), python.uq.transformation.JointTransformation.JointTransformation.toJson(), python.uq.transformation.LinearTransformation.LinearTransformation.toJson(), python.uq.transformation.RosenblattTransformation.RosenblattTransformation.toJson(), and python.uq.uq_setting.UQSetting.UQSetting.toJson().

Referenced by python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.writeToFile(), and python.uq.uq_setting.UQSetting.UQSetting.writeToFile().

fromJson()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.fromJson	(		cls,
			jsonObject
	)

Restores the ASGC object from the json object with its
attributes.
@param jsonObject: json object
@return: the restored ASGC object

Referenced by python.learner.Learner.Learner.setMemento(), python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.setMemento(), python.uq.learner.Learner.Learner.setMemento(), python.uq.sampler.asgc.ASGCSampler.ASGCSampler.setMemento(), and python.uq.uq_setting.UQSetting.UQSetting.setMemento().

getAlpha()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getAlpha	(		self,
			qoi = '_',
			t = 0,
			dtype = KnowledgeTypes.SIMPLE,
			iteration = None
	)

Get the coefficient vector for the given configuration
@param qoi: string quantity of interest
@param t: float time step
@param dtype: KnowledgeType
@param iteration: int, iteration number

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__iteration, python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__iteration, and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.hasAlpha().

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

getAlphas()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getAlphas

(

self

)

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas.

getAlphasByQoI()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getAlphasByQoI	(		self,
			qoi = '_',
			dtype = KnowledgeTypes.SIMPLE,
			iteration = None
	)

Get all coefficient vectors for the given quantity of interest
@param qoi: string quantity of interest
@param iteration: int, iteration number

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__iteration, and python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__iteration.

getAvailableIterations()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getAvailableIterations

(

self

)

get available iterations
@return: sorted list of integes

References python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__grids.

getAvailableKnowledgeTypes()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getAvailableKnowledgeTypes

(

self

)

@return list of available KnowledgeTypes

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas.

getAvailableQoI()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getAvailableQoI

(

self

)

get available quantities of interest
@return: list of strings identifying the quantities of interest

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas.

Referenced by python.uq.uq_setting.UQSetting.UQSetting.getResult(), and python.uq.uq_setting.UQSetting.UQSetting.getTimeDependentResults().

getAvailableTimeSteps()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getAvailableTimeSteps

(

self

)

get available time steps
@return: sorted list of floats

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__iteration, and python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__iteration.

getGrid()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getGrid	(		self,
			qoi = '_',
			iteration = None
	)

Get the grid for the given configuration
@param qoi: string quantity of interest
@param iteration: int, iteration number

References python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__grids, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__iteration, python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__iteration, and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.hasGrid().

Referenced by python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getSparseGridFunction(), python.uq.learner.SimulationLearner.SimulationLearner.learnData(), python.uq.learner.SimulationLearner.SimulationLearner.learnDataWithFolding(), python.uq.learner.SimulationLearner.SimulationLearner.learnDataWithTest(), and python.uq.learner.SimulationLearner.SimulationLearner.refineGrid().

getGrids()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getGrids

(

self

)

References python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__grids.

getIteration()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getIteration

(

self

)

get current iteration number

References python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__iteration, and python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__iteration.

getSparseGridFunction()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getSparseGridFunction	(		self,
			qoi = '_',
			t = 0,
			dtype = KnowledgeTypes.SIMPLE,
			iteration = None
	)

Get the sparse grid function (grid, alpha) for the given setting
@param qoi: string quantity of interest
@param t: float time step
@param dtype: KnowledgeType
@param iteration: int, iteration number

References python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__iteration, python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__iteration, sgpp::datadriven::LearnerBase.getAlpha(), sgpp::base::InterpolantScalarFunction.getAlpha(), sgpp::base::InterpolantScalarFunctionGradient.getAlpha(), sgpp::base::InterpolantScalarFunctionHessian.getAlpha(), sgpp::base::InterpolantVectorFunction.getAlpha(), sgpp::base::InterpolantVectorFunctionGradient.getAlpha(), sgpp::base::InterpolantVectorFunctionHessian.getAlpha(), sgpp::optimization::ASInterpolantScalarFunction.getAlpha(), sgpp::optimization::ASInterpolantScalarFunctionGradient.getAlpha(), sgpp::optimization::optimizer::NelderMead.getAlpha(), python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getAlpha(), sgpp::base::HierarchisationSLE.getGrid(), sgpp::datadriven::LearnerBase.getGrid(), sgpp::datadriven::LearnerSGDE.getGrid(), sgpp::datadriven::RegressionLearner.getGrid(), sgpp::datadriven::SparseGridDensityEstimator.getGrid(), sgpp::datadriven::ModelFittingBase.getGrid(), sgpp::datadriven::ModelFittingBaseSingleGrid.getGrid(), sgpp::optimization::SplineResponseSurface.getGrid(), sgpp::optimization::SplineResponseSurfaceVector.getGrid(), sgpp::solver::OperationParabolicPDESolverSystem.getGrid(), sgpp::combigrid::OperationEvalCombinationGrid.getGrid(), sgpp::combigrid::OperationEvalFullGrid.getGrid(), sgpp::combigrid::OperationUPCombinationGrid.getGrid(), sgpp::combigrid::OperationUPFullGrid.getGrid(), sgpp::optimization::IterativeGridGenerator.getGrid(), sgpp::pde::PDESolver.getGrid(), python.uq.analysis.asgc.ASGCAnalysis.ASGCAnalysis.getGrid(), python.uq.learner.SimulationLearner.SimulationLearner.getGrid(), python.uq.manager.ASGCUQManager.ASGCUQManager.getGrid(), python.uq.sampler.asgc.ASGCSampler.ASGCSampler.getGrid(), python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getGrid(), sgpp::datadriven::LearnerSGDEOnOffParallel.getGrid(), python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.hasAlpha(), and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.hasGrid().

hasAlpha()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.hasAlpha	(		self,
			iteration,
			qoi,
			t,
			dtype
	)

Check if there is a coefficient vector for the given
configuration.
@param iteration: int iteration number
@param qoi: string quantity of interest
@param t: float time step
@param dtype: KnowledgeType

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas.

Referenced by python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getAlpha(), and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getSparseGridFunction().

hasGrid()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.hasGrid	(		self,
			iteration,
			qoi
	)

Check if there is a grid available for the given configuration
@param iteration: int iteration number
@param qoi: string quantity of interest

References python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__grids.

Referenced by python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getGrid(), and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.getSparseGridFunction().

initWithStandardValues()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.initWithStandardValues	(		cls,
			grid,
			alpha
	)

setAlphas()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.setAlphas	(		self,
			alphas
	)

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, and python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas.

setGrids()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.setGrids	(		self,
			grids
	)

References python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__grids.

setIteration()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.setIteration	(		self,
			iteration
	)

set current iteration number

References python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__iteration, and python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__iteration.

setMemento()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.setMemento	(		self,
			memento
	)

Restores the state which is saved in the given memento
@param memento: the memento object

References python.data.DataContainer.DataContainer.fromJson(), python.learner.solver.CGSolver.CGSolver.fromJson(), python.learner.TrainingSpecification.TrainingSpecification.fromJson(), python.learner.TrainingStopPolicy.TrainingStopPolicy.fromJson(), python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.fromJson(), python.uq.dists.Beta.Beta.fromJson(), python.uq.dists.Corr.Corr.fromJson(), python.uq.dists.CorrBeta.CorrBeta.fromJson(), python.uq.dists.DataDist.DataDist.fromJson(), python.uq.dists.Dist.Dist.fromJson(), python.uq.dists.J.J.fromJson(), python.uq.dists.KDEDist.KDEDist.fromJson(), python.uq.dists.Lognormal.Lognormal.fromJson(), python.uq.dists.MultivariateNormal.MultivariateNormal.fromJson(), python.uq.dists.NatafDist.NatafDist.fromJson(), python.uq.dists.Normal.Normal.fromJson(), python.uq.dists.SGDEdist.SGDEdist.fromJson(), python.uq.dists.TLognormal.TLognormal.fromJson(), python.uq.dists.TNormal.TNormal.fromJson(), python.uq.dists.Uniform.Uniform.fromJson(), python.uq.estimators.SparseGridEstimationStrategy.SparseGridEstimationStrategy.fromJson(), python.uq.parameters.Parameter.Parameter.fromJson(), python.uq.parameters.ParameterSet.ParameterSet.fromJson(), python.uq.sampler.asgc.ASGCSampler.ASGCSampler.fromJson(), python.uq.sampler.asgc.ASGCSamplerStopPolicy.ASGCSamplerStopPolicy.fromJson(), python.uq.sampler.Sample.Sample.fromJson(), python.uq.transformation.JointTransformation.JointTransformation.fromJson(), python.uq.transformation.LinearTransformation.LinearTransformation.fromJson(), python.uq.transformation.RosenblattTransformation.RosenblattTransformation.fromJson(), python.uq.transformation.Transformation.Transformation.fromJson(), python.uq.uq_setting.UQSetting.UQSetting.fromJson(), python.learner.Learner.Learner.fromJson(), and python.uq.learner.Learner.Learner.fromJson().

toJson()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.toJson

(

self

)

@return: a string that represents the object

Referenced by python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__str__(), python.uq.uq_setting.UQSetting.UQSetting.__str__(), python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.createMemento(), python.uq.sampler.asgc.ASGCSampler.ASGCSampler.createMemento(), and python.uq.uq_setting.UQSetting.UQSetting.createMemento().

update()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.update	(		self,
			grid,
			alpha,
			qoi,
			t,
			dtype,
			iteration
	)

Update the knowledge
@param grid: Grid
@param alpha: numpy array surplus vector
@param qoi: string quantity of interest
@param t: float time step
@param dtype: KnowledgeType
@param iteration: int iteration number

References python.learner.LearnedKnowledge.LearnedKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__alphas, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__grids, python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.__iteration, and python.uq.sampler.asgc.ASGCSampler.ASGCSampler.__iteration.

Referenced by python.uq.refinement.RefinementStrategy.Ranking.rank(), and python.learner.LearnedKnowledge.LearnedKnowledge.setMemento().

writeToFile()

python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.writeToFile	(		self,
			filename
	)

Write knowledge object to file

References python.learner.LearnedKnowledge.LearnedKnowledge.createMemento(), python.learner.Learner.Learner.createMemento(), python.uq.analysis.asgc.ASGCKnowledge.ASGCKnowledge.createMemento(), python.uq.learner.Learner.Learner.createMemento(), python.uq.sampler.asgc.ASGCSampler.ASGCSampler.createMemento(), and python.uq.uq_setting.UQSetting.UQSetting.createMemento().

Referenced by python.uq.uq_setting.UQSetting.UQSetting.run(), and python.uq.uq_setting.UQSetting.UQSetting.waitForResults().

---

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

• datadriven/python/uq/analysis/asgc/ASGCKnowledge.py