python.learner.TrainingSpecification.TrainingSpecification Class Reference

Collection of parameters, which specify the learning process. More...

Inheritance diagram for python.learner.TrainingSpecification.TrainingSpecification:

Public Member Functions
	fromJson (cls, jsonObject)
	Restores the TrainingSpecification object from the json object with attributes.
	getAdaptPoints (self)
	Getter for Number of points to refine.
	getAdaptRate (self)
	Getter for Rate of points to refine.
	getAdaptThreshold (self)
	Getter for refinement threshold only the points with greater to equal absolute values of the refinement criterion (e.g.
	getBOperator (self, name="train")
	Getter for B operator.
	getCOperator (self)
	Getter for C operator.
	getCOperatorType (self)
	Returns the type of the C operator.
	getL (self)
	Getter for Regularization parameter.
	getNumOfPointsToRefine (self, refinablePoints)
	Calculates the number of points which should be refined.
	setAdaptPoints (self, value)
	Setter for Number of points to refine.
	setAdaptRate (self, value)
	Setter for Rate of points to refine.
	setAdaptThreshold (self, value)
	Setter for refinement threshold only the points with greater to equal absolute values of the refinement criterion (e.g.
	setBOperator (self, value, name="train")
	Setter for B operator.
	setCOperator (self, value)
	Setter for C operator.
	setCOperatorType (self, value)
	Sets the type of the C operator.
	setL (self, value)
	Setter for Regularization parameter.
	toString (self)
	Returns a string that represents the object.

Detailed Description

Collection of parameters, which specify the learning process.

An object of the class is aggregated by the Learner object.

Member Function Documentation

fromJson()

python.learner.TrainingSpecification.TrainingSpecification.fromJson	(		cls,
			jsonObject
	)

Restores the TrainingSpecification object from the json object with attributes.

Parameters

cls	python keyword (do not specify)
jsonObject	A json object.

Returns

The restored TrainingSpecification 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().

getAdaptPoints()

python.learner.TrainingSpecification.TrainingSpecification.getAdaptPoints

(

self

)

Getter for Number of points to refine.

Returns

: integer Number of points to refine

References python.learner.TrainingSpecification.TrainingSpecification.__adaptPoints.

getAdaptRate()

python.learner.TrainingSpecification.TrainingSpecification.getAdaptRate

(

self

)

Getter for Rate of points to refine.

Returns

: double in [0,1] Rate of points to refine

References python.learner.TrainingSpecification.TrainingSpecification.__adaptRate.

getAdaptThreshold()

python.learner.TrainingSpecification.TrainingSpecification.getAdaptThreshold

(

self

)

Getter for refinement threshold only the points with greater to equal absolute values of the refinement criterion (e.g.

alpha or error) will be refined

Returns

float threshold

References python.learner.TrainingSpecification.TrainingSpecification.__adaptThreshold.

Referenced by python.uq.learner.Regressor.Regressor.refineGrid().

getBOperator()

python.learner.TrainingSpecification.TrainingSpecification.getBOperator	(		self,
			name = "train"
	)

Getter for B operator.

Parameters

name	operator identifier

Returns

: OperationB

References python.learner.TrainingSpecification.TrainingSpecification.__bOperator.

Referenced by python.uq.learner.Regressor.Regressor.evalError().

getCOperator()

python.learner.TrainingSpecification.TrainingSpecification.getCOperator

(

self

)

Getter for C operator.

Returns

: OperationMatrix

References python.learner.TrainingSpecification.TrainingSpecification.__cOperator.

getCOperatorType()

python.learner.TrainingSpecification.TrainingSpecification.getCOperatorType

(

self

)

Returns the type of the C operator.

Returns

: the type of the C operator as a string

References python.learner.TrainingSpecification.TrainingSpecification.__cOperatorType.

getL()

python.learner.TrainingSpecification.TrainingSpecification.getL

(

self

)

Getter for Regularization parameter.

Returns

: double Regularization parameter

References python.learner.TrainingSpecification.TrainingSpecification.__l, and python.uq.dists.Beta.Beta.__l.

getNumOfPointsToRefine()

python.learner.TrainingSpecification.TrainingSpecification.getNumOfPointsToRefine	(		self,
			refinablePoints
	)

Calculates the number of points which should be refined.

Parameters

refinablePoints

integer number of points which can be refined

Returns

: integer number of point which should be refined

References python.learner.TrainingSpecification.TrainingSpecification.__adaptPoints, and python.learner.TrainingSpecification.TrainingSpecification.__adaptRate.

Referenced by python.uq.refinement.RefinementManager.RefinementManager.candidates(), and python.uq.learner.Regressor.Regressor.refineGrid().

setAdaptPoints()

python.learner.TrainingSpecification.TrainingSpecification.setAdaptPoints	(		self,
			value
	)

Setter for Number of points to refine.

Parameters

value

integer Number of points to refine

References python.learner.TrainingSpecification.TrainingSpecification.__adaptPoints.

setAdaptRate()

python.learner.TrainingSpecification.TrainingSpecification.setAdaptRate	(		self,
			value
	)

Setter for Rate of points to refine.

Parameters

value

double in [0,1] Rate of points to refine

References python.learner.TrainingSpecification.TrainingSpecification.__adaptRate.

setAdaptThreshold()

python.learner.TrainingSpecification.TrainingSpecification.setAdaptThreshold	(		self,
			value
	)

Setter for refinement threshold only the points with greater to equal absolute values of the refinement criterion (e.g.

alpha or error) will be refined

Parameters

value

float threshold

References python.learner.TrainingSpecification.TrainingSpecification.__adaptThreshold.

setBOperator()

python.learner.TrainingSpecification.TrainingSpecification.setBOperator	(		self,
			value,
			name = "train"
	)

Setter for B operator.

Parameters

value	OperationB
name	operator identifier

References python.learner.TrainingSpecification.TrainingSpecification.__bOperator.

setCOperator()

python.learner.TrainingSpecification.TrainingSpecification.setCOperator	(		self,
			value
	)

Setter for C operator.

Parameters

value

OperationMatrix

References python.learner.TrainingSpecification.TrainingSpecification.__cOperator.

setCOperatorType()

python.learner.TrainingSpecification.TrainingSpecification.setCOperatorType	(		self,
			value
	)

Sets the type of the C operator.

Parameters

value

string type of the C operator

References python.learner.TrainingSpecification.TrainingSpecification.__cOperatorType.

setL()

python.learner.TrainingSpecification.TrainingSpecification.setL	(		self,
			value
	)

Setter for Regularization parameter.

Parameters

value

double Regularization parameter

References python.learner.TrainingSpecification.TrainingSpecification.__l, and python.uq.dists.Beta.Beta.__l.

toString()

python.learner.TrainingSpecification.TrainingSpecification.toString

(

self

)

Returns a string that represents the object.

Returns

A string that represents the object.

Referenced by python.controller.LearnerEventController.LearnerEventController.__repr__(), python.controller.SolverEventController.SolverEventController.__repr__(), python.learner.Learner.Learner.createMemento(), python.uq.learner.Learner.Learner.createMemento(), and python.learner.formatter.GridFormatter.GridFormatter.serialize().

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

• datadriven/python/learner/TrainingSpecification.py