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

Inheritance diagram for python.learner.TrainingSpecification.TrainingSpecification:

Public Member Functions
def	fromJson (cls, jsonObject)
	Restores the TrainingSpecification object from the json object with attributes. More...

def	getAdaptPoints (self)
	Getter for Number of points to refine. More...

def	getAdaptRate (self)
	Getter for Rate of points to refine. More...

def	getAdaptThreshold (self)
	Getter for refinement threshold only the points with greater to equal absolute values of the refinement criterion (e.g. More...

def	getBOperator (self, name="train")
	Getter for B operator. More...

def	getCOperator (self)
	Getter for C operator. More...

def	getCOperatorType (self)
	Returns the type of the C operator. More...

def	getL (self)
	Getter for Regularization parameter. More...

def	getNumOfPointsToRefine (self, refinablePoints)
	Calculates the number of points which should be refined. More...

def	setAdaptPoints (self, value)
	Setter for Number of points to refine. More...

def	setAdaptRate (self, value)
	Setter for Rate of points to refine. More...

def	setAdaptThreshold (self, value)
	Setter for refinement threshold only the points with greater to equal absolute values of the refinement criterion (e.g. More...

def	setBOperator (self, value, name="train")
	Setter for B operator. More...

def	setCOperator (self, value)
	Setter for C operator. More...

def	setCOperatorType (self, value)
	Sets the type of the C operator. More...

def	setL (self, value)
	Setter for Regularization parameter. More...

def	toString (self)
	Returns a string that represents the object. More...

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()

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

◆ getAdaptPoints()

def 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()

def 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()

def 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()

def 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()

def python.learner.TrainingSpecification.TrainingSpecification.getCOperator ( self )

Getter for C operator.

Returns: : OperationMatrix

References python.learner.TrainingSpecification.TrainingSpecification.__cOperator.

◆ getCOperatorType()

def 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()

def python.learner.TrainingSpecification.TrainingSpecification.getL ( self )

Getter for Regularization parameter.

Returns: : double Regularization parameter

References python.learner.TrainingSpecification.TrainingSpecification.__l.

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

◆ getNumOfPointsToRefine()

def 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()

def 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()

def 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()

def 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()

def 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()

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

Setter for C operator.

Parameters

value OperationMatrix

References python.learner.TrainingSpecification.TrainingSpecification.__cOperator.

◆ setCOperatorType()

def 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()

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

Setter for Regularization parameter.

Parameters

value double Regularization parameter

References python.learner.TrainingSpecification.TrainingSpecification.__l.

◆ toString()

def python.learner.TrainingSpecification.TrainingSpecification.toString ( self )

Returns a string that represents the object.

Returns: A string that represents the object.

References create_dataset.type.

Referenced by python.uq.learner.Learner.Learner.createMemento().

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

datadriven/python/learner/TrainingSpecification.py

Public Member Functions

Detailed Description

Member Function Documentation

◆ fromJson()

◆ getAdaptPoints()

◆ getAdaptRate()

◆ getAdaptThreshold()

◆ getBOperator()

◆ getCOperator()

◆ getCOperatorType()

◆ getL()

◆ getNumOfPointsToRefine()

◆ setAdaptPoints()

◆ setAdaptRate()

◆ setAdaptThreshold()

◆ setBOperator()

◆ setCOperator()

◆ setCOperatorType()

◆ setL()

◆ toString()