SG++
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

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 getVectorizationType (self)
 
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 setVectorizationType (self, vecType)
 
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

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

Restores the TrainingSpecification object from the json object with attributes.

Parameters
clspython keyword (do not specify)
jsonObjectA 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().

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.

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.

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().

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

Getter for B operator.

Parameters
nameoperator identifier
Returns
: OperationB

References python.learner.TrainingSpecification.TrainingSpecification.__bOperator.

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

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

Getter for C operator.

Returns
: OperationMatrix

References python.learner.TrainingSpecification.TrainingSpecification.__cOperator.

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.

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().

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

Calculates the number of points which should be refined.

Parameters
refinablePointsinteger 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().

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

References python.learner.TrainingSpecification.TrainingSpecification.__vecType.

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

Setter for Number of points to refine.

Parameters
valueinteger Number of points to refine

References python.learner.TrainingSpecification.TrainingSpecification.__adaptPoints.

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

Setter for Rate of points to refine.

Parameters
valuedouble in [0,1] Rate of points to refine

References python.learner.TrainingSpecification.TrainingSpecification.__adaptRate.

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
valuefloat threshold

References python.learner.TrainingSpecification.TrainingSpecification.__adaptThreshold.

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

Setter for B operator.

Parameters
valueOperationB
nameoperator identifier

References python.learner.TrainingSpecification.TrainingSpecification.__bOperator.

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

Setter for C operator.

Parameters
valueOperationMatrix

References python.learner.TrainingSpecification.TrainingSpecification.__cOperator.

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

Sets the type of the C operator.

Parameters
valuestring type of the C operator

References python.learner.TrainingSpecification.TrainingSpecification.__cOperatorType.

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

Setter for Regularization parameter.

Parameters
valuedouble Regularization parameter

References python.learner.TrainingSpecification.TrainingSpecification.__l.

def python.learner.TrainingSpecification.TrainingSpecification.setVectorizationType (   self,
  vecType 
)

References python.learner.TrainingSpecification.TrainingSpecification.__vecType.

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

Returns a string that represents the object.

Returns
A string that represents the object.

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


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