Package net.sf.tweety.logics.pcl.analysis

Class SmoothedPenalizingCreepingMachineShop

java.lang.Object

net.sf.tweety.logics.pcl.analysis.AbstractCreepingMachineShop

net.sf.tweety.logics.pcl.analysis.SmoothedPenalizingCreepingMachineShop

All Implemented Interfaces:

BeliefBaseMachineShop

public class SmoothedPenalizingCreepingMachineShop
extends AbstractCreepingMachineShop

This class is capable of restoring consistency of a possible inconsistent probabilistic conditional belief set. Restoring consistency is performed by a smoothed penalizing creeping of the original belief set towards an uniform belief set, see [PhD thesis, Thimm] for details.

Author:

Matthias Thimm

Field Summary

Fields

Modifier and Type	Field	Description
private CulpabilityMeasure<ProbabilisticConditional,PclBeliefSet>	culpabilityMeasure	The culpability measure used by this machine shop.
private double	scalingParameter	The scaling parameter for the function 'v'

Fields inherited from class net.sf.tweety.logics.pcl.analysis.AbstractCreepingMachineShop

MAX_ITERATIONS, PRECISION

Constructor Summary

Constructors

Constructor	Description
SmoothedPenalizingCreepingMachineShop(CulpabilityMeasure<ProbabilisticConditional,PclBeliefSet> culpabilityMeasure)	Creates a new creeping machine shop based on the given culpability measure.

Method Summary

Modifier and Type	Method	Description
protected double	getLowerBound()	Retrieves the lower bound for delta for this machine shop.
protected double	getUpperBound()	Retrieves the upper bound for delta for this machine shop.
protected java.util.Map<ProbabilisticConditional,Probability>	getValues(double delta, PclBeliefSet beliefSet)	Computes the values of the conditionals for step delta
protected void	init(PclBeliefSet beliefSet)	Performs some optional initializations before beginning to restore consistency.
private java.lang.Double	v(double a, double b1, double b2, double x)	This method implements a weighted linear approach from 'b2' to 'b1' with gradient 'a'; 'x' is the parameter in [0,1].

Methods inherited from class net.sf.tweety.logics.pcl.analysis.AbstractCreepingMachineShop

characteristicFunction, repair

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

scalingParameter

private double scalingParameter

The scaling parameter for the function 'v'

culpabilityMeasure

private CulpabilityMeasure<ProbabilisticConditional,PclBeliefSet> culpabilityMeasure

The culpability measure used by this machine shop.

Constructor Detail

SmoothedPenalizingCreepingMachineShop

public SmoothedPenalizingCreepingMachineShop(CulpabilityMeasure<ProbabilisticConditional,PclBeliefSet> culpabilityMeasure)

Creates a new creeping machine shop based on the given culpability measure.

Parameters:

culpabilityMeasure - a culpability measure.

Method Detail

getValues

protected java.util.Map<ProbabilisticConditional,Probability> getValues(double delta,
                                                                              PclBeliefSet beliefSet)

Description copied from class: AbstractCreepingMachineShop

Computes the values of the conditionals for step delta

Specified by:

getValues in class AbstractCreepingMachineShop

Parameters:

delta - the step parameter.

beliefSet - the belief set.

Returns:

a map mapping conditionals to probabilities.

getLowerBound

protected double getLowerBound()

Description copied from class: AbstractCreepingMachineShop

Retrieves the lower bound for delta for this machine shop.

Specified by:

getLowerBound in class AbstractCreepingMachineShop

Returns:

the lower bound for delta for this machine shop.

getUpperBound

protected double getUpperBound()

Description copied from class: AbstractCreepingMachineShop

Retrieves the upper bound for delta for this machine shop.

Specified by:

getUpperBound in class AbstractCreepingMachineShop

Returns:

the upper bound for delta for this machine shop.

init

protected void init(PclBeliefSet beliefSet)

Description copied from class: AbstractCreepingMachineShop

Performs some optional initializations before beginning to restore consistency.

Overrides:

init in class AbstractCreepingMachineShop

Parameters:

beliefSet - a PCL belief set.

v

private java.lang.Double v(double a,
                           double b1,
                           double b2,
                           double x)

This method implements a weighted linear approach from 'b2' to 'b1' with gradient 'a'; 'x' is the parameter in [0,1].

Parameters:

a - a double

b1 - a double

b2 - a double

x - a double

Returns:

a double