Package org.tweetyproject.arg.dung.learning

Class OptimizedParallelAFLearner

java.lang.Object

org.tweetyproject.arg.dung.learning.OptimizedParallelAFLearner

All Implemented Interfaces:

AFLearner

public class OptimizedParallelAFLearner
extends Object
implements AFLearner

Implementation of the iterative algorithm for learning argumentation frameworks from labelings utilizing the parallelization possibilities

Author:

Lars Bengel

Constructor Summary

Constructors

Constructor	Description
OptimizedParallelAFLearner(Collection<Argument> args)	initialize the Learner with a set of arguments

Method Summary

Modifier and Type	Method	Description
DungTheory	getModel()	compute a single model that satisfies all attack constraints computation for each argument constraint is independent and thus can be done in parallel
Collection<DungTheory>	getModels()	compute all argumentation frameworks that satisfy the internal acceptance conditions
long	getNumberOfFrameworks()	compute the number of argumentation frameworks that satisfy the internal acceptance conditions
long	getNumberOfFrameworks(boolean shortcut)
boolean	learnLabeling(Input labeling)	learn a single labeling i.e.
boolean	learnLabelings(Collection<Input> inputs)
void	printStatus()	prints status
void	printStatus(boolean dnf)	print the current status of the acceptance conditions

Methods inherited from class java.lang.Object

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

Constructor Details

OptimizedParallelAFLearner

public OptimizedParallelAFLearner(Collection<Argument> args)

initialize the Learner with a set of arguments

Parameters:

args - a set of arguments

Method Details

learnLabeling

public boolean learnLabeling(Input labeling)

learn a single labeling i.e. compute the acceptance condition for each argument a wrt to the given labeling and combine it with the previous acceptance condition of a

Specified by:

learnLabeling in interface AFLearner

Parameters:

labeling - some labeling of the set of arguments

Returns:

true if the labeling was learned successfully

getModels

public Collection<DungTheory> getModels()

Description copied from interface: AFLearner

compute all argumentation frameworks that satisfy the internal acceptance conditions

Specified by:

getModels in interface AFLearner

Returns:

the set of computed argumentation frameworks

learnLabelings

public boolean learnLabelings(Collection<Input> inputs)

Parameters:

inputs - inputs

Returns:

the learned labels

getModel

public DungTheory getModel()

compute a single model that satisfies all attack constraints computation for each argument constraint is independent and thus can be done in parallel

Specified by:

getModel in interface AFLearner

Returns:

a dung theory for which all attack constraints are satisfied

printStatus

public void printStatus()

prints status

Specified by:

printStatus in interface AFLearner

printStatus

public void printStatus(boolean dnf)

print the current status of the acceptance conditions

Parameters:

dnf - whether dnf holds

getNumberOfFrameworks

public long getNumberOfFrameworks()

Description copied from interface: AFLearner

compute the number of argumentation frameworks that satisfy the internal acceptance conditions

Specified by:

getNumberOfFrameworks in interface AFLearner

Returns:

number of afs that produce all processed input labelings

getNumberOfFrameworks

public long getNumberOfFrameworks(boolean shortcut)

Specified by:

getNumberOfFrameworks in interface AFLearner

Parameters:

shortcut - whether shortcut is applied

Returns:

number of frameworks