org.dllearner.core.configurators
Class ClassLearningProblemConfigurator

java.lang.Object
  org.dllearner.core.configurators.ClassLearningProblemConfigurator

All Implemented Interfaces:

Configurator

public class ClassLearningProblemConfigurator
extends Object
implements Configurator

automatically generated, do not edit manually. run org.dllearner.scripts.ConfigJavaGenerator to update

Constructor Summary

ClassLearningProblemConfigurator(ClassLearningProblem classLearningProblem)

Method Summary

String	getAccuracyMethod() accuracyMethod Specifies, which method/function to use for computing accuracy..
double	getApproxAccuracy() approxAccuracy accuracy of the approximation (only for expert use).
double	getBetaEq() betaEq Higher values of beta rate recall higher than precision or in other words, covering the instances of the class to describe is more important even at the cost of covering additional instances.
double	getBetaSC() betaSC Higher values of beta rate recall higher than precision or in other words, covering the instances of the class to describe is more important even at the cost of covering additional instances.
boolean	getCheckConsistency() checkConsistency Specify whether to check consistency for solution candidates.
static ClassLearningProblem	getClassLearningProblem(AbstractReasonerComponent reasoningService, URL classToDescribe)
URL	getClassToDescribe() classToDescribe class of which a description should be learned.
int	getMaxExecutionTimeInSeconds() maxExecutionTimeInSeconds algorithm will stop after specified seconds.
String	getType() type whether to learn an equivalence class or super class axiom.
boolean	getUseApproximations() useApproximations whether to use stochastic approximations for computing accuracy.
boolean	isReinitNecessary() true, if this component needs reinitializsation.
void	setAccuracyMethod(String accuracyMethod)
void	setApproxAccuracy(double approxAccuracy)
void	setBetaEq(double betaEq)
void	setBetaSC(double betaSC)
void	setCheckConsistency(boolean checkConsistency)
void	setClassToDescribe(URL classToDescribe)
void	setMaxExecutionTimeInSeconds(int maxExecutionTimeInSeconds)
void	setType(String type)
void	setUseApproximations(boolean useApproximations)

Methods inherited from class java.lang.Object

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

Constructor Detail

ClassLearningProblemConfigurator

public ClassLearningProblemConfigurator(ClassLearningProblem classLearningProblem)

Parameters:

classLearningProblem - see ClassLearningProblem

Method Detail

getClassLearningProblem

public static ClassLearningProblem getClassLearningProblem(AbstractReasonerComponent reasoningService,
                                                           URL classToDescribe)

Parameters:

reasoningService - see reasoningService

classToDescribe - class of which a description should be learned

Returns:

ClassLearningProblem

getClassToDescribe

public URL getClassToDescribe()

classToDescribe class of which a description should be learned. mandatory: true| reinit necessary: false default value: null

Returns:

URL

getType

public String getType()

type whether to learn an equivalence class or super class axiom. mandatory: false| reinit necessary: true default value: equivalence

Returns:

String

getUseApproximations

public boolean getUseApproximations()

useApproximations whether to use stochastic approximations for computing accuracy. mandatory: false| reinit necessary: true default value: true

Returns:

boolean

getApproxAccuracy

public double getApproxAccuracy()

approxAccuracy accuracy of the approximation (only for expert use). mandatory: false| reinit necessary: true default value: 0.05

Returns:

double

getAccuracyMethod

public String getAccuracyMethod()

accuracyMethod Specifies, which method/function to use for computing accuracy.. mandatory: false| reinit necessary: true default value: standard

Returns:

String

getCheckConsistency

public boolean getCheckConsistency()

checkConsistency Specify whether to check consistency for solution candidates. This is convenient for user interfaces, but can be performance intensive.. mandatory: false| reinit necessary: true default value: true

Returns:

boolean

getMaxExecutionTimeInSeconds

public int getMaxExecutionTimeInSeconds()

maxExecutionTimeInSeconds algorithm will stop after specified seconds. mandatory: false| reinit necessary: true default value: 10

Returns:

int

getBetaSC

public double getBetaSC()

betaSC Higher values of beta rate recall higher than precision or in other words, covering the instances of the class to describe is more important even at the cost of covering additional instances. The actual implementation depends on the selected heuristic. This values is used only for super class learning.. mandatory: false| reinit necessary: true default value: 3.0

Returns:

double

getBetaEq

public double getBetaEq()

betaEq Higher values of beta rate recall higher than precision or in other words, covering the instances of the class to describe is more important even at the cost of covering additional instances. The actual implementation depends on the selected heuristic. This values is used only for equivalence class learning.. mandatory: false| reinit necessary: true default value: 1.0

Returns:

double

setClassToDescribe

public void setClassToDescribe(URL classToDescribe)

Parameters:

classToDescribe - class of which a description should be learned. mandatory: true| reinit necessary: false default value: null

setType

public void setType(String type)

Parameters:

type - whether to learn an equivalence class or super class axiom. mandatory: false| reinit necessary: true default value: equivalence

setUseApproximations

public void setUseApproximations(boolean useApproximations)

Parameters:

useApproximations - whether to use stochastic approximations for computing accuracy. mandatory: false| reinit necessary: true default value: true

setApproxAccuracy

public void setApproxAccuracy(double approxAccuracy)

Parameters:

approxAccuracy - accuracy of the approximation (only for expert use). mandatory: false| reinit necessary: true default value: 0.05

setAccuracyMethod

public void setAccuracyMethod(String accuracyMethod)

Parameters:

accuracyMethod - Specifies, which method/function to use for computing accuracy.. mandatory: false| reinit necessary: true default value: standard

setCheckConsistency

public void setCheckConsistency(boolean checkConsistency)

Parameters:

checkConsistency - Specify whether to check consistency for solution candidates. This is convenient for user interfaces, but can be performance intensive.. mandatory: false| reinit necessary: true default value: true

setMaxExecutionTimeInSeconds

public void setMaxExecutionTimeInSeconds(int maxExecutionTimeInSeconds)

Parameters:

maxExecutionTimeInSeconds - algorithm will stop after specified seconds. mandatory: false| reinit necessary: true default value: 10

setBetaSC

public void setBetaSC(double betaSC)

Parameters:

betaSC - Higher values of beta rate recall higher than precision or in other words, covering the instances of the class to describe is more important even at the cost of covering additional instances. The actual implementation depends on the selected heuristic. This values is used only for super class learning.. mandatory: false| reinit necessary: true default value: 3.0

setBetaEq

public void setBetaEq(double betaEq)

Parameters:

betaEq - Higher values of beta rate recall higher than precision or in other words, covering the instances of the class to describe is more important even at the cost of covering additional instances. The actual implementation depends on the selected heuristic. This values is used only for equivalence class learning.. mandatory: false| reinit necessary: true default value: 1.0

isReinitNecessary

public boolean isReinitNecessary()

true, if this component needs reinitializsation.

Returns:

boolean