public class PropertyAxiomLearningProblem<T extends org.semanticweb.owlapi.model.OWLPropertyAxiom> extends AbstractLearningProblem<AxiomScore,T,EvaluatedAxiom<T>>
Constructor and Description |
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PropertyAxiomLearningProblem() |
Modifier and Type | Method and Description |
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AxiomScore |
computeScore(T hypothesis,
double noise)
Computes the
Score of a given hypothesis
with respect to this learning problem. |
double |
getAccuracyOrTooWeak(T hypothesis,
double noise)
This method computes the accuracy and returns -1 instead of the accuracy if
the accuracy of the hypothesis is below the given threshold and
the accuracy of all more special w.r.t.
|
void |
init()
Method to be called after the component has been configured.
|
changeReasonerComponent, computeScore, evaluate, evaluate, getAccuracyOrTooWeak, getReasoner, setReasoner
isInitialized
public PropertyAxiomLearningProblem()
public void init() throws ComponentInitException
Component
ComponentInitException
- This exception is thrown if any
exceptions occur within the initialisation process of this
component. As component developer, you are encouraged to
re-throw occurring exception as ComponentInitException and
giving an error message as well as the actually exception by
using the constructor ComponentInitException(String, Throwable)
.public AxiomScore computeScore(T hypothesis, double noise)
AbstractLearningProblem
Score
of a given hypothesis
with respect to this learning problem.
This can (but does not need to) be used by learning algorithms
to measure how good the hypothesis fits the learning problem.
Score objects are used to store e.g. covered examples, accuracy etc.,
so often it is more efficient to only create score objects for
promising hypotheses.computeScore
in class AbstractLearningProblem<AxiomScore,T extends org.semanticweb.owlapi.model.OWLPropertyAxiom,EvaluatedAxiom<T extends org.semanticweb.owlapi.model.OWLPropertyAxiom>>
hypothesis
- A hypothesis (as solution candidate for this learning problem).noise
- the (approximated) value of noise within the examplesScore
object.public double getAccuracyOrTooWeak(T hypothesis, double noise)
AbstractLearningProblem
getAccuracyOrTooWeak
in class AbstractLearningProblem<AxiomScore,T extends org.semanticweb.owlapi.model.OWLPropertyAxiom,EvaluatedAxiom<T extends org.semanticweb.owlapi.model.OWLPropertyAxiom>>
DL-Learner is licenced under the terms of the GNU General Public License.
Copyright © 2007-2019 Jens Lehmann