I
- input symbol typepublic class BlueFringeEDSMDFA<I> extends BlueFringeRPNIDFA<I>
BlueFringeRPNIDFA
algorithm. However, whereas the basic RPNI approach merges the very first
pair of nodes that resemble a valid merge, the EDSM variant prioritizes the promotion of states (to be unmergable)
and only proceeds to merge states, if there exists at least one mergable blue state for every red state. If such a
situation occurs, the algorithm merges the two states whose merge would yield the biggest score (see EDSMUtil.score(UniversalDeterministicAutomaton, List, List)
). Thus the behavior of this algorithm is more passive,
or as the name suggest evidence-driven.PassiveLearningAlgorithm.PassiveAcceptorLearner<M extends net.automatalib.automata.fsa.FiniteStateAcceptor<?,I>,I>, PassiveLearningAlgorithm.PassiveDFALearner<I>, PassiveLearningAlgorithm.PassiveMealyLearner<I,O>, PassiveLearningAlgorithm.PassiveNFALearner<I>
negative, positive
alphabet, alphabetSize, deterministic, order, parallel
Constructor and Description |
---|
BlueFringeEDSMDFA(net.automatalib.words.Alphabet<I> alphabet)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
net.automatalib.automata.fsa.DFA<?,I> |
computeModel() |
addSamples, initializePTA, ptaToModel
decideOnValidMerge, setDeterministic, setParallel, tryMerge
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
addNegativeSample, addNegativeSamples, addNegativeSamples, addPositiveSample, addPositiveSamples, addPositiveSamples
addSample, addSample, addSamples, addSamples, addSamples
public BlueFringeEDSMDFA(net.automatalib.words.Alphabet<I> alphabet)
alphabet
- the alphabetpublic net.automatalib.automata.fsa.DFA<?,I> computeModel()
computeModel
in interface PassiveLearningAlgorithm<net.automatalib.automata.fsa.DFA<?,I>,I,Boolean>
computeModel
in class AbstractBlueFringeRPNI<I,Boolean,Boolean,Void,net.automatalib.automata.fsa.DFA<?,I>>
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