List of learnMachine() Examples

Examples of learnMachine()

statechum.analysis.learning.ErlangOracleLearner.learnMachine()
statechum.analysis.learning.Learner.learnMachine()
Learns the machine. When different learning events occur, the learner calls its listeners (which are the decorators); these listeners are given a chance to inspect the arguments, do something and pass the call further or not pass. At the bottom level, there is default processing performed by the learner itself.
For internal use only. @param topLevelDecorator the first listener in a chain of them.
statechum.analysis.learning.RPNIBlueFringeLearnerTestComponentOpt.learnMachine()
statechum.analysis.learning.RPNIUniversalLearner.learnMachine()
statechum.analysis.learning.experiments.PairSelection.ASE2014.EDSM_MarkovLearner.learnMachine()
statechum.analysis.learning.experiments.PairSelection.Cav2014.KTailsReferenceLearner.learnMachine()
statechum.analysis.learning.experiments.PairSelection.Cav2014.LearnerMarkovPassive.learnMachine()
statechum.analysis.learning.experiments.PairSelection.PairQualityLearner.LearnerThatCanClassifyPairs.learnMachine()
statechum.analysis.learning.experiments.PairSelection.PairQualityLearner.ReferenceLearner.learnMachine()
statechum.analysis.learning.observers.AutoAnswers.learnMachine()
statechum.analysis.learning.observers.Learner.learnMachine()
Learns the machine. When different learning events occur, the learner calls its listeners (which are the decorators); these listeners are given a chance to inspect the arguments, do something and pass the call further or not pass. At the bottom level, there is default processing performed by the learner itself.
For internal use only. @param topLevelDecorator the first listener in a chain of them.
statechum.analysis.learning.observers.LearningConvergenceObserver.learnMachine()
statechum.analysis.learning.observers.QuestionAndRestartCounter.learnMachine()
statechum.analysis.learning.observers.RecordProgressDecorator.learnMachine()

Examples of statechum.analysis.learning.RPNIUniversalLearner.learnMachine()

                              
                            };
                            if (learnerInitConfiguration.config.getAskQuestions()) // only generate initial traces if we are permited to ask questions.
                              learner.init(learner.GenerateInitialTraces(learnerInitConfiguration.config.getErlangInitialTraceLength()),0,0);
                            System.out.println("random trace generation complete");
                            LearnerGraph graphLearnt = learner.learnMachine(),
                                graphWithTrimmedLabels = new LearnerGraph(learnerInitConfiguration.config);
                            
                            if (learnerInitConfiguration.config.getErlangStripModuleNamesFromFunctionsInNonGenModules())
                              convertLabelsToStrings(graphLearnt,graphWithTrimmedLabels);
                            else

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Examples of statechum.analysis.learning.experiments.PairSelection.ASE2014.EDSM_MarkovLearner.learnMachine()

                            if (learnerInitConfiguration.graph != null)
                            {
                              learnerInitConfiguration.graph.clearColours();learnerInitConfiguration.graph.getInit().setColour(JUConstants.RED);
                              LearnerGraph.copyGraphs(learnerInitConfiguration.graph,learner.getTentativeAutomaton());
                            }
                            LearnerGraph graphLearnt = learner.learnMachine(new LinkedList<List<Label>>(),new LinkedList<List<Label>>());
                            outcome = new OtpErlangTuple(new OtpErlangObject[]{ref,msgOk,  constructFSM(graphLearnt)});
                          }
                          catch(AskedToTerminateException e)
                          {
                            outcome = new OtpErlangTuple(new OtpErlangObject[]{ref,msgTerminate});

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Examples of statechum.analysis.learning.experiments.PairSelection.ASE2014.EDSM_MarkovLearner.learnMachine()

                              
                            };
                            if (learnerInitConfiguration.config.getAskQuestions()) // only generate initial traces if we are permited to ask questions.
                              learner.init(learner.GenerateInitialTraces(learnerInitConfiguration.config.getErlangInitialTraceLength()),0,0);
                            System.out.println("random trace generation complete");
                            LearnerGraph graphLearnt = learner.learnMachine(),
                                graphWithTrimmedLabels = new LearnerGraph(learnerInitConfiguration.config);
                            
                            if (learnerInitConfiguration.config.getErlangStripModuleNamesFromFunctionsInNonGenModules())
                              convertLabelsToStrings(graphLearnt,graphWithTrimmedLabels);
                            else

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Examples of statechum.analysis.learning.experiments.PairSelection.ASE2014.EDSM_MarkovLearner.learnMachine()

                            if (learnerInitConfiguration.graph != null)
                            {
                              learnerInitConfiguration.graph.clearColours();learnerInitConfiguration.graph.getInit().setColour(JUConstants.RED);
                              LearnerGraph.copyGraphs(learnerInitConfiguration.graph,learner.getTentativeAutomaton());
                            }
                            LearnerGraph graphLearnt = learner.learnMachine(new LinkedList<List<Label>>(),new LinkedList<List<Label>>());
                            outcome = new OtpErlangTuple(new OtpErlangObject[]{ref,msgOk,  constructFSM(graphLearnt)});
                          }
                          catch(AskedToTerminateException e)
                          {
                            outcome = new OtpErlangTuple(new OtpErlangObject[]{ref,msgTerminate});

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Examples of statechum.analysis.learning.experiments.PairSelection.ASE2014.EDSM_MarkovLearner.learnMachine()

                              
                            };
                            if (learnerInitConfiguration.config.getAskQuestions()) // only generate initial traces if we are permited to ask questions.
                              learner.init(learner.GenerateInitialTraces(learnerInitConfiguration.config.getErlangInitialTraceLength()),0,0);
                            System.out.println("random trace generation complete");
                            LearnerGraph graphLearnt = learner.learnMachine(),
                                graphWithTrimmedLabels = new LearnerGraph(learnerInitConfiguration.config);
                            
                            if (learnerInitConfiguration.config.getErlangStripModuleNamesFromFunctionsInNonGenModules())
                              convertLabelsToStrings(graphLearnt,graphWithTrimmedLabels);
                            else

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Examples of statechum.analysis.learning.experiments.PairSelection.Cav2014.KTailsReferenceLearner.learnMachine()

                            if (learnerInitConfiguration.graph != null)
                            {
                              learnerInitConfiguration.graph.clearColours();learnerInitConfiguration.graph.getInit().setColour(JUConstants.RED);
                              LearnerGraph.copyGraphs(learnerInitConfiguration.graph,learner.getTentativeAutomaton());
                            }
                            LearnerGraph graphLearnt = learner.learnMachine(new LinkedList<List<Label>>(),new LinkedList<List<Label>>());
                            outcome = new OtpErlangTuple(new OtpErlangObject[]{ref,msgOk,  constructFSM(graphLearnt)});
                          }
                          catch(AskedToTerminateException e)
                          {
                            outcome = new OtpErlangTuple(new OtpErlangObject[]{ref,msgTerminate});

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Examples of statechum.analysis.learning.experiments.PairSelection.Cav2014.LearnerMarkovPassive.learnMachine()

    System.out.println("Alphabet of "+pta.getCache().getAlphabet().size()+" : "+pta.getCache().getAlphabet());


    final Configuration deepCopy = pta.config.copy();deepCopy.setLearnerCloneGraph(true);
    LearnerGraph ptaCopy = new LearnerGraph(deepCopy);LearnerGraph.copyGraphs(pta, ptaCopy);
    
    LearnerGraph actualAutomaton = learnerOfPairs.learnMachine(new LinkedList<List<Label>>(),new LinkedList<List<Label>>());
    LearnerGraph edsm2Outcome = new EDSMReferenceLearner(learnerEval,ptaCopy,1).learnMachine(new LinkedList<List<Label>>(),new LinkedList<List<Label>>());
    System.out.println("Outcome states: "+edsm2Outcome.getStateNumber());
    Visualiser.updateFrame(actualAutomaton, edsm2Outcome);
    Visualiser.waitForKey();

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Examples of statechum.analysis.learning.experiments.PairSelection.PairQualityLearner.LearnerThatCanClassifyPairs.learnMachine()

      Helper.throwUnchecked("failed to augment using if-then", e);
    }// we only need  to augment our PTA once (refer to the explanation above).
      LearnerThatCanClassifyPairs learner =  c != null? new PairQualityLearner.LearnerThatUsesWekaResults(ifDepth,learnerInitConfiguration,referenceGraph,c,initPTA):
          new PairQualityLearner.ReferenceLearner(learnerInitConfiguration,referenceGraph,initPTA);
      learner.setLabelsLeadingToStatesToBeMerged(labelsToMergeTo);learner.setLabelsLeadingFromStatesToBeMerged(labelsToMergeFrom);learner.setAlphabetUsedForIfThen(referenceGraph.pathroutines.computeAlphabet());
        LearnerGraph actualAutomaton = learner.learnMachine(new LinkedList<List<Label>>(),new LinkedList<List<Label>>());
        
        // Now merge everything that we need to merge
        LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();
    List<StatePair> pairsList = LearnerThatCanClassifyPairs.buildVerticesToMerge(actualAutomaton,learner.getLabelsLeadingToStatesToBeMerged(),learner.getLabelsLeadingFromStatesToBeMerged());
    if (!pairsList.isEmpty())

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Examples of statechum.analysis.learning.experiments.PairSelection.PairQualityLearner.LearnerThatCanClassifyPairs.learnMachine()

    Transform.augmentFromIfThenAutomaton(initialPTA, null, ifthenAutomata, initConfiguration.config.getHowManyStatesToAddFromIFTHEN());// we only need  to augment our PTA once (refer to the explanation above).
    System.out.println(new Date().toString()+" if-then states added, now "+initialPTA.getStateNumber()+" states");
    WekaDataCollector dataCollector = PairQualityLearner.createDataCollector(ifDepth);
    // Run the learner that will find out how to select the correct pairs.
    LearnerThatCanClassifyPairs learnerOfPairs = new PairQualityLearner.LearnerThatUpdatesWekaResults(initConfiguration,referenceGraph,dataCollector,initialPTA);
    LearnerGraph actualAutomaton = learnerOfPairs.learnMachine(new LinkedList<List<Label>>(),new LinkedList<List<Label>>());
    
    // final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
    FileWriter wekaInstances= null;
    try
    {

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Examples of statechum.analysis.learning.experiments.PairSelection.PairQualityLearner.LearnerThatCanClassifyPairs.learnMachine()

       final InitialConfigurationAndData initialConfigAndData = PairQualityLearner.loadInitialAndPopulateInitialConfiguration(PairQualityLearner.largePTAFileName, learnerConfig, new Transform.InternStringLabel());


    LearnerGraph referenceGraph = new LearnerGraph(initialConfigAndData.initial.graph.config);AbstractPersistence.loadGraph("resources/largePTA/outcome_correct", referenceGraph, initialConfigAndData.learnerInitConfiguration.getLabelConverter());
      WekaDataCollector dataCollector = PairQualityLearner.createDataCollector(ifDepth);
      LearnerThatCanClassifyPairs learnerOfPairs = new PairQualityLearner.LearnerThatUpdatesWekaResults(initialConfigAndData.learnerInitConfiguration,referenceGraph,dataCollector,initialConfigAndData.initial.graph);
    learnerOfPairs.learnMachine(new LinkedList<List<Label>>(),new LinkedList<List<Label>>());
    
    FileWriter wekaInstances=new FileWriter("resources/largePTA/pairsEncountered3.arff");
    wekaInstances.write(dataCollector.trainingData.toString());
    wekaInstances.close();
  }

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