Examples of statechum.Configuration

statechum.Configuration
Represents a configuration for a learner. The purpose is a possibility of a global customisation of all objects used by a learner in the course of learning by the same object.
When you add to this class, please add the corresponding entries to hashCode and equals.
This class is using the built-in clone method, hence all attributes have to be either primitives or immutable (such as String).

   */
  public final void testChooseStatePairs(String fsm, String [] initialReds, String [][] expectedReds, List<PairScore> expectedPairs, String graphName)
  {
    final DirectedSparseGraph gB = TestFSMAlgo.buildGraph(fsm, graphName);
    // check how the reference pair selection function performs
    Configuration conf = (Configuration)testConfig.clone();conf.setLearnerUseStrings(false);conf.setLearnerCloneGraph(false);
    testChooseStatePairsInternal(gB,new LearnerGraph(gB, conf), initialReds, expectedReds, expectedPairs, new InterfaceChooserToTest() {
      public Stack<StatePair> choosePairs() {// Here I need to convert the old type of pairs to the new one.
        Stack<OrigStatePair> pairs = chooseStatePairs(gB, new HashSet<List<String>>(), new HashSet<List<String>>());
        Stack<StatePair> result = new Stack<StatePair>();
        for(OrigStatePair pair:pairs) result.add(new StatePair((CmpVertex)pair.getQ(),(CmpVertex)pair.getR()));

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      // It is not hard to calculate what blue states will directly surround a specific state chosen to become red, however those blue states may in turn immediately become red after evaluation and the same would apply
      // to the newly-discovered red states, so we effectively have to re-implement blue state calculation here. For this reason, it was decided not to do this but instead clone the state machine (possibly in a trimmed form)
      // and ask it for a list of pairs. 
      // In practice, this algorithm turned out to be rather slow because there could be many red states to choose from and among those, many would lead to many pairs, all of which have to be scored.
      Configuration configCloneAll = coregraph.config.copy();configCloneAll.setLearnerCloneGraph(true);// we need to clone vertices because chooseStatePairs would colour some of the vertices red or blue.
      for(CmpVertex tentativeRed:tentativeRedNodes)
      {
        LearnerGraph graph = new LearnerGraph(configCloneAll);LearnerGraph.copyGraphs(coregraph,graph);
        graph.findVertex(tentativeRed).setColour(JUConstants.RED);// mark the tentative blue as red
        Stack<PairScore> pairs = graph.pairscores.chooseStatePairs(null);// calculate the subsequent red-blue pairs

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      // It is not hard to calculate what blue states will directly surround a specific state chosen to become red, however those blue states may in turn immediately become red after evaluation and the same would apply
      // to the newly-discovered red states, so we effectively have to re-implement blue state calculation here. For this reason, it was decided not to do this but instead clone the state machine (possibly in a trimmed form)
      // and ask it for a list of pairs. 
      // Assuming that we received red states in the same order as they are encountered by Statechum, it is appropriate to return the first state that has the highest number of reds after mergers,
      // because where it is actually 
      Configuration configCloneAll = coregraph.config.copy();configCloneAll.setLearnerCloneGraph(true);// we need to clone vertices because chooseStatePairs would colour some of the vertices red or blue.
      for(CmpVertex tentativeRed:tentativeRedNodes)
      {
        LearnerGraph graph = new LearnerGraph(configCloneAll);LearnerGraph.copyGraphs(coregraph,graph);
        graph.findVertex(tentativeRed).setColour(JUConstants.RED);// mark the tentative blue as red
        graph.pairscores.chooseStatePairs(null);// calculate the subsequent red-blue pairs

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  {
    Set<List<String>> plusStrings = buildSet(new String[][] { new String[] {"a","b","c"},new String[]{"a","d","c"} });
    DirectedSparseGraph actualA = new RPNIBlueFringeLearnerOrig(null,Configuration.getDefaultConfiguration()).augmentPTA(DeterministicDirectedSparseGraph.initialise(), plusStrings, true),
      actualC = null;
    DeterministicDirectedSparseGraph.numberVertices(actualA);// Numbering is necessary to ensure uniqueness of labels used by LearnerGraph constructor.
    Configuration config = (Configuration)Configuration.getDefaultConfiguration().clone();config.setLearnerIdMode(Configuration.IDMode.POSITIVE_NEGATIVE);
    config.setAllowedToCloneNonCmpVertex(true);
    LearnerGraph l = new LearnerGraph(config);
    actualC = l.paths.augmentPTA(plusStrings, true).paths.getGraph();
    DeterministicDirectedSparseGraph.numberVertices(actualA);
    String expectedPTA = "A-a->B--b->C-c->End1\nB--d->C2-c->End2";
    TestFSMAlgo.checkM(actualA, expectedPTA,config);

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      eA = e;
    }


    try
    {
      Configuration config = (Configuration)Configuration.getDefaultConfiguration().clone();
      RPNIBlueFringeLearnerTestComponentOpt l = new RPNIBlueFringeLearnerTestComponentOpt(null,config);
      config.setLearnerIdMode(Configuration.IDMode.POSITIVE_NEGATIVE);
      l.init(plusStrings, minusStrings);
      actualC = l.scoreComputer.paths.getGraph();
    }
    catch(IllegalArgumentException e)
    {
      // ignore this - it might be expected.
      eC = e;
    }


    try
    {
      Configuration config = (Configuration)Configuration.getDefaultConfiguration().clone();
      RPNIBlueFringeLearnerTestComponentOpt l = new RPNIBlueFringeLearnerTestComponentOpt(null,config);
      config.setLearnerIdMode(Configuration.IDMode.POSITIVE_NEGATIVE);
      PTASequenceEngine engine = buildPTA(plusStrings, minusStrings);
      checkPTAConsistency(engine, plusStrings, true);if (engine.numberOfLeafNodes()>0) checkPTAConsistency(engine, minusStrings, false);
      l.init(engine,0,0);
      actualD = l.scoreComputer.paths.getGraph();
    }
    catch(IllegalArgumentException e)
    {
      // ignore this - it might be expected.
      eD = e;
    }


    try
    {
      Configuration config = (Configuration)Configuration.getDefaultConfiguration().clone();
      RPNIBlueFringeLearnerTestComponentOpt l = new RPNIBlueFringeLearnerTestComponentOpt(null,config);
      config.setLearnerIdMode(Configuration.IDMode.POSITIVE_NEGATIVE);
      l.init(buildPTA(plusStrings, buildSet(new String[][] {})),0,0);
      for(List<String> seq:minusStrings)
        l.scoreComputer.paths.augmentPTA(buildPTA(buildSet(new String[][] {}),buildSet(new String[][] { (String [])seq.toArray()})));
      actualE = l.scoreComputer.paths.getGraph();
    }

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      eA = e;
    }


    try
    {
      Configuration config = (Configuration)Configuration.getDefaultConfiguration().clone();
      RPNIBlueFringeLearnerTestComponentOpt l = new RPNIBlueFringeLearnerTestComponentOpt(null,config);
      config.setLearnerIdMode(Configuration.IDMode.POSITIVE_NEGATIVE);
      l.init(plusStrings, minusStrings);
      actualC = l.scoreComputer.paths.getGraph();
    }
    catch(IllegalArgumentException e)
    {
      // ignore this - it might be expected.
      eC = e;
    }
    
    try
    {
      Configuration config = (Configuration)Configuration.getDefaultConfiguration().clone();
      RPNIBlueFringeLearnerTestComponentOpt l = new RPNIBlueFringeLearnerTestComponentOpt(null,config);
      config.setLearnerIdMode(Configuration.IDMode.POSITIVE_NEGATIVE);
      PTASequenceEngine engine = buildPTA(plusStrings, minusStrings);
      checkPTAConsistency(engine, plusStrings, true);if (engine.numberOfLeafNodes()>0) checkPTAConsistency(engine, minusStrings, false);
      l.init(engine,0,0);
      actualD = l.scoreComputer.paths.getGraph();
    }
    catch(IllegalArgumentException e)
    {
      // ignore this - it might be expected.
      eD = e;
    }


    try
    {
      Configuration config = (Configuration)Configuration.getDefaultConfiguration().clone();
      RPNIBlueFringeLearnerTestComponentOpt l = new RPNIBlueFringeLearnerTestComponentOpt(null,config);
      config.setLearnerIdMode(Configuration.IDMode.POSITIVE_NEGATIVE);
      l.init(buildPTA(plusStrings, buildSet(new String[][] {})),0,0);
      for(List<String> seq:minusStrings)
        l.scoreComputer.paths.augmentPTA(buildPTA(buildSet(new String[][] {}),buildSet(new String[][] { (String [])seq.toArray()})));
      actualE = l.scoreComputer.paths.getGraph();
    }
    catch(IllegalArgumentException e)
    {
      // ignore this - it might be expected.
      eE = e;
    }


    if (eA != null)
    {
      Assert.assertNotNull(eC);
      Assert.assertNotNull(eD);
      Assert.assertNotNull(eE);
      throw eA;
    }


    Assert.assertNull(eA);
    Assert.assertNull(eC);
    Assert.assertNull(eD);
    Assert.assertNull(eE);


    Configuration config = (Configuration)Configuration.getDefaultConfiguration().clone();
    config.setAllowedToCloneNonCmpVertex(true);
    TestFSMAlgo.checkM(actualA, expectedPTA,config);
    TestFSMAlgo.checkM(actualC, expectedPTA,config);
    //Visualiser.updateFrame(actualE,TestFSMAlgo.buildGraph(expectedPTA,"expected graph"));Visualiser.waitForKey();
    TestFSMAlgo.checkM(actualD,expectedPTA ,config);
    TestFSMAlgo.checkM(actualE,expectedPTA ,config);

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          SequenceHandler stackHandler = new ExtendedSequenceHandler(namesToMethods, filesToHandlers.get(files[i]));
          parser.parse((File)files[i], stackHandler);
          System.out.println(stackHandler.getFunctionString(3));
          sPlus.add(stackHandler.getArrayListFunctionString(3));
        }
        Configuration config = Configuration.getDefaultConfiguration();
        PickNegativesVisualiser.setSimpleConfiguration(config, true,0);
        new PickNegativesVisualiser(split,ans).construct(sPlus, new HashSet<List<String>>(),null,config);
      }
      catch(Exception ex){
        ex.printStackTrace();

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    } catch (IOException e) {
      e.printStackTrace();
    }
    // new PickNegativesVisualiser(new
    // SootCallGraphOracle()).construct(sPlus, sMinus,null, active);
    Configuration config = Configuration.getDefaultConfiguration();
    String AutoName = System.getProperty(VIZ_ENV_PROPERTIES.VIZ_AUTOFILENAME.toString());
    if (AutoName != null) config.setAutoAnswerFileName(AutoName);
    if(textoutput) config.setGenerateTextOutput(true);
    if(dotoutput) config.setGenerateDotOutput(true);
    PickNegativesVisualiser.setSimpleConfiguration(config, active, k);
    PickNegativesVisualiser pnv = new PickNegativesVisualiser();
    if (!includeLTL)
      pnv.construct(sPlus, sMinus, null, config);
    else

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    LearnerGraph.testMode = true;
    setAutoOracle();
    Map<Integer, AtomicInteger> whichScoresWereUsedForMerging = new HashMap<Integer, AtomicInteger>(), restartScoreDistribution = new HashMap<Integer, AtomicInteger>();
    Map<PairScore, Integer> scoresToIterations = new HashMap<PairScore, Integer>();
    Map<PairScore, Integer> restartsToIterations = new HashMap<PairScore, Integer>();
    final Configuration shallowCopy = (Configuration)scoreComputer.config.clone();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraph ptaHardFacts = scoreComputer.copy(shallowCopy);// this is cloned to eliminate counter-examples added to ptaSoftFacts by Spin
    LearnerGraph ptaSoftFacts = scoreComputer;


    if (!SpinUtil.check(ptaHardFacts.paths.getGraph(), ltl))
      throw new IllegalArgumentException(getHardFactsContradictionErrorMessage(ltl));

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public class Benchmarklinear {
  static LearnerGraph graph = null;


  public static void main(String[] args)
  {
    Configuration config = Configuration.getDefaultConfiguration();
    //config.setConsistencyCheckMode(true);
    //config.setQuestionGenerator(QuestionGeneratorKind.SYMMETRIC);
    synchronized (LearnerGraph.syncObj) 
    {// ensure that the calls to Jung's vertex-creation routines do not occur on different threads.
        GraphMLFile graphmlFile = new GraphMLFile();

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