Examples of statechum.analysis.learning.rpnicore.LearnerGraphND

Package statechum.analysis.learning.rpnicore

Examples of statechum.analysis.learning.rpnicore.LearnerGraphND

statechum.analysis.learning.rpnicore.LearnerGraphND
This is a non-deterministic graph. Strictly speaking, all the methods here are applicable to the generalised graph too and thus could have been placed in AbstractPathRoutines. The reason they are here is that there does not seem to be much use in running such methods on deterministic graphs. In case one needs to do this, the relevant routines can be moved. @author kirill

        {
          ExperimentResult outcome = new ExperimentResult();
          while(!outcome.experimentValid)
          {
            int mutations = mutationsPerStage * (mutationStage+1);
            LearnerGraphND origGraph = mg.nextMachine(alphabet, experiment,config,converter);
            GraphMutator<List<CmpVertex>,LearnerGraphNDCachedData> mutator = new GraphMutator<List<CmpVertex>,LearnerGraphNDCachedData>(origGraph,r);
            mutator.mutate(mutations);
            LearnerGraphND origAfterRenaming = new LearnerGraphND(origGraph.config);
            Map<CmpVertex,CmpVertex> origToNew = copyStatesAndTransitions(origGraph,origAfterRenaming);
            LearnerGraphND mutated = (LearnerGraphND)mutator.getMutated();
            Set<Transition> appliedMutations = new HashSet<Transition>();
            for(Transition tr:mutator.getDiff())
            {
              CmpVertex renamedFrom = origToNew.get(tr.getFrom());if (renamedFrom == null) renamedFrom = tr.getFrom();
              CmpVertex renamedTo = origToNew.get(tr.getTo());if (renamedTo == null) renamedTo = tr.getTo();

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          }
          ++artificialTargetSize;
        }
      }
      
      LearnerGraphND outcome = new LearnerGraphND(machine.config);AbstractLearnerGraph.copyGraphs(machine,outcome);
      return outcome;
    }

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      }
  }
  
  public static void printTraceLeadingTo(LearnerGraph graph, String vert)
  {
    LearnerGraphND ptaInverse = new LearnerGraphND(graph.config);
    AbstractPathRoutines.buildInverse(graph, new StatesToConsider() {
      @Override public boolean stateToConsider(@SuppressWarnings("unused") CmpVertex v) { return true; }}
      , ptaInverse);
    CmpVertex v=ptaInverse.findVertex(vert);
    for(int i=0;i<10;++i)
    {
      Map<Label,List<CmpVertex>> transitions = ptaInverse.transitionMatrix.get(v);
      if (transitions.size() > 1)
      {

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   * @return an association between vertices and deterministic graphs. Where built as an inverse of the original graph at each of its vertices, each graph can be large but this does not have to be so
   * even in the worst case since we can trim all graphs at the number of transitions corresponding to the length of the paths at the point of construction. 
   */
  public static Map<CmpVertex,LearnerGraph> constructPathsFromEachState(LearnerGraph graph, boolean directionForward)
  {
    LearnerGraphND forwardOrInverseGraph = null;
    
    if (directionForward)
    {
      forwardOrInverseGraph = new LearnerGraphND(graph,graph.config);
    }
    else
    {
      Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
      forwardOrInverseGraph = new LearnerGraphND(shallowCopy);forwardOrInverseGraph.initEmpty();
      AbstractPathRoutines.buildInverse(graph,LearnerGraphND.ignoreNone,forwardOrInverseGraph);  // do the inverse to the tentative graph
    }


    Map<CmpVertex,LearnerGraph> outcome = new TreeMap<CmpVertex,LearnerGraph>();
    for(CmpVertex v:graph.transitionMatrix.keySet())

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      }
  }
  
  public static void printTraceLeadingTo(LearnerGraph graph, String vert)
  {
    LearnerGraphND ptaInverse = new LearnerGraphND(graph.config);
    AbstractPathRoutines.buildInverse(graph, new StatesToConsider() {
      @Override public boolean stateToConsider(@SuppressWarnings("unused") CmpVertex v) { return true; }}
      , ptaInverse);
    CmpVertex v=ptaInverse.findVertex(vert);
    for(int i=0;i<10;++i)
    {
      Map<Label,List<CmpVertex>> transitions = ptaInverse.transitionMatrix.get(v);
      if (transitions.size() > 1)
      {

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   * @return inverted graph
   */
  public static LearnerGraphND computeInverseGraph(LearnerGraph graph)
  {
    Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraphND inverseGraph = new LearnerGraphND(shallowCopy);inverseGraph.initEmpty();
    AbstractPathRoutines.buildInverse(graph,LearnerGraphND.ignoreNone,inverseGraph);  // do the inverse to the tentative graph
    return inverseGraph;
  }

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  public void testParseAutomataAndComputeDiff2()
  {
    ConvertALabel converter = null;
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setGdFailOnDuplicateNames(false);
    Random rnd=new Random(0);
    LearnerGraphND previous = null;
    
    for(int states=1;states < 100;states++)
    {
      final int alphabet = 2*states;
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      LearnerGraphND graph = mg.nextMachine(alphabet,-states, config, converter);
      
      LearnerGraphND parsedOutcome = new LearnerGraphND(config);
      StatechumProcess.parseStatemachine(StatechumProcess.constructFSM(graph), parsedOutcome, converter,true);
      DifferentFSMException diffException = WMethod.checkM(graph, parsedOutcome);
      Assert.assertNull(diffException);


      Map<VertID,VertID> map = new TreeMap<VertID,VertID>();
      for(int i=0;i<20;++i) map.put(graph.pathroutines.pickRandomState(rnd),graph.pathroutines.pickRandomState(rnd));
      Map<VertID,VertID> mapOutcome = StatechumProcess.parseMap(StatechumProcess.mapToObject(map));
      Assert.assertEquals(map,mapOutcome);
      
      if (previous != null)
      {
        LearnerGraphND shouldBeLikePrevious = new LearnerGraphND(graph,config);
        OtpErlangObject difference = DifferenceVisualiser.ChangesToGraph.computeGD(graph, previous, config);
        DifferenceVisualiser.ChangesToGraph.load(difference).applyDiff(shouldBeLikePrevious, config);
        DifferentFSMException ex = WMethod.checkM(previous, shouldBeLikePrevious);
        Assert.assertNull(ex);
      }

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  @Test
  public void testSetRed1()
  {
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setGdFailOnDuplicateNames(false);
    LearnerGraphND grA = new LearnerGraphND(config);
    OtpErlangObject grAerlang = ErlangLabel.parseText("{statemachine,['P1000','P1002','N1000'],"+
              "[{'P1000',a,'P1000'},"+
              " {'P1000',b,'P1002'},"+
              " {'P1000',c,'N1000'},"+
              " {'P1002',c,'P1002'},"+
              " {'P1002',d,'P1002'}],"+
              "'P1000',"+
              "[d,b,c,a]}");
    Synapse.StatechumProcess.parseStatemachine(grAerlang,grA,null,true);
    Assert.assertEquals(0, grA.getRedStateNumber());
    Synapse.StatechumProcess.setReds(new OtpErlangList(new OtpErlangObject[]{new OtpErlangAtom("P1002")}), grA);
    Assert.assertEquals(JUConstants.RED,grA.findVertex("P1002").getColour());
    Assert.assertEquals(1, grA.getRedStateNumber());
  }

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  @Test
  public void testSetRed2()
  {
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setGdFailOnDuplicateNames(false);
    final LearnerGraphND grA = new LearnerGraphND(config);
    OtpErlangObject grAerlang = ErlangLabel.parseText("{statemachine,['P1000','P1002','N1000'],"+
              "[{'P1000',a,'P1000'},"+
              " {'P1000',b,'P1002'},"+
              " {'P1000',c,'N1000'},"+
              " {'P1002',c,'P1002'},"+
              " {'P1002',d,'P1002'}],"+
              "'P1000',"+
              "[d,b,c,a]}");
    Synapse.StatechumProcess.parseStatemachine(grAerlang,grA,null,true);
    Assert.assertEquals(0, grA.getRedStateNumber());
    checkForCorrectException(new whatToRun() { public @Override void run() {
      Synapse.StatechumProcess.setReds(new OtpErlangTuple(new OtpErlangObject[]{new OtpErlangAtom("P1002")}), grA);
    }},ClassCastException.class,"OtpErlangTuple");
  }

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  @Test
  public void testSetRed3()
  {
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setGdFailOnDuplicateNames(false);
    final LearnerGraphND grA = new LearnerGraphND(config);
    OtpErlangObject grAerlang = ErlangLabel.parseText("{statemachine,['P1000','P1002','N1000'],"+
              "[{'P1000',a,'P1000'},"+
              " {'P1000',b,'P1002'},"+
              " {'P1000',c,'N1000'},"+
              " {'P1002',c,'P1002'},"+
              " {'P1002',d,'P1002'}],"+
              "'P1000',"+
              "[d,b,c,a]}");
    Synapse.StatechumProcess.parseStatemachine(grAerlang,grA,null,true);
    Assert.assertEquals(0, grA.getRedStateNumber());
    checkForCorrectException(new whatToRun() { public @Override void run() {
      Synapse.StatechumProcess.setReds(new OtpErlangList(new OtpErlangObject[]{new OtpErlangString("P1002")}), grA);
    }},ClassCastException.class,"OtpErlangString");
  }

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