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).

  }
  
  @Test
  public final void testGD_nondetA()
  {
    Configuration config = Configuration.getDefaultConfiguration();
    final LearnerGraphND 
      grA=buildLearnerGraphND("A-x->A-a->C-u->C-v->C-c->F\nC-c->G\nC-c->A\nC-b->A\n"+
        "G-b->A\nG-a->C\nG-b->F\n"+
        "F-a->A\nF-a->C\nF-a->G\n"
        , "TestGD_MultipleCasesOfRenamingA",config,converter),

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  /** Clashes between disconnected vertices and the original ones. */
  @Test
  public final void testGD_nondetB()
  {
    Configuration config = Configuration.getDefaultConfiguration();
    final LearnerGraphND 
      grA=buildLearnerGraphND("A-x->A-a->C-u->C-v->C-c->F\nC-c->G\nC-c->A\nC-b->A\n"+
        "G-b->A\nG-a->C\nG-b->F\n"+
        "F-a->A\nF-a->C\nF-a->G\n"
        , "TestGD_MultipleCasesOfRenamingA",config,converter),

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  /** Graph B is slightly different now, hence duplicates are different too. */
  @Test
  public final void testGD_nondetC()
  {
    Configuration config = Configuration.getDefaultConfiguration().copy();
    final LearnerGraphND 
      grA=buildLearnerGraphND("A-x->A-a->C-u->C-c->F\nC-c->G\nC-c->A\nC-b->A\n"+
        "G-b->A\nG-a->C\nG-b->F\n"+
        "F-a->A\nF-a->C\nF-a->G\n"
        , "TestGD_MultipleCasesOfRenamingA",config,converter),

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    runTest(grA,grB, stateC,expectedDuplicates);
  }
  
  private LearnerGraphND runTest(LearnerGraphND grA, LearnerGraphND grB, String secondStateInKeyPair, String [] duplicatesExpectedString)
  {
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setGdFailOnDuplicateNames(false);config.setGdKeyPairThreshold(.1);
    GD<List<CmpVertex>,List<CmpVertex>,LearnerGraphNDCachedData,LearnerGraphNDCachedData> gd = new GD<List<CmpVertex>,List<CmpVertex>,LearnerGraphNDCachedData,LearnerGraphNDCachedData>();
    gd.init(grA, grB, threadNumber,config);gd.identifyKeyPairs();
    ChangesRecorder recorder = new ChangesRecorder(null);
    gd.makeSteps();gd.computeDifference(recorder);
    //Visualiser.updateFrame(grA,grB);Visualiser.updateFrame(gd.showGD(grA, grB, 1), null);
    Assert.assertEquals(2,gd.aTOb.size());
    Set<CmpVertex> keyPairsLeft = new TreeSet<CmpVertex>(),keyPairsRight = new TreeSet<CmpVertex>();
    keyPairsLeft.addAll(Arrays.asList(new CmpVertex[]{grA.findVertex(VertexID.parseID("A")),grA.findVertex(VertexID.parseID("C"))}));
    keyPairsRight.addAll(Arrays.asList(new CmpVertex[]{gd.origToNewB.get(grB.findVertex(VertexID.parseID("B"))),gd.origToNewB.get(grB.findVertex(VertexID.parseID(secondStateInKeyPair)))}));
    Assert.assertEquals(keyPairsLeft, gd.aTOb.keySet());
    Set<CmpVertex> actual = new TreeSet<CmpVertex>();actual.addAll(gd.aTOb.values());Assert.assertEquals(keyPairsRight, actual);
    Set<CmpVertex> duplicatesExpected = new TreeSet<CmpVertex>();
    for(String dup:duplicatesExpectedString) duplicatesExpected.add(gd.origToNewB.get(grB.findVertex(VertexID.parseID(dup))));
    
    Assert.assertEquals(duplicatesExpected,gd.duplicates); 
    Configuration cloneConfig = config.copy();cloneConfig.setLearnerCloneGraph(true);
    LearnerGraphND graph = new LearnerGraphND(cloneConfig);AbstractLearnerGraph.copyGraphs(grA, graph);
    ChangesRecorder.applyGD(graph, recorder.writeGD(TestGD.createDoc()), converter);
    LearnerGraphND outcome = new LearnerGraphND(config);AbstractLearnerGraph.copyGraphs(graph, outcome);
    Assert.assertNull(WMethod.checkM(grB, graph));


    // Now do the same as above, but renumber states to match grB
    AbstractLearnerGraph.copyGraphs(grA, graph);
    Configuration configMut = Configuration.getDefaultConfiguration().copy();config.setLearnerCloneGraph(false);
    LearnerGraphMutator<List<CmpVertex>,LearnerGraphNDCachedData> graphPatcher = new LearnerGraphMutator<List<CmpVertex>,LearnerGraphNDCachedData>(graph,configMut,null);
    ChangesRecorder.loadDiff(graphPatcher, recorder.writeGD(TestGD.createDoc()), converter);
    graphPatcher.removeDanglingStates();
    LearnerGraphND result = new LearnerGraphND(configMut);
    graphPatcher.relabel(result);

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  }
  
  @Test
  public final void testGD_nondet_incompatibles()
  {
    Configuration config = Configuration.getDefaultConfiguration();
    final LearnerGraphND 
      grA=buildLearnerGraphND("A-x->A-a->C-u->C-c->F\nC-v->C\nC-c->G\nC-c->A\nC-b->A\n"+
        "G-b->A\nG-b->C\nG-b->F\n"+
        "F-a->A\nF-a->C\nF-a->G\n"
        , "TestGD_MultipleCasesOfRenamingA",config,converter),

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   * @param graph what to invert.
   * @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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    /** Maps states to a function associating labels to a sum of probabilities from a Markov matrix. Such a sum is supposed to predict the likelyhood of a transition with this label from every state. */
    Map<CmpVertex,Map<Label,UpdatablePairInteger>> state_outgoing_occurence=new HashMap<CmpVertex,Map<Label,UpdatablePairInteger>>();
    /** Maps states to a function associating labels to a probability of a transition with the label of interest from a state of interest. Computed from {@link MarkovUniversalLearner#state_outgoing_occurence}. */
    Map<CmpVertex,Map<Label,UpdatablePairDouble>> state_outgoing=new HashMap<CmpVertex,Map<Label,UpdatablePairDouble>>();


    final Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraph outcome = new LearnerGraph(shallowCopy);
    LearnerGraph.copyGraphs(graph, outcome);
    final Set<Label> allElementsOfAlphabet = graph.learnerCache.getAlphabet(); 
    // mapping map to store all paths leave each state in different length
      for(CmpVertex vert:graph.transitionMatrix.keySet())

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      throw new IllegalArgumentException("predictions are only supported in the forward direction, not inverse");


    /** Maps states to a function associating labels to a probability of a transition with the label of interest from a state of interest. Computed from {@link MarkovUniversalLearner#state_outgoing_occurence}. */
    Map<CmpVertex,Map<Label,MarkovOutcome>> state_outgoing=predictTransitions();


    final Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraph graphWithPredictedTransitions = new LearnerGraph(shallowCopy);
    LearnerGraph.copyGraphs(graph, graphWithPredictedTransitions);


    // in this part the tree is extended depend on their outgoing transition probabilities
     for(Entry<CmpVertex, Map<Label, MarkovOutcome>> outgoing:state_outgoing.entrySet())

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{
  public static DirectedSparseGraph obtainDifferenceGraph(String graphA, String graphB,int counter,boolean display)
  {
    GD<List<CmpVertex>,List<CmpVertex>,LearnerGraphNDCachedData,LearnerGraphNDCachedData> gd = 
      new GD<List<CmpVertex>,List<CmpVertex>,LearnerGraphNDCachedData,LearnerGraphNDCachedData>();
    Configuration config = Configuration.getDefaultConfiguration();
    LearnerGraphND grA=new LearnerGraphND(buildGraph(graphA, "labellingDemo_A_"+counter),config),grB=
    new LearnerGraphND(buildGraph(graphB, "labellingDemo_B_"+counter),config);
    DirectedSparseGraph gr = gd.showGD(
        grA,grB,
        ExperimentRunner.getCpuNumber());

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   * @param graph what to invert.
   * @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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