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

  /** Tests non-deterministic case of {@link MarkovClassifier#tracePath}, deterministic case is tested with {@link TestPathTracing}. */
  @Test
  public void testTracePath3()
  {
    final LearnerGraph fsm = FsmParser.buildLearnerGraph("A-a->B-b->C", "testTracePath1",config,converter);
    LearnerGraphND ndFSM = new LearnerGraphND(fsm,config);
    synchronized(AbstractLearnerGraph.syncObj)
    {
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("A"), true, lblA),true,lblA),true,lblA),true,lblA);
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("A"), true, lblA),true,lblA),true,lblA);
    }
    Assert.assertEquals(10,ndFSM.getStateNumber());
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","b"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertFalse(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a","a","a","a"}),config,converter),fsm.findVertex("A")));

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  /** Tests non-deterministic case of {@link MarkovClassifier#tracePath}, deterministic case is tested with {@link TestPathTracing}. */
  @Test
  public void testTracePath4()
  {
    final LearnerGraph fsm = FsmParser.buildLearnerGraph("A-t->B-b->C", "testTracePath1",config,converter);
    LearnerGraphND ndFSM = new LearnerGraphND(fsm,config);
    synchronized(AbstractLearnerGraph.syncObj)
    {
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("B"), true, lblA),true,lblA),true,lblA),true,lblA);
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("B"), true, lblA),true,lblA),true,lblC);
    }
    Assert.assertEquals(10,ndFSM.getStateNumber());
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","b"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","c"}),config,converter),fsm.findVertex("A")));

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  /** Tests non-deterministic case of {@link MarkovClassifier#tracePath}, deterministic case is tested with {@link TestPathTracing}. */
  @Test
  public void testTracePath5()
  {
    final LearnerGraph fsm = FsmParser.buildLearnerGraph("A-t->B-b->C", "testTracePath1",config,converter);
    LearnerGraphND ndFSM = new LearnerGraphND(fsm,config);
    CmpVertex Brej=null;
    synchronized(AbstractLearnerGraph.syncObj)
    {
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("B"), true, lblA),true,lblA),true,lblA),true,lblA);
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("B"), true, lblA),true,lblA),false,lblC);
      Brej=ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("B"), true, lblA),false,lblB);
      ndFSM.addVertex(Brej,false,lblC);
    }
    Assert.assertEquals(13,ndFSM.getStateNumber());
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","b"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertFalse(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","c"}),config,converter),fsm.findVertex("A")));

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            fileA1 = files[fileNum], 
            fileA2 = files[(fileNum+1)%files.length],
            fileB1 = files[(fileNum+2)%files.length],
            fileB2 = files[(fileNum+3)%files.length];
        
          LearnerGraphND grA = null, grB = null;
          {
            LearnerGraphND loadedA1 = new LearnerGraphND(config);AbstractPersistence.loadGraph(fileA1, loadedA1,null);
            LearnerGraph loadedA2 = new LearnerGraph(config);AbstractPersistence.loadGraph(fileA2, loadedA2,null);
            grA = LearnerGraphND.UniteTransitionMatrices(loadedA1,loadedA2);
            addColourAndTransitionsRandomly(grA, new Random(0));
          }
          
          {
            LearnerGraphND loadedB1 = new LearnerGraphND(config);AbstractPersistence.loadGraph(fileB1, loadedB1,null);
            LearnerGraph loadedB2 = new LearnerGraph(config);AbstractPersistence.loadGraph(fileB2, loadedB2,null);
            grB = LearnerGraphND.UniteTransitionMatrices(loadedB1,loadedB2);
            addColourAndTransitionsRandomly(grB, new Random(1));
          }

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  {
    Configuration config = Configuration.getDefaultConfiguration().copy();
    config.setGdKeyPairThreshold(1);config.setGdLowToHighRatio(1);
    String name = "testVisual5";
    String common = "A-a->B-p->B\nA-a->C-q->C\nA-a->D-r->D\nS-a-#T";
    LearnerGraphND grA = new LearnerGraphND(FsmParser.buildGraph("A-a->E-s->E\nA-a->F-v->F\nU-a-#V\n"+common,name+"A"),config);
    LearnerGraphND grB = new LearnerGraphND(FsmParser.buildGraph("A-a->G-u->G\nA-a->H-t->H\n"+common,name+"B"),config);
    LearnerGraphND grA_reduced = new LearnerGraphND(config), grB_reduced = new LearnerGraphND(config);
    AbstractPathRoutines.removeRejectStates(grA, grA_reduced);
    AbstractPathRoutines.removeRejectStates(grB, grB_reduced);
    GD<List<CmpVertex>,List<CmpVertex>,LearnerGraphNDCachedData,LearnerGraphNDCachedData> gd = 
      new GD<List<CmpVertex>,List<CmpVertex>,LearnerGraphNDCachedData,LearnerGraphNDCachedData>();
    DirectedSparseGraph graph = gd.showGD(grA_reduced,grB_reduced,  ExperimentRunner.getCpuNumber());

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  public static void main(String[] args) throws IOException{
    //File graphDir = new File(args[0]);//new File(System.getProperty("user.dir")+System.getProperty("file.separator")+"resources"+
    //System.getProperty("file.separator")+"TestGraphs"+System.getProperty("file.separator") +args[0]);
    //String wholePath = graphDir.getAbsolutePath()+File.separator;
    LearnerGraphND graph0 = new LearnerGraphND(Configuration.getDefaultConfiguration().copy()),graph1 = null;
    AbstractPersistence.loadGraph(args[0], graph0);
    if (args.length > 1)
    {
      graph1 = new LearnerGraphND(Configuration.getDefaultConfiguration().copy());;
      AbstractPersistence.loadGraph(args[1], graph1);
    }
    Visualiser.updateFrame(graph0, graph1);Visualiser.waitForKey();
  }

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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());
    if (display)
    {

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  public static DirectedSparseGraph obtainDifferenceGraph(String graphA, String graphB,int counter,boolean display, Configuration config)
  {
    GD<List<CmpVertex>,List<CmpVertex>,LearnerGraphNDCachedData,LearnerGraphNDCachedData> gd = 
      new GD<List<CmpVertex>,List<CmpVertex>,LearnerGraphNDCachedData,LearnerGraphNDCachedData>();


    LearnerGraphND grA=buildLearnerGraphND(graphA, "labellingDemo_A_"+counter,config),grB=
    buildLearnerGraphND(graphB, "labellingDemo_B_"+counter,config);
    DirectedSparseGraph gr = gd.showGD(
        grA,grB,
        ExperimentRunner.getCpuNumber());
    if (display)

View Full Code Here

  /** Tests non-deterministic case of {@link MarkovClassifier#tracePath}, deterministic case is tested with {@link TestPathTracing}. */
  @Test
  public void testTracePath1()
  {
    final LearnerGraph fsm = FsmParser.buildLearnerGraph("A-a->B-b->C", "testTracePath1",config,converter);
    LearnerGraphND ndFSM = new LearnerGraphND(fsm,config);
    synchronized(AbstractLearnerGraph.syncObj)
    {
      ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("A"), true, lblA),true,lblA);
    }
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","b"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a"}),config,converter),fsm.findVertex("A")));
  }

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  /** Tests non-deterministic case of {@link MarkovClassifier#tracePath}, deterministic case is tested with {@link TestPathTracing}. */
  @Test
  public void testTracePath2()
  {
    final LearnerGraph fsm = FsmParser.buildLearnerGraph("A-a->B-b->C", "testTracePath1",config,converter);
    LearnerGraphND ndFSM = new LearnerGraphND(fsm,config);
    synchronized(AbstractLearnerGraph.syncObj)
    {
      CmpVertex AA=ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("A"), true, lblA),true,lblA);
      ndFSM.addVertex(AA, true, lblA);ndFSM.addVertex(AA, true, lblC);
    }
    Assert.assertEquals(7,ndFSM.getStateNumber());
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a","c"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertFalse(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a","b"}),config,converter),fsm.findVertex("A")));
    Assert.assertFalse(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a","a","a"}),config,converter),fsm.findVertex("A")));
  }

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