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 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")));
  }

View Full Code Here

  /** 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")));
  }

View Full Code Here

  /** 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")));

View Full Code Here

  /** 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")));

View Full Code Here

  /** 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")));

View Full Code Here

  {
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setTransitionMatrixImplType(STATETREE.STATETREE_SLOWTREE);
    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 = FsmParser.buildLearnerGraphND("A-a->E-s->E\nA-a->F-v->F\nU-a-#V\n"+common,name+"A",config,null);
    LearnerGraphND grB = FsmParser.buildLearnerGraphND("A-a->G-u->G\nA-a->H-t->H\n"+common,name+"B",config,null);
    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());

View Full Code Here

  {
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setTransitionMatrixImplType(STATETREE.STATETREE_SLOWTREE);
    config.setGdKeyPairThreshold(1);config.setGdLowToHighRatio(1);config.setGdMaxNumberOfStatesInCrossProduct(0);
    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 = FsmParser.buildLearnerGraphND("A-a->E-s->E\nA-a->F-v->F\nU-a-#V\n"+common,name+"A",config,null);
    LearnerGraphND grB = FsmParser.buildLearnerGraphND("A-a->G-u->G\nA-a->H-t->H\n"+common,name+"B",config,null);
    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());

View Full Code Here

   * @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;
  }

View Full Code Here

      }
  }
  
  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)
      {

View Full Code Here

    GDLearnerGraph(AbstractLearnerGraph<TARGET_A_TYPE,CACHE_A_TYPE> coregraph,
    StatesToConsider stateFilter, 
    boolean direction)
  {
    alphabet = coregraph.learnerCache.getAlphabet();config=coregraph.config;filter=stateFilter;
    matrixInverse = new LearnerGraphND(config);matrixInverse.initEmpty();
    matrixForward = new LearnerGraphND(config);matrixForward.initEmpty();
    pairCompatibility = coregraph.pairCompatibility;
    
    stateToNumberMap = new TreeMap<CmpVertex,Integer>();
    numberToStateArray = coregraph.buildStateToIntegerMap(filter,stateToNumberMap);
    assert numberToStateArray.length == stateToNumberMap.size();

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

Related Classes of statechum.analysis.learning.rpnicore.LearnerGraphND

edu.uci.ics.jung.graph.impl.DirectedSparseEdge

statechum.analysis.Erlang.Synapse$StatechumProcess

statechum.analysis.Erlang.TestSynapseAuxiliaryFunctions

statechum.analysis.learning.ErlangOracleVisualiser

statechum.analysis.learning.experiments.ForestFireNDStateMachineGenerator

statechum.analysis.learning.experiments.mutation.DiffExperiments

statechum.analysis.learning.experiments.mutation.DiffExperiments$MachineGenerator

statechum.analysis.learning.experiments.mutation.GenerateRandomFSM

statechum.analysis.learning.experiments.mutation.TestDiffExperimentWithLogs$SystemTestDiffExperiments

statechum.analysis.learning.experiments.mutation.TestGraphMutator

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.