Package statechum.analysis.learning.rpnicore.AbstractLearnerGraph

Examples of statechum.analysis.learning.rpnicore.AbstractLearnerGraph.StatesToConsider

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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)
    {
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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)
    {
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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)
    {
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  public <TARGET_A_TYPE,CACHE_A_TYPE extends CachedData<TARGET_A_TYPE,CACHE_A_TYPE>> void removeReachableStatesFromWhichInitIsNotReachable(AbstractLearnerGraph<TARGET_A_TYPE,CACHE_A_TYPE> outcome)
  {
    final Set<CmpVertex> reachableStates = computeReachableStatesFromWhichInitIsNotReachable();
   
    buildForward(coregraph, new StatesToConsider(){

      @Override
      public boolean stateToConsider(CmpVertex vert) {
        return !reachableStates.contains(vert);
      }}, outcome);
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            // Perhaps I should be numbering states directly here instead of using numberNonNegativeElements afterwards,
            // but this is not simple to do: I have to give numbers in the order in which triangular traversal visits states.
          }
        });
      GDLearnerGraph.performRowTasks(handlerList, ThreadNumber, grCombined.transitionMatrix,new StatesToConsider() {
        @Override public boolean stateToConsider(CmpVertex vert) {
          return statesOfA.contains(vert);
        }
      }, GDLearnerGraph.partitionWorkLoadLinear(ThreadNumber,statesOfA.size()));
      final int numberOfPairs = GDLearnerGraph.numberNonNegativeElements(pairScores);
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            assert highState != null;
            wave.add(new PairScore(entryA.getKey(),highState,(int)(multiplier*scoreHigh),(int)(multiplier*scoreLow)));
          }
        });
      }
      GDLearnerGraph.performRowTasks(handlerList, ThreadNumber, grCombined.transitionMatrix,new StatesToConsider() {
        @Override public boolean stateToConsider(CmpVertex vert) {
          return statesOfA.contains(vert);
        }
      }, GDLearnerGraph.partitionWorkLoadLinear(ThreadNumber,statesOfA.size()));
     
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    LearnerGraphND expected = buildLearnerGraphND("C-c->A\nU-a->V-b->U","testBuildInverse7",config,converter);
    expected.transitionMatrix.put(AbstractLearnerGraph.generateNewCmpVertex(VertexID.parseID("D"), graph.config), graph.createNewRow());
    expected.setInit(expected.findVertex("A"));
   
    LearnerGraphND actual = new LearnerGraphND(Configuration.getDefaultConfiguration());actual.initEmpty();
    AbstractPathRoutines.buildInverse(graph, new StatesToConsider() {

      @Override
      public boolean stateToConsider(CmpVertex vert) {
        return !vert.getStringId().equals("B");
      }
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  {
    LearnerGraphND graph = buildLearnerGraphND("A-a->B-a->C\nA<-c-B-b-#D\nB-d->A-c->C\nU-b->V-a->U",
      "testConvertToND6",config,converter);
   
    LearnerGraphND actual = new LearnerGraphND(Configuration.getDefaultConfiguration());actual.initEmpty();
    AbstractPathRoutines.buildInverse(graph, new StatesToConsider() {

      @Override
      public boolean stateToConsider(@SuppressWarnings("unused") CmpVertex vert) {
        return false;
      }
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    LearnerGraphND graph = buildLearnerGraphND("A-a->B-a->C\nA<-c-B-b-#D\nB-d->A-c->C\nU-b->V-a->U","testConvertToND6",config,converter);
    LearnerGraphND expected = buildLearnerGraphND("A-c->C\nU-b->V-a->U","testBuildForward7",config,converter);
    expected.transitionMatrix.put(AbstractLearnerGraph.generateNewCmpVertex(VertexID.parseID("D"), graph.config), graph.createNewRow());
   
    LearnerGraphND actual = new LearnerGraphND(Configuration.getDefaultConfiguration());actual.initEmpty();
    AbstractPathRoutines.buildForward(graph, new StatesToConsider() {

      @Override
      public boolean stateToConsider(CmpVertex vert) {
        return !vert.getStringId().equals("B");
      }
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  public final void testBuildForward8()
  {
    final LearnerGraphND graph = buildLearnerGraphND("A-a->B-a->C\nA<-c-B-b-#D\nB-d->A-c->C\nU-b->V-a->U","testConvertToND6",config,converter);
    final LearnerGraphND actual = new LearnerGraphND(Configuration.getDefaultConfiguration());actual.initEmpty();
   
    AbstractPathRoutines.buildForward(graph, new StatesToConsider() {
      @Override
      public boolean stateToConsider(@SuppressWarnings("unused") CmpVertex vert) {
        return false;
      }
    },actual);
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Related Classes of statechum.analysis.learning.rpnicore.AbstractLearnerGraph.StatesToConsider

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  • statechum.analysis.learning.linear.GD
  • statechum.analysis.learning.linear.TestLinear
  • statechum.analysis.learning.linear.TestLinearWithMultipleThreads
  • statechum.analysis.learning.linear.TestMatrixComputationWithMultipleThreads
  • statechum.analysis.learning.rpnicore.AbstractPathRoutines
  • statechum.analysis.learning.rpnicore.GD
  • statechum.analysis.learning.rpnicore.TestLearnerGraphND
  • statechum.analysis.learning.rpnicore.TestLinear
  • statechum.analysis.learning.rpnicore.TestLinearWithMultipleThreads
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