Package statechum.analysis.learning.rpnicore.Transform

Examples of statechum.analysis.learning.rpnicore.Transform.ConvertALabel

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  {
    Writer randomFSM = null;
    try
    {
      Configuration config = Configuration.getDefaultConfiguration().copy();
      ConvertALabel converter = null;
      int initStates = 25, graphComplexityMax=6, experimentsPerCategory = 100;
      for(int graphComplexity=0;graphComplexity < graphComplexityMax;graphComplexity++)
      {
        int states=initStates+graphComplexity*20;
        int alphabet = states/2;
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    final int minStateNumber = 30;
    final int chunkLen = 3;

    Configuration config = Configuration.getDefaultConfiguration().copy();config.setAskQuestions(false);config.setDebugMode(false);config.setGdLowToHighRatio(0.7);config.setRandomPathAttemptFudgeThreshold(1000);
    config.setTransitionMatrixImplType(STATETREE.STATETREE_LINKEDHASH);config.setLearnerScoreMode(ScoreMode.ONLYOVERRIDE);
    ConvertALabel converter = new Transform.InternStringLabel();
    GlobalConfiguration.getConfiguration().setProperty(G_PROPERTIES.LINEARWARNINGS, "false");
   
    final int states=minStateNumber;
    final int alphabet = (int)(alphabetMultiplier*states);
    final int seed = traceQuantity;
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  // AbstractLearnerGraph is polymorphic in this context: I can use both deterministic and non-deterministic graphs.
  @Parameters
  public static Collection<Object[]> data()
  {
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setTransitionMatrixImplType(STATETREE.STATETREE_SLOWTREE);
    ConvertALabel converter = new Transform.InternStringLabel();
    final String fsmA = "S-a->S1-b->"+"A-a->A1-a-#AR\nA1-d->A2-d->A3\nA1-c->A2-c->A3"+TestRpniLearner.PTA3;
    final String fsmB = "S-a->S\nA1-a->A2\nS-a->S1-b->"+"A-a->A1-a-#AR\nA1-d->A2-d->A3\nA1-c->A2-c->A3"+TestRpniLearner.PTA3;
    LearnerGraph graphD=FsmParser.buildLearnerGraph(fsmA, "testCopyGraph2",config,converter);
    LearnerGraphND graphND=FsmParser.buildLearnerGraphND(fsmB, "testCopyGraph4",config,converter);
    LearnerGraph graphDNotIntern=FsmParser.buildLearnerGraph(fsmA, "testCopyGraph2",config,null);
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  // AbstractLearnerGraph is polymorphic in this context: I can use both deterministic and non-deterministic graphs.
  @Parameters
  public static Collection<Object[]> data()
  {
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setTransitionMatrixImplType(STATETREE.STATETREE_SLOWTREE);
    ConvertALabel converter = new Transform.InternStringLabel();
    final String fsmA = "S-a->S1-b->"+"A-a->A1-a-#AR\nA1-d->A2-d->A3\nA1-c->A2-c->A3"+TestRpniLearner.PTA3;
    final String fsmB = "S-a->S\nA1-a->A2\nS-a->S1-b->"+"A-a->A1-a-#AR\nA1-d->A2-d->A3\nA1-c->A2-c->A3"+TestRpniLearner.PTA3;
    LearnerGraph graphD=FsmParser.buildLearnerGraph(fsmA, "testCopyGraph2",config,converter);
    LearnerGraphND graphND=FsmParser.buildLearnerGraphND(fsmB, "testCopyGraph4",config,converter);
    LearnerGraph graphDNotIntern=FsmParser.buildLearnerGraph(fsmA, "testCopyGraph2",config,null);
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        }
      }
    }
   

    ConvertALabel converter = new Transform.InternStringLabel();
    LearnerGraph differentGraphA = new LearnerGraph(graphD,graphD.config);differentGraphA.transitionMatrix.get(differentGraphA.findVertex("A2"))
      .put(AbstractLearnerGraph.generateNewLabel("t",differentGraphA.config,converter),
          differentGraphA.findVertex("A3"));
    differentGraphA.setName("differentA");
    LearnerGraphND differentGraphB = new LearnerGraphND(graphND,graphD.config);differentGraphB.transitionMatrix.get(differentGraphB.findVertex("A2"))
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  public static void main(String []args)
  {
    final Configuration config = Configuration.getDefaultConfiguration().copy();config.setAskQuestions(false);config.setDebugMode(false);config.setGdLowToHighRatio(0.7);config.setRandomPathAttemptFudgeThreshold(1000);
    config.setTransitionMatrixImplType(STATETREE.STATETREE_LINKEDHASH);config.setLearnerScoreMode(ScoreMode.COMPATIBILITY);config.setLabelKind(LABELKIND.LABEL_STRING);
    final ConvertALabel converter = new Transform.InternStringLabel();
    MarkovModel m=new MarkovModel(3,true,true);
   
    TraceLoader tool = new TraceLoader(config,converter);
    tool.loadConfig(args[0]);
    LearnerGraph pta = tool.getPTA();
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  // AbstractLearnerGraph is polymorphic in this context: I can use both deterministic and non-deterministic graphs.
  @Parameters
  public static Collection<Object[]> data()
  {
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setTransitionMatrixImplType(STATETREE.STATETREE_SLOWTREE);
    ConvertALabel converter = new Transform.InternStringLabel();
    final String fsmA = "S-a->S1-b->"+"A-a->A1-a-#AR\nA1-d->A2-d->A3\nA1-c->A2-c->A3"+TestRpniLearner.PTA3;
    final String fsmB = "S-a->S\nA1-a->A2\nS-a->S1-b->"+"A-a->A1-a-#AR\nA1-d->A2-d->A3\nA1-c->A2-c->A3"+TestRpniLearner.PTA3;
    LearnerGraph graphD=FsmParser.buildLearnerGraph(fsmA, "testCopyGraph2",config,converter);
    LearnerGraphND graphND=FsmParser.buildLearnerGraphND(fsmB, "testCopyGraph4",config,converter);
    LearnerGraph graphDNotIntern=FsmParser.buildLearnerGraph(fsmA, "testCopyGraph2",config,null);
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  public static void runExperiment() throws Exception
  {
    DrawGraphs gr = new DrawGraphs();
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setAskQuestions(false);config.setDebugMode(false);config.setGdLowToHighRatio(0.7);config.setRandomPathAttemptFudgeThreshold(1000);
    config.setTransitionMatrixImplType(STATETREE.STATETREE_LINKEDHASH);config.setLearnerScoreMode(ScoreMode.COMPATIBILITY);
    ConvertALabel converter = new Transform.InternStringLabel();
    GlobalConfiguration.getConfiguration().setProperty(G_PROPERTIES.LINEARWARNINGS, "false");
    final int ThreadNumber = ExperimentRunner.getCpuNumber(); 
    ExecutorService executorService = Executors.newFixedThreadPool(ThreadNumber);
    final int minStateNumber = 20;
    final int samplesPerFSM = 5;
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  public static void runExperiment() throws Exception
  {
    DrawGraphs gr = new DrawGraphs();
    Configuration config = Configuration.getDefaultConfiguration().copy();config.setAskQuestions(false);config.setDebugMode(false);config.setGdLowToHighRatio(0.7);config.setRandomPathAttemptFudgeThreshold(1000);
    config.setTransitionMatrixImplType(STATETREE.STATETREE_LINKEDHASH);
    ConvertALabel converter = new Transform.InternStringLabel();
    //gr_NewToOrig.setLimit(7000);
    GlobalConfiguration.getConfiguration().setProperty(G_PROPERTIES.LINEARWARNINGS, "false");
    final int ThreadNumber = ExperimentRunner.getCpuNumber();
   
    ExecutorService executorService = Executors.newFixedThreadPool(ThreadNumber);
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  public static void main(String []args)
  {
    final Configuration config = Configuration.getDefaultConfiguration().copy();config.setAskQuestions(false);config.setDebugMode(false);config.setGdLowToHighRatio(0.7);config.setRandomPathAttemptFudgeThreshold(1000);
    config.setTransitionMatrixImplType(STATETREE.STATETREE_LINKEDHASH);config.setLearnerScoreMode(ScoreMode.COMPATIBILITY);config.setLabelKind(LABELKIND.LABEL_STRING);
    final ConvertALabel converter = new Transform.InternStringLabel();
    MarkovModel m=new MarkovModel(3,true,true,false);
   
    TraceLoader tool = new TraceLoader(config,converter);
    tool.loadConfig(args[0]);
    LearnerGraph pta = tool.getPTA();
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  • statechum.analysis.learning.experiments.PairSelection.LearnFromTracesUsingExperimentalLearners
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