Examples of MachineGenerator

Examples of forestry.energy.gadgets.MachineGenerator

  }


  @Override
  protected void drawGuiContainerBackgroundLayer(float var1, int mouseX, int mouseY) {
    super.drawGuiContainerBackgroundLayer(var1, mouseX, mouseY);
    MachineGenerator boiler = tile;


    int progress = boiler.getStoredScaled(49);
    if (progress > 0)
      drawTexturedModalRect(guiLeft + 108, guiTop + 38, 176, 91, progress, 18);
  }

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Examples of statechum.analysis.learning.experiments.mutation.DiffExperiments.MachineGenerator

    {
      final int alphabet = 2*states;
      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      Label uniqueFromInitial = null;
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      do
      {
        referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
        if (pickUniqueFromInitial)
        {
          Map<Label,CmpVertex> uniques = uniqueFromState(referenceGraph);
          if(!uniques.isEmpty())
          {

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Examples of statechum.analysis.learning.experiments.mutation.DiffExperiments.MachineGenerator

      final int alphabet = (int)(alphabetMultiplier*states);
      final int tracesAlphabet = (int)(tracesAlphabetMultiplier*states);
      
      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
      
      LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,makeEven(states*tracesAlphabet));


      for(int attempt=0;attempt<2;++attempt)

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Examples of statechum.analysis.learning.experiments.mutation.DiffExperiments.MachineGenerator

    public ThreadResult call() throws Exception 
    {
      final int alphabet = (int)(alphabetMultiplier*states);
      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
      
      LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,makeEven(states*alphabet));


      for(int attempt=0;attempt<2;++attempt)

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Examples of statechum.analysis.learning.experiments.mutation.DiffExperiments.MachineGenerator

      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      WekaDataCollector dataCollector = createDataCollector(ifDepth);
      Label uniqueFromInitial = null;
      Timer timerToDetectLongRunningAutomata = new Timer("timer_to_detect_lengthy_tasks");
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      do
      {
        referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
        if (pickUniqueFromInitial)
        {
          Map<Label,CmpVertex> uniques = uniqueFromState(referenceGraph);
          if(!uniques.isEmpty())
          {

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Examples of statechum.analysis.learning.experiments.mutation.DiffExperiments.MachineGenerator

      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      WekaDataCollector dataCollector = createDataCollector(ifDepth);
      Label uniqueFromInitial = null;
      Timer timerToDetectLongRunningAutomata = new Timer("timer_to_detect_lengthy_tasks");
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      do
      {
        referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
        if (pickUniqueFromInitial)
        {
          Map<Label,CmpVertex> uniques = uniqueFromState(referenceGraph);
          if(!uniques.isEmpty())
          {

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Examples of statechum.analysis.learning.experiments.mutation.DiffExperiments.MachineGenerator

    // merges some of them (randomly) in one go. Such a merge should generate the same number of states and give the same score.
    @Test 
    public final void testRandomFSMMergers() throws IncompatibleStatesException
    {
      final int states = 50;
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      LearnerGraph referenceGraph = mg.nextMachine(states/2,fsmNumber, config,getLabelConverter()).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, in the mergers below we can still attempt to merge arbitrary subsets of states.


      for(CmpVertex a:referenceGraph.transitionMatrix.keySet())
        for(CmpVertex b:referenceGraph.transitionMatrix.keySet())
        {
          Collection<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();

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Examples of statechum.analysis.learning.experiments.mutation.DiffExperiments.MachineGenerator

    // merges some of them (randomly) in one go. Such a merge should generate the same number of states and give the same score.
    @Test 
    public final void testRandomFSMMergers() throws IncompatibleStatesException
    {
      final int states = 50;
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      LearnerGraph referenceGraph = mg.nextMachine(states/2,fsmNumber, config,getLabelConverter()).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, in the mergers below we can still attempt to merge arbitrary subsets of states.


      for(CmpVertex a:referenceGraph.transitionMatrix.keySet())
        for(CmpVertex b:referenceGraph.transitionMatrix.keySet())
        {
          Collection<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>> verticesToMerge = new LinkedList<AMEquivalenceClass<CmpVertex,LearnerGraphCachedData>>();

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Examples of statechum.analysis.learning.experiments.mutation.DiffExperiments.MachineGenerator

    Configuration config = Configuration.getDefaultConfiguration().copy();
    Random rnd=new Random(0);
    for(int states=1;states < 100;states++)
    {
      final int alphabet = 2*states;
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      LearnerGraph graph = mg.nextMachine(alphabet,-states, config, converter).pathroutines.buildDeterministicGraph();
            
      LearnerGraph parsedOutcome = new LearnerGraph(config);
      StatechumProcess.parseStatemachine(StatechumProcess.constructFSM(graph), parsedOutcome, converter,true);
      DifferentFSMException diffException = WMethod.checkM(graph, parsedOutcome);
      Assert.assertNull(diffException);

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Examples of statechum.analysis.learning.experiments.mutation.DiffExperiments.MachineGenerator

    LearnerGraphND previous = null;
    
    for(int states=1;states < 100;states++)
    {
      final int alphabet = 2*states;
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      LearnerGraphND graph = mg.nextMachine(alphabet,-states, config, converter);
      
      LearnerGraphND parsedOutcome = new LearnerGraphND(config);
      StatechumProcess.parseStatemachine(StatechumProcess.constructFSM(graph), parsedOutcome, converter,true);
      DifferentFSMException diffException = WMethod.checkM(graph, parsedOutcome);
      Assert.assertNull(diffException);

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