Examples of statechum.Configuration

statechum.Configuration
Represents a configuration for a learner. The purpose is a possibility of a global customisation of all objects used by a learner in the course of learning by the same object.
When you add to this class, please add the corresponding entries to hashCode and equals.
This class is using the built-in clone method, hence all attributes have to be either primitives or immutable (such as String).

    {
      forwardOrInverseGraph = new LearnerGraphND(graph,graph.config);
    }
    else
    {
      Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
      forwardOrInverseGraph = new LearnerGraphND(shallowCopy);forwardOrInverseGraph.initEmpty();
      AbstractPathRoutines.buildInverse(graph,LearnerGraphND.ignoreNone,forwardOrInverseGraph);  // do the inverse to the tentative graph
    }


    Map<CmpVertex,LearnerGraph> outcome = new TreeMap<CmpVertex,LearnerGraph>();

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  {
    InitialConfigurationAndData outcome = new InitialConfigurationAndData();
    
    final java.io.FileInputStream inputStream = new java.io.FileInputStream(argPTAFileName);
    final LearnerSimulator simulator = new LearnerSimulator(inputStream,true,converter);
    Configuration defaultConfig = configToUse.copy();
    //defaultConfig.setRejectPositivePairsWithScoresLessThan(1);
    assert converter != null : "we expect this methods to be used with large graphs and Array matrix types hence converter must be set";
    outcome.learnerInitConfiguration = simulator.readLearnerConstructionData(defaultConfig);
    outcome.learnerInitConfiguration.setLabelConverter(converter);

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  @SuppressWarnings("null")
  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();

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       PTASequenceEngine engine = collectionOfTraces.get(UAVAllSeeds).tracesForUAVandFrame.get(UAVAllSeeds).get(maxFrameNumber);
       
       FileOutputStream log = new java.io.FileOutputStream("resources/largePTA/VeryLargePTA.zip");
       RPNIBlueFringeVariability ourLearner = new RPNIBlueFringeVariability(learnerInitConfiguration,true,null,null);
       LearnerGraph automatonAfterInit = ourLearner.getLearner().init(engine,0,0);
       final Configuration shallowCopy = automatonAfterInit.config.copy();shallowCopy.setLearnerCloneGraph(false);
       LearnerGraph copyOfAutomaton = new LearnerGraph(shallowCopy);LearnerGraph.copyGraphs(automatonAfterInit, copyOfAutomaton);
       ourLearner.setInitPta(copyOfAutomaton);
       RecordProgressDecorator recorder = new RecordProgressDecorator(ourLearner.getLearner(),log,1,learnerInitConfiguration.config,true);
       learnerInitConfiguration.graph = smallPTA;learnerInitConfiguration.testSet = new LinkedList<List<statechum.Label>>();
    recorder.writeLearnerEvaluationData(learnerInitConfiguration);

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   */
  public static void mainSingleHugeAutomaton(String[] args) throws Exception 
  {
    PaperUAS paper = new PaperUAS();
      paper.learnerInitConfiguration.setLabelConverter(new Transform.InternStringLabel());
        final Configuration learnerConfig = paper.learnerInitConfiguration.config;learnerConfig.setGeneralisationThreshold(0);learnerConfig.setGdFailOnDuplicateNames(false);
        learnerConfig.setGdLowToHighRatio(0.75);learnerConfig.setGdKeyPairThreshold(0.5);learnerConfig.setTransitionMatrixImplType(STATETREE.STATETREE_ARRAY);
        learnerConfig.setAskQuestions(false);learnerConfig.setDebugMode(false);
        paper.loadReducedConfigurationFile(args[0]);
        
    final int offset=1;
      Reader []inputFiles = new Reader[args.length-offset];for(int i=offset;i<args.length;++i) inputFiles[i-offset]=new FileReader(args[i]); 
      int maxFrame = paper.getMaxFrame(inputFiles);

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   * @param graph what to invert.
   * @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;
  }

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    /** Maps states to a function associating labels to a sum of probabilities from a Markov matrix. Such a sum is supposed to predict the likelyhood of a transition with this label from every state. */
    Map<CmpVertex,Map<Label,UpdatablePairInteger>> state_outgoing_occurence=new HashMap<CmpVertex,Map<Label,UpdatablePairInteger>>();
    /** Maps states to a function associating labels to a probability of a transition with the label of interest from a state of interest. Computed from {@link MarkovUniversalLearner#state_outgoing_occurence}. */
    Map<CmpVertex,Map<Label,UpdatablePairDouble>> state_outgoing=new HashMap<CmpVertex,Map<Label,UpdatablePairDouble>>();


    final Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraph outcome = new LearnerGraph(shallowCopy);
    LearnerGraph.copyGraphs(graph, outcome);
    final Set<Label> allElementsOfAlphabet = graph.learnerCache.getAlphabet(); 
    // mapping map to store all paths leave each state in different length
      for(CmpVertex vert:graph.transitionMatrix.keySet())

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   */
  public static void mainSmallAutomata(String[] args) throws Exception 
  {
    PaperUAS paper = new PaperUAS();
    paper.learnerInitConfiguration.setLabelConverter(new Transform.InternStringLabel());
        final Configuration learnerConfig = paper.learnerInitConfiguration.config;learnerConfig.setGeneralisationThreshold(0);learnerConfig.setGdFailOnDuplicateNames(false);
        learnerConfig.setGdLowToHighRatio(0.75);learnerConfig.setGdKeyPairThreshold(0.5);learnerConfig.setTransitionMatrixImplType(STATETREE.STATETREE_ARRAY);
        learnerConfig.setAskQuestions(false);learnerConfig.setDebugMode(false);
        learnerConfig.setLearnerScoreMode(Configuration.ScoreMode.GENERAL);
        paper.loadReducedConfigurationFile(args[0]);
        
    final int offset=1;
      Reader []inputFiles = new Reader[args.length-offset];for(int i=offset;i<args.length;++i) inputFiles[i-offset]=new FileReader(args[i]); 
      int maxFrame = paper.getMaxFrame(inputFiles);

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      throw new IllegalArgumentException("predictions are only supported in the forward direction, not inverse");


    /** Maps states to a function associating labels to a probability of a transition with the label of interest from a state of interest. Computed from {@link MarkovUniversalLearner#state_outgoing_occurence}. */
    Map<CmpVertex,Map<Label,MarkovOutcome>> state_outgoing=predictTransitions();


    final Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraph graphWithPredictedTransitions = new LearnerGraph(shallowCopy);
    LearnerGraph.copyGraphs(graph, graphWithPredictedTransitions);


    // in this part the tree is extended depend on their outgoing transition probabilities
     for(Entry<CmpVertex, Map<Label, MarkovOutcome>> outgoing:state_outgoing.entrySet())

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  public Map<CmpVertex, Map<Label, MarkovOutcome>> predictTransitions()
  {
    /** Maps states to a function associating labels to a probability of a transition with the label of interest from a state of interest. Computed from {@link MarkovUniversalLearner#state_outgoing_occurence}. */
    Map<CmpVertex,Map<Label,MarkovOutcome>> state_outgoing=new HashMap<CmpVertex,Map<Label,MarkovOutcome>>();


    final Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);


      for(CmpVertex vert:graph.transitionMatrix.keySet())
        if(vert.isAccept() )
            {
             Map<Label,MarkovOutcome> outgoing_labels_probabilities=predictTransitionsFromState(vert,null,model.getChunkLen(),null);

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statechum.analysis.Erlang.Signatures.TestTypes

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statechum.analysis.Erlang.TestErlangGraphs

statechum.analysis.Erlang.TestErlangModule

statechum.analysis.Erlang.TestSynapseAuxiliaryFunctions

statechum.analysis.learning.experiments.Benchmarklinear

statechum.analysis.learning.experiments.ExperimentRunner$GeneratorConfiguration

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