@Override
  public void init(String prefix) {
    // initialize global arm model
    GlobalArmModel.initialize(prefix);

    // initialize static fields
    K = (double) GlobalArmModel.numberOfArms();
    d = GlobalArmModel.getDValue();

    // initialize counters
    n = new double[GlobalArmModel.numberOfArms()];
    Arrays.fill(n, 0.0);
    sumN = 0.0;

    // model
    s = Arrays.copyOf(n, n.length);
    w = Arrays.copyOf(n, n.length);
    r = Arrays.copyOf(n, n.length);
    q = Arrays.copyOf(n, n.length);
    f = Arrays.copyOf(n, n.length);
    g = Arrays.copyOf(n, n.length);

    // parameter
    c = Configuration.getDouble(prefix + ".p2greedy.C");
  }
Ejemplo n.º 2
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 ProtocolData(String prefix) {
   pid = CommonState.getPid();
   lid = Configuration.getPid(prefix + "." + PAR_LINKABLE);
   tid = Configuration.getPid(prefix + "." + PAR_TRANSPORT);
   period = Configuration.getInt(prefix + "." + PAR_PERIOD);
   prob = Configuration.getDouble(prefix + "." + PAR_PROB);
 }
Ejemplo n.º 3
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  /**
   * Loads configuration. It verifies the constraints defined in {@link WireByMethod}.
   *
   * @param prefix the configuration prefix for this class
   */
  public WireByMethod(String prefix) {
    super(prefix);

    // get the method
    try {
      final Class wire =
          Configuration.getClass(
              prefix + "." + PAR_CLASS, Class.forName("peersim.graph.GraphFactory"));
      method =
          WireByMethod.getMethod(wire, Configuration.getString(prefix + "." + PAR_METHOD, "wire"));
    } catch (Exception e) {
      throw new RuntimeException(e);
    }

    // set the constant args (those other than 0th)
    Class[] argt = method.getParameterTypes();
    args = new Object[argt.length];
    for (int i = 1; i < args.length; ++i) {

      if (argt[i] == int.class) args[i] = Configuration.getInt(prefix + "." + PAR_ARG + i);
      else if (argt[i] == long.class) args[i] = Configuration.getLong(prefix + "." + PAR_ARG + i);
      else if (argt[i] == double.class)
        args[i] = Configuration.getDouble(prefix + "." + PAR_ARG + i);
      else if (i == args.length - 1 && argt[i].isInstance(CommonState.r)) args[i] = CommonState.r;
      else {
        // we should neve get here
        throw new RuntimeException(
            "Unexpected error, please " + "report this problem to the peersim team");
      }
    }
  }
Ejemplo n.º 4
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  public RenaterInitializer(String prefix) {
    pid = Configuration.getPid(prefix + "." + PAR_PROT);
    phid = FastConfig.getLinkable(pid);
    cpid = Configuration.getPid(prefix + "." + PAR_CHORD_PROT);

    nrDC = Configuration.getInt("NR_DC", 1);
    nrNodePerDC = Configuration.getInt("NR_NODE_PER_DC", 1);

    dc_file = (Configuration.getString(prefix + "." + PAR_DC_FILE, "nofilewiththisname"));
    distance = (Configuration.getDouble(prefix + "." + PAR_DC_DISTANCE, 0.01));
  }
Ejemplo n.º 5
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 /**
  * Standard constructor that reads the configuration parameters. Invoked by the simulation engine.
  *
  * @param prefix the configuration prefix for this class
  */
 public DynamicNetwork(String prefix) {
   add = Configuration.getDouble(prefix + "." + PAR_ADD);
   substitute = Configuration.contains(prefix + "." + PAR_SUBST);
   Object[] tmp = Configuration.getInstanceArray(prefix + "." + PAR_INIT);
   inits = new NodeInitializer[tmp.length];
   for (int i = 0; i < tmp.length; ++i) {
     // System.out.println("Inits " + tmp[i]);
     inits[i] = (NodeInitializer) tmp[i];
   }
   maxsize = Configuration.getInt(prefix + "." + PAR_MAX, Integer.MAX_VALUE);
   minsize = Configuration.getInt(prefix + "." + PAR_MIN, 0);
 }
Ejemplo n.º 6
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  @Override
  public boolean execute() {
    double alpha = Configuration.getDouble("ALPHA", -2.0);
    System.out.println("ALPHA set to " + alpha);

    System.out.println("\nSetting up renater nodes, positioning and gateways ");
    // initialize the gson parser
    List<String> lines = null;

    if (Files.exists(Paths.get(dc_file))) {
      lines = getDcCordsFromFile();
      if (lines != null) nrDC = lines.size();
    }

    KoalaJsonParser.intitialize();
    NodeUtilities.initialize();
    NodeUtilities.setNodePIDs(phid, pid, cpid);
    NodeUtilities.intializeDCCenters(lines);

    //        if(Network.size() < nrDC)
    //        	nrDC = Network.size();

    NodeUtilities.ACTUAL_NR_DC = Network.size();

    //        assigning nodes an id and setting their coordinates according to their data-center
    initializationMode = true;
    for (int i = 0; i < Network.size(); i++) {
      initialize(Network.get(i));
    }
    initializationMode = false;

    //        System.setErr(new PrintStream(new OutputStream() {
    //            public void write(int b) {
    //                //DO NOTHING
    //            }
    //        }));

    return false;
  }
  public static void main(String[] args) throws Exception {
    String configName = args[0];
    Configuration.setConfig(new ParsedProperties(configName));
    // System.err.println("#Reading datasets from files");
    File tFile = new File(Configuration.getString("trainingFile"));
    File eFile = new File(Configuration.getString("evaluationFile"));
    String dbReaderName = "gossipLearning.RecSysDataBaseReader";

    seed = Configuration.getInt("SEED");
    numOfClusters = Configuration.getInt("numOfClusters");
    divErr = Configuration.getDouble("divErr");
    numberOfRatings = Configuration.getDouble("numOfRatings");
    numberOfAllUsers = Configuration.getDouble("numOfAllUsers");

    classifierName = Configuration.getString("classifier", null);

    prefix = "model";
    modelName = Configuration.getString(prefix, null);

    boolean isNormalize = Configuration.getBoolean("normalize");
    int polyDegree = Configuration.getInt("polyDegree");

    DataBaseReader dbReader = DataBaseReader.createDataBaseReader(dbReaderName, tFile, eFile);

    // System.err.println("#Preprocessing datasets");
    // pre-processing database
    getNumberOfUsers = new double[dbReader.getTrainingSet().getNumberOfFeatures()];
    getAverageRating = new double[dbReader.getTrainingSet().getNumberOfFeatures()];
    getNumberOfLikeableUsers = new double[dbReader.getTrainingSet().getNumberOfFeatures()];
    for (int i = 0; i < dbReader.getTrainingSet().size(); i++) {
      for (VectorEntry e : dbReader.getTrainingSet().getInstance(i)) {
        getNumberOfUsers[e.index]++;
        getAverageRating[e.index] += e.value;
      }
    }
    for (int i = 0; i < dbReader.getTrainingSet().getNumberOfFeatures(); i++) {
      getAverageRating[i] =
          getNumberOfUsers[i] == 0.0 ? 0.0 : getAverageRating[i] / getNumberOfUsers[i];
    }

    for (int i = 0; i < dbReader.getTrainingSet().size(); i++) {
      for (VectorEntry e : dbReader.getTrainingSet().getInstance(i)) {
        if (e.value > getAverageRating[e.index]) {
          getNumberOfLikeableUsers[e.index]++;
        }
      }
    }

    // System.err.println("#Extracting features");
    // feature extraction
    InstanceHolder trainingSet = new InstanceHolder((int) numberOfRatings, 11);
    for (int userID = 0; userID < dbReader.getTrainingSet().size(); userID++) {
      for (VectorEntry e : dbReader.getTrainingSet().getInstance(userID)) {
        int itemID = e.index;
        double label = e.value;
        double[] features = new double[11];
        computeUserSpecificFeatureVectorPart(
            dbReader.getTrainingSet().getInstance(userID), features);
        computeItemSpecificFinalFeatureVector(itemID, features);
        SparseVector instance = new SparseVector(features);
        trainingSet.add(instance, label);
      }
    }
    InstanceHolder testSet = new InstanceHolder((int) numberOfRatings, 11);
    for (int userID = 0; userID < dbReader.getEvalSet().size(); userID++) {
      for (VectorEntry e : dbReader.getEvalSet().getInstance(userID)) {
        int itemID = e.index;
        double label = e.value;
        double[] features = new double[11];
        computeUserSpecificFeatureVectorPart(
            dbReader.getTrainingSet().getInstance(userID), features);
        computeItemSpecificFinalFeatureVector(itemID, features);
        SparseVector instance = new SparseVector(features);
        testSet.add(instance, label);
      }
    }

    File tout = new File("tr.dat");
    File eout = new File("ev.dat");

    trainingSet.writeToFile(tout);
    testSet.writeToFile(eout);

    DataBaseReader dbr =
        DataBaseReader.createDataBaseReader("gossipLearning.DataBaseReader", tout, eout);

    if (polyDegree > 1) {
      // dbr.polynomize(polyDegree);
      // dbr.writeToFile(new File("bookcrossing_fn_" + polyDegree + ".train"), new
      // File("bookcrossing_fn_" + polyDegree + ".test"));
      // System.exit(0);
    }

    if (isNormalize) {
      dbr.standardize();
    }

    if (classifierName != null) {
      weka(dbr.getTrainingSet(), dbr.getEvalSet());
      // weka(trainingSet, testSet);
    }

    if (modelName != null) {
      golf(dbr.getTrainingSet(), dbr.getEvalSet());
      // golf(trainingSet, testSet);
    }

    tout.deleteOnExit();
    eout.deleteOnExit();
  }