Examples of umontreal.iro.lecuyer.stat.Tally

umontreal.iro.lecuyer.stat.Tally
This type of statistical collector takes a sequence of real-valued observations and can return the average, the variance, a confidence interval for the theoretical mean, etc. Each call to {@link #add add} provides a new observation.When the broadcasting to observers is activated, the method {@link #add add} will also pass this new information to itsregistered observers. This type of collector does not memorize the individual observations, but only their number, sum, sum of squares, maximum, and minimum. The subclass {@link TallyStore} offers a collector that memorizesthe observations.

      public Tally      statSojourn; // Stats on sojourn times.


      // Reads data for new task type and creates data structures.
      TaskType() throws IOException {
         StringTokenizer line = new StringTokenizer (input.readLine());
         statSojourn = new Tally (name = line.nextToken()); 
         arrivalRate = Double.parseDouble (line.nextToken());
         nbOper = Integer.parseInt (line.nextToken());
         machOper = new Resource[nbOper];
         lengthOper = new double[nbOper];
         for (int i = 0; i < nbOper; i++) {

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      int s = 12;
      double[] zeta = new double[s+1];   zeta[0] = 0.0;
      for (int j=1; j<=s; j++)
         zeta[j] = (double)j / (double)s;
      Asian process = new Asian (0.05, 0.5, 100.0, 100.0, s, zeta);
      Tally statValue = new Tally ("Stats on value of Asian option");


      Chrono timer = new Chrono();
      int n = 100000;
      process.simulateRuns (n, new MRG32k3a(), statValue);
      statValue.setConfidenceIntervalStudent();
      System.out.println (statValue.report (0.95, 3));
      System.out.println ("Total CPU time:      " + timer.format() + "\n");
   }

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   // Makes m independent randomizations of the digital net p using stream
   // noise. For each of them, performs one simulation run for each point
   // of p, and adds the average over these points to the collector statQMC.
   public void simulateQMC (int m, PointSet p,
                            RandomStream noise, Tally statQMC) {
      Tally statValue  = new Tally ("stat on value of Asian option");
      PointSetIterator stream = p.iterator ();
      for (int j=0; j<m; j++) {
          p.randomize (noise);
          stream.resetStartStream();
          simulateRuns (p.getNumPoints(), stream, statValue);
          statQMC.add (statValue.average());
      }
   }

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      int s = 12;
      double[] zeta = new double[s+1];
      for (int j=0; j<=s; j++)
         zeta[j] = (double)j / (double)s;
      AsianQMCk process = new AsianQMCk (0.05, 0.5, 100.0, 100.0, s, zeta);
      Tally statValue  = new Tally ("value of Asian option");
      Tally statQMC = new Tally ("QMC averages for Asian option");


      Chrono timer = new Chrono();
      int n = 100000;
      System.out.println ("Ordinary MC:\n");
      process.simulateRuns (n, new MRG32k3a(), statValue);
      statValue.setConfidenceIntervalStudent();
      System.out.println (statValue.report (0.95, 3));
      System.out.println ("Total CPU time: " + timer.format());
      double varMC = statValue.variance();
      double cpuMC = timer.getSeconds() / n;  // CPU seconds per run.
      System.out.println ("------------------------\n");


      timer.init();
      KorobovLattice p = new KorobovLattice (65521, 944, s); // approx. 2^{16} points.
      BakerTransformedPointSet pb = new BakerTransformedPointSet (p);


      n = p.getNumPoints();
      int m = 20;                     // Number of QMC randomizations.
      process.simulateQMC (m, pb, new MRG32k3a(), statQMC);
      System.out.println ("QMC with Korobov point set with " + n +
          " points and random shift + baker:\n");
      statQMC.setConfidenceIntervalStudent();
      System.out.println (statQMC.report (0.95, 3));
      System.out.println ("Total CPU time: " + timer.format() + "\n");
      double varQMC = p.getNumPoints() * statQMC.variance();
      double cpuQMC = timer.getSeconds() / (m * n);
      System.out.printf ("Variance ratio:   %9.4g%n", varMC/varQMC);
      System.out.printf ("Efficiency ratio: %9.4g%n",
           (varMC * cpuMC) / (varQMC * cpuQMC));
   }

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  public void setStatCollecting (boolean b) {
    if (b && !stats) {
      if (blockSize == null)
      blockSize = new Accumulate(sim, "List Size " + name);
      if (blockSojourn == null)
      blockSojourn = new Tally("List Sojourn " + name);
      blockSize.update (size());
      stats = true;
      initStat();
    } else
    stats = false;

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    if (statAvail == null) throw new IllegalStateException ("Asking a report for a bin "
                          +"for which setStatCollecting (true) has not been called");


    Accumulate sizeWait = waitingList.statSize();
    Tally sojWait = waitingList.statSojourn();


    PrintfFormat str = new PrintfFormat();


    str.append ("REPORT ON BIN : ").append (name).append (PrintfFormat.NEWLINE);
    str.append ("   From time : ").append (7, 2, 2, initStatTime);
    str.append ("   to time : ");
    str.append (10,2, 2, sim.time());
    str.append (PrintfFormat.NEWLINE + "                    min        max     average  ");
    str.append ("standard dev.  nb. obs.");


    str.append (PrintfFormat.NEWLINE + "   Available tokens ");
    str.append (8, (int)(statAvail.min()+0.5));
    str.append (11, (int)(statAvail.max()+0.5));
    str.append (12, 3, 2, statAvail.average());


    str.append (PrintfFormat.NEWLINE + "   Queue Size  ");
    str.append (8, (int)(sizeWait.min()+0.5));
    str.append (11, (int)(sizeWait.max()+0.5));
    str.append (12, 3, 2, sizeWait.average());


    str.append (PrintfFormat.NEWLINE + "   Wait    ");
    str.append (12, 3, 2, sojWait.min()).append (' ');
    str.append (10, 3, 2, sojWait.max()).append (' ');
    str.append (11, 3, 2, sojWait.average()).append (' ');
    str.append (10, 3, 2, sojWait.standardDeviation());
    str.append (10, sojWait.numberObs());


    return str.toString();
   }

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  new ProcGenArr().schedule (0.0);
  Sim.start();
    }


    public Cafeteria(){
  AttClient[Hotfood]  = new Tally("Temps d'attente repas chauds");
  AttClient[Sandwich] = new Tally("Temps d'attente sandwich");
  AttClient[Drinks]   = new Tally("Temps d'attente breuvages");
  for (int i=0; i<3; i++) {
      Caisse[i] = new Resource (1, "Une caisse");
      Caisse[i].setStatCollecting (true);
  }
  HotServ.setStatCollecting (true);

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   // Makes m independent randomizations of the digital net p using stream
   // noise. For each of them, performs one simulation run for each point
   // of p, and adds the average over these points to the collector statQMC.
   public void simulateQMC (int m, DigitalNet p,
                            RandomStream noise, Tally statQMC) {
      Tally statValue  = new Tally ("stat on value of Asian option");
      PointSetIterator stream = p.iterator ();
      for (int j=0; j<m; j++) {
          p.leftMatrixScramble (noise);
          p.addRandomShift (0, p.getDimension(), noise);
          stream.resetStartStream();
          simulateRuns (p.getNumPoints(), stream, statValue);
          statQMC.add (statValue.average());
      }
   }

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      int s = 12;
      double[] zeta = new double[s+1];
      for (int j=0; j<=s; j++)
         zeta[j] = (double)j / (double)s;
      AsianQMC process = new AsianQMC (0.05, 0.5, 100.0, 100.0, s, zeta);
      Tally statValue  = new Tally ("value of Asian option");
      Tally statQMC = new Tally ("QMC averages for Asian option");


      Chrono timer = new Chrono();
      int n = 100000;
      System.out.println ("Ordinary MC:\n");
      process.simulateRuns (n, new MRG32k3a(), statValue);
      statValue.setConfidenceIntervalStudent();
      System.out.println (statValue.report (0.95, 3));
      System.out.println ("Total CPU time: " + timer.format());
      double varMC = statValue.variance();
      double cpuMC = timer.getSeconds() / n;  // CPU seconds per run.
      System.out.println ("------------------------\n");


      timer.init();
      DigitalNet p = new SobolSequence (16, 31, s); // 2^{16} points.
      n = p.getNumPoints();
      int m = 20;                     // Number of QMC randomizations.
      process.simulateQMC (m, p, new MRG32k3a(), statQMC);
      System.out.println ("QMC with Sobol point set with " + n +
          " points and affine matrix scramble:\n");
      statQMC.setConfidenceIntervalStudent();
      System.out.println (statQMC.report (0.95, 3));
      System.out.println ("Total CPU time: " + timer.format() + "\n");
      double varQMC = p.getNumPoints() * statQMC.variance();
      double cpuQMC = timer.getSeconds() / (m * n);
      System.out.printf ("Variance ratio:   %9.4g%n", varMC/varQMC);
      System.out.printf ("Efficiency ratio: %9.4g%n",
           (varMC * cpuMC) / (varQMC * cpuQMC));
   }

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         else {
            statUtil = new Accumulate (sim, "StatOnUtil");
            statUtil.update (capacity - available);
            statCapacity = new Accumulate (sim, "StatOnCapacity");
            statCapacity.update (capacity);
            statSojourn = new Tally ("StatOnSojourn");
         }
      }
      else {
         if (stats)
           throw new IllegalStateException ("Not collecting statistics for this resource");

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Related Classes of umontreal.iro.lecuyer.stat.Tally

umontreal.iro.lecuyer.simevents.ListWithStat

umontreal.iro.lecuyer.simprocs.Bin

umontreal.iro.lecuyer.simprocs.Resource

umontreal.iro.lecuyer.stat.list.ListOfTallies

umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance

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