Package com.google.common.base

Examples of com.google.common.base.Stopwatch.reset()

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          p.add(HConstants.CATALOG_FAMILY, b, b);
          table.put(p);
        }
        if (i % printInterval == 0) {
          LOG.info("Put " + printInterval + "/" + stopWatch.elapsedMillis());
          stopWatch.reset();
          stopWatch.start();
        }
      }
      LOG.info("Finished a cycle putting " + namespaceSpan + " in " +
          (System.currentTimeMillis() - startTime) + "ms");
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    @Override
    public void run() {
      Stopwatch sw = new Stopwatch();
      Map<String, GcTimes> gcTimesBeforeSleep = getGcTimes();
      while (shouldRun) {
        sw.reset().start();
        try {
          Thread.sleep(SLEEP_INTERVAL_MS);
        } catch (InterruptedException ie) {
          return;
        }
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        }

        if (i > numPreRuns) {

          if (i > 0) {
            wfWatch.reset();
            wfWatch.start();
            wfFilter.update(wfDistribution, obsState);
            wfWatch.stop();
            final long latency = wfWatch.elapsed(TimeUnit.MILLISECONDS);
            wfLatency.accumulate(new MutableDouble(latency));
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        final Vector y = simulation.get(i).getObservedValue();

        if (i > numPreRuns) {

          if (i > 0) {
            wfWatch.reset();
            wfWatch.start();
            wfFilter.update(wfDistribution, simulation.get(i));
            wfWatch.stop();
            final long latency = wfWatch.elapsed(TimeUnit.MILLISECONDS);
            wfLatency.accumulate(new MutableDouble(latency));
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      for (int i = 0; i < T; i++) {
 
        if (i > numPreRuns) {

          if (i > 0) {
            wfWatch.reset();
            wfWatch.start();
            wfFilter.update(wfDistribution, simulations.get(i));
            wfWatch.stop();
            final long latency = wfWatch.elapsed(TimeUnit.MILLISECONDS);
            wfLatency.accumulate(new MutableDouble(latency));
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        new UnivariateGaussian.SufficientStatistic();

    for (int i = 0; i < T; i++) {
      final ObservedValue<Vector, Matrix> observation = observations.get(i);

      watch.reset();
      watch.start();
        plFilter.update(currentMixtureDistribution, observation);
      watch.stop();
      final long latency = watch.elapsed(TimeUnit.MILLISECONDS);
      latencyStats.update(latency);
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        final Vector y = simulation.get(i).getObservedValue();

        if (i > numPreRuns) {

          if (i > 0) {
            wfWatch.reset();
            wfWatch.start();
            wfFilter.update(wfDistribution, simulation.get(i));
            wfWatch.stop();
            final long latency = wfWatch.elapsed(TimeUnit.MILLISECONDS);
            wfLatency.accumulate(new MutableDouble(latency));
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        final Vector y = simulation.get(i).getObservedValue();

        if (i > numPreRuns) {

          if (i > 0) {
            wfWatch.reset();
            wfWatch.start();
            wfFilter.update(wfDistribution, simulation.get(i));
            wfWatch.stop();
            final long latency = wfWatch.elapsed(TimeUnit.MILLISECONDS);
            wfLatency.accumulate(new MutableDouble(latency));
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        new UnivariateGaussian.SufficientStatistic();

    for (int i = 0; i < T; i++) {
      final ObservedValue<Vector, Matrix> observation = observations.get(i);

      watch.reset();
      watch.start();
        plFilter.update(currentMixtureDistribution, observation);
      watch.stop();
      final long latency = watch.elapsed(TimeUnit.MILLISECONDS);
      latencyStats.update(latency);
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      for (int i = 0; i < T; i++) {
 
        if (i > numPreRuns) {

          if (i > 0) {
            wfWatch.reset();
            wfWatch.start();
            wfFilter.update(wfDistribution, simulations.get(i));
            wfWatch.stop();
            final long latency = wfWatch.elapsed(TimeUnit.MILLISECONDS);
            wfLatency.accumulate(new MutableDouble(latency));
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