Examples of org.apache.commons.math.stat.descriptive.DescriptiveStatistics

Package org.apache.commons.math.stat.descriptive

Examples of org.apache.commons.math.stat.descriptive.DescriptiveStatistics

org.apache.commons.math.stat.descriptive.DescriptiveStatistics
Maintains a dataset of values of a single variable and computes descriptive statistics based on stored data. The {@link #getWindowSize() windowSize}property sets a limit on the number of values that can be stored in the dataset. The default value, INFINITE_WINDOW, puts no limit on the size of the dataset. This value should be used with caution, as the backing store will grow without bound in this case. For very large datasets, {@link SummaryStatistics}, which does not store the dataset, should be used instead of this class. If windowSize is not INFINITE_WINDOW and more values are added than can be stored in the dataset, new values are added in a "rolling" manner, with new values replacing the "oldest" values in the dataset.
Note: this class is not threadsafe. Use {@link SynchronizedDescriptiveStatistics} if concurrent access from multiplethreads is required.
@version $Revision: 1054186 $ $Date: 2011-01-01 03:28:46 +0100 (sam. 01 janv. 2011) $

  /**
   * Test StorelessDescriptiveStatistics
   */
  public void testStoredUnivariateImpl() throws Exception {


    DescriptiveStatistics u = DescriptiveStatistics.newInstance();
    
    loadStats("data/PiDigits.txt", u);
    assertEquals("PiDigits: std", std, u.getStandardDeviation(), .0000000000001);
    assertEquals("PiDigits: mean", mean, u.getMean(), .0000000000001);
    
    loadStats("data/Mavro.txt", u);
    assertEquals("Mavro: std", std, u.getStandardDeviation(), .00000000000001);
    assertEquals("Mavro: mean", mean, u.getMean(), .00000000000001);    
    
    //loadStats("data/Michelso.txt");
    //assertEquals("Michelso: std", std, u.getStandardDeviation(), .00000000000001);
    //assertEquals("Michelso: mean", mean, u.getMean(), .00000000000001);  


    loadStats("data/NumAcc1.txt", u);
    assertEquals("NumAcc1: std", std, u.getStandardDeviation(), .00000000000001);
    assertEquals("NumAcc1: mean", mean, u.getMean(), .00000000000001);
    
    //loadStats("data/NumAcc2.txt");
    //assertEquals("NumAcc2: std", std, u.getStandardDeviation(), .000000001);
    //assertEquals("NumAcc2: mean", mean, u.getMean(), .00000000000001);
  }

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   * @param file
   * @param statistical summary
   */
  private void loadStats(String resource, Object u) throws Exception {
      
      DescriptiveStatistics d = null;
      SummaryStatistics s = null;
      if (u instanceof DescriptiveStatistics) {
          d = (DescriptiveStatistics) u;
      } else {
          s = (SummaryStatistics) u;
      }
      
      u.getClass().getDeclaredMethod("clear", null).invoke(u, null);
      mean = Double.NaN;
      std = Double.NaN;
      
      BufferedReader in =
          new BufferedReader(
                  new InputStreamReader(
                          getClass().getResourceAsStream(resource)));
      
      String line = null;
      
      for (int j = 0; j < 60; j++) {
          line = in.readLine();
          if (j == 40) {
              mean =
                  Double.parseDouble(
                          line.substring(line.lastIndexOf(":") + 1).trim());
          }
          if (j == 41) {
              std =
                  Double.parseDouble(
                          line.substring(line.lastIndexOf(":") + 1).trim());
          }
      }
      
      line = in.readLine();
      
      while (line != null) {
          if (d != null) {
              d.addValue(Double.parseDouble(line.trim()));
          }  else {
              s.addValue(Double.parseDouble(line.trim()));
          }
          line = in.readLine();
      }

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                mk = createMicroKernel(microKernel);
                repository = createRepository(mk);


                // Run the test
                DescriptiveStatistics statistics = runTest(test, repository);
                if (statistics.getN() > 0) {
                    writeReport(test.toString(), name, microKernel, statistics);
                }
            } catch (RepositoryException re) {
                re.printStackTrace();
            } catch (Exception e) {

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        }
    }


    private DescriptiveStatistics runTest(AbstractTest test,
            Repository repository) throws Exception {
        DescriptiveStatistics statistics = new DescriptiveStatistics();


        test.setUp(repository, credentials);
        try {
            // Run a few iterations to warm up the system
            if (warmup > 0) {
                long warmupEnd = System.currentTimeMillis() + warmup;
                while (System.currentTimeMillis() < warmupEnd) {
                    test.execute();
                }
            }


            // Run test iterations, and capture the execution times
            long runtimeEnd = System.currentTimeMillis() + runtime;
            while (System.currentTimeMillis() < runtimeEnd) {
                statistics.addValue(test.execute());
            }
        } finally {
            test.tearDown();
        }

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      resultsWithReplica.add(new PerfEvalCallable(util.getHBaseAdmin(), replicaReadOpts).call());
      // TODO: sleep to let cluster stabilize, though monkey continues. is it necessary?
      Thread.sleep(5000l);
    }


    DescriptiveStatistics withoutReplicaStats = new DescriptiveStatistics();
    for (TimingResult tr : resultsWithoutReplica) {
      withoutReplicaStats.addValue(tr.elapsedTime);
    }
    DescriptiveStatistics withReplicaStats = new DescriptiveStatistics();
    for (TimingResult tr : resultsWithReplica) {
      withReplicaStats.addValue(tr.elapsedTime);
    }


    LOG.info(Objects.toStringHelper("testName")
      .add("withoutReplicas", resultsWithoutReplica)
      .add("withReplicas", resultsWithReplica)
      .add("withoutReplicasMean", withoutReplicaStats.getMean())
      .add("withReplicasMean", withReplicaStats.getMean())
      .toString());


    assertTrue(
      "Running with region replicas under chaos should be as fast or faster than without. "
      + "withReplicas.mean: " + withReplicaStats.getMean() + "ms "
      + "withoutReplicas.mean: " + withoutReplicaStats.getMean() + "ms.",
      withReplicaStats.getMean() <= withoutReplicaStats.getMean());
  }

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  }


  public Map<String, DescriptiveStatistics> getCoprocessorExecutionStatistics() {
    Map<String, DescriptiveStatistics> results = new HashMap<String, DescriptiveStatistics>();
    for (RegionEnvironment env : coprocessors) {
      DescriptiveStatistics ds = new DescriptiveStatistics();
      if (env.getInstance() instanceof RegionObserver) {
        for (Long time : env.getExecutionLatenciesNanos()) {
          ds.addValue(time);
        }
        // Ensures that web ui circumvents the display of NaN values when there are zero samples.
        if (ds.getN() == 0) {
          ds.addValue(0);
        }
        results.put(env.getInstance().getClass().getSimpleName(), ds);
      }
    }
    return results;

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        MetricsRegionSource.NUM_FILES_COMPACTED_DESC),
        this.regionWrapper.getNumFilesCompacted());
    for (Map.Entry<String, DescriptiveStatistics> entry : this.regionWrapper
        .getCoprocessorExecutionStatistics()
        .entrySet()) {
      DescriptiveStatistics ds = entry.getValue();
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "Min: "), ds.getMin() / 1000);
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "Mean: "), ds.getMean() / 1000);
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "Max: "), ds.getMax() / 1000);
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "90th percentile: "), ds
          .getPercentile(90d) / 1000);
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "95th percentile: "), ds
          .getPercentile(95d) / 1000);
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "99th percentile: "), ds
          .getPercentile(99d) / 1000);
    }


  }

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  }


  public Map<String, DescriptiveStatistics> getCoprocessorExecutionStatistics() {
    Map<String, DescriptiveStatistics> results = new HashMap<String, DescriptiveStatistics>();
    for (RegionEnvironment env : coprocessors) {
      DescriptiveStatistics ds = new DescriptiveStatistics();
      if (env.getInstance() instanceof RegionObserver) {
        for (Long time : env.getExecutionLatenciesNanos()) {
          ds.addValue(time);
        }
        // Ensures that web ui circumvents the display of NaN values when there are zero samples.
        if (ds.getN() == 0) {
          ds.addValue(0);
        }
        results.put(env.getInstance().getClass().getSimpleName(), ds);
      }
    }
    return results;

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        MetricsRegionSource.NUM_FILES_COMPACTED_DESC),
        this.regionWrapper.getNumFilesCompacted());
    for (Map.Entry<String, DescriptiveStatistics> entry : this.regionWrapper
        .getCoprocessorExecutionStatistics()
        .entrySet()) {
      DescriptiveStatistics ds = entry.getValue();
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "Min: "), ds.getMin() / 1000);
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "Mean: "), ds.getMean() / 1000);
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "Max: "), ds.getMax() / 1000);
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "90th percentile: "), ds
          .getPercentile(90d) / 1000);
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "95th percentile: "), ds
          .getPercentile(95d) / 1000);
      mrb.addGauge(Interns.info(regionNamePrefix + " " + entry.getKey() + " "
          + MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS,
        MetricsRegionSource.COPROCESSOR_EXECUTION_STATISTICS_DESC + "99th percentile: "), ds
          .getPercentile(99d) / 1000);
    }


  }

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            //Initialization of outer statistic agregators
            DescriptiveStatistics[] statsMean = new DescriptiveStatistics[maxThread];
            DescriptiveStatistics[] statsSTD = new DescriptiveStatistics[maxThread];
            for (int threads = 1; threads <= maxThread; ++threads) {
                statsMean[threads - 1] = new DescriptiveStatistics();
                statsSTD[threads - 1] = new DescriptiveStatistics();
            }


            for (; trys-- > 0;) { //Each try == one random sum
                DescriptiveStatistics[] stats = new DescriptiveStatistics[maxThread];
                Sum[] sums = random2Sums();
                System.out.println("try " + trys);
                for (int threads = 1; threads <= maxThread; ++threads) {
                    DescriptiveStatistics ds = new DescriptiveStatistics();


                    for (int i = 0; i < 10; ++i) { //Each random sum repeatedly tested 1000 times
                        Sum[] sums_ = new Sum[]{sums[0].clone(), sums[1].clone()};
                        long start = System.nanoTime();
                        PairEC pec = new PairEC(sums_[0], sums_[1], NaiveSumCollectIP.FACTORY, ScalarsSplitter.INSTANCE, threads);
                        Sum res = (Sum) pec.result();
//                        System.out.println(res.size() + " " + res);
                        ds.addValue(System.nanoTime() - start);
                    }


                    stats[threads - 1] = ds; //Saving statistics for one thread
                }

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