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) $

            }
        }
    }


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


        setUp(repository, CREDENTIALS);
        try {
            
            // Run a few iterations to warm up the system
            long warmupEnd = System.currentTimeMillis() + WARMUP;
            while (System.currentTimeMillis() < warmupEnd) {
                execute();
            }


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

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      scoresForEntity.get(surf).add(ne.getConfidence());
    }
    averageScores = new HashMap<String,Double>();
    maxScores = new HashMap<String,Double>();
    for(String surf : scoresForEntity.keySet()) {
      DescriptiveStatistics descStats = new DescriptiveStatisticsImpl();
      for(double score : scoresForEntity.get(surf)) {
        descStats.addValue(score);
      }
      maxScores.put(surf, descStats.getMax());
      averageScores.put(surf, descStats.getMean());
    }
    
    Map<Token,NamedEntity> neByLastToken = new HashMap<Token,NamedEntity>();
    for(NamedEntity ne : entities) {
      if(!ne.isBlocked()) {

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     * @param sample sample to normalize
     * @return normalized (standardized) sample
     * @since 2.2
     */
    public static double[] normalize(final double[] sample) {
        DescriptiveStatistics stats = new DescriptiveStatistics();


        // Add the data from the series to stats
        for (int i = 0; i < sample.length; i++) {
            stats.addValue(sample[i]);
        }


        // Compute mean and standard deviation
        double mean = stats.getMean();
        double standardDeviation = stats.getStandardDeviation();


        // initialize the standardizedSample, which has the same length as the sample
        double[] standardizedSample = new double[sample.length];


        for (int i = 0; i < sample.length; i++) {

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        String[] columns = new String[] {"Fitness", "StdDev+", "StdDev-", "Max", "Min"};
        
        this.chartNames.add(chartName);
        
        DescriptiveStatistics  stat = new DescriptiveStatistics();
        
        for (RobEA rob : env.getAgents()) {
            stat.addValue(rob.getFitSum());
        }
        
        ChartEvent event1 = new ChartEvent(chartName, columns[0], stat.getMean());
        ChartEvent event2 = new ChartEvent(chartName, columns[1], stat.getMean() + stat.getStandardDeviation());
        ChartEvent event3 = new ChartEvent(chartName, columns[2], stat.getMean() - stat.getStandardDeviation());
        ChartEvent event4 = new ChartEvent(chartName, columns[3], stat.getMax());
        ChartEvent event5 = new ChartEvent(chartName, columns[4], stat.getMin());
        event1.setyAxisLabel("Fitness");
        event1.setLineColor(Color.black);
        event1.setLineStroke(new BasicStroke(3));
        event2.setLineColor(Color.gray);
        event3.setLineColor(Color.gray);

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    }


    private MutSeqVerf measureMut;
    
    public void createUniversalityCharts(EnvironmentEA env, ParCollection params) {
        DescriptiveStatistics stat = new DescriptiveStatistics();
        String chartNamePrefix = "UnivChart-Level";
        String[] columns = new String[] {"Fitness", "StdDev+", "StdDev-", "Max", "Min"};


        for (int level = 1; level < this.levelAnzahl() - 1; level++) {
            String chartName = chartNamePrefix + level;
            
            this.chartNames.add(chartName);
            
            MeasureTranslatorGenerality measure = new MeasureTranslatorGenerality(this.measureMut, 99, 100, 100, rand, params);
            
            for (RobEA rob : env.getAgents()) {
                List<Integer> werte = measure.measureManyTimes(this.getAutomat(rob.id(), level, env), 3);
                double sum = 0;
                
                // Durchschnitt berechnen.
                for (double d : werte) {
                    sum += d;
                }
                
                stat.addValue(1 / (sum / werte.size()));
            }


            ChartEvent event1 = new ChartEvent(chartName, columns[0], stat.getMean());
            ChartEvent event2 = new ChartEvent(chartName, columns[1], stat.getMean() + stat.getStandardDeviation());
            ChartEvent event3 = new ChartEvent(chartName, columns[2], stat.getMean() - stat.getStandardDeviation());
            ChartEvent event4 = new ChartEvent(chartName, columns[3], stat.getMax());
            ChartEvent event5 = new ChartEvent(chartName, columns[4], stat.getMin());
            
            event1.setyAxisLabel("Universality");
            event1.setLineColor(Color.black);
            event1.setLineStroke(new BasicStroke(3));
            event2.setLineColor(Color.gray);

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        for (RepositoryFixture fixture : fixtures) {
            try {
                Repository[] cluster = fixture.setUpCluster(1);
                try {
                    // Run the test
                    DescriptiveStatistics statistics = runTest(cluster[0]);
                    if (statistics.getN() > 0) {
                        System.out.format(
                                "%-28.28s  %6.0f  %6.0f  %6.0f  %6.0f  %6.0f  %6d%n",
                                fixture.toString(),
                                statistics.getMin(),
                                statistics.getPercentile(10.0),
                                statistics.getPercentile(50.0),
                                statistics.getPercentile(90.0),
                                statistics.getMax(),
                                statistics.getN());
                    }
                } finally {
                    fixture.tearDownCluster();
                }
            } catch (Exception e) {

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    private static final Credentials CREDENTIALS =
            new SimpleCredentials("admin", "admin".toCharArray());


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


        setUp(repository, CREDENTIALS);
        try {
            // Run a few iterations to warm up the system
            for (int i = 0; i < 5; i++) {
                execute();
            }


            // Run test iterations, and capture the execution times
            int iterations = 0;
            long runtimeEnd =
                    System.currentTimeMillis()
                    + TimeUnit.MILLISECONDS.convert(1, TimeUnit.MINUTES);
            while (iterations++ < 10
                    || System.currentTimeMillis() < runtimeEnd) {
                statistics.addValue(execute());
            }
        } finally {
            tearDown();
        }

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        this.gets.clear();
      }
      super.testTakedown();
      if (opts.reportLatency) {
        Arrays.sort(times);
        DescriptiveStatistics ds = new DescriptiveStatistics();
        for (double t : times) {
          ds.addValue(t);
        }
        LOG.info("randomRead latency log (ms), on " + times.length + " measures");
        LOG.info("99.9999% = " + ds.getPercentile(99.9999d));
        LOG.info(" 99.999% = " + ds.getPercentile(99.999d));
        LOG.info("  99.99% = " + ds.getPercentile(99.99d));
        LOG.info("   99.9% = " + ds.getPercentile(99.9d));
        LOG.info("     99% = " + ds.getPercentile(99d));
        LOG.info("     95% = " + ds.getPercentile(95d));
        LOG.info("     90% = " + ds.getPercentile(90d));
        LOG.info("     80% = " + ds.getPercentile(80d));
        LOG.info("Standard Deviation = " + ds.getStandardDeviation());
        LOG.info("Mean = " + ds.getMean());
      }
    }

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   * Return a commons math DescriptiveStatistics class which is contructed from double array d
   * @param d samples
   * @return a DescriptiveStatistics class which is contructed from double array d
   */
  public static DescriptiveStatistics getStats(double[] d){
    DescriptiveStatistics stats = DescriptiveStatistics.newInstance();
    for(int i =0; i < d.length; i++){
      stats.addValue(d[i]);
    }
    return stats;
  }

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        this.gets.clear();
      }
      super.testTakedown();
      if (opts.reportLatency) {
        Arrays.sort(times);
        DescriptiveStatistics ds = new DescriptiveStatistics();
        for (double t : times) {
          ds.addValue(t);
        }
        LOG.info("randomRead latency log (ms), on " + times.length + " measures");
        LOG.info("99.9999% = " + ds.getPercentile(99.9999d));
        LOG.info(" 99.999% = " + ds.getPercentile(99.999d));
        LOG.info("  99.99% = " + ds.getPercentile(99.99d));
        LOG.info("   99.9% = " + ds.getPercentile(99.9d));
        LOG.info("     99% = " + ds.getPercentile(99d));
        LOG.info("     95% = " + ds.getPercentile(95d));
        LOG.info("     90% = " + ds.getPercentile(90d));
        LOG.info("     80% = " + ds.getPercentile(80d));
        LOG.info("Standard Deviation = " + ds.getStandardDeviation());
        LOG.info("Mean = " + ds.getMean());
      }
    }

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