Example usage for org.apache.commons.math3.stat.descriptive SummaryStatistics addValue

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.descriptive SummaryStatistics addValue.

Prototype

public void addValue(double value) 

Source Link

Document

Add a value to the data

Usage

From source file:com.sop4j.SimpleStatistics.java

public static void main(String[] args) {
    final MersenneTwister rng = new MersenneTwister(); // used for RNG... READ THE DOCS!!!
    final int[] values = new int[NUM_VALUES];

    final DescriptiveStatistics descriptiveStats = new DescriptiveStatistics(); // stores values
    final SummaryStatistics summaryStats = new SummaryStatistics(); // doesn't store values
    final Frequency frequency = new Frequency();

    // add numbers into our stats
    for (int i = 0; i < NUM_VALUES; ++i) {
        values[i] = rng.nextInt(MAX_VALUE);

        descriptiveStats.addValue(values[i]);
        summaryStats.addValue(values[i]);
        frequency.addValue(values[i]);/*  w  w w .  java  2s  . c om*/
    }

    // print out some standard stats
    System.out.println("MIN: " + summaryStats.getMin());
    System.out.println("AVG: " + String.format("%.3f", summaryStats.getMean()));
    System.out.println("MAX: " + summaryStats.getMax());

    // get some more complex stats only offered by DescriptiveStatistics
    System.out.println("90%: " + descriptiveStats.getPercentile(90));
    System.out.println("MEDIAN: " + descriptiveStats.getPercentile(50));
    System.out.println("SKEWNESS: " + String.format("%.4f", descriptiveStats.getSkewness()));
    System.out.println("KURTOSIS: " + String.format("%.4f", descriptiveStats.getKurtosis()));

    // quick and dirty stats (need a little help from Guava to convert from int[] to double[])
    System.out.println("MIN: " + StatUtils.min(Doubles.toArray(Ints.asList(values))));
    System.out.println("AVG: " + String.format("%.4f", StatUtils.mean(Doubles.toArray(Ints.asList(values)))));
    System.out.println("MAX: " + StatUtils.max(Doubles.toArray(Ints.asList(values))));

    // some stats based upon frequencies
    System.out.println("NUM OF 7s: " + frequency.getCount(7));
    System.out.println("CUMULATIVE FREQUENCY OF 7: " + frequency.getCumFreq(7));
    System.out.println("PERCENTAGE OF 7s: " + frequency.getPct(7));
}

---

From source file:com.github.rinde.dynurg.PoissonDynamismExperiment.java

static void createDynamismHistogram(TimeSeriesGenerator generator, long seed, File file, int repetitions) {
    try {/*from  w w  w.  j  a  v a2  s.  c o  m*/
        Files.createParentDirs(file);
    } catch (final IOException e1) {
        throw new IllegalStateException(e1);
    }
    final RandomGenerator rng = new MersenneTwister(seed);
    final List<Double> values = newArrayList();
    final SummaryStatistics ss = new SummaryStatistics();
    for (int i = 0; i < repetitions; i++) {
        final List<Double> times = generator.generate(rng.nextLong());
        ss.addValue(times.size());
        final double dynamism = Metrics.measureDynamism(times, LENGTH_OF_DAY);
        values.add(dynamism);
    }
    System.out.println(
            file.getName() + " has #events: mean: " + ss.getMean() + " +- " + ss.getStandardDeviation());

    final StringBuilder sb = new StringBuilder();
    sb.append(Joiner.on("\n").join(values));
    try {
        Files.write(sb.toString(), file, Charsets.UTF_8);
    } catch (final IOException e) {
        throw new IllegalStateException(e);
    }
}

---

From source file:de.tudarmstadt.ukp.dkpro.core.performance.PerformanceTestUtil.java

public static SummaryStatistics measurePerformance(AnalysisEngineDescription aWriterDesc,
        Iterable<JCas> aTestData) throws ResourceInitializationException, AnalysisEngineProcessException {
    AnalysisEngine writer = createEngine(aWriterDesc);

    SummaryStatistics stats = new SummaryStatistics();

    for (JCas jcas : aTestData) {
        long begin = System.currentTimeMillis();
        writer.process(jcas);//from  w  w  w  . j a v a 2s.  c o  m
        stats.addValue(System.currentTimeMillis() - begin);
    }

    writer.collectionProcessComplete();
    writer.destroy();

    return stats;
}

---

From source file:net.recommenders.rival.evaluation.statistics.EffectSize.java

/**
 * Computes Cohen's d, either the classical formulation (dividing the pooled
 * standard deviation by the sum of the number of samples) or using the
 * least squares estimation (substracting 2 to the sum of the number of
 * samples when normalizing the pooled standard deviation).
 *
 * @param <V> type of the keys of each map.
 * @param baselineMetricPerDimension map for the baseline method, one value
 * for each user (dimension)//from  w ww. j a  v a  2s  . co m
 * @param testMetricPerDimension map for the test method, one value for each
 * user (dimension)
 * @param doLeastSquares flag to use one formulation or the other (see
 * description above)
 * @return the computed Cohen's d as estimation of the effect size..
 */
public static <V> double getCohenD(final Map<V, Double> baselineMetricPerDimension,
        final Map<V, Double> testMetricPerDimension, final boolean doLeastSquares) {
    SummaryStatistics statsBaseline = new SummaryStatistics();
    for (double d : baselineMetricPerDimension.values()) {
        statsBaseline.addValue(d);
    }
    SummaryStatistics statsTest = new SummaryStatistics();
    for (double d : testMetricPerDimension.values()) {
        statsTest.addValue(d);
    }
    if (doLeastSquares) {
        return getCohenDLeastSquares((int) statsBaseline.getN(), statsBaseline.getMean(),
                statsBaseline.getStandardDeviation(), (int) statsTest.getN(), statsTest.getMean(),
                statsTest.getStandardDeviation());
    }
    return getCohenD((int) statsBaseline.getN(), statsBaseline.getMean(), statsBaseline.getStandardDeviation(),
            (int) statsTest.getN(), statsTest.getMean(), statsTest.getStandardDeviation());
}

---

From source file:iDynoOptimizer.MOEAFramework26.src.org.moeaframework.util.Timing.java

/**
 * Stops the timer with the specified name.
 * /*from   w ww  .j av a 2 s  . com*/
 * @param name the name of the timer to stop
 */
public static void stopTimer(String name) {
    long stopTime = System.nanoTime();

    Long startTime = openTimers.remove(name);
    if (startTime == null) {
        throw new IllegalArgumentException("timer does not exist");
    }

    SummaryStatistics statistics = data.get(name);
    if (statistics == null) {
        statistics = new SummaryStatistics();
        data.put(name, statistics);
    }

    statistics.addValue(stopTime - startTime);
}

---

From source file:net.recommenders.rival.evaluation.statistics.EffectSize.java

/**
 *
 * Estimation of effect size based on the distribution of score differences
 * (from paired samples)./* w w w. j a v a 2s.co m*/
 *
 * @param <V> type of the keys of each map.
 * @param baselineMetricPerDimension map for the baseline method, one value
 * for each user (dimension)
 * @param testMetricPerDimension map for the test method, one value for each
 * user (dimension)
 * @return the effect size.
 */
public static <V> double getEffectSizePairedT(final Map<V, Double> baselineMetricPerDimension,
        final Map<V, Double> testMetricPerDimension) {
    Set<V> overlap = new HashSet<V>(baselineMetricPerDimension.keySet());
    overlap.retainAll(testMetricPerDimension.keySet());

    SummaryStatistics differences = new SummaryStatistics();
    for (V key : overlap) {
        double diff = testMetricPerDimension.get(key) - baselineMetricPerDimension.get(key);
        differences.addValue(diff);
    }

    return getEffectSizePairedT(differences.getMean(), Math.sqrt(differences.getVariance()));
}

---

From source file:cn.edu.suda.core.stats.StatsUtils.java

public static DataMatrix addTTest(DataMatrix dm, int m, int n) {
    int col = dm.getDcol();
    dm.addCols(2);/*from w ww .jav a2 s .c o  m*/
    dm.setColname(col, "ABS_t_value");
    dm.setColname(col + 1, "P_value");
    for (int i = 0; i < dm.getDrow(); i++) {
        double[] array = dm.getRow(i);
        SummaryStatistics stats1 = new SummaryStatistics();
        SummaryStatistics stats2 = new SummaryStatistics();
        for (int j = 0; j < m; j++) {
            stats1.addValue(array[j]);
        }
        for (int j = m; j < m + n; j++) {
            stats2.addValue(array[j]);
        }
        double var1 = stats1.getVariance();
        double var2 = stats2.getVariance();
        if (var1 == 0 && var2 == 0) {
            dm.setValue(i, col, 0);
            dm.setValue(i, col + 1, 1);
        } else {
            double t = Math.abs(TestUtils.t(stats1, stats2));
            double p = TestUtils.tTest(stats1, stats2);
            t = Utils.formatNumber(t, 4);
            p = Utils.formatNumber(p, 4);
            dm.setValue(i, col, t);
            dm.setValue(i, col + 1, p);
        }
    }
    return dm;
}

---

From source file:model.experiments.WhenDoesAveragingMatters.java

public static void learnedRun(int competitors, Class<? extends AskPricingStrategy> pricing,
        int weightedAverageSize) {

    final MacroII macroII = new MacroII(System.currentTimeMillis());
    final TripolistScenario scenario1 = new TripolistScenario(macroII);
    scenario1.setSalesDepartmentType(SalesDepartmentOneAtATime.class);
    scenario1.setAskPricingStrategy(pricing);
    scenario1.setControlType(MonopolistScenario.MonopolistScenarioIntegratedControlEnum.MARGINAL_PLANT_CONTROL);
    scenario1.setAdditionalCompetitors(competitors);
    scenario1.setWorkersToBeRehiredEveryDay(true);
    scenario1.setDemandIntercept(102);/*w  ww. j  a v a 2  s  .com*/

    scenario1.setSalesPricePreditorStrategy(FixedDecreaseSalesPredictor.class);

    //assign scenario
    macroII.setScenario(scenario1);

    macroII.start();

    macroII.schedule.step(macroII);
    for (Firm firm : scenario1.getCompetitors()) {
        for (HumanResources hr : firm.getHRs())
            hr.setPredictor(new FixedIncreasePurchasesPredictor(0));

        final SalesDepartment salesDepartment = firm.getSalesDepartment(UndifferentiatedGoodType.GENERIC);
        salesDepartment.setPriceAverager(new WeightedPriceAverager(weightedAverageSize));
        salesDepartment.setPredictorStrategy(new FixedDecreaseSalesPredictor(0));
    }

    while (macroII.schedule.getTime() < 5000) {
        macroII.schedule.step(macroII);

    }

    SummaryStatistics prices = new SummaryStatistics();
    SummaryStatistics quantities = new SummaryStatistics();
    for (int j = 0; j < 500; j++) {
        macroII.schedule.step(macroII);
        assert !Float.isNaN(macroII.getMarket(UndifferentiatedGoodType.GENERIC).getTodayAveragePrice());
        prices.addValue(macroII.getMarket(UndifferentiatedGoodType.GENERIC).getTodayAveragePrice());
        quantities.addValue(macroII.getMarket(UndifferentiatedGoodType.GENERIC).getTodayVolume());

    }

    System.out.println(prices.getMean() + " - " + quantities.getMean() + "----" + macroII.seed() + " | "
            + macroII.getMarket(UndifferentiatedGoodType.GENERIC).getLastDaysAveragePrice());
    System.out.println("standard deviations: price : " + prices.getStandardDeviation() + " , quantity: "
            + quantities.getStandardDeviation());

}

---

From source file:gr.aueb.cs.nlp.wordtagger.data.structure.features.FeatureBuilder.java

/**
 * normalize feature vectors of words using mean and stadard devaition
 *  provided the vectora values are/*w ww . j a v  a 2 s .c  om*/
 * higher than 1.
 * @param words
 * @param indeces
 */
private static void normalize(List<Word> words, List<Integer> indeces) {
    Map<Integer, Double> means = new WeakHashMap<>();
    Map<Integer, Double> stds = new WeakHashMap<>();
    for (Integer i : indeces) {
        SummaryStatistics smt = new SummaryStatistics();
        for (Word w : words) {
            smt.addValue(w.getFeatureVec().getValues()[i]);
        }
        means.put(i, smt.getMean());
        stds.put(i, smt.getStandardDeviation());
    }
    for (Integer i : indeces) {
        for (Word w : words) {
            double value = w.getFeatureVec().getValues()[i];
            w.getFeatureVec().getValues()[i] = (value - means.get(i)) / stds.get(i);
        }
    }
}

---

From source file:model.experiments.tuningRuns.CompetitiveAveragingGridSearch.java

public static CompetitiveAveragingResult intervalRuns(float hrWeight, float salesWeight) {

    SummaryStatistics averageResultingPrice = new SummaryStatistics();
    SummaryStatistics averageResultingQuantity = new SummaryStatistics();
    SummaryStatistics averageStandardDeviation = new SummaryStatistics();
    for (int i = 0; i < 5; i++) {
        final MacroII macroII = new MacroII(System.currentTimeMillis());
        final TripolistScenario scenario1 = new TripolistScenario(macroII);

        scenario1.setSalesDepartmentType(SalesDepartmentOneAtATime.class);
        scenario1.setAskPricingStrategy(SalesControlWithFixedInventoryAndPID.class);
        scenario1.setControlType(//from  w ww  .  jav  a  2s .com
                MonopolistScenario.MonopolistScenarioIntegratedControlEnum.MARGINAL_PLANT_CONTROL);
        scenario1.setAdditionalCompetitors(4);
        scenario1.setWorkersToBeRehiredEveryDay(true);
        scenario1.setDemandIntercept(102);

        scenario1.setSalesPricePreditorStrategy(FixedDecreaseSalesPredictor.class);

        //assign scenario
        macroII.setScenario(scenario1);

        macroII.start();

        macroII.schedule.step(macroII);
        for (Firm firm : scenario1.getCompetitors()) {
            for (HumanResources hr : firm.getHRs()) {
                hr.setPredictor(new FixedIncreasePurchasesPredictor(0));
                hr.setPriceAverager(new AveragerOverSmallIntervalOnly(hrWeight));
            }
            firm.getSalesDepartment(UndifferentiatedGoodType.GENERIC)
                    .setPriceAverager(new AveragerOverSmallIntervalOnly(salesWeight));
            firm.getSalesDepartment(UndifferentiatedGoodType.GENERIC)
                    .setPredictorStrategy(new FixedDecreaseSalesPredictor(0));
        }

        while (macroII.schedule.getTime() < 10000) {
            macroII.schedule.step(macroII);
        }

        SummaryStatistics prices = new SummaryStatistics();
        SummaryStatistics quantities = new SummaryStatistics();
        for (int j = 0; j < 500; j++) {
            macroII.schedule.step(macroII);
            prices.addValue(macroII.getMarket(UndifferentiatedGoodType.GENERIC).getTodayAveragePrice());
            quantities.addValue(macroII.getMarket(UndifferentiatedGoodType.GENERIC).getTodayVolume());

        }

        //okay?
        averageResultingPrice.addValue(prices.getMean());
        averageResultingQuantity.addValue(quantities.getMean());
        averageStandardDeviation.addValue(prices.getStandardDeviation());

    }

    //okay?
    return new CompetitiveAveragingResult(averageResultingPrice.getMean(), averageResultingQuantity.getMean(),
            averageStandardDeviation.getMean());

}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• »