Example usage for org.apache.commons.math3.stat.descriptive DescriptiveStatistics getMean

Introduction

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

Prototype

public double getMean() 

Source Link

Document

Returns the <a href="http://www.xycoon.com/arithmetic_mean.htm"> arithmetic mean </a> of the available values

Usage

From source file:com.intuit.tank.persistence.databases.BucketDataItemTest.java

/**
 * Run the DescriptiveStatistics getStats() method test.
 * // w  w w . ja  v a  2  s  .co  m
 * @throws Exception
 * 
 * @generatedBy CodePro at 9/10/14 10:32 AM
 */
@Test
public void testGetStats_1() throws Exception {
    BucketDataItem fixture = new BucketDataItem(1, new Date(), new DescriptiveStatistics());

    DescriptiveStatistics result = fixture.getStats();

    assertNotNull(result);
    assertEquals(
            "DescriptiveStatistics:\nn: 0\nmin: NaN\nmax: NaN\nmean: NaN\nstd dev: NaN\nmedian: NaN\nskewness: NaN\nkurtosis: NaN\n",
            result.toString());
    assertEquals(Double.NaN, result.getMax(), 1.0);
    assertEquals(Double.NaN, result.getVariance(), 1.0);
    assertEquals(Double.NaN, result.getMean(), 1.0);
    assertEquals(-1, result.getWindowSize());
    assertEquals(0.0, result.getSumsq(), 1.0);
    assertEquals(Double.NaN, result.getKurtosis(), 1.0);
    assertEquals(0.0, result.getSum(), 1.0);
    assertEquals(Double.NaN, result.getSkewness(), 1.0);
    assertEquals(Double.NaN, result.getPopulationVariance(), 1.0);
    assertEquals(Double.NaN, result.getStandardDeviation(), 1.0);
    assertEquals(Double.NaN, result.getGeometricMean(), 1.0);
    assertEquals(0L, result.getN());
    assertEquals(Double.NaN, result.getMin(), 1.0);
}

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

public String getPerformanceaAnalysis() {
    StringBuilder sb = new StringBuilder();

    long sumMillis = 0;
    for (double timeValue : times) {
        sumMillis += timeValue;//  w  ww.ja  v  a2 s.  c o m
    }

    DescriptiveStatistics statTimes = new DescriptiveStatistics();
    for (Long timeValue : times) {
        statTimes.addValue((double) timeValue / 1000);
    }

    sb.append("Estimate after processing " + times.size() + " documents.");
    sb.append(LF);

    Formatter formatter = new Formatter(sb, Locale.US);

    formatter.format("Time / Document:       %,.3f (%,.3f)\n", statTimes.getMean(),
            statTimes.getStandardDeviation());
    formatter.format("Time / 10^4 Token:     %,.3f\n", getNormalizedTime(sumMillis, nrofTokens, 1000));
    formatter.format("Time / 10^4 Sentences: %,.3f\n", getNormalizedTime(sumMillis, nrofSentences, 1000));

    formatter.close();

    return sb.toString();
}

---

From source file:com.facebook.stats.cardinality.TestHyperLogLog.java

@Test(groups = "slow")
public void testError() throws Exception {
    DescriptiveStatistics stats = new DescriptiveStatistics();
    int buckets = 2048;
    for (int i = 0; i < 10000; ++i) {
        HyperLogLog estimator = new HyperLogLog(buckets);
        Set<Long> randomSet = makeRandomSet(5 * buckets);
        for (Long value : randomSet) {
            estimator.add(value);/* w ww .j a  v  a 2 s. c o m*/
        }

        double error = (estimator.estimate() - randomSet.size()) * 1.0 / randomSet.size();
        stats.addValue(error);
    }

    assertTrue(stats.getMean() < 1e-2);
    assertTrue(stats.getStandardDeviation() < 1.04 / Math.sqrt(buckets));
}

---

From source file:info.financialecology.finance.utilities.datastruct.DoubleTimeSeries.java

public double mean() {
    DescriptiveStatistics stats = new DescriptiveStatistics();

    for (int i = 0; i < this.values.size(); i++)
        stats.addValue(this.values.get(i));

    return stats.getMean();
}

---

From source file:com.duy.pascal.interperter.libraries.math.MathLib.java

@PascalMethod(description = "")
public double Mean(double... arr) {
    DescriptiveStatistics descriptiveStatistics1 = new DescriptiveStatistics(arr);
    return descriptiveStatistics1.getMean();
}

---

From source file:main.java.repartition.SimpleTr.java

static double getDeltaLb(Cluster cluster, SimpleTr t, MigrationPlan m) {

    // Before migration
    DescriptiveStatistics current_server_data = new DescriptiveStatistics();
    for (Server s : cluster.getServers())
        if (t.serverDataSet.containsKey(s.getServer_id()))
            current_server_data.addValue(s.getServer_total_data());

    // After migration      
    DescriptiveStatistics expected_server_data = new DescriptiveStatistics();
    for (Server s : cluster.getServers()) {
        if (t.serverDataSet.containsKey(s.getServer_id())) {

            if (m.fromSet.contains(s.getServer_id())) {
                int data_count = s.getServer_total_data() - t.serverDataSet.get(s.getServer_id()).size();
                expected_server_data.addValue(data_count);

            } else if (m.to == s.getServer_id()) {
                int data_count = s.getServer_total_data() + t.serverDataSet.get(s.getServer_id()).size();
                expected_server_data.addValue(data_count);
            }//  www.  j a v  a  2 s.  com
        }
    }

    // Calculate total lb
    double variance = current_server_data.getVariance() - expected_server_data.getVariance();

    // Calculate delta lb
    double mu = current_server_data.getMean();
    double delta_lb = (double) (variance / (mu * Math.sqrt(Global.servers - 1)));

    return delta_lb;
    //return ((double) delta_lb/m.req_data_mgr);
}

---

From source file:com.fpuna.preproceso.PreprocesoTS.java

private static void calculoFeatures(Registro[] muestras, String activity) {

    DescriptiveStatistics stats_x = new DescriptiveStatistics();
    DescriptiveStatistics stats_y = new DescriptiveStatistics();
    DescriptiveStatistics stats_z = new DescriptiveStatistics();
    //DescriptiveStatistics stats_m1 = new DescriptiveStatistics();
    //DescriptiveStatistics stats_m2 = new DescriptiveStatistics();
    double[] fft_x;
    double[] fft_y;
    double[] fft_z;
    double[] AR_4;

    for (int i = 0; i < muestras.length; i++) {
        stats_x.addValue(muestras[i].getValor_x());
        stats_y.addValue(muestras[i].getValor_y());
        stats_z.addValue(muestras[i].getValor_z());
    }// www.j  a  v  a  2 s  . c  o m

    //********* FFT *********
    fft_x = Util.transform(stats_x.getValues());
    fft_y = Util.transform(stats_y.getValues());
    fft_z = Util.transform(stats_z.getValues());

    //******************* Eje X *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_x.getMean() + ",");
    //std(s) - Standard deviation
    System.out.print(stats_x.getStandardDeviation() + ",");
    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_x.getMax() + ",");
    //min(s) - Smallest value in array
    System.out.print(stats_x.getMin() + ",");
    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_x.getSkewness() + ",");
    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_x.getKurtosis() + ",");
    //energy(s) - Average sum of the squares
    System.out.print(stats_x.getSumsq() / stats_x.getN() + ",");
    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_x) + ",");
    //iqr (s) Interquartile range
    System.out.print(stats_x.getPercentile(75) - stats_x.getPercentile(25) + ",");
    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_x.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_x, stats_x.getValues()) + ",");

    //******************* Eje Y *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_y.getMean() + ",");
    //std(s) - Standard deviation
    System.out.print(stats_y.getStandardDeviation() + ",");
    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_y.getMax() + ",");
    //min(s) - Smallest value in array
    System.out.print(stats_y.getMin() + ",");
    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_y.getSkewness() + ",");
    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_y.getKurtosis() + ",");
    //energy(s) - Average sum of the squares
    System.out.print(stats_y.getSumsq() / stats_y.getN() + ",");
    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_y) + ",");
    //iqr (s) Interquartile range
    System.out.print(stats_y.getPercentile(75) - stats_y.getPercentile(25) + ",");
    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_y.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_y, stats_y.getValues()) + ",");

    //******************* Eje Z *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_z.getMean() + ",");
    //std(s) - Standard deviation
    System.out.print(stats_z.getStandardDeviation() + ",");
    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_z.getMax() + ",");
    //min(s) - Smallest value in array
    System.out.print(stats_z.getMin() + ",");
    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_z.getSkewness() + ",");
    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_z.getKurtosis() + ",");
    //energy(s) - Average sum of the squares
    System.out.print(stats_z.getSumsq() / stats_z.getN() + ",");
    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_z) + ",");
    //iqr (s) Interquartile range
    System.out.print(stats_z.getPercentile(75) - stats_z.getPercentile(25) + ",");
    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_z.getValues(), 4, true);
        System.out.print(AR_4[0] + ",");
        System.out.print(AR_4[1] + ",");
        System.out.print(AR_4[2] + ",");
        System.out.print(AR_4[3] + ",");
    } catch (Exception ex) {
        Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex);
    }
    //meanFreq(s) - Frequency signal weighted average
    System.out.print(Util.meanFreq(fft_z, stats_z.getValues()) + ",");

    //******************* Feature combinados *******************/
    //sma(s1; s2; s3) - Signal magnitude area
    System.out.print(Util.sma(stats_x.getValues(), stats_y.getValues(), stats_z.getValues()) + ",");
    //correlation(s1; s2) - Pearson Correlation coefficient
    System.out.print(new PearsonsCorrelation().correlation(stats_x.getValues(), stats_y.getValues()) + ",");
    System.out.print(new PearsonsCorrelation().correlation(stats_x.getValues(), stats_z.getValues()) + ",");
    System.out.print(new PearsonsCorrelation().correlation(stats_y.getValues(), stats_z.getValues()) + ",");

    //******************* Actividad *******************/
    System.out.print(activity);
    System.out.print("\n");
}

---

From source file:com.caseystella.analytics.util.DistributionUtil.java

public void summary(String title, DescriptiveStatistics statistics, PrintStream pw) {
    pw.println(title + ": " + "\n\tMin: " + statistics.getMin() + "\n\t1th: " + statistics.getPercentile(1)
            + "\n\t5th: " + statistics.getPercentile(5) + "\n\t10th: " + statistics.getPercentile(10)
            + "\n\t25th: " + statistics.getPercentile(25) + "\n\t50th: " + statistics.getPercentile(50)
            + "\n\t90th: " + statistics.getPercentile(90) + "\n\t95th: " + statistics.getPercentile(95)
            + "\n\t99th: " + statistics.getPercentile(99) + "\n\tMax: " + statistics.getMax() + "\n\tMean: "
            + statistics.getMean() + "\n\tStdDev: " + statistics.getStandardDeviation());
}

---

From source file:com.facebook.presto.operator.aggregation.AbstractTestApproximateCountDistinct.java

@Test(dataProvider = "provideStandardErrors")
public void testMultiplePositions(double maxStandardError) throws Exception {
    DescriptiveStatistics stats = new DescriptiveStatistics();

    for (int i = 0; i < 500; ++i) {
        int uniques = ThreadLocalRandom.current().nextInt(20000) + 1;

        List<Object> values = createRandomSample(uniques, (int) (uniques * 1.5));

        long actual = estimateGroupByCount(values, maxStandardError);
        double error = (actual - uniques) * 1.0 / uniques;

        stats.addValue(error);//  w w  w.  ja  va 2s. c  o  m
    }

    assertLessThan(stats.getMean(), 1.0e-2);
    assertLessThan(Math.abs(stats.getStandardDeviation() - maxStandardError), 1.0e-2);
}

---

From source file:knop.psfj.heatmap.FractionnedSpace.java

/**
 * Gets the mean number of beads.// w  ww .  ja va  2s . c o m
 *
 * @return the mean number of beads
 */
public double getMeanNumberOfBeads() {
    DescriptiveStatistics stats = new DescriptiveStatistics();

    for (Fraction f : this) {
        stats.addValue(f.size());
    }
    return stats.getMean();
}

---

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