Example usage for org.apache.commons.math.stat.descriptive.rank Max Max

Introduction

In this page you can find the example usage for org.apache.commons.math.stat.descriptive.rank Max Max.

Prototype

public Max() 

Source Link

Document

Create a Max instance

Usage

From source file:com.discursive.jccook.math.StatExample.java

public static void main(String[] args) {
    double[] values = new double[] { 2.3, 5.4, 6.2, 7.3, 23.3 };

    System.out.println("min: " + StatUtils.min(values));
    System.out.println("max: " + StatUtils.max(values));
    System.out.println("mean: " + StatUtils.mean(values));
    System.out.println("product: " + StatUtils.product(values));
    System.out.println("sum: " + StatUtils.sum(values));
    System.out.println("variance: " + StatUtils.variance(values));

    // Measures from previous example
    Min min = new Min();
    System.out.println("min: " + min.evaluate(values));
    Max max = new Max();
    System.out.println("max: " + max.evaluate(values));
    Mean mean = new Mean();
    System.out.println("mean: " + mean.evaluate(values));
    Product product = new Product();
    System.out.println("product: " + product.evaluate(values));
    Sum sum = new Sum();
    System.out.println("sum: " + sum.evaluate(values));
    Variance variance = new Variance();
    System.out.println("variance: " + variance.evaluate(values));

    // New measures
    Percentile percentile = new Percentile();
    System.out.println("80 percentile value: " + percentile.evaluate(values, 80.0));
    GeometricMean geoMean = new GeometricMean();
    System.out.println("geometric mean: " + geoMean.evaluate(values));
    StandardDeviation stdDev = new StandardDeviation();
    System.out.println("standard dev: " + stdDev.evaluate(values));
    Skewness skewness = new Skewness();
    System.out.println("skewness: " + skewness.evaluate(values));
    Kurtosis kurtosis = new Kurtosis();
    System.out.println("kurtosis: " + kurtosis.evaluate(values));

}

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From source file:fr.ens.transcriptome.corsen.util.StatTest.java

public void testMax() {

    Max max = new Max();

    for (int i = 0; i < 1000; i++) {

        List<DataDouble> list = generate();
        assertEquals(max.evaluate(Stats.toDouble(list)), Stats.max(list));
    }// www .  ja  va  2  s .co  m
}

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From source file:net.sf.jtmt.clustering.Cluster.java

/**
 * Gets the complete linkage distance.//from www.  jav  a  2s  .c  om
 *
 * @param doc the doc
 * @return the complete linkage distance
 */
public double getCompleteLinkageDistance(RealMatrix doc) {
    Max max = new Max();
    if (docs.size() == 0) {
        return 0.0D;
    }
    double[] distances = new double[docs.size()];
    for (int i = 0; i < distances.length; i++) {
        RealMatrix clusterDoc = docs.get(docNames.get(i));
        distances[i] = clusterDoc.subtract(doc).getFrobeniusNorm();
    }
    return max.evaluate(distances);
}

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From source file:org.apache.accumulo.core.util.Stat.java

public Stat() {
    min = new Min();
    max = new Max();
    sum = new Sum();
    mean = new Mean();
    sd = new StandardDeviation();

    stats = new StorelessUnivariateStatistic[] { min, max, sum, mean, sd };
}

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From source file:org.beedraz.semantics_II.expression.number.real.double64.stat.DoubleMaxBeed.java

/**
 * @post  getSource() == null;//from  w  w w.  ja v a 2  s. com
 * @post  getDouble() == null;
 * @post  owner != null ? owner.registerAggregateElement(this);
 */
public DoubleMaxBeed(AggregateBeed owner) {
    super(new Max(), owner);
}

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From source file:org.matsim.contrib.common.stats.DescriptivePiStatistics.java

/**
 * Creates a new descriptive statistics object initialized with dummy
 * implementations that return {@link Double#NaN} (except
 * min/max-implementations).//  w  ww  . jav a  2s. c o m
 */
public DescriptivePiStatistics() {
    DummyPiStatistics dummyStats = new DummyPiStatistics();
    setMeanImpl(dummyStats);
    setGeometricMeanImpl(dummyStats);
    setKurtosisImpl(dummyStats);
    setMaxImpl(new StatisticsWrapper(new Max()));
    setMinImpl(new StatisticsWrapper(new Min()));
    setPercentileImpl(new DummyPiPercentile());
    setSkewnessImpl(dummyStats);
    setVarianceImpl(dummyStats);
    setSumsqImpl(dummyStats);
    setSumImpl(dummyStats);
}

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