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

Introduction

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

Prototype

public double getVariance() 

Source Link

Document

Returns the (sample) variance of the available values.

Usage

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

private static double getLbGain(Cluster cluster, SwappingCandidate sc) {

    DescriptiveStatistics sc_partition_data = new DescriptiveStatistics();

    for (Partition p : cluster.getPartitions())
        if (sc.p_pair.x == p.getPartition_id() || sc.p_pair.y == p.getPartition_id())
            sc_partition_data.addValue(p.getPartition_dataSet().size());

    return sc_partition_data.getVariance();
}

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From source file:com.intuit.tank.vm.common.util.ReportUtil.java

public static final String[] getSummaryData(String key, DescriptiveStatistics stats) {
    String[] ret = new String[ReportUtil.SUMMARY_HEADERS.length + PERCENTILES.length];
    int i = 0;//  w ww  . j a v  a2s.com
    ret[i++] = key;// Page ID
    ret[i++] = INT_NF.format(stats.getN());// Sample Size
    ret[i++] = DOUBLE_NF.format(stats.getMean());// Mean
    ret[i++] = INT_NF.format(stats.getPercentile(50));// Meadian
    ret[i++] = INT_NF.format(stats.getMin());// Min
    ret[i++] = INT_NF.format(stats.getMax());// Max
    ret[i++] = DOUBLE_NF.format(stats.getStandardDeviation());// Std Dev
    ret[i++] = DOUBLE_NF.format(stats.getKurtosis());// Kurtosis
    ret[i++] = DOUBLE_NF.format(stats.getSkewness());// Skewness
    ret[i++] = DOUBLE_NF.format(stats.getVariance());// Varience
    for (int n = 0; n < PERCENTILES.length; n++) {
        ret[i++] = INT_NF.format(stats.getPercentile((Integer) PERCENTILES[n][1]));// Percentiles
    }
    return ret;
}

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From source file:com.github.aptd.simulation.core.statistic.local.CStatistic.java

/**
 * write data//  w  ww  . j a  v a 2 s.c o  m
 *
 * @param p_writer writer instance
 * @param p_name section name
 * @param p_statistic statistic value
 */
private static void apply(final IWriter p_writer, final String p_name,
        final DescriptiveStatistics p_statistic) {
    p_writer.section(1, p_name);

    p_writer.value("geometricmean", p_statistic.getGeometricMean());
    p_writer.value("kurtosis", p_statistic.getKurtosis());
    p_writer.value("max", p_statistic.getMax());
    p_writer.value("min", p_statistic.getMin());
    p_writer.value("mean", p_statistic.getMean());
    p_writer.value("count", p_statistic.getN());
    p_writer.value("25-percentile", p_statistic.getPercentile(0.25));
    p_writer.value("75-percentile", p_statistic.getPercentile(0.75));
    p_writer.value("populationvariance", p_statistic.getPopulationVariance());
    p_writer.value("quadraticmean", p_statistic.getQuadraticMean());
    p_writer.value("standdeviation", p_statistic.getStandardDeviation());
    p_writer.value("skewness", p_statistic.getSkewness());
    p_writer.value("sum", p_statistic.getSum());
    p_writer.value("sumsequared", p_statistic.getSumsq());
    p_writer.value("variance", p_statistic.getVariance());
}

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From source file:ijfx.core.overlay.PixelStatisticsBase.java

public PixelStatisticsBase(DescriptiveStatistics stats) {

    setMean(stats.getMean());//from   w  w w .  j av  a  2  s.com
    setMax(stats.getMax());
    setStandardDeviation(stats.getStandardDeviation());
    setVariance(stats.getVariance());
    setMedian(stats.getPercentile(50));
    setPixelCount(stats.getN());
    setMin(stats.getMin());

}

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From source file:com.intuit.tank.service.impl.v1.report.SummaryReportRunner.java

/**
 * @param key//  w  w  w. j  a va2s  .  com
 * @param value
 * @return
 */
private static SummaryData getSummaryData(int jobId, String key, DescriptiveStatistics stats) {
    SummaryData ret = SummaryDataBuilder.summaryData().withJobId(jobId)
            .withKurtosis(!Double.isNaN(stats.getKurtosis()) ? stats.getKurtosis() : 0).withMax(stats.getMax())
            .withMean(stats.getMean()).withMin(stats.getMin()).withPageId(key)
            .withPercentile10(stats.getPercentile(10)).withPercentile20(stats.getPercentile(20))
            .withPercentile30(stats.getPercentile(30)).withPercentile40(stats.getPercentile(40))
            .withPercentile50(stats.getPercentile(50)).withPercentile60(stats.getPercentile(60))
            .withPercentile70(stats.getPercentile(70)).withPercentile80(stats.getPercentile(80))
            .withPercentile90(stats.getPercentile(90)).withPercentile95(stats.getPercentile(95))
            .withPercentile99(stats.getPercentile(99)).withSampleSize((int) stats.getN())
            .withSkewness(!Double.isNaN(stats.getSkewness()) ? stats.getSkewness() : 0)
            .withSttDev(!Double.isNaN(stats.getStandardDeviation()) ? stats.getStandardDeviation() : 0)
            .withVarience(!Double.isNaN(stats.getVariance()) ? stats.getVariance() : 0).build();
    return ret;
}

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From source file:algorithms.quality.JndRegionSize.java

@Override
public double getQuality(Colormap2D colormap) {
    JndRegionComputer computer = new JndRegionComputer(colormap, sampling, 3.0);

    DescriptiveStatistics stats = new DescriptiveStatistics();

    for (Point2D center : computer.getPoints()) {
        List<Point2D> poly = computer.getRegion(center);
        double area = computeArea(poly, center);

        stats.addValue(area);/*  ww  w .j  ava2 s .c  o m*/
    }

    // TODO: find a better scaling factor
    return stats.getVariance() * 10000000.d;
}

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From source file:ijfx.core.overlay.DefaultPixelStatistics.java

public DefaultPixelStatistics(ImageDisplay display, Overlay overlay, Context context) {

    context.inject(this);

    Double[] valueList = overlayStatService.getValueListFromImageDisplay(display, overlay);

    DescriptiveStatistics statistics = new DescriptiveStatistics(ArrayUtils.toPrimitive(valueList));

    this.mean = statistics.getMean();
    this.max = statistics.getMax();
    this.min = statistics.getMin();
    this.standardDeviation = statistics.getStandardDeviation();
    this.variance = statistics.getVariance();
    this.median = statistics.getPercentile(MEDIAN_PERCENTILE);
    this.pixelCount = valueList.length;
}

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From source file:ijfx.service.overlay.DefaultPixelStatistics.java

public DefaultPixelStatistics(ImageDisplay display, Overlay overlay, Context context) {

    context.inject(this);

    Double[] valueList = overlayStatService.getValueList(display, overlay);

    DescriptiveStatistics statistics = new DescriptiveStatistics(ArrayUtils.toPrimitive(valueList));

    this.mean = statistics.getMean();
    this.max = statistics.getMax();
    this.min = statistics.getMin();
    this.standardDeviation = statistics.getStandardDeviation();
    this.variance = statistics.getVariance();
    this.median = statistics.getPercentile(MEDIAN_PERCENTILE);
    this.pixelCount = valueList.length;
}

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From source file:algorithms.quality.AttentionQuality.java

@Override
public double getQuality(Colormap2D colormap) {
    // max L + max c (which is the same as a or b)
    double normFac = Math.sqrt(100 * 100 + 150 * 150);

    DescriptiveStatistics stats = new DescriptiveStatistics();

    for (Point2D pt : sampling.getPoints()) {
        Color color = colormap.getColor(pt.getX(), pt.getY());
        double[] lch = new CIELABLch().fromColor(color);
        double attention = Math.sqrt(lch[0] * lch[0] + lch[1] * lch[1]) / normFac;

        stats.addValue(attention);//  w ww . j  av a 2s.c  o m
    }

    return stats.getVariance();
}

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

/**
 * Run the DescriptiveStatistics getStats() method test.
 * // ww  w  . j av  a2 s .com
 * @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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