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:de.tudarmstadt.ukp.experiments.argumentation.convincingness.sampling.Step6GraphTransitivityCleaner.java

@SuppressWarnings("unchecked")
public static void printResultStatistics(File xmlFile) throws IllegalAccessException {
    Map<String, Map<String, GraphCleaningResults>> results = (Map<String, Map<String, GraphCleaningResults>>) XStreamTools
            .getXStream().fromXML(xmlFile);

    //        System.out.println(results);

    SortedMap<String, List<GraphCleaningResults>> resultsGroupedByMethod = new TreeMap<>();

    for (Map.Entry<String, Map<String, GraphCleaningResults>> entry : results.entrySet()) {
        //            System.out.println(entry.getKey());

        for (Map.Entry<String, GraphCleaningResults> e : entry.getValue().entrySet()) {
            //                System.out.println(e.getKey());
            //                System.out.println(e.getValue());

            if (!resultsGroupedByMethod.containsKey(e.getKey())) {
                resultsGroupedByMethod.put(e.getKey(), new ArrayList<GraphCleaningResults>());
            }/*from w ww.ja  v  a2s  .  c o m*/

            resultsGroupedByMethod.get(e.getKey()).add(e.getValue());
        }
    }

    String header = null;

    // collect statistics
    for (Map.Entry<String, List<GraphCleaningResults>> entry : resultsGroupedByMethod.entrySet()) {
        List<GraphCleaningResults> value = entry.getValue();
        SortedMap<String, DescriptiveStatistics> stringDescriptiveStatisticsMap = collectStatisticsOverGraphCleaningResults(
                value);

        if (header == null) {
            header = StringUtils.join(stringDescriptiveStatisticsMap.keySet(), "\t");
            System.out.println("\t\t" + header);
        }

        List<Double> means = new ArrayList<>();
        List<Double> stdDevs = new ArrayList<>();
        for (DescriptiveStatistics statistics : stringDescriptiveStatisticsMap.values()) {
            means.add(statistics.getMean());
            stdDevs.add(statistics.getStandardDeviation());
        }

        List<String> meansString = new ArrayList<>();
        for (Double mean : means) {
            meansString.add(String.format(Locale.ENGLISH, "%.2f", mean));
        }

        List<String> stdDevString = new ArrayList<>();
        for (Double stdDev : stdDevs) {
            stdDevString.add(String.format(Locale.ENGLISH, "%.2f", stdDev));
        }

        System.out.println(entry.getKey() + "\tmean\t" + StringUtils.join(meansString, "\t"));
        //            System.out.println(entry.getKey() + "\tstdDev\t" + StringUtils.join(stdDevString, "\t"));
    }
}

---

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

public PixelStatisticsBase(DescriptiveStatistics stats) {

    setMean(stats.getMean());
    setMax(stats.getMax());/*from   w w w.j  a va 2  s  .c o m*/
    setStandardDeviation(stats.getStandardDeviation());
    setVariance(stats.getVariance());
    setMedian(stats.getPercentile(50));
    setPixelCount(stats.getN());
    setMin(stats.getMin());

}

---

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

private static TrainingSetFeature calculoFeaturesMagnitud(List<Registro> muestras, String activity) {

    TrainingSetFeature Feature = new TrainingSetFeature();
    DescriptiveStatistics stats_m = new DescriptiveStatistics();

    double[] fft_m;
    double[] AR_4;

    muestras = Util.calcMagnitud(muestras);

    for (int i = 0; i < muestras.size(); i++) {
        stats_m.addValue(muestras.get(i).getM_1());
    }/*from   ww  w  . ja v a  2  s .c om*/

    //********* FFT *********
    //fft_m = Util.transform(stats_m.getValues());
    fft_m = FFTMixedRadix.fftPowerSpectrum(stats_m.getValues());

    //******************* Calculos Magnitud *******************//
    //mean(s) - Arithmetic mean
    System.out.print(stats_m.getMean() + ",");
    Feature.setMeanX((float) stats_m.getMean());

    //std(s) - Standard deviation
    System.out.print(stats_m.getStandardDeviation() + ",");
    Feature.setStdX((float) stats_m.getStandardDeviation());

    //mad(s) - Median absolute deviation
    //
    //max(s) - Largest values in array
    System.out.print(stats_m.getMax() + ",");
    Feature.setMaxX((float) stats_m.getMax());

    //min(s) - Smallest value in array
    System.out.print(stats_m.getMin() + ",");
    Feature.setMinX((float) stats_m.getMin());

    //skewness(s) - Frequency signal Skewness
    System.out.print(stats_m.getSkewness() + ",");
    Feature.setSkewnessX((float) stats_m.getSkewness());

    //kurtosis(s) - Frequency signal Kurtosis
    System.out.print(stats_m.getKurtosis() + ",");
    Feature.setKurtosisX((float) stats_m.getKurtosis());

    //energy(s) - Average sum of the squares
    System.out.print(stats_m.getSumsq() / stats_m.getN() + ",");
    Feature.setEnergyX((float) (stats_m.getSumsq() / stats_m.getN()));

    //entropy(s) - Signal Entropy
    System.out.print(Util.calculateShannonEntropy(fft_m) + ",");
    Feature.setEntropyX(Util.calculateShannonEntropy(fft_m).floatValue());

    //iqr (s) Interquartile range
    System.out.print(stats_m.getPercentile(75) - stats_m.getPercentile(25) + ",");
    Feature.setIqrX((float) (stats_m.getPercentile(75) - stats_m.getPercentile(25)));

    try {
        //autoregression (s) -4th order Burg Autoregression coefficients
        AR_4 = AutoRegression.calculateARCoefficients(stats_m.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] + ",");
        Feature.setArX1((float) AR_4[0]);
        Feature.setArX2((float) AR_4[1]);
        Feature.setArX3((float) AR_4[2]);
        Feature.setArX4((float) 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_m, stats_m.getValues()) + ",");
    Feature.setMeanFreqx((float) Util.meanFreq(fft_m, stats_m.getValues()));

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

    return Feature;
}

---

From source file:com.insightml.data.features.stats.FeatureStatistics.java

public Double getMean(final String feature) {
    final DescriptiveStatistics stat = stats.get(feature);
    return stat == null ? null : stat.getMean();
}

---

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;
}

---

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;
}

---

From source file:com.insightml.models.meta.VoteModel.java

private double resolve(final DescriptiveStatistics stats) {
    switch (strategy) {
    case AVERAGE:
        return stats.getMean();
    case MEDIAN:/* ww w  .  ja va 2  s .  c o  m*/
        return stats.getPercentile(50);
    case GEOMETRIC:
        return stats.getGeometricMean();
    case HARMONIC:
        double sum = 0;
        for (final double value : stats.getValues()) {
            sum += 1 / value;
        }
        return stats.getN() * 1.0 / sum;
    default:
        throw new IllegalStateException();
    }
}

---

From source file:cc.kave.commons.pointsto.evaluation.PointsToSetEvaluation.java

public void run(Path contextsDir) throws IOException {
    StatementCounterVisitor stmtCounterVisitor = new StatementCounterVisitor();
    List<Context> contexts = getSamples(contextsDir).stream()
            .filter(cxt -> cxt.getSST().accept(stmtCounterVisitor, null) > 0).collect(Collectors.toList());
    log("Using %d contexts for evaluation\n", contexts.size());

    PointsToUsageExtractor extractor = new PointsToUsageExtractor();
    for (Context context : contexts) {
        PointstoSetSizeAnalysis analysis = new PointstoSetSizeAnalysis();
        extractor.extract(analysis.compute(context));
        results.addAll(analysis.getSetSizes());
    }/*from  w w w.ja v a 2  s. c o m*/

    DescriptiveStatistics statistics = new DescriptiveStatistics();
    for (Integer setSize : results) {
        statistics.addValue(setSize.doubleValue());
    }
    log("mean: %.2f\n", statistics.getMean());
    log("stddev: %.2f\n", statistics.getStandardDeviation());
    log("min/max: %.2f/%.2f\n", statistics.getMin(), statistics.getMax());
}

---

From source file:cz.cuni.mff.d3s.tools.perfdoc.server.measuring.statistics.Statistics.java

public long computeMedian() {
    if (measurementResults.isEmpty()) {
        return -1;
    }/*from ww w  . java  2s  . c o  m*/

    DescriptiveStatistics stats = new DescriptiveStatistics();
    for (Long l : measurementResults) {
        stats.addValue(l);
    }
    return (long) stats.getMean();
}

---

From source file:com.datatorrent.netlet.benchmark.util.BenchmarkResults.java

private String getResults() {
    DescriptiveStatistics statistics = getDescriptiveStatistics();
    final StringBuilder sb = new StringBuilder();
    sb.append("Iterations: ").append(statistics.getN());
    sb.append(" | Avg Time: ").append(fromNanoTime(statistics.getMean()));
    sb.append(" | Min Time: ").append(fromNanoTime(statistics.getMin()));
    sb.append(" | Max Time: ").append(fromNanoTime(statistics.getMax()));
    sb.append(" | 75% Time: ").append(fromNanoTime(statistics.getPercentile(75d)));
    sb.append(" | 90% Time: ").append(fromNanoTime(statistics.getPercentile(90d)));
    sb.append(" | 99% Time: ").append(fromNanoTime(statistics.getPercentile(99d)));
    sb.append(" | 99.9% Time: ").append(fromNanoTime(statistics.getPercentile(99.9d)));
    sb.append(" | 99.99% Time: ").append(fromNanoTime(statistics.getPercentile(99.99d)));
    sb.append(" | 99.999% Time: ").append(fromNanoTime(statistics.getPercentile(99.999d)));

    return sb.toString();

}

---

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