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

Introduction

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

Prototype

public SummaryStatistics()

Source Link

Document

Construct a SummaryStatistics instance

Usage

From source file:uk.ac.diamond.scisoft.ncd.calibration.NCDAbsoluteCalibration.java

public void calibrate() {
    qMin = Math.max(absQ.min().doubleValue(), dataQ.min().doubleValue());
    qMax = Math.min(absQ.max().doubleValue(), dataQ.max().doubleValue());
    if (!(qMin < qMax)) {
        throw new IllegalArgumentException(
                "No calibration data found for the selected scattering vector range");
    }/*from   ww  w  .  j ava  2s.  c om*/

    int dataQStart = Math.min(dataQ.getSize() - 1, DatasetUtils.findIndexGreaterThanOrEqualTo(dataQ, qMin));
    int dataQStop = Math.min(dataQ.getSize() - 1, DatasetUtils.findIndexGreaterThanOrEqualTo(dataQ, qMax));

    SummaryStatistics stats = new SummaryStatistics();
    for (int i = dataQStart; i <= dataQStop; i++) {
        double qval = dataQ.getDouble(i);
        stats.addValue(absInterpolate.value(qval) / dataI.getDouble(i));
    }

    absScale = stats.getMean();
    absScaleStdDev = stats.getStandardDeviation();

    String msg = StringUtils.join(new String[] { "scale", Double.toString(absScale) }, " : ");
    System.out.println(msg);

    System.out.println("NCD Absolute Instensity Scaler");
    System.out.println(Double.toString(absScale));
    System.out.println("standard deviation:" + Double.toString(absScaleStdDev));

    calibratedData(dataI);
}

From source file:wsattacker.library.intelligentdos.success.TTestSuccessDecider.java

private double calculateProbability(Long[] run1, Long[] run2) {
    SummaryStatistics statisticsX = new SummaryStatistics();
    for (double value : run1) {
        statisticsX.addValue(value);/* w w  w.  j a  v a  2 s.co  m*/
    }

    SummaryStatistics statisticsY = new SummaryStatistics();
    for (double value : run2) {
        statisticsY.addValue(value);
    }

    // two-sample
    double t = TestUtils.t(statisticsX, statisticsY);
    long degreesOfFreedom = statisticsX.getN() + statisticsY.getN() - 2;
    // p-value = 0.3002
    TDistribution tDistribution = new TDistribution(degreesOfFreedom);
    double pValue = tDistribution.cumulativeProbability(t);
    return pValue;
}

From source file:wsattacker.plugin.dos.dosExtension.mvc.model.AttackModel.java

private SummaryStatistics createNetworkStatistics() {
    int toSkip = 5;
    // generate Array from LogResults
    // loop all NetworkTestRequests and write to array
    int j = 0;//from  w ww .  j a  va  2  s  .  c o m
    SummaryStatistics statistics = new SummaryStatistics();
    for (LogEntryRequest currentLogEntry : this.logListNetworktestRequests) {
        // skip first entries
        if (j >= toSkip) {
            statistics.addValue(currentLogEntry.getDuration());
            System.out.println("----add:" + (j - toSkip) + " - " + currentLogEntry.getDuration());
        }
        j++;
    }
    return statistics;
}

From source file:wsattacker.plugin.intelligentdos.StatisticTest.java

private static void statistics(double[] x, double[] y) {

    DecimalFormat df = new DecimalFormat("#.######");

    SummaryStatistics statisticsX = new SummaryStatistics();
    for (double value : x) {
        statisticsX.addValue(value);/*www  .  j av a2  s.  c  o  m*/
    }

    SummaryStatistics statisticsY = new SummaryStatistics();
    for (double value : y) {
        statisticsY.addValue(value);
    }

    // t = -0.5331
    double t = TestUtils.t(statisticsX, statisticsY);
    // df = 18
    long degreesOfFreedom = statisticsX.getN() + statisticsY.getN() - 2;
    // p-value = 0.3002
    TDistribution tDistribution = new TDistribution(degreesOfFreedom);
    double pValue = tDistribution.cumulativeProbability(t);

    // t = -0.5331, df = 18, p-value = 0.3002
    System.out.print("t = " + df.format(t));
    System.out.print(", df = " + degreesOfFreedom);
    System.out.println(", p-value = " + df.format(pValue));

    System.out.println("mean of x mean of y");
    System.out.println(df.format(statisticsX.getMean()) + " - " + df.format(statisticsY.getMean()));

    // Calculate 95% confidence interval
    double ci = calcMeanCI(statisticsY, 0.95);
    System.out.println(String.format("Confidence inteval 95%%: %f", ci));
}