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

Introduction

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

Prototype

public double getMean() 

Source Link

Document

Returns the mean of the values that have been added.

Usage

From source file:org.recommender101.recommender.extensions.mahout.impl.AbstractFactorizer.java

protected double getAveragePreference() throws TasteException {
    SummaryStatistics stat = new SummaryStatistics();

    for (Rating rating : dataModel.getRatings()) {
        stat.addValue(rating.rating);/*from w w w . j  a va2s .  c  o m*/
    }

    return stat.getMean();
}

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From source file:org.spotter.ext.detection.highmessaging.analyze.Analyzer.java

protected double mean(List<Double> values) {
    SummaryStatistics stats = new SummaryStatistics();
    for (double val : values) {
        stats.addValue(val);
    }/*  w  ww .ja  va  2s .  com*/
    return stats.getMean();
}

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From source file:org.spotter.ext.detection.highmessaging.analyze.LinearAnalyzer.java

@Override
public AnalyzeResult analyze() {
    // Values smoothed
    List<Double> valuesSmoothed = smooth(yValues, smoothingWide);

    // Values smoothed and normalized relative to the first
    List<Double> valuesSmoothedNormalized = normalize(valuesSmoothed, valuesSmoothed.get(0));

    // Check if linear increasing
    List<Double> slopes = new ArrayList<Double>();
    SummaryStatistics statsSlopesSmoothedNormalized = new SummaryStatistics();
    for (int i = 0; i < xValues.size(); i++) {
        double slope = 1;

        if (i != 0) {
            slope = (valuesSmoothedNormalized.get(i) - valuesSmoothedNormalized.get(i - 1))
                    / (xValues.get(i) - xValues.get(i - 1));
        }/*from w ww  .j  ava2s . c om*/

        statsSlopesSmoothedNormalized.addValue(slope);
        slopes.add(slope);
        // System.out.println((slope + "").replaceAll("\\.", ","));
    }

    List<Double> slopesSmoothed = smooth(slopes, 2);

    int outlierCount = 0;
    double lowerBound = statsSlopesSmoothedNormalized.getMean()
            - statsSlopesSmoothedNormalized.getStandardDeviation();
    lowerBound = 0.5;
    for (double val : slopesSmoothed) {
        // System.out.println((val + "").replaceAll("\\.", ","));
        if (lowerBound > val) {
            outlierCount++;
        }
    }

    double pctOutlier = 1D / slopes.size() * outlierCount;
    //      System.out.println(String.format("> Outlier: %d Pct Outlier: %.2f", outlierCount, pctOutlier));

    if (pctOutlier > 1D - pctThreshold) {
        // Too much outside
        return AnalyzeResult.NEGATIVE;
    } else {
        // Alles ok
        return AnalyzeResult.POSITIVE;
    }
}

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From source file:playground.acmarmol.matsim2030.microcensus2010.MZPopulationUtils.java

public static void analyzeActivityTypesAndLengths(Population population) throws ExecutionException {
    LoadingCache<String, SummaryStatistics> activityDuration = CacheBuilder.newBuilder()
            .build(CacheLoader.from(new Supplier<SummaryStatistics>() {
                @Override/*from   ww w.ja  v a 2  s  .co m*/
                public SummaryStatistics get() {
                    return new SummaryStatistics();
                }
            }));

    for (Person p : population.getPersons().values()) {
        if (p.getPlans().size() == 0)
            continue;

        Plan plan = p.getPlans().get(0);

        List<PlanElement> planElements = plan.getPlanElements();
        for (PlanElement pe : planElements) {
            if (!(pe instanceof Activity))
                continue;
            Activity activity = (Activity) pe;

            double startTime = activity.getStartTime();
            double endTime = activity.getEndTime();

            SummaryStatistics typeStats = activityDuration.get(activity.getType());
            if (endTime != Time.UNDEFINED_TIME) {
                if (startTime == Time.UNDEFINED_TIME)
                    startTime = 0;
                typeStats.addValue(endTime - startTime);
            }
        }
    }

    ConcurrentMap<String, SummaryStatistics> activityDurationMap = activityDuration.asMap();
    {
        int i = 0;
        final StringBuffer s = new StringBuffer();
        for (final String actType : activityDurationMap.keySet()) {
            final SummaryStatistics stats = activityDurationMap.get(actType);

            s.append(String.format("<param name=\"activityType_%d\" value=\"%s\" />\n", i, actType));
            s.append(String.format("<param name=\"activityPriority_%d\" value=\"1\" />\n", i));
            s.append(String.format("<param name=\"activityTypicalDuration_%d\" value=\"%s\" />\n", i,
                    Time.writeTime(stats.getMean())));
            s.append("\n");
            i++;
        }
        log.info("All activities:\n" + s.toString());
    }
}

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From source file:tech.tablesaw.columns.numbers.Stats.java

private static Stats getStats(NumericColumn<?> values, SummaryStatistics summaryStatistics) {
    Stats stats = new Stats("Column: " + values.name());
    stats.min = summaryStatistics.getMin();
    stats.max = summaryStatistics.getMax();
    stats.n = summaryStatistics.getN();/*ww  w.j  a  va  2  s.com*/
    stats.sum = summaryStatistics.getSum();
    stats.variance = summaryStatistics.getVariance();
    stats.populationVariance = summaryStatistics.getPopulationVariance();
    stats.quadraticMean = summaryStatistics.getQuadraticMean();
    stats.geometricMean = summaryStatistics.getGeometricMean();
    stats.mean = summaryStatistics.getMean();
    stats.standardDeviation = summaryStatistics.getStandardDeviation();
    stats.sumOfLogs = summaryStatistics.getSumOfLogs();
    stats.sumOfSquares = summaryStatistics.getSumsq();
    stats.secondMoment = summaryStatistics.getSecondMoment();
    return stats;
}

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From source file:tools.descartes.bungee.evaluation.ScalabilityReproducibilityEvaluation.java

@Override
public void run() {
    System.out.println("Analysis started...");
    List<Double> firstStepResults = new LinkedList<Double>();
    Host host = new Host(cloudSettings.getIp(), cloudSettings.getPrivatePort());
    analysis.setMaxResources(cloudSettings.getMaxInstances());
    int runs = 0;
    boolean finished = false;

    while (runs < minRuns || (runs < maxRuns && !finished)) {
        runs++;/*from  w  w w. j  av a2s  .c  o  m*/
        System.out.println("run number: " + runs);
        IntensityDemandMapping mapping = analysis.analyzeSystem(host, request, slo);
        double result = mapping.getMaxIntensity(1);
        firstStepResults.add(result);
        writeAnalysisResultsToFile(firstStepResults);

        if (runs > 1) {
            SummaryStatistics summaryStats = createSummaryStatistics(firstStepResults);
            double wantedLower = (1 - diffPercent) * summaryStats.getMean();
            double wantedUpper = (1 + diffPercent) * summaryStats.getMean();
            double width = getConfidenceIntervalWidth(summaryStats, confidence);
            double lowerConfidence = summaryStats.getMean() - width;
            double upperConfidence = summaryStats.getMean() + width;
            finished = lowerConfidence > wantedLower;
            printStatistics(summaryStats, lowerConfidence, upperConfidence, wantedLower, wantedUpper, finished);
            writeEvaluationResultsToFile(firstStepResults, summaryStats, lowerConfidence, upperConfidence,
                    wantedLower, wantedUpper, finished);
        }
    }

}

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From source file:tools.descartes.bungee.evaluation.ScalabilityReproducibilityEvaluation.java

private void printStatistics(SummaryStatistics summaryStats, double lowerConfidence, double upperConfidence,
        double wantedLower, double wantedUpper, boolean finished) {
    System.out.println("total runs: " + summaryStats.getN());
    System.out.println("mean: " + summaryStats.getMean());
    System.out.println("stdDev: " + summaryStats.getStandardDeviation());
    System.out.println("confidence interval [" + lowerConfidence + "," + upperConfidence + "]");
    System.out.println("wanted interval [" + wantedLower + "," + wantedUpper + "]");
    System.out.println("success: " + finished);
}

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From source file:tools.descartes.bungee.evaluation.ScalabilityReproducibilityEvaluation.java

private void writeEvaluationResultsToFile(List<Double> firstStepResults, SummaryStatistics summaryStats,
        double lowerConfidence, double upperConfidence, double wantedLower, double wantedUpper,
        boolean finished) {
    String resultsString = analysisResultCSVString(firstStepResults);
    PrintWriter writer;// w w w.  j a  va  2  s  .c  o m
    try {
        writer = new PrintWriter(
                new File(measurementFolder, cloudSettings.getOffering() + "-evaluationResults.csv"),
                FileUtility.ENOCDING);
        writer.println(resultsString);
        writer.println("runs: " + FileUtility.CSV_SPLIT_BY + summaryStats.getN());
        writer.println("mean" + FileUtility.CSV_SPLIT_BY + summaryStats.getMean());
        writer.println("stdDev" + FileUtility.CSV_SPLIT_BY + summaryStats.getStandardDeviation());
        writer.println(Double.toString(diffPercent * 100) + "%-interval" + FileUtility.CSV_SPLIT_BY
                + Double.toString(wantedLower) + FileUtility.CSV_SPLIT_BY + Double.toString(wantedUpper));
        writer.println(Double.toString(confidence * 100) + "% confidence interval" + FileUtility.CSV_SPLIT_BY
                + Double.toString(lowerConfidence) + FileUtility.CSV_SPLIT_BY
                + Double.toString(upperConfidence));
        writer.println("confidence interval small enough" + FileUtility.CSV_SPLIT_BY + finished);
        writer.close();
    } catch (FileNotFoundException e) {
        e.printStackTrace();
    } catch (UnsupportedEncodingException e) {
        e.printStackTrace();
    }
}

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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");
    }/*  ww  w .j  a va  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);
}

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

/**
 * Prints the result of the network stability Test
 *///from  w  w w.j  a  v  a  2s.  c om
public void generateNetworktestResult() {
    SummaryStatistics statistics = createNetworkStatistics();

    double standarddeviationResult = statistics.getStandardDeviation();
    double meanResult = statistics.getMean();

    // get Coefficient of variation
    this.networkTestResult = (standarddeviationResult / meanResult);
    this.networkTestResult = Math.round(networkTestResult * 100.0) / 100.0;
    System.out.println("--------------------Ergebnis NETWORK:" + standarddeviationResult + " - " + meanResult
            + " - " + (standarddeviationResult / meanResult));

    // get Result String
    if (this.networkTestResult < 0.5) {
        this.networkTestResultString = "stable";
    } else if (this.networkTestResult >= 0.5 && this.networkTestResult < 2.0) {
        this.networkTestResultString = "noisy";
    } else {
        this.networkTestResultString = "unstable";
    }

    this.networkTestFinished = true;

    fireModelChanged();
}

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