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:uk.ac.cam.cl.dtg.teaching.programmingtest.java.FittedCurve.java

/**
 * Attempt to fit this curve.// w w  w  .j a  v a 2s . c  om
 */
public void fit() {
    List<Point> mapped = mapValues(xs, ys);
    DescriptiveStatistics s = new DescriptiveStatistics();
    for (int i = 0; i < 20; ++i) {
        double r = fit(mapped, 0.5);
        s.addValue(r);
    }
    rsq = s.getMean();
}

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From source file:uk.ac.diamond.scisoft.analysis.diffraction.PowderRingsUtils.java

/**
 * Find major axes by looking along thick line given by relative coordinates to centre for
 * maximum intensity values/*from  www .  java2  s. c o m*/
 * @param mon
 * @param axes
 * @param image
 * @param mask
 * @param roi
 * @param offset minimum position of peaks
 * @param width of line
 * @param centre
 * @param dx
 * @param dy
 */
private static void findMajorAxes(IMonitor mon, TreeSet<Double> axes, Dataset image, Dataset mask,
        EllipticalROI roi, double offset, double width, double[] centre, double dx, double dy) {
    RectangularROI rroi = new RectangularROI();
    rroi.setPoint(centre);
    rroi.setAngle(Math.atan2(dy, dx));
    rroi.setLengths(Math.hypot(dx, dy), width);
    rroi.translate(0, -0.5);
    rroi.setClippingCompensation(true);
    Dataset profile = ROIProfile.maxInBox(image, mask, rroi)[0];

    List<IdentifiedPeak> peaks = Generic1DFitter
            .findPeaks(DatasetFactory.createRange(profile.getSize(), Dataset.INT), profile, PEAK_SMOOTHING);
    if (mon != null)
        mon.worked(profile.getSize());

    System.err.printf("\n");
    DescriptiveStatistics stats = new DescriptiveStatistics();
    int[] pb = new int[1];
    int[] pe = new int[1];
    for (IdentifiedPeak p : peaks) {
        if (p.getPos() < offset) {
            continue;
        }
        pb[0] = (int) p.getMinXVal();
        pe[0] = (int) p.getMaxXVal();
        p.setArea((Double) profile.getSlice(pb, pe, null).sum());
        stats.addValue(p.getArea());
        System.err.printf("P %f A %f W %f H %f\n", p.getPos(), p.getArea(), p.getFWHM(), p.getHeight());
    }

    double area = stats.getMean() + 0.4 * (stats.getPercentile(75) - stats.getPercentile(25));
    logger.debug("Area: {}", stats);
    logger.debug("Minimum threshold: {}", area);

    double majorFactor = roi.getSemiAxis(0) / roi.getDistance(rroi.getAngle());
    double maxFWHM = MAX_FWHM_FACTOR * width;
    for (IdentifiedPeak p : peaks) {
        double l = p.getPos();
        if (l < offset) {
            continue;
        }
        //         System.err.println(p);
        // filter on area and FWHM
        if (p.getFWHM() > maxFWHM) {
            continue;
        }
        if (p.getArea() < area) {
            break;
        }
        axes.add(l * majorFactor);
    }
    if (mon != null)
        mon.worked(peaks.size());

}

---

From source file:uk.ac.soton.itinnovation.ecc.service.utils.ExplorerDemoData.java

public EccINTRATSummary getINTRATDistDataDiscrete(UUID attrID, ArrayList<Date> stamps) {
    EccINTRATSummary result = null;//from w  ww  .  j a va  2  s .  c  o m

    // Safety
    if (attrID != null && stamps != null) {
        EccAttributeInfo info = qosAttributesByID.get(attrID);
        EccINTRATSeries srcSeries = qosSeries.get(attrID);

        if (info != null && srcSeries != null) {
            // Copy source series data with just those timestamps
            ArrayList<EccMeasurement> targMeasures = new ArrayList<>();

            for (EccMeasurement srcM : srcSeries.getValues())
                for (Date targDate : stamps)
                    if (srcM.getTimestamp().equals(targDate))
                        targMeasures.add(new EccMeasurement(srcM));

            // If we've got any data, create a summary
            if (!targMeasures.isEmpty()) {
                DescriptiveStatistics ds = new DescriptiveStatistics();

                for (EccMeasurement m : targMeasures)
                    ds.addValue(Double.parseDouble(m.getValue()));

                result = new EccINTRATSummary(info, ds.getMin(), ds.getMean(), ds.getMax());
            }
        }
    }

    return result;
}

---

From source file:uk.ac.soton.itinnovation.ecc.service.utils.ExplorerDemoData.java

public EccINTRATSummary getQosSummary(UUID attrID) {
    EccINTRATSummary result = null;//  w  w w .j av  a 2s.  c om

    if (attrID != null) {
        // Find QoS series and calculate summary data
        EccINTRATSeries series = qosSeries.get(attrID);

        if (series != null) {
            DescriptiveStatistics ds = new DescriptiveStatistics();

            for (EccMeasurement m : series.getValues())
                ds.addValue(Double.parseDouble(m.getValue()));

            result = new EccINTRATSummary(qosAttributesByID.get(attrID), ds.getMin(), ds.getMean(),
                    ds.getMax());
        }
    }

    return result;
}

---

From source file:uk.ac.soton.itinnovation.ecc.service.utils.MetricCalculator.java

public static Properties calcINTRATSummary(MeasurementSet ms) throws Exception {
    Properties result = new Properties();

    try {//from  www . ja  v  a 2 s .c o  m
        if (validateINTRATMeasurementSet(ms)) {
            DescriptiveStatistics ds = new DescriptiveStatistics();

            for (Measurement m : ms.getMeasurements())
                ds.addValue(Double.parseDouble(m.getValue()));

            result.put("floor", ds.getMin());
            result.put("mean", ds.getMean());
            result.put("ceiling", ds.getMax());
        }
    } catch (Exception ex) {
        throw ex;
    }

    return result;
}

---

From source file:util.Statistics.java

public Statistics(List<Integer> list) {
    scores = intsToDoubles(list);//  w ww . java 2s .c om
    DescriptiveStatistics dStats = new DescriptiveStatistics(scores);

    summaryStatistics.put("min", dStats.getMin()); // Minimum
    summaryStatistics.put("q1", dStats.getPercentile(25)); // Lower Quartile (Q1)
    summaryStatistics.put("q2", dStats.getPercentile(50)); // Middle Quartile (Median - Q2)
    summaryStatistics.put("q3", dStats.getPercentile(75)); // High Quartile (Q3)
    summaryStatistics.put("max", dStats.getMax()); // Maxiumum

    summaryStatistics.put("mean", dStats.getMean()); // Mean
    summaryStatistics.put("sd", dStats.getStandardDeviation()); // Standard Deviation

    EmpiricalDistribution distribution = new EmpiricalDistribution(NUM_BINS);
    distribution.load(scores);
    List<SummaryStatistics> binStats = distribution.getBinStats();
    double[] upperBounds = distribution.getUpperBounds();

    Double lastUpperBound = upperBounds[0];
    bins.add(new Pair<Pair<Double, Double>, Long>(
            new Pair<Double, Double>(summaryStatistics.get("min"), lastUpperBound), binStats.get(0).getN()));
    for (int i = 1; i < binStats.size(); i++) {
        bins.add(new Pair<Pair<Double, Double>, Long>(new Pair<Double, Double>(lastUpperBound, upperBounds[i]),
                binStats.get(i).getN()));
        lastUpperBound = upperBounds[i];
    }

    if (list.size() > 5 && dStats.getStandardDeviation() > 0) // Only remove outliers if relatively normal
    {
        double mean = dStats.getMean();
        double stDev = dStats.getStandardDeviation();
        NormalDistribution normalDistribution = new NormalDistribution(mean, stDev);

        Iterator<Integer> listIterator = list.iterator();
        double significanceLevel = .50 / list.size(); // Chauvenet's Criterion for Outliers
        while (listIterator.hasNext()) {
            int num = listIterator.next();
            double pValue = normalDistribution.cumulativeProbability(num);
            if (pValue < significanceLevel) {
                outliers.add(num);
                listIterator.remove();
            }
        }

        if (list.size() != dStats.getN()) // If and only if outliers have been removed
        {
            double[] significantData = intsToDoubles(list);
            dStats = new DescriptiveStatistics(significantData);

            summaryStatistics.put("min", dStats.getMin());
            summaryStatistics.put("max", dStats.getMax());
            summaryStatistics.put("mean", dStats.getMean());
            summaryStatistics.put("sd", dStats.getStandardDeviation());
        }
    }
}

---

From source file:weka.classifiers.timeseries.eval.TSEvaluation.java

public double CalcOutlierTreshold(DescriptiveStatistics MAPEstats) throws Exception {
    double tresh = 0;
    mape = MAPEstats.getMean();//result[i];
    std = MAPEstats.getStandardDeviation();
    median = MAPEstats.getPercentile(50);
    upperPercentile = MAPEstats.getPercentile(85);
    if (readTreshsFromFile == false) {
        if (mape < 1) {
            if (std < 5) {
                tresh = upperPercentile + 3 * std + mape + median;
                ;//from   w w  w  .jav  a  2 s . com
            } else if ((std >= 5) && (std < 10)) {
                tresh = upperPercentile + 1.5 * std + mape + median;
            } else if ((std >= 10)) {
                tresh = upperPercentile + 1 * std + mape + median;
            }
        } else if ((mape >= 1) && (mape < 2)) {
            if (std < 5) {
                tresh = upperPercentile + 3 * std + mape + median;
                ;
            } else if ((std >= 5) && (std < 10)) {
                tresh = upperPercentile + 2.5 * std + mape + median;
            } else if ((std >= 10)) {
                tresh = upperPercentile + 1 * std + mape + median;
            }

        } else if ((mape >= 2) && (mape < 5)) {
            if (std < 5) {
                tresh = 2 * upperPercentile + std + mape + 1.25 * median;
                ;
            } else if ((std >= 5) && (std < 10)) {
                tresh = 1 * upperPercentile + std + mape + 2.75 * median;
            } else if ((std >= 10)) {
                tresh = 0.5 * upperPercentile + std + mape + median;
            }

        } else if ((mape >= 5) && (mape < 10)) {
            if (std < 5) {
                tresh = 1.5 * upperPercentile + std + mape + 1.25 * median;
            } else if ((std >= 5) && (std < 7.5)) {
                tresh = 0.85 * upperPercentile + std + mape + 2.5 * median;
            } else if ((std >= 7.5) && (std < 10)) {
                tresh = 0.85 * upperPercentile + std + mape + 2.5 * median;
            } else if ((std >= 10)) {
                tresh = 0.85 * upperPercentile + std + mape + 2 * median;
            }
        } else if ((mape >= 10) && (mape < 15)) {
            if (std < 5) {
                tresh = 0.75 * upperPercentile + std + mape + 2 * median;
            } else if ((std >= 5) && (std < 10)) {
                tresh = 0.5 * upperPercentile + std + mape + 1.65 * median;
            } else if ((std >= 10) && (std < 15)) {
                tresh = 2 * upperPercentile + std + mape + 2.5 * median;
            } else if ((std >= 15) && (std < 20)) {
                tresh = 2 * upperPercentile + std + mape + 2 * median;
            } else if ((std >= 20)) {
                tresh = 0.5 * upperPercentile + std + mape + 2 * median;
            }
        } else if ((mape >= 15) && (mape < 20)) {
            if (std < 10) {
                tresh = 1 * upperPercentile + std + mape + 2 * median;
            } else if ((std >= 10) && (std < 15)) {
                tresh = 1 * upperPercentile + std + mape + 3 * median;
            } else if ((std >= 15) && (std < 20)) {
                tresh = 0.5 * upperPercentile + std + mape + 2 * median;
            } else if ((std >= 20)) {
                tresh = 1 * upperPercentile + std + mape + 2 * median;
            }
        } else if ((mape >= 20) && (mape < 30)) {
            if (std < 10) {
                tresh = 1 * upperPercentile + std + mape + 2 * median;
            } else if ((std >= 10) && (std < 20)) {
                tresh = 1.25 * upperPercentile + std + mape + 2 * median;
            } else if ((std >= 20)) {
                tresh = 1 * upperPercentile + std + mape + 2 * median;
            }
            //if (tresh>(3.5*mape))
            //   tresh=3.5*mape;
        } else {
            if (std < 10) {
                tresh = 1 * upperPercentile + std + mape + median;
            } else if ((std >= 10) && (std < 20)) {
                tresh = 1 * upperPercentile + std + mape + median;
            } else if ((std >= 20)) {
                tresh = 0.5 * upperPercentile + std + mape + median;
            }
            //if (tresh>(3*mape))
            //   tresh=3*mape;
        }
    } else {
        tresh = treshold;
    }
    return tresh;
    /*       if (upperPercentile>25)
              factor=3;
           else if ((upperPercentile>15)&&(upperPercentile>25))
              factor=2;
           else{
              factor=1.5;
           }
           if (mape<15){
              if ((mape<4)&&(median<=2)&&(std>7)){
     tresh= 4*factor*upperPercentile+2*std+mape+median;//+
              }
              else if ((median<4.0)&&(std<=7)){
     tresh= factor*upperPercentile+std+mape+median;//+
              }
              else if ((mape<10)&&(std>12)){
     tresh= factor*upperPercentile+mape+std;
              }
              else if ((mape<10)){
     tresh= upperPercentile+std+2*median;//factor*upperPercentile;//+2*median
              }
              else { 
     tresh =factor*upperPercentile+mape;//2.5*(std+mape);//+median
     if (std>=10)
        tresh=tresh+std;
              }
              if (tresh<1)
     tresh=2*tresh;
            
           }
           else{
              tresh =upperPercentile+2*(mape+median)+(std/2);
              if (tresh>(3.5*mape))
        tresh=3.5*mape;
           }
    */
    //  return tresh;
}

---

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