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

Introduction

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

Prototype

public double getMax()

Source Link

Document

Returns the maximum of the available values

Usage

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 w w w  .j av  a 2 s. co 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);/*from   w  w w .j a  v  a 2  s . com*/
    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());
        }
    }
}