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

Introduction

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

Prototype

public double getMin()

Source Link

Document

Returns the minimum of the available values

Usage

From source file:util.Statistics.java

public Statistics(List<Integer> list) {
    scores = intsToDoubles(list);/*from www  .j a v  a 2s . 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());
        }
    }
}