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

List of usage examples for org.apache.commons.math3.stat.descriptive DescriptiveStatistics addValue

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Introduction

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

Prototype

public void addValue(double v)

Source Link

Document

Adds the value to the dataset.

Usage

From source file:net.adamjak.thomas.graph.application.commons.StatisticsUtils.java

public static DescriptiveStatistics statisticsWithoutExtremes(DescriptiveStatistics inputStatistics,
        GrubbsLevel grubbsLevel) throws IllegalArgumentException {
    if (inputStatistics == null || grubbsLevel == null)
        throw new IllegalArgumentException("Params inputStatistics and grubbsLevel can not be null.");

    int countInput = inputStatistics.getValues().length;
    Double avgInput = inputStatistics.getMean();
    Double stdInput = inputStatistics.getStandardDeviation();
    Double s = stdInput * Math.sqrt((countInput - 1.0) / countInput);
    Double criticalValue = grubbsLevel.getCriticalValue(countInput);

    DescriptiveStatistics outputStatistic = new DescriptiveStatistics();

    for (double inpVal : inputStatistics.getValues()) {
        double test = Math.abs(inpVal - avgInput) / s;

        if (test <= criticalValue) {
            outputStatistic.addValue(inpVal);
        }//from  w ww .j  av  a 2  s.  c o  m
    }

    return outputStatistic;
}

From source file:nars.truth.Truth.java

/** provides a statistics summary (mean, min, max, variance, etc..) of a particular TruthValue component across a given list of Truthables (sentences, TruthValue's, etc..).  null values in the iteration are ignored */
@NotNull//from w w w  . j a  v a 2  s . c om
static DescriptiveStatistics statistics(@NotNull Iterable<? extends Truthed> t,
        @NotNull TruthComponent component) {
    DescriptiveStatistics d = new DescriptiveStatistics();
    for (Truthed x : t) {
        Truth v = x.truth();
        if (v != null)
            d.addValue(v.getComponent(component));
    }
    return d;
}

From source file:com.caseystella.analytics.distribution.Distribution.java

public static double getMadScore(Iterable<Double> vals, Double val) {
    DescriptiveStatistics stats = new DescriptiveStatistics();
    DescriptiveStatistics medianStats = new DescriptiveStatistics();
    for (Double v : vals) {
        stats.addValue(v);
    }//  ww w. j  a  v  a2s.  c  o  m
    double median = stats.getPercentile(50);
    for (Double v : vals) {
        medianStats.addValue(Math.abs(v - median));
    }
    double mad = medianStats.getPercentile(50);
    return Math.abs(0.6745 * (val - median) / mad);
}

From source file:com.caseystella.analytics.outlier.streaming.mad.ConfusionMatrix.java

public static Map<ConfusionEntry, Long> getConfusionMatrix(Set<Long> expectedOutliers,
        Set<Long> computedOutliers, long numObservations, long meanDiffBetweenTs, int timeBounds,
        Map<Long, Outlier> outlierMap, DescriptiveStatistics globalExpectedOutlierScoreStats) {
    Map<ConfusionEntry, Long> ret = new HashMap<>();
    for (ResultType r : ResultType.values()) {
        for (ResultType s : ResultType.values()) {
            ret.put(new ConfusionEntry(r, s), 0L);
        }//from  w  ww  .  jav a 2  s .com
    }
    int unionSize = 0;
    DescriptiveStatistics expectedOutlierScoreStats = new DescriptiveStatistics();
    for (Long expectedOutlier : expectedOutliers) {
        Outlier o = outlierMap.get(expectedOutlier);
        if (o.getScore() != null) {
            expectedOutlierScoreStats.addValue(o.getScore());
            globalExpectedOutlierScoreStats.addValue(o.getScore());
        }
        if (setContains(computedOutliers, expectedOutlier, meanDiffBetweenTs, timeBounds)) {
            ConfusionEntry entry = new ConfusionEntry(ResultType.OUTLIER, ResultType.OUTLIER);
            ConfusionEntry.increment(entry, ret);
            unionSize++;
        } else {
            ConfusionEntry entry = new ConfusionEntry(ResultType.NON_OUTLIER, ResultType.OUTLIER);
            long closest = closest(expectedOutlier, computedOutliers);
            long delta = Math.abs(expectedOutlier - closest);
            if (closest != Long.MAX_VALUE) {
                System.out.println("Missed an outlier (" + expectedOutlier + ") wasn't in computed outliers ("
                        + o + "), closest point is " + closest + " which is " + timeConversion(delta)
                        + "away. - E[delta t] " + timeConversion(meanDiffBetweenTs) + "");
            } else {
                System.out.println("Missed an outlier (" + expectedOutlier + ") wasn't in computed outliers ("
                        + o + "), which is empty. - E[delta t] " + timeConversion(meanDiffBetweenTs) + "");
            }
            ConfusionEntry.increment(entry, ret);
            unionSize++;
        }
    }
    printStats("Expected Outlier Score Stats", expectedOutlierScoreStats);
    DescriptiveStatistics computedOutlierScoreStats = new DescriptiveStatistics();
    for (Long computedOutlier : computedOutliers) {
        if (!setContains(expectedOutliers, computedOutlier, meanDiffBetweenTs, timeBounds)) {
            Outlier o = outlierMap.get(computedOutlier);
            if (o.getScore() != null) {
                computedOutlierScoreStats.addValue(o.getScore());
            }
            ConfusionEntry entry = new ConfusionEntry(ResultType.OUTLIER, ResultType.NON_OUTLIER);
            ConfusionEntry.increment(entry, ret);
            unionSize++;
        }
    }
    printStats("Computed Outlier Scores", computedOutlierScoreStats);
    ret.put(new ConfusionEntry(ResultType.NON_OUTLIER, ResultType.NON_OUTLIER), numObservations - unionSize);
    Assert.assertEquals(numObservations, getTotalNum(ret));
    return ret;
}

From source file:main.java.metric.Metric.java

public static double getMeanServerData(Cluster cluster) {
    DescriptiveStatistics server_data = new DescriptiveStatistics();

    for (Server server : cluster.getServers())
        server_data.addValue(server.getServer_total_data());

    return server_data.getMean();
}

From source file:main.java.metric.Metric.java

public static double getCVServerData(Cluster cluster) {
    DescriptiveStatistics server_data = new DescriptiveStatistics();

    for (Server server : cluster.getServers())
        server_data.addValue(server.getServer_total_data());

    double c_v = server_data.getStandardDeviation() / server_data.getMean();
    return c_v;// w  w  w.  j av a  2  s  .  co  m
}

From source file:main.java.metric.Metric.java

public static double getMeanServerData(Cluster cluster, Transaction tr) {
    DescriptiveStatistics server_data = new DescriptiveStatistics();

    for (Entry<Integer, HashSet<Integer>> entry : tr.getTr_serverSet().entrySet()) {
        server_data.addValue(entry.getValue().size());
    }/*from  ww w.  j  a  v a2s  .  c  o  m*/

    return server_data.getMean();
}

From source file:main.java.metric.Metric.java

public static double getCVServerData(Cluster cluster, Transaction tr) {
    DescriptiveStatistics server_data = new DescriptiveStatistics();

    for (Entry<Integer, HashSet<Integer>> entry : tr.getTr_serverSet().entrySet()) {
        server_data.addValue(entry.getValue().size());
    }//  ww w .  j  a  va 2s  .c  om

    double c_v = server_data.getStandardDeviation() / server_data.getMean();
    return c_v;
}

From source file:main.java.repartition.RBPTA.java

private static double getLbGain(Cluster cluster, SwappingCandidate sc) {

    DescriptiveStatistics sc_partition_data = new DescriptiveStatistics();

    for (Partition p : cluster.getPartitions())
        if (sc.p_pair.x == p.getPartition_id() || sc.p_pair.y == p.getPartition_id())
            sc_partition_data.addValue(p.getPartition_dataSet().size());

    return sc_partition_data.getVariance();
}

From source file:cc.kave.commons.pointsto.evaluation.runners.ProjectStoreRunner.java

private static void countRecvCallSites(Collection<ICoReTypeName> types, ProjectUsageStore store)
        throws IOException {
    DescriptiveStatistics statistics = new DescriptiveStatistics();
    for (ICoReTypeName type : types) {
        if (store.getProjects(type).size() < 10) {
            continue;
        }/* w  ww . j a va2s  . c om*/

        int numDistinctRecvCallsite = store.load(type, new PointsToUsageFilter()).stream()
                .flatMap(usage -> usage.getReceiverCallsites().stream()).map(CallSite::getMethod)
                .collect(Collectors.toSet()).size();
        if (numDistinctRecvCallsite > 0) {
            statistics.addValue(numDistinctRecvCallsite);
            System.out.printf(Locale.US, "%s: %d\n", CoReNames.vm2srcQualifiedType(type),
                    numDistinctRecvCallsite);
        }
    }
    System.out.println();
    System.out.printf(Locale.US, "mean: %.3f, stddev: %.3f, median: %.1f\n", statistics.getMean(),
            statistics.getStandardDeviation(), statistics.getPercentile(50));
}