Example usage for org.apache.commons.math.stat.descriptive.moment Mean Mean

Introduction

In this page you can find the example usage for org.apache.commons.math.stat.descriptive.moment Mean Mean.

Prototype

public Mean() 

Source Link

Document

Constructs a Mean.

Usage

From source file:com.discursive.jccook.math.StatExample.java

public static void main(String[] args) {
    double[] values = new double[] { 2.3, 5.4, 6.2, 7.3, 23.3 };

    System.out.println("min: " + StatUtils.min(values));
    System.out.println("max: " + StatUtils.max(values));
    System.out.println("mean: " + StatUtils.mean(values));
    System.out.println("product: " + StatUtils.product(values));
    System.out.println("sum: " + StatUtils.sum(values));
    System.out.println("variance: " + StatUtils.variance(values));

    // Measures from previous example
    Min min = new Min();
    System.out.println("min: " + min.evaluate(values));
    Max max = new Max();
    System.out.println("max: " + max.evaluate(values));
    Mean mean = new Mean();
    System.out.println("mean: " + mean.evaluate(values));
    Product product = new Product();
    System.out.println("product: " + product.evaluate(values));
    Sum sum = new Sum();
    System.out.println("sum: " + sum.evaluate(values));
    Variance variance = new Variance();
    System.out.println("variance: " + variance.evaluate(values));

    // New measures
    Percentile percentile = new Percentile();
    System.out.println("80 percentile value: " + percentile.evaluate(values, 80.0));
    GeometricMean geoMean = new GeometricMean();
    System.out.println("geometric mean: " + geoMean.evaluate(values));
    StandardDeviation stdDev = new StandardDeviation();
    System.out.println("standard dev: " + stdDev.evaluate(values));
    Skewness skewness = new Skewness();
    System.out.println("skewness: " + skewness.evaluate(values));
    Kurtosis kurtosis = new Kurtosis();
    System.out.println("kurtosis: " + kurtosis.evaluate(values));

}

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From source file:com.ibm.streamsx.transportation.sfpark.ParkingFill.java

public ParkingFill aggregate(Iterable<ParkingOccupancy> items) {

    Mean mean = new Mean();

    int count = 0;
    for (ParkingOccupancy occupancy : items) {
        ospid = occupancy.getOspid();/*from w  w w . j ava  2  s. c o m*/
        // maintain the last values, as that's all
        // that matters for parking now!
        occ = occupancy.getOcc();
        oper = occupancy.getOper();
        setTs(occupancy.getTs());
        if (oper == 0)
            continue;
        count++;
        double fill = ((double) occ) / ((double) oper);
        mean.increment(fill);
    }

    if (ospid == null || oper == 0) {
        return null;
    }

    if (count > 5) {
        double[] values = new double[count * 2];
        int i = 0;
        for (ParkingOccupancy occupancy : items) {
            int occl = occupancy.getOcc();
            int operl = occupancy.getOper();
            long tsl = occupancy.getTs();
            if (operl == 0)
                continue;

            // y, then x
            // spaces (y) vs time (x)
            values[i++] = occl;
            values[i++] = tsl;
        }

        OLSMultipleLinearRegression ols = new OLSMultipleLinearRegression();
        ols.newSampleData(values, count, 1);

        double[] coe = ols.estimateRegressionParameters();
        if (coe.length >= 2)
            setTrend(coe[1] * 1000.0 * 60.0); // cars per minute
    }

    fill = (int) (mean.getResult() * 100.0);
    if (fill > 100)
        fill = 100;
    else if (fill < 0)
        fill = 0;

    return this;
}

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From source file:edu.harvard.med.screensaver.analysis.ZScoreFunction.java

public void initializeAggregates(Collection<Double> valuesToNormalizeOverController) {
    ResizableDoubleArray tmpDoubleValues = new ResizableDoubleArray();
    for (Double value : valuesToNormalizeOverController) {
        tmpDoubleValues.addElement(value);
    }// w w w. j a v  a2  s  . co  m
    _stdDev = new StandardDeviation().evaluate(tmpDoubleValues.getElements());
    _mean = new Mean().evaluate(tmpDoubleValues.getElements());
    _initialized = true;
}

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From source file:com.joliciel.jochre.stats.MeanAbsoluteDeviation.java

public double getResult() {
    if (dirty) {// www  . j a v  a 2s .  c o m
        Mean mean = new Mean();
        for (double value : values)
            mean.increment(value);
        double meanResult = mean.getResult();

        Mean deviationMean = new Mean();
        for (double value : values) {
            double deviation = value - meanResult;
            if (deviation < 0)
                deviation = 0 - deviation;
            deviationMean.increment(deviation);
        }

        meanAbsoluteDeviation = deviationMean.getResult();

        dirty = false;
    }
    return meanAbsoluteDeviation;
}

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From source file:de.tudarmstadt.ukp.dkpro.tc.weka.evaluation.MekaEvaluationUtils.java

/**
 * Calculates a number of evaluation measures for multi-label classification, without class-wise measures.
 * //from ww w.  java2 s . c  o m
 * @param predictions
 *            predictions by the classifier (ranking)
 * @param goldStandard
 *            gold standard (bipartition)
 * @param t
 *            a threshold to create bipartitions from rankings
 * @return the evaluation statistics
 */
public static HashMap<String, Double> calcMLStats(double predictions[][], int goldStandard[][], double t[]) {
    int N = goldStandard.length;
    int L = goldStandard[0].length;
    int Ypred[][] = ThresholdUtils.threshold(predictions, t);

    HashMap<String, Double> results = new LinkedHashMap<String, Double>();
    Mean mean = new Mean();

    results.put(NUMBER_LABELS, (double) L);
    results.put(NUMBER_EXAMPLES, (double) N);
    results.put(ZERO_ONE_LOSS, Metrics.L_ZeroOne(goldStandard, Ypred));
    results.put(LABEL_CARDINALITY_PRED, MLUtils.labelCardinality(Ypred));
    results.put(LABEL_CARDINALITY_REAL, MLUtils.labelCardinality(goldStandard));
    results.put(AVERAGE_THRESHOLD, mean.evaluate(t, 0, t.length)); // average
    results.put(EMPTY_VECTORS, MLUtils.emptyVectors(Ypred));

    return results;
}

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From source file:com.srotya.sidewinder.core.analytics.TestMathUtils.java

@Test
public void testMean() {
    double[] a = new double[] { 2, 3, 4, 5, 6 };
    double mean = MathUtils.mean(a);
    Mean cmean = new Mean();
    assertEquals(cmean.evaluate(a), mean, 0.0001);
}

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From source file:com.joliciel.jochre.graphics.features.EmptySectorsBinaryFeature.java

@Override
public FeatureResult<Boolean> checkInternal(ShapeWrapper shapeWrapper, RuntimeEnvironment env) {
    Shape shape = shapeWrapper.getShape();
    double[][] totals = shape.getBrightnessBySection(5, 5, 1, SectionBrightnessMeasurementMethod.RAW);

    Mean testMean = new Mean();
    Mean otherMean = new Mean();

    for (int i = 0; i < totals.length; i++) {
        for (int j = 0; j < totals[0].length; j++) {
            double brightness = totals[i][j];
            if (testSectors[i][j])
                testMean.increment(brightness);
            else if (brightness > shape.getBrightnessMeanBySection(5, 5, 1,
                    SectionBrightnessMeasurementMethod.RAW))
                otherMean.increment(brightness);
        }//from   w  ww. ja  va 2  s  .c  o  m
    }

    double testMeanValue = testMean.getResult();
    double otherMeanValue = otherMean.getResult();

    if (LOG.isDebugEnabled())
        LOG.trace("Test mean: " + testMeanValue + " (* threshold = " + testMeanValue * THRESHOLD
                + "), Other mean: " + otherMeanValue);

    boolean result = (testMeanValue * THRESHOLD < otherMeanValue);
    FeatureResult<Boolean> outcome = this.generateResult(result);
    return outcome;
}

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From source file:net.sf.jdmf.util.MathCalculator.java

/**
 * Calculates the mean of all attribute values.
 * //from w w  w . j ava  2  s.  c  o  m
 * @param attributeValues attribute values
 * @return the mean
 */
public Double calculateMean(Comparable[] attributeValues) {
    Mean mean = new Mean();

    Double evaluatedMean = mean.evaluate(convertToPrimitives(attributeValues));

    log.debug("mean = " + evaluatedMean);

    return evaluatedMean;
}

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From source file:fr.ens.transcriptome.corsen.util.StatTest.java

public void testMean() {

    Mean mean = new Mean();

    for (int i = 0; i < 1000; i++) {

        List<DataDouble> list = generate();
        assertEquals(mean.evaluate(Stats.toDouble(list)), Stats.mean(list));
    }//w  w  w  . j  a v a2 s.  c  o m
}

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From source file:com.linkedin.pinot.transport.common.routing.RandomRoutingTableTest.java

@Test
public void testHelixExternalViewBasedRoutingTable() throws Exception {
    String tableName = "testTable_OFFLINE";
    String fileName = RandomRoutingTableTest.class.getClassLoader().getResource("SampleExternalView.json")
            .getFile();//from  w  w w .  j a v  a 2  s .c om
    System.out.println(fileName);
    InputStream evInputStream = new FileInputStream(fileName);
    ZNRecordSerializer znRecordSerializer = new ZNRecordSerializer();
    ZNRecord externalViewRecord = (ZNRecord) znRecordSerializer.deserialize(IOUtils.toByteArray(evInputStream));
    int totalRuns = 10000;
    RoutingTableBuilder routingStrategy = new BalancedRandomRoutingTableBuilder(10);
    HelixExternalViewBasedRouting routingTable = new HelixExternalViewBasedRouting(null, null);
    Field offlineRTBField = HelixExternalViewBasedRouting.class.getDeclaredField("_offlineRoutingTableBuilder");
    offlineRTBField.setAccessible(true);
    offlineRTBField.set(routingTable, routingStrategy);

    ExternalView externalView = new ExternalView(externalViewRecord);

    routingTable.markDataResourceOnline(tableName, externalView, getInstanceConfigs(externalView));

    double[] globalArrays = new double[9];

    for (int numRun = 0; numRun < totalRuns; ++numRun) {
        RoutingTableLookupRequest request = new RoutingTableLookupRequest(tableName);
        Map<ServerInstance, SegmentIdSet> serversMap = routingTable.findServers(request);
        TreeSet<ServerInstance> serverInstances = new TreeSet<ServerInstance>(serversMap.keySet());

        int i = 0;

        double[] arrays = new double[9];
        for (ServerInstance serverInstance : serverInstances) {
            globalArrays[i] += serversMap.get(serverInstance).getSegments().size();
            arrays[i++] = serversMap.get(serverInstance).getSegments().size();
        }
        for (int j = 0; i < arrays.length; ++j) {
            Assert.assertTrue(arrays[j] / totalRuns <= 31);
            Assert.assertTrue(arrays[j] / totalRuns >= 28);
        }
        //System.out.println(Arrays.toString(arrays) + " : " + new StandardDeviation().evaluate(arrays) + " : " + new Mean().evaluate(arrays));
    }
    for (int i = 0; i < globalArrays.length; ++i) {
        Assert.assertTrue(globalArrays[i] / totalRuns <= 31);
        Assert.assertTrue(globalArrays[i] / totalRuns >= 28);
    }
    System.out.println(Arrays.toString(globalArrays) + " : " + new StandardDeviation().evaluate(globalArrays)
            + " : " + new Mean().evaluate(globalArrays));
}

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