Example usage for org.apache.commons.math3.stat.correlation KendallsCorrelation KendallsCorrelation

List of usage examples for org.apache.commons.math3.stat.correlation KendallsCorrelation KendallsCorrelation

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Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.correlation KendallsCorrelation KendallsCorrelation.

Prototype

public KendallsCorrelation()

Source Link

Document

Create a KendallsCorrelation instance without data.

Usage

From source file:edu.umd.umiacs.clip.tools.math.CorrelationUtils.java

public static void main(String[] args) {
    double[] x = { 0.9, 0.6, 0.4, 0.3, 0.2, 0.1 };
    double[] y = { 0.5, 0.6, 0.4, 0.3, 0.2, 0.1 };
    System.out.println(new PearsonsCorrelation().correlation(x, y));
    System.out.println(new KendallsCorrelation().correlation(x, y));
    System.out.println(tauAP(x, y));
    System.out.println(tauGAP(x, y));
    System.out.println(pr(x, y));
}

From source file:elaborate.tag_analysis.feature.KendallsCorrelationDistanceCalculatorImpl.java

@Override
public double getDistance(double[] feature1, double[] feature2) {
    KendallsCorrelation p = new KendallsCorrelation();
    double cor = p.correlation(feature1, feature2);
    return 1.0 / (cor + 2);//prevent negative value
}

From source file:Evaluator.StatCalculator.java

public double kendalTauCalc(HashMap<String, Double> kderunQidMap, HashMap<String, Double> aprunQidMap) {
    double xArray[], yArray[];
    int count = 0;

    xArray = new double[kderunQidMap.size()];
    yArray = new double[kderunQidMap.size()];

    Iterator it = kderunQidMap.keySet().iterator();
    while (it.hasNext()) {
        String run = (String) it.next();
        xArray[count] = kderunQidMap.get(run);
        yArray[count++] = aprunQidMap.get(run);
    }/*from w  w  w . ja  v a 2  s.co  m*/

    KendallsCorrelation kc = new KendallsCorrelation();
    return kc.correlation(xArray, yArray);

}

From source file:eu.eexcess.diversityasurement.CorrelationTest.java

@Test
public void kendallsTao_givenAKartesianQuadrandIandIIISymmetraleParallelLine() {
    CorrelationValues c = newCorrelationValue3();
    KendallsCorrelation kCorrelation = new KendallsCorrelation();
    System.out.println("kendall's tao correlation t(x, y)=" + kCorrelation.correlation(c.x, c.y));
    assertEquals(c.kendallTaoB, kCorrelation.correlation(c.x, c.y), c.epsilon);
}

From source file:eu.eexcess.diversityasurement.CorrelationTest.java

@Test
public void kendallsTao_givenAKartesianQuadrandIIandIVSymmetralePrallelLine() {
    CorrelationValues c = newCorrelationValue2();
    KendallsCorrelation kCorrelation = new KendallsCorrelation();
    System.out.println("kendall's tao correlation t(x, y)=" + kCorrelation.correlation(c.x, c.y));
    assertEquals(c.kendallTaoB, kCorrelation.correlation(c.x, c.y), c.epsilon);
}

From source file:eu.eexcess.diversityasurement.CorrelationTest.java

@Test
public void kendallsTao_givenDefaultValues_expectCorrectResult() {
    CorrelationValues c = newCorrelationValue1();
    KendallsCorrelation kCorrelation = new KendallsCorrelation();
    System.out.println("kendall's tao correlation t(x, y)=" + kCorrelation.correlation(c.x, c.y));
    assertEquals(c.kendallTaoB, kCorrelation.correlation(c.x, c.y), c.epsilon);
}

From source file:org.apache.solr.client.solrj.io.eval.CorrelationEvaluator.java

@Override
public Object doWork(Object... values) throws IOException {

    if (values.length == 2) {
        Object first = values[0];
        Object second = values[1];

        if (null == first) {
            throw new IOException(
                    String.format(Locale.ROOT, "Invalid expression %s - null found for the first value",
                            toExpression(constructingFactory)));
        }// w ww . jav  a  2s  . c  o m
        if (null == second) {
            throw new IOException(
                    String.format(Locale.ROOT, "Invalid expression %s - null found for the second value",
                            toExpression(constructingFactory)));
        }
        if (!(first instanceof List<?>)) {
            throw new IOException(String.format(Locale.ROOT,
                    "Invalid expression %s - found type %s for the first value, expecting a list of numbers",
                    toExpression(constructingFactory), first.getClass().getSimpleName()));
        }
        if (!(second instanceof List<?>)) {
            throw new IOException(String.format(Locale.ROOT,
                    "Invalid expression %s - found type %s for the second value, expecting a list of numbers",
                    toExpression(constructingFactory), first.getClass().getSimpleName()));
        }

        if (type.equals(CorrelationType.pearsons)) {
            PearsonsCorrelation pearsonsCorrelation = new PearsonsCorrelation();
            return pearsonsCorrelation.correlation(
                    ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(),
                    ((List) second).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue())
                            .toArray());
        } else if (type.equals(CorrelationType.kendalls)) {
            KendallsCorrelation kendallsCorrelation = new KendallsCorrelation();
            return kendallsCorrelation.correlation(
                    ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(),
                    ((List) second).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue())
                            .toArray());

        } else if (type.equals(CorrelationType.spearmans)) {
            SpearmansCorrelation spearmansCorrelation = new SpearmansCorrelation();
            return spearmansCorrelation.correlation(
                    ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(),
                    ((List) second).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue())
                            .toArray());
        } else {
            return null;
        }
    } else if (values.length == 1) {
        if (values[0] instanceof Matrix) {
            Matrix matrix = (Matrix) values[0];
            double[][] data = matrix.getData();
            if (type.equals(CorrelationType.pearsons)) {
                PearsonsCorrelation pearsonsCorrelation = new PearsonsCorrelation(data);
                RealMatrix corrMatrix = pearsonsCorrelation.getCorrelationMatrix();
                double[][] corrMatrixData = corrMatrix.getData();
                Matrix realMatrix = new Matrix(corrMatrixData);
                realMatrix.addToContext("corr", pearsonsCorrelation);
                return realMatrix;
            } else if (type.equals(CorrelationType.kendalls)) {
                KendallsCorrelation kendallsCorrelation = new KendallsCorrelation(data);
                RealMatrix corrMatrix = kendallsCorrelation.getCorrelationMatrix();
                double[][] corrMatrixData = corrMatrix.getData();
                Matrix realMatrix = new Matrix(corrMatrixData);
                realMatrix.addToContext("corr", kendallsCorrelation);
                return realMatrix;
            } else if (type.equals(CorrelationType.spearmans)) {
                SpearmansCorrelation spearmansCorrelation = new SpearmansCorrelation(
                        new Array2DRowRealMatrix(data));
                RealMatrix corrMatrix = spearmansCorrelation.getCorrelationMatrix();
                double[][] corrMatrixData = corrMatrix.getData();
                Matrix realMatrix = new Matrix(corrMatrixData);
                realMatrix.addToContext("corr", spearmansCorrelation.getRankCorrelation());
                return realMatrix;
            } else {
                return null;
            }
        } else {
            throw new IOException(
                    "corr function operates on either two numeric arrays or a single matrix as parameters.");
        }
    } else {
        throw new IOException(
                "corr function operates on either two numeric arrays or a single matrix as parameters.");
    }
}

From source file:org.apache.solr.client.solrj.io.eval.KendallsCorrelationEvaluator.java

@Override
public Object doWork(Object first, Object second) throws IOException {
    if (null == first) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - null found for the first value", toExpression(constructingFactory)));
    }//from w w  w  .jav a 2  s .  c om
    if (null == second) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - null found for the second value", toExpression(constructingFactory)));
    }
    if (!(first instanceof List<?>)) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - found type %s for the first value, expecting a list of numbers",
                toExpression(constructingFactory), first.getClass().getSimpleName()));
    }
    if (!(second instanceof List<?>)) {
        throw new IOException(String.format(Locale.ROOT,
                "Invalid expression %s - found type %s for the second value, expecting a list of numbers",
                toExpression(constructingFactory), first.getClass().getSimpleName()));
    }

    KendallsCorrelation kendallsCorrelation = new KendallsCorrelation();

    return kendallsCorrelation.correlation(
            ((List) first).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray(),
            ((List) second).stream().mapToDouble(value -> ((BigDecimal) value).doubleValue()).toArray());
}

From source file:org.meteoinfo.math.stats.StatsUtil.java

/**
 * Calculates Kendall's tau, a correlation measure for ordinal data.
 *
 * @param x X data/*w ww .j av a2s  .c o m*/
 * @param y Y data
 * @return Kendall's tau correlation.
 */
public static double kendalltau(Array x, Array y) {
    double[] xd = (double[]) ArrayUtil.copyToNDJavaArray(x);
    double[] yd = (double[]) ArrayUtil.copyToNDJavaArray(y);
    KendallsCorrelation kc = new KendallsCorrelation();
    double r = kc.correlation(xd, yd);
    return r;
}

From source file:tech.tablesaw.api.NumberColumnTest.java

@Test
public void testCorrelation() {
    double[] x = new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
    double[] y = new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };

    DoubleColumn xCol = DoubleColumn.create("x", x);
    DoubleColumn yCol = DoubleColumn.create("y", y);

    double resultP = xCol.pearsons(yCol);
    double resultS = xCol.spearmans(yCol);
    double resultK = xCol.kendalls(yCol);
    assertEquals(new PearsonsCorrelation().correlation(x, y), resultP, 0.0001);
    assertEquals(new SpearmansCorrelation().correlation(x, y), resultS, 0.0001);
    assertEquals(new KendallsCorrelation().correlation(x, y), resultK, 0.0001);
}