Example usage for org.apache.commons.math.stat.correlation PearsonsCorrelation correlation

Introduction

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

Prototype

public double correlation(final double[] xArray, final double[] yArray) throws IllegalArgumentException 

Source Link

Document

Computes the Pearson's product-moment correlation coefficient between the two arrays.

Usage

From source file:com.test.zmisc.App.java

public static void main(String[] args) {
    double[] a = new double[32768];
    double[] b = new double[32768];

    ThreadLocalRandom rand = ThreadLocalRandom.current();

    for (int i = 0; i < a.length; i++) {
        a[i] = rand.nextDouble();/*from  w ww .  j  a va2  s  .c om*/
        b[i] = a[i];
    }

    long ts = System.currentTimeMillis();
    PearsonsCorrelation corr = new PearsonsCorrelation();
    corr.correlation(a, b);
    ts = System.currentTimeMillis() - ts;
    System.out.println("Time elapsed:" + ts + "ms");

    // standard deviation is the sqrt(mean((X-avg)^2))

    ts = System.currentTimeMillis();
    double amean = average(a);
    double aStdDev = standardDeviation(a, amean);

    StandardDeviation sd = new StandardDeviation();
    sd.setBiasCorrected(false);

    double bmean = average(b);
    double bStdDev = standardDeviation(b, bmean);

    double cor = (covariance(a, aStdDev, b, bmean)) / (aStdDev * bStdDev);
    ts = System.currentTimeMillis() - ts;
    System.out.println("Time elapsed:" + ts + "ms");
}

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From source file:com.ibm.bluej.commonutil.DenseVectors.java

public static double pearsonsR(double[] x, double[] y) {
    PearsonsCorrelation pc = new PearsonsCorrelation();
    double corrValue = pc.correlation(x, y);

    if (new Double(corrValue).isNaN())
        corrValue = 0.0;/*from  w w w .j ava 2 s .  c om*/

    return corrValue;
}

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

@Test
public void testPPMCC() {
    double[] a = new double[] { 2, 3, 4, 5, 6 };
    double[] b = new double[] { 2.2, 33.2, 44.4, 55.5, 66.6 };
    PearsonsCorrelation corr = new PearsonsCorrelation();
    double ppmcc = MathUtils.ppmcc(a, b);
    double correlation = corr.correlation(a, b);
    assertEquals(correlation, ppmcc, 0.001);
}

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

@Test
public void testPPMCCMulti() {
    double[][] data = new double[][] { new double[] { 2, 3, 4, 5, 6 },
            new double[] { 2.2, 33.2, 44.4, 55.5, 66.6 }, new double[] { 2, 3, 4, 5, 6 } };
    PearsonsCorrelation corr = new PearsonsCorrelation();
    double[] ppmcc = MathUtils.ppmcc(data, 0);
    for (int i = 0; i < data.length; i++) {
        double correlation = corr.correlation(data[0], data[i]);
        assertEquals(ppmcc[i], correlation, 0.001);
    }//from  w w  w.j  av  a 2  s  .c o m
}

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From source file:dkpro.similarity.algorithms.structure.TokenPairDistanceMeasure.java

@Override
public double getSimilarity(Collection<String> stringList1, Collection<String> stringList2)
        throws SimilarityException {
    // Transform input lists into lowercase string lists
    List<String> sl1 = new ArrayList<String>();
    for (String s : stringList1) {
        sl1.add(s.toLowerCase());/* w  ww .  j a  va  2 s. c o  m*/
    }

    List<String> sl2 = new ArrayList<String>();
    for (String s : stringList2) {
        sl2.add(s.toLowerCase());
    }

    // Get word sets
    Set<String> strings1 = new HashSet<String>(sl1);
    Set<String> strings2 = new HashSet<String>(sl2);

    // Get a common word list
    List<String> commonStrings = new ArrayList<String>(strings1);
    commonStrings.retainAll(strings2);

    // Build up pairs (ignoring order, i.e. a-b or b-a)
    Set<Pair> pairs = new HashSet<Pair>();
    for (String s1 : commonStrings) {
        for (String s2 : commonStrings) {
            if (!s1.equals(s2)) {
                Pair p = new Pair(s1, s2);
                pairs.add(p);
            }
        }
    }

    if (pairs.size() > 1) {
        double[] v1 = new double[pairs.size()];
        double[] v2 = new double[pairs.size()];

        List<Pair> pairList = new ArrayList<Pair>(pairs);

        for (int i = 0; i < pairList.size(); i++) {
            Pair p = pairList.get(i);

            int idx1a = sl1.indexOf(p.getString1());
            int idx1b = sl1.indexOf(p.getString2());
            int idx1diff = transform(idx1a - idx1b);

            int idx2a = sl2.indexOf(p.getString1());
            int idx2b = sl2.indexOf(p.getString2());
            int idx2diff = transform(idx2a - idx2b);

            v1[i] = idx1diff;
            v2[i] = idx2diff;
        }

        PearsonsCorrelation pearson = new PearsonsCorrelation();
        return pearson.correlation(v1, v2);
    }

    return 0.0;
}

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From source file:cal.binBased.Calculate_BinSpectrum_Similarity.java

/**
 * This method calculates correlation coefficients between two MSnSpectrum
 * object while taking into account of intensities of the mutual peaks. A
 * perfect score is 1 (or -1). And value range is [-1:1]
 *
 *//*from  w ww  .  j  ava 2 s  . c  o m*/
public void calculateCorrelation() {
    // TODO: Make sure if any of binSpectra is empty, score i NaN!       
    if (sim_method.equals(SimilarityMethods.SPEARMANS_CORRELATION)) {
        SpearmansCorrelation sc = new SpearmansCorrelation();
        score = sc.correlation(bin_specA.getBin_spectrum(), bin_specB.getBin_spectrum());
    } else if (sim_method.equals(SimilarityMethods.PEARSONS_CORRELATION)) {
        PearsonsCorrelation pc = new PearsonsCorrelation();
        score = pc.correlation(bin_specA.getBin_spectrum(), bin_specB.getBin_spectrum());
    }
}

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From source file:org.dkpro.similarity.algorithms.structure.TokenPairDistanceMeasure.java

@Override
public double getSimilarity(Collection<String> stringList1, Collection<String> stringList2)
        throws SimilarityException {

    // Transform input lists into lowercase string lists
    List<String> sl1 = new LinkedList<String>();
    for (String s : stringList1) {
        sl1.add(s.toLowerCase());//  w w  w  . j a va  2s  .  co  m
    }

    List<String> sl2 = new LinkedList<String>();
    for (String s : stringList2) {
        sl2.add(s.toLowerCase());
    }

    // Get word sets
    Set<String> strings1 = new LinkedHashSet<String>(sl1);
    Set<String> strings2 = new LinkedHashSet<String>(sl2);

    // Get a common word list
    List<String> commonStrings = new LinkedList<String>(strings1);
    commonStrings.retainAll(strings2);

    // Build up pairs (ignoring order, i.e. a-b or b-a)
    Set<Pair> pairs = new LinkedHashSet<Pair>();
    for (String s1 : commonStrings) {
        for (String s2 : commonStrings) {
            if (!s1.equals(s2)) {
                Pair p = new Pair(s1, s2);
                pairs.add(p);
            }
        }
    }

    if (pairs.size() > 1) {
        double[] v1 = new double[pairs.size()];
        double[] v2 = new double[pairs.size()];

        List<Pair> pairList = new LinkedList<Pair>(pairs);

        for (int i = 0; i < pairList.size(); i++) {
            Pair p = pairList.get(i);

            int idx1a = sl1.indexOf(p.getString1());
            int idx1b = sl1.indexOf(p.getString2());
            int idx1diff = transform(idx1a - idx1b);

            int idx2a = sl2.indexOf(p.getString1());
            int idx2b = sl2.indexOf(p.getString2());
            int idx2diff = transform(idx2a - idx2b);

            v1[i] = idx1diff;
            v2[i] = idx2diff;
        }

        PearsonsCorrelation pearson = new PearsonsCorrelation();
        return pearson.correlation(v1, v2);
    }

    return 0.0;
}

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From source file:org.dkpro.similarity.algorithms.style.FunctionWordFrequenciesMeasure.java

@Override
public double getSimilarity(JCas jcas1, JCas jcas2) throws SimilarityException {
    double[] v1 = getDocumentVector(jcas1);
    double[] v2 = getDocumentVector(jcas2);

    PearsonsCorrelation p = new PearsonsCorrelation();
    return p.correlation(v1, v2);
}

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From source file:org.dkpro.similarity.algorithms.style.FunctionWordFrequenciesMeasure.java

@Override
public double getSimilarity(JCas jcas1, JCas jcas2, Annotation coveringAnnotation1,
        Annotation coveringAnnotation2) throws SimilarityException {
    double[] v1 = getDocumentVector(jcas1, coveringAnnotation1);
    double[] v2 = getDocumentVector(jcas2, coveringAnnotation2);

    PearsonsCorrelation p = new PearsonsCorrelation();
    return p.correlation(v1, v2);
}

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From source file:org.dkpro.similarity.experiments.sts2013.util.Evaluator.java

@SuppressWarnings("unchecked")
public static void runEvaluationMetric(Mode mode, EvaluationMetric metric, Dataset... datasets)
        throws IOException {
    StringBuilder sb = new StringBuilder();

    // Compute Pearson correlation for the specified datasets
    for (Dataset dataset : datasets) {
        computePearsonCorrelation(mode, dataset);
    }/*w  ww  . ja va 2  s.co  m*/

    if (metric == PearsonAll) {
        List<Double> concatExp = new ArrayList<Double>();
        List<Double> concatGS = new ArrayList<Double>();

        // Concat the scores
        for (Dataset dataset : datasets) {
            File expScoresFile = new File(
                    OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv");

            List<String> lines = FileUtils.readLines(expScoresFile);

            for (String line : lines) {
                concatExp.add(Double.parseDouble(line));
            }
        }

        // Concat the gold standard
        for (Dataset dataset : datasets) {
            String gsScoresFilePath = GOLDSTANDARD_DIR + "/" + mode.toString().toLowerCase() + "/" + "STS.gs."
                    + dataset.toString() + ".txt";

            PathMatchingResourcePatternResolver r = new PathMatchingResourcePatternResolver();
            Resource res = r.getResource(gsScoresFilePath);
            File gsScoresFile = res.getFile();

            List<String> lines = FileUtils.readLines(gsScoresFile);

            for (String line : lines) {
                concatGS.add(Double.parseDouble(line));
            }
        }

        double[] concatExpArray = ArrayUtils.toPrimitive(concatExp.toArray(new Double[concatExp.size()]));
        double[] concatGSArray = ArrayUtils.toPrimitive(concatGS.toArray(new Double[concatGS.size()]));

        PearsonsCorrelation pearson = new PearsonsCorrelation();
        Double correl = pearson.correlation(concatExpArray, concatGSArray);

        sb.append(correl.toString());
    } else if (metric == PearsonMean) {
        List<Double> scores = new ArrayList<Double>();

        for (Dataset dataset : datasets) {
            File resultFile = new File(
                    OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".txt");
            double score = Double.parseDouble(FileUtils.readFileToString(resultFile));

            scores.add(score);
        }

        double mean = 0.0;
        for (Double score : scores) {
            mean += score;
        }
        mean = mean / scores.size();

        sb.append(mean);
    } else if (metric == PearsonWeightedMean) {
        List<Double> scores = new ArrayList<Double>();
        List<Integer> weights = new ArrayList<Integer>();

        for (Dataset dataset : datasets) {
            File resultFile = new File(
                    OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".txt");
            double score = Double.parseDouble(FileUtils.readFileToString(resultFile));

            File scoresFile = new File(
                    OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv");
            int weight = FileUtils.readLines(scoresFile).size();

            scores.add(score);
            weights.add(weight);
        }

        double mean = 0.0;
        int weightsum = 0;
        for (int i = 0; i < scores.size(); i++) {
            Double score = scores.get(i);
            Integer weight = weights.get(i);

            mean += weight * score;
            weightsum += weight;
        }
        mean = mean / weightsum;

        sb.append(mean);
    }

    FileUtils.writeStringToFile(
            new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + metric.toString() + ".txt"),
            sb.toString());
}

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