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

Introduction

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

Prototype

public PearsonsCorrelation() 

Source Link

Document

Create a PearsonsCorrelation instance without data

Usage

From source file:PCC.java

/**
 * @param args the command line arguments
 * @throws java.io.IOException//from ww w  . j a  va  2s. c  o  m
 */

public static void main(String[] args) throws IOException {
    // TODO code application logic here

    PearsonsCorrelation corel = new PearsonsCorrelation();
    PCC method = new PCC();
    ArrayList<String> name = new ArrayList<>();
    Multimap<String, String> genes = ArrayListMultimap.create();
    BufferedWriter bw = new BufferedWriter(new FileWriter(args[1]));
    BufferedReader br = new BufferedReader(new FileReader(args[0]));
    String str;
    while ((str = br.readLine()) != null) {
        String[] a = str.split("\t");
        name.add(a[0]);
        for (int i = 1; i < a.length; i++) {
            genes.put(a[0], a[i]);
        }
    }
    for (String key : genes.keySet()) {
        double[] first = new double[genes.get(key).size()];
        int element1 = 0;
        for (String value : genes.get(key)) {
            double d = Double.parseDouble(value);
            first[element1] = d;
            element1++;
        }
        for (String key1 : genes.keySet()) {
            if (!key.equals(key1)) {
                double[] second = new double[genes.get(key1).size()];
                int element2 = 0;
                for (String value : genes.get(key1)) {
                    double d = Double.parseDouble(value);
                    second[element2] = d;
                    element2++;

                }
                double corrlation = corel.correlation(first, second);
                if (corrlation > 0.5) {
                    bw.write(key + "\t" + key1 + "\t" + corrlation + "\t"
                            + method.pvalue(corrlation, second.length) + "\n");
                }
            }
        }
    }
    br.close();
    bw.close();
}

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From source file:com.medlog.webservice.util.MathUtl.java

public static void main(String[] args) {
    BigDecimal d;//from   w  ww  . j ava  2 s  . c  om
    d = new BigDecimal(0);
    double[] xs = { 30, 80, 90, 70, 70, 80, 50, 50, 50, 100, 10, 40, 80, 100 };

    double ys[] = { 52.9676423, 28.14739166, 45.22978518, 60.54520645, 58.43671379, 15.94587142, 58.869,
            57.8465, 58.686, 40.38026666, 55.8178, 60.3812, 88.1389, 61.1895 };

    double r = new PearsonsCorrelation().correlation(xs, ys);
    double rb = new SpearmansCorrelation().correlation(xs, ys);
    System.out.println("com.medlog.webservice.util.MathUtl.main()" + r * r);

    System.out.println("com.medlog.webservice.util.MathUtl.main()" + rb * rb);

}

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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));
}

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From source file:edu.indiana.soic.ts.streaming.dataflow.utils.DistanceFunction.java

public static double calculateDistance(VectorPoint vp1, VectorPoint vp2) {
    //FIXME This should be replaced by the actual distance calculation
    PearsonsCorrelation pearsonsCorrelation = new PearsonsCorrelation();
    return pearsonsCorrelation.correlation(vp1.getNumbers(), vp2.getNumbers());
}

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

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

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From source file:com.davidbracewell.ml.regression.Regression.java

/**
 * Pearson's correlation coefficient.//from w  ww. j  a v  a 2s  .co m
 *
 * @param model the model
 * @param data  the data
 * @return the double
 */
public static double correlationCoefficient(RegressionModel model, List<Instance> data) {
    PearsonsCorrelation correlation = new PearsonsCorrelation();
    List<Double> gold = Lists.newArrayList();
    List<Double> pred = Lists.newArrayList();
    for (Instance instance : data) {
        if (instance.hasTargetValue()) {
            gold.add(instance.getTargetValue());
            pred.add(model.estimate(instance));
        }
    }
    return correlation.correlation(Doubles.toArray(gold), Doubles.toArray(pred));
}

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From source file:jgap.SpearmansFitnessFunction.java

/**
 * Determine the fitness of the given Chromosome instance. The higher the
 * return value, the more fit the instance. This method should always
 * return the same fitness value for two equivalent Chromosome instances.
 *
 * @param a_subject the Chromosome instance to evaluate
 * @return positive double reflecting the fitness rating of the given
 * Chromosome/*from  w w w. ja v a 2 s . c om*/
 * @author Neil Rotstan, Klaus Meffert, John Serri
 * @since 2.0 (until 1.1: return type int)
 */
@Override
public double evaluate(IChromosome a_subject) {

    double[] newAhpResult = runAHP(a_subject);

    //        double correlation = new SpearmansCorrelation().correlation(this.original, newAhpResult);
    double correlation = new PearsonsCorrelation().correlation(this.original, newAhpResult);
    //        System.out.println(correlation);
    return correlation < 0.0d ? (correlation * -1) : correlation;

}

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From source file:com.facebook.presto.operator.aggregation.TestFloatCorrelationAggregation.java

@Override
public Object getExpectedValue(int start, int length) {
    if (length <= 1) {
        return null;
    }/*from w  w  w  .  j a  v  a  2  s  .  c om*/
    PearsonsCorrelation corr = new PearsonsCorrelation();
    return (float) corr.correlation(constructDoublePrimitiveArray(start + 2, length),
            constructDoublePrimitiveArray(start, length));
}

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From source file:com.facebook.presto.operator.aggregation.TestCorrelationAggregation.java

@Override
public Object getExpectedValue(int start, int length) {
    if (length <= 1) {
        return null;
    }//from w w w . ja  va 2 s.co  m
    PearsonsCorrelation corr = new PearsonsCorrelation();
    return corr.correlation(constructDoublePrimitiveArray(start + 2, length),
            constructDoublePrimitiveArray(start, length));
}

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From source file:com.insightml.math.statistics.Correlation.java

public Correlation(final double[] x, final double[] y) {
    arrays = new double[2][x.length];
    for (int i = 0; i < x.length; ++i) {
        arrays[0][i] = x[i];/*w  ww .j  av a  2 s  .  c om*/
        arrays[1][i] = y[i];
    }
    final DoubleLinkedList x2 = new DoubleLinkedList();
    final DoubleLinkedList y2 = new DoubleLinkedList();
    for (int i = 0; i < x.length; ++i) {
        if (!Double.isNaN(x[i])) {
            x2.add(x[i]);
            y2.add(y[i]);
        }
    }
    final double[] x2arr = x2.toArray();
    final double[] y2arr = y2.toArray();
    covariance = new Covariance().covariance(x2arr, y2arr);
    pearson = new PearsonsCorrelation().correlation(x2arr, y2arr);
    spearman = new SpearmansCorrelation().correlation(x2arr, y2arr);
    mean = (Math.abs(pearson) + Math.abs(spearman)) / 2;
}

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