org.apache.commons.math.stat.correlation.PearsonsCorrelation Java Examples

The following examples show how to use org.apache.commons.math.stat.correlation.PearsonsCorrelation. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: RowVsRowScoreGC.java    From mzmine3 with GNU General Public License v2.0 5 votes vote down vote up 
public static double computeSimilarityScore(double[] vec1, double[] vec2,
    SimilarityMethodType simMethodType) throws IllegalArgumentException {

  double simScore = 0.0;

  try {

    if (simMethodType == SimilarityMethodType.DOT) {
      double[] vec1_norm = new double[vec1.length];
      double[] vec2_norm = new double[vec2.length];

      double div1 = 0.0, div2 = 0.0;
      for (int i = 0; i < vec1.length; ++i) {
        div1 += vec1[i] * vec1[i];
        div2 += vec2[i] * vec2[i];
      }
      for (int i = 0; i < vec1.length; ++i) {
        vec1_norm[i] = vec1[i] / Math.sqrt(div1);
        vec2_norm[i] = vec2[i] / Math.sqrt(div2);
      }
      simScore = (new ArrayRealVector(vec1_norm)).dotProduct(vec2_norm);
    } else if (simMethodType == SimilarityMethodType.PEARSON) {
      simScore = new PearsonsCorrelation().correlation(vec1, vec2);
    }
  } catch (IllegalArgumentException e) {
    throw new IllegalArgumentException("Failed to compute similarity score for vec1.length="
        + vec1.length + " and vec2.length=" + vec2.length);
  }

  return simScore;
}
Example #2
Source File: Step10bUpperBoundStatistics.java    From argument-reasoning-comprehension-task with Apache License 2.0 5 votes vote down vote up 
private static double[] computeCorrelation(List<Double> allX, List<Double> allY)
{
    if (allX.size() < 2) {
        return new double[] { Double.NaN, Double.NaN };
    }

    double[][] matrix = new double[allX.size()][];
    for (int i = 0; i < allX.size(); i++) {
        matrix[i] = new double[2];
        matrix[i][0] = allX.get(i);
        matrix[i][1] = allY.get(i);
    }

    PearsonsCorrelation pearsonsCorrelation = new PearsonsCorrelation(matrix);

    try {
        double pValue = pearsonsCorrelation.getCorrelationPValues().getEntry(0, 1);
        double correlation = pearsonsCorrelation.getCorrelationMatrix().getEntry(0, 1);

        SpearmansCorrelation sc = new SpearmansCorrelation(new Array2DRowRealMatrix(matrix));

        double[] result = new double[2];

        double pValSC = sc.getRankCorrelation().getCorrelationPValues().getEntry(0, 1);
        double corrSC = sc.getCorrelationMatrix().getEntry(0, 1);

        result[0] = corrSC;
        result[1] = pValSC;

        return result;
    }
    catch (MathException e) {
        throw new RuntimeException(e);
    }
}
Example #3
Source File: RowVsRowScoreGC.java    From mzmine2 with GNU General Public License v2.0 5 votes vote down vote up 
public static double computeSimilarityScore(double[] vec1, double[] vec2,
    SimilarityMethodType simMethodType) throws IllegalArgumentException {

  double simScore = 0.0;

  try {

    if (simMethodType == SimilarityMethodType.DOT) {
      double[] vec1_norm = new double[vec1.length];
      double[] vec2_norm = new double[vec2.length];

      double div1 = 0.0, div2 = 0.0;
      for (int i = 0; i < vec1.length; ++i) {
        div1 += vec1[i] * vec1[i];
        div2 += vec2[i] * vec2[i];
      }
      for (int i = 0; i < vec1.length; ++i) {
        vec1_norm[i] = vec1[i] / Math.sqrt(div1);
        vec2_norm[i] = vec2[i] / Math.sqrt(div2);
      }
      simScore = (new ArrayRealVector(vec1_norm)).dotProduct(vec2_norm);
    } else if (simMethodType == SimilarityMethodType.PEARSON) {
      simScore = new PearsonsCorrelation().correlation(vec1, vec2);
    }
  } catch (IllegalArgumentException e) {
    throw new IllegalArgumentException("Failed to compute similarity score for vec1.length="
        + vec1.length + " and vec2.length=" + vec2.length);
  }

  return simScore;
}
Example #4
Source File: MillerUpdatingRegressionTest.java    From astor with GNU General Public License v2.0 4 votes vote down vote up 
@Test
public void testPCorr() {
    MillerUpdatingRegression instance = new MillerUpdatingRegression(4, false);
    double[][] x = new double[airdata[0].length][];
    double[] y = new double[airdata[0].length];
    double[] cp = new double[10];
    double[] yxcorr = new double[4];
    double[] diag = new double[4];
    double sumysq = 0.0;
    int off = 0;
    for (int i = 0; i < airdata[0].length; i++) {
        x[i] = new double[4];
        x[i][0] = 1.0;
        x[i][1] = Math.log(airdata[3][i]);
        x[i][2] = Math.log(airdata[4][i]);
        x[i][3] = airdata[5][i];
        y[i] = Math.log(airdata[2][i]);
        off = 0;
        for (int j = 0; j < 4; j++) {
            double tmp = x[i][j];
            for (int k = 0; k <= j; k++, off++) {
                cp[off] += tmp * x[i][k];
            }
            yxcorr[j] += tmp * y[i];
        }
        sumysq += y[i] * y[i];
    }
    PearsonsCorrelation pearson = new PearsonsCorrelation(x);
    RealMatrix corr = pearson.getCorrelationMatrix();
    off = 0;
    for (int i = 0; i < 4; i++, off += (i + 1)) {
        diag[i] = FastMath.sqrt(cp[off]);
    }

    instance.addObservations(x, y);
    double[] pc = instance.getPartialCorrelations(0);
    int idx = 0;
    off = 0;
    int off2 = 6;
    for (int i = 0; i < 4; i++) {
        for (int j = 0; j < i; j++) {
            if (Math.abs(pc[idx] - cp[off] / (diag[i] * diag[j])) > 1.0e-8) {
                fail("Failed cross products... i = " + i + " j = " + j);
            }
            ++idx;
            ++off;
        }
        ++off;
        if (Math.abs(pc[i+off2] - yxcorr[ i] / (FastMath.sqrt(sumysq) * diag[i])) > 1.0e-8) {
            fail("failed cross product i = " + i + " y");
        }
    }
    double[] pc2 = instance.getPartialCorrelations(1);

    idx = 0;

    for (int i = 1; i < 4; i++) {
        for (int j = 1; j < i; j++) {
            if (Math.abs(pc2[idx] - corr.getEntry(j, i)) > 1.0e-8) {
                fail("Failed cross products... i = " + i + " j = " + j);
            }
            ++idx;
        }
    }
    double[] pc3 = instance.getPartialCorrelations(2);
    if (pc3 == null) {
        fail("Should not be null");
    }
    return;
}
Example #5
Source File: MillerUpdatingRegressionTest.java    From astor with GNU General Public License v2.0 4 votes vote down vote up 
@Test
public void testPCorr() {
    MillerUpdatingRegression instance = new MillerUpdatingRegression(4, false);
    double[][] x = new double[airdata[0].length][];
    double[] y = new double[airdata[0].length];
    double[] cp = new double[10];
    double[] yxcorr = new double[4];
    double[] diag = new double[4];
    double sumysq = 0.0;
    int off = 0;
    for (int i = 0; i < airdata[0].length; i++) {
        x[i] = new double[4];
        x[i][0] = 1.0;
        x[i][1] = Math.log(airdata[3][i]);
        x[i][2] = Math.log(airdata[4][i]);
        x[i][3] = airdata[5][i];
        y[i] = Math.log(airdata[2][i]);
        off = 0;
        for (int j = 0; j < 4; j++) {
            double tmp = x[i][j];
            for (int k = 0; k <= j; k++, off++) {
                cp[off] += tmp * x[i][k];
            }
            yxcorr[j] += tmp * y[i];
        }
        sumysq += y[i] * y[i];
    }
    PearsonsCorrelation pearson = new PearsonsCorrelation(x);
    RealMatrix corr = pearson.getCorrelationMatrix();
    off = 0;
    for (int i = 0; i < 4; i++, off += (i + 1)) {
        diag[i] = FastMath.sqrt(cp[off]);
    }

    instance.addObservations(x, y);
    double[] pc = instance.getPartialCorrelations(0);
    int idx = 0;
    off = 0;
    int off2 = 6;
    for (int i = 0; i < 4; i++) {
        for (int j = 0; j < i; j++) {
            if (Math.abs(pc[idx] - cp[off] / (diag[i] * diag[j])) > 1.0e-8) {
                fail("Failed cross products... i = " + i + " j = " + j);
            }
            ++idx;
            ++off;
        }
        ++off;
        if (Math.abs(pc[i+off2] - yxcorr[ i] / (FastMath.sqrt(sumysq) * diag[i])) > 1.0e-8) {
            fail("failed cross product i = " + i + " y");
        }
    }
    double[] pc2 = instance.getPartialCorrelations(1);

    idx = 0;

    for (int i = 1; i < 4; i++) {
        for (int j = 1; j < i; j++) {
            if (Math.abs(pc2[idx] - corr.getEntry(j, i)) > 1.0e-8) {
                fail("Failed cross products... i = " + i + " j = " + j);
            }
            ++idx;
        }
    }
    double[] pc3 = instance.getPartialCorrelations(2);
    if (pc3 == null) {
        fail("Should not be null");
    }
    return;
}
Example #6
Source File: DoubleSeries.java    From incubator-pinot with Apache License 2.0 4 votes vote down vote up 
public static double corr(Series a, Series b) {
  if(a.hasNull() || b.hasNull())
    return NULL;
  return new PearsonsCorrelation().correlation(a.getDoubles().values(), b.getDoubles().values());
}