Examples of PearsonsCorrelation

org.apache.commons.math.stat.correlation.PearsonsCorrelation
Computes Pearson's product-moment correlation coefficients for pairs of arrays or columns of a matrix.
The constructors that take RealMatrix or double[][] arguments generate correlation matrices. The columns of the input matrices are assumed to represent variable values. Correlations are given by the formula
cor(X, Y) = Σ[(x_i - E(X))(y_i - E(Y))] / [(n - 1)s(X)s(Y)] where E(X) is the mean of X, E(Y) is the mean of the Y values and s(X), s(Y) are standard deviations. @version $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $ @since 2.0
org.apache.commons.math3.stat.correlation.PearsonsCorrelation
Computes Pearson's product-moment correlation coefficients for pairs of arrays or columns of a matrix.
The constructors that take RealMatrix or double[][] arguments generate correlation matrices. The columns of the input matrices are assumed to represent variable values. Correlations are given by the formula

cor(X, Y) = Σ[(x_i - E(X))(y_i - E(Y))] / [(n - 1)s(X)s(Y)] where E(X) is the mean of X, E(Y) is the mean of the Y values and s(X), s(Y) are standard deviations.

To compute the correlation coefficient for a single pair of arrays, use {@link #PearsonsCorrelation()}to construct an instance with no data and then {@link #correlation(double[],double[])}. Correlation matrices can also be computed directly from an instance with no data using {@link #computeCorrelationMatrix(double[][])}. In order to use {@link #getCorrelationMatrix()}, {@link #getCorrelationPValues()}, or {@link #getCorrelationStandardErrors()}; however, one of the constructors supplying data or a covariance matrix must be used to create the instance.
@since 2.0

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

      }
      
      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)
    {

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

    }
    
    double[] expArray = ArrayUtils.toPrimitive(expScores.toArray(new Double[expScores.size()])); 
    double[] gsArray = ArrayUtils.toPrimitive(gsScores.toArray(new Double[gsScores.size()]));


    PearsonsCorrelation pearson = new PearsonsCorrelation();
    Double correl = pearson.correlation(expArray, gsArray);
    
    FileUtils.writeStringToFile(
        new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".txt"),
        correl.toString());
  }

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

      }
      
      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)
    {

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

    }
    
    double[] expArray = ArrayUtils.toPrimitive(expScores.toArray(new Double[expScores.size()])); 
    double[] gsArray = ArrayUtils.toPrimitive(gsScores.toArray(new Double[gsScores.size()]));


    PearsonsCorrelation pearson = new PearsonsCorrelation();
    Double correl = pearson.correlation(expArray, gsArray);
    
    FileUtils.writeStringToFile(
        new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".txt"),
        correl.toString());
  }

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Examples of org.apache.commons.math3.stat.correlation.PearsonsCorrelation

                }
                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]);
        }

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Examples of org.apache.commons.math3.stat.correlation.PearsonsCorrelation

      y[index] = resultPair[1];
      index++;
    }
    double mse = meanSquareError(x, y);
    double spearmans = new SpearmansCorrelation().correlation(x, y);
    double pearsons = new PearsonsCorrelation().correlation(x, y);
    double cosine = cosine(x, y);
    mse = round(mse);
    spearmans = round(spearmans);
    pearsons = round(pearsons);
    cosine = round(cosine);

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Examples of org.apache.commons.math3.stat.correlation.PearsonsCorrelation

                }
                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]);
        }

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Examples of org.apache.commons.math3.stat.correlation.PearsonsCorrelation

                }
                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]);
        }

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