Examples of Covariance

org.apache.commons.math.stat.correlation.Covariance
Computes covariances for pairs of arrays or columns of a matrix.
The constructors that take RealMatrix or double[][] arguments generate covariance matrices. The columns of the input matrices are assumed to represent variable values.

The constructor argument biasCorrected determines whether or not computed covariances are bias-corrected.

Unbiased covariances are given by the formula
cov(X, Y) = Σ[(x_i - E(X))(y_i - E(Y))] / (n - 1) where E(X) is the mean of X and E(Y) is the mean of the Y values.
Non-bias-corrected estimates use n in place of n - 1 @version $Revision: 983921 $ $Date: 2010-08-10 12:46:06 +0200 (mar. 10 août 2010) $ @since 2.0
org.apache.commons.math3.stat.correlation.Covariance
Computes covariances for pairs of arrays or columns of a matrix.
The constructors that take RealMatrix or double[][] arguments generate covariance matrices. The columns of the input matrices are assumed to represent variable values.

The constructor argument biasCorrected determines whether or not computed covariances are bias-corrected.

Unbiased covariances are given by the formula
cov(X, Y) = Σ[(x_i - E(X))(y_i - E(Y))] / (n - 1) where E(X) is the mean of X and E(Y) is the mean of the Y values.
Non-bias-corrected estimates use n in place of n - 1 @since 2.0

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

    return values.real(new org.apache.commons.math.stat.correlation.SpearmansCorrelation().correlation(xvalues, yvalues));
  }
  
  public IValue covariance(IList dataValues){
    make(dataValues);
    return values.real(new Covariance().covariance(xvalues, yvalues, false));
  }

View Full Code Here

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

                errorSeeds.setEntry(i, j, rg.nextGaussian() * sigma[j]);
            }
        }
        
        // Get covariance matrix for columns
        RealMatrix cov = (new Covariance(errorSeeds)).getCovarianceMatrix();
          
        // Create a CorrelatedRandomVectorGenerator to use to generate correlated errors
        GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
        double[] errorMeans = new double[nObs];  // Counting on init to 0 here
        CorrelatedRandomVectorGenerator gen = new CorrelatedRandomVectorGenerator(errorMeans, cov,

View Full Code Here

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


            MathArrays.scaleInPlace(1d / numBinRows, columnMeans);


            // covariance matrix for this bin
            final double[][] covMat
                = new Covariance(binData).getCovarianceMatrix().getData();
            final MultivariateNormalDistribution mvn
                = new MultivariateNormalDistribution(columnMeans, covMat);


            components.add(new Pair<Double, MultivariateNormalDistribution>(weight, mvn));
        }

View Full Code Here

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


            MathArrays.scaleInPlace(1d / numBinRows, columnMeans);


            // covariance matrix for this bin
            final double[][] covMat
                = new Covariance(binData).getCovarianceMatrix().getData();
            final MultivariateNormalDistribution mvn
                = new MultivariateNormalDistribution(columnMeans, covMat);


            components.add(new Pair<Double, MultivariateNormalDistribution>(weight, mvn));
        }

View Full Code Here

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

        for (int j = 0; j < dim; j++) {
            sampleMeans[j] /= samples.length;
            Assert.assertEquals(mu[j], sampleMeans[j], sampledValueTolerance);
        }


        final double[][] sampleSigma = new Covariance(samples).getCovarianceMatrix().getData();
        for (int i = 0; i < dim; i++) {
            for (int j = 0; j < dim; j++) {
                Assert.assertEquals(sigma[i][j], sampleSigma[i][j], sampledValueTolerance);
            }
        }

View Full Code Here

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

        for (int j = 0; j < dim; j++) {
            sampleMeans[j] /= samples.length;
            Assert.assertEquals(mu[j], sampleMeans[j], sampledValueTolerance);
        }


        final double[][] sampleSigma = new Covariance(samples).getCovarianceMatrix().getData();
        for (int i = 0; i < dim; i++) {
            for (int j = 0; j < dim; j++) {
                Assert.assertEquals(sigma[i][j], sampleSigma[i][j], sampledValueTolerance);
            }
        }

View Full Code Here

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


            MathArrays.scaleInPlace(1d / numBinRows, columnMeans);


            // covariance matrix for this bin
            final double[][] covMat
                = new Covariance(binData).getCovarianceMatrix().getData();
            final MultivariateNormalDistribution mvn
                = new MultivariateNormalDistribution(columnMeans, covMat);


            components.add(new Pair<Double, MultivariateNormalDistribution>(weight, mvn));
        }

View Full Code Here

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

        for (int j = 0; j < dim; j++) {
            sampleMeans[j] /= samples.length;
            Assert.assertEquals(mu[j], sampleMeans[j], sampledValueTolerance);
        }


        final double[][] sampleSigma = new Covariance(samples).getCovarianceMatrix().getData();
        for (int i = 0; i < dim; i++) {
            for (int j = 0; j < dim; j++) {
                Assert.assertEquals(sigma[i][j], sampleSigma[i][j], sampledValueTolerance);
            }
        }

View Full Code Here

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

                errorSeeds.setEntry(i, j, rg.nextGaussian() * sigma[j]);
            }
        }
        
        // Get covariance matrix for columns
        RealMatrix cov = (new Covariance(errorSeeds)).getCovarianceMatrix();
          
        // Create a CorrelatedRandomVectorGenerator to use to generate correlated errors
        GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
        double[] errorMeans = new double[nObs];  // Counting on init to 0 here
        CorrelatedRandomVectorGenerator gen = new CorrelatedRandomVectorGenerator(errorMeans, cov,

View Full Code Here

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

     * @return Returns the BICValue of the Gaussian model that approximates the
     *         the feature vectors data samples
     */
    public static double getBICValue(Array2DRowRealMatrix mat) {
        double ret = 0;
        EigenDecomposition ed = new EigenDecomposition(new Covariance(mat).getCovarianceMatrix());
        double[] re = ed.getRealEigenvalues();
        for (int i = 0; i < re.length; i++)
            ret += Math.log(re[i]);
        return ret * (mat.getRowDimension() / 2);
    }

View Full Code Here

0 1

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.