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

Package org.apache.commons.math3.stat.correlation

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

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

     * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
     * @throws NotStrictlyPositiveException if {@code mean <= 0}.
     * @since 2.1
     */
    public ExponentialDistribution(double mean, double inverseCumAccuracy) {
        this(new Well19937c(), mean, inverseCumAccuracy);
    }

View Full Code Here

     * @param upper Upper bound (inclusive) of this distribution.
     * @throws NumberIsTooLargeException if {@code lower >= upper}.
     */
    public UniformIntegerDistribution(int lower, int upper)
        throws NumberIsTooLargeException {
        this(new Well19937c(), lower, upper);
    }

View Full Code Here

     * @throws NumberIsTooLargeException if {@code a >= b} or if {@code c > b}.
     * @throws NumberIsTooSmallException if {@code c < a}.
     */
    public TriangularDistribution(double a, double c, double b)
        throws NumberIsTooLargeException, NumberIsTooSmallException {
        this(new Well19937c(), a, c, b);
    }

View Full Code Here


            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


            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

        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

        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


            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

        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

                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

0 1 2 3 4 5 6 7 8 9

TOP

Related Classes of org.apache.commons.math3.stat.correlation.Covariance

org.apache.commons.math3.analysis.differentiation.DerivativeStructure

org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction

org.apache.commons.math3.analysis.function.HarmonicOscillator

org.apache.commons.math3.analysis.function.Sin

org.apache.commons.math3.analysis.function.Sinc

org.apache.commons.math3.analysis.MultivariateFunction

org.apache.commons.math3.analysis.QuinticFunction

org.apache.commons.math3.analysis.solvers.BrentSolver

org.apache.commons.math3.analysis.UnivariateFunction

org.apache.commons.math3.complex.Complex

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.