Examples of org.apache.commons.math3.stat.descriptive.moment.FirstMoment

Package org.apache.commons.math3.stat.descriptive.moment

Examples of org.apache.commons.math3.stat.descriptive.moment.FirstMoment

org.apache.commons.math3.linear.QRDecomposition
Computes the first moment (arithmetic mean). Uses the definitional formula:
mean = sum(x_i) / n

where n is the number of observations.

To limit numeric errors, the value of the statistic is computed using the following recursive updating algorithm:
1. Initialize m = the first value
2. For each additional value, update using
  m = m + (new value - m) / (number of observations)
Returns Double.NaN if the dataset is empty.

Note that this implementation is not synchronized. If multiple threads access an instance of this class concurrently, and at least one of the threads invokes the increment() or clear() method, it must be synchronized externally.


        // Compute transpose(J)J.
        final RealMatrix jTj = j.transpose().multiply(j);


        // Compute the covariances matrix.
        final DecompositionSolver solver
            = new QRDecomposition(jTj, threshold).getSolver();
        return solver.getInverse().getData();
    }

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     * Creates a diagonal weight matrix.
     *
     * @param weight List of the values of the diagonal.
     */
    public Weight(double[] weight) {
        weightMatrix = new DiagonalMatrix(weight);
    }

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     * @return the square-root of the weight matrix.
     */
    private RealMatrix squareRoot(RealMatrix m) {
        if (m instanceof DiagonalMatrix) {
            final int dim = m.getRowDimension();
            final RealMatrix sqrtM = new DiagonalMatrix(dim);
            for (int i = 0; i < dim; i++) {
                sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
            }
            return sqrtM;
        } else {
            final EigenDecomposition dec = new EigenDecomposition(m);
            return dec.getSquareRoot();

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     * Creates a diagonal weight matrix.
     *
     * @param weight List of the values of the diagonal.
     */
    public Weight(double[] weight) {
        weightMatrix = new DiagonalMatrix(weight);
    }

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     * Creates a diagonal weight matrix.
     *
     * @param weight List of the values of the diagonal.
     */
    public Weight(double[] weight) {
        weightMatrix = new DiagonalMatrix(weight);
    }

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            for (int i = 0; i < dim; i++) {
                sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
            }
            return sqrtM;
        } else {
            final EigenDecomposition dec = new EigenDecomposition(m);
            return dec.getSquareRoot();
        }
    }

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     * @throws NonSquareMatrixException if the argument is not
     * a square matrix.
     */
    public Weight(RealMatrix weight) {
        if (weight.getColumnDimension() != weight.getRowDimension()) {
            throw new NonSquareMatrixException(weight.getColumnDimension(),
                                               weight.getRowDimension());
        }


        weightMatrix = weight.copy();
    }

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     * @throws NonSquareMatrixException if the argument is not
     * a square matrix.
     */
    public Weight(RealMatrix weight) {
        if (weight.getColumnDimension() != weight.getRowDimension()) {
            throw new NonSquareMatrixException(weight.getColumnDimension(),
                                               weight.getRowDimension());
        }


        weightMatrix = weight.copy();
    }

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     * @throws NonSquareMatrixException if the argument is not
     * a square matrix.
     */
    public Weight(RealMatrix weight) {
        if (weight.getColumnDimension() != weight.getRowDimension()) {
            throw new NonSquareMatrixException(weight.getColumnDimension(),
                                               weight.getRowDimension());
        }


        weightMatrix = weight.copy();
    }

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        // Compute transpose(J)J.
        final RealMatrix jTj = j.transpose().multiply(j);


        // Compute the covariances matrix.
        final DecompositionSolver solver
            = new QRDecomposition(jTj, threshold).getSolver();
        return solver.getInverse().getData();
    }

View Full Code Here

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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

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