Examples of org.apache.commons.math3.linear.RealMatrix

org.apache.commons.math3.linear.RealMatrix
Interface defining a real-valued matrix with basic algebraic operations.
Matrix element indexing is 0-based -- e.g., getEntry(0, 0) returns the element in the first row, first column of the matrix.
@version $Id: RealMatrix.java 1244107 2012-02-14 16:17:55Z erans $

     * if the covariance matrix cannot be computed (singular problem).
     */
    public double[][] computeCovariances(double[] params,
                                         double threshold) {
        // Set up the Jacobian.
        final RealMatrix j = computeWeightedJacobian(params);


        // 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

     * @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();

View Full Code Here

      
      List<SiteWithPolynomial> nearestSites =
          nearestSiteMap.get(site);
      
      RealVector vector = new ArrayRealVector(SITES_FOR_APPROX);
      RealMatrix matrix = new Array2DRowRealMatrix(
          SITES_FOR_APPROX, DefaultPolynomial.NUM_COEFFS);
      
      for (int row = 0; row < SITES_FOR_APPROX; row++) {
        SiteWithPolynomial nearSite = nearestSites.get(row);
        DefaultPolynomial.populateMatrix(matrix, row, nearSite.pos.x, nearSite.pos.z);

View Full Code Here

     * @param matrix matrix with columns representing variables to correlate
     * @return correlation matrix
     */
    public RealMatrix computeCorrelationMatrix(final RealMatrix matrix) {
        int nVars = matrix.getColumnDimension();
        RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
        for (int i = 0; i < nVars; i++) {
            for (int j = 0; j < i; j++) {
                double corr = correlation(matrix.getColumn(i), matrix.getColumn(j));
                outMatrix.setEntry(i, j, corr);
                outMatrix.setEntry(j, i, corr);
            }
            outMatrix.setEntry(i, i, 1d);
        }
        return outMatrix;
    }

View Full Code Here


        // solve the rectangular system in the least square sense
        // to get the best estimate of the Nordsieck vector [s2 ... sk]
        QRDecomposition decomposition;
        decomposition = new QRDecomposition(new Array2DRowRealMatrix(a, false));
        RealMatrix x = decomposition.getSolver().solve(new Array2DRowRealMatrix(b, false));
        return new Array2DRowRealMatrix(x.getData(), false);
    }

View Full Code Here

        double[] qtf     = new double[nR];
        double[] work1   = new double[nC];
        double[] work2   = new double[nC];
        double[] work3   = new double[nC];


        final RealMatrix weightMatrixSqrt = getWeightSquareRoot();


        // Evaluate the function at the starting point and calculate its norm.
        double[] currentObjective = computeObjectiveValue(currentPoint);
        double[] currentResiduals = computeResiduals(currentObjective);
        PointVectorValuePair current = new PointVectorValuePair(currentPoint, currentObjective);
        double currentCost = computeCost(currentResiduals);


        // Outer loop.
        lmPar = 0;
        boolean firstIteration = true;
        final ConvergenceChecker<PointVectorValuePair> checker = getConvergenceChecker();
        while (true) {
            incrementIterationCount();


            final PointVectorValuePair previous = current;


            // QR decomposition of the jacobian matrix
            qrDecomposition(computeWeightedJacobian(currentPoint));


            weightedResidual = weightMatrixSqrt.operate(currentResiduals);
            for (int i = 0; i < nR; i++) {
                qtf[i] = weightedResidual[i];
            }


            // compute Qt.res

View Full Code Here

        double[] qtf     = new double[nR];
        double[] work1   = new double[nC];
        double[] work2   = new double[nC];
        double[] work3   = new double[nC];


        final RealMatrix weightMatrixSqrt = getWeightSquareRoot();


        // Evaluate the function at the starting point and calculate its norm.
        double[] currentObjective = computeObjectiveValue(currentPoint);
        double[] currentResiduals = computeResiduals(currentObjective);
        PointVectorValuePair current = new PointVectorValuePair(currentPoint, currentObjective);
        double currentCost = computeCost(currentResiduals);


        // Outer loop.
        lmPar = 0;
        boolean firstIteration = true;
        int iter = 0;
        final ConvergenceChecker<PointVectorValuePair> checker = getConvergenceChecker();
        while (true) {
            ++iter;
            final PointVectorValuePair previous = current;


            // QR decomposition of the jacobian matrix
            qrDecomposition(computeWeightedJacobian(currentPoint));


            weightedResidual = weightMatrixSqrt.operate(currentResiduals);
            for (int i = 0; i < nR; i++) {
                qtf[i] = weightedResidual[i];
            }


            // compute Qt.res

View Full Code Here

     * if the covariance matrix cannot be computed (singular problem).
     */
    public double[][] computeCovariances(double[] params,
                                         double threshold) {
        // Set up the Jacobian.
        final RealMatrix j = computeWeightedJacobian(params);


        // 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

     * @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();

View Full Code Here

     * @throws NullPointerException unless method {@code newSampleData} has been
     * called beforehand.
     */
    public RealMatrix calculateHat() {
        // Create augmented identity matrix
        RealMatrix Q = qr.getQ();
        final int p = qr.getR().getColumnDimension();
        final int n = Q.getColumnDimension();
        // No try-catch or advertised NotStrictlyPositiveException - NPE above if n < 3
        Array2DRowRealMatrix augI = new Array2DRowRealMatrix(n, n);
        double[][] augIData = augI.getDataRef();
        for (int i = 0; i < n; i++) {
            for (int j =0; j < n; j++) {
                if (i == j && i < p) {
                    augIData[i][j] = 1d;
                } else {
                    augIData[i][j] = 0d;
                }
            }
        }


        // Compute and return Hat matrix
        // No DME advertised - args valid if we get here
        return Q.multiply(augI).multiply(Q.transpose());
    }

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

Related Classes of org.apache.commons.math3.linear.RealMatrix

com.cloudera.oryx.common.math.LinearSystemSolverTest

com.cloudera.oryx.ml.speed.als.ALSSpeedModel

net.myrrix.common.math.MatrixUtils

net.myrrix.common.math.SolverLoadTest

net.myrrix.online.factorizer.als.AlternatingLeastSquares

net.myrrix.online.factorizer.als.AlternatingLeastSquares$Worker

net.myrrix.online.factorizer.als.AlternatingLeastSquaresTest

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

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

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

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.