Package org.apache.commons.math3.linear

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

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                }
            }

            try {
                // solve the linearized least squares problem
                RealMatrix mA = new BlockRealMatrix(a);
                DecompositionSolver solver = useLU ?
                        new LUDecomposition(mA).getSolver() :
                        new QRDecomposition(mA).getSolver();
                final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
                // update the estimated parameters
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        if (getN() < 1) {
            Arrays.fill(stdDev, Double.NaN);
        } else if (getN() < 2) {
            Arrays.fill(stdDev, 0.0);
        } else {
            RealMatrix matrix = covarianceImpl.getResult();
            for (int i = 0; i < k; ++i) {
                stdDev[i] = FastMath.sqrt(matrix.getEntry(i, i));
            }
        }
        return stdDev;
    }
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     *
     * @return the hat matrix
     */
    public RealMatrix calculateHat() {
        // Create augmented identity matrix
        RealMatrix Q = qr.getQ();
        final int p = qr.getR().getColumnDimension();
        final int n = Q.getColumnDimension();
        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
        return Q.multiply(augI).multiply(Q.transpose());
    }
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     * @return The beta variance-covariance matrix
     */
    @Override
    protected RealMatrix calculateBetaVariance() {
        int p = getX().getColumnDimension();
        RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
        RealMatrix Rinv = new LUDecomposition(Raug).getSolver().getInverse();
        return Rinv.multiply(Rinv.transpose());
    }
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     * </pre>
     * @return beta
     */
    @Override
    protected RealVector calculateBeta() {
        RealMatrix OI = getOmegaInverse();
        RealMatrix XT = getX().transpose();
        RealMatrix XTOIX = XT.multiply(OI).multiply(getX());
        RealMatrix inverse = new LUDecomposition(XTOIX).getSolver().getInverse();
        return inverse.multiply(XT).multiply(OI).operate(getY());
    }
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     * </pre>
     * @return The beta variance matrix
     */
    @Override
    protected RealMatrix calculateBetaVariance() {
        RealMatrix OI = getOmegaInverse();
        RealMatrix XTOIX = getX().transpose().multiply(OI).multiply(getX());
        return new LUDecomposition(XTOIX).getSolver().getInverse();
    }
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        /*
         * Here the rounding part comes into play: use
         * RealMatrix instead of FieldMatrix<BigFraction>
         */
        final RealMatrix H = new Array2DRowRealMatrix(m, m);

        for (int i = 0; i < m; ++i) {
            for (int j = 0; j < m; ++j) {
                H.setEntry(i, j, HBigFraction.getEntry(i, j).doubleValue());
            }
        }

        final RealMatrix Hpower = H.power(n);

        double pFrac = Hpower.getEntry(k - 1, k - 1);

        for (int i = 1; i <= n; ++i) {
            pFrac *= (double) i / (double) n;
        }

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     * @return covariance matrix
     */
    protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) {
        int dimension = matrix.getColumnDimension();
        Variance variance = new Variance(biasCorrected);
        RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension);
        for (int i = 0; i < dimension; i++) {
            for (int j = 0; j < i; j++) {
              double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected);
              outMatrix.setEntry(i, j, cov);
              outMatrix.setEntry(j, i, cov);
            }
            outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));
        }
        return outMatrix;
    }
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     * @return covariance matrix
     */
    public RealMatrix getResult() {

        int dimension = sums.length;
        RealMatrix result = MatrixUtils.createRealMatrix(dimension, dimension);

        if (n > 1) {
            double c = 1.0 / (n * (isBiasCorrected ? (n - 1) : n));
            int k = 0;
            for (int i = 0; i < dimension; ++i) {
                for (int j = 0; j <= i; ++j) {
                    double e = c * (n * productsSums[k++] - sums[i] * sums[j]);
                    result.setEntry(i, j, e);
                    result.setEntry(j, i, e);
                }
            }
        }

        return result;
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     *
     * @param matrix matrix with columns representing variables to correlate
     * @return correlation matrix
     */
    public RealMatrix computeCorrelationMatrix(RealMatrix matrix) {
        RealMatrix matrixCopy = matrix.copy();
        rankTransform(matrixCopy);
        return new PearsonsCorrelation().computeCorrelationMatrix(matrixCopy);
    }
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