Examples of Jama.QRDecomposition

Jama.QRDecomposition
QR Decomposition.
For an m-by-n matrix A with m >= n, the QR decomposition is an m-by-n orthogonal matrix Q and an n-by-n upper triangular matrix R so that A = Q*R.
The QR decompostion always exists, even if the matrix does not have full rank, so the constructor will never fail. The primary use of the QR decomposition is in the least squares solution of nonsquare systems of simultaneous linear equations. This will fail if isFullRank() returns false.

     * inversion/qr decomposition until the preferable
     * Apache commons class catches up. Matrix is the Jama version
     * RealMatrixImpl is the commons version.
     */
    Matrix v = new Matrix(new VandermondeMatrix(x,order+1).getMatrix().getData());
    QRDecomposition qr = new QRDecomposition(v);
    rMatrix = new RealMatrixImpl(qr.getR().getArray());
    qMatrix = new RealMatrixImpl(qr.getQ().getArray());
    RealMatrix qMatrixTransposed = qMatrix.transpose();
    RealMatrix qByY = qMatrixTransposed.multiply(yMatrix);
    RealMatrix  rMatrixInverse = new RealMatrixImpl(new Matrix(rMatrix.getData()).inverse().getArray());
    RealMatrix resultMatrix = rMatrixInverse.multiply(qByY);
    coefficiants= resultMatrix.getColumn(0);

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            errorCount = try_failure(errorCount, "times(double)...",
                    "incorrect Matrix-scalar product calculation");
        }


        A = new Matrix(columnwise, 4);
        QRDecomposition QR = A.qr();


        R = QR.getR();
        try {
            check(A, QR.getQ().times(R));
            try_success("QRDecomposition...", "");
        } catch (java.lang.RuntimeException e) {
            errorCount = try_failure(errorCount, "QRDecomposition...",
                    "incorrect QR decomposition calculation");
        }

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            Matrix R = L.times(U).minus(M.getMatrix(p, 0, n - 1));
            double res = R.norm1() / (n * eps);


            print(fixedWidthDoubletoString(res, 12, 3));


            QRDecomposition QR = new QRDecomposition(M);
            Matrix Q = QR.getQ();


            R = QR.getR();
            R = Q.times(R).minus(M);
            res = R.norm1() / (n * eps);
            print(fixedWidthDoubletoString(res, 12, 3));


            print("\n");

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            }
            yMatrix.set(row, 0, dataRow.getIdeal()[0]);
        }


        // Calculate the least squares solution
        final QRDecomposition qr = new QRDecomposition(xMatrix);
        final Matrix beta = qr.solve(yMatrix);


        double sum = 0.0;
        for (int i = 0; i < inputColCount; i++)
            sum += yMatrix.get(i, 0);
        final double mean = sum / inputColCount;

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