Java 类org.apache.commons.math3.linear.QRDecomposition 实例源码

项目：morpheus-core 文件：AlgebraTests.java

@Test(dataProvider = "styles")
public void testPseudoInverse(DataFrameAlgebra.Lib lib, boolean parallel) {
    DataFrameAlgebra.LIBRARY.set(lib);
    final DataFrame<Integer,String> source = DataFrame.read().csv("./src/test/resources/pca/svd/poppet-svd-eigenvectors.csv");
    Array.of(20, 77, 95, 135, 233, 245).forEach(count -> {
        final DataFrame<Integer,String> frame = source.cols().select(col -> col.ordinal() < count);
        final DataFrame<Integer,Integer> inverse = frame.inverse();
        final RealMatrix matrix = new QRDecomposition(toMatrix(frame)).getSolver().getInverse();
        assertEquals(inverse, matrix);
    });
}

项目：cy-dataseries 文件：BSPlineRegressionSmoothingProvider.java

@Override
public double[] smooth(double[] sourceX, double[] noisyY, double[] estimateX, double parameter) {
    int numDivisions = (int)Math.round(parameter);

    SmoothingHelper.SmoothingInput filteredInput = SmoothingHelper.filterInvalidValues(sourceX, noisyY);
    sourceX = filteredInput.getSourceX();
    noisyY = filteredInput.getSourceY();

    if(numDivisions >= sourceX.length) {
        throw new IllegalArgumentException("Cannot fit with " + numDivisions + " knots as the input data (after removing NAs) has < " + (numDivisions + 1) + " data points.");
    }


    double[][] sourceBasis = createBasis(numDivisions, splineDegree, sourceX);
    RealMatrix matrix = new Array2DRowRealMatrix(sourceBasis);      
    QRDecomposition decomposition = new QRDecomposition(matrix);
    RealVector coefficients = decomposition.getSolver().solve(new ArrayRealVector(noisyY));

    double[][] estimateBasis = createBasis((int)parameter, splineDegree, estimateX);
    RealMatrix estimateBasisMatrix = new Array2DRowRealMatrix(estimateBasis);

    double[] result = estimateBasisMatrix.transpose().preMultiply(coefficients).toArray();
    return result;
}

项目：MeteoInfoLib 文件：LinalgUtil.java

/**
 * Calculates the QR-decomposition of a matrix. The QR-decomposition of a
 * matrix A consists of two matrices Q and R that satisfy: A = QR, Q is
 * orthogonal (QTQ = I), and R is upper triangular. If A is m×n, Q is m×m
 * and R m×n.
 *
 * @param a Given matrix.
 * @return Result Q/R arrays.
 */
public static Array[] qr(Array a) {
    int m = a.getShape()[0];
    int n = a.getShape()[1];
    Array Qa = Array.factory(DataType.DOUBLE, new int[]{m, m});
    Array Ra = Array.factory(DataType.DOUBLE, a.getShape());
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    QRDecomposition decomposition = new QRDecomposition(matrix);
    RealMatrix Q = decomposition.getQ();
    RealMatrix R = decomposition.getR();
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < m; j++) {
            Qa.setDouble(i * m + j, Q.getEntry(i, j));
        }
    }
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n; j++) {
            Ra.setDouble(i * n + j, R.getEntry(i, j));
        }
    }

    return new Array[]{Qa, Ra};
}

项目：systemml 文件：LibCommonsMath.java

/**
 * Function to solve a given system of equations.
 * 
 * @param in1 matrix object 1
 * @param in2 matrix object 2
 * @return matrix block
 * @throws DMLRuntimeException if DMLRuntimeException occurs
 */
private static MatrixBlock computeSolve(MatrixObject in1, MatrixObject in2) 
    throws DMLRuntimeException 
{
    Array2DRowRealMatrix matrixInput = DataConverter.convertToArray2DRowRealMatrix(in1);
    Array2DRowRealMatrix vectorInput = DataConverter.convertToArray2DRowRealMatrix(in2);

    /*LUDecompositionImpl ludecompose = new LUDecompositionImpl(matrixInput);
    DecompositionSolver lusolver = ludecompose.getSolver();
    RealMatrix solutionMatrix = lusolver.solve(vectorInput);*/

    // Setup a solver based on QR Decomposition
    QRDecomposition qrdecompose = new QRDecomposition(matrixInput);
    DecompositionSolver solver = qrdecompose.getSolver();
    // Invoke solve
    RealMatrix solutionMatrix = solver.solve(vectorInput);

    return DataConverter.convertToMatrixBlock(solutionMatrix.getData());
}

项目：systemml 文件：LibCommonsMath.java

/**
 * Function to perform QR decomposition on a given matrix.
 * 
 * @param in matrix object
 * @return array of matrix blocks
 * @throws DMLRuntimeException if DMLRuntimeException occurs
 */
private static MatrixBlock[] computeQR(MatrixObject in) 
    throws DMLRuntimeException 
{
    Array2DRowRealMatrix matrixInput = DataConverter.convertToArray2DRowRealMatrix(in);

    // Perform QR decomposition
    QRDecomposition qrdecompose = new QRDecomposition(matrixInput);
    RealMatrix H = qrdecompose.getH();
    RealMatrix R = qrdecompose.getR();

    // Read the results into native format
    MatrixBlock mbH = DataConverter.convertToMatrixBlock(H.getData());
    MatrixBlock mbR = DataConverter.convertToMatrixBlock(R.getData());

    return new MatrixBlock[] { mbH, mbR };
}

项目：astor 文件：AbstractLeastSquaresOptimizer.java

/**
 * Get the covariance matrix of the optimized parameters.
 * <br/>
 * Note that this operation involves the inversion of the
 * <code>J<sup>T</sup>J</code> matrix, where {@code J} is the
 * Jacobian matrix.
 * The {@code threshold} parameter is a way for the caller to specify
 * that the result of this computation should be considered meaningless,
 * and thus trigger an exception.
 *
 * @param threshold Singularity threshold.
 * @return the covariance matrix.
 * @throws org.apache.commons.math3.linear.SingularMatrixException
 * if the covariance matrix cannot be computed (singular problem).
 */
public double[][] getCovariances(double threshold) {
    // Set up the jacobian.
    updateJacobian();

    // Compute transpose(J)J, without building intermediate matrices.
    double[][] jTj = new double[cols][cols];
    for (int i = 0; i < cols; ++i) {
        for (int j = i; j < cols; ++j) {
            double sum = 0;
            for (int k = 0; k < rows; ++k) {
                sum += weightedResidualJacobian[k][i] * weightedResidualJacobian[k][j];
            }
            jTj[i][j] = sum;
            jTj[j][i] = sum;
        }
    }

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

项目：morpheus-core 文件：XDataFrameLeastSquares.java

/**
 * Computes model parameters and parameter variance using a QR decomposition of the X matrix
 * @param y     the response vector
 * @param x     the design matrix
 */
private RealMatrix computeBetaQR(RealVector y, RealMatrix x) {
    final int n = x.getRowDimension();
    final int p = x.getColumnDimension();
    final int offset = hasIntercept() ? 1 : 0;
    final QRDecomposition decomposition = new QRDecomposition(x, threshold);
    final RealVector betaVector = decomposition.getSolver().solve(y);
    final RealVector residuals = y.subtract(x.operate(betaVector));
    this.rss = residuals.dotProduct(residuals);
    this.errorVariance = rss / (n - p);
    this.stdError = Math.sqrt(errorVariance);
    this.residuals = createResidualsFrame(residuals);
    final RealMatrix rAug = decomposition.getR().getSubMatrix(0, p - 1, 0, p - 1);
    final RealMatrix rInv = new LUDecomposition(rAug).getSolver().getInverse();
    final RealMatrix covMatrix = rInv.multiply(rInv.transpose()).scalarMultiply(errorVariance);
    final RealMatrix result = new Array2DRowRealMatrix(p, 2);
    if (hasIntercept()) {
        result.setEntry(0, 0, betaVector.getEntry(0));      //Intercept coefficient
        result.setEntry(0, 1, covMatrix.getEntry(0, 0));    //Intercept variance
    }
    for (int i = 0; i < getRegressors().size(); i++) {
        final int index = i + offset;
        final double variance = covMatrix.getEntry(index, index);
        result.setEntry(index, 1, variance);
        result.setEntry(index, 0, betaVector.getEntry(index));
    }
    return result;
}

项目：SME 文件：AbstractEvaluation.java

/** {@inheritDoc} */
public RealMatrix getCovariances(double threshold) {
    // Set up the Jacobian.
    final RealMatrix j = this.getJacobian();

    // 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();
}

项目：CARMA 文件：AbstractEvaluation.java

/** {@inheritDoc} */
public RealMatrix getCovariances(double threshold) {
    // Set up the Jacobian.
    final RealMatrix j = this.getJacobian();

    // 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();
}

项目：Rugged 文件：SensorMeanPlaneCrossing.java

/** Guess a start line using the last four results.
 * @param n number of cached results available
 * @param target target ground point
 * @return guessed start line
 */
private double guessStartLine(final int n, final Vector3D target) {
    try {

        // assume a linear model of the form: l = ax + by + cz + d
        final RealMatrix m = new Array2DRowRealMatrix(n, 4);
        final RealVector v = new ArrayRealVector(n);
        for (int i = 0; i < n; ++i) {
            m.setEntry(i, 0, cachedResults[i].getTarget().getX());
            m.setEntry(i, 1, cachedResults[i].getTarget().getY());
            m.setEntry(i, 2, cachedResults[i].getTarget().getZ());
            m.setEntry(i, 3, 1.0);
            v.setEntry(i, cachedResults[i].getLine());
        }

        final DecompositionSolver solver = new QRDecomposition(m, Precision.SAFE_MIN).getSolver();
        final RealVector coefficients = solver.solve(v);

        // apply the linear model
        return target.getX() * coefficients.getEntry(0) +
               target.getY() * coefficients.getEntry(1) +
               target.getZ() * coefficients.getEntry(2) +
               coefficients.getEntry(3);

    } catch (SingularMatrixException sme) {
        // the points are not independent
        return Double.NaN;
    }
}

项目：systemml 文件：LibCommonsMath.java

/**
 * Function to compute matrix inverse via matrix decomposition.
 * 
 * @param in commons-math3 Array2DRowRealMatrix
 * @return matrix block
 * @throws DMLRuntimeException if DMLRuntimeException occurs
 */
private static MatrixBlock computeMatrixInverse(Array2DRowRealMatrix in) 
    throws DMLRuntimeException 
{   
    if ( !in.isSquare() )
        throw new DMLRuntimeException("Input to inv() must be square matrix -- given: a " + in.getRowDimension() + "x" + in.getColumnDimension() + " matrix.");

    QRDecomposition qrdecompose = new QRDecomposition(in);
    DecompositionSolver solver = qrdecompose.getSolver();
    RealMatrix inverseMatrix = solver.getInverse();

    return DataConverter.convertToMatrixBlock(inverseMatrix.getData());
}

项目：gatk-protected 文件：DecomposeSingularValuesIntegrationTest.java

/**
 * Assert that the given matrix is unitary.
 * @param m
 */
public static void assertUnitaryMatrix(final RealMatrix m){
    final RealMatrix mInv = new QRDecomposition(m).getSolver().getInverse();
    final RealMatrix mT = m.transpose();

    for (int i = 0; i < mInv.getRowDimension(); i ++) {
        for (int j = 0; j < mInv.getColumnDimension(); j ++) {
            Assert.assertEquals(mInv.getEntry(i, j), mT.getEntry(i, j), 1e-7);
        }
    }
}

项目：astor 文件：AbstractEvaluation.java

/** {@inheritDoc} */
public RealMatrix getCovariances(double threshold) {
    // Set up the Jacobian.
    final RealMatrix j = this.getJacobian();

    // 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();
}

项目：astor 文件：AbstractEvaluation.java

/** {@inheritDoc} */
public RealMatrix getCovariances(double threshold) {
    // Set up the Jacobian.
    final RealMatrix j = this.getJacobian();

    // 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();
}

项目：gatk 文件：SingularValueDecomposerUnitTest.java

/**
 * Check that the given matrix is unitary.
 */
public static boolean isUnitaryMatrix(final RealMatrix m){
    //Note can't use MatrixUtils.inverse because m may not be square
    final RealMatrix mInv = new QRDecomposition(m).getSolver().getInverse();
    final RealMatrix mT = m.transpose();

    for (int i = 0; i < mInv.getRowDimension(); i ++) {
        for (int j = 0; j < mInv.getColumnDimension(); j ++) {
            if (Math.abs(mInv.getEntry(i, j) - mT.getEntry(i, j)) > 1.0e-7){
                return false;
            }
        }
    }
    return true;
}

项目：Strata 文件：QRDecompositionCommonsResult.java

/**
 * Creates an instance.
 * 
 * @param qr The result of the QR decomposition, not null
 */
public QRDecompositionCommonsResult(QRDecomposition qr) {
  ArgChecker.notNull(qr, "qr");
  _q = CommonsMathWrapper.unwrap(qr.getQ());
  _r = CommonsMathWrapper.unwrap(qr.getR());
  _qTranspose = _q.transpose();
  _solver = qr.getSolver();
}

项目：Strata 文件：QRDecompositionCommons.java

@Override
public QRDecompositionResult apply(DoubleMatrix x) {
  ArgChecker.notNull(x, "x");
  RealMatrix temp = CommonsMathWrapper.wrap(x);
  QRDecomposition qr = new QRDecomposition(temp);
  return new QRDecompositionCommonsResult(qr);
}

项目：pacioli 文件：Matrix.java

public PacioliTuple qrZeroSub() throws MVMException {

    // Collect the non-zero numbers
     NonZeroSubMatrix sub = new NonZeroSubMatrix(numbers);

     // Do the QR decomposition on the non-zero numbers.
     QRDecomposition decomposition = new QRDecomposition(sub.numbers);
     RealMatrix numbersQ = decomposition.getQ();
     RealMatrix numbersR = decomposition.getR();

     Pacioli.logln("mat=%s", sub.numbers.toString());

     // Create the full result matrices
     Matrix matrixQ = new Matrix(shape.leftIdentity());
     Matrix matrixR = new Matrix(shape);

     // Fill the result matrices from the decomposition result
     for (int i = 0; i < sub.numbers.getRowDimension(); i++) {
         for (int j = 0; j < sub.numbers.getRowDimension(); j++) {
            matrixQ.numbers.setEntry(sub.originalRow(i), sub.originalRow(j), numbersQ.getEntry(i, j));
         }
     }
     for (int i = 0; i < sub.numbers.getRowDimension(); i++) {
         for (int j = 0; j < sub.numbers.getColumnDimension(); j++) {
            matrixR.numbers.setEntry(sub.originalRow(i), sub.originalColumn(j), numbersR.getEntry(i, j));
         }
     }

     // Return a tuple with the result matrices
     List<PacioliValue> items = new ArrayList<PacioliValue>();
     items.add(matrixQ);
     items.add(matrixR);
     return new PacioliTuple(items);
 }

项目：finmath-lib 文件：LinearAlgebra.java

/**
 * Find a solution of the linear equation A x = b where
 * <ul>
 * <li>A is an n x m - matrix given as double[n][m]</li>
 * <li>b is an m - vector given as double[m],</li>
 * <li>x is an n - vector given as double[n],</li>
 * </ul>
 * 
 * @param A The matrix (left hand side of the linear equation).
 * @param b The vector (right hand of the linear equation).
 * @return A solution x to A x = b.
 */
public static double[] solveLinearEquation(double[][] A, double[] b) {

    if(isSolverUseApacheCommonsMath) {
        Array2DRowRealMatrix matrix = new Array2DRowRealMatrix(A);

        DecompositionSolver solver;
        if(matrix.getColumnDimension() == matrix.getRowDimension()) {
            solver = new LUDecomposition(matrix).getSolver();           
        }
        else {
            solver = new QRDecomposition(new Array2DRowRealMatrix(A)).getSolver();          
        }

        // Using SVD - very slow
        //          solver = new SingularValueDecomposition(new Array2DRowRealMatrix(A)).getSolver();

        return solver.solve(new Array2DRowRealMatrix(b)).getColumn(0);
    }
    else {
        return org.jblas.Solve.solve(new org.jblas.DoubleMatrix(A), new org.jblas.DoubleMatrix(b)).data;

        // For use of colt:
        // cern.colt.matrix.linalg.Algebra linearAlgebra = new cern.colt.matrix.linalg.Algebra();
        // return linearAlgebra.solve(new DenseDoubleMatrix2D(A), linearAlgebra.transpose(new DenseDoubleMatrix2D(new double[][] { b }))).viewColumn(0).toArray();

        // For use of parallel colt:
        // return new cern.colt.matrix.tdouble.algo.decomposition.DenseDoubleLUDecomposition(new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D(A)).solve(new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix1D(b)).toArray();
    }
}

项目：finmath-lib 文件：LinearAlgebra.java

/**
 * Find a solution of the linear equation A x = b where
 * <ul>
 * <li>A is an n x m - matrix given as double[n][m]</li>
 * <li>b is an m - vector given as double[m],</li>
 * <li>x is an n - vector given as double[n],</li>
 * </ul>
 * 
 * @param A The matrix (left hand side of the linear equation).
 * @param b The vector (right hand of the linear equation).
 * @return A solution x to A x = b.
 */
public static double[] solveLinearEquation(double[][] A, double[] b) {

    if(isSolverUseApacheCommonsMath) {
        Array2DRowRealMatrix matrix = new Array2DRowRealMatrix(A);

        DecompositionSolver solver;
        if(matrix.getColumnDimension() == matrix.getRowDimension()) {
            solver = new LUDecomposition(matrix).getSolver();           
        }
        else {
            solver = new QRDecomposition(new Array2DRowRealMatrix(A)).getSolver();          
        }

        // Using SVD - very slow
        //          solver = new SingularValueDecomposition(new Array2DRowRealMatrix(A)).getSolver();

        return solver.solve(new Array2DRowRealMatrix(b)).getColumn(0);
    }
    else {
        return org.jblas.Solve.solve(new org.jblas.DoubleMatrix(A), new org.jblas.DoubleMatrix(b)).data;

        // For use of colt:
        // cern.colt.matrix.linalg.Algebra linearAlgebra = new cern.colt.matrix.linalg.Algebra();
        // return linearAlgebra.solve(new DenseDoubleMatrix2D(A), linearAlgebra.transpose(new DenseDoubleMatrix2D(new double[][] { b }))).viewColumn(0).toArray();

        // For use of parallel colt:
        // return new cern.colt.matrix.tdouble.algo.decomposition.DenseDoubleLUDecomposition(new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D(A)).solve(new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix1D(b)).toArray();
    }
}

项目：autoredistrict 文件：AbstractEvaluation.java

/** {@inheritDoc} */
public RealMatrix getCovariances(double threshold) {
    // Set up the Jacobian.
    final RealMatrix j = this.getJacobian();

    // 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();
}

项目：morpheus-core 文件：XDataFrameAlgebraApache.java

/**
 * Constructor
 * @param matrix    the input matrix
 */
XQRD(RealMatrix matrix) {
    this.qrd = new QRDecomposition(matrix);
    this.q = LazyValue.of(() -> toDataFrame(qrd.getQ()));
    this.r = LazyValue.of(() -> toDataFrame(qrd.getR()));
}

项目：SME 文件：AdamsNordsieckTransformer.java

/** Initialize the high order scaled derivatives at step start.
 * @param h step size to use for scaling
 * @param t first steps times
 * @param y first steps states
 * @param yDot first steps derivatives
 * @return Nordieck vector at start of first step (h<sup>2</sup>/2 y''<sub>n</sub>,
 * h<sup>3</sup>/6 y'''<sub>n</sub> ... h<sup>k</sup>/k! y<sup>(k)</sup><sub>n</sub>)
 */

public Array2DRowRealMatrix initializeHighOrderDerivatives(final double h, final double[] t,
                                                           final double[][] y,
                                                           final double[][] yDot) {

    // using Taylor series with di = ti - t0, we get:
    //  y(ti)  - y(t0)  - di y'(t0) =   di^2 / h^2 s2 + ... +   di^k     / h^k sk + O(h^k)
    //  y'(ti) - y'(t0)             = 2 di   / h^2 s2 + ... + k di^(k-1) / h^k sk + O(h^(k-1))
    // we write these relations for i = 1 to i= 1+n/2 as a set of n + 2 linear
    // equations depending on the Nordsieck vector [s2 ... sk rk], so s2 to sk correspond
    // to the appropriately truncated Taylor expansion, and rk is the Taylor remainder.
    // The goal is to have s2 to sk as accurate as possible considering the fact the sum is
    // truncated and we don't want the error terms to be included in s2 ... sk, so we need
    // to solve also for the remainder
    final double[][] a     = new double[c1.length + 1][c1.length + 1];
    final double[][] b     = new double[c1.length + 1][y[0].length];
    final double[]   y0    = y[0];
    final double[]   yDot0 = yDot[0];
    for (int i = 1; i < y.length; ++i) {

        final double di    = t[i] - t[0];
        final double ratio = di / h;
        double dikM1Ohk    =  1 / h;

        // linear coefficients of equations
        // y(ti) - y(t0) - di y'(t0) and y'(ti) - y'(t0)
        final double[] aI    = a[2 * i - 2];
        final double[] aDotI = (2 * i - 1) < a.length ? a[2 * i - 1] : null;
        for (int j = 0; j < aI.length; ++j) {
            dikM1Ohk *= ratio;
            aI[j]     = di      * dikM1Ohk;
            if (aDotI != null) {
                aDotI[j]  = (j + 2) * dikM1Ohk;
            }
        }

        // expected value of the previous equations
        final double[] yI    = y[i];
        final double[] yDotI = yDot[i];
        final double[] bI    = b[2 * i - 2];
        final double[] bDotI = (2 * i - 1) < b.length ? b[2 * i - 1] : null;
        for (int j = 0; j < yI.length; ++j) {
            bI[j]    = yI[j] - y0[j] - di * yDot0[j];
            if (bDotI != null) {
                bDotI[j] = yDotI[j] - yDot0[j];
            }
        }

    }

    // solve the linear system to get the best estimate of the Nordsieck vector [s2 ... sk],
    // with the additional terms s(k+1) and c grabbing the parts after the truncated Taylor expansion
    final QRDecomposition decomposition = new QRDecomposition(new Array2DRowRealMatrix(a, false));
    final RealMatrix x = decomposition.getSolver().solve(new Array2DRowRealMatrix(b, false));

    // extract just the Nordsieck vector [s2 ... sk]
    final Array2DRowRealMatrix truncatedX = new Array2DRowRealMatrix(x.getRowDimension() - 1, x.getColumnDimension());
    for (int i = 0; i < truncatedX.getRowDimension(); ++i) {
        for (int j = 0; j < truncatedX.getColumnDimension(); ++j) {
            truncatedX.setEntry(i, j, x.getEntry(i, j));
        }
    }
    return truncatedX;

}

项目：SME 文件：OLSMultipleLinearRegression.java

/**
 * {@inheritDoc}
 * <p>This implementation computes and caches the QR decomposition of the X matrix.</p>
 */
@Override
public void newSampleData(double[] data, int nobs, int nvars) {
    super.newSampleData(data, nobs, nvars);
    qr = new QRDecomposition(getX(), threshold);
}

项目：SME 文件：OLSMultipleLinearRegression.java

/**
 * {@inheritDoc}
 * <p>This implementation computes and caches the QR decomposition of the X matrix
 * once it is successfully loaded.</p>
 */
@Override
protected void newXSampleData(double[][] x) {
    super.newXSampleData(x);
    qr = new QRDecomposition(getX(), threshold);
}

项目：CARMA 文件：OLSMultipleLinearRegression.java

/**
 * {@inheritDoc}
 * <p>This implementation computes and caches the QR decomposition of the X matrix.</p>
 */
@Override
public void newSampleData(double[] data, int nobs, int nvars) {
    super.newSampleData(data, nobs, nvars);
    qr = new QRDecomposition(getX(), threshold);
}

项目：CARMA 文件：OLSMultipleLinearRegression.java

/**
 * {@inheritDoc}
 * <p>This implementation computes and caches the QR decomposition of the X matrix
 * once it is successfully loaded.</p>
 */
@Override
protected void newXSampleData(double[][] x) {
    super.newXSampleData(x);
    qr = new QRDecomposition(getX(), threshold);
}