Example usage for org.apache.commons.math3.linear RealMatrix setSubMatrix

List of usage examples for org.apache.commons.math3.linear RealMatrix setSubMatrix

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Introduction

In this page you can find the example usage for org.apache.commons.math3.linear RealMatrix setSubMatrix.

Prototype

void setSubMatrix(double[][] subMatrix, int row, int column)
        throws NoDataException, OutOfRangeException, DimensionMismatchException, NullArgumentException;

Source Link

Document

Replace the submatrix starting at row, column using data in the input subMatrix array.

Usage

From source file:edu.cudenver.bios.matrix.MatrixUtils.java

/**
 * Horizontally append two matrices//from  w ww  .  j av  a  2 s. com
 * @param matrix
 * @param column
 * @return the combined matrix
 * @throws IllegalArgumentException
 */
public static RealMatrix getHorizontalAppend(RealMatrix m1, RealMatrix m2) throws IllegalArgumentException {
    if (m1 == null || m2 == null)
        throw new IllegalArgumentException("Missing required argument");
    if (m1.getRowDimension() != m2.getRowDimension())
        throw new IllegalArgumentException("Row dimensions must be equal");

    RealMatrix newMatrix = new Array2DRowRealMatrix(m1.getRowDimension(),
            m1.getColumnDimension() + m2.getColumnDimension());
    newMatrix.setSubMatrix(m1.getData(), 0, 0);
    newMatrix.setSubMatrix(m2.getData(), 0, m1.getColumnDimension());

    return newMatrix;
}

From source file:edu.cudenver.bios.matrix.MatrixUtils.java

/**
 * Calculate the Kronecker product of two matrices
 *
 * @param matrix1 first matrix/*  w w  w.  java2 s .  c o  m*/
 * @param matrix2 second matrix
 * @return Kronecker product of matrix 1 and matrix 2
 */
public static RealMatrix getKroneckerProduct(RealMatrix matrix1, RealMatrix matrix2) {
    if (matrix1 == null || matrix2 == null)
        throw new IllegalArgumentException("null input matrix");

    int m1Rows = matrix1.getRowDimension();
    int m1Cols = matrix1.getColumnDimension();
    int m2Rows = matrix2.getRowDimension();
    int m2Cols = matrix2.getColumnDimension();

    double[][] productData = new double[m1Rows * m2Rows][m1Cols * m2Cols];
    RealMatrix productMatrix = new Array2DRowRealMatrix(productData);
    for (int col = 0; col < m1Cols; col++) {
        for (int row = 0; row < m1Rows; row++) {
            productMatrix.setSubMatrix((matrix2.scalarMultiply(matrix1.getEntry(row, col))).getData(),
                    row * m2Rows, col * m2Cols);
        }
    }

    return productMatrix;
}

From source file:edu.cudenver.bios.matrix.MatrixUtils.java

/**
 * Calculate the horizontal direct product of two matrices
 *
 * @param matrix1 first matrix/*w ww  .j ava 2  s  .  co m*/
 * @param matrix2 second matrix
 * @return horizontal direct product of matrix 1 and matrix 2
 */
public static RealMatrix getHorizontalDirectProduct(RealMatrix matrix1, RealMatrix matrix2)
        throws IllegalArgumentException {
    if (matrix1 == null || matrix2 == null)
        throw new IllegalArgumentException("null input matrix");
    if (matrix1.getRowDimension() != matrix2.getRowDimension())
        throw new IllegalArgumentException("input matrices must have equal row dimension");

    int mRows = matrix1.getRowDimension();
    int m1Cols = matrix1.getColumnDimension();
    int m2Cols = matrix2.getColumnDimension();

    double[][] productData = new double[mRows][m1Cols * m2Cols];
    RealMatrix productMatrix = new Array2DRowRealMatrix(productData);
    for (int col = 0; col < m1Cols; col++) {
        for (int row = 0; row < mRows; row++) {
            RealMatrix m2Row = matrix2.getRowMatrix(row);
            productMatrix.setSubMatrix((m2Row.scalarMultiply(matrix1.getEntry(row, col))).getData(), row,
                    col * m2Cols);
        }
    }
    return productMatrix;
}

From source file:hivemall.utils.math.MatrixUtils.java

@Nonnull
public static RealMatrix combinedMatrices(@Nonnull final RealMatrix[][] grid, final int dimensions) {
    Preconditions.checkArgument(grid.length >= 1, "The number of rows must be greater than 1");
    Preconditions.checkArgument(grid[0].length >= 1, "The number of cols must be greater than 1");
    Preconditions.checkArgument(dimensions > 0, "Dimension should be more than 0: ", dimensions);

    final int rows = grid.length;
    final int cols = grid[0].length;

    final RealMatrix combined = new BlockRealMatrix(rows * dimensions, cols * dimensions);
    for (int row = 0; row < grid.length; row++) {
        for (int col = 0; col < grid[row].length; col++) {
            combined.setSubMatrix(grid[row][col].getData(), row * dimensions, col * dimensions);
        }/*w  w  w .  j a v  a2s  .  c o  m*/
    }
    return combined;
}

From source file:hivemall.utils.math.MatrixUtils.java

/**
 * QR decomposition for a tridiagonal matrix T.
 *
 * @see https://gist.github.com/lightcatcher/8118181
 * @see http://www.ericmart.in/blog/optimizing_julia_tridiag_qr
 * @param T target tridiagonal matrix//from www  . j  av  a  2s.  c o m
 * @param R output matrix for R which is the same shape as T
 * @param Qt output matrix for Q.T which is the same shape an T
 */
public static void tridiagonalQR(@Nonnull final RealMatrix T, @Nonnull final RealMatrix R,
        @Nonnull final RealMatrix Qt) {
    int n = T.getRowDimension();
    Preconditions.checkArgument(n == R.getRowDimension() && n == R.getColumnDimension(),
            "T and R must be the same shape");
    Preconditions.checkArgument(n == Qt.getRowDimension() && n == Qt.getColumnDimension(),
            "T and Qt must be the same shape");

    // initial R = T
    R.setSubMatrix(T.getData(), 0, 0);

    // initial Qt = identity
    Qt.setSubMatrix(eye(n), 0, 0);

    for (int i = 0; i < n - 1; i++) {
        // Householder projection for a vector x
        // https://en.wikipedia.org/wiki/Householder_transformation
        RealVector x = T.getSubMatrix(i, i + 1, i, i).getColumnVector(0);
        x = unitL2norm(x);

        RealMatrix subR = R.getSubMatrix(i, i + 1, 0, n - 1);
        R.setSubMatrix(subR.subtract(x.outerProduct(subR.preMultiply(x)).scalarMultiply(2)).getData(), i, 0);

        RealMatrix subQt = Qt.getSubMatrix(i, i + 1, 0, n - 1);
        Qt.setSubMatrix(subQt.subtract(x.outerProduct(subQt.preMultiply(x)).scalarMultiply(2)).getData(), i, 0);
    }
}

From source file:hivemall.utils.math.MatrixUtils.java

@Nonnull
public static RealMatrix combinedMatrices(@Nonnull final RealMatrix[] grid) {
    Preconditions.checkArgument(grid.length >= 1, "The number of rows must be greater than 0: " + grid.length);

    final int rows = grid.length;
    final int rowDims = grid[0].getRowDimension();
    final int colDims = grid[0].getColumnDimension();

    final RealMatrix combined = new BlockRealMatrix(rows * rowDims, colDims);
    for (int row = 0; row < grid.length; row++) {
        RealMatrix cell = grid[row];//from w  ww  .j av a 2  s.  c o  m
        Preconditions.checkArgument(cell.getRowDimension() == rowDims, "Mismatch in row dimensions at row ",
                row);
        Preconditions.checkArgument(cell.getColumnDimension() == colDims, "Mismatch in col dimensions at row ",
                row);
        combined.setSubMatrix(cell.getData(), row * rowDims, 0);
    }
    return combined;
}

From source file:hivemall.utils.math.MatrixUtils.java

/**
 * Lanczos tridiagonalization for a symmetric matrix C to make s * s tridiagonal matrix T.
 *
 * @see http://www.cas.mcmaster.ca/~qiao/publications/spie05.pdf
 * @param C target symmetric matrix//from   www. j  a va  2s .c  om
 * @param a initial vector
 * @param T result is stored here
 */
public static void lanczosTridiagonalization(@Nonnull final RealMatrix C, @Nonnull final double[] a,
        @Nonnull final RealMatrix T) {
    Preconditions.checkArgument(Arrays.deepEquals(C.getData(), C.transpose().getData()),
            "Target matrix C must be a symmetric matrix");
    Preconditions.checkArgument(C.getColumnDimension() == a.length,
            "Column size of A and length of a should be same");
    Preconditions.checkArgument(T.getRowDimension() == T.getColumnDimension(), "T must be a square matrix");

    int s = T.getRowDimension();

    // initialize T with zeros
    T.setSubMatrix(new double[s][s], 0, 0);

    RealVector a0 = new ArrayRealVector(a.length);
    RealVector r = new ArrayRealVector(a);

    double beta0 = 1.d;

    for (int i = 0; i < s; i++) {
        RealVector a1 = r.mapDivide(beta0);
        RealVector Ca1 = C.operate(a1);

        double alpha1 = a1.dotProduct(Ca1);

        r = Ca1.add(a1.mapMultiply(-1.d * alpha1)).add(a0.mapMultiply(-1.d * beta0));

        double beta1 = r.getNorm();

        T.setEntry(i, i, alpha1);
        if (i - 1 >= 0) {
            T.setEntry(i, i - 1, beta0);
        }
        if (i + 1 < s) {
            T.setEntry(i, i + 1, beta1);
        }

        a0 = a1.copy();
        beta0 = beta1;
    }
}

From source file:fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurement.java

@Override
public void addAlternative(Alternative alt) {
    alternatives.add(alt);/*from  ww  w.  j  a v  a2 s  .  c  o  m*/
    RealMatrix matrix = MatrixUtils.createRealIdentityMatrix(alternatives.size());
    final RealMatrix newCovarianceMatrix = matrix;
    newCovarianceMatrix.setSubMatrix(covarianceMatrix.getData(), 0, 0);
    final RealVector newMeanVector = meanVector.append(0);
    fireEvents(setMeanVectorInternal(newMeanVector), setCovarianceMatrixInternal(newCovarianceMatrix));
}

From source file:com.joptimizer.functions.SOCPLogarithmicBarrier.java

private RealMatrix buildJ(SOCPConstraintParameters param, RealVector X) {
    RealMatrix J = new Array2DRowRealMatrix(dim, param.getA().getRowDimension() + 1);
    J.setSubMatrix(param.getA().transpose().getData(), 0, 0);
    J.setColumnVector(param.getA().getRowDimension(), param.getC());
    return J;/*www. jav a 2 s . co  m*/
}

From source file:com.joptimizer.functions.SOCPLogarithmicBarrier.java

/**
 * Create the barrier function for the Phase I.
 * It is an instance of this class for the constraints: 
 * <br>||Ai.x+bi|| < ci.x+di+t, i=1,...,m
 * @see "S.Boyd and L.Vandenberghe, Convex Optimization, 11.6.2"
 *//*  w  w  w  .ja  v a  2 s .  c  o m*/
public BarrierFunction createPhase1BarrierFunction() {

    final int dimPh1 = dim + 1;
    List<SOCPConstraintParameters> socpConstraintParametersPh1List = new ArrayList<SOCPConstraintParameters>();
    SOCPLogarithmicBarrier bfPh1 = new SOCPLogarithmicBarrier(socpConstraintParametersPh1List, dimPh1);

    for (int i = 0; i < socpConstraintParametersList.size(); i++) {
        SOCPConstraintParameters param = socpConstraintParametersList.get(i);
        RealMatrix A = param.getA();
        RealVector b = param.getB();
        RealVector c = param.getC();
        double d = param.getD();

        RealMatrix APh1 = MatrixUtils.createRealMatrix(A.getRowDimension(), dimPh1);
        APh1.setSubMatrix(A.getData(), 0, 0);
        RealVector bPh1 = b;
        RealVector cPh1 = new ArrayRealVector(c.getDimension() + 1);
        cPh1.setSubVector(0, c);
        cPh1.setEntry(c.getDimension(), 1);
        double dPh1 = d;

        SOCPConstraintParameters paramsPh1 = new SOCPConstraintParameters(APh1.getData(), bPh1.toArray(),
                cPh1.toArray(), dPh1);
        socpConstraintParametersPh1List.add(socpConstraintParametersPh1List.size(), paramsPh1);
    }

    return bfPh1;
}