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

Introduction

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

Prototype

int getColumnDimension();

Source Link

Document

Returns the number of columns in the matrix.

Usage

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

@Nonnull
public static double[] flatten(@Nonnull final RealMatrix[][] grid) {
    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");

    final int rows = grid.length;
    final int cols = grid[0].length;
    RealMatrix grid00 = grid[0][0];
    Preconditions.checkNotNull(grid00);/*  ww w  .  j  av a 2  s.  c o  m*/
    int cellRows = grid00.getRowDimension();
    int cellCols = grid00.getColumnDimension();

    final DoubleArrayList list = new DoubleArrayList(rows * cols * cellRows * cellCols);
    final RealMatrixPreservingVisitor visitor = new DefaultRealMatrixPreservingVisitor() {
        @Override
        public void visit(int row, int column, double value) {
            list.add(value);
        }
    };

    for (int row = 0; row < rows; row++) {
        for (int col = 0; col < cols; col++) {
            RealMatrix cell = grid[row][col];
            cell.walkInRowOrder(visitor);
        }
    }

    return list.toArray();
}

---

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   w ww  .jav  a 2  s  .  co  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:edu.cudenver.bios.matrix.MatrixUtils.java

/**
 * This method will return vech(matrix).
 * matrix must be symmetric in order to perform vech operation.
 * @param RealMatrix matrix//from  ww  w .j ava2  s . c  o  m
 * @return vech(matrix)
 */
public static RealMatrix getVechMatrix(RealMatrix matrix) throws IllegalArgumentException {
    if (matrix == null || !isSymmetric(matrix)) {
        throw new IllegalArgumentException("Matrix must be non-null and " + "symmetrical.");
    }
    int newRow = 0;
    int numRows = matrix.getRowDimension();
    RealMatrix vech = new Array2DRowRealMatrix(numRows * (numRows + 1) / 2, 1);
    for (int c = 0; c < matrix.getColumnDimension(); c++) {
        for (int r = c; r < matrix.getRowDimension(); r++, newRow++) {
            vech.setEntry(newRow, 0, matrix.getEntry(r, c));
        }
    }
    return vech;
}

---

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

/**
 * Find the first singular vector/value of a matrix A based on the Power method.
 *
 * @see http/*from w  w w .  j a  v  a 2  s  .c  o  m*/
 *      ://www.cs.yale.edu/homes/el327/datamining2013aFiles/07_singular_value_decomposition.pdf
 * @param A target matrix
 * @param x0 initial vector
 * @param nIter number of iterations for the Power method
 * @param u 1st left singular vector
 * @param v 1st right singular vector
 * @return 1st singular value
 */
public static double power1(@Nonnull final RealMatrix A, @Nonnull final double[] x0, final int nIter,
        @Nonnull final double[] u, @Nonnull final double[] v) {
    Preconditions.checkArgument(A.getColumnDimension() == x0.length,
            "Column size of A and length of x should be same");
    Preconditions.checkArgument(A.getRowDimension() == u.length,
            "Row size of A and length of u should be same");
    Preconditions.checkArgument(x0.length == v.length, "Length of x and u should be same");
    Preconditions.checkArgument(nIter >= 1, "Invalid number of iterations: " + nIter);

    RealMatrix AtA = A.transpose().multiply(A);

    RealVector x = new ArrayRealVector(x0);
    for (int i = 0; i < nIter; i++) {
        x = AtA.operate(x);
    }

    double xNorm = x.getNorm();
    for (int i = 0, n = v.length; i < n; i++) {
        v[i] = x.getEntry(i) / xNorm;
    }

    RealVector Av = new ArrayRealVector(A.operate(v));
    double s = Av.getNorm();

    for (int i = 0, n = u.length; i < n; i++) {
        u[i] = Av.getEntry(i) / s;
    }

    return s;
}

---

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

/**
 * Horizontally append two matrices//from w  w w .j  av a2s  .  c  o  m
 * @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: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];
        Preconditions.checkArgument(cell.getRowDimension() == rowDims, "Mismatch in row dimensions at row ",
                row);/*from  ww w. ja  v a 2 s.c om*/
        Preconditions.checkArgument(cell.getColumnDimension() == colDims, "Mismatch in col dimensions at row ",
                row);
        combined.setSubMatrix(cell.getData(), row * rowDims, 0);
    }
    return combined;
}

---

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

/**
 * Calculate the horizontal direct product of two matrices
 *
 * @param matrix1 first matrix/*  www.  ja v a2s.c  o 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:edu.cudenver.bios.matrix.MatrixUtils.java

/**
 * This method will return the element-wise product of matrixA
 * and matrixB.// w  w  w  .jav  a2s. co m
 * In order to perform element-wise multiplication, the incoming
 * matrices must have the same dimensions.
 * @param matrixA
 * @param matrixB
 * @return RealMatrix which is the element-wise product of A and B.
 */
public static RealMatrix getElementWiseProduct(RealMatrix matrixA, RealMatrix matrixB)
        throws IllegalArgumentException {
    if (matrixA == null || matrixB == null || !areDimensionsEqual(matrixA, matrixB)) {
        throw new IllegalArgumentException("Both matrices must be non-null "
                + "and matrix dimensions must be equal" + " for element-wise multiplication.");
    }

    int numRows = matrixA.getRowDimension();
    int numCols = matrixA.getColumnDimension();
    RealMatrix product = new Array2DRowRealMatrix(numRows, numCols);
    double aVal, bVal;

    //loop through each row
    for (int r = 0; r < numRows; r++) {
        //multiply each element of A by same element of B
        for (int c = 0; c < numCols; c++) {
            aVal = matrixA.getEntry(r, c);
            bVal = matrixB.getEntry(r, c);
            product.setEntry(r, c, aVal * bVal);
        }
    }
    return product;
}

---

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

/**
 * Find eigenvalues and eigenvectors of given tridiagonal matrix T.
 *
 * @see http://web.csulb.edu/~tgao/math423/s94.pdf
 * @see http://stats.stackexchange.com/questions/20643/finding-matrix-eigenvectors-using-qr-
 *      decomposition//w  w w .j av a  2 s .co  m
 * @param T target tridiagonal matrix
 * @param nIter number of iterations for the QR method
 * @param eigvals eigenvalues are stored here
 * @param eigvecs eigenvectors are stored here
 */
public static void tridiagonalEigen(@Nonnull final RealMatrix T, @Nonnull final int nIter,
        @Nonnull final double[] eigvals, @Nonnull final RealMatrix eigvecs) {
    Preconditions.checkArgument(Arrays.deepEquals(T.getData(), T.transpose().getData()),
            "Target matrix T must be a symmetric (tridiagonal) matrix");
    Preconditions.checkArgument(eigvecs.getRowDimension() == eigvecs.getColumnDimension(),
            "eigvecs must be a square matrix");
    Preconditions.checkArgument(T.getRowDimension() == eigvecs.getRowDimension(),
            "T and eigvecs must be the same shape");
    Preconditions.checkArgument(eigvals.length == eigvecs.getRowDimension(),
            "Number of eigenvalues and eigenvectors must be same");

    int nEig = eigvals.length;

    // initialize eigvecs as an identity matrix
    eigvecs.setSubMatrix(eye(nEig), 0, 0);

    RealMatrix T_ = T.copy();

    for (int i = 0; i < nIter; i++) {
        // QR decomposition for the tridiagonal matrix T
        RealMatrix R = new Array2DRowRealMatrix(nEig, nEig);
        RealMatrix Qt = new Array2DRowRealMatrix(nEig, nEig);
        tridiagonalQR(T_, R, Qt);

        RealMatrix Q = Qt.transpose();
        T_ = R.multiply(Q);
        eigvecs.setSubMatrix(eigvecs.multiply(Q).getData(), 0, 0);
    }

    // diagonal elements correspond to the eigenvalues
    for (int i = 0; i < nEig; i++) {
        eigvals[i] = T_.getEntry(i, i);
    }
}

---

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/* w  w  w .j av  a2s .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;
    }
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• 21
• 22
• 23
• 24
• 25
• 26
• 27
• 28
• 29
• 30
• 31
• 32
• »