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

Introduction

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

Prototype

int getRowDimension();

Source Link

Document

Returns the number of rows in the matrix.

Usage

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

/**
 * Calculate the horizontal direct product of two matrices
 *
 * @param matrix1 first matrix//from   w  w  w .ja va 2 s  .  c om
 * @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;
}

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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 www.  j a  v a 2 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;
}

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From source file:lirmm.inria.fr.math.TestUtils.java

/** verifies that two matrices are close (1-norm) */
public static void assertEquals(String msg, RealMatrix expected, RealMatrix observed, double tolerance) {

    Assert.assertNotNull(msg + "\nObserved should not be null", observed);

    if (expected.getColumnDimension() != observed.getColumnDimension()
            || expected.getRowDimension() != observed.getRowDimension()) {
        StringBuilder messageBuffer = new StringBuilder(msg);
        messageBuffer.append("\nObserved has incorrect dimensions.");
        messageBuffer/*from w w  w.j a  v  a2 s .  c o m*/
                .append("\nobserved is " + observed.getRowDimension() + " x " + observed.getColumnDimension());
        messageBuffer
                .append("\nexpected " + expected.getRowDimension() + " x " + expected.getColumnDimension());
        Assert.fail(messageBuffer.toString());
    }

    RealMatrix delta = expected.subtract(observed);
    if (delta.getNorm() >= tolerance) {
        StringBuilder messageBuffer = new StringBuilder(msg);
        messageBuffer.append("\nExpected: " + expected);
        messageBuffer.append("\nObserved: " + observed);
        messageBuffer.append("\nexpected - observed: " + delta);
        Assert.fail(messageBuffer.toString());
    }
}

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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   w w  w.jav  a  2  s  .c o  m*/
        Preconditions.checkArgument(cell.getColumnDimension() == colDims, "Mismatch in col dimensions at row ",
                row);
        combined.setSubMatrix(cell.getData(), row * rowDims, 0);
    }
    return combined;
}

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

/**
 * Regenerates the design matrix and fills any random columns with a new
 * realization of random values based on a normal distribution.
 *
 * @return full design matrix/* w ww .  ja va2  s.  c  om*/
 */
public static RealMatrix getFullDesignMatrix(RealMatrix designEssence, RealMatrix sigmaGaussianRandom,
        int sampleSize, int seed) {
    RealMatrix fixed = MatrixUtils.getKroneckerProduct(designEssence,
            MatrixUtils.getRealMatrixWithFilledValue(sampleSize, 1, 1));
    // GLMM(F, g), so we need to append a random column
    return MatrixUtils.getHorizontalAppend(fixed, MatrixUtils.getRandomColumnMatrix(fixed.getRowDimension(), 0,
            sigmaGaussianRandom.getEntry(0, 0), seed));
}

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From source file:edu.ucdenver.bios.powersvc.resource.ContrastHelper.java

/**
 * Create a trend test fpr the specified factor of interest
 * @param factorOfInterestMap factor of interest plus trend type information
 * @param factorList list of all between participant factors
 * @return trend contrast//w  w  w  .j a  va  2  s .c  o m
 */
public static RealMatrix trendBetween(HypothesisBetweenParticipantMapping factorOfInterestMap,
        List<BetweenParticipantFactor> factorList) {

    // build contrast component for the effect of interest 
    BetweenParticipantFactor factorOfInterest = factorOfInterestMap.getBetweenParticipantFactor();
    HypothesisTrendTypeEnum trendType = factorOfInterestMap.getType();
    int levels = factorOfInterest.getCategoryList().size();
    if (levels > 1) {
        // create even spacing
        double[] spacing = new double[levels];
        for (int i = 0; i < levels; i++) {
            spacing[i] = i;
        }
        RealMatrix trendContrast = getTrendContrast(spacing, trendType, true);

        // Kronecker product the trend contrast with average contrasts for remaining
        // factors
        RealMatrix contrast = MatrixUtils.getRealMatrixWithFilledValue(1, 1, 1);
        int df = trendContrast.getRowDimension();
        for (BetweenParticipantFactor factor : factorList) {
            if (factor.getPredictorName().equals(factorOfInterest.getPredictorName())) {
                contrast = MatrixUtils.getKroneckerProduct(contrast, trendContrast);
            } else {
                List<Category> categoryList = factor.getCategoryList();
                if (categoryList.size() > 0) {
                    int dimension = categoryList.size();
                    contrast = MatrixUtils.getKroneckerProduct(contrast,
                            MatrixUtils.getRealMatrixWithFilledValue(1, dimension, 1 / (double) dimension));
                }
            }
        }
        return contrast;
    } else {
        return ContrastHelper.grandMeanBetween(factorList);
    }
}

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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");

    final int rows = grid.length;
    RealMatrix grid0 = grid[0];
    Preconditions.checkNotNull(grid0);/*from ww  w  . ja  v  a2 s  .c o  m*/
    int cellRows = grid0.getRowDimension();
    int cellCols = grid0.getColumnDimension();

    final DoubleArrayList list = new DoubleArrayList(rows * 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++) {
        RealMatrix cell = grid[row];
        cell.walkInRowOrder(visitor);
    }

    return list.toArray();
}

---

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);/*w  w w  . j  a va2s  .co  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:edu.ucdenver.bios.powersvc.resource.ContrastHelper.java

/**
 * Create a trend test fpr the specified within participant of interest
 * @param factorOfInterestMap factor of interest plus trend type information
 * @param factorList list of all within participant factors
 * @return trend contrast// w ww  .  ja  v  a 2 s  .  c om
 */
public static RealMatrix trendWithin(HypothesisRepeatedMeasuresMapping factorOfInterestMap,
        List<RepeatedMeasuresNode> factorList, List<ResponseNode> responseList) {

    // build contrast component for the effect of interest 
    RepeatedMeasuresNode factorOfInterest = factorOfInterestMap.getRepeatedMeasuresNode();
    HypothesisTrendTypeEnum trendType = factorOfInterestMap.getType();
    int levels = factorOfInterest.getNumberOfMeasurements();
    if (levels > 1) {
        List<Spacing> spacingList = factorOfInterest.getSpacingList();
        double[] spacingArray;
        if (spacingList != null) {
            spacingArray = new double[spacingList.size()];
            for (int i = 0; i < spacingList.size(); i++) {
                spacingArray[i] = spacingList.get(i).getValue();
            }
        } else {
            int size = factorOfInterest.getNumberOfMeasurements();
            spacingArray = new double[size];
            for (int i = 0; i < size; i++) {
                spacingArray[i] = i + 1;
            }
        }
        RealMatrix trendContrast = getTrendContrast(spacingArray, trendType, false);

        // horizontal direct product the trend contrast with average contrasts for remaining
        // factors
        RealMatrix contrast = MatrixUtils.getRealMatrixWithFilledValue(1, 1, 1);
        int df = trendContrast.getRowDimension();
        for (RepeatedMeasuresNode factor : factorList) {
            if (factor.getDimension().equals(factorOfInterest.getDimension())) {
                contrast = MatrixUtils.getKroneckerProduct(contrast, trendContrast);
            } else {
                int size = factor.getNumberOfMeasurements();
                if (size > 0) {
                    contrast = MatrixUtils.getKroneckerProduct(contrast,
                            MatrixUtils.getRealMatrixWithFilledValue(1, size, 1 / (double) size));
                }
            }
        }
        // multiply on an identity matrix with dimension equal to the number of multivariate responses
        if (responseList != null && responseList.size() > 1) {
            contrast = MatrixUtils.getKroneckerProduct(contrast,
                    org.apache.commons.math3.linear.MatrixUtils.createRealIdentityMatrix(responseList.size()));
        }
        return contrast;
    } else {
        return ContrastHelper.grandMeanWithin(factorList, responseList);
    }
}

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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 .  j av  a2  s. 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);
    }
}

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