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:edu.washington.gs.skyline.model.quantification.DesignMatrix.java

private static RealMatrix matrixFromColumnVectors(double[][] columnVectors) {
    RealMatrix realMatrix = MatrixUtils.createRealMatrix(columnVectors[0].length, columnVectors.length);
    for (int iRow = 0; iRow < realMatrix.getRowDimension(); iRow++) {
        for (int iCol = 0; iCol < realMatrix.getColumnDimension(); iCol++) {
            realMatrix.setEntry(iRow, iCol, columnVectors[iCol][iRow]);
        }/*www.  j a v a  2  s  . c om*/
    }
    return realMatrix;
}

From source file:ch.zhaw.iamp.rct.weights.Weights.java

/**
 * Calculates a pseudo-inverse using the values in the given files. They are
 * first read, then converted to a matrix, and finally used for the
 * calculation of the inverse, using Singular value decomposition (SVD).
 *
 * @param pathToA The file which contains the matrix A, represented in
 * comma-separated-value format.//from w  w  w  .j  av a  2  s.c o m
 * @param targetTrajectoryFile The file which contains the target
 * trajectory, represented in comma-separated-value format.
 * @param weightsFile The file, to which the calculated weights should be
 * written to.
 * @param offset The numbers of first steps to ignore (to skip fading-memory
 * initialization steps).
 */
public static void calculateWeights(final String pathToA, final String targetTrajectoryFile,
        final String weightsFile, final int offset) {
    try {
        RealMatrix A = csvToMatrix(pathToA);
        // cut first n elements
        A = A.getSubMatrix(offset, A.getRowDimension() - 1, 0, A.getColumnDimension() - 1);
        A = addNoise(A);

        RealMatrix b = csvToMatrix(targetTrajectoryFile);

        // adjust b to cutting
        int n = offset % b.getRowDimension();

        if (n > 0) {
            RealMatrix tmp = b.getSubMatrix(n, b.getRowDimension() - 1, 0, b.getColumnDimension() - 1);
            b = b.getSubMatrix(0, n - 1, 0, b.getColumnDimension() - 1);
            double[][] tmpArray = tmp.getData();
            double[][] tmpArray2 = b.getData();
            b = MatrixUtils.createRealMatrix(concat(tmpArray, tmpArray2));
            tmpArray = b.getData();

            for (int i = 0; tmpArray.length < A.getRowDimension(); ++i) {
                tmpArray2 = new double[1][tmpArray[0].length];

                for (int j = 0; j < tmpArray[i].length; ++j) {
                    tmpArray2[0][j] = tmpArray[i][j];
                }

                tmpArray = concat(tmpArray, tmpArray2);
            }

            b = MatrixUtils.createRealMatrix(tmpArray);
        }

        DecompositionSolver solver = new SingularValueDecomposition(A).getSolver();
        RealMatrix x = solver.solve(b).transpose();
        matrixToCsv(x, weightsFile);

    } catch (IOException ex) {
        JOptionPane.showMessageDialog(null, "Could not read a file: " + ex.getMessage(), "File Error",
                JOptionPane.ERROR_MESSAGE);
        Logger.getLogger(Weights.class.getName()).log(Level.WARNING, "Could not read a file: {0}", ex);
    } catch (DimensionMismatchException ex) {
        JOptionPane.showMessageDialog(null,
                "<html>Could not calculate the " + "pseudo-inverse since a dimension mismatch occurred.<br />"
                        + "Please make sure that all lines of the CSV file posses "
                        + "the same amount of entries.<br />Hint: Remove the last "
                        + "line and try it again.</html>",
                "Matrix Dimension Mismatch", JOptionPane.ERROR_MESSAGE);
        Logger.getLogger(Weights.class.getName()).log(Level.WARNING, "A dimension mismatch occurred: {0}", ex);
    }
}

From source file:lsafunctions.LSA.java

public static RealMatrix calculateTfIdf(RealMatrix M) {
    int tf;/* w w w  .jav a2 s .  com*/
    double idf;
    int df;
    double ndf;
    for (int j = 0; j < M.getRowDimension(); j++) {

        df = calcDf(j, M);
        // System.out.println("J:"+j+"  df:"+df);

        for (int k = 0; k < M.getColumnDimension(); k++) {
            tf = (int) M.getEntry(j, k);
            ndf = M.getColumnDimension() / df;
            idf = Math.log(ndf) / Math.log(2);
            M.setEntry(j, k, idf * tf);
        }
    }
    //M.print(NumberFormat.INTEGER_FIELD, M.getColumnDimension());
    M = normalizeMatrix(M);
    return M;
}

From source file:dataGen.DataGen.java

/**
 * Do the yet created data stored in the matrix fulfill the Anti-Correlation condition?
 * Returns true if all correlation coefficients are below the Supremum correlation coefficient.
 * @param corrMatrix//from w w  w  .  j a  va 2  s.  co  m
 *       The Data-Matrix
 * @param corrCoeff
 *       the Supremum correlation coefficient
 * @param dimensions
 *       how many dimensions does the test file have?
 * @return
 *       true if all correlation coefficients are below the Supremum correlation coefficient.
 */
public static boolean isCorrMatrixCompleteValid(RealMatrix corrMatrix, double corrCoeff, int dimensions) {
    for (int i = 1; i < corrMatrix.getColumnDimension(); i++) {
        for (int j = 0; j < i; j++) {
            if (corrMatrix.getEntry(i, j) > corrCoeff) {
                return false;
            }
        }
    }
    return true;
}

From source file:edu.macalester.tagrelatedness.KendallsCorrelation.java

/**
 * This code is taken from a patch submitted for the Apache Commons 3.3
 * library by The Apache Software Foundation 
 * at: https://issues.apache.org/jira/browse/MATH-814
 * //from  ww w. j  a v a2  s  .  c  o m
 * 
 * Computes the Kendall's Tau rank correlation matrix for the columns of
 * the input matrix.
 *
 * @param matrix matrix with columns representing variables to correlate
 * @return correlation matrix
 * @author Matt Adereth
 */
static public RealMatrix computeCorrelationMatrix(final RealMatrix matrix) {
    int nVars = matrix.getColumnDimension();
    RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
    for (int i = 0; i < nVars; i++) {
        for (int j = 0; j < i; j++) {
            double corr = correlation(matrix.getColumn(i), matrix.getColumn(j));
            outMatrix.setEntry(i, j, corr);
            outMatrix.setEntry(j, i, corr);
        }
        outMatrix.setEntry(i, i, 1d);
    }
    return outMatrix;
}

From source file:com.vsthost.rnd.commons.math.ext.linear.EMatrixUtils.java

/**
 * Returns the standard deviations of columns.
 *
 * @param matrix The matrix of which the standard deviations of columns to be computed
 * @return A double array of column standard deviations.
 *//*w  w w. j  av a 2 s .com*/
public static double[] columnStdDevs(RealMatrix matrix) {
    double[] retval = new double[matrix.getColumnDimension()];
    for (int i = 0; i < retval.length; i++) {
        retval[i] = new DescriptiveStatistics(matrix.getColumn(i)).getStandardDeviation();
    }
    return retval;
}

From source file:ch.zhaw.iamp.rct.weights.Weights.java

private static void matrixToCsv(final RealMatrix matrix, final String targetFilePath) throws IOException {
    Writer out = new FileWriter(targetFilePath);

    for (int i = 0; i < matrix.getRowDimension(); ++i) {
        for (int j = 0; j < matrix.getColumnDimension(); ++j) {
            out.append("" + matrix.getEntry(i, j));
            if (j < matrix.getColumnDimension() - 1) {
                out.append(",");
            }/*w ww.ja v a 2 s.  co m*/
        }
        out.append("\n");
        out.flush();
    }

}

From source file:edu.mit.genecircuits.GcUtils.java

/** Convert dense Apache Commons to Colt matrix */
static public DoubleMatrix2D apache2colt(RealMatrix apache) {

    // Convert apache commons matrix to colt
    DoubleMatrix2D colt = new DenseDoubleMatrix2D(apache.getRowDimension(), apache.getColumnDimension());
    for (int i = 0; i < apache.getRowDimension(); i++)
        for (int j = 0; j < apache.getColumnDimension(); j++)
            colt.set(i, j, apache.getEntry(i, j));

    return colt;//from w ww .j  a  v  a2 s . c om
}

From source file:com.yahoo.egads.utilities.SpectralMethods.java

public static RealMatrix createHankelMatrix(RealMatrix data, int windowSize) {

    int n = data.getRowDimension();
    int m = data.getColumnDimension();
    int k = n - windowSize + 1;

    RealMatrix res = MatrixUtils.createRealMatrix(k, m * windowSize);
    double[] buffer = {};

    for (int i = 0; i < n; ++i) {
        double[] row = data.getRow(i);
        buffer = ArrayUtils.addAll(buffer, row);

        if (i >= windowSize - 1) {
            RealMatrix mat = MatrixUtils.createRowRealMatrix(buffer);
            res.setRowMatrix(i - windowSize + 1, mat);
            buffer = ArrayUtils.subarray(buffer, m, buffer.length);
        }//from  ww w.  j a v a2 s  .  co  m
    }

    return res;
}

From source file:com.vsthost.rnd.commons.math.ext.linear.EMatrixUtils.java

/**
 * Returns the row range from the matrix as a new matrix.
 *
 * @param matrix The input matrix/*w  w  w. java2s.c  o  m*/
 * @param start The index of the row to start with (inclusive)
 * @param end The index of the row to end with (inclusive)
 * @return A new matrix with rows specified
 */
public static RealMatrix getRowRange(RealMatrix matrix, int start, int end) {
    return matrix.getSubMatrix(start, end, 0, matrix.getColumnDimension() - 1);
}