Example usage for org.apache.commons.math.linear RealMatrix getData

Introduction

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

Prototype

double[][] getData();

Source Link

Document

Returns matrix entries as a two-dimensional array.

Usage

From source file:com.opengamma.analytics.math.util.wrapper.CommonsMathWrapper.java

/**
 * @param x A Commons matrix, not null//  w w w  . j a  v a2 s  .  c  om
 * @return An OG 2-D matrix of doubles
 */
public static DoubleMatrix2D unwrap(final RealMatrix x) {
    Validate.notNull(x);
    return new DoubleMatrix2D(x.getData());
}

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From source file:juicebox.tools.utils.juicer.apa.APAUtils.java

public static double peakEnhancement(RealMatrix matrix) {
    int rows = matrix.getRowDimension();
    int center = rows / 2;
    double centerVal = matrix.getEntry(center, center);
    double remainingSum = APARegionStatistics.sum(matrix.getData()) - centerVal;
    double remainingAverage = remainingSum / (rows * rows - 1);
    return centerVal / remainingAverage;
}

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From source file:juicebox.tools.utils.common.MatrixTools.java

/**
 * print for matrix
 */
public static void print(RealMatrix matrix) {
    print(matrix.getData());
}

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From source file:juicebox.tools.utils.common.MatrixTools.java

/**
 * @return minimal positive entry in the matrix greater than 0
 *//*from ww w .j  a  v a 2  s  .c  o  m*/
public static double minimumPositive(RealMatrix data) {
    return minimumPositive(data.getData());
}

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From source file:juicebox.tools.utils.common.MatrixTools.java

/**
 * @return mean of matrix/*from  ww  w  .  j av  a 2 s  . com*/
 */
public static double mean(RealMatrix matrix) {
    return APARegionStatistics.statistics(matrix.getData()).getMean();
}

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From source file:juicebox.tools.utils.juicer.apa.APAPlotter.java

/**
 * Method for plotting apa data/*from w ww. j a v a  2 s  .  co m*/
 *
 * @param data       for heat map
 * @param axesRange  initial values and increments to annotate axes [x0, dx, y0, dy]
 * @param outputFile where image will saved
 */
public static void plot(RealMatrix data, int[] axesRange, File outputFile, String title) {

    APARegionStatistics apaStats = new APARegionStatistics(data);
    DecimalFormat df = new DecimalFormat("0.000");
    title += ", P2LL = " + df.format(apaStats.getPeak2LL());

    // initialize heat map
    HeatChart map = new HeatChart(data.getData());
    map.setLowValueColour(Color.WHITE);
    map.setHighValueColour(Color.RED);
    map.setXValues(axesRange[0], axesRange[1]);
    map.setYValues(axesRange[2], axesRange[3]);
    map.setTitle(title);

    try {
        // calculate dimensions for plot wrapper
        initializeSizes(map);

        // create blank white image
        BufferedImage apaImage = new BufferedImage(fullWidth, fullHeight, BufferedImage.TYPE_INT_ARGB);
        Graphics2D g2 = apaImage.createGraphics();
        g2.setBackground(Color.WHITE);
        g2.fillRect(0, 0, fullWidth, fullHeight);

        // plot in heat map, color bar, etc
        g2.drawImage(map.getChartImage(), 0, 0, heatmapWidth, fullHeight, null);
        drawHeatMapBorder(g2, map);
        plotColorScaleBar(g2);
        plotColorScaleValues(g2, map);

        // top left, top right, bottom left, bottom right values (from apa)

        drawCornerRegions(g2, map, new Dimension(APA.regionWidth, APA.regionWidth),
                apaStats.getRegionCornerValues());

        // save data
        ImageIO.write(apaImage, "png", outputFile);

    } catch (IOException e) {
        e.printStackTrace();
    }
}

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From source file:juicebox.tools.utils.common.MatrixTools.java

/**
 * Write data from matrix out to specified file with each row on a separate line
 *
 * @param filename/*from   w  ww  . j  a va2s.c  om*/
 * @param realMatrix
 */
public static void saveMatrixText(String filename, RealMatrix realMatrix) {
    Writer writer = null;
    try {
        writer = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(filename), "utf-8"));
        double[][] matrix = realMatrix.getData();
        for (double[] row : matrix) {
            writer.write(Arrays.toString(row) + "\n");
        }
    } catch (IOException ex) {
        ex.printStackTrace();
    } finally {
        try {
            if (writer != null)
                writer.close();
        } catch (Exception ex) {
            ex.printStackTrace();
        }
    }
}

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From source file:edu.cudenver.bios.matrixsvc.application.NamedRealMatrix.java

public NamedRealMatrix(RealMatrix matrix) {
    super();
    this.data = matrix.getData();
}

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From source file:com.opengamma.analytics.math.util.wrapper.CommonsMathWrapperTest.java

@Test
public void testVectorAsMatrix() {
    final RealMatrix commons = CommonsMathWrapper.wrapAsMatrix(OG_VECTOR);
    final double[][] data = commons.getData();
    assertEquals(data.length, OG_VECTOR.getNumberOfElements());
    assertEquals(data[0].length, 1);/*from  w  w w  .j a  v a 2s  . c o m*/
}

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From source file:com.cloudera.science.ml.client.cmd.KMeansOutlierCommand.java

@Override
public int execute(Configuration conf) throws IOException {

    List<MLClusterCovariance> ccov = AvroIO.read(MLClusterCovariance.class, new File(covFile));
    Map<ClusterKey, MahalanobisDistance> distances = Maps.newHashMap();
    for (MLClusterCovariance cc : ccov) {
        int dim = cc.getMeans().size();
        RealMatrix m = MatrixUtils.toRealMatrix(dim, dim, cc.getCov(), true);
        RealMatrix im = Inverter.SVD.apply(m); // pseudo-inverse works fine for this
        MahalanobisDistance md = new MahalanobisDistance(toArray(cc.getMeans()), im.getData(), cc.getCount());
        distances.put(new ClusterKey(cc.getClusteringId(), cc.getCenterId()), md);
    }/*from www .j a  v  a  2 s  .  co  m*/

    Pipeline p = pipelineParams.create(KMeansOutlierCommand.class, conf);
    PCollection<NamedVector> vecs = inputParams.getVectors(p);
    KMeansParallel kmp = new KMeansParallel();

    List<Centers> centers = centersParams.getCenters();
    List<Integer> centerIds = centersParams.getCenterIds();
    validate(centers, centerIds, distances);

    Records outliers = kmp.computeOutliers(vecs, centers, approx, centerIds, distances);
    outputParams.writeRecords(outliers.get(), outliers.getSpec(), outliersPath);
    p.done();
    return 0;
}

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