Example usage for org.apache.mahout.math Matrix rowSize

Introduction

In this page you can find the example usage for org.apache.mahout.math Matrix rowSize.

Prototype

int rowSize();

Source Link

Usage

From source file:edu.indiana.d2i.htrc.skmeans.StreamingKMeansAdapterTest.java

License:Apache License

@Test
public static void testCluster() {
    int dimension = 500;

    // construct data samplers centered on the corners of a unit cube
    Matrix mean = new DenseMatrix(8, dimension);
    List<MultiNormal> rowSamplers = Lists.newArrayList();
    for (int i = 0; i < 8; i++) {
        //         mean.viewRow(i).assign(
        //               new double[] { 0.25 * (i & 4), 0.5 * (i & 2), i & 1 });

        double[] random = new double[dimension];
        for (int j = 0; j < random.length; j++) {
            random[j] = Math.random();
        }//w w w.j a  va  2s. c  o m
        mean.viewRow(i).assign(random);
        rowSamplers.add(new MultiNormal(0.01, mean.viewRow(i)));
    }

    // sample a bunch of data points
    Matrix data = new DenseMatrix(10000, dimension);
    for (MatrixSlice row : data) {
        row.vector().assign(rowSamplers.get(row.index() % 8).sample());
    }

    // cluster the data
    long t0 = System.currentTimeMillis();

    double cutoff = StreamingKMeansAdapter.estimateCutoff(data, 100);
    Configuration conf = new Configuration();
    conf.setInt(StreamingKMeansConfigKeys.MAXCLUSTER, 1000);
    conf.setFloat(StreamingKMeansConfigKeys.CUTOFF, (float) cutoff);
    conf.setClass(StreamingKMeansConfigKeys.DIST_MEASUREMENT, EuclideanDistanceMeasure.class,
            DistanceMeasure.class);
    conf.setInt(StreamingKMeansConfigKeys.VECTOR_DIMENSION, dimension);
    StreamingKMeansAdapter skmeans = new StreamingKMeansAdapter(conf);
    // for (MatrixSlice row : Iterables.skip(data, 1)) {
    // skmeans.cluster(row.vector());
    // }
    for (MatrixSlice row : data) {
        skmeans.cluster(row.vector());
    }

    // validate
    Searcher r = skmeans.getCentroids();

    // StreamingKMeansAdapter skmeans = new StreamingKMeansAdapter();
    // Searcher r = skmeans.cluster(data, 1000, centroidFactory);

    long t1 = System.currentTimeMillis();

    assertEquals("Total weight not preserved", totalWeight(data), totalWeight(r), 1e-9);

    // and verify that each corner of the cube has a centroid very nearby
    for (MatrixSlice row : mean) {
        WeightedVector v = r.search(row.vector(), 1).get(0);
        assertTrue(v.getWeight() < 0.05);
    }
    System.out.printf("%.2f for clustering\n%.1f us per row\n", (t1 - t0) / 1000.0,
            (t1 - t0) / 1000.0 / data.rowSize() * 1e6);

    System.out.println("Done??");
}

---

From source file:edu.snu.dolphin.bsp.examples.ml.algorithms.clustering.em.EMMainCmpTask.java

License:Apache License

/**
 * Compute the inverse of a given matrix.
 *//*from ww  w.  j  ava 2  s  .co  m*/
private Matrix inverse(final Matrix matrix) {
    final int dimension = matrix.rowSize();
    final QRDecomposition qr = new QRDecomposition(matrix);
    return qr.solve(DiagonalMatrix.identity(dimension));
}

---

From source file:org.carrot2.matrix.factorization.PartialSingularValueDecomposition.java

License:Open Source License

private static DenseDoubleMatrix2D toColtMatrix(Matrix m) {
    DenseDoubleMatrix2D result = new DenseDoubleMatrix2D(m.rowSize(), m.columnSize());
    for (int r = 0; r < result.rows(); r++) {
        for (int c = 0; c < result.columns(); c++) {
            result.setQuick(r, c, m.getQuick(r, c));
        }//  w  w  w.jav a 2s  .c om
    }
    return result;
}

---