Package org.apache.mahout.math

Examples of org.apache.mahout.math.Vector.assign()

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    PartialRowEmitter btEmitter = new PartialRowEmitter() {
      @Override
      public void emitRow(int rowNum, Vector row) {
        Vector btRow = btRows.get(rowNum);
        if (btRow != null) {
          btRow.assign(row, Functions.PLUS);
        }
        btRows.put(rowNum, btRow == null ? new DenseVector(row) : btRow);
      }
    };
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      @Override
      public void emitRow(int rowNum, Vector row) {
        Vector bbtRow = bbt.get(rowNum);
        if (bbtRow != null) {
          bbtRow.assign(row, Functions.PLUS);
        }
        bbt.put(rowNum, bbtRow == null ? new DenseVector(row) : bbtRow);
      }
    };
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  private static List<VectorWritable> getPointsWritable() {
    List<VectorWritable> points = Lists.newArrayList();
    for (double[] fr : RAW) {
      Vector vec = new RandomAccessSparseVector(fr.length);
      vec.assign(fr);
      points.add(new VectorWritable(vec));
    }
    return points;
  }
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  private static List<Vector> getPoints() {
    List<Vector> points = Lists.newArrayList();
    for (double[] fr : RAW) {
      Vector vec = new RandomAccessSparseVector(fr.length);
      vec.assign(fr);
      points.add(vec);
    }
    return points;
  }
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   * For the distributed case, the best guess at a useful initialization state for Lanczos we'll chose to be
   * uniform over all input dimensions, L_2 normalized.
   */
  public Vector getInitialVector(VectorIterable corpus) {
    Vector initialVector = new DenseVector(corpus.numCols());
    initialVector.assign(1.0 / Math.sqrt(corpus.numCols()));
    return initialVector;
  }

  public LanczosState runJob(Configuration originalConfig,
                             LanczosState state,
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  @Test
  public void testCanopyAsFormatStringSparse() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Cluster cluster = new Canopy(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    assertEquals("C-123{n=0 c=[0:1.100, 2:3.300] r=[]}", formatString);
  }
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  @Test
  public void testCanopyAsFormatStringSparseWithBindings() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Cluster cluster = new Canopy(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    assertEquals("C-123{n=0 c=[0:1.100, 2:3.300] r=[]}", formatString);
  }
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  @Test
  public void testClusterAsFormatStringSparse() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Cluster cluster = new org.apache.mahout.clustering.kmeans.Cluster(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    assertEquals("CL-123{n=0 c=[0:1.100, 2:3.300] r=[]}", formatString);
  }
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  @Test
  public void testClusterAsFormatStringSparseWithBindings() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Cluster cluster = new org.apache.mahout.clustering.kmeans.Cluster(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    assertEquals("CL-123{n=0 c=[0:1.100, 2:3.300] r=[]}", formatString);
  }
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  @Test
  public void testMSCanopyAsFormatStringSparse() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Cluster cluster = new MeanShiftCanopy(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    assertEquals("MSC-123{n=0 c=[0:1.100, 2:3.300] r=[]}", formatString);
  }
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