Package org.apache.mahout.math

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

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      if (r.nextBoolean() || numRows == nonNullRows) {
        m.assignRow(numRows == nonNullRows ? i : c, v);
      } else {
        Vector other = m.getRow(r.nextInt(numRows));
        if (other != null && other.getLengthSquared() > 0) {
          m.assignRow(c, other.clone());
        }
      }
      n += m.getRow(c).getLengthSquared();
    }
    return m;
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  @Override
  public void observe(VectorWritable x) {
    s0++;
    Vector v = x.get();
    if (s1 == null) {
      s1 = v.clone();
    } else {
      s1 = s1.plus(v);
    }
    if (s2 == null) {
      s2 = v.times(v);
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  @Override
  public void observe(VectorWritable v) {
    Vector x = v.get();
    s0++;
    if (s1 == null) {
      s1 = x.clone();
    } else {
      s1 = s1.plus(x);
    }
    if (s2 == null) {
      s2 = x.times(x);
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    initData(1, 0.25, measure);
    Canopy cluster = new Canopy(new DenseVector(new double[] { 0, 0 }), 19, measure);
    clusters.add(cluster);
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    Vector delta = new DenseVector(new double[] { 0, Double.MIN_NORMAL });
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    representativePoints.put(cluster.getId(), points);
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    Canopy cluster = new Canopy(new DenseVector(new double[] { 0, 0 }), 19, measure);
    clusters.add(cluster);
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    Vector delta = new DenseVector(new double[] { 0, Double.MIN_NORMAL });
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    representativePoints.put(cluster.getId(), points);
    CDbwEvaluator evaluator = new CDbwEvaluator(representativePoints, clusters, measure);
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    clusters.add(cluster);
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    Vector delta = new DenseVector(new double[] { 0, Double.MIN_NORMAL });
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    representativePoints.put(cluster.getId(), points);
    CDbwEvaluator evaluator = new CDbwEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.0, evaluator.interClusterDensity(), EPSILON);
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    List<VectorWritable> points = new ArrayList<VectorWritable>();
    Vector delta = new DenseVector(new double[] { 0, Double.MIN_NORMAL });
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    representativePoints.put(cluster.getId(), points);
    CDbwEvaluator evaluator = new CDbwEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.0, evaluator.interClusterDensity(), EPSILON);
    assertEquals("separation", 28.970562748477143, evaluator.separation(), EPSILON);
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    Vector delta = new DenseVector(new double[] { 0, Double.MIN_NORMAL });
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    points.add(new VectorWritable(delta.clone()));
    representativePoints.put(cluster.getId(), points);
    CDbwEvaluator evaluator = new CDbwEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.0, evaluator.interClusterDensity(), EPSILON);
    assertEquals("separation", 28.970562748477143, evaluator.separation(), EPSILON);
    assertEquals("intra cluster density", 1.8, evaluator.intraClusterDensity(), EPSILON);
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      String clusterId = clusterIdBuilder.toString();
      clusterId = clusterId.substring(0, clusterId.lastIndexOf('-'));
      Text cluster = new Text(clusterId);
      Writable point;
      if (debugOutput) {
        point = new VectorWritable(featureVector.clone());
      } else {
        point = new Text(item.toString());
      }
      context.write(cluster, point);
    }
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      if (r.nextBoolean() || numRows == nonNullRows) {
        m.assignRow(numRows == nonNullRows ? i : c, v);
      } else {
        Vector other = m.viewRow(r.nextInt(numRows));
        if (other != null && other.getLengthSquared() > 0) {
          m.assignRow(c, other.clone());
        }
      }
      //n += m.getRow(c).getLengthSquared();
    }
    return m;
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