Package org.apache.mahout.matrix

Examples of org.apache.mahout.matrix.Vector

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public class FuzzyKMeansClusterMapper extends FuzzyKMeansMapper {
  @Override
  public void map(WritableComparable<?> key, Text values,
      OutputCollector<Text, Text> output, Reporter reporter) throws IOException
  {
    Vector point = AbstractVector.decodeVector(values.toString());
    SoftCluster.outputPointWithClusterProbabilities(key.toString(), point, clusters, values, output);
  } 
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  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;

    Vector dv = new DenseVector(2);
    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        AsymmetricSampledNormalModel mm = (AsymmetricSampledNormalModel) m;
        dv.set(0, mm.sd.get(0) * 3);
        dv.set(1, mm.sd.get(1) * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, dv);
      }
    }
  }
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    String center = formattedString.substring(beginIndex);
    char firstChar = id.charAt(0);
    boolean startsWithV = firstChar == 'V';
    if (firstChar == 'C' || startsWithV) {
      int clusterId = new Integer(formattedString.substring(1, beginIndex - 2));
      Vector clusterCenter = AbstractVector.decodeVector(center);

      SoftCluster cluster = new SoftCluster(clusterCenter, clusterId);
      cluster.converged = startsWithV;
      return cluster;
    }
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   * Return if the cluster is converged by comparing its center and centroid.
   *
   * @return if the cluster is converged
   */
  public boolean computeConvergence() {
    Vector centroid = computeCentroid();
    converged = measure.distance(center, centroid) <= convergenceDelta;
    return converged;
  }
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  @Override
  public void reduce(Text key, Iterator<Text> values,
                     OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
    while (values.hasNext()) {
      Text value = values.next();
      Vector point = AbstractVector.decodeVector(value.toString());
      Canopy.addPointToCanopies(point, canopies);
    }
    for (Canopy canopy : canopies)
      output.collect(new Text(canopy.getIdentifier()), new Text(Canopy
              .formatCanopy(canopy)));
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  private final List<Canopy> canopies = new ArrayList<Canopy>();

  @Override
  public void map(WritableComparable<?> key, Text values,
                  OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
    Vector point = AbstractVector.decodeVector(values.toString());
    Canopy.emitPointToNewCanopies(point, canopies, output);
  }
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  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;

    Vector dv = new DenseVector(2);
    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        AsymmetricSampledNormalModel mm = (AsymmetricSampledNormalModel) m;
        dv.assign(mm.sd.times(3));
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, dv);
      }
    }
  }
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  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;

    Vector dv = new DenseVector(2);
    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        NormalModel mm = (NormalModel) m;
        dv.assign(mm.sd * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, dv);
      }
    }
  }
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public class WeightedEuclideanDistanceMeasure extends WeightedDistanceMeasure {

  @Override
  public double distance(Vector p1, Vector p2) {
    double result = 0;
    Vector res = p2.minus(p1);
    if (weights == null) {
      for (int i = 0; i < p1.cardinality(); i++) {
        result += res.get(i) * res.get(i);
      }
    } else {
      for (int i = 0; i < p1.cardinality(); i++) {
        result += res.get(i) * res.get(i) * weights.get(i);  // todo this is where the weights goes, right?
      }
    }
    return Math.sqrt(result);
  }
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  protected List<SoftCluster> clusters;

  @Override
  public void map(WritableComparable<?> key, Text values,
      OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
    Vector point = AbstractVector.decodeVector(values.toString());
    SoftCluster.emitPointProbToCluster(point, clusters, values, output);
  }
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Related Classes of org.apache.mahout.matrix.Vector

  • org.apache.mahout.clustering.canopy.Canopy
  • org.apache.mahout.clustering.canopy.CanopyCombiner
  • org.apache.mahout.clustering.canopy.CanopyMapper
  • org.apache.mahout.clustering.canopy.CanopyReducer
  • org.apache.mahout.clustering.canopy.ClusterMapper
  • org.apache.mahout.clustering.canopy.DisplayCanopy
  • org.apache.mahout.clustering.canopy.TestCanopyCreation
  • org.apache.mahout.clustering.dirichlet.DirichletClusterer
  • org.apache.mahout.clustering.dirichlet.DirichletMapper
  • org.apache.mahout.clustering.dirichlet.DirichletReducer
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