Package org.apache.mahout.math.neighborhood

Examples of org.apache.mahout.math.neighborhood.ProjectionSearch

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   * index is i.
   */
  public static List<OnlineSummarizer> summarizeClusterDistances(Iterable<? extends Vector> datapoints,
                                                                 Iterable<? extends Vector> centroids,
                                                                 DistanceMeasure distanceMeasure) {
    UpdatableSearcher searcher = new ProjectionSearch(distanceMeasure, 3, 1);
    searcher.addAll(centroids);
    List<OnlineSummarizer> summarizers = Lists.newArrayList();
    if (searcher.size() == 0) {
      return summarizers;
    }
    for (int i = 0; i < searcher.size(); ++i) {
      summarizers.add(new OnlineSummarizer());
    }
    for (Vector v : datapoints) {
      Centroid closest = (Centroid)searcher.search(v,  1).get(0).getValue();
      OnlineSummarizer summarizer = summarizers.get(closest.getIndex());
      summarizer.add(distanceMeasure.distance(v, closest));
    }
    return summarizers;
  }
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   * @param centroids iterable of Centroids.
   * @return the total cost described above.
   */
  public static double totalClusterCost(Iterable<? extends Vector> datapoints, Iterable<? extends Vector> centroids) {
    DistanceMeasure distanceMeasure = new EuclideanDistanceMeasure();
    UpdatableSearcher searcher = new ProjectionSearch(distanceMeasure, 3, 1);
    searcher.addAll(centroids);
    return totalClusterCost(datapoints, searcher);
  }
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  }

  @Parameters
  public static List<Object[]> generateData() {
    return Arrays.asList(new Object[][] {
        {new ProjectionSearch(new SquaredEuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE), true},
        {new FastProjectionSearch(new SquaredEuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE),
            true},
        {new ProjectionSearch(new SquaredEuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE), false},
        {new FastProjectionSearch(new SquaredEuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE),
            false},
    });
  }
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   * index is i.
   */
  public static List<OnlineSummarizer> summarizeClusterDistances(Iterable<? extends Vector> datapoints,
                                                                 Iterable<? extends Vector> centroids,
                                                                 DistanceMeasure distanceMeasure) {
    UpdatableSearcher searcher = new ProjectionSearch(distanceMeasure, 3, 1);
    searcher.addAll(centroids);
    List<OnlineSummarizer> summarizers = Lists.newArrayList();
    if (searcher.size() == 0) {
      return summarizers;
    }
    for (int i = 0; i < searcher.size(); ++i) {
      summarizers.add(new OnlineSummarizer());
    }
    for (Vector v : datapoints) {
      Centroid closest = (Centroid)searcher.search(v,  1).get(0).getValue();
      OnlineSummarizer summarizer = summarizers.get(closest.getIndex());
      summarizer.add(distanceMeasure.distance(v, closest));
    }
    return summarizers;
  }
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   * @param centroids iterable of Centroids.
   * @return the total cost described above.
   */
  public static double totalClusterCost(Iterable<? extends Vector> datapoints, Iterable<? extends Vector> centroids) {
    DistanceMeasure distanceMeasure = new EuclideanDistanceMeasure();
    UpdatableSearcher searcher = new ProjectionSearch(distanceMeasure, 3, 1);
    searcher.addAll(centroids);
    return totalClusterCost(datapoints, searcher);
  }
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  }

  @Parameters
  public static List<Object[]> generateData() {
    return Arrays.asList(new Object[][] {
        {new ProjectionSearch(new SquaredEuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE), true},
        {new FastProjectionSearch(new SquaredEuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE),
            true},
        {new ProjectionSearch(new SquaredEuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE), false},
        {new FastProjectionSearch(new SquaredEuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE),
            false},
    });
  }
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   * index is i.
   */
  public static List<OnlineSummarizer> summarizeClusterDistances(Iterable<? extends Vector> datapoints,
                                                                 Iterable<? extends Vector> centroids,
                                                                 DistanceMeasure distanceMeasure) {
    UpdatableSearcher searcher = new ProjectionSearch(distanceMeasure, 3, 1);
    searcher.addAll(centroids);
    List<OnlineSummarizer> summarizers = Lists.newArrayList();
    if (searcher.size() == 0) {
      return summarizers;
    }
    for (int i = 0; i < searcher.size(); ++i) {
      summarizers.add(new OnlineSummarizer());
    }
    for (Vector v : datapoints) {
      Centroid closest = (Centroid)searcher.search(v,  1).get(0).getValue();
      OnlineSummarizer summarizer = summarizers.get(closest.getIndex());
      summarizer.add(distanceMeasure.distance(v, closest));
    }
    return summarizers;
  }
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   * @param centroids iterable of Centroids.
   * @return the total cost described above.
   */
  public static double totalClusterCost(Iterable<? extends Vector> datapoints, Iterable<? extends Vector> centroids) {
    DistanceMeasure distanceMeasure = new EuclideanDistanceMeasure();
    UpdatableSearcher searcher = new ProjectionSearch(distanceMeasure, 3, 1);
    searcher.addAll(centroids);
    return totalClusterCost(datapoints, searcher);
  }
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Related Classes of org.apache.mahout.math.neighborhood.ProjectionSearch

  • org.apache.mahout.clustering.ClusteringUtils
  • org.apache.mahout.clustering.streaming.cluster.StreamingKMeansTest
  • org.apache.mahout.math.Vector
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