Package org.apache.mahout.math.neighborhood

Examples of org.apache.mahout.math.neighborhood.UpdatableSearcher.search()

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    }
    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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    // Verify that each corner of the cube has a centroid very nearby.
    // This is probably FALSE for large-dimensional spaces!
    OnlineSummarizer summarizer = new OnlineSummarizer();
    for (Vector mean : syntheticData.getSecond()) {
      WeightedThing<Vector> v = searcher.search(mean, 1).get(0);
      summarizer.add(v.getWeight());
    }
    assertTrue(String.format("Median weight [%f] too large [>%f]", summarizer.getMedian(),
        DISTRIBUTION_RADIUS), summarizer.getMedian() < DISTRIBUTION_RADIUS);
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    }
    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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    // Verify that each corner of the cube has a centroid very nearby.
    // This is probably FALSE for large-dimensional spaces!
    OnlineSummarizer summarizer = new OnlineSummarizer();
    for (Vector mean : syntheticData.getSecond()) {
      WeightedThing<Vector> v = searcher.search(mean, 1).get(0);
      summarizer.add(v.getWeight());
    }
    assertTrue(String.format("Median weight [%f] too large [>%f]", summarizer.getMedian(),
        DISTRIBUTION_RADIUS), summarizer.getMedian() < DISTRIBUTION_RADIUS);
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    }
    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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