Package org.apache.mahout.clustering.streaming.cluster

Examples of org.apache.mahout.clustering.streaming.cluster.StreamingKMeans.cluster()

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      estimateDistanceCutoff = ClusteringUtils.estimateDistanceCutoff(estimatePoints, searcher.getDistanceMeasure());
    }

    StreamingKMeans clusterer = new StreamingKMeans(searcher, numClusters, estimateDistanceCutoff);
    while (datapointsIterator.hasNext()) {
      clusterer.cluster(datapointsIterator.next());
    }
    clusterer.reindexCentroids();

    return clusterer;
  }
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    // Clusters the data using local batch StreamingKMeans.
    StreamingKMeans batchClusterer =
        new StreamingKMeans(StreamingKMeansUtilsMR.searcherFromConfiguration(configuration),
            mapDriver.getConfiguration().getInt("estimatedNumMapClusters", -1), DISTANCE_CUTOFF);
    batchClusterer.cluster(syntheticData.getFirst());
    List<Centroid> batchCentroids = Lists.newArrayList();
    for (Vector v : batchClusterer) {
      batchCentroids.add((Centroid) v);
    }
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    // Clusters the data using point by point StreamingKMeans.
    StreamingKMeans perPointClusterer =
        new StreamingKMeans(StreamingKMeansUtilsMR.searcherFromConfiguration(configuration),
            (1 << NUM_DIMENSIONS) * (int)Math.log(NUM_DATA_POINTS), DISTANCE_CUTOFF);
    for (Centroid datapoint : syntheticData.getFirst()) {
      perPointClusterer.cluster(datapoint);
    }
    List<Centroid> perPointCentroids = Lists.newArrayList();
    for (Vector v : perPointClusterer) {
      perPointCentroids.add((Centroid) v);
    }
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    StreamingKMeans clusterer =
        new StreamingKMeans(StreamingKMeansUtilsMR .searcherFromConfiguration(configuration),
            (1 << NUM_DIMENSIONS) * (int)Math.log(NUM_DATA_POINTS), DISTANCE_CUTOFF);

    long start = System.currentTimeMillis();
    clusterer.cluster(syntheticData.getFirst());
    long end = System.currentTimeMillis();

    System.out.printf("%f [s]\n", (end - start) / 1000.0);
    List<CentroidWritable> reducerInputs = Lists.newArrayList();
    int postMapperTotalWeight = 0;
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    if (!dataPointsIterator.hasNext()) {
      dataPointsIterator = dataPoints.iterator();
    }

    while (dataPointsIterator.hasNext()) {
      streamingKMeans.cluster(dataPointsIterator.next());
    }

    streamingKMeans.reindexCentroids();
    return streamingKMeans;
  }
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    // Clusters the data using local batch StreamingKMeans.
    StreamingKMeans batchClusterer =
        new StreamingKMeans(StreamingKMeansUtilsMR.searcherFromConfiguration(configuration),
            mapDriver.getConfiguration().getInt("estimatedNumMapClusters", -1), DISTANCE_CUTOFF);
    batchClusterer.cluster(syntheticData.getFirst());
    List<Centroid> batchCentroids = Lists.newArrayList();
    for (Vector v : batchClusterer) {
      batchCentroids.add((Centroid) v);
    }
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    // Clusters the data using point by point StreamingKMeans.
    StreamingKMeans perPointClusterer =
        new StreamingKMeans(StreamingKMeansUtilsMR.searcherFromConfiguration(configuration),
            (1 << NUM_DIMENSIONS) * (int)Math.log(NUM_DATA_POINTS), DISTANCE_CUTOFF);
    for (Centroid datapoint : syntheticData.getFirst()) {
      perPointClusterer.cluster(datapoint);
    }
    List<Centroid> perPointCentroids = Lists.newArrayList();
    for (Vector v : perPointClusterer) {
      perPointCentroids.add((Centroid) v);
    }
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    StreamingKMeans clusterer =
        new StreamingKMeans(StreamingKMeansUtilsMR .searcherFromConfiguration(configuration),
            (1 << NUM_DIMENSIONS) * (int)Math.log(NUM_DATA_POINTS), DISTANCE_CUTOFF);

    long start = System.currentTimeMillis();
    clusterer.cluster(syntheticData.getFirst());
    long end = System.currentTimeMillis();

    System.out.printf("%f [s]\n", (end - start) / 1000.0);
    List<CentroidWritable> reducerInputs = Lists.newArrayList();
    int postMapperTotalWeight = 0;
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