Package org.apache.mahout.common.distance

Examples of org.apache.mahout.common.distance.CosineDistanceMeasure

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    return new ClusterClassifier(models, new KMeansClusteringPolicy());
  }
 
  private static ClusterClassifier newCosineKlusterClassifier() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new CosineDistanceMeasure();
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2), 1, measure));
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2).assign(-1), 2, measure));
    return new ClusterClassifier(models, new KMeansClusteringPolicy());
  }
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    clusterDumper.printClusters(termDictionary);
  }
 
  @Test
  public void testMeanShift() throws Exception {
    DistanceMeasure measure = new CosineDistanceMeasure();
    IKernelProfile kernelProfile = new TriangularKernelProfile();
    Path output = getTestTempDirPath("output");
    Configuration conf = new Configuration();
    MeanShiftCanopyDriver.run(conf, getTestTempDirPath("testdata"), output,
        measure, kernelProfile, 0.5, 0.01, 0.05, 10, false, true, true);
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    return new ClusterClassifier(models, new KMeansClusteringPolicy());
  }
 
  private static ClusterClassifier newCosineKlusterClassifier() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new CosineDistanceMeasure();
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2), 1, measure));
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2).assign(-1), 2, measure));
    return new ClusterClassifier(models, new KMeansClusteringPolicy());
  }
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      mark.incrementalCreateBenchmark();
      mark.cloneBenchmark();
      mark.dotBenchmark();
      mark.serializeBenchmark();
      mark.deserializeBenchmark();
      mark.distanceMeasureBenchmark(new CosineDistanceMeasure());
      mark.distanceMeasureBenchmark(new SquaredEuclideanDistanceMeasure());
      mark.distanceMeasureBenchmark(new EuclideanDistanceMeasure());
      mark.distanceMeasureBenchmark(new ManhattanDistanceMeasure());
      mark.distanceMeasureBenchmark(new TanimotoDistanceMeasure());
     
      mark.closestCentroidBenchmark(new CosineDistanceMeasure());
      mark.closestCentroidBenchmark(new SquaredEuclideanDistanceMeasure());
      mark.closestCentroidBenchmark(new EuclideanDistanceMeasure());
      mark.closestCentroidBenchmark(new ManhattanDistanceMeasure());
      mark.closestCentroidBenchmark(new TanimotoDistanceMeasure());
     
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    return new ClusterClassifier(models, new KMeansClusteringPolicy());
  }
 
  private static ClusterClassifier newCosineKlusterClassifier() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new CosineDistanceMeasure();
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2), 1, measure));
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2).assign(-1), 2, measure));
    return new ClusterClassifier(models, new KMeansClusteringPolicy());
  }
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