Package org.apache.mahout.common.distance

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

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  }

  @Test
  public void testCanopy() throws Exception {
    ClusteringTestUtils.writePointsToFile(sampleData, getTestTempFilePath("testdata/file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    CanopyDriver.run(new Configuration(), testdata, output, measure, 3.1, 2.1, true, true);
    int numIterations = 10;
    Path clustersIn = new Path(output, "clusters-0");
    RepresentativePointsDriver.run(conf, clustersIn, new Path(output, "clusteredPoints"), output, measure, numIterations, true);
    CDbwEvaluator evaluator = new CDbwEvaluator(conf, clustersIn);
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  }

  @Test
  public void testKmeans() throws Exception {
    ClusteringTestUtils.writePointsToFile(sampleData, getTestTempFilePath("testdata/file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    // now run the Canopy job to prime kMeans canopies
    CanopyDriver.run(new Configuration(), testdata, output, measure, 3.1, 2.1, false, true);
    // now run the KMeans job
    KMeansDriver.run(testdata, new Path(output, "clusters-0"), output, measure, 0.001, 10, true, true);
    int numIterations = 10;
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  }

  @Test
  public void testFuzzyKmeans() throws Exception {
    ClusteringTestUtils.writePointsToFile(sampleData, getTestTempFilePath("testdata/file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    // now run the Canopy job to prime kMeans canopies
    CanopyDriver.run(new Configuration(), testdata, output, measure, 3.1, 2.1, false, true);
    // now run the KMeans job
    FuzzyKMeansDriver.run(testdata, new Path(output, "clusters-0"), output, measure, 0.001, 10, 2, true, true, 0, true);
    int numIterations = 10;
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  }

  @Test
  public void testMeanShift() throws Exception {
    ClusteringTestUtils.writePointsToFile(sampleData, getTestTempFilePath("testdata/file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    new MeanShiftCanopyDriver().run(conf, testdata, output, measure, 2.1, 1.0, 0.001, 10, false, true, true);
    int numIterations = 10;
    Path clustersIn = new Path(output, "clusters-2");
    RepresentativePointsDriver.run(conf, clustersIn, new Path(output, "clusteredPoints"), output, measure, numIterations, true);
    CDbwEvaluator evaluator = new CDbwEvaluator(conf, clustersIn);
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      }

      // run mapper
      FuzzyKMeansMapper mapper = new FuzzyKMeansMapper();
      mapper.config(clusterList);
      DistanceMeasure measure = new EuclideanDistanceMeasure();
      Configuration conf = new Configuration();
      conf.set(FuzzyKMeansConfigKeys.DISTANCE_MEASURE_KEY, measure.getClass().getName());
      conf.set(FuzzyKMeansConfigKeys.CLUSTER_CONVERGENCE_KEY, "0.001");
      conf.set(FuzzyKMeansConfigKeys.M_KEY, "2");
      conf.set(FuzzyKMeansConfigKeys.EMIT_MOST_LIKELY_KEY, "true");
      conf.set(FuzzyKMeansConfigKeys.THRESHOLD_KEY, "0");
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    }
    int numDims = Integer.parseInt(parsedArgs.get("--dimensions"));
    int clusters = Integer.parseInt(parsedArgs.get("--clusters"));
    String measureClass = getOption(DefaultOptionCreator.DISTANCE_MEASURE_OPTION);
    ClassLoader ccl = Thread.currentThread().getContextClassLoader();
    DistanceMeasure measure = ccl.loadClass(measureClass).asSubclass(DistanceMeasure.class).newInstance();
    double convergenceDelta = Double.parseDouble(getOption(DefaultOptionCreator.CONVERGENCE_DELTA_OPTION));
    int maxIterations = Integer.parseInt(getOption(DefaultOptionCreator.MAX_ITERATIONS_OPTION));

    run(conf, input, output, numDims, clusters, measure, convergenceDelta, maxIterations);
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      }

      // run mapper
      FuzzyKMeansMapper mapper = new FuzzyKMeansMapper();
      mapper.config(clusterList);
      DistanceMeasure measure = new EuclideanDistanceMeasure();
      Configuration conf = new Configuration();
      conf.set(FuzzyKMeansConfigKeys.DISTANCE_MEASURE_KEY, measure.getClass().getName());
      conf.set(FuzzyKMeansConfigKeys.CLUSTER_CONVERGENCE_KEY, "0.001");
      conf.set(FuzzyKMeansConfigKeys.M_KEY, "2");
      conf.set(FuzzyKMeansConfigKeys.EMIT_MOST_LIKELY_KEY, "true");
      conf.set(FuzzyKMeansConfigKeys.THRESHOLD_KEY, "0");
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      }

      // run mapper
      FuzzyKMeansMapper mapper = new FuzzyKMeansMapper();
      mapper.config(clusterList);
      DistanceMeasure measure = new EuclideanDistanceMeasure();

      Configuration conf = new Configuration();
      conf.set(FuzzyKMeansConfigKeys.DISTANCE_MEASURE_KEY, measure.getClass().getName());
      conf.set(FuzzyKMeansConfigKeys.CLUSTER_CONVERGENCE_KEY, "0.001");
      conf.set(FuzzyKMeansConfigKeys.M_KEY, "2");
      conf.set(FuzzyKMeansConfigKeys.EMIT_MOST_LIKELY_KEY, "true");
      conf.set(FuzzyKMeansConfigKeys.THRESHOLD_KEY, "0");
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    double convergenceDelta = Double.parseDouble(getOption(DefaultOptionCreator.CONVERGENCE_DELTA_OPTION));
    int maxIterations = Integer.parseInt(getOption(DefaultOptionCreator.MAX_ITERATIONS_OPTION));
    boolean inputIsCanopies = hasOption(INPUT_IS_CANOPIES_OPTION);
    boolean runSequential = getOption(DefaultOptionCreator.METHOD_OPTION).equalsIgnoreCase(DefaultOptionCreator.SEQUENTIAL_METHOD);
    ClassLoader ccl = Thread.currentThread().getContextClassLoader();
    DistanceMeasure measure = ccl.loadClass(measureClass).asSubclass(DistanceMeasure.class).newInstance();

    run(getConf(), input, output, measure, t1, t2, convergenceDelta, maxIterations, inputIsCanopies, runClustering, runSequential);
    return 0;
  }
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public final class TestVectorModelClassifier extends MahoutTestCase {

  @Test
  public void testDMClusterClassification() {
    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new DistanceMeasureCluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new DistanceMeasureCluster(new DenseVector(2), 1, measure));
    models.add(new DistanceMeasureCluster(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
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Related Classes of org.apache.mahout.common.distance.DistanceMeasure

  • mia.clustering.ch10.InterClusterDistances
  • org.apache.mahout.clustering.canopy.CanopyConfigKeys
  • org.apache.mahout.clustering.canopy.CanopyDriver
  • org.apache.mahout.clustering.cdbw.TestCDbwEvaluator
  • org.apache.mahout.clustering.classify.ClusterClassificationDriver
  • org.apache.mahout.clustering.classify.ClusterClassificationMapper
  • org.apache.mahout.clustering.ClusteringUtils
  • org.apache.mahout.clustering.dirichlet.DirichletDriver
  • org.apache.mahout.clustering.dirichlet.models.DistributionDescription
  • org.apache.mahout.clustering.display.DisplayFuzzyKMeans
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