Examples of DistanceMeasure

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

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

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

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

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

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

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  }

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

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  }

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

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  }

  @Test
  public void testMSCanopyClassification() {
    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new MeanShiftCanopy(new DenseVector(2).assign(1), 0, measure));
    models.add(new MeanShiftCanopy(new DenseVector(2), 1, measure));
    models.add(new MeanShiftCanopy(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    try {
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Examples of org.apache.mahout.common.distance.DistanceMeasure

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  }

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

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  }

  @Test
  public void testRepresentativePoints() throws Exception {
    ClusteringTestUtils.writePointsToFile(referenceData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    Configuration conf = new Configuration();
    // run using MR reference point calculation
    CanopyDriver.run(conf, testdata, output, measure, 3.1, 1.1, true, true);
    int numIterations = 2;
    Path clustersIn = new Path(output, "clusters-0");
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