Examples of DistanceMeasure

com.aamend.hadoop.clustering.distance.DistanceMeasure
Author: antoine.amend@gmail.com Date: 21/03/14
org.apache.commons.math3.ml.distance.DistanceMeasure
Interface for distance measures of n-dimensional vectors. @since 3.2
org.apache.mahout.common.distance.DistanceMeasure
This interface is used for objects which can determine a distance metric between two points
org.apache.mahout.utils.DistanceMeasure
This interface is used for objects which can determine a distance metric between two points

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

  }


  @Test
  public void testCluster0() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    initData(1, 0.25, measure);
    ClusterEvaluator evaluator = new ClusterEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.33333333333333315, evaluator.interClusterDensity(), EPSILON);
    assertEquals("intra cluster density", 0.3656854249492381, evaluator.intraClusterDensity(), EPSILON);
  }

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

  }


  @Test
  public void testCluster1() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    initData(1, 0.5, measure);
    ClusterEvaluator evaluator = new ClusterEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.33333333333333315, evaluator.interClusterDensity(), EPSILON);
    assertEquals("intra cluster density", 0.3656854249492381, evaluator.intraClusterDensity(), EPSILON);
  }

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

  }


  @Test
  public void testCluster2() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    initData(1, 0.75, measure);
    ClusterEvaluator evaluator = new ClusterEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.33333333333333315, evaluator.interClusterDensity(), EPSILON);
    assertEquals("intra cluster density", 0.3656854249492381, evaluator.intraClusterDensity(), EPSILON);
  }

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

  }


  @Test
  public void testEmptyCluster() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    initData(1, 0.25, measure);
    Canopy cluster = new Canopy(new DenseVector(new double[] { 10, 10 }), 19, measure);
    clusters.add(cluster);
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    representativePoints.put(cluster.getId(), points);

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

  }


  @Test
  public void testSingleValueCluster() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    initData(1, 0.25, measure);
    Canopy cluster = new Canopy(new DenseVector(new double[] { 0, 0 }), 19, measure);
    clusters.add(cluster);
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    points.add(new VectorWritable(cluster.getCenter().plus(new DenseVector(new double[] { 1, 1 }))));

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

      HadoopUtil.overwriteOutput(output);
    }
    boolean emitMostLikely = Boolean.parseBoolean(getOption(DefaultOptionCreator.EMIT_MOST_LIKELY_OPTION));
    double threshold = Double.parseDouble(getOption(DefaultOptionCreator.THRESHOLD_OPTION));
    ClassLoader ccl = Thread.currentThread().getContextClassLoader();
    DistanceMeasure measure = ccl.loadClass(measureClass).asSubclass(DistanceMeasure.class).newInstance();


    if (hasOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)) {
      clusters = RandomSeedGenerator.buildRandom(input, clusters, Integer.parseInt(parseArguments(args)
          .get(DefaultOptionCreator.NUM_CLUSTERS_OPTION)), measure);
    }

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

    Constructor<? extends Vector> v = vcl.getConstructor(int.class);
    modelDistribution.setModelPrototype(new VectorWritable(v.newInstance(prototypeSize)));


    if (modelDistribution instanceof DistanceMeasureClusterDistribution) {
      Class<? extends DistanceMeasure> measureCl = ccl.loadClass(distanceMeasure).asSubclass(DistanceMeasure.class);
      DistanceMeasure measure = measureCl.newInstance();
      ((DistanceMeasureClusterDistribution) modelDistribution).setMeasure(measure);
    }
    return modelDistribution;
  }

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

    return null;
  }


  @Test
  public void testCanopy() throws Exception { // now run the Job
    DistanceMeasure measure = new EuclideanDistanceMeasure();


    Path output = getTestTempDirPath("output");
    CanopyDriver.run(new Configuration(), getTestTempDirPath("testdata"), output, measure, 8, 4, true, false);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(new Path(output, "clusters-0"), new Path(output, "clusteredPoints"));

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

    clusterDumper.printClusters(termDictionary);
  }


  @Test
  public void testKmeans() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    // now run the Canopy job to prime kMeans canopies
    Path output = getTestTempDirPath("output");
    Configuration conf = new Configuration();
    CanopyDriver.run(conf, getTestTempDirPath("testdata"), output, measure, 8, 4, false, false);
    // now run the KMeans job

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

    clusterDumper.printClusters(termDictionary);
  }


  @Test
  public void testFuzzyKmeans() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    // now run the Canopy job to prime kMeans canopies
    Path output = getTestTempDirPath("output");
    Configuration conf = new Configuration();
    CanopyDriver.run(conf, getTestTempDirPath("testdata"), output, measure, 8, 4, false, false);
    // now run the Fuzzy KMeans job

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