Examples of EuclideanDistanceMeasure

org.apache.mahout.common.distance.EuclideanDistanceMeasure
This class implements a Euclidean distance metric by summing the square root of the squared differences between each coordinate.
If you don't care about the true distance and only need the values for comparison, then the base class, {@link SquaredEuclideanDistanceMeasure}, will be faster since it doesn't do the actual square root of the squared differences.
org.apache.mahout.utils.EuclideanDistanceMeasure
This class implements a Euclidian distance metric by summing the square root of the squared differences between each coordinate

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

    Path clustersIn = new Path(output, "clusters-5");
    RepresentativePointsDriver.run(conf,
                                   clustersIn,
                                   new Path(output, "clusteredPoints"),
                                   output,
                                   new EuclideanDistanceMeasure(),
                                   numIterations,
                                   true);
    ClusterEvaluator evaluator = new ClusterEvaluator(conf, clustersIn);
    // now print out the Results
    System.out.println("Intra-cluster density = " + evaluator.intraClusterDensity());

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

  }


  @Test
  public void testCDbw0() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, getTestTempFilePath("testdata/file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    initData(1, 0.25, measure);
    CDbwEvaluator evaluator = new CDbwEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.0, evaluator.interClusterDensity(), EPSILON);
    assertEquals("separation", 20.485281374238568, evaluator.separation(), EPSILON);
    assertEquals("intra cluster density", 0.8, evaluator.intraClusterDensity(), EPSILON);

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

  }


  @Test
  public void testCDbw1() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, getTestTempFilePath("testdata/file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    initData(1, 0.5, measure);
    CDbwEvaluator evaluator = new CDbwEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 1.2, evaluator.interClusterDensity(), EPSILON);
    assertEquals("separation", 6.207661022496537, evaluator.separation(), EPSILON);
    assertEquals("intra cluster density", 0.4, evaluator.intraClusterDensity(), EPSILON);

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

  }


  @Test
  public void testCDbw2() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, getTestTempFilePath("testdata/file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    initData(1, 0.75, measure);
    CDbwEvaluator evaluator = new CDbwEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.682842712474619, evaluator.interClusterDensity(), EPSILON);
    assertEquals("separation", 4.0576740025245694, evaluator.separation(), EPSILON);
    assertEquals("intra cluster density", 0.26666666666666666, evaluator.intraClusterDensity(), EPSILON);

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

  }


  @Test
  public void testEmptyCluster() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, getTestTempFilePath("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.EuclideanDistanceMeasure

  }


  @Test
  public void testSingleValueCluster() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, getTestTempFilePath("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.EuclideanDistanceMeasure

   * @throws IOException 
   */
  @Test
  public void testAllSameValueCluster() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, getTestTempFilePath("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()));

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

   * @throws IOException 
   */
  @Test
  public void testAlmostSameValueCluster() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, getTestTempFilePath("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>();
    Vector delta = new DenseVector(new double[] { 0, Double.MIN_NORMAL });

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

  }


  @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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Examples of org.apache.mahout.common.distance.EuclideanDistanceMeasure

  }


  @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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