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

  }


  @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.EuclideanDistanceMeasure

  }


  @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.EuclideanDistanceMeasure

  }


  @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.EuclideanDistanceMeasure

  }


  @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.EuclideanDistanceMeasure

  }


  @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.EuclideanDistanceMeasure

   * @throws IOException 
   */
  @Test
  public void testAllSameValueCluster() 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()));

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

  }


  @Test
  public void testCanopy() throws Exception {
    ClusteringTestUtils.writePointsToFile(sampleData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    Configuration conf = new Configuration();
    CanopyDriver.run(conf, testdata, output, measure, 3.1, 1.1, true, true);
    int numIterations = 10;
    Path clustersIn = new Path(output, "clusters-0");
    RepresentativePointsDriver.run(conf, clustersIn, new Path(output, "clusteredPoints"), output, measure, numIterations, true);

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

  }


  @Test
  public void testKmeans() throws Exception {
    ClusteringTestUtils.writePointsToFile(sampleData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    // now run the Canopy job to prime kMeans canopies
    Configuration conf = new Configuration();
    CanopyDriver.run(conf, testdata, output, measure, 3.1, 1.1, false, true);
    // now run the KMeans job
    KMeansDriver.run(testdata, new Path(output, "clusters-0"), output, measure, 0.001, 10, true, true);

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

  }


  @Test
  public void testFuzzyKmeans() throws Exception {
    ClusteringTestUtils.writePointsToFile(sampleData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    // now run the Canopy job to prime kMeans canopies
    Configuration conf = new Configuration();
    CanopyDriver.run(conf, testdata, output, measure, 3.1, 1.1, 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);

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

  }


  @Test
  public void testMeanShift() throws Exception {
    ClusteringTestUtils.writePointsToFile(sampleData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    Configuration conf = new Configuration();
    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-10");
    RepresentativePointsDriver.run(conf, clustersIn, new Path(output, "clusteredPoints"), output, measure, numIterations, true);

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