Package org.apache.mahout.common.distance

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

67
68
69
70
71
72
73
74
75
76
77
    String vectorsFolder = outputDir + "/tfidf-vectors";
    String canopyCentroids = outputDir + "/canopy-centroids";
    String clusterOutput = outputDir + "/clusters/";
   
    CanopyDriver.run(conf, new Path(vectorsFolder), new Path(canopyCentroids),
      new ManhattanDistanceMeasure(), 3000.0, 2000.0, false, false);
   
    FuzzyKMeansDriver.run(conf, new Path(vectorsFolder), new Path(canopyCentroids, "clusters-0"), new Path(clusterOutput),
      new TanimotoDistanceMeasure(), 0.01, 20, 2.0f, true, true, 0.0, false);
   
    SequenceFile.Reader reader = new SequenceFile.Reader(fs, new Path(
View Full Code Here
192
193
194
195
196
197
198
199
200
201
  }
 
  /** Story: User can cluster points without instantiating them all in memory at once */
  public void testIterativeManhattan() throws Exception {
    List<Vector> points = getPoints(raw);
    List<Canopy> canopies = CanopyClusterer.createCanopies(points, new ManhattanDistanceMeasure(), 3.1, 2.1);
    System.out.println("testIterativeManhattan");
    printCanopies(canopies);
    verifyManhattanCanopies(canopies);
  }
View Full Code Here
65
66
67
68
69
70
71
72
73
74
75
    RandomUtils.useTestSeed();
    DisplayDirichlet.generateSamples();
    List<Vector> points = new ArrayList<Vector>();
    for (VectorWritable sample : sampleData)
      points.add(sample.get());
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    List<SoftCluster> initialClusters = new ArrayList<SoftCluster>();
   
    k = 3;
    int i = 0;
    for (Vector point : points) {
View Full Code Here
67
68
69
70
71
72
73
74
75
    RandomUtils.useTestSeed();
    DisplayDirichlet.generateSamples();
    List<Vector> points = new ArrayList<Vector>();
    for (VectorWritable sample : sampleData)
      points.add(sample.get());
    canopies = CanopyClusterer.createCanopies(points, new ManhattanDistanceMeasure(), t1, t2);
    CanopyClusterer.updateCentroids(canopies);
    new DisplayCanopy();
  }
View Full Code Here
63
64
65
66
67
68
69
70
71
72
73
    RandomUtils.useTestSeed();
    DisplayDirichlet.generateSamples();
    List<Vector> points = new ArrayList<Vector>();
    for (VectorWritable sample : sampleData)
      points.add(sample.get());
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    List<Cluster> initialClusters = new ArrayList<Cluster>();
    k = 3;
    int i = 0;
    for (Vector point : points) {
      if (initialClusters.size() < Math.min(k, points.size())) {
View Full Code Here
39
40
41
42
43
44
45
46
47
48
49
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));
View Full Code Here
53
54
55
56
57
58
59
60
61
62
63
  }

  @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));
View Full Code Here
67
68
69
70
71
72
73
74
75
76
77
  }

  @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));
View Full Code Here
81
82
83
84
85
86
87
88
89
90
91
  }

  @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 {
View Full Code Here
96
97
98
99
100
101
102
103
104
105
106
  }

  @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));
View Full Code Here
TOP

Related Classes of org.apache.mahout.common.distance.ManhattanDistanceMeasure

  • mia.clustering.ch09.NewsFuzzyKMeansClustering
  • org.apache.mahout.benchmark.VectorBenchmarks
  • org.apache.mahout.clustering.canopy.DisplayCanopy
  • org.apache.mahout.clustering.canopy.TestCanopyCreation
  • org.apache.mahout.clustering.classify.ClusterClassificationDriverTest
  • org.apache.mahout.clustering.dirichlet.models.DistanceMeasureClusterDistribution
  • org.apache.mahout.clustering.display.DisplayCanopy
  • org.apache.mahout.clustering.display.DisplayFuzzyKMeans
  • org.apache.mahout.clustering.display.DisplayKMeans
  • org.apache.mahout.clustering.display.DisplaySpectralKMeans
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.