Package org.apache.mahout.clustering.classify

Examples of org.apache.mahout.clustering.classify.ClusterClassifier.classify()

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    while (iteration <= numIterations) {
      for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(inPath, PathType.LIST,
          PathFilters.logsCRCFilter(), conf)) {
        Vector vector = vw.get();
        // classification yields probabilities
        Vector probabilities = classifier.classify(vector);
        // policy selects weights for models given those probabilities
        Vector weights = classifier.getPolicy().select(probabilities);
        // training causes all models to observe data
        for (Vector.Element e : weights.nonZeroes()) {
          int index = e.index();
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  }
 
  @Test
  public void testDMClusterClassification() {
    ClusterClassifier classifier = newDMClassifier();
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.200, 0.600, 0.200]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.493, 0.296, 0.211]", AbstractCluster.formatVector(pdf, null));
  }
 
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  @Test
  public void testDMClusterClassification() {
    ClusterClassifier classifier = newDMClassifier();
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.200, 0.600, 0.200]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.493, 0.296, 0.211]", AbstractCluster.formatVector(pdf, null));
  }
 
  @Test
  public void testCanopyClassification() {
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    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));
    ClusterClassifier classifier = new ClusterClassifier(models, new CanopyClusteringPolicy());
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.200, 0.600, 0.200]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.493, 0.296, 0.211]", AbstractCluster.formatVector(pdf, null));
  }
 
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    models.add(new Canopy(new DenseVector(2), 1, measure));
    models.add(new Canopy(new DenseVector(2).assign(-1), 2, measure));
    ClusterClassifier classifier = new ClusterClassifier(models, new CanopyClusteringPolicy());
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.200, 0.600, 0.200]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.493, 0.296, 0.211]", AbstractCluster.formatVector(pdf, null));
  }
 
  @Test
  public void testClusterClassification() {
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  }
 
  @Test
  public void testClusterClassification() {
    ClusterClassifier classifier = newKlusterClassifier();
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.200, 0.600, 0.200]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.493, 0.296, 0.211]", AbstractCluster.formatVector(pdf, null));
  }
 
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  @Test
  public void testClusterClassification() {
    ClusterClassifier classifier = newKlusterClassifier();
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.200, 0.600, 0.200]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.493, 0.296, 0.211]", AbstractCluster.formatVector(pdf, null));
  }
 
  @Test(expected = UnsupportedOperationException.class)
  public void testMSCanopyClassification() {
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    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));
    ClusterClassifier classifier = new ClusterClassifier(models, new MeanShiftClusteringPolicy());
    classifier.classify(new DenseVector(2));
  }
 
  @Test
  public void testSoftClusterClassification() {
    ClusterClassifier classifier = newSoftClusterClassifier();
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  }
 
  @Test
  public void testSoftClusterClassification() {
    ClusterClassifier classifier = newSoftClusterClassifier();
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.000, 1.000, 0.000]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.735, 0.184, 0.082]", AbstractCluster.formatVector(pdf, null));
  }
 
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  @Test
  public void testSoftClusterClassification() {
    ClusterClassifier classifier = newSoftClusterClassifier();
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.000, 1.000, 0.000]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.735, 0.184, 0.082]", AbstractCluster.formatVector(pdf, null));
  }
 
  @Test
  public void testGaussianClusterClassification() {
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