Examples of classify()

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

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    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));
    assertEquals("[0,0]", "[0.107, 0.787, 0.107]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.867, 0.117, 0.016]", AbstractCluster.formatVector(pdf, null));
  }
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Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

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    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));
    assertEquals("[0,0]", "[0.107, 0.787, 0.107]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.867, 0.117, 0.016]", AbstractCluster.formatVector(pdf, null));
  }

  @Test
  public void testCanopyClassification() {
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Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

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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));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.107, 0.787, 0.107]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.867, 0.117, 0.016]", AbstractCluster.formatVector(pdf, null));
  }
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Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

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    AbstractVectorClassifier classifier = new StandardNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

    Vector prediction = classifier.classify(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }
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Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

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    AbstractVectorClassifier classifier = new ComplementaryNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

    Vector prediction = classifier.classify(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
  }
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Examples of org.apache.mahout.classifier.bayes.BayesClassifier.classify()

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      line.append(token).append(' ');
    }
    List<String> doc = Model.generateNGramsWithoutLabel(line.toString(), gramSize) ;
    log.info("Done converting");
    log.info("Classifying document: {}", docPath);
    ClassifierResult category = classifier.classify(model, doc.toArray(new String[doc.size()]), defaultCat);
    log.info("Category for {} is {}", docPath, category);

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

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

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

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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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