Package org.apache.mahout.classifier

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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    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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    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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    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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    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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