Package org.apache.mahout.classifier

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

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    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    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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    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    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));
View Full Code Here
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    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    AbstractVectorClassifier classifier = new StandardNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

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

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
View Full Code Here
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    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    AbstractVectorClassifier classifier = new ComplementaryNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

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

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
View Full Code Here
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    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    AbstractVectorClassifier classifier = new StandardNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

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

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
View Full Code Here
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    NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);

    AbstractVectorClassifier classifier = new ComplementaryNaiveBayesClassifier(naiveBayesModel);

    assertEquals(2, classifier.numCategories());

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

    // should be classified as not stolen
    assertTrue(prediction.get(0) < prediction.get(1));
View Full Code Here
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