Package org.apache.mahout.classifier.bayes.model

Examples of org.apache.mahout.classifier.bayes.model.ClassifierContext.initialize()

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    } else {
      throw new IllegalArgumentException("Unrecognized dataSource type: " + params.get("dataSource"));
    }
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    ResultAnalyzer resultAnalyzer = new ResultAnalyzer(classifier.getLabels(), params.get("defaultCat"));
    TimingStatistics totalStatistics = new TimingStatistics();
    if (subdirs != null) {
     
      for (File file : subdirs) {
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    } else {
      throw new IllegalArgumentException("Unrecognized dataSource type: " + dataSource);
    }
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    String defaultCat = "unknown";
    if (cmdLine.hasOption(defaultCatOpt)) {
      defaultCat = (String) cmdLine.getValue(defaultCatOpt);
    }
    File docPath = new File((String) cmdLine.getValue(classifyOpt));
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    params.set("alpha_i", "1.0");
   
    Algorithm algorithm = new BayesAlgorithm();
    Datastore datastore = new InMemoryBayesDatastore(params);
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    ResultAnalyzer resultAnalyzer = new ResultAnalyzer(classifier.getLabels(), params.get("defaultCat"));
   
    for (String[] entry : ClassifierData.DATA) {
      List<String> document = new NGrams(entry[1], Integer.parseInt(params.get("gramSize")))
          .generateNGramsWithoutLabel();
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    params.set("alpha_i", "1.0");
   
    Algorithm algorithm = new CBayesAlgorithm();
    Datastore datastore = new InMemoryBayesDatastore(params);
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    ResultAnalyzer resultAnalyzer = new ResultAnalyzer(classifier.getLabels(), params.get("defaultCat"));
    for (String[] entry : ClassifierData.DATA) {
      List<String> document = new NGrams(entry[1], Integer.parseInt(params.get("gramSize")))
          .generateNGramsWithoutLabel();
      assertEquals(3, classifier.classifyDocument(document.toArray(new String[document.size()]),
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    } else {
      throw new IllegalArgumentException("Unrecognized dataSource type: " + params.get("dataSource"));
    }
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    ResultAnalyzer resultAnalyzer = new ResultAnalyzer(classifier.getLabels(), params.get("defaultCat"));
    TimingStatistics totalStatistics = new TimingStatistics();
    if (subdirs != null) {
     
      for (File file : subdirs) {
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    params.set("alpha_i", "1.0");
   
    Algorithm algorithm = new BayesAlgorithm();
    Datastore datastore = new InMemoryBayesDatastore(params);
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    ResultAnalyzer resultAnalyzer = new ResultAnalyzer(classifier.getLabels(), params.get("defaultCat"));
   
    for (String[] entry : ClassifierData.DATA) {
      List<String> document = new NGrams(entry[1], Integer.parseInt(params.get("gramSize")))
          .generateNGramsWithoutLabel();
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    params.set("alpha_i", "1.0");
   
    Algorithm algorithm = new CBayesAlgorithm();
    Datastore datastore = new InMemoryBayesDatastore(params);
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    ResultAnalyzer resultAnalyzer = new ResultAnalyzer(classifier.getLabels(), params.get("defaultCat"));
    for (String[] entry : ClassifierData.DATA) {
      List<String> document = new NGrams(entry[1], Integer.parseInt(params.get("gramSize")))
          .generateNGramsWithoutLabel();
      assertEquals(3, classifier.classifyDocument(document.toArray(new String[document.size()]),
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    } else {
      throw new IllegalArgumentException("Unrecognized dataSource type: " + dataSource);
    }
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    String defaultCat = "unknown";
    if (cmdLine.hasOption(defaultCatOpt)) {
      defaultCat = (String) cmdLine.getValue(defaultCatOpt);
    }
    File docPath = new File((String) cmdLine.getValue(classifyOpt));
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    } else {
      throw new IllegalArgumentException("Unrecognized dataSource type: "
          + params.get("dataSource"));
    }
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    ResultAnalyzer resultAnalyzer = new ResultAnalyzer(classifier.getLabels(),
        params.get("defaultCat"));
    final TimingStatistics totalStatistics = new TimingStatistics();
    if (subdirs != null) {
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    } else {
      throw new IllegalArgumentException("Unrecognized dataSource type: "
          + dataSource);
    }
    ClassifierContext classifier = new ClassifierContext(algorithm, datastore);
    classifier.initialize();
    String defaultCat = "unknown";
    if (cmdLine.hasOption(defaultCatOpt)) {
      defaultCat = (String) cmdLine.getValue(defaultCatOpt);
    }
    File docPath = new File((String) cmdLine.getValue(classifyOpt));
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