Package org.apache.mahout.classifier.bayes.interfaces

Examples of org.apache.mahout.classifier.bayes.interfaces.Datastore

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        return s.startsWith(".") == false;
      }
    });
   
    Algorithm algorithm;
    Datastore datastore;
   
    if (params.get("dataSource").equals("hdfs")) {
      if (params.get("classifierType").equalsIgnoreCase("bayes")) {
        log.info("Testing Bayes Classifier");
        algorithm = new BayesAlgorithm();
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    String modelBasePath = (String) cmdLine.getValue(pathOpt);
   
    log.info("Loading model from: {}", params.print());
   
    Algorithm algorithm;
    Datastore datastore;
   
    String classifierType = (String) cmdLine.getValue(typeOpt);
   
    String dataSource = (String) cmdLine.getValue(dataSourceOpt);
    if (dataSource.equals("hdfs")) {
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    try {
      log.info("Bayes Parameter {}", job.get("bayes.parameters"));
      Parameters params = Parameters.fromString(job.get("bayes.parameters", ""));
      log.info("{}", params.print());
      Algorithm algorithm;
      Datastore datastore;
     
      if (params.get("dataSource").equals("hdfs")) {
        if (params.get("classifierType").equalsIgnoreCase("bayes")) {
          log.info("Testing Bayes Classifier");
          algorithm = new BayesAlgorithm();
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    params.set("defaultCat", "unknown");
    params.set("encoding", "UTF-8");
    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) {
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    params.set("defaultCat", "unknown");
    params.set("encoding", "UTF-8");
    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")))
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    try {
      Parameters params = Parameters.fromString(job.get("bayes.parameters", ""));
      log.info("Bayes Parameter {}", params.print());
      log.info("{}", params.print());
      Algorithm algorithm;
      Datastore datastore;
     
      if (params.get("dataSource").equals("hdfs")) {
        if (params.get("classifierType").equalsIgnoreCase("bayes")) {
          log.info("Testing Bayes Classifier");
          algorithm = new BayesAlgorithm();
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        return !s.startsWith(".");
      }
    });
   
    Algorithm algorithm;
    Datastore datastore;
   
    if (params.get("dataSource").equals("hdfs")) {
      if (params.get("classifierType").equalsIgnoreCase("bayes")) {
        log.info("Testing Bayes Classifier");
        algorithm = new BayesAlgorithm();
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    params.set("defaultCat", "unknown");
    params.set("encoding", "UTF-8");
    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) {
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    params.set("defaultCat", "unknown");
    params.set("encoding", "UTF-8");
    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")))
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    String modelBasePath = (String) cmdLine.getValue(pathOpt);
   
    log.info("Loading model from: {}", params.print());
   
    Algorithm algorithm;
    Datastore datastore;
   
    String classifierType = (String) cmdLine.getValue(typeOpt);
   
    String dataSource = (String) cmdLine.getValue(dataSourceOpt);
    if ("hdfs".equals(dataSource)) {
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Related Classes of org.apache.mahout.classifier.bayes.interfaces.Datastore

  • org.apache.mahout.classifier.bayes.BayesClassifierSelfTest
  • org.apache.mahout.classifier.bayes.mapreduce.bayes.BayesClassifierMapper
  • org.apache.mahout.classifier.bayes.TestClassifier
  • org.apache.mahout.classifier.Classify
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