Package org.apache.mahout.classifier.bayes

Examples of org.apache.mahout.classifier.bayes.BayesParameters

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  }

  public void initClassifierContext() {
    try {
      //<start id="mahout.bayes.setup"/>
      BayesParameters p = new BayesParameters();
      p.set("basePath", modelDir.getCanonicalPath());
      Datastore ds = new InMemoryBayesDatastore(p);
      Algorithm a  = new BayesAlgorithm();
      ClassifierContext ctx = new ClassifierContext(a,ds);
      ctx.initialize();
      //<end id="mahout.bayes.setup"/>
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      if (!modelDir.isDirectory()) {
        throw new IllegalArgumentException(modelDir + " does not exist or is not a directory");
      }
     
      BayesParameters p = new BayesParameters();
      p.set("basePath", modelDir.getCanonicalPath());
      Datastore ds = new InMemoryBayesDatastore(p);
      Algorithm a  = new BayesAlgorithm();
      ClassifierContext ctx = new ClassifierContext(a,ds);
      ctx.initialize();
     
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    if (cmdLine.hasOption(gramSizeOpt)) {
      gramSize = Integer.parseInt((String) cmdLine.getValue(gramSizeOpt));
     
    }
   
    BayesParameters params = new BayesParameters();
    params.setGramSize(gramSize);
    String modelBasePath = (String) cmdLine.getValue(pathOpt);
    params.setBasePath(modelBasePath);

    log.info("Loading model from: {}", params.print());
   
    Algorithm algorithm;
    Datastore datastore;
   
    String classifierType = (String) cmdLine.getValue(typeOpt);
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  }
 
  @Override
  public void configure(JobConf job) {
    try {
      BayesParameters params = new BayesParameters(job.get("bayes.parameters", ""));
      log.info("Bayes Parameter {}", params.print());
      gramSize = params.getGramSize();
     
    } catch (IOException ex) {
      log.warn(ex.toString(), ex);
    }
  }
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  }

  @Override
  public void configure(JobConf job) {
    try {
      BayesParameters params = new BayesParameters(job.get("bayes.parameters", ""));
      log.info("Bayes Parameter {}", params.print());
      minSupport = params.getMinSupport();
      minDf      = params.getMinDF();
    } catch (IOException ex) {
      log.warn(ex.toString(), ex);
    }
  }
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  }
 
  @Override
  public void configure(JobConf job) {
    try {
      BayesParameters params = new BayesParameters(job.get("bayes.parameters", ""));
      log.info("Bayes Parameter {}", params.print());
      log.info("{}", params.print());
      Algorithm algorithm;
      Datastore datastore;
     
      if ("hdfs".equals(params.get("dataSource"))) {
        if ("bayes".equalsIgnoreCase(params.get("classifierType"))) {
          log.info("Testing Bayes Classifier");
          algorithm = new BayesAlgorithm();
          datastore = new InMemoryBayesDatastore(params);
        } else if ("cbayes".equalsIgnoreCase(params.get("classifierType"))) {
          log.info("Testing Complementary Bayes Classifier");
          algorithm = new CBayesAlgorithm();
          datastore = new InMemoryBayesDatastore(params);
        } else {
          throw new IllegalArgumentException("Unrecognized classifier type: " + params.get("classifierType"));
        }
       
      } else {
        throw new IllegalArgumentException("Unrecognized dataSource type: " + params.get("dataSource"));
      }
      classifier = new ClassifierContext(algorithm, datastore);
      classifier.initialize();
     
      defaultCategory = params.get("defaultCat");
      gramSize = params.getGramSize();
    } catch (IOException ex) {
      log.warn(ex.toString(), ex);
    } catch (InvalidDatastoreException e) {
      log.error(e.toString(), e);
    }
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      }
     
      Path input = new Path(cmdLine.getValue(inputOpt).toString());
      Path output = new Path(cmdLine.getValue(outputOpt).toString());

      BayesParameters bayesParams = new BayesParameters();
      bayesParams.setGramSize(1);
      job.runJob(input, output, bayesParams);
    } catch (OptionException e) {
      log.error(e.getMessage());
      CommandLineUtil.printHelp(group);
    }
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  • org.apache.mahout.classifier.bayes.mapreduce.common.BayesFeatureMapper
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