Examples of BayesParameters

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

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

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

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

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

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

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

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      int gramSize = 1;
      if (cmdLine.hasOption(gramSizeOpt)) {
        gramSize = Integer.parseInt((String) cmdLine.getValue(gramSizeOpt));
       
      }
      BayesParameters params = new BayesParameters(gramSize);
     
      String modelBasePath = (String) cmdLine.getValue(pathOpt);
     
      String classifierType = (String) cmdLine.getValue(typeOpt);
      String dataSource = (String) cmdLine.getValue(dataSourceOpt);
     
      String defaultCat = "unknown";
      if (cmdLine.hasOption(defaultCatOpt)) {
        defaultCat = (String) cmdLine.getValue(defaultCatOpt);
      }
     
      String encoding = "UTF-8";
      if (cmdLine.hasOption(encodingOpt)) {
        encoding = (String) cmdLine.getValue(encodingOpt);
      }
     
      String alphaI = "1.0";
      if (cmdLine.hasOption(alphaOpt)) {
        alphaI = (String) cmdLine.getValue(alphaOpt);
      }
     
      boolean verbose = cmdLine.hasOption(verboseOutputOpt);
     
      String testDirPath = (String) cmdLine.getValue(dirOpt);
     
      String classificationMethod = "sequential";
      if (cmdLine.hasOption(methodOpt)) {
        classificationMethod = (String) cmdLine.getValue(methodOpt);
      }
     
      params.set("verbose", Boolean.toString(verbose));
      params.set("basePath", modelBasePath);
      params.set("classifierType", classifierType);
      params.set("dataSource", dataSource);
      params.set("defaultCat", defaultCat);
      params.set("encoding", encoding);
      params.set("alpha_i", alphaI);
      params.set("testDirPath", testDirPath);
     
      if (classificationMethod.equalsIgnoreCase("sequential")) {
        classifySequential(params);
      } else if (classificationMethod.equalsIgnoreCase("mapreduce")) {
        classifyParallel(params);
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Examples of org.apache.mahout.classifier.bayes.common.BayesParameters

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    if (cmdLine.hasOption(gramSizeOpt)) {
      gramSize = Integer.parseInt((String) cmdLine.getValue(gramSizeOpt));
     
    }
   
    BayesParameters params = new BayesParameters(gramSize);
   
    String modelBasePath = (String) cmdLine.getValue(pathOpt);
   
    log.info("Loading model from: {}", params.print());
   
    Algorithm algorithm;
    Datastore datastore;
   
    String classifierType = (String) cmdLine.getValue(typeOpt);
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Examples of org.apache.mahout.classifier.bayes.common.BayesParameters

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      }
     
      String classifierType = (String) cmdLine.getValue(typeOpt);
      String dataSourceType = (String) cmdLine.getValue(dataSourceOpt);
     
      BayesParameters params = new BayesParameters(Integer.parseInt((String) cmdLine.getValue(gramSizeOpt)));
     
      String alphaI = "1.0";
      if (cmdLine.hasOption(alphaOpt)) {
        alphaI = (String) cmdLine.getValue(alphaOpt);
      }
     
      params.set("alpha_i", alphaI);
     
      if (dataSourceType.equals("hbase")) {
        params.set("dataSource", "hbase");
      } else {
        params.set("dataSource", "hdfs");
      }
     
      if (classifierType.equalsIgnoreCase("bayes")) {
        log.info("Training Bayes Classifier");
        trainNaiveBayes((String) cmdLine.getValue(inputDirOpt), (String) cmdLine
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