读代码-TrainClassifier和TestClassifier
package org.apache.mahout.classifier.bayes;
public final class TrainClassifier
bayes和cbyes的入口类

两个分支

  public static void trainNaiveBayes(Path dir, Path outputDir, BayesParameters params) throws IOException {    BayesDriver driver = new BayesDriver();    driver.runJob(dir, outputDir, params);  }    public static void trainCNaiveBayes(Path dir, Path outputDir, BayesParameters params) throws IOException {    CBayesDriver driver = new CBayesDriver();    driver.runJob(dir, outputDir, params);  }

先设定所有默认参数,如果有非默认项再覆盖
由于参数过多,定义一个类封装参数,便于后续传递

      BayesParameters params = new BayesParameters();      // Setting all the default parameter values      params.setGramSize(1);      params.setMinDF(1);      params.set("alpha_i","1.0");      params.set("dataSource", "hdfs");            if (cmdLine.hasOption(gramSizeOpt)) {        params.setGramSize(Integer.parseInt((String) cmdLine.getValue(gramSizeOpt)));      }            if (cmdLine.hasOption(minDfOpt)) {        params.setMinDF(Integer.parseInt((String) cmdLine.getValue(minDfOpt)));      }

      Path inputPath = new Path((String) cmdLine.getValue(inputDirOpt));      Path outputPath = new Path((String) cmdLine.getValue(outputOpt));      if ("cbayes".equalsIgnoreCase(classifierType)) {        log.info("Training Complementary Bayes Classifier");        trainCNaiveBayes(inputPath, outputPath, params);      } else {        log.info("Training Bayes Classifier");        // setup the HDFS and copy the files there, then run the trainer        trainNaiveBayes(inputPath, outputPath, params);      }

package org.apache.mahout.classifier.bayes;
public final class TestClassifier

入口

  public static void classifyParallel(BayesParameters params) throws IOException {    BayesClassifierDriver.runJob(params);  }

分并行和非并行两种实现

      if ("sequential".equalsIgnoreCase(classificationMethod)) {        classifySequential(params);      } else if ("mapreduce".equalsIgnoreCase(classificationMethod)) {        classifyParallel(params);      }