首先是wordcount
package org.lukey.hadoop.classifyBayes; import java.io.IOException; import java.util.ArrayList; import java.util.HashMap; import java.util.List; import java.util.Map; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FSDataOutputStream; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IOUtils; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Counter; import org.apache.hadoop.mapreduce.Counters; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileSplit; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs; /** * * 一次将需要的结果都统计到对应的文件夹中 AFRICA 484017newsML.txt afford 1 * * 按照这个格式输出给后面处理得到需要的： 1. AFRICA 484017newsML.txt AFRICA 487141newsML.txt * 类别中的文本数， ---> 计算先验概率(单独解决这个) 所有类别中的文本总数， ---> 可以由上面得到，计算先验概率 * * 2. AFRICA afford 1 AFRICA boy 3 每个类中的每个单词的个数，---> 计算各个类中单词的概率 * * 3. AFRICA 768 类中单词总数， ---> 将2中的第一个key相同的第三个数相加即可 * * 4. AllWORDS 12345 所有类别中单词种类数 ---> 将1中的第三个key归并，计算个数 * */ public class MyWordCount { private static MultipleOutputs<Text, IntWritable> mos; static String baseOutputPath = "/user/hadoop/test_out"; // 设计两个map分别计算每个类别的文本数//和每个类别的单词总数 static Map<String, List<String>> fileCountMap = new HashMap<String, List<String>>(); static Map<String, Integer> fileCount = new HashMap<String, Integer>(); // static Map<String, List<String>> wordsCountInClassMap = new // HashMap<String, List<String>>(); static enum WordsNature { CLSASS_NUMBER, CLASS_WORDS, TOTALWORDS } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = { "/user/hadoop/test", "/user/hadoop/mid/wordsFrequence" }; /* * String[] otherArgs = new GenericOptionsParser(conf, * args).getRemainingArgs(); * * if (otherArgs.length != 2) { System.out.println("Usage <in> <out>"); * System.exit(-1); } */ Job job = new Job(conf, "file count"); job.setJarByClass(MyWordCount.class); // job.setInputFormatClass(CustomInputFormat.class); job.setMapperClass(First_Mapper.class); job.setReducerClass(First_Reducer.class); Path inputpath = new Path(otherArgs[0]); // 调用自己写的方法 MyUtils.addInputPath(job, inputpath, conf); // CustomInputFormat.setInputPaths(job, inputpath); // FileInputFormat.addInputPath(job, inputpath); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); int exitCode = job.waitForCompletion(true) ? 0 : 1; // 调用计数器 Counters counters = job.getCounters(); Counter c1 = counters.findCounter(WordsNature.TOTALWORDS); System.out.println("-------------->>>>: " + c1.getDisplayName() + ":" + c1.getName() + ": " + c1.getValue()); // 将单词种类数写入文件中 Path totalWordsPath = new Path("/user/hadoop/output/totalwords.txt"); FileSystem fs = FileSystem.get(conf); FSDataOutputStream outputStream = fs.create(totalWordsPath); outputStream.writeBytes(c1.getDisplayName() + ":" + c1.getValue()); // 将每个类的文本个数写入文件中 Path priorPath = new Path("/user/hadoop/output/priorPro.txt"); // 先验概率 for (Map.Entry<String, List<String>> entry : fileCountMap.entrySet()) { fileCount.put(entry.getKey(), entry.getValue().size()); } // 求文本总数 int fileSum = 0; for (Integer num : fileCount.values()) { fileSum += num; } System.out.println("fileSum = " + fileSum); FSDataOutputStream priorStream = fs.create(priorPath); // 计算每个类的先验概率并写入文件 for (Map.Entry<String, Integer> entry : fileCount.entrySet()) { double p = (double) entry.getValue() / fileSum; priorStream.writeBytes(entry.getKey() + ":" + p); } IOUtils.closeStream(priorStream); IOUtils.closeStream(outputStream); // 下次求概率是尝试单词总种类数写到configuration中 // // conf.set("TOTALWORDS", totalWords.toString()); System.exit(exitCode); } // Mapper static class First_Mapper extends Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private final static IntWritable zero = new IntWritable(0); private Text className = new Text(); private Text countryName = new Text(); @Override protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException { // TODO Auto-generated method stub FileSplit fileSplit = (FileSplit) context.getInputSplit(); // 文件名 String fileName = fileSplit.getPath().getName(); // 文件夹名(即类别名) String dirName = fileSplit.getPath().getParent().getName(); className.set(dirName + "\t" + value.toString()); countryName.set(dirName + "\t" + fileName + "\t" + value.toString()); // 将文件名添加到map中用于统计文本个数 if (fileCountMap.containsKey(dirName)) { fileCountMap.get(dirName).add(fileName); } else { List<String> oneList = new ArrayList<String>(); oneList.add(fileName); fileCountMap.put(dirName, oneList); } context.write(className, one); // 每个类别的每个单词数 // ABDBI hello 1 context.write(new Text(dirName), one);// 统计每个类中的单词总数 //ABDBI 1 context.write(value, zero); // 用于统计所有类中单词个数 } } // Reducer static class First_Reducer extends Reducer<Text, IntWritable, Text, IntWritable> { // result 表示每个类别中每个单词的个数 IntWritable result = new IntWritable(); Map<String, List<String>> classMap = new HashMap<String, List<String>>(); Map<String, List<String>> fileMap = new HashMap<String, List<String>>(); @Override protected void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable value : values) { sum += value.get(); } // sum为0，总得单词数加1，统计所有单词的种类 if (sum == 0) { context.getCounter(WordsNature.TOTALWORDS).increment(1); } else {// sum不为0时，通过key的长度来判断， String[] temp = key.toString().split("\t"); if (temp.length == 2) { // 用tab分隔类别和单词 result.set(sum); context.write(key, result); // mos.write(new Text(temp[1]), result, temp[0]); }else{ //类别中单词总数 result.set(sum); mos.write(key, result, "wordsInClass"); } /* // 先处理类中的单词数 String[] temp = key.toString().split("\t"); if (temp.length == 2) { // 用tab分隔类别和单词 if (classMap.containsKey(temp[0])) { classMap.get(temp[0]).add(temp[1]); } else { List<String> oneList = new ArrayList<String>(); oneList.add(temp[1]); classMap.put(temp[0], oneList); } // mos.write(temp[0], temp[1], result); result.set(sum); context.write(key, result); // 保存每个类别名，单词名以及个数 // mos.write(temp[0], temp[1], result); } else if (temp.length == 1) { // 统计文件个数,每个map保存的是一个类别的文件名和文件名列表，list的长度就是个数 if (fileMap.containsKey(temp[0])) { fileMap.get(temp[0]).add(temp[1]); } else { List<String> oneList = new ArrayList<String>(); oneList.add(temp[1]); fileMap.put(temp[0], oneList); } } // 计算先验概率 int fileNumberSum = 0; for (List<String> list : classMap.values()) { fileNumberSum += list.size(); System.out.println(fileNumberSum);// test } // 保存先验概率 Map<String, Double> priorMap = new HashMap<>(); Iterator<Map.Entry<String, List<String>>> iterators = classMap.entrySet().iterator(); while (iterators.hasNext()) { Map.Entry<String, List<String>> iterator = iterators.next(); double prior = (double) iterator.getValue().size() / fileNumberSum; priorMap.put(iterator.getKey(), prior); } */ // result.set(sum); // context.write(key, result); } } @Override protected void cleanup(Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException { // TODO Auto-generated method stub mos.close(); } @Override protected void setup(Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException { // TODO Auto-generated method stub mos = new MultipleOutputs<Text, IntWritable>(context); } } }