请基于华为云的云主机服务实验环境，回答以下问题：是否可以修改MapReduce任务，使之完成多个文档的TF-iDF计算？若是，请提交修改后的程序并说明计算步骤。若否，请说明原因

是的，可以修改MapReduce任务，使之完成多个文档的TF-iDF计算。

以下是修改后的程序：

Mapper：

public static class TFIDFMapper extends Mapper<LongWritable, Text, Text, Text> {

    private final static Text word = new Text();
    private final static Text document = new Text();

    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        String[] tokens = line.split(",");
        String documentName = tokens[0];
        String wordValue = tokens[1];
        String wordCount = tokens[2];
        word.set(wordValue);
        document.set(documentName + "=" + wordCount);
        context.write(word, document);
    }
}

Reducer：

public static class TFIDFReducer extends Reducer<Text, Text, Text, Text> {

    private final static Text word = new Text();

    public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        int numberOfDocumentsInCorpus = context.getConfiguration().getInt("numberOfDocumentsInCorpus", 1);
        int numberOfDocumentsContainingKey = 0;
        Map<String, Integer> documents = new HashMap<String, Integer>();
        for (Text value : values) {
            String[] documentAndCount = value.toString().split("=");
            String documentName = documentAndCount[0];
            int count = Integer.parseInt(documentAndCount[1]);
            numberOfDocumentsContainingKey++;
            documents.put(documentName, count);
        }
        double idf = Math.log10((double) numberOfDocumentsInCorpus / (double) numberOfDocumentsContainingKey);
        StringBuilder documentAndTFIDF = new StringBuilder();
        for (String documentName : documents.keySet()) {
            int count = documents.get(documentName);
            double tf = (double) count / (double) getTotalNumberOfWordsInDocument(documentName, context);
            double tfidf = tf * idf;
            documentAndTFIDF.append(documentName).append("=").append(tfidf).append("\t");
        }
        word.set(key);
        context.write(word, new Text(documentAndTFIDF.toString()));
    }

    private int getTotalNumberOfWordsInDocument(String documentName, Context context) throws IOException {
        Path[] inputPaths = context.getInputPaths();
        for (Path inputPath : inputPaths) {
            FileSystem fileSystem = inputPath.getFileSystem(context.getConfiguration());
            Path filePath = new Path(inputPath, documentName);
            if (fileSystem.exists(filePath)) {
                FSDataInputStream inputStream = fileSystem.open(filePath);
                BufferedReader reader = new BufferedReader(new InputStreamReader(inputStream));
                String line;
                int count = 0;
                while ((line = reader.readLine()) != null) {
                    count += line.split(" ").length;
                }
                reader.close();
                return count;
            }
        }
        return 1;
    }
}

在修改后的程序中，Mapper将每个文档中的单词与其出现次数映射到键值对中，键为单词，值为文档名和出现次数。Reducer计算TF-IDF，将每个文档及其TF-IDF值作为字符串追加到StringBuilder中，最后以单词为键，文档名和TF-IDF字符串为值写入输出。

需要注意的是，需要在运行作业之前将所有文档存储在HDFS中，并在作业运行时将它们作为输入路径提供给作业。

修改后的程序计算步骤如下：

1. Mapper将每个文档中的单词与其出现次数映射到键值对中，键为单词，值为文档名和出现次数。
2. Reducer遍历每个键，并计算TF-IDF值。
3. 对于每个键，Reducer遍历其所有值，并将每个值解析为文档名和出现次数。
4. Reducer计算文档中每个单词的TF-IDF值，并将文档名和TF-IDF值作为字符串追加到StringBuilder中。
5. 对于每个键，Reducer以单词为键，文档名和TF-IDF字符串为值写入输出。