FSDirectory directory = SimpleFSDirectory.open(new File("d:/nrtTest2")); IndexReader reader = IndexReader.open(directory);IndexSearcher searcher = new IndexSearcher(reader);//MoreLikeThis mlt = new MoreLikeThis(reader);mlt.setFieldNames(new String[] { "ab" }); //用于计算的字段//int docNum = 1;
//      TermFreqVector vector = reader.getTermFreqVector(docNum, "ab");
//      System.out.println(vector.toString());Query query = mlt.like(docNum);//试图找到与docnum=1相似的documentsSystem.out.println(reader.document(docNum));System.out.println(query.toString());//查看构造的query，后面的就是常规的lucene的检索过程。TopDocs topDocs = searcher.search(query, 10);ScoreDoc[] scoreDocs = topDocs.scoreDocs;for (ScoreDoc sdoc : scoreDocs) {Document doc = reader.document(sdoc.doc);System.out.println(doc.get("ti"));// System.out.println(doc.get("ti"));}

public Query like(int docNum) throws IOException {if (fieldNames == null) {// gather list of valid fields from luceneCollection<String> fields = ReaderUtil.getIndexedFields(ir);fieldNames = fields.toArray(new String[fields.size()]);}return createQuery(retrieveTerms(docNum));}

public PriorityQueue<Object[]> retrieveTerms(int docNum) throws IOException {Map<String,Int> termFreqMap = new HashMap<String,Int>();for (int i = 0; i < fieldNames.length; i++) {String fieldName = fieldNames[i];TermFreqVector vector = ir.getTermFreqVector(docNum, fieldName);//取出term向量// 如果当前字段没有存储termVector，那么需要重新计算。其实这里就是分词，并计算term词频的过程，注意他默认使用的是StandardAnalyzer分词器！！！if (vector == null) {Document d = ir.document(docNum);String text[] = d.getValues(fieldName);if (text != null) {for (int j = 0; j < text.length; j++) {addTermFrequencies(new StringReader(text[j]), termFreqMap,fieldName);}}} else {//如果之前保存了termVector那么就方便多了。addTermFrequencies(termFreqMap, vector);}}

private void addTermFrequencies(Map<String,Int> termFreqMap,TermFreqVector vector) {String[] terms = vector.getTerms();int freqs[] = vector.getTermFrequencies();for (int j = 0; j < terms.length; j++) {String term = terms[j];if (isNoiseWord(term)) {continue;}// increment frequencyInt cnt = termFreqMap.get(term);if (cnt == null) {cnt = new Int();termFreqMap.put(term, cnt);cnt.x = freqs[j];} else {cnt.x += freqs[j];}}}

private boolean isNoiseWord(String term) {int len = term.length();if (minWordLen > 0 && len < minWordLen) {return true;}if (maxWordLen > 0 && len > maxWordLen) {return true;}if (stopWords != null && stopWords.contains(term)) {return true;}return false;}

private PriorityQueue<Object[]> createQueue(Map<String,Int> words)throws IOException {// 获取当前index的文档总数。int numDocs = ir.numDocs();FreqQ res = new FreqQ(words.size()); // 按照term的得分进行存放Iterator<String> it = words.keySet().iterator();while (it.hasNext()) { // 对所有term进行遍历String word = it.next();int tf = words.get(word).x; // 对应term的tfif (minTermFreq > 0 && tf < minTermFreq) {continue; // 和去噪类似，tf太小的term直接过掉。}// 对于同一个term，找到df最大的那个字段，存放到topField。String topField = fieldNames[0];int docFreq = 0;for (int i = 0; i < fieldNames.length; i++) {int freq = ir.docFreq(new Term(fieldNames[i], word));topField = (freq > docFreq) ? fieldNames[i] : topField;docFreq = (freq > docFreq) ? freq : docFreq;}//df太小的term也要直接过掉if (minDocFreq > 0 && docFreq < minDocFreq) {continue; // filter out words that don't occur in enough docs}//df太大的term也要直接过掉if (docFreq > maxDocFreq) {continue; // filter out words that occur in too many docs}//df==0的term也要直接过掉，怎么会有df的term？？？这里说是index文件的问题if (docFreq == 0) {continue; // index update problem?}//经典的idf、tf又来了float idf = similarity.idf(docFreq, numDocs);float score = tf * idf;//将结果存放到优先队列中。res.insertWithOverflow(new Object[] {word, // the wordtopField, // the top fieldFloat.valueOf(score), // overall scoreFloat.valueOf(idf), // idfInteger.valueOf(docFreq), // freq in all docsInteger.valueOf(tf)});}return res;}

private Query createQuery(PriorityQueue<Object[]> q) {BooleanQuery query = new BooleanQuery();Object cur;int qterms = 0;float bestScore = 0;while (((cur = q.pop()) != null)) {Object[] ar = (Object[]) cur;TermQuery tq = new TermQuery(new Term((String) ar[1], (String) ar[0]));//这里还可以对termquery进行boost的设置。默认为falseif (boost) {if (qterms == 0) {bestScore = ((Float) ar[2]).floatValue();}float myScore = ((Float) ar[2]).floatValue();tq.setBoost(boostFactor * myScore / bestScore);}//构建boolean query，should关联。try {query.add(tq, BooleanClause.Occur.SHOULD);} catch (BooleanQuery.TooManyClauses ignore) {break;}qterms++;if (maxQueryTerms > 0 && qterms >= maxQueryTerms) {//限定参与运算的term的数量break;}}return query;}