import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
private void createIndexWriter(List<URLContentObject> urlContentList,
	Directory ramDirectory) throws IOException
	{
Set<String> stopWords = new HashSet<String>(FileUtils.readLines(new File(baseDir+
		"data/stopwords/stopwords_en.txt")));  
Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_36,stopWords);


IndexWriter indexWriter = new IndexWriter(ramDirectory, new IndexWriterConfig(Version.LUCENE_36, analyzer));
for(URLContentObject d:urlContentList)
{
	Document document = new Document();
	StringReader reader=new StringReader(d.getContent());
	document.add(new Field("id", d.getId(), Field.Store.YES, Field.Index.ANALYZED,TermVector.YES));
	document.add(new Field("contents", reader, TermVector.YES));
	//document.add(new Field("id",Integer.toString(d.getAutoIncrementId()),Field.Store.YES, Field.Index.ANALYZED));
	indexWriter.addDocument(document);
	reader.close();
}

indexWriter.commit();
indexWriter.close();



	}
 

import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
private Document createDocument(Term term, String value, boolean synonym) {
	Document doc = new Document();
	
	Field ontologyField = new Field(FIELD_ONTOLOGY, 
    		term.getOntology().getName(), 
    		Field.Store.YES, 
    		Field.Index.NO,
    		TermVector.NO);
	ontologyField.setOmitNorms(true);
	ontologyField.setOmitTermFreqAndPositions(true);
	doc.add(ontologyField);
	
	Field idField = new Field(FIELD_ID, 
    		term.getReferenceId(), 
    		Field.Store.YES, 
    		Field.Index.NOT_ANALYZED,
    		TermVector.NO);
	idField.setOmitNorms(true);
	idField.setOmitTermFreqAndPositions(true);
	doc.add(idField);
	
	Field nameField = new Field(FIELD_TERM, 
	   		value, 
	   		Field.Store.YES, 
	   		Field.Index.ANALYZED,
	   		TermVector.WITH_POSITIONS_OFFSETS);
	//nameField.setOmitNorms(true);
	doc.add(nameField);
	
	doc.add(new Field(FIELD_SYNONYM,
			synonym ? new byte[] {1} : new byte[] {0},	
			Field.Store.YES));
	
	return doc;
}
 

import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
/**
 * 
 */
public NonDictionaryField(String name, Store store, Index index, TermVector termVector, boolean multiValued)
{
    this.name = name;
    this.store = store;
    this.index = index;
    this.termVector = termVector;
    this.multiValued = multiValued;
}
 

import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
public static DocVector[] getCosineSimilarityMatrix(List<String> fileSentences) throws IOException{

		RAMDirectory ramDir = new RAMDirectory();
		FileReader fr=new FileReader(new File("lib/stoplists/en.txt"));

		//	Set<String> stopWords = new HashSet<String>(FileUtils.readLines(new File("stop-words.txt")));  
		Analyzer analyzer = new StopAnalyzer(Version.LUCENE_36, fr );
		//Index the full text of both documents
		//IndexWriter writer = new IndexWriter(ramDir, new StandardAnalyzer(Version.LUCENE_36), true, IndexWriter.MaxFieldLength.UNLIMITED);
		IndexWriter writer =new IndexWriter(ramDir, new IndexWriterConfig(Version.LUCENE_36, analyzer));
		for (String s:fileSentences)
		{
			Document doc1 = new Document();
			StringReader d1reader=new StringReader(s);
			doc1.add(new Field("contents", d1reader, TermVector.YES));
			writer.addDocument(doc1);
		}


		//  writer.commit();
		writer.close();

		DocVector[] docs = new DocVector[fileSentences.size()];
		//Build a term vector for each document
		IndexReader RAMreader = IndexReader.open(ramDir);
		Map<String,Integer> terms = new HashMap<String,Integer>();
		TermEnum termEnum = RAMreader.terms(new Term("contents"));

		//System.out.println(RAMreader.numDocs());
		int pos = 0;
	    while (termEnum.next()) {
	      Term term = termEnum.term();
	      if (!"contents".equals(term.field())) 
	        break;
	      terms.put(term.text(), pos++);
	    }
	    
	    //System.out.println("Num terms:"+terms.size());
   
		for(int i=0;i<fileSentences.size();i++)
		{
			TermFreqVector[] tfvs = RAMreader.getTermFreqVectors(i);
			docs[i]=new DocVector(terms);
			if (tfvs==null)
					continue;
			for (TermFreqVector tfv : tfvs) 
			{
				String[] termTexts = tfv.getTerms();
				int[] termFreqs = tfv.getTermFrequencies();
				for (int j = 0; j < termTexts.length; j++) {
					double idfValue=getIDF(RAMreader,termTexts[j]);
					double tfIdfValue=termFreqs[j]*idfValue;
					docs[i].setEntry(termTexts[j], tfIdfValue);
				}
			       
			}
			docs[i].normalize();
			
			
		}
		

		RAMreader.close();
		ramDir.close();
		//ramDir.close();
		//System.out.println(RAMreader.numDocs());
		//System.out.println("Similarity:" + calcCosineSimilarity(docs[5], docs[19]));
		return docs;

	}
 

import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
private static void addNonDictionaryField(String name, Store store, Index index, TermVector termVector, boolean multiValued)
{
    nonDictionaryFields.put(name, new NonDictionaryField(name, store, index, termVector, multiValued));
}
 

import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
private static void addAdditionalContentField(String name, Store store, Index index, TermVector termVector, boolean multiValued)
{
    additionalContentFields.put(name, new NonDictionaryField(name, store, index, termVector, multiValued));
}
 

import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
private static void addAdditionalTextField(String name, Store store, Index index, TermVector termVector, boolean multiValued)
{
    additionalTextFields.put(name, new NonDictionaryField(name, store, index, termVector, multiValued));
}
 

import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
private static void addAdditionalMlTextField(String name, Store store, Index index, TermVector termVector, boolean multiValued)
{
    additionalMlTextFields.put(name, new NonDictionaryField(name, store, index, termVector, multiValued));
}
 

import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
/**
 * @param field
 * @return
 */
public TermVector getFieldTermVec(SchemaField field)
{
    return TermVector.NO;
}
 

import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
@Override
protected TermVector getFieldTermVec(SchemaField field, String internalVal)
{
    return AlfrescoSolrDataModel.getInstance(id).getFieldTermVec(field);
}
 

import org.apache.lucene.document.Field.TermVector; //导入依赖的package包/类
public double run(String doc1,String doc2) throws IOException 
    {
        // index strings
    	s[0]=doc1;
    	s[1]=doc2;
    	//System.out.print(s[0]+"\n"+s[1]+"\n");
        Directory index = new RAMDirectory();
        StandardAnalyzer analyzer = new StandardAnalyzer(Version.LUCENE_36);
        IndexWriterConfig config = new IndexWriterConfig(Version.LUCENE_36, analyzer);
        IndexWriter writer = new IndexWriter(index, config);        
        for (String si : s) {
            Document doc = new Document();
            doc.add(new Field("content", si, Field.Store.YES, Field.Index.ANALYZED,TermVector.WITH_POSITIONS_OFFSETS));
            writer.addDocument(doc);
        }
        writer.close();
        
        // read the index
        IndexReader reader = IndexReader.open(index);
        
        // calculate tf/idf
        Map<String,Integer> terms = new HashMap<String,Integer>();
        TermEnum termEnum = reader.terms(new Term("content"));
        int pos = 0;
        while (termEnum.next()) {
            Term term = termEnum.term();
            if (! "content".equals(term.field())) break;
                terms.put(term.text(), pos++);
        }
        
//        for (int i=0; i<reader.maxDoc(); i++) {
//            if (reader.isDeleted(i))
//                continue;
//
//            Document doc = reader.document(i);
//            System.out.println(doc);
//            TermFreqVector tfvs = reader.getTermFreqVector(i,"content");
//            System.out.println(tfvs);
//        }
//        
        // apply cosine similarity
        DocVector[] docs = new DocVector[s.length];
        for (int i=0; i<s.length; i++) {
            TermFreqVector[] tfvs = reader.getTermFreqVectors(i);
            //String strip_str=tfvs.toString();
            //strip_str.replaceAll("null", "");
            
            docs[i] = new DocVector(terms); 
            //System.out.print(tfvs);
        //}
            
           for (TermFreqVector tfv : tfvs) {
                String[] termTexts = tfv.getTerms();
                int[] termFreqs = tfv.getTermFrequencies();
                for (int j = 0; j < termTexts.length; j++) {
                docs[i].setEntry(termTexts[j], termFreqs[j]);
              }
            }
            docs[i].normalize();
           
          }
        
        // now get similarity between doc[0] and doc[1]
        double cosim01 = getCosineSimilarity(docs[0], docs[1]);
        //System.out.println("cosim(0,1)=" + cosim01);
        // between doc[0] and doc[2]
       // double cosim02 = getCosineSimilarity(docs[0], docs[3]);
        //System.out.println("cosim(0,2)=" + cosim02);
        // between doc[1] and doc[3]
        //double cosim03 = getCosineSimilarity(docs[1], docs[2]);
        //System.out.println("cosim(1,2)=" + cosim03);
       
       // }
        //double cosim01=10.0;
        reader.close();
        return cosim01;
    }