Examples of org.apache.lucene.index.Term.text()

org.apache.lucene.index.Term.text()
Returns the text of this term. In the case of words, this is simply the text of the word. In the case of dates and other types, this is an encoding of the object as a string.

      TermEnum termEnum = reader.terms (new Term (field));
      try {
        do {
          Term term = termEnum.term();
          if (term==null || term.field() != field) break;
          double termval = parser.parseDouble(term.text());
          if (retArray == null) // late init
            retArray = new double[reader.maxDoc()];
          termDocs.seek (termEnum);
          while (termDocs.next()) {
            retArray[termDocs.doc()] = termval;

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    try {
      int count = 0;
      do {
        final Term t = termEnum.term();
        if (t != null) {
          final int val = NumericUtils.prefixCodedToInt(t.text());
          assertTrue("value not in bounds", val >= lower && val <= upper);
          count++;
        } else break;
      } while (termEnum.next());
      assertFalse(termEnum.next());

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          query.append(" ");
        }


        Term term = terms.get(i);


        query.append(term.text());
      }
      query.append('\"');


      Explanation idfExpl =
        new Explanation(idf, "idf(" + field + ":" + docFreqs + ")");

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      }
      int df = te.docFreq();
      if (df < minDf || df > percent){
        continue;
      }
      TermEntry entry = new TermEntry(term.text(), count++, df);
      termEntries.put(entry.term, entry);
    } while (te.next());
    te.close();
  }

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      // AND the term's bitset with cluster doc bitset to get the term's in-cluster frequency.
      // This modifies the termBitset, but that's fine as we are not using it anywhere else.
      termBitset.and(clusterDocBitset);
      int inclusterDF = (int) termBitset.cardinality();
      
      TermEntry entry = new TermEntry(term.text(), count++, inclusterDF);
      termEntryMap.put(entry.term, entry);
    } while (te.next());
    te.close();
    
    List<TermInfoClusterInOut> clusteredTermInfo = new LinkedList<TermInfoClusterInOut>();

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                for (TermEnum terms : rangeScans) {
                    do {
                        Term t = terms.term();
                        if (t != null) {
                            currentTerm.setBase(t.text());
                            int compare = currentTerm.compareTo(termText);
                            if (compare == 0) {
                                orderedTerms.put(t, terms.docFreq());
                            } else if (compare < 0) {
                                // try next one

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                           {
                              num = idx.removeDocument(idTerm);
                              if (num > 0)
                              {
                                 if (log.isDebugEnabled())
                                    log.debug(idTerm.text() + " has been found in the persisted index " + i);
                                 break;
                              }
                           }
                        }
                     }

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                           }
                        }
                     }
                     else if (log.isDebugEnabled())
                     {
                        log.debug(idTerm.text() + " has been found in the volatile index");
                     }
                  }


                  // try to avoid getting index reader for each doc
                  IndexReader indexReader = null;

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            // there are always two range scanse: one with an initial
            // lower case character and another one with an initial upper case
            // character
            List rangeScans = new ArrayList(2);
            nameLength = FieldNames.getNameLength(term.text());
            String propName = term.text().substring(0, nameLength);
            this.termText = new OffsetCharSequence(nameLength, term.text());
            this.currentTerm = new OffsetCharSequence(nameLength, term.text(), transform);


            try {

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            // there are always two range scanse: one with an initial
            // lower case character and another one with an initial upper case
            // character
            List rangeScans = new ArrayList(2);
            nameLength = FieldNames.getNameLength(term.text());
            String propName = term.text().substring(0, nameLength);
            this.termText = new OffsetCharSequence(nameLength, term.text());
            this.currentTerm = new OffsetCharSequence(nameLength, term.text(), transform);


            try {
                // start with a term using the lower case character for the first

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