content: package org.apache.lucene.search;
  
  /**
   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements.  See the NOTICE file distributed with
   * this work for additional information regarding copyright ownership.
   * The ASF licenses this file to You under the Apache License, Version 2.0
   * (the "License"); you may not use this file except in compliance with
   * the License.  You may obtain a copy of the License at
   *
   *     http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  import java.io.IOException;
  
  import org.apache.lucene.index.TermDocs;
  
  /** Expert: A <code>Scorer</code> for documents matching a <code>Term</code>.
   */
  final class TermScorer extends Scorer {
    private final TermDocs termDocs;
    private final byte[] norms;
    private float weightValue;
    private int doc = -1;
    private int freq;
  
    private final int[] docs = new int[32];         // buffered doc numbers
    private final int[] freqs = new int[32];        // buffered term freqs
    private int pointer;
    private int pointerMax;
  
    private static final int SCORE_CACHE_SIZE = 32;
    private final float[] scoreCache = new float[SCORE_CACHE_SIZE];
  
    /**
     * Construct a <code>TermScorer</code>.
     * 
     * @param weight
     *          The weight of the <code>Term</code> in the query.
     * @param td
     *          An iterator over the documents matching the <code>Term</code>.
     * @param similarity
     *          The </code>Similarity</code> implementation to be used for score
     *          computations.
     * @param norms
     *          The field norms of the document fields for the <code>Term</code>.
     */
    TermScorer(Weight weight, TermDocs td, Similarity similarity, byte[] norms) {
      super(similarity, weight);
      
      this.termDocs = td;
      this.norms = norms;
      this.weightValue = weight.getValue();
  
      for (int i = 0; i < SCORE_CACHE_SIZE; i++)
        scoreCache[i] = getSimilarity().tf(i) * weightValue;
    }
  
    @Override
    public void score(Collector c) throws IOException {
      score(c, Integer.MAX_VALUE, nextDoc());
    }
  
    // firstDocID is ignored since nextDoc() sets 'doc'
    @Override
    protected boolean score(Collector c, int end, int firstDocID) throws IOException {
      c.setScorer(this);
      while (doc < end) {                           // for docs in window
        c.collect(doc);                      // collect score
          
        if (++pointer >= pointerMax) {
          pointerMax = termDocs.read(docs, freqs);  // refill buffers
          if (pointerMax != 0) {
            pointer = 0;
          } else {
            termDocs.close();                       // close stream
            doc = Integer.MAX_VALUE;                // set to sentinel value
            return false;
          }
        } 
        doc = docs[pointer];
        freq = freqs[pointer];
      }
      return true;
    }
  
    @Override
    public int docID() { return doc; }
  
    @Override
    public float freq() {
      return freq;
    }
  
    /**
     * Advances to the next document matching the query. <br>
     * The iterator over the matching documents is buffered using
     * {@link TermDocs#read(int[],int[])}.
     * 
     * @return the document matching the query or NO_MORE_DOCS if there are no more documents.
     */
    @Override
    public int nextDoc() throws IOException {
      pointer++;
      if (pointer >= pointerMax) {
        pointerMax = termDocs.read(docs, freqs);    // refill buffer
        if (pointerMax != 0) {
          pointer = 0;
        } else {
          termDocs.close();                         // close stream
          return doc = NO_MORE_DOCS;
        }
      } 
      doc = docs[pointer];
      freq = freqs[pointer];
      return doc;
    }
    
    @Override
    public float score() {
      assert doc != -1;
      float raw =                                   // compute tf(f)*weight
        freq < SCORE_CACHE_SIZE                        // check cache
        ? scoreCache[freq]                             // cache hit
        : getSimilarity().tf(freq)*weightValue;        // cache miss
  
      return norms == null ? raw : raw * getSimilarity().decodeNormValue(norms[doc]); // normalize for field
    }
  
    /**
     * Advances to the first match beyond the current whose document number is
     * greater than or equal to a given target. <br>
     * The implementation uses {@link TermDocs#skipTo(int)}.
     * 
     * @param target
     *          The target document number.
     * @return the matching document or NO_MORE_DOCS if none exist.
     */
    @Override
    public int advance(int target) throws IOException {
      // first scan in cache
      for (pointer++; pointer < pointerMax; pointer++) {
        if (docs[pointer] >= target) {
      	freq = freqs[pointer];
          return doc = docs[pointer];
        }
      }
  
      // not found in cache, seek underlying stream
      boolean result = termDocs.skipTo(target);
      if (result) {
        pointerMax = 1;
        pointer = 0;
        docs[pointer] = doc = termDocs.doc();
        freqs[pointer] = freq = termDocs.freq();
      } else {
        doc = NO_MORE_DOCS;
      }
      return doc;
    }
    
    /** Returns a string representation of this <code>TermScorer</code>. */
    @Override
    public String toString() { return "scorer(" + weight + ")"; }
  
  }
  