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.IndexReader;
  import org.apache.lucene.index.Term;
  
  /** Subclass of FilteredTermEnum for enumerating all terms that are similar
   * to the specified filter term.
   *
   * <p>Term enumerations are always ordered by Term.compareTo().  Each term in
   * the enumeration is greater than all that precede it.
   */
  public final class FuzzyTermEnum extends FilteredTermEnum {
  
    /* Allows us save time required to create a new array
     * every time similarity is called.
     */
    private int[] p;
    private int[] d;
  
    private float similarity;
    private boolean endEnum = false;
  
    private Term searchTerm = null;
    private final String field;
    private final char[] text;
    private final String prefix;
  
    private final float minimumSimilarity;
    private final float scale_factor;
  
    /**
     * Creates a FuzzyTermEnum with an empty prefix and a minSimilarity of 0.5f.
     * <p>
     * After calling the constructor the enumeration is already pointing to the first 
     * valid term if such a term exists. 
     * 
     * @param reader
     * @param term
     * @throws IOException
     * @see #FuzzyTermEnum(IndexReader, Term, float, int)
     */
    public FuzzyTermEnum(IndexReader reader, Term term) throws IOException {
      this(reader, term, FuzzyQuery.defaultMinSimilarity, FuzzyQuery.defaultPrefixLength);
    }
      
    /**
     * Creates a FuzzyTermEnum with an empty prefix.
     * <p>
     * After calling the constructor the enumeration is already pointing to the first 
     * valid term if such a term exists. 
     * 
     * @param reader
     * @param term
     * @param minSimilarity
     * @throws IOException
     * @see #FuzzyTermEnum(IndexReader, Term, float, int)
     */
    public FuzzyTermEnum(IndexReader reader, Term term, float minSimilarity) throws IOException {
      this(reader, term, minSimilarity, FuzzyQuery.defaultPrefixLength);
    }
      
    /**
     * Constructor for enumeration of all terms from specified <code>reader</code> which share a prefix of
     * length <code>prefixLength</code> with <code>term</code> and which have a fuzzy similarity &gt;
     * <code>minSimilarity</code>.
     * <p>
     * After calling the constructor the enumeration is already pointing to the first 
     * valid term if such a term exists. 
     * 
     * @param reader Delivers terms.
     * @param term Pattern term.
     * @param minSimilarity Minimum required similarity for terms from the reader. Default value is 0.5f.
     * @param prefixLength Length of required common prefix. Default value is 0.
     * @throws IOException
     */
    public FuzzyTermEnum(IndexReader reader, Term term, final float minSimilarity, final int prefixLength) throws IOException {
      super();
      
      if (minSimilarity >= 1.0f)
        throw new IllegalArgumentException("minimumSimilarity cannot be greater than or equal to 1");
      else if (minSimilarity < 0.0f)
        throw new IllegalArgumentException("minimumSimilarity cannot be less than 0");
      if(prefixLength < 0)
        throw new IllegalArgumentException("prefixLength cannot be less than 0");
  
      this.minimumSimilarity = minSimilarity;
      this.scale_factor = 1.0f / (1.0f - minimumSimilarity);
      this.searchTerm = term;
      this.field = searchTerm.field();
  
      //The prefix could be longer than the word.
      //It's kind of silly though.  It means we must match the entire word.
      final int fullSearchTermLength = searchTerm.text().length();
      final int realPrefixLength = prefixLength > fullSearchTermLength ? fullSearchTermLength : prefixLength;
  
      this.text = searchTerm.text().substring(realPrefixLength).toCharArray();
      this.prefix = searchTerm.text().substring(0, realPrefixLength);
  
      this.p = new int[this.text.length+1];
      this.d = new int[this.text.length+1];
  
      setEnum(reader.terms(new Term(searchTerm.field(), prefix)));
    }
  
    /**
     * The termCompare method in FuzzyTermEnum uses Levenshtein distance to 
     * calculate the distance between the given term and the comparing term. 
     */
    @Override
    protected final boolean termCompare(Term term) {
      if (field == term.field() && term.text().startsWith(prefix)) {
          final String target = term.text().substring(prefix.length());
          this.similarity = similarity(target);
          return (similarity > minimumSimilarity);
      }
      endEnum = true;
      return false;
    }
    
    /** {@inheritDoc} */
    @Override
    public final float difference() {
      return (similarity - minimumSimilarity) * scale_factor;
    }
    
    /** {@inheritDoc} */
    @Override
    public final boolean endEnum() {
      return endEnum;
    }
    
    /******************************
     * Compute Levenshtein distance
     ******************************/
    
    /**
     * <p>Similarity returns a number that is 1.0f or less (including negative numbers)
     * based on how similar the Term is compared to a target term.  It returns
     * exactly 0.0f when
     * <pre>
     *    editDistance &gt; maximumEditDistance</pre>
     * Otherwise it returns:
     * <pre>
     *    1 - (editDistance / length)</pre>
     * where length is the length of the shortest term (text or target) including a
     * prefix that are identical and editDistance is the Levenshtein distance for
     * the two words.</p>
     *
     * <p>Embedded within this algorithm is a fail-fast Levenshtein distance
     * algorithm.  The fail-fast algorithm differs from the standard Levenshtein
     * distance algorithm in that it is aborted if it is discovered that the
     * minimum distance between the words is greater than some threshold.
     *
     * <p>To calculate the maximum distance threshold we use the following formula:
     * <pre>
     *     (1 - minimumSimilarity) * length</pre>
     * where length is the shortest term including any prefix that is not part of the
     * similarity comparison.  This formula was derived by solving for what maximum value
     * of distance returns false for the following statements:
     * <pre>
     *   similarity = 1 - ((float)distance / (float) (prefixLength + Math.min(textlen, targetlen)));
     *   return (similarity > minimumSimilarity);</pre>
     * where distance is the Levenshtein distance for the two words.
     * </p>
     * <p>Levenshtein distance (also known as edit distance) is a measure of similarity
     * between two strings where the distance is measured as the number of character
     * deletions, insertions or substitutions required to transform one string to
     * the other string.
     * @param target the target word or phrase
     * @return the similarity,  0.0 or less indicates that it matches less than the required
     * threshold and 1.0 indicates that the text and target are identical
     */
    private float similarity(final String target) {
      final int m = target.length();
      final int n = text.length;
      if (n == 0)  {
        //we don't have anything to compare.  That means if we just add
        //the letters for m we get the new word
        return prefix.length() == 0 ? 0.0f : 1.0f - ((float) m / prefix.length());
      }
      if (m == 0) {
        return prefix.length() == 0 ? 0.0f : 1.0f - ((float) n / prefix.length());
      }
  
      final int maxDistance = calculateMaxDistance(m);
  
      if (maxDistance < Math.abs(m-n)) {
        //just adding the characters of m to n or vice-versa results in
        //too many edits
        //for example "pre" length is 3 and "prefixes" length is 8.  We can see that
        //given this optimal circumstance, the edit distance cannot be less than 5.
        //which is 8-3 or more precisely Math.abs(3-8).
        //if our maximum edit distance is 4, then we can discard this word
        //without looking at it.
        return 0.0f;
      }
  
      // init matrix d
      for (int i = 0; i<=n; ++i) {
        p[i] = i;
      }
  
      // start computing edit distance
      for (int j = 1; j<=m; ++j) { // iterates through target
        int bestPossibleEditDistance = m;
        final char t_j = target.charAt(j-1); // jth character of t
        d[0] = j;
  
        for (int i=1; i<=n; ++i) { // iterates through text
          // minimum of cell to the left+1, to the top+1, diagonally left and up +(0|1)
          if (t_j != text[i-1]) {
            d[i] = Math.min(Math.min(d[i-1], p[i]),  p[i-1]) + 1;
  		} else {
            d[i] = Math.min(Math.min(d[i-1]+1, p[i]+1),  p[i-1]);
  		}
          bestPossibleEditDistance = Math.min(bestPossibleEditDistance, d[i]);
        }
  
        //After calculating row i, the best possible edit distance
        //can be found by found by finding the smallest value in a given column.
        //If the bestPossibleEditDistance is greater than the max distance, abort.
  
        if (j > maxDistance && bestPossibleEditDistance > maxDistance) {  //equal is okay, but not greater
          //the closest the target can be to the text is just too far away.
          //this target is leaving the party early.
          return 0.0f;
        }
  
        // copy current distance counts to 'previous row' distance counts: swap p and d
        int _d[] = p;
        p = d;
        d = _d;
      }
  
      // our last action in the above loop was to switch d and p, so p now
      // actually has the most recent cost counts
  
      // this will return less than 0.0 when the edit distance is
      // greater than the number of characters in the shorter word.
      // but this was the formula that was previously used in FuzzyTermEnum,
      // so it has not been changed (even though minimumSimilarity must be
      // greater than 0.0)
      return 1.0f - ((float)p[n] / (float) (prefix.length() + Math.min(n, m)));
    }
  
    /**
     * The max Distance is the maximum Levenshtein distance for the text
     * compared to some other value that results in score that is
     * better than the minimum similarity.
     * @param m the length of the "other value"
     * @return the maximum levenshtein distance that we care about
     */
    private int calculateMaxDistance(int m) {
      return (int) ((1-minimumSimilarity) * (Math.min(text.length, m) + prefix.length()));
    }
  
    /** {@inheritDoc} */
    @Override
    public void close() throws IOException {
      p = d = null;
      searchTerm = null;
      super.close();  //call super.close() and let the garbage collector do its work.
    }
    
  }
  