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 String 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 >
* <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);
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 > 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.charAt(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.
}
}