package org.apache.lucene.sandbox.queries;
/*
* 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.Term;
import org.apache.lucene.index.Terms;
import org.apache.lucene.index.TermsEnum;
import org.apache.lucene.index.FilteredTermsEnum;
import org.apache.lucene.search.BoostAttribute;
import org.apache.lucene.search.FuzzyTermsEnum;
import org.apache.lucene.util.AttributeSource;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.IntsRef;
import org.apache.lucene.util.StringHelper;
import org.apache.lucene.util.UnicodeUtil;
/** Potentially slow fuzzy TermsEnum for enumerating all terms that are similar
* to the specified filter term.
* <p> If the minSimilarity or maxEdits is greater than the Automaton's
* allowable range, this backs off to the classic (brute force)
* fuzzy terms enum method by calling FuzzyTermsEnum's getAutomatonEnum.
* </p>
* <p>Term enumerations are always ordered by
* {@link BytesRef#compareTo}. Each term in the enumeration is
* greater than all that precede it.</p>
*
* @deprecated Use {@link FuzzyTermsEnum} instead.
*/
@Deprecated
public final class SlowFuzzyTermsEnum extends FuzzyTermsEnum {
public SlowFuzzyTermsEnum(Terms terms, AttributeSource atts, Term term,
float minSimilarity, int prefixLength) throws IOException {
super(terms, atts, term, minSimilarity, prefixLength, false);
}
@Override
protected void maxEditDistanceChanged(BytesRef lastTerm, int maxEdits, boolean init)
throws IOException {
TermsEnum newEnum = getAutomatonEnum(maxEdits, lastTerm);
if (newEnum != null) {
setEnum(newEnum);
} else if (init) {
setEnum(new LinearFuzzyTermsEnum());
}
}
/**
* Implement fuzzy enumeration with linear brute force.
*/
private class LinearFuzzyTermsEnum extends FilteredTermsEnum {
/* Allows us save time required to create a new array
* every time similarity is called.
*/
private int[] d;
private int[] p;
// this is the text, minus the prefix
private final int[] text;
private final BoostAttribute boostAtt =
attributes().addAttribute(BoostAttribute.class);
/**
* 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.
*
* @throws IOException If there is a low-level I/O error.
*/
public LinearFuzzyTermsEnum() throws IOException {
super(terms.iterator(null));
this.text = new int[termLength - realPrefixLength];
System.arraycopy(termText, realPrefixLength, text, 0, text.length);
final String prefix = UnicodeUtil.newString(termText, 0, realPrefixLength);
prefixBytesRef = new BytesRef(prefix);
this.d = new int[this.text.length + 1];
this.p = new int[this.text.length + 1];
setInitialSeekTerm(prefixBytesRef);
}
private final BytesRef prefixBytesRef;
// used for unicode conversion from BytesRef byte[] to int[]
private final IntsRef utf32 = new IntsRef(20);
/**
* <p>The termCompare method in FuzzyTermEnum uses Levenshtein distance to
* calculate the distance between the given term and the comparing term.
* </p>
* <p>If the minSimilarity is >= 1.0, this uses the maxEdits as the comparison.
* Otherwise, this method uses the following logic to calculate similarity.
* <pre>
* similarity = 1 - ((float)distance / (float) (prefixLength + Math.min(textlen, targetlen)));
* </pre>
* where distance is the Levenshtein distance for the two words.
* </p>
*
*/
@Override
protected final AcceptStatus accept(BytesRef term) {
if (StringHelper.startsWith(term, prefixBytesRef)) {
UnicodeUtil.UTF8toUTF32(term, utf32);
final int distance = calcDistance(utf32.ints, realPrefixLength, utf32.length - realPrefixLength);
//Integer.MIN_VALUE is the sentinel that Levenshtein stopped early
if (distance == Integer.MIN_VALUE){
return AcceptStatus.NO;
}
//no need to calc similarity, if raw is true and distance > maxEdits
if (raw == true && distance > maxEdits){
return AcceptStatus.NO;
}
final float similarity = calcSimilarity(distance, (utf32.length - realPrefixLength), text.length);
//if raw is true, then distance must also be <= maxEdits by now
//given the previous if statement
if (raw == true ||
(raw == false && similarity > minSimilarity)) {
boostAtt.setBoost((similarity - minSimilarity) * scale_factor);
return AcceptStatus.YES;
} else {
return AcceptStatus.NO;
}
} else {
return AcceptStatus.END;
}
}
/******************************
* Compute Levenshtein distance
******************************/
/**
* <p>calcDistance returns the Levenshtein distance between the query term
* and the target term.</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>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
* @param offset the offset at which to start the comparison
* @param length the length of what's left of the string to compare
* @return the number of edits or Integer.MIN_VALUE if the edit distance is
* greater than maxDistance.
*/
private final int calcDistance(final int[] target, int offset, int length) {
final int m = 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 m;
}
if (m == 0) {
return n;
}
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 Integer.MIN_VALUE;
}
// 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 int t_j = target[offset+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 Integer.MIN_VALUE;
}
// 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
return p[n];
}
private float calcSimilarity(int edits, int m, int n){
// 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)edits / (float) (realPrefixLength + 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 raw ? maxEdits : Math.min(maxEdits,
(int)((1-minSimilarity) * (Math.min(text.length, m) + realPrefixLength)));
}
}
}