package com.flaptor.org.apache.lucene.util.automaton; /** * 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.util.Iterator; import java.util.SortedSet; import java.util.TreeSet; /** * Class to construct DFAs that match a word within some edit distance. * <p> * Implements the algorithm described in: * Schulz and Mihov: Fast String Correction with Levenshtein Automata * <p> * @lucene.experimental */ public class LevenshteinAutomata { /** @lucene.internal */ public static final int MAXIMUM_SUPPORTED_DISTANCE = 2; /* input word */ final String input; final int word[]; /* the automata alphabet. */ final int alphabet[]; /* the unicode ranges outside of alphabet */ final int rangeLower[]; final int rangeUpper[]; int numRanges = 0; ParametricDescription descriptions[]; /** * Create a new LevenshteinAutomata for some input String. */ public LevenshteinAutomata(String input) { this.input = input; int length = Character.codePointCount(input, 0, input.length()); word = new int[length]; for (int i = 0, j = 0, cp = 0; i < input.length(); i += Character.charCount(cp)) { word[j++] = cp = input.codePointAt(i); } // calculate the alphabet SortedSet<Integer> set = new TreeSet<Integer>(); for (int i = 0; i < word.length; i++) set.add(word[i]); alphabet = new int[set.size()]; Iterator<Integer> iterator = set.iterator(); for (int i = 0; i < alphabet.length; i++) alphabet[i] = iterator.next(); rangeLower = new int[alphabet.length + 2]; rangeUpper = new int[alphabet.length + 2]; // calculate the unicode range intervals that exclude the alphabet // these are the ranges for all unicode characters not in the alphabet int lower = 0; for (int i = 0; i < alphabet.length; i++) { int higher = alphabet[i]; if (higher > lower) { rangeLower[numRanges] = lower; rangeUpper[numRanges] = higher - 1; numRanges++; } lower = higher + 1; } /* add the final endpoint */ if (lower <= Character.MAX_CODE_POINT) { rangeLower[numRanges] = lower; rangeUpper[numRanges] = Character.MAX_CODE_POINT; numRanges++; } descriptions = new ParametricDescription[] { null, /* for n=0, we do not need to go through the trouble */ new Lev1ParametricDescription(word.length), new Lev2ParametricDescription(word.length), }; } /** * Compute a DFA that accepts all strings within an edit distance of <code>n</code>. * <p> * All automata have the following properties: * <ul> * <li>They are deterministic (DFA). * <li>There are no transitions to dead states. * <li>They are not minimal (some transitions could be combined). * </ul> * </p> */ public Automaton toAutomaton(int n) { if (n == 0) return BasicAutomata.makeString(input); if (n >= descriptions.length) return null; final int range = 2*n+1; ParametricDescription description = descriptions[n]; // the number of states is based on the length of the word and n State states[] = new State[description.size()]; // create all states, and mark as accept states if appropriate for (int i = 0; i < states.length; i++) { states[i] = new State(); states[i].number = i; states[i].setAccept(description.isAccept(i)); } // create transitions from state to state for (int k = 0; k < states.length; k++) { final int xpos = description.getPosition(k); if (xpos < 0) continue; final int end = xpos + Math.min(word.length - xpos, range); for (int x = 0; x < alphabet.length; x++) { final int ch = alphabet[x]; // get the characteristic vector at this position wrt ch final int cvec = getVector(ch, xpos, end); int dest = description.transition(k, xpos, cvec); if (dest >= 0) states[k].addTransition(new Transition(ch, states[dest])); } // add transitions for all other chars in unicode // by definition, their characteristic vectors are always 0, // because they do not exist in the input string. int dest = description.transition(k, xpos, 0); // by definition if (dest >= 0) for (int r = 0; r < numRanges; r++) states[k].addTransition(new Transition(rangeLower[r], rangeUpper[r], states[dest])); } Automaton a = new Automaton(states[0]); a.setDeterministic(true); // we create some useless unconnected states, and its a net-win overall to remove these, // as well as to combine any adjacent transitions (it makes later algorithms more efficient). // so, while we could set our numberedStates here, its actually best not to, and instead to // force a traversal in reduce, pruning the unconnected states while we combine adjacent transitions. //a.setNumberedStates(states); a.reduce(); // we need not trim transitions to dead states, as they are not created. //a.restoreInvariant(); return a; } /** * Get the characteristic vector <code>X(x, V)</code> * where V is <code>substring(pos, end)</code> */ int getVector(int x, int pos, int end) { int vector = 0; for (int i = pos; i < end; i++) { vector <<= 1; if (word[i] == x) vector |= 1; } return vector; } /** * A ParametricDescription describes the structure of a Levenshtein DFA for some degree n. * <p> * There are four components of a parametric description, all parameterized on the length * of the word <code>w</code>: * <ol> * <li>The number of states: {@link #size()} * <li>The set of final states: {@link #isAccept(int)} * <li>The transition function: {@link #transition(int, int, int)} * <li>Minimal boundary function: {@link #getPosition(int)} * </ol> */ static abstract class ParametricDescription { protected final int w; protected final int n; private final int[] minErrors; ParametricDescription(int w, int n, int[] minErrors) { this.w = w; this.n = n; this.minErrors = minErrors; } /** * Return the number of states needed to compute a Levenshtein DFA */ int size() { return minErrors.length * (w+1); }; /** * Returns true if the <code>state</code> in any Levenshtein DFA is an accept state (final state). */ boolean isAccept(int absState) { // decode absState -> state, offset int state = absState/(w+1); int offset = absState%(w+1); assert offset >= 0; return w - offset + minErrors[state] <= n; } /** * Returns the position in the input word for a given <code>state</code>. * This is the minimal boundary for the state. */ int getPosition(int absState) { return absState % (w+1); } /** * Returns the state number for a transition from the given <code>state</code>, * assuming <code>position</code> and characteristic vector <code>vector</code> */ abstract int transition(int state, int position, int vector); private final static long[] MASKS = new long[] {0x1,0x3,0x7,0xf, 0x1f,0x3f,0x7f,0xff, 0x1ff,0x3ff,0x7ff,0xfff, 0x1fff,0x3fff,0x7fff,0xffff, 0x1ffff,0x3ffff,0x7ffff,0xfffff, 0x1fffff,0x3fffff,0x7fffff,0xffffff, 0x1ffffff,0x3ffffff,0x7ffffff,0xfffffff, 0x1fffffff,0x3fffffff,0x7fffffffL,0xffffffffL, 0x1ffffffffL,0x3ffffffffL,0x7ffffffffL,0xfffffffffL, 0x1fffffffffL,0x3fffffffffL,0x7fffffffffL,0xffffffffffL, 0x1ffffffffffL,0x3ffffffffffL,0x7ffffffffffL,0xfffffffffffL, 0x1fffffffffffL,0x3fffffffffffL,0x7fffffffffffL,0xffffffffffffL, 0x1ffffffffffffL,0x3ffffffffffffL,0x7ffffffffffffL,0xfffffffffffffL, 0x1fffffffffffffL,0x3fffffffffffffL,0x7fffffffffffffL,0xffffffffffffffL, 0x1ffffffffffffffL,0x3ffffffffffffffL,0x7ffffffffffffffL,0xfffffffffffffffL, 0x1fffffffffffffffL,0x3fffffffffffffffL,0x7fffffffffffffffL}; protected int unpack(long[] data, int index, int bitsPerValue) { final long bitLoc = bitsPerValue * index; final int dataLoc = (int) (bitLoc >> 6); final int bitStart = (int) (bitLoc & 63); //System.out.println("index=" + index + " dataLoc=" + dataLoc + " bitStart=" + bitStart + " bitsPerV=" + bitsPerValue); if (bitStart + bitsPerValue <= 64) { // not split return (int) ((data[dataLoc] >> bitStart) & MASKS[bitsPerValue-1]); } else { // split final int part = 64-bitStart; return (int) (((data[dataLoc] >> bitStart) & MASKS[part-1]) + ((data[1+dataLoc] & MASKS[bitsPerValue-part-1]) << part)); } } } }