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Implementation of various string similarity and distance algorithms: Levenshtein, Jaro-winkler, n-Gram, Q-Gram, Jaccard index, Longest Common Subsequence edit distance, cosine similarity...
/*
* The MIT License
*
* Copyright 2015 Thibault Debatty.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
package info.debatty.java.stringsimilarity;
import info.debatty.java.stringsimilarity.interfaces.StringDistance;
/**
* Implementation of Levenshtein that allows to define different weights for
* different character substitutions.
*
* @author Thibault Debatty
*/
public class WeightedLevenshtein implements StringDistance {
public static void main(String[] args) {
WeightedLevenshtein wl = new WeightedLevenshtein(
new CharacterSubstitutionInterface() {
public double cost(char c1, char c2) {
// The cost for substituting 't' and 'r' is considered
// smaller as these 2 are located next to each other
// on a keyboard
if (c1 == 't' && c2 == 'r') {
return 0.5;
}
// For most cases, the cost of substituting 2 characters
// is 1.0
return 1.0;
}
});
System.out.println(wl.distance("String1", "Srring2"));
}
private final CharacterSubstitutionInterface charsub;
public WeightedLevenshtein(CharacterSubstitutionInterface charsub) {
this.charsub = charsub;
}
public double distance(String s1, String s2) {
if (s1.equals(s2)){
return 0;
}
if (s1.length() == 0) {
return s2.length();
}
if (s2.length() == 0) {
return s1.length();
}
// create two work vectors of integer distances
double[] v0 = new double[s2.length() + 1];
double[] v1 = new double[s2.length() + 1];
double[] vtemp;
// initialize v0 (the previous row of distances)
// this row is A[0][i]: edit distance for an empty s
// the distance is just the number of characters to delete from t
for (int i = 0; i < v0.length; i++) {
v0[i] = i;
}
for (int i = 0; i < s1.length(); i++) {
// calculate v1 (current row distances) from the previous row v0
// first element of v1 is A[i+1][0]
// edit distance is delete (i+1) chars from s to match empty t
v1[0] = i + 1;
// use formula to fill in the rest of the row
for (int j = 0; j < s2.length(); j++) {
double cost = (s1.charAt(i) == s2.charAt(j)) ? 0 : charsub.cost(s1.charAt(i), s2.charAt(j));
v1[j + 1] = Math.min(
v1[j] + 1, // Cost of insertion
Math.min(
v0[j + 1] + 1, // Cost of remove
v0[j] + cost)); // Cost of substitution
}
// copy v1 (current row) to v0 (previous row) for next iteration
//System.arraycopy(v1, 0, v0, 0, v0.length);
// Flip references to current and previous row
vtemp = v0;
v0 = v1;
v1 = vtemp;
}
return v0[s2.length()];
}
}