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/*
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 * Simmetrics Core
 * %%
 * Copyright (C) 2014 - 2016 Simmetrics Authors
 * %%
 * Licensed 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
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 */

package org.simmetrics.metrics;

import static com.google.common.base.Preconditions.checkArgument;
import static java.lang.Math.max;
import static org.simmetrics.metrics.Math.max;
import static org.simmetrics.metrics.Math.min;

import org.simmetrics.StringDistance;
import org.simmetrics.StringMetric;

/**
 * Calculates the Damerau-Levenshtein similarity and distance measure between
 * two strings.
 * 

* Insert/delete, substitute and transpose operations can be weighted. When the * cost for substitution and/or transposition are zero Damerau-Levenshtein does * not satisfy the coincidence property. *

* This class is immutable and thread-safe. * * @see Wikipedia * - Damerau-Levenshtein distance * @see Levenshtein * */ public final class DamerauLevenshtein implements StringMetric, StringDistance { private final float maxCost; private final float insertDelete; private final float substitute; private final float transpose; /** * Constructs a new Damerau-Levenshtein metric. */ public DamerauLevenshtein() { this(1.0f, 1.0f, 1.0f); } /** * Constructs a new weighted Damerau-Levenshtein metric. When the cost for * substitution and/or transposition are zero Damerau-Levenshtein does not * satisfy the coincidence property. * * @param insertDelete * positive non-zero cost of an insert or deletion operation * @param substitute * positive cost of a substitute operation * @param transpose * positive cost of a transpose operation */ public DamerauLevenshtein(float insertDelete, float substitute, float transpose) { checkArgument(insertDelete > 0); checkArgument(substitute >= 0); checkArgument(transpose >= 0); this.maxCost = max(insertDelete, substitute, transpose); this.insertDelete = insertDelete; this.substitute = substitute; this.transpose = transpose; } @Override public float compare(final String a, final String b) { if (a.isEmpty() && b.isEmpty()) { return 1.0f; } return 1.0f - (distance(a, b) / (maxCost * max(a.length(), b.length()))); } @Override public float distance(final String s, final String t) { if (s.isEmpty()) return t.length() * insertDelete; if (t.isEmpty()) return s.length() * insertDelete; if (s.equals(t)) return 0; final int tLength = t.length(); final int sLength = s.length(); float[] swap; float[] v0 = new float[tLength + 1]; float[] v1 = new float[tLength + 1]; float[] v2 = new float[tLength + 1]; // initialize v1 (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 < v1.length; i++) { v1[i] = i * insertDelete; } for (int i = 0; i < sLength; i++) { // first element of v2 is A[i+1][0] // edit distance is delete (i+1) chars from s to match empty t v2[0] = (i + 1) * insertDelete; for (int j = 0; j < tLength; j++) { if (j > 0 && i > 0 && s.charAt(i - 1) == t.charAt(j) && s.charAt(i) == t.charAt(j - 1)) { v2[j + 1] = min(v2[j] + insertDelete, v1[j + 1] + insertDelete, v1[j] + (s.charAt(i) == t.charAt(j) ? 0.0f : substitute), v0[j - 1] + transpose); } else { v2[j + 1] = min(v2[j] + insertDelete, v1[j + 1] + insertDelete, v1[j] + (s.charAt(i) == t.charAt(j) ? 0.0f : substitute)); } } swap = v0; v0 = v1; v1 = v2; v2 = swap; } // latest results was in v2 which was swapped to v1 return v1[tLength]; } @Override public String toString() { return "DamerauLevenshtein [insertDelete=" + insertDelete + ", substitute=" + substitute + ", transpose=" + transpose + "]"; } }





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