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/*
 * 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.
 */
package org.apache.commons.text.similarity;

import java.util.Arrays;
import java.util.Objects;

/**
 * A similarity algorithm indicating the percentage of matched characters between two character sequences.
 *
 * 

* The Jaro measure is the weighted sum of percentage of matched characters from each file and transposed characters. Winkler increased this measure for * matching initial characters. *

*

* This implementation is based on the Jaro Winkler similarity algorithm from * https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance. *

*

* This code has been adapted from Apache Commons Lang 3.3. *

* * @since 1.7 */ public class JaroWinklerSimilarity implements SimilarityScore { /** * Singleton instance. */ static final JaroWinklerSimilarity INSTANCE = new JaroWinklerSimilarity(); /** * Computes the Jaro-Winkler string matches, half transpositions, prefix array. * * @param first the first input to be matched. * @param second the second input to be matched. * @return mtp array containing: matches, half transpositions, and prefix. */ protected static int[] matches(final CharSequence first, final CharSequence second) { return matches(SimilarityInput.input(first), SimilarityInput.input(second)); } /** * Computes the Jaro-Winkler string matches, half transpositions, prefix array. * * @param The type of similarity score unit. * @param first the first input to be matched. * @param second the second input to be matched. * @return mtp array containing: matches, half transpositions, and prefix. * @since 1.13.0 */ protected static int[] matches(final SimilarityInput first, final SimilarityInput second) { final SimilarityInput max; final SimilarityInput min; if (first.length() > second.length()) { max = first; min = second; } else { max = second; min = first; } final int range = Math.max(max.length() / 2 - 1, 0); final int[] matchIndexes = new int[min.length()]; Arrays.fill(matchIndexes, -1); final boolean[] matchFlags = new boolean[max.length()]; int matches = 0; for (int mi = 0; mi < min.length(); mi++) { final E c1 = min.at(mi); for (int xi = Math.max(mi - range, 0), xn = Math.min(mi + range + 1, max.length()); xi < xn; xi++) { if (!matchFlags[xi] && c1.equals(max.at(xi))) { matchIndexes[mi] = xi; matchFlags[xi] = true; matches++; break; } } } final Object[] ms1 = new Object[matches]; final Object[] ms2 = new Object[matches]; for (int i = 0, si = 0; i < min.length(); i++) { if (matchIndexes[i] != -1) { ms1[si] = min.at(i); si++; } } for (int i = 0, si = 0; i < max.length(); i++) { if (matchFlags[i]) { ms2[si] = max.at(i); si++; } } int halfTranspositions = 0; for (int mi = 0; mi < ms1.length; mi++) { if (!ms1[mi].equals(ms2[mi])) { halfTranspositions++; } } int prefix = 0; for (int mi = 0; mi < Math.min(4, min.length()); mi++) { if (!first.at(mi).equals(second.at(mi))) { break; } prefix++; } return new int[] { matches, halfTranspositions, prefix }; } /** * Computes the Jaro Winkler Similarity between two character sequences. * *
     * sim.apply(null, null)          = IllegalArgumentException
     * sim.apply("foo", null)         = IllegalArgumentException
     * sim.apply(null, "foo")         = IllegalArgumentException
     * sim.apply("", "")              = 1.0
     * sim.apply("foo", "foo")        = 1.0
     * sim.apply("foo", "foo ")       = 0.94
     * sim.apply("foo", "foo  ")      = 0.91
     * sim.apply("foo", " foo ")      = 0.87
     * sim.apply("foo", "  foo")      = 0.51
     * sim.apply("", "a")             = 0.0
     * sim.apply("aaapppp", "")       = 0.0
     * sim.apply("frog", "fog")       = 0.93
     * sim.apply("fly", "ant")        = 0.0
     * sim.apply("elephant", "hippo") = 0.44
     * sim.apply("hippo", "elephant") = 0.44
     * sim.apply("hippo", "zzzzzzzz") = 0.0
     * sim.apply("hello", "hallo")    = 0.88
     * sim.apply("ABC Corporation", "ABC Corp") = 0.91
     * sim.apply("D N H Enterprises Inc", "D & H Enterprises, Inc.") = 0.95
     * sim.apply("My Gym Children's Fitness Center", "My Gym. Childrens Fitness") = 0.92
     * sim.apply("PENNSYLVANIA", "PENNCISYLVNIA") = 0.88
     * 
* * @param left the first input, must not be null. * @param right the second input, must not be null. * @return result similarity. * @throws IllegalArgumentException if either CharSequence input is {@code null}. */ @Override public Double apply(final CharSequence left, final CharSequence right) { return apply(SimilarityInput.input(left), SimilarityInput.input(right)); } /** * Computes the Jaro Winkler Similarity between two character sequences. * *
     * sim.apply(null, null)          = IllegalArgumentException
     * sim.apply("foo", null)         = IllegalArgumentException
     * sim.apply(null, "foo")         = IllegalArgumentException
     * sim.apply("", "")              = 1.0
     * sim.apply("foo", "foo")        = 1.0
     * sim.apply("foo", "foo ")       = 0.94
     * sim.apply("foo", "foo  ")      = 0.91
     * sim.apply("foo", " foo ")      = 0.87
     * sim.apply("foo", "  foo")      = 0.51
     * sim.apply("", "a")             = 0.0
     * sim.apply("aaapppp", "")       = 0.0
     * sim.apply("frog", "fog")       = 0.93
     * sim.apply("fly", "ant")        = 0.0
     * sim.apply("elephant", "hippo") = 0.44
     * sim.apply("hippo", "elephant") = 0.44
     * sim.apply("hippo", "zzzzzzzz") = 0.0
     * sim.apply("hello", "hallo")    = 0.88
     * sim.apply("ABC Corporation", "ABC Corp") = 0.91
     * sim.apply("D N H Enterprises Inc", "D & H Enterprises, Inc.") = 0.95
     * sim.apply("My Gym Children's Fitness Center", "My Gym. Childrens Fitness") = 0.92
     * sim.apply("PENNSYLVANIA", "PENNCISYLVNIA") = 0.88
     * 
* * @param The type of similarity score unit. * @param left the first input, must not be null. * @param right the second input, must not be null. * @return result similarity. * @throws IllegalArgumentException if either CharSequence input is {@code null}. * @since 1.13.0 */ public Double apply(final SimilarityInput left, final SimilarityInput right) { final double defaultScalingFactor = 0.1; if (left == null || right == null) { throw new IllegalArgumentException("CharSequences must not be null"); } if (Objects.equals(left, right)) { return 1d; } final int[] mtp = matches(left, right); final double m = mtp[0]; if (m == 0) { return 0d; } final double j = (m / left.length() + m / right.length() + (m - (double) mtp[1] / 2) / m) / 3; return j < 0.7d ? j : j + defaultScalingFactor * mtp[2] * (1d - j); } }




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