blob: 22db4fe70e7579719ab06a504cc52197e3d018b9 [file] [log] [blame]
 // Copyright 2011 Google Inc. All Rights Reserved. // // 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 // limitations under the License. #include "edit_distance.h" #include int EditDistance(const StringPiece& s1, const StringPiece& s2, bool allow_replacements, int max_edit_distance) { // The algorithm implemented below is the "classic" // dynamic-programming algorithm for computing the Levenshtein // distance, which is described here: // // http://en.wikipedia.org/wiki/Levenshtein_distance // // Although the algorithm is typically described using an m x n // array, only two rows are used at a time, so this implemenation // just keeps two separate vectors for those two rows. int m = s1.len_; int n = s2.len_; std::vector previous(n + 1); std::vector current(n + 1); for (int i = 0; i <= n; ++i) previous[i] = i; for (int y = 1; y <= m; ++y) { current[0] = y; int best_this_row = current[0]; for (int x = 1; x <= n; ++x) { if (allow_replacements) { current[x] = min(previous[x-1] + (s1.str_[y-1] == s2.str_[x-1] ? 0 : 1), min(current[x-1], previous[x])+1); } else { if (s1.str_[y-1] == s2.str_[x-1]) current[x] = previous[x-1]; else current[x] = min(current[x-1], previous[x]) + 1; } best_this_row = min(best_this_row, current[x]); } if (max_edit_distance && best_this_row > max_edit_distance) return max_edit_distance + 1; current.swap(previous); } return previous[n]; }