/*
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Copyright (c) 2011 Andrei Mackenzie
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Permission is hereby granted, free of charge, to any person obtaining a copy of
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this software and associated documentation files (the "Software"), to deal in
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the Software without restriction, including without limitation the rights to
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use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
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the Software, and to permit persons to whom the Software is furnished to do so,
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subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
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IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
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CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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*/
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// levenshtein distance algorithm, pulled from Andrei Mackenzie's MIT licensed.
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// gist, which can be found here: https://gist.github.com/andrei-m/982927
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'use strict'
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// Compute the edit distance between the two given strings
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module.exports = function levenshtein (a, b) {
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if (a.length === 0) return b.length
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if (b.length === 0) return a.length
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const matrix = []
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// increment along the first column of each row
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let i
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for (i = 0; i <= b.length; i++) {
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matrix[i] = [i]
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}
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// increment each column in the first row
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let j
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for (j = 0; j <= a.length; j++) {
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matrix[0][j] = j
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}
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// Fill in the rest of the matrix
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for (i = 1; i <= b.length; i++) {
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for (j = 1; j <= a.length; j++) {
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if (b.charAt(i - 1) === a.charAt(j - 1)) {
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matrix[i][j] = matrix[i - 1][j - 1]
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} else {
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matrix[i][j] = Math.min(matrix[i - 1][j - 1] + 1, // substitution
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Math.min(matrix[i][j - 1] + 1, // insertion
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matrix[i - 1][j] + 1)) // deletion
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}
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}
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}
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return matrix[b.length][a.length]
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}
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