/*
Copyright (c) 2011 Andrei Mackenzie
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.
*/
// levenshtein distance algorithm, pulled from Andrei Mackenzie's MIT licensed.
// gist, which can be found here: https://gist.github.com/andrei-m/982927
'use strict'
// Compute the edit distance between the two given strings
module.exports = function levenshtein (a, b) {
if (a.length === 0) return b.length
if (b.length === 0) return a.length
const matrix = []
// increment along the first column of each row
let i
for (i = 0; i <= b.length; i++) {
matrix[i] = [i]
}
// increment each column in the first row
let j
for (j = 0; j <= a.length; j++) {
matrix[0][j] = j
}
// Fill in the rest of the matrix
for (i = 1; i <= b.length; i++) {
for (j = 1; j <= a.length; j++) {
if (b.charAt(i - 1) === a.charAt(j - 1)) {
matrix[i][j] = matrix[i - 1][j - 1]
} else {
matrix[i][j] = Math.min(matrix[i - 1][j - 1] + 1, // substitution
Math.min(matrix[i][j - 1] + 1, // insertion
matrix[i - 1][j] + 1)) // deletion
}
}
}
return matrix[b.length][a.length]
}
