A UMD build of fast-levenshtein
Este script não deve ser instalado diretamente. É uma biblioteca destinada a ser incluída por outros scripts através da diretiva de metadados // @require https://update.greasyfork.org/scripts/525938/1532043/fast-levenshtein-umd.js
(function (global, factory) {
typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
typeof define === 'function' && define.amd ? define(factory) :
(global = typeof globalThis !== 'undefined' ? globalThis : global || self, global.Levenshtein = factory());
})(this, (function () { 'use strict';
const peq = new Uint32Array(0x10000);
const myers_32 = (a, b) => {
const n = a.length;
const m = b.length;
const lst = 1 << (n - 1);
let pv = -1;
let mv = 0;
let sc = n;
let i = n;
while (i--) {
peq[a.charCodeAt(i)] |= 1 << i;
}
for (i = 0; i < m; i++) {
let eq = peq[b.charCodeAt(i)];
const xv = eq | mv;
eq |= ((eq & pv) + pv) ^ pv;
mv |= ~(eq | pv);
pv &= eq;
if (mv & lst) sc++;
if (pv & lst) sc--;
mv = (mv << 1) | 1;
pv = (pv << 1) | ~(xv | mv);
mv &= xv;
}
i = n;
while (i--) {
peq[a.charCodeAt(i)] = 0;
}
return sc;
};
const myers_x = (b, a) => {
const n = a.length;
const m = b.length;
const mhc = [];
const phc = [];
const hsize = Math.ceil(n / 32);
const vsize = Math.ceil(m / 32);
for (let i = 0; i < hsize; i++) {
phc[i] = -1;
mhc[i] = 0;
}
let j = 0;
for (; j < vsize - 1; j++) {
let mv = 0;
let pv = -1;
const start = j * 32;
const vlen = Math.min(32, m) + start;
for (let k = start; k < vlen; k++) {
peq[b.charCodeAt(k)] |= 1 << k;
}
for (let i = 0; i < n; i++) {
const eq = peq[a.charCodeAt(i)];
const pb = (phc[(i / 32) | 0] >>> i) & 1;
const mb = (mhc[(i / 32) | 0] >>> i) & 1;
const xv = eq | mv;
const xh = ((((eq | mb) & pv) + pv) ^ pv) | eq | mb;
let ph = mv | ~(xh | pv);
let mh = pv & xh;
if ((ph >>> 31) ^ pb) {
phc[(i / 32) | 0] ^= 1 << i;
}
if ((mh >>> 31) ^ mb) {
mhc[(i / 32) | 0] ^= 1 << i;
}
ph = (ph << 1) | pb;
mh = (mh << 1) | mb;
pv = mh | ~(xv | ph);
mv = ph & xv;
}
for (let k = start; k < vlen; k++) {
peq[b.charCodeAt(k)] = 0;
}
}
let mv = 0;
let pv = -1;
const start = j * 32;
const vlen = Math.min(32, m - start) + start;
for (let k = start; k < vlen; k++) {
peq[b.charCodeAt(k)] |= 1 << k;
}
let score = m;
for (let i = 0; i < n; i++) {
const eq = peq[a.charCodeAt(i)];
const pb = (phc[(i / 32) | 0] >>> i) & 1;
const mb = (mhc[(i / 32) | 0] >>> i) & 1;
const xv = eq | mv;
const xh = ((((eq | mb) & pv) + pv) ^ pv) | eq | mb;
let ph = mv | ~(xh | pv);
let mh = pv & xh;
score += (ph >>> (m - 1)) & 1;
score -= (mh >>> (m - 1)) & 1;
if ((ph >>> 31) ^ pb) {
phc[(i / 32) | 0] ^= 1 << i;
}
if ((mh >>> 31) ^ mb) {
mhc[(i / 32) | 0] ^= 1 << i;
}
ph = (ph << 1) | pb;
mh = (mh << 1) | mb;
pv = mh | ~(xv | ph);
mv = ph & xv;
}
for (let k = start; k < vlen; k++) {
peq[b.charCodeAt(k)] = 0;
}
return score;
};
const distance = (a, b) => {
if (a.length < b.length) {
const tmp = b;
b = a;
a = tmp;
}
if (b.length === 0) return a.length;
if (a.length <= 32) return myers_32(a, b);
return myers_x(a, b);
};
const collator = Intl.Collator("generic", { sensitivity: "base" });
const prevRow = [];
const str2Char = [];
const Levenshtein = {
get(str1, str2, options={useCollator:false}) {
const useCollator = (options && collator && options.useCollator);
if (useCollator) {
const str1Len = str1.length;
const str2Len = str2.length;
if (str1Len === 0) return str2Len;
if (str2Len === 0) return str1Len;
for (i=0; i<str2Len; ++i) {
prevRow[i] = i;
str2Char[i] = str2.charCodeAt(i);
}
prevRow[str2Len] = str2Len;
var strCmp;
for (let i = 0; i < str1Len; ++i) {
let nextCol = i + 1;
for (let j = 0; j < str2Len; ++j) {
let curCol = nextCol;
strCmp = 0 === collator.compare(str1.charAt(i), String.fromCharCode(str2Char[j]));
nextCol = prevRow[j] + (strCmp ? 0 : 1);
let tmp = curCol + 1;
if (nextCol > tmp) {
nextCol = tmp;
}
tmp = prevRow[j + 1] + 1;
if (nextCol > tmp) {
nextCol = tmp;
}
prevRow[j] = curCol;
}
prevRow[j] = nextCol;
}
return nextCol;
}
return distance(str1, str2);
}
};
return Levenshtein;
}));