fast-fuzzy/lib/fuzzy.mjs

import split from 'graphemesplit';

function _defineProperty(obj, key, value) {
  if (key in obj) {
    Object.defineProperty(obj, key, {
      value: value,
      enumerable: true,
      configurable: true,
      writable: true
    });
  } else {
    obj[key] = value;
  }

  return obj;
}

function ownKeys(object, enumerableOnly) {
  var keys = Object.keys(object);

  if (Object.getOwnPropertySymbols) {
    var symbols = Object.getOwnPropertySymbols(object);
    if (enumerableOnly) symbols = symbols.filter(function (sym) {
      return Object.getOwnPropertyDescriptor(object, sym).enumerable;
    });
    keys.push.apply(keys, symbols);
  }

  return keys;
}

function _objectSpread2(target) {
  for (var i = 1; i < arguments.length; i++) {
    var source = arguments[i] != null ? arguments[i] : {};

    if (i % 2) {
      ownKeys(Object(source), true).forEach(function (key) {
        _defineProperty(target, key, source[key]);
      });
    } else if (Object.getOwnPropertyDescriptors) {
      Object.defineProperties(target, Object.getOwnPropertyDescriptors(source));
    } else {
      ownKeys(Object(source)).forEach(function (key) {
        Object.defineProperty(target, key, Object.getOwnPropertyDescriptor(source, key));
      });
    }
  }

  return target;
}

const whitespaceRegex = /^\s+$/;
const nonWordRegex = /^[`~!@#$%^&*()\-=_+{}[\]\|\\;':",./<>?]+$/; // the default options, which will be used for any unset option

const defaultOptions = {
  keySelector: s => s,
  threshold: .6,
  ignoreCase: true,
  ignoreSymbols: true,
  normalizeWhitespace: true,
  returnMatchData: false,
  useDamerau: true,
  useSellers: true
};

const noop = () => {};

const arrayWrap = item => item instanceof Array ? item : [item]; // return normalized string, with map included


function normalize(string, options) {
  const lower = options.ignoreCase ? string.toLocaleLowerCase() : string; // track transformations

  const normal = [];
  const map = [];
  let lastWasWhitespace = true;
  let length = 0;

  for (const grapheme of split(lower)) {
    whitespaceRegex.lastIndex = 0;
    nonWordRegex.lastIndex = 0;

    if (options.normalizeWhitespace && whitespaceRegex.test(grapheme)) {
      if (!lastWasWhitespace) {
        normal.push(" ");
        map.push(length);
        lastWasWhitespace = true;
      }
    } else if (!(options.ignoreSymbols && nonWordRegex.test(grapheme))) {
      normal.push(grapheme.normalize());
      map.push(length);
      lastWasWhitespace = false;
    }

    length += grapheme.length;
  } // add the end of the string


  map.push(string.length);

  while (normal[normal.length - 1] === " ") {
    normal.pop();
    map.pop();
  }

  return {
    original: string,
    normal,
    map
  };
} // translates a match to the original string


function denormalizeMatchPosition(match, map) {
  return {
    index: map[match.start],
    length: map[match.end + 1] - map[match.start]
  };
} // walks back up the matrix to find the match index and length


function walkBack(rows, scoreIndex) {
  if (scoreIndex === 0) {
    return {
      index: 0,
      length: 0
    };
  }

  let start = scoreIndex;

  for (let i = rows.length - 2; i > 0 && start > 1; i--) {
    const row = rows[i];
    start = row[start] < row[start - 1] ? start : start - 1;
  }

  return {
    start: start - 1,
    end: scoreIndex - 1
  };
}

const levUpdateScore = () => true;

const sellersUpdateScore = (cur, min) => cur < min;

function getLevScore(rows, length) {
  const lastRow = rows[rows.length - 1];
  const lastCell = lastRow[length - 1];
  const scoreLength = Math.max(rows.length, length);
  return {
    score: 1 - lastCell / (scoreLength - 1),
    scoreIndex: length - 1
  };
}

function getSellersScore(rows, length) {
  // search term was empty string, return perfect score
  if (rows.length === 1) {
    return {
      score: 1,
      scoreIndex: 0
    };
  }

  const lastRow = rows[rows.length - 1];
  let minValue = lastRow[0];
  let minIndex = 0;

  for (let i = 1; i < length; i++) {
    const val = lastRow[i];

    if (val < minValue) {
      minValue = val;
      minIndex = i;
    }
  }

  return {
    score: 1 - minValue / (rows.length - 1),
    scoreIndex: minIndex
  };
}

function initLevRows(rowCount, columnCount) {
  const rows = new Array(rowCount);

  for (let i = 0; i < rowCount; i++) {
    rows[i] = new Array(columnCount);
    rows[i][0] = i;
  }

  for (let i = 0; i < columnCount; i++) {
    rows[0][i] = i;
  }

  return rows;
}

function initSellersRows(rowCount, columnCount) {
  const rows = new Array(rowCount);
  rows[0] = new Array(columnCount).fill(0);

  for (let i = 1; i < rowCount; i++) {
    rows[i] = new Array(columnCount);
    rows[i][0] = i;
  }

  return rows;
} // the content of the innermost loop of levenshtein


function levCore(term, candidate, rows, i, j) {
  const rowA = rows[i];
  const rowB = rows[i + 1];
  const cost = term[i] === candidate[j] ? 0 : 1;
  let m;
  let min = rowB[j] + 1; // insertion

  if ((m = rowA[j + 1] + 1) < min) min = m; // deletion

  if ((m = rowA[j] + cost) < min) min = m; // substitution

  rowB[j + 1] = min;
} // runtime complexity: O(mn) where m and n are the lengths of term and candidate, respectively
// Note: this method only runs on a single column


function levenshtein(term, candidate, rows, j) {
  for (let i = 0; i < term.length; i++) {
    levCore(term, candidate, rows, i, j);
  }
} // has all the runtime characteristics of the above, but punishes transpositions less,
// resulting in better tolerance to those types of typos
// Note: this method only runs on a single column


function damerauLevenshtein(term, candidate, rows, j) {
  // if j === 0, we can't check for transpositions,
  // so use normal levenshtein instead
  if (j === 0) {
    levenshtein(term, candidate, rows, j);
    return;
  } // for i === 0, we also can't check for transpositions, so calculate
  // the first row using normal levenshtein as well


  if (term.length > 0) {
    levCore(term, candidate, rows, 0, j);
  }

  for (let i = 1; i < term.length; i++) {
    const rowA = rows[i - 1];
    const rowB = rows[i];
    const rowC = rows[i + 1];
    const cost = term[i] === candidate[j] ? 0 : 1;
    let m; // insertion

    let min = rowC[j] + 1; // deletion

    if ((m = rowB[j + 1] + 1) < min) min = m; // substitution

    if ((m = rowB[j] + cost) < min) min = m; // transposition

    if (term[i] === candidate[j - 1] && term[i - 1] === candidate[j] && (m = rowA[j - 1] + cost) < min) min = m;
    rowC[j + 1] = min;
  }
} // method for creating a trie from search candidates
// using a trie can significantly improve search time


function trieInsert(trie, string, item) {
  let walker = trie;

  for (let i = 0; i < string.length; i++) {
    const char = string[i]; // add child node if not already present

    if (walker.children[char] == null) {
      walker.children[char] = {
        children: {},
        candidates: [],
        depth: 0
      };
    } // log max depth of this subtree


    walker.depth = Math.max(walker.depth, string.length - i); // step into child node

    walker = walker.children[char];
  }

  walker.candidates.push(item);
} // transforms a list of candidates into objects with normalized search keys,
// and inserts them into a trie
// the keySelector is used to pick strings from an object to search by


function createSearchTrie(trie, index, items, options) {
  for (const item of items) {
    const candidates = arrayWrap(options.keySelector(item)).map((key, keyIndex) => ({
      index,
      keyIndex,
      item,
      normalized: normalize(key, options)
    }));
    index++;

    for (const candidate of candidates) {
      trieInsert(trie, candidate.normalized.normal, candidate);
    }
  }
} // scored item comparator


function compareItems(a, b) {
  const scoreDiff = b.score - a.score;

  if (scoreDiff !== 0) {
    return scoreDiff;
  }

  const keyIndexDiff = a.keyIndex - b.keyIndex;

  if (keyIndexDiff !== 0) {
    return keyIndexDiff;
  }

  const lengthDiff = a.lengthDiff - b.lengthDiff;

  if (lengthDiff !== 0) {
    return lengthDiff;
  }

  return a.index - b.index;
} // dedupes and adds results to the results list/map


function addResult(results, resultMap, candidate, score, match, lengthDiff) {
  const scoredItem = {
    item: candidate.item,
    normalized: candidate.normalized,
    score,
    match,
    index: candidate.index,
    keyIndex: candidate.keyIndex,
    lengthDiff
  };

  if (resultMap[candidate.index] == null) {
    resultMap[candidate.index] = results.length;
    results.push(scoredItem);
  } else if (compareItems(scoredItem, results[resultMap[candidate.index]]) < 0) {
    results[resultMap[candidate.index]] = scoredItem;
  }
}

const getLevLength = Math.max;

const getSellersLength = termLength => termLength; // skip any subtrees for which it is impossible to score >= threshold


function levShouldContinue(node, pos, term, threshold, sValue) {
  // earliest point (length) at which sValue could return to 0
  const p1 = pos + sValue; // point (length) at which string lengths would match

  const p2 = Math.min(term.length, pos + node.depth + 1); // the best score possible is the string which minimizes the value
  // max(sValue, strLenDiff), which is always halfway between p1 and p2

  const length = Math.ceil((p1 + p2) / 2);
  const bestPossibleValue = length - p2;
  return 1 - bestPossibleValue / length >= threshold;
}

function sellersShouldContinue(node, _, term, threshold, sValue, lastValue) {
  const bestPossibleValue = Math.min(sValue, lastValue - (node.depth + 1));
  return 1 - bestPossibleValue / term.length >= threshold;
} // recursively walk the trie


function searchRecurse(node, acc, len, term, scoreMethods, rows, results, resultMap, options, sIndex, sValue) {
  // build rows
  scoreMethods.score(term, acc, rows, len - 1); // track best score and position

  const lastIndex = len;
  const lastValue = rows[rows.length - 1][lastIndex];

  if (scoreMethods.shouldUpdateScore(lastValue, sValue)) {
    sIndex = lastIndex;
    sValue = lastValue;
  } // insert results


  if (node.candidates.length > 0) {
    const length = scoreMethods.getLength(term.length, len);
    const score = 1 - sValue / length;

    if (score >= options.threshold) {
      const match = scoreMethods.walkBack(rows, sIndex);
      const lengthDiff = Math.abs(len - term.length);

      for (const candidate of node.candidates) {
        addResult(results, resultMap, candidate, score, match, lengthDiff);
      }
    }
  } // recurse for children


  for (const key in node.children) {
    const child = node.children[key];

    if (scoreMethods.shouldContinue(child, len, term, options.threshold, sValue, lastValue)) {
      acc[len] = key;
      searchRecurse(child, acc, len + 1, term, scoreMethods, rows, results, resultMap, options, sIndex, sValue);
    }
  }
} // the core match finder: returns a sorted, filtered list of matches
// this does not normalize input, requiring users to normalize themselves


function searchCore(term, trie, options) {
  const initMethod = options.useSellers ? initSellersRows : initLevRows;
  const scoreMethods = {
    score: options.useDamerau ? damerauLevenshtein : levenshtein,
    getLength: options.useSellers ? getSellersLength : getLevLength,
    shouldUpdateScore: options.useSellers ? sellersUpdateScore : levUpdateScore,
    shouldContinue: options.useSellers ? sellersShouldContinue : levShouldContinue,
    walkBack: options.returnMatchData && options.useSellers ? walkBack : noop
  }; // walk the trie, scoring and storing the candidates

  const resultMap = {};
  const results = [];
  const rows = initMethod(term.length + 1, trie.depth + 1);

  if (options.threshold <= 0 || term.length === 0) {
    for (const candidate of trie.candidates) {
      addResult(results, resultMap, candidate, 0, {
        index: 0,
        length: 0
      }, term.length);
    }
  }

  for (const key in trie.children) {
    const value = trie.children[key];
    const acc = new Array(trie.depth);
    acc[0] = key;
    searchRecurse(value, acc, 1, term, scoreMethods, rows, results, resultMap, options, 0, term.length);
  }

  const sorted = results.sort(compareItems);

  if (options.returnMatchData) {
    const denormalize = options.useSellers ? denormalizeMatchPosition : noop;
    return sorted.map(candidate => ({
      item: candidate.item,
      original: candidate.normalized.original,
      key: candidate.normalized.normal.join(""),
      score: candidate.score,
      match: denormalize(candidate.match, candidate.normalized.map)
    }));
  }

  return sorted.map(candidate => candidate.item);
} // wrapper for exporting sellers while allowing options to be passed in


function fuzzy(term, candidate, options) {
  options = _objectSpread2(_objectSpread2({}, defaultOptions), options);
  const initMethod = options.useSellers ? initSellersRows : initLevRows;
  const scoreMethod = options.useDamerau ? damerauLevenshtein : levenshtein;
  const getScore = options.useSellers ? getSellersScore : getLevScore;
  term = normalize(term, options).normal;
  const normalized = normalize(candidate, options);
  const rows = initMethod(term.length + 1, normalized.normal.length + 1);

  for (let j = 0; j < normalized.normal.length; j++) {
    scoreMethod(term, normalized.normal, rows, j);
  }

  const scoreResult = getScore(rows, normalized.normal.length + 1);
  return options.returnMatchData ? {
    item: candidate,
    original: normalized.original,
    key: normalized.normal.join(""),
    score: scoreResult.score,
    match: options.useSellers ? denormalizeMatchPosition(walkBack(rows, scoreResult.scoreIndex), normalized.map) : noop()
  } : scoreResult.score;
} // simple one-off search. Useful if you don't expect to use the same candidate list again

function search(term, candidates, options) {
  options = _objectSpread2(_objectSpread2({}, defaultOptions), options);
  const trie = {
    children: {},
    candidates: [],
    depth: 0
  };
  createSearchTrie(trie, 0, candidates, options);
  return searchCore(normalize(term, options).normal, trie, options);
} // class that improves performance of searching the same set multiple times
// normalizes the strings and caches the result for future calls

class Searcher {
  constructor(candidates, options) {
    this.options = Object.assign({}, defaultOptions, options);
    this.trie = {
      children: {},
      candidates: [],
      depth: 0
    };
    createSearchTrie(this.trie, 0, candidates, this.options);
    this.count = candidates.length;
  }

  add(...candidates) {
    createSearchTrie(this.trie, this.count, candidates, this.options);
    this.count += candidates.length;
  }

  search(term, options) {
    options = Object.assign({}, this.options, options);
    return searchCore(normalize(term, this.options).normal, this.trie, options);
  }

}

export { Searcher, fuzzy, search };