react-native-web-headroom/docs/node_modules/fuse.js/src/index.js

React Native Web Headroom

const Bitap = require('./bitap')
const deepValue = require('./helpers/deep_value')
const isArray = require('./helpers/is_array')

class Fuse {
  constructor (list, {
    // Approximately where in the text is the pattern expected to be found?
    location = 0,
    // Determines how close the match must be to the fuzzy location (specified above).
    // An exact letter match which is 'distance' characters away from the fuzzy location
    // would score as a complete mismatch. A distance of '0' requires the match be at
    // the exact location specified, a threshold of '1000' would require a perfect match
    // to be within 800 characters of the fuzzy location to be found using a 0.8 threshold.
    distance = 100,
    // At what point does the match algorithm give up. A threshold of '0.0' requires a perfect match
    // (of both letters and location), a threshold of '1.0' would match anything.
    threshold = 0.6,
    // Machine word size
    maxPatternLength = 32,
    // Indicates whether comparisons should be case sensitive.
    caseSensitive = false,
    // Regex used to separate words when searching. Only applicable when `tokenize` is `true`.
    tokenSeparator = / +/g,
    // When true, the algorithm continues searching to the end of the input even if a perfect
    // match is found before the end of the same input.
    findAllMatches = false,
    // Minimum number of characters that must be matched before a result is considered a match
    minMatchCharLength = 1,
    // The name of the identifier property. If specified, the returned result will be a list
    // of the items' dentifiers, otherwise it will be a list of the items.
    id = null,
    // List of properties that will be searched. This also supports nested properties.
    keys = [],
    // Whether to sort the result list, by score
    shouldSort = true,
    // The get function to use when fetching an object's properties.
    // The default will search nested paths *ie foo.bar.baz*
    getFn = deepValue,
    // Default sort function
    sortFn = (a, b) => (a.score - b.score),
    // When true, the search algorithm will search individual words **and** the full string,
    // computing the final score as a function of both. Note that when `tokenize` is `true`,
    // the `threshold`, `distance`, and `location` are inconsequential for individual tokens.
    tokenize = false,
    // When true, the result set will only include records that match all tokens. Will only work
    // if `tokenize` is also true.
    matchAllTokens = false,

    includeMatches = false,
    includeScore = false,

    // Will print to the console. Useful for debugging.
    verbose = false
  }) {
    this.options = {
      location,
      distance,
      threshold,
      maxPatternLength,
      isCaseSensitive: caseSensitive,
      tokenSeparator,
      findAllMatches,
      minMatchCharLength,
      id,
      keys,
      includeMatches,
      includeScore,
      shouldSort,
      getFn,
      sortFn,
      verbose,
      tokenize,
      matchAllTokens
    }

    this.setCollection(list)
  }

  setCollection (list) {
    this.list = list
    return list
  }

  search (pattern) {
    this._log(`---------\nSearch pattern: "${pattern}"`)

    const {
      tokenSearchers,
      fullSearcher
    } = this._prepareSearchers(pattern)

    let { weights, results } = this._search(tokenSearchers, fullSearcher)

    this._computeScore(weights, results)

    if (this.options.shouldSort) {
      this._sort(results)
    }

    return this._format(results)
  }

  _prepareSearchers (pattern = '') {
    const tokenSearchers = []

    if (this.options.tokenize) {
      // Tokenize on the separator
      const tokens = pattern.split(this.options.tokenSeparator)
      for (let i = 0, len = tokens.length; i < len; i += 1) {
        tokenSearchers.push(new Bitap(tokens[i], this.options))
      }
    }

    let fullSearcher = new Bitap(pattern, this.options)

    return { tokenSearchers, fullSearcher }
  }

  _search (tokenSearchers = [], fullSearcher) {
    const list = this.list
    const resultMap = {}
    const results = []

    // Check the first item in the list, if it's a string, then we assume
    // that every item in the list is also a string, and thus it's a flattened array.
    if (typeof list[0] === 'string') {
      // Iterate over every item
      for (let i = 0, len = list.length; i < len; i += 1) {
        this._analyze({
          key: '',
          value: list[i],
          record: i,
          index: i
        }, {
          resultMap,
          results,
          tokenSearchers,
          fullSearcher
        })
      }

      return { weights: null, results }
    }

    // Otherwise, the first item is an Object (hopefully), and thus the searching
    // is done on the values of the keys of each item.
    const weights = {}
    for (let i = 0, len = list.length; i < len; i += 1) {
      let item = list[i]
      // Iterate over every key
      for (let j = 0, keysLen = this.options.keys.length; j < keysLen; j += 1) {
        let key = this.options.keys[j]
        if (typeof key !== 'string') {
          weights[key.name] = {
            weight: (1 - key.weight) || 1
          }
          if (key.weight <= 0 || key.weight > 1) {
            throw new Error('Key weight has to be > 0 and <= 1')
          }
          key = key.name
        } else {
          weights[key] = {
            weight: 1
          }
        }

        this._analyze({
          key,
          value: this.options.getFn(item, key),
          record: item,
          index: i
        }, {
          resultMap,
          results,
          tokenSearchers,
          fullSearcher
        })
      }
    }

    return { weights, results }
  }

  _analyze ({ key, arrayIndex = -1, value, record, index }, { tokenSearchers = [], fullSearcher = [], resultMap = {}, results = [] }) {
    // Check if the texvaluet can be searched
    if (value === undefined || value === null) {
      return
    }

    let exists = false
    let averageScore = -1
    let numTextMatches = 0

    if (typeof value === 'string') {
      this._log(`\nKey: ${key === '' ? '-' : key}`)

      let mainSearchResult = fullSearcher.search(value)
      this._log(`Full text: "${value}", score: ${mainSearchResult.score}`)

      if (this.options.tokenize) {
        let words = value.split(this.options.tokenSeparator)
        let scores = []

        for (let i = 0; i < tokenSearchers.length; i += 1) {
          let tokenSearcher = tokenSearchers[i]

          this._log(`\nPattern: "${tokenSearcher.pattern}"`)

          // let tokenScores = []
          let hasMatchInText = false

          for (let j = 0; j < words.length; j += 1) {
            let word = words[j]
            let tokenSearchResult = tokenSearcher.search(word)
            let obj = {}
            if (tokenSearchResult.isMatch) {
              obj[word] = tokenSearchResult.score
              exists = true
              hasMatchInText = true
              scores.push(tokenSearchResult.score)
            } else {
              obj[word] = 1
              if (!this.options.matchAllTokens) {
                scores.push(1)
              }
            }
            this._log(`Token: "${word}", score: ${obj[word]}`)
            // tokenScores.push(obj)
          }

          if (hasMatchInText) {
            numTextMatches += 1
          }
        }

        averageScore = scores[0]
        let scoresLen = scores.length
        for (let i = 1; i < scoresLen; i += 1) {
          averageScore += scores[i]
        }
        averageScore = averageScore / scoresLen

        this._log('Token score average:', averageScore)
      }

      let finalScore = mainSearchResult.score
      if (averageScore > -1) {
        finalScore = (finalScore + averageScore) / 2
      }

      this._log('Score average:', finalScore)

      let checkTextMatches = (this.options.tokenize && this.options.matchAllTokens) ? numTextMatches >= tokenSearchers.length : true

      this._log(`\nCheck Matches: ${checkTextMatches}`)

      // If a match is found, add the item to <rawResults>, including its score
      if ((exists || mainSearchResult.isMatch) && checkTextMatches) {
        // Check if the item already exists in our results
        let existingResult = resultMap[index]
        if (existingResult) {
          // Use the lowest score
          // existingResult.score, bitapResult.score
          existingResult.output.push({
            key,
            arrayIndex,
            value,
            score: finalScore,
            matchedIndices: mainSearchResult.matchedIndices
          })
        } else {
          // Add it to the raw result list
          resultMap[index] = {
            item: record,
            output: [{
              key,
              arrayIndex,
              value,
              score: finalScore,
              matchedIndices: mainSearchResult.matchedIndices
            }]
          }

          results.push(resultMap[index])
        }
      }
    } else if (isArray(value)) {
      for (let i = 0, len = value.length; i < len; i += 1) {
        this._analyze({
          key,
          arrayIndex: i,
          value: value[i],
          record,
          index
        }, {
          resultMap,
          results,
          tokenSearchers,
          fullSearcher
        })
      }
    }
  }

  _computeScore (weights, results) {
    this._log('\n\nComputing score:\n')

    for (let i = 0, len = results.length; i < len; i += 1) {
      const output = results[i].output
      const scoreLen = output.length

      let totalScore = 0
      let bestScore = 1

      for (let j = 0; j < scoreLen; j += 1) {
        let weight = weights ? weights[output[j].key].weight : 1
        let score = weight === 1 ? output[j].score : (output[j].score || 0.001)
        let nScore = score * weight

        if (weight !== 1) {
          bestScore = Math.min(bestScore, nScore)
        } else {
          output[j].nScore = nScore
          totalScore += nScore
        }
      }

      results[i].score = bestScore === 1 ? totalScore / scoreLen : bestScore

      this._log(results[i])
    }
  }

  _sort (results) {
    this._log('\n\nSorting....')
    results.sort(this.options.sortFn)
  }

  _format (results) {
    const finalOutput = []

    this._log('\n\nOutput:\n\n', JSON.stringify(results))

    let transformers = []

    if (this.options.includeMatches) {
      transformers.push((result, data) => {
        const output = result.output
        data.matches = []

        for (let i = 0, len = output.length; i < len; i += 1) {
          let item = output[i]

          if (item.matchedIndices.length === 0) {
            continue
          }

          let obj = {
            indices: item.matchedIndices,
            value: item.value
          }
          if (item.key) {
            obj.key = item.key
          }
          if (item.hasOwnProperty('arrayIndex') && item.arrayIndex > -1) {
            obj.arrayIndex = item.arrayIndex
          }
          data.matches.push(obj)
        }
      })
    }

    if (this.options.includeScore) {
      transformers.push((result, data) => {
        data.score = result.score
      })
    }

    for (let i = 0, len = results.length; i < len; i += 1) {
      const result = results[i]

      if (this.options.id) {
        result.item = this.options.getFn(result.item, this.options.id)[0]
      }

      if (!transformers.length) {
        finalOutput.push(result.item)
        continue
      }

      const data = {
        item: result.item
      }

      for (let j = 0, len = transformers.length; j < len; j += 1) {
        transformers[j](result, data)
      }

      finalOutput.push(data)
    }

    return finalOutput
  }

  _log () {
    if (this.options.verbose) {
      console.log(...arguments)
    }
  }
}

module.exports = Fuse