lunr.Builder.prototype.createFieldVectors = function()

in assets/js/lunr/lunr.js [2585:2635]
36 lines of code
6 McCabe index (conditional complexity)

lunr.Builder.prototype.createFieldVectors = function () {
  var fieldVectors = {},
      fieldRefs = Object.keys(this.fieldTermFrequencies),
      fieldRefsLength = fieldRefs.length,
      termIdfCache = Object.create(null)

  for (var i = 0; i < fieldRefsLength; i++) {
    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),
        fieldName = fieldRef.fieldName,
        fieldLength = this.fieldLengths[fieldRef],
        fieldVector = new lunr.Vector,
        termFrequencies = this.fieldTermFrequencies[fieldRef],
        terms = Object.keys(termFrequencies),
        termsLength = terms.length


    var fieldBoost = this._fields[fieldName].boost || 1,
        docBoost = this._documents[fieldRef.docRef].boost || 1

    for (var j = 0; j < termsLength; j++) {
      var term = terms[j],
          tf = termFrequencies[term],
          termIndex = this.invertedIndex[term]._index,
          idf, score, scoreWithPrecision

      if (termIdfCache[term] === undefined) {
        idf = lunr.idf(this.invertedIndex[term], this.documentCount)
        termIdfCache[term] = idf
      } else {
        idf = termIdfCache[term]
      }

      score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)
      score *= fieldBoost
      score *= docBoost
      scoreWithPrecision = Math.round(score * 1000) / 1000
      // Converts 1.23456789 to 1.234.
      // Reducing the precision so that the vectors take up less
      // space when serialised. Doing it now so that they behave
      // the same before and after serialisation. Also, this is
      // the fastest approach to reducing a number's precision in
      // JavaScript.

      fieldVector.insert(termIndex, scoreWithPrecision)
    }

    fieldVectors[fieldRef] = fieldVector
  }

  this.fieldVectors = fieldVectors
}