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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | 42x 42x 30x 30x 52x 52x 953x 10x 10x 1x 9x 9x 47x 9x 8x 8x 1x 5x 42x 42x | /* * Copyright (c) AXA Group Operations Spain S.A. * * 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. */ const BaseNLU = require('./base-nlu'); const LogisticRegressionClassifier = require('../classifiers/logistic-regression-classifier'); /** * Class for the Logistic Regression NLU */ class LogisticRegressionNLU extends BaseNLU { /** * Constructor of the class. * @param {Object} settings Settings for this instance. */ constructor(settings) { super(settings); this.classifier = this.settings.classifier || new LogisticRegressionClassifier(); } /** * Given an utterance, tokenize and steam the utterance and convert it * to a vector of binary values, where each position is a feature (a word * stemmed) and the value means if the utterance has this feature. * The input utterance can be an string or an array of tokens. * @param {String} utterance Utterance to be converted to features vector. * @returns {Number[]} Features vector of the utterance. */ textToFeatures(utterance) { const tokens = this.tokenizeAndStem(utterance); const keys = Object.keys(this.features); return keys.map(key => (tokens.indexOf(key) > -1 ? 1 : 0)); } /** * Train the classifier */ async train() { const mustTrain = this.isEditing ? this.endEdit() : true; if (!mustTrain) { return false; } this.classifier.clear(); this.docs.forEach(doc => { this.classifier.addObservation( this.textToFeatures(doc.tokens), doc.intent ); }); if (this.classifier.observationCount > 0) { await this.classifier.train(); return true; } return false; } /** * Get all the labels and score for each label from this utterance. * @param {String} utterance Utterance to be classified. * @returns {Object[]} Sorted array of classifications, with label and score. */ getClassifications(utterance) { return this.classifier.getClassifications( this.textToFeatures(this.tokenizeAndStem(utterance)) ); } } BaseNLU.classes.LogisticRegressionNLU = LogisticRegressionNLU; module.exports = LogisticRegressionNLU; |