node-nlp

<!doctype html> <html lang="en"> <head> <title>Code coverage report for lib/classifiers/binary-neural-network-classifier.js</title> <meta charset="utf-8" /> <link rel="stylesheet" href="..\..\prettify.css" /> <link rel="stylesheet" href="..\..\base.css" /> <meta name="viewport" content="width=device-width, initial-scale=1"> <style type='text/css'> .coverage-summary .sorter { background-image: url(..\..\sort-arrow-sprite.png); } </style> </head> <body> <div class='wrapper'> <div class='pad1'> <h1> <a href="..\..\index.html">All files</a> / <a href="index.html">lib/classifiers</a> binary-neural-network-classifier.js </h1> <div class='clearfix'> <div class='fl pad1y space-right2'> 100% Statements 39/39 </div> <div class='fl pad1y space-right2'> 100% Branches 26/26 </div> <div class='fl pad1y space-right2'> 100% Functions 11/11 </div> <div class='fl pad1y space-right2'> 100% Lines 37/37 </div> </div> </div> <div class='status-line high'></div> <pre><table class="coverage"> <tr><td class="line-count quiet">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 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119</td><td class="line-coverage quiet">                                              31x                     194x 194x 193x             194x 194x 194x 194x               884x 617x 617x 6x 3x 3x         611x 608x   617x                 52x 366x 52x 366x 999x 99x   999x           52x 52x 99x   52x                 57x 57x 1x 3x     56x 453x 453x     1066x       31x  </td><td class="text"><pre class="prettyprint lang-js">/* * Copyright (c) AXA Shared Services 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 { NeuralNetwork } = require('brain.js');   /** * Classifier using Brain.js Neural Network */ class BinaryNeuralNetworkClassifier { /** * Constructor of the class. * @param {Object} settings Settings for the instance. */ constructor(settings) { this.settings = settings || {}; if (!this.settings.config) { this.settings.config = { activation: 'leaky-relu', hiddenLayers: [], learningRate: 0.1, errorThresh: 0.0005, }; } this.totalTimeout = this.settings.totalTimeout || 2 * 60 * 1000; this.labelTimeout = this.settings.labelTimeout; this.labels = []; this.classifierMap = {}; }   /** * If a trainer does not exists for a label, create it. * @param {*} label */ addTrainer(label) { if (!this.classifierMap[label]) { this.labels.push(label); if (this.labelTimeout && this.labelTimeout > 0) { if (this.totalTimeout && this.totalTimeout > 0) { const partialTimeout = this.totalTimeout / this.labels.length; this.settings.config.timeout = Math.min( this.totalTimeout, partialTimeout ); } } else if (this.totalTimeout && this.totalTimeout > 0) { this.settings.config.timeout = this.totalTimeout / this.labels.length; } this.classifierMap[label] = new NeuralNetwork(this.settings.config); } }   /** * Train the classifier given a dataset. * @param {Object} dataset Dataset with features and outputs. */ async trainBatch(dataset) { const datasetMap = {}; dataset.forEach(item => this.addTrainer(item.output)); dataset.forEach(item => { this.labels.forEach(label => { if (!datasetMap[label]) { datasetMap[label] = []; } datasetMap[label].push({ input: item.input, output: [item.output === label ? 1 : 0], }); }); }); const promises = []; Object.keys(datasetMap).forEach(label => { promises.push(this.classifierMap[label].trainAsync(datasetMap[label])); }); return Promise.all(promises); }   /** * Given a sample, return the classification. * @param {Object} sample Input sample. * @returns {Object} Classification output. */ classify(sample) { const scores = []; if (Object.keys(sample).length === 0) { this.labels.forEach(label => { scores.push({ label, value: 0.5 }); }); } else { Object.keys(this.classifierMap).forEach(label => { const score = this.classifierMap[label].run(sample); scores.push({ label, value: score[0] }); }); } return scores.sort((x, y) => y.value - x.value); } }   module.exports = BinaryNeuralNetworkClassifier;  </pre></td></tr> </table></pre> <div class='push'></div> </div> <div class='footer quiet pad2 space-top1 center small'> Code coverage generated by <a href="https://istanbul.js.org/" target="_blank">istanbul</a> at Fri Jan 25 2019 22:43:23 GMT+0100 (GMT+01:00) </div> </div> <script src="..\..\prettify.js"></script> <script> window.onload = function () { if (typeof prettyPrint === 'function') { prettyPrint(); } }; </script> <script src="..\..\sorter.js"></script> </body> </html>