node-nlp

<!doctype html> <html lang="en"> <head> <title>Code coverage report for lib/classifiers/logistic-regression-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> logistic-regression-classifier.js </h1> <div class='clearfix'> <div class='fl pad1y space-right2'> 100% Statements 18/18 </div> <div class='fl pad1y space-right2'> 100% Branches 2/2 </div> <div class='fl pad1y space-right2'> 100% Functions 3/3 </div> <div class='fl pad1y space-right2'> 100% Lines 18/18 </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</td><td class="line-coverage quiet">                                              31x 31x                           46x 46x 46x 46x 99x 99x 366x 366x 366x     46x                     541x                           616x 616x 74x 541x           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 Classifier = require('./classifier'); const { Mathops } = require('../math');   /** * Class for a Logistic Regression Classifier. */ class LogisticRegressionClassifier extends Classifier { /** * Train the logistic regression clasifier, that means * that it calculates the thetas that relates all the features * with the classifications, so when a new vector of features * is the input to classify, these thetas are the weights for the * calculation of the scores of each classification. */ async train() { const observations = []; const classifications = this.createClassificationMatrix(); let currentObservation = 0; for (let i = 0, li = this.labels.length; i < li; i += 1) { const classificationObservations = this.observations[this.labels[i]]; for (let j = 0, lj = classificationObservations.length; j < lj; j += 1) { observations.push(classificationObservations[j]); classifications[currentObservation][i] = 1; currentObservation += 1; } } this.theta = await Mathops.computeThetas(observations, classifications); }   /** * Given an observation vector and the index of one of the classifications, * it returns an object that contains the label of the classification and * the score of the vector for this classification. * @param {Vector} observation Observation vector. * @param {Number} indexClassification Index of the classification. */ newClassification(observation, indexClassification) { return { label: this.labels[indexClassification], value: Mathops.sigmoid(observation.dot(this.theta[indexClassification])), }; }   /** * Given an observation and an array for inserting the results, * it calculates the score of the observation for each of the classifications * and fills the array with the result objects. * @param {Object} srcObservation Source observation. * @param {Object[]} classifications Array of classifications. */ classifyObservation(srcObservation, classifications) { const observation = Mathops.asVector(srcObservation); if (this.theta) { for (let i = 0; i < this.theta.length; i += 1) { classifications.push(this.newClassification(observation, i)); } } } }   module.exports = LogisticRegressionClassifier;  </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>