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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 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | 42x 42x 17x 17x 17x 17x 36x 36x 107x 107x 107x 17x 54x 23x 23x 22x 54x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 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 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)); } } } /** * Clone the object properties. * @returns {Object} Cloned object. */ toObj() { const result = {}; result.className = this.constructor.name; result.observations = this.observations; result.labels = this.labels; result.classifications = this.classifications; result.observationCount = this.observationCount; result.theta = this.theta; return result; } /** * Fills the instance from another object. * @param {Object} obj Source object. */ fromObj(obj) { this.labels = obj.labels; this.classifications = obj.classifications; this.observationCount = obj.observationCount; this.theta = obj.theta; this.observations = obj.observations; } } Classifier.classes.LogisticRegressionClassifier = LogisticRegressionClassifier; module.exports = LogisticRegressionClassifier; |