Press n or j to go to the next uncovered block, b, p or k for the previous block.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | 42x 42x 1x 41x 41x 41x 41x 463x 157x 157x 476x 157x 157x 41x 41x 16x 16x 41x 41x 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 Bayes Classifier. */ class BayesClassifier extends Classifier { /** * Sets the smoothing * @param {number} newSmoothing New smoothing value */ setSmoothing(newSmoothing) { this.smoothing = newSmoothing; } /** * Calculate the probability of a class (label) given an observation. * * @param {Vector} observation Observation vector. * @param {String} label Label of the class. * @returns {Number} Value of probability of class. * @memberof BayesClassifier */ getProbabilityOfClass(observation, label) { const smoothing = this.smoothing || 1.0; let probability = 0; const classTotal = this.observations[label].length; observation.forEach((feature, index) => { if (feature) { let count = 0; this.observations[label].forEach(classObservation => { count += classObservation[index]; }); const value = count || smoothing; probability += Math.log(value / classTotal); } }); probability = (classTotal / this.observationCount) * Math.exp(probability); return probability; } /** * 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. * @memberof BayesClassifier */ classifyObservation(srcObservation, classifications) { const observation = Mathops.asVector(srcObservation); Object.keys(this.observations).forEach(label => { const value = this.getProbabilityOfClass(observation, label); classifications.push({ label, value, }); }); } train() { // Do nothing } /** * Clone the object properties. * @returns {Object} Cloned object. */ toObj() { const result = {}; result.className = this.constructor.name; result.settings = this.settings; result.labels = this.labels; result.observations = this.observations; result.smoothing = this.smoothing; result.observationCount = this.observationCount; return result; } /** * Fills the instance from another object. * @param {Object} obj Source object. */ fromObj(obj) { this.settings = obj.settings; this.labels = obj.labels; this.observations = obj.observations; this.smoothing = obj.smoothing; this.observationCount = obj.observationCount; } } Classifier.classes.BayesClassifier = BayesClassifier; module.exports = BayesClassifier; |