/*
* 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;
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