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Library for NLU (Natural Language Understanding) done in Node.js

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<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <title>JSDoc: Source: math/mathops.js</title> <script src="scripts/prettify/prettify.js"> </script> <script src="scripts/prettify/lang-css.js"> </script> <!--[if lt IE 9]> <script src="//html5shiv.googlecode.com/svn/trunk/html5.js"></script> <![endif]--> <link type="text/css" rel="stylesheet" href="styles/prettify-tomorrow.css"> <link type="text/css" rel="stylesheet" href="styles/jsdoc-default.css"> </head> <body> <div id="main"> <h1 class="page-title">Source: math/mathops.js</h1> <section> <article> <pre class="prettyprint source linenums"><code>/* * 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 Vector = require('./vector'); const Matrix = require('./matrix'); let job; let IS_WORKET_THREADS_ENABLED; /* eslint-disable */ { /* istanbul ignore next */ IS_WORKET_THREADS_ENABLED = false; job = fn => fn(); try { require("worker_threads"); IS_WORKET_THREADS_ENABLED = true; job = require("microjob").job; } catch (e) { /* */ } } /* eslint-enable */ /* istanbul ignore next */ const mtComputeThetasIterator = ( Mathops, srcExamples, srcClassifications, num ) => job( () => { let ret; try { ret = Mathops.computeThetasHelper(srcExamples, srcClassifications, num); } catch (e) { /* eslint-disable no-console */ console.error('Error in job', e); } return ret; }, { ctx: { srcExamples, srcClassifications, num, Mathops, Vector, Matrix, }, } ); const stComputeThetasIterator = ( Mathops, srcExamples, srcClassifications, num ) => Mathops.computeThetasHelper(srcExamples, srcClassifications, num); /* istanbul ignore next */ const computeThetasIterator = IS_WORKET_THREADS_ENABLED ? mtComputeThetasIterator : stComputeThetasIterator; class Mathops { /** * Calculates the sigmoid defined as: * S(x) = 1/(1+e^(-x)) * @param {Number} x Input value. * @returns {Number} Sigmoid of x. */ static sigmoid(x) { let result = 1.0 / (1 + Math.exp(-x)); if (result === 1) { result = 0.99999999999999; } else if (result === 0) { result = 1e-14; } return result; } /** * Calculate the hypothesis from the observations. * @param {Matrix} theta Theta matrix. * @param {Matrix} observations Observations. * @returns {Matrix} Hypothesis result. */ static hypothesis(theta, observations) { return observations.multiply(theta, Mathops.sigmoid); } /** * Cost function * @param {Matrix} theta Theta matrix. * @param {Matrix} observations Observations. * @param {Matrix} classifications Classification matrix. * @param {Matrix} srcHypothesis Hypothesis. If not provided is calculated. * @return {number} Calculated cost based on the hypothesis. */ static cost(theta, observations, classifications, srcHypothesis) { const hypothesis = srcHypothesis || Mathops.hypothesis(theta, observations); const ones = Vector.one(observations.rowCount()); const costOne = Vector.zero(observations.rowCount()) .subtract(classifications) .elementMultiply(hypothesis.log()); const costZero = ones .subtract(classifications) .elementMultiply(ones.subtract(hypothesis).log()); return (1 / observations.rowCount()) * costOne.subtract(costZero).sum(); } /** * Descend the gradient based on the cost function. * @param {Matrix} srcTheta Theta matrix. * @param {Vector} srcExamples Examples. * @param {Matrix} classifications Classification matrix. * @param {Object} srcOptions Settings for the descend. */ static descendGradient(srcTheta, srcExamples, classifications, srcOptions) { const options = srcOptions || {}; const maxIterationFactor = options.maxIterationFactor || Mathops.maxIterationFactor; const learningRateStart = options.learningRateStart || Mathops.learningRateStart; const maxCostDelta = options.maxCostDelta || Mathops.maxCostDelta; const learningRateDivisor = options.learningRateDivisor || Mathops.learningRateDivisor; const maxIterations = maxIterationFactor * srcExamples.rowCount(); const examples = Matrix.one(srcExamples.rowCount(), 1).augment(srcExamples); const examplesRowCountInverse = 1 / examples.rowCount(); const transposed = examples.transpose(); let learningRate = learningRateStart; let multiplyFactor = examplesRowCountInverse * learningRate; let learningRateFound = false; let theta = srcTheta.augment([0]); while (!learningRateFound || learningRate === 0) { let i = 0; let lastCost = null; while (i &lt; maxIterations) { const hypothesis = Mathops.hypothesis(theta, examples); theta = theta.subtract( transposed .multiply(hypothesis.subtract(classifications)) .multiply(multiplyFactor) ); const currentCost = Mathops.cost( theta, examples, classifications, hypothesis ); i += 1; if (lastCost) { if (currentCost >= lastCost) { break; } learningRateFound = true; if (lastCost - currentCost &lt; maxCostDelta) { break; } } if (i >= maxIterations) { throw new Error('Unable to find minimum'); } lastCost = currentCost; } learningRate /= learningRateDivisor; multiplyFactor = examplesRowCountInverse * learningRate; } return theta.chomp(1); } /** * Return a vector representing x. * @param {Number[]} x Input array. * @returns {Vector} Vector representing x. */ static asVector(x) { return new Vector(x); } /** * Returns a matrix representing x. * @param {Number[][]} x Input array. * @return {Matrix} Matrix representing x. */ static asMatrix(x) { return new Matrix(x); } /** * Function returning 0. * @returns {number} Returns 0. */ static zero() { return 0; } /** * Compute the thetas of the examples and classifications. * @param {Vector} srcExamples Vector of examples. * @param {Matrix} srcClassifications Matrix of classifications. */ static async computeThetas(srcExamples, srcClassifications) { if (!srcClassifications || srcClassifications.length === 0) { return []; } const result = []; await Promise.all( srcClassifications[0].map(async (_, i) => { try { const item = await computeThetasIterator( Mathops, srcExamples, srcClassifications, i ); result.push(item); } catch (e) { console.error('Error in loop', e); } }) ).catch(console.error); return result; } static computeThetasHelper(srcExamples, srcClassifications, num) { const examples = this.asMatrix(srcExamples); const classifications = this.asMatrix(srcClassifications); const row = examples.row(0); const theta = row.map(this.zero); return this.descendGradient(theta, examples, classifications.column(num)); } } Mathops.learningRateStart = 3; Mathops.learningRateDivisor = 3; Mathops.maxIterationFactor = 500; Mathops.maxCostDelta = 0.0001; module.exports = Mathops; </code></pre> </article> </section> </div> <nav> <h2><a href="index.html">Home</a></h2><h3>Classes</h3><ul><li><a href="BinaryNeuralNetworkClassifier.html">BinaryNeuralNetworkClassifier</a></li><li><a href="Classifier.html">Classifier</a></li><li><a href="ConversationContext.html">ConversationContext</a></li><li><a href="DutchStemmer.html">DutchStemmer</a></li><li><a href="EnglishStemmer.html">EnglishStemmer</a></li><li><a href="EnumNamedEntity.html">EnumNamedEntity</a></li><li><a href="Evaluator.html">Evaluator</a></li><li><a href="HungarianStemmer.html">HungarianStemmer</a></li><li><a href="ItalianStemmer.html">ItalianStemmer</a></li><li><a href="Language.html">Language</a></li><li><a href="LogisticRegressionClassifier.html">LogisticRegressionClassifier</a></li><li><a href="Matrix.html">Matrix</a></li><li><a href="MemoryConversationContext.html">MemoryConversationContext</a></li><li><a href="NamedEntity.html">NamedEntity</a></li><li><a href="NerManager.html">NerManager</a></li><li><a href="NlgManager.html">NlgManager</a></li><li><a href="NlpClassifier.html">NlpClassifier</a></li><li><a href="NlpManager.html">NlpManager</a></li><li><a href="NorwegianStemmer.html">NorwegianStemmer</a></li><li><a href="PortugueseStemmer.html">PortugueseStemmer</a></li><li><a href="Recognizer.html">Recognizer</a></li><li><a href="RegexNamedEntity.html">RegexNamedEntity</a></li><li><a href="RomanianStemmer.html">RomanianStemmer</a></li><li><a href="RussianStemmer.html">RussianStemmer</a></li><li><a href="SentimentAnalyzer.html">SentimentAnalyzer</a></li><li><a href="SentimentManager.html">SentimentManager</a></li><li><a href="SimilarSearch.html">SimilarSearch</a></li><li><a href="SlotManager.html">SlotManager</a></li><li><a href="StemmerJa.html">StemmerJa</a></li><li><a href="SwedishStemmer.html">SwedishStemmer</a></li><li><a href="Tokenizer.html">Tokenizer</a></li><li><a href="TrimNamedEntity.html">TrimNamedEntity</a></li><li><a href="TurkishStemmer.html">TurkishStemmer</a></li><li><a href="Vector.html">Vector</a></li><li><a href="XTable.html">XTable</a></li></ul><h3>Global</h3><ul><li><a href="global.html#endsinArr">endsinArr</a></li><li><a href="global.html#prelude">prelude</a></li><li><a href="global.html#regions">regions</a></li><li><a href="global.html#stem">stem</a></li><li><a href="global.html#stopwords">stopwords</a></li></ul> </nav> <br class="clear"> <footer> Documentation generated by <a href="https://github.com/jsdoc3/jsdoc">JSDoc 3.5.5</a> on Sat Oct 13 2018 19:14:51 GMT+0200 (CEST) </footer> <script> prettyPrint(); 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