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| /*
* 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) {
return new Promise((resolve, reject) => {
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 < 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) {
Iif (currentCost >= lastCost) {
break;
}
learningRateFound = true;
if (lastCost - currentCost < maxCostDelta) {
break;
}
}
Iif (i >= maxIterations) {
return reject(new Error('Unable to find minimum'));
}
lastCost = currentCost;
}
learningRate /= learningRateDivisor;
multiplyFactor = examplesRowCountInverse * learningRate;
}
return resolve(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 async 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;
|