Adds required files
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Roman Galochkin
parent
7bc62d3934
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204
src/utility/lib/continuousDistribution.js
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204
src/utility/lib/continuousDistribution.js
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const _ = require('lodash');
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const { interpolate, range, min, max } = require('./functions');
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class ContinuousDistribution {
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/**
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* @param {number[]} xs
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* @param {number[]} ys
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*/
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constructor(xs, ys) {
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if (!_.isArray(xs)) {
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throw new Error('XS should be an array.');
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}
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if (!_.isArray(ys)) {
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throw new Error('YS should be an array.');
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}
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if (!this.validateHasLength(xs)) {
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throw new Error('You need at least one element.');
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}
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if (!this.validateSize(xs, ys)) {
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throw new Error('Arrays of "xs" and "ys" have different sizes.');
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}
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const sorted = this.order(xs, ys);
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this.xs = sorted.xs;
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this.ys = sorted.ys;
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}
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/**
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* Order them to make sure that xs are increasing
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* @param {number[]} xs
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* @param {number[]} ys
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* @return {{ys: number[], xs: number[]}}
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*/
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order(xs, ys) {
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const xsYs = xs.map((v, i) => ({ ys: ys[i], xs: v }));
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const sorted = xsYs.sort((a, b) => {
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if (a.xs > b.xs) return 1;
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if (a.xs < b.xs) return -1;
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return 0;
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});
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const XS = sorted.map(v => v.xs);
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const YS = sorted.map(v => v.ys);
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return { xs: XS, ys: YS };
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}
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/**
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* @param {number[]} xs
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* @param {number[]} ys
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* @return {boolean}
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*/
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validateSize(xs, ys) {
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return xs.length === ys.length;
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}
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/**
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* @param xs
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* @returns {boolean}
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*/
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validateHasLength(xs) {
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return xs.length > 0;
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}
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/**
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* @returns {number}
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*/
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minX() {
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return this.xs[0]
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}
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/**
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* @returns {number}
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*/
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maxX() {
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return this.xs[this.xs.length - 1]
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}
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/**
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* If xs=[1,2,3], and ys=[5,6,7],
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* then findY(1) = 5, findY(3) = 7, findY(1.5) = 5.5
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* @param {number} x
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* @return {number}
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*/
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findY(x) {
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let firstHigherIndex = this.xs.findIndex(X => X >= x);
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if (firstHigherIndex < 0) return this.ys[this.ys.length - 1];
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if (firstHigherIndex === 0) return this.ys[0];
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let lowerOrEqualIndex = firstHigherIndex - 1;
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if (lowerOrEqualIndex < 0) lowerOrEqualIndex = 0;
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let needsInterpolation = this.xs[lowerOrEqualIndex] !== x;
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if (needsInterpolation) {
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return interpolate(
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this.xs[lowerOrEqualIndex],
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this.xs[firstHigherIndex],
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this.ys[lowerOrEqualIndex],
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this.ys[firstHigherIndex],
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x
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);
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} else {
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return this.ys[lowerOrEqualIndex];
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}
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}
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/**
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* If xs=[1,2,3], and ys=[5,6,7],
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* then findX(5) = 1, findX(7) = 3, findY(5.5) = 1.5
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* This should do the same thing as `findY`, but for Y.
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* @param {number} y
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* @return {number}
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*/
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findX(y) {
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let firstHigherIndex = this.ys.findIndex(Y => Y >= y);
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if (firstHigherIndex < 0) return this.xs[this.xs.length - 1];
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if (firstHigherIndex === 0) return this.xs[0];
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let lowerOrEqualIndex = firstHigherIndex - 1;
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if (lowerOrEqualIndex < 0) lowerOrEqualIndex = 0;
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let needsInterpolation = this.ys[lowerOrEqualIndex] !== y;
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if (needsInterpolation) {
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return interpolate(
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this.ys[lowerOrEqualIndex],
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this.ys[firstHigherIndex],
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this.xs[lowerOrEqualIndex],
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this.xs[firstHigherIndex],
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y
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);
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} else {
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return this.xs[lowerOrEqualIndex];
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}
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}
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/**
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* @param {number[]} xs
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* @return {ContinuousDistribution}
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*/
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convertWithAlternativeXs(xs) {
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const ys = xs.map(x => this.findY(x));
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return new ContinuousDistribution(xs, ys);
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}
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/**
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* @param {number} newLength
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* @return {ContinuousDistribution}
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*/
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convertToNewLength(newLength) {
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const _range = range(min(this.xs), max(this.xs), newLength);
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return this.convertWithAlternativeXs(_range);
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}
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/**
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* @return {number}
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*/
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sampleSingle() {
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const y = Math.random();
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return this.findX(y);
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}
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/**
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* Poduce n samples, using ``sampleSingle`` for each.
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* @param size
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* @return {number[]}
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*/
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sample(size) {
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return Array.from(Array(size), () => this.sampleSingle());
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}
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/**
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* Finds the integral. Takes the average Y value between points,
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* treating them like a triangle.
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* @return {number[]}
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*/
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integral(params = { filterOutNaNs: false }) {
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let integral = 0;
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if (!params.filterOutNaNs && _.includes(this.ys, NaN)) {
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return NaN;
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} else if (_.includes(this.ys, Infinity) && _.includes(this.ys, -Infinity)) {
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return NaN;
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} else if (_.includes(this.ys, Infinity)) {
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return Infinity;
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} else if (_.includes(this.ys, -Infinity)) {
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return -Infinity;
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}
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for (let i = 1; i < this.ys.length; i++) {
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let thisY = this.ys[i];
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let lastY = this.ys[i - 1];
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let thisX = this.xs[i];
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let lastX = this.xs[i - 1];
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if (
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_.isFinite(thisY) && _.isFinite(lastY) &&
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_.isFinite(thisX) && _.isFinite(lastX)
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) {
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let sectionInterval = ((thisY + lastY) / 2) * (thisX - lastX);
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integral = integral + sectionInterval;
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}
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}
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return integral;
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}
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}
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module.exports = {
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ContinuousDistribution,
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};
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111
src/utility/lib/continuousDistribution.spec.js
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111
src/utility/lib/continuousDistribution.spec.js
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@ -0,0 +1,111 @@
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const { ContinuousDistribution } = require('./continuousDistribution');
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const { up, down } = require('./functions');
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describe('ContinuousDistribution Class', () => {
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it('constructor()', () => {
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const xs = up(1, 9);
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const ys = up(1, 8);
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expect(() => {
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new ContinuousDistribution(xs, ys);
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}).toThrow(/^Arrays of "xs" and "ys" have different sizes.$/);
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});
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it('order()', () => {
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const xs = down(9, 1);
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const ys = down(9, 1);
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const cdf = new ContinuousDistribution(xs, ys);
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expect(cdf.xs).toEqual(up(1, 9));
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expect(cdf.ys).toEqual(up(1, 9));
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});
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it('findY()', () => {
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const xs = [1, 2, 3];
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const ys = [5, 6, 7];
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const cdf = new ContinuousDistribution(xs, ys);
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expect(cdf.findY(1)).toEqual(5);
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expect(cdf.findY(1.5)).toEqual(5.5);
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expect(cdf.findY(3)).toEqual(7);
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expect(cdf.findY(4)).toEqual(7);
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expect(cdf.findY(15)).toEqual(7);
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expect(cdf.findY(-1)).toEqual(5);
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});
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it('findX()', () => {
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const xs = [1, 2, 3];
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const ys = [5, 6, 7];
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const cdf = new ContinuousDistribution(xs, ys);
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expect(cdf.findX(5)).toEqual(1);
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expect(cdf.findX(7)).toEqual(3);
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expect(cdf.findX(5.5)).toEqual(1.5);
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expect(cdf.findX(8)).toEqual(3);
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expect(cdf.findX(4)).toEqual(1);
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});
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it('convertWithAlternativeXs() when "XS" within "xs"', () => {
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const xs = up(1, 9);
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const ys = up(20, 28);
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const cdf = new ContinuousDistribution(xs, ys);
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const XS = up(3, 7);
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const CDF = cdf.convertWithAlternativeXs(XS);
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expect(CDF.xs).toEqual([3, 4, 5, 6, 7]);
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expect(CDF.ys).toEqual([22, 23, 24, 25, 26]);
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});
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it('convertToNewLength()', () => {
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const xs = up(1, 9);
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const ys = up(50, 58);
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const cdf = new ContinuousDistribution(xs, ys);
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const CDF = cdf.convertToNewLength(3);
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expect(CDF.xs).toEqual([1, 5, 9]);
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expect(CDF.ys).toEqual([50, 54, 58]);
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});
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it('sample()', () => {
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const xs = up(1, 9);
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const ys = up(70, 78);
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const cdf = new ContinuousDistribution(xs, ys);
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const XS = cdf.sample(3);
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expect(Number.isInteger(XS[0])).toBe(true);
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expect(Number.isInteger(XS[1])).toBe(true);
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expect(Number.isInteger(XS[2])).toBe(true);
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});
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describe('integral()', () => {
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it('with regular inputs', () => {
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const xs = [0,1,2,4];
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const ys = [0.0, 1.0, 2.0, 2.0];
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const cdf = new ContinuousDistribution(xs, ys);
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const integral = cdf.integral();
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expect(integral).toEqual(6);
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});
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it('with an infinity', () => {
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const xs = [0,1,2,4];
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const ys = [0.0, 1.0, Infinity, 2.0];
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const cdf = new ContinuousDistribution(xs, ys);
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const integral = cdf.integral();
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expect(integral).toEqual(Infinity);
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});
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it('with negative infinity', () => {
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const xs = [0,1,2,4];
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const ys = [0.0, 1.0, -Infinity, 2.0];
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const cdf = new ContinuousDistribution(xs, ys);
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const integral = cdf.integral();
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expect(integral).toEqual(-Infinity);
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});
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it('with both positive and negative infinities', () => {
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const xs = [0,1,2,4];
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const ys = [0.0, 1.0, -Infinity, Infinity];
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const cdf = new ContinuousDistribution(xs, ys);
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const integral = cdf.integral();
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expect(integral).toEqual(NaN);
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});
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it('with a NaN and filterOutNaNs set to false', () => {
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const xs = [0,1,2,4];
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const ys = [0.0, 1.0, 2.0, NaN];
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const cdf = new ContinuousDistribution(xs, ys);
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const integral = cdf.integral({filterOutNaNs: false});
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expect(integral).toEqual(NaN);
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});
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it('with a NaN and filterOutNaNs set to true', () => {
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const xs = [0,1,2,4];
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const ys = [0.0, 1.0, 2.0, NaN];
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const cdf = new ContinuousDistribution(xs, ys);
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const integral = cdf.integral({filterOutNaNs: true});
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expect(integral).toEqual(2);
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});
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})
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});
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136
src/utility/lib/functions.js
Normal file
136
src/utility/lib/functions.js
Normal file
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@ -0,0 +1,136 @@
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/**
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* @param {number} xMin
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* @param {number} xMax
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* @param {number} yMin
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* @param {number} yMax
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* @param {number} xIntended
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* @return {number}
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*/
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function interpolate(xMin, xMax, yMin, yMax, xIntended) {
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const minProportion = (xMax - xIntended) / (xMax - xMin);
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const maxProportion = (xIntended - xMin) / (xMax - xMin);
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return (yMin * minProportion) + (yMax * maxProportion);
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}
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/**
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* This should return an array of n evenly-spaced items
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* between min and max, including min and max.
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* range(1,5,3) = [1, 3, 5];
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* range(1,5,5) = [1, 2, 3, 4, 5];
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* @param {number} min
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* @param {number} max
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* @param {number} n
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* @return {number[]}
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*/
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function range(min, max, n) {
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if (n <= 0) throw new RangeError('n is less then zero');
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if (n === Infinity) throw new RangeError('n is Infinity');
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if (n === 0) return [];
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if (n === 1) return [min];
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if (n === 2) return [min, max];
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if (min === max) return Array(n).fill(min);
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n -= 1;
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const diff = min - max;
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const interval = Math.abs(diff / n);
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const result = [];
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let item = min;
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do {
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result.push(item);
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item += interval;
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} while (item <= max);
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// corrects results because of math errors
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if ((n + 1) - result.length === 1) {
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result.push(max);
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}
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return result;
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}
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/**
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* @param {number[]} arr
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* @return {number}
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*/
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function sum(arr) {
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return arr.reduce((acc, val) => acc + val, 0);
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}
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/**
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* @param {number[]} arr
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* @return {number}
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*/
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function mean(arr) {
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return sum(arr) / arr.length;
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}
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/**
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* @param {number[]} arr
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* @return {number}
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*/
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function min(arr) {
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let val = arr[0];
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for (let i = 1; i < arr.length; i++) {
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if (arr[i] < val) {
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val = arr[i];
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}
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}
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return val;
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}
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/**
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* @param {number[]} arr
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* @return {number}
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*/
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function max(arr) {
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let val = arr[0];
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for (let i = 1; i < arr.length; i++) {
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if (arr[i] > val) {
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val = arr[i];
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}
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}
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return val;
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}
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/**
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* @param {number} min
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* @param {number} max
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* @return {number}
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*/
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function random(min, max) {
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return Math.floor(Math.random() * (max - min + 1) + min);
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}
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/**
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* @param {number} from
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* @param {number} to
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* @return {number[]}
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*/
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function up(from, to) {
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const arr = [];
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for (let i = from; i <= to; i++) arr.push(i);
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return arr;
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}
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/**
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* @param {number} from
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* @param {number} to
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* @return {number[]}
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*/
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function down(from, to) {
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const arr = [];
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for (let i = from; i >= to; i--) arr.push(i);
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return arr;
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}
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module.exports = {
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interpolate,
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min,
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max,
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range,
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mean,
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random,
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up,
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down,
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};
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55
src/utility/lib/functions.spec.js
Normal file
55
src/utility/lib/functions.spec.js
Normal file
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@ -0,0 +1,55 @@
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const { interpolate } = require('./functions');
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const { range } = require('./functions');
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const { mean } = require('./functions');
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const { min } = require('./functions');
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const { max } = require('./functions');
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const { random } = require('./functions');
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const { up, down } = require('./functions');
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describe('Functions', () => {
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it('interpolate()', () => {
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expect(interpolate(
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10, 20,
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1, 2,
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15
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)).toBe(1.5);
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});
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it('range()', () => {
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expect(range(1, 5, 3)).toEqual([1, 3, 5]);
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expect(range(1, 5, 5)).toEqual([1, 2, 3, 4, 5]);
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expect(range(-10, 15, 2)).toEqual([-10, 15]);
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expect(range(-10, 15, 3)).toEqual([-10, 2.5, 15]);
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expect(range(-10.3, 17, 3)).toEqual([-10.3, 3.3499999999999996, 17]);
|
||||
expect(range(-10.3, 17, 5)).toEqual([-10.3, -3.4750000000000005, 3.3499999999999996, 10.175, 17]);
|
||||
expect(range(-10.3, 17.31, 3)).toEqual([-10.3, 3.504999999999999, 17.31]);
|
||||
expect(range(1, 1, 3)).toEqual([1, 1, 1]);
|
||||
});
|
||||
it('mean()', () => {
|
||||
expect(mean([1, 2, 3])).toBe(2);
|
||||
expect(mean([1, 2, 3, -2])).toBe(1);
|
||||
expect(mean([1, 2, 3, -2, -10])).toBe(-1.2);
|
||||
});
|
||||
it('min()', () => {
|
||||
expect(min([1, 2, 3])).toBe(1);
|
||||
expect(min([-1, -2, 0, 20])).toBe(-2);
|
||||
expect(min([-1, -2, 0, 20, -2.2])).toBe(-2.2);
|
||||
});
|
||||
it('max()', () => {
|
||||
expect(max([1, 2, 3])).toBe(3);
|
||||
expect(max([-1, -2, 0, 20])).toBe(20);
|
||||
expect(max([-1, -2, 0, -2.2])).toBe(0);
|
||||
});
|
||||
it('random()', () => {
|
||||
const num = random(1, 5);
|
||||
expect(num).toBeLessThanOrEqual(5);
|
||||
expect(num).toBeGreaterThanOrEqual(1);
|
||||
});
|
||||
it('up()', () => {
|
||||
expect(up(1, 5)).toEqual([1, 2, 3, 4, 5]);
|
||||
expect(up(-1, 5)).toEqual([-1, 0, 1, 2, 3, 4, 5]);
|
||||
});
|
||||
it('down()', () => {
|
||||
expect(down(5, 1)).toEqual([5, 4, 3, 2, 1]);
|
||||
expect(down(5, -1)).toEqual([5, 4, 3, 2, 1, 0, -1]);
|
||||
});
|
||||
});
|
||||
Loading…
Reference in New Issue
Block a user