547 lines
21 KiB
JavaScript
547 lines
21 KiB
JavaScript
'use strict'
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/*
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Open Rowing Monitor, https://github.com/laberning/openrowingmonitor
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This tests the Quadratic Theil-Senn Regression algorithm. As regression is an estimation and methods have biasses,
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we need to accept some slack with respect to real-life examples
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*/
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import { test } from 'uvu'
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import * as assert from 'uvu/assert'
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import { createTSQuadraticSeries } from './FullTSQuadraticSeries.js'
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test('Quadratic Approximation startup behaviour', () => {
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const dataSeries = createTSQuadraticSeries(10)
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testCoefficientA(dataSeries, 0)
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testCoefficientB(dataSeries, 0)
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testCoefficientC(dataSeries, 0)
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dataSeries.push(-1, 2)
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testCoefficientA(dataSeries, 0)
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testCoefficientB(dataSeries, 0)
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testCoefficientC(dataSeries, 0)
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dataSeries.push(0, 2)
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testCoefficientA(dataSeries, 0)
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testCoefficientB(dataSeries, 0)
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testCoefficientC(dataSeries, 0)
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dataSeries.push(1, 6)
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testCoefficientA(dataSeries, 2)
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testCoefficientB(dataSeries, 2)
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testCoefficientC(dataSeries, 2)
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})
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test('Quadratic Approximation on a perfect noisefree function y = 2 * Math.pow(x, 2) + 2 * x + 2, 21 datapoints', () => {
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// Data based on 2 x^2 + 2 x + 2
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const dataSeries = createTSQuadraticSeries(21)
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dataSeries.push(-10, 182)
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dataSeries.push(-9, 146)
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dataSeries.push(-8, 114)
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dataSeries.push(-7, 86)
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dataSeries.push(-6, 62)
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dataSeries.push(-5, 42)
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dataSeries.push(-4, 26)
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dataSeries.push(-3, 14) // Pi ;)
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dataSeries.push(-2, 6)
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dataSeries.push(-1, 2)
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dataSeries.push(0, 2)
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dataSeries.push(1, 6)
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dataSeries.push(2, 14)
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dataSeries.push(3, 26)
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dataSeries.push(4, 42)
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dataSeries.push(5, 62)
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dataSeries.push(6, 86)
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dataSeries.push(7, 114)
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dataSeries.push(8, 146)
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dataSeries.push(9, 182)
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dataSeries.push(10, 222)
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testCoefficientA(dataSeries, 2)
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testCoefficientB(dataSeries, 2)
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testCoefficientC(dataSeries, 2)
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})
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test('Quadratic Approximation on a perfect noisefree function y = 2 * Math.pow(x, 2) + 2 * x + 2, with 10 datapoints and some shifting in the series', () => {
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// Data based on 2 x^2 + 2 x + 2, split the dataset in two to see its behaviour when it is around the Vertex
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const dataSeries = createTSQuadraticSeries(10)
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dataSeries.push(-10, 182)
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dataSeries.push(-9, 146)
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dataSeries.push(-8, 114)
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dataSeries.push(-7, 86)
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dataSeries.push(-6, 62)
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dataSeries.push(-5, 42)
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dataSeries.push(-4, 26)
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dataSeries.push(-3, 14) // Pi ;)
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dataSeries.push(-2, 6)
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dataSeries.push(-1, 2)
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dataSeries.push(0, 2)
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testCoefficientA(dataSeries, 2)
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testCoefficientB(dataSeries, 2)
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testCoefficientC(dataSeries, 2)
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dataSeries.push(1, 6)
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dataSeries.push(2, 14)
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dataSeries.push(3, 26)
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dataSeries.push(4, 42)
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dataSeries.push(5, 62)
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dataSeries.push(6, 86)
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dataSeries.push(7, 114)
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dataSeries.push(8, 146)
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dataSeries.push(9, 182)
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dataSeries.push(10, 222)
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testCoefficientA(dataSeries, 2)
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testCoefficientB(dataSeries, 2)
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testCoefficientC(dataSeries, 2)
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})
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test('Quadratic Approximation on function y = 4 * Math.pow(x, 2) + 4 * x + 4, noisefree', () => {
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// Data based on 4 x^2 + 4 x + 4
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const dataSeries = createTSQuadraticSeries(11)
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dataSeries.push(-11, 444)
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dataSeries.push(-10, 364)
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dataSeries.push(-9, 292)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-8, 228)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-7, 172)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-6, 124)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-5, 84)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-4, 52)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-3, 28)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-2, 12)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-1, 4)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(0, 4)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(1, 12)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(2, 28)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(3, 52)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(4, 84)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(5, 124)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(6, 172)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(7, 228)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(8, 292)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(9, 364)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(10, 444)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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})
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test('Quadratic Approximation on function y = 4 * Math.pow(x, 2) + 4 * x + 4, with some noise (+/- 1)', () => {
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// Data based on 4 x^2 + 4 x + 4
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const dataSeries = createTSQuadraticSeries(11)
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dataSeries.push(-11, 443)
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dataSeries.push(-10, 365)
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dataSeries.push(-9, 291)
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testCoefficientA(dataSeries, 2)
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testCoefficientB(dataSeries, -36)
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testCoefficientC(dataSeries, -195)
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dataSeries.push(-8, 229)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 3.6666666666666643) // This is quite acceptable as ORM ignores the C
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dataSeries.push(-7, 171)
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testCoefficientA(dataSeries, 3.666666666666667)
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testCoefficientB(dataSeries, -1.8333333333333335)
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testCoefficientC(dataSeries, -20.916666666666682)
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dataSeries.push(-6, 125)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 3.799999999999997) // This is quite acceptable as ORM ignores the C
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dataSeries.push(-5, 83)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-4, 53)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 3.8571428571428577) // This is quite acceptable as ORM ignores the C
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dataSeries.push(-3, 27)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-2, 13)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 3.8888888888888893) // This is quite acceptable as ORM ignores the C
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dataSeries.push(-1, 3)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(0, 5)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(1, 11)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(2, 29)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(3, 51)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(4, 85)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(5, 123)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(6, 173)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(7, 227)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(8, 293)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(9, 363)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(10, 444)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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})
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test('Quadratic Approximation on function y = 4 * Math.pow(x, 2) + 4 * x + 4, with some noise (+/- 1) and spikes (+/- 9)', () => {
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// Data based on 4 x^2 + 4 x + 4
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const dataSeries = createTSQuadraticSeries(11)
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dataSeries.push(-11, 443)
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dataSeries.push(-10, 365)
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dataSeries.push(-9, 291)
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dataSeries.push(-8, 229)
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dataSeries.push(-7, 171)
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dataSeries.push(-6, 125)
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dataSeries.push(-5, 83)
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dataSeries.push(-4, 53)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 3.8571428571428577)
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dataSeries.push(-3, 37) // FIRST SPIKE +9
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(-2, 3) // SECOND SPIKE -9
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4.142857142857142) // Coefficient B seems to take a hit anyway
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testCoefficientC(dataSeries, 5.9999999999999964) // We get a 5.9999999999999964 instead of 4, which is quite acceptable (especially since ORM ignores the C)
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dataSeries.push(-1, 3)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(0, 5)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(1, 11)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(2, 29)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(3, 51)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(4, 85)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(5, 123)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(6, 173)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(7, 227)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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dataSeries.push(8, 293)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 3) // This is quite acceptable as ORM ignores the C
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dataSeries.push(9, 363)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4) // We get a 3 instead of 4, which is quite acceptable (especially since ORM ignores the C)
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dataSeries.push(10, 444)
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testCoefficientA(dataSeries, 4)
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testCoefficientB(dataSeries, 4)
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testCoefficientC(dataSeries, 4)
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})
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test('Quadratic TS Estimation should be decent for standard real-life example from MathBits with some noise', () => {
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// Data based on https://mathbits.com/MathBits/TISection/Statistics2/quadratic.html
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const dataSeries = createTSQuadraticSeries(13)
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dataSeries.push(10, 115.6)
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dataSeries.push(15, 157.2)
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dataSeries.push(20, 189.2)
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dataSeries.push(24, 220.8)
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dataSeries.push(30, 253.8)
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dataSeries.push(34, 269.2)
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dataSeries.push(40, 284.8)
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dataSeries.push(45, 285.0)
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dataSeries.push(48, 277.4)
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dataSeries.push(50, 269.2)
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dataSeries.push(58, 244.2)
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dataSeries.push(60, 231.4)
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dataSeries.push(64, 180.4)
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testCoefficientA(dataSeries, -0.17785023090944152) // In the example, the TI084 results in -0.1737141137, which we consider acceptably close
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testCoefficientB(dataSeries, 15.115602960635863) // In the example, the TI084 results in 14.52117133, which we consider acceptably close
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testCoefficientC(dataSeries, -35.639987946994665) // In the example, the TI084 results in -21.89774466, which we consider acceptably close
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})
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test('Quadratic TS Estimation should be decent for standard real-life example from VarsityTutors with some noise', () => {
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// Test based on https://www.varsitytutors.com/hotmath/hotmath_help/topics/quadratic-regression
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const dataSeries = createTSQuadraticSeries(7)
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dataSeries.push(-3, 7.5)
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dataSeries.push(-2, 3)
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dataSeries.push(-1, 0.5)
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dataSeries.push(0, 1)
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dataSeries.push(1, 3)
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dataSeries.push(2, 6)
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dataSeries.push(3, 14)
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testCoefficientA(dataSeries, 1.1166666666666667) // The example results in 1.1071 for OLS, which we consider acceptably close
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testCoefficientB(dataSeries, 0.966666666666667) // The example results in 1 for OLS, which we consider acceptably close
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testCoefficientC(dataSeries, 0.44722222222222213) // The example results in 0.5714 for OLS, which we consider acceptably close
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})
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test('Quadratic TS Estimation should be decent for standard example from VTUPulse with some noise, without the vertex being part of the dataset', () => {
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// Test based on https://www.vtupulse.com/machine-learning/quadratic-polynomial-regression-model-solved-example/
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const dataSeries = createTSQuadraticSeries(5)
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dataSeries.push(3, 2.5)
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dataSeries.push(4, 3.3)
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dataSeries.push(5, 3.8)
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dataSeries.push(6, 6.5)
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dataSeries.push(7, 11.5)
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testCoefficientA(dataSeries, 0.9500000000000005) // The example results in 0.7642857 for OLS, which we consider acceptably close given the small sample size
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testCoefficientB(dataSeries, -7.483333333333338) // The example results in -5.5128571 for OLS, which we consider acceptably close given the small sample size
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testCoefficientC(dataSeries, 17.275000000000006) // The example results in 12.4285714 for OLS, which we consider acceptably close given the small sample size
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})
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test('Quadratic TS Estimation should be decent for standard real-life example from Uni Berlin with some noise without the vertex being part of the dataset', () => {
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// Test based on https://www.geo.fu-berlin.de/en/v/soga/Basics-of-statistics/Linear-Regression/Polynomial-Regression/Polynomial-Regression---An-example/index.html
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const dataSeries = createTSQuadraticSeries(25)
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dataSeries.push(0.001399613, -0.23436656)
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dataSeries.push(0.971629779, 0.64689524)
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dataSeries.push(0.579119475, -0.92635765)
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dataSeries.push(0.335693937, 0.13000706)
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dataSeries.push(0.736736086, -0.89294863)
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dataSeries.push(0.492572335, 0.33854780)
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dataSeries.push(0.737133774, -1.24171910)
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dataSeries.push(0.563693769, -0.22523318)
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dataSeries.push(0.877603280, -0.12962722)
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dataSeries.push(0.141426545, 0.37632006)
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dataSeries.push(0.307203910, 0.30299077)
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dataSeries.push(0.024509308, -0.21162739)
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dataSeries.push(0.843665029, -0.76468719)
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dataSeries.push(0.771206067, -0.90455412)
|
|
dataSeries.push(0.149670258, 0.77097952)
|
|
dataSeries.push(0.359605608, 0.56466366)
|
|
dataSeries.push(0.049612895, 0.18897607)
|
|
dataSeries.push(0.409898906, 0.32531750)
|
|
dataSeries.push(0.935457898, -0.78703491)
|
|
dataSeries.push(0.149476207, 0.80585375)
|
|
dataSeries.push(0.234315216, 0.62944986)
|
|
dataSeries.push(0.455297119, 0.02353327)
|
|
dataSeries.push(0.102696671, 0.27621694)
|
|
dataSeries.push(0.715372314, -1.20379729)
|
|
dataSeries.push(0.681745393, -0.83059624)
|
|
testCoefficientA(dataSeries, -3.13052236289358)
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|
testCoefficientB(dataSeries, 1.5907039702198331)
|
|
testCoefficientC(dataSeries, 0.12896850914578195)
|
|
})
|
|
|
|
test('Quadratic TS Estimation should be decent for standard real-life example from Statology.org with some noise and chaotic X values', () => {
|
|
// Test based on https://www.statology.org/quadratic-regression-r/
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|
const dataSeries = createTSQuadraticSeries(11)
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|
dataSeries.push(6, 14)
|
|
dataSeries.push(9, 28)
|
|
dataSeries.push(12, 50)
|
|
dataSeries.push(14, 70)
|
|
dataSeries.push(30, 89)
|
|
dataSeries.push(35, 94)
|
|
dataSeries.push(40, 90)
|
|
dataSeries.push(47, 75)
|
|
dataSeries.push(51, 59)
|
|
dataSeries.push(55, 44)
|
|
dataSeries.push(60, 27)
|
|
testCoefficientA(dataSeries, -0.10466531440162272) // The example results in -0.1012 for R after two rounds, which we consider acceptably close
|
|
testCoefficientB(dataSeries, 6.98670916642519) // The example results in 6.7444 for R after two rounds, which we consider acceptably close
|
|
testCoefficientC(dataSeries, -21.826295759683177) // The example results in 18.2536 for R after two rounds, but for ORM, this factor is irrelevant
|
|
})
|
|
|
|
test('Quadratic TS Estimation should be decent for standard real-life example from StatsDirect.com with some noise and chaotic X values', () => {
|
|
// Test based on https://www.statsdirect.com/help/regression_and_correlation/polynomial.htm
|
|
const dataSeries = createTSQuadraticSeries(10)
|
|
dataSeries.push(1290, 1182)
|
|
dataSeries.push(1350, 1172)
|
|
dataSeries.push(1470, 1264)
|
|
dataSeries.push(1600, 1493)
|
|
dataSeries.push(1710, 1571)
|
|
dataSeries.push(1840, 1711)
|
|
dataSeries.push(1980, 1804)
|
|
dataSeries.push(2230, 1840)
|
|
dataSeries.push(2400, 1956)
|
|
dataSeries.push(2930, 1954)
|
|
testCoefficientA(dataSeries, -0.0004480669511301859) // The example results in -0.00045 through QR decomposition by Givens rotations, which we consider acceptably close
|
|
testCoefficientB(dataSeries, 2.373459636061883) // The example results in 2.39893 for QR decomposition by Givens rotations, which we consider acceptably close
|
|
testCoefficientC(dataSeries, -1178.1630473732216) // The example results in -1216.143887 for QR decomposition by Givens rotations, but for ORM, this factor is irrelevant
|
|
})
|
|
|
|
test('Quadratic Approximation with a clean function and a reset', () => {
|
|
// Data based on 2 x^2 + 2 x + 2
|
|
const dataSeries = createTSQuadraticSeries(10)
|
|
dataSeries.push(-10, 182)
|
|
dataSeries.push(-9, 146)
|
|
dataSeries.push(-8, 114)
|
|
dataSeries.push(-7, 86)
|
|
dataSeries.push(-6, 62)
|
|
dataSeries.push(-5, 42)
|
|
testCoefficientA(dataSeries, 2)
|
|
testCoefficientB(dataSeries, 2)
|
|
testCoefficientC(dataSeries, 2)
|
|
dataSeries.push(-4, 26)
|
|
dataSeries.push(-3, 14) // Pi ;)
|
|
dataSeries.push(-2, 6)
|
|
dataSeries.push(-1, 2)
|
|
dataSeries.push(0, 2)
|
|
dataSeries.push(1, 6)
|
|
dataSeries.push(2, 14)
|
|
testCoefficientA(dataSeries, 2)
|
|
testCoefficientB(dataSeries, 2)
|
|
testCoefficientC(dataSeries, 2)
|
|
dataSeries.push(3, 26)
|
|
dataSeries.push(4, 42)
|
|
dataSeries.push(5, 62)
|
|
dataSeries.push(6, 86)
|
|
dataSeries.push(7, 114)
|
|
dataSeries.push(8, 146)
|
|
dataSeries.push(9, 182)
|
|
dataSeries.push(10, 222)
|
|
testCoefficientA(dataSeries, 2)
|
|
testCoefficientB(dataSeries, 2)
|
|
testCoefficientC(dataSeries, 2)
|
|
dataSeries.reset()
|
|
testCoefficientA(dataSeries, 0)
|
|
testCoefficientB(dataSeries, 0)
|
|
testCoefficientC(dataSeries, 0)
|
|
dataSeries.push(-1, 2)
|
|
testCoefficientA(dataSeries, 0)
|
|
testCoefficientB(dataSeries, 0)
|
|
testCoefficientC(dataSeries, 0)
|
|
dataSeries.push(0, 2)
|
|
testCoefficientA(dataSeries, 0)
|
|
testCoefficientB(dataSeries, 0)
|
|
testCoefficientC(dataSeries, 0)
|
|
dataSeries.push(1, 6)
|
|
testCoefficientA(dataSeries, 2)
|
|
testCoefficientB(dataSeries, 2)
|
|
testCoefficientC(dataSeries, 2)
|
|
})
|
|
|
|
test('Quadratic TS Estimation should result in a straight line for function y = x', () => {
|
|
// As ORM will encounter straight lines (when forces are balanced on the flywheel, there is no acceleration/deceleration), so we need to test this as well
|
|
const dataSeries = createTSQuadraticSeries(7)
|
|
dataSeries.push(0, 0)
|
|
dataSeries.push(1, 1)
|
|
dataSeries.push(2, 2)
|
|
dataSeries.push(3, 3)
|
|
dataSeries.push(4, 4)
|
|
dataSeries.push(5, 5)
|
|
dataSeries.push(6, 6)
|
|
testCoefficientA(dataSeries, 0)
|
|
testCoefficientB(dataSeries, 1)
|
|
testCoefficientC(dataSeries, 0)
|
|
})
|
|
|
|
function testCoefficientA (series, expectedValue) {
|
|
assert.ok(series.coefficientA() === expectedValue, `Expected value for coefficientA at X-position ${series.xAtSeriesEnd()} is ${expectedValue}, encountered a ${series.coefficientA()}`)
|
|
}
|
|
|
|
function testCoefficientB (series, expectedValue) {
|
|
assert.ok(series.coefficientB() === expectedValue, `Expected value for coefficientB at X-position ${series.xAtSeriesEnd()} is ${expectedValue}, encountered a ${series.coefficientB()}`)
|
|
}
|
|
|
|
function testCoefficientC (series, expectedValue) {
|
|
assert.ok(series.coefficientC() === expectedValue, `Expected value for coefficientC at X-position ${series.xAtSeriesEnd()} is ${expectedValue}, encountered a ${series.coefficientC()}`)
|
|
}
|
|
|
|
/*
|
|
function testSlope (series, position, expectedValue) {
|
|
assert.ok(series.slope(position) === expectedValue, `Expected value for Slope-${position} at X-position ${series.xAtSeriesEnd()} (slope at X-position ${series.xAtPosition(position)}) is ${expectedValue}, encountered a ${series.slope(position)}`)
|
|
}
|
|
|
|
function reportAll (series) {
|
|
assert.ok(series.coefficientA() === 99, `time: ${series.xAtSeriesEnd()}, coefficientA: ${series.coefficientA()}, coefficientB: ${series.coefficientB()}, coefficientC: ${series.coefficientC()}, Slope-10: ${series.slope(10)}, Slope-9: ${series.slope(9)}, Slope-8: ${series.slope(8)}, Slope-7: ${series.slope(7)}, Slope-6: ${series.slope(6)}, Slope-5: ${series.slope(5)}, Slope-4: ${series.slope(4)}, Slope-3: ${series.slope(3)}, Slope-2: ${series.slope(2)}, Slope-1: ${series.slope(1)}, Slope-0: ${series.slope(0)}`)
|
|
}
|
|
*/
|
|
|
|
test.run()
|