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Update FullTSQuadraticSeries.test.js
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@JaapvanEkris
JaapvanEkris authored Nov 27, 2023
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140 changes: 70 additions & 70 deletions app/engine/utils/FullTSQuadraticSeries.test.js
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'use strict'
/*
Open Rowing Monitor, https://github.com/jaapvanekris/openrowingmonitor
Open Rowing Monitor, https://github.com/laberning/openrowingmonitor
This tests the Quadratic Theil-Senn estimator Regression algorithm. As regression is an estimation and methods have biasses,
This tests the Quadratic Theil-Senn Regression algorithm. As regression is an estimation and methods have biasses,
we need to accept some slack with respect to real-life examples
*/
import { test } from 'uvu'
Expand Down Expand Up @@ -188,80 +188,80 @@ test('Quadratic Approximation on function y = 4 * Math.pow(x, 2) + 4 * x + 4, wi
testCoefficientC(dataSeries, -195)
dataSeries.push(-8, 229)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.666666666666666)
testCoefficientC(dataSeries, -48.333333333333336)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.6666666666666643) // This is quite acceptable as ORM ignores the C
dataSeries.push(-7, 171)
testCoefficientA(dataSeries, 3.666666666666667)
testCoefficientB(dataSeries, -2.3333333333333335)
testCoefficientC(dataSeries, -22.666666666666668)
testCoefficientB(dataSeries, -1.8333333333333335)
testCoefficientC(dataSeries, -20.916666666666682)
dataSeries.push(-6, 125)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.533333333333333)
testCoefficientC(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.799999999999997) // This is quite acceptable as ORM ignores the C
dataSeries.push(-5, 83)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 3.3333333333333335)
testCoefficientC(dataSeries, 3)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(-4, 53)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.4)
testCoefficientC(dataSeries, 3)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.8571428571428577) // This is quite acceptable as ORM ignores the C
dataSeries.push(-3, 27)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 3.5999999999999996)
testCoefficientC(dataSeries, 3)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(-2, 13)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.285714285714286)
testCoefficientC(dataSeries, 4.428571428571429)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.8888888888888893) // This is quite acceptable as ORM ignores the C
dataSeries.push(-1, 3)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 3.7142857142857144)
testCoefficientC(dataSeries, 3)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(0, 5)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.253968253968254)
testCoefficientC(dataSeries, 5)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(1, 11)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 3.7777777777777777)
testCoefficientC(dataSeries, 3.857142857142857)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(2, 29)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.222222222222222)
testCoefficientC(dataSeries, 3.933333333333333)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(3, 51)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 3.888888888888889)
testCoefficientC(dataSeries, 4.022222222222222)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(4, 85)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4.022222222222222)
testCoefficientC(dataSeries, 4)
dataSeries.push(5, 123)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.933333333333333)
testCoefficientC(dataSeries, 4)
dataSeries.push(6, 173)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4.111111111111111)
testCoefficientC(dataSeries, 4)
dataSeries.push(7, 227)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.933333333333333)
testCoefficientC(dataSeries, 4)
dataSeries.push(8, 293)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4.111111111111111)
testCoefficientC(dataSeries, 4)
dataSeries.push(9, 363)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.933333333333333)
testCoefficientC(dataSeries, 4)
dataSeries.push(10, 444)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4.111111111111111) // We consider this quite acceptable as ORM ignores the C coefficient anyways
testCoefficientC(dataSeries, 4)
})

test('Quadratic Approximation on function y = 4 * Math.pow(x, 2) + 4 * x + 4, with some noise (+/- 1) and spikes (+/- 9)', () => {
Expand All @@ -276,64 +276,64 @@ test('Quadratic Approximation on function y = 4 * Math.pow(x, 2) + 4 * x + 4, wi
dataSeries.push(-5, 83)
dataSeries.push(-4, 53)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.4)
testCoefficientC(dataSeries, 3)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.8571428571428577)
dataSeries.push(-3, 37) // FIRST SPIKE +9
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.666666666666666) // Coefficient B seems to take a hit anyway
testCoefficientC(dataSeries, 12.644444444444446) // We get a 12.6 instead of 4, which is quite acceptable (especially since ORM ignores the C)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(-2, 3) // SECOND SPIKE -9
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.342857142857143)
testCoefficientC(dataSeries, 3)
testCoefficientB(dataSeries, 4.142857142857142) // Coefficient B seems to take a hit anyway
testCoefficientC(dataSeries, 5.9999999999999964) // We get a 5.9999999999999964 instead of 4, which is quite acceptable (especially since ORM ignores the C)
dataSeries.push(-1, 3)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 3.8476190476190473) // Coefficient B seems to take a hit anyway
testCoefficientC(dataSeries, 2.3333333333333335) // We get a 2.3333333333333357 instead of 4, which is quite acceptable (especially since ORM ignores the C)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(0, 5)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.2253968253968255)
testCoefficientC(dataSeries, 4.238095238095238)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(1, 11)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.72) // We get a 3.719999999999997 instead of 4, which is quite acceptable (especially since ORM ignores the C)
testCoefficientC(dataSeries, 4)
dataSeries.push(2, 29)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.165079365079365)
testCoefficientC(dataSeries, 5) // We get a 5 instead of 4, which is quite acceptable (especially since ORM ignores the C)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(3, 51)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.648888888888889) // This is quite acceptable as ORM ignores the C
testCoefficientC(dataSeries, 4)
dataSeries.push(4, 85)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.156190476190476)
testCoefficientC(dataSeries, 3.648888888888889) // This is quite acceptable as ORM ignores the C
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(5, 123)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.72) // This is quite acceptable as ORM ignores the C
testCoefficientC(dataSeries, 4)
dataSeries.push(6, 173)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.152380952380953)
testCoefficientC(dataSeries, 4.111111111111111) // This is quite acceptable as ORM ignores the C
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
dataSeries.push(7, 227)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.7885714285714287) // This is quite acceptable as ORM ignores the C
testCoefficientC(dataSeries, 4)
dataSeries.push(8, 293)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 3.8666666666666663)
testCoefficientC(dataSeries, 3.4380952380952383) // This is quite acceptable as ORM ignores the C
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3) // This is quite acceptable as ORM ignores the C
dataSeries.push(9, 363)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 3.72) // We get a 3 instead of 4, which is quite acceptable (especially since ORM ignores the C)
testCoefficientC(dataSeries, 4) // We get a 3 instead of 4, which is quite acceptable (especially since ORM ignores the C)
dataSeries.push(10, 444)
testCoefficientA(dataSeries, 4)
testCoefficientB(dataSeries, 4.16)
testCoefficientC(dataSeries, 4.533333333333333)
testCoefficientB(dataSeries, 4)
testCoefficientC(dataSeries, 4)
})

test('Quadratic TS Estimation should be decent for standard real-life example from MathBits with some noise', () => {
Expand All @@ -353,8 +353,8 @@ test('Quadratic TS Estimation should be decent for standard real-life example fr
dataSeries.push(60, 231.4)
dataSeries.push(64, 180.4)
testCoefficientA(dataSeries, -0.17785023090944152) // In the example, the TI084 results in -0.1737141137, which we consider acceptably close
testCoefficientB(dataSeries, 15.005518925518913) // In the example, the TI084 results in 14.52117133, which we consider acceptably close
testCoefficientC(dataSeries, -32.561632653064834) // In the example, the TI084 results in -21.89774466, which we consider acceptably close
testCoefficientB(dataSeries, 15.115602960635863) // In the example, the TI084 results in 14.52117133, which we consider acceptably close
testCoefficientC(dataSeries, -35.639987946994665) // In the example, the TI084 results in -21.89774466, which we consider acceptably close
})

test('Quadratic TS Estimation should be decent for standard real-life example from VarsityTutors with some noise', () => {
Expand All @@ -368,8 +368,8 @@ test('Quadratic TS Estimation should be decent for standard real-life example fr
dataSeries.push(2, 6)
dataSeries.push(3, 14)
testCoefficientA(dataSeries, 1.1166666666666667) // The example results in 1.1071 for OLS, which we consider acceptably close
testCoefficientB(dataSeries, 1.125) // The example results in 1 for OLS, which we consider acceptably close
testCoefficientC(dataSeries, 1) // The example results in 0.5714 for OLS, which we consider acceptably close
testCoefficientB(dataSeries, 0.966666666666667) // The example results in 1 for OLS, which we consider acceptably close
testCoefficientC(dataSeries, 0.44722222222222213) // The example results in 0.5714 for OLS, which we consider acceptably close
})

test('Quadratic TS Estimation should be decent for standard example from VTUPulse with some noise, without the vertex being part of the dataset', () => {
Expand All @@ -381,8 +381,8 @@ test('Quadratic TS Estimation should be decent for standard example from VTUPuls
dataSeries.push(6, 6.5)
dataSeries.push(7, 11.5)
testCoefficientA(dataSeries, 0.9500000000000005) // The example results in 0.7642857 for OLS, which we consider acceptably close given the small sample size
testCoefficientB(dataSeries, -7.574999999999999) // The example results in -5.5128571 for OLS, which we consider acceptably close given the small sample size
testCoefficientC(dataSeries, 17.92500000000001) // The example results in 12.4285714 for OLS, which we consider acceptably close given the small sample size
testCoefficientB(dataSeries, -7.483333333333338) // The example results in -5.5128571 for OLS, which we consider acceptably close given the small sample size
testCoefficientC(dataSeries, 17.275000000000006) // The example results in 12.4285714 for OLS, which we consider acceptably close given the small sample size
})

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', () => {
Expand Down Expand Up @@ -414,8 +414,8 @@ test('Quadratic TS Estimation should be decent for standard real-life example fr
dataSeries.push(0.715372314, -1.20379729)
dataSeries.push(0.681745393, -0.83059624)
testCoefficientA(dataSeries, -3.13052236289358)
testCoefficientB(dataSeries, 0.8866733959812066)
testCoefficientC(dataSeries, 0.09073933224254299)
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', () => {
Expand All @@ -433,8 +433,8 @@ test('Quadratic TS Estimation should be decent for standard real-life example fr
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, 7.049898580121704) // The example results in 6.7444 for R after two rounds, which we consider acceptably close
testCoefficientC(dataSeries, -23.701915708812262) // The example results in 18.2536 for R after two rounds, but for ORM, this factor is irrelevant
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', () => {
Expand All @@ -451,8 +451,8 @@ test('Quadratic TS Estimation should be decent for standard real-life example fr
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, 2.3216877058559673) // The example results in 2.39893 for QR decomposition by Givens rotations, which we consider acceptably close
testCoefficientC(dataSeries, -1613.0695243192479) // The example results in -1216.143887 for QR decomposition by Givens rotations, but for ORM, this factor is irrelevant
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', () => {
Expand Down

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