Not much progress on this, got distracted by:
Everything’s better under the C (a quick poke at the frame flex value).
But I did try a quick manual iteration of doing short descents (100) multiple times, weighted average of the results, then using that as the input for another round, with the area the initial sample is taken from decreasing each time.
I started with a roughly-right frame measurement (in unstretched-belt-mm), and only did 10 samples-with-100-descents in each step as this seemed like the lowest I was likely to get anything from. And I did 3 iterations, sample ± 50mm in the first, then ±25mm, then ±12mm.
| Fitness | TL X | TL Y | TR X | TR Y | BL X | BL Y | BR X | BR Y |
|---|---|---|---|---|---|---|---|---|
| 1st round | 100 | |||||||
| plus minus 50mm | ||||||||
| 0.5715 | -10.3 | 2314 | 2868.6 | 2327.9 | 0 | 0 | 2890.1 | 0 |
| 0.5041 | 3.9 | 2330.4 | 2875.4 | 2320.8 | 0 | 0 | 2890.9 | 0 |
| 0.4197 | 7.8 | 2319.8 | 2899 | 2291.6 | 0 | 0 | 2903.3 | 0 |
| 0.3712 | 7.6 | 2293.5 | 2924.9 | 2258.8 | 0 | 0 | 2924.6 | 0 |
| 0.6179 | -16 | 2298.2 | 2878.9 | 2315.7 | 0 | 0 | 2897.2 | 0 |
| 0.4399 | -10.7 | 2264.1 | 2898.2 | 2292.5 | 0 | 0 | 2929.9 | 0 |
| 0.2687 | 14 | 2307.9 | 2924.8 | 2258.9 | 0 | 0 | 2918.8 | 0 |
| 0.6162 | -10.9 | 2286.6 | 2887.6 | 2305.1 | 0 | 0 | 2911.5 | 0 |
| 0.6118 | -0.8 | 2324.3 | 2876.3 | 2319.3 | 0 | 0 | 2891.3 | 0 |
| 0.9893 | -7.2 | 2273.6 | 2910.8 | 2276.3 | 0 | 0 | 2925.8 | 0 |
| Weight total | ||||||||
| 5.4103 | ||||||||
| Weighted | ||||||||
| -5.88645 | 1322.451 | 1639.4049 | 1330.39485 | 0 | 0 | 1651.69215 | 0 | |
| 1.96599 | 1174.75464 | 1449.48914 | 1169.91528 | 0 | 0 | 1457.30269 | 0 | |
| 3.27366 | 973.62006 | 1216.7103 | 961.78452 | 0 | 0 | 1218.51501 | 0 | |
| 2.82112 | 851.3472 | 1085.72288 | 838.46656 | 0 | 0 | 1085.61152 | 0 | |
| -9.8864 | 1420.05778 | 1778.87231 | 1430.87103 | 0 | 0 | 1790.17988 | 0 | |
| -4.70693 | 995.97759 | 1274.91818 | 1008.47075 | 0 | 0 | 1288.86301 | 0 | |
| 3.7618 | 620.13273 | 785.89376 | 606.96643 | 0 | 0 | 784.28156 | 0 | |
| -6.71658 | 1409.00292 | 1779.33912 | 1420.40262 | 0 | 0 | 1794.0663 | 0 | |
| -0.48944 | 1422.00674 | 1759.72034 | 1418.94774 | 0 | 0 | 1768.89734 | 0 | |
| -7.12296 | 2249.27248 | 2879.65444 | 2251.94359 | 0 | 0 | 2894.49394 | 0 | |
| Weighted Totals | ||||||||
| -22.98619 | 12438.62314 | 15649.72537 | 12438.16337 | 0 | 0 | 15733.9034 | 0 | |
| Weighted Averages | ||||||||
| -4.248598044 | 2299.063479 | 2892.579962 | 2298.978498 | 0 | 0 | 2908.138809 | 0 | |
| 2nd round | 100 | |||||||
| plus minus 25mm | ||||||||
| 0.8812 | 12 | 2308.4 | 2881.8 | 2280 | 0 | 0 | 2932.3 | 0 |
| 0.8812 | -1.6 | 2304.6 | 2868.4 | 2314.6 | 0 | 0 | 2930.1 | 0 |
| 0.8812 | -11 | 2308.7 | 2898.3 | 2308.1 | 0 | 0 | 2927.9 | 0 |
| 0.8812 | 14.7 | 2324 | 2869.7 | 2273.5 | 0 | 0 | 2931.8 | 0 |
| 0.9488 | -5.5 | 2285.4 | 2909.1 | 2278.4 | 0 | 0 | 2918 | 0 |
| 1.0708 | -7.2 | 2294.5 | 2895.3 | 2295.6 | 0 | 0 | 2909.1 | 0 |
| 0.8877 | -4.3 | 2308.1 | 2888.3 | 2304.3 | 0 | 0 | 2901 | 0 |
| 0.8812 | 5.7 | 2311.3 | 2915.8 | 2277.1 | 0 | 0 | 2908.8 | 0 |
| 0.8812 | -5.2 | 2276.4 | 2908 | 2307.9 | 0 | 0 | 2904.9 | 0 |
| 0.9302 | -4.6 | 2292 | 2904.8 | 2283.8 | 0 | 0 | 2913.6 | 0 |
| Weight total | ||||||||
| 9.1247 | ||||||||
| Weighted | ||||||||
| 10.5744 | 2034.16208 | 2539.44216 | 2009.136 | 0 | 0 | 2583.94276 | 0 | |
| -1.40992 | 2030.81352 | 2527.63408 | 2039.62552 | 0 | 0 | 2582.00412 | 0 | |
| -9.6932 | 2034.42644 | 2553.98196 | 2033.89772 | 0 | 0 | 2580.06548 | 0 | |
| 12.95364 | 2047.9088 | 2528.77964 | 2003.4082 | 0 | 0 | 2583.50216 | 0 | |
| -5.2184 | 2168.38752 | 2760.15408 | 2161.74592 | 0 | 0 | 2768.5984 | 0 | |
| -7.70976 | 2456.9506 | 3100.28724 | 2458.12848 | 0 | 0 | 3115.06428 | 0 | |
| -3.81711 | 2048.90037 | 2563.94391 | 2045.52711 | 0 | 0 | 2575.2177 | 0 | |
| 5.02284 | 2036.71756 | 2569.40296 | 2006.58052 | 0 | 0 | 2563.23456 | 0 | |
| -4.58224 | 2005.96368 | 2562.5296 | 2033.72148 | 0 | 0 | 2559.79788 | 0 | |
| -4.27892 | 2132.0184 | 2702.04496 | 2124.39076 | 0 | 0 | 2710.23072 | 0 | |
| Weighted Totals | ||||||||
| -8.15867 | 20996.24897 | 26408.20059 | 20916.16171 | 0 | 0 | 26621.65806 | 0 | |
| Weighted Averages | ||||||||
| -0.894130218 | 2301.034442 | 2894.14453 | 2292.257467 | 0 | 0 | 2917.537898 | 0 | |
| 3rd round | 100 | |||||||
| plus minus 12mm | ||||||||
| 1.0259 | -4.5 | 2291.4 | 2902.7 | 2286.6 | 0 | 0 | 2914.4 | 0 |
| 0.9889 | -4.5 | 2291.9 | 2903.3 | 2285.6 | 0 | 0 | 2913.8 | 0 |
| 0.9364 | -3.4 | 2300.3 | 2897.2 | 2293.3 | 0 | 0 | 2908.2 | 0 |
| 0.8446 | -1.8 | 2301.4 | 2898.4 | 2291.9 | 0 | 0 | 2908.8 | 0 |
| 0.8978 | -5.6 | 2307.4 | 2888.2 | 2304.1 | 0 | 0 | 2900.2 | 0 |
| 1.003 | -6.4 | 2300.7 | 2891.3 | 2300.5 | 0 | 0 | 2904.7 | 0 |
| 0.8056 | -1.1 | 2301.8 | 2899 | 2291.1 | 0 | 0 | 2909.1 | 0 |
| 1.0501 | -5 | 2293.5 | 2900.2 | 2289.4 | 0 | 0 | 2912.1 | 0 |
| 0.8971 | -3.5 | 2306.8 | 2890.2 | 2302 | 0 | 0 | 2902.9 | 0 |
| 1.0043 | -5.3 | 2287.3 | 2906.4 | 2281.8 | 0 | 0 | 2916.6 | 0 |
| Weight total | ||||||||
| 9.4537 | ||||||||
| Weighted | ||||||||
| -4.61655 | 2350.74726 | 2977.87993 | 2345.82294 | 0 | 0 | 2989.88296 | 0 | |
| -4.45005 | 2266.45991 | 2871.07337 | 2260.22984 | 0 | 0 | 2881.45682 | 0 | |
| -3.18376 | 2154.00092 | 2712.93808 | 2147.44612 | 0 | 0 | 2723.23848 | 0 | |
| -1.52028 | 1943.76244 | 2447.98864 | 1935.73874 | 0 | 0 | 2456.77248 | 0 | |
| -5.02768 | 2071.58372 | 2593.02596 | 2068.62098 | 0 | 0 | 2603.79956 | 0 | |
| -6.4192 | 2307.6021 | 2899.9739 | 2307.4015 | 0 | 0 | 2913.4141 | 0 | |
| -0.88616 | 1854.33008 | 2335.4344 | 1845.71016 | 0 | 0 | 2343.57096 | 0 | |
| -5.2505 | 2408.40435 | 3045.50002 | 2404.09894 | 0 | 0 | 3057.99621 | 0 | |
| -3.13985 | 2069.43028 | 2592.79842 | 2065.1242 | 0 | 0 | 2604.19159 | 0 | |
| -5.32279 | 2297.13539 | 2918.89752 | 2291.61174 | 0 | 0 | 2929.14138 | 0 | |
| Weighted Totals | ||||||||
| -39.81682 | 21723.45645 | 27395.51024 | 21671.80516 | 0 | 0 | 27503.46454 | 0 | |
| Weighted Averages | ||||||||
| -4.211771053 | 2297.878762 | 2897.86118 | 2292.415156 | 0 | 0 | 2909.280445 | 0 |
The output of the 3rd round (equiv to 3000 descents in the normal algorithm) Is pretty good, the TLX is slightly off - looking at the data it looks like you get a spread of potential values, possibly bimodal, which is interesting. I have wondered if doing a grid of points is giving us sampling artifacts and we should be doing (still stratified) random sample points for the calibration, that’s for another day though.