Once I have played a series of games and I have the file data.log, if I run the test again and use the file, in each run I have different results and the results seem quite random.
Code: Select all
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Using [8.0, 90.0, 5.0, 114.0] => 0.5 from log-file
Using [1.0, 144.0, 1.0, 121.0] => 0.0 from log-file
Using [9.0, 107.0, 6.0, 99.0] => 0.0 from log-file
Using [8.0, 158.0, 7.0, 125.0] => -0.5 from log-file
Using [8.0, 101.0, 9.0, 134.0] => -1.0 from log-file
Using [3.0, 116.0, 9.0, 112.0] => -0.5 from log-file
Using [1.0, 133.0, 3.0, 142.0] => -1.0 from log-file
Using [6.0, 154.0, 8.0, 145.0] => 0.0 from log-file
Using [3.0, 156.0, 7.0, 115.0] => 0.0 from log-file
Using [1.0, 135.0, 4.0, 144.0] => 0.0 from log-file
Using [5.0, 148.0, 10.0, 126.0] => -0.5 from log-file
Using [2.0, 90.0, 4.0, 111.0] => -1.0 from log-file
Using [4.0, 115.0, 3.0, 84.0] => -0.5 from log-file
Using [8.0, 92.0, 1.0, 123.0] => 0.0 from log-file
Using [8.0, 100.0, 4.0, 129.0] => 0.0 from log-file
Using [3.0, 104.0, 0.0, 128.0] => -0.5 from log-file
Using [2.0, 128.0, 9.0, 134.0] => 0.5 from log-file
Using [6.0, 100.0, 4.0, 104.0] => -0.5 from log-file
Using [5.0, 133.0, 3.0, 107.0] => 0.0 from log-file
Using [6.0, 117.0, 10.0, 105.0] => 0.5 from log-file
Using [3.0, 100.0, 8.0, 97.0] => -0.5 from log-file
Fitting first model
Summarizing best values
Best expectation (κ=0): [ 1. 83. 2. 135.] = -0.000 ± 0.375 (ELO-diff -0.000 ± 338.039)
Best expectation (κ=1): [ 9. 155. 2. 92.] = -0.000 ± 0.255 (ELO-diff -0.000 ± 195.793)
Best expectation (κ=2): [ 9. 155. 2. 92.] = -0.000 ± 0.255 (ELO-diff -0.000 ± 195.793)
Best expectation (κ=3): [ 9. 155. 2. 92.] = -0.000 ± 0.255 (ELO-diff -0.000 ± 195.793)
Code: Select all
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Using [8.0, 90.0, 5.0, 114.0] => 0.5 from log-file
Using [1.0, 144.0, 1.0, 121.0] => 0.0 from log-file
Using [9.0, 107.0, 6.0, 99.0] => 0.0 from log-file
Using [8.0, 158.0, 7.0, 125.0] => -0.5 from log-file
Using [8.0, 101.0, 9.0, 134.0] => -1.0 from log-file
Using [3.0, 116.0, 9.0, 112.0] => -0.5 from log-file
Using [1.0, 133.0, 3.0, 142.0] => -1.0 from log-file
Using [6.0, 154.0, 8.0, 145.0] => 0.0 from log-file
Using [3.0, 156.0, 7.0, 115.0] => 0.0 from log-file
Using [1.0, 135.0, 4.0, 144.0] => 0.0 from log-file
Using [5.0, 148.0, 10.0, 126.0] => -0.5 from log-file
Using [2.0, 90.0, 4.0, 111.0] => -1.0 from log-file
Using [4.0, 115.0, 3.0, 84.0] => -0.5 from log-file
Using [8.0, 92.0, 1.0, 123.0] => 0.0 from log-file
Using [8.0, 100.0, 4.0, 129.0] => 0.0 from log-file
Using [3.0, 104.0, 0.0, 128.0] => -0.5 from log-file
Using [2.0, 128.0, 9.0, 134.0] => 0.5 from log-file
Using [6.0, 100.0, 4.0, 104.0] => -0.5 from log-file
Using [5.0, 133.0, 3.0, 107.0] => 0.0 from log-file
Using [6.0, 117.0, 10.0, 105.0] => 0.5 from log-file
Using [3.0, 100.0, 8.0, 97.0] => -0.5 from log-file
Fitting first model
Summarizing best values
Best expectation (κ=0): [ 0. 95. 9. 113.] = -0.000 ± 0.375 (ELO-diff -0.000 ± 338.039)
Best expectation (κ=1): [ 10. 149. 1. 120.] = -0.000 ± 0.255 (ELO-diff -0.000 ± 195.793)
Best expectation (κ=2): [ 10. 149. 1. 120.] = -0.000 ± 0.255 (ELO-diff -0.000 ± 195.793)
Best expectation (κ=3): [ 10. 149. 1. 120.] = -0.000 ± 0.255 (ELO-diff -0.000 ± 195.793)
Code: Select all
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Using [8.0, 90.0, 5.0, 114.0] => 0.5 from log-file
Using [1.0, 144.0, 1.0, 121.0] => 0.0 from log-file
Using [9.0, 107.0, 6.0, 99.0] => 0.0 from log-file
Using [8.0, 158.0, 7.0, 125.0] => -0.5 from log-file
Using [8.0, 101.0, 9.0, 134.0] => -1.0 from log-file
Using [3.0, 116.0, 9.0, 112.0] => -0.5 from log-file
Using [1.0, 133.0, 3.0, 142.0] => -1.0 from log-file
Using [6.0, 154.0, 8.0, 145.0] => 0.0 from log-file
Using [3.0, 156.0, 7.0, 115.0] => 0.0 from log-file
Using [1.0, 135.0, 4.0, 144.0] => 0.0 from log-file
Using [5.0, 148.0, 10.0, 126.0] => -0.5 from log-file
Using [2.0, 90.0, 4.0, 111.0] => -1.0 from log-file
Using [4.0, 115.0, 3.0, 84.0] => -0.5 from log-file
Using [8.0, 92.0, 1.0, 123.0] => 0.0 from log-file
Using [8.0, 100.0, 4.0, 129.0] => 0.0 from log-file
Using [3.0, 104.0, 0.0, 128.0] => -0.5 from log-file
Using [2.0, 128.0, 9.0, 134.0] => 0.5 from log-file
Using [6.0, 100.0, 4.0, 104.0] => -0.5 from log-file
Using [5.0, 133.0, 3.0, 107.0] => 0.0 from log-file
Using [6.0, 117.0, 10.0, 105.0] => 0.5 from log-file
Using [3.0, 100.0, 8.0, 97.0] => -0.5 from log-file
Fitting first model
Summarizing best values
Best expectation (κ=0): [ 5. 97. 10. 88.] = -0.000 ± 0.375 (ELO-diff -0.000 ± 338.039)
Best expectation (κ=1): [ 5. 159. 6. 140.] = -0.000 ± 0.255 (ELO-diff -0.000 ± 195.793)
Best expectation (κ=2): [ 5. 159. 6. 140.] = -0.000 ± 0.255 (ELO-diff -0.000 ± 195.793)
Best expectation (κ=3): [ 5. 159. 6. 140.] = -0.000 ± 0.255 (ELO-diff -0.000 ± 195.793)
I thought you could reuse data, whether there is a crash or if you want to expand the number of iterations, but I am confused that in each run once all the games have been played I have different results.