Re: [Computer-go] CGOS source on github
Hi, The Elo statistical model is wrong when different kind of programs play against I have a similar experience. I calculated Japanese Shogi women pro rating before. The strongest woman, Ichiyo Shimizu, her rating is 1578 Elo. Her winrate against men pros is 18%(163 games), and against women pro is 65%(523 games). Her rating without women pros game is 1286 Elo. There is 292(=1578 - 1286) Elo difference. It is because usually women pros play with women pros. Women pros vs men pros are rare. I think similar thing happens on CGOS. There are three eras, Zen, LeelaZero and KataGo. Number of Zen vs LeelaZero games are a little. CrazyStone-18.04 rate maybe depend on Zen-15.7-3c1g. http://www.yss-aya.com/cgos/19x19/cross/CrazyStone-18.04.html Zen's absence is maybe a reason of this drift. Thanks, Hiroshi Yamashita ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
Re: [Computer-go] CGOS source on github
It's a relative ranking versus who you actually get to play against. Sparsity of actual skill will lead to that kind of clumping. The only way that a rating could meaningfully climb by playing gnugo or your direct peers is going to happen exponentially slowly -- you'd need to lose to gnugo twice less often (or win all the time over twice as many games) to get more points. So although it would eventually increase, it would flatten out pretty quickly. Good point about mcmc. A more dramatic approach would be to remove gnugo altogether. On Mon, Jan 18, 2021, 6:41 AM Rémi Coulom wrote: > Hi, > > Thanks to you for taking care of CGOS. > > I have just connected CrazyStone-57-TiV. It is not identical, but should > be similar to the old CrazyStone-18.04. CrazyStone-18.04 was the last > version of my program that used tensorflow. CrazyStone-57 is the first > neural network that did not use tensorflow, running with my current code. > So it should be stronger than CrazyStone-18.04, and I expect it will get a > much lower rating. > A possible explanation for the rating drift may be that most of the old MC > programs have disappeared. They won easily against GNU Go, and were easily > beaten by the CNN programs. The Elo statistical model is wrong when > different kind of programs play against each other. When the CNN program > had to get a rating by playing directly against GNU Go, they did not manage > to climb as high as when they had the MC programs between them and GNU Go. > I'll try to investigate this hypothesis more with the data. > > Rémi > ___ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go > ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
Re: [Computer-go] CGOS source on github
Hi, Thanks to you for taking care of CGOS. I have just connected CrazyStone-57-TiV. It is not identical, but should be similar to the old CrazyStone-18.04. CrazyStone-18.04 was the last version of my program that used tensorflow. CrazyStone-57 is the first neural network that did not use tensorflow, running with my current code. So it should be stronger than CrazyStone-18.04, and I expect it will get a much lower rating. A possible explanation for the rating drift may be that most of the old MC programs have disappeared. They won easily against GNU Go, and were easily beaten by the CNN programs. The Elo statistical model is wrong when different kind of programs play against each other. When the CNN program had to get a rating by playing directly against GNU Go, they did not manage to climb as high as when they had the MC programs between them and GNU Go. I'll try to investigate this hypothesis more with the data. Rémi ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
[Computer-go] CGOS source on github
Hi,
I have published current CGOS source on github.
https://github.com/yssaya/CGOS
There are some changes. Like
1. Recent 300 games on cross-table page.
2. WGo viewer
3. 7.0 komi and handling draw for rating calculation.
4. Shorter pgn file for BayesElo (cgosBayes).
5. Forbid number only account.
6. Bug fixed to send info all 'catch {puts $soc "info $msg"}'
7. badusers.txt for not removing dead stones or too many timeout.
Thanks,
Hiroshi Yamashita
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