#jja — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #jja, aggregated by home.social.
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No #Zobrist hash collisions were found in unique #chess positions from lichess games (2013-2014). The study will expand to 2023. Despite optimizing #SQLite for the expanding data, import times remain high, prompting a potential switch to #PostgreSQL for parallel import. Final results will be in #SQLite format. #jja
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Improving cache efficiency has made #Stockfish hash almost double as fast for #jja: https://git.sr.ht/~alip/jja/commit/604366959060cffa8a02f14a039353f15732fa09 #rust #rustlang #chess
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To remove entries with zero depth from a #BrainLearn experience file you may do: "jja dump experience.exp | perl -F, -lane 'print if $F[1] > 0' | jja restore experience-nonull.exp" #jja #chess
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To remove entries with zero depth from a #BrainLearn experience file you may do: "jja dump experience.exp | perl -F, -lane 'print if $F[1] > 0' | jja restore experience-nonull.exp" #jja #chess
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In converting #lichess database #PGN's to #polyglot books, I process only BOT games over a certain rating threshold, and arrange score using #jja's --min-{game,score} parameters to create strong chess books. Much obliged to @lichess for releasing these databases to public domain. I have also released some opening books generated by #jja to public domain here: https://www.chesswob.org/jja/books
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In converting #lichess database #PGN's to #polyglot books, I process only BOT games over a certain rating threshold, and arrange score using #jja's --min-{game,score} parameters to create strong chess books. Much obliged to @lichess for releasing these databases to public domain. I have also released some opening books generated by #jja to public domain here: https://www.chesswob.org/jja/books
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In converting #lichess database #PGN's to #polyglot books, I process only BOT games over a certain rating threshold, and arrange score using #jja's --min-{game,score} parameters to create strong chess books. Much obliged to @lichess for releasing these databases to public domain. I have also released some opening books generated by #jja to public domain here: https://www.chesswob.org/jja/books
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In converting #lichess database #PGN's to #polyglot books, I process only BOT games over a certain rating threshold, and arrange score using #jja's --min-{game,score} parameters to create strong chess books. Much obliged to @lichess for releasing these databases to public domain. I have also released some opening books generated by #jja to public domain here: https://www.chesswob.org/jja/books
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@lichess I want to nominate https://lichess.org/wNOZEVrh which is a very beautiful attacking game by my bot caissa-test. The funny story is I've been generating opening books using #jja and BOT games from lichess database and in this game the opening book line ends with an evaluation of +5.71!! I am willing to provide more information on the opening book and the game if you people find it interesting. Thanks lichess! #jja #chess
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The last #jja bug concerning #BrainLearn experience files was incorrect deserializing of promotion moves. I have found, corrected the bug and tested it with all types of promotions. It works beautifully now! https://git.sr.ht/~alip/jja/commit/02e9aa49c8ff79434efbb57bb18f1097f1872438 Time to release a new #jja version!
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The last #jja bug concerning #BrainLearn experience files was incorrect deserializing of promotion moves. I have found, corrected the bug and tested it with all types of promotions. It works beautifully now! https://git.sr.ht/~alip/jja/commit/02e9aa49c8ff79434efbb57bb18f1097f1872438 Time to release a new #jja version!
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Too bad #pgn-extract has a flag --underpromotions to find games with underpromotions however no flag to find games with _any_ type of promotion. #grep to the rescue! "grep -Ei '=[QRBN]' caissa-ai.pgn -B18 > promotions.pgn" finds a game with any type of promotion and prints the whole #PGN. The good thing is this way I found a promotion entry in a #BrainLearn experience file and I found a bug in #jja's promotion handling!
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Too bad #pgn-extract has a flag --underpromotions to find games with underpromotions however no flag to find games with _any_ type of promotion. #grep to the rescue! "grep -Ei '=[QRBN]' caissa-ai.pgn -B18 > promotions.pgn" finds a game with any type of promotion and prints the whole #PGN. The good thing is this way I found a promotion entry in a #BrainLearn experience file and I found a bug in #jja's promotion handling!
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A reasonable theoretical opening line with an en-passant capture goes like this: 1.d4 f5 2.c4 Nf6 3.Nc3 g6 4.g3 Bg7 5.Bg2 d6 6.Nf3 O-O 7.O-O c6 8.d5 e5 9.dxe6 e.p. Thanks to kosmik on #chess channel at #Libera for remembering that! #jja #git can correctly deserialize this position and the en-passant capture from a #BrainLearn experience file.
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A reasonable theoretical opening line with an en-passant capture goes like this: 1.d4 f5 2.c4 Nf6 3.Nc3 g6 4.g3 Bg7 5.Bg2 d6 6.Nf3 O-O 7.O-O c6 8.d5 e5 9.dxe6 e.p. Thanks to kosmik on #chess channel at #Libera for remembering that! #jja #git can correctly deserialize this position and the en-passant capture from a #BrainLearn experience file.
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#jja's deserialization of en-passant and castling moves are tested to work for #BrainLearn experience files. Now I need to find a reasonable opening line that has a promotion so as to test if promotion is deserialized correctly. Then we can release a new version :-)
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#jja's deserialization of en-passant and castling moves are tested to work for #BrainLearn experience files. Now I need to find a reasonable opening line that has a promotion so as to test if promotion is deserialized correctly. Then we can release a new version :-)
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I have also fixed #jja's deserializing for castling moves of #BrainLearn experience file entries. Now I have to test en-passant and promotion before releasing a new version, however those two are relatively hard to find in opening books than castling. So I have also added #BrainLearn to #pgn export support to #jja edit subcommand.
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I have also fixed #jja's deserializing for castling moves of #BrainLearn experience file entries. Now I have to test en-passant and promotion before releasing a new version, however those two are relatively hard to find in opening books than castling. So I have also added #BrainLearn to #pgn export support to #jja edit subcommand.
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#jja #git now has proper support for #Stockfish compatible #Zobrist hashes and querying #BrainLearn files works as expected. I'll release this as 0.6.1 after some testing.
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#jja #git now has proper support for #Stockfish compatible #Zobrist hashes and querying #BrainLearn files works as expected. I'll release this as 0.6.1 after some testing.
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Turns out #BrainLearn experience files use #Zobrist hashes but with different seends than #Polyglot. I'm working on implementing #Stockfish compatible #Zobrist hashes in #jja, it is almost done.
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Turns out #BrainLearn experience files use #Zobrist hashes but with different seends than #Polyglot. I'm working on implementing #Stockfish compatible #Zobrist hashes in #jja, it is almost done.
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so the newest version of #jja, which is 0.6.0, has read access for #BrainLearn experience files. Yet I have a riddle. I could not find a way to test the query interface because given a #BrainLearn experience entry, which is #Zobrist hash, move, depth, score, and performance, i don't have enough info to constitute a legit chess position. #Zobrist hash works one way as all hash functions do...
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so the newest version of #jja, which is 0.6.0, has read access for #BrainLearn experience files. Yet I have a riddle. I could not find a way to test the query interface because given a #BrainLearn experience entry, which is #Zobrist hash, move, depth, score, and performance, i don't have enough info to constitute a legit chess position. #Zobrist hash works one way as all hash functions do...
