Horizontalx: i love clash of code
IKEJIME: is c99 good to learn while you are learning c
Superhero25e: Hi. i am new to coding. I heard they also teach coding here apart from the challanges
DaNinja: select Activities > Learn from the site menu
FriendlyKh: hey guý
FriendlyKh: hey uys
FriendlyKh: my gods
phucdeptrai: hey
FriendlyKh: hey
FriendlyKh: any one here joins coding escape?
helloeason: hi
FriendlyKh: hi
helloeason: i leran C
FriendlyKh: oh
FriendlyKh: ok
patrisia_X: play now?
FBF_Luis: hey guys, does anyone here knows where I can learn Qt5?
FBF_Luis: tutorials/discord groups/books etc...
KiwiTae: https://doc.qt.io/ ^
FBF_Luis: thanks :)
Socksfor2: https://www.codingame.com/clashofcode/clash/1862505a12c95a8a06e955f923ded094621cdc6
Socksfor2: join
derjack: oO
Marchete: Oo
NaSaPaKri: https://www.codingame.com/contribute/view/71265f437558a2e1cdd0c3d071ff33a8efd7
dbdr: \o
dbdr: https://www.codingame.com/replay/569960510
dbdr: strange result, scores are usually closer at the top. I wonder if Agade has a bug
AntiSquid: did anyone join when you ordered them to? Socksfor2
derjack: well this noob also has low score https://www.codingame.com/share-replay/569860986
dbdr: :D
I expected I would be the noob
AntiSquid: does it have something to do with picking wrong branch on the search tree ?
derjack: picks wrong seeds from right branch
Marchete: unvisited branches
Marchete: when I got something like "NEW ROOT: Visits:1/22463 = 0%"
Marchete: that's a bad thing
Marchete: this means the enemy did a move I barely tested
Marchete: neither on this game and probably not on training
derjack: and that move was good?
Marchete: he won, so it was good
Marchete: oware is good for "prediction"
Marchete: I usually have +80% of tree reuse
Marchete: meaning that my move+enemy move were well predicted
Marchete: but at some cases it drops a lot
Marchete: to <7%
Marchete: so it's a terrible prediction
Marchete: maybe it's a bad exploration, maybe more matches fixed that
Marchete: but I'm unsure
martinpapa69: Marchete I too tried your alphazero implementation. my pc has a 10 years old processor, and it does not have avx2, but intel provides an emulator, so now it looks like it's training :D but i assume the training will be much slower than on a native avx processor
dbdr: policy_categorical_accuracy: 0.7526
dbdr: so that's 75% just from the NN, search should help. but then again it's against itself, a real opponent might behave differently :D
Marchete: :thumbsup:
Marchete: martinpapa69 if winrate is +90% at first generations it's working
dbdr: I think it reaches 80% after more training
Marchete: dbdr that can be a lot of inbreeding
dbdr: yeah
Marchete: and it doesn't ensure at all exploration
Marchete: in fact I'm having troubles to reach the same winrate than in arena
Marchete: I'm trying other things
Marchete: but the bot seems to see all possible start moves as losses
Marchete: no matter the position
Marchete: losses as 0.0x
dbdr: all moves are losses? as in the starting position is a loss?
Marchete: are you still working on it dbdr?
Marchete: yes
dbdr: I stopped during the week-end, but looking at it again now
Marchete: I'm trying other stuff
dbdr: that sounds like a bug, no?
Marchete: regularization, batchnorm etc
martinpapa69: okay. curretly im at the training sample generation step. I set MATCHES_PER_GENERATION to 1k. how much does it take on your processor to generate this many samples ?
Marchete: it's clearly a bug
Marchete: 1k matches? not a lot
Marchete: it depends on TRAIN_MCTS_ITER
Marchete: but less than 5 min?
Marchete: around that
Marchete: also depends on THREADS
Marchete: I usually use 6 to 9 threads
dbdr: wasn't TRAIN_MCTS_ITER set with a default value?
Marchete: dunno
martinpapa69: i dont think it was
Marchete: I usually use 1000 .. 1500
martinpapa69: i set it to 2k
Marchete: it's ok
martinpapa69: THREADS=6
Marchete: that depends on your CPU
martinpapa69: its a 4 core HT processor
Marchete: could be
derjack: and it doesnt have avx2 eh
martinpapa69: but im rly curioes much slower the avx2 emulation will be than the native one
Marchete: you can use AVX
Marchete: check if fma exists on AVX
Marchete: but if you have an emulator that's easier
Marchete: as I said, just ensure it's learning at first generations
Marchete: usually >90%
Marchete: if not, something is off
martinpapa69: my processor does support avx. does it require code modification to run it on avx, instead of avx2?
dbdr: as recurs@ said, even if something is off it will probably learn, just less well. the 'magic' of NNs
Marchete: I know
Marchete: but to be sure it works, better expect >80% winrate at gen1
Marchete: at least without any weird config
dbdr: I always get 100%
Marchete: sometimes is 99.99%, like 1 draw
dbdr: right
Marchete: but that's a good initial sign
Marchete: even with near zero training (1 epoch to speed up)
martinpapa69: i tried two different alphazero implementations to integrate oware. but none seemed to work for me
martinpapa69: days of training, still was not able to beat mcts bot
dbdr: which other implementations?
martinpapa69: alphazero-general and alphazero-gomoku on github
dbdr: alphazero-general is pure python, no?
dbdr: search will be really slow
dbdr: the NN might not compensate that
martinpapa69: yes, but i assume after days of training it should slow some signs of intelligence :D
dbdr: if it improves it has intelligence. it's just one part of the whole bot
martinpapa69: ye it rly slow it takes like 10-20 seconds to run a game until the end with 300 mcts simulations/round
dbdr: ah ok, you don't use time control
dbdr: then speed does not affect playing strength
martinpapa69: *the framework i use. but ye its a constant
dbdr: oh, csircsip changed name again :D
derjack: hm?
pavlik17: this website it 2hard for me, any website i can practice JavaScript for beginers?
Marchete: alphazero-general winrates:
Marchete: https://github.com/suragnair/alpha-zero-general/raw/master/pretrained_models/6x6.png
Marchete: a damn 90% winrate vs a random bot in 5 generations
Marchete: 40% winrate in gen2
Marchete: like... worse than a random
Marchete: who is csircsip now?
derjack: oh my
Marchete: how can you train and be worse than a random?
Marchete: that's only mean... it's wrong at some point
dbdr: maybe it overfitted badly early on
dbdr: I agree it does not look good
Marchete: yet the repo has like 2500 stars
dbdr: it has good keywords
Marchete: also, 3 days on a nvidia tesla K80...
Marchete: they are from Stanford, I would expect a better paper than https://github.com/suragnair/alpha-zero-general/raw/master/pretrained_models/writeup.pdf
Marchete: pitplay vs a random is a nonsense
dbdr: students
Marchete: from Stanford
Marchete: but yeah, students
dbdr: they compare with "minimax agent with hand-crafted feature"
dbdr: they beat it too
Wontonimo: it really bugs me when papers don't put a published date anywhere convenient grrr
dbdr: yeah. you need to guess from the references
derjack: there is some a0 in julia and they made effort to make it available to test against vanilla mcts
Wontonimo: you mentioned a repo, plz share a link
derjack: repo?
derjack: welp https://github.com/jonathan-laurent/AlphaZero.jl
derjack: they test c4 against 1000 mcts and 5-ply minimax
TheMadSuperstarEater_2483: hi
Marchete: that repo seemed good, I've read it sometimes and it seems very complete
derjack: and gets 100% against them in 5 iterations (generations?)
TheMadSuperstarEater_2483: are you real or bot?
Marchete: each iteration was 1 hour if I recall
derjack: still better than beating random 60% after few hours
Marchete: 3 days*
Wontonimo: TheMadSuperstarEater_2483 , we are a hybrid real-bot
RoboStac: yeah, python's slow
Wontonimo: ha ha ... is it ever!
RoboStac: my original oware version was based on that alphazero-general, with the gameplaying done externally
TheMadSuperstarEater_2483: Wontonimo, good to hear that <3
Wontonimo: my first attempt at MCTS was in UTTT using python. 550 rollouts per turn. Compared to my c++ which is 55k per turn
Marchete: http://numba.pydata.org/
Marchete: that thing boosts python a LOT
Wontonimo: :thumbsup:
Marchete: I've tested it on some samples and it was like c++
TheMadSuperstarEater_2483: I am a bot and here to help you. Press 9 for English.
Marchete: but it has its limitations
derjack: is this like pypy?
Marchete: a jit compiler
Marchete: I don't know if pypy has numpy acceleration
Marchete: if yes , then it's the same
Wontonimo: back to training a NN and it doing worse than random when using RL, you can definitely get model collapse.
Wontonimo: there are a few anti-patterns to combat that
Marchete: you have a sh*t RL
Marchete: it's impossible to do it worse than random
Marchete: you must purposely do it worse than random
dbdr: sign error :)
Wontonimo: if your nn is only battling its prior self, and if its prior self has a horrible cognitive blind spot that it exploits and starts a feedback of focusing on just that one blind spot. Reasonable regularization helps.
Marchete: then stop, review, debug and once you are sure it works
Marchete: then publish
Wontonimo: i agree Marchete, it would be pretty sh*t RL
dbdr: what's regularization?
Wontonimo: constraining the weights of the NN such that they stay within "regular" values
Marchete: tldr: for me it's some way to avoid complex explanations for simple stuff
Marchete: L2 regularization: Add some kind of noise depending on weight values,
Marchete: squared noise
Wontonimo: square cost*
Marchete: and on loss calculation the system adjust it (if done with model.fit)
Marchete: I mean Wontonimo, I explain it for dummies (no offense :D)
Marchete: many explanations are math hardcore
Wontonimo: :D
dbdr: Marchete you timed out: https://www.codingame.com/replay/569974310
Uljahn: for dummies i'd recommend this blog https://machinelearningjourney.com/index.php/blog
Marchete: and I was winning
dbdr: maybe. I have a small stack :)
Wontonimo: oh, that's a nice link Uljahn !
Wontonimo: there are many pages of articles ... nice
Marchete: https://machinelearningjourney.com/index.php/2021/01/15/max-norm-regularization/
Marchete: one Q
Marchete: max norm works for negatives?
Marchete: like if I put 2.0 it tries to limit to -2.0 to 2.0?
RoboStac: are you trying to 8bit your network? :)
Marchete: I'm trying to achieve reasonable winrates with limited ranges, yes
Marchete: but it stays at like 5th-10th
Marchete: also I'm still trying my 16bit
Marchete: still not 8 bit
Marchete: but as a consequence of that quantization limits
Marchete: I was checking different ways to limit the operating range
Marchete: I've read about kernel constraints like max norm
Marchete: but I'm unsure
Marchete: maybe I broke my bot with all these changes
Wontonimo: have you looked at this https://www.tensorflow.org/lite/performance/post_training_integer_quant
Marchete: yes, but still never used it
Marchete: maybe I'd go that way
Marchete: but I still think that pure int16 can bea feasible
Marchete: it's just a matter of putting the right range and adjust clipping relu to the factors
dbdr: lol: https://machinelearningjourney.com/index.php/blog/
dbdr: https://machinelearningjourney.com/index.php/2018/12/26/quick-eye-makeup/
Wontonimo: yeah, i saw that too ! too funny.
Wontonimo: looks like test data
dbdr: machine learning make up, that should work
dbdr: just need data
Wontonimo: if it is an RL make up machine, there will be millions of ugly applications first
Marchete: it seems codingame's chat
Marchete: 1 line it's about beards, then suddenly goes to a very deep ML chat
dbdr: chat model collapse
Marchete: NN chat collapsing, and next is AutomatonNN asking euler about something
rootZero: exec(bytes('㵲湩⡴湩異⡴⤩⼯〹㐥⨊Ɱ洽灡猨牴爬湡敧ㄨ椬瑮椨灮瑵⤨⬩⤱㵣✧椊╲㨲㵣尧❮椊㹲㨱㵮孮㨺ㄭ牰湩⡴潪湩渨⤩','u16')[2:])
rootZero: what language is this?
eulerscheZahl: python
dbdr: python
dbdr: darn
eulerscheZahl: at least one thing where i'm still above you ;)
RoboStac: https://github.com/clemg/pythongolfer/
dbdr: :P
eulerscheZahl: oh, nice link
dbdr: god, someone published a tool for it? :/
rootZero: is thiss allowed?
eulerscheZahl: i always felt too lazy to script that myself. time to improve some golfing solutions
eulerscheZahl: if it compiles, it's allowed
Q12: Wow, this is mean
I'm doing it right away :smiling_imp:
martinpapa69: are you still here Marchete ?
Westicles: come after me bro
Marchete: more or less
Marchete: what's up?
martinpapa69: is there any reason why the "exp256_ps" function is used, instead of its "fast" version? the second one looks easier to convert to avx
Marchete: second one has much less accuracy
Marchete: I'd like some intermediate function
Marchete: also, it's used only once in each NN prediction
Marchete: it doesn't seem a bottleneck right now
Marchete: probably tanh is more costly rn
martinpapa69: i see
RoboStac: I don't even bother trying to avx the softmax.
RoboStac: it's such a tiny part of the total time
Marchete: this guy knows
martinpapa69: for me the main "bottleneck" is to convert the avx2 calls to avx ones, so i might end up with a non-vectorized activation func
martinpapa69: but i mcurious how much does this difference of accuracy between the two implementations affect the final result of the training
Marchete: you can remove all AVX
Marchete: and let the compiler autovectorize
Marchete: in fact I think jace_k does that
Marchete: just a note, when compiling to send to CG maybe you need to force the target machine to have avx2
Marchete: to optimize the sent binary
Marchete: it doesn't need to be runnable in your pc
martinpapa69: ye sure. run the train code on windows
martinpapa69: *I
Marchete: I usually code/debug/profile on win
fbaker: yoooo, how do I disable this chat window?
Marchete: but sometimes VS C++ is slower
struct: almost everytime
Marchete: but almost everytime VS C++ is slower*
struct: :D
martinpapa69: ye but the debugging expericence is not comparable between visual stion and the IDE-s that are available on linux
struct: true
struct: this is why i also use vs
AllYourTrees: rust + vs code <3
Marchete: :regional_indicator_s::regional_indicator_e::regional_indicator_n::regional_indicator_d: :regional_indicator_n::regional_indicator_u::regional_indicator_e::regional_indicator_s:
Marchete: :regional_indicator_s::regional_indicator_e::regional_indicator_n::regional_indicator_d: :regional_indicator_n::regional_indicator_n::regional_indicator_u::regional_indicator_e::regional_indicator_s:
Marchete: I forgot an N
AllYourTrees: lol
christian-zunalargo: who's sending those?
Marchete: nnue's? no one, I wanted some of these
dbdr: :regional_indicator_s::regional_indicator_e::regional_indicator_n::regional_indicator_d: :regional_indicator_n::regional_indicator_u::regional_indicator_d::regional_indicator_e::regional_indicator_s:
Marchete?
Marchete: no no no
Marchete: nnues!
Marchete: efficiently updatable neural networks
RoboStac: pretty sure they'd be less effiecient for oware anyway
RoboStac: well, I guess they work for 1/2 seed moves that don't capture
Marchete: and connect4? :innocent:
RoboStac: would probably make a lot of sense there
Marchete: send C4 nnues
Marchete: imo nnues in 1st layer when one-hot are dead simple
Marchete: on board games with 1 bit change
Marchete: just add weights for that input
Marchete: done
Marchete: and non one-hot are (newvalue-previousvalue)*weight[input]
Marchete: I think
jacek: for c4 only 1 bit is changed per move
jacek: for bt, at most 6 squares are changed (with my inputs)
Marchete: 6?
Marchete: pawn + eaten?
jacek: attacked/defended squares
Marchete: ok
dbdr: is oware balanced for p1/p2?
RoboStac: I think p1 has a slight advantage
Marchete: robo vs robo = win p1
RoboStac: not got any real proof but it usually seems to be slightly favoured
Marchete: I agree
RoboStac: from lots of training
Marchete: when I sometimes win robo or jacek it's usually p1
Marchete: p2 I just consider ties as a "win" vs top players
dbdr: it's annoying that arena matches are deleted so fast now
struct: i dont believe in ties
BlaiseEbuth: Ties don't believe in you
Pythonista_TN: print("hello world!")
BlaiseEbuth: clear()
Wontonimo: Pythonista_TN.send("what up")
Pythonista_TN: Wontonimo.reply("I am good :smile: and you?)
TheRealKai: hi
jacek: yeah id say 1p in oware has slight advantage
Westicles: Is there a name for programs like read|sed|grep... or .join.split.flat_map... where you don't store anything?
erionhasani: Hello
Wontonimo: no, hello isn't what it's called
Wontonimo: stateless ?
Westicles: aha, thanks
jacek: pure functions?
Wontonimo: extra virgin lambdas ?
jacek: oO
Marchete: pipe commands?
Wontonimo: you can pipe any command in unix/linux
Wontonimo: so that isn't a good name since it doesn't discriminate
Wontonimo: stateless mutators
Marchete: sed can read and save on files
Marchete: I won't say it's stateless
Astrobytes: command line utilities?
Wontonimo: yes, so an a lot of those linux command
Marchete: wontapps
Wontonimo: oh, yes, i'd definitely call them command line utilities
Wontonimo: +1 Astrobytes
Astrobytes: that's what I've always known them as
Wontonimo: i thought we were specifically trying to come up with an alternate name for just the ones that can be used to not effect state (disk)
jacek: pure apps
Astrobytes: Stateless command line utilities :P
Marchete: bash binary stateless command line operational utilities
Astrobytes: *shell
Marchete: bash binary stateless command line operational utilities and shell
Marchete: :rofl:
Astrobytes: shell not bash :P
Wontonimo: BBSCLOUS
Marchete: I know
Astrobytes: I'll go with it because that acronym is awesome
Astrobytes: Shabs
Astrobytes: or SHABS, Stateless Helpful Applications Based in Shells
Westicles: functional programming?
dbdr: Transformers
BlaiseEbuth: Rollout!
jacek: trollout?
elderlybeginner: A code of ice and fire: I cannot find a way to prograssing. Any ideas as to algorithm, evaluation or what to use?
Astrobytes: Did you look at the forum thread? For the contest.
elderlybeginner: yep, it's 'thin'
Astrobytes: This one?
https://www.codingame.com/forum/t/a-code-of-ice-fire-feedbacks-and-strategies/105722/4
jacek: very thin
elderlybeginner: no, this one was not linked to bot_prog. Thanks
Astrobytes: elderlybeginner: the previous contest multis are sometimes linked to the multi forum thread rather than the contest one, it's worthwhile using the search in the forum to find them
elderlybeginner: Astrobytes: Thanks again
Astrobytes: no worries
refaat0: :wave:
hey82: hi
flame568: hi
akramBENghanem: hi
flame568: how are you
akramBENghanem: fine
flame568: thats quite great
akramBENghanem: and u ?
flame568: i am fine aswell
akramBENghanem: goood
Marchete: :thinking:
flame568: i am new here and i am i have no idea about basics of c++ i known python a lit but i donot think i can use python here
Astrobytes: You can use python here.
flame568: oh thankyou for telling me
jacek: python++ eh
Astrobytes: python++ = Black Mamba
danonchik: hello, use python#
danonchik: python++ is a bit old
ArtisticLuis: python is deprecated, use Cobra
Marchete: Cobra is deprecated, use Hydra
Astrobytes: Everything is deprecated. Use Medusa.
doomento: Medusa is deprecated. Use Hades
Stanley1337: Medusa is deprecated. Use Zeus
Dragon84: Zeus is deprecated. Use Hera
doomento: Hey guys, can you give me some tips on "The Descent"? I'm stuck right now and just completed the first test case but I can't complete the other ones.
KiwiTae: its hard to guess what you did wrong doomento
KiwiTae: show us you rcode
doomento: ok
doomento: while True: http://chat.codingame.com/pastebin/cd952fe1-039a-4bbe-8768-a32b45cc6c30
KiwiTae: don't worry it get some time to get use to it
doomento: :grin: ok
KiwiTae: your code is not doing what they ask
KiwiTae: you need to print the id of the highest mountain
doomento: so I get the mountain ID then print it... ok
KiwiTae: you can send me mp if you want i can walk you through
doomento: message?
doomento: ok
doomento: ok I sent you messages
jbollman7: Does clash of code still allow you to limit types of code challenges/ languages allowed? Or did they get rid of that?
FriendlyKh: they get rid of that
DaNinja: you can select languages and modes for Private Clashes
beginnerto2: bor this shit is hard
beginnerto2: im so new
beginnerto2: what do i do someone send me the answer
DaNinja: 42
anon8023849: halo guys do you know if perl6 is supported here
DaNinja: perl5
DaNinja: https://www.codingame.com/faq
kakaru: hello
Horizontalx: i love clash of code
