lately i've got interested into optimization algorithms
and i thought that it would be possible to use them on AC...
i think that when we choose a sensitivity parameter we not are maximazing our real weapon control but only the perception of it (which most of the times is wrong)
i'm aware that after a long time playing with the same config one is going to get used to the choice taken long before (the sensitivity and mouse accel)
so what if it would be possible to estimate our best sensitivity in a mathematical way?
i made an attempt at this:
modification to AC that gets this data and writes it to a logfile:
Mouse-DX , Optimal-DX, curtime
then after playing a few minutes:
i quit AC and i run my optimization algorithm (sthocastic hillclimber) on the dataset with the objective
of getting a sensitivity and a mouseaccel parameter that minimizes the difference between my aim and what would have been "the perfect aim" in that instant.
and after milliseconds or so of computation on the dataset i get those two parameters:
my original sens was: 1,8 and i got 34% accuracy with SMG on a ac_desert bot deathmatch on medium difficulty in the training game
then i decided to try the estimated parameters
sensitivity 2.58
mouseaccel 0 (strangely the algorithm found that mouseaccel for me is best left at 0)
and i got a 41% accuracy on the same mode map nbots ecc. (my percieved control over the weapon was less than before but getting used was very fast!)
conclusion i got a free almost 7% boost on accuracy with this. (it's too soon to say it's actually that way but i still got VERY surprised, i was suddenly tracking moving bots with much more easy, kinda obvious you would say the sens is higher now)
so is anyone with coding experience and good intention interested?
edit..
after making more tests it looks like it doesnt settle near some sensitivity it jumps whener i recreate the dataset with another starting sensitivity...
--
RE-EDIT: reformulated the problem, and fixed the above bug!! :D
Download link and readme : http://forum.cubers.net/thread-3814-post...l#pid66465
and i thought that it would be possible to use them on AC...
i think that when we choose a sensitivity parameter we not are maximazing our real weapon control but only the perception of it (which most of the times is wrong)
i'm aware that after a long time playing with the same config one is going to get used to the choice taken long before (the sensitivity and mouse accel)
so what if it would be possible to estimate our best sensitivity in a mathematical way?
i made an attempt at this:
modification to AC that gets this data and writes it to a logfile:
Mouse-DX , Optimal-DX, curtime
then after playing a few minutes:
i quit AC and i run my optimization algorithm (sthocastic hillclimber) on the dataset with the objective
of getting a sensitivity and a mouseaccel parameter that minimizes the difference between my aim and what would have been "the perfect aim" in that instant.
and after milliseconds or so of computation on the dataset i get those two parameters:
my original sens was: 1,8 and i got 34% accuracy with SMG on a ac_desert bot deathmatch on medium difficulty in the training game
then i decided to try the estimated parameters
sensitivity 2.58
mouseaccel 0 (strangely the algorithm found that mouseaccel for me is best left at 0)
and i got a 41% accuracy on the same mode map nbots ecc. (my percieved control over the weapon was less than before but getting used was very fast!)
conclusion i got a free almost 7% boost on accuracy with this. (it's too soon to say it's actually that way but i still got VERY surprised, i was suddenly tracking moving bots with much more easy, kinda obvious you would say the sens is higher now)
so is anyone with coding experience and good intention interested?
edit..
after making more tests it looks like it doesnt settle near some sensitivity it jumps whener i recreate the dataset with another starting sensitivity...
--
RE-EDIT: reformulated the problem, and fixed the above bug!! :D
Download link and readme : http://forum.cubers.net/thread-3814-post...l#pid66465