Yesss! by reformulating the problem, i simplified the dataset structure to:
DX PDX CurTime
where:
DX = mouse delta X
PDX = perfect aim mouse delta X
CurTime = TimeFrame
this fixed almost all of the issues i was having
so now the inputs are:
DX PDX CurTime
and the outputs are:
sensitivity mouseaccel
now the objective function to be minimized is this:
sum from 0 to n of: pow(DX(n)/33.0*e_sens-PDX(n)*CurTime(n)/9,2)
with e_sens being the estimated sensitivity plus the maccel component.
*CurTime/9 this is needed because PDX value is an instantaneus complete adjustement which a human cannot make so it has to be normalized to obtain a useful sensitivity value
anyway that /9 constant has to be reviewed and set to more accurate values,
the best thing for that would be to run a modded server and see what the mean mouse movement velocity is, but i think i got it very close anyway (i obtained it doing the inverse problem, trying to make the PDX as close as possible to the DX and in this issue i used my best sensitivity).
now the algorithm is converging to the same sensitivity value when started on datasets that had different sensitivitys (atleast for me)
now i need someone to test it :) and to tell me if they get improvements in their accuracy.
@Viper: dpi is not needed in the model since it's approximately a scalar applied to DX and the optimization is made to bring DX to PDX as closely as possible using one scalar "sensitivity" and a non linear adjustement "mouseaccel".
premise:
my best sensitivity is around 2.6 (and in the first version of the algorithm i was lucky enuff to get to that value)
RESULTS:
using a dataset produced with sens=1: sens:2.65596749951308 maccel:7.37441540697647e-15
using a dataset produced with sens=2.5: sens:2.67095337700015 maccel:2.79989582531623e-16
using a dataset produced with sens=4: sens:2.55510040757476 maccel:4.19500044802589
in the first two maccel = 0
in the one pruduced at sens = 4:
the sensitivity is the same but accel = 4.2??? well i think that's actually a measure of the uncertainty my hand had, it was very hard controlling the aim at sens =4 for me so i produced a dataset with a lot of noise and that noise has been "absorbed" by the non linear component of the model.
i would like to know if i can post the source code directly here...
there are some lines in it which produce the PDX value those lines could be used to produce a aimbot (a very stupid one but still... i dont want to take risks with the mods)
i saw a many intelligent posts in here, if someone steps in PM i would be gratefull i need a tester :)
PS.
if i dont make any sense please try to correct me, i'm new on this subjects :)
DX PDX CurTime
where:
DX = mouse delta X
PDX = perfect aim mouse delta X
CurTime = TimeFrame
this fixed almost all of the issues i was having
so now the inputs are:
DX PDX CurTime
and the outputs are:
sensitivity mouseaccel
now the objective function to be minimized is this:
sum from 0 to n of: pow(DX(n)/33.0*e_sens-PDX(n)*CurTime(n)/9,2)
with e_sens being the estimated sensitivity plus the maccel component.
*CurTime/9 this is needed because PDX value is an instantaneus complete adjustement which a human cannot make so it has to be normalized to obtain a useful sensitivity value
anyway that /9 constant has to be reviewed and set to more accurate values,
the best thing for that would be to run a modded server and see what the mean mouse movement velocity is, but i think i got it very close anyway (i obtained it doing the inverse problem, trying to make the PDX as close as possible to the DX and in this issue i used my best sensitivity).
now the algorithm is converging to the same sensitivity value when started on datasets that had different sensitivitys (atleast for me)
now i need someone to test it :) and to tell me if they get improvements in their accuracy.
@Viper: dpi is not needed in the model since it's approximately a scalar applied to DX and the optimization is made to bring DX to PDX as closely as possible using one scalar "sensitivity" and a non linear adjustement "mouseaccel".
premise:
my best sensitivity is around 2.6 (and in the first version of the algorithm i was lucky enuff to get to that value)
RESULTS:
using a dataset produced with sens=1: sens:2.65596749951308 maccel:7.37441540697647e-15
using a dataset produced with sens=2.5: sens:2.67095337700015 maccel:2.79989582531623e-16
using a dataset produced with sens=4: sens:2.55510040757476 maccel:4.19500044802589
in the first two maccel = 0
in the one pruduced at sens = 4:
the sensitivity is the same but accel = 4.2??? well i think that's actually a measure of the uncertainty my hand had, it was very hard controlling the aim at sens =4 for me so i produced a dataset with a lot of noise and that noise has been "absorbed" by the non linear component of the model.
i would like to know if i can post the source code directly here...
there are some lines in it which produce the PDX value those lines could be used to produce a aimbot (a very stupid one but still... i dont want to take risks with the mods)
i saw a many intelligent posts in here, if someone steps in PM i would be gratefull i need a tester :)
PS.
if i dont make any sense please try to correct me, i'm new on this subjects :)