does bin smoothing make guns better or worse

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Instead of applying methods of estimating the correct amounts of bin smoothing, people tend to switch to kernel density and tune the kernel function. There was a lot of discussion about the best kernel function and the best function width. The optimal changes for each opponent and some kind of averaging is needed, which is usually estimated through genetic tuning.

In guns, smoothing usually has no effect because you don't need to estimate the PDF, you only need to find the peak. But when you superpose many PDFs together (swarm targeting), things change.

MN (talk)01:51, 25 November 2013

Estimating the PDF can still a useful component of finding the peak when not superimposing things, particular when the density of observations is sufficiently low. The main reasons you don't see much effect in targeting is that the usual bin sizes inherently act similar to a certain amount of smoothing anyway, and for targeting you have a larger number of observations than movement which reduces the amount of smoothing that makes sense as well. Consider what happens when your bins are significantly smaller than what is typical without any additional smoothing. (A targeting system that accounts for botwidth also reduces the amount of smoothing that makes sense, but that's a bit of a different matter)

Rednaxela (talk)14:41, 25 November 2013

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Return to Thread:User talk:Tmservo/does bin smoothing make guns better or worse/reply (7).

re: why in movement, agreed 100%.

With WaveSim, I've tested different kernel densities (effectively smoothing formulas) in my main gun over a huge data set. There were differences, but IIRC on the order of thousandths of a percent in hit percentage (eg 12.004% vs 12.002%). Not sure of the margin of error, either... 5k battles * ~25k ticks = millions of records, and both algorithms were running on the same data set.

Voidious (talk)01:56, 26 November 2013