Density Calculations

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Indeed, I use a pretty low K in my anti-surfer gun. Higher than 1 though.

Also, make sure you're running enough battles to get an accurate measurement. It might be more battles than you think. More than a few bad Robocode conclusions have come from not gathering enough data.

Voidious (talk)18:40, 15 December 2013

How low is your k? I've only run a few battles, but when my hitrate goes under 8% vs a bot which can't shoot and can't flatten, and normally its above 12%, it makes me think there is something wrong. Ill run a few more to make sure.

Straw (talk)21:33, 15 December 2013

My Anti-Surfer gun uses 4 data sets. They are k=3 of last {125 | 400 | 1500 | 4000} data points. So the least-decaying one has ~5 rounds of data, and many of the selected data points will overlap.

8% vs 12% of raw hit rate over a few battles doesn't could still be just fluctuation. If it's a normalized hit rate that might be significant... I generally run a few hundred to a few thousand battles to test a change, for what it's worth.

Voidious (talk)21:53, 15 December 2013

Its normalized calculated by DrussGT. I just did 15 battles, it seems unlikely due to random fluctuation. Is your anti-surfer gun a composite of multiple other guns? How does it work?

Straw (talk)22:10, 15 December 2013

Well, I consider it one gun. :-) But instead of a single KNN search, it does 4 of them and combines the results.

You should be able to hazard a guess at the margin of error by looking at the distribution of those 15 values. Isn't it 1.96 * standard deviation = 95% confidence interval? Edit: Er, then divided by sqrt(num battles)? I don't know, but it's not hard to find. :-)

Voidious (talk)22:20, 15 December 2013

Does it use different trees for the different searches? Isn't that assuming a normal distribution? The mean and stdev for k = 1 are 12.12 and .34, for k = 100 (at max) its 9.26 and .36. Seems significant. Haven't tried intermediate values, k = 2 seemed to decrease performance, but I didn't do a careful study.

Straw (talk)22:55, 15 December 2013

Yes, different trees. It does assume a normal distribution, but I think that's at least in the ballpark.

I'm not sure, but if going from k=1 to k=2+ produces a significantly different result, that seems strange. I'd probably spend some time trying to suss out bugs. Like what if you do k=2 and only consider the 2nd point, so there's still only 1 point in your kernel density? Or what if you do k=1 but then feed two of that point into your kernel density?

Voidious (talk)19:54, 16 December 2013