Danger function
Yes it should help in guns, but I think it would increase the hit rate by at most 1 percent vs a good VCS gun (Targeting_Challenge_RM/Results), which should translate to less than 1 APS if your movement is good (I think, not sure on that, but it's my general impression). Also I think I can prove (ie. I didn't bother to write it down and double check) that the benefit is incidental, and if you perfectly configured a VCS GF gun, it would be better. The catch is that setting up a KNN gun is a lot easier than setting up a VCS gun perfectly.
As AW says, movement is always more important than guns. If you can dodge every enemy bullet it will get you a higher score than if you can hit them 100%. What strikes me as strange is that there are only 5 bots above Cotillion in the minirumble, despite having double the available codesize.
That's an interesting comment - I actually think VCS guns are much easier to configure, which is why it took a long time (years?) for top KNN guns to catch up to top VCS guns even after a lot of us were playing with them. I agree that the top guns of each style are not too different at this point, though I think DrussGT's and Diamond's guns are pretty far ahead of the rest and are both KNN.
Maybe I just had a better idea of what I was doing when I made my KNN gun, but I really think there's more to it than that. In KNN, you just decide what attributes are important, and then, if you want to, try to guess the importance. In VCS you decide which attributes to use in each set of segmentation, then how fine that segmentation should be, and the conditions for using that segmentation.
A quick outline of my proof that a perfect VCS dominates (or should dominate, in that it has identical or better data) KNN. Assuming infinite memory and CPU resources, you create every possible set of segmentations for VCS given certain attributes. When aiming, select the histogram that is centered on the current data point with the size of each dimension's bin so that it includes all points that would be included in the KNN search. It contains at least all points from the KNN search (it may contain more if there are multiple points exactly the same distance from the query point) Obviously, this assumes a range search with a hyper rectangle, but a more complicated algorithm could do the same thing for a hypersphere.
In my opinion KNN is actually easier to get good results with but much more difficult to get spectacular results with. My current KNN gun in Nene is pretty much bare bones (being from Mint). But I had started to hit a tweaking wall with it despite only having 'good' performance.
I strongly doubt that you hit a tweaking wall with your attributes. Try:
making sure all of them are calculated correctly
working on your segmentation for wall proximity
perhaps tweaking how your bandwidth and size of k
