# Hard-coded segmentation

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I know what you mean about those Hard-coded segmented bin-based statistical algorithms. I never liked those, I went to great lengths to try and make those easier to work with.

Now we just need to find a way to get rid of the hard coding in the kNN algorithms, that exist in the form of carefully tuned weights and states.

I suspect we'd need much longer battles for dynamic weighting to ever out-perform hand-tuned weights. I'm not even sure enemy behavior has a non-negligible impact on the optimal weights, as we generally assume. Has anyone proven a certain set of weights to be optimal against one bot, and a different set optimal against another, beyond margin of error, in gun or movement?

I don't know if that's been proved either. However, just the way that stats are gathered combined with robocode physics are going to make some weights more relevant than others.

Of course, I can choose two different bots that I know will be optimal with different kNN weights. Think a surfer vs a random-movement bot, and the weight of the rolling average between them. Or a bot that bounces off walls, and the effect of a wall-distance attribute vs a bot that smooths against walls.

The best would probably be to have multiple weighting sets, and the gun chooses the one with the highest hitrate, or possibly with some classifier.