← Thread:User talk:MN/Hard-coded segmentation/reply (14)
Many people seem to be talking about adding different sets of weights in the form of a set of virtual guns, each with a separate kD-Tree. It seems you could dynamically change them if you didn't weight your predictors when putting them into the kD-Tree, but included the weights in the distance function. I don't know if this would reduce the efficiency of the tree, but its probably faster than running one kD-Tree for each set of weights. Also, it would allow you to constantly change your weights. This might be useful when surfing as if you notice that the opponent is using a high data decay rate (hitting you a lot) you could slow down your own data decay rate to make you less vulnerable to their antisurfer targeting.
This has been done a few times in the past. Various kD-Tree implementations have support for dynamically setting weightings for the distance function.
IIRC Diamond is one example of a bot that makes use of this feature of the kD-Tree implementation, though I think it does still use a pre-defined list rather than dynamic tweaking.
IIRC there have been experiments in dynamic weight tweaking but I'm not sure how successful they've been off hand.
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Data decay (time classification) is one of many classifications in k-NN search. You need to mirror all of them to achieve perfect dodging.
You need a lot more data than is available to estimate all weights, unless you want to get shot a lot.
I believe multiple people have noted that the weight on new vs old data is significantly more important than weights on other predictors.(In a gun) I believe Skilgannon said something along the lines of: I can change the weights by tenfold (except the one on shots fired) and I get very little difference in performance. So if you could match that weight in your flattener, you could (hopefully) get very low hitrates against you.