DC and VCS

So, I have been working on my movement, and I theorized that VCS movements would have a small advantage in the rumble because VCS guns are so common. However, I dislike VCS because I have found KNN so much more intuitive. (Yes, I probably just had a lot of bugs in my VCS gun, but I still like KNN better.) The problem I have is that I can guess how important attributes would be for a random movement fairly well, but I can't guess what people would use in their guns so I can't do as good of a job with those. (Which basically means I just copy Diamond) So I wanted to try to make my KNN classifiers match VCS guns as much as possible (except for the flattener where being different is probably beneficial). Step one is to make classifiers that would match "typical" guns, like raiko, or GFTargeting tutorial, etc. Any thoughts on what is "typical" for VCS guns? Also I can't think of how the weights should be chosen. Any ideas?

Well, the same dilemma of "guess how important attributes would be" exists with VCS as well.

Anyway, fun thing is, you can actually determine in a fairly algorithmic manner, how to make a KNN search that behaves as closely as possible to a given VCS configuration (i.e. Raiko's like you mention) while still being continuious.

1. For each dimension, examine the VCS bins. If the size of the bins are non-equal (be careful to note how the minimum/maximum values of the dimension interact with the first/last bins), then you need to divise a continious function which curve fits the bin-size as a function of the value of the dimension. You can use any form of regression you like for this, provided the function stays above 0 for all.
2. For each dimension's "bin-size" approximation function, take it's integral. The resulting function should now be monotonic. We'll call this the "scaling function"
3. Feed each "scaling function" the minimum/maximum values for it's associated dimension and make note of the minimum/maximum values out of the function. Then divide the number of bins in the dimension by the difference between the maximum/minimum values, to get the scaling factor to make the range of the "scaling function" proportional to the number of bins. Multiply your "scaling function" by this.
4. Now, before inserting values into the kd-tree, you put them through this "scaling function", which should weight them approximately the same as how the VCS did :)

(Hmm... maybe I should write code to perform this procedure some time...)