Thread:Talk:Rolling Averages/Rolling KD-Tree?

So I am trying to work out how you deal with rolling averages in a KD-Tree DC gun implementation? I'd like a low rolling average anti-surfer gun. Silly question but I have no idea how to do it. I understand you can use time as a dimension in the tree, but then there is the problem of distance. Time is a continuous variable, which means you can't just go "get me the last 3 Guess Factors that match the current situation" as the time dimension in the tree will mean that you are not guaranteed the latest? I'm probably not explaining it very well. If you weight the time the same as other dimensions then you might get recent values, but, given the weighting of time in a 0-1 value distance matching, pulling 3 values out of the tree will not necessarily give you the 3 latest values, just the 3 closest values which might match latest or might be closer in non-time values?

This is a pretty terrible explanation/question sorry, but I'm trying to understand how to integrate low rolling averages with DC.

If you don't put time as a dimension and just pull out the say, 100 closest matches, you are not necessarily going to get any recent matches, so even if you put time-stamps in the data to filter those 100 results, you might not get any at all which are recent?