Difference between revisions of "Thread:Talk:DrussGT/Head-to-head/reply (9)"
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(We might just move this thread to [[Talk:Wave Surfing]] at some point...) | (We might just move this thread to [[Talk:Wave Surfing]] at some point...) | ||
| − | Well, I agree that much of the value of multiple VCS buffers is covered inherently by a DC system: smoothing of the data and scaling to different amounts of data (eg, no need for a quick- | + | Well, I agree that much of the value of multiple VCS buffers is covered inherently by a DC system: smoothing of the data and scaling to different amounts of data (eg, no need for a quick-learning unsegmented buffer in DC). So while my best VCS gun has a few stat buffers (and your best does anti-aliasing / interpolation), my best DC gun has only one tree. But while I wouldn't expect to have 100 trees in a DC movement, I do have more than one and I think there's value in it. |
As far as data decay in a DC system, I think the progression from simplistic to sophisticated goes something like this: | As far as data decay in a DC system, I think the progression from simplistic to sophisticated goes something like this: | ||
Latest revision as of 18:55, 12 September 2011
(We might just move this thread to Talk:Wave Surfing at some point...)
Well, I agree that much of the value of multiple VCS buffers is covered inherently by a DC system: smoothing of the data and scaling to different amounts of data (eg, no need for a quick-learning unsegmented buffer in DC). So while my best VCS gun has a few stat buffers (and your best does anti-aliasing / interpolation), my best DC gun has only one tree. But while I wouldn't expect to have 100 trees in a DC movement, I do have more than one and I think there's value in it.
As far as data decay in a DC system, I think the progression from simplistic to sophisticated goes something like this:
- Weighting by age. This is not without merit, but is pretty crude. An old piece of data may still be the most recent for that situation.
- Capping number of data points, deleting old ones. Also pretty crude, but effective if it's very important to emphasize recent data. I still do this in parts of Diamond.
- Within the set of nearest points, sort chronologically and weight by rank. This is about as close as you can get to how rolling average works in a VCS segment. I weight data by 1 / (base ^ sort position). So with a base of 2, they're weighted 1, .5, .25, ... . A base of 2.4 is about equivalent to a rolling average of 0.7.
- Use multiple, exponentially increasing values of k (say 1/4/15/50), with each set of data weighted by chronological order. This emulates having stat buffers of increasing segmentation depths, each with a rolling average. The deepest set of segmentation is akin to taking a low k nearest neighbors search, while an unsegmented buffer would use the max value of k.
Lastly, this is just a hunch, but I think another value of combining many different views of your data is that you achieve a safe pseudo-randomness. That is, simply surfing one set of data will make you move more predictably than the sum of a diverse set of viewpoints - at least with a True Surfing algorithm. But surfing that sum of viewpoints is still going to err on the side of dodging bullets accurately, in contrast to a truly random movement.
