View source for User talk:Chase-san/Kd-Tree

NullPointerExceptions and inaccuracy

Trying to test your Kd-Tree in the benchmark framework, I'm getting some NullPointerExceptions due to right.rect.getNearest(k) returning null sometimes. I don't know if it's the proper fix, I changed the relevant lines to be like:

PointKD p = right.rect.getNearest(k);
if(p != null && k.distanceSq(p) < t) {

Something else is wrong also I believe, because it's only getting "88% accuracy" out of the benchmark results, so it's not finding all of the nearest neighbors correctly. --Rednaxela 22:23, 1 March 2010 (UTC)

That should only happen if the rectangle hasn't gotten expanded ever, which shouldn't happen, unless you haven't added anything to the tree at all (darn me and removing all my safeties). Since this gets set during the add. Can you give me some idea of the data you are feeding into it? --Chase 22:44, 1 March 2010 (UTC)

Well, it's adding to the tree before it's searching. See, File:KNN.jar along with Diamond vs CunobelinDC gun data, a recording I made of every kd-tree operation performed by an older version of Voidious's Diamond during a battle. I can upload an updated KNN.jar that includes the testing of your tree if you wish. Personally I've found the framework invaluable for testing my tree, making it easy to see when something I just did added a bug. --Rednaxela 22:55, 1 March 2010 (UTC)

I see, I got it running, and I get the error too. Let me see whats going on. --Chase 23:25, 1 March 2010 (UTC)

Well I found parts of the problem, it gets 100% now, but its about pretty slow (not as slow as Linear search), I suppose that is to be expected, seeing how hard it is on the GC. Still not sure why Rect is returning null, since it shouldn't (probably something stupid per usual). Well I based this new one off my old one, maybe I should write one from scratch. :P --Chase 00:02, 2 March 2010 (UTC)

K-NEAREST NEIGHBOURS ALGORITHMS BENCHMARK
-----------------------------------------
Reading data from data.csv
Read 25621 saves and 10300 searches.

Running 10 repetition(s) for k-nearest neighbours searching:
:: 13 dimension(s); 40 neighbour(s)
Warming up the JIT with 5 repetitions first...

Running tests... COMPLETED.

RESULT << k-nearest neighbours search with Voidious' Linear search >>
: Average searching time       = 2.007 miliseconds
: Average worst searching time = 37.627 miliseconds
: Average adding time          = 3.96 microseconds
: Accuracy                     = 100%

RESULT << k-nearest neighbours search with Chase-san's Bucket PR k-d tree >>
: Average searching time       = 1.023 miliseconds
: Average worst searching time = 29.937 miliseconds
: Average adding time          = 8.38 microseconds
: Accuracy                     = 100%

RESULT << k-nearest neighbours search with Simonton's Bucket PR k-d tree >>
: Average searching time       = 0.493 miliseconds
: Average worst searching time = 45.444 miliseconds
: Average adding time          = 7.15 microseconds
: Accuracy                     = 100%

RESULT << k-nearest neighbours search with Voidious' Bucket PR k-d tree >>
: Average searching time       = 0.836 miliseconds
: Average worst searching time = 35.729 miliseconds
: Average adding time          = 6.39 microseconds
: Accuracy                     = 100%

RESULT << k-nearest neighbours search with Rednaxela's Bucket kd-tree >>
: Average searching time       = 0.152 miliseconds
: Average worst searching time = 38.736 miliseconds
: Average adding time          = 7.43 microseconds
: Accuracy                     = 100%


BEST RESULT: 
 - #1 Rednaxela's Bucket kd-tree [0.1518]
 - #2 Simonton's Bucket PR k-d tree [0.4928]
 - #3 Voidious' Bucket PR k-d tree [0.8362]
 - #4 Chase-san's Bucket PR k-d tree [1.0227]
 - #5 Voidious' Linear search [2.0067]

Benchmark running time: 493.49 seconds

Some results... All I can say is.. this current implementation, the worst search time is the lowest of all of them (though not by a ton, I suppose). But I wonder why that is. --Chase 00:29, 2 March 2010 (UTC)

RESULT << k-nearest neighbours search with Chase-san's Bucket PR k-d tree >>
: Average searching time       = 0.649 miliseconds
: Average worst searching time = 34.114 miliseconds
: Average adding time          = 8.92 microseconds
: Accuracy                     = 100%

It seems Caching the distance cut the time by about 40%. (other times are close to what they are above). Oh well this is probably my last post on the wiki tonight. --Chase 00:45, 2 March 2010 (UTC)

Nice stuff! With that graph, it looks like the place you have the most room to squeeze out more performance if you wish to, is by refactoring the code so that the JIT gets it optimized sooner. Having the lowest time in those first few searches is certainly neat, and perhaps is the most important thing in the robocode context as that's the most likely time for a kd-tree to lead to a skipped turn. I wonder what it took to get that down and how much that is dataset specific.

But on the inverse you have a smaller data set earlier on. So it might balance out to a point. But I do intend to work on that a bit. I actually originally tuned with a random data set.

Also the current tree is based off C but I removed the Item class and altered the return to use a heap. Though I was considering having it interface with SortedMap and just returning that interface. So that I could avoid the issue of defining an external class.

I am mulling around how to redesign my tree so that it works better with the JIT, and so I create/destroy fewer objects (which should save some time).