4 ms·

Some more details: * I used the complearn package from http://complearn.org/ http://complearn.org/ - it hasn't been updated in a while and needed a few minor c

Some more details: * I used the complearn package from http://complearn.org/ http://complearn.org/ - it hasn't been updated in a while and needed a few minor changes to compile on Ubuntu 14; drop me a line if you'd like the patch. * Similarity clustering with NCD works best when you can visually inspect the items and get some level of understanding as to what they are doing. I recently clustered all of our web logs on a monthly basis and found that, while e.g. summer months were clustered together (around releases and conferences typically) there were a couple of unexpected winter months in there too. The logs were too large to inspect for differences by hand - in this case it is more useful to cluster e.g. individual requests, or summaries tables of each month. * Wesnoth is a great example of an open source game that continues to grow and develop and the team are friendly and welcoming. I can highly recommend contributing to it!

cultureulterior 12y ago
Could you upload a svg example?
- moconnor 12y ago
  Sure, here's the AI after a change introduced a pretty severe bug: https://www.dropbox.com/s/lqclj3toywnh8ak/losing.svg?dl=1 https://www.dropbox.com/s/lqclj3toywnh8ak/losing.svg?dl=1 In this case looking at the replays for the most similar win/loss runs (15 and 4) and the most severe losses (31+32) made the cause absolutely clear. Here is the current state of the AI: https://www.dropbox.com/s/e1n3wevn2gggbic/winning.svg?dl=1 https://www.dropbox.com/s/e1n3wevn2gggbic/winning.svg?dl=1 In both the cases in which is loses its opponent gets lucky in combat and has a critical unit level up twice. There was no need for the AI to risk this happening though, so the next improvement might be to make it target units that are gaining XP more aggressively earlier on when winning, or simply avoid combat with units likely to level up and move straight in for the kill. Of course, there are a wide range of more general improvements that can be made as well - this version of the AI only recruits skeletons, for example!
__Joker 12y ago
Similarity clustering with NCD works best when you can visually inspect the items.. Doesn't this apply to all the clustering algorithms ? And this is what we want to avoid for clustering, because it is not easy to visualize in higher dimensions.
- moconnor 12y ago
  It's a particular problem with NCD because you don't always know what the compressor is measuring. At least with the L2 distance on a vector you know that those points were close to each other for a well-defined definition of 'close'. If it is surprising that they are close, you might want to investigate whether your feature selection really makes sense for these items. With NCD the compressor selects its own features in a largely opaque way. This makes it fun to use but difficult to debug!

cultureulterior 12y ago

Could you upload a svg example?

moconnor 12y ago
Sure, here's the AI after a change introduced a pretty severe bug: https://www.dropbox.com/s/lqclj3toywnh8ak/losing.svg?dl=1 https://www.dropbox.com/s/lqclj3toywnh8ak/losing.svg?dl=1 In this case looking at the replays for the most similar win/loss runs (15 and 4) and the most severe losses (31+32) made the cause absolutely clear. Here is the current state of the AI: https://www.dropbox.com/s/e1n3wevn2gggbic/winning.svg?dl=1 https://www.dropbox.com/s/e1n3wevn2gggbic/winning.svg?dl=1 In both the cases in which is loses its opponent gets lucky in combat and has a critical unit level up twice. There was no need for the AI to risk this happening though, so the next improvement might be to make it target units that are gaining XP more aggressively earlier on when winning, or simply avoid combat with units likely to level up and move straight in for the kill. Of course, there are a wide range of more general improvements that can be made as well - this version of the AI only recruits skeletons, for example!

__Joker 12y ago

Similarity clustering with NCD works best when you can visually inspect the items.. Doesn't this apply to all the clustering algorithms ? And this is what we want to avoid for clustering, because it is not easy to visualize in higher dimensions.

moconnor 12y ago
It's a particular problem with NCD because you don't always know what the compressor is measuring. At least with the L2 distance on a vector you know that those points were close to each other for a well-defined definition of 'close'. If it is surprising that they are close, you might want to investigate whether your feature selection really makes sense for these items. With NCD the compressor selects its own features in a largely opaque way. This makes it fun to use but difficult to debug!