Virtual Guns with Swarm Targeting?

Fragment of a discussion from Talk:Virtual Guns
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I think the best would be having individual guns, and using leave-one-out cross correlation to test which gun actually gives better results on the historical data at fire time. This would skip needing to track separate hitrates for guns that you don't know where they would fire.

Skilgannon (talk)17:32, 31 December 2017

If I understand correctly, for n firing waves w = {w1..wn}, LOOCV involves training the gun with w - {wi}, then test with wi, for all i. For knn guns, this means doing n “(k+1)nn” searches (then exclude wi from result), and for neural networks, this means feeding the network n(n-1) waves in total. Isn’t that costly for robocode? Or may there be some faster ways?

Xor (talk)05:05, 1 January 2018

For KNN I think this is fine. I know personally I use a very fast kernel density function (square), so these kind of tests aren't expensive. That said, I haven't tried this technique yet. It might not work well due to other things we haven't spoken of yet.

Agreed though for NN, it is already pushing the limit of what can be processed in a single tick, so there is no way you can take 2x or 3x the processing power.

Skilgannon (talk)22:41, 2 January 2018

So do you mean only 2~3 waves need to be tested? I thought we need to iterate through every fired waves and do a knn search for each.

Xor (talk)15:14, 6 January 2018