57 commits

Author	SHA1	Message	Date
Starbeamrainbowlabs	b53c77a2cb	index.py: call static function name run	2022-07-26 19:51:28 +01:00
Starbeamrainbowlabs	a7ed58fc03	ai: move requirements.txt to the right place	2022-07-26 19:25:11 +01:00
Starbeamrainbowlabs	e93a95f1b3	ai dataset: add if main == main	2022-07-26 19:24:40 +01:00
Starbeamrainbowlabs	de4c3dab17	typo	2022-07-26 19:14:55 +01:00
Starbeamrainbowlabs	18a7d3674b	ai: create (untested) dataset	2022-07-26 19:14:10 +01:00
Starbeamrainbowlabs	dac6919fcd	ai: start creating initial scaffolding	2022-07-25 19:01:10 +01:00
Starbeamrainbowlabs	8a9cd6c1c0	Lay out some basic scaffolding ... I *really* hope this works. This is the 3rd major revision of this model. I've learnt a ton of stuff between now and my last attempt, so here's hoping that all goes well :D The basic idea behind this attempt is *Contrastive Learning*. If we don't get anything useful with this approach, then we can assume that it's not really possible / feasible. Something we need to watch out for is the variance (or rather lack thereof) in the dataset. We have 1.5M timesteps, but not a whole lot will be happening in most of those.... We may need to analyse the variance of the water depth data and extract a subsample that's more balanced.	2022-05-13 19:06:15 +01:00