7 commits

Author	SHA1	Message	Date
Starbeamrainbowlabs	1a657bd653	add new uniq subcommand ... It deduplicates lines in the files, with the potential to add the ability to filter on a specific property later. The reasoningf or this is thus: 1. There will naturally be periods of time where nothing happens 2. Too many duplicates will interfere and confuse with the contrastive learning algorithm, as in each batch it will have less variance in samples This is especially important because contrastive learning causes it to compare every item in each batch with every othear item in the batch.	2022-07-04 19:46:06 +01:00
Starbeamrainbowlabs	1297f41105	.tfrecord files are too much hassle ... let's go with a standard of .jsonl.gz instead	2022-07-01 18:28:39 +01:00
Starbeamrainbowlabs	f5f267c6b6	Update dependencies	2022-07-01 16:56:51 +01:00
Starbeamrainbowlabs	bb018c53f6	Fix many bugs ... Many bugs remain though	2022-05-19 17:54:14 +01:00
Starbeamrainbowlabs	cc5efbae8a	Implement tfrecodify subcommand. ... It's all still untested, but that's the next step	2022-05-19 17:15:15 +01:00
Starbeamrainbowlabs	9411ad3218	tweak licence	2022-05-13 19:08:04 +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