12 commits

Author	SHA1	Message	Date
Starbeamrainbowlabs	1cbb241786	README: tidy up	2023-11-30 17:02:08 +00:00
Starbeamrainbowlabs	60674fb6b3	README: finish filling out	2023-11-30 16:32:46 +00:00
Starbeamrainbowlabs	df621fd7d2	README: Continue filling out, but we're not there yet.	2023-11-29 17:31:47 +00:00
Starbeamrainbowlabs	a1a2b105e6	README: Add under construction notice	2023-11-29 15:15:07 +00:00
Starbeamrainbowlabs	1d8807793f	README: start expanding, buti ts not finished yet	2023-11-28 16:46:02 +00:00
Starbeamrainbowlabs	d7fdf8f038	README: fix diagram	2023-07-06 15:31:13 +01:00
Starbeamrainbowlabs	d93365a191	Update license to AGPL.	2023-07-06 15:16:11 +01:00
Starbeamrainbowlabs	0c11ddca4b	rainfallwrangler does NOT mess up the ordering of the data	2022-10-18 19:07:14 +01:00
Starbeamrainbowlabs	f2312c1184	fix crash	2022-08-31 16:25:27 +01:00
Starbeamrainbowlabs	fe7a8b3fc0	Add 9 simple steps to use the rainfall radar model..... oh boy.	2022-08-31 16:18:28 +01:00
Starbeamrainbowlabs	d9b9a4f9fc	note tos elf	2022-07-22 19:04:41 +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