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research-rainfallradar/rainfallwrangler/package.json

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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 18:06:15 +00:00
{
"name": "rainfallwrangler",
"version": "1.0.0",
"description": "Wrangles rainfall radar and water depth data into something sensible.",
"main": "src/index.mjs",
"scripts": {
"test": "echo \"No tests have been implemented yet\""
},
"repository": {
"type": "git",
"url": "https://git.starbeamrainbowlabs.com/sbrl/PhD-Rainfall-Radar.git"
},
"author": "Starbeamrainbowlabs",
Update license to AGPL.
2023-07-06 14:16:11 +00:00
"license": "AGPL-3.0-or-later",
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 18:06:15 +00:00
"dependencies": {
Update dependencies
2022-07-01 15:56:51 +00:00
"@tensorflow/tfjs-node-gpu": "^3.18.0",
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 18:06:15 +00:00
"applause-cli": "^1.8.1",
"gunzip-maybe": "^1.4.2",
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 18:46:06 +00:00
"nexline": "^1.2.2",
"p-map": "^5.5.0",
"p-reflect": "^3.0.0",
Update dependencies
2022-07-01 15:56:51 +00:00
"pretty-ms": "^8.0.0",
.tfrecord files are too much hassle let's go with a standard of .jsonl.gz instead
2022-07-01 17:28:39 +00:00
"spawn-stream": "^1.0.2",
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 18:46:06 +00:00
"terrain50": "^1.10.1",
"workerpool": "^6.2.1"
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 18:06:15 +00:00
}
}
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