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Backup mirror of https://github.com/sbrl/research-rainfallradar A model to predict water depth data from rainfall radar information. https://github.com/sbrl/research-rainfallradar
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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
.vscode .tfrecord files are too much hassle 2022-07-01 18:28:39 +01:00
aimodel/src Lay out some basic scaffolding 2022-05-13 19:06:15 +01:00
rainfallwrangler add new uniq subcommand 2022-07-04 19:46:06 +01:00
.gitignore .tfrecord files are too much hassle 2022-07-01 18:28:39 +01:00
LICENSE Initial commit 2022-05-13 19:08:14 +01:00
package-lock.json it works 2022-07-01 19:08:36 +01:00
package.json it works 2022-07-01 19:08:36 +01:00
README.md Initial commit 2022-05-13 19:08:14 +01:00

README.md

Rainfall Radar

A model to predict water depth data from rainfall radar information.

This is the 3rd major version of this model.

rainfallwrangler

rainfallwrangler is a Node.js application to wrangle the dataset into something more appropriate for training an AI efficiently. The rainfall radar and water depth data are considered temporally to be regular time steps. Here's a diagram explaining the terminology:

NOW
│                       │         │Water depth
│▼ Rainfall Radar Data ▼│[Offset] │▼
├─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┬─┼─┬─┬─┬─┬─┼─┐
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
│ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │
└─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┴─┼─┴─┴─┴─┴─┴─┘
                        │
◄────────── Timesteps ─────────────►