Comments (4)
As an FYI, I renamed fsrs-wasm
to https://github.com/AlexErrant/fsrs-browser because it very much only runs in the browser. Also, I got that project demoing compute_weights
, which is nice. I think all the hard work/proof of concept is done, will iterate as needed.
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It's feasible, but the framework doesn't provides a document for that: https://burn-rs.github.io/book/advanced/index.html
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I would support a migration of Anki Web (frontend) to WASM. However, I understand that it should be entirely rewritten in RUST (another framework).
But I think if it were to move forward, it would be a project for the end of 2024/ beginning of 2025.
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Technically speaking, as of this PR, fsrs-rs compiles to WASM, satisfying this issue. However, more work needs to be done to make it usable from Javascript, which is probably what you want. I started work on a wrapper for fsrs-rs here https://github.com/AlexErrant/fsrs-wasm but it's very much a WIP. I got next_interval
exposed to JS, but I'm missing the ability to create an "FSRS" object with weights, so you can't even call it (next_interval
). I stopped working on it to chase other shiny objects, but I'll return to it eventually. (Currently trying - and failing - to learn Rust 😬.)
If you're just looking to run the scheduling algorithm, a pure JS solution exists https://github.com/open-spaced-repetition/fsrs.js You only need this repo if you want to run training of the ML model. There's also https://github.com/open-spaced-repetition/rs-fsrs if you're just looking for a scheduler.
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Related Issues (20)
- [Enhancement] Use more splits while training with larger datasets HOT 3
- Request: Ignore reviews before "Forget" HOT 9
- Enhancement: Include incomplete revlogs even when training HOT 4
- Consider time-frame limitation? HOT 3
- TODO: speed up finding optimal retention via Brent's method
- Better outlier filter for trainset HOT 25
- Skip reviews with time = 0 when calculating average answer times HOT 1
- What's the difference between this repo and rs-fsrs? HOT 1
- User guide HOT 3
- Add an option to turn off outlier filter when benchmark HOT 1
- Inference.rs uses the new power curve, but the default parameters are from v4 HOT 17
- Add a example file HOT 4
- Reference usage? HOT 5
- Pre-training Only when the number of reviews is less than 1000 HOT 5
- [BUG] Potential inconsistency in optimal_retention.rs HOT 20
- [Question] How to choose "Days to simulate"? HOT 14
- [Feature Request[ Use two different sets of initial parameters, then average out the results HOT 4
- Use the first revlog in the "known" review history for converting SM-2 ivl & ease to memory states HOT 13
- Achieve parity with the Python optimizer HOT 10
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