Comments (5)
Oops. I made a mistake. I will fix it. And use const.
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I want to keep consistency between the Python version and Rust version. Here is the schema:
Column Name | Data Type | Description | Constraints |
---|---|---|---|
review_state | integer | The state of the card at the time of review. This describes the learning phase of the card | Optional, Values: {0 (New), 1 (Learning), 2 (Review), 3 (Relearning)} |
https://github.com/open-spaced-repetition/fsrs-optimizer#review-logs-schema
from fsrs-rs.
Won't the current Rust code (the second line I showed) always fail, because review_kind will never be 0 after the addition? What if instead of changing the numbers, you used constants like "Review" instead of comparing numbers? Then it should be clear in both.
from fsrs-rs.
Eg const Review = 1; if review_kind == Review { ... }
from fsrs-rs.
Closed by #38
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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
- support WASM HOT 4
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