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Refactor reviews_for_fsrs function for improved performance

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Replaced the previous implementation with a more efficient approach using a single loop and pre-allocated vectors. This change reduces the complexity of creating FSRSItems and enhances overall performance, especially for larger datasets.
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Jarrett Ye 2025-09-17 12:28:24 +08:00
parent b97fb45e06
commit ecd1cf45e9
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1 changed files with 16 additions and 18 deletions
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34
rslib/src/scheduler/fsrs/params.rs
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@ -481,24 +481,22 @@ pub(crate) fn reviews_for_fsrs(
let skip = if training { 1 } else { 0 }; let skip = if training { 1 } else { 0 };
// Convert the remaining entries into separate FSRSItems, where each item // Convert the remaining entries into separate FSRSItems, where each item
// contains all reviews done until then. // contains all reviews done until then.
let items: Vec<(RevlogId, FSRSItem)> = entries let mut items = Vec::with_capacity(entries.len());
.iter() let mut current_reviews = Vec::with_capacity(entries.len());
.enumerate() for (idx, (entry, &delta_t)) in entries.iter().zip(delta_ts.iter()).enumerate() {
.skip(skip) current_reviews.push(FSRSReview {
.map(|(outer_idx, entry)| { rating: entry.button_chosen as u32,
let reviews = entries delta_t,
.iter() });
.take(outer_idx + 1) if idx >= skip {
.enumerate() if !training || current_reviews.last().unwrap().delta_t > 0 {
.map(|(inner_idx, r)| FSRSReview { let item = FSRSItem {
rating: r.button_chosen as u32, reviews: current_reviews.clone(),
delta_t: delta_ts[inner_idx], };
}) items.push((entry.id, item));
.collect(); }
(entry.id, FSRSItem { reviews }) }
}) }
.filter(|(_, item)| !training || item.reviews.last().unwrap().delta_t > 0)
.collect_vec();
if items.is_empty() { if items.is_empty() {
None None
} else { } else {