skip revlog with time = 0 when calc recall cost (#2870)
* skip revlog with time = 0 when calc recall cost * Update retention.rs
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a5bdb28849
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@ -121,7 +121,11 @@ impl Collection {
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let mut arr = default;
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let mut arr = default;
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revlogs
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revlogs
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.iter()
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.iter()
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.filter(|r| r.review_kind == RevlogReviewKind::Review && r.button_chosen > 0)
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.filter(|r| {
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r.review_kind == RevlogReviewKind::Review
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&& r.button_chosen > 0
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&& r.taken_millis > 0
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})
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.sorted_by(|a, b| a.button_chosen.cmp(&b.button_chosen))
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.sorted_by(|a, b| a.button_chosen.cmp(&b.button_chosen))
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.group_by(|r| r.button_chosen)
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.group_by(|r| r.button_chosen)
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.into_iter()
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.into_iter()
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@ -139,10 +143,15 @@ impl Collection {
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let learn_cost = {
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let learn_cost = {
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let revlogs_filter = revlogs
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let revlogs_filter = revlogs
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.iter()
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.iter()
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.filter(|r| r.review_kind == RevlogReviewKind::Learning && r.button_chosen >= 1)
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.filter(|r| {
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r.review_kind == RevlogReviewKind::Learning
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&& r.button_chosen >= 1
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&& r.taken_millis > 0
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})
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.map(|r| r.taken_millis);
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.map(|r| r.taken_millis);
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if total_first > 0.0 {
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let length = revlogs_filter.clone().count() as f64;
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revlogs_filter.sum::<u32>() as f64 / total_first / 1000.0
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if length > 0.0 {
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revlogs_filter.sum::<u32>() as f64 / length / 1000.0
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} else {
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} else {
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return Err(AnkiError::FsrsInsufficientData);
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return Err(AnkiError::FsrsInsufficientData);
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}
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}
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