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update optimal retention and parameters tooltip (#3148)
* update optimal retention and parameters tooltip
* Revert "update optimal retention and parameters tooltip"
This reverts commit 32fdc5c1c3.
* update optimal retention and parameters tooltip
* check num of revlogs at first
* use new translation string
* Update deck-config.ftl
Co-authored-by: user1823 <92206575+user1823@users.noreply.github.com>
* Update deck-config.ftl
Co-authored-by: user1823 <92206575+user1823@users.noreply.github.com>
* Update deck-config.ftl
Co-authored-by: user1823 <92206575+user1823@users.noreply.github.com>
* Update ftl/core/deck-config.ftl
Co-authored-by: user1823 <92206575+user1823@users.noreply.github.com>
* fix position of translation string
* Update deck-config.ftl
Co-authored-by: Damien Elmes <dae@users.noreply.github.com>
* Update deck-config.ftl
Co-authored-by: Damien Elmes <dae@users.noreply.github.com>
* Update deck-config.ftl
---------
Co-authored-by: user1823 <92206575+user1823@users.noreply.github.com>
Co-authored-by: Damien Elmes <dae@users.noreply.github.com>
This commit is contained in:
Jarrett Ye
GitHub
parent
790c52f012
commit
2c9accf595
3 changed files with 43 additions and 24 deletions
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@ -394,10 +394,9 @@ deck-config-historical-retention-tooltip =
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The latter is quite rare, so unless you've used the former option, you probably don't need to adjust
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this setting.
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deck-config-weights-tooltip =
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FSRS parameters affect how cards are scheduled. Anki will start with default parameters. Once
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you've accumulated 1000+ reviews, you can use the option below to optimize the parameters to best
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match your performance in decks using this preset.
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deck-config-weights-tooltip2 =
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FSRS parameters affect how cards are scheduled. Anki will start with default parameters. You can use
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the option below to optimize the parameters to best match your performance in decks using this preset.
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deck-config-reschedule-cards-on-change-tooltip =
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Affects the entire collection, and is not saved.
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@ -414,22 +413,22 @@ deck-config-reschedule-cards-warning =
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deck-config-ignore-before-tooltip =
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If set, reviews before the provided date will be ignored when optimizing & evaluating FSRS parameters.
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This can be useful if you imported someone else's scheduling data, or have changed the way you use the answer buttons.
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deck-config-compute-optimal-weights-tooltip =
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Once you've done 1000+ reviews in Anki, you can use the Optimize button to analyze your review history,
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and automatically generate parameters that are optimal for your memory and the content you're studying.
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If you have decks that vary wildly in difficulty, it is recommended to assign them separate presets, as
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the parameters for easy decks and hard decks will be different. There is no need to optimize your parameters
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frequently - once every few months is sufficient.
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deck-config-compute-optimal-weights-tooltip2 =
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When you click the Optimize button, FSRS will analyze your review history, and generate parameters that are
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optimal for your memory and the content you're studying. If your decks vary wildly in difficulty, it
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is recommended to assign them separate presets, as the parameters for easy decks and hard decks will be different.
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You don't need to optimize your parameters frequently - once every few months is sufficient.
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By default, parameters will be calculated from the review history of all decks using the current preset. You can
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optionally adjust the search before calculating the parameters, if you'd like to alter which cards are used for
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optimizing the parameters.
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deck-config-compute-optimal-retention-tooltip2 =
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This tool assumes that you’re starting with 0 learned cards, and will attempt to find the desired retention
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value that will lead to the most material learnt, in the least amount of time. This number can be used as a
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reference when deciding what to set your desired retention to. You may wish to choose a higher desired retention,
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if you’re willing to trade more study time for a greater recall rate. Setting your desired retention lower than
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the minimum is not recommended, as it will lead to more work without benefit.
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deck-config-compute-optimal-retention-tooltip3 =
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This tool assumes that you’re starting with 0 learned cards, and will attempt to find the desired retention value
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that will lead to the most material learnt, in the least amount of time. To accurately simulate your learning process,
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this feature requires a minimum of 400+ reviews. The calculated number can serve as a reference when deciding what to
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set your desired retention to. You may wish to choose a higher desired retention, if you’re willing to trade more study
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time for a greater recall rate. Setting your desired retention lower than the minimum is not recommended, as it will
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lead to a higher workload, because of the high forgetting rate.
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deck-config-please-save-your-changes-first = Please save your changes first.
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deck-config-a-100-day-interval =
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{ $days ->
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@ -480,3 +479,23 @@ deck-config-compute-optimal-retention-tooltip =
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deck-config-compute-optimal-retention = Compute minimum recommended retention
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deck-config-predicted-optimal-retention = Minimum recommended retention: { $num }
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deck-config-weights-tooltip =
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FSRS parameters affect how cards are scheduled. Anki will start with default parameters. Once
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you've accumulated 1000+ reviews, you can use the option below to optimize the parameters to best
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match your performance in decks using this preset.
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deck-config-compute-optimal-weights-tooltip =
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Once you've done 1000+ reviews in Anki, you can use the Optimize button to analyze your review history,
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and automatically generate parameters that are optimal for your memory and the content you're studying.
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If you have decks that vary wildly in difficulty, it is recommended to assign them separate presets, as
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the parameters for easy decks and hard decks will be different. There is no need to optimize your parameters
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frequently - once every few months is sufficient.
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By default, parameters will be calculated from the review history of all decks using the current preset. You can
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optionally adjust the search before calculating the parameters, if you'd like to alter which cards are used for
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optimizing the parameters.
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deck-config-compute-optimal-retention-tooltip2 =
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This tool assumes that you’re starting with 0 learned cards, and will attempt to find the desired retention
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value that will lead to the most material learnt, in the least amount of time. This number can be used as a
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reference when deciding what to set your desired retention to. You may wish to choose a higher desired retention,
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if you’re willing to trade more study time for a greater recall rate. Setting your desired retention lower than
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the minimum is not recommended, as it will lead to more work without benefit.
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@ -79,6 +79,11 @@ impl Collection {
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&mut self,
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revlogs: Vec<RevlogEntry>,
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) -> Result<OptimalRetentionParameters> {
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if revlogs.len() < 400 {
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return Err(AnkiError::FsrsInsufficientReviews {
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count: revlogs.len(),
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});
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}
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let first_rating_count = revlogs
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.iter()
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.group_by(|r| r.cid)
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@ -108,11 +113,6 @@ impl Collection {
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.filter(|r| r.review_kind == RevlogReviewKind::Review && r.button_chosen != 1)
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.counts_by(|r| r.button_chosen);
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let total_reviews = review_rating_count.values().sum::<usize>();
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if total_reviews < 400 {
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return Err(AnkiError::FsrsInsufficientReviews {
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count: total_reviews,
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});
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}
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let review_rating_prob = if total_reviews as f64 > 0.0 {
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let mut arr = [0.0; 3];
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review_rating_count
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@ -40,9 +40,9 @@ License: GNU AGPL, version 3 or later; http://www.gnu.org/licenses/agpl.html
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modelWeights: {
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title: tr.deckConfigWeights(),
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help:
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tr.deckConfigWeightsTooltip() +
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tr.deckConfigWeightsTooltip2() +
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"\n\n" +
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tr.deckConfigComputeOptimalWeightsTooltip(),
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tr.deckConfigComputeOptimalWeightsTooltip2(),
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sched: HelpItemScheduler.FSRS,
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},
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ignoreRevlogsBeforeMs: {
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@ -57,7 +57,7 @@ License: GNU AGPL, version 3 or later; http://www.gnu.org/licenses/agpl.html
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},
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computeOptimalRetention: {
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title: tr.deckConfigComputeOptimalRetention(),
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help: tr.deckConfigComputeOptimalRetentionTooltip2(),
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help: tr.deckConfigComputeOptimalRetentionTooltip3(),
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sched: HelpItemScheduler.FSRS,
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},
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};
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