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0a37d366f6
* Feat/FSRS-5 * adapt the SimulatorConfig of FSRS-5 * update parameters from FSRS-4.5 * udpate to FSRS-rs v1.1.0 * ./ninja fix:minilints * pass ci * update cargo-deny to 0.14.24 * udpate to FSRS-rs v1.1.1 * update to fsrs-rs v1.1.2
86 lines
3.1 KiB
Rust
86 lines
3.1 KiB
Rust
// Copyright: Ankitects Pty Ltd and contributors
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// License: GNU AGPL, version 3 or later; http://www.gnu.org/licenses/agpl.html
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use anki_proto::scheduler::SimulateFsrsReviewRequest;
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use anki_proto::scheduler::SimulateFsrsReviewResponse;
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use fsrs::simulate;
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use fsrs::SimulatorConfig;
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use itertools::Itertools;
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use crate::prelude::*;
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use crate::search::SortMode;
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impl Collection {
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pub fn simulate_review(
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&mut self,
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req: SimulateFsrsReviewRequest,
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) -> Result<SimulateFsrsReviewResponse> {
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let guard = self.search_cards_into_table(&req.search, SortMode::NoOrder)?;
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let revlogs = guard
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.col
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.storage
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.get_revlog_entries_for_searched_cards_in_card_order()?;
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let cards = guard.col.storage.all_searched_cards()?;
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drop(guard);
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let p = self.get_optimal_retention_parameters(revlogs)?;
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let config = SimulatorConfig {
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deck_size: req.deck_size as usize,
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learn_span: req.days_to_simulate as usize,
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max_cost_perday: f32::MAX,
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max_ivl: req.max_interval as f32,
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learn_costs: p.learn_costs,
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review_costs: p.review_costs,
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first_rating_prob: p.first_rating_prob,
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review_rating_prob: p.review_rating_prob,
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first_rating_offsets: p.first_rating_offsets,
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first_session_lens: p.first_session_lens,
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forget_rating_offset: p.forget_rating_offset,
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forget_session_len: p.forget_session_len,
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loss_aversion: 1.0,
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learn_limit: req.new_limit as usize,
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review_limit: req.review_limit as usize,
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};
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let days_elapsed = self.timing_today().unwrap().days_elapsed as i32;
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let (
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accumulated_knowledge_acquisition,
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daily_review_count,
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daily_new_count,
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daily_time_cost,
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) = simulate(
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&config,
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&req.weights,
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req.desired_retention,
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None,
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Some(
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cards
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.into_iter()
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.filter_map(|c| Card::convert(c, days_elapsed))
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.collect_vec(),
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),
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);
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Ok(SimulateFsrsReviewResponse {
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accumulated_knowledge_acquisition: accumulated_knowledge_acquisition.to_vec(),
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daily_review_count: daily_review_count.iter().map(|x| *x as u32).collect_vec(),
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daily_new_count: daily_new_count.iter().map(|x| *x as u32).collect_vec(),
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daily_time_cost: daily_time_cost.to_vec(),
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})
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}
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}
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impl Card {
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fn convert(card: Card, days_elapsed: i32) -> Option<fsrs::Card> {
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match card.memory_state {
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Some(state) => {
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let due = card.original_or_current_due();
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let relative_due = due - days_elapsed;
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Some(fsrs::Card {
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difficulty: state.difficulty,
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stability: state.stability,
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last_date: (relative_due - card.interval as i32) as f32,
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due: relative_due as f32,
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})
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}
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None => None,
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}
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}
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}
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