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|
// SM2-Anki
// https://gist.github.com/riceissa/1ead1b9881ffbb48793565ce69d7dbdd
use crate::model::difficulty::{Difficulty, Difficulty::*};
use serde::{Deserialize, Serialize};
// Learning
const LEARNING_INTERVALS: [f32; 2] = [
1.0 / 60.0 / 24.0, // 1 minute
10.0 / 60.0 / 24.0, // 10 minutes
];
// Ease
const EASE_INIT: f32 = 2.5;
const EASE_MIN: f32 = 1.3;
// Interval
const INTERVAL_INIT: f32 = 1.0;
const INTERVAL_INIT_EASY: f32 = 4.0;
const INTERVAL_MIN: f32 = 0.1;
const INTERVAL_MAX: f32 = 36500.0;
// Learned
const EASE_AGAIN_SUB: f32 = 0.2;
const EASE_HARD_SUB: f32 = 0.15;
const EASE_EASY_ADD: f32 = 0.15;
const INTERVAL_AGAIN_MUL: f32 = 0.7;
const INTERVAL_HARD_MUL: f32 = 1.2;
const INTERVAL_EASY_MUL: f32 = 1.3;
// Relearning
const RELEARNING_INTERVALS: [f32; 1] = [
10.0 / 60.0 / 24.0, // 10 minutes
];
#[derive(Debug, PartialEq, Deserialize, Serialize)]
pub enum State {
Learning {
step: usize,
},
Learned {
ease: f32, // ratio
interval: f32, // in days
},
Relearning {
step: usize,
ease: f32,
interval: f32,
},
}
pub fn init() -> State {
State::Learning { step: 0 }
}
impl State {
pub fn get_interval_seconds(&self) -> u64 {
let days = match self {
State::Learning { step } => LEARNING_INTERVALS[*step],
State::Learned { interval, .. } => *interval,
State::Relearning { step, .. } => RELEARNING_INTERVALS[*step],
};
(days * 24.0 * 60.0 * 60.0).round() as u64
}
pub fn difficulties(&self) -> Vec<Difficulty> {
match self {
State::Learning { .. } => [Again, Good, Easy].to_vec(),
State::Learned { .. } => [Again, Hard, Good, Easy].to_vec(),
State::Relearning { .. } => [Again, Good].to_vec(),
}
}
}
pub fn update(state: State, difficulty: Difficulty) -> State {
match state {
State::Learning { step } => match difficulty {
Again => State::Learning { step: 0 },
Good => {
let new_step = step + 1;
if new_step < LEARNING_INTERVALS.len() {
State::Learning { step: new_step }
} else {
State::Learned {
ease: EASE_INIT,
interval: INTERVAL_INIT,
}
}
}
Easy => State::Learned {
ease: EASE_INIT,
interval: INTERVAL_INIT_EASY,
},
_ => panic!("Learning is incompatible with {:?}", difficulty),
},
State::Learned { ease, interval } => match difficulty {
Again => State::Relearning {
step: 0,
ease: clamp_ease(ease - EASE_AGAIN_SUB),
interval: clamp_interval(interval * INTERVAL_AGAIN_MUL),
},
Hard => State::Learned {
ease: clamp_ease(ease - EASE_HARD_SUB),
interval: clamp_interval(interval * INTERVAL_HARD_MUL),
},
Good => State::Learned {
ease,
interval: clamp_interval(interval * ease),
},
Easy => State::Learned {
ease: clamp_ease(ease + EASE_EASY_ADD),
interval: clamp_interval(interval * ease * INTERVAL_EASY_MUL),
},
},
State::Relearning {
step,
ease,
interval,
} => match difficulty {
Again => State::Relearning {
step: 0,
ease,
interval,
},
Good => {
let new_step = step + 1;
if new_step < RELEARNING_INTERVALS.len() {
State::Relearning {
step: new_step,
ease,
interval,
}
} else {
State::Learned { ease, interval }
}
}
_ => panic!("Relearning is incompatible with {:?}.", difficulty),
},
}
}
fn clamp_ease(f: f32) -> f32 {
if f < EASE_MIN {
EASE_MIN
} else {
f
}
}
fn clamp_interval(i: f32) -> f32 {
if i < INTERVAL_MIN {
INTERVAL_MIN
} else if i > INTERVAL_MAX {
INTERVAL_MAX
} else {
i
}
}
#[cfg(test)]
mod tests {
use super::{State::*, *};
#[test]
fn learning_again() {
assert_eq!(update(Learning { step: 1 }, Again), Learning { step: 0 });
}
#[test]
fn learning_good() {
assert_eq!(update(Learning { step: 0 }, Good), Learning { step: 1 });
assert_eq!(
update(
Learning {
step: LEARNING_INTERVALS.len() - 1
},
Good
),
Learned {
ease: EASE_INIT,
interval: INTERVAL_INIT
}
);
}
#[test]
fn learning_easy() {
assert_eq!(
update(Learning { step: 0 }, Easy),
Learned {
ease: EASE_INIT,
interval: INTERVAL_INIT_EASY
}
);
}
#[test]
fn learned_again() {
assert_eq!(
update(
Learned {
ease: EASE_MIN,
interval: INTERVAL_MIN
},
Again
),
Relearning {
step: 0,
ease: EASE_MIN,
interval: INTERVAL_MIN
}
);
assert_eq!(
update(
Learned {
ease: EASE_INIT,
interval: INTERVAL_INIT
},
Again
),
Relearning {
step: 0,
ease: EASE_INIT - EASE_AGAIN_SUB,
interval: INTERVAL_INIT * INTERVAL_AGAIN_MUL
}
);
}
#[test]
fn learned_hard() {
assert_eq!(
update(
Learned {
ease: EASE_MIN,
interval: INTERVAL_MAX
},
Hard
),
Learned {
ease: EASE_MIN,
interval: INTERVAL_MAX
}
);
assert_eq!(
update(
Learned {
ease: EASE_INIT,
interval: INTERVAL_INIT
},
Hard
),
Learned {
ease: EASE_INIT - EASE_HARD_SUB,
interval: INTERVAL_INIT * INTERVAL_HARD_MUL
}
);
}
#[test]
fn learned_good() {
assert_eq!(
update(
Learned {
ease: EASE_INIT,
interval: INTERVAL_MAX
},
Good
),
Learned {
ease: EASE_INIT,
interval: INTERVAL_MAX
}
);
assert_eq!(
update(
Learned {
ease: EASE_INIT,
interval: INTERVAL_INIT
},
Good
),
Learned {
ease: EASE_INIT,
interval: INTERVAL_INIT * EASE_INIT
}
);
}
#[test]
fn learned_easy() {
assert_eq!(
update(
Learned {
ease: EASE_INIT,
interval: INTERVAL_MAX
},
Easy
),
Learned {
ease: EASE_INIT + EASE_EASY_ADD,
interval: INTERVAL_MAX
}
);
assert_eq!(
update(
Learned {
ease: EASE_INIT,
interval: INTERVAL_INIT
},
Easy
),
Learned {
ease: EASE_INIT + EASE_EASY_ADD,
interval: INTERVAL_INIT * EASE_INIT * INTERVAL_EASY_MUL
}
);
}
#[test]
fn relearning_again() {
let ease = EASE_INIT + EASE_EASY_ADD;
let interval = INTERVAL_INIT * ease;
assert_eq!(
update(
Relearning {
step: 1,
ease,
interval,
},
Again
),
Relearning {
step: 0,
ease,
interval
}
);
}
#[test]
fn relearning_good() {
let ease = EASE_INIT + EASE_EASY_ADD;
let interval = INTERVAL_INIT * ease;
assert_eq!(
update(
Relearning {
step: RELEARNING_INTERVALS.len() - 1,
ease,
interval,
},
Good
),
Learned { ease, interval }
);
}
}
|
