Class FSRSAlgorithm

constructor

• new FSRSAlgorithm(params): FSRSAlgorithm

• Parameters

  • params: Partial<FSRSParameters>

  Returns FSRSAlgorithm

forgetting_curve

interval_modifier

• get interval_modifier(): number

• Returns number

parameters

• get parameters(): FSRSParameters

• Get the parameters of the algorithm.

  Returns FSRSParameters
• set parameters(params): void

• Set the parameters of the algorithm.

  Parameters

  • params: Partial<FSRSParameters>

    Partial

  Returns void

seed

• set seed(seed): void

• Parameters

  • seed: string

  Returns void

apply_fuzz

• apply_fuzz(ivl, elapsed_days): int

• If fuzzing is disabled or ivl is less than 2.5, it returns the original interval.

  Parameters

  • ivl: number

    The interval to be fuzzed.
  • elapsed_days: number

    t days since the last review

  Returns int

  • The fuzzed interval.

calculate_interval_modifier

• calculate_interval_modifier(request_retention): number

• Parameters

  • request_retention: number

    0<request_retention<=1,Requested retention rate

  Returns number

  See

  https://github.com/open-spaced-repetition/fsrs4anki/wiki/The-Algorithm#fsrs-45

  The formula used is: $$I(r,s) = (r^{\frac{1}{DECAY}} - 1) / FACTOR \times s$$

  Throws

  Requested retention rate should be in the range (0,1]

constrain_difficulty

• constrain_difficulty(difficulty): number

• The formula used is : $$\min \lbrace \max \lbrace D_0,1 \rbrace,10\rbrace$$

  Parameters

  • difficulty: number

    $$D \in [1,10]$$

  Returns number

init_difficulty

• init_difficulty(g): number

• The formula used is : $$D_0(G) = w_4 - e^{(G-1) \cdot w_5} + 1$$ $$D_0 = \min \lbrace \max \lbrace D_0(G),1 \rbrace,10 \rbrace$$ where the $$D_0(1)=w_4$$ when the first rating is good.

  Parameters

  • g: Grade

    Grade (rating at Anki) [1.again,2.hard,3.good,4.easy]

  Returns number

  Difficulty $$D \in [1,10]$$

init_stability

• init_stability(g): number

• The formula used is : $$S_0(G) = w_{G-1}$$ $$S_0 = \max \lbrace S_0,0.1\rbrace$$

  Parameters

  • g: Grade

    Grade (rating at Anki) [1.again,2.hard,3.good,4.easy]

  Returns number

  Stability (interval when R=90%)

linear_damping

• linear_damping(delta_d, old_d): number

• Parameters

  • delta_d: number
  • old_d: number

  Returns number

  See

  https://github.com/open-spaced-repetition/fsrs4anki/issues/697

mean_reversion

• mean_reversion(init, current): number

• The formula used is : $$w_7 \cdot \text{init} +(1 - w_7) \cdot \text{current}$$

  Parameters

  • init: number

    $$w_2 : D_0(3) = w_2 + (R-2) \cdot w_3= w_2$$
  • current: number

    $$D - w_6 \cdot (R - 2)$$

  Returns number

  difficulty

next_difficulty

• next_difficulty(d, g): number

• The formula used is : $$\text{delta}_d = -w_6 \cdot (g - 3)$$ $$\text{next}_d = D + \text{linear damping}(\text{delta}_d , D)$$ $$D^\prime(D,R) = w_7 \cdot D_0(4) +(1 - w_7) \cdot \text{next}_d$$

  Parameters

  • d: number

    Difficulty $$D \in [1,10]$$
  • g: Grade

    Grade (rating at Anki) [1.again,2.hard,3.good,4.easy]

  Returns number

  $$\text{next}_D$$

next_forget_stability

• next_forget_stability(d, s, r): number

• The formula used is : $$S^\prime_f(D,S,R) = w_{11}\cdot D^{-w_{12}}\cdot ((S+1)^{w_{13}}-1) \cdot e^{w_{14}\cdot(1-R)}$$ enable_short_term = true : $$S^\prime_f \in \min \lbrace \max \lbrace S^\prime_f,0.01\rbrace, \frac{S}{e^{w_{17} \cdot w_{18}}} \rbrace$$ enable_short_term = false : $$S^\prime_f \in \min \lbrace \max \lbrace S^\prime_f,0.01\rbrace, S \rbrace$$

  Parameters

  • d: number

    Difficulty D \in [1,10]
  • s: number

    Stability (interval when R=90%)
  • r: number

    Retrievability (probability of recall)

  Returns number

  S^\prime_f new stability after forgetting

next_interval

• next_interval(s, elapsed_days): int

• Parameters

  • s: number

    Stability (interval when R=90%)
  • elapsed_days: number

    t days since the last review

  Returns int

  See

  The formula used is : FSRSAlgorithm.calculate_interval_modifier

next_recall_stability

• next_recall_stability(d, s, r, g): number

• The formula used is : $$S^\prime_r(D,S,R,G) = S\cdot(e^{w_8}\cdot (11-D)\cdot S^{-w_9}\cdot(e^{w_{10}\cdot(1-R)}-1)\cdot w_{15}(\text{if} G=2) \cdot w_{16}(\text{if} G=4)+1)$$

  Parameters

  • d: number

    Difficulty D \in [1,10]
  • s: number

    Stability (interval when R=90%)
  • r: number

    Retrievability (probability of recall)
  • g: Grade

    Grade (Rating[0.again,1.hard,2.good,3.easy])

  Returns number

  S^\prime_r new stability after recall

next_short_term_stability

• next_short_term_stability(s, g): number

• The formula used is : $$S^\prime_s(S,G) = S \cdot e^{w_{17} \cdot (G-3+w_{18})}$$

  Parameters

  • s: number

    Stability (interval when R=90%)
  • g: Grade

    Grade (Rating[0.again,1.hard,2.good,3.easy])

  Returns number

next_state

• next_state(memory_state, t, g): FSRSState

• Calculates the next state of memory based on the current state, time elapsed, and grade.

  Parameters

  • memory_state: null | FSRSState

    The current state of memory, which can be null.
  • t: number

    The time elapsed since the last review.
  • g: number

    Grade (Rating[0.Manual,1.Again,2.Hard,3.Good,4.Easy])

  Returns FSRSState

  The next state of memory with updated difficulty and stability.