Crate fsrs 

FSRS-rs

The Free Spaced Repetition Scheduler (FSRS) is a modern spaced repetition algorithm. It springs from MaiMemo’s DHP model, which is a variant of the DSR model proposed by Piotr Wozniak.

FSRS-rs is a Rust implementation of FSRS. It also provides simulation capabilities and basic scheduling functionality.

For more information about the algorithm, please refer to the wiki page of FSRS.

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Most of the functions in this crate require a struct input. e.g. ComputeParametersInput and FSRSItem.

The most common struct is FSRS which holds the user’s parameters.

Examples

use chrono::{Duration, Utc};
use fsrs::{FSRS, MemoryState};

let fsrs = FSRS::default();
let desired_retention = 0.9;
let previous_state: Option<MemoryState> = None;
let elapsed_days = 0;

let next_states = fsrs.next_states(previous_state, desired_retention, elapsed_days).unwrap();
let review = next_states.good;

let interval_days = review.interval.round().max(1.0) as u32;
let due = Utc::now() + Duration::days(interval_days as i64);

There are more functions and structures. You can find them here.

Structs

CMRRTargetFn

A struct holding a function which evaluation the quality.

Card

Holds the full state of a memory card.

CombinedProgressState

Progress for the compute_parameters function.

ComputeParametersInput

Input parameters for computing FSRS parameters.

FSRS

This is the main structure provided by this crate.

FSRSItem

Stores a list of reviews for a card, in chronological order.

FSRSReview

A single review for a card, including the user’s rating and the number of days that passed.

ItemProgress

The learning progress.

ItemState

The state of an item after a review.

MemoryState

Represents the memory state of an item in the FSRS system.

ModelEvaluation

The evaluation metrics of a model.

NextStates

The next states of an item after a review.

PostSchedulingContext

Context for post scheduling operations.

PostSchedulingFn

Function type for post scheduling operations that takes a scheduling context and returns a new interval.

ReviewPriorityFn

Function type for review priority calculation that takes a card reference and returns a priority value (lower value means higher priority)

RevlogEntry

Represents a single review log entry.

SimulationResult

All output data you can get after the simulation ends.

SimulatorConfig

TrainingConfig

Hyperparameters used when training FSRS parameters.

Enums

FSRSError

Error types and Result alias for the crate. Represents an error that can occur during FSRS operations.

RevlogReviewKind

Represents the review kind of a review log entry.

Constants

FSRS5_DEFAULT_DECAY

The default decay for FSRS 5.

FSRS6_DEFAULT_DECAY

The default decay for FSRS 6.

Statics

DEFAULT_PARAMETERS

The default parameters. Fits the average person’s learning habits.

Functions

benchmark

Assess how good or bad the current parameters are.

check_and_fill_parameters

Checks and fills the parameters with default values if necessary.

compute_parameters

Computes parameters for the FSRS model based on provided training data.

current_retrievability

Calculate the current retrievability for a memory state.

evaluate_with_time_series_splits

Evaluates the model using time series cross-validation.

expected_workload

expected_workload_with_existing_cards

Evaluate expected workload when there are already in-flight cards.

extract_simulator_config

Extracts the simulator configuration from the given review log entries.

filter_outlier

Filters out outlier reviews from two Vec<FSRSItem>.

optimal_retention

For the given simulator parameters and parameters, determine the suggested desired_retention value.

simulate

Type Aliases

Result

Error types and Result alias for the crate. A type alias for Result with FSRSError as the error type.