Dart-FSRS

🧠🔄 Build your own Spaced Repetition System in Dart 🧠🔄

Dart-FSRS is a dart package that allows developers to easily create their own spaced repetition system using the Free Spaced Repetition Scheduler algorithm.

Table of Contents

• Installation
• Quickstart
• Usage
• Reference
• License
• More Info
• Online development

Installation

Add the package to your pubspec.yaml:

dependencies:
  fsrs: ^2.0.0

and then run:

dart pub get

Or just install it with dart cli:

dart pub add fsrs

Quickstart

Import and initialize the FSRS scheduler

import 'package:fsrs/fsrs.dart';

var scheduler = Scheduler();

Create a new Card object

// note: all new cards are 'due' immediately upon creation
final card = Card(cardId: 1);
// alternatively, you can let fsrs generate a unique ID for you
final card = await Card.create();

Choose a rating and review the card with the scheduler

// Rating.Again (==1) forgot the card
// Rating.Hard (==2) remembered the card with serious difficulty
// Rating.Good (==3) remembered the card after a hesitation
// Rating.Easy (==4) remembered the card easily

final rating = Rating.good;

final (:card, :reviewLog) = scheduler.reviewCard(card, rating);

print("Card rated ${reviewLog.rating} at ${reviewLog.reviewDateTime}");
// > Card rated 3 at 2024-11-30 17:46:58.856497Z

See when the card is due next

final due = card.due;

// how much time between when the card is due and now
final timeDelta = due.difference(DateTime.now());

print("Card due on $due");
print("Card due in ${timeDelta.inSeconds} seconds");

// > Card due on 2024-12-01 17:46:58.856497Z
// > Card due in 599 seconds

Usage

Custom parameters

You can initialize the FSRS scheduler with your own custom parameters.

// note: the following arguments are also the defaults
scheduler = Scheduler(
  parameters: [
    0.2172,
    1.1771,
    3.2602,
    16.1507,
    7.0114,
    0.57,
    2.0966,
    0.0069,
    1.5261,
    0.112,
    1.0178,
    1.849,
    0.1133,
    0.3127,
    2.2934,
    0.2191,
    3.0004,
    0.7536,
    0.3332,
    0.1437,
    0.2,
  ],
  desiredRetention: 0.9,
  learningSteps: [
    Duration(minutes: 1),
    Duration(minutes: 10),
  ],
  relearningSteps: [
    Duration(minutes: 10),
  ],
  maximumInterval: 36500,
  enableFuzzing: true,
);

Explanation of parameters

parameters are a set of 21 model weights that affect how the FSRS scheduler will schedule future reviews. If you're not familiar with optimizing FSRS, it is best not to modify these default values.

desired_retention is a value between 0 and 1 that sets the desired minimum retention rate for cards when scheduled with the scheduler. For example, with the default value of desired_retention=0.9, a card will be scheduled at a time in the future when the predicted probability of the user correctly recalling that card falls to 90%. A higher desired_retention rate will lead to more reviews and a lower rate will lead to fewer reviews.

learning_steps are custom time intervals that schedule new cards in the Learning state. By default, cards in the Learning state have short intervals of 1 minute then 10 minutes. You can also disable learning_steps with Scheduler(learning_steps=())

relearning_steps are analogous to learning_steps except they apply to cards in the Relearning state. Cards transition to the Relearning state if they were previously in the Review state, then were rated Again - this is also known as a 'lapse'. If you specify Scheduler(relearning_steps=()), cards in the Review state, when lapsed, will not move to the Relearning state, but instead stay in the Review state.

maximum_interval sets the cap for the maximum days into the future the scheduler is capable of scheduling cards. For example, if you never want the scheduler to schedule a card more than one year into the future, you'd set Scheduler(maximum_interval=365).

enable_fuzzing, if set to True, will apply a small amount of random 'fuzz' to calculated intervals. For example, a card that would've been due in 50 days, after fuzzing, might be due in 49, or 51 days.

Timezone

Dart-FSRS uses UTC only.

You can still specify custom datetimes, but they must use the UTC timezone.

Retrievability

You can calculate the current probability of correctly recalling a card (its 'retrievability') with

final retrievability = scheduler.getCardRetrievability(card);

print("There is a $retrievability probability that this card is remembered.");
// > There is a 0.94 probability that this card is remembered.

Serialization

Scheduler, Card and ReviewLog classes are all JSON-serializable via their toMap and fromMap methods for easy database storage:

// serialize before storage
final schedulerDict = scheduler.toMap();
final cardDict = card.toMap();
final reviewLogDict = reviewLog.toMap();

// deserialize from dict
final newScheduler = Scheduler.fromMap(schedulerDict);
final newCard = Card.fromMap(cardDict);
final newReviewLog = ReviewLog.fromMap(reviewLogDict);

Reference

Card objects have one of three possible states

State.Learning # (==1) new card being studied for the first time
State.Review # (==2) card that has "graduated" from the Learning state
State.Relearning # (==3) card that has "lapsed" from the Review state

There are four possible ratings when reviewing a card object:

Rating.Again # (==1) forgot the card
Rating.Hard # (==2) remembered the card with serious difficulty
Rating.Good # (==3) remembered the card after a hesitation
Rating.Easy # (==4) remembered the card easily

License

Distributed under the MIT License. See LICENSE for more information.

More Info:

Port from open-spaced-repetition/py-fsrs@6fd0857

Online development

https://idx.google.com/import