# sm2 0.1.6

Simple spaced repetition algorithm. It calculates the number of days to wait before reviewing a piece of information based on how easily the the information was remembered today.

Implementation of SM-2 in DART.

SM-2 is a simple spaced repetition algorithm. It calculates the number of days to wait before reviewing a piece of information based on how easily the information was remembered today.

# Links #

- Spaced repetition on Wikipedia
- Official algorithm description
- Official implementation of SM-2 algorithm in SuperMemo 2
- http://www.supermemo.com
- http://www.supermemo.eu

# The algorithm requires four inputs #

The algorithm requires four inputs: `quality`

, `repetitions`

, `previous ease factor`

, and `previous interval`

. The last three inputs are taken from the output of a previous call to SM-2. (On the first call, default values are used.)

## Quality #

An integer from 0-5 indicating how easily the information was remembered today. This could correspond to a button such as "Difficult" or "Very Easy."

The official algorithm description explains the meaning of each number:

```
5 - perfect response
4 - correct response after a hesitation
3 - correct response recalled with serious difficulty
2 - incorrect response; where the correct one seemed easy to recall
1 - incorrect response; the correct one remembered
0 - complete blackout.
```

## Repetitions (integer) #

The number of times the information has been reviewed prior to this review. `repetitions`

should equal zero for the first review.

SM-2 uses this value to define specific intervals for the first and second reviews. SM-2 will also reset this value to zero when `quality`

is less than 3.

## Previous ease factor (float) #

A floating point number (≥ 1.3) generated by the last iteration of the SM-2 algorithm. `previous ease factor`

should equal 2.5 for the first review.

The ease factor is used to determine the number of days to wait before reviewing again. Each call to SM-2 adjusts this number up or down based on `quality`

.

## Previous interval (integer) #

Generated by the last iteration of the SM-2 algorithm. Indicates the number of days to wait between reviews.

This previous interval is used when calculating the new interval. `previous interval`

should equal zero for the first review.

# Outputs #

The algorithm returns three outputs: `interval`

, `repetitions`

, and `ease factor`

. All three values should be saved and passed to the next call to SM-2 as inputs.

## Interval (integer) #

An integer number indicating the number of days to wait before the next review.

## Repetitions (integer) #

The number of times the information has been reviewed as of this review.

This value is maintained between calls to the algorithm and used for calculating `interval`

. The number increments after each successful review. SM-2 will reset `repetitions`

to zero if `quality`

is less than 3.

## Ease factor #

A floating point number (≥ 1.3) which is adjusted up or down based on how easily the information was remembered.

This value is maintained between calls to the algorithm and is used for calculating `interval`

.

# Steps #

If `quality`

is greater than or equal to 3, indicating a correct response:

- If
`repetitions`

is 0 (first review), set`interval`

to 1 day. - If
`repetitions`

is 1 (second review), set`interval`

to 6 days. - If
`repetitions`

is greater than 1 (subsequent reviews), set`interval`

to`previous interval * previous ease factor`

. (See note about recursion below.) - Round
`interval`

up to the next whole number. - Increment
`repetitions`

by 1. - Set
`ease factor`

to`previous ease factor + (0.1 - (5 - quality) * (0.08 + (5 - quality ) * 0.02))`

. (See formula description below.)

If `quality`

is less than 3, indicating an incorrect response:

- Set
`repetitions`

to 0. - Set
`interval`

to 1. - Set
`ease factor`

to`previous ease factor`

(no change).

If `ease factor`

is less than 1.3:

- Set
`ease factor`

to 1.3.

Return `interval`

, `repetitions`

and `ease factor`

.

# Ease factor formula #

After the first two reviews, `ease factor`

is adjusted using this formula:

`previous ease factor + (0.1 - (5 - quality) * (0.08 + (5 - quality) * 0.02))`

The magic numbers come from the official algorithm description.

This increases `ease factor`

when `quality`

is 5, makes no change when `quality`

is 4, and decreases `ease factor`

by varying amounts when `quality`

is lower than 4. The lower `quality`

is, the more `ease factor`

is decreased.