201 lines
6.2 KiB
Markdown
201 lines
6.2 KiB
Markdown
<div align="center">
|
|
<!-- spacer -->
|
|
<img height="15px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>
|
|
|
|
|
|
<img src="https://cloud.githubusercontent.com/assets/399657/23590290/ede73772-01aa-11e7-8915-181ef21027bc.png" />
|
|
|
|
<div>find the optimal transormations between sets of words</div>
|
|
|
|
<!-- npm version -->
|
|
<a href="https://npmjs.org/package/suffix-thumb">
|
|
<img src="https://img.shields.io/npm/v/suffix-thumb.svg?style=flat-square" />
|
|
</a>
|
|
|
|
<!-- file size -->
|
|
<a href="https://unpkg.com/suffix-thumb/builds/suffix-thumb.min.js">
|
|
<img src="https://badge-size.herokuapp.com/spencermountain/suffix-thumb/master/builds/suffix-thumb.min.js" />
|
|
</a>
|
|
|
|
<div align="center">
|
|
<code>npm install suffix-thumb</code>
|
|
</div>
|
|
|
|
<!-- spacer -->
|
|
<img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>
|
|
|
|
|
|
</div>
|
|
|
|
discover the minimal rules for mapping two sets of words to one another, according to changes in their suffix.
|
|
|
|
It was built for learning rules about verb conjugations, but in a way, it is just a generic compression algorithm.
|
|
|
|
The assumption is that a word's _suffix_ is the most-often changed part of a word.
|
|
|
|
<!--  -->
|
|
|
|
|
|
|
|
|
|
<!-- spacer -->
|
|
<img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>
|
|
|
|
### Learn → Convert
|
|
|
|
```js
|
|
import { learn, convert } from 'suffix-thumb'
|
|
|
|
let pairs = [
|
|
['walk', 'walked'],
|
|
['talk', 'talked'],
|
|
['go', 'went'],
|
|
]
|
|
let model = learn(pairs)
|
|
/* {
|
|
rules: { k: [ [ 'alk', 'alked' ] ] },
|
|
exceptions: { go: 'went' },
|
|
}*/
|
|
|
|
let out = convert('walk', model)
|
|
// 'walked'
|
|
```
|
|
|
|
you can pass-in options:
|
|
```js
|
|
let opts={
|
|
threshold:80, //how sloppy our initial rules can be
|
|
min:0, //rule must satisfy # of pairs
|
|
reverse:true, //compute backward transformation, too
|
|
}
|
|
let model = learn(pairs, opts)
|
|
```
|
|
<!-- spacer -->
|
|
<img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>
|
|
|
|
### Reverse
|
|
the model also works transforming the words the other way:
|
|
```js
|
|
import { learn, reverse, convert } from 'suffix-thumb'
|
|
|
|
let pairs = [
|
|
['walk', 'walked'],
|
|
['talk', 'talked'],
|
|
['go', 'went'],
|
|
]
|
|
let model = learn(pairs)
|
|
let rev = reverse(model)
|
|
let out = convert('walked', rev)
|
|
// 'walk'
|
|
```
|
|
by default, the model ensures all two-way transformation - if you only require 1-way, you can do:
|
|
```js
|
|
learn(pairs, {reverse: false})
|
|
```
|
|
you can expect the model to be 5% smaller or so - not much.
|
|
|
|
<!-- spacer -->
|
|
<img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>
|
|
|
|
### Compress
|
|
by default, the model is small, but remains human-readable (and human-editable).
|
|
We can compress it further, turning it into a snowball inscrutible characters:
|
|
|
|
```js
|
|
import { learn, compress, uncompress, convert } from 'suffix-thumb'
|
|
|
|
let pairs = [
|
|
['walk', 'walked'],
|
|
['talk', 'talked'],
|
|
['go', 'went'],
|
|
]
|
|
let model = learn(pairs)
|
|
// shrink it
|
|
model = compress(shrink)
|
|
// {rules:'LSKs3H2-LNL.S3DH'}
|
|
// pop it back
|
|
model = uncompress(model)
|
|
let out = convert('walk', model)
|
|
// 'walked'
|
|
|
|
```
|
|
The models must be uncompressed before they are used, or reversed.
|
|
|
|
<!-- spacer -->
|
|
<img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>
|
|
|
|
### Validation
|
|
sometimes you can accidentally send in an impossible set of transformations. This library quietly ignores duplicates, by default.
|
|
You can use `{verbose:true}` to log warnings about this, or validate your input manually:
|
|
```js
|
|
import { validate } from 'suffix-thumb'
|
|
let pairs = [
|
|
['left', 'right'],
|
|
['left', 'right-two'],
|
|
['ok', 'right'],
|
|
]
|
|
pairs = validate(pairs) //remove dupes (on both sides)
|
|
```
|
|
|
|
If you are just doing one-way transformation, and not reverse, you may want to allow duplicates on the right side:
|
|
```js
|
|
let pairs = [
|
|
['left', 'right'],
|
|
['ok', 'right'],
|
|
]
|
|
let model = learn(pairs, {reverse: false})
|
|
let out = convert('ok', model)
|
|
// 'right'
|
|
```
|
|
<!--
|
|
### Classify
|
|
the model can also be used to classify whether a given word belongs to either Left or Right sides.
|
|
|
|
```js
|
|
import { learn, classify } from 'suffix-thumb'
|
|
let pairs = [
|
|
['walk', 'walked'],
|
|
['talk', 'talked'],
|
|
['go', 'went'],
|
|
]
|
|
let model = learn(pairs)
|
|
let out = classify('stalked', model)
|
|
// 'Right'
|
|
out = classify('waited', model)
|
|
// null
|
|
```
|
|
Unlike convert, the classifier is not guarnteed to return 100% on the training data.
|
|
The classifier will generally hit high-90s on the given dataset, but how-well it generalizes to novel input is up-to the dataset. -->
|
|
|
|
<!-- spacer -->
|
|
<img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>
|
|
|
|
## How it works
|
|
|
|
For each word-pair, it generates all **n-suffixes** of the left-side, and **n-suffixes** of the right-side.
|
|
|
|
any good correlations between the two suffix pairs begins to pop out. Exceptions to these rules are remembered. It then exhaustively reduces any redundancies in these rules.
|
|
|
|
There are some compromises, magic-numbers, and opinionated decisions - in-order to allow productive, but imperfect rules.
|
|
|
|
* The library is meant optimize for file-size of the model
|
|
* compression is slow, uncompression is fast
|
|
* it should always return a perfect result
|
|
|
|
The library drops case-information - and numbers and some characters[1](https://github.com/spencermountain/efrt) will not compress properly.
|
|
|
|
There may be wordlists with few helpful patterns. Conjugation datasets in English and French tend to get ~85% filesize compression.
|
|
|
|
<!-- spacer -->
|
|
<img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>
|
|
|
|
|
|
### See also
|
|
* [efrt](https://github.com/spencermountain/efrt) - trie-based prefix compression for JSON
|
|
|
|
<!-- spacer -->
|
|
<img height="50px" src="https://user-images.githubusercontent.com/399657/68221862-17ceb980-ffb8-11e9-87d4-7b30b6488f16.png"/>
|
|
|
|
|
|
MIT
|