https://github.com/jchambers/jbktree
A generic BK-tree implemented as a Java Collection
https://github.com/jchambers/jbktree
Last synced: about 2 months ago
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A generic BK-tree implemented as a Java Collection
- Host: GitHub
- URL: https://github.com/jchambers/jbktree
- Owner: jchambers
- Created: 2018-07-14T03:54:21.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2021-05-12T03:20:53.000Z (about 4 years ago)
- Last Synced: 2025-02-10T00:47:08.948Z (3 months ago)
- Language: Java
- Size: 13.7 KB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# jbktree
[](https://travis-ci.org/jchambers/jbktree)
jbktree provides a [generic](https://docs.oracle.com/javase/tutorial/java/generics/) [BK-tree](https://signal-to-noise.xyz/post/bk-tree/) implemented as a [`java.util.Collection`](https://docs.oracle.com/javase/8/docs/api/java/util/Collection.html). A BK-tree is a kind of [metric tree](https://en.wikipedia.org/wiki/Metric_tree) designed for use in discrete [metric spaces](https://en.wikipedia.org/wiki/Metric_space). BK-trees are generally used to efficiently conduct [_k_-nearest neighbor](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) searches.
A common use case for BK-trees (but certainly not the only use case) is "fuzzy matching" for strings, or "spell-checking." In this use case, we add a list of known words into a BK-tree, and can then search the tree for words that are within a certain [edit distance](https://en.wikipedia.org/wiki/Edit_distance) of a query term. To demonstrate this use case, we can start by loading a list of words into a `List`.
```java
final List words;
// Under macOS, /usr/share/dict/words contains a list of 235,886 English words
try (final BufferedReader reader = new BufferedReader(new FileReader("/usr/share/dict/words"))) {
words = reader.lines().collect(Collectors.toList());
}
```
Next, we define the distance function we'd like to use to calculate the edit distance between words in the list. In this case, we'll use the [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) implementation from [Commons Text](https://commons.apache.org/proper/commons-text/):
```java
final DiscreteDistanceFunction distanceFunction = (first, second) ->
LevenshteinDistance.getDefaultInstance().apply(first, second);
```
With a distance function and a collection of words, constructing a BK-tree is staightforward:
```java
final BKTree bkTree = new BKTree<>(distanceFunction, words);
```
Now, let's say we have a word that we think is misspelled (`"exaple"`), and we want to find some possible replacements. We can search the BK-tree for all other words that are within a certain edit distance ("radius") of our query term to get some suggestions:
```java
final PriorityQueue results = bkTree.getNearestNeighbors("exaple", 2);
```
That gives us the following results:
| Neighbor | Distance |
|----------|----------|
| example | 1 |
| hexapla | 2 |
| vexable | 2 |
| exciple | 2 |
| exile | 2 |
| exhale | 2 |
| staple | 2 |
| epaule | 2 |
| enable | 2 |
| elapse | 2 |
| eagle | 2 |
| saple | 2 |
| maple | 2 |
| exalt | 2 |