https://github.com/fcanas/bayes
Naive Bayes Classifier in Swift for Mac and iOS
https://github.com/fcanas/bayes
bayes naive-bayes-classifier swift
Last synced: 2 months ago
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Naive Bayes Classifier in Swift for Mac and iOS
- Host: GitHub
- URL: https://github.com/fcanas/bayes
- Owner: fcanas
- License: mit
- Created: 2015-05-11T18:19:06.000Z (about 10 years ago)
- Default Branch: main
- Last Pushed: 2022-01-03T15:23:55.000Z (over 3 years ago)
- Last Synced: 2025-04-06T17:12:50.269Z (3 months ago)
- Topics: bayes, naive-bayes-classifier, swift
- Language: Swift
- Homepage:
- Size: 115 KB
- Stars: 31
- Watchers: 3
- Forks: 7
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Bayes [](https://github.com/fcanas/Bayes/actions/workflows/swift.yml)
Bayes is a [Naive Bayes Classifier](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) for iOS and Mac platforms.
Bayes is implemented in Swift and takes advantage of generics to enable any `Hashable`, `Equatable` type of your choosing or creation for use as category or feature.
## Installation
### Swift Package
Bayes is built as a Swift package.
### CocoaPods
Bayes is available as a [CocoaPod](http://cocoapods.org), but has not yet been versioned and submitted to trunk. So to use it:
```ruby
pod 'Bayes', git: 'https://github.com/fcanas/Bayes.git'
```
Since Bayes is written in Swift, you will need to be using a recent version of CocoaPods (>0.36) and you may need to add `use_frameworks!` to your Podfile. See [this blog post](http://blog.cocoapods.org/CocoaPods-0.36/) for more information.
### Framework
The project includes framework targets for iOS and Mac. Pull the repository and build the appropriate target for you.
When Bayes reaches a release milestone, it will be appropriately tagged and should be easily available via [Carthage](https://github.com/Carthage/Carthage).
## Use
```swift
var eventSpace = EventSpace()
eventSpace.observe("Cat", features: ["paw", "tail", "claw"])
eventSpace.observe("Cat", features: ["stripe", "tail", "whisker", "ear"])
eventSpace.observe("Cat", features: ["meow", "vertical pupil"])
eventSpace.observe("Dog", features: ["paw", "tail", "bark"])
eventSpace.observe("Dog", features: ["wag", "fetch", "tail", "paw"])
var classifier = BayesianClassifier(eventSpace: eventSpace)
XCTAssertEqual(classifier.classify(["claw", "tail"])!, "Cat", "Should categorize as Cat, due to claw")
XCTAssertEqual(classifier.classify(["bark", "tail"])!, "Dog", "Should categorize as Dog, due to bark")
XCTAssertEqual(classifier.classify(["tail"])!, "Cat", "Should categorize as Cat, due to base rate")
XCTAssertEqual(classifier.classify(["paw", "tail"])!, "Dog", "Should categorize as Dog, due to prevalence of paw")
```
## Contribute
Contributions are welcome. Please note the following guidelines
* Test public functionality
* Make everything as private as possible; use `private` and `internal` aggressively and only expose what is necessary for external functionality
* Favor structs over classes
* Extract standard mathematical functions that may simplify implementation of future models, _e.g._ `product`, `argmax`