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https://github.com/winkjs/wink-perceptron
Multi-class classifier
https://github.com/winkjs/wink-perceptron
averaged-perceptron hactoberfest machine-learning multi-class-perceptron perceptron
Last synced: about 2 months ago
JSON representation
Multi-class classifier
- Host: GitHub
- URL: https://github.com/winkjs/wink-perceptron
- Owner: winkjs
- License: mit
- Created: 2018-06-21T15:15:02.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-02-28T00:24:16.000Z (almost 2 years ago)
- Last Synced: 2024-08-10T21:17:18.655Z (5 months ago)
- Topics: averaged-perceptron, hactoberfest, machine-learning, multi-class-perceptron, perceptron
- Language: JavaScript
- Homepage: http://winkjs.org/wink-perceptron/
- Size: 1.48 MB
- Stars: 13
- Watchers: 5
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# wink-perceptron
Multi-class averaged perceptron
### [](https://travis-ci.org/winkjs/wink-perceptron) [](https://coveralls.io/github/winkjs/wink-perceptron?branch=master) [](http://inch-ci.org/github/winkjs/wink-perceptron) [](https://david-dm.org/winkjs/wink-perceptron) [](https://david-dm.org/winkjs/wink-perceptron?type=dev) [](https://gitter.im/winkjs/Lobby)
[
](http://winkjs.org/)
Wink Perceptron is a fast and effective way to learn linearly separable patterns from either dense or sparse data. Its averaging function results in better generalization compared to the vanilla implementation of perceptron.
### Installation
Use [npm](https://www.npmjs.com/package/wink-perceptron) to install:
npm install wink-perceptron --save
### Getting Started
Here is an example of predicting type of iris plant from the [Iris Data Set](https://archive.ics.uci.edu/ml/datasets/iris).
```javascript
// Load training data — the Iris Data Set obtained from
// UCI Machine Learning Repository; it has been converted
// into JSON format.
// You may need to update the path in the "require" statement
// according to your working directory.
const trainingExamples = require( 'wink-perceptron/sample-data/iris-train.json' );
// Initialize a test data sample.
const testData = {
setosa: { sepalLength: 4.9, sepalWidth: 3, petalLength: 1.4, petalWidth: 0.2 },
versicolor: { sepalLength: 6.4, sepalWidth: 3.2, petalLength: 4.5, petalWidth: 1.5 },
virginica: { sepalLength: 7.2, sepalWidth: 3.6, petalLength: 6.1, petalWidth: 2.5 }
};
// Load wink perceptron.
var winkPerceptron = require( 'wink-perceptron' );
// Instantiate wink perceptron.
var perceptron = winkPerceptron();
// Define configurtaion.
perceptron.defineConfig( { shuffleData: true, maxIterations: 21 } );
// Learn from training data.
perceptron.learn( trainingExamples );
// Attempt prediction for each iris plant type.
console.log( perceptron.predict( testData.setosa ) );
// -> Iris-setosa
console.log( perceptron.predict( testData.versicolor ) );
// -> Iris-versicolor
console.log( perceptron.predict( testData.virginica ) );
// -> Iris-virginica
```
Try [experimenting with this example on Runkit](https://npm.runkit.com/wink-perceptron) in the browser.
### Documentation
Check out the [perceptron API documentation](http://winkjs.org/wink-perceptron/) to learn more.
### Need Help?
If you spot a bug and the same has not yet been reported, raise a new [issue](https://github.com/winkjs/wink-perceptron/issues) or consider fixing it and sending a pull request.
### About wink
[Wink](http://winkjs.org/) is a family of open source packages for **Statistical Analysis**, **Natural Language Processing** and **Machine Learning** in NodeJS. The code is **thoroughly documented** for easy human comprehension and has a **test coverage of ~100%** for reliability to build production grade solutions.
### Copyright & License
**wink-perceptron** is copyright 2017-18 [GRAYPE Systems Private Limited](http://graype.in/).
It is licensed under the terms of the MIT License.