https://github.com/mljs/svm
Support Vector Machine in Javascript
https://github.com/mljs/svm
javascript support-vector-machine svm svm-classifier svm-model
Last synced: 2 months ago
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Support Vector Machine in Javascript
- Host: GitHub
- URL: https://github.com/mljs/svm
- Owner: mljs
- License: mit
- Created: 2015-06-08T07:18:32.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2018-04-26T10:42:36.000Z (about 7 years ago)
- Last Synced: 2025-04-17T16:56:08.674Z (2 months ago)
- Topics: javascript, support-vector-machine, svm, svm-classifier, svm-model
- Language: JavaScript
- Size: 1.2 MB
- Stars: 39
- Watchers: 11
- Forks: 7
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: History.md
- License: LICENSE
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README
# ml-svm
[![NPM version][npm-image]][npm-url]
[![build status][travis-image]][travis-url]
[![David deps][david-image]][david-url]
[![npm download][download-image]][download-url]
Support Vector Machines in Javascript
## :warning: :warning: This is a simplified implementation of SVM, primarily meant for students to understand the algorithm. For real world applications, please check out [libsvm-js](https://github.com/mljs/libsvm) :warning: :warning:
Implementation of [this simplified Sequential Minimization Optimization algorithm](http://cs229.stanford.edu/materials/smo.pdf)
## Installation
`npm install ml-svm`
## API
[API documentation](https://mljs.github.io/svm)
## Example
```js
// Instantiate the svm classifier
var SVM = require('ml-svm');
var options = {
C: 0.01,
tol: 10e-4,
maxPasses: 10,
maxIterations: 10000,
kernel: 'rbf',
kernelOptions: {
sigma: 0.5
}
};
var svm = new SVM(options);
// Train the classifier - we give him an xor
var features = [[0,0],[0,1],[1,1],[1,0]];
var labels = [1, -1, 1, -1];
svm.train(features, labels);
// Let's see how narrow the margin is
var margins = svm.margin(features);
// Let's see if it is separable by testing on the training data
svm.predict(features); // [1, -1, 1, -1]
// I want to see what my support vectors are
var supportVectors = svm.supportVectors();
// Now we want to save the model for later use
var model = svm.toJSON();
/// ... later, you can make predictions without retraining the model
var importedSvm = SVM.load(model);
importedSvm.predict(features); // [1, -1, 1, -1]
```
## Authors
- [Miguel Asencio](https://github.com/maasencioh)
- [Daniel Kostro](https://github.com/stropitek)
## License
[MIT](./LICENSE)
[npm-image]: https://img.shields.io/npm/v/ml-svm.svg?style=flat-square
[npm-url]: https://npmjs.org/package/ml-svm
[travis-image]: https://img.shields.io/travis/mljs/svm/master.svg?style=flat-square
[travis-url]: https://travis-ci.org/mljs/svm
[david-image]: https://img.shields.io/david/mljs/svm.svg?style=flat-square
[david-url]: https://david-dm.org/mljs/svm
[download-image]: https://img.shields.io/npm/dm/ml-svm.svg?style=flat-square
[download-url]: https://npmjs.org/package/ml-svm