# svm

**Repository Path**: mirrors_mljs/svm

## Basic Information

- **Project Name**: svm
- **Description**: Support Vector Machine in Javascript
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-22
- **Last Updated**: 2026-03-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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)

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