https://github.com/mljs/kernel
A factory for kernel functions
https://github.com/mljs/kernel
Last synced: 10 months ago
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A factory for kernel functions
- Host: GitHub
- URL: https://github.com/mljs/kernel
- Owner: mljs
- License: mit
- Created: 2015-11-18T13:19:33.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2019-06-29T12:28:34.000Z (almost 7 years ago)
- Last Synced: 2024-10-04T19:06:09.138Z (over 1 year ago)
- Language: JavaScript
- Homepage:
- Size: 59.6 KB
- Stars: 1
- Watchers: 5
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: History.md
- License: LICENSE
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README
# ml-kernel
[![NPM version][npm-image]][npm-url]
[![build status][travis-image]][travis-url]
[![npm download][download-image]][download-url]
A factory for kernel functions.
## Installation
`$ npm i ml-kernel`
## Usage
### new Kernel(type, options)
This function can be called with a matrix of input vectors.
and optional landmarks. If no landmark is provided, the input vectors will be used.
**Available kernels**:
- `linear` - Linear kernel
- `gaussian` or `rbf` - [Gaussian (radial basis function) kernel](https://github.com/mljs/kernel-gaussian)
- `polynomial` or `poly` - [Polynomial kernel](https://github.com/mljs/kernel-polynomial)
- `exponential` - [Exponential kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#exponential)
- `laplacian` - [Laplacian kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#laplacian)
- `anova` - [ANOVA kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#anova)
- `rational` - [Rational Quadratic kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#rational)
- `multiquadratic` - [Multiquadratic kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#multiquadric)
- `cauchy` - [Cauchy kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#cauchy)
- `histogram` or `min` - [Histogram Intersection kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#histogram)
- `sigmoid` or `mlp' - [Sigmoid (hyperbolic tangent) kernel](https://github.com/mljs/kernel-sigmoid)
### kernel.compute(inputs, landmarks)
This function can be called with a matrix of input vectors and optional landmarks.
If no landmark is provided, the input vectors will be used.
The function returns a kernel matrix of feature space vectors.
## License
[MIT](./LICENSE)
[npm-image]: https://img.shields.io/npm/v/ml-kernel.svg?style=flat-square
[npm-url]: https://npmjs.org/package/ml-kernel
[travis-image]: https://img.shields.io/travis/mljs/kernel/master.svg?style=flat-square
[travis-url]: https://travis-ci.org/mljs/kernel
[download-image]: https://img.shields.io/npm/dm/ml-kernel.svg?style=flat-square
[download-url]: https://npmjs.org/package/ml-kernel