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with a matrix of input vectors.\nand optional landmarks. If no landmark is provided, the input vectors will be used.\n\n**Available kernels**:\n\n- `linear` - Linear kernel\n- `gaussian` or `rbf` - [Gaussian (radial basis function) kernel](https://github.com/mljs/kernel-gaussian)\n- `polynomial` or `poly` - [Polynomial kernel](https://github.com/mljs/kernel-polynomial)\n- `exponential` - [Exponential kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#exponential)\n- `laplacian` - [Laplacian kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#laplacian)\n- `anova` - [ANOVA kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#anova)\n- `rational` - [Rational Quadratic kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#rational)\n- `multiquadratic` - [Multiquadratic kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#multiquadric)\n- `cauchy` - [Cauchy kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#cauchy)\n- `histogram` or `min` - [Histogram Intersection kernel](http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#histogram)\n- `sigmoid` or `mlp` - [Sigmoid (hyperbolic tangent) kernel](https://github.com/mljs/kernel-sigmoid)\n\n### kernel.compute(inputs, landmarks)\n\nThis function can be called with a matrix of input vectors and optional landmarks.\nIf no landmark is provided, the input vectors will be used.  \nThe function returns a kernel matrix of feature space vectors.\n\n## Example\n\n```js\nimport { Kernel } from 'ml-kernel';\n\nconst kernel = new Kernel('gaussian', { sigma: 1 });\nconst result = kernel.compute([\n  [1, 2],\n  [3, 4],\n]);\n// result is an ml-matrix Matrix instance\n```\n\n## License\n\n[MIT](./LICENSE)\n\n[npm-image]: https://img.shields.io/npm/v/ml-kernel.svg?style=flat-square\n[npm-url]: https://npmjs.org/package/ml-kernel\n[ci-image]: https://github.com/mljs/kernel/actions/workflows/nodejs.yml/badge.svg?branch=main\n[ci-url]: https://github.com/mljs/kernel/actions/workflows/nodejs.yml\n[download-image]: https://img.shields.io/npm/dm/ml-kernel.svg?style=flat-square\n[download-url]: https://npmjs.org/package/ml-kernel\n","readmeFilename":"README.md"}