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package.json
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{
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"_where": "C:\\Users\\nica_\\Desktop\\nodejs\\node_modules\\ml-regression",
"author": {
"name": "Michaël Zasso"
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"url": "https://github.com/mljs/kernel/issues"
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"description": "A factory for kernel functions",
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"keywords": [
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"gaussian",
"rbf",
"polynomial",
"fisher",
"data",
"mining",
"datamining",
"machine",
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],
"license": "MIT",
"main": "./src/kernel.js",
"maintainers": [
{
"name": "andcastillo",
"email": "andcastillo@gmail.com"
},
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"name": "ml-kernel",
"optionalDependencies": {},
"readme": "# ml-kernel\n\n[![NPM version][npm-image]][npm-url]\n[![build status][travis-image]][travis-url]\n[![npm download][download-image]][download-url]\n\nA factory for kernel functions.\n\n## Installation\n\n`$ npm i ml-kernel`\n\n## Usage\n\n### new Kernel(type, options)\n\nThis function can be called 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## 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[travis-image]: https://img.shields.io/travis/mljs/kernel/master.svg?style=flat-square\n[travis-url]: https://travis-ci.org/mljs/kernel\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",
"repository": {
"type": "git",
"url": "git+https://github.com/mljs/kernel.git"
},
"scripts": {
"eslint": "eslint src",
"eslint-fix": "npm run eslint -- --fix",
"test": "npm run test-coverage && npm run eslint",
"test-coverage": "jest --coverage",
"test-only": "jest"
},
"version": "3.0.0"
}