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Status](https://magnum-ci.com/status/2c2313c29387e744fb7c1c438443b20c.png)](https://magnum-ci.com/public/3a5d88927a03611f50d6/builds)\r\n\r\n[![NPM](https://nodei.co/npm/nlearn.png?downloads=true&downloadRank=true&stars=true)](https://nodei.co/npm/nlearn/)\r\n\r\n# Advantages #\r\n\r\n* Pure JS implementation\r\n* .d.ts file is provided for implementing in your TypeScript project\r\n* Internal computations implemented with Float64Array for performance\r\n\r\n# Alternative Libraries #\r\n\r\n* ml-js: Implements more learning models currently. However, a number of native dependencies are required\r\n* node-svm: Implements SVM classifier only, while nlearn is designed to provide a consistent interface for multiple classifiers\r\n* node-fann: Again, a single classifier type. Also depends on native libraries with special installation requirements.\r\n* machine_learning: Implements many supervised learning models. You might want to choose this for now until this library is robust enough.\r\n\r\n# Basic Usage #\r\n\r\n    var nlearn = require(\"nlearn\");\r\n    var Example = nlearn.Example;\r\n    var BinaryLabel = nlearn.BinaryLabel;\r\n\r\n    var data = [\r\n                new Example(new Float64Array([1,2,3]), BinaryLabel.UP),\r\n                new Example(new Float64Array([1,3,7]), BinaryLabel.DOWN),\r\n                new Example(new Float64Array([4,5,8]), BinaryLabel.DOWN)\r\n                ];\r\n\r\n    var kernel = new nlearn.RbfKernel(10);\r\n\r\n    var classifier = nlearn.SvmFactory.create(kernel);\r\n    classifier.fit(data);\r\n    var label = classifier.classify([1,2,3]); //BinaryLabel.UP\r\n\r\n\r\n# Choosing a Best Fit Classifier #\r\n\r\nSome classifiers will fit your data better than others. You can use an Optimizer to run analysis on a set of classifier parameters and\r\nchoose the best fit. 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