# node-svm > Support Vector Machine for nodejs Latest version **2.1.9** (published 2018-09-07) · MIT license · 0 weekly downloads ## Install ```sh npm install node-svm pnpm add node-svm yarn add node-svm bun add node-svm ``` Provides the command `node-svm`. ## Health **Score 15/100 (F)** — status: abandoned. Positive: no vulnerabilities. Warnings: low downloads; no types; no esm support. Negative: abandoned; low maintenance score. ## Facts | | | |---|---| | Version | 2.1.9 | | Published | 2018-09-07 | | First published | 2014-03-29 | | Weekly downloads | 0 | | License | MIT | | TypeScript types | none | | Module format | CommonJS | | Dependencies | 18 | | Unpacked size | 798.9 KB | | Known vulnerabilities | 0 (+11 in 2 direct dependencies) | | Install scripts | yes | | GitHub stars | 301 | | Author | Nicolas Panel | | Maintainers | nicolaspanel | | Keywords | svm, machine learning, libsvm | ## Links - npm: https://www.npmjs.com/package/node-svm - Repository: https://github.com/nicolaspanel/node-svm - Issues: https://github.com/nicolaspanel/node-svm/issues - npm.io page: https://npm.io/package/node-svm ## Dependencies (18) - [q](https://npm.io/package/q.md) ^1.1.2 - [nan](https://npm.io/package/nan.md) ^2.3.3 - [mout](https://npm.io/package/mout.md) ^0.11.0 - [nopt](https://npm.io/package/nopt.md) ~3.0.0 - [chalk](https://npm.io/package/chalk.md) ^0.5.1 - [osenv](https://npm.io/package/osenv.md) ^0.1.0 - [abbrev](https://npm.io/package/abbrev.md) ^1.0.5 - [moment](https://npm.io/package/moment.md) ^2.8.4 - [numeric](https://npm.io/package/numeric.md) ^1.2.6 - [cardinal](https://npm.io/package/cardinal.md) ^0.4.4 - [inquirer](https://npm.io/package/inquirer.md) ^0.8.0 - [node-gyp](https://npm.io/package/node-gyp.md) ~1.0.2 - [optimist](https://npm.io/package/optimist.md) ^0.6.1 - [promptly](https://npm.io/package/promptly.md) ^0.2.0 - [handlebars](https://npm.io/package/handlebars.md) ^2.0.0 - [underscore](https://npm.io/package/underscore.md) ^1.7.0 - [graceful-fs](https://npm.io/package/graceful-fs.md) ^4.1.0 - [stringify-object](https://npm.io/package/stringify-object.md) ^1.0.0 ## Recent versions - 2.1.9 (latest) — 2018-09-07 - 2.1.8 — 2016-05-26 - 2.1.7 — 2015-12-12 - 2.1.6 — 2015-10-08 - 2.1.5 — 2015-05-11 - 2.1.4 — 2015-02-09 - 2.1.3 — 2015-01-12 - 2.1.2 — 2015-01-09 - 2.1.1 — 2015-01-05 - 2.1.0 — 2015-01-04 - 2.0.0 — 2014-12-23 - 2.0.0-beta2 — 2014-12-22 - 2.0.0-beta — 2014-12-19 - 1.3.0 — 2014-06-21 - 1.2.1 — 2014-04-10 - … 21 more at https://npm.io/package/node-svm/versions ## README # node-svm Support Vector Machine (SVM) library for [nodejs](http://nodejs.org/). [![NPM](https://nodei.co/npm/node-svm.png)](https://nodei.co/npm/node-svm/) [![Build Status](https://travis-ci.org/nicolaspanel/node-svm.png)](https://travis-ci.org/nicolaspanel/node-svm) [![Coverage Status](https://coveralls.io/repos/nicolaspanel/node-svm/badge.png?branch=master)](https://coveralls.io/r/nicolaspanel/node-svm?branch=master) # Support Vector Machines [Wikipedia](http://en.wikipedia.org/wiki/Support_vector_machine) : >Support vector machines are supervised learning models that analyze data and recognize patterns. >A special property is that they simultaneously minimize the empirical classification error and maximize the geometric margin; hence they are also known as maximum margin classifiers. >[![Wikipedia image](http://upload.wikimedia.org/wikipedia/commons/1/1b/Kernel_Machine.png)](http://en.wikipedia.org/wiki/File:Kernel_Machine.png) # Installation `npm install --save node-svm` # Quick start If you are not familiar with SVM I highly recommend this [guide](http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf). Here's an example of using [node-svm](https://github.com/nicolaspanel/node-svm) to approximate the XOR function : ```javascript var svm = require('node-svm'); var xor = [ [[0, 0], 0], [[0, 1], 1], [[1, 0], 1], [[1, 1], 0] ]; // initialize a new predictor var clf = new svm.CSVC(); clf.train(xor).done(function () { // predict things xor.forEach(function(ex){ var prediction = clf.predictSync(ex[0]); console.log('%d XOR %d => %d', ex[0][0], ex[0][1], prediction); }); }); /******** CONSOLE ******** 0 XOR 0 => 0 0 XOR 1 => 1 1 XOR 0 => 1 1 XOR 1 => 0 */ ``` More examples are available [here](https://github.com/nicolaspanel/node-svm/tree/master/examples). __Note__: There's no reason to use SVM to figure out XOR BTW... # API ## Classifiers Possible classifiers are: | Classifier | Type | Params | Initialization | |-------------|------------------------|----------------|-------------------------------| | C_SVC | multi-class classifier | `c` | `= new svm.CSVC(opts)` | | NU_SVC | multi-class classifier | `nu` | `= new svm.NuSVC(opts)` | | ONE_CLASS | one-class classifier | `nu` | `= new svm.OneClassSVM(opts)` | | EPSILON_SVR | regression | `c`, `epsilon` | `= new svm.EpsilonSVR(opts)` | | NU_SVR | regression | `c`, `nu` | `= new svm.NuSVR(opts)` | ## Kernels Possible kernels are: | Kernel | Parameters | |---------|--------------------------------| | LINEAR | No parameter | | POLY | `degree`, `gamma`, `r` | | RBF |`gamma` | | SIGMOID | `gamma`, `r` | ## Parameters and options Possible parameters/options are: | Name | Default value(s) | Description | |------------------|------------------------|-------------------------------------------------------------------------------------------------------| | svmType | `C_SVC` | Used classifier | | kernelType | `RBF` | Used kernel | | c | `[0.01,0.125,0.5,1,2]` | Cost for `C_SVC`, `EPSILON_SVR` and `NU_SVR`. Can be a `Number` or an `Array` of numbers | | nu | `[0.01,0.125,0.5,1]` | For `NU_SVC`, `ONE_CLASS` and `NU_SVR`. Can be a `Number` or an `Array` of numbers | | epsilon | `[0.01,0.125,0.5,1]` | For `EPSILON_SVR`. Can be a `Number` or an `Array` of numbers | | degree | `[2,3,4]` | For `POLY` kernel. Can be a `Number` or an `Array` of numbers | | gamma | `[0.001,0.01,0.5]` | For `POLY`, `RBF` and `SIGMOID` kernels. Can be a `Number` or an `Array` of numbers | | r | `[0.125,0.5,0,1]` | For `POLY` and `SIGMOID` kernels. Can be a `Number` or an `Array` of numbers | | kFold | `4` | `k` parameter for [k-fold cross validation]( http://en.wikipedia.org/wiki/Cross-validation_(statistics)#k-fold_cross-validation). `k` must be >= 1. If `k===1` then entire dataset is use for both testing and training. | | normalize | `true` | Whether to use [mean normalization](http://en.wikipedia.org/wiki/Normalization_(statistics)) during data pre-processing | | reduce | `true` | Whether to use [PCA](http://en.wikipedia.org/wiki/Principal_component_analysis) to reduce dataset's dimensions during data pre-processing | | retainedVariance | `0.99` | Define the acceptable impact on data integrity (require `reduce` to be `true`) | | eps | `1e-3` | Tolerance of termination criterion | | cacheSize | `200` | Cache size in MB. | | shrinking | `true` | Whether to use the shrinking heuristics | | probability | `false` | Whether to train a SVC or SVR model for probability estimates | The example below shows how to use them: ```javascript var svm = require('node-svm'); var clf = new svm.SVM({ svmType: 'C_SVC', c: [0.03125, 0.125, 0.5, 2, 8], // kernels parameters kernelType: 'RBF', gamma: [0.03125, 0.125, 0.5, 2, 8], // training options kFold: 4, normalize: true, reduce: true, retainedVariance: 0.99, eps: 1e-3, cacheSize: 200, shrinking : true, probability : false }); ``` __Notes__ : * You can override default values by creating a `.nodesvmrc` file (JSON) at the root of your project. * If at least one parameter has multiple values, [node-svm](https://github.com/nicolaspanel/node-svm/) will go through all possible combinations to see which one gives the best results (it performs grid-search to maximize [f-score](http://en.wikipedia.org/wiki/F1_score) for classification and minimize [Mean Squared Error](http://en.wikipedia.org/wiki/Mean_squared_error) for regression). ##Training SVMs can be trained using `svm#train(dataset)` method. Pseudo code : ```javascript var clf = new svm.SVM(options); clf .train(dataset) .progress(function(rate){ // ... }) .spread(function(trainedModel, trainingReport){ // ... }); ``` __Notes__ : * `trainedModel` can be used to restore the predictor later (see [this example](https://github.com/nicolaspanel/node-svm/blob/master/examples/save-prediction-model-example.js) for more information). * `trainingReport` contains information about predictor's accuracy (such as MSE, precison, recall, fscore, retained variance etc.) ## Prediction Once trained, you can use the classifier object to predict values for new inputs. You can do so : * Synchronously using `clf#predictSync(inputs)` * Asynchronously using `clf#predict(inputs).then(function(predicted){ ... });` **If you enabled probabilities during initialization** you can also predict probabilities for each class : * Synchronously using `clf#predictProbabilitiesSync(inputs)`. * Asynchronously using `clf#predictProbabilities(inputs).then(function(probabilities){ ... })`. __Note__ : `inputs` must be a 1d array of numbers ## Model evaluation Once the predictor is trained it can be evaluated against a test set. Pseudo code : ```javascript var svm = require('node-svm'); var clf = new svm.SVM(options); svm.read(trainFile) .then(function(dataset){ return clf.train(dataset); }) .then(function(trainedModel, trainingReport){ return svm.read(testFile); }) .then(function(testset){ return clf.evaluate(testset); }) .done(function(report){ console.log(report); }); ``` # CLI [node-svm](https://github.com/nicolaspanel/node-svm/) comes with a build-in Command Line Interpreter. To use it you have to install [node-svm](https://github.com/nicolaspanel/node-svm/) globally using `npm install -g node-svm`. See `$ node-svm -h` for complete command line reference. ## help ```shell $ node-svm help [] ``` Display help information about [node-svm](https://github.com/nicolaspanel/node-svm/) ## train ```shell $ node-svm train [] [] ``` Train a new model with given data set __Note__: use `$ node-svm train -i` to set parameters values dynamically. ## evaluate ```shell $ node-svm evaluate [] ``` Evaluate model's accuracy against a test set # How it work `node-svm` uses the official libsvm C++ library, version 3.20. For more information see also : * [libsvm web site](http://www.csie.ntu.edu.tw/~cjlin/libsvm/) * Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. * [Wikipedia article about SVM](https://en.wikipedia.org/wiki/Support_vector_machine) * [node addons](http://nodejs.org/api/addons.html) # Contributions Feel free to fork and improve/enhance `node-svm` in any way your want. If you feel that the community will benefit from your changes, please send a pull request : * Fork the project. * Make your feature addition or bug fix. * Add documentation if necessary. * Add tests for it. This is important so I don't break it in a future version unintentionally (run `grunt` or `npm test`). * Send a pull request to the `develop` branch. #FAQ ###Segmentation fault Q : Node returns 'segmentation fault' error during training. What's going on? A1 : Your dataset is empty or its format is incorrect. A2 : Your dataset is too big. ###Difference between nu-SVC and C-SVC Q : What is the difference between nu-SVC and C-SVC? A : [Answer here](http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#f411) ###Other questions * Take a look at [libsvm's FAQ](http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html). * Create [an issue](https://github.com/nicolaspanel/node-svm/issues) # License MIT [![githalytics.com alpha](https://cruel-carlota.pagodabox.com/92d9dd8573d8b458d19a240629fea97a "githalytics.com")](http://githalytics.com/nicolaspanel/node-svm) --- _Source: https://npm.io/package/node-svm · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._