Support Vector Machine for nodejs


Support Vector Machine (SVM) library for nodejs & io.js .

Support Vector Machines

Wikipedia :

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.


npm install --save node-svm

Quick start

If you are not familiar with SVM I highly recommend this guide.

Here's an example of using node-svm to approximate the XOR function :

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 
        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.

Note: There's no reason to use SVM to figure out XOR BTW...


Possible classifiers are:

C_SVCmulti-class classifierc= new svm.CSVC(opts)
NU_SVCmulti-class classifiernu= new svm.NuSVC(opts)
ONE_CLASSone-class classifiernu= new svm.OneClassSVM(opts)
EPSILON_SVRregressionc, epsilon= new svm.EpsilonSVR(opts)
NU_SVRregressionc, nu= new svm.NuSVR(opts)

Possible kernels are:

LINEARNo parameter
POLYdegree, gamma, r
SIGMOIDgamma, r

Possible parameters/options are:

NameDefault value(s)Description
svmTypeC_SVCUsed classifier
kernelTypeRBFUsed 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
kFold4k parameter for k-fold cross validation. k must be >= 1. If k===1 then entire dataset is use for both testing and training.
normalizetrueWhether to use mean normalization during data pre-processing
reducetrueWhether to use PCA to reduce dataset's dimensions during data pre-processing
retainedVariance0.99Define the acceptable impact on data integrity (require reduce to be true)
eps1e-3Tolerance of termination criterion
cacheSize200Cache size in MB.
shrinkingtrueWhether to use the shrinking heuristics
probabilityfalseWhether to train a SVC or SVR model for probability estimates

The example below shows how to use them:

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 will go through all possible combinations to see which one gives the best results (it performs grid-search to maximize f-score for classification and minimize Mean Squared Error for regression).


SVMs can be trained using svm#train(dataset) method.

Pseudo code :

var clf = new svm.SVM(options);
    // ... 
    // ... 

Notes :

  • trainedModel can be used to restore the predictor later (see this example for more information).
  • trainingReport contains information about predictor's accuracy (such as MSE, precison, recall, fscore, retained variance etc.)

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

Once the predictor is trained it can be evaluated against a test set.

Pseudo code :

var svm = require('node-svm');
var clf = new svm.SVM(options);
    return clf.train(dataset);
    return clf.evaluate(testset);


node-svm comes with a build-in Command Line Interpreter.

To use it you have to install node-svm globally using npm install -g node-svm.

See $ node-svm -h for complete command line reference.

$ node-svm help [<command>]

Display help information about node-svm

$ node-svm train <dataset file> [<where to save the prediction model>] [<options>]

Train a new model with given data set

Note: use $ node-svm train <dataset file> -i to set parameters values dynamically.

$ node-svm evaluate <model file> <testset file> [<options>]

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 :


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

###Other questions