svm

Support Vector Machines

svmjs

Andrej Karpathy July 2012

svmjs is a lightweight implementation of the SMO algorithm to train a binary Support Vector Machine. As this uses the dual formulation, it also supports arbitrary kernels. Correctness test, together with MATLAB reference code are in /test.

Can be found here: http://cs.stanford.edu/~karpathy/svmjs/demo/

Corresponding code is inside /demo directory.

The simplest use case:

// include the library 
<script src="./svmjs/lib/svm.js"></script>
<script>
svm = new svmjs.SVM();
svm.train(data, labels);
testlabels = svm.predict(testdata);
</script>

Here, data and testdata are a 2D, NxD array of floats, labels and testlabels is an array of size N that contains 1 or -1. You can also query for the raw margins:

margins = svm.margins(testdata);
margin = svm.marginOne(testadata[0]);

The library supports arbitrary kernels, but currently comes with linear and rbf kernel:

svm.train(data, labels, { kernelfunction(v1,v2){ /* return K(v1, v2) */} }); // arbitrary function 
svm.train(data, labels, { kernel: svmjs.linearKernel });
svm.train(data, labels, { kernel: svmjs.makeRbfKernel(0.5) }); // sigma = 0.5 

For linear kernels, you can also query the weights and offset directly:

wb= svm.getWeights();
//wb.w is array of weights and wb.b is the bias term 

For training you can pass in several options. Here are the defaults:

var options = {};
/* For C, Higher = you trust your data more. Lower = more regularization.
Should be in range of around 1e-2 ... 1e5 at most. */
options.= 1.0;
options.tol = 1e-4; // do not touch this unless you're pro 
options.maxiter = 10000; // if you have a larger problem, you may need to increase this 
options.kernel = svmjs.linearKernel; // discussed above 
options.numpasses = 10; // increase this for higher precision of the result. (but slower) 
svm.train(data, labels, options);

To use this library in node.js, install with npm:

npm install svm

And use like so:

var svm = require("svm");
var SVM = new svm.SVM();
SVM.train(data, labels);

The SMO algorithm is very space efficient, so you need not worry about running out of space no matter how large your problem is. However, you do need to worry about runtime efficiency. In practice, there are many heuristics one can use to select the pair of alphas (i,j) to optimize and this uses a rather naive approach. If you have a large and complex problem, you will need to increase maxiter a lot. (or don't use Javascript!)

If you intend to use only linear SVM and are worried about efficiency, I recommend you train it, get the weights out with getWeights(), and use them directly in your code from then on.

MIT