npm install svmsvmjs 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:
``javascript`
// include the library
Here, 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:javascript`
margins = svm.margins(testdata);
margin = svm.marginOne(testadata[0]);
The library supports arbitrary kernels, but currently comes with linear and rbf kernel:
`javascript`
svm.train(data, labels, { kernel: function(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:
`javascript`
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:
`javascript`
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.C = 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);
:`
npm install svm
And use like so:
javascript
var svm = require("svm");
var SVM = new svm.SVM();
SVM.train(data, labels);
``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.