js-svm

Package provides javascript implementation of linear SVM and SVM with gaussian kernel

Features

* Support for binary classification
* Support for multi-class classification

Install

``bash
npm install js-svm
`

Usage

$3

The sample code below show how to use SVM binary classifier on the iris datsets to classify whether a data row belong to species Iris-virginica:

`javascript
var jssvm = require('js-svm');
var iris = require('js-datasets-iris');

var svm = new jssvm.BinarySvmClassifier();

iris.shuffle();

var trainingDataSize = Math.round(iris.rowCount * 0.9);
var trainingData = [];
var testingData = [];
for(var i=0; i < iris.rowCount ; ++i) {
var row = [];
row.push(iris.data[i][0]); // sepalLength;
row.push(iris.data[i][1]); // sepalWidth;
row.push(iris.data[i][2]); // petalLength;
row.push(iris.data[i][3]); // petalWidth;
row.push(iris.data[i][4] == "Iris-virginica" ? 1.0 : 0.0); // output which is 1 if species is Iris-virginica; 0 otherwise
if(i < trainingDataSize){
trainingData.push(row);
} else {
testingData.push(row);
}
}


var result = svm.fit(trainingData);

console.log(result);

for(var i=0; i < testingData.length; ++i){
var predicted = svm.transform(testingData[i]);
console.log("actual: " + testingData[i][4] + " predicted: " + predicted);
}
`

To configure the BinarySvmClassifier, use the following code when it is created:

`javascript
var svm = new jssvm.BinarySvmClassifier({
alpha: 0.01, // learning rate
iterations: 1000, // maximum iterations
C: 5.0, // panelty term
trace: false // debug tracing
});
`

$3

The sample code below illustrates how to run the multi-class classifier on the iris datasets to classifiy the species of each data row:

`javascript
var jssvm = require('js-svm');
var iris = require('js-datasets-iris');

var classifier = new jssvm.MultiClassSvmClassifier();

iris.shuffle();

var trainingDataSize = Math.round(iris.rowCount * 0.9);
var trainingData = [];
var testingData = [];
for(var i=0; i < iris.rowCount ; ++i) {
var row = [];
row.push(iris.data[i][0]); // sepalLength;
row.push(iris.data[i][1]); // sepalWidth;
row.push(iris.data[i][2]); // petalLength;
row.push(iris.data[i][3]); // petalWidth;
row.push(iris.data[i][4]); // output is species
if(i < trainingDataSize){
trainingData.push(row);
} else {
testingData.push(row);
}
}


var result = classifier.fit(trainingData);

console.log(result);

for(var i=0; i < testingData.length; ++i){
var predicted = classifier.transform(testingData[i]);
console.log("svm prediction testing: actual: " + testingData[i][4] + " predicted: " + predicted);
}
`

To configure the MultiClassSvmClassifier, use the following code when it is created:

`javascript
var classifier = new jssvm.MultiClassSvmClassifier({
alpha: 0.01, // learning rate
iterations: 1000, // maximum iterations
C: 5.0 // panelty term
sigma: 1.0 // the standard deviation for the gaussian kernel
});
`

$3

By default the kernel used by the binary and multi-class classifier is "linear" which can be printed by:

`javascript
console.log(classifier.kernel);
`

To switch to use gaussian kernel, put the property 'kernel: "gaussian"' in the config data when the classifier is created:

`javascript
var svm = new jssvm.BinarySvmClassifier({
...,
kernel: 'gaussian'
});

....

var svm = new jssvm.MultiClassSvmClassifier({
...,
kernel: 'gaussian'
});

``

$3

Include the "node_modules/js-svm/build/jssvm.min.js" (or "node_modules/js-svm/src/jssvm.js") in your HTML \ tag

The demo code in HTML can be found in the following files within the package:

* example-binary-classifier.html
* example-multi-class-classifier.html