DEMO

Port of libsvm v3.22 using emscripten and it targets browser or nodejs using asm and WebAssembly.

What is libsvm? libsvm is a c++ library developped by Chih-Chung Chang and Chih-Jen Lin that allows to do support vector machine (aka SVM) classification and regression.

Resources about libsvm:

libsvm website
libsvm github repository
Wikipedia article
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.

Usage

Install using npm

npm install libsvm-ts

Install using yarn

yarn add libsvm-ts

Load in nodejs

The main entry point loads the WebAssembly build and is asynchronous.

import { SVM } from 'libsvm-ts';
const svm = new SVM(); // ...

Load in the browser

The npm package contains a bundle for the browser that works with AMD and browser globals. There is one bundle for the asm build and another for the web assembly build. They are located in the dist/browser directory of the package. You can load them into your web page with a script tag. For the web assembly module, make sure that the libsvm.wasm file is served from the same relative path as the js file.

Basic usage

This example illustrates how to use the library to train and use an SVM classifier.

import { SVM } from 'libsvm-ts';

const svm = new SVM({
  type: 'C_SVC',
  kernel: 'RBF',
  gamma: 1,
  cost: 1
});

// This is the xor problem
//  1  0
//  0  1
const samples = [[0, 0], [1, 1], [1, 0], [0, 1]];
const labels = [0, 0, 1, 1];

svm.loadWASM().then((loadedSVM) => {
  // Also you can loadASM() instead of loadWASM()
  loadedSVM.train({ samples, labels });
  const predictedLabel = loadedSVM.predictOne([0.7, 0.8]);
  console.log(predictedLabel) // 0
});

Development

We welcome new contributors of all level of experience. The development guide will be added to assist new contributors to easily join the project.

You want to participate in a Machine Learning project, which will boost your Machine Learning skills and knowledge
Looking to be part of a growing community
You want to learn Machine Learning
You like Typescript ❤️ Machine Learning

Checkout CONTRIBUTIONS.md for more information

Benchmarks

You can compare the performance of the library in various environments. Run npm run benchmark to run the benchmarks with native c/c++ code and with the compiled code with your local version of node.js. For browser performance, go to the web benchmark page.

Speed is mainly affected by the javascript engine that compiles it. Since WebAssembly has been stabilized and is an optimization phase, more recent engines are almost always faster.

Speed is also affected by the version of emscripten that generated the build or the options used in the build. I will try to keep up with any improvement that might significantly impact the performance.

Cross-validation benchmark

I report the results here for the cross-validation benchmark on the iris dataset to get a feeling for how performance compares on different platforms. There are other benchmarks that can be run from the terminal in node.js or in the browser. The performance results are given relative to how they run natively (with compiled c++ code). The benchmarks only consider runtime performance, not load and parse performance.

Platform	Rel asm perf	Rel wasm perf
Native	100%	100%
Node.js 8.1.2	34.2%	52.6%
Node.js v7.10.0	14.4%	N/A
Chrome 59.0.3071.115	36.2%	51.3%
Firefox 54.0	35.5%	70.4%

What are asm and WebAssembly ?

From asmjs.org

asm is an optimizable subset of javascript.

From webassembly.org

WebAssembly or wasm is a new portable, size- and load-time-efficient format suitable for compilation to the web

Should I use asm or WebAssembly ?

Both. You should try to use WebAssembly first and fall back to asm in order to support all browsers.

WebAssembly is currently supported in the latest stable versions of Chrome, Firefox and on preview versions of Safari and Edge.

API Documentation

SVM

Kind: global class

SVM
- new SVM(options)
- instance
  - .train(samples, labels)
  - .crossValidation(samples, labels, kFold) ⇒ Array.<number>
  - .free()
  - .predictOne(sample) ⇒ number
  - .predict(samples) ⇒ Array.<number>
  - .predictProbability(samples) ⇒ Array.<object>
  - .predictOneProbability(sample) ⇒ object
  - .predictOneInterval(sample, confidence) ⇒ object
  - .predictInterval(samples, confidence) ⇒ Array.<object>
  - .getLabels() ⇒ Array.<number>
  - .getSVIndices() ⇒ Array.<number>
  - .serializeModel() ⇒ string
- static
  - .SVM_TYPES : Object
  - .KERNEL_TYPES : Object
  - .load(serializedModel) ⇒ SVM

new SVM(options)

Param	Type	Default	Description
options	`object`
[options.type]	`number`	`SVM_TYPES.C_SVC`	Type of SVM to perform,
[options.kernel]	`number`	`KERNEL_TYPES.RBF`	Kernel function,
[options.degree]	`number`	`3`	Degree of polynomial, for polynomial kernel
[options.gamma]	`number`		Gamma parameter of the RBF, Polynomial and Sigmoid kernels. Default value is 1/num_features
[options.coef0]	`number`	`0`	coef0 parameter for Polynomial and Sigmoid kernels
[options.cost]	`number`	`1`	Cost parameter, for C SVC, Epsilon SVR and NU SVR
[options.nu]	`number`	`0.5`	For NU SVC and NU SVR
[options.epsilon]	`number`	`0.1`	For epsilon SVR
[options.cacheSize]	`number`	`100`	Cache size in MB
[options.tolerance]	`number`	`0.001`	Tolerance
[options.shrinking]	`boolean`	`true`	Use shrinking euristics (faster),
[options.probabilityEstimates]	`boolean`	`false`	weather to train SVC/SVR model for probability estimates,
[options.weight]	`object`		Set weight for each possible class
[options.quiet]	`boolean`	`true`	Print info during training if false

svM.train(samples, labels)

Trains the SVM model.

Kind: instance method of SVM
Throws:

if SVM instance was instantiated from SVM.load.

Param	Type	Description
samples	`Array.<Array.<number>>`	The training samples. First level of array are the samples, second level are the individual features
labels	`Array.<number>`	The training labels. It should have the same size as the samples. If you are training a classification model, the labels should be distinct integers for each class. If you are training a regression model, each label should be the value of the predicted variable.

svM.crossValidation(samples, labels, kFold) ⇒ `Array.<number>`

Performs k-fold cross-validation (KF-CV). KF-CV separates the data-set into kFold random equally sized partitions, and uses each as a validation set, with all other partitions used in the training set. Observations left over from if kFold does not divide the number of observations are left out of the cross-validation process. If kFold is one, this is equivalent to a leave-on-out cross-validation

Kind: instance method of SVM
Returns: Array.<number> - The array of predicted labels produced by the cross validation. Has a size equal to the number of samples provided as input.
Throws:

if SVM instance was instantiated from SVM.loadWASM() or SVM.loadASK().

Param	Type	Description
samples	`Array.<Array.<number>>`	The training samples.
labels	`Array.<number>`	The training labels.
kFold	`number`	Number of datasets into which to split the training set.

svM.free()

Free the memory allocated for the model. Since this memory is stored in the memory model of emscripten, it is allocated within an ArrayBuffer and WILL NOT BE GARBARGE COLLECTED, you have to explicitly free it. So not calling this will result in memory leaks. As of today in the browser, there is no way to hook the garbage collection of the SVM object to free it automatically. Free the memory that was created by the compiled libsvm library to. store the model. This model is reused every time the predict method is called.

Kind: instance method of SVM

svM.predictOne(sample) ⇒ `number`

Predict the label of one sample.

Kind: instance method of SVM
Returns: number - - The predicted label.

Param	Type	Description
sample	`Array.<number>`	The sample to predict.

svM.predict(samples) ⇒ `Array.<number>`

Predict the label of many samples.

Kind: instance method of SVM
Returns: Array.<number> - - The predicted labels.

Param	Type	Description
samples	`Array.<Array.<number>>`	The samples to predict.

svM.predictProbability(samples) ⇒ `Array.<object>`

Predict the label with probability estimate of many samples.

Kind: instance method of SVM
Returns: Array.<object> - - An array of objects containing the prediction label and the probability estimates for each label

Param	Type	Description
samples	`Array.<Array.<number>>`	The samples to predict.

svM.predictOneProbability(sample) ⇒ `object`

Predict the label with probability estimate.

Kind: instance method of SVM
Returns: object - - An object containing the prediction label and the probability estimates for each label

Param	Type
sample	`Array.<number>`

svM.predictOneInterval(sample, confidence) ⇒ `object`

Predict a regression value with a confidence interval

Kind: instance method of SVM
Returns: object - - An object containing the prediction value and the lower and upper bounds of the confidence interval

Param	Type	Description
sample	`Array.<number>`
confidence	`number`	A value between 0 and 1. For example, a value 0.95 will give you the 95% confidence interval of the predicted value.

svM.predictInterval(samples, confidence) ⇒ `Array.<object>`

Predict regression values with confidence intervals

Kind: instance method of SVM
Returns: Array.<object> - - An array of objects each containing the prediction label and the probability estimates for each label

Param	Type	Description
samples	`Array.<Array.<number>>`	An array of samples.
confidence	`number`	A value between 0 and 1. For example, a value 0.95 will give you the 95% confidence interval of the predicted value.

svM.getLabels() ⇒ `Array.<number>`

Get the array of labels from the model. Useful when creating an SVM instance with SVM.load

Kind: instance method of SVM
Returns: Array.<number> - - The list of labels.

svM.getSVIndices() ⇒ `Array.<number>`

Get the indices of the support vectors from the training set passed to the train method.

Kind: instance method of SVM
Returns: Array.<number> - - The list of indices from the training samples.

svM.serializeModel() ⇒ `string`

Uses libsvm's serialization method of the model.

Kind: instance method of SVM
Returns: string - The serialization string.

SVM.SVM_TYPES : `Object`

SVM classification and regression types

Kind: static property of SVM
Properties

Name	Description
C_SVC	The C support vector classifier type
NU_SVC	The nu support vector classifier type
ONE_CLASS	The one-class support vector classifier type
EPSILON_SVR	The epsilon support vector regression type
NU_SVR	The nu support vector regression type

SVM.KERNEL_TYPES : `Object`

SVM kernel types

Kind: static property of SVM
Properties

Name	Description
LINEAR	Linear kernel
POLYNOMIAL	Polynomial kernel
RBF	Radial basis function (gaussian) kernel
SIGMOID	Sigmoid kernel

SVM.loadASM() ⇒ `SVM`

Create a SVM instance using ASM.

Kind: a member method of SVM
Returns: SVM - - SVM instance that contains the model.

Param	Type	Description
serializedModel	`string`	The serialized model.

SVM.loadWASM() ⇒ `SVM`

Create a SVM instance using WASM.

Kind: a member method of SVM
Returns: SVM - - SVM instance that contains the model.

Param	Type	Description
serializedModel	`string`	The serialized model.

LICENSE

BSD-3-Clause

machinelearnjs/libsvm-ts

DEMO

Usage

Install using npm

Install using yarn

Load in nodejs

Load in the browser

Basic usage

Development

Benchmarks

Cross-validation benchmark

What are asm and WebAssembly ?

Should I use asm or WebAssembly ?

API Documentation

SVM

new SVM(options)

svM.train(samples, labels)

svM.crossValidation(samples, labels, kFold) ⇒ Array.<number>

svM.free()

svM.predictOne(sample) ⇒ number

svM.predict(samples) ⇒ Array.<number>

svM.predictProbability(samples) ⇒ Array.<object>

svM.predictOneProbability(sample) ⇒ object

svM.predictOneInterval(sample, confidence) ⇒ object

svM.predictInterval(samples, confidence) ⇒ Array.<object>

svM.getLabels() ⇒ Array.<number>

svM.getSVIndices() ⇒ Array.<number>

svM.serializeModel() ⇒ string

SVM.SVM_TYPES : Object

SVM.KERNEL_TYPES : Object

SVM.loadASM() ⇒ SVM

SVM.loadWASM() ⇒ SVM