glm

glm

Responsive generalized linear models in pure Javascript. This tool is useful for responsive feedback when doing data cleansing or adjusting parameters that actually change the data.

The purpose of this library is to have one self contained generalized linear model library. Other javascript libraries can be used for to create additional models. See the execellent brain library for neural networks and classifier.js library for Bayesian classifiers.

var glm_model = glm.GLM(GLM.families.Gaussian());
var feature_vectors = [[1], [2]];
var target_values = [1, 2];
glm_model.fit(target_values, feature_vectors);

Applications

• Data visualizations
• Interactive data cleansing tools (example: outlier removal)
• Interactive feature manipulation or discretization
• Fitting models with Node.js
• Teaching & education with linear model examples

Future Plans

We will soon have support for regularization and Probit regression. After this, we plan on optimizing the runtime performance of the system. It would be neat to have support for MAP or fully Bayesian GLMs, but we currently don't see any reason to work on this functionality.

API changes will probably be made to make GLM.js more in line with the other popular Javascript Machine Learning libraries.

Usage & Architecture

There is one main function called GLM which expects a distribution to be passed in to it. The families can be found in the families attribute of this GLM function. For example: GLM.families.Gaussian() will initialize a gaussian distribution object.

This is essentially a port of a python GLM implementation that uses the iteratively reweighted least squares algorithm in the excellent statsmodels library.

Usage in Node

$ npm install node
$ node
> var glm = require('glm');

Usage in browser

Just include 'glm.js' as a script in your HTML code.

Examples

Run python -m SimpleHTTPServer in the root of this repo and navigate your browser to http://localhost:8000/examples/

Compiling

To compile, first install the dependencies with npm and then run make. To test, run make test.