goml is a machine learning library written entirely in Golang which lets the average developer include machine learning into their applications. (pronounced like the data format 'toml')
While models include traditional, batch learning interfaces,
goml includes many models which let you learn in an online, reactive manner by passing data to streams held on channels.
The library includes comprehensive tests, extensive documentation, and clean, expressive, modular source code. Community contribution is heavily encouraged.
Each package (mentioned below) includes individual README's to learn more about the function, and purpose of the models. Above all, if you want to learn about models, read the GoDoc reference for the package. All models are, as mentioned above, heavily documented.
go get github.com/cdipaolo/goml/base
# This could be any other model package if you want
# Also, the base package is imported already
# by many of the packages so you might not even
# need to `go get` the package explicitly
go get github.com/cdipaolo/goml/perceptron
All the code is well documented, and the source is/should be really readable if you'd like to make sense of it all! Look at each package (like right now, in GitHub,) and you will see a link to Godoc as well as an explanation of the package and an example usage. You can even click on the main bullets below and it'll take you to those packages. Also you could just use the Godoc link at the top of this README and navigate to the package you'd like to see more about.
Sub-bullets below will take you directly to the source code of the model.
I'd love help with any of this if anybody thinks that they would like to implement a model that isn't here, or if they have improvements to current models implemented, or if they want to help with documentation (this would be greatly appreciated, believe me, writing great documentation takes time! :+1:)