https://github.com/danieldk/golinear

liblinear bindings for Go
https://github.com/danieldk/golinear

classifier go liblinear linear-models svm

Last synced: about 2 months ago
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liblinear bindings for Go

• Host: GitHub
• URL: https://github.com/danieldk/golinear
• Owner: danieldk
• License: other
• Archived: true
• Created: 2013-04-05T15:37:01.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2018-08-29T10:30:44.000Z (over 6 years ago)
• Last Synced: 2024-10-25T05:23:09.123Z (about 2 months ago)
• Topics: classifier, go, liblinear, linear-models, svm
• Language: Go
• Size: 70.3 KB
• Stars: 45
• Watchers: 7
• Forks: 12
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## Introduction

[![Report card](http://goreportcard.com/badge/danieldk/golinear)](http://goreportcard.com/report/danieldk/golinear)

[![GoDoc](https://godoc.org/gopkg.in/danieldk/golinear.v1?status.svg)](https://godoc.org/gopkg.in/danieldk/golinear.v1)

golinear is a package for training and using linear classifiers in the Go

programming language (golang).

## Installation

To use this package, you need the

[liblinear](http://www.csie.ntu.edu.tw/~cjlin/liblinear/) library. On Mac

OS X, you can install this library with

[Homebrew](http://mxcl.github.com/homebrew/):

    brew install liblinear

Ubuntu and Debian provide packages for *liblinear*. However, at the time of

writing (July 2, 2014), these were serverly outdated. This package requires

version 1.9 or later.

This latest API-stable version (v1) can be installed with the go

command:

    go get gopkg.in/danieldk/golinear.v1

or included in your source code:

    import "gopkg.in/danieldk/golinear.v1"

The package documentation is available at: http://godoc.org/gopkg.in/danieldk/golinear.v1

### OpenMP

If you wish to use *liblinear* with OpenMP support for multicore processing, 

please use this command to install the package:

    CGO_LDFLAGS="-lgomp" CGO_CFLAGS="-DCV_OMP" go get github.com/danieldk/golinear

## Plans

1. Port classification to Go.

2. Port training to Go.

We will take a pragmatic approach to porting code to Go: if the performance penalty is minor,

ported code will flow to the main branch. Otherwise, we will keep it around until the performance

is good enough.

## Examples

Examples for using golinear can be found at:

https://github.com/danieldk/golinear-examples