This is a Golang wrapper for LIBLINEAR (C.-J. Lin et al.) (GitHub). Note that the interface of this package might be slightly different from liblinear C interface because of Go convention. Yet, I'll try to align the function name and functionality to liblinear C library.

GoDoc: Document.

LIBLINEAR is a linear classifier for data with millions of instances and features. It supports

• L2-regularized classifiers
• L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
• L1-regularized classifiers (after version 1.4)
• L2-loss linear SVM and logistic regression (LR)
• L2-regularized support vector regression (after version 1.9)
• L2-loss linear SVR and L1-loss linear SVR.

This package depends on LIBLINEAR 2.01+. Please install it first via Homebrew or other package managers on your OS:

brew update
brew info liblinear # make sure your formula will install version higher than 2.01
brew install liblinear

After liblinear installation, just go get this package

go get github.com/lazywei/liblinear

The package is based on mat64.

import linear "github.com/lazywei/liblinear"

// ReadLibsvm(filepath string, oneBased bool) (X, y *mat64.Dense)
X, y := linear.ReadLibsvm("heart_scale", true)

// Train(X, y *mat64.Dense, bias float64, solverType int,
// 	eps, C_, p float64,
// 	classWeights map[int]float64) (*Model)
// Please checkout liblinear's doc for the explanation for these parameters.
model := Train(X, y, -1, 0, 0.01, 1.0, 0.1, map[int]float64{1: 1, -1: 1})
y_pred:= linear.Predict(model, X)

fmt.Println(linear.Accuracy(y, y_pred))

This package is mainly built because of mockingbird, which is a programming language classifier in Go. Mockingbird is my Google Summer of Code 2015 Project with GitHub and linguist. If you like it, please feel free to follow linguist, mockingbird, and this library.

• Wrap core functions
  • Train
  • Predict
  • LIBSVM format reader
  • Predict Probability
  • Cross Validation
  • Save Model / Load Model
• Better Wrapper for liblinear's C struct
  • Model (wrap struct model)
  • FeatureNode (wrap struct feature_node)
  • Parameter (wrap struct parameter)
  • Problem (wrap struct problem)
• Abstraction for classifier: encapsulate model