11 Stars
Updated Last
1 Year Ago
Started In
October 2015


Build Status Build status

Julia bindings for LIBLINEAR.

using Statistics, RDatasets, LIBLINEAR

# Load Fisher's classic iris data
iris = dataset("datasets", "iris")

# LIBLINEAR handles multi-class data automatically using a one-against-the rest strategy
labels = iris.Species

# First dimension of input data is features; second is instances
data = convert(Matrix, iris[:, 1:4])'

# Train SVM on half of the data using default parameters. See the linear_train
# function in LIBLINEAR.jl for optional parameter settings.
model = linear_train(labels[1:2:end], data[:, 1:2:end], verbose=true);

# Test model on the other half of the data.
(predicted_labels, decision_values) = linear_predict(model, data[:, 2:2:end]);

# Compute accuracy
println("Accuracy: $(mean(predicted_labels .== labels[2:2:end])*100)")


Created by Zhizhong Li.

This package is adapted from the LIBSVM Julia package by Simon Kornblith.

Required Packages