This is a Julia interface for LIBSVM.
- Supports all LIBSVM models: classification C-SVC, nu-SVC, regression: epsilon-SVR, nu-SVR and distribution estimation: one-class SVM
- Model objects are represented by Julia type SVM which gives you easy access to model features and can be saved e.g. as JLD file
- Supports ScikitLearn.jl API
This provides a lower level API similar to LIBSVM C-interface. See
using LIBSVM using RDatasets using Printf using Statistics # Load Fisher's classic iris data iris = dataset("datasets", "iris") # First four dimension of input data is features X = Matrix(iris[:, 1:4])' # LIBSVM handles multi-class data automatically using a one-against-one strategy y = iris.Species # Split the dataset into training set and testing set Xtrain = X[:, 1:2:end] Xtest = X[:, 2:2:end] ytrain = y[1:2:end] ytest = y[2:2:end] # Train SVM on half of the data using default parameters. See documentation # of svmtrain for options model = svmtrain(Xtrain, ytrain) # Test model on the other half of the data. ŷ, decision_values = svmpredict(model, Xtest); # Compute accuracy @printf "Accuracy: %.2f%%\n" mean(ŷ .== ytest) * 100
You can alternatively use
ScikitLearn.jl API with same options as
using LIBSVM using RDatasets # Classification C-SVM iris = dataset("datasets", "iris") X = Matrix(iris[:, 1:4]) y = iris.Species Xtrain = X[1:2:end, :] Xtest = X[2:2:end, :] ytrain = y[1:2:end] ytest = y[2:2:end] model = fit!(SVC(), Xtrain, ytrain) ŷ = predict(model, Xtest)
# Epsilon-Regression whiteside = RDatasets.dataset("MASS", "whiteside") X = Matrix(whiteside[:, 3:3]) # the `Gas` column y = whiteside.Temp model = fit!(EpsilonSVR(cost = 10., gamma = 1.), X, y) ŷ = predict(model, X)
The library is currently developed and maintained by Matti Pastell. It was originally developed by Simon Kornblith.
LIBSVM by Chih-Chung Chang and Chih-Jen Lin