This is a Julia interface for LIBSVM.
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
It is possible to use different kernels than those that are provided. In such a case, it is required to provide a matrix filled with precomputed kernel values.
For training, a symmetric matrix is expected:
K = [k(x_1, x_1) k(x_1, x_2) ... k(x_1, x_l); k(x_2, x_1) ... ... k(x_l, x_1) ... k(x_l, x_l)]
i-th training instance and
l is the number of training
n instances, a matrix of shape
(l, n) is expected:
KK = [k(x_1, t_1) k(x_1, t_2) ... k(x_1, t_n); k(x_2, t_1) ... ... k(x_l, t_1) ... k(x_l, t_n)]
i-th instance to be predicted.
# Training data X = [-2 -1 -1 1 1 2; -1 -1 -2 1 2 1] y = [1, 1, 1, 2, 2, 2] # Testing data T = [-1 2 3; -1 2 2] # Precomputed matrix for training (corresponds to linear kernel) K = X' * X model = svmtrain(K, y, kernel=Kernel.Precomputed) # Precomputed matrix for prediction KK = X' * T ỹ, _ = svmpredict(model, KK)
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
4 days ago