Linear SVM using LIBLINEAR

svmpredict{T,U<:Real}(model::SVM{T}, X::AbstractMatrix{U})

Predict values using model based on data X. The shape of X needs to be (nfeatures, nsamples). The method returns tuple (predictions, decisionvalues).

svmtrain{T, U<:Real}(X::AbstractMatrix{U}, y::AbstractVector{T}=[];
    svmtype::Type=SVC, kernel::Kernel.KERNEL=Kernel.RadialBasis, degree::Integer=3,
    gamma::Float64=1.0/size(X, 1), coef0::Float64=0.0,
    cost::Float64=1.0, nu::Float64=0.5, epsilon::Float64=0.1,
    tolerance::Float64=0.001, shrinking::Bool=true,
    probability::Bool=false, weights::Union{Dict{T, Float64}, Compat.Nothing}=nothing,
    cachesize::Float64=200.0, verbose::Bool=false)

Train Support Vector Machine using LIBSVM using response vector y and training data X. The shape of X needs to be (nfeatures, nsamples). For one-class SVM use only X.

Arguments

• svmtype::Type=LIBSVM.SVC: Type of SVM to train SVC (for C-SVM), NuSVCOneClassSVM, EpsilonSVR or NuSVR. Defaults to OneClassSVM if y is not used.
• kernel::Kernels.KERNEL=Kernel.RadialBasis: Model kernel Linear, polynomial, RadialBasis, Sigmoid or Precomputed.
• degree::Integer=3: Kernel degree. Used for polynomial kernel
• gamma::Float64=1.0/size(X, 1) : γ for kernels
• coef0::Float64=0.0: parameter for sigmoid and polynomial kernel
• cost::Float64=1.0: cost parameter C of C-SVC, epsilon-SVR, and nu-SVR
• nu::Float64=0.5: parameter nu of nu-SVC, one-class SVM, and nu-SVR
• epsilon::Float64=0.1: epsilon in loss function of epsilon-SVR
• tolerance::Float64=0.001: tolerance of termination criterion
• shrinking::Bool=true: whether to use the shrinking heuristics
• probability::Bool=false: whether to train a SVC or SVR model for probability estimates
• weights::Union{Dict{T, Float64}, Compat.Nothing}=nothing: dictionary of class weights
• cachesize::Float64=100.0: cache memory size in MB
• verbose::Bool=false: print training output from LIBSVM if true
• nt::Integer=0: number of OpenMP cores to use, if 0 it is set to OMPNUMTHREADS, if negative it is set to the max number of threads

Consult LIBSVM documentation for advice on the choise of correct parameters and model tuning.

Convert SVM model to libsvm struct for prediction