Does anyone have experience of training a support vector machine (SVM) in Julia (1.4.1) ?

I tried the LIBSVM interface, but the example on the gituhub page gave an error :

```
# Load Fisher's classic iris data
iris = dataset("datasets", "iris")
# LIBSVM handles multi-class data automatically using a one-against-one strategy
labels = convert(Vector, iris[:Species])
# First dimension of input data is features; second is instances
instances = convert(Array, iris[:, 1:4])'
# Train SVM on half of the data using default parameters. See documentation
# of svmtrain for options
model = svmtrain(instances[:, 1:2:end], labels[1:2:end]);```

ERROR: MethodError: no method matching LIBSVM.SupportVectors(::Int32, ::Array{Int32,1}, ::CategoricalArray{String,1,UInt8,String,CategoricalValue{String,UInt8},Union{}}, ::Array{Float64,2}, ::Array{Int32,1}, ::Array{LIBSVM.SVMNode,1})
Closest candidates are:
LIBSVM.SupportVectors(::Int32, ::Array{Int32,1}, ::Array{T,1}, ::AbstractArray{U,2}, ::Array{Int32,1}, ::Array{LIBSVM.SVMNode,1}) where {T, U} at /home/benny/.julia/packages/LIBSVM/5Z99T/src/LIBSVM.jl:18
LIBSVM.SupportVectors(::LIBSVM.SVMModel, ::Any, ::Any) at /home/benny/.julia/packages/LIBSVM/5Z99T/src/LIBSVM.jl:27 

```