Learning Machine Learning (SVM) with Julia

Specific Domains Machine Learning
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10

This is a feeback of my first attempt to learn SVM
I read the nice guide published in LIBSVM page. Then I tried the example published in LIBSVM.jl webpage and I obtained this result with julia 1.3.1 and LIBSVM v0.4.0 :confused:


julia> labels = convert(Vector, iris[:Species])
┌ Warning: `getindex(df::DataFrame, col_ind::ColumnIndex)` is deprecated, use `df[!, col_ind]` instead.
│   caller = top-level scope at REPL[9]:1
â”” @ Core REPL[9]:1
150-element Array{CategoricalString{UInt8},1}:
 "setosa"   
 "setosa"       
 "setosa"   
 â‹®          
 "virginica"
 "virginica"
 "virginica"

julia> instances = convert(Array, iris[:, 1:4])'
4×150 LinearAlgebra.Adjoint{Float64,Array{Float64,2}}:
 5.1  4.9  4.7  4.6  5.0  5.4  4.6  5.0  4.4  4.9  …  6.9  5.8  6.8  6.7  6.7  6.3  6.5  6.2  5.9
 3.5  3.0  3.2  3.1  3.6  3.9  3.4  3.4  2.9  3.1     3.1  2.7  3.2  3.3  3.0  2.5  3.0  3.4  3.0
 1.4  1.4  1.3  1.5  1.4  1.7  1.4  1.5  1.4  1.5     5.1  5.1  5.9  5.7  5.2  5.0  5.2  5.4  5.1
 0.2  0.2  0.2  0.2  0.2  0.4  0.3  0.2  0.2  0.1     2.3  1.9  2.3  2.5  2.3  1.9  2.0  2.3  1.8

julia> 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,CategoricalString{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/fred/.julia/packages/LIBSVM/5Z99T/src/LIBSVM.jl:18
  LIBSVM.SupportVectors(::LIBSVM.SVMModel, ::Any, ::Any) at /home/fred/.julia/packages/LIBSVM/5Z99T/src/LIBSVM.jl:27