PerceptronClassifier

mutable struct PerceptronClassifier <: MLJModelInterface.Probabilistic

The classical perceptron algorithm using one-vs-all for multiclass, from the Beta Machine Learning Toolkit (BetaML).

Hyperparameters:

• initial_coefficients::Union{Nothing, Matrix{Float64}}: N-classes by D-dimensions matrix of initial linear coefficients [def: nothing, i.e. zeros]
• initial_constant::Union{Nothing, Vector{Float64}}: N-classes vector of initial contant terms [def: nothing, i.e. zeros]
• epochs::Int64: Maximum number of epochs, i.e. passages trough the whole training sample [def: 1000]
• shuffle::Bool: Whether to randomly shuffle the data at each iteration (epoch) [def: true]
• force_origin::Bool: Whether to force the parameter associated with the constant term to remain zero [def: false]
• return_mean_hyperplane::Bool: Whether to return the average hyperplane coefficients instead of the final ones [def: false]
• rng::Random.AbstractRNG: A Random Number Generator to be used in stochastic parts of the code [deafult: Random.GLOBAL_RNG]

Example:

julia> using MLJ

julia> X, y        = @load_iris;

julia> modelType   = @load PerceptronClassifier pkg = "BetaML"
[ Info: For silent loading, specify `verbosity=0`. 
import BetaML ✔
BetaML.Perceptron.PerceptronClassifier

julia> model       = modelType()
PerceptronClassifier(
  initial_coefficients = nothing, 
  initial_constant = nothing, 
  epochs = 1000, 
  shuffle = true, 
  force_origin = false, 
  return_mean_hyperplane = false, 
  rng = Random._GLOBAL_RNG())

julia> mach        = machine(model, X, y);

julia> fit!(mach);
[ Info: Training machine(PerceptronClassifier(initial_coefficients = nothing, …), …).
*** Avg. error after epoch 2 : 0.0 (all elements of the set has been correctly classified)
julia> est_classes = predict(mach, X)
150-element CategoricalDistributions.UnivariateFiniteVector{Multiclass{3}, String, UInt8, Float64}:
 UnivariateFinite{Multiclass{3}}(setosa=>1.0, versicolor=>2.53e-34, virginica=>0.0)
 UnivariateFinite{Multiclass{3}}(setosa=>1.0, versicolor=>1.27e-18, virginica=>1.86e-310)
 ⋮
 UnivariateFinite{Multiclass{3}}(setosa=>2.77e-57, versicolor=>1.1099999999999999e-82, virginica=>1.0)
 UnivariateFinite{Multiclass{3}}(setosa=>3.09e-22, versicolor=>4.03e-25, virginica=>1.0)