dmetivie / expectationmaximization.jl Goto Github PK

using ExpectationMaximization

# Try to fit a beta mixture.
N = 50_000
α₁ = 10
β₁ = 5
α₂ = 5
β₂ = 10
π = 0.3

# Mixture Model of two betas.
mix_true = MixtureModel([Beta(α₁, β₁), Beta(α₂, β₂)], [π, 1 - π]) 

# Generate N samples from the mixture.
y = rand(mix_true, N)
histogram(y)

# Initial guess.
mix_guess = MixtureModel([Beta(1, 1), Beta(1, 1)], [0.5, 1 - 0.5])
test = rand(mix_guess, N)

# Fit the MLE with the EM algorithm:
mix_mle = fit_mle(mix_guess, y)
# ERROR: suffstats is not implemented for (Beta{Float64}, Vector{Float64}, Vector{Float64}).

julia> X = randn(10);

julia> mix = MixtureModel([Normal(100, 0.001), Normal(200, 0.001)], [0.5, 0.5]);

julia> logpdf.(components(mix), X')
2×10 Matrix{Float64}:
 -4.92479e9   -4.97741e9   -5.02964e9   -5.15501e9   -5.05792e9   …  -5.16391e9   -4.88617e9   -4.93348e9   -5.09162e9
 -1.98493e10  -1.99548e10  -2.00592e10  -2.03088e10  -2.01157e10     -2.03265e10  -1.97717e10  -1.98667e10  -2.01828e10

julia> fit_mle(mix, X)
ERROR: DomainError with NaN:
Normal: the condition σ >= zero(σ) is not satisfied.
Stacktrace:
  [1] #371
    @ ~/.julia/dev/Distributions/src/univariate/continuous/normal.jl:37 [inlined]
  [2] check_args
    @ ~/.julia/dev/Distributions/src/utils.jl:89 [inlined]
  [3] #Normal#370
    @ ~/.julia/dev/Distributions/src/univariate/continuous/normal.jl:37 [inlined]
  [4] Normal
    @ ~/.julia/dev/Distributions/src/univariate/continuous/normal.jl:36 [inlined]
  [5] fit_mle
    @ ~/.julia/dev/Distributions/src/univariate/continuous/normal.jl:229 [inlined]
  [6] fit_mle(::Type{Normal{Float64}}, x::Vector{Float64}, w::Vector{Float64}; mu::Float64, sigma::Float64)
    @ Distributions ~/.julia/dev/Distributions/src/univariate/continuous/normal.jl:256
  [7] fit_mle
    @ ~/.julia/dev/Distributions/src/univariate/continuous/normal.jl:253 [inlined]
  [8] fit_mle
    @ ~/.julia/dev/ExpectationMaximization/src/that_should_be_in_Distributions.jl:17 [inlined]
  [9] (::ExpectationMaximization.var"#2#3"{Vector{Normal{Float64}}, Vector{Float64}, Matrix{Float64}})(k::Int64)
    @ ExpectationMaximization ./none:0
 [10] iterate(::Base.Generator{Vector{Any}, DualNumbers.var"#1#3"})
    @ Base ./generator.jl:47 [inlined]
 [11] collect_to!(dest::AbstractArray{T}, itr::Any, offs::Any, st::Any) where T
    @ Base ./array.jl:890 [inlined]
 [12] collect_to_with_first!(dest::AbstractArray, v1::Any, itr::Any, st::Any)
    @ Base ./array.jl:868 [inlined]
 [13] collect(itr::Base.Generator{UnitRange{Int64}, ExpectationMaximization.var"#2#3"{Vector{…}, Vector{…}, Matrix{…}}})
    @ Base ./array.jl:842
 [14] fit_mle!(α::Vector{…}, dists::Vector{…}, y::Vector{…}, method::ClassicEM; display::Symbol, maxiter::Int64, atol::Float64, robust::Bool)
    @ ExpectationMaximization ~/.julia/dev/ExpectationMaximization/src/classic_em.jl:48
 [15] fit_mle!
    @ ~/.julia/dev/ExpectationMaximization/src/classic_em.jl:14 [inlined]
 [16] fit_mle(::MixtureModel{…}, ::Vector{…}; method::ClassicEM, display::Symbol, maxiter::Int64, atol::Float64, robust::Bool,
 infos::Bool)
    @ ExpectationMaximization ~/.julia/dev/ExpectationMaximization/src/fit_em.jl:30
 [17] fit_mle(::MixtureModel{Univariate, Continuous, Normal{Float64}, Categorical{Float64, Vector{Float64}}}, ::Vector{Float64})
    @ ExpectationMaximization ~/.julia/dev/ExpectationMaximization/src/fit_em.jl:12
 [18] top-level scope
    @ REPL[8]:1
Some type information was truncated. Use `show(err)` to see complete types.