Comments (2)
Hi,
Indeed, suffstats
is not implemented for MvLogNormal
in Distributions.jl.
However MvLogNormal
is implemented on top of MvNormal
[1], so the following should do the trick:
# Add this before calling HMMBase.fit_mle(...)
using Distributions
function Distributions.fit_mle(D::Type{MvLogNormal{T,Cov,Mean}}, x::AbstractMatrix) where {T,Cov,Mean}
MvLogNormal(fit_mle(MvNormal{T,Cov,Mean}, log.(x)))
end
function Distributions.fit_mle(D::Type{MvLogNormal{T,Cov,Mean}}, x::AbstractMatrix, w::AbstractVector) where {T,Cov,Mean}
MvLogNormal(fit_mle(MvNormal{T,Cov,Mean}, log.(x), w))
end
Quick test:
d = MvLogNormal(rand(2), rand(2))
# MvLogNormal{Float64,PDMats.PDiagMat{Float64,Array{Float64,1}},Array{Float64,1}}(
# dim: 2
# μ: [0.6279736031524341, 0.37199017227979225]
# Σ: [0.3469761710542945 0.0; 0.0 0.17073815831522066]
# )
fit_mle(typeof(d), rand(d, 1000))
# MvLogNormal{Float64,PDMats.PDiagMat{Float64,Array{Float64,1}},Array{Float64,1}}(
# dim: 2
# μ: [0.6259224730939991, 0.3911089992097486]
# Σ: [0.36060896050009134 0.0; 0.0 0.17540136901320552]
# )
[1] https://github.com/JuliaStats/Distributions.jl/blob/master/src/multivariate/mvlognormal.jl
from hmmbase.jl.
Perfect, thanks!
from hmmbase.jl.
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