Open rbeeli opened 2 years ago
Hi,
This is currently unsupported out-of-the-box as the MixtureModel
type from Distributions.jl does not implement fit_mle
. Something like this could work:
using Distributions
using HMMBase
function Distributions.fit_mle(::Type{MixtureModel{Univariate, Continuous, Normal{Float64}, Categorical{Float64, Vector{Float64}}}}, x::Matrix, w::Vector)
# Implement ML estimator here and return fitted mixture model.
end
A = [0.9 0.1; 0.1 0.9]
B = [
MixtureModel([Normal(0, 1), Normal(1, 1)]),
MixtureModel([Normal(10, 1), Normal(11, 1)])
]
hmm = HMM(A, B)
y = rand(hmm, 500)
fit_mle(hmm, y)
Hi,
do you have an example for a multivariate features Gaussian Mixture Model HMM, similar to GMMHMM from the hmmlearn Python package? Extending the docs with such an example would help a lot.
Many thanks.