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This repository has been archived by the owner on Jun 14, 2023. It is now read-only.
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.
The text was updated successfully, but these errors were encountered:
rbeeli
changed the title
Example for GMMHMM, include in docs
Example for multivariate features GMMHMM, include in docs
Jun 13, 2022
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 = [0.90.1; 0.10.9]
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.
The text was updated successfully, but these errors were encountered: