Comments (3)
Nice work. I use hmmlearn with PyCall in Julia and want to change to HMMBase. Do you have any idea when the Baum-Welch will be added to HMMBase?
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I've added an initial implementation in v0.0.11
. See the example.
There is room for performance improvements but it works well with univariate distributions (as long as fit_mle
is implemented for the distribution).
For now, initialization is up to the user.
And for some reason it does not work with multivariate observations yet. I'll investigate :-)
Edit: fixed in master
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I've made many changes in my fork.
Let me know if you're interested in a PR: I've changed many things... I apologise for that.
For example I've changed the HMM parameters \pi0
, \pi
, D
to a
and A
and B
.
Baum Welch is now tested and re-written.
There's lot of room for improvement, in particular some choices of Distributions.jl
are not so well suited for fast updating of the parameters (you have to construct distributions at each iterations and you could avoid that). But maybe they will change it in the future...
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Related Issues (20)
- Error in mle.jl? HOT 1
- Benchmarks vs MS_HMMBase & question about messages_backwards_log
- 2 state and 3 observation HMM HOT 3
- Multiple sequence with different length HOT 17
- Stable documentation is not up-to-date HOT 1
- HMM with observations as probabilities HOT 13
- viterbi(hmm, y) got "ERROR: BoundsError: attempt to access T×5 Array{Int64,2} at index [T-1, 0]" HOT 2
- Unable to fit using Multivariate LogNormal distribution HOT 2
- I can not recover the original parameters HOT 1
- Running into an argument error in Distributions when using fit_mle HOT 1
- Product of Discrete, Bernoulli HOT 1
- kmeans initialization doesn't support a user defined estimator
- Possible error in `fit_mle!`?
- Compat for Distributions.jl v0.25? HOT 1
- TagBot trigger issue HOT 4
- Improve documentation for novices HOT 1
- Example for multivariate features GMMHMM, include in docs HOT 1
- Application for new maintainer HOT 6
- Implement serialization/deserialization HOT 1
- Em Algorithm Speedup HOT 3
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