Fit CategoricalHMM with available data?
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- Python
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Description
I have walked through the example usage in： https://probml.github.io/dynamax/notebooks/hmm/casino_hmm_learning.html
However, the params and promps are all generated by initialize function in the example, and if I have ready-to-use lists, one input lists (X in general ML), and one label list (y in general ML), how could I use the fit function?
I know this question could be naive, but I'm relatively new to Python. I greatly appreciate someone could help.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
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Research direction
Start with the linked casino HMM learning notebook and the CategoricalHMM fit API. Compare the example’s initialize-generated params and prompts with the reported X and y lists. Done means the supported usage for ready-to-use inputs and labels is documented or clearly explained.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100