tslearn-team / tslearn-team/tslearn
GP module
- Dominant language
- Python
- Stars
- 3.2k
- Forks
- 384
- Avg merge
- 3d 12h
- Merged PRs (30d)
- 11
Description
Support for gaussian process may be a great addition to this library
e.g. normalizing sparse/irregular time series, uncertainty estimation, etc..
Regression/classification can probably be easily done similarly to the svm/svc module using sklearn, but here is an example of a more sophisticated gp library for inspiration: https://github.com/cornellius-gp/gpytorch
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.
- Open a pull request that references the issue number.
Research direction
Review the open pull request #241 first, then compare its proposed scope with the existing SVM/SVC module and the sklearn and gpytorch references in the issue. The work is done when the project has an agreed, tested Gaussian process module covering the selected regression or classification use cases.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch, scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100