tensorflow / tensorflow/probability
Contributing Faddeeva (complex erfcx)
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Description
We would be interested to have the Faddeeva function available (erfcx for complex arguments) and would like to implement and then contribute it (as we will have a GSoC student).
So the questions
- is there already any work done in this direction?
- is that contribution to TFP possible? any specific ideas/requirements?
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
Start by reviewing the existing issue discussion and checking whether related Faddeeva or complex erfcx work already exists in TensorFlow Probability. Clarify the contribution requirements and intended API before implementation; done would mean an accepted Faddeeva function contribution.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100