JuliaDataCubes / JuliaDataCubes/YAXArrays.jl
Sampling the data cube for machine learning
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 132
- Forks
- 25
- PR merge metrics
- No merged PRs in 30d
Description
I have written a simple block sampler in space. Because there is quite a lot of work going on with deep learning this seems like something this should be available somehow.
Where should something like this go?
What is the best sampling strategy on an irregular grid?
Is this a generic enough problem so that not everyone will end up writing their own sampler anyways?
Contributor guide
No contributing guide indexed for this repository
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
No source file, test, or entry point is identified in the issue. Start by locating the existing block sampler and reviewing how YAXArrays.jl handles irregular grids, then clarify where the sampler belongs and which sampling strategy and completion criteria the maintainers want.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100