JuliaGaussianProcesses / JuliaGaussianProcesses/AbstractGPs.jl
Improve show output for AbstractGPs objects
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good first issue
- Dominant language
- Julia
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- 287
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- 27
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Description
Right now this is disastrous mess, we should agree on what is the essential info and how to print it.
For example this is what the output of a FiniteGP returns:
AbstractGPs.FiniteGP{GP{AbstractGPs.ZeroMean{Float64}, TransformedKernel{SqExponentialKernel{Distances.Euclidean}, ScaleTransform{Float64}}}, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Diagonal{Float64, FillArrays.Fill{Float64, 1, Tuple{Base.OneTo{Int64}}}}}(
f: GP{AbstractGPs.ZeroMean{Float64}, TransformedKernel{SqExponentialKernel{Distances.Euclidean}, ScaleTransform{Float64}}}(AbstractGPs.ZeroMean{Float64}(), Squared Exponential Kernel (metric = Distances.Euclidean(0.0))
- Scale Transform (s = 0.5))
x: -10.0:0.20202020202020202:10.0
Σy: [1.0e-8 0.0 … 0.0 0.0; 0.0 1.0e-8 … 0.0 0.0; … ; 0.0 0.0 … 1.0e-8 0.0; 0.0 0.0 … 0.0 1.0e-8]
)
Contributor guide
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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 reproducing the shown FiniteGP output and inspecting the existing display methods for AbstractGPs objects. Because the issue does not specify the essential information or target format, first agree on the intended concise representation; done means the agreed output is implemented and verified for the example.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100