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Home Page: https://iastro-pt.github.io/gpyrn/
License: MIT License
Modelling stellar activity signals with Gaussian process regression networks
Home Page: https://iastro-pt.github.io/gpyrn/
License: MIT License
Update the simple examples and add them to the docs.
Hello ! It's me again :)
I just wanted to know if there is a way to not use weighting just as we do for standard Gaussian Process Regression in the case of a single dataset ?
I mean, this is possible by using a constant and fixed weighting but my question is can we use no weighting at all?
Cheers.
Paul
Due to scipy's requirements we are "stuck" with python = ">=3.8, <3.11"
>The current project's Python requirement (>3.0,<3.11) is not compatible with some of the required packages Python requirement:
- scipy requires Python >=3.8,<3.11, so it will not be satisfied for Python >3.0,<3.8
Because no versions of scipy match >1.8.0,<2.0.0
and scipy (1.8.0) requires Python >=3.8,<3.11, scipy is forbidden.
So, because gpyrn depends on scipy (^1.8.0), version solving failed.
at ~/.local/share/pypoetry/venv/lib/python3.8/site-packages/poetry/puzzle/solver.py:241 in _solve
237│ packages = result.packages
238│ except OverrideNeeded as e:
239│ return self.solve_in_compatibility_mode(e.overrides, use_latest=use_latest)
240│ except SolveFailure as e:
→ 241│ raise SolverProblemError(e)
242│
243│ results = dict(
244│ depth_first_search(
245│ PackageNode(self._package, packages), aggregate_package_nodes
• Check your dependencies Python requirement: The Python requirement can be specified via the `python` or `markers` properties
For scipy, a possible solution would be to set the `python` property to ">=3.8,<3.11"
https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies,
https://python-poetry.org/docs/dependency-specification/#using-environment-markers
Should we go with more recent python versions or should we search for a scipy version that allows to use older python versions @j-faria ?
Maybe it's worth trying other minimization algorithms, depending on cost.
Hello João ! Hello Team !
Wonderful work.
I just wanted to know how do you optimize the hyperparameters when using several datasets with MCMC or even with the default optimization when doing gprn.optimize
? I've tried to mimic the way it used to be with a single parameter but I didn't succeed.
Cheers !
Paul.
PS: I really think this could be very useful for my research so I will be very interested in having a discussion with you guys or even to collaborate.
There is a problem with re-writing muF
in this line, that only applies to more than one node
Line 631 in 9682cba
This is fixed in the dev
branch.
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