Comments (1)
basnijholt
commented on July 17, 2024
originally posted by Jorn Hoofwijk ([gitlab:@Jorn)](https://gitlab.kwant-project.org/Jorn](https://gitlab.kwant-project.org/Jorn))) at 2018-07-13T07:48:30.616Z on GitLab
Joe and I discussed this yesterday (in real life), we made the tests stronger, with all possible special points (like on an edge, collinear with an edge, coplanar with a face, on a face, inside and outside, etcetera) and instead removed some randomness to make the tests easier to understand.
I do see that applying a transform could be interesting to do, but then for each case we should watch out that it is actually still correct, because a triangulation may change when scaling x and y with different factors.
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Related Issues (20)
- learner tests fail HOT 2
- use a ItemSortedDict for the loss in the LearnerND
- divide by zero warnings in LearnerND
- Issues that can potentially be closed
- Improvements to plotting of the LearnerND
- Learner.load does not raise an exception if the provided filename was not found HOT 5
- Specify an API for defining the scale of point
- (LearnerND) use direct neighbours in loss
- (LearnerND) add advanced usage example HOT 1
- Document and test loss function signatures HOT 4
- Balancing learner does not work with Integrator learner HOT 5
- make triangulation tests stronger with more randomness HOT 1
- learner tests fail HOT 4
- deprecate Learner2D HOT 3
- use a ItemSortedDict for the loss in the LearnerND
- suggested points lie outside of domain HOT 2
- use a ItemSortedDict for the loss in the LearnerND
- Specify an API for defining the scale of point
- Runners should tell learner about remaining points at end of run HOT 1
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from adaptive.