Comments (3)
basnijholt
commented on July 17, 2024
originally posted by Bas Nijholt (@basnijholt) at 2018-07-09T16:40:11.612Z on GitLab
If good enough == better than.
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basnijholt
commented on July 17, 2024
originally posted by Joseph Weston (@jbweston) at 2018-07-09T16:57:09.452Z on GitLab
good enough == as good as?
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basnijholt
commented on July 17, 2024
originally posted by Jorn Hoofwijk (@Jorn) at 2018-07-13T07:59:33.461Z on GitLab
I think good enough == better than the Learner2d.
- Because right now the learner2d takes all the points into account when computing the loss for each triangle. Although this is something we want to lose, this has some benefits, as you can for example, take the second derivative of the function into account (or as the default loss is currently implemented, taking the deviation from the linear interpolation + gradient)
- Also I am not sure if people have written custom loss functions, which are going to be different for the LearnerND, so it is not a drop-in replacement.
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Related Issues (20)
- make triangulation tests stronger with more randomness HOT 1
- 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
- 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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