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[ENH] Logistic distribution #241
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Interesting - I would just have used the standard logarithm rules to cancel exps etc. Do you have an opinion on which representation is more stable, numerically? Intuitively, at least, I would think cancelling as many exps as possible is better. |
PS: no need to close PR and reopen, if you update your branch on your fork, the PR automatically gets updated. |
Previously, I incorrectly named the branch, it should be logistic, but I named the branch as weibull, sorry about that 😅😅 |
Thank you so much on your thoughts about this! Based on my manual calculation it should be
I'm uncertain myself about which representation is numerically more stable 😅😅 Previously I leaned towards the result from Wolfram Alpha as it offered a relatively simpler equation, but I am also not really sure whether it is the best solution After some careful consideration, I think using the standard logarithm rule would be more intuitive to use and to be read. I will change it accordingly 😅😅 |
Looks correct to me. I'd use |
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Thanks!
- could you kindly add the distribution to the api reference in
docs
? - not blocking, but could you kindly look at the comment re logs and cancellation?
Sure, I have adjusted the code to use the function
Sure, I have added the new class to the API reference
I am not sure I understand this, what does comment re logs and cancellation mean? Thank you! Please let me know if there is any concern or feedback, thank you so much! |
The above is what I meant, yes |
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I'm assuming you're no longer working on this, so I'm wrapping this up now.
Hi @fkiraly, I very much apologize for the delay in completing the PR without notifying you further. Thank you so much for wrapping this PR up. Please kindly let me know if there's anything I can assist with or if there are any adjustments needed 🙏🙏 |
no worries - a message would have been helpful, but it is not unusual that PRs do not get completed, and we wrap those up that are close to the finish line. Thanks for your contibutions with the scipy adapter, these are really nice! |
Reference Issues/PRs
#22
What does this implement/fix? Explain your changes.
Logistic probability distribution
Does your contribution introduce a new dependency? If yes, which one?
no
What should a reviewer concentrate their feedback on?
Did you add any tests for the change?
pip install --editable .[dev,test]
and create simple driver program to use this new distributionAny other comments?
PR checklist
For all contributions
How to: add yourself to the all-contributors file in the
skpro
root directory (not theCONTRIBUTORS.md
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- fixing a bug, or adding code logic.doc
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- reporting or diagnosing a bug (get this pluscode
if you also fixed the bug in the PR).maintenance
- CI, test framework, release.See here for full badge reference
For new estimators
docs/source/api_reference/taskname.rst
, follow the pattern.Examples
section.python_dependencies
tag and ensureddependency isolation, see the estimator dependencies guide.