[MRG] Add UNHaP experiments and documentation #21
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@tomMoral, now would be a good time for a review, before the number of lines changes is too big :) The experiment files |
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Jul 8, 2025
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A few comments but overall LGTM
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
…ne_, alpha_, kernel_, baseline_noise
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Jul 15, 2025
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A few comment but otherwise, lgtm to merge like this
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| experiments = [ | ||
| "dicodile>=0.3", |
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Why do we need dicodile? To get the gait data?
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Yes exactly. There are a few packages we need just for the ECG and gait experiments. I'll add them here.
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
Co-authored-by: Thomas Moreau <thomas.moreau.2010@gmail.com>
vloison
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This PR is now ready to merge on my side, @tomMoral do you want to check the latest updates or is it good to go? :) |
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To do :
[x] homogenize docstring of FaDIn and UNHaP
[x] Add solvers benchmarked in the UNHaP paper
[x] Add UNHaP experiments
[x] Update README
[ ] Update pyproject.toml with optional dependencies for experiments.
[ ] More beginner-friendly tutorials.
[ ] More doc for UNHaP and its utility functions.
To do in a future PR:
[ ] Factorize UNHaP and FaDIn utility functions
This will not be so easy. For instance, during optimization, model parameters are stored as a list. And UNHaP has one more model parameter, the noise baseline. Another example: for fadin, events are floats, for unhap, they are arrays of size two. This means that between the two solvers, several functions have lots of code in common but are tricky to factorize robustly.
[x] add tests for UNHaP and FaDIn: