Refactor UKF Components to use linearize() #635
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pnkraemer
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pnkraemer
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Feb 11, 2022
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| compute_jacobian_at = ( | ||
| _linearise_at | ||
| if _linearise_at is not None | ||
| else randvars.Constant(realization) | ||
| ) | ||
| linearized_model = self.linearize(t=t, at_this_rv=compute_jacobian_at) | ||
| return linearized_model.forward_realization( | ||
| realization=realization, | ||
| t=t, | ||
| dt=dt, | ||
| compute_gain=compute_gain, | ||
| _diffusion=_diffusion, | ||
| ) |
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very soon, this whole class can go, and the linearization is done from the outside in, e.g., an ExtendedKalman() object.
Codecov Report
@@ Coverage Diff @@
## main #635 +/- ##
=======================================
Coverage 89.83% 89.84%
=======================================
Files 186 186
Lines 7063 7020 -43
Branches 1109 1104 -5
=======================================
- Hits 6345 6307 -38
+ Misses 472 468 -4
+ Partials 246 245 -1
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pnkraemer
commented
Feb 15, 2022
| @@ -1,264 +0,0 @@ | |||
| """Unscented Transform.""" | |||
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the unscented transform can finally go
tests/test_filtsmooth/test_gaussian/test_approx/_linearization_test_interface.py
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schmidtjonathan
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Feb 15, 2022
…ssian/approx/_interface.py
pnkraemer
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Feb 15, 2022
pnkraemer
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Feb 15, 2022
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In a Nutshell
With this PR, forward and backward propagation in UKFComponents is handled via explicit linearisation.
Detailed Description
Instead of implementing forward* and backward*, the UKF now only uses a linearise() method, which handles the forward and backward propagation.
This makes a bunch of code obsolete, and simplifies further refactoring steps mentioned in #627.