Comments (2)
I would go the route recomputing the k_n, k_a
variables here. The expressions for those are very cheap to compute compared to the computations that take place inside the filter. An UKF or EKF will perform matrix factorizations that scale as
A side note,
maximum([a_n .* (1 .+ abs(c_D)) .^ b_n .+ k_offset1, 0])
is more efficiently computed using
max(a_n * (1 + abs(c_D)) ^ b_n + k_offset1, 0)
This avoids allocating an array.
I see that you use broadcasting in a lot of places where I'd expect scalars to appear, is there a reason for this? For example, why c_D = D ./ V
instead of c_D = D / V
? Using broadcasting when it's not needed increases compile times and can also fail to catch mistakes.
from lowlevelparticlefilters.jl.
Yes, when I think about it, it makes sense that more states increase the complexity even more.
Thank you also for the other recommendations ;)
from lowlevelparticlefilters.jl.
Related Issues (20)
- Typo in DAEUnscentedKalmanFilter docstring
- LowLevelParticleFilters won't compile on Julia 1.8/macOS HOT 4
- Passing dynamics noise density makes stochastic dynamics less expressive HOT 3
- Incremental precomiplation fatally broken HOT 1
- More flexible noise in UKF
- Next major version
- Compatibility to ModelingToolkit.jl? HOT 4
- Installing this package is downgrading many other packages
- Improve documentation HOT 4
- Particle filter with second order Markov model HOT 3
- loglik bug for ParticleFilter with resampling
- Does `correct!()` support `missing` samples as explained in docs? HOT 4
- Struct of Arrays and BLAS2->3 optimization HOT 1
- Dependency Deprecated - Yeppp HOT 9
- TagBot trigger issue HOT 32
- notation + documentation HOT 2
- v3.0
- Ensemble Kalman filters? HOT 2
- Support for non-uniform observations? HOT 8
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