Merge pull request #163 from baggepinnen/detect_ip

more robust detection of inplace measurement for MeasurementOop

Project.toml

@@ -1,7 +1,7 @@
 name = "LowLevelParticleFilters"
 uuid = "d9d29d28-c116-5dba-9239-57a5fe23875b"
 authors = ["baggepinnen <[email protected]>"]
-version = "3.10.0"
+version = "3.10.1"
 
 [deps]
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"

docs/src/neural_network.md

@@ -149,8 +149,8 @@ ukf = UnscentedKalmanFilter(dynamics, measurement, R1, R2, SimpleMvNormal(s0, 10
 @time sole = forward_trajectory(ekf, data.u, data.x)
 @time solu = forward_trajectory(ukf, data.u, data.x)
 
-plot(sole, plotx=false, plotxt=false, plotyh=true, plotyht=false, plotu=false, plote=true, name="EKF", layout=(nx, 1))
-plot!(solu, plotx=false, plotxt=false, plotyh=true, plotyht=false, plotu=false, plote=true, name="UKF", ploty=false, size=(1200, 1500))
+plot(sole, plotx=false, plotxt=false, plotyh=true, plotyht=false, plotu=false, plote=true, name="EKF", layout=(nx, 1), size=(1200, 1500))
+plot!(solu, plotx=false, plotxt=false, plotyh=true, plotyht=false, plotu=false, plote=true, name="UKF", ploty=false)
 DisplayAs.PNG(Plots.current()) # hide
 ```
 
@@ -180,12 +180,12 @@ Performing the estimation using the Extended Kalman Filter took
 ```julia
 using BenchmarkTools
 @btime forward_trajectory(ekf, data.u, data.x);
-  46.034 ms (77872 allocations: 123.45 MiB)
+  # 46.034 ms (77872 allocations: 123.45 MiB)
 ```
 and with the Unscented Kalman Filter
 ```julia
 @btime forward_trajectory(ukf, data.u, data.x);
-  142.608 ms (2134370 allocations: 224.82 MiB)
+  # 142.608 ms (2134370 allocations: 224.82 MiB)
 ```
 
 The EKF is a bit faster, which is to be expected. Both methods are very fast from a neural-network training perspective, but the performance will not scale favorably to very large network sizes.

src/kalman.jl

@@ -122,7 +122,7 @@ end
 function (kfm::MeasurementOop)(x,u,p,t)
     kf = kfm.kf
     mfun = measurement(kf)
-    if has_ip(mfun)
+    if kf isa UnscentedKalmanFilter{<:Any,true} || kf isa ExtendedKalmanFilter{<:Any,true}
         y = zeros(kf.ny)
         mfun(y,x,u,p,t)
         return y