Replace matrix model dynamics with scalar

test/linear-gaussian.jl

@@ -19,9 +19,7 @@ function test_algorithm(rng, algorithm, model, N_SAMPLES, Xf)
  particles = hcat([chain.trajectory.model.X for chain in chains]...)
  final_particles = particles[:, end]
 
- test = ExactOneSampleKSTest(
- final_particles, Normal(Xf.x[end].μ[1], sqrt(Xf.x[end].Σ[1, 1]))
- )
+ test = ExactOneSampleKSTest(final_particles, Normal(Xf.x[end].μ, sqrt(Xf.x[end].Σ)))
  return pvalue(test)
 end
 
@@ -30,18 +28,18 @@ end
  N_PARTICLES = 20
  N_SAMPLES = 50
 
- # Model dynamics (in matrix form, despite being one-dimensional, to work with Kalman.jl)
- Φ = [0.5;;]
- b = [0.2]
- Q = [0.1;;]
- E = LinearEvolution(Φ, Gaussian(b, Q))
+ # Model dynamics
+ a = 0.5
+ b = 0.2
+ q = 0.1
+ E = LinearEvolution(a, Gaussian(b, q))
 
- H = [1.0;;]
- R = [0.1;;]
+ H = 1.0
+ R = 0.1
  Obs = LinearObservationModel(H, R)
 
- x0 = [0.0]
- P0 = [1.0;;]
+ x0 = 0.0
+ P0 = 1.0
  G0 = Gaussian(x0, P0)
 
  M = LinearStateSpaceModel(E, Obs)
@@ -86,7 +84,7 @@ end
 
  AdvancedPS.isdone(::LinearGaussianModel, step) = step > T
 
- params = LinearGaussianParams(Φ[1, 1], b[1], Q[1, 1], H[1, 1], R[1, 1], x0[1], P0[1, 1])
+ params = LinearGaussianParams(a, b, q, H, R, x0, P0)
  model = LinearGaussianModel(params)
 
  @testset "PGAS" begin