test

Author: FredericWantiez

src/algorithms/apf.jl

@@ -7,7 +7,7 @@ mutable struct AuxiliaryParticleFilter{RS<:AbstractConditionalResampler} <: Abst
 end
 
 function AuxiliaryParticleFilter(
-    N::Integer; threshold::Real=1.0, resampler::AbstractResampler=Systematic()
+    N::Integer; threshold::Real=0., resampler::AbstractResampler=Systematic()
 )
     conditional_resampler = ESSResampler(threshold, resampler)
     return AuxiliaryParticleFilter(N, conditional_resampler, zeros(N))

test/runtests.jl

@@ -109,7 +109,9 @@ end
     _, _, data = sample(rng, model, 20)
 
     bf = BF(2^12; threshold=0.8)
+    apf = APF(2^10, threshold=1.)
     bf_state, llbf = GeneralisedFilters.filter(rng, model, bf, data)
+    _, llapf= GeneralisedFilters.filter(rng, model, apf, data)
     kf_state, llkf = GeneralisedFilters.filter(rng, model, KF(), data)
 
     xs = bf_state.filtered.particles
@@ -120,48 +122,7 @@ end
 
     # since this is log valued, we can up the tolerance
     @test llkf ≈ llbf atol = 0.1
-end
-
-@testitem "APF filter test" begin
-    using GeneralisedFilters
-    using SSMProblems
-    using StableRNGs
-    using PDMats
-    using LinearAlgebra
-    using Random: randexp
-
-    T = Float32
-    rng = StableRNG(1234)
-    σx², σy² = randexp(rng, T, 2)
-
-    # initial state distribution
-    μ0 = zeros(T, 2)
-    Σ0 = PDMat(T[1 0; 0 1])
-
-    # state transition equation
-    A = T[1 1; 0 1]
-    b = T[0; 0]
-    Q = PDiagMat([σx²; 0])
-
-    # observation equation
-    H = T[1 0]
-    c = T[0;]
-    R = [σy²;;]
-
-    # when working with PDMats, the Kalman filter doesn't play nicely without this
-    function Base.convert(::Type{PDMat{T,MT}}, mat::MT) where {MT<:AbstractMatrix,T<:Real}
-        return PDMat(Symmetric(mat))
-    end
-
-    model = create_homogeneous_linear_gaussian_model(μ0, Σ0, A, b, Q, H, c, R)
-    _, _, data = sample(rng, model, 20)
-
-    bf = APF(2^10, threshold=0.8)
-    _, llbf = GeneralisedFilters.filter(rng, model, bf, data)
-    _, llkf = GeneralisedFilters.filter(rng, model, KF(), data)
-
-    # since this is log valued, we can up the tolerance
-    @test llkf ≈ llbf atol = 2
+    @test llkf ≈ llapf atol = 2
 end
 
 @testitem "Forward algorithm test" begin