Guided Filter Implementation

GeneralisedFilters/src/GeneralisedFilters.jl

@@ -54,7 +54,7 @@ function initialise(model, alg; kwargs...)
     return initialise(default_rng(), model, alg; kwargs...)
 end
 
-function predict(model, alg, step, filtered; kwargs...)
+function predict(model, alg, step, filtered, observation; kwargs...)
     return predict(default_rng(), model, alg, step, filtered; kwargs...)
 end
 
@@ -108,7 +108,7 @@ function step(
     callback::Union{AbstractCallback,Nothing}=nothing,
     kwargs...,
 )
-    state = predict(rng, model, alg, iter, state; kwargs...)
+    state = predict(rng, model, alg, iter, state, observation; kwargs...)
     isnothing(callback) ||
         callback(model, alg, iter, state, observation, PostPredict; kwargs...)
 
@@ -132,10 +132,12 @@ include("models/discrete.jl")
 include("models/hierarchical.jl")
 
 # Filtering/smoothing algorithms
-include("algorithms/bootstrap.jl")
+include("algorithms/particles.jl")
+# include("algorithms/bootstrap.jl")
 include("algorithms/kalman.jl")
 include("algorithms/forward.jl")
 include("algorithms/rbpf.jl")
+# include("algorithms/guided.jl")
 
 # Unit-testing helper module
 include("GFTest/GFTest.jl")

GeneralisedFilters/src/algorithms/forward.jl

@@ -14,7 +14,8 @@ function predict(
     model::DiscreteStateSpaceModel{T},
     filter::ForwardAlgorithm,
     step::Integer,
-    states::AbstractVector;
+    states::AbstractVector,
+    observation;
     kwargs...,
 ) where {T}
     P = calc_P(model.dyn, step; kwargs...)

GeneralisedFilters/src/algorithms/kalman.jl

@@ -1,6 +1,7 @@
 export KalmanFilter, filter, BatchKalmanFilter
 using GaussianDistributions
 using CUDA: i32
+import LinearAlgebra: Symmetric
 
 export KalmanFilter, KF, KalmanSmoother, KS
 
@@ -18,9 +19,10 @@ end
 function predict(
     rng::AbstractRNG,
     model::LinearGaussianStateSpaceModel,
-    filter::KalmanFilter,
+    algo::KalmanFilter,
     step::Integer,
-    filtered::Gaussian;
+    filtered::Gaussian,
+    observation=nothing;
     kwargs...,
 )
     μ, Σ = GaussianDistributions.pair(filtered)
@@ -30,7 +32,7 @@ end
 
 function update(
     model::LinearGaussianStateSpaceModel,
-    filter::KalmanFilter,
+    algo::KalmanFilter,
     step::Integer,
     proposed::Gaussian,
     obs::AbstractVector;
@@ -42,12 +44,9 @@ function update(
     # Update state
     m = H * μ + c
     y = obs - m
-    S = H * Σ * H' + R
+    S = Symmetric(H * Σ * H' + R)
     K = Σ * H' / S
 
-    # HACK: force the covariance to be positive definite
-    S = (S + S') / 2
-
     filtered = Gaussian(μ + K * y, Σ - K * H * Σ)
 
     # Compute log-likelihood
@@ -75,7 +74,8 @@ function predict(
     model::LinearGaussianStateSpaceModel{T},
     algo::BatchKalmanFilter,
     step::Integer,
-    state::BatchGaussianDistribution;
+    state::BatchGaussianDistribution,
+    observation;
     kwargs...,
 ) where {T}
     μs, Σs = state.μs, state.Σs
@@ -175,7 +175,7 @@ end
 function smooth(
     rng::AbstractRNG,
     model::LinearGaussianStateSpaceModel{T},
-    alg::KalmanSmoother,
+    algo::KalmanSmoother,
     observations::AbstractVector;
     t_smooth=1,
     callback=nothing,
@@ -190,7 +190,7 @@ function smooth(
     back_state = filtered
     for t in (length(observations) - 1):-1:t_smooth
         back_state = backward(
-            rng, model, alg, t, back_state, observations[t]; states_cache=cache, kwargs...
+            rng, model, algo, t, back_state, observations[t]; states_cache=cache, kwargs...
         )
     end
 
@@ -200,7 +200,7 @@ end
 function backward(
     rng::AbstractRNG,
     model::LinearGaussianStateSpaceModel{T},
-    alg::KalmanSmoother,
+    algo::KalmanSmoother,
     iter::Integer,
     back_state,
     obs;