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Merge duplicate Ag/eks_imlplementation into original ag/eks-implementation (with new commits) #84

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merged 1 commit into from
Nov 12, 2020
Merged

Merge duplicate Ag/eks_imlplementation into original ag/eks-implementation (with new commits) #84

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7 changes: 6 additions & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,9 +9,14 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
GaussianProcesses = "891a1506-143c-57d2-908e-e1f8e92e6de9"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
ScikitLearn = "3646fa90-6ef7-5e7e-9f22-8aca16db6324"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

[extras]
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"

[targets]
test = ["Test", "Plots"]
16 changes: 0 additions & 16 deletions src/EKP.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -72,14 +72,6 @@ function EKObj(parameters::Array{FT, 2},
process::P;
Δt=FT(1)) where {FT<:AbstractFloat, P<:Process}

# Throw an error if an attempt is made to instantiate a `Sampler` EKObj
# EK Sampler implementation is not finalized yet, so its use is prohibited
# TODO: Finalize EKS implementation (can be done as soon as we know the
# correct EKS update equation, which apparently is different from
# Eq. (2.8) in Cleary et al. (2019))
# err_msg = "Ensemble Kalman Sampler is not fully implemented yet. Use Ensemble Kalman Inversion instead."
# typeof(process) != Sampler{FT} || error(err_msg)

# ensemble size
N_ens = size(parameters)[1]
IT = typeof(N_ens)
Expand Down Expand Up @@ -252,11 +244,6 @@ function update_ensemble!(ek::EKObj{FT, IT, Sampler{FT}}, g) where {FT, IT}

noise = MvNormal(u_cov)


###########################################################################
############### TODO: Implement correct equation here ################
###########################################################################

implicit = (1 * Matrix(I, size(u)[2], size(u)[2]) + Δt * (ek.process.prior_cov' \ u_cov')') \
(u'
.- Δt * ( u' .- u_mean) * D
Expand All @@ -265,9 +252,6 @@ function update_ensemble!(ek::EKObj{FT, IT, Sampler{FT}}, g) where {FT, IT}

u = implicit' + sqrt(2*Δt) * rand(noise, N_ens)'

###########################################################################
###########################################################################

# store new parameters (and observations)
push!(ek.u, u) # N_ens x N_params
push!(ek.g, g) # N_ens x N_data
Expand Down