Fix errors relating to RecursiveArrayTools, Base.stack, and GaussianDistributions.jl

Project.toml

@@ -13,7 +13,6 @@ FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 FiniteHorizonGramians = "b59a298d-d283-4a37-9369-85a9f9a111a5"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 FunctionWrappersWrappers = "77dc65aa-8811-40c2-897b-53d922fa7daf"
-GaussianDistributions = "43dcc890-d446-5863-8d1a-14597580bb8d"
 Kronecker = "2c470bb0-bcc8-11e8-3dad-c9649493f05e"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MatrixEquations = "99c1a7ee-ab34-5fd5-8076-27c950a045f4"
@@ -22,6 +21,7 @@ OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 PSDMatrices = "fe68d972-6fd8-4755-bdf0-97d4c54cefdc"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
@@ -56,7 +56,6 @@ FillArrays = "1.9"
 FiniteHorizonGramians = "0.2"
 ForwardDiff = "0.10"
 FunctionWrappersWrappers = "0.1.3"
-GaussianDistributions = "0.5"
 Kronecker = "0.5.4"
 LinearAlgebra = "1"
 MatrixEquations = "2"
@@ -65,6 +64,7 @@ OrdinaryDiffEq = "6.52"
 PSDMatrices = "0.4.7"
 PrecompileTools = "1"
 Printf = "1"
+Random = "1"
 RecipesBase = "1"
 RecursiveArrayTools = "2, 3"
 Reexport = "1"

ext/DiffEqDevToolsExt.jl

@@ -7,7 +7,7 @@ using Statistics
 using LinearAlgebra
 
 function chi2(gaussian_estimate, actual_value)
-    μ, Σ = gaussian_estimate
+    μ, Σ = mean(gaussian_estimate), cov(gaussian_estimate)
     d = length(μ)
     diff = μ - actual_value
     if iszero(Σ)

ext/RecipesBaseExt.jl

@@ -36,8 +36,8 @@ end
     x::Matrix{<:Gaussian}, y::Matrix{<:Gaussian},
 )
     @warn "This plot does not visualize any uncertainties"
-    xmeans = mean.(x)
-    ymeans = mean.(y)
+    xmeans = mean.(x)'
+    ymeans = mean.(y)'
     return xmeans, ymeans
 end
 @recipe function f(

src/ProbNumDiffEq.jl

@@ -3,11 +3,13 @@ __precompile__()
 module ProbNumDiffEq
 
 import Base:
-    copy, copy!, show, size, ndims, similar, isapprox, isequal, iterate, ==, length, zero
+    copy, copy!, show, size, ndims, similar, isapprox, isequal, iterate, ==, length, zero,
+    eltype
 
 using LinearAlgebra
-import LinearAlgebra: mul!
+import LinearAlgebra: mul!, norm_sqr
 import Statistics: mean, var, std, cov
+import Random: rand, GLOBAL_RNG, AbstractRNG
 using Printf
 using DocStringExtensions
 
@@ -33,7 +35,7 @@ using FillArrays
 using MatrixEquations
 using DiffEqCallbacks
 
-@reexport using GaussianDistributions
+# @reexport using GaussianDistributions
 
 @reexport using PSDMatrices
 import PSDMatrices: X_A_Xt, X_A_Xt!, unfactorize
@@ -69,6 +71,7 @@ export IsometricKroneckerCovariance, DenseCovariance, BlockDiagonalCovariance
 abstract type AbstractODEFilterCache <: OrdinaryDiffEq.OrdinaryDiffEqCache end
 
 include("gaussians.jl")
+export Gaussian
 
 include("priors/common.jl")
 include("priors/iwp.jl")

src/callbacks/manifoldupdate.jl

@@ -1,5 +1,5 @@
 function manifoldupdate!(cache, residualf; maxiters=100, ϵ₁=1e-25, ϵ₂=1e-15)
-    m, C = cache.x
+    m, C = mean(cache.x), cov(cache.x)
 
     # Create some caches
     @unpack SolProj, tmp, H, x_tmp = cache
@@ -11,7 +11,7 @@ function manifoldupdate!(cache, residualf; maxiters=100, ϵ₁=1e-25, ϵ₂=1e-1
     _K1, _K2 = cache.C_DxD[:, 1:d], cache.C_2DxD[1:D, 1:d]
 
     S = PSDMatrix(C.R[:, 1:d])
-    m_tmp, C_tmp = x_tmp
+    m_tmp, C_tmp = mean(x_tmp), cov(x_tmp)
 
     m_i = copy(m)
     local m_i_new, C_i_new

src/data_likelihoods/fenrir.jl

@@ -127,7 +127,7 @@ function fit_pnsolution_to_data!(
 end
 
 function measure_and_update!(x, u, H, R::PSDMatrix, cache)
-    z, S = cache.m_tmp
+    z, S = mean(cache.m_tmp), cov(cache.m_tmp)
     _matmul!(z, H, x.μ)
     z .-= u
     S = PSDMatrix(make_obscov_sqrt(x.Σ.R, H, R.R))

src/derivative_utils.jl

@@ -13,7 +13,16 @@ function calc_H!(H, integ, cache)
     elseif integ.alg isa DiagonalEK1
         calc_H_EK0!(H, integ, cache)
         OrdinaryDiffEq.calc_J!(ddu, integ, cache, true)
-        ddu_diag = Diagonal(ddu)
+        _ddu = size(ddu, 2) != d ? view(ddu, 1:d, :) : ddu
+        ddu_diag = if size(ddu, 2) == d
+            # the normal case: just extract the diagonal
+            Diagonal(_ddu)
+        else
+            # the "SecondOrderODEProblem" case: since f(ddu, du, u, p, t) we need a bit more
+            topleft = view(ddu, 1:d, 1:d)
+            topright = view(ddu, 1:d, d+1:2d)
+            BlocksOfDiagonals([[topleft[i, i] topright[i, i];] for i in 1:d])
+        end
         _matmul!(H, ddu_diag, cache.SolProj, -1.0, 1.0)
     else
         error("Unknown algorithm")

src/diffusions/calibration.jl

@@ -151,7 +151,7 @@ function local_diagonal_diffusion(cache)
     @unpack d, q, H, Qh, measurement, m_tmp = cache
     tmp = m_tmp.μ
     @unpack local_diffusion = cache
-    @assert (H == cache.E1) || (H == cache.E2)
+    @assert (H == cache.E1) || (H == cache.E2) || H isa BlocksOfDiagonals
 
     z = measurement.μ
     # HQH = H * unfactorize(Qh) * H'

src/filtering/update.jl

@@ -21,8 +21,8 @@
 For better performance, we recommend to use the non-allocating [`update!`](@ref).
 """
 function update(x::Gaussian, measurement::Gaussian, H::AbstractMatrix)
-    m, C = x
-    z, S = measurement
+    m, C = mean(x), cov(x)
+    z, S = mean(measurement), cov(measurement)
 
     K = C * H' * inv(S)
     m_new = m - K * z
@@ -31,8 +31,8 @@
     return Gaussian(m_new, C_new)
 end
 function update(x::SRGaussian, measurement::Gaussian, H::AbstractMatrix)
-    m, C = x
-    z, S = measurement
+    m, C = mean(x), cov(x)
+    z, S = mean(measurement), cov(measurement)
 
     K = C * H' * inv(S)
     m_new = m - K * z

src/gaussians.jl

@@ -1,30 +1,132 @@
 ############################################################################################
-# Useful things when working with GaussianDistributions.Gaussian
+# `Gaussian` distributions
+# Based on @mschauer's GaussianDistributions.jl
 ############################################################################################
+"""
+    Gaussian(μ, Σ) -> P
+
+Gaussian distribution with mean `μ` and covariance `Σ`. Defines `rand(P)` and `(log-)pdf(P, x)`.
+Designed to work with `Number`s, `UniformScaling`s, `StaticArrays` and `PSD`-matrices.
+
+Implementation details: On `Σ` the functions `logdet`, `whiten` and `unwhiten`
+(or `chol` as fallback for the latter two) are called.
+"""
+struct Gaussian{T,S}
+    μ::T
+    Σ::S
+    Gaussian(μ::T, Σ::S) where {T,S} = new{T,S}(μ, Σ)
+end
+Base.convert(::Type{Gaussian{T,S}}, g::Gaussian) where {T,S} =
+    Gaussian(convert(T, g.μ), convert(S, g.Σ))
+
+# Base
+Base.:(==)(g1::Gaussian, g2::Gaussian) = g1.μ == g2.μ && g1.Σ == g2.Σ
+Base.isapprox(g1::Gaussian, g2::Gaussian; kwargs...) =
+    isapprox(g1.μ, g2.μ; kwargs...) && isapprox(g1.Σ, g2.Σ; kwargs...)
 copy(P::Gaussian) = Gaussian(copy(P.μ), copy(P.Σ))
 similar(P::Gaussian) = Gaussian(similar(P.μ), similar(P.Σ))
+Base.copyto!(P::AbstractArray{<:Gaussian}, idx::Integer, el::Gaussian) =
+    (P[idx] = copy(el); P)
 function Base.copy!(dst::Gaussian, src::Gaussian)
     copy!(dst.μ, src.μ)
     copy!(dst.Σ, src.Σ)
     return dst
 end
-RecursiveArrayTools.recursivecopy(P::Gaussian) = copy(P)
-RecursiveArrayTools.recursivecopy!(dst::Gaussian, src::Gaussian) = copy!(dst, src)
-show(io::IO, g::Gaussian) = print(io, "Gaussian($(g.μ), $(g.Σ))")
-show(io::IO, ::MIME"text/plain", g::Gaussian{T,S}) where {T,S} =
-    print(io, "Gaussian{$T,$S}($(g.μ), $(g.Σ))")
+length(P::Gaussian) = length(mean(P))
 size(g::Gaussian) = size(g.μ)
-ndims(g::Gaussian) = ndims(g.μ)
+eltype(::Type{G}) where {G<:Gaussian} = G
+Base.@propagate_inbounds Base.getindex(P::Gaussian, i::Integer) =
+    Gaussian(P.μ[i], diag(P.Σ)[i])
+
+# Statistics
+mean(P::Gaussian) = P.μ
+cov(P::Gaussian) = P.Σ
+var(P::Gaussian{<:Number}) = P.Σ
+std(P::Gaussian{<:Number}) = sqrt(var(P))
 var(g::Gaussian) = diag(g.Σ)
 std(g::Gaussian) = sqrt.(diag(g.Σ))
 
+dim(P::Gaussian) = length(P.μ)
+
+# whiten(Σ::PSD, z) = Σ.σ\z
+whiten(Σ, z) = cholesky(Σ).U' \ z
+whiten(Σ::Number, z) = sqrt(Σ) \ z
+whiten(Σ::UniformScaling, z) = sqrt(Σ.λ) \ z
+
+# unwhiten(Σ::PSD, z) = Σ.σ*z
+unwhiten(Σ, z) = cholesky(Σ).U' * z
+unwhiten(Σ::Number, z) = sqrt(Σ) * z
+unwhiten(Σ::UniformScaling, z) = sqrt(Σ.λ) * z
+
+sqmahal(P::Gaussian, x) = norm_sqr(whiten(P.Σ, x - P.μ))
+
+rand(P::Gaussian) = rand(GLOBAL_RNG, P)
+rand(RNG::AbstractRNG, P::Gaussian) = P.μ + unwhiten(P.Σ, randn(RNG, typeof(P.μ)))
+rand(RNG::AbstractRNG, P::Gaussian{Vector{T}}) where {T} =
+    P.μ + unwhiten(P.Σ, randn(RNG, T, length(P.μ)))
+rand(RNG::AbstractRNG, P::Gaussian{<:Number}) =
+    P.μ + sqrt(P.Σ) * randn(RNG, typeof(one(P.μ)))
+
+_logdet(Σ, d) = logdet(Σ)
+_logdet(J::UniformScaling, d) = log(J.λ) * d
+logpdf(P::Gaussian, x) = -(sqmahal(P, x) + _logdet(P.Σ, dim(P)) + dim(P) * log(2pi)) / 2
+pdf(P::Gaussian, x) = exp(logpdf(P::Gaussian, x))
+
+Base.:+(g::Gaussian, vec) = Gaussian(g.μ + vec, g.Σ)
+Base.:+(vec, g::Gaussian) = g + vec
+Base.:-(g::Gaussian, vec) = g + (-vec)
+Base.:*(M, g::Gaussian) = Gaussian(M * g.μ, X_A_Xt(g.Σ, M))
+
+function rand_scalar(RNG::AbstractRNG, P::Gaussian{T}, dims) where {T}
+    X = zeros(T, dims)
+    for i in 1:length(X)
+        X[i] = rand(RNG, P)
+    end
+    X
+end
+
+function rand_vector(
+    RNG::AbstractRNG,
+    P::Gaussian{Vector{T}},
+    dims::Union{Integer,NTuple},
+) where {T}
+    X = zeros(T, dim(P), dims...)
+    for i in 1:prod(dims)
+        X[:, i] = rand(RNG, P)
+    end
+    X
+end
+rand(RNG::AbstractRNG, P::Gaussian, dim::Integer) = rand_scalar(RNG, P, dim)
+rand(RNG::AbstractRNG, P::Gaussian, dims::Tuple{Vararg{Int64,N}} where {N}) =
+    rand_scalar(RNG, P, dims)
+
+rand(RNG::AbstractRNG, P::Gaussian{Vector{T}}, dim::Integer) where {T} =
+    rand_vector(RNG, P, dim)
+rand(
+    RNG::AbstractRNG,
+    P::Gaussian{Vector{T}},
+    dims::Tuple{Vararg{Int64,N}} where {N},
+) where {T} = rand_vector(RNG, P, dims)
+rand(P::Gaussian, dims::Tuple{Vararg{Int64,N}} where {N}) = rand(GLOBAL_RNG, P, dims)
+rand(P::Gaussian, dim::Integer) = rand(GLOBAL_RNG, P, dim)
+
+# RecursiveArrayTools
+RecursiveArrayTools.recursivecopy(P::Gaussian) = copy(P)
+RecursiveArrayTools.recursivecopy!(dst::Gaussian, src::Gaussian) = copy!(dst, src)
+
+# Print
+show(io::IO, g::Gaussian) = print(io, "Gaussian(μ=$(g.μ), Σ=$(g.Σ))")
+show(io::IO, ::MIME"text/plain", g::Gaussian{T,S}) where {T,S} = begin
+    println(io, "Gaussian{$T,$S}(")
+    println(io, "  μ=$(g.μ),")
+    println(io, "  Σ=$(g.Σ)")
+    print(io, ")")
+end
+
 ############################################################################################
 # `SRGaussian`: Gaussians with PDFMatrix covariances
 ############################################################################################
 const SRGaussian{T,S} = Gaussian{VM,PSDMatrix{T,S}} where {VM<:AbstractVecOrMat{T}}
-Base.:*(M::AbstractMatrix, g::SRGaussian) = Gaussian(M * g.μ, X_A_Xt(g.Σ, M))
-# GaussianDistributions.whiten(Σ::PSDMatrix, z) = Σ.L\z
-
 function _gaussian_mul!(g_out::SRGaussian, M::AbstractMatrix, g_in::SRGaussian)
     _matmul!(g_out.μ, M, g_in.μ)
     fast_X_A_Xt!(g_out.Σ, g_in.Σ, M)

src/solution.jl

@@ -281,6 +281,32 @@
     u = proj * x
     return Gaussian(u.μ[idxs], diag(u.Σ)[idxs])
 end
+function (interp::ODEFilterPosterior)(
+    t::Real,
+    idxs::AbstractVector{<:Integer},
+    ::Type{deriv},
+    p,
+    continuity,
+) where {deriv}
+    q = interp.cache.q
+    dv = deriv.parameters[1]
+    proj = if deriv == Val{0}
+        interp.cache.SolProj
+    elseif dv <= q
+        interp.cache.Proj(dv)
+    else
+        throw(ArgumentError("We can only provide derivatives up to $q but you requested $dv"))
+    end
+    x = interpolate(
+        t, interp.ts, interp.x_filt, interp.x_smooth, interp.diffusions, interp.cache;
+        smoothed=interp.smooth)
+    u = proj * x
+    P = zeros(Bool, length(idxs), length(u))
+    for (i, idx) in enumerate(idxs)
+        P[i, idx] = 1
+    end
+    return P * u
+end
 function (interp::ODEFilterPosterior)(
     t::AbstractVector{<:Real},
     idxs,

test/core/filtering.jl

@@ -8,7 +8,7 @@ using LinearAlgebra
 import ProbNumDiffEq: IsometricKroneckerProduct, BlocksOfDiagonals
 import ProbNumDiffEq as PNDE
 using FillArrays
-import ProbNumDiffEq.GaussianDistributions: logpdf
+import ProbNumDiffEq: logpdf
 
 @testset "PREDICT" begin
     # Setup