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**BREAKING** improve structs & constructors #306

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7075809
be more specific about what types work
st-- Mar 30, 2022
f168433
Update src/finite_gp_projection.jl
st-- Mar 30, 2022
ca3cfba
add test
st-- Mar 31, 2022
0ee4a39
add constructor for UniformScaling
st-- Mar 31, 2022
d6fc701
Update test/finite_gp_projection.jl
st-- Mar 31, 2022
1b74c95
Merge branch 'master' into constructors
st-- Mar 31, 2022
c56c412
Merge branch 'master' into constructors
st-- Mar 31, 2022
213b346
protect line
st-- Apr 1, 2022
ac78254
Merge branch 'master' into constructors
st-- Apr 1, 2022
28fbcfb
Merge branch 'master' into constructors
yebai May 28, 2025
ddbd5f0
Create ad.jl
yebai May 28, 2025
3f9897e
Update AbstractGPs.jl
yebai May 28, 2025
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3 changes: 2 additions & 1 deletion src/finite_gp_projection.jl
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Expand Up @@ -4,7 +4,8 @@
The finite-dimensional projection of the AbstractGP `f` at `x`. Assumed to be observed under
Gaussian noise with zero mean and covariance matrix `Σy`
"""
struct FiniteGP{Tf<:AbstractGP,Tx<:AbstractVector,TΣ} <: AbstractMvNormal
struct FiniteGP{Tf<:AbstractGP,Tx<:AbstractVector,TΣ<:AbstractMatrix{<:Real}} <:
AbstractMvNormal
f::Tf
x::Tx
Σy::TΣ
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9 changes: 9 additions & 0 deletions test/finite_gp_projection.jl
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Expand Up @@ -243,6 +243,15 @@ end
first(FiniteDifferences.grad(central_fdm(3, 1), Base.Fix1(logpdf, fx), y))
@test Distributions.sqmahal!(r, fx, Y) ≈ Distributions.sqmahal(fx, Y)
end

@testset "FiniteGP with UniformScaling" begin
f = GP(SqExponentialKernel())
fx = f(rand(10), 2.0*I)
# for now, just check that it runs
_ = mean(fx)
_ = mean_and_cov(fx)
_ = rand(fx)
end
end

@testset "Docs" begin
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