Skip to content

Add _sum for KernelSum{<:AbstractVector} and associated tests #547

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
theogf merged 4 commits into from
Feb 8, 2024
Merged

Add _sum for KernelSum{<:AbstractVector} and associated tests #547

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
f16b783
add sum for vectors and add tests for it
theogf Feb 6, 2024
8bab761
Update test/kernels/kernelsum.jl
theogf Feb 7, 2024
a80ce21
Merge branch 'master' into tgf/fix-sum
theogf Feb 7, 2024
42316d9
Merge branch 'master' into tgf/fix-sum
theogf Feb 7, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions src/kernels/kernelsum.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,7 @@ Base.length(k::KernelSum) = length(k.kernels)

_sum(f, ks::Tuple, args...) = f(first(ks), args...) + _sum(f, Base.tail(ks), args...)
_sum(f, ks::Tuple{Tx}, args...) where {Tx} = f(only(ks), args...)
_sum(f, ks::AbstractVector, args...) = sum(k -> f(k, args...), ks)
Copy link
Member

@devmotion devmotion Feb 7, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Codecov says the line is not covered by tests?

Copy link
Member Author

@theogf theogf Feb 7, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I see it being covered? https://app.codecov.io/gh/JuliaGaussianProcesses/KernelFunctions.jl/pull/547?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=checks&utm_campaign=pr+comments&utm_term=JuliaGaussianProcesses#431d7fc89a2f6028dfd213ee63091cca-L49

Copy link
Member

@devmotion devmotion Feb 7, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Strange, the Github annotation was complaining about this line.

Copy link
Member

@devmotion devmotion Feb 7, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I can still see it here: https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/pull/547/files

Copy link
Member

@devmotion devmotion Feb 7, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

But I trust the codecov webpage more, maybe the annotation is just outdated.


(κ::KernelSum)(x, y) = _sum((k, x, y) -> k(x, y), κ.kernels, x, y)

Expand Down
13 changes: 8 additions & 5 deletions test/kernels/kernelsum.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,21 +2,24 @@
k1 = LinearKernel()
k2 = SqExponentialKernel()
k = KernelSum(k1, k2)
@test k == KernelSum([k1, k2]) == KernelSum((k1, k2))
kvec = KernelSum([k1, k2])
@test k == kvec == KernelSum((k1, k2))
for (_k1, _k2) in Iterators.product(
(k1, KernelSum((k1,)), KernelSum([k1])), (k2, KernelSum((k2,)), KernelSum([k2]))
)
@test k == _k1 + _k2
@test kvec == _k1 + _k2
end
@test length(k) == 2
@test repr(k) == (
@test length(k) == length(kvec) == 2
@test repr(k) ==
repr(kvec) ==
"Sum of 2 kernels:\n" *
"\tLinear Kernel (c = 0.0)\n" *
"\tSquared Exponential Kernel (metric = Euclidean(0.0))"
)
"\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))"

# Standardised tests.
test_interface(k, Float64)
test_interface(kvec, Float64)
test_interface(ConstantKernel(; c=1.5) + WhiteKernel(), Vector{String})
test_ADs(x -> KernelSum(SqExponentialKernel(), LinearKernel(; c=exp(x[1]))), rand(1))
test_interface_ad_perf(2.4, StableRNG(123456)) do c
Expand Down