Adding a Link object

.gitignore

@@ -3,6 +3,7 @@
 *.jl.mem
 .DS_Store
 /Manifest.toml
+test/Manifest.toml
 /dev/
 /docs/build/
 /docs/site/

Project.toml

@@ -8,9 +8,11 @@ AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 
 [compat]
 AbstractGPs = "0.2"
 Distributions = "0.19, 0.20, 0.21, 0.22, 0.23"
 Functors = "0.1"
+StatsFuns = "0.9"
 julia = "1.3"

src/GPLikelihoods.jl

@@ -4,13 +4,16 @@ using Distributions
 using AbstractGPs
 using Random
 using Functors
+using StatsFuns: logistic
 
 import Distributions
 
 export GaussianLikelihood, PoissonLikelihood
+export Link, LogisticLink
 
+# Links
+include(joinpath("likelihoods", "link.jl"))
 # Likelihoods
-include("likelihoods/gaussian.jl")
-include("likelihoods/poisson.jl")
-
+include(joinpath("likelihoods", "gaussian.jl"))
+include(joinpath("likelihoods", "poisson.jl"))
 end # module

src/likelihoods/gaussian.jl

@@ -1,7 +1,7 @@
 """
     GaussianLikelihood(σ²)
 
-Gaussian likelihood with `σ²` variance. This is to be used if we assume that the 
+Gaussian likelihood with `σ²` variance. This is to be used if we assume that the
 uncertainity associated with the data follows a Gaussian distribution.
 
 ```math
@@ -10,13 +10,15 @@ uncertainity associated with the data follows a Gaussian distribution.
 On calling, this would return a normal distribution with mean `f` and variance σ².
 """
 struct GaussianLikelihood{T<:Real}
-    σ²::T
+    σ²::Vector{T}
 end
 
 GaussianLikelihood() = GaussianLikelihood(1e-6)
 
+GaussianLikelihood(σ²::Real) = GaussianLikelihood([σ²])
+
 @functor GaussianLikelihood
 
-(l::GaussianLikelihood)(f::Real) = Normal(f, sqrt(l.σ²))
+(l::GaussianLikelihood)(f::Real) = Normal(f, sqrt(first(l.σ²)))
 
-(l::GaussianLikelihood)(fs::AbstractVector{<:Real}) = MvNormal(fs, sqrt(l.σ²))
+(l::GaussianLikelihood)(fs::AbstractVector{<:Real}) = MvNormal(fs, sqrt(first(l.σ²)))

src/likelihoods/link.jl

@@ -0,0 +1,21 @@
+abstract type AbstractLink end
+
+(f::AbstractLink)(x) = apply(f, x)
+
+struct Link{F} <: AbstractLink
+    f::F
+end
+
+apply(l::Link, x) = l.f(x)
+
+struct LogisticLink{T<:Real} <: AbstractLink
+    λ::Vector{T}
+end
+
+LogisticLink() = LogisticLink(1.0)
+
+LogisticLink(λ::Real) = LogisticLink([λ])
+
+@functor LogisticLink
+
+apply(l::LogisticLink, x) = first(l.λ) * logistic(x)

src/likelihoods/poisson.jl

@@ -4,8 +4,16 @@
 Poisson likelihood with rate as exponential of samples from GP `f`. This is to be used if
 we assume that the uncertainity associated with the data follows a Poisson distribution.
 """
-struct PoissonLikelihood end
+struct PoissonLikelihood{L<:AbstractLink}
+    link::L
+end
 
-(l::PoissonLikelihood)(f::Real) = Poisson(exp(f))
+PoissonLikelihood() = PoissonLikelihood(Link(exp))
 
-(l::PoissonLikelihood)(fs::AbstractVector{<:Real}) = Product(Poisson.(exp.(fs)))
+PoissonLikelihood(f::Function)  = PoissonLikelihood(Link(f))
+
+@functor PoissonLikelihood
+
+(l::PoissonLikelihood)(f::Real) = Poisson(l.link(f))
+
+(l::PoissonLikelihood)(fs::AbstractVector{<:Real}) = Product(Poisson.(l.link.(fs)))

test/Project.toml

@@ -3,10 +3,12 @@ AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 AbstractGPs = "0.2"
 Distributions = "0.19, 0.20, 0.21, 0.22, 0.23"
 Functors = "0.1"
+StatsFuns = "0.9"
 julia = "1.3"

test/likelihoods/gaussian.jl

@@ -10,4 +10,8 @@
     @test typeof(lik(rand(rng, lfgp.fx))) <: Distribution
     @test length(rand(rng, lik(rand(rng, lfgp.fx)))) == 10
     @test keys(Functors.functor(lik)[1]) == (:σ²,)
+
+    # Test default constructore
+    l = GaussianLikelihood()
+    @test l.σ² == [1e-6]
 end

test/likelihoods/link.jl

@@ -0,0 +1,13 @@
+@testset "link" begin
+    using GPLikelihoods: AbstractLink, Link, LogisticLink
+    f = sin
+    l = Link(f)
+    x = rand()
+    @test l(x) == f(x)
+
+    λ = 2.0
+    l = LogisticLink(λ)
+    @test l(x) == λ * logistic(x)
+    l = LogisticLink()
+    @test l(x) == logistic(x)
+end

test/likelihoods/poisson.jl

@@ -6,8 +6,12 @@
     lik = PoissonLikelihood()
     lgp = LatentGP(gp, lik, 1e-5)
     lfgp = lgp(x)
-    
+
     @test typeof(lik(rand(rng, lfgp.fx))) <: Distribution
     @test length(rand(rng, lik(rand(rng, lfgp.fx)))) == 10
-    @test Functors.functor(lik)[1] == ()
+    @test lik.link == Link(exp)
+    @test Functors.functor(lik)[1] == (link = Link{typeof(exp)}(exp),)
+
+    lik = PoissonLikelihood(LogisticLink())
+    @test lik.link isa LogisticLink
 end

test/runtests.jl

@@ -4,12 +4,14 @@ using Test
 using Random
 using Functors
 using Distributions
+using StatsFuns
 
 @testset "GPLikelihoods.jl" begin
 
     @testset "likelihoods" begin
-        include("likelihoods/gaussian.jl")
-        include("likelihoods/poisson.jl")
+        include(joinpath("likelihoods", "gaussian.jl"))
+        include(joinpath("likelihoods", "poisson.jl"))
+        include(joinpath("likelihoods", "link.jl"))
     end
 
 end