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means.jl
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means.jl
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module TestMeans
using GaussianProcesses, Calculus
using Test, Statistics, Random
Random.seed!(1)
@testset "Means" begin
n = 5 # number of observations
d = 4 # dimension
D = 3 # degree of polynomial mean function
means = [MeanZero(),
MeanConst(3.0),
MeanLin(rand(d)),
MeanPoly(rand(d, D)),
MeanPeriodic(randn(d), randn(d), randn(d)),
MeanConst(3.0) * MeanLin(rand(d)),
MeanLin(rand(d)) + MeanPoly(rand(d, D))]
X = rand(d, n)
x = view(X, :, 1)
@testset "Mean $(typeof(m))" for m in means
println("\tTesting ", nameof(typeof(m)), "...")
means = mean(m, X)
params = GaussianProcesses.get_params(m)
@testset "Run" begin
mean(m, x)
GaussianProcesses.grad_mean(m, x)
GaussianProcesses.grad_stack(m, X)
show(devnull, m)
GaussianProcesses.get_param_names(m)
set_params!(m, params)
end
@testset "Consistency" begin
@test means ≈ invoke(mean, Tuple{GaussianProcesses.Mean, Matrix{Float64}}, m, X)
end
@testset "Gradients" begin
@testset "Observation #$i" for i in 1:n
Xi = view(X, :, i)
theor_grad = GaussianProcesses.grad_mean(m, Xi)
num_grad = Calculus.gradient(params) do params
set_params!(m, params)
mean(m, Xi)
end
@test theor_grad ≈ num_grad rtol=1e-5 atol=1e-5
end
end
end
end
end