|
8 | 8 | x2 = MOInput([rand(rng, in_dim) for _ in 1:N], out_dim)
|
9 | 9 |
|
10 | 10 | k = LatentFactorMOKernel(
|
11 |
| - [MaternKernel(), SqExponentialKernel(), FBMKernel()], |
| 11 | + [Matern32Kernel(), SqExponentialKernel(), FBMKernel()], |
12 | 12 | IndependentMOKernel(GaussianKernel()),
|
13 | 13 | rand(rng, out_dim, 3),
|
14 | 14 | )
|
|
23 | 23 | @test string(k) == "Semi-parametric Latent Factor Multi-Output Kernel"
|
24 | 24 | @test repr("text/plain", k) == (
|
25 | 25 | "Semi-parametric Latent Factor Multi-Output Kernel\n\tgᵢ: " *
|
26 |
| - "Matern Kernel (ν = 1.5)\n\t\tSquared Exponential Kernel\n" * |
| 26 | + "Matern 3/2 Kernel\n\t\tSquared Exponential Kernel\n" * |
27 | 27 | "\t\tFractional Brownian Motion Kernel (h = 0.5)\n\teᵢ: " *
|
28 | 28 | "Independent Multi-Output Kernel\n\tSquared Exponential Kernel"
|
29 | 29 | )
|
30 | 30 |
|
31 | 31 | # AD test
|
32 | 32 | function test_slfm(A::AbstractMatrix, x1, x2)
|
33 | 33 | k = LatentFactorMOKernel(
|
34 |
| - [MaternKernel(), SqExponentialKernel(), FBMKernel()], |
| 34 | + [Matern32Kernel(), SqExponentialKernel(), FBMKernel()], |
35 | 35 | IndependentMOKernel(GaussianKernel()),
|
36 | 36 | A,
|
37 | 37 | )
|
|
40 | 40 |
|
41 | 41 | a = rand()
|
42 | 42 | @test all(
|
43 |
| - FiniteDifferences.j′vp(FDM, test_slfm, a, k.A, x1[1][1], x2[1][1]) .≈ |
| 43 | + FiniteDifferences.j′vp(FDM, test_slfm, a, k.A, x1[1][1], x2[1][1]) .≈ |
44 | 44 | Zygote.pullback(test_slfm, k.A, x1[1][1], x2[1][1])[2](a)
|
45 |
| - ) |
46 |
| - |
| 45 | + ) |
47 | 46 | end
|
0 commit comments