|
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 |
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