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1.4.0-DEV-81899bf99e.log
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1.4.0-DEV-81899bf99e.log
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Julia Version 1.4.0-DEV.634
Commit 81899bf99e (2019-12-18 10:13 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-8.0.1 (ORCJIT, skylake)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
Resolving package versions...
Installed AxisAlgorithms ────────────── v1.0.0
Installed PDMats ────────────────────── v0.9.10
Installed NLSolversBase ─────────────── v7.5.0
Installed NearestNeighbors ──────────── v0.4.4
Installed GradDescent ───────────────── v0.3.1
Installed Parameters ────────────────── v0.12.0
Installed StatsBase ─────────────────── v0.32.0
Installed AugmentedGaussianProcesses ── v0.6.0
Installed MKL_jll ───────────────────── v2019.0.117+0
Installed LineSearches ──────────────── v7.0.1
Installed OrderedCollections ────────── v1.1.0
Installed CommonSubexpressions ──────── v0.2.0
Installed FFTW_jll ──────────────────── v3.3.9+3
Installed DataFrames ────────────────── v0.19.4
Installed Calculus ──────────────────── v0.5.1
Installed Tables ────────────────────── v0.2.11
Installed Missings ──────────────────── v0.4.3
Installed RecipesBase ───────────────── v0.7.0
Installed Rmath ─────────────────────── v0.6.0
Installed AbstractFFTs ──────────────── v0.5.0
Installed MCMCChains ────────────────── v0.3.15
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed DiffResults ───────────────── v1.0.1
Installed Interpolations ────────────── v0.12.5
Installed Distances ─────────────────── v0.8.2
Installed BinDeps ───────────────────── v1.0.0
Installed InvertedIndices ───────────── v1.0.0
Installed InplaceOps ────────────────── v0.3.0
Installed QuadGK ────────────────────── v2.3.1
Installed PositiveFactorizations ────── v0.2.3
Installed StatsFuns ─────────────────── v0.8.0
Installed Optim ─────────────────────── v0.19.7
Installed Reexport ──────────────────── v0.2.0
Installed FillArrays ────────────────── v0.8.2
Installed MacroTools ────────────────── v0.5.3
Installed DataAPI ───────────────────── v1.1.0
Installed AxisArrays ────────────────── v0.3.3
Installed Ratios ────────────────────── v0.3.1
Installed Parsers ───────────────────── v0.3.10
Installed Requires ──────────────────── v1.0.0
Installed RangeArrays ───────────────── v0.3.1
Installed BinaryProvider ────────────── v0.5.8
Installed FastGaussQuadrature ───────── v0.4.1
Installed SortingAlgorithms ─────────── v0.3.1
Installed IntelOpenMP_jll ───────────── v2018.0.3+0
Installed Distributions ─────────────── v0.21.11
Installed Arpack_jll ────────────────── v3.5.0+2
Installed Compat ────────────────────── v2.2.0
Installed DiffEqDiffTools ───────────── v1.6.0
Installed KernelFunctions ───────────── v0.2.2
Installed Showoff ───────────────────── v0.3.1
Installed IntervalSets ──────────────── v0.3.2
Installed DataStructures ────────────── v0.17.6
Installed ArrayInterface ────────────── v2.1.0
Installed LazyArrays ────────────────── v0.14.10
Installed CategoricalArrays ─────────── v0.7.4
Installed IterTools ─────────────────── v1.3.0
Installed OpenBLAS_jll ──────────────── v0.3.7+1
Installed ArgCheck ──────────────────── v1.0.1
Installed PooledArrays ──────────────── v0.5.2
Installed Arpack ────────────────────── v0.4.0
Installed KernelDensity ─────────────── v0.5.1
Installed SpecialFunctions ──────────── v0.8.0
Installed DataValueInterfaces ───────── v1.0.0
Installed URIParser ─────────────────── v0.4.0
Installed ProgressMeter ─────────────── v1.2.0
Installed Clustering ────────────────── v0.13.3
Installed ArrayLayouts ──────────────── v0.1.5
Installed WoodburyMatrices ──────────── v0.4.1
Installed NaNMath ───────────────────── v0.3.3
Installed OffsetArrays ──────────────── v0.11.4
Installed StaticArrays ──────────────── v0.12.1
Installed JSON ──────────────────────── v0.21.0
Installed TableTraits ───────────────── v1.0.0
Installed DiffRules ─────────────────── v1.0.0
Installed ForwardDiff ───────────────── v0.10.8
Installed AdvancedHMC ───────────────── v0.2.14
Installed FFTW ──────────────────────── v1.2.0
Updating `~/.julia/environments/v1.4/Project.toml`
[38eea1fd] + AugmentedGaussianProcesses v0.6.0
Updating `~/.julia/environments/v1.4/Manifest.toml`
[621f4979] + AbstractFFTs v0.5.0
[0bf59076] + AdvancedHMC v0.2.14
[dce04be8] + ArgCheck v1.0.1
[7d9fca2a] + Arpack v0.4.0
[68821587] + Arpack_jll v3.5.0+2
[4fba245c] + ArrayInterface v2.1.0
[4c555306] + ArrayLayouts v0.1.5
[38eea1fd] + AugmentedGaussianProcesses v0.6.0
[13072b0f] + AxisAlgorithms v1.0.0
[39de3d68] + AxisArrays v0.3.3
[9e28174c] + BinDeps v1.0.0
[b99e7846] + BinaryProvider v0.5.8
[49dc2e85] + Calculus v0.5.1
[324d7699] + CategoricalArrays v0.7.4
[aaaa29a8] + Clustering v0.13.3
[bbf7d656] + CommonSubexpressions v0.2.0
[34da2185] + Compat v2.2.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.19.4
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[01453d9d] + DiffEqDiffTools v1.6.0
[163ba53b] + DiffResults v1.0.1
[b552c78f] + DiffRules v1.0.0
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.21.11
[7a1cc6ca] + FFTW v1.2.0
[f5851436] + FFTW_jll v3.3.9+3
[442a2c76] + FastGaussQuadrature v0.4.1
[1a297f60] + FillArrays v0.8.2
[f6369f11] + ForwardDiff v0.10.8
[e1397348] + GradDescent v0.3.1
[505f98c9] + InplaceOps v0.3.0
[1d5cc7b8] + IntelOpenMP_jll v2018.0.3+0
[a98d9a8b] + Interpolations v0.12.5
[8197267c] + IntervalSets v0.3.2
[41ab1584] + InvertedIndices v1.0.0
[c8e1da08] + IterTools v1.3.0
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[5ab0869b] + KernelDensity v0.5.1
[ec8451be] + KernelFunctions v0.2.2
[5078a376] + LazyArrays v0.14.10
[d3d80556] + LineSearches v7.0.1
[c7f686f2] + MCMCChains v0.3.15
[856f044c] + MKL_jll v2019.0.117+0
[1914dd2f] + MacroTools v0.5.3
[e1d29d7a] + Missings v0.4.3
[d41bc354] + NLSolversBase v7.5.0
[77ba4419] + NaNMath v0.3.3
[b8a86587] + NearestNeighbors v0.4.4
[6fe1bfb0] + OffsetArrays v0.11.4
[4536629a] + OpenBLAS_jll v0.3.7+1
[429524aa] + Optim v0.19.7
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[d96e819e] + Parameters v0.12.0
[69de0a69] + Parsers v0.3.10
[2dfb63ee] + PooledArrays v0.5.2
[85a6dd25] + PositiveFactorizations v0.2.3
[92933f4c] + ProgressMeter v1.2.0
[1fd47b50] + QuadGK v2.3.1
[b3c3ace0] + RangeArrays v0.3.1
[c84ed2f1] + Ratios v0.3.1
[3cdcf5f2] + RecipesBase v0.7.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v1.0.0
[79098fc4] + Rmath v0.6.0
[992d4aef] + Showoff v0.3.1
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.8.0
[90137ffa] + StaticArrays v0.12.1
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.8.0
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[30578b45] + URIParser v0.4.0
[efce3f68] + WoodburyMatrices v0.4.1
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building Rmath ───────────→ `~/.julia/packages/Rmath/BoBag/deps/build.log`
Path `/home/pkgeval/.julia/packages/Rmath/BoBag` exists and looks like the correct package. Using existing path.
Updating `/tmp/jl_GpnD9n/Project.toml`
[79098fc4] + Rmath v0.6.0 [`~/.julia/packages/Rmath/BoBag`]
Updating `/tmp/jl_GpnD9n/Manifest.toml`
[79098fc4] ~ Rmath v0.6.0 ⇒ v0.6.0 [`~/.julia/packages/Rmath/BoBag`]
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Path `/home/pkgeval/.julia/packages/SpecialFunctions/ne2iw` exists and looks like the correct package. Using existing path.
Updating `/tmp/jl_n3dVK9/Project.toml`
[276daf66] + SpecialFunctions v0.8.0 [`~/.julia/packages/SpecialFunctions/ne2iw`]
Updating `/tmp/jl_n3dVK9/Manifest.toml`
[276daf66] ~ SpecialFunctions v0.8.0 ⇒ v0.8.0 [`~/.julia/packages/SpecialFunctions/ne2iw`]
Building FFTW ────────────→ `~/.julia/packages/FFTW/qqcBj/deps/build.log`
Path `/home/pkgeval/.julia/packages/FFTW/qqcBj` exists and looks like the correct package. Using existing path.
Updating `/tmp/jl_2HZYQa/Project.toml`
[7a1cc6ca] + FFTW v1.2.0 [`~/.julia/packages/FFTW/qqcBj`]
Updating `/tmp/jl_2HZYQa/Manifest.toml`
[7a1cc6ca] ~ FFTW v1.2.0 ⇒ v1.2.0 [`~/.julia/packages/FFTW/qqcBj`]
Testing AugmentedGaussianProcesses
Path `/home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ` exists and looks like the correct package. Using existing path.
Updating `/tmp/jl_RHnkbk/Project.toml`
[38eea1fd] + AugmentedGaussianProcesses v0.6.0 [`~/.julia/packages/AugmentedGaussianProcesses/8kAgJ`]
Updating `/tmp/jl_RHnkbk/Manifest.toml`
[38eea1fd] ~ AugmentedGaussianProcesses v0.6.0 ⇒ v0.6.0 [`~/.julia/packages/AugmentedGaussianProcesses/8kAgJ`]
Running sandbox
Status `/tmp/jl_RHnkbk/Project.toml`
[0bf59076] AdvancedHMC v0.2.14
[38eea1fd] AugmentedGaussianProcesses v0.6.0 [`~/.julia/packages/AugmentedGaussianProcesses/8kAgJ`]
[aaaa29a8] Clustering v0.13.3
[a93c6f00] DataFrames v0.19.4
[31c24e10] Distributions v0.21.11
[442a2c76] FastGaussQuadrature v0.4.1
[f6369f11] ForwardDiff v0.10.8
[e1397348] GradDescent v0.3.1
[ec8451be] KernelFunctions v0.2.2
[c7f686f2] MCMCChains v0.3.15
[90014a1f] PDMats v0.9.10
[92933f4c] ProgressMeter v1.2.0
[3cdcf5f2] RecipesBase v0.7.0
[276daf66] SpecialFunctions v0.8.0
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.8.0
[ade2ca70] Dates
[37e2e46d] LinearAlgebra
[9a3f8284] Random
[10745b16] Statistics
[8dfed614] Test
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = lstirling_asym(::BigFloat) at misc.jl:56
└ @ StatsFuns ~/.julia/packages/StatsFuns/2QE7p/src/misc.jl:56
WARNING: Method definition deepcopy(GradDescent.Optimizer) in module GradDescent at /home/pkgeval/.julia/packages/GradDescent/C4qjb/src/AbstractOptimizer.jl:22 overwritten in module AugmentedGaussianProcesses at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/functions/utils.jl:71.
** incremental compilation may be fatally broken for this module **
Starting training Gaussian Process with a Gaussian likelihood infered by Analytic Inference with 100 samples with 2 features and 1 latent GP
GP Testing: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_GP.jl:21
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._GP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:22
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._GP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true},1}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::GP{Float64,GaussianLikelihood{Float64},Analytic{Float64},AugmentedGaussianProcesses._GP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:3
[12] train!(::GP{Float64,GaussianLikelihood{Float64},Analytic{Float64},AugmentedGaussianProcesses._GP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::GP{Float64,GaussianLikelihood{Float64},Analytic{Float64},AugmentedGaussianProcesses._GP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_GP.jl:21 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_GP.jl:18
Starting training Variational Gaussian Process with a Student-t likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._VGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:22
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._VGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true},1}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::VGP{Float64,StudentTLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:3
[12] train!(::VGP{Float64,StudentTLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::VGP{Float64,StudentTLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:44 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:42 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:40 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:38
┌ Info: Regression Error
└ err = 0.3204166298368535
Starting training Variational Gaussian Process with a Laplace likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._VGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:22
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._VGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true},1}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::VGP{Float64,LaplaceLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:3
[12] train!(::VGP{Float64,LaplaceLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::VGP{Float64,LaplaceLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:44 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:42 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:40 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:38
┌ Info: Regression Error
└ err = 0.2864630542722869
Starting training Variational Gaussian Process with a Gaussian likelihood with heteroscedastic noise infered by Analytic Variational Inference with 100 samples with 2 features and 2 latent GPs
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._VGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:22
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._VGP{Float64},AugmentedGaussianProcesses._VGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true},1}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::VGP{Float64,HeteroscedasticLikelihood{Float64},AnalyticVI{Float64,2},AugmentedGaussianProcesses._VGP{Float64},2}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:3
[12] train!(::VGP{Float64,HeteroscedasticLikelihood{Float64},AnalyticVI{Float64,2},AugmentedGaussianProcesses._VGP{Float64},2}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::VGP{Float64,HeteroscedasticLikelihood{Float64},AnalyticVI{Float64,2},AugmentedGaussianProcesses._VGP{Float64},2}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:44 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:42 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:40 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:38
┌ Info: Regression Error
└ err = 0.6299998467930292
Starting training Variational Gaussian Process with a Bayesian SVM infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._VGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:22
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._VGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true},1}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::VGP{Float64,BayesianSVM{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:3
[12] train!(::VGP{Float64,BayesianSVM{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::VGP{Float64,BayesianSVM{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:44 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:42 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:40 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:38
┌ Info: Classification Error
└ err = 0.02
Starting training Variational Gaussian Process with a Bernoulli Likelihood with Logistic Link infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._VGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:22
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._VGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true},1}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::VGP{Float64,LogisticLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:3
[12] train!(::VGP{Float64,LogisticLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::VGP{Float64,LogisticLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:44 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:42 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:40 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:38
┌ Info: Classification Error
└ err = 0.03
Starting training Variational Gaussian Process with a Logistic-Softmax Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 5 latent GPs
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._VGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:22
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{NTuple{5,AugmentedGaussianProcesses._VGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true},1}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::VGP{Float64,LogisticSoftMaxLikelihood{Float64},AnalyticVI{Float64,5},AugmentedGaussianProcesses._VGP{Float64},5}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:3
[12] train!(::VGP{Float64,LogisticSoftMaxLikelihood{Float64},AnalyticVI{Float64,5},AugmentedGaussianProcesses._VGP{Float64},5}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::VGP{Float64,LogisticSoftMaxLikelihood{Float64},AnalyticVI{Float64,5},AugmentedGaussianProcesses._VGP{Float64},5}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:44 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:42 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:40 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:38
┌ Info: Multiclass Error
└ err = 0.12
Starting training Variational Gaussian Process with a Poisson Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._VGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:22
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._VGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true},1}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::VGP{Float64,PoissonLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:3
[12] train!(::VGP{Float64,PoissonLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::VGP{Float64,PoissonLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:44 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:42 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:40 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:38
┌ Info: Event Error
└ err = 0.6750192052189938
Starting training Variational Gaussian Process with a Negative Binomial Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._VGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:22
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._VGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}}},true},1}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::VGP{Float64,NegBinomialLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:3
[12] train!(::VGP{Float64,NegBinomialLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::VGP{Float64,NegBinomialLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._VGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:48 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:44 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:42 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:40 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_VGP.jl:38
┌ Info: Event Error
└ err = 17.45416371976427
Starting training Sparse Variational Gaussian Process with a Gaussian likelihood infered by Analytic Stochastic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticSVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,1}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false},1},Tuple{Array{Float64,1}},Tuple{Array{Float64,1}},Array{Any,1},Tuple{AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,GaussianLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,GaussianLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,GaussianLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Regression Error
└ err = 0.20327533827481392
Starting training Sparse Variational Gaussian Process with a Gaussian likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,1}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false},1},Tuple{Array{Float64,1}},Tuple{Array{Float64,1}},Array{Any,1},Tuple{AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,GaussianLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,GaussianLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,GaussianLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Regression Error
└ err = 0.18982400152692944
Starting training Sparse Variational Gaussian Process with a Student-t likelihood infered by Analytic Stochastic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticSVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,1}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false},1},Tuple{Array{Float64,1}},Tuple{Array{Float64,1}},Array{Any,1},Tuple{AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,StudentTLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,StudentTLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,StudentTLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Regression Error
└ err = 0.5019948443607585
Starting training Sparse Variational Gaussian Process with a Student-t likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,1}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false},1},Tuple{Array{Float64,1}},Tuple{Array{Float64,1}},Array{Any,1},Tuple{AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,StudentTLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,StudentTLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,StudentTLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Regression Error
└ err = 0.34748563863145415
Starting training Sparse Variational Gaussian Process with a Laplace likelihood infered by Analytic Stochastic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticSVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,1}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false},1},Tuple{Array{Float64,1}},Tuple{Array{Float64,1}},Array{Any,1},Tuple{AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,LaplaceLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,LaplaceLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,LaplaceLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Regression Error
└ err = 0.5190201914808079
Starting training Sparse Variational Gaussian Process with a Laplace likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,1}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false},1},Tuple{Array{Float64,1}},Tuple{Array{Float64,1}},Array{Any,1},Tuple{AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,LaplaceLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,LaplaceLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,LaplaceLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Regression Error
└ err = 0.33710468199922416
Starting training Sparse Variational Gaussian Process with a Bayesian SVM infered by Analytic Stochastic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticSVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,1}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false},1},Tuple{Array{Float64,1}},Tuple{Array{Float64,1}},Array{Any,1},Tuple{AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,BayesianSVM{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,BayesianSVM{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,BayesianSVM{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Classification Error
└ err = 0.09
Starting training Sparse Variational Gaussian Process with a Bayesian SVM infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,1}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false},1},Tuple{Array{Float64,1}},Tuple{Array{Float64,1}},Array{Any,1},Tuple{AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,BayesianSVM{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,BayesianSVM{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,BayesianSVM{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Classification Error
└ err = 0.08
Starting training Sparse Variational Gaussian Process with a Bernoulli Likelihood with Logistic Link infered by Analytic Stochastic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticSVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,1}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false},1},Tuple{Array{Float64,1}},Tuple{Array{Float64,1}},Array{Any,1},Tuple{AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,LogisticLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,LogisticLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,LogisticLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Classification Error
└ err = 0.1
Starting training Sparse Variational Gaussian Process with a Bernoulli Likelihood with Logistic Link infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,1}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{Tuple{AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false},1},Tuple{Array{Float64,1}},Tuple{Array{Float64,1}},Array{Any,1},Tuple{AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,LogisticLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,LogisticLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,LogisticLikelihood{Float64},AnalyticVI{Float64,1},AugmentedGaussianProcesses._SVGP{Float64},1}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Classification Error
└ err = 0.09
Starting training Sparse Variational Gaussian Process with a Logistic-Softmax Likelihood infered by Analytic Stochastic Variational Inference with 100 samples with 2 features and 5 latent GPs
AnalyticSVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,5}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{NTuple{5,AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{Array{Int64,1},Base.Slice{Base.OneTo{Int64}}},false},1},Array{Array{Float64,1},1},Array{Array{Float64,1},1},Array{Any,1},NTuple{5,AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,LogisticSoftMaxLikelihood{Float64},AnalyticVI{Float64,5},AugmentedGaussianProcesses._SVGP{Float64},5}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,LogisticSoftMaxLikelihood{Float64},AnalyticVI{Float64,5},AugmentedGaussianProcesses._SVGP{Float64},5}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,LogisticSoftMaxLikelihood{Float64},AnalyticVI{Float64,5},AugmentedGaussianProcesses._SVGP{Float64},5}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Multiclass Error
└ err = 0.15
Starting training Sparse Variational Gaussian Process with a Logistic-Softmax Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 5 latent GPs
AnalyticVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50
Test threw exception
Expression: train!(model, 50)
MethodError: no method matching kernelderivative(::SqExponentialKernel{Float64,ScaleTransform{Float64}}, ::Array{Float64,2})
Closest candidates are:
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:38
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:(AbstractArray{T,1} where T)), ::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:44
kernelderivative(!Matched::Kernel{T,#s150} where #s150<:(ScaleTransform{#s149} where #s149<:Base.RefValue), ::Any, !Matched::Any) where T at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning_utils.jl:50
...
Stacktrace:
[1] update_hyperparameters!(::AugmentedGaussianProcesses._SVGP{Float64}, ::SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false}, ::Array{Float64,1}, ::Array{Float64,1}, ::AnalyticVI{Float64,5}, ::AugmentedGaussianProcesses.AVIOptimizer{Float64}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:49
[2] _broadcast_getindex_evalf at ./broadcast.jl:630 [inlined]
[3] _broadcast_getindex at ./broadcast.jl:603 [inlined]
[4] getindex at ./broadcast.jl:563 [inlined]
[5] macro expansion at ./broadcast.jl:909 [inlined]
[6] macro expansion at ./simdloop.jl:77 [inlined]
[7] copyto! at ./broadcast.jl:908 [inlined]
[8] copyto! at ./broadcast.jl:863 [inlined]
[9] copy(::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Tuple{Base.OneTo{Int64}},typeof(AugmentedGaussianProcesses.update_hyperparameters!),Tuple{NTuple{5,AugmentedGaussianProcesses._SVGP{Float64}},Array{SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Base.Slice{Base.OneTo{Int64}}},false},1},Array{Array{Float64,1},1},Array{Array{Float64,1},1},Array{Any,1},NTuple{5,AugmentedGaussianProcesses.AVIOptimizer{Float64}}}}) at ./broadcast.jl:839
[10] materialize at ./broadcast.jl:819 [inlined]
[11] update_hyperparameters!(::SVGP{Float64,LogisticSoftMaxLikelihood{Float64},AnalyticVI{Float64,5},AugmentedGaussianProcesses._SVGP{Float64},5}) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/autotuning.jl:8
[12] train!(::SVGP{Float64,LogisticSoftMaxLikelihood{Float64},AnalyticVI{Float64,5},AugmentedGaussianProcesses._SVGP{Float64},5}, ::Int64; callback::Nothing, convergence::Nothing) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:30
[13] train!(::SVGP{Float64,LogisticSoftMaxLikelihood{Float64},AnalyticVI{Float64,5},AugmentedGaussianProcesses._SVGP{Float64},5}, ::Int64) at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/src/training.jl:13
[14] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50 [inlined]
[15] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[16] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:46 [inlined]
[17] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[18] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:43 [inlined]
[19] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[20] macro expansion at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:41 [inlined]
[21] macro expansion at /workspace/srcdir/usr/share/julia/stdlib/v1.4/Test/src/Test.jl:1116 [inlined]
[22] top-level scope at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:39
┌ Info: Multiclass Error
└ err = 0.12
Starting training Sparse Variational Gaussian Process with a Poisson Likelihood infered by Analytic Stochastic Variational Inference with 100 samples with 2 features and 1 latent GP
AnalyticSVI: Error During Test at /home/pkgeval/.julia/packages/AugmentedGaussianProcesses/8kAgJ/test/test_SVGP.jl:50