If you think that there is an error in how your package is being tested or represented, please file an issue at NewPkgEval.jl , making sure to read the FAQ first.
Results with Julia v1.2.0
Testing was successful .
Last evaluation was ago and took 39 minutes, 51 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed Missings ──────────────────── v0.4.3
Installed DataAPI ───────────────────── v1.1.0
Installed Showoff ───────────────────── v0.3.1
Installed TableTraits ───────────────── v1.0.0
Installed NearestNeighbors ──────────── v0.4.4
Installed LineSearches ──────────────── v7.0.1
Installed DataFrames ────────────────── v0.19.4
Installed AugmentedGaussianProcesses ── v0.6.0
Installed BinaryProvider ────────────── v0.5.8
Installed StatsBase ─────────────────── v0.32.0
Installed AbstractFFTs ──────────────── v0.5.0
Installed RangeArrays ───────────────── v0.3.1
Installed PDMats ────────────────────── v0.9.10
Installed StatsFuns ─────────────────── v0.8.0
Installed CommonSubexpressions ──────── v0.2.0
Installed Conda ─────────────────────── v1.3.0
Installed URIParser ─────────────────── v0.4.0
Installed ArrayLayouts ──────────────── v0.1.5
Installed AxisAlgorithms ────────────── v1.0.0
Installed Requires ──────────────────── v0.5.2
Installed DataValueInterfaces ───────── v1.0.0
Installed InplaceOps ────────────────── v0.3.0
Installed NLSolversBase ─────────────── v7.5.0
Installed Reexport ──────────────────── v0.2.0
Installed InvertedIndices ───────────── v1.0.0
Installed PooledArrays ──────────────── v0.5.2
Installed Compat ────────────────────── v2.2.0
Installed OffsetArrays ──────────────── v0.11.2
Installed OrderedCollections ────────── v1.1.0
Installed ForwardDiff ───────────────── v0.10.7
Installed Rmath ─────────────────────── v0.6.0
Installed IterTools ─────────────────── v1.3.0
Installed AdvancedHMC ───────────────── v0.2.14
Installed Tables ────────────────────── v0.2.11
Installed Calculus ──────────────────── v0.5.1
Installed KernelDensity ─────────────── v0.5.1
Installed RecipesBase ───────────────── v0.7.0
Installed Parsers ───────────────────── v0.3.10
Installed DataStructures ────────────── v0.17.6
Installed ArgCheck ──────────────────── v1.0.1
Installed Distributions ─────────────── v0.21.9
Installed FillArrays ────────────────── v0.8.2
Installed DiffEqDiffTools ───────────── v1.5.0
Installed DiffRules ─────────────────── v0.1.0
Installed Ratios ────────────────────── v0.3.1
Installed JSON ──────────────────────── v0.21.0
Installed FastGaussQuadrature ───────── v0.4.1
Installed NaNMath ───────────────────── v0.3.3
Installed ArrayInterface ────────────── v2.0.0
Installed StaticArrays ──────────────── v0.12.1
Installed DiffResults ───────────────── v0.0.4
Installed MCMCChains ────────────────── v0.3.15
Installed FFTW ──────────────────────── v1.1.0
Installed Parameters ────────────────── v0.12.0
Installed IntervalSets ──────────────── v0.3.2
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed QuadGK ────────────────────── v2.1.1
Installed CategoricalArrays ─────────── v0.7.3
Installed WoodburyMatrices ──────────── v0.4.1
Installed GradDescent ───────────────── v0.3.1
Installed Interpolations ────────────── v0.12.5
Installed SortingAlgorithms ─────────── v0.3.1
Installed VersionParsing ────────────── v1.1.3
Installed Clustering ────────────────── v0.13.3
Installed LazyArrays ────────────────── v0.14.10
Installed KernelFunctions ───────────── v0.2.1
Installed MacroTools ────────────────── v0.5.2
Installed PositiveFactorizations ────── v0.2.3
Installed Distances ─────────────────── v0.8.2
Installed BinDeps ───────────────────── v0.8.10
Installed AxisArrays ────────────────── v0.3.3
Installed SpecialFunctions ──────────── v0.8.0
Installed Arpack ────────────────────── v0.3.1
Installed Optim ─────────────────────── v0.19.5
Installed ProgressMeter ─────────────── v1.2.0
Updating `~/.julia/environments/v1.2/Project.toml`
[38eea1fd] + AugmentedGaussianProcesses v0.6.0
Updating `~/.julia/environments/v1.2/Manifest.toml`
[621f4979] + AbstractFFTs v0.5.0
[0bf59076] + AdvancedHMC v0.2.14
[dce04be8] + ArgCheck v1.0.1
[7d9fca2a] + Arpack v0.3.1
[4fba245c] + ArrayInterface v2.0.0
[4c555306] + ArrayLayouts v0.1.5
[38eea1fd] + AugmentedGaussianProcesses v0.6.0
[13072b0f] + AxisAlgorithms v1.0.0
[39de3d68] + AxisArrays v0.3.3
[9e28174c] + BinDeps v0.8.10
[b99e7846] + BinaryProvider v0.5.8
[49dc2e85] + Calculus v0.5.1
[324d7699] + CategoricalArrays v0.7.3
[aaaa29a8] + Clustering v0.13.3
[bbf7d656] + CommonSubexpressions v0.2.0
[34da2185] + Compat v2.2.0
[8f4d0f93] + Conda v1.3.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.19.4
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[01453d9d] + DiffEqDiffTools v1.5.0
[163ba53b] + DiffResults v0.0.4
[b552c78f] + DiffRules v0.1.0
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.21.9
[7a1cc6ca] + FFTW v1.1.0
[442a2c76] + FastGaussQuadrature v0.4.1
[1a297f60] + FillArrays v0.8.2
[f6369f11] + ForwardDiff v0.10.7
[e1397348] + GradDescent v0.3.1
[505f98c9] + InplaceOps v0.3.0
[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 v0.2.1
[5078a376] + LazyArrays v0.14.10
[d3d80556] + LineSearches v7.0.1
[c7f686f2] + MCMCChains v0.3.15
[1914dd2f] + MacroTools v0.5.2
[e1d29d7a] + Missings v0.4.3
[d41bc354] + NLSolversBase v7.5.0
[77ba4419] + NaNMath v0.3.3
[b8a86587] + NearestNeighbors v0.4.4
[6fe1bfb0] + OffsetArrays v0.11.2
[429524aa] + Optim v0.19.5
[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 v2.1.1
[b3c3ace0] + RangeArrays v0.3.1
[c84ed2f1] + Ratios v0.3.1
[3cdcf5f2] + RecipesBase v0.7.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v0.5.2
[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
[81def892] + VersionParsing v1.1.3
[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 Conda ───────────→ `~/.julia/packages/Conda/kLXeC/deps/build.log`
Building Rmath ───────────→ `~/.julia/packages/Rmath/BoBag/deps/build.log`
Building FFTW ────────────→ `~/.julia/packages/FFTW/loJ3F/deps/build.log`
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Building Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Testing AugmentedGaussianProcesses
Resolving package versions...
Status `/tmp/jl_sjNHeF/Manifest.toml`
[621f4979] AbstractFFTs v0.5.0
[0bf59076] AdvancedHMC v0.2.14
[dce04be8] ArgCheck v1.0.1
[7d9fca2a] Arpack v0.3.1
[4fba245c] ArrayInterface v2.0.0
[4c555306] ArrayLayouts v0.1.5
[38eea1fd] AugmentedGaussianProcesses v0.6.0
[13072b0f] AxisAlgorithms v1.0.0
[39de3d68] AxisArrays v0.3.3
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[49dc2e85] Calculus v0.5.1
[324d7699] CategoricalArrays v0.7.3
[aaaa29a8] Clustering v0.13.3
[bbf7d656] CommonSubexpressions v0.2.0
[34da2185] Compat v2.2.0
[8f4d0f93] Conda v1.3.0
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[01453d9d] DiffEqDiffTools v1.5.0
[163ba53b] DiffResults v0.0.4
[b552c78f] DiffRules v0.1.0
[b4f34e82] Distances v0.8.2
[31c24e10] Distributions v0.21.9
[7a1cc6ca] FFTW v1.1.0
[442a2c76] FastGaussQuadrature v0.4.1
[1a297f60] FillArrays v0.8.2
[f6369f11] ForwardDiff v0.10.7
[e1397348] GradDescent v0.3.1
[505f98c9] InplaceOps v0.3.0
[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 v0.2.1
[5078a376] LazyArrays v0.14.10
[d3d80556] LineSearches v7.0.1
[c7f686f2] MCMCChains v0.3.15
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[d41bc354] NLSolversBase v7.5.0
[77ba4419] NaNMath v0.3.3
[b8a86587] NearestNeighbors v0.4.4
[6fe1bfb0] OffsetArrays v0.11.2
[429524aa] Optim v0.19.5
[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 v2.1.1
[b3c3ace0] RangeArrays v0.3.1
[c84ed2f1] Ratios v0.3.1
[3cdcf5f2] RecipesBase v0.7.0
[189a3867] Reexport v0.2.0
[ae029012] Requires v0.5.2
[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
[81def892] VersionParsing v1.1.3
[efce3f68] WoodburyMatrices v0.4.1
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
┌ 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 /root/.julia/packages/GradDescent/C4qjb/src/AbstractOptimizer.jl:22 overwritten in module AugmentedGaussianProcesses at /root/.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
Training Progress: 2%|▋ | ETA: 0:06:52[K
iter: 10
ELBO: 624.055311605656
[A
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Training Progress: 4%|█▎ | ETA: 0:04:01[K
iter: 20
ELBO: 635.674952676726
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Training Progress: 6%|█▉ | ETA: 0:02:55[K
iter: 30
ELBO: 589.8717573550398
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Training Progress: 8%|██▌ | ETA: 0:02:31[K
iter: 40
ELBO: 662.1781749152855
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[ATraining ended after 50 iterations. Total number of iterations 50
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Training Progress: 10%|███▎ | ETA: 0:02:09[K
iter: 50
ELBO: 623.4231417439881
[A
[AStarting training Variational Gaussian Process with a Student-t likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:24:08[K
iter: 10
ELBO: -152.22527095522128
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Training Progress: 4%|█▎ | ETA: 0:14:44[K
iter: 20
ELBO: -150.83076430626713
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Training Progress: 6%|█▉ | ETA: 0:10:17[K
iter: 30
ELBO: -149.87410204825002
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Training Progress: 8%|██▌ | ETA: 0:09:30[K
iter: 40
ELBO: -149.3256010303905
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Training Progress: 10%|███▎ | ETA: 0:09:00[K
iter: 50
ELBO: -149.09330240808055
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.4242331022347892
Starting training Variational Gaussian Process with a Laplace likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:11:16[K
iter: 10
ELBO: -566.225785866745
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Training Progress: 4%|█▎ | ETA: 0:11:49[K
iter: 20
ELBO: -568.6837211638668
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Training Progress: 6%|█▉ | ETA: 0:11:11[K
iter: 30
ELBO: -570.2704750617578
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Training Progress: 8%|██▌ | ETA: 0:10:32[K
iter: 40
ELBO: -571.5108565720788
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[ATraining ended after 50 iterations. Total number of iterations 50
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Training Progress: 10%|███▎ | ETA: 0:10:18[K
iter: 50
ELBO: -572.6758898614879
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[A┌ Info: Regression Error
└ err = 0.4018312504927594
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
Training Progress: 2%|▋ | ETA: 0:17:41[K
iter: 10
ELBO: -151.91804200759827
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Training Progress: 4%|█▎ | ETA: 0:18:21[K
iter: 20
ELBO: -148.2628097629236
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Training Progress: 6%|█▉ | ETA: 0:16:56[K
iter: 30
ELBO: -145.8411732543865
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Training Progress: 8%|██▌ | ETA: 0:17:41[K
iter: 40
ELBO: -143.96603507455967
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Training Progress: 10%|███▎ | ETA: 0:17:32[K
iter: 50
ELBO: -142.6234705895817
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.6697049944690828
Starting training Variational Gaussian Process with a Bayesian SVM infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:06:59[K
iter: 10
ELBO: 69.36594915002166
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Training Progress: 4%|█▎ | ETA: 0:07:25[K
iter: 20
ELBO: 81.76959815148342
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Training Progress: 6%|█▉ | ETA: 0:07:21[K
iter: 30
ELBO: 93.11305693561144
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Training Progress: 8%|██▌ | ETA: 0:06:35[K
iter: 40
ELBO: 102.88460405829255
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Training Progress: 10%|███▎ | ETA: 0:06:07[K
iter: 50
ELBO: 111.76775053363693
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ 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
Training Progress: 2%|▋ | ETA: 0:10:45[K
iter: 10
ELBO: -15.45496403516649
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Training Progress: 4%|█▎ | ETA: 0:10:19[K
iter: 20
ELBO: -12.921811247704284
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Training Progress: 6%|█▉ | ETA: 0:08:03[K
iter: 30
ELBO: -10.525979508342859
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Training Progress: 8%|██▌ | ETA: 0:07:45[K
iter: 40
ELBO: -8.358200907239684
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[ATraining ended after 50 iterations. Total number of iterations 50
[K[A
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Training Progress: 10%|███▎ | ETA: 0:06:59[K
iter: 50
ELBO: -6.4397859300417934
[A
[A┌ Info: Classification Error
└ err = 0.06
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
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lgamma), ::typeof(Base.add_sum), ::Array{Float64,1}, ::Int64, ::Int64, ::Int64) at reduce.jl:163
└ @ Base ./reduce.jl:163
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lgamma), ::typeof(Base.add_sum), ::Array{Float64,1}, ::Int64, ::Int64, ::Int64) at reduce.jl:163
└ @ Base ./reduce.jl:163
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = macro expansion at reduce.jl:166 [inlined]
└ @ Core ./reduce.jl:166
Training Progress: 2%|▋ | ETA: 0:33:54[K
iter: 10
ELBO: -35.3935785497838
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Training Progress: 4%|█▎ | ETA: 0:35:29[K
iter: 20
ELBO: -34.450206095421095
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Training Progress: 6%|█▉ | ETA: 0:34:21[K
iter: 30
ELBO: -33.48761577222891
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Training Progress: 8%|██▌ | ETA: 0:36:55[K
iter: 40
ELBO: -32.511984642637856
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Training Progress: 10%|███▎ | ETA: 0:40:36[K
iter: 50
ELBO: -31.53238843149282
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Multiclass Error
└ err = 0.27
Starting training Variational Gaussian Process with a Poisson Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::SubArray{Int64,1,Array{Int64,1},Tuple{UnitRange{Int64}},true}, ::Int64, ::Int64, ::Int64) at reduce.jl:163
└ @ Base ./reduce.jl:163
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::SubArray{Int64,1,Array{Int64,1},Tuple{UnitRange{Int64}},true}, ::Int64, ::Int64, ::Int64) at reduce.jl:163
└ @ Base ./reduce.jl:163
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = macro expansion at reduce.jl:166 [inlined]
└ @ Core ./reduce.jl:166
Training Progress: 2%|▋ | ETA: 0:10:29[K
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iter: 50
ELBO: -125.97784291753513
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 0.7098173135397451
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
Training Progress: 2%|▋ | ETA: 0:10:41[K
iter: 10
ELBO: -721.0880180049668
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iter: 50
ELBO: -736.0449100226631
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┌ Info: Event Error
└ err = 17.843017416604514
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
Training Progress: 2%|▋ | ETA: 0:17:41[K
iter: 10
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iter: 50
ELBO: -2128.361384061914
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┌ Info: Regression Error
└ err = 0.19166431205933365
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
Training Progress: 2%|▋ | ETA: 0:09:54[K
iter: 10
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iter: 50
ELBO: -1244.9903501916276
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┌ Info: Regression Error
└ err = 0.19240798383401675
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
Training Progress: 2%|▋ | ETA: 0:04:39[K
iter: 10
ELBO: -154.46487995526215
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iter: 50
ELBO: -149.6070600895724
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┌ Info: Regression Error
└ err = 0.37437043455267
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
Training Progress: 2%|▋ | ETA: 0:03:05[K
iter: 10
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iter: 50
ELBO: -145.29013276252704
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[A┌ Info: Regression Error
└ err = 0.41590173469563896
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
Training Progress: 2%|▋ | ETA: 0:06:07[K
iter: 10
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iter: 50
ELBO: -539.2752458689331
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┌ Info: Regression Error
└ err = 0.36679289894619643
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
Training Progress: 2%|▋ | ETA: 0:05:25[K
iter: 10
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iter: 50
ELBO: -562.3300006862518
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┌ Info: Regression Error
└ err = 0.4045807937532465
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
Training Progress: 2%|▋ | ETA: 0:07:29[K
iter: 10
ELBO: 65.9218130002265
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iter: 50
ELBO: 97.50413919760484
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┌ Info: Classification Error
└ err = 0.08
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
Training Progress: 2%|▋ | ETA: 0:07:22[K
iter: 10
ELBO: 68.98355584076893
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iter: 40
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iter: 50
ELBO: 99.92516828116736
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└ err = 0.1
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
Training Progress: 2%|▋ | ETA: 0:07:58[K
iter: 10
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iter: 40
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iter: 50
ELBO: -24.39375109216409
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[A┌ Info: Classification Error
└ err = 0.09
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
Training Progress: 2%|▋ | ETA: 0:04:08[K
iter: 10
ELBO: -26.85480220115716
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iter: 20
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iter: 40
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iter: 50
ELBO: -22.567557185060824
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┌ Info: Classification Error
└ err = 0.12
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
Training Progress: 2%|▋ | ETA: 0:34:40[K
iter: 10
ELBO: -49.35338984675457
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iter: 20
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iter: 50
ELBO: -44.020038336192954
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┌ 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
Training Progress: 2%|▋ | ETA: 0:31:07[K
iter: 10
ELBO: -36.0747705571992
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iter: 20
ELBO: -34.4478201922481
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iter: 30
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iter: 40
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iter: 50
ELBO: -29.50121146723589
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Multiclass Error
└ err = 0.33
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
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapfoldl_impl at reduce.jl:301 [inlined]
└ @ Core ./reduce.jl:301
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapfoldl_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::NamedTuple{(:init,),Tuple{Float64}}, ::SubArray{Int64,1,Array{Int64,1},Tuple{Array{Int64,1}},false}, ::Tuple{Base.OneTo{Int64},Int64}) at reduce.jl:45
└ @ Base ./reduce.jl:45
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapfoldl_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::NamedTuple{(:init,),Tuple{Float64}}, ::SubArray{Int64,1,Array{Int64,1},Tuple{Array{Int64,1}},false}, ::Tuple{Base.OneTo{Int64},Int64}) at reduce.jl:49
└ @ Base ./reduce.jl:49
Training Progress: 2%|▋ | ETA: 0:08:03[K
iter: 10
ELBO: -139.15902420996977
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iter: 50
ELBO: -141.88303814571918
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┌ Info: Event Error
└ err = 0.7510338044696269
Starting training Sparse Variational Gaussian Process with a Poisson Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:09:25[K
iter: 10
ELBO: -145.46229622545312
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iter: 50
ELBO: -141.27613432017748
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 0.7455536649032818
Starting training Sparse Variational Gaussian Process with a Negative Binomial Likelihood infered by Analytic Stochastic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:09:30[K
iter: 10
ELBO: -545.1836920779126
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iter: 50
ELBO: -463.387603307715
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 7.459339822755598
Starting training Sparse Variational Gaussian Process with a Negative Binomial Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:06:28[K
iter: 10
ELBO: -432.4950813465152
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iter: 40
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iter: 50
ELBO: -408.2247063170046
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 7.530005362003821
Test Summary: | Pass Total
Augmented Gaussian Process Testing | 79 79
Testing AugmentedGaussianProcesses tests passed
Results with Julia v1.3.0
Testing was successful .
Last evaluation was ago and took 33 minutes, 9 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed URIParser ─────────────────── v0.4.0
Installed Ratios ────────────────────── v0.3.1
Installed KernelDensity ─────────────── v0.5.1
Installed SortingAlgorithms ─────────── v0.3.1
Installed GradDescent ───────────────── v0.3.1
Installed DiffResults ───────────────── v0.0.4
Installed Arpack ────────────────────── v0.3.1
Installed AugmentedGaussianProcesses ── v0.6.0
Installed AxisAlgorithms ────────────── v1.0.0
Installed DataStructures ────────────── v0.17.6
Installed LineSearches ──────────────── v7.0.1
Installed FFTW ──────────────────────── v1.1.0
Installed LazyArrays ────────────────── v0.14.10
Installed StaticArrays ──────────────── v0.12.1
Installed KernelFunctions ───────────── v0.2.1
Installed FastGaussQuadrature ───────── v0.4.1
Installed QuadGK ────────────────────── v2.1.1
Installed Compat ────────────────────── v2.2.0
Installed StatsFuns ─────────────────── v0.8.0
Installed BinaryProvider ────────────── v0.5.8
Installed CategoricalArrays ─────────── v0.7.3
Installed InvertedIndices ───────────── v1.0.0
Installed Parsers ───────────────────── v0.3.10
Installed MCMCChains ────────────────── v0.3.15
Installed NLSolversBase ─────────────── v7.5.0
Installed IterTools ─────────────────── v1.3.0
Installed ProgressMeter ─────────────── v1.2.0
Installed Missings ──────────────────── v0.4.3
Installed Rmath ─────────────────────── v0.6.0
Installed NearestNeighbors ──────────── v0.4.4
Installed MacroTools ────────────────── v0.5.2
Installed Parameters ────────────────── v0.12.0
Installed Distributions ─────────────── v0.21.9
Installed TableTraits ───────────────── v1.0.0
Installed PositiveFactorizations ────── v0.2.3
Installed RangeArrays ───────────────── v0.3.1
Installed IntervalSets ──────────────── v0.3.2
Installed SpecialFunctions ──────────── v0.8.0
Installed ArgCheck ──────────────────── v1.0.1
Installed OrderedCollections ────────── v1.1.0
Installed ArrayLayouts ──────────────── v0.1.5
Installed Showoff ───────────────────── v0.3.1
Installed JSON ──────────────────────── v0.21.0
Installed AdvancedHMC ───────────────── v0.2.14
Installed RecipesBase ───────────────── v0.7.0
Installed CommonSubexpressions ──────── v0.2.0
Installed BinDeps ───────────────────── v0.8.10
Installed InplaceOps ────────────────── v0.3.0
Installed DataAPI ───────────────────── v1.1.0
Installed Distances ─────────────────── v0.8.2
Installed NaNMath ───────────────────── v0.3.3
Installed Tables ────────────────────── v0.2.11
Installed DataValueInterfaces ───────── v1.0.0
Installed ForwardDiff ───────────────── v0.10.7
Installed DiffEqDiffTools ───────────── v1.5.0
Installed DiffRules ─────────────────── v0.1.0
Installed Optim ─────────────────────── v0.19.5
Installed Calculus ──────────────────── v0.5.1
Installed Interpolations ────────────── v0.12.5
Installed ArrayInterface ────────────── v2.0.0
Installed VersionParsing ────────────── v1.1.3
Installed Conda ─────────────────────── v1.3.0
Installed Requires ──────────────────── v0.5.2
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed OffsetArrays ──────────────── v0.11.2
Installed PooledArrays ──────────────── v0.5.2
Installed DataFrames ────────────────── v0.19.4
Installed Reexport ──────────────────── v0.2.0
Installed FillArrays ────────────────── v0.8.2
Installed PDMats ────────────────────── v0.9.10
Installed AxisArrays ────────────────── v0.3.3
Installed WoodburyMatrices ──────────── v0.4.1
Installed AbstractFFTs ──────────────── v0.5.0
Installed StatsBase ─────────────────── v0.32.0
Installed Clustering ────────────────── v0.13.3
Updating `~/.julia/environments/v1.3/Project.toml`
[38eea1fd] + AugmentedGaussianProcesses v0.6.0
Updating `~/.julia/environments/v1.3/Manifest.toml`
[621f4979] + AbstractFFTs v0.5.0
[0bf59076] + AdvancedHMC v0.2.14
[dce04be8] + ArgCheck v1.0.1
[7d9fca2a] + Arpack v0.3.1
[4fba245c] + ArrayInterface v2.0.0
[4c555306] + ArrayLayouts v0.1.5
[38eea1fd] + AugmentedGaussianProcesses v0.6.0
[13072b0f] + AxisAlgorithms v1.0.0
[39de3d68] + AxisArrays v0.3.3
[9e28174c] + BinDeps v0.8.10
[b99e7846] + BinaryProvider v0.5.8
[49dc2e85] + Calculus v0.5.1
[324d7699] + CategoricalArrays v0.7.3
[aaaa29a8] + Clustering v0.13.3
[bbf7d656] + CommonSubexpressions v0.2.0
[34da2185] + Compat v2.2.0
[8f4d0f93] + Conda v1.3.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.19.4
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[01453d9d] + DiffEqDiffTools v1.5.0
[163ba53b] + DiffResults v0.0.4
[b552c78f] + DiffRules v0.1.0
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.21.9
[7a1cc6ca] + FFTW v1.1.0
[442a2c76] + FastGaussQuadrature v0.4.1
[1a297f60] + FillArrays v0.8.2
[f6369f11] + ForwardDiff v0.10.7
[e1397348] + GradDescent v0.3.1
[505f98c9] + InplaceOps v0.3.0
[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 v0.2.1
[5078a376] + LazyArrays v0.14.10
[d3d80556] + LineSearches v7.0.1
[c7f686f2] + MCMCChains v0.3.15
[1914dd2f] + MacroTools v0.5.2
[e1d29d7a] + Missings v0.4.3
[d41bc354] + NLSolversBase v7.5.0
[77ba4419] + NaNMath v0.3.3
[b8a86587] + NearestNeighbors v0.4.4
[6fe1bfb0] + OffsetArrays v0.11.2
[429524aa] + Optim v0.19.5
[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 v2.1.1
[b3c3ace0] + RangeArrays v0.3.1
[c84ed2f1] + Ratios v0.3.1
[3cdcf5f2] + RecipesBase v0.7.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v0.5.2
[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
[81def892] + VersionParsing v1.1.3
[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 Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Building Conda ───────────→ `~/.julia/packages/Conda/kLXeC/deps/build.log`
Building FFTW ────────────→ `~/.julia/packages/FFTW/loJ3F/deps/build.log`
Building Rmath ───────────→ `~/.julia/packages/Rmath/BoBag/deps/build.log`
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Testing AugmentedGaussianProcesses
Resolving package versions...
Status `/tmp/jl_ngP2ob/Manifest.toml`
[621f4979] AbstractFFTs v0.5.0
[0bf59076] AdvancedHMC v0.2.14
[dce04be8] ArgCheck v1.0.1
[7d9fca2a] Arpack v0.3.1
[4fba245c] ArrayInterface v2.0.0
[4c555306] ArrayLayouts v0.1.5
[38eea1fd] AugmentedGaussianProcesses v0.6.0
[13072b0f] AxisAlgorithms v1.0.0
[39de3d68] AxisArrays v0.3.3
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[49dc2e85] Calculus v0.5.1
[324d7699] CategoricalArrays v0.7.3
[aaaa29a8] Clustering v0.13.3
[bbf7d656] CommonSubexpressions v0.2.0
[34da2185] Compat v2.2.0
[8f4d0f93] Conda v1.3.0
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[01453d9d] DiffEqDiffTools v1.5.0
[163ba53b] DiffResults v0.0.4
[b552c78f] DiffRules v0.1.0
[b4f34e82] Distances v0.8.2
[31c24e10] Distributions v0.21.9
[7a1cc6ca] FFTW v1.1.0
[442a2c76] FastGaussQuadrature v0.4.1
[1a297f60] FillArrays v0.8.2
[f6369f11] ForwardDiff v0.10.7
[e1397348] GradDescent v0.3.1
[505f98c9] InplaceOps v0.3.0
[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 v0.2.1
[5078a376] LazyArrays v0.14.10
[d3d80556] LineSearches v7.0.1
[c7f686f2] MCMCChains v0.3.15
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[d41bc354] NLSolversBase v7.5.0
[77ba4419] NaNMath v0.3.3
[b8a86587] NearestNeighbors v0.4.4
[6fe1bfb0] OffsetArrays v0.11.2
[429524aa] Optim v0.19.5
[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 v2.1.1
[b3c3ace0] RangeArrays v0.3.1
[c84ed2f1] Ratios v0.3.1
[3cdcf5f2] RecipesBase v0.7.0
[189a3867] Reexport v0.2.0
[ae029012] Requires v0.5.2
[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
[81def892] VersionParsing v1.1.3
[efce3f68] WoodburyMatrices v0.4.1
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
┌ 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 /root/.julia/packages/GradDescent/C4qjb/src/AbstractOptimizer.jl:22 overwritten in module AugmentedGaussianProcesses at /root/.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
Training Progress: 2%|▋ | ETA: 0:07:05[K
iter: 10
ELBO: 624.055311605656
[A
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Training Progress: 4%|█▎ | ETA: 0:03:55[K
iter: 20
ELBO: 635.674952676726
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Training Progress: 6%|█▉ | ETA: 0:02:57[K
iter: 30
ELBO: 589.8717573550398
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Training Progress: 8%|██▌ | ETA: 0:02:15[K
iter: 40
ELBO: 662.1781749152855
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Training Progress: 10%|███▎ | ETA: 0:01:49[K
iter: 50
ELBO: 623.4231417439881
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[ATraining ended after 50 iterations. Total number of iterations 50
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
Training Progress: 2%|▋ | ETA: 0:26:20[K
iter: 10
ELBO: -152.22527095522128
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Training Progress: 4%|█▎ | ETA: 0:17:16[K
iter: 20
ELBO: -150.83076430626713
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Training Progress: 6%|█▉ | ETA: 0:12:26[K
iter: 30
ELBO: -149.87410204825002
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Training Progress: 8%|██▌ | ETA: 0:10:33[K
iter: 40
ELBO: -149.3256010303905
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[ATraining ended after 50 iterations. Total number of iterations 50
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Training Progress: 10%|███▎ | ETA: 0:09:13[K
iter: 50
ELBO: -149.09330240808055
[A
[A┌ Info: Regression Error
└ err = 0.4242331022347892
Starting training Variational Gaussian Process with a Laplace likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:09:35[K
iter: 10
ELBO: -566.225785866745
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Training Progress: 4%|█▎ | ETA: 0:08:45[K
iter: 20
ELBO: -568.6837211638668
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Training Progress: 6%|█▉ | ETA: 0:08:47[K
iter: 30
ELBO: -570.2704750617578
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Training Progress: 8%|██▌ | ETA: 0:08:45[K
iter: 40
ELBO: -571.5108565720788
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Training Progress: 10%|███▎ | ETA: 0:08:33[K
iter: 50
ELBO: -572.6758898614879
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.4018312504927594
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
Training Progress: 2%|▋ | ETA: 0:17:16[K
iter: 10
ELBO: -151.91804200759827
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Training Progress: 4%|█▎ | ETA: 0:19:20[K
iter: 20
ELBO: -148.2628097629236
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Training Progress: 6%|█▉ | ETA: 0:19:22[K
iter: 30
ELBO: -145.8411732543865
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Training Progress: 8%|██▌ | ETA: 0:17:34[K
iter: 40
ELBO: -143.96603507455967
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[ATraining ended after 50 iterations. Total number of iterations 50
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Training Progress: 10%|███▎ | ETA: 0:16:03[K
iter: 50
ELBO: -142.6234705895817
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[A┌ Info: Regression Error
└ err = 0.6697049944690828
Starting training Variational Gaussian Process with a Bayesian SVM infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:07:50[K
iter: 10
ELBO: 69.36594915002166
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Training Progress: 4%|█▎ | ETA: 0:06:27[K
iter: 20
ELBO: 81.76959815148342
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Training Progress: 6%|█▉ | ETA: 0:06:42[K
iter: 30
ELBO: 93.11305693561144
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Training Progress: 8%|██▌ | ETA: 0:07:34[K
iter: 40
ELBO: 102.88460405829255
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Training Progress: 10%|███▎ | ETA: 0:07:30[K
iter: 50
ELBO: 111.76775053363693
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ 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
Training Progress: 2%|▋ | ETA: 0:10:30[K
iter: 10
ELBO: -15.45496403516649
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Training Progress: 4%|█▎ | ETA: 0:08:17[K
iter: 20
ELBO: -12.921811247704284
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Training Progress: 6%|█▉ | ETA: 0:08:17[K
iter: 30
ELBO: -10.525979508342859
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Training Progress: 8%|██▌ | ETA: 0:07:43[K
iter: 40
ELBO: -8.358200907239684
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Training Progress: 10%|███▎ | ETA: 0:06:48[K
iter: 50
ELBO: -6.4397859300417934
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Classification Error
└ err = 0.06
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
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lgamma), ::typeof(Base.add_sum), ::Array{Float64,1}, ::Int64, ::Int64, ::Int64) at reduce.jl:155
└ @ Base ./reduce.jl:155
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lgamma), ::typeof(Base.add_sum), ::Array{Float64,1}, ::Int64, ::Int64, ::Int64) at reduce.jl:155
└ @ Base ./reduce.jl:155
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = macro expansion at reduce.jl:158 [inlined]
└ @ Core ./reduce.jl:158
Training Progress: 2%|▋ | ETA: 0:29:25[K
iter: 10
ELBO: -35.3935785497838
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Training Progress: 4%|█▎ | ETA: 0:29:46[K
iter: 20
ELBO: -34.450206095421095
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Training Progress: 6%|█▉ | ETA: 0:31:38[K
iter: 30
ELBO: -33.48761577222891
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Training Progress: 8%|██▌ | ETA: 0:31:57[K
iter: 40
ELBO: -32.511984642637856
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Training Progress: 10%|███▎ | ETA: 0:31:56[K
iter: 50
ELBO: -31.53238843149282
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Multiclass Error
└ err = 0.27
Starting training Variational Gaussian Process with a Poisson Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::SubArray{Int64,1,Array{Int64,1},Tuple{UnitRange{Int64}},true}, ::Int64, ::Int64, ::Int64) at reduce.jl:155
└ @ Base ./reduce.jl:155
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::SubArray{Int64,1,Array{Int64,1},Tuple{UnitRange{Int64}},true}, ::Int64, ::Int64, ::Int64) at reduce.jl:155
└ @ Base ./reduce.jl:155
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = macro expansion at reduce.jl:158 [inlined]
└ @ Core ./reduce.jl:158
Training Progress: 2%|▋ | ETA: 0:08:58[K
iter: 10
ELBO: -127.88298349900538
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Training Progress: 4%|█▎ | ETA: 0:09:26[K
iter: 20
ELBO: -127.29460423970174
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Training Progress: 6%|█▉ | ETA: 0:08:11[K
iter: 30
ELBO: -126.78849653826994
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Training Progress: 8%|██▌ | ETA: 0:07:40[K
iter: 40
ELBO: -126.35342619265279
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Training Progress: 10%|███▎ | ETA: 0:06:34[K
iter: 50
ELBO: -125.97784291753513
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 0.7098173135397451
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
Training Progress: 2%|▋ | ETA: 0:07:30[K
iter: 10
ELBO: -721.0880180049668
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Training Progress: 4%|█▎ | ETA: 0:06:41[K
iter: 20
ELBO: -722.0593597319065
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Training Progress: 6%|█▉ | ETA: 0:06:34[K
iter: 30
ELBO: -724.9942368244377
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Training Progress: 8%|██▌ | ETA: 0:06:28[K
iter: 40
ELBO: -729.9041714833297
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Training Progress: 10%|███▎ | ETA: 0:07:01[K
iter: 50
ELBO: -736.0449100226631
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 17.843017416604514
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
Training Progress: 2%|▋ | ETA: 0:18:01[K
iter: 10
ELBO: -17079.797227277842
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iter: 20
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iter: 30
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iter: 40
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iter: 50
ELBO: -2128.361384061914
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┌ Info: Regression Error
└ err = 0.19166431205933365
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
Training Progress: 2%|▋ | ETA: 0:10:42[K
iter: 10
ELBO: -9899.639904103531
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ELBO: -5426.539173392311
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iter: 30
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iter: 50
ELBO: -1244.9903501916276
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┌ Info: Regression Error
└ err = 0.19240798383401675
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
Training Progress: 2%|▋ | ETA: 0:06:36[K
iter: 10
ELBO: -154.46487995526215
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iter: 20
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iter: 30
ELBO: -148.27646371341282
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Training Progress: 8%|██▌ | ETA: 0:03:32[K
iter: 40
ELBO: -144.721419278619
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Training Progress: 10%|███▎ | ETA: 0:03:14[K
iter: 50
ELBO: -149.6070600895724
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[A┌ Info: Regression Error
└ err = 0.37437043455267
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
Training Progress: 2%|▋ | ETA: 0:00:15[K
iter: 10
ELBO: -153.04344847573242
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iter: 20
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iter: 30
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iter: 40
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iter: 50
ELBO: -145.29013276252704
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┌ Info: Regression Error
└ err = 0.41590173469563896
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
Training Progress: 2%|▋ | ETA: 0:06:45[K
iter: 10
ELBO: -484.54426228743625
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iter: 20
ELBO: -508.1824957786268
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iter: 30
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iter: 40
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iter: 50
ELBO: -539.2752458689331
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┌ Info: Regression Error
└ err = 0.36679289894619643
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
Training Progress: 2%|▋ | ETA: 0:03:14[K
iter: 10
ELBO: -532.2261019516801
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iter: 20
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iter: 50
ELBO: -562.3300006862518
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┌ Info: Regression Error
└ err = 0.4045807937532465
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
Training Progress: 2%|▋ | ETA: 0:05:10[K
iter: 10
ELBO: 65.9218130002265
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iter: 20
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iter: 30
ELBO: 73.98349532366115
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iter: 40
ELBO: 74.00915532136801
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iter: 50
ELBO: 97.50413919760484
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└ err = 0.08
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
Training Progress: 2%|▋ | ETA: 0:04:11[K
iter: 10
ELBO: 68.98355584076893
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iter: 20
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iter: 40
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iter: 50
ELBO: 99.92516828116736
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┌ Info: Classification Error
└ err = 0.1
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
Training Progress: 2%|▋ | ETA: 0:06:33[K
iter: 10
ELBO: -27.751042546926456
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iter: 20
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iter: 30
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iter: 40
ELBO: -30.514751102705365
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iter: 50
ELBO: -24.39375109216409
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└ err = 0.09
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
Training Progress: 2%|▋ | ETA: 0:04:27[K
iter: 10
ELBO: -26.85480220115716
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iter: 20
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iter: 30
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iter: 40
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iter: 50
ELBO: -22.567557185060824
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┌ Info: Classification Error
└ err = 0.12
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
Training Progress: 2%|▋ | ETA: 0:33:10[K
iter: 10
ELBO: -49.35338984675457
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iter: 20
ELBO: -41.16411651416668
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iter: 30
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iter: 40
ELBO: -43.97645560619186
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iter: 50
ELBO: -44.020038336192954
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┌ 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
Training Progress: 2%|▋ | ETA: 0:26:00[K
iter: 10
ELBO: -36.0747705571992
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iter: 20
ELBO: -34.4478201922481
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iter: 30
ELBO: -32.80267257984208
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Training Progress: 8%|██▌ | ETA: 0:29:39[K
iter: 40
ELBO: -31.148706915774156
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Training Progress: 10%|███▎ | ETA: 0:27:13[K
iter: 50
ELBO: -29.50121146723589
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[A┌ Info: Multiclass Error
└ err = 0.33
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
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapreduce_first at reduce.jl:293 [inlined]
└ @ Core ./reduce.jl:293
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapfoldl_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::NamedTuple{(:init,),Tuple{Float64}}, ::SubArray{Int64,1,Array{Int64,1},Tuple{Array{Int64,1}},false}, ::Tuple{Base.OneTo{Int64},Int64}) at reduce.jl:45
└ @ Base ./reduce.jl:45
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapfoldl_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::NamedTuple{(:init,),Tuple{Float64}}, ::SubArray{Int64,1,Array{Int64,1},Tuple{Array{Int64,1}},false}, ::Tuple{Base.OneTo{Int64},Int64}) at reduce.jl:49
└ @ Base ./reduce.jl:49
Training Progress: 2%|▋ | ETA: 0:06:38[K
iter: 10
ELBO: -139.15902420996977
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iter: 50
ELBO: -141.88303814571918
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┌ Info: Event Error
└ err = 0.7510338044696269
Starting training Sparse Variational Gaussian Process with a Poisson Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:04:45[K
iter: 10
ELBO: -145.46229622545312
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iter: 20
ELBO: -144.11699398013732
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iter: 30
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iter: 50
ELBO: -141.27613432017748
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┌ Info: Event Error
└ err = 0.7455536649032818
Starting training Sparse Variational Gaussian Process with a Negative Binomial Likelihood infered by Analytic Stochastic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:06:49[K
iter: 10
ELBO: -545.1836920779126
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iter: 20
ELBO: -431.2933407778883
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iter: 30
ELBO: -410.62566331071366
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Training Progress: 8%|██▌ | ETA: 0:05:40[K
iter: 40
ELBO: -416.3963375083516
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Training Progress: 10%|███▎ | ETA: 0:05:28[K
iter: 50
ELBO: -463.387603307715
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 7.459339822755598
Starting training Sparse Variational Gaussian Process with a Negative Binomial Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:05:31[K
iter: 10
ELBO: -432.4950813465152
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iter: 20
ELBO: -421.048391009173
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Training Progress: 6%|█▉ | ETA: 0:05:38[K
iter: 30
ELBO: -413.58238983648727
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Training Progress: 8%|██▌ | ETA: 0:05:30[K
iter: 40
ELBO: -409.5425285298352
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Training Progress: 10%|███▎ | ETA: 0:05:21[K
iter: 50
ELBO: -408.2247063170046
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 7.530005362003821
Test Summary: | Pass Total
Augmented Gaussian Process Testing | 79 79
Testing AugmentedGaussianProcesses tests passed
Results with Julia v1.3.1-pre-7704df0a5a
Testing was successful .
Last evaluation was ago and took 34 minutes, 4 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed Ratios ────────────────────── v0.3.1
Installed FastGaussQuadrature ───────── v0.4.1
Installed Conda ─────────────────────── v1.3.0
Installed QuadGK ────────────────────── v2.1.1
Installed DataStructures ────────────── v0.17.6
Installed KernelFunctions ───────────── v0.2.1
Installed AugmentedGaussianProcesses ── v0.6.0
Installed Optim ─────────────────────── v0.19.5
Installed NaNMath ───────────────────── v0.3.3
Installed ArrayLayouts ──────────────── v0.1.5
Installed SpecialFunctions ──────────── v0.8.0
Installed LazyArrays ────────────────── v0.14.10
Installed Tables ────────────────────── v0.2.11
Installed DataFrames ────────────────── v0.19.4
Installed ArgCheck ──────────────────── v1.0.1
Installed Compat ────────────────────── v2.2.0
Installed DiffResults ───────────────── v0.0.4
Installed Showoff ───────────────────── v0.3.1
Installed BinDeps ───────────────────── v0.8.10
Installed StatsBase ─────────────────── v0.32.0
Installed IterTools ─────────────────── v1.3.0
Installed FillArrays ────────────────── v0.8.2
Installed StaticArrays ──────────────── v0.12.1
Installed MacroTools ────────────────── v0.5.2
Installed ForwardDiff ───────────────── v0.10.7
Installed AxisAlgorithms ────────────── v1.0.0
Installed LineSearches ──────────────── v7.0.1
Installed OffsetArrays ──────────────── v0.11.2
Installed Calculus ──────────────────── v0.5.1
Installed FFTW ──────────────────────── v1.1.0
Installed AxisArrays ────────────────── v0.3.3
Installed URIParser ─────────────────── v0.4.0
Installed Missings ──────────────────── v0.4.3
Installed Arpack ────────────────────── v0.3.1
Installed TableTraits ───────────────── v1.0.0
Installed AdvancedHMC ───────────────── v0.2.14
Installed CommonSubexpressions ──────── v0.2.0
Installed StatsFuns ─────────────────── v0.8.0
Installed BinaryProvider ────────────── v0.5.8
Installed PooledArrays ──────────────── v0.5.2
Installed GradDescent ───────────────── v0.3.1
Installed ProgressMeter ─────────────── v1.2.0
Installed Parameters ────────────────── v0.12.0
Installed InplaceOps ────────────────── v0.3.0
Installed Rmath ─────────────────────── v0.6.0
Installed NLSolversBase ─────────────── v7.5.0
Installed InvertedIndices ───────────── v1.0.0
Installed Requires ──────────────────── v0.5.2
Installed DiffRules ─────────────────── v0.1.0
Installed Interpolations ────────────── v0.12.5
Installed IntervalSets ──────────────── v0.3.2
Installed ArrayInterface ────────────── v2.0.0
Installed AbstractFFTs ──────────────── v0.5.0
Installed Distances ─────────────────── v0.8.2
Installed PositiveFactorizations ────── v0.2.3
Installed KernelDensity ─────────────── v0.5.1
Installed DataValueInterfaces ───────── v1.0.0
Installed Distributions ─────────────── v0.21.9
Installed Reexport ──────────────────── v0.2.0
Installed CategoricalArrays ─────────── v0.7.3
Installed RangeArrays ───────────────── v0.3.1
Installed RecipesBase ───────────────── v0.7.0
Installed PDMats ────────────────────── v0.9.10
Installed Clustering ────────────────── v0.13.3
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed DataAPI ───────────────────── v1.1.0
Installed WoodburyMatrices ──────────── v0.4.1
Installed OrderedCollections ────────── v1.1.0
Installed MCMCChains ────────────────── v0.3.15
Installed NearestNeighbors ──────────── v0.4.4
Installed JSON ──────────────────────── v0.21.0
Installed Parsers ───────────────────── v0.3.10
Installed VersionParsing ────────────── v1.1.3
Installed SortingAlgorithms ─────────── v0.3.1
Installed DiffEqDiffTools ───────────── v1.5.0
Updating `~/.julia/environments/v1.3/Project.toml`
[38eea1fd] + AugmentedGaussianProcesses v0.6.0
Updating `~/.julia/environments/v1.3/Manifest.toml`
[621f4979] + AbstractFFTs v0.5.0
[0bf59076] + AdvancedHMC v0.2.14
[dce04be8] + ArgCheck v1.0.1
[7d9fca2a] + Arpack v0.3.1
[4fba245c] + ArrayInterface v2.0.0
[4c555306] + ArrayLayouts v0.1.5
[38eea1fd] + AugmentedGaussianProcesses v0.6.0
[13072b0f] + AxisAlgorithms v1.0.0
[39de3d68] + AxisArrays v0.3.3
[9e28174c] + BinDeps v0.8.10
[b99e7846] + BinaryProvider v0.5.8
[49dc2e85] + Calculus v0.5.1
[324d7699] + CategoricalArrays v0.7.3
[aaaa29a8] + Clustering v0.13.3
[bbf7d656] + CommonSubexpressions v0.2.0
[34da2185] + Compat v2.2.0
[8f4d0f93] + Conda v1.3.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.19.4
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[01453d9d] + DiffEqDiffTools v1.5.0
[163ba53b] + DiffResults v0.0.4
[b552c78f] + DiffRules v0.1.0
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.21.9
[7a1cc6ca] + FFTW v1.1.0
[442a2c76] + FastGaussQuadrature v0.4.1
[1a297f60] + FillArrays v0.8.2
[f6369f11] + ForwardDiff v0.10.7
[e1397348] + GradDescent v0.3.1
[505f98c9] + InplaceOps v0.3.0
[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 v0.2.1
[5078a376] + LazyArrays v0.14.10
[d3d80556] + LineSearches v7.0.1
[c7f686f2] + MCMCChains v0.3.15
[1914dd2f] + MacroTools v0.5.2
[e1d29d7a] + Missings v0.4.3
[d41bc354] + NLSolversBase v7.5.0
[77ba4419] + NaNMath v0.3.3
[b8a86587] + NearestNeighbors v0.4.4
[6fe1bfb0] + OffsetArrays v0.11.2
[429524aa] + Optim v0.19.5
[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 v2.1.1
[b3c3ace0] + RangeArrays v0.3.1
[c84ed2f1] + Ratios v0.3.1
[3cdcf5f2] + RecipesBase v0.7.0
[189a3867] + Reexport v0.2.0
[ae029012] + Requires v0.5.2
[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
[81def892] + VersionParsing v1.1.3
[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 Conda ───────────→ `~/.julia/packages/Conda/kLXeC/deps/build.log`
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Building Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Building FFTW ────────────→ `~/.julia/packages/FFTW/loJ3F/deps/build.log`
Building Rmath ───────────→ `~/.julia/packages/Rmath/BoBag/deps/build.log`
Testing AugmentedGaussianProcesses
Resolving package versions...
Status `/tmp/jl_xnv7AD/Manifest.toml`
[621f4979] AbstractFFTs v0.5.0
[0bf59076] AdvancedHMC v0.2.14
[dce04be8] ArgCheck v1.0.1
[7d9fca2a] Arpack v0.3.1
[4fba245c] ArrayInterface v2.0.0
[4c555306] ArrayLayouts v0.1.5
[38eea1fd] AugmentedGaussianProcesses v0.6.0
[13072b0f] AxisAlgorithms v1.0.0
[39de3d68] AxisArrays v0.3.3
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[49dc2e85] Calculus v0.5.1
[324d7699] CategoricalArrays v0.7.3
[aaaa29a8] Clustering v0.13.3
[bbf7d656] CommonSubexpressions v0.2.0
[34da2185] Compat v2.2.0
[8f4d0f93] Conda v1.3.0
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[01453d9d] DiffEqDiffTools v1.5.0
[163ba53b] DiffResults v0.0.4
[b552c78f] DiffRules v0.1.0
[b4f34e82] Distances v0.8.2
[31c24e10] Distributions v0.21.9
[7a1cc6ca] FFTW v1.1.0
[442a2c76] FastGaussQuadrature v0.4.1
[1a297f60] FillArrays v0.8.2
[f6369f11] ForwardDiff v0.10.7
[e1397348] GradDescent v0.3.1
[505f98c9] InplaceOps v0.3.0
[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 v0.2.1
[5078a376] LazyArrays v0.14.10
[d3d80556] LineSearches v7.0.1
[c7f686f2] MCMCChains v0.3.15
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[d41bc354] NLSolversBase v7.5.0
[77ba4419] NaNMath v0.3.3
[b8a86587] NearestNeighbors v0.4.4
[6fe1bfb0] OffsetArrays v0.11.2
[429524aa] Optim v0.19.5
[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 v2.1.1
[b3c3ace0] RangeArrays v0.3.1
[c84ed2f1] Ratios v0.3.1
[3cdcf5f2] RecipesBase v0.7.0
[189a3867] Reexport v0.2.0
[ae029012] Requires v0.5.2
[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
[81def892] VersionParsing v1.1.3
[efce3f68] WoodburyMatrices v0.4.1
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
┌ 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 /root/.julia/packages/GradDescent/C4qjb/src/AbstractOptimizer.jl:22 overwritten in module AugmentedGaussianProcesses at /root/.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
Training Progress: 2%|▋ | ETA: 0:12:25[K
iter: 10
ELBO: 624.055311605656
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Training Progress: 4%|█▎ | ETA: 0:06:59[K
iter: 20
ELBO: 635.674952676726
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Training Progress: 6%|█▉ | ETA: 0:05:11[K
iter: 30
ELBO: 589.8717573550398
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Training Progress: 8%|██▌ | ETA: 0:04:20[K
iter: 40
ELBO: 662.1781749152855
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Training Progress: 10%|███▎ | ETA: 0:03:47[K
iter: 50
ELBO: 623.4231417439881
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[ATraining ended after 50 iterations. Total number of iterations 50
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
Training Progress: 2%|▋ | ETA: 0:29:56[K
iter: 10
ELBO: -152.22527095522128
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Training Progress: 4%|█▎ | ETA: 0:20:22[K
iter: 20
ELBO: -150.83076430626713
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Training Progress: 6%|█▉ | ETA: 0:18:36[K
iter: 30
ELBO: -149.87410204825002
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Training Progress: 8%|██▌ | ETA: 0:17:23[K
iter: 40
ELBO: -149.3256010303905
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Training Progress: 10%|███▎ | ETA: 0:15:34[K
iter: 50
ELBO: -149.09330240808055
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.4242331022347892
Starting training Variational Gaussian Process with a Laplace likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:07:52[K
iter: 10
ELBO: -566.225785866745
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Training Progress: 4%|█▎ | ETA: 0:09:04[K
iter: 20
ELBO: -568.6837211638668
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Training Progress: 6%|█▉ | ETA: 0:07:45[K
iter: 30
ELBO: -570.2704750617578
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Training Progress: 8%|██▌ | ETA: 0:07:13[K
iter: 40
ELBO: -571.5108565720788
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Training Progress: 10%|███▎ | ETA: 0:06:29[K
iter: 50
ELBO: -572.6758898614879
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.4018312504927594
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
Training Progress: 2%|▋ | ETA: 0:19:05[K
iter: 10
ELBO: -151.91804200759827
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Training Progress: 4%|█▎ | ETA: 0:16:54[K
iter: 20
ELBO: -148.2628097629236
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Training Progress: 6%|█▉ | ETA: 0:17:23[K
iter: 30
ELBO: -145.8411732543865
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Training Progress: 8%|██▌ | ETA: 0:15:44[K
iter: 40
ELBO: -143.96603507455967
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Training Progress: 10%|███▎ | ETA: 0:14:34[K
iter: 50
ELBO: -142.6234705895817
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.6697049944690828
Starting training Variational Gaussian Process with a Bayesian SVM infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:10:26[K
iter: 10
ELBO: 69.36594915002166
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Training Progress: 4%|█▎ | ETA: 0:09:45[K
iter: 20
ELBO: 81.76959815148342
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Training Progress: 6%|█▉ | ETA: 0:07:39[K
iter: 30
ELBO: 93.11305693561144
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Training Progress: 8%|██▌ | ETA: 0:07:20[K
iter: 40
ELBO: 102.88460405829255
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Training Progress: 10%|███▎ | ETA: 0:07:18[K
iter: 50
ELBO: 111.76775053363693
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ 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
Training Progress: 2%|▋ | ETA: 0:07:52[K
iter: 10
ELBO: -15.45496403516649
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Training Progress: 4%|█▎ | ETA: 0:07:10[K
iter: 20
ELBO: -12.921811247704284
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Training Progress: 6%|█▉ | ETA: 0:07:49[K
iter: 30
ELBO: -10.525979508342859
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Training Progress: 8%|██▌ | ETA: 0:08:29[K
iter: 40
ELBO: -8.358200907239684
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Training Progress: 10%|███▎ | ETA: 0:07:52Training ended after 50 iterations. Total number of iterations 50
[K
iter: 50
ELBO: -6.4397859300417934
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[A┌ Info: Classification Error
└ err = 0.06
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
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lgamma), ::typeof(Base.add_sum), ::Array{Float64,1}, ::Int64, ::Int64, ::Int64) at reduce.jl:155
└ @ Base ./reduce.jl:155
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lgamma), ::typeof(Base.add_sum), ::Array{Float64,1}, ::Int64, ::Int64, ::Int64) at reduce.jl:155
└ @ Base ./reduce.jl:155
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = macro expansion at reduce.jl:158 [inlined]
└ @ Core ./reduce.jl:158
Training Progress: 2%|▋ | ETA: 0:46:16[K
iter: 10
ELBO: -35.3935785497838
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Training Progress: 4%|█▎ | ETA: 0:49:37[K
iter: 20
ELBO: -34.450206095421095
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Training Progress: 6%|█▉ | ETA: 0:47:17[K
iter: 30
ELBO: -33.48761577222891
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Training Progress: 8%|██▌ | ETA: 0:43:03[K
iter: 40
ELBO: -32.511984642637856
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Training Progress: 10%|███▎ | ETA: 0:38:58[K
iter: 50
ELBO: -31.53238843149282
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Multiclass Error
└ err = 0.27
Starting training Variational Gaussian Process with a Poisson Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::SubArray{Int64,1,Array{Int64,1},Tuple{UnitRange{Int64}},true}, ::Int64, ::Int64, ::Int64) at reduce.jl:155
└ @ Base ./reduce.jl:155
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapreduce_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::SubArray{Int64,1,Array{Int64,1},Tuple{UnitRange{Int64}},true}, ::Int64, ::Int64, ::Int64) at reduce.jl:155
└ @ Base ./reduce.jl:155
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = macro expansion at reduce.jl:158 [inlined]
└ @ Core ./reduce.jl:158
Training Progress: 2%|▋ | ETA: 0:05:26[K
iter: 10
ELBO: -127.88298349900538
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Training Progress: 4%|█▎ | ETA: 0:05:42[K
iter: 20
ELBO: -127.29460423970174
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Training Progress: 6%|█▉ | ETA: 0:05:58[K
iter: 30
ELBO: -126.78849653826994
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Training Progress: 8%|██▌ | ETA: 0:05:25[K
iter: 40
ELBO: -126.35342619265279
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Training Progress: 10%|███▎ | ETA: 0:05:38[K
iter: 50
ELBO: -125.97784291753513
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 0.7098173135397451
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
Training Progress: 2%|▋ | ETA: 0:02:44[K
iter: 10
ELBO: -721.0880180049668
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Training Progress: 4%|█▎ | ETA: 0:02:29[K
iter: 20
ELBO: -722.0593597319065
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Training Progress: 6%|█▉ | ETA: 0:02:03[K
iter: 30
ELBO: -724.9942368244377
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Training Progress: 8%|██▌ | ETA: 0:01:39[K
iter: 40
ELBO: -729.9041714833297
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Training Progress: 10%|███▎ | ETA: 0:01:47[K
iter: 50
ELBO: -736.0449100226631
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 17.843017416604514
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
Training Progress: 2%|▋ | ETA: 0:17:56[K
iter: 10
ELBO: -17079.797227277842
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Training Progress: 4%|█▎ | ETA: 0:11:32[K
iter: 20
ELBO: -8599.597977234811
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Training Progress: 6%|█▉ | ETA: 0:09:26[K
iter: 30
ELBO: -5242.010884223601
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Training Progress: 8%|██▌ | ETA: 0:07:50[K
iter: 40
ELBO: -3289.1278946429925
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Training Progress: 10%|███▎ | ETA: 0:06:51[K
iter: 50
ELBO: -2128.361384061914
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.19166431205933365
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
Training Progress: 2%|▋ | ETA: 0:07:24[K
iter: 10
ELBO: -9899.639904103531
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Training Progress: 4%|█▎ | ETA: 0:04:08[K
iter: 20
ELBO: -5426.539173392311
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Training Progress: 6%|█▉ | ETA: 0:03:46[K
iter: 30
ELBO: -2843.9384952479813
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Training Progress: 8%|██▌ | ETA: 0:04:06[K
iter: 40
ELBO: -1750.2504312954447
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Training Progress: 10%|███▎ | ETA: 0:03:47[K
iter: 50
ELBO: -1244.9903501916276
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.19240798383401675
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
Training Progress: 2%|▋ | ETA: 0:04:01[K
iter: 10
ELBO: -154.46487995526215
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Training Progress: 4%|█▎ | ETA: 0:03:43[K
iter: 20
ELBO: -154.20442389905529
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Training Progress: 6%|█▉ | ETA: 0:04:15[K
iter: 30
ELBO: -148.27646371341282
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Training Progress: 8%|██▌ | ETA: 0:03:39[K
iter: 40
ELBO: -144.721419278619
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Training Progress: 10%|███▎ | ETA: 0:03:22[K
iter: 50
ELBO: -149.6070600895724
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.37437043455267
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
Training Progress: 2%|▋ | ETA: 0:02:39[K
iter: 10
ELBO: -153.04344847573242
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Training Progress: 4%|█▎ | ETA: 0:04:24[K
iter: 20
ELBO: -149.99639403772224
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Training Progress: 6%|█▉ | ETA: 0:03:57[K
iter: 30
ELBO: -147.7602417610451
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Training Progress: 8%|██▌ | ETA: 0:03:38[K
iter: 40
ELBO: -146.24357901950046
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Training Progress: 10%|███▎ | ETA: 0:03:15[K
iter: 50
ELBO: -145.29013276252704
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.41590173469563896
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
Training Progress: 2%|▋ | ETA: 0:00:48[K
iter: 10
ELBO: -484.54426228743625
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Training Progress: 6%|█▉ | ETA: 0:00:19[K
iter: 30
ELBO: -529.6422203331739
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Training Progress: 10%|███▎ | ETA: 0:00:16[K
iter: 50
ELBO: -539.2752458689331
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.36679289894619643
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
Training Progress: 2%|▋ | ETA: 0:03:30[K
iter: 10
ELBO: -532.2261019516801
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Training Progress: 4%|█▎ | ETA: 0:04:25[K
iter: 20
ELBO: -541.9117945690705
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Training Progress: 6%|█▉ | ETA: 0:03:42[K
iter: 30
ELBO: -550.4257458267432
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Training Progress: 8%|██▌ | ETA: 0:03:35[K
iter: 40
ELBO: -557.2638921738726
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Training Progress: 10%|███▎ | ETA: 0:03:12[K
iter: 50
ELBO: -562.3300006862518
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Regression Error
└ err = 0.4045807937532465
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
Training Progress: 2%|▋ | ETA: 0:03:08[K
iter: 10
ELBO: 65.9218130002265
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Training Progress: 4%|█▎ | ETA: 0:04:25[K
iter: 20
ELBO: 76.82715598619467
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Training Progress: 6%|█▉ | ETA: 0:03:48[K
iter: 30
ELBO: 73.98349532366115
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Training Progress: 8%|██▌ | ETA: 0:03:48[K
iter: 40
ELBO: 74.00915532136801
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Training Progress: 10%|███▎ | ETA: 0:04:04[K
iter: 50
ELBO: 97.50413919760484
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Classification Error
└ err = 0.08
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
Training Progress: 2%|▋ | ETA: 0:05:10[K
iter: 10
ELBO: 68.98355584076893
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Training Progress: 4%|█▎ | ETA: 0:03:40[K
iter: 20
ELBO: 76.78745658806402
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Training Progress: 6%|█▉ | ETA: 0:04:02[K
iter: 30
ELBO: 84.52423904711736
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Training Progress: 8%|██▌ | ETA: 0:03:24[K
iter: 40
ELBO: 92.2066783805736
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Training Progress: 10%|███▎ | ETA: 0:03:33[K
iter: 50
ELBO: 99.92516828116736
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┌ Info: Classification Error
└ err = 0.1
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
Training Progress: 2%|▋ | ETA: 0:05:50[K
iter: 10
ELBO: -27.751042546926456
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Training Progress: 4%|█▎ | ETA: 0:05:03[K
iter: 20
ELBO: -28.644634401176216
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Training Progress: 6%|█▉ | ETA: 0:04:36[K
iter: 30
ELBO: -27.546234465921838
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Training Progress: 8%|██▌ | ETA: 0:04:31[K
iter: 40
ELBO: -30.514751102705365
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Training Progress: 10%|███▎ | ETA: 0:04:33[K
iter: 50
ELBO: -24.39375109216409
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Classification Error
└ err = 0.09
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
Training Progress: 2%|▋ | ETA: 0:04:54[K
iter: 10
ELBO: -26.85480220115716
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iter: 20
ELBO: -25.465444295531924
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iter: 30
ELBO: -24.217839663708514
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Training Progress: 8%|██▌ | ETA: 0:03:19[K
iter: 40
ELBO: -23.223133523714495
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Training Progress: 10%|███▎ | ETA: 0:03:35[K
iter: 50
ELBO: -22.567557185060824
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Classification Error
└ err = 0.12
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
Training Progress: 2%|▋ | ETA: 0:21:08[K
iter: 10
ELBO: -49.35338984675457
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iter: 20
ELBO: -41.16411651416668
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iter: 30
ELBO: -44.22525228539834
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Training Progress: 8%|██▌ | ETA: 0:22:53[K
iter: 40
ELBO: -43.97645560619186
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Training Progress: 10%|███▎ | ETA: 0:19:49[K
iter: 50
ELBO: -44.020038336192954
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ 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
Training Progress: 2%|▋ | ETA: 0:11:55[K
iter: 10
ELBO: -36.0747705571992
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Training Progress: 4%|█▎ | ETA: 0:13:22[K
iter: 20
ELBO: -34.4478201922481
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Training Progress: 6%|█▉ | ETA: 0:11:09[K
iter: 30
ELBO: -32.80267257984208
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Training Progress: 8%|██▌ | ETA: 0:10:22[K
iter: 40
ELBO: -31.148706915774156
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Training Progress: 10%|███▎ | ETA: 0:11:28[K
iter: 50
ELBO: -29.50121146723589
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[A┌ Info: Multiclass Error
└ err = 0.33
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
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapreduce_first at reduce.jl:293 [inlined]
└ @ Core ./reduce.jl:293
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapfoldl_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::NamedTuple{(:init,),Tuple{Float64}}, ::SubArray{Int64,1,Array{Int64,1},Tuple{Array{Int64,1}},false}, ::Tuple{Base.OneTo{Int64},Int64}) at reduce.jl:45
└ @ Base ./reduce.jl:45
┌ Warning: `lfactorial(x)` is deprecated, use `logfactorial(x)` instead.
│ caller = mapfoldl_impl(::typeof(SpecialFunctions.lfactorial), ::typeof(Base.add_sum), ::NamedTuple{(:init,),Tuple{Float64}}, ::SubArray{Int64,1,Array{Int64,1},Tuple{Array{Int64,1}},false}, ::Tuple{Base.OneTo{Int64},Int64}) at reduce.jl:49
└ @ Base ./reduce.jl:49
Training Progress: 2%|▋ | ETA: 0:05:50[K
iter: 10
ELBO: -139.15902420996977
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Training Progress: 4%|█▎ | ETA: 0:04:39[K
iter: 20
ELBO: -133.03364964276952
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Training Progress: 6%|█▉ | ETA: 0:04:15[K
iter: 30
ELBO: -140.48091147463396
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Training Progress: 8%|██▌ | ETA: 0:04:39[K
iter: 40
ELBO: -144.3885844875022
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Training Progress: 10%|███▎ | ETA: 0:04:36[K
iter: 50
ELBO: -141.88303814571918
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 0.7510338044696269
Starting training Sparse Variational Gaussian Process with a Poisson Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:00:33[K
iter: 10
ELBO: -145.46229622545312
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Training Progress: 4%|█▎ | ETA: 0:02:36[K
iter: 20
ELBO: -144.11699398013732
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Training Progress: 6%|█▉ | ETA: 0:01:56[K
iter: 30
ELBO: -142.97528946376272
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Training Progress: 8%|██▌ | ETA: 0:01:37[K
iter: 40
ELBO: -142.03652823774968
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Training Progress: 10%|███▎ | ETA: 0:01:36[K
iter: 50
ELBO: -141.27613432017748
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 0.7455536649032818
Starting training Sparse Variational Gaussian Process with a Negative Binomial Likelihood infered by Analytic Stochastic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:04:53[K
iter: 10
ELBO: -545.1836920779126
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Training Progress: 4%|█▎ | ETA: 0:04:35[K
iter: 20
ELBO: -431.2933407778883
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Training Progress: 6%|█▉ | ETA: 0:03:51[K
iter: 30
ELBO: -410.62566331071366
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Training Progress: 8%|██▌ | ETA: 0:03:49[K
iter: 40
ELBO: -416.3963375083516
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Training Progress: 10%|███▎ | ETA: 0:03:38[K
iter: 50
ELBO: -463.387603307715
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 7.459339822755598
Starting training Sparse Variational Gaussian Process with a Negative Binomial Likelihood infered by Analytic Variational Inference with 100 samples with 2 features and 1 latent GP
Training Progress: 2%|▋ | ETA: 0:04:47[K
iter: 10
ELBO: -432.4950813465152
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Training Progress: 4%|█▎ | ETA: 0:05:45[K
iter: 20
ELBO: -421.048391009173
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Training Progress: 6%|█▉ | ETA: 0:04:53[K
iter: 30
ELBO: -413.58238983648727
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Training Progress: 8%|██▌ | ETA: 0:04:01[K
iter: 40
ELBO: -409.5425285298352
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Training Progress: 10%|███▎ | ETA: 0:03:43[K
iter: 50
ELBO: -408.2247063170046
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[ATraining ended after 50 iterations. Total number of iterations 50
┌ Info: Event Error
└ err = 7.530005362003821
Test Summary: | Pass Total
Augmented Gaussian Process Testing | 79 79
Testing AugmentedGaussianProcesses tests passed