Package evaluation of GaussianMixtures on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T13:52:49.049 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.26s ################################################################################ # Installation # Installing GaussianMixtures... Resolving package versions... Updating `~/.julia/environments/v1.11/Project.toml` [cc18c42c] + GaussianMixtures v0.3.12 Updating `~/.julia/environments/v1.11/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [7d9fca2a] + Arpack v0.5.4 [aaaa29a8] + Clustering v0.15.8 [34da2185] + Compat v4.16.0 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.18.22 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.118 [ffbed154] + DocStringExtensions v0.9.4 [5789e2e9] + FileIO v1.17.0 [1a297f60] + FillArrays v1.13.0 [cc18c42c] + GaussianMixtures v0.3.12 [34004b35] + HypergeometricFunctions v0.3.28 [92d709cd] + IrrationalConstants v0.2.4 ⌅ [033835bb] + JLD2 v0.4.54 [692b3bcd] + JLLWrappers v1.7.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [1914dd2f] + MacroTools v0.5.15 [e1d29d7a] + Missings v1.2.0 [b8a86587] + NearestNeighbors v0.4.21 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.33 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.8.0 [6e75b9c4] + ScikitLearnBase v0.5.0 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.0 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.0 [2913bbd2] + StatsBase v0.34.4 [4c63d2b9] + StatsFuns v1.4.0 [3bb67fe8] + TranscodingStreams v0.11.3 ⌅ [68821587] + Arpack_jll v3.5.1+1 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.11.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.11.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.6.0+0 [e37daf67] + LibGit2_jll v1.7.2+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.6+0 [14a3606d] + MozillaCACerts_jll v2023.12.12 [4536629a] + OpenBLAS_jll v0.3.27+1 [05823500] + OpenLibm_jll v0.8.5+0 [bea87d4a] + SuiteSparse_jll v7.7.0+0 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.59.0+0 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 4.5s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 29.18s ################################################################################ # Testing # Testing GaussianMixtures Status `/tmp/jl_zllsy8/Project.toml` [7d9fca2a] Arpack v0.5.4 [aaaa29a8] Clustering v0.15.8 [34da2185] Compat v4.16.0 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.118 [5789e2e9] FileIO v1.17.0 [cc18c42c] GaussianMixtures v0.3.12 ⌅ [033835bb] JLD2 v0.4.54 [90014a1f] PDMats v0.11.33 [ce6b1742] RDatasets v0.7.7 [6e75b9c4] ScikitLearnBase v0.5.0 [276daf66] SpecialFunctions v2.5.0 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.4 [8ba89e20] Distributed v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_zllsy8/Manifest.toml` [66dad0bd] AliasTables v1.1.3 [7d9fca2a] Arpack v0.5.4 [336ed68f] CSV v0.10.15 [324d7699] CategoricalArrays v0.10.8 [aaaa29a8] Clustering v0.15.8 [944b1d66] CodecZlib v0.7.8 [34da2185] Compat v4.16.0 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.0 [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.118 [ffbed154] DocStringExtensions v0.9.4 [e2ba6199] ExprTools v0.1.10 [5789e2e9] FileIO v1.17.0 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.13.0 [cc18c42c] GaussianMixtures v0.3.12 [34004b35] HypergeometricFunctions v0.3.28 [842dd82b] InlineStrings v1.4.3 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [82899510] IteratorInterfaceExtensions v1.0.0 ⌅ [033835bb] JLD2 v0.4.54 [692b3bcd] JLLWrappers v1.7.0 [b964fa9f] LaTeXStrings v1.4.0 [2ab3a3ac] LogExpFunctions v0.3.29 [1914dd2f] MacroTools v0.5.15 [e1d29d7a] Missings v1.2.0 [78c3b35d] Mocking v0.8.1 [b8a86587] NearestNeighbors v0.4.21 [bac558e1] OrderedCollections v1.8.0 [90014a1f] PDMats v0.11.33 [69de0a69] Parsers v2.8.1 [2dfb63ee] PooledArrays v1.4.3 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [08abe8d2] PrettyTables v2.4.0 [43287f4e] PtrArrays v1.3.0 [1fd47b50] QuadGK v2.11.2 ⌅ [df47a6cb] RData v0.8.3 [ce6b1742] RDatasets v0.7.7 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [79098fc4] Rmath v0.8.0 [6e75b9c4] ScikitLearnBase v0.5.0 [6c6a2e73] Scratch v1.2.1 [91c51154] SentinelArrays v1.4.8 [a2af1166] SortingAlgorithms v1.2.1 [276daf66] SpecialFunctions v2.5.0 [90137ffa] StaticArrays v1.9.13 [1e83bf80] StaticArraysCore v1.4.3 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.0 [2913bbd2] StatsBase v0.34.4 [4c63d2b9] StatsFuns v1.4.0 [892a3eda] StringManipulation v0.4.1 [dc5dba14] TZJData v1.5.0+2025b [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.0 [f269a46b] TimeZones v1.21.3 [3bb67fe8] TranscodingStreams v0.11.3 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 ⌅ [68821587] Arpack_jll v3.5.1+1 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [f50d1b31] Rmath_jll v0.5.1+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.6.0+0 [e37daf67] LibGit2_jll v1.7.2+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.6+0 [14a3606d] MozillaCACerts_jll v2023.12.12 [4536629a] OpenBLAS_jll v0.3.27+1 [05823500] OpenLibm_jll v0.8.5+0 [bea87d4a] SuiteSparse_jll v7.7.0+0 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.59.0+0 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... Precompiling GaussianMixtures... 9458.5 ms ✓ GaussianMixtures 1 dependency successfully precompiled in 12 seconds. 81 already precompiled. Precompiling RDatasets... 28931.2 ms ✓ CSV 17754.8 ms ✓ RData 41552.5 ms ✓ RDatasets 3 dependencies successfully precompiled in 89 seconds. 67 already precompiled. (100000, -669137.3368700745, [385.0805617709906, 99614.91943822902], [179.28570010549691 615.1016617523773 845.0647277681136; 30.740610631539475 46.42115344192193 -866.2269964769705], [[448.20655643834755 234.73681750455307 366.15800532064657; 234.73681750455307 1278.2140840104546 1126.7417627978236; 366.15800532064657 1126.7417627978236 2046.2047937116865], [100175.69604694839 -258.4491058420986 -272.7687605868842; -258.44910584209856 98391.5787736294 -1288.3625671084715; -272.7687605868841 -1288.3625671084715 98980.46823413511]]) Test Summary: | Pass Total Time data.jl | 8 8 6m54.6s [ Info: Initializing GMM, 2 Gaussians diag covariance 1 dimensions using 272 data points K-means converged with 4 iterations (objv = 8855.79069767458) ┌ Info: K-means with 272 data points using 4 iterations └ 68.0 data points per parameter [ Info: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points K-means converged with 4 iterations (objv = 801.0714694903645) ┌ Info: K-means with 272 data points using 4 iterations └ 11.3 data points per parameter ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = lowerbound(vg::VGMM{Float64}, N::Vector{Float64}, mx::Matrix{Float64}, S::Vector{Matrix{Float64}}, Elogπ::Vector{Float64}, ElogdetΛ::Vector{Float64}, ElogpZπqZ::Float64) at bayes.jl:221 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/bayes.jl:221 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = lowerbound(vg::VGMM{Float64}, N::Vector{Float64}, mx::Matrix{Float64}, S::Vector{Matrix{Float64}}, Elogπ::Vector{Float64}, ElogdetΛ::Vector{Float64}, ElogpZπqZ::Float64) at bayes.jl:221 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/bayes.jl:221 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:678 [inlined] └ @ Core ./broadcast.jl:678 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = lowerbound(vg::VGMM{Float64}, N::Vector{Float64}, mx::Matrix{Float64}, S::Vector{Matrix{Float64}}, Elogπ::Vector{Float64}, ElogdetΛ::Vector{Float64}, ElogpZπqZ::Float64) at bayes.jl:230 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/bayes.jl:230 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:678 [inlined] └ @ Core ./broadcast.jl:678 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:678 [inlined] └ @ Core ./broadcast.jl:678 History[Tue Apr 8 14:04:22 2025: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points , Tue Apr 8 14:04:26 2025: K-means with 272 data points using 4 iterations 11.3 data points per parameter , Tue Apr 8 14:04:29 2025: EM with 272 data points 0 iterations avll -2.047832 5.8 data points per parameter , Tue Apr 8 14:04:49 2025: GMM converted to Variational GMM , Tue Apr 8 14:05:06 2025: iteration 1, lowerbound -3.796972 , Tue Apr 8 14:05:06 2025: iteration 2, lowerbound -3.666419 , Tue Apr 8 14:05:06 2025: iteration 3, lowerbound -3.511265 , Tue Apr 8 14:05:06 2025: iteration 4, lowerbound -3.321408 , Tue Apr 8 14:05:06 2025: iteration 5, lowerbound -3.120869 , Tue Apr 8 14:05:06 2025: iteration 6, lowerbound -2.947309 , Tue Apr 8 14:05:07 2025: dropping number of Gaussions to 7 , Tue Apr 8 14:05:07 2025: iteration 7, lowerbound -2.826167 , Tue Apr 8 14:05:07 2025: dropping number of Gaussions to 6 , Tue Apr 8 14:05:07 2025: iteration 8, lowerbound -2.753621 , Tue Apr 8 14:05:07 2025: dropping number of Gaussions to 5 , Tue Apr 8 14:05:07 2025: iteration 9, lowerbound -2.705616 , Tue Apr 8 14:05:07 2025: dropping number of Gaussions to 3 , Tue Apr 8 14:05:07 2025: iteration 10, lowerbound -2.642964 , Tue Apr 8 14:05:07 2025: iteration 11, lowerbound -2.572257 , Tue Apr 8 14:05:07 2025: iteration 12, lowerbound -2.503398 , Tue Apr 8 14:05:07 2025: iteration 13, lowerbound -2.440231 , Tue Apr 8 14:05:07 2025: iteration 14, lowerbound -2.389729 , Tue Apr 8 14:05:07 2025: iteration 15, lowerbound -2.351895 , Tue Apr 8 14:05:07 2025: iteration 16, lowerbound -2.324819 , Tue Apr 8 14:05:07 2025: iteration 17, lowerbound -2.309720 , Tue Apr 8 14:05:07 2025: iteration 18, lowerbound -2.308454 , Tue Apr 8 14:05:07 2025: dropping number of Gaussions to 2 , Tue Apr 8 14:05:07 2025: iteration 19, lowerbound -2.302915 , Tue Apr 8 14:05:07 2025: iteration 20, lowerbound -2.299259 , Tue Apr 8 14:05:07 2025: iteration 21, lowerbound -2.299256 , Tue Apr 8 14:05:07 2025: iteration 22, lowerbound -2.299254 , Tue Apr 8 14:05:07 2025: iteration 23, lowerbound -2.299254 , Tue Apr 8 14:05:07 2025: iteration 24, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 25, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 26, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 27, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 28, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 29, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 30, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 31, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 32, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 33, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 34, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 35, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 36, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 37, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 38, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 39, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 40, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 41, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 42, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 43, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 44, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 45, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 46, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 47, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 48, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 49, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: iteration 50, lowerbound -2.299253 , Tue Apr 8 14:05:07 2025: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253 ] α = [178.04509222601408, 95.95490777398592] β = [178.04509222601408, 95.95490777398592] m = [4.250300733269908 79.2868669443618; 2.0002292577753686 53.85198717246127] ν = [180.04509222601408, 97.95490777398592] W = LinearAlgebra.UpperTriangular{Float64, Matrix{Float64}}[[0.18404155547484877 -0.007644049042327725; 0.0 0.00858170516633351], [0.3758763611948455 -0.008953123827346074; 0.0 0.012748664777409213]] Test Summary: | Pass Total Time bayes.jl | 3 3 2m33.6s Kind: diag, size256 nx: 100000 sum(zeroth order stats): 100000.00000000006 avll from stats: -0.9981833533693495 avll from llpg: -0.9981833533693494 avll direct: -0.9981833533693494 sum posterior: 100000.0 Kind: full, size16 nx: 100000 sum(zeroth order stats): 100000.00000000001 avll from stats: -1.0121418368589683 avll from llpg: -1.012141836858968 avll direct: -1.012141836858968 sum posterior: 100000.0 kind diag, method split ┌ Info: 0: avll = └ tll[1] = -1.390102448175211 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.3902139833849494 │ -1.3901099462015842 │ -1.3893775156277464 │ -1.382048672036264 │ ⋮ │ -1.3538669779048018 │ -1.3538534575094057 └ -1.3538327852669454 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.3539452791593263 │ -1.3537375402370517 │ -1.3529180455538712 │ -1.3466840613205495 │ ⋮ │ -1.3031132284780265 │ -1.3030912700047577 └ -1.3030711391183891 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.3032415264836907 │ -1.3030511346779092 │ -1.3026938426885644 │ -1.2995918996473272 │ ⋮ │ -1.2360383508832378 │ -1.2360383490555296 └ -1.2360383476632064 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 8 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 6 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.236294504560475 │ -1.2360357812346392 │ -1.2351246431549752 │ -1.224360097817576 │ ⋮ │ -1.1381912955563454 │ -1.1319894663479695 └ -1.1539375050920624 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 4 │ 11 │ 12 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 4 │ 10 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 4 │ 9 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 10 │ 11 │ ⋮ │ 28 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 4 │ 9 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 10 │ 28 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 9 │ 11 │ 12 │ 16 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 4 │ 10 │ 15 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 9 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 4 │ 10 │ 11 │ 12 │ 16 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 6 │ 9 │ 15 │ 27 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 4 │ 10 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 16 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 4 │ 15 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 27 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 6 │ 16 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 15 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 4 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ 11 │ 12 │ 16 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 15 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 6 │ 9 │ 10 │ 11 │ 12 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 4 │ 16 │ 27 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ 11 │ 12 │ 15 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 4 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 16 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 6 │ 15 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ 11 │ 12 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 16 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ 11 │ 12 │ 15 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 4 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 6 │ 9 │ ⋮ │ 12 │ 16 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 4 │ 15 │ 27 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ 11 │ 12 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 16 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 15 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 4 │ 6 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 16 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 15 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ 11 │ 12 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 16 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 6 │ 9 │ ⋮ │ 12 │ 15 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 27 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ 11 │ 12 │ 16 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 15 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ 11 │ 12 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 4 │ 6 │ 16 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 15 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 4 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.1419794701732373 │ -1.1354324539665563 │ -1.1296558847981197 │ -1.120453654242449 │ ⋮ │ -1.060406708967472 │ -1.0552291826498355 └ -1.0700324600466316 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.390102448175211 │ -1.3902139833849494 │ -1.3901099462015842 │ -1.3893775156277464 │ ⋮ │ -1.060406708967472 │ -1.0552291826498355 └ -1.0700324600466316 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ 11 │ 12 │ 16 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 27 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 3 │ 4 │ 6 │ 9 │ ⋮ │ 16 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 16 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 3 │ 4 │ 6 │ 9 │ ⋮ │ 27 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 27 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 27 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 3 │ 4 │ 6 │ 9 │ ⋮ │ 16 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 3 │ 4 │ 9 │ 10 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 kind diag, method kmeans [ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points K-means terminated without convergence after 50 iterations (objv = 581380.042213642) ┌ Info: K-means with 32000 data points using 50 iterations └ 37.0 data points per parameter ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 21 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 2 │ 9 │ 16 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 6 │ 24 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 8 │ 15 │ 20 │ 21 │ 27 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 16 │ 22 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 6 │ 9 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 15 │ 16 │ 21 │ 24 │ 27 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 5 │ 6 │ 8 │ 9 │ 20 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 14 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 11 │ 21 │ 24 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 15 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 5 │ 6 │ 9 │ 20 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 8 │ 11 │ 14 │ 24 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 16 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 5 │ 6 │ 15 │ 22 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 9 │ 11 │ 14 │ 20 │ 21 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 16 │ 24 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 5 │ 6 │ 9 │ 11 │ 15 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 14 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 8 │ 20 │ 24 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 11 │ 16 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 5 │ 6 │ 14 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 8 │ 9 │ 15 │ 24 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 11 │ 20 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 3 │ 5 │ 6 │ 9 │ ⋮ │ 22 │ 24 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 8 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 6 │ 11 │ 14 │ 21 │ 24 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 5 │ 8 │ 9 │ ⋮ │ 30 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 9 │ 14 │ 16 │ 21 │ 22 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 5 │ 6 │ 8 │ 15 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 20 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 14 │ 22 │ 24 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 6 │ 8 │ 9 │ 15 │ 16 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 5 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 11 │ 20 │ 21 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 9 │ 14 │ 22 │ 27 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 6 │ 15 │ 16 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 8 │ 11 │ 20 │ 21 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 3 │ 6 │ 8 │ 9 │ ⋮ │ 30 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 8 │ 20 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 8 │ 11 │ 21 │ 24 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 3 │ 5 │ 6 │ 8 │ ⋮ │ 24 │ 27 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 8 │ 11 │ 16 │ 24 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 8 │ 11 │ 20 │ 21 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 3 │ 8 │ 9 │ 11 │ ⋮ │ 27 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 6 │ 8 │ 20 │ 24 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 kind full, method split ┌ Info: 0: avll = └ tll[1] = -1.4186147999714431 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.418643146993657 │ -1.4185805150731745 │ -1.4185257162630516 │ -1.4184201792457074 │ ⋮ │ -1.4131059689510341 │ -1.4131056490801333 └ -1.4131053884708389 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4131314911314263 │ -1.4130688352526877 │ -1.413017554667135 │ -1.4129308491641035 │ ⋮ │ -1.3976252794120931 │ -1.3976044080295051 └ -1.3975835191581505 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.39759915836926 │ -1.3974702062070234 │ -1.397345845793284 │ -1.3971398486859057 │ ⋮ │ -1.359683197010966 │ -1.3596450496569155 └ -1.3596066724793376 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.35962781543947 │ -1.3593866093290132 │ -1.3590929002420133 │ -1.3577511740114705 │ ⋮ │ -1.3237664228783463 │ -1.3236702306348502 └ -1.3234791836093682 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.3231257664978457 │ -1.321878120770103 │ -1.319809871922517 │ -1.3171563719503487 │ ⋮ │ -1.2963482928332453 │ -1.296336048250087 └ -1.2963248652195618 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.4186147999714431 │ -1.418643146993657 │ -1.4185805150731745 │ -1.4185257162630516 │ ⋮ │ -1.2963482928332453 │ -1.296336048250087 └ -1.2963248652195618 kind full, method kmeans [ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points K-means terminated without convergence after 50 iterations (objv = 678050.2669240106) ┌ Info: K-means with 32000 data points using 50 iterations └ 37.0 data points per parameter Test Summary: | Broken Total Time train.jl | 1 1 8m57.0s [ Info: Initializing GMM, 2 Gaussians diag covariance 2 dimensions using 900 data points K-means converged with 2 iterations (objv = 7869.867369234178) ┌ Info: K-means with 900 data points using 2 iterations └ 150.0 data points per parameter Test Summary: | Pass Total Time ScikitLearnBase | 1 1 4.1s Testing GaussianMixtures tests passed Testing completed after 1237.7s PkgEval succeeded after 1292.35s