Package evaluation of GaussianMixtures on Julia 1.13.0-DEV.140 (fac1ce7906*) started at 2025-03-02T12:10:45.298 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 7.62s ################################################################################ # Installation # Installing GaussianMixtures... Resolving package versions... Updating `~/.julia/environments/v1.13/Project.toml` [cc18c42c] + GaussianMixtures v0.3.12 Updating `~/.julia/environments/v1.13/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 v0.18.20 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.117 [ffbed154] + DocStringExtensions v0.9.3 [5789e2e9] + FileIO v1.17.0 [1a297f60] + FillArrays v1.13.0 [cc18c42c] + GaussianMixtures v0.3.12 [34004b35] + HypergeometricFunctions v0.3.27 [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 v0.11.32 [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 v1.3.2 [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 v1.7.0 [7b1f6079] + FileWatching v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.12.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.12.0 [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 v1.12.0 [f489334b] + StyledStrings 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 v1.3.0+1 [deac9b47] + LibCURL_jll v8.11.1+1 [e37daf67] + LibGit2_jll v1.9.0+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2024.12.31 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.5+0 [458c3c95] + OpenSSL_jll v3.0.16+0 [bea87d4a] + SuiteSparse_jll v7.8.3+2 [83775a58] + Zlib_jll v1.3.1+2 [8e850b90] + libblastrampoline_jll v5.12.0+0 [8e850ede] + nghttp2_jll v1.64.0+1 [3f19e933] + p7zip_jll v17.5.0+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 3.07s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 92.56s ################################################################################ # Testing # Testing GaussianMixtures Status `/tmp/jl_KUfauA/Project.toml` [7d9fca2a] Arpack v0.5.4 [aaaa29a8] Clustering v0.15.8 [34da2185] Compat v4.16.0 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.117 [5789e2e9] FileIO v1.17.0 [cc18c42c] GaussianMixtures v0.3.12 ⌅ [033835bb] JLD2 v0.4.54 [90014a1f] PDMats v0.11.32 [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 v1.12.0 [56ddb016] Logging v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_KUfauA/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 v0.18.20 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.117 [ffbed154] DocStringExtensions v0.9.3 [e2ba6199] ExprTools v0.1.10 [5789e2e9] FileIO v1.17.0 [48062228] FilePathsBase v0.9.23 [1a297f60] FillArrays v1.13.0 [cc18c42c] GaussianMixtures v0.3.12 [34004b35] HypergeometricFunctions v0.3.27 [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 v0.11.32 [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 v1.3.2 [892a3eda] StringManipulation v0.4.1 [dc5dba14] TZJData v1.4.0+2025a [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.0 [f269a46b] TimeZones v1.21.2 [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 v1.7.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.12.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.12.0 [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 v1.12.0 [f489334b] StyledStrings 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 v1.3.0+1 [deac9b47] LibCURL_jll v8.11.1+1 [e37daf67] LibGit2_jll v1.9.0+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2024.12.31 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.5+0 [458c3c95] OpenSSL_jll v3.0.16+0 [bea87d4a] SuiteSparse_jll v7.8.3+2 [83775a58] Zlib_jll v1.3.1+2 [8e850b90] libblastrampoline_jll v5.12.0+0 [8e850ede] nghttp2_jll v1.64.0+1 [3f19e933] p7zip_jll v17.5.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... Precompiling packages... 6034.0 ms ✓ CategoricalArrays → CategoricalArraysSentinelArraysExt 32170.5 ms ✓ CSV 28010.9 ms ✓ RData 57782.3 ms ✓ RDatasets 4 dependencies successfully precompiled in 125 seconds. 68 already precompiled. (100000, -987544.3386309583, [64321.744883313906, 35678.2551166861], [-30981.316290300354 -1037.4702967742799 -11303.065014677837; 31182.15266254432 1060.8756730904902 11686.970909665108], [[56259.01055544951 1126.1133615843721 -3157.8473357182133; 1126.1133615843721 82568.03575533644 -2658.838176019739; -3157.847335718214 -2658.838176019739 60907.30138158828], [43313.36019603931 -1132.7480475136217 3154.3015832211945; -1132.7480475136217 16928.30631120345 2627.8339809827785; 3154.301583221194 2627.8339809827785 39279.48405921354]]) Test Summary: | Pass Total Time data.jl | 8 8 5m36.4s [ Info: Initializing GMM, 2 Gaussians diag covariance 1 dimensions using 272 data points K-means converged with 3 iterations (objv = 8855.79069767458) ┌ Info: K-means with 272 data points using 3 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 = 969.7502852101079) ┌ 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 └ @ Core ~/.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 └ @ Core ~/.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:699 [inlined] └ @ Core ./broadcast.jl:699 ┌ 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 └ @ Core ~/.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:699 [inlined] └ @ Core ./broadcast.jl:699 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:699 [inlined] └ @ Core ./broadcast.jl:699 History[Sun Mar 2 12:22:48 2025: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points , Sun Mar 2 12:22:50 2025: K-means with 272 data points using 4 iterations 11.3 data points per parameter , Sun Mar 2 12:22:50 2025: EM with 272 data points 0 iterations avll -2.078096 5.8 data points per parameter , Sun Mar 2 12:22:58 2025: GMM converted to Variational GMM , Sun Mar 2 12:23:14 2025: iteration 1, lowerbound -3.795199 , Sun Mar 2 12:23:18 2025: iteration 2, lowerbound -3.663965 , Sun Mar 2 12:23:22 2025: iteration 3, lowerbound -3.525373 , Sun Mar 2 12:23:26 2025: iteration 4, lowerbound -3.368265 , Sun Mar 2 12:23:30 2025: iteration 5, lowerbound -3.199616 , Sun Mar 2 12:23:31 2025: dropping number of Gaussions to 7 , Sun Mar 2 12:23:35 2025: iteration 6, lowerbound -3.025085 , Sun Mar 2 12:23:38 2025: iteration 7, lowerbound -2.854944 , Sun Mar 2 12:23:38 2025: dropping number of Gaussions to 6 , Sun Mar 2 12:23:41 2025: iteration 8, lowerbound -2.699405 , Sun Mar 2 12:23:41 2025: dropping number of Gaussions to 5 , Sun Mar 2 12:23:44 2025: iteration 9, lowerbound -2.561528 , Sun Mar 2 12:23:44 2025: dropping number of Gaussions to 4 , Sun Mar 2 12:23:46 2025: iteration 10, lowerbound -2.459585 , Sun Mar 2 12:23:48 2025: iteration 11, lowerbound -2.387469 , Sun Mar 2 12:23:50 2025: iteration 12, lowerbound -2.343982 , Sun Mar 2 12:23:50 2025: dropping number of Gaussions to 3 , Sun Mar 2 12:23:52 2025: iteration 13, lowerbound -2.318799 , Sun Mar 2 12:23:53 2025: iteration 14, lowerbound -2.307414 , Sun Mar 2 12:23:53 2025: dropping number of Gaussions to 2 , Sun Mar 2 12:23:55 2025: iteration 15, lowerbound -2.302952 , Sun Mar 2 12:23:56 2025: iteration 16, lowerbound -2.299262 , Sun Mar 2 12:23:58 2025: iteration 17, lowerbound -2.299257 , Sun Mar 2 12:23:59 2025: iteration 18, lowerbound -2.299255 , Sun Mar 2 12:24:01 2025: iteration 19, lowerbound -2.299254 , Sun Mar 2 12:24:02 2025: iteration 20, lowerbound -2.299253 , Sun Mar 2 12:24:04 2025: iteration 21, lowerbound -2.299253 , Sun Mar 2 12:24:05 2025: iteration 22, lowerbound -2.299253 , Sun Mar 2 12:24:07 2025: iteration 23, lowerbound -2.299253 , Sun Mar 2 12:24:08 2025: iteration 24, lowerbound -2.299253 , Sun Mar 2 12:24:10 2025: iteration 25, lowerbound -2.299253 , Sun Mar 2 12:24:11 2025: iteration 26, lowerbound -2.299253 , Sun Mar 2 12:24:13 2025: iteration 27, lowerbound -2.299253 , Sun Mar 2 12:24:14 2025: iteration 28, lowerbound -2.299253 , Sun Mar 2 12:24:16 2025: iteration 29, lowerbound -2.299253 , Sun Mar 2 12:24:17 2025: iteration 30, lowerbound -2.299253 , Sun Mar 2 12:24:18 2025: iteration 31, lowerbound -2.299253 , Sun Mar 2 12:24:20 2025: iteration 32, lowerbound -2.299253 , Sun Mar 2 12:24:21 2025: iteration 33, lowerbound -2.299253 , Sun Mar 2 12:24:23 2025: iteration 34, lowerbound -2.299253 , Sun Mar 2 12:24:24 2025: iteration 35, lowerbound -2.299253 , Sun Mar 2 12:24:26 2025: iteration 36, lowerbound -2.299253 , Sun Mar 2 12:24:27 2025: iteration 37, lowerbound -2.299253 , Sun Mar 2 12:24:29 2025: iteration 38, lowerbound -2.299253 , Sun Mar 2 12:24:30 2025: iteration 39, lowerbound -2.299253 , Sun Mar 2 12:24:31 2025: iteration 40, lowerbound -2.299253 , Sun Mar 2 12:24:33 2025: iteration 41, lowerbound -2.299253 , Sun Mar 2 12:24:34 2025: iteration 42, lowerbound -2.299253 , Sun Mar 2 12:24:35 2025: iteration 43, lowerbound -2.299253 , Sun Mar 2 12:24:37 2025: iteration 44, lowerbound -2.299253 , Sun Mar 2 12:24:38 2025: iteration 45, lowerbound -2.299253 , Sun Mar 2 12:24:40 2025: iteration 46, lowerbound -2.299253 , Sun Mar 2 12:24:41 2025: iteration 47, lowerbound -2.299253 , Sun Mar 2 12:24:43 2025: iteration 48, lowerbound -2.299253 , Sun Mar 2 12:24:44 2025: iteration 49, lowerbound -2.299253 , Sun Mar 2 12:24:46 2025: iteration 50, lowerbound -2.299253 , Sun Mar 2 12:24:46 2025: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253 ] α = [95.954907773986, 178.04509222601402] β = [95.954907773986, 178.04509222601402] m = [2.000229257775369 53.85198717246129; 4.250300733269908 79.28686694436182] ν = [97.954907773986, 180.04509222601402] W = LinearAlgebra.UpperTriangular{Float64, Matrix{Float64}}[[0.375876361194843 -0.008953123827346162; 0.0 0.012748664777409434], [0.184041555474846 -0.007644049042327518; 0.0 0.008581705166333458]] Test Summary: | Pass Total Time bayes.jl | 3 3 3m49.5s Kind: diag, size256 nx: 100000 sum(zeroth order stats): 99999.99999999996 avll from stats: -1.0094251618160643 avll from llpg: -1.0094251618160641 avll direct: -1.0094251618160641 sum posterior: 100000.0 Kind: full, size16 nx: 100000 sum(zeroth order stats): 100000.0 avll from stats: -0.990924218107497 avll from llpg: -0.990924218107497 avll direct: -0.990924218107497 sum posterior: 100000.0 kind diag, method split ┌ Info: 0: avll = └ tll[1] = -1.4232965184450872 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4233791155779723 │ -1.4232963126687768 │ -1.422468549416278 │ -1.414619842741144 │ ⋮ │ -1.3863728731755052 │ -1.386372851125304 └ -1.3863728316461463 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.3865195571120355 │ -1.38639093638649 │ -1.3857278164019626 │ -1.378738656019017 │ ⋮ │ -1.341491804119457 │ -1.34146837209436 └ -1.3414499007912382 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.341661475675855 │ -1.341442545237322 │ -1.340767437830061 │ -1.3334667969668133 │ ⋮ │ -1.2757677363056872 │ -1.2757677221699704 └ -1.2757677117082717 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 7 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.2760886326138878 │ -1.2757605122020457 │ -1.2750825776720616 │ -1.2691410060769235 │ ⋮ │ -1.199787331567537 │ -1.2045778031023082 └ -1.1956260044895834 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 7 │ 8 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 7 │ 8 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 7 │ 8 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 7 │ 8 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 6 │ 7 │ 8 │ 13 │ 14 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 14 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 7 │ 8 │ 14 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 7 │ 8 │ 14 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 7 │ 8 │ 13 │ 14 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 6 │ 7 │ 8 │ 14 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 7 │ 8 │ 13 │ 25 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 6 │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 2 │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 7 │ 8 │ 13 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 7 │ 8 │ 13 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 6 │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 2 │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 7 │ 8 │ 13 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 7 │ 8 │ 13 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 6 │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 2 │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 7 │ 8 │ 13 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 7 │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.2015811127259284 │ -1.19303698253887 │ -1.1994481238236077 │ -1.179102076030884 │ ⋮ │ -1.1362063874878983 │ -1.1375295899519546 └ -1.1448171191167376 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.4232965184450872 │ -1.4233791155779723 │ -1.4232963126687768 │ -1.422468549416278 │ ⋮ │ -1.1362063874878983 │ -1.1375295899519546 └ -1.1448171191167376 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 4 │ 6 │ 7 │ 8 │ 13 │ 25 │ 27 └ @ 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 = 611194.0965372317) ┌ Info: K-means with 32000 data points using 50 iterations └ 37.0 data points per parameter ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 5 │ 9 │ 18 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 12 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 11 │ 13 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 9 │ 18 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 5 │ 8 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 12 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 9 │ 11 │ 23 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 13 │ 18 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 2 │ 5 │ 12 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 8 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 6 │ 9 │ 18 │ 23 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 5 │ 13 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 2 │ 11 │ 12 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 5 │ 6 │ 18 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 12 │ 13 │ 23 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 5 │ 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 = │ 6-element Vector{Int64}: │ 2 │ 12 │ 18 │ 23 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 5 │ 9 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 11 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 12 │ 18 │ 23 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 8 │ 13 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 5 │ 9 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 2 │ 3 │ 6 │ 11 │ ⋮ │ 27 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 5 │ 13 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 3 │ 5 │ 12 │ 18 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 9 │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 8 │ 13 │ 23 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 3 │ 5 │ 12 │ 15 │ 18 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 8 │ 9 │ 11 │ 23 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 3 │ 5 │ 12 │ 18 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 8 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 13 │ 18 │ 23 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 3 │ 5 │ 11 │ 12 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 3 │ 5 │ 9 │ 11 │ 12 │ 15 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 2 │ 3 │ 5 │ 6 │ ⋮ │ 23 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 2 │ 3 │ 5 │ 8 │ ⋮ │ 13 │ 15 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 3 │ 5 │ 11 │ 12 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 2 │ 3 │ 5 │ 6 │ ⋮ │ 23 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 3 │ 5 │ 8 │ 11 │ 12 │ 13 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 2 │ 3 │ 5 │ 9 │ ⋮ │ 15 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 2 │ 3 │ 5 │ 6 │ ⋮ │ 23 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 2 │ 3 │ 5 │ 8 │ ⋮ │ 13 │ 15 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 kind full, method split ┌ Info: 0: avll = └ tll[1] = -1.410775017088718 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4108037147202113 │ -1.4107453015773956 │ -1.4107015257205915 │ -1.4106288188481513 │ ⋮ │ -1.4053503115161652 │ -1.405344666499767 └ -1.4053403421732775 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4053649203497545 │ -1.4053020186515601 │ -1.4052573222645706 │ -1.4051908279850525 │ ⋮ │ -1.3978147942532846 │ -1.397781877019897 └ -1.3977404101937716 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.397722464243852 │ -1.3974919332244122 │ -1.3971533580371218 │ -1.3960091037273938 │ ⋮ │ -1.3464074168895932 │ -1.3463237555419565 └ -1.346260469868781 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.3462706235521011 │ -1.3459884928060606 │ -1.3451625443671658 │ -1.3400327690345637 │ ⋮ │ -1.2964828865664184 │ -1.2963164518448125 └ -1.2959533718247056 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.2952000414532738 │ -1.2930571766924286 │ -1.2901078720224926 │ -1.287292107897464 │ ⋮ │ -1.2529241572049636 │ -1.25291791190816 └ -1.2529124655421353 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.410775017088718 │ -1.4108037147202113 │ -1.4107453015773956 │ -1.4107015257205915 │ ⋮ │ -1.2529241572049636 │ -1.25291791190816 └ -1.2529124655421353 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 = 665771.4814113993) ┌ 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 8m25.0s [ 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 2.9s Testing GaussianMixtures tests passed Testing completed after 1242.89s PkgEval succeeded after 1363.99s