Package evaluation of GaussianMixtures on Julia 1.13.0-DEV.111 (ce76dbf07d*) started at 2025-02-28T01:15:26.877 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 7.86s ################################################################################ # 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 v1.9.12 [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 3.08s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 43.84s ################################################################################ # Testing # Testing GaussianMixtures Status `/tmp/jl_j3bpxf/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_j3bpxf/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 v1.9.12 [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... 73827.4 ms ✓ GaussianMixtures 1 dependency successfully precompiled in 75 seconds. 83 already precompiled. Precompiling packages... 89987.6 ms ✓ DataFrames 25199.0 ms ✓ RData 22700.1 ms ✓ RDatasets 3 dependencies successfully precompiled in 139 seconds. 69 already precompiled. (100000, -1.5481651575143533e6, [98804.5631192339, 1195.4368807660956], [2373.46937388648 -588.3746155160069 -772.415518404832; -2395.26511317416 599.0596663526569 703.0209743896107], [[95328.44810307842 676.6774787036311 1085.0738884607815; 676.6774787036311 99049.79200559 337.59167011990195; 1085.0738884607815 337.591670119902 98500.8239534373], [4889.391384799535 -1089.425785766311 -1142.5964150341936; -1089.425785766311 1410.4547463321805 245.40218518030895; -1142.5964150341938 245.40218518030898 1548.442619557629]]) Test Summary: | Pass Total Time data.jl | 8 8 6m20.8s [ 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 3 iterations (objv = 905.7749369017811) ┌ Info: K-means with 272 data points using 3 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[Fri Feb 28 01:28:49 2025: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points , Fri Feb 28 01:28:51 2025: K-means with 272 data points using 3 iterations 11.3 data points per parameter , Fri Feb 28 01:28:51 2025: EM with 272 data points 0 iterations avll -2.073692 5.8 data points per parameter , Fri Feb 28 01:28:59 2025: GMM converted to Variational GMM , Fri Feb 28 01:29:14 2025: iteration 1, lowerbound -3.835139 , Fri Feb 28 01:29:18 2025: iteration 2, lowerbound -3.687218 , Fri Feb 28 01:29:22 2025: iteration 3, lowerbound -3.522416 , Fri Feb 28 01:29:26 2025: iteration 4, lowerbound -3.332461 , Fri Feb 28 01:29:30 2025: iteration 5, lowerbound -3.141395 , Fri Feb 28 01:29:34 2025: iteration 6, lowerbound -2.980921 , Fri Feb 28 01:29:35 2025: dropping number of Gaussions to 7 , Fri Feb 28 01:29:38 2025: iteration 7, lowerbound -2.867958 , Fri Feb 28 01:29:38 2025: dropping number of Gaussions to 6 , Fri Feb 28 01:29:41 2025: iteration 8, lowerbound -2.785232 , Fri Feb 28 01:29:41 2025: dropping number of Gaussions to 4 , Fri Feb 28 01:29:43 2025: iteration 9, lowerbound -2.719500 , Fri Feb 28 01:29:46 2025: iteration 10, lowerbound -2.663874 , Fri Feb 28 01:29:46 2025: dropping number of Gaussions to 3 , Fri Feb 28 01:29:48 2025: iteration 11, lowerbound -2.616330 , Fri Feb 28 01:29:50 2025: iteration 12, lowerbound -2.564962 , Fri Feb 28 01:29:51 2025: iteration 13, lowerbound -2.516811 , Fri Feb 28 01:29:53 2025: iteration 14, lowerbound -2.471306 , Fri Feb 28 01:29:55 2025: iteration 15, lowerbound -2.430346 , Fri Feb 28 01:29:56 2025: iteration 16, lowerbound -2.394089 , Fri Feb 28 01:29:58 2025: iteration 17, lowerbound -2.361763 , Fri Feb 28 01:30:00 2025: iteration 18, lowerbound -2.333873 , Fri Feb 28 01:30:02 2025: iteration 19, lowerbound -2.314168 , Fri Feb 28 01:30:04 2025: iteration 20, lowerbound -2.307413 , Fri Feb 28 01:30:04 2025: dropping number of Gaussions to 2 , Fri Feb 28 01:30:06 2025: iteration 21, lowerbound -2.302936 , Fri Feb 28 01:30:07 2025: iteration 22, lowerbound -2.299260 , Fri Feb 28 01:30:08 2025: iteration 23, lowerbound -2.299256 , Fri Feb 28 01:30:10 2025: iteration 24, lowerbound -2.299255 , Fri Feb 28 01:30:11 2025: iteration 25, lowerbound -2.299254 , Fri Feb 28 01:30:13 2025: iteration 26, lowerbound -2.299253 , Fri Feb 28 01:30:14 2025: iteration 27, lowerbound -2.299253 , Fri Feb 28 01:30:16 2025: iteration 28, lowerbound -2.299253 , Fri Feb 28 01:30:17 2025: iteration 29, lowerbound -2.299253 , Fri Feb 28 01:30:19 2025: iteration 30, lowerbound -2.299253 , Fri Feb 28 01:30:20 2025: iteration 31, lowerbound -2.299253 , Fri Feb 28 01:30:22 2025: iteration 32, lowerbound -2.299253 , Fri Feb 28 01:30:23 2025: iteration 33, lowerbound -2.299253 , Fri Feb 28 01:30:25 2025: iteration 34, lowerbound -2.299253 , Fri Feb 28 01:30:26 2025: iteration 35, lowerbound -2.299253 , Fri Feb 28 01:30:28 2025: iteration 36, lowerbound -2.299253 , Fri Feb 28 01:30:29 2025: iteration 37, lowerbound -2.299253 , Fri Feb 28 01:30:31 2025: iteration 38, lowerbound -2.299253 , Fri Feb 28 01:30:32 2025: iteration 39, lowerbound -2.299253 , Fri Feb 28 01:30:34 2025: iteration 40, lowerbound -2.299253 , Fri Feb 28 01:30:35 2025: iteration 41, lowerbound -2.299253 , Fri Feb 28 01:30:37 2025: iteration 42, lowerbound -2.299253 , Fri Feb 28 01:30:38 2025: iteration 43, lowerbound -2.299253 , Fri Feb 28 01:30:39 2025: iteration 44, lowerbound -2.299253 , Fri Feb 28 01:30:41 2025: iteration 45, lowerbound -2.299253 , Fri Feb 28 01:30:42 2025: iteration 46, lowerbound -2.299253 , Fri Feb 28 01:30:44 2025: iteration 47, lowerbound -2.299253 , Fri Feb 28 01:30:45 2025: iteration 48, lowerbound -2.299253 , Fri Feb 28 01:30:47 2025: iteration 49, lowerbound -2.299253 , Fri Feb 28 01:30:48 2025: iteration 50, lowerbound -2.299253 , Fri Feb 28 01:30:48 2025: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253 ] α = [178.04509222601502, 95.95490777398503] β = [178.04509222601502, 95.95490777398503] m = [4.250300733269899 79.28686694436168; 2.0002292577753606 53.85198717246123] ν = [180.04509222601502, 97.95490777398503] W = LinearAlgebra.UpperTriangular{Float64, Matrix{Float64}}[[0.18404155547484463 -0.007644049042327498; 0.0 0.008581705166333097], [0.37587636119485496 -0.008953123827346131; 0.0 0.01274866477740925]] Test Summary: | Pass Total Time bayes.jl | 3 3 3m46.2s Kind: diag, size256 nx: 100000 sum(zeroth order stats): 99999.99999999996 avll from stats: -1.01367453008451 avll from llpg: -1.01367453008451 avll direct: -1.01367453008451 sum posterior: 100000.0 Kind: full, size16 nx: 100000 sum(zeroth order stats): 100000.0 avll from stats: -1.0014948591331412 avll from llpg: -1.001494859133141 avll direct: -1.001494859133141 sum posterior: 100000.00000000001 kind diag, method split ┌ Info: 0: avll = └ tll[1] = -1.3956807629848282 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.3957503735865575 │ -1.3956734809282192 │ -1.3947385008158446 │ -1.3848157055261883 │ ⋮ │ -1.3559375517507688 │ -1.3559375010013759 └ -1.3559374601787941 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.3560424006331353 │ -1.3559335707567652 │ -1.3550945638862673 │ -1.3473792598676868 │ ⋮ │ -1.3014467307572146 │ -1.3014387639436207 └ -1.3014313532534258 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.3015873747117241 │ -1.3014098136874417 │ -1.3006379392703555 │ -1.2939457064900202 │ ⋮ │ -1.236389780613154 │ -1.236382885122729 └ -1.2363774871224154 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 4 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 7 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 4 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 4 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.2365875092732435 │ -1.236306530389165 │ -1.2339422628215884 │ -1.2129384493181554 │ ⋮ │ -1.1556961147628908 │ -1.1456896606168407 └ -1.1499079227969187 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 1 │ 2 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 1 │ 2 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 2 │ 7 │ 8 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 2 │ 13 │ 14 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 21 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 2 │ 5 │ 16 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 21 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 16 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ 16 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 13 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 21 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 2 │ 5 │ 16 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 21 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 16 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ 16 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 13 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 21 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 2 │ 5 │ 16 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 21 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 16 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ 16 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 13 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 21 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 2 │ 5 │ 16 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 21 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 5 │ 16 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 1 │ 2 │ 5 │ 12 │ 16 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.1382904445822553 │ -1.132295476633886 │ -1.1259833709821119 │ -1.113357713082992 │ ⋮ │ -1.0307743186884786 │ -1.0430651460855156 └ -1.0446957296470005 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.3956807629848282 │ -1.3957503735865575 │ -1.3956734809282192 │ -1.3947385008158446 │ ⋮ │ -1.0307743186884786 │ -1.0430651460855156 └ -1.0446957296470005 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 16-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 17-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 16-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 17-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 16-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 28 │ 29 │ 30 └ @ 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 = 585042.2542254122) ┌ 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}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 4 │ 12 │ 14 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 9 │ 18 │ 19 │ 21 │ 24 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 13 │ 20 └ @ 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 = │ 6-element Vector{Int64}: │ 2 │ 4 │ 9 │ 12 │ 14 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 18 │ 21 │ 23 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 8 │ 13 │ 15 │ 19 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 6 │ 9 │ 14 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 4 │ 23 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 12 │ 18 │ 24 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 8 │ 9 │ 13 │ 21 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 14 │ 19 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 9 │ 12 │ 15 │ 18 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 8 │ 13 │ 14 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 19 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 4 │ 9 │ 25 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 12 │ 14 │ 15 │ 18 │ ⋮ │ 23 │ 24 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 6 │ 8 │ 13 │ 19 └ @ 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 = │ 3-element Vector{Int64}: │ 4 │ 25 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 12 │ 14 │ 18 │ 19 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 6 │ 9 │ 15 │ 20 │ 23 │ 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 = │ 5-element Vector{Int64}: │ 4 │ 14 │ 24 │ 25 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 9 │ 12 │ 18 │ 19 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 13 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 8 │ 14 │ 15 │ 20 │ 25 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 4 │ 9 │ 19 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 12 │ 18 │ 21 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 13 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 8 │ 9 │ 14 │ 15 │ 19 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 2 │ 4 │ 20 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 12 │ 13 │ 18 │ 21 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 6 │ 9 │ 14 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 8 │ 15 │ 19 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 4 │ 12 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 9 │ 13 │ 14 │ 18 │ 20 │ 21 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 6 │ 8 │ 23 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 19 └ @ 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 = │ 6-element Vector{Int64}: │ 9 │ 13 │ 14 │ 18 │ 21 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 6 │ 19 │ 20 │ 23 │ 25 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 8 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 9 │ 14 │ 15 │ 24 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 12 │ 13 │ 18 │ 21 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 9 │ 12 │ 13 │ 18 │ 20 │ 21 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 2 │ 4 │ 6 │ 8 │ ⋮ │ 23 │ 24 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 12 │ 13 │ 18 │ 20 │ 21 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 9 │ 12 │ 13 │ 18 │ 21 │ 28 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 16-element Vector{Int64}: │ 2 │ 4 │ 6 │ 8 │ ⋮ │ 24 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 9 │ 12 │ 13 │ 18 │ 21 │ 25 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 12 │ 13 │ 18 │ 20 │ 21 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 2 │ 4 │ 6 │ 8 │ ⋮ │ 23 │ 24 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 12 │ 13 │ 18 │ 20 │ 21 │ 28 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 kind full, method split ┌ Info: 0: avll = └ tll[1] = -1.4161234074462994 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4161529815065417 │ -1.4160949629091564 │ -1.4160541997977998 │ -1.4159856955886139 │ ⋮ │ -1.4104283137203464 │ -1.4104282622184003 └ -1.4104282234458443 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4104566974576467 │ -1.4103983107073992 │ -1.410361994813398 │ -1.4103087091438926 │ ⋮ │ -1.4028413330884402 │ -1.4027682007486435 └ -1.4027048490676945 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.4026895805854804 │ -1.4024992727154164 │ -1.4022792879724209 │ -1.4015447710781068 │ ⋮ │ -1.3409960964880057 │ -1.3408679351583295 └ -1.340748540371587 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.3406856454207254 │ -1.3403494981988613 │ -1.3399990244487705 │ -1.3390443085340042 │ ⋮ │ -1.3083050459125534 │ -1.308274855381448 └ -1.3082361778822198 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.3082585009749452 │ -1.3078664758307361 │ -1.307400267325312 │ -1.3064380694426285 │ ⋮ │ -1.261451210671594 │ -1.261443352612968 └ -1.2614355514140942 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.4161234074462994 │ -1.4161529815065417 │ -1.4160949629091564 │ -1.4160541997977998 │ ⋮ │ -1.261451210671594 │ -1.261443352612968 └ -1.2614355514140942 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 = 674277.5320177476) ┌ 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 8m01.8s [ 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 2.8s Testing GaussianMixtures tests passed Testing completed after 1348.3s PkgEval succeeded after 1420.67s