Package evaluation to test GaussianMixtures on Julia 1.13.0-DEV.1324 (c1d413cf8a*) started at 2025-10-17T19:41:27.892 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 10.02s ################################################################################ # Installation # Installing GaussianMixtures... Resolving package versions... Updating `~/.julia/environments/v1.13/Project.toml` [cc18c42c] + GaussianMixtures v0.3.13 Updating `~/.julia/environments/v1.13/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [7d9fca2a] + Arpack v0.5.4 [aaaa29a8] + Clustering v0.15.8 [34da2185] + Compat v4.18.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.1 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.122 [ffbed154] + DocStringExtensions v0.9.5 [5789e2e9] + FileIO v1.17.1 [1a297f60] + FillArrays v1.14.0 [cc18c42c] + GaussianMixtures v0.3.13 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [92d709cd] + IrrationalConstants v0.2.6 ⌅ [033835bb] + JLD2 v0.5.15 [692b3bcd] + JLLWrappers v1.7.1 [2ab3a3ac] + LogExpFunctions v0.3.29 [1914dd2f] + MacroTools v0.5.16 [e1d29d7a] + Missings v1.2.0 [b8a86587] + NearestNeighbors v0.4.22 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.35 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.0 [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 [7e506255] + ScopedValues v1.5.0 [a2af1166] + SortingAlgorithms v1.2.2 [276daf66] + SpecialFunctions v2.6.1 [90137ffa] + StaticArrays v1.9.15 [1e83bf80] + StaticArraysCore v1.4.3 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.6 [4c63d2b9] + StatsFuns v1.5.0 [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 v1.7.0 [7b1f6079] + FileWatching 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 v1.13.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.13.0 [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 v1.13.0 [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 v8.16.0+0 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.9.9 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.4+0 [efcefdf7] + PCRE2_jll v10.46.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.1+2 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.67.1+0 [3f19e933] + p7zip_jll v17.6.0+0 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 5.95s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 31.07s ################################################################################ # Testing # Testing GaussianMixtures Status `/tmp/jl_xiE4Ac/Project.toml` [7d9fca2a] Arpack v0.5.4 [aaaa29a8] Clustering v0.15.8 [34da2185] Compat v4.18.1 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.122 [5789e2e9] FileIO v1.17.1 [cc18c42c] GaussianMixtures v0.3.13 ⌅ [033835bb] JLD2 v0.5.15 [90014a1f] PDMats v0.11.35 [ce6b1742] RDatasets v0.7.7 [6e75b9c4] ScikitLearnBase v0.5.0 [276daf66] SpecialFunctions v2.6.1 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.6 [8ba89e20] Distributed v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [56ddb016] Logging v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_xiE4Ac/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 v4.18.1 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.8.0 [864edb3b] DataStructures v0.19.1 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.122 [ffbed154] DocStringExtensions v0.9.5 [e2ba6199] ExprTools v0.1.10 [5789e2e9] FileIO v1.17.1 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.14.0 [cc18c42c] GaussianMixtures v0.3.13 [076d061b] HashArrayMappedTries v0.2.0 [34004b35] HypergeometricFunctions v0.3.28 [842dd82b] InlineStrings v1.4.5 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [82899510] IteratorInterfaceExtensions v1.0.0 ⌅ [033835bb] JLD2 v0.5.15 [692b3bcd] JLLWrappers v1.7.1 [b964fa9f] LaTeXStrings v1.4.0 [2ab3a3ac] LogExpFunctions v0.3.29 [1914dd2f] MacroTools v0.5.16 [e1d29d7a] Missings v1.2.0 [78c3b35d] Mocking v0.8.1 [b8a86587] NearestNeighbors v0.4.22 [bac558e1] OrderedCollections v1.8.1 [90014a1f] PDMats v0.11.35 [69de0a69] Parsers v2.8.3 [2dfb63ee] PooledArrays v1.4.3 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.0 [08abe8d2] PrettyTables v3.1.0 [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 [7e506255] ScopedValues v1.5.0 [6c6a2e73] Scratch v1.3.0 [91c51154] SentinelArrays v1.4.8 [a2af1166] SortingAlgorithms v1.2.2 [276daf66] SpecialFunctions v2.6.1 [90137ffa] StaticArrays v1.9.15 [1e83bf80] StaticArraysCore v1.4.3 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.1 [2913bbd2] StatsBase v0.34.6 [4c63d2b9] StatsFuns v1.5.0 [892a3eda] StringManipulation v0.4.1 [dc5dba14] TZJData v1.5.0+2025b [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [f269a46b] TimeZones v1.22.1 [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 v1.13.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.13.0 [de0858da] Printf v1.11.0 [3fa0cd96] REPL 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 v1.13.0 [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 v8.16.0+0 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.9.9 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.4+0 [efcefdf7] PCRE2_jll v10.46.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.1+2 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.15.0+0 [8e850ede] nghttp2_jll v1.67.1+0 [3f19e933] p7zip_jll v17.6.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... (100000, -2.729464713971464e6, [32588.24472216927, 67411.75527783074], [14218.357161530292 -18964.022176246403 -4576.262381724333; -14526.6174887495 18270.8694719699 4558.3030775533225], [[23122.02431423467 5952.942249881894 -293.08756668319006; 5952.942249881894 30328.666154712188 461.80497555554354; -293.08756668319 461.8049755555437 32031.906143402764], [76637.20169129285 -6018.974659784903 -338.898318036347; -6018.974659784903 70233.3839827863 -492.01157347317775; -338.89831803634695 -492.01157347317775 67042.28576718942]]) Test Summary: | Pass Total Time data.jl | 8 8 4m33.9s [ 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 5 iterations (objv = 879.1420738741881) ┌ Info: K-means with 272 data points using 5 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/RYvNa/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/RYvNa/src/bayes.jl:221 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:698 [inlined] └ @ Core ./broadcast.jl:698 ┌ 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/RYvNa/src/bayes.jl:230 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:698 [inlined] └ @ Core ./broadcast.jl:698 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:698 [inlined] └ @ Core ./broadcast.jl:698 History[Fri Oct 17 19:49:27 2025: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points , Fri Oct 17 19:49:29 2025: K-means with 272 data points using 5 iterations 11.3 data points per parameter , Fri Oct 17 19:49:29 2025: EM with 272 data points 0 iterations avll -2.080070 5.8 data points per parameter , Fri Oct 17 19:49:37 2025: GMM converted to Variational GMM , Fri Oct 17 19:49:47 2025: iteration 1, lowerbound -3.836381 , Fri Oct 17 19:49:47 2025: iteration 2, lowerbound -3.695696 , Fri Oct 17 19:49:47 2025: iteration 3, lowerbound -3.551695 , Fri Oct 17 19:49:47 2025: iteration 4, lowerbound -3.395263 , Fri Oct 17 19:49:47 2025: iteration 5, lowerbound -3.235199 , Fri Oct 17 19:49:47 2025: iteration 6, lowerbound -3.082426 , Fri Oct 17 19:49:48 2025: dropping number of Gaussions to 7 , Fri Oct 17 19:49:48 2025: iteration 7, lowerbound -2.952158 , Fri Oct 17 19:49:48 2025: dropping number of Gaussions to 6 , Fri Oct 17 19:49:48 2025: iteration 8, lowerbound -2.857490 , Fri Oct 17 19:49:48 2025: dropping number of Gaussions to 5 , Fri Oct 17 19:49:48 2025: iteration 9, lowerbound -2.797454 , Fri Oct 17 19:49:48 2025: iteration 10, lowerbound -2.771958 , Fri Oct 17 19:49:48 2025: dropping number of Gaussions to 3 , Fri Oct 17 19:49:48 2025: iteration 11, lowerbound -2.743127 , Fri Oct 17 19:49:48 2025: iteration 12, lowerbound -2.705350 , Fri Oct 17 19:49:48 2025: iteration 13, lowerbound -2.661275 , Fri Oct 17 19:49:48 2025: iteration 14, lowerbound -2.602899 , Fri Oct 17 19:49:48 2025: iteration 15, lowerbound -2.535016 , Fri Oct 17 19:49:48 2025: iteration 16, lowerbound -2.467961 , Fri Oct 17 19:49:48 2025: iteration 17, lowerbound -2.411372 , Fri Oct 17 19:49:48 2025: iteration 18, lowerbound -2.368064 , Fri Oct 17 19:49:48 2025: iteration 19, lowerbound -2.336124 , Fri Oct 17 19:49:48 2025: iteration 20, lowerbound -2.315088 , Fri Oct 17 19:49:48 2025: iteration 21, lowerbound -2.307409 , Fri Oct 17 19:49:48 2025: dropping number of Gaussions to 2 , Fri Oct 17 19:49:48 2025: iteration 22, lowerbound -2.302952 , Fri Oct 17 19:49:48 2025: iteration 23, lowerbound -2.299262 , Fri Oct 17 19:49:48 2025: iteration 24, lowerbound -2.299257 , Fri Oct 17 19:49:48 2025: iteration 25, lowerbound -2.299255 , Fri Oct 17 19:49:48 2025: iteration 26, lowerbound -2.299254 , Fri Oct 17 19:49:48 2025: iteration 27, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 28, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 29, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 30, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 31, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 32, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 33, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 34, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 35, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 36, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 37, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 38, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 39, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 40, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 41, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 42, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 43, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 44, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 45, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 46, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 47, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 48, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 49, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: iteration 50, lowerbound -2.299253 , Fri Oct 17 19:49:48 2025: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253 ] α = [178.0450922260171, 95.95490777398292] β = [178.0450922260171, 95.95490777398292] m = [4.250300733269884 79.28686694436145; 2.0002292577753433 53.85198717246116] ν = [180.0450922260171, 97.95490777398292] W = LinearAlgebra.UpperTriangular{Float64, Matrix{Float64}}[[0.18404155547484277 -0.0076440490423277194; 0.0 0.008581705166333031], [0.3758763611948862 -0.00895312382734672; 0.0 0.012748664777409687]] Test Summary: | Pass Total Time bayes.jl | 3 3 2m11.2s Kind: diag, size256 nx: 100000 sum(zeroth order stats): 99999.99999999994 avll from stats: -0.9746046975169066 avll from llpg: -0.9746046975169066 avll direct: -0.9746046975169066 sum posterior: 100000.0 Kind: full, size16 nx: 100000 sum(zeroth order stats): 100000.00000000003 avll from stats: -1.0163847734963487 avll from llpg: -1.0163847734963485 avll direct: -1.0163847734963485 sum posterior: 100000.0 kind diag, method split ┌ Info: 0: avll = └ tll[1] = -1.409488823931738 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4095536674058786 │ -1.4094909313432793 │ -1.409023112185307 │ -1.4035356816460196 │ ⋮ │ -1.3711099192941618 │ -1.3711099079045093 └ -1.3711098992322157 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.3712171889824423 │ -1.3710959308577428 │ -1.370440762458948 │ -1.364902577566098 │ ⋮ │ -1.3214890279662546 │ -1.3214887259446395 └ -1.3214884872925925 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.3216422945041004 │ -1.3214615231004117 │ -1.32046944782231 │ -1.3115877604387922 │ ⋮ │ -1.2532625255951961 │ -1.2532292521682857 └ -1.253201496646587 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 1 │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 3 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.253359816537119 │ -1.2530728311934947 │ -1.2508096250923924 │ -1.2346079125610299 │ ⋮ │ -1.1734959519485828 │ -1.1672922715749048 └ -1.1702221657242442 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 10 │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 18 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 7 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 11 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 5 │ 7 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 18 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 7 │ 9 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 11 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 16 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 18 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 7 │ 10 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 11 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 7 │ 10 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 18 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ 7 │ 10 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 23 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.1735347423695253 │ -1.1692121175642 │ -1.1662964802493716 │ -1.1528426716229736 │ ⋮ │ -1.0966001347373548 │ -1.0917453450527779 └ -1.073444330724298 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.409488823931738 │ -1.4095536674058786 │ -1.4094909313432793 │ -1.409023112185307 │ ⋮ │ -1.0966001347373548 │ -1.0917453450527779 └ -1.073444330724298 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 16-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 2 │ 3 │ 4 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/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 = 602369.4841209666) ┌ Info: K-means with 32000 data points using 50 iterations └ 37.0 data points per parameter ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 10 │ 12 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 4 │ 11 │ 21 │ 25 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 14 │ 22 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 6 │ 20 │ 24 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 12 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 3 │ 11 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 21 │ 22 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 12 │ 14 │ 20 │ 24 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 10 │ 11 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 21 │ 24 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 12 │ 20 │ 22 │ 25 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 14 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 5 │ 11 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 21 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 12 │ 14 │ 16 │ 20 │ 24 │ 29 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 11 │ 22 │ 25 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 10 │ 12 │ 21 │ 24 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 14 │ 20 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 11 │ 22 │ 25 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 12 │ 16 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 21 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 10 │ 11 │ 14 │ 20 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 12 │ 22 │ 24 │ 25 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 16 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 10 │ 11 │ 14 │ 20 │ 21 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 12 │ 22 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 24 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 10 │ 11 │ 12 │ 14 │ 16 │ 20 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 22 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 12 │ 25 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 10 │ 11 │ 14 │ 20 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 16 │ 21 │ 22 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 12 │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 11 │ 14 │ 20 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 16 │ 21 │ 22 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 11 │ 12 │ 24 │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 10 │ 14 │ 20 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 21 │ 23 │ 24 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 11 │ 12 │ 14 │ 16 │ ⋮ │ 29 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 12 │ 21 │ 24 │ 25 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 11 │ 12 │ 14 │ 16 │ ⋮ │ 29 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 12 │ 21 │ 23 │ 24 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 5 │ 7 │ 11 │ 12 │ ⋮ │ 29 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 7 │ 12 │ 21 │ 23 │ 24 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 7 │ 11 │ 12 │ 14 │ ⋮ │ 29 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 7 │ 12 │ 21 │ 24 │ 25 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 7 │ 11 │ 12 │ 14 │ ⋮ │ 29 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 kind full, method split ┌ Info: 0: avll = └ tll[1] = -1.4230608445304753 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4230899013355975 │ -1.4230264415174274 │ -1.4229656598100224 │ -1.422841634821636 │ ⋮ │ -1.4171163432239684 │ -1.417116222924944 └ -1.4171161213592312 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4171426988578701 │ -1.417078631420407 │ -1.4170232543510102 │ -1.4169275190698098 │ ⋮ │ -1.4101359686984356 │ -1.4100922874101922 └ -1.4100438970541764 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.410034309579212 │ -1.4098205648477817 │ -1.4093929032586174 │ -1.4075467017135066 │ ⋮ │ -1.3439472514531525 │ -1.3439321084098053 └ -1.343917025919481 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.3439686754288247 │ -1.3437439301126874 │ -1.3435436908766976 │ -1.3432328193855367 │ ⋮ │ -1.3242869729198359 │ -1.324219082708894 └ -1.3241421341636876 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.3241313909769357 │ -1.3237218050681043 │ -1.3232749902473824 │ -1.3224363932864511 │ ⋮ │ -1.29457737269202 │ -1.294553438779635 └ -1.2945344431217884 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.4230608445304753 │ -1.4230899013355975 │ -1.4230264415174274 │ -1.4229656598100224 │ ⋮ │ -1.29457737269202 │ -1.294553438779635 └ -1.2945344431217884 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 = 679902.3803168832) ┌ Info: K-means with 32000 data points using 50 iterations └ 37.0 data points per parameter ┌ Warning: Too low occupancy count 24.8 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 12.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 8.3 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 6.9 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 6.5 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 6.5 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 6.6 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 6.7 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 6.8 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 6.6 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 6.2 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 5.7 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 5.4 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 5.1 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 4.7 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 4.4 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 4.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.5 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.1 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.9 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.9 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.9 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.8 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.8 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.7 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.5 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.2 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.2 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.4 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 2.9 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 3.0 for Gausian 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 Test Summary: | Broken Total Time train.jl | 1 1 6m54.5s [ 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 2.7s Testing GaussianMixtures tests passed Testing completed after 870.14s PkgEval succeeded after 930.13s