Package evaluation of GaussianMixtures on Julia 1.12.0-rc2.1 (084dab1917*) started at 2025-09-12T09:11:05.135 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.96s ################################################################################ # Installation # Installing GaussianMixtures... Resolving package versions... Updating `~/.julia/environments/v1.12/Project.toml` [cc18c42c] + GaussianMixtures v0.3.13 Updating `~/.julia/environments/v1.12/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [7d9fca2a] + Arpack v0.5.4 [aaaa29a8] + Clustering v0.15.8 [34da2185] + Compat v4.18.0 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.1 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.120 [ffbed154] + DocStringExtensions v0.9.5 [5789e2e9] + FileIO v1.17.0 [1a297f60] + FillArrays v1.14.0 [cc18c42c] + GaussianMixtures v0.3.13 [076d061b] + HashArrayMappedTries v0.2.0 [34004b35] + HypergeometricFunctions v0.3.28 [92d709cd] + IrrationalConstants v0.2.4 ⌅ [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 v2.5.1 [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 v1.6.0 [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 v1.12.0 [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 v2025.5.20 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.1+0 [bea87d4a] + SuiteSparse_jll v7.8.3+2 [83775a58] + Zlib_jll v1.3.1+2 [8e850b90] + libblastrampoline_jll v5.13.1+1 [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 4.96s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 36.95s ################################################################################ # Testing # Testing GaussianMixtures Status `/tmp/jl_furSc3/Project.toml` [7d9fca2a] Arpack v0.5.4 [aaaa29a8] Clustering v0.15.8 [34da2185] Compat v4.18.0 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.120 [5789e2e9] FileIO v1.17.0 [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 v2.5.1 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.6 [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_furSc3/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 v4.18.0 [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 v0.25.120 [ffbed154] DocStringExtensions v0.9.5 [e2ba6199] ExprTools v0.1.10 [5789e2e9] FileIO v1.17.0 [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 v0.2.4 [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 v3.0.8 [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 v2.5.1 [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 v1.22.0 [3bb67fe8] TranscodingStreams v0.11.3 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 ⌅ [68821587] Arpack_jll v3.5.1+1 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [f50d1b31] Rmath_jll v0.5.1+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [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 [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 v1.12.0 [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 v2025.5.20 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.1+0 [bea87d4a] SuiteSparse_jll v7.8.3+2 [83775a58] Zlib_jll v1.3.1+2 [8e850b90] libblastrampoline_jll v5.13.1+1 [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... 14818.6 ms ✓ GaussianMixtures 1 dependency successfully precompiled in 17 seconds. 84 already precompiled. Precompiling packages... 28550.0 ms ✓ RData 20439.7 ms ✓ RDatasets 2 dependencies successfully precompiled in 50 seconds. 72 already precompiled. (100000, -968793.8619780128, [83791.99922419102, 16208.000775808989], [-21443.671538024493 10165.983137376854 -2746.7170401115773; 21314.142281976383 -9865.44316280858 2803.6215547312645], [[65144.87439385142 8986.812024333012 -3498.232366512306; 8986.812024333012 79973.63292510698 1418.423568552953; -3498.232366512306 1418.423568552953 78008.23556142514], [34853.14591431714 -8641.795616213398 3657.1217279014227; -8641.795616213398 19928.881726549607 -758.4794985401765; 3657.1217279014227 -758.4794985401765 22908.002862537363]]) Test Summary: | Pass Total Time data.jl | 8 8 4m02.7s [ Info: Initializing GMM, 2 Gaussians diag covariance 1 dimensions using 272 data points K-means converged with 3 iterations (objv = 8855.79069767458) ┌ Info: K-means with 272 data points using 3 iterations └ 68.0 data points per parameter [ Info: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points K-means converged with 4 iterations (objv = 1104.4259962158103) ┌ Info: K-means with 272 data points using 4 iterations └ 11.3 data points per parameter ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = lowerbound(vg::VGMM{Float64}, N::Vector{Float64}, mx::Matrix{Float64}, S::Vector{Matrix{Float64}}, Elogπ::Vector{Float64}, ElogdetΛ::Vector{Float64}, ElogpZπqZ::Float64) at bayes.jl:221 └ @ Core ~/.julia/packages/GaussianMixtures/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: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/RYvNa/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 Sep 12 09:19:33 2025: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points , Fri Sep 12 09:19:35 2025: K-means with 272 data points using 4 iterations 11.3 data points per parameter , Fri Sep 12 09:19:35 2025: EM with 272 data points 0 iterations avll -2.078485 5.8 data points per parameter , Fri Sep 12 09:19:45 2025: GMM converted to Variational GMM , Fri Sep 12 09:19:57 2025: iteration 1, lowerbound -3.673964 , Fri Sep 12 09:19:57 2025: iteration 2, lowerbound -3.510022 , Fri Sep 12 09:19:57 2025: iteration 3, lowerbound -3.357130 , Fri Sep 12 09:19:57 2025: iteration 4, lowerbound -3.210920 , Fri Sep 12 09:19:58 2025: dropping number of Gaussions to 7 , Fri Sep 12 09:19:58 2025: iteration 5, lowerbound -3.072794 , Fri Sep 12 09:19:58 2025: dropping number of Gaussions to 6 , Fri Sep 12 09:19:58 2025: iteration 6, lowerbound -2.944115 , Fri Sep 12 09:19:58 2025: iteration 7, lowerbound -2.834878 , Fri Sep 12 09:19:58 2025: dropping number of Gaussions to 5 , Fri Sep 12 09:19:58 2025: iteration 8, lowerbound -2.748721 , Fri Sep 12 09:19:58 2025: iteration 9, lowerbound -2.680958 , Fri Sep 12 09:19:58 2025: dropping number of Gaussions to 4 , Fri Sep 12 09:19:58 2025: iteration 10, lowerbound -2.618076 , Fri Sep 12 09:19:58 2025: dropping number of Gaussions to 3 , Fri Sep 12 09:19:58 2025: iteration 11, lowerbound -2.541852 , Fri Sep 12 09:19:58 2025: iteration 12, lowerbound -2.470844 , Fri Sep 12 09:19:58 2025: iteration 13, lowerbound -2.413575 , Fri Sep 12 09:19:58 2025: iteration 14, lowerbound -2.369637 , Fri Sep 12 09:19:58 2025: iteration 15, lowerbound -2.337221 , Fri Sep 12 09:19:58 2025: iteration 16, lowerbound -2.315692 , Fri Sep 12 09:19:58 2025: iteration 17, lowerbound -2.307444 , Fri Sep 12 09:19:58 2025: dropping number of Gaussions to 2 , Fri Sep 12 09:19:58 2025: iteration 18, lowerbound -2.302963 , Fri Sep 12 09:19:58 2025: iteration 19, lowerbound -2.299262 , Fri Sep 12 09:19:58 2025: iteration 20, lowerbound -2.299257 , Fri Sep 12 09:19:58 2025: iteration 21, lowerbound -2.299255 , Fri Sep 12 09:19:58 2025: iteration 22, lowerbound -2.299254 , Fri Sep 12 09:19:58 2025: iteration 23, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 24, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 25, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 26, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 27, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 28, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 29, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 30, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 31, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 32, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 33, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 34, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 35, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 36, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 37, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 38, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 39, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 40, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 41, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 42, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 43, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 44, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 45, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 46, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 47, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 48, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 49, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: iteration 50, lowerbound -2.299253 , Fri Sep 12 09:19:58 2025: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253 ] α = [95.95490777398598, 178.04509222601402] β = [95.95490777398598, 178.04509222601402] m = [2.0002292577753695 53.85198717246128; 4.250300733269907 79.28686694436182] ν = [97.95490777398598, 180.04509222601402] W = LinearAlgebra.UpperTriangular{Float64, Matrix{Float64}}[[0.3758763611948444 -0.008953123827345989; 0.0 0.012748664777409345], [0.1840415554748438 -0.007644049042327608; 0.0 0.008581705166333558]] Test Summary: | Pass Total Time bayes.jl | 3 3 2m00.4s Kind: diag, size256 nx: 100000 sum(zeroth order stats): 99999.9999999999 avll from stats: -0.9938200998166422 avll from llpg: -0.993820099816641 avll direct: -0.9938200998166411 sum posterior: 100000.0 Kind: full, size16 nx: 100000 sum(zeroth order stats): 100000.00000000003 avll from stats: -0.9824737657002256 avll from llpg: -0.9824737657002256 avll direct: -0.9824737657002256 sum posterior: 100000.0 kind diag, method split ┌ Info: 0: avll = └ tll[1] = -1.4307842791777658 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4308587603491987 │ -1.4307581552642692 │ -1.429316429640822 │ -1.4159281888087107 │ ⋮ │ -1.3876259977151415 │ -1.3876259930153194 └ -1.3876259898254748 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.3877433198191502 │ -1.3876257645425585 │ -1.3870676547111 │ -1.3807909335686936 │ ⋮ │ -1.3365397701992665 │ -1.3365393705375093 └ -1.336539042533742 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.3367382752523522 │ -1.3365621115139263 │ -1.3361847870438737 │ -1.3330075665215295 │ ⋮ │ -1.2752344316490891 │ -1.2752092288756316 └ -1.2751931723206558 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 1 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.2754457730268218 │ -1.2751095838754685 │ -1.2733158001312341 │ -1.255403143074522 │ ⋮ │ -1.175491172265063 │ -1.176407724534349 └ -1.1822275240544058 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 13 │ 17 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 14 │ 18 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 17 │ 19 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 13 │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 17 │ 18 │ 19 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 5 │ 14 │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 13 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 10 │ 17 │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 18 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 13 │ 14 │ 17 │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 5 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 17 │ 18 │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 1 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 13 │ 14 │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 17 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 5 │ 18 │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 17 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 13 │ 14 │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 17 │ 18 │ 19 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 17 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 2 │ 5 │ 13 │ 14 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 17 │ 18 │ 19 │ 21 │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 3 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 13 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 14 │ 17 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 13 │ 18 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 17 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 3 │ 13 │ 14 │ 17 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 18 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 17 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 13 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 14 │ 17 │ 18 │ 19 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 13 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 13 │ 14 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 17 │ 18 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 1 │ 2 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 13 │ 17 │ 19 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 2 │ 14 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 13 │ 17 │ 18 │ 19 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 1 │ 2 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 13 │ 14 │ 17 │ 19 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 3 │ 17 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 18 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 13 │ 17 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.1754549231997464 │ -1.1720712989327458 │ -1.1729396852040603 │ -1.1486470287598143 │ ⋮ │ -1.0760839883304527 │ -1.075771580777055 └ -1.072013794056303 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.4307842791777658 │ -1.4308587603491987 │ -1.4307581552642692 │ -1.429316429640822 │ ⋮ │ -1.0760839883304527 │ -1.075771580777055 └ -1.072013794056303 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 14 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 13 │ 14 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 14 │ 17 │ 19 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 13 │ 14 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 14 │ 17 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 1 │ 2 │ 3 │ 13 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 14 │ 19 │ 21 │ 22 │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 13 │ 14 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 14 │ 17 │ 19 │ ⋮ │ 23 │ 24 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 13 │ 14 │ ⋮ │ 23 │ 24 │ 25 └ @ 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 = 623066.1651489116) ┌ Info: K-means with 32000 data points using 50 iterations └ 37.0 data points per parameter ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 11 │ 17 │ 24 │ 28 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 3 │ 20 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 18 │ 19 │ 26 │ 28 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 1 │ 5 │ 6 │ 14 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 11 │ 17 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 28 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 6 │ 14 │ 19 │ 20 │ 22 │ 26 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 5 │ 16 │ 17 │ 18 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 11 │ 28 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 5 │ 6 │ 9 │ ⋮ │ 26 │ 28 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 1 │ 11 │ 16 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 5 │ 6 │ 11 │ 17 │ ⋮ │ 26 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 1 │ 3 │ 14 │ 16 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 9 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 5 │ 6 │ 11 │ 16 │ ⋮ │ 26 │ 28 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 3 │ 14 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 5 │ 9 │ 11 │ 16 │ ⋮ │ 24 │ 26 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 1 │ 14 │ 18 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 3 │ 6 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 5 │ 11 │ 14 │ 16 │ ⋮ │ 26 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 1 │ 6 │ 9 │ 18 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 3 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 28 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 5 │ 6 │ 11 │ ⋮ │ 24 │ 26 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 3 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 28 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 3 │ 5 │ 6 │ ⋮ │ 26 │ 28 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 18 │ 19 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 9 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 5 │ 11 │ 14 │ 16 │ ⋮ │ 26 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 19 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 18 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 9 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 5 │ 11 │ 14 │ ⋮ │ 24 │ 26 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 19 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 6 │ 9 │ 14 │ 18 │ 24 │ 28 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 1 │ 5 │ 6 │ 11 │ ⋮ │ 26 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 6 │ 9 │ 14 │ 24 │ 28 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 18 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 16-element Vector{Int64}: │ 1 │ 5 │ 6 │ 9 │ ⋮ │ 26 │ 28 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 3 │ 6 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 6 │ 9 │ 14 │ 18 │ 28 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 5 │ 6 │ 11 │ 16 │ ⋮ │ 24 │ 26 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 6 │ 9 │ 14 │ 28 │ 29 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 kind full, method split ┌ Info: 0: avll = └ tll[1] = -1.4152250779266673 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4152546283891414 │ -1.4151925643068837 │ -1.415138706345649 │ -1.4150317146985165 │ ⋮ │ -1.4094436480291255 │ -1.4094434713710546 └ -1.4094433428437352 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4094720400804959 │ -1.4094074754451542 │ -1.409356335470524 │ -1.4092669485558462 │ ⋮ │ -1.4026167340126283 │ -1.4025355361531517 └ -1.4024596014657802 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.4024312644512464 │ -1.402243221875561 │ -1.4020355364716743 │ -1.4013455731938447 │ ⋮ │ -1.351996948946434 │ -1.351912231577261 └ -1.3518385803982258 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.3518351384598537 │ -1.3515283556543924 │ -1.3510634457199873 │ -1.3483457994990695 │ ⋮ │ -1.2907376173762133 │ -1.2907312756243146 └ -1.2907242562586094 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.2907927831573165 │ -1.2904420933073746 │ -1.2899513363968653 │ -1.2872533864484845 │ ⋮ │ -1.2473724186737547 │ -1.2473648875880738 └ -1.247356795485663 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.4152250779266673 │ -1.4152546283891414 │ -1.4151925643068837 │ -1.415138706345649 │ ⋮ │ -1.2473724186737547 │ -1.2473648875880738 └ -1.247356795485663 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 = 671775.5726569551) ┌ 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 9m31.9s [ 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 1053.68s PkgEval succeeded after 1121.89s