Package evaluation of GaussianMixtures on Julia 1.12.0-DEV.1805 (a080deafdd*) started at 2025-03-24T17:28:33.041 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.06s ################################################################################ # Installation # Installing GaussianMixtures... Resolving package versions... Updating `~/.julia/environments/v1.12/Project.toml` [cc18c42c] + GaussianMixtures v0.3.12 Updating `~/.julia/environments/v1.12/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [7d9fca2a] + Arpack v0.5.4 [aaaa29a8] + Clustering v0.15.8 [34da2185] + Compat v4.16.0 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.18.22 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.118 [ffbed154] + DocStringExtensions v0.9.3 [5789e2e9] + FileIO v1.17.0 [1a297f60] + FillArrays v1.13.0 [cc18c42c] + GaussianMixtures v0.3.12 [34004b35] + HypergeometricFunctions v0.3.28 [92d709cd] + IrrationalConstants v0.2.4 ⌅ [033835bb] + JLD2 v0.4.54 [692b3bcd] + JLLWrappers v1.7.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [1914dd2f] + MacroTools v0.5.15 [e1d29d7a] + Missings v1.2.0 [b8a86587] + NearestNeighbors v0.4.21 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.32 [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [79098fc4] + Rmath v0.8.0 [6e75b9c4] + ScikitLearnBase v0.5.0 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.0 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.0 [2913bbd2] + StatsBase v0.34.4 [4c63d2b9] + StatsFuns v1.3.2 [3bb67fe8] + TranscodingStreams v0.11.3 ⌅ [68821587] + Arpack_jll v3.5.1+1 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [dc6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.11.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.12.0 [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 v1.2.0+0 [deac9b47] + LibCURL_jll v8.6.0+0 [e37daf67] + LibGit2_jll v1.8.0+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.6+1 [14a3606d] + MozillaCACerts_jll v2024.11.26 [4536629a] + OpenBLAS_jll v0.3.28+3 [05823500] + OpenLibm_jll v0.8.1+3 [bea87d4a] + SuiteSparse_jll v7.8.0+1 [83775a58] + Zlib_jll v1.3.1+1 [8e850b90] + libblastrampoline_jll v5.11.2+0 [8e850ede] + nghttp2_jll v1.63.0+1 [3f19e933] + p7zip_jll v17.5.0+1 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 4.36s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 46.6s ################################################################################ # Testing # Testing GaussianMixtures Status `/tmp/jl_bT13r5/Project.toml` [7d9fca2a] Arpack v0.5.4 [aaaa29a8] Clustering v0.15.8 [34da2185] Compat v4.16.0 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.118 [5789e2e9] FileIO v1.17.0 [cc18c42c] GaussianMixtures v0.3.12 ⌅ [033835bb] JLD2 v0.4.54 [90014a1f] PDMats v0.11.32 [ce6b1742] RDatasets v0.7.7 [6e75b9c4] ScikitLearnBase v0.5.0 [276daf66] SpecialFunctions v2.5.0 [10745b16] Statistics v1.11.1 [2913bbd2] StatsBase v0.34.4 [8ba89e20] Distributed v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_bT13r5/Manifest.toml` [66dad0bd] AliasTables v1.1.3 [7d9fca2a] Arpack v0.5.4 [336ed68f] CSV v0.10.15 [324d7699] CategoricalArrays v0.10.8 [aaaa29a8] Clustering v0.15.8 [944b1d66] CodecZlib v0.7.8 [34da2185] Compat v4.16.0 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.0 [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.118 [ffbed154] DocStringExtensions v0.9.3 [e2ba6199] ExprTools v0.1.10 [5789e2e9] FileIO v1.17.0 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.13.0 [cc18c42c] GaussianMixtures v0.3.12 [34004b35] HypergeometricFunctions v0.3.28 [842dd82b] InlineStrings v1.4.3 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [82899510] IteratorInterfaceExtensions v1.0.0 ⌅ [033835bb] JLD2 v0.4.54 [692b3bcd] JLLWrappers v1.7.0 [b964fa9f] LaTeXStrings v1.4.0 [2ab3a3ac] LogExpFunctions v0.3.29 [1914dd2f] MacroTools v0.5.15 [e1d29d7a] Missings v1.2.0 [78c3b35d] Mocking v0.8.1 [b8a86587] NearestNeighbors v0.4.21 [bac558e1] OrderedCollections v1.8.0 [90014a1f] PDMats v0.11.32 [69de0a69] Parsers v2.8.1 [2dfb63ee] PooledArrays v1.4.3 [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [08abe8d2] PrettyTables v2.4.0 [43287f4e] PtrArrays v1.3.0 [1fd47b50] QuadGK v2.11.2 ⌅ [df47a6cb] RData v0.8.3 [ce6b1742] RDatasets v0.7.7 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [79098fc4] Rmath v0.8.0 [6e75b9c4] ScikitLearnBase v0.5.0 [6c6a2e73] Scratch v1.2.1 [91c51154] SentinelArrays v1.4.8 [a2af1166] SortingAlgorithms v1.2.1 [276daf66] SpecialFunctions v2.5.0 [90137ffa] StaticArrays v1.9.13 [1e83bf80] StaticArraysCore v1.4.3 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.0 [2913bbd2] StatsBase v0.34.4 [4c63d2b9] StatsFuns v1.3.2 [892a3eda] StringManipulation v0.4.1 [dc5dba14] TZJData v1.4.0+2025a [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.0 [f269a46b] TimeZones v1.21.3 [3bb67fe8] TranscodingStreams v0.11.3 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 ⌅ [68821587] Arpack_jll v3.5.1+1 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [f50d1b31] Rmath_jll v0.5.1+0 [0dad84c5] ArgTools v1.1.2 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [dc6e5ff7] JuliaSyntaxHighlighting v1.12.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.12.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.12.0 [f489334b] StyledStrings v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.2.0+0 [deac9b47] LibCURL_jll v8.6.0+0 [e37daf67] LibGit2_jll v1.8.0+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.6+1 [14a3606d] MozillaCACerts_jll v2024.11.26 [4536629a] OpenBLAS_jll v0.3.28+3 [05823500] OpenLibm_jll v0.8.1+3 [bea87d4a] SuiteSparse_jll v7.8.0+1 [83775a58] Zlib_jll v1.3.1+1 [8e850b90] libblastrampoline_jll v5.11.2+0 [8e850ede] nghttp2_jll v1.63.0+1 [3f19e933] p7zip_jll v17.5.0+1 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... (100000, -5.315760327839807e6, [35193.70725147195, 64806.29274852805], [13497.35452428127 24754.661747796436 24220.623291975367; -13268.501169819063 -24930.970507359372 -23870.67394241748], [[37479.80062320856 4198.53815548724 2407.8314808219593; 4198.53815548724 41785.05060898163 5488.872198160147; 2407.8314808219593 5488.872198160147 42635.05555149111], [62300.903192904734 -3973.413147499254 -2737.771896929412; -3973.413147499254 58667.5193914528 -5303.292626282414; -2737.771896929412 -5303.292626282414 57199.99943040374]]) Test Summary: | Pass Total Time data.jl | 8 8 3m29.3s [ 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 7 iterations (objv = 968.5637437444784) ┌ Info: K-means with 272 data points using 7 iterations └ 11.3 data points per parameter ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = lowerbound(vg::VGMM{Float64}, N::Vector{Float64}, mx::Matrix{Float64}, S::Vector{Matrix{Float64}}, Elogπ::Vector{Float64}, ElogdetΛ::Vector{Float64}, ElogpZπqZ::Float64) at bayes.jl:221 └ @ Core ~/.julia/packages/GaussianMixtures/tYojF/src/bayes.jl:221 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = lowerbound(vg::VGMM{Float64}, N::Vector{Float64}, mx::Matrix{Float64}, S::Vector{Matrix{Float64}}, Elogπ::Vector{Float64}, ElogdetΛ::Vector{Float64}, ElogpZπqZ::Float64) at bayes.jl:221 └ @ Core ~/.julia/packages/GaussianMixtures/tYojF/src/bayes.jl:221 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:694 [inlined] └ @ Core ./broadcast.jl:694 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = lowerbound(vg::VGMM{Float64}, N::Vector{Float64}, mx::Matrix{Float64}, S::Vector{Matrix{Float64}}, Elogπ::Vector{Float64}, ElogdetΛ::Vector{Float64}, ElogpZπqZ::Float64) at bayes.jl:230 └ @ Core ~/.julia/packages/GaussianMixtures/tYojF/src/bayes.jl:230 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:694 [inlined] └ @ Core ./broadcast.jl:694 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:694 [inlined] └ @ Core ./broadcast.jl:694 History[Mon Mar 24 17:39:12 2025: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points , Mon Mar 24 17:39:14 2025: K-means with 272 data points using 7 iterations 11.3 data points per parameter , Mon Mar 24 17:39:14 2025: EM with 272 data points 0 iterations avll -2.068810 5.8 data points per parameter , Mon Mar 24 17:39:24 2025: GMM converted to Variational GMM , Mon Mar 24 17:39:37 2025: iteration 1, lowerbound -3.873434 , Mon Mar 24 17:39:39 2025: iteration 2, lowerbound -3.772019 , Mon Mar 24 17:39:42 2025: iteration 3, lowerbound -3.661915 , Mon Mar 24 17:39:44 2025: iteration 4, lowerbound -3.525865 , Mon Mar 24 17:39:46 2025: iteration 5, lowerbound -3.376991 , Mon Mar 24 17:39:49 2025: iteration 6, lowerbound -3.240225 , Mon Mar 24 17:39:49 2025: dropping number of Gaussions to 7 , Mon Mar 24 17:39:51 2025: iteration 7, lowerbound -3.125878 , Mon Mar 24 17:39:53 2025: iteration 8, lowerbound -3.035785 , Mon Mar 24 17:39:53 2025: dropping number of Gaussions to 6 , Mon Mar 24 17:39:55 2025: iteration 9, lowerbound -2.967829 , Mon Mar 24 17:39:55 2025: dropping number of Gaussions to 5 , Mon Mar 24 17:39:56 2025: iteration 10, lowerbound -2.908911 , Mon Mar 24 17:39:56 2025: dropping number of Gaussions to 4 , Mon Mar 24 17:39:58 2025: iteration 11, lowerbound -2.851783 , Mon Mar 24 17:39:59 2025: iteration 12, lowerbound -2.812148 , Mon Mar 24 17:40:00 2025: iteration 13, lowerbound -2.793464 , Mon Mar 24 17:40:00 2025: dropping number of Gaussions to 3 , Mon Mar 24 17:40:01 2025: iteration 14, lowerbound -2.783077 , Mon Mar 24 17:40:02 2025: iteration 15, lowerbound -2.770462 , Mon Mar 24 17:40:03 2025: iteration 16, lowerbound -2.756741 , Mon Mar 24 17:40:05 2025: iteration 17, lowerbound -2.735461 , Mon Mar 24 17:40:06 2025: iteration 18, lowerbound -2.703304 , Mon Mar 24 17:40:07 2025: iteration 19, lowerbound -2.657298 , Mon Mar 24 17:40:08 2025: iteration 20, lowerbound -2.597259 , Mon Mar 24 17:40:09 2025: iteration 21, lowerbound -2.528728 , Mon Mar 24 17:40:10 2025: iteration 22, lowerbound -2.462320 , Mon Mar 24 17:40:11 2025: iteration 23, lowerbound -2.407046 , Mon Mar 24 17:40:12 2025: iteration 24, lowerbound -2.364936 , Mon Mar 24 17:40:13 2025: iteration 25, lowerbound -2.333932 , Mon Mar 24 17:40:14 2025: iteration 26, lowerbound -2.313913 , Mon Mar 24 17:40:16 2025: iteration 27, lowerbound -2.307406 , Mon Mar 24 17:40:16 2025: dropping number of Gaussions to 2 , Mon Mar 24 17:40:16 2025: iteration 28, lowerbound -2.302935 , Mon Mar 24 17:40:17 2025: iteration 29, lowerbound -2.299261 , Mon Mar 24 17:40:18 2025: iteration 30, lowerbound -2.299257 , Mon Mar 24 17:40:19 2025: iteration 31, lowerbound -2.299255 , Mon Mar 24 17:40:20 2025: iteration 32, lowerbound -2.299254 , Mon Mar 24 17:40:21 2025: iteration 33, lowerbound -2.299253 , Mon Mar 24 17:40:21 2025: iteration 34, lowerbound -2.299253 , Mon Mar 24 17:40:22 2025: iteration 35, lowerbound -2.299253 , Mon Mar 24 17:40:23 2025: iteration 36, lowerbound -2.299253 , Mon Mar 24 17:40:24 2025: iteration 37, lowerbound -2.299253 , Mon Mar 24 17:40:25 2025: iteration 38, lowerbound -2.299253 , Mon Mar 24 17:40:26 2025: iteration 39, lowerbound -2.299253 , Mon Mar 24 17:40:27 2025: iteration 40, lowerbound -2.299253 , Mon Mar 24 17:40:27 2025: iteration 41, lowerbound -2.299253 , Mon Mar 24 17:40:28 2025: iteration 42, lowerbound -2.299253 , Mon Mar 24 17:40:29 2025: iteration 43, lowerbound -2.299253 , Mon Mar 24 17:40:30 2025: iteration 44, lowerbound -2.299253 , Mon Mar 24 17:40:31 2025: iteration 45, lowerbound -2.299253 , Mon Mar 24 17:40:32 2025: iteration 46, lowerbound -2.299253 , Mon Mar 24 17:40:33 2025: iteration 47, lowerbound -2.299253 , Mon Mar 24 17:40:33 2025: iteration 48, lowerbound -2.299253 , Mon Mar 24 17:40:34 2025: iteration 49, lowerbound -2.299253 , Mon Mar 24 17:40:35 2025: iteration 50, lowerbound -2.299253 , Mon Mar 24 17:40:35 2025: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253 ] α = [95.95490777358289, 178.04509222641715] β = [95.95490777358289, 178.04509222641715] m = [2.000229257771983 53.85198717244364; 4.250300733266638 79.28686694431373] ν = [97.95490777358289, 180.04509222641715] W = LinearAlgebra.UpperTriangular{Float64, Matrix{Float64}}[[0.3758763612004715 -0.008953123827413024; 0.0 0.012748664777426439], [0.18404155547439655 -0.007644049042370374; 0.0 0.008581705166272883]] Test Summary: | Pass Total Time bayes.jl | 3 3 2m57.1s Kind: diag, size256 nx: 100000 sum(zeroth order stats): 99999.99999999997 avll from stats: -1.0156145716709948 avll from llpg: -1.0156145716709946 avll direct: -1.0156145716709946 sum posterior: 100000.0 Kind: full, size16 nx: 100000 sum(zeroth order stats): 100000.00000000001 avll from stats: -0.9797442513597314 avll from llpg: -0.9797442513597316 avll direct: -0.9797442513597315 sum posterior: 100000.0 kind diag, method split ┌ Info: 0: avll = └ tll[1] = -1.414475877998421 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.414561975520116 │ -1.4144862847922646 │ -1.414081094016212 │ -1.409366089336729 │ ⋮ │ -1.370723789960185 │ -1.3707233343817158 └ -1.3707229260020342 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.3708473826129401 │ -1.370710906047938 │ -1.3699123781050984 │ -1.3632390694954264 │ ⋮ │ -1.3235839168347774 │ -1.3235839160682648 └ -1.323583915523363 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.3237177525305674 │ -1.323563864433253 │ -1.3226092056527063 │ -1.3144838480868986 │ ⋮ │ -1.265168959320548 │ -1.2651163389044717 └ -1.2650866400436107 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 3 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 3 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 3 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 3 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 3 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 3 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 3 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 3 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.265277289065805 │ -1.2650291171545474 │ -1.2637077848952525 │ -1.248845428063256 │ ⋮ │ -1.1793947089295076 │ -1.1778409463365762 └ -1.1892992922608925 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 6 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 6 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 6 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 7 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 7 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 5 │ 6 │ 7 │ 8 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 6 │ 7 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 7 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 7 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 5 │ 6 │ 7 │ 8 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 6 │ 7 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 7 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 7 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 5 │ 6 │ 7 │ 8 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 6 │ 7 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 7 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 7 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 8 │ 11 │ 12 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 5 │ 6 │ 7 │ 8 │ ⋮ │ 18 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.1761224506071974 │ -1.170691206611047 │ -1.174375500197894 │ -1.1509707409910228 │ ⋮ │ -1.073257867997668 │ -1.068031230592438 └ -1.0409365092012999 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.414475877998421 │ -1.414561975520116 │ -1.4144862847922646 │ -1.414081094016212 │ ⋮ │ -1.073257867997668 │ -1.068031230592438 └ -1.0409365092012999 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 5 │ 6 │ 8 │ 9 │ ⋮ │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 7 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 5 │ 6 │ 8 │ 9 │ ⋮ │ 24 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 5 │ 6 │ 7 │ 8 │ ⋮ │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 5 │ 6 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 5 │ 6 │ 8 │ 9 │ ⋮ │ 24 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 7 │ 9 │ 17 │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 5 │ 6 │ 8 │ 9 │ ⋮ │ 18 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 kind diag, method kmeans [ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points K-means converged with 50 iterations (objv = 608864.9966909342) ┌ Info: K-means with 32000 data points using 50 iterations └ 37.0 data points per parameter ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 5 │ 16 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 20 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 5 │ 18 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 16 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 5 │ 8 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 20 │ 26 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 16 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 8 │ 18 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 20 │ 26 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 16 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 5 │ 8 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 16 │ 20 │ 21 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 4 │ 5 │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 8 │ 18 │ 26 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 5 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 4 │ 18 │ 21 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 8 │ 20 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 5 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 4 │ 18 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 5 │ 16 │ 20 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 5 │ 8 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 16 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 6 │ 18 │ 20 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 5 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 18 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 4 │ 5 │ 6 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 18 │ 20 │ 26 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 5 │ 8 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 16 │ 20 │ 21 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 8 │ 20 │ 21 │ 26 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 4 │ 16 │ 20 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 20 │ 21 │ 22 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 5 │ 6 │ 8 │ 16 │ 20 │ 21 │ 26 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 4 │ 21 │ 22 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 16 │ 20 │ 21 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 4 │ 5 │ 6 │ 8 │ ⋮ │ 26 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 16 │ 20 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 kind full, method split ┌ Info: 0: avll = └ tll[1] = -1.4128058500572573 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.412833024554647 │ -1.4127735994859651 │ -1.412715663771674 │ -1.4125940960082013 │ ⋮ │ -1.4070546084310622 │ -1.4070545346653147 └ -1.4070544689178506 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4070780635384492 │ -1.4070173845474896 │ -1.4069612055393903 │ -1.406862731593852 │ ⋮ │ -1.4001863474478795 │ -1.4001633785003642 └ -1.4001420684513906 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.4001607094157953 │ -1.3999937043343988 │ -1.399113533444912 │ -1.3938622842868393 │ ⋮ │ -1.353159878971607 │ -1.3529585322453803 └ -1.3525069839971375 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.351553812108312 │ -1.34950302543661 │ -1.347573827754321 │ -1.346687493159153 │ ⋮ │ -1.303322728642041 │ -1.3021961367931123 └ -1.3001160445400346 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.2974805501073166 │ -1.2952104350902542 │ -1.294732157505578 │ -1.2941314832861994 │ ⋮ │ -1.277216192391878 │ -1.2751910087190665 └ -1.2744205399654067 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.4128058500572573 │ -1.412833024554647 │ -1.4127735994859651 │ -1.412715663771674 │ ⋮ │ -1.277216192391878 │ -1.2751910087190665 └ -1.2744205399654067 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 = 671814.5568123668) ┌ 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 9m28.4s [ Info: Initializing GMM, 2 Gaussians diag covariance 2 dimensions using 900 data points K-means converged with 2 iterations (objv = 7869.867369234178) ┌ Info: K-means with 900 data points using 2 iterations └ 150.0 data points per parameter Test Summary: | Pass Total Time ScikitLearnBase | 1 1 2.9s Testing GaussianMixtures tests passed Testing completed after 1229.7s PkgEval succeeded after 1306.79s