Package evaluation of GaussianMixtures on Julia 1.13.0-DEV.820 (69a2a571e8*) started at 2025-07-07T14:53:06.294 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 8.13s ################################################################################ # 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 v4.17.0 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.18.22 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.120 [ffbed154] + DocStringExtensions v0.9.5 [5789e2e9] + FileIO v1.17.0 [1a297f60] + FillArrays v1.13.0 [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 v1.7.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [1914dd2f] + MacroTools v0.5.16 [e1d29d7a] + Missings v1.2.0 [b8a86587] + NearestNeighbors v0.4.21 [bac558e1] + OrderedCollections v1.8.1 [90014a1f] + PDMats v0.11.35 [aea7be01] + PrecompileTools v1.3.2 [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 [7e506255] + ScopedValues v1.3.0 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.1 [90137ffa] + StaticArrays v1.9.13 [1e83bf80] + StaticArraysCore v1.4.3 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.1 [2913bbd2] + StatsBase v0.34.5 [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 [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 v1.13.0 [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 v1.12.0 [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 v8.14.1+1 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.5.20 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.5+0 [458c3c95] + OpenSSL_jll v3.5.1+0 [efcefdf7] + PCRE2_jll v10.45.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.1+2 [8e850b90] + libblastrampoline_jll v5.13.1+0 [8e850ede] + nghttp2_jll v1.65.0+0 [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 4.07s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 30.08s ################################################################################ # Testing # Testing GaussianMixtures Status `/tmp/jl_NhroYP/Project.toml` [7d9fca2a] Arpack v0.5.4 [aaaa29a8] Clustering v0.15.8 [34da2185] Compat v4.17.0 [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 v0.34.5 [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_NhroYP/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 v4.17.0 [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 v0.25.120 [ffbed154] DocStringExtensions v0.9.5 [e2ba6199] ExprTools v0.1.10 [5789e2e9] FileIO v1.17.0 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.13.0 [cc18c42c] GaussianMixtures v0.3.13 [076d061b] HashArrayMappedTries v0.2.0 [34004b35] HypergeometricFunctions v0.3.28 [842dd82b] InlineStrings v1.4.4 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [82899510] IteratorInterfaceExtensions v1.0.0 [033835bb] JLD2 v0.5.15 [692b3bcd] JLLWrappers v1.7.0 [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 v0.4.21 [bac558e1] OrderedCollections v1.8.1 [90014a1f] PDMats v0.11.35 [69de0a69] Parsers v2.8.3 [2dfb63ee] PooledArrays v1.4.3 [aea7be01] PrecompileTools v1.3.2 [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 [7e506255] ScopedValues v1.3.0 [6c6a2e73] Scratch v1.3.0 [91c51154] SentinelArrays v1.4.8 [a2af1166] SortingAlgorithms v1.2.1 [276daf66] SpecialFunctions v2.5.1 [90137ffa] StaticArrays v1.9.13 [1e83bf80] StaticArraysCore v1.4.3 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.1 [2913bbd2] StatsBase v0.34.5 [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 v1.21.3 [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 v1.7.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.12.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.13.0 [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 v1.12.0 [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 v8.14.1+1 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.5.20 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.5+0 [458c3c95] OpenSSL_jll v3.5.1+0 [efcefdf7] PCRE2_jll v10.45.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.1+2 [8e850b90] libblastrampoline_jll v5.13.1+0 [8e850ede] nghttp2_jll v1.65.0+0 [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... 50048.0 ms ✓ JLD2 11436.6 ms ✓ GaussianMixtures 2 dependencies successfully precompiled in 63 seconds. 84 already precompiled. Precompiling packages... 4738.8 ms ✓ Mocking 85239.3 ms ✓ DataFrames 1127.7 ms ✓ InlineStrings → ParsersExt 7752.2 ms ✓ TimeZones 2567.2 ms ✓ WeakRefStrings 22234.8 ms ✓ RData 29427.6 ms ✓ CSV 20596.7 ms ✓ RDatasets 8 dependencies successfully precompiled in 175 seconds. 65 already precompiled. (100000, -3.6489564480482736e6, [3629.6470850725345, 96370.35291492747], [-4497.416177942617 -5083.211992681541 4163.675211884048; 5027.258348919431 5365.709033518361 -4215.110606027102], [[8099.110299919238 4985.13711719173 -4584.143286173558; 4985.13711719173 9897.715629510234 -4521.275878517387; -4584.143286173558 -4521.275878517387 7512.433846380394], [91865.53654334634 -5129.6121544742455 4479.220372685747; -5129.6121544742455 90273.23406022631 4486.263583031789; 4479.220372685748 4486.263583031788 92901.53072846144]]) Test Summary: | Pass Total Time data.jl | 8 8 4m40.8s [ Info: Initializing GMM, 2 Gaussians diag covariance 1 dimensions using 272 data points K-means converged with 4 iterations (objv = 8855.79069767458) ┌ Info: K-means with 272 data points using 4 iterations └ 68.0 data points per parameter [ Info: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points K-means converged with 10 iterations (objv = 953.6436005331543) ┌ Info: K-means with 272 data points using 10 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:701 [inlined] └ @ Core ./broadcast.jl:701 ┌ 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:701 [inlined] └ @ Core ./broadcast.jl:701 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:701 [inlined] └ @ Core ./broadcast.jl:701 History[Mon Jul 7 15:05:07 2025: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points , Mon Jul 7 15:05:09 2025: K-means with 272 data points using 10 iterations 11.3 data points per parameter , Mon Jul 7 15:05:09 2025: EM with 272 data points 0 iterations avll -2.061328 5.8 data points per parameter , Mon Jul 7 15:05:16 2025: GMM converted to Variational GMM , Mon Jul 7 15:05:27 2025: iteration 1, lowerbound -3.693306 , Mon Jul 7 15:05:27 2025: iteration 2, lowerbound -3.566990 , Mon Jul 7 15:05:27 2025: iteration 3, lowerbound -3.444771 , Mon Jul 7 15:05:27 2025: iteration 4, lowerbound -3.312674 , Mon Jul 7 15:05:28 2025: dropping number of Gaussions to 7 , Mon Jul 7 15:05:28 2025: iteration 5, lowerbound -3.169197 , Mon Jul 7 15:05:28 2025: iteration 6, lowerbound -3.022533 , Mon Jul 7 15:05:28 2025: iteration 7, lowerbound -2.892113 , Mon Jul 7 15:05:28 2025: dropping number of Gaussions to 6 , Mon Jul 7 15:05:28 2025: iteration 8, lowerbound -2.775657 , Mon Jul 7 15:05:28 2025: dropping number of Gaussions to 5 , Mon Jul 7 15:05:28 2025: iteration 9, lowerbound -2.663977 , Mon Jul 7 15:05:28 2025: dropping number of Gaussions to 4 , Mon Jul 7 15:05:28 2025: iteration 10, lowerbound -2.562223 , Mon Jul 7 15:05:28 2025: iteration 11, lowerbound -2.482020 , Mon Jul 7 15:05:28 2025: dropping number of Gaussions to 3 , Mon Jul 7 15:05:28 2025: iteration 12, lowerbound -2.419893 , Mon Jul 7 15:05:28 2025: iteration 13, lowerbound -2.371261 , Mon Jul 7 15:05:28 2025: iteration 14, lowerbound -2.338194 , Mon Jul 7 15:05:28 2025: iteration 15, lowerbound -2.316200 , Mon Jul 7 15:05:28 2025: iteration 16, lowerbound -2.307487 , Mon Jul 7 15:05:28 2025: dropping number of Gaussions to 2 , Mon Jul 7 15:05:28 2025: iteration 17, lowerbound -2.302973 , Mon Jul 7 15:05:28 2025: iteration 18, lowerbound -2.299262 , Mon Jul 7 15:05:28 2025: iteration 19, lowerbound -2.299257 , Mon Jul 7 15:05:28 2025: iteration 20, lowerbound -2.299255 , Mon Jul 7 15:05:28 2025: iteration 21, lowerbound -2.299254 , Mon Jul 7 15:05:28 2025: iteration 22, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 23, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 24, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 25, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 26, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 27, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 28, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 29, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 30, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 31, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 32, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 33, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 34, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 35, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 36, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 37, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 38, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 39, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 40, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 41, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 42, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 43, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: iteration 44, lowerbound -2.299253 , Mon Jul 7 15:05:28 2025: 45 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253 ] α = [178.04509222601746, 95.95490777398248] β = [178.04509222601746, 95.95490777398248] m = [4.2503007332698814 79.28686694436142; 2.00022925777534 53.85198717246116] ν = [180.04509222601746, 97.95490777398248] W = LinearAlgebra.UpperTriangular{Float64, Matrix{Float64}}[[0.18404155547484152 -0.007644049042327754; 0.0 0.008581705166333076], [0.3758763611948915 -0.008953123827346659; 0.0 0.012748664777409746]] Test Summary: | Pass Total Time bayes.jl | 3 3 2m11.4s Kind: diag, size256 nx: 100000 sum(zeroth order stats): 99999.99999999993 avll from stats: -0.9932729925134413 avll from llpg: -0.99327299251344 avll direct: -0.99327299251344 sum posterior: 100000.0 Kind: full, size16 nx: 100000 sum(zeroth order stats): 100000.0 avll from stats: -0.9975555420287862 avll from llpg: -0.9975555420287864 avll direct: -0.9975555420287864 sum posterior: 100000.0 kind diag, method split ┌ Info: 0: avll = └ tll[1] = -1.442276140333176 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4423637031338985 │ -1.4422784095517687 │ -1.4415561028650923 │ -1.4329218220747513 │ ⋮ │ -1.4027897945492882 │ -1.402789782907539 └ -1.4027897738466923 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4029286560714813 │ -1.4028024157122385 │ -1.4022846603126935 │ -1.3977483844079597 │ ⋮ │ -1.3600444796716125 │ -1.360034331414757 └ -1.3600221850086975 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.3601985256222704 │ -1.3599815055004838 │ -1.3590650155295707 │ -1.350617285916225 │ ⋮ │ -1.2942785598735749 │ -1.2942726771957485 └ -1.2942647466780894 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ 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 = │ 4-element Vector{Int64}: │ 2 │ 4 │ 6 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 7 │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ 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 = │ 2-element Vector{Int64}: │ 2 │ 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 = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 7 │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 4 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 7 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 4 │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 4 │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ 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 = │ 3-element Vector{Int64}: │ 2 │ 4 │ 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 = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 7 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 2 │ 11 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.2944923632205683 │ -1.294175974204789 │ -1.2918332687826217 │ -1.2692618746861937 │ ⋮ │ -1.1917044211570245 │ -1.186973385370498 └ -1.1838410614341468 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 4 │ 7 │ 8 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 7 │ 8 │ 13 │ 14 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 7 │ 8 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 7 │ 8 │ 13 │ 14 │ 20 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 20 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 20 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 23 │ 24 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 13 │ 14 │ 20 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 23 │ 24 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 9 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 3 │ 4 │ 5 │ 7 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 13 │ 14 │ 19 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 14 │ 19 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 3 │ 4 │ 5 │ 7 │ ⋮ │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 10 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 13 │ 14 │ 19 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 3 │ 4 │ 5 │ 7 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 14 │ 19 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 10 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 13 │ 14 │ 19 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 5 │ 7 │ ⋮ │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 14 │ 19 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 10 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 3 │ 4 │ 5 │ 7 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ 13 │ 14 │ 19 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.1704373126390528 │ -1.1632313076186938 │ -1.1674287659317013 │ -1.1485914129645163 │ ⋮ │ -1.0968919709244846 │ -1.0779878243860603 └ -1.0691410669278858 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.442276140333176 │ -1.4423637031338985 │ -1.4422784095517687 │ -1.4415561028650923 │ ⋮ │ -1.0968919709244846 │ -1.0779878243860603 └ -1.0691410669278858 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 16-element Vector{Int64}: │ 3 │ 4 │ 5 │ 7 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 24 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 4 │ 7 │ 8 │ ⋮ │ 22 │ 23 │ 24 └ @ 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 = 642363.8246400584) ┌ Info: K-means with 32000 data points using 50 iterations └ 37.0 data points per parameter ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 20 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 1 │ 10 │ 19 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 11 │ 13 │ 16 │ 18 │ 21 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 5 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 3 │ 9 │ 14 │ 22 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 6 │ 7 │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 10 │ 11 │ 16 │ 18 │ 19 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 5 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 1 │ 3 │ 9 │ 22 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 14 │ 26 │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 10 │ 11 │ 16 │ 18 │ 19 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 5 │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 1 │ 3 │ 9 │ 13 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 2 │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 7 │ 10 │ 11 │ 16 │ ⋮ │ 21 │ 26 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 6 │ 14 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 3 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 13 │ 27 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 10 │ 18 │ 21 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 3 │ 6 │ 9 │ ⋮ │ 19 │ 22 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 7 │ 14 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 13 │ 29 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 9 │ 10 │ 18 │ 21 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 1 │ 2 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 11 │ 16 │ 19 │ 26 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 6 │ 9 │ 10 │ ⋮ │ 29 │ 30 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 14 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 10 │ 16 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 3 │ 5 │ 7 │ 9 │ ⋮ │ 26 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 1 │ 2 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 10 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 9 │ 14 │ 16 │ 21 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 1 │ 18 │ 19 │ 22 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 5 │ 11 │ 13 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 6 │ 9 │ 10 │ 16 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 7 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 1 │ 14 │ 18 │ 19 │ 22 │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 6 │ 10 │ 11 │ 16 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 3 │ 9 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 1 │ 10 │ 18 │ 22 │ 25 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 3 │ 6 │ 9 │ 19 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 7 │ 11 │ 14 │ 16 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 5 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 1 │ 6 │ 10 │ 18 │ 22 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 2 │ 3 │ 6 │ ⋮ │ 22 │ 27 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 1 │ 5 │ 6 │ 7 │ ⋮ │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 1 │ 3 │ 6 │ 9 │ ⋮ │ 25 │ 26 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 1 │ 2 │ 6 │ 10 │ ⋮ │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 16-element Vector{Int64}: │ 1 │ 3 │ 5 │ 6 │ ⋮ │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 6 │ 7 │ 10 │ ⋮ │ 22 │ 26 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 1 │ 2 │ 3 │ 6 │ ⋮ │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 1 │ 5 │ 6 │ 10 │ ⋮ │ 27 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 1 │ 3 │ 6 │ 7 │ ⋮ │ 22 │ 26 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 kind full, method split ┌ Info: 0: avll = └ tll[1] = -1.4138381104919626 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4138657990751928 │ -1.413804566763166 │ -1.4137519606734876 │ -1.413654781218137 │ ⋮ │ -1.408151233156792 │ -1.4081508909301643 └ -1.4081506532368173 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4081747564766458 │ -1.4081138029062652 │ -1.4080630940334795 │ -1.4079811359494117 │ ⋮ │ -1.401634051436732 │ -1.4015680500692211 └ -1.4015049805915034 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.4014885823135939 │ -1.401298138292471 │ -1.4010953444169603 │ -1.4006365990013367 │ ⋮ │ -1.3441599654254268 │ -1.3441148838575716 └ -1.3440740119896541 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.3440929485994997 │ -1.3437895810657257 │ -1.3433449488804523 │ -1.3417231675008352 │ ⋮ │ -1.3029780029024138 │ -1.302970272503151 └ -1.3029627926147374 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.303028858352649 │ -1.302716462468212 │ -1.302404202006302 │ -1.301918978384898 │ ⋮ │ -1.2772430564748352 │ -1.2772298060506835 └ -1.2772174192547148 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.4138381104919626 │ -1.4138657990751928 │ -1.413804566763166 │ -1.4137519606734876 │ ⋮ │ -1.2772430564748352 │ -1.2772298060506835 └ -1.2772174192547148 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 = 672592.6843270705) ┌ 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 8m17.0s [ Info: Initializing GMM, 2 Gaussians diag covariance 2 dimensions using 900 data points K-means converged with 2 iterations (objv = 7869.867369234178) ┌ Info: K-means with 900 data points using 2 iterations └ 150.0 data points per parameter Test Summary: | Pass Total Time ScikitLearnBase | 1 1 2.8s Testing GaussianMixtures tests passed Testing completed after 1191.57s PkgEval succeeded after 1253.66s