Package evaluation of GaussianMixtures on Julia 1.13.0-DEV.1244 (c841b5fe7d*) started at 2025-10-02T21:20:44.960 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.74s ################################################################################ # Installation # Installing GaussianMixtures... Resolving package versions... Updating `~/.julia/environments/v1.13/Project.toml` [cc18c42c] + GaussianMixtures v0.3.13 Updating `~/.julia/environments/v1.13/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [7d9fca2a] + Arpack v0.5.4 [aaaa29a8] + Clustering v0.15.8 [34da2185] + Compat v4.18.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.1 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.120 [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 v1.7.0 [7b1f6079] + FileWatching v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.12.0 [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.13.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] + LibCURL_jll v8.16.0+0 [e37daf67] + LibGit2_jll v1.9.1+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.9.9 [4536629a] + OpenBLAS_jll v0.3.29+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.4+0 [efcefdf7] + PCRE2_jll v10.46.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.1+2 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.13.1+0 [8e850ede] + nghttp2_jll v1.67.1+0 [3f19e933] + p7zip_jll v17.6.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 6.02s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 88.68s ################################################################################ # Testing # Testing GaussianMixtures Status `/tmp/jl_UEDEFM/Project.toml` [7d9fca2a] Arpack v0.5.4 [aaaa29a8] Clustering v0.15.8 [34da2185] Compat v4.18.1 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.120 [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 v1.13.0 [56ddb016] Logging v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_UEDEFM/Manifest.toml` [66dad0bd] AliasTables v1.1.3 [7d9fca2a] Arpack v0.5.4 [336ed68f] CSV v0.10.15 ⌅ [324d7699] CategoricalArrays v0.10.8 [aaaa29a8] Clustering v0.15.8 [944b1d66] CodecZlib v0.7.8 [34da2185] Compat v4.18.1 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.8.0 [864edb3b] DataStructures v0.19.1 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.120 [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 v3.0.11 [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 v1.7.0 [7b1f6079] FileWatching v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [ac6e5ff7] JuliaSyntaxHighlighting v1.12.0 [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [ca575930] NetworkOptions v1.3.0 [44cfe95a] Pkg v1.13.0 [de0858da] Printf v1.11.0 [3fa0cd96] REPL v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.13.0 [f489334b] StyledStrings v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] LibCURL_jll v8.16.0+0 [e37daf67] LibGit2_jll v1.9.1+0 [29816b5a] LibSSH2_jll v1.11.3+1 [14a3606d] MozillaCACerts_jll v2025.9.9 [4536629a] OpenBLAS_jll v0.3.29+0 [05823500] OpenLibm_jll v0.8.7+0 [458c3c95] OpenSSL_jll v3.5.4+0 [efcefdf7] PCRE2_jll v10.46.0+0 [bea87d4a] SuiteSparse_jll v7.10.1+0 [83775a58] Zlib_jll v1.3.1+2 [3161d3a3] Zstd_jll v1.5.7+1 [8e850b90] libblastrampoline_jll v5.13.1+0 [8e850ede] nghttp2_jll v1.67.1+0 [3f19e933] p7zip_jll v17.6.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... (100000, -1.8750935336718415e6, [58560.042748191205, 41439.9572518088], [15454.051274595675 -709.3933328937229 3611.525050240823; -14817.489846842018 633.4824232529472 -3595.124453203748], [[54863.36622897048 -13022.21020034944 5630.916827779983; -13022.21020034944 26159.413727308645 12390.842026061599; 5630.916827779983 12390.842026061599 53278.78889259177], [45461.49195671371 13215.116944900174 -5721.471520063656; 13215.116944900174 73058.25939319239 -12939.252871920986; -5721.471520063656 -12939.252871920988 46554.55923621011]]) Test Summary: | Pass Total Time data.jl | 8 8 5m11.6s [ 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 4 iterations (objv = 916.2792023378811) ┌ 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: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[Thu Oct 2 21:31:42 2025: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points , Thu Oct 2 21:31:43 2025: K-means with 272 data points using 4 iterations 11.3 data points per parameter , Thu Oct 2 21:31:43 2025: EM with 272 data points 0 iterations avll -2.082423 5.8 data points per parameter , Thu Oct 2 21:31:50 2025: GMM converted to Variational GMM , Thu Oct 2 21:32:01 2025: iteration 1, lowerbound -3.893765 , Thu Oct 2 21:32:01 2025: iteration 2, lowerbound -3.782480 , Thu Oct 2 21:32:01 2025: iteration 3, lowerbound -3.642818 , Thu Oct 2 21:32:01 2025: iteration 4, lowerbound -3.449819 , Thu Oct 2 21:32:01 2025: iteration 5, lowerbound -3.213510 , Thu Oct 2 21:32:01 2025: iteration 6, lowerbound -2.960127 , Thu Oct 2 21:32:01 2025: dropping number of Gaussions to 7 , Thu Oct 2 21:32:01 2025: iteration 7, lowerbound -2.724222 , Thu Oct 2 21:32:01 2025: dropping number of Gaussions to 6 , Thu Oct 2 21:32:01 2025: iteration 8, lowerbound -2.537699 , Thu Oct 2 21:32:02 2025: iteration 9, lowerbound -2.418742 , Thu Oct 2 21:32:02 2025: dropping number of Gaussions to 5 , Thu Oct 2 21:32:02 2025: iteration 10, lowerbound -2.357027 , Thu Oct 2 21:32:02 2025: dropping number of Gaussions to 4 , Thu Oct 2 21:32:02 2025: iteration 11, lowerbound -2.322778 , Thu Oct 2 21:32:02 2025: dropping number of Gaussions to 3 , Thu Oct 2 21:32:02 2025: iteration 12, lowerbound -2.311456 , Thu Oct 2 21:32:02 2025: dropping number of Gaussions to 2 , Thu Oct 2 21:32:02 2025: iteration 13, lowerbound -2.302928 , Thu Oct 2 21:32:02 2025: iteration 14, lowerbound -2.299264 , Thu Oct 2 21:32:02 2025: iteration 15, lowerbound -2.299258 , Thu Oct 2 21:32:02 2025: iteration 16, lowerbound -2.299255 , Thu Oct 2 21:32:02 2025: iteration 17, lowerbound -2.299254 , Thu Oct 2 21:32:02 2025: iteration 18, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 19, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 20, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 21, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 22, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 23, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 24, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 25, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 26, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 27, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 28, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 29, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 30, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 31, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 32, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 33, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 34, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 35, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 36, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 37, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 38, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 39, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 40, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 41, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 42, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 43, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 44, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 45, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 46, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 47, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 48, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 49, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: iteration 50, lowerbound -2.299253 , Thu Oct 2 21:32:02 2025: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253 ] α = [178.04509222601396, 95.95490777398604] β = [178.04509222601396, 95.95490777398604] m = [4.250300733269909 79.28686694436183; 2.0002292577753695 53.851987172461264] ν = [180.04509222601396, 97.95490777398604] W = LinearAlgebra.UpperTriangular{Float64, Matrix{Float64}}[[0.1840415554748462 -0.007644049042327484; 0.0 0.00858170516633351], [0.37587636119484236 -0.008953123827345939; 0.0 0.012748664777409165]] Test Summary: | Pass Total Time bayes.jl | 3 3 1m52.9s Kind: diag, size256 nx: 100000 sum(zeroth order stats): 100000.00000000007 avll from stats: -0.9877151012525618 avll from llpg: -0.9877151012525625 avll direct: -0.9877151012525626 sum posterior: 100000.0 Kind: full, size16 nx: 100000 sum(zeroth order stats): 100000.0 avll from stats: -0.9852347057664742 avll from llpg: -0.9852347057664743 avll direct: -0.9852347057664743 sum posterior: 100000.0 kind diag, method split ┌ Info: 0: avll = └ tll[1] = -1.3805583968498627 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.3806194839768464 │ -1.3805468740434403 │ -1.3797900245778527 │ -1.3714588926163533 │ ⋮ │ -1.3397648446466974 │ -1.33976484358716 └ -1.3397648428606896 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.3398456953453401 │ -1.3397671086204102 │ -1.3393953146135176 │ -1.3355790015099764 │ ⋮ │ -1.3034554400097973 │ -1.303442109869324 └ -1.303426879338804 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.303548863132832 │ -1.303405739756516 │ -1.3031888782170857 │ -1.301392889986779 │ ⋮ │ -1.2336901031270997 │ -1.23368182912676 └ -1.2336743370297272 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.2338775623377973 │ -1.2336113183979682 │ -1.2322684329485507 │ -1.220965556732178 │ ⋮ │ -1.151753678933161 │ -1.1505422070379832 └ -1.158120334797942 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 29 │ 30 └ @ 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 = │ 8-element Vector{Int64}: │ 10 │ 11 │ 14 │ 19 │ 20 │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 13 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 10 │ 14 │ 19 │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 12 │ 13 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 10 │ 11 │ 14 │ 19 │ ⋮ │ 22 │ 29 │ 30 └ @ 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}: │ 10 │ 14 │ 19 │ 20 │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 11 │ 13 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 8 │ 10 │ 12 │ 14 │ 19 │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 13 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 10 │ 11 │ 14 │ 19 │ ⋮ │ 22 │ 29 │ 30 └ @ 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 = │ 8-element Vector{Int64}: │ 10 │ 12 │ 14 │ 19 │ 20 │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 8 │ 11 │ 13 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 10 │ 19 │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 13 │ 14 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 10 │ 11 │ 12 │ 19 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 8 │ 10 │ 13 │ 14 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 11 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 10 │ 12 │ 13 │ 14 │ 19 │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 10 │ 11 │ 13 │ 19 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 8 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 10 │ 12 │ 13 │ 19 │ 20 │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 11 │ 14 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 10 │ 13 │ 19 │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 14 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 8 │ 10 │ 11 │ 12 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 10 │ 13 │ 19 │ 20 │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 11 │ 14 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 10 │ 12 │ 13 │ 19 │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 8 │ 14 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 10 │ 11 │ 13 │ 19 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 10 │ 12 │ 13 │ 19 │ 20 │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 11 │ 14 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 8 │ 10 │ 13 │ 19 │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 14 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 10 │ 11 │ 12 │ 13 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 10 │ 13 │ 19 │ 20 │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 11 │ 14 │ 21 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 8 │ 10 │ 12 │ 13 │ 19 │ 20 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 14 │ 22 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.1529269167037248 │ -1.1504291419447676 │ -1.1479051441702457 │ -1.137887993738934 │ ⋮ │ -1.071307350907347 │ -1.0504409411001896 └ -1.0852544193212488 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.3805583968498627 │ -1.3806194839768464 │ -1.3805468740434403 │ -1.3797900245778527 │ ⋮ │ -1.071307350907347 │ -1.0504409411001896 └ -1.0852544193212488 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 10 │ 11 │ 13 │ 19 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 10 │ 11 │ 13 │ 14 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 10 │ 11 │ 12 │ 13 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 8 │ 10 │ 11 │ 13 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 10 │ 11 │ 13 │ 19 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 10 │ 11 │ 13 │ 14 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 10 │ 11 │ 12 │ 13 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 8 │ 10 │ 11 │ 13 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 10 │ 11 │ 13 │ 19 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 10 │ 11 │ 12 │ 13 │ ⋮ │ 22 │ 29 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 kind diag, method kmeans [ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points K-means converged with 41 iterations (objv = 573946.5477304081) ┌ Info: K-means with 32000 data points using 41 iterations └ 37.0 data points per parameter ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 19 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 12 │ 16 │ 21 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 5 │ 6 │ 22 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 9 │ 11 │ 19 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 12 │ 14 │ 16 │ 20 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 21 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 22 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 7 │ 19 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 5 │ 12 │ 14 │ 16 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 9 │ 22 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 12 │ 19 │ 23 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 7 │ 11 │ 14 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 6 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 5 │ 21 │ 22 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 12 │ 19 │ 23 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 2 │ 9 │ 14 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 3 │ 6 │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 2 │ 5 │ 11 │ 12 │ ⋮ │ 25 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 16 └ @ 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 = │ 7-element Vector{Int64}: │ 5 │ 6 │ 9 │ 12 │ 19 │ 23 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 2 │ 7 │ 14 │ 21 │ 22 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 16 └ @ 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 = │ 4-element Vector{Int64}: │ 12 │ 19 │ 23 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 5 │ 6 │ 7 │ 11 │ 14 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 2 │ 3 │ 16 │ 21 │ 22 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 9 │ 12 │ 19 │ 23 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 5 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 6 │ 7 │ 16 │ 21 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 2 │ 22 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 12 │ 19 │ 23 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 11 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 5 │ 6 │ 16 │ 21 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 2 │ 7 │ 9 │ 12 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 19 │ 22 │ 23 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 12 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 2 │ 5 │ 6 │ 7 │ ⋮ │ 21 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 9 │ 19 │ 22 │ 23 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 3 │ 12 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 5 │ 6 │ 12 │ 14 │ 21 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 2 │ 3 │ 7 │ 9 │ ⋮ │ 23 │ 25 │ 27 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 3 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 6 │ 12 │ 14 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 5 │ 11 │ 12 │ 14 │ 16 │ 27 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 2 │ 3 │ 7 │ 14 │ 19 │ 22 │ 23 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 6 │ 12 │ 14 │ 21 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 12 │ 14 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:260 kind full, method split ┌ Info: 0: avll = └ tll[1] = -1.4218872742341857 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4219145382150153 │ -1.4218660368567753 │ -1.4218320701616247 │ -1.4217661545764622 │ ⋮ │ -1.4161508536933052 │ -1.41615075331823 └ -1.416150665650382 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4161750191206908 │ -1.4161189762855875 │ -1.4160769584127462 │ -1.416016172807118 │ ⋮ │ -1.4092107280379351 │ -1.4091101889158975 └ -1.4090246220587463 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.409000499489465 │ -1.4088001794362692 │ -1.4082967458979496 │ -1.4046845784119677 │ ⋮ │ -1.352525593594756 │ -1.3511269963212234 └ -1.3497247580223726 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.3479776870197469 │ -1.3466398519892953 │ -1.3463455628525482 │ -1.345511784336108 │ ⋮ │ -1.3190531418352718 │ -1.3189210296256237 └ -1.318787633845599 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.3187297709376489 │ -1.3182134292926897 │ -1.317162355082538 │ -1.3128919694520003 │ ⋮ │ -1.259150033173284 │ -1.2591231013997612 └ -1.2590948852941046 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.4218872742341857 │ -1.4219145382150153 │ -1.4218660368567753 │ -1.4218320701616247 │ ⋮ │ -1.259150033173284 │ -1.2591231013997612 └ -1.2590948852941046 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 = 682242.3378490527) ┌ Info: K-means with 32000 data points using 50 iterations └ 37.0 data points per parameter ┌ Warning: Too low occupancy count 19.0 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 14.5 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 12.4 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 11.4 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 10.8 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 10.4 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 10.2 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 10.0 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.9 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.8 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.8 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.8 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.7 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.7 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.7 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.7 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.7 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.7 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.7 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.7 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.7 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.7 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.6 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.6 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.6 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.6 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.6 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.6 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.6 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.5 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.5 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.5 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.5 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.4 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.4 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.4 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.3 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.3 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.3 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.2 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.2 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.1 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.1 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.1 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 ┌ Warning: Too low occupancy count 9.0 for Gausian 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RYvNa/src/train.jl:266 Test Summary: | Broken Total Time train.jl | 1 1 8m24.5s [ Info: Initializing GMM, 2 Gaussians diag covariance 2 dimensions using 900 data points K-means converged with 2 iterations (objv = 7869.867369234178) ┌ Info: K-means with 900 data points using 2 iterations └ 150.0 data points per parameter Test Summary: | Pass Total Time ScikitLearnBase | 1 1 2.8s Testing GaussianMixtures tests passed Testing completed after 1078.09s PkgEval succeeded after 1196.21s