Package evaluation of GaussianMixtures on Julia 1.10.8 (92f03a4775*) started at 2025-02-25T09:20:19.069 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 4.96s ################################################################################ # Installation # Installing GaussianMixtures... Resolving package versions... Updating `~/.julia/environments/v1.10/Project.toml` [cc18c42c] + GaussianMixtures v0.3.12 Updating `~/.julia/environments/v1.10/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [7d9fca2a] + Arpack v0.5.4 [aaaa29a8] + Clustering v0.15.8 [34da2185] + Compat v4.16.0 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.18.20 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [31c24e10] + Distributions v0.25.117 [ffbed154] + DocStringExtensions v0.9.3 [5789e2e9] + FileIO v1.16.6 [1a297f60] + FillArrays v1.13.0 [cc18c42c] + GaussianMixtures v0.3.12 [34004b35] + HypergeometricFunctions v0.3.27 [92d709cd] + IrrationalConstants v0.2.4 ⌅ [033835bb] + JLD2 v0.4.54 [692b3bcd] + JLLWrappers v1.7.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [1914dd2f] + MacroTools v0.5.15 [e1d29d7a] + Missings v1.2.0 [b8a86587] + NearestNeighbors v0.4.21 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.32 [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.0 [79098fc4] + Rmath v0.8.0 [6e75b9c4] + ScikitLearnBase v0.5.0 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.0 [90137ffa] + StaticArrays v1.9.12 [1e83bf80] + StaticArraysCore v1.4.3 [82ae8749] + StatsAPI v1.7.0 [2913bbd2] + StatsBase v0.34.4 [4c63d2b9] + StatsFuns v1.3.2 [3bb67fe8] + TranscodingStreams v0.11.3 ⌅ [68821587] + Arpack_jll v3.5.1+1 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [0dad84c5] + ArgTools v1.1.1 [56f22d72] + Artifacts [2a0f44e3] + Base64 [ade2ca70] + Dates [8ba89e20] + Distributed [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching [b77e0a4c] + InteractiveUtils [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 [8f399da3] + Libdl [37e2e46d] + LinearAlgebra [56ddb016] + Logging [d6f4376e] + Markdown [a63ad114] + Mmap [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.10.0 [de0858da] + Printf [3fa0cd96] + REPL [9a3f8284] + Random [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization [6462fe0b] + Sockets [2f01184e] + SparseArrays v1.10.0 [10745b16] + Statistics v1.10.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [cf7118a7] + UUIDs [4ec0a83e] + Unicode [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.4.0+0 [e37daf67] + LibGit2_jll v1.6.4+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.2+1 [14a3606d] + MozillaCACerts_jll v2023.1.10 [4536629a] + OpenBLAS_jll v0.3.23+4 [05823500] + OpenLibm_jll v0.8.1+4 [bea87d4a] + SuiteSparse_jll v7.2.1+1 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.52.0+1 [3f19e933] + p7zip_jll v17.4.0+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 6.86s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 24.21s ################################################################################ # Testing # Testing GaussianMixtures Status `/tmp/jl_2E8es1/Project.toml` [7d9fca2a] Arpack v0.5.4 [aaaa29a8] Clustering v0.15.8 [34da2185] Compat v4.16.0 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.117 [5789e2e9] FileIO v1.16.6 [cc18c42c] GaussianMixtures v0.3.12 ⌅ [033835bb] JLD2 v0.4.54 [90014a1f] PDMats v0.11.32 [ce6b1742] RDatasets v0.7.7 [6e75b9c4] ScikitLearnBase v0.5.0 [276daf66] SpecialFunctions v2.5.0 [2913bbd2] StatsBase v0.34.4 [8ba89e20] Distributed [37e2e46d] LinearAlgebra [56ddb016] Logging [de0858da] Printf [9a3f8284] Random [10745b16] Statistics v1.10.0 [8dfed614] Test Status `/tmp/jl_2E8es1/Manifest.toml` [66dad0bd] AliasTables v1.1.3 [7d9fca2a] Arpack v0.5.4 [336ed68f] CSV v0.10.15 [324d7699] CategoricalArrays v0.10.8 [aaaa29a8] Clustering v0.15.8 [944b1d66] CodecZlib v0.7.8 [34da2185] Compat v4.16.0 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.0 [864edb3b] DataStructures v0.18.20 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [31c24e10] Distributions v0.25.117 [ffbed154] DocStringExtensions v0.9.3 [e2ba6199] ExprTools v0.1.10 [5789e2e9] FileIO v1.16.6 [48062228] FilePathsBase v0.9.23 [1a297f60] FillArrays v1.13.0 [cc18c42c] GaussianMixtures v0.3.12 [34004b35] HypergeometricFunctions v0.3.27 [842dd82b] InlineStrings v1.4.3 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [82899510] IteratorInterfaceExtensions v1.0.0 ⌅ [033835bb] JLD2 v0.4.54 [692b3bcd] JLLWrappers v1.7.0 [b964fa9f] LaTeXStrings v1.4.0 [2ab3a3ac] LogExpFunctions v0.3.29 [1914dd2f] MacroTools v0.5.15 [e1d29d7a] Missings v1.2.0 [78c3b35d] Mocking v0.8.1 [b8a86587] NearestNeighbors v0.4.21 [bac558e1] OrderedCollections v1.8.0 [90014a1f] PDMats v0.11.32 [69de0a69] Parsers v2.8.1 [2dfb63ee] PooledArrays v1.4.3 [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [08abe8d2] PrettyTables v2.4.0 [43287f4e] PtrArrays v1.3.0 [1fd47b50] QuadGK v2.11.2 ⌅ [df47a6cb] RData v0.8.3 [ce6b1742] RDatasets v0.7.7 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.0 [79098fc4] Rmath v0.8.0 [6e75b9c4] ScikitLearnBase v0.5.0 [6c6a2e73] Scratch v1.2.1 [91c51154] SentinelArrays v1.4.8 [a2af1166] SortingAlgorithms v1.2.1 [276daf66] SpecialFunctions v2.5.0 [90137ffa] StaticArrays v1.9.12 [1e83bf80] StaticArraysCore v1.4.3 [82ae8749] StatsAPI v1.7.0 [2913bbd2] StatsBase v0.34.4 [4c63d2b9] StatsFuns v1.3.2 [892a3eda] StringManipulation v0.4.1 [dc5dba14] TZJData v1.4.0+2025a [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.0 [f269a46b] TimeZones v1.21.2 [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 v1.1.1 [56f22d72] Artifacts [2a0f44e3] Base64 [ade2ca70] Dates [8ba89e20] Distributed [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching [9fa8497b] Future [b77e0a4c] InteractiveUtils [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 [8f399da3] Libdl [37e2e46d] LinearAlgebra [56ddb016] Logging [d6f4376e] Markdown [a63ad114] Mmap [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.10.0 [de0858da] Printf [3fa0cd96] REPL [9a3f8284] Random [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization [6462fe0b] Sockets [2f01184e] SparseArrays v1.10.0 [10745b16] Statistics v1.10.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test [cf7118a7] UUIDs [4ec0a83e] Unicode [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.4.0+0 [e37daf67] LibGit2_jll v1.6.4+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.2+1 [14a3606d] MozillaCACerts_jll v2023.1.10 [4536629a] OpenBLAS_jll v0.3.23+4 [05823500] OpenLibm_jll v0.8.1+4 [bea87d4a] SuiteSparse_jll v7.2.1+1 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.52.0+1 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... (100000, -2.6536872628779244e6, [39002.16336416242, 60997.83663583758], [28107.438468576504 -3392.666728471371 -9660.342935542463; -28350.222576773034 3196.9560238667173 9538.603485154725], [[39149.42961522398 11782.215003584812 574.4687453649864; 11782.215003584812 41612.84960607213 -613.9748894833758; 574.4687453649863 -613.9748894833757 37998.77467591387], [60296.70758760517 -11826.63523352951 -448.57618295625883; -11826.63523352951 58649.84305563406 -4.310570968808899; -448.57618295625895 -4.310570968809004 62304.071646175384]]) Test Summary: | Pass Total Time data.jl | 8 8 5m57.4s [ Info: Initializing GMM, 2 Gaussians diag covariance 1 dimensions using 272 data points K-means converged with 2 iterations (objv = 8855.79069767458) ┌ Info: K-means with 272 data points using 2 iterations └ 68.0 data points per parameter [ Info: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points K-means converged with 5 iterations (objv = 817.273023560112) ┌ Info: K-means with 272 data points using 5 iterations └ 11.3 data points per parameter ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = lowerbound(vg::VGMM{Float64}, N::Vector{Float64}, mx::Matrix{Float64}, S::Vector{Matrix{Float64}}, Elogπ::Vector{Float64}, ElogdetΛ::Vector{Float64}, ElogpZπqZ::Float64) at bayes.jl:221 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/bayes.jl:221 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = lowerbound(vg::VGMM{Float64}, N::Vector{Float64}, mx::Matrix{Float64}, S::Vector{Matrix{Float64}}, Elogπ::Vector{Float64}, ElogdetΛ::Vector{Float64}, ElogpZπqZ::Float64) at bayes.jl:221 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/bayes.jl:221 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:709 [inlined] └ @ Core ./broadcast.jl:709 ┌ 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 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/bayes.jl:230 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:709 [inlined] └ @ Core ./broadcast.jl:709 ┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead. │ caller = _broadcast_getindex_evalf at broadcast.jl:709 [inlined] └ @ Core ./broadcast.jl:709 History[Tue Feb 25 09:30:02 2025: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points , Tue Feb 25 09:30:03 2025: K-means with 272 data points using 5 iterations 11.3 data points per parameter , Tue Feb 25 09:30:07 2025: EM with 272 data points 0 iterations avll -2.048466 5.8 data points per parameter , Tue Feb 25 09:30:10 2025: GMM converted to Variational GMM , Tue Feb 25 09:30:19 2025: iteration 1, lowerbound -3.750447 , Tue Feb 25 09:30:20 2025: iteration 2, lowerbound -3.633969 , Tue Feb 25 09:30:20 2025: iteration 3, lowerbound -3.520725 , Tue Feb 25 09:30:21 2025: iteration 4, lowerbound -3.408684 , Tue Feb 25 09:30:22 2025: iteration 5, lowerbound -3.310704 , Tue Feb 25 09:30:23 2025: iteration 6, lowerbound -3.232734 , Tue Feb 25 09:30:23 2025: dropping number of Gaussions to 7 , Tue Feb 25 09:30:24 2025: iteration 7, lowerbound -3.159100 , Tue Feb 25 09:30:24 2025: dropping number of Gaussions to 6 , Tue Feb 25 09:30:24 2025: iteration 8, lowerbound -3.081445 , Tue Feb 25 09:30:24 2025: dropping number of Gaussions to 5 , Tue Feb 25 09:30:25 2025: iteration 9, lowerbound -2.983516 , Tue Feb 25 09:30:25 2025: iteration 10, lowerbound -2.885075 , Tue Feb 25 09:30:26 2025: iteration 11, lowerbound -2.804924 , Tue Feb 25 09:30:26 2025: dropping number of Gaussions to 4 , Tue Feb 25 09:30:26 2025: iteration 12, lowerbound -2.745096 , Tue Feb 25 09:30:27 2025: iteration 13, lowerbound -2.699697 , Tue Feb 25 09:30:27 2025: dropping number of Gaussions to 3 , Tue Feb 25 09:30:27 2025: iteration 14, lowerbound -2.663324 , Tue Feb 25 09:30:27 2025: iteration 15, lowerbound -2.622455 , Tue Feb 25 09:30:28 2025: iteration 16, lowerbound -2.581137 , Tue Feb 25 09:30:28 2025: iteration 17, lowerbound -2.537674 , Tue Feb 25 09:30:28 2025: iteration 18, lowerbound -2.494612 , Tue Feb 25 09:30:29 2025: iteration 19, lowerbound -2.454097 , Tue Feb 25 09:30:29 2025: iteration 20, lowerbound -2.417084 , Tue Feb 25 09:30:30 2025: iteration 21, lowerbound -2.383321 , Tue Feb 25 09:30:30 2025: iteration 22, lowerbound -2.352553 , Tue Feb 25 09:30:30 2025: iteration 23, lowerbound -2.326680 , Tue Feb 25 09:30:31 2025: iteration 24, lowerbound -2.310565 , Tue Feb 25 09:30:31 2025: iteration 25, lowerbound -2.308089 , Tue Feb 25 09:30:31 2025: dropping number of Gaussions to 2 , Tue Feb 25 09:30:31 2025: iteration 26, lowerbound -2.302915 , Tue Feb 25 09:30:31 2025: iteration 27, lowerbound -2.299259 , Tue Feb 25 09:30:32 2025: iteration 28, lowerbound -2.299256 , Tue Feb 25 09:30:32 2025: iteration 29, lowerbound -2.299254 , Tue Feb 25 09:30:32 2025: iteration 30, lowerbound -2.299254 , Tue Feb 25 09:30:33 2025: iteration 31, lowerbound -2.299253 , Tue Feb 25 09:30:33 2025: iteration 32, lowerbound -2.299253 , Tue Feb 25 09:30:33 2025: iteration 33, lowerbound -2.299253 , Tue Feb 25 09:30:33 2025: iteration 34, lowerbound -2.299253 , Tue Feb 25 09:30:34 2025: iteration 35, lowerbound -2.299253 , Tue Feb 25 09:30:34 2025: iteration 36, lowerbound -2.299253 , Tue Feb 25 09:30:34 2025: iteration 37, lowerbound -2.299253 , Tue Feb 25 09:30:35 2025: iteration 38, lowerbound -2.299253 , Tue Feb 25 09:30:35 2025: iteration 39, lowerbound -2.299253 , Tue Feb 25 09:30:35 2025: iteration 40, lowerbound -2.299253 , Tue Feb 25 09:30:35 2025: iteration 41, lowerbound -2.299253 , Tue Feb 25 09:30:36 2025: iteration 42, lowerbound -2.299253 , Tue Feb 25 09:30:36 2025: iteration 43, lowerbound -2.299253 , Tue Feb 25 09:30:36 2025: iteration 44, lowerbound -2.299253 , Tue Feb 25 09:30:36 2025: iteration 45, lowerbound -2.299253 , Tue Feb 25 09:30:37 2025: iteration 46, lowerbound -2.299253 , Tue Feb 25 09:30:37 2025: iteration 47, lowerbound -2.299253 , Tue Feb 25 09:30:37 2025: iteration 48, lowerbound -2.299253 , Tue Feb 25 09:30:38 2025: iteration 49, lowerbound -2.299253 , Tue Feb 25 09:30:38 2025: iteration 50, lowerbound -2.299253 , Tue Feb 25 09:30:38 2025: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253 ] α = [178.04509222607192, 95.95490777392823] β = [178.04509222607192, 95.95490777392823] m = [4.250300733269438 79.28686694435488; 2.0002292577748824 53.851987172458735] ν = [180.04509222607192, 97.95490777392823] W = LinearAlgebra.UpperTriangular{Float64, Matrix{Float64}}[[0.18404155547478024 -0.007644049042333395; 0.0 0.008581705166324614], [0.37587636119564727 -0.008953123827355611; 0.0 0.012748664777411734]] Test Summary: | Pass Total Time bayes.jl | 3 3 1m55.1s Kind: diag, size256 nx: 100000 sum(zeroth order stats): 99999.99999999999 avll from stats: -1.0103601866304495 avll from llpg: -1.0103601866304344 avll direct: -1.0103601866304344 sum posterior: 100000.0 Kind: full, size16 nx: 100000 sum(zeroth order stats): 100000.0 avll from stats: -1.0239757949838877 avll from llpg: -1.0239757949838877 avll direct: -1.0239757949838877 sum posterior: 100000.0 kind diag, method split ┌ Info: 0: avll = └ tll[1] = -1.4509183235501029 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4509808626440206 │ -1.4509227158312625 │ -1.4505399381241293 │ -1.4457824560827204 │ ⋮ │ -1.4082531918165089 │ -1.4082531462325947 └ -1.4082531144171386 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4083669792244657 │ -1.4082547141374044 │ -1.407872544260582 │ -1.404051020253173 │ ⋮ │ -1.3703613033254611 │ -1.370319134458848 └ -1.3702748537850333 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.3703954323200922 │ -1.3701713212332658 │ -1.3695629510219134 │ -1.3649716490780461 │ ⋮ │ -1.3060520023443578 │ -1.318576357192043 └ -1.3131991061949086 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 13 │ 14 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 13 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 4 │ 9 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 14 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 9 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 4 │ 13 │ 14 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 16 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 4 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.3111652298571093 │ -1.3100080872891522 │ -1.3083274323798846 │ -1.2974750648586517 │ ⋮ │ -1.227378650417732 │ -1.2206278622251194 └ -1.2211412265330954 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 25 │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 7 │ 8 │ 25 │ 26 │ 27 │ 28 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 7 │ 8 │ 18 │ 25 │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 7 │ 8 │ 17 │ 25 │ ⋮ │ 28 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 1 │ 2 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 5 │ 6 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 7 │ 8 │ 9 │ 14 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 1 │ 2 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 6 │ 7 │ 8 │ 9 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 5 │ 7 │ 8 │ 17 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 6 │ 7 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 5 │ 7 │ 8 │ 12 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 6 │ 7 │ 8 │ 9 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 1 │ 2 │ 5 │ 7 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 6 │ 7 │ 8 │ 9 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 5 │ 7 │ 8 │ 14 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 1 │ 2 │ 6 │ 7 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 5 │ 7 │ 8 │ 12 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 17-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 16-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 12-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 10-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 17-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 18-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 18-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 1 │ 5 │ 7 │ 8 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.20604971932304 │ -1.1974060051238153 │ -1.2018049018138448 │ -1.1810578146689632 │ ⋮ │ -1.0852051328013783 │ -1.1356783654740346 └ -1.0962360683378998 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.4509183235501029 │ -1.4509808626440206 │ -1.4509227158312625 │ -1.4505399381241293 │ ⋮ │ -1.0852051328013783 │ -1.1356783654740346 └ -1.0962360683378998 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 21-element Vector{Int64}: │ 1 │ 2 │ 5 │ 6 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 21-element Vector{Int64}: │ 1 │ 2 │ 5 │ 6 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 21-element Vector{Int64}: │ 1 │ 2 │ 5 │ 6 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 21-element Vector{Int64}: │ 1 │ 2 │ 5 │ 6 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 13-element Vector{Int64}: │ 2 │ 6 │ 7 │ 8 │ ⋮ │ 26 │ 27 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 21-element Vector{Int64}: │ 1 │ 2 │ 5 │ 6 │ ⋮ │ 29 │ 31 │ 32 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 kind diag, method kmeans [ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points K-means terminated without convergence after 50 iterations (objv = 654409.6988769623) ┌ 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}: │ 7 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 6 │ 11 │ 23 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 10 │ 16 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 17 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 5 │ 8 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 6 │ 14 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 4 │ 9 │ 13 │ 18 │ 25 │ 28 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 7 │ 10 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 8 │ 17 │ 23 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 3 │ 4 │ 9 │ 14 │ 16 │ 18 │ 25 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 7 │ 10 │ 11 │ 20 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 8 │ 15 │ 17 │ 23 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 9 │ 13 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 9-element Vector{Int64}: │ 3 │ 4 │ 7 │ 9 │ ⋮ │ 23 │ 25 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 8 │ 11 │ 14 │ 17 │ 20 │ 26 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 6 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 4 │ 7 │ 9 │ 10 │ 13 │ 15 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 8 │ 17 │ 25 │ 26 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 14 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 11 │ 15 │ 20 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 4 │ 7 │ 9 │ 18 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 8 │ 10 │ 13 │ 26 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 3 │ 6 │ 15 │ 17 │ 23 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 14 │ 20 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 4 │ 7 │ 9 │ 18 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 8 │ 11 │ 23 │ 26 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 10 │ 15 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 3 │ 13 │ 17 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 4 │ 7 │ 9 │ 14 │ 18 │ 20 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 8 │ 15 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 6 │ 11 │ 13 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 3 │ 10 │ 17 │ 25 │ 26 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 4 │ 7 │ 9 │ 15 │ 18 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 8 │ 14 │ 20 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 3-element Vector{Int64}: │ 6 │ 11 │ 13 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 25 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 4 │ 7 │ 9 │ 10 │ 15 │ 17 │ 18 │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 6 │ 8 │ 13 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 14 │ 25 │ 26 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 3 │ 7 │ 11 │ 15 │ 20 │ 23 │ 28 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 2-element Vector{Int64}: │ 4 │ 9 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 8-element Vector{Int64}: │ 6 │ 8 │ 10 │ 13 │ 17 │ 18 │ 25 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 1-element Vector{Int64}: │ 23 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 6-element Vector{Int64}: │ 7 │ 9 │ 14 │ 15 │ 23 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 14-element Vector{Int64}: │ 3 │ 4 │ 6 │ 7 │ ⋮ │ 28 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 5-element Vector{Int64}: │ 9 │ 14 │ 15 │ 23 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 7 │ 17 │ 23 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 15-element Vector{Int64}: │ 3 │ 4 │ 6 │ 7 │ ⋮ │ 28 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 4-element Vector{Int64}: │ 8 │ 10 │ 23 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 7 │ 9 │ 14 │ 15 │ 17 │ 23 │ 30 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 11-element Vector{Int64}: │ 3 │ 4 │ 6 │ 7 │ ⋮ │ 28 │ 30 │ 31 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 ┌ Warning: Variances had to be floored │ ind = │ 7-element Vector{Int64}: │ 8 │ 9 │ 10 │ 14 │ 15 │ 23 │ 25 └ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/tYojF/src/train.jl:260 kind full, method split ┌ Info: 0: avll = └ tll[1] = -1.424566917345559 ┌ Info: 1 │ avll = │ 50-element Vector{Float64}: │ -1.4245976699845009 │ -1.4245370950322456 │ -1.4244963828493407 │ -1.4244318189895266 │ ⋮ │ -1.4189344646951847 │ -1.4189344450554917 └ -1.4189344275457656 ┌ Info: 2 │ avll = │ 50-element Vector{Float64}: │ -1.4189611098788621 │ -1.4188992044382225 │ -1.4188552604256577 │ -1.4187916063894892 │ ⋮ │ -1.4122832231318887 │ -1.4121894568181805 └ -1.41210109505828 ┌ Info: 3 │ avll = │ 50-element Vector{Float64}: │ -1.4120634902160356 │ -1.4118503759063024 │ -1.411606106098777 │ -1.4109918813107167 │ ⋮ │ -1.3596528486533426 │ -1.3583424863608313 └ -1.3563083397159423 ┌ Info: 4 │ avll = │ 50-element Vector{Float64}: │ -1.3544103858204068 │ -1.3521293410598376 │ -1.3499980469236508 │ -1.3437761838816529 │ ⋮ │ -1.3085745145736716 │ -1.3085547337715127 └ -1.3085361563032338 ┌ Info: 5 │ avll = │ 50-element Vector{Float64}: │ -1.3085996684495769 │ -1.3082552016761606 │ -1.3079268865344646 │ -1.3074697550561343 │ ⋮ │ -1.2856374314394963 │ -1.2856047137507158 └ -1.2855729461978505 ┌ Info: Total log likelihood: │ tll = │ 251-element Vector{Float64}: │ -1.424566917345559 │ -1.4245976699845009 │ -1.4245370950322456 │ -1.4244963828493407 │ ⋮ │ -1.2856374314394963 │ -1.2856047137507158 └ -1.2855729461978505 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 = 687050.1049915567) ┌ 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 6m48.4s [ Info: Initializing GMM, 2 Gaussians diag covariance 2 dimensions using 900 data points K-means converged with 2 iterations (objv = 7869.867369234178) ┌ Info: K-means with 900 data points using 2 iterations └ 150.0 data points per parameter Test Summary: | Pass Total Time ScikitLearnBase | 1 1 2.4s Testing GaussianMixtures tests passed Testing completed after 992.83s PkgEval succeeded after 1034.95s