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1.5.0-DEV-c9d183757a.log
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Julia Version 1.5.0-DEV.62
Commit c9d183757a (2020-01-13 22:37 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-8.0.1 (ORCJIT, skylake)
Environment:
JULIA_DEPOT_PATH = ::/usr/local/share/julia
Resolving package versions...
Installed PDMats ───────────── v0.9.10
Installed LegacyStrings ────── v0.4.1
Installed Missings ─────────── v0.4.3
Installed GaussianMixtures ─── v0.3.0
Installed Compat ───────────── v2.2.0
Installed Arpack ───────────── v0.4.0
Installed FileIO ───────────── v1.2.1
Installed StatsBase ────────── v0.32.0
Installed BinaryProvider ───── v0.5.8
Installed OrderedCollections ─ v1.1.0
Installed HDF5 ─────────────── v0.12.5
Installed QuadGK ───────────── v2.3.1
Installed OpenSpecFun_jll ──── v0.5.3+1
Installed CMake ────────────── v1.1.2
Installed Blosc ────────────── v0.5.1
Installed StaticArrays ─────── v0.12.1
Installed Distances ────────── v0.8.2
Installed DataStructures ───── v0.17.7
Installed Arpack_jll ───────── v3.5.0+2
Installed JLD ──────────────── v0.9.1
Installed CMakeWrapper ─────── v0.2.3
Installed Rmath ────────────── v0.6.0
Installed SpecialFunctions ─── v0.9.0
Installed ScikitLearnBase ──── v0.5.0
Installed Distributions ────── v0.22.1
Installed DataAPI ──────────── v1.1.0
Installed SortingAlgorithms ── v0.3.1
Installed FillArrays ───────── v0.8.4
Installed URIParser ────────── v0.4.0
Installed BinDeps ──────────── v1.0.0
Installed NearestNeighbors ─── v0.4.4
Installed Parameters ───────── v0.12.0
Installed Clustering ───────── v0.13.3
Installed OpenBLAS_jll ─────── v0.3.7+4
Installed StatsFuns ────────── v0.9.3
Updating `~/.julia/environments/v1.5/Project.toml`
[cc18c42c] + GaussianMixtures v0.3.0
Updating `~/.julia/environments/v1.5/Manifest.toml`
[7d9fca2a] + Arpack v0.4.0
[68821587] + Arpack_jll v3.5.0+2
[9e28174c] + BinDeps v1.0.0
[b99e7846] + BinaryProvider v0.5.8
[a74b3585] + Blosc v0.5.1
[631607c0] + CMake v1.1.2
[d5fb7624] + CMakeWrapper v0.2.3
[aaaa29a8] + Clustering v0.13.3
[34da2185] + Compat v2.2.0
[9a962f9c] + DataAPI v1.1.0
[864edb3b] + DataStructures v0.17.7
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.22.1
[5789e2e9] + FileIO v1.2.1
[1a297f60] + FillArrays v0.8.4
[cc18c42c] + GaussianMixtures v0.3.0
[f67ccb44] + HDF5 v0.12.5
[4138dd39] + JLD v0.9.1
[1b4a561d] + LegacyStrings v0.4.1
[e1d29d7a] + Missings v0.4.3
[b8a86587] + NearestNeighbors v0.4.4
[4536629a] + OpenBLAS_jll v0.3.7+4
[efe28fd5] + OpenSpecFun_jll v0.5.3+1
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[d96e819e] + Parameters v0.12.0
[1fd47b50] + QuadGK v2.3.1
[79098fc4] + Rmath v0.6.0
[6e75b9c4] + ScikitLearnBase v0.5.0
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.9.0
[90137ffa] + StaticArrays v0.12.1
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.9.3
[30578b45] + URIParser v0.4.0
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[9abbd945] + Profile
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building CMake → `~/.julia/packages/CMake/nSK2r/deps/build.log`
Updating `/tmp/jl_NkIi2C/Project.toml`
[no changes]
Updating `/tmp/jl_NkIi2C/Manifest.toml`
[no changes]
Building Blosc → `~/.julia/packages/Blosc/lzFr0/deps/build.log`
Updating `/tmp/jl_IecC1s/Project.toml`
[no changes]
Updating `/tmp/jl_IecC1s/Manifest.toml`
[no changes]
Building HDF5 ─→ `~/.julia/packages/HDF5/Zh9on/deps/build.log`
Updating `/tmp/jl_rSRtyl/Project.toml`
[no changes]
Updating `/tmp/jl_rSRtyl/Manifest.toml`
[no changes]
Building Rmath → `~/.julia/packages/Rmath/BoBag/deps/build.log`
Updating `/tmp/jl_Wtd6vy/Project.toml`
[no changes]
Updating `/tmp/jl_Wtd6vy/Manifest.toml`
[no changes]
Testing GaussianMixtures
Updating `/tmp/jl_7vCaK7/Project.toml`
[no changes]
Updating `/tmp/jl_7vCaK7/Manifest.toml`
[no changes]
Running sandbox
Status `/tmp/jl_7vCaK7/Project.toml`
[aaaa29a8] Clustering v0.13.3
[34da2185] Compat v2.2.0
[31c24e10] Distributions v0.22.1
[5789e2e9] FileIO v1.2.1
[cc18c42c] GaussianMixtures v0.3.0
[4138dd39] JLD v0.9.1
[90014a1f] PDMats v0.9.10
[6e75b9c4] ScikitLearnBase v0.5.0
[276daf66] SpecialFunctions v0.9.0
[2913bbd2] StatsBase v0.32.0
[8bb1440f] DelimitedFiles
[8ba89e20] Distributed
[37e2e46d] LinearAlgebra
[56ddb016] Logging
[de0858da] Printf
[9a3f8284] Random
[10745b16] Statistics
[ Info: Testing Data
(100000, -2.7085920633016257e6, [170.7315687474653, 99829.26843125254], [540.2531951352156 -119.43850237076155 -4.369004374689611; -943.2804406586101 45.26773570871264 -181.43137268907722], [[1729.9548530161017 -344.2937588346763 -2.013311530766238; -344.2937588346763 236.71858788180134 2.0969510002261256; -2.0133115307662415 2.0969510002261242 158.04085826720274], [98482.65697853512 -183.65310313673984 44.67053342230133; -183.65310313673984 99536.88462676932 -396.6416726433704; 44.6705334223013 -396.6416726433704 100302.32348472645]])
┌ Warning: rmprocs: process 1 not removed
└ @ Distributed /workspace/srcdir/usr/share/julia/stdlib/v1.5/Distributed/src/cluster.jl:1015
[ Info: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 1.195581e+03
1 8.663623e+02 -3.292185e+02 | 6
2 8.414020e+02 -2.496028e+01 | 0
3 8.414020e+02 0.000000e+00 | 0
K-means converged with 3 iterations (objv = 841.4020160132131)
┌ Info: K-means with 272 data points using 3 iterations
└ 11.3 data points per parameter
[ Info: Running 0 iterations EM on full cov GMM with 8 Gaussians in 2 dimensions
┌ Info: EM with 272 data points 0 iterations avll -2.078067
└ 5.8 data points per parameter
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = lowerbound(::VGMM{Float64}, ::Array{Float64,1}, ::Array{Float64,2}, ::Array{Array{Float64,2},1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Float64) at bayes.jl:221
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/bayes.jl:221
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = lowerbound(::VGMM{Float64}, ::Array{Float64,1}, ::Array{Float64,2}, ::Array{Array{Float64,2},1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Float64) at bayes.jl:221
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/bayes.jl:221
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = _broadcast_getindex_evalf at broadcast.jl:631 [inlined]
└ @ Core ./broadcast.jl:631
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = lowerbound(::VGMM{Float64}, ::Array{Float64,1}, ::Array{Float64,2}, ::Array{Array{Float64,2},1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Float64) at bayes.jl:230
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/bayes.jl:230
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = _broadcast_getindex_evalf at broadcast.jl:631 [inlined]
└ @ Core ./broadcast.jl:631
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = _broadcast_getindex_evalf at broadcast.jl:631 [inlined]
└ @ Core ./broadcast.jl:631
[ Info: iteration 1, lowerbound -3.806892
[ Info: iteration 2, lowerbound -3.671685
[ Info: iteration 3, lowerbound -3.513013
[ Info: iteration 4, lowerbound -3.314503
[ Info: iteration 5, lowerbound -3.099595
[ Info: iteration 6, lowerbound -2.904015
[ Info: dropping number of Gaussions to 7
[ Info: iteration 7, lowerbound -2.746088
[ Info: dropping number of Gaussions to 6
[ Info: iteration 8, lowerbound -2.631124
[ Info: dropping number of Gaussions to 5
[ Info: iteration 9, lowerbound -2.544140
[ Info: dropping number of Gaussions to 3
[ Info: iteration 10, lowerbound -2.467195
[ Info: iteration 11, lowerbound -2.405028
[ Info: iteration 12, lowerbound -2.363316
[ Info: iteration 13, lowerbound -2.332732
[ Info: iteration 14, lowerbound -2.313282
[ Info: iteration 15, lowerbound -2.307444
[ Info: dropping number of Gaussions to 2
[ Info: iteration 16, lowerbound -2.302929
[ Info: iteration 17, lowerbound -2.299261
[ Info: iteration 18, lowerbound -2.299256
[ Info: iteration 19, lowerbound -2.299255
[ Info: iteration 20, lowerbound -2.299254
[ Info: iteration 21, lowerbound -2.299253
[ Info: iteration 22, lowerbound -2.299253
[ Info: iteration 23, lowerbound -2.299253
[ Info: iteration 24, lowerbound -2.299253
[ Info: iteration 25, lowerbound -2.299253
[ Info: iteration 26, lowerbound -2.299253
[ Info: iteration 27, lowerbound -2.299253
[ Info: iteration 28, lowerbound -2.299253
[ Info: iteration 29, lowerbound -2.299253
[ Info: iteration 30, lowerbound -2.299253
[ Info: iteration 31, lowerbound -2.299253
[ Info: iteration 32, lowerbound -2.299253
[ Info: iteration 33, lowerbound -2.299253
[ Info: iteration 34, lowerbound -2.299253
[ Info: iteration 35, lowerbound -2.299253
[ Info: iteration 36, lowerbound -2.299253
[ Info: iteration 37, lowerbound -2.299253
[ Info: iteration 38, lowerbound -2.299253
[ Info: iteration 39, lowerbound -2.299253
[ Info: iteration 40, lowerbound -2.299253
[ Info: iteration 41, lowerbound -2.299253
[ Info: iteration 42, lowerbound -2.299253
[ Info: iteration 43, lowerbound -2.299253
[ Info: iteration 44, lowerbound -2.299253
[ Info: iteration 45, lowerbound -2.299253
[ Info: iteration 46, lowerbound -2.299253
[ Info: iteration 47, lowerbound -2.299253
[ Info: iteration 48, lowerbound -2.299253
[ Info: iteration 49, lowerbound -2.299253
[ Info: iteration 50, lowerbound -2.299253
[ Info: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253
History[Tue Jan 14 14:02:27 2020: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points
, Tue Jan 14 14:02:35 2020: K-means with 272 data points using 3 iterations
11.3 data points per parameter
, Tue Jan 14 14:02:37 2020: EM with 272 data points 0 iterations avll -2.078067
5.8 data points per parameter
, Tue Jan 14 14:02:39 2020: GMM converted to Variational GMM
, Tue Jan 14 14:02:48 2020: iteration 1, lowerbound -3.806892
, Tue Jan 14 14:02:48 2020: iteration 2, lowerbound -3.671685
, Tue Jan 14 14:02:48 2020: iteration 3, lowerbound -3.513013
, Tue Jan 14 14:02:48 2020: iteration 4, lowerbound -3.314503
, Tue Jan 14 14:02:48 2020: iteration 5, lowerbound -3.099595
, Tue Jan 14 14:02:48 2020: iteration 6, lowerbound -2.904015
, Tue Jan 14 14:02:49 2020: dropping number of Gaussions to 7
, Tue Jan 14 14:02:49 2020: iteration 7, lowerbound -2.746088
, Tue Jan 14 14:02:49 2020: dropping number of Gaussions to 6
, Tue Jan 14 14:02:49 2020: iteration 8, lowerbound -2.631124
, Tue Jan 14 14:02:49 2020: dropping number of Gaussions to 5
, Tue Jan 14 14:02:49 2020: iteration 9, lowerbound -2.544140
, Tue Jan 14 14:02:49 2020: dropping number of Gaussions to 3
, Tue Jan 14 14:02:49 2020: iteration 10, lowerbound -2.467195
, Tue Jan 14 14:02:49 2020: iteration 11, lowerbound -2.405028
, Tue Jan 14 14:02:49 2020: iteration 12, lowerbound -2.363316
, Tue Jan 14 14:02:49 2020: iteration 13, lowerbound -2.332732
, Tue Jan 14 14:02:49 2020: iteration 14, lowerbound -2.313282
, Tue Jan 14 14:02:49 2020: iteration 15, lowerbound -2.307444
, Tue Jan 14 14:02:49 2020: dropping number of Gaussions to 2
, Tue Jan 14 14:02:49 2020: iteration 16, lowerbound -2.302929
, Tue Jan 14 14:02:49 2020: iteration 17, lowerbound -2.299261
, Tue Jan 14 14:02:49 2020: iteration 18, lowerbound -2.299256
, Tue Jan 14 14:02:49 2020: iteration 19, lowerbound -2.299255
, Tue Jan 14 14:02:49 2020: iteration 20, lowerbound -2.299254
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, Tue Jan 14 14:02:49 2020: iteration 47, lowerbound -2.299253
, Tue Jan 14 14:02:49 2020: iteration 48, lowerbound -2.299253
, Tue Jan 14 14:02:49 2020: iteration 49, lowerbound -2.299253
, Tue Jan 14 14:02:49 2020: iteration 50, lowerbound -2.299253
, Tue Jan 14 14:02:49 2020: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253
]
α = [178.04509222601396, 95.95490777398611]
β = [178.04509222601396, 95.95490777398611]
m = [4.250300733269907 79.2868669443618; 2.00022925777537 53.85198717246129]
ν = [180.04509222601396, 97.95490777398611]
W = LinearAlgebra.UpperTriangular{Float64,Array{Float64,2}}[[0.18404155547484738 -0.0076440490423277125; 0.0 0.008581705166333308], [0.37587636119484563 -0.008953123827346287; 0.0 0.012748664777409473]]
Kind: diag, size256
nx: 100000 sum(zeroth order stats): 99999.99999999996
avll from stats: -0.9753570978529966
avll from llpg: -0.9753570978529948
avll direct: -0.9753570978529947
sum posterior: 99999.99999999999
Kind: full, size16
nx: 100000 sum(zeroth order stats): 100000.0
avll from stats: -1.018478701464081
avll from llpg: -1.0184787014640808
avll direct: -1.0184787014640808
sum posterior: 100000.0
32×26 Array{Float64,2}:
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0.00834586 0.262107 -0.0923873 0.0696676 0.0792656 -0.0119476 0.0256211 -0.0292817 0.0722196 0.0236742 -0.0205315 -0.127816 -0.0419022 -0.0771125 0.171923 0.0842687 -0.0164148 -0.0569906 -0.0519219 -0.0123144 -0.011585 0.0252618 0.0419222 0.14362 -0.0294072 -0.027667
0.0821251 -0.0165481 -0.157028 -0.0736152 0.0968648 -0.111564 -0.114259 0.012896 0.183084 -0.270751 -0.0448954 -0.00612569 0.103122 -0.0398217 -0.115933 -0.0626628 0.0658996 0.0249781 -0.109565 -0.0423923 -0.081863 0.0129798 0.056513 -0.00231253 0.125487 0.031855
-0.0122236 0.0634039 0.0797192 -0.00603995 -0.029178 -0.0604655 -0.016173 0.124665 0.229051 -0.18457 -0.0315995 0.02416 -0.0186908 -0.0852336 0.115481 -0.256423 0.0149395 0.0855429 0.061538 0.130701 -0.0796587 -0.100274 0.124248 -0.0173805 0.184711 -0.0794893
0.00470986 -0.0437409 -0.126377 0.0595295 -0.0330537 -0.0573768 0.0240574 -0.185679 -0.0266091 0.0361499 -0.0713093 0.0418451 -0.0680576 0.0525222 0.0835552 -0.112004 0.0259936 0.0197818 -0.0920407 -0.134345 -0.147235 0.0128831 0.0909334 0.111911 0.107094 -0.0296271
0.00723199 -0.0055539 0.00521552 0.1167 0.149383 0.227044 -0.046122 0.0374744 0.00370067 -0.0358928 -0.0811006 -0.0174866 0.0736399 0.0118534 -0.022648 -0.0199208 0.0547935 0.189525 -0.120458 0.0448616 -0.169545 0.0370553 0.233877 0.0233919 -0.0828498 -0.107595
0.122859 -0.0254538 -0.0476416 -0.0556912 0.0693471 0.102325 -0.0351232 0.114819 0.184235 0.0167418 0.0378765 -0.119081 0.0772324 -0.116641 0.119343 -0.119138 0.0456909 -0.172918 0.0564734 -0.0469061 0.0349631 0.0603883 0.138232 -0.14658 -0.112202 0.0068859
-0.0590978 0.240258 0.0445543 -0.000106262 -0.030669 0.0328804 0.178591 0.151958 0.0355936 -0.142905 -0.103663 0.0613992 -0.0757829 0.00112481 -0.0501491 -0.109213 0.0198885 0.168318 0.0757253 0.0264436 -0.0384716 0.0538574 -0.108015 -0.0844557 -0.0220308 0.110082
-0.176293 -0.0841602 0.0393378 -0.186617 -0.0294243 0.0244806 -0.111466 -0.161059 0.173366 0.0653654 0.140289 0.0977904 -0.0688827 -0.00755966 -0.100765 0.0848015 -0.111426 0.0172285 -0.0287957 0.0362096 -0.096034 0.0443155 -0.0278546 -0.362811 0.0532176 0.183572
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0.0711126 0.107471 -0.103064 -0.0660933 0.0282898 0.0681035 -0.162323 -0.0955832 -0.0540981 -0.0185964 -0.279098 0.087642 -0.104385 0.0040518 -0.32582 -0.0523847 -0.0955752 0.0539486 0.107851 -0.0910048 -0.120568 -0.18417 -0.0038572 0.0796848 -0.0264144 0.0419999
0.0252018 0.107631 0.079023 -0.126999 -0.136845 -0.0311833 0.089916 0.131271 -0.105044 -0.126669 0.0183927 0.148 -0.10657 -0.0219206 0.0829235 -0.0407394 0.0120876 -0.00860156 0.103103 0.0598043 0.126404 -0.0245102 0.0142444 -0.28077 0.158717 0.0364821
0.151438 -0.00689697 -0.033255 -0.0821683 0.033281 0.0710374 0.036052 -0.0288709 0.082781 0.0800394 0.00143209 -0.0260845 0.191386 -0.0116206 -0.00292576 0.100621 -0.145463 -0.0458452 0.0625469 0.01376 -0.0914514 0.0331013 0.0847526 0.324883 -0.0477189 0.0122967
-0.0944911 -0.060431 0.028657 0.136405 0.0608969 -0.0221248 0.0634825 -0.0285191 0.184417 0.168454 -0.0942206 -0.141332 -0.0198977 -0.0661331 0.252516 0.156281 0.0212146 0.0765799 -0.0798843 -0.156135 -0.0436299 -0.0715992 0.0436113 -0.0104703 -0.106878 0.0545451
-0.199185 0.0853504 0.0980759 0.0231877 0.0753122 0.088993 -0.339014 -0.106706 0.0558779 -0.0392132 0.0430866 0.180189 0.0907073 0.113719 0.00260689 0.0283824 0.036738 -0.16191 0.301934 -0.0739134 -0.151867 0.158705 -0.0416424 -0.0450826 -0.0676783 0.107425
0.0459129 0.0877302 -0.119002 0.0469258 -0.0988542 0.123839 0.0575379 0.0624517 -0.072819 -0.00240058 -0.116055 -0.00865912 0.0840823 0.0821455 -0.149276 -0.131732 0.102352 0.116319 -0.0813749 0.0195649 0.0760793 -0.0309793 0.0580953 -0.0746908 -0.0391085 -0.104791
0.0392264 0.0301294 0.180297 0.138003 0.123626 -0.0534354 -0.00405907 -0.202842 -0.0741243 0.112667 -0.0785073 0.0227708 -0.0695263 -0.147911 -0.209294 0.213374 0.114002 -0.18799 -0.0642291 0.0106781 0.150683 0.14988 0.0112937 0.0273094 -0.239393 0.0483612kind diag, method split
┌ Info: 0: avll =
└ tll[1] = -1.4112907471425078
[ Info: Running 50 iterations EM on diag cov GMM with 2 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.411345
[ Info: iteration 2, average log likelihood -1.411263
[ Info: iteration 3, average log likelihood -1.410360
[ Info: iteration 4, average log likelihood -1.403046
[ Info: iteration 5, average log likelihood -1.390721
[ Info: iteration 6, average log likelihood -1.386772
[ Info: iteration 7, average log likelihood -1.385948
[ Info: iteration 8, average log likelihood -1.385565
[ Info: iteration 9, average log likelihood -1.385329
[ Info: iteration 10, average log likelihood -1.385170
[ Info: iteration 11, average log likelihood -1.385053
[ Info: iteration 12, average log likelihood -1.384963
[ Info: iteration 13, average log likelihood -1.384888
[ Info: iteration 14, average log likelihood -1.384823
[ Info: iteration 15, average log likelihood -1.384763
[ Info: iteration 16, average log likelihood -1.384707
[ Info: iteration 17, average log likelihood -1.384653
[ Info: iteration 18, average log likelihood -1.384598
[ Info: iteration 19, average log likelihood -1.384538
[ Info: iteration 20, average log likelihood -1.384471
[ Info: iteration 21, average log likelihood -1.384398
[ Info: iteration 22, average log likelihood -1.384320
[ Info: iteration 23, average log likelihood -1.384234
[ Info: iteration 24, average log likelihood -1.384134
[ Info: iteration 25, average log likelihood -1.384008
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[ Info: iteration 27, average log likelihood -1.383616
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[ Info: iteration 29, average log likelihood -1.383084
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[ Info: iteration 31, average log likelihood -1.382433
[ Info: iteration 32, average log likelihood -1.382049
[ Info: iteration 33, average log likelihood -1.381675
[ Info: iteration 34, average log likelihood -1.381320
[ Info: iteration 35, average log likelihood -1.380973
[ Info: iteration 36, average log likelihood -1.380614
[ Info: iteration 37, average log likelihood -1.380241
[ Info: iteration 38, average log likelihood -1.379874
[ Info: iteration 39, average log likelihood -1.379538
[ Info: iteration 40, average log likelihood -1.379238
[ Info: iteration 41, average log likelihood -1.378954
[ Info: iteration 42, average log likelihood -1.378644
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[ Info: iteration 45, average log likelihood -1.376951
[ Info: iteration 46, average log likelihood -1.375813
[ Info: iteration 47, average log likelihood -1.375381
[ Info: iteration 48, average log likelihood -1.375214
[ Info: iteration 49, average log likelihood -1.375136
[ Info: iteration 50, average log likelihood -1.375095
┌ Info: EM with 100000 data points 50 iterations avll -1.375095
└ 952.4 data points per parameter
┌ Info: 1
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4113451803360544
│ -1.4112628837081553
│ ⋮
└ -1.375094946869731
[ Info: Running 50 iterations EM on diag cov GMM with 4 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.375167
[ Info: iteration 2, average log likelihood -1.375037
[ Info: iteration 3, average log likelihood -1.374072
[ Info: iteration 4, average log likelihood -1.366319
[ Info: iteration 5, average log likelihood -1.350277
[ Info: iteration 6, average log likelihood -1.341761
[ Info: iteration 7, average log likelihood -1.338854
[ Info: iteration 8, average log likelihood -1.337656
[ Info: iteration 9, average log likelihood -1.337057
[ Info: iteration 10, average log likelihood -1.336714
[ Info: iteration 11, average log likelihood -1.336503
[ Info: iteration 12, average log likelihood -1.336369
[ Info: iteration 13, average log likelihood -1.336280
[ Info: iteration 14, average log likelihood -1.336219
[ Info: iteration 15, average log likelihood -1.336174
[ Info: iteration 16, average log likelihood -1.336140
[ Info: iteration 17, average log likelihood -1.336112
[ Info: iteration 18, average log likelihood -1.336088
[ Info: iteration 19, average log likelihood -1.336068
[ Info: iteration 20, average log likelihood -1.336050
[ Info: iteration 21, average log likelihood -1.336035
[ Info: iteration 22, average log likelihood -1.336022
[ Info: iteration 23, average log likelihood -1.336012
[ Info: iteration 24, average log likelihood -1.336003
[ Info: iteration 25, average log likelihood -1.335996
[ Info: iteration 26, average log likelihood -1.335990
[ Info: iteration 27, average log likelihood -1.335985
[ Info: iteration 28, average log likelihood -1.335981
[ Info: iteration 29, average log likelihood -1.335978
[ Info: iteration 30, average log likelihood -1.335975
[ Info: iteration 31, average log likelihood -1.335973
[ Info: iteration 32, average log likelihood -1.335971
[ Info: iteration 33, average log likelihood -1.335969
[ Info: iteration 34, average log likelihood -1.335968
[ Info: iteration 35, average log likelihood -1.335967
[ Info: iteration 36, average log likelihood -1.335966
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[ Info: iteration 38, average log likelihood -1.335964
[ Info: iteration 39, average log likelihood -1.335964
[ Info: iteration 40, average log likelihood -1.335963
[ Info: iteration 41, average log likelihood -1.335963
[ Info: iteration 42, average log likelihood -1.335962
[ Info: iteration 43, average log likelihood -1.335962
[ Info: iteration 44, average log likelihood -1.335961
[ Info: iteration 45, average log likelihood -1.335961
[ Info: iteration 46, average log likelihood -1.335960
[ Info: iteration 47, average log likelihood -1.335960
[ Info: iteration 48, average log likelihood -1.335959
[ Info: iteration 49, average log likelihood -1.335959
[ Info: iteration 50, average log likelihood -1.335958
┌ Info: EM with 100000 data points 50 iterations avll -1.335958
└ 473.9 data points per parameter
┌ Info: 2
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.3751671924970397
│ -1.375037005131449
│ ⋮
└ -1.3359579739151881
[ Info: Running 50 iterations EM on diag cov GMM with 8 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.336108
[ Info: iteration 2, average log likelihood -1.335968
[ Info: iteration 3, average log likelihood -1.335584
[ Info: iteration 4, average log likelihood -1.332128
[ Info: iteration 5, average log likelihood -1.317935
[ Info: iteration 6, average log likelihood -1.302319
[ Info: iteration 7, average log likelihood -1.294993
[ Info: iteration 8, average log likelihood -1.291407
[ Info: iteration 9, average log likelihood -1.289248
[ Info: iteration 10, average log likelihood -1.287657
[ Info: iteration 11, average log likelihood -1.286232
[ Info: iteration 12, average log likelihood -1.284727
[ Info: iteration 13, average log likelihood -1.282978
[ Info: iteration 14, average log likelihood -1.280928
[ Info: iteration 15, average log likelihood -1.279374
[ Info: iteration 16, average log likelihood -1.278774
[ Info: iteration 17, average log likelihood -1.278545
[ Info: iteration 18, average log likelihood -1.278382
[ Info: iteration 19, average log likelihood -1.278200
[ Info: iteration 20, average log likelihood -1.277913
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[ Info: iteration 50, average log likelihood -1.272502
┌ Info: EM with 100000 data points 50 iterations avll -1.272502
└ 236.4 data points per parameter
┌ Info: 3
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.3361082476400459
│ -1.3359681077633203
│ ⋮
└ -1.272502328725007
[ Info: Running 50 iterations EM on diag cov GMM with 16 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.272682
[ Info: iteration 2, average log likelihood -1.272465
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -1.193439
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -1.173245
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 18, average log likelihood -1.169533
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 20, average log likelihood -1.162354
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 21, average log likelihood -1.169962
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 23, average log likelihood -1.161049
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 25, average log likelihood -1.165402
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 26, average log likelihood -1.168360
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 27, average log likelihood -1.170044
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 29, average log likelihood -1.160215
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 32, average log likelihood -1.166753
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[ Info: iteration 35, average log likelihood -1.159568
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 37, average log likelihood -1.163102
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[ Info: iteration 38, average log likelihood -1.166172
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[ Info: iteration 47, average log likelihood -1.157825
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 49, average log likelihood -1.161456
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 50, average log likelihood -1.164828
┌ Info: EM with 100000 data points 50 iterations avll -1.164828
└ 118.1 data points per parameter
┌ Info: 4
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.2726819919488797
│ -1.27246545729513
│ ⋮
└ -1.1648278933721452
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
┌ Warning: Variances had to be floored
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│ 2-element Array{Int64,1}:
│ 3
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 1, average log likelihood -1.166248
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
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[ Info: iteration 6, average log likelihood -1.093483
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
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[ Info: iteration 8, average log likelihood -1.096866
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 12, average log likelihood -1.095127
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 13, average log likelihood -1.082592
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 14, average log likelihood -1.103718
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 15, average log likelihood -1.077524
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[ Info: iteration 16, average log likelihood -1.095683
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 17, average log likelihood -1.093032
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 18, average log likelihood -1.097486
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 19, average log likelihood -1.083125
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[ Info: iteration 20, average log likelihood -1.104265
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 21, average log likelihood -1.078250
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 22, average log likelihood -1.096212
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 23, average log likelihood -1.093040
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 24, average log likelihood -1.097489
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255