If you think that there is an error in how your package is being tested or represented, please file an issue at NewPkgEval.jl , making sure to read the FAQ first.
Results with Julia v1.2.0
Testing was successful .
Last evaluation was ago and took 8 minutes, 21 seconds.
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Resolving package versions...
Installed Missings ─────────── v0.4.3
Installed GaussianMixtures ─── v0.3.0
Installed DataAPI ──────────── v1.1.0
Installed PDMats ───────────── v0.9.10
Installed FileIO ───────────── v1.1.0
Installed NearestNeighbors ─── v0.4.4
Installed StatsBase ────────── v0.32.0
Installed BinaryProvider ───── v0.5.8
Installed ScikitLearnBase ──── v0.5.0
Installed Blosc ────────────── v0.5.1
Installed URIParser ────────── v0.4.0
Installed StatsFuns ────────── v0.9.0
Installed HDF5 ─────────────── v0.12.5
Installed Rmath ────────────── v0.5.1
Installed JLD ──────────────── v0.9.1
Installed Compat ───────────── v2.2.0
Installed OrderedCollections ─ v1.1.0
Installed DataStructures ───── v0.17.6
Installed Parameters ───────── v0.12.0
Installed QuadGK ───────────── v2.1.1
Installed Distributions ────── v0.21.9
Installed StaticArrays ─────── v0.12.1
Installed CMake ────────────── v1.1.2
Installed SortingAlgorithms ── v0.3.1
Installed CMakeWrapper ─────── v0.2.3
Installed Distances ────────── v0.8.2
Installed LegacyStrings ────── v0.4.1
Installed Clustering ───────── v0.13.3
Installed BinDeps ──────────── v0.8.10
Installed Arpack ───────────── v0.3.1
Installed SpecialFunctions ─── v0.8.0
Updating `~/.julia/environments/v1.2/Project.toml`
[cc18c42c] + GaussianMixtures v0.3.0
Updating `~/.julia/environments/v1.2/Manifest.toml`
[7d9fca2a] + Arpack v0.3.1
[9e28174c] + BinDeps v0.8.10
[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 v0.17.6
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.21.9
[5789e2e9] + FileIO v1.1.0
[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
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[d96e819e] + Parameters v0.12.0
[1fd47b50] + QuadGK v2.1.1
[79098fc4] + Rmath v0.5.1
[6e75b9c4] + ScikitLearnBase v0.5.0
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.8.0
[90137ffa] + StaticArrays v0.12.1
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.9.0
[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`
Building Blosc ───────────→ `~/.julia/packages/Blosc/lzFr0/deps/build.log`
Building HDF5 ────────────→ `~/.julia/packages/HDF5/Zh9on/deps/build.log`
Building Rmath ───────────→ `~/.julia/packages/Rmath/4wt82/deps/build.log`
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Building Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Testing GaussianMixtures
Status `/tmp/jl_IbwimU/Manifest.toml`
[7d9fca2a] Arpack v0.3.1
[9e28174c] BinDeps v0.8.10
[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 v0.17.6
[b4f34e82] Distances v0.8.2
[31c24e10] Distributions v0.21.9
[5789e2e9] FileIO v1.1.0
[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
[bac558e1] OrderedCollections v1.1.0
[90014a1f] PDMats v0.9.10
[d96e819e] Parameters v0.12.0
[1fd47b50] QuadGK v2.1.1
[79098fc4] Rmath v0.5.1
[6e75b9c4] ScikitLearnBase v0.5.0
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.8.0
[90137ffa] StaticArrays v0.12.1
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.9.0
[30578b45] URIParser v0.4.0
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[9abbd945] Profile [`@stdlib/Profile`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
[ Info: Testing Data
(100000, -2.114878172691752e6, [96223.37099167121, 3776.6290083287804], [1909.2768727377727 7007.681893981676 2057.751876771842; -2297.413238961541 -7148.41306425312 -1925.5434208540764], Array{Float64,2}[[95379.39787914659 -3623.4900105113147 -84.24382146875247; -3623.4900105113156 85013.48872674159 -2699.9411671975677; -84.2438214687525 -2699.9411671975677 96252.5067471903], [4967.194975203896 3437.1217262247465 347.3823538918625; 3437.1217262247465 14918.008311395191 2530.88007238308; 347.38235389186246 2530.88007238308 4159.433108632379]])
┌ Warning: rmprocs: process 1 not removed
└ @ Distributed /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.2/Distributed/src/cluster.jl:1005
[ Info: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 1.053910e+03
1 9.421860e+02 -1.117237e+02 | 6
2 9.005112e+02 -4.167474e+01 | 0
3 9.005112e+02 0.000000e+00 | 0
K-means converged with 3 iterations (objv = 900.5112473160834)
┌ 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 -2.075865
└ 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:625 [inlined]
└ @ Core ./broadcast.jl:625
┌ 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:625 [inlined]
└ @ Core ./broadcast.jl:625
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = _broadcast_getindex_evalf at broadcast.jl:625 [inlined]
└ @ Core ./broadcast.jl:625
[ Info: iteration 1, lowerbound -3.794054
[ Info: iteration 2, lowerbound -3.645538
[ Info: iteration 3, lowerbound -3.484707
[ Info: iteration 4, lowerbound -3.300915
[ Info: iteration 5, lowerbound -3.118718
[ Info: iteration 6, lowerbound -2.970408
[ Info: dropping number of Gaussions to 7
[ Info: iteration 7, lowerbound -2.867358
[ Info: dropping number of Gaussions to 6
[ Info: iteration 8, lowerbound -2.810434
[ Info: dropping number of Gaussions to 5
[ Info: iteration 9, lowerbound -2.797870
[ Info: dropping number of Gaussions to 3
[ Info: iteration 10, lowerbound -2.779378
[ Info: iteration 11, lowerbound -2.763700
[ Info: iteration 12, lowerbound -2.750304
[ Info: iteration 13, lowerbound -2.729211
[ Info: iteration 14, lowerbound -2.697057
[ Info: iteration 15, lowerbound -2.650881
[ Info: iteration 16, lowerbound -2.590663
[ Info: iteration 17, lowerbound -2.522281
[ Info: iteration 18, lowerbound -2.456572
[ Info: iteration 19, lowerbound -2.402338
[ Info: iteration 20, lowerbound -2.361246
[ Info: iteration 21, lowerbound -2.331239
[ Info: iteration 22, lowerbound -2.312529
[ Info: iteration 23, lowerbound -2.307533
[ Info: dropping number of Gaussions to 2
[ Info: iteration 24, lowerbound -2.302923
[ Info: iteration 25, lowerbound -2.299260
[ Info: iteration 26, lowerbound -2.299256
[ Info: iteration 27, lowerbound -2.299254
[ Info: iteration 28, lowerbound -2.299254
[ 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[Mon Dec 2 17:37:16 2019: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points
, Mon Dec 2 17:37:23 2019: K-means with 272 data points using 3 iterations
11.3 data points per parameter
, Mon Dec 2 17:37:24 2019: EM with 272 data points 0 iterations avll -2.075865
5.8 data points per parameter
, Mon Dec 2 17:37:26 2019: GMM converted to Variational GMM
, Mon Dec 2 17:37:32 2019: iteration 1, lowerbound -3.794054
, Mon Dec 2 17:37:32 2019: iteration 2, lowerbound -3.645538
, Mon Dec 2 17:37:32 2019: iteration 3, lowerbound -3.484707
, Mon Dec 2 17:37:32 2019: iteration 4, lowerbound -3.300915
, Mon Dec 2 17:37:32 2019: iteration 5, lowerbound -3.118718
, Mon Dec 2 17:37:32 2019: iteration 6, lowerbound -2.970408
, Mon Dec 2 17:37:33 2019: dropping number of Gaussions to 7
, Mon Dec 2 17:37:33 2019: iteration 7, lowerbound -2.867358
, Mon Dec 2 17:37:33 2019: dropping number of Gaussions to 6
, Mon Dec 2 17:37:33 2019: iteration 8, lowerbound -2.810434
, Mon Dec 2 17:37:33 2019: dropping number of Gaussions to 5
, Mon Dec 2 17:37:33 2019: iteration 9, lowerbound -2.797870
, Mon Dec 2 17:37:33 2019: dropping number of Gaussions to 3
, Mon Dec 2 17:37:33 2019: iteration 10, lowerbound -2.779378
, Mon Dec 2 17:37:33 2019: iteration 11, lowerbound -2.763700
, Mon Dec 2 17:37:33 2019: iteration 12, lowerbound -2.750304
, Mon Dec 2 17:37:33 2019: iteration 13, lowerbound -2.729211
, Mon Dec 2 17:37:33 2019: iteration 14, lowerbound -2.697057
, Mon Dec 2 17:37:33 2019: iteration 15, lowerbound -2.650881
, Mon Dec 2 17:37:33 2019: iteration 16, lowerbound -2.590663
, Mon Dec 2 17:37:33 2019: iteration 17, lowerbound -2.522281
, Mon Dec 2 17:37:33 2019: iteration 18, lowerbound -2.456572
, Mon Dec 2 17:37:33 2019: iteration 19, lowerbound -2.402338
, Mon Dec 2 17:37:33 2019: iteration 20, lowerbound -2.361246
, Mon Dec 2 17:37:33 2019: iteration 21, lowerbound -2.331239
, Mon Dec 2 17:37:33 2019: iteration 22, lowerbound -2.312529
, Mon Dec 2 17:37:33 2019: iteration 23, lowerbound -2.307533
, Mon Dec 2 17:37:33 2019: dropping number of Gaussions to 2
, Mon Dec 2 17:37:33 2019: iteration 24, lowerbound -2.302923
, Mon Dec 2 17:37:33 2019: iteration 25, lowerbound -2.299260
, Mon Dec 2 17:37:33 2019: iteration 26, lowerbound -2.299256
, Mon Dec 2 17:37:33 2019: iteration 27, lowerbound -2.299254
, Mon Dec 2 17:37:33 2019: iteration 28, lowerbound -2.299254
, Mon Dec 2 17:37:33 2019: iteration 29, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 30, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 31, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 32, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 33, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 34, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 35, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 36, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 37, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 38, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 39, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 40, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 41, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 42, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 43, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 44, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 45, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 46, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 47, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 48, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 49, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: iteration 50, lowerbound -2.299253
, Mon Dec 2 17:37:33 2019: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253
]
α = [95.95490777397131, 178.04509222602871]
β = [95.95490777397131, 178.04509222602871]
m = [2.000229257775246 53.85198717246062; 4.250300733269789 79.28686694436004]
ν = [97.95490777397131, 180.04509222602871]
W = LinearAlgebra.UpperTriangular{Float64,Array{Float64,2}}[[0.37587636119504886 -0.008953123827348369; 0.0 0.012748664777409751], [0.1840415554748283 -0.007644049042328595; 0.0 0.008581705166331017]]
Kind: diag, size256
nx: 100000 sum(zeroth order stats): 99999.99999999999
avll from stats: -0.9990036717744462
avll from llpg: -0.9990036717744452
avll direct: -0.9990036717744452
sum posterior: 100000.0
Kind: full, size16
nx: 100000 sum(zeroth order stats): 100000.0
avll from stats: -0.9573164636528305
avll from llpg: -0.957316463652831
avll direct: -0.957316463652831
sum posterior: 100000.0
32×26 Array{Float64,2}:
0.0222097 0.176742 -0.0231408 -0.039351 0.215101 0.053445 -0.016615 -0.0392146 -0.114455 0.0340123 0.185065 -0.0329518 -0.0190561 -0.0155323 0.225663 -0.0552715 0.094904 0.113927 0.017948 -0.110629 -0.0316003 0.0826712 -0.0144637 -0.0107549 -0.00195681 0.121887
-0.0102852 -0.0861489 -0.0784853 0.0867545 0.0701538 0.0701644 -0.0105751 0.0553872 -0.0316545 -0.0665026 -0.0736675 0.0865245 0.0414358 -0.00430916 -0.00731767 0.0125473 0.159586 -0.00648283 -0.0290379 -0.181819 -0.120316 -0.189294 -0.106103 0.103116 -0.000550234 -0.00330515
0.210259 0.0516378 -0.0212542 -0.02393 -0.0855034 -0.116948 -0.0638983 -0.210206 0.128015 0.121677 -0.0241252 -0.128016 -0.0408318 0.31654 0.0424094 -0.255963 0.124758 -0.0453534 0.0387677 0.175968 0.0231617 -0.0653336 0.0105226 -0.0521201 -0.046576 -0.0689671
0.119503 0.0223266 -0.0485108 0.158633 -0.0272659 0.177086 -0.0282571 0.023781 -0.0562304 0.168692 -0.06817 0.200126 -0.0739513 0.0317089 0.0836056 0.050799 -0.179269 0.0499351 -0.00073226 -0.069721 0.101987 -0.0147747 0.0928411 0.0377047 -0.127327 0.00414756
0.0796082 0.219068 -0.0854412 0.103737 0.155255 0.000820188 0.198768 0.16737 0.0719577 -0.142188 0.122491 -0.0114656 -0.069444 0.147943 -0.186161 -0.0264207 -0.181996 0.119395 0.0571309 -0.0027217 0.0501695 -0.0162199 0.0689366 0.0817008 -0.0751605 -0.0796727
-0.015696 -0.149564 -0.052656 -0.099632 0.137773 -0.101755 -0.00651788 0.0701845 0.092817 -0.00252082 -0.127901 0.12028 0.0578009 -0.0290246 0.0752596 -0.0169988 0.201283 0.018061 -0.0223743 0.0747355 -0.0614807 -0.140416 0.0270851 -0.115974 0.246433 -0.0186691
0.064692 0.0963104 -0.0669936 0.123625 -0.0965399 0.0845628 -0.0329131 0.0920518 0.0353875 -0.122661 -0.0660994 -0.00384992 0.111671 -0.159431 -0.0755593 -0.242515 -0.22465 -0.0576846 -0.0400945 0.0442749 -0.0704225 -0.0803433 0.081897 0.117158 -0.0893726 -0.0320291
-0.0234764 -0.102635 -0.105611 0.0764303 -0.0127636 0.0503632 0.147752 0.0107797 -0.030951 -0.0681123 -0.0903873 -0.104058 0.226137 0.286718 0.00664695 -0.0277398 0.030615 -0.0119022 -0.08029 -0.113634 -0.01792 0.0543432 0.201538 0.061854 -0.0939748 0.00825803
0.100383 -0.023946 -0.0199448 0.0943937 -0.0747605 0.0665176 -0.0613324 0.0818778 -0.0920329 -0.0203531 -0.00098559 0.0385797 -0.00152315 -0.0456925 -0.0752642 0.01309 0.182756 -0.0673023 0.133757 0.0339956 0.0028058 0.0115032 -0.0545524 -0.0714531 -0.0591645 0.00645575
0.117859 -0.0716838 -0.0296914 0.0325418 -0.0404203 0.013899 0.0970878 0.16296 0.052219 -0.0236529 -0.0169793 -0.00893926 -0.123915 -0.0888516 0.0691074 -0.228122 -0.0288173 0.157068 0.083874 -0.0482551 0.0960371 -0.0294223 -0.0278333 -0.0389106 0.14821 0.218291
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0.0494427 0.0397772 0.121424 -0.0757257 -0.142581 -0.0740529 -0.0756197 0.0238335 0.0361107 -0.0346224 0.0422445 0.00499342 0.176407 -0.0685585 -0.0996214 -0.0539451 -0.00993551 0.111119 0.10589 0.115113 0.0169306 0.0719264 -0.118264 0.13092 -0.00820628 -0.0103846
0.00610146 -0.129988 -0.0273662 -0.0249311 0.0901247 0.10068 0.0406753 -0.0483176 0.152669 -0.0325414 -0.0272537 -0.196347 0.0488286 0.131154 -0.112255 -0.030456 0.191861 0.198226 -0.0548744 -0.035932 0.154452 -0.0623246 -0.116862 -0.0200627 -0.100665 0.0314509
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0.0265216 0.23223 -0.10253 0.161718 -0.107146 -0.0867443 -0.0338805 -0.0601722 -0.171333 -0.000575663 0.0768967 -0.00628956 0.102055 0.0823353 -0.0641972 -0.0214682 0.0376788 0.130583 0.198582 0.0107426 0.101249 -0.129923 -0.0475545 -0.12156 0.0230799 -0.0485312
-0.0770597 -0.0291846 0.216291 0.102172 -0.0942685 0.00453586 -0.0585655 -0.000683499 0.052166 0.0333258 -0.226368 -0.0846456 0.103095 0.0135459 0.190892 0.123377 -0.0291639 -0.0298075 -0.0604525 -0.0827 -0.110256 -0.217909 0.0482874 -0.0892359 0.129752 -0.19189
-0.0529829 0.0648953 0.0189889 -0.0930767 -0.135949 0.00284567 0.0885369 -0.0504685 0.0769622 -0.0293731 0.0685646 0.121655 -0.0452124 -0.104323 -0.177429 0.0675385 0.048104 -0.0740086 0.0498632 0.158035 -0.100446 0.090954 -0.0816228 0.00501795 -0.172154 0.132932
0.023183 0.0357988 0.0348258 -0.188952 -0.102996 -0.0647111 -0.155396 -0.0902789 0.0455435 0.0454895 -0.0886994 0.041398 0.0439369 0.0443977 0.144041 -0.0881071 0.0856944 0.115834 0.045976 -0.0322765 -0.235426 0.0476719 -0.13185 -0.0557694 0.000609982 0.101224
-0.00564584 -0.053865 -0.0289672 0.0525274 0.22538 0.161977 -0.033069 0.00253017 0.00381851 -0.177716 -0.0993129 0.161963 -0.1052 0.0923348 -0.0785064 -0.0719027 -0.1835 -0.0957025 0.00649774 0.0320148 0.00285347 0.0738335 -0.113451 0.0944876 -0.163195 -0.0528892
-0.0599292 0.00270709 -0.138172 0.114137 -0.115297 -0.0455774 0.150355 -0.0846045 -0.0521967 -0.0911186 0.0813916 0.0697489 0.060314 -0.0356075 0.103682 -0.0540574 -0.0287193 0.0060039 -0.103871 -0.325934 0.0913295 0.0864778 -0.121449 0.0566949 0.163161 0.088983
0.0354993 0.179528 0.0814844 0.0422044 -0.0462181 -0.118314 0.0807364 0.161176 0.0124375 0.130271 -0.0109455 -0.113467 0.147878 -0.195453 0.0872559 0.120772 0.0203895 0.0214172 0.0582247 0.109537 -0.0647072 0.0042534 0.116298 -0.135891 -0.0648587 0.0425753
0.149447 0.0305382 -0.0260324 0.149099 -0.155173 -0.126095 -0.0921249 -0.127369 -0.0227696 0.0662471 0.25228 -0.104184 0.0521551 -0.0774472 -0.0591458 -0.0163746 0.114206 0.14943 -0.042548 -0.106812 -0.0605524 -0.0566245 -0.166322 0.0271689 -0.194901 0.106371
0.195075 -0.0375584 0.0781004 -0.00514496 -0.0997586 -0.093269 -0.00296478 -0.0229808 0.130479 0.03585 0.0984036 0.0266917 0.0604929 -0.062825 0.0575171 -0.0975946 -0.0624631 -0.0547993 -0.0133333 0.0344919 -0.095144 -0.124902 -0.0408598 0.15002 -0.0337472 -0.0185671
-0.155419 -0.118121 0.00184268 -0.110584 0.0108835 0.0221302 0.25283 -0.0159275 -0.0417499 0.143607 0.0036374 0.0118472 -0.023772 -0.0427541 -0.0182648 0.128088 0.0713701 -0.0840751 0.0587288 -0.102675 -0.154755 -0.0503586 0.0675112 0.106613 -0.0892131 -0.111939
-0.107046 -0.0715789 0.000554391 0.0490423 0.0421613 0.0559648 -0.125724 -0.00018038 0.0698416 0.0527655 -0.000158263 0.0350306 -0.0209922 -0.0570214 -0.0550226 0.0259002 -0.157599 -0.0114701 0.00298455 -0.256175 0.00404165 0.0303636 -0.148282 -0.0218999 -0.153841 -0.0963326
-0.143847 0.186713 0.0497352 -0.0583975 -0.0647204 0.0979032 0.0608547 -0.0450038 0.0434281 -0.024315 -0.157534 0.0907487 0.0890454 0.126329 -0.149528 0.0800626 0.113975 -0.205569 0.0280373 -0.056085 0.0403242 -0.0955746 0.0123845 0.0968324 0.0214686 -0.146532
0.16015 -0.0746503 -0.113972 -0.0144278 -0.170203 -0.173938 -0.0585456 0.134789 -0.0646417 -0.121454 -0.0920971 0.075831 -0.15344 -0.0118915 0.0164275 0.07181 -0.0113809 -0.0953973 0.00702353 -0.0862027 0.016661 0.210375 -0.047496 -0.114642 0.0510965 -0.0323217
0.04662 -0.192991 -0.000775945 -0.0420062 0.0715233 0.115579 0.0239538 -0.102647 -0.0570604 -0.16044 -0.0207042 0.0294442 -0.0376625 0.0314389 0.236716 0.00203644 -0.252459 0.0291861 -0.056588 -0.0455894 0.0342278 -0.085846 -0.0265002 -0.0620294 0.0908761 -0.0321034
-0.196684 -0.0224511 0.134921 -0.0962217 -0.00284376 -0.022332 0.0678901 -0.151742 -0.19453 0.161035 0.150356 0.122639 0.0682973 0.12859 0.149177 0.0609064 -0.0431283 -0.193743 0.0300906 -0.0224187 0.0239757 0.0999714 0.0290359 -0.000294335 -0.0513268 -0.0576123
0.148532 -0.0702002 -0.0557627 -0.0164084 0.0613394 0.0763719 0.0530511 -0.0533868 -0.210168 0.195094 -0.0481003 -0.0713035 -0.064836 0.103694 -0.00955869 -0.00828504 -0.0622801 0.0107943 -0.056398 -0.0278557 0.0969544 0.0167522 0.0174254 0.0171642 -0.0496808 0.134206 kind diag, method split
┌ Info: 0: avll =
└ tll[1] = -1.3697428266623226
[ Info: Running 50 iterations EM on diag cov GMM with 2 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.369815
[ Info: iteration 2, average log likelihood -1.369750
[ Info: iteration 3, average log likelihood -1.369354
[ Info: iteration 4, average log likelihood -1.364902
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[ Info: iteration 50, average log likelihood -1.330202
┌ Info: EM with 100000 data points 50 iterations avll -1.330202
└ 952.4 data points per parameter
┌ Info: 1
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.369814750081125
│ -1.369750325810214
│ ⋮
└ -1.330202421360847
[ Info: Running 50 iterations EM on diag cov GMM with 4 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.330310
[ Info: iteration 2, average log likelihood -1.330185
[ Info: iteration 3, average log likelihood -1.329471
[ Info: iteration 4, average log likelihood -1.322619
[ Info: iteration 5, average log likelihood -1.305274
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[ Info: iteration 14, average log likelihood -1.291248
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[ Info: iteration 16, average log likelihood -1.291023
[ Info: iteration 17, average log likelihood -1.290914
[ Info: iteration 18, average log likelihood -1.290803
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[ Info: iteration 50, average log likelihood -1.289750
┌ Info: EM with 100000 data points 50 iterations avll -1.289750
└ 473.9 data points per parameter
┌ Info: 2
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.3303099452325347
│ -1.3301849331043167
│ ⋮
└ -1.289750255357867
[ Info: Running 50 iterations EM on diag cov GMM with 8 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.289890
[ Info: iteration 2, average log likelihood -1.289709
[ Info: iteration 3, average log likelihood -1.289015
[ Info: iteration 4, average log likelihood -1.282907
[ Info: iteration 5, average log likelihood -1.264828
[ Info: iteration 6, average log likelihood -1.250941
[ Info: iteration 7, average log likelihood -1.245756
[ Info: iteration 8, average log likelihood -1.243160
[ Info: iteration 9, average log likelihood -1.241423
[ Info: iteration 10, average log likelihood -1.240009
[ Info: iteration 11, average log likelihood -1.238827
[ Info: iteration 12, average log likelihood -1.237959
[ Info: iteration 13, average log likelihood -1.237342
[ Info: iteration 14, average log likelihood -1.236928
[ Info: iteration 15, average log likelihood -1.236670
[ Info: iteration 16, average log likelihood -1.236507
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[ Info: iteration 48, average log likelihood -1.232073
[ Info: iteration 49, average log likelihood -1.232058
[ Info: iteration 50, average log likelihood -1.232048
┌ Info: EM with 100000 data points 50 iterations avll -1.232048
└ 236.4 data points per parameter
┌ Info: 3
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.2898898428295709
│ -1.2897094448309159
│ ⋮
└ -1.2320480111190375
[ Info: Running 50 iterations EM on diag cov GMM with 16 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.232252
[ Info: iteration 2, average log likelihood -1.231950
[ Info: iteration 3, average log likelihood -1.229306
[ Info: iteration 4, average log likelihood -1.208860
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 2
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -1.176978
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 7
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -1.164027
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.153305
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 2
│ 15
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -1.140138
[ Info: iteration 9, average log likelihood -1.153671
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 7
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.137291
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 2
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 11, average log likelihood -1.141699
[ Info: iteration 12, average log likelihood -1.150169
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 15
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 13, average log likelihood -1.131899
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 2
│ 7
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 14, average log likelihood -1.136921
[ Info: iteration 15, average log likelihood -1.153039
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 16, average log likelihood -1.133266
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 2
│ 7
│ 15
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 17, average log likelihood -1.135456
[ Info: iteration 18, average log likelihood -1.161189
[ Info: iteration 19, average log likelihood -1.145534
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 2
│ 7
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 20, average log likelihood -1.132248
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 21, average log likelihood -1.143847
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 5
│ 15
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 22, average log likelihood -1.130387
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 2
│ 7
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 23, average log likelihood -1.144969
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 24, average log likelihood -1.153338
[ Info: iteration 25, average log likelihood -1.140271
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 2
│ 7
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 26, average log likelihood -1.126955
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 15
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 27, average log likelihood -1.137527
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 5
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 28, average log likelihood -1.136294
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 2
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[ Info: iteration 29, average log likelihood -1.142131
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 31, average log likelihood -1.132346
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[ Info: iteration 32, average log likelihood -1.123137
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[ Info: iteration 33, average log likelihood -1.149725
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[ Info: iteration 34, average log likelihood -1.143996
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[ Info: iteration 35, average log likelihood -1.134665
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[ Info: iteration 37, average log likelihood -1.123796
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[ Info: iteration 38, average log likelihood -1.137870
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[ Info: iteration 45, average log likelihood -1.135135
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[ Info: iteration 48, average log likelihood -1.137483
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[ Info: iteration 49, average log likelihood -1.149441
[ Info: iteration 50, average log likelihood -1.141035
┌ Info: EM with 100000 data points 50 iterations avll -1.141035
└ 118.1 data points per parameter
┌ Info: 4
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.2322516833785015
│ -1.2319496232027665
│ ⋮
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[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
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[ Info: iteration 20, average log likelihood -1.044778
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[ Info: iteration 22, average log likelihood -1.051757
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[ Info: iteration 23, average log likelihood -1.046527
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[ Info: iteration 24, average log likelihood -1.046329
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[ Info: iteration 25, average log likelihood -1.056032
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[ Info: iteration 26, average log likelihood -1.049494
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[ Info: iteration 27, average log likelihood -1.045829
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[ Info: iteration 28, average log likelihood -1.056546
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[ Info: iteration 29, average log likelihood -1.049046
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[ Info: iteration 30, average log likelihood -1.046330
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[ Info: iteration 31, average log likelihood -1.056026
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[ Info: iteration 32, average log likelihood -1.049497
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 33, average log likelihood -1.045801
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 34, average log likelihood -1.056550
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 35, average log likelihood -1.049013
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 36, average log likelihood -1.046342
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 37, average log likelihood -1.055995
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[ Info: iteration 38, average log likelihood -1.049499
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[ Info: iteration 39, average log likelihood -1.045782
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 40, average log likelihood -1.056552
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[ Info: iteration 41, average log likelihood -1.048985
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 42, average log likelihood -1.046354
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 43, average log likelihood -1.055970
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 44, average log likelihood -1.049502
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 45, average log likelihood -1.045767
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 46, average log likelihood -1.056555
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 47, average log likelihood -1.048963
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 48, average log likelihood -1.046365
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 49, average log likelihood -1.055949
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 50, average log likelihood -1.049506
┌ Info: EM with 100000 data points 50 iterations avll -1.049506
└ 59.0 data points per parameter
┌ Info: 5
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.12690473570304
│ -1.118582345256897
│ ⋮
└ -1.0495062978252185
┌ Info: Total log likelihood:
│ tll =
│ 251-element Array{Float64,1}:
│ -1.3697428266623226
│ -1.369814750081125
│ -1.369750325810214
│ -1.369353921643935
│ ⋮
│ -1.046364881503956
│ -1.0559490061798955
└ -1.0495062978252185
32×26 Array{Float64,2}:
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-0.0949352 -0.0661992 0.10656 -0.0335159 0.000609046 0.0619512 -0.111176 0.00418843 0.0661659 0.0129577 -0.000757894 0.036529 -0.0845138 -0.0568593 -0.0562334 0.0644602 -0.24727 -0.0238123 0.0594475 -0.177596 0.0413536 0.0765036 -0.205477 -0.072611 -0.575648 -0.0566812
-0.0983079 -0.0648251 -0.235307 0.175865 0.100351 0.0561333 -0.109551 -0.0258122 0.0640163 0.13176 -0.00119409 0.0372364 0.153396 -0.0543545 -0.0557314 -0.16565 0.109075 0.0156565 -0.15233 -0.298357 -0.0179237 -0.0479027 -0.044356 0.0274613 0.771407 -0.159701
-0.0315498 0.043384 -0.108756 0.0752043 -0.055697 0.0662485 0.0246958 0.0508731 -0.00340814 -0.173227 -0.000944437 0.0990435 0.103761 -0.0759136 -0.0396627 -0.176949 -0.077716 -0.0270668 0.00148142 -0.0111669 -0.0705769 -0.0305485 0.00876697 0.145985 -0.0959209 -0.0178515
0.153245 -0.0174261 -0.022861 0.0015973 -0.056788 -0.0543765 0.000275622 -0.0406675 0.0890531 0.0555368 -0.0259428 -0.0680272 -0.0850631 0.138942 0.0698539 -0.242592 0.0461382 0.054937 0.0639779 0.0539748 0.0763644 -0.0507347 -0.0364718 -0.0455474 0.0526956 0.065448
0.19466 0.0360111 -0.124149 -0.0245237 -1.3232 -0.220148 0.113636 -0.0310059 0.0827018 0.184894 0.481834 0.0183907 0.0418902 -0.0814416 0.0592241 -0.0979888 -0.0376921 -0.0552137 -0.0941409 0.0669984 -0.0993337 -0.460192 0.0671371 0.119488 0.00707324 -0.00876681
0.195286 -0.0456314 0.0877009 -0.00905908 0.164332 -0.047694 -0.0260865 -0.0253357 0.147224 0.0135879 0.0405687 0.0230355 0.0683234 -0.0403685 0.0568462 -0.09834 -0.0712604 -0.0545491 0.00308539 0.00978843 -0.0973712 -0.0603058 -0.0811042 0.144169 -0.0297749 -0.0143966
0.0588895 -0.208465 -0.0204018 -0.170036 0.0611871 0.177805 0.0253691 -0.0795076 -0.147281 -0.137625 -0.031442 0.0201342 0.0850402 -0.275587 0.285333 -0.0357014 -0.248907 0.0317045 -0.170251 -0.0756407 0.0673494 -0.095952 -0.0267112 -0.194508 0.0987867 -0.0487129
0.0299116 -0.165834 0.0209252 0.0745759 0.0839631 0.0015062 0.0230332 -0.144673 0.000195797 -0.16836 -0.00393732 0.0366149 -0.139986 0.283848 0.233643 0.00868025 -0.254305 0.0262637 0.051285 -0.0162355 -0.00121234 -0.00426186 -0.0269439 0.0123699 0.0822019 -0.00888441
-0.154621 0.202767 0.0389096 -0.0551703 -0.0771912 0.102838 0.0564518 -0.024456 0.0433035 -0.0211066 -0.157291 0.0840982 0.0885647 0.143502 -0.158185 0.0514275 0.10952 -0.206505 0.048216 -0.0546248 0.0451085 -0.0957862 -0.00826171 0.0978853 0.00541964 -0.14213
-0.00219841 -0.0858685 -0.0141942 -0.0140367 0.0860132 0.0622579 0.00906805 -0.0110417 0.137127 -0.0289792 -0.0512593 -0.118738 0.0479182 0.111143 -0.0808762 -0.0413733 0.158139 0.190038 -0.0154096 -0.0792104 0.1332 -0.0631326 -0.108891 -0.0232207 -0.0930141 0.0015948
0.0617355 -0.0986145 0.163021 -0.0281089 -0.0546665 -0.155021 -0.247921 -0.0737957 0.133157 0.0464098 0.0891379 0.195454 0.022205 -0.197321 -0.149596 0.174687 -0.10751 -0.0626369 -0.0358506 -0.0521508 -0.0633999 0.182049 0.0779236 0.134206 -0.0445491 -0.275548
0.0256558 0.0149001 -0.0647861 0.0779016 -0.019692 0.029814 0.0405919 -0.0237034 -0.0865748 -0.0260106 0.0651607 0.0310239 0.018526 -0.0392336 0.0912591 -0.0310458 0.0846877 0.0182268 0.00605218 -0.139833 0.0213877 0.0331951 -0.0741742 -0.00765784 0.0393194 0.0711089
0.0542616 0.0661768 0.0422298 -0.00837417 0.100911 0.0389659 -0.0217718 -0.0329218 -0.0152984 -0.102507 -0.0457852 0.0592042 -0.0637451 0.0173461 -0.0633779 -0.0424182 -0.192995 -0.0350416 0.0908849 0.00828922 -0.0322679 0.0635643 0.0331118 0.0272119 -0.140109 -0.0810395
-0.0441745 0.0375852 0.10548 -0.0763753 -0.108981 -0.0169374 -0.0121474 -0.0374946 0.0558705 0.00408494 -0.0643704 0.0324743 0.0184542 -0.025478 0.0211384 0.0522484 0.0253932 0.0159501 0.0227389 0.0230506 -0.14929 -0.0256975 -0.0251351 -0.0459275 -0.0455351 -0.00265833
-0.0652272 0.0314852 0.126216 -0.0969671 -0.0564826 -0.0486594 -0.0194977 -0.0756265 -0.0600042 0.0440837 0.0631381 0.0688532 0.117895 0.0281839 0.0357002 -0.00769035 -0.0342713 -0.037238 0.0768594 0.0701394 -0.002014 0.047068 -0.0346455 0.0403883 -0.0237651 -0.0169457
0.0433634 0.247976 -0.122113 0.160662 -0.108176 -0.0898536 -0.0315076 -0.058033 -0.169797 -0.00280097 0.0532811 0.00707717 0.1021 0.0784596 -0.0626989 -0.00973479 0.0252899 0.134163 0.197663 0.0118348 0.101676 -0.128705 -0.0455529 -0.120372 0.0237033 -0.0250785
0.199454 -0.226113 0.104341 -0.0333492 0.0443764 -0.161578 0.0256738 -0.0970429 -0.0876844 -0.0336515 0.0693317 -0.0820073 -0.0282839 0.126322 -0.207523 -0.0177079 0.176876 0.0184545 0.0555497 -0.0302999 -0.138053 0.0442708 -0.029398 -0.0419358 0.00609621 0.251509
0.0531068 -0.109812 -0.0926771 0.0822793 -0.00617784 0.0609741 0.153279 0.00322829 -0.0301634 -0.0656772 -0.0697383 -0.0293801 0.226255 0.284386 0.0165664 -0.0239051 0.0399742 -0.0168811 -0.0820791 -0.11148 -0.0180778 0.0111826 0.220133 0.074831 -0.0848964 0.00932999
-0.0422155 -0.0812833 -0.0435563 -0.0996937 0.134319 -0.0940636 -0.00318772 0.0204924 -0.114536 -0.000400085 -0.218106 -0.0792465 0.0391336 0.00733006 0.0641796 -0.107783 0.196132 0.0262729 -0.0231914 0.0689998 -0.134492 -0.186851 -0.0307967 -0.0769454 0.128628 0.0243831
0.0117585 -0.188355 -0.0601155 -0.105585 0.155692 -0.104331 0.0228082 0.125474 0.32577 -0.00217497 -0.0381744 0.240267 0.115263 -0.0662555 0.046954 0.00333695 0.204472 0.00977192 -0.0194599 0.0776496 0.0907478 -0.110808 0.0429737 -0.17686 0.393832 -0.0379994
-0.347874 0.362404 0.100626 0.0422608 0.0849221 -0.117267 -0.0446892 0.161095 0.0541932 0.148329 -0.0296801 -0.185107 0.0988324 -0.200475 0.0786247 0.123003 0.0219946 0.0216397 -0.0315188 0.0593645 0.0486775 -0.643764 0.119981 -0.142253 -0.0636188 0.0694036
0.468941 0.0337246 0.0655176 0.0424254 -0.143137 -0.11614 0.225464 0.161169 -0.0337716 0.0519002 0.00443581 -0.0696101 0.170608 -0.196263 0.0960638 0.109626 0.0275693 0.0212471 0.15492 0.159795 -0.168558 0.596002 0.0800106 -0.127434 -0.0662848 0.00876151
0.0739481 -0.0917565 -0.111474 -0.335884 -0.253097 -0.173704 -0.0709069 0.0680379 -0.142768 -0.209516 -0.19682 0.050026 -0.128142 -0.0517894 0.0201477 0.348768 0.0346083 -0.102624 -0.0537643 -0.0616583 0.285112 0.029877 -0.00147943 -0.117548 0.115985 -0.024293
0.196737 -0.0328039 -0.112967 0.459781 -0.0965858 -0.173032 -0.0405498 0.206569 0.0627302 -0.0512402 0.0249005 0.0803581 -0.180786 0.0123994 0.0164364 -0.205116 -0.0476837 -0.0738375 0.0963563 -0.124495 -0.376261 0.334062 -0.0198764 -0.0991814 -0.0103331 -0.0452335 [ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
┌ Warning: Variances had to be floored
│ ind =
│ 11-element Array{Int64,1}:
│ 3
│ 4
│ 6
│ 13
│ ⋮
│ 30
│ 31
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 1, average log likelihood -1.045753
┌ Warning: Variances had to be floored
│ ind =
│ 13-element Array{Int64,1}:
│ 3
│ 4
│ 6
│ 9
│ ⋮
│ 30
│ 31
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 2, average log likelihood -1.041270
┌ Warning: Variances had to be floored
│ ind =
│ 11-element Array{Int64,1}:
│ 3
│ 4
│ 6
│ 13
│ ⋮
│ 30
│ 31
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 3, average log likelihood -1.045742
┌ Warning: Variances had to be floored
│ ind =
│ 13-element Array{Int64,1}:
│ 3
│ 4
│ 6
│ 9
│ ⋮
│ 30
│ 31
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 4, average log likelihood -1.041256
┌ Warning: Variances had to be floored
│ ind =
│ 11-element Array{Int64,1}:
│ 3
│ 4
│ 6
│ 13
│ ⋮
│ 30
│ 31
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -1.045739
┌ Warning: Variances had to be floored
│ ind =
│ 13-element Array{Int64,1}:
│ 3
│ 4
│ 6
│ 9
│ ⋮
│ 30
│ 31
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -1.041257
┌ Warning: Variances had to be floored
│ ind =
│ 11-element Array{Int64,1}:
│ 3
│ 4
│ 6
│ 13
│ ⋮
│ 30
│ 31
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.045736
┌ Warning: Variances had to be floored
│ ind =
│ 13-element Array{Int64,1}:
│ 3
│ 4
│ 6
│ 9
│ ⋮
│ 30
│ 31
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -1.041259
┌ Warning: Variances had to be floored
│ ind =
│ 11-element Array{Int64,1}:
│ 3
│ 4
│ 6
│ 13
│ ⋮
│ 30
│ 31
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -1.045733
┌ Warning: Variances had to be floored
│ ind =
│ 13-element Array{Int64,1}:
│ 3
│ 4
│ 6
│ 9
│ ⋮
│ 30
│ 31
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.041261
┌ Info: EM with 100000 data points 10 iterations avll -1.041261
└ 59.0 data points per parameter
[ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points
kind diag, method kmeans
Iters objv objv-change | affected
-------------------------------------------------------------
0 7.821785e+05
1 6.368946e+05 -1.452839e+05 | 32
2 6.086117e+05 -2.828289e+04 | 32
3 5.926905e+05 -1.592118e+04 | 32
4 5.820642e+05 -1.062635e+04 | 32
5 5.757068e+05 -6.357370e+03 | 32
6 5.719461e+05 -3.760750e+03 | 32
7 5.698008e+05 -2.145326e+03 | 32
8 5.683587e+05 -1.442083e+03 | 32
9 5.671791e+05 -1.179532e+03 | 32
10 5.660698e+05 -1.109302e+03 | 32
11 5.649560e+05 -1.113865e+03 | 32
12 5.639417e+05 -1.014256e+03 | 32
13 5.630938e+05 -8.479370e+02 | 32
14 5.624368e+05 -6.569258e+02 | 32
15 5.619870e+05 -4.498779e+02 | 32
16 5.616506e+05 -3.363292e+02 | 32
17 5.613596e+05 -2.910175e+02 | 32
18 5.611671e+05 -1.925403e+02 | 32
19 5.610575e+05 -1.095652e+02 | 32
20 5.609909e+05 -6.665375e+01 | 31
21 5.609546e+05 -3.630351e+01 | 32
22 5.609290e+05 -2.555296e+01 | 32
23 5.609009e+05 -2.810115e+01 | 32
24 5.608741e+05 -2.681049e+01 | 31
25 5.608524e+05 -2.167026e+01 | 28
26 5.608335e+05 -1.890256e+01 | 32
27 5.608152e+05 -1.835893e+01 | 30
28 5.607956e+05 -1.958717e+01 | 32
29 5.607736e+05 -2.201754e+01 | 32
30 5.607473e+05 -2.630413e+01 | 30
31 5.607203e+05 -2.694454e+01 | 32
32 5.606864e+05 -3.389546e+01 | 32
33 5.606542e+05 -3.217294e+01 | 32
34 5.606109e+05 -4.330537e+01 | 31
35 5.605721e+05 -3.888017e+01 | 31
36 5.605382e+05 -3.381426e+01 | 31
37 5.604930e+05 -4.522240e+01 | 32
38 5.604563e+05 -3.671652e+01 | 32
39 5.604198e+05 -3.654633e+01 | 32
40 5.603921e+05 -2.767309e+01 | 27
41 5.603728e+05 -1.925337e+01 | 30
42 5.603538e+05 -1.900520e+01 | 32
43 5.603421e+05 -1.174536e+01 | 25
44 5.603310e+05 -1.107228e+01 | 21
45 5.603232e+05 -7.767585e+00 | 28
46 5.603126e+05 -1.065350e+01 | 26
47 5.602994e+05 -1.316181e+01 | 28
48 5.602883e+05 -1.114307e+01 | 29
49 5.602717e+05 -1.653785e+01 | 25
50 5.602518e+05 -1.995165e+01 | 28
K-means terminated without convergence after 50 iterations (objv = 560251.7944513058)
┌ Info: K-means with 32000 data points using 50 iterations
└ 37.0 data points per parameter
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.285711
[ Info: iteration 2, average log likelihood -1.257906
[ Info: iteration 3, average log likelihood -1.231533
[ Info: iteration 4, average log likelihood -1.198311
[ Info: iteration 5, average log likelihood -1.158973
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 4
│ 15
│ 26
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -1.106426
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 10
│ 27
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.098903
[ Info: iteration 8, average log likelihood -1.061625
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 7
│ 15
│ 16
│ 17
│ 28
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -1.013097
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 4
│ 26
│ 27
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.050162
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 10
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 11, average log likelihood -1.043920
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 7
│ 17
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 12, average log likelihood -1.034042
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 13
│ 15
│ 28
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 13, average log likelihood -1.022091
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 4
│ 10
│ 16
│ 26
│ 27
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 14, average log likelihood -1.028552
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 7
│ 22
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 15, average log likelihood -1.064349
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 16, average log likelihood -1.047197
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 13
│ 15
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 17, average log likelihood -1.017536
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 18, average log likelihood -1.035892
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 7
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 19, average log likelihood -1.053342
┌ Warning: Variances had to be floored
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│ 1-element Array{Int64,1}:
│ 27
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 20, average log likelihood -1.037564
┌ Warning: Variances had to be floored
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│ 13
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 21, average log likelihood -0.989351
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 4
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 22, average log likelihood -1.035440
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 22
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 23, average log likelihood -1.068727
┌ Warning: Variances had to be floored
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│ 1-element Array{Int64,1}:
│ 28
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 24, average log likelihood -1.023292
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 7
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 25, average log likelihood -0.985099
┌ Warning: Variances had to be floored
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│ 4-element Array{Int64,1}:
│ 4
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 26, average log likelihood -1.038632
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 22
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 27, average log likelihood -1.058329
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 7
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 28, average log likelihood -1.019189
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 10
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│ 15
│ 16
│ 25
│ 28
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 29, average log likelihood -0.981092
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 4
│ 17
│ 26
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 30, average log likelihood -1.055054
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 7
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 31, average log likelihood -1.047106
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 32, average log likelihood -1.015345
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
│ 4
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│ 13
│ 15
│ 16
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 33, average log likelihood -0.973182
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 7
│ 17
│ 26
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 34, average log likelihood -1.043605
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 22
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 35, average log likelihood -1.044221
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 36, average log likelihood -1.012435
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 4
│ 7
│ 10
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 37, average log likelihood -0.969629
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 17
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 38, average log likelihood -1.057752
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 22
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 39, average log likelihood -1.027331
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 7
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│ 13
│ 15
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 40, average log likelihood -0.986797
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 25
│ 26
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 41, average log likelihood -1.027928
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 4
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 42, average log likelihood -1.029827
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 7
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│ 22
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 43, average log likelihood -0.998566
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 13
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 44, average log likelihood -1.006139
[ Info: iteration 45, average log likelihood -1.056317
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 4
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 46, average log likelihood -0.992914
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 10
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│ 26
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 47, average log likelihood -0.993960
[ Info: iteration 48, average log likelihood -1.075488
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 7
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 49, average log likelihood -1.015232
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 4
│ 10
│ 15
│ 16
│ ⋮
│ 25
│ 26
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 50, average log likelihood -0.972380
┌ Info: EM with 100000 data points 50 iterations avll -0.972380
└ 59.0 data points per parameter
32×26 Array{Float64,2}:
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0.0550257 -0.110277 -0.0953964 0.0911421 -0.00616651 0.0612245 0.159975 0.00450148 -0.0301352 -0.0669179 -0.0711624 -0.0321535 0.226341 0.286767 0.014914 -0.0231759 0.0386973 -0.0177538 -0.0859636 -0.112844 -0.0157319 0.0104546 0.222353 0.0763083 -0.087701 0.00931174
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0.15372 -0.0310916 -0.0854744 0.00510152 -0.126872 0.0424372 -0.0852308 0.207976 -0.179804 0.0845457 0.0141099 0.1956 0.0252438 0.0175923 -0.0981193 -0.0658423 0.211865 -0.0547521 -0.549916 0.00542882 0.009247 0.00957934 0.00118541 -0.100882 -0.165576 0.0048553
0.00454566 -0.0953188 -0.0285841 -0.0147958 0.12583 0.0817756 0.0301156 -0.00875007 0.148901 -0.0326669 -0.0434678 -0.144336 0.0454769 0.124026 -0.110473 -0.0327387 0.173816 0.195305 -0.0134252 -0.0871807 0.172416 -0.0625039 -0.122385 -0.0220172 -0.112278 0.00384848
0.202331 0.0155488 -0.0431142 0.151054 -0.0240651 0.218386 -0.0381926 0.0327138 -0.0532141 0.166552 -0.0561543 0.201314 -0.0719687 0.0313094 0.0792425 -0.0342992 -0.181589 0.0358387 0.00633381 -0.0389729 0.0987481 -0.0196558 0.0859325 0.042682 -0.133634 -0.00844894
-0.0609474 -0.0433345 -0.136142 0.131276 -0.0982224 -0.0369761 0.146423 -0.0773183 -0.0465772 -0.0962374 0.0731993 0.0491515 0.0609269 -0.0524228 0.156003 -0.0858059 -0.0211479 0.0243048 -0.100606 -0.320939 0.0885884 0.05562 -0.127289 0.0585151 0.160184 0.0924529
0.176121 -0.0508722 -0.0573032 -0.0129576 0.0623634 0.0716824 0.061401 -0.0466492 -0.190374 0.158822 -0.0381697 -0.0576716 -0.0649896 0.0997313 -0.000254423 -0.0429438 -0.08651 0.0179545 -0.0490863 -0.0287322 0.110331 0.0172119 -0.0266021 0.0383785 -0.0505853 0.141622
0.195385 -0.0293443 0.0530212 -0.0114476 -0.0824844 -0.0777381 -0.00443892 -0.0262661 0.138816 0.0406969 0.115965 0.0239514 0.0656832 -0.0487908 0.0558388 -0.0980853 -0.0651801 -0.054941 -0.0133048 0.0229408 -0.0977641 -0.128515 -0.0580866 0.14338 -0.026727 -0.013707
0.0176852 0.0335231 0.0338885 -0.234593 -0.119435 -0.0513798 -0.154238 -0.0846543 0.0615524 0.0423949 -0.10081 0.0429108 0.0451987 0.0448169 0.141297 -0.0960283 0.0371444 0.135455 0.0495932 -0.0285901 -0.248098 0.0510709 -0.105098 -0.0577263 0.00766186 0.103312
0.196855 0.061426 -0.0197464 -0.0151634 -0.0897534 -0.116156 -0.0879368 -0.24666 0.123105 0.116343 -0.0247591 -0.128844 -0.0394631 0.355895 0.0361313 -0.257884 0.113235 -0.0496899 0.0383261 0.145798 0.0755743 -0.0758033 -0.0337305 -0.0594745 -0.0500887 -0.0830792
-0.184959 -0.120207 0.00705868 -0.0974773 0.026383 0.0252101 0.228807 -0.0209913 -0.0311683 0.143253 -0.0700654 0.00616272 -0.0343086 -0.0412294 -0.01749 0.139531 0.057374 -0.0843126 0.0473321 -0.10962 -0.177627 -0.0500325 0.0586914 0.101581 -0.0295009 -0.109451 [ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 13
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 1, average log likelihood -1.073725
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 7
│ 13
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 2, average log likelihood -1.025701
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 13
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 3, average log likelihood -0.984348
┌ Warning: Variances had to be floored
│ ind =
│ 10-element Array{Int64,1}:
│ 4
│ 7
│ 10
│ 13
│ ⋮
│ 25
│ 26
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 4, average log likelihood -0.939563
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 13
│ 17
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -1.063045
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 7
│ 13
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -1.023609
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 13
│ 15
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -0.976148
┌ Warning: Variances had to be floored
│ ind =
│ 8-element Array{Int64,1}:
│ 4
│ 7
│ 10
│ 13
│ 16
│ 22
│ 26
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -0.972217
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 13
│ 17
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -1.039429
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 7
│ 13
│ 15
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.008283
┌ Info: EM with 100000 data points 10 iterations avll -1.008283
└ 59.0 data points per parameter
32×26 Array{Float64,2}:
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-0.078177 -0.0784807 0.033158 -0.0739009 0.120891 -0.146561 0.0607101 0.0711641 0.139252 0.0633357 0.0660971 0.0646615 0.0185769 -0.044019 -0.0422151 -0.109149 0.133284 -0.00253921 -0.222656 0.283187 0.0403829 -0.0627766 0.0735082 -0.0998898 -0.0601356 -0.0113962
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-0.134698 0.125809 -0.0397577 -0.15921 -0.108139 0.0754011 0.0949287 -0.129934 0.0466325 -0.0638618 -0.00721124 0.0380835 0.0490442 0.135678 -0.134327 -0.101333 -0.169836 -0.0401033 -0.07307 -0.0528094 -0.0846364 -0.0730748 -0.193177 -0.0500143 0.00103127 -0.0528283
0.134638 -0.00221488 0.165488 0.00612338 -0.0366256 0.0077341 0.0689058 -0.132011 -0.0870728 0.0717198 -0.104664 -0.105746 0.180054 -0.00731562 -0.0456502 -0.121574 -0.04762 -0.0428203 0.0334944 -0.153409 0.0306087 0.055948 0.0815278 0.0174708 0.192126 -0.11626
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-0.0500498 0.00234824 0.08609 0.056347 -0.047615 -0.0209122 0.069851 -0.0676556 0.0306691 0.0306343 0.0816705 -0.0671477 0.0546575 -0.179896 0.130734 -0.0819829 0.107287 -0.032454 -0.0894662 -0.164943 -0.128776 -0.0655849 0.0374987 -0.102393 -0.130545 0.0182284
0.00941461 0.105067 0.138569 0.0173381 0.0472794 -0.112611 0.252625 -0.0191546 0.0500095 0.109562 0.0636414 -0.0369295 0.0284634 -0.0296224 0.143161 -0.124215 -0.101331 -0.0562247 -0.164825 -0.148603 -0.0139978 0.153239 0.304355 -0.0291654 0.0641022 0.0972843
-0.0383383 -0.0487447 -0.0850408 0.0722634 0.0151722 -0.00406727 0.0334349 0.0602093 0.170915 0.080406 -0.164305 0.151336 0.00520804 -0.0029604 -0.0517673 -0.250616 -0.0981408 0.0498077 0.0666835 0.115486 0.182992 -0.0194626 0.0470159 0.00338595 0.0304496 -0.0124415
0.217809 -0.11017 0.0248037 0.113771 0.0111945 -0.0251462 0.00739596 -0.0652484 -0.0504971 0.0695666 -0.121258 -0.0975096 0.155945 -0.0601015 -0.0571722 -0.116266 -0.0597713 -0.112051 -0.0155478 0.105314 0.144641 -0.0477481 -0.0242431 -0.117837 -0.0135788 0.012339 kind full, method split
┌ Info: 0: avll =
└ tll[1] = -1.4228581805290048
[ Info: Running 50 iterations EM on diag cov GMM with 2 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.422877
[ Info: iteration 2, average log likelihood -1.422812
[ Info: iteration 3, average log likelihood -1.422760
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[ Info: iteration 50, average log likelihood -1.417406
┌ Info: EM with 100000 data points 50 iterations avll -1.417406
└ 952.4 data points per parameter
┌ Info: 1
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4228771432273186
│ -1.4228122112138295
│ ⋮
└ -1.4174061036192254
[ Info: Running 50 iterations EM on diag cov GMM with 4 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.417421
[ Info: iteration 2, average log likelihood -1.417356
[ Info: iteration 3, average log likelihood -1.417298
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[ Info: iteration 50, average log likelihood -1.416175
┌ Info: EM with 100000 data points 50 iterations avll -1.416175
└ 473.9 data points per parameter
┌ Info: 2
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4174212726599202
│ -1.4173556354141132
│ ⋮
└ -1.416174856319443
[ Info: Running 50 iterations EM on diag cov GMM with 8 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.416184
[ Info: iteration 2, average log likelihood -1.416136
[ Info: iteration 3, average log likelihood -1.416097
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[ Info: iteration 50, average log likelihood -1.414710
┌ Info: EM with 100000 data points 50 iterations avll -1.414710
└ 236.4 data points per parameter
┌ Info: 3
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4161835782526506
│ -1.416136376992469
│ ⋮
└ -1.4147099841116832
[ Info: Running 50 iterations EM on diag cov GMM with 16 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.414709
[ Info: iteration 2, average log likelihood -1.414640
[ Info: iteration 3, average log likelihood -1.414573
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[ Info: iteration 9, average log likelihood -1.413885
[ Info: iteration 10, average log likelihood -1.413755
[ Info: iteration 11, average log likelihood -1.413636
[ Info: iteration 12, average log likelihood -1.413530
[ Info: iteration 13, average log likelihood -1.413435
[ Info: iteration 14, average log likelihood -1.413353
[ Info: iteration 15, average log likelihood -1.413283
[ Info: iteration 16, average log likelihood -1.413222
[ Info: iteration 17, average log likelihood -1.413170
[ Info: iteration 18, average log likelihood -1.413125
[ Info: iteration 19, average log likelihood -1.413086
[ Info: iteration 20, average log likelihood -1.413052
[ Info: iteration 21, average log likelihood -1.413022
[ Info: iteration 22, average log likelihood -1.412994
[ Info: iteration 23, average log likelihood -1.412969
[ Info: iteration 24, average log likelihood -1.412946
[ Info: iteration 25, average log likelihood -1.412925
[ Info: iteration 26, average log likelihood -1.412905
[ Info: iteration 27, average log likelihood -1.412887
[ Info: iteration 28, average log likelihood -1.412869
[ Info: iteration 29, average log likelihood -1.412853
[ Info: iteration 30, average log likelihood -1.412837
[ Info: iteration 31, average log likelihood -1.412823
[ Info: iteration 32, average log likelihood -1.412808
[ Info: iteration 33, average log likelihood -1.412795
[ Info: iteration 34, average log likelihood -1.412782
[ Info: iteration 35, average log likelihood -1.412770
[ Info: iteration 36, average log likelihood -1.412758
[ Info: iteration 37, average log likelihood -1.412746
[ Info: iteration 38, average log likelihood -1.412735
[ Info: iteration 39, average log likelihood -1.412725
[ Info: iteration 40, average log likelihood -1.412715
[ Info: iteration 41, average log likelihood -1.412705
[ Info: iteration 42, average log likelihood -1.412695
[ Info: iteration 43, average log likelihood -1.412686
[ Info: iteration 44, average log likelihood -1.412677
[ Info: iteration 45, average log likelihood -1.412669
[ Info: iteration 46, average log likelihood -1.412660
[ Info: iteration 47, average log likelihood -1.412652
[ Info: iteration 48, average log likelihood -1.412645
[ Info: iteration 49, average log likelihood -1.412637
[ Info: iteration 50, average log likelihood -1.412630
┌ Info: EM with 100000 data points 50 iterations avll -1.412630
└ 118.1 data points per parameter
┌ Info: 4
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4147094830649485
│ -1.4146398813876038
│ ⋮
└ -1.4126298090301337
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.412631
[ Info: iteration 2, average log likelihood -1.412563
[ Info: iteration 3, average log likelihood -1.412498
[ Info: iteration 4, average log likelihood -1.412423
[ Info: iteration 5, average log likelihood -1.412330
[ Info: iteration 6, average log likelihood -1.412214
[ Info: iteration 7, average log likelihood -1.412075
[ Info: iteration 8, average log likelihood -1.411916
[ Info: iteration 9, average log likelihood -1.411747
[ Info: iteration 10, average log likelihood -1.411577
[ Info: iteration 11, average log likelihood -1.411415
[ Info: iteration 12, average log likelihood -1.411264
[ Info: iteration 13, average log likelihood -1.411128
[ Info: iteration 14, average log likelihood -1.411006
[ Info: iteration 15, average log likelihood -1.410899
[ Info: iteration 16, average log likelihood -1.410806
[ Info: iteration 17, average log likelihood -1.410724
[ Info: iteration 18, average log likelihood -1.410652
[ Info: iteration 19, average log likelihood -1.410589
[ Info: iteration 20, average log likelihood -1.410532
[ Info: iteration 21, average log likelihood -1.410482
[ Info: iteration 22, average log likelihood -1.410437
[ Info: iteration 23, average log likelihood -1.410396
[ Info: iteration 24, average log likelihood -1.410359
[ Info: iteration 25, average log likelihood -1.410324
[ Info: iteration 26, average log likelihood -1.410293
[ Info: iteration 27, average log likelihood -1.410263
[ Info: iteration 28, average log likelihood -1.410236
[ Info: iteration 29, average log likelihood -1.410210
[ Info: iteration 30, average log likelihood -1.410186
[ Info: iteration 31, average log likelihood -1.410163
[ Info: iteration 32, average log likelihood -1.410141
[ Info: iteration 33, average log likelihood -1.410120
[ Info: iteration 34, average log likelihood -1.410100
[ Info: iteration 35, average log likelihood -1.410081
[ Info: iteration 36, average log likelihood -1.410063
[ Info: iteration 37, average log likelihood -1.410045
[ Info: iteration 38, average log likelihood -1.410028
[ Info: iteration 39, average log likelihood -1.410011
[ Info: iteration 40, average log likelihood -1.409995
[ Info: iteration 41, average log likelihood -1.409980
[ Info: iteration 42, average log likelihood -1.409964
[ Info: iteration 43, average log likelihood -1.409950
[ Info: iteration 44, average log likelihood -1.409935
[ Info: iteration 45, average log likelihood -1.409921
[ Info: iteration 46, average log likelihood -1.409908
[ Info: iteration 47, average log likelihood -1.409894
[ Info: iteration 48, average log likelihood -1.409881
[ Info: iteration 49, average log likelihood -1.409868
[ Info: iteration 50, average log likelihood -1.409856
┌ Info: EM with 100000 data points 50 iterations avll -1.409856
└ 59.0 data points per parameter
┌ Info: 5
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4126313860696968
│ -1.4125627107909937
│ ⋮
└ -1.409856094077938
┌ Info: Total log likelihood:
│ tll =
│ 251-element Array{Float64,1}:
│ -1.4228581805290048
│ -1.4228771432273186
│ -1.4228122112138295
│ -1.4227596367746478
│ ⋮
│ -1.4098811549005763
│ -1.409868464102259
└ -1.409856094077938
32×26 Array{Float64,2}:
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0.794313 0.330556 0.199918 0.208015 0.0853269 0.149569 -0.336467 -0.0355373 0.385639 0.662656 0.409408 0.186165 0.226732 -0.186405 0.520545 -0.647929 -0.0820761 0.0366115 -0.0691201 0.0486865 0.237588 0.260288 0.849429 0.195131 0.0830657 -0.288288
0.389642 0.329546 -0.424639 0.261078 -0.171869 -0.199976 0.179754 -0.151141 -0.209583 -0.22456 0.365306 -0.218161 0.353752 -0.158881 0.0766385 0.376841 -0.0761789 1.04598 -0.0260167 0.156196 -0.531061 -0.221938 0.101773 -0.482927 0.373948 0.473671
0.103943 0.644677 0.654545 -0.334218 -0.233267 0.153911 -0.922607 0.177692 0.213065 -0.210091 -0.0287765 -0.268186 0.0105422 0.14274 0.202102 -0.451887 -0.272895 -0.0636895 -0.340292 -0.30687 -0.26544 0.0405398 0.178546 -0.417982 0.00120749 1.11894
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0.108434 -0.21125 -0.792984 0.404377 0.655938 0.0355151 0.285404 -0.730322 0.494823 -0.00866143 -0.278781 0.193461 0.654294 0.279673 -0.0829109 0.182404 0.608351 0.0491936 -0.023341 -0.608699 -0.430629 0.685281 0.250557 0.40841 -0.0021111 0.0571324
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-0.598296 0.0592178 -0.386543 -0.253037 0.196271 -0.262742 0.454668 -0.220729 -0.089939 -0.0941496 -0.235738 0.0811016 -0.152545 -0.0593558 -0.0741726 0.12461 0.351017 0.238683 0.232184 0.332716 -0.80258 -0.108038 -0.677644 -0.0277993 0.172724 -0.289504
-0.086223 -0.369356 -0.0693115 0.0241982 0.20812 -0.135269 0.63925 -0.0551637 -0.292838 -0.259113 -0.157468 -0.0359982 0.0804952 -0.0452512 -0.252131 0.344785 0.148201 0.0203398 -0.0624609 0.461672 0.751605 -0.0810785 -0.550364 0.0426132 0.223429 0.0233163
-0.0402553 -0.0894167 -0.0136889 0.0189062 -0.0953503 0.119215 -0.0940662 0.306061 -0.113197 -0.0249359 -0.0941734 -0.245371 -0.00608292 -0.0536296 -0.165749 0.140478 -0.000624626 -0.196631 0.119138 -0.131031 0.334892 -0.0414926 0.156512 0.100402 -0.0672937 -0.115994
0.11925 -0.0258724 -0.0109448 -0.0446458 0.14182 -0.0438083 0.0945991 -0.219876 0.0280409 0.0684298 0.103713 0.223354 0.0318516 0.0897066 0.100986 -0.22534 0.0747826 0.139558 -0.0623995 0.11753 -0.170178 0.0674808 0.0132175 0.0217998 0.00056404 -0.0190709
-0.526739 0.155465 0.089061 -0.153954 0.00705902 -0.479846 -0.224668 -0.228493 0.249986 -0.122033 -0.193761 0.0228479 0.332675 -0.0356911 -0.340967 0.22362 -0.240794 -0.335815 -0.168794 -0.129476 -0.362765 -0.286152 -0.171955 -0.360592 -0.323558 0.199903
-0.165991 0.395927 0.209533 0.333026 -0.184603 0.699517 -0.613262 0.057493 0.0122881 -0.373347 -0.043092 -0.205054 0.17457 0.115214 0.123301 0.292877 -0.0490711 0.170804 -0.168047 0.152567 -0.188482 0.0487772 -0.16884 -0.0634246 0.193498 0.409336
-0.18967 0.0566953 -0.144963 0.161601 0.359388 0.00520491 -0.724978 0.134567 -0.154165 0.249113 0.157669 -0.0736647 0.0149136 0.0671468 0.683192 -0.724623 0.104098 -0.18105 0.160942 0.0429961 -0.338701 0.256927 0.129541 -0.163438 -0.347487 0.0197295
-0.277542 -0.195515 0.134522 -0.264292 -0.2033 0.402675 -0.224517 -0.10687 -0.178707 0.496848 0.120228 -0.496768 -0.30473 -0.186403 0.447642 0.166915 -0.0726692 -0.00451919 -0.14054 -0.173876 -0.278552 0.746696 0.329977 0.46874 0.129714 -0.0819844
0.737096 -0.52292 -0.291241 0.171047 -0.211801 0.167903 -0.140772 0.361825 -0.873955 -0.0547286 0.223937 0.195296 -0.0610534 0.109303 0.486977 -0.259412 -0.2089 0.0612716 0.300687 0.345206 0.282945 0.270862 0.339888 0.0679111 0.431375 -0.268943
0.74602 -0.346874 -0.500489 -0.0252858 0.0207727 0.25018 0.223199 0.371594 0.384496 -0.441248 0.0817344 -0.108311 -0.0892228 0.87226 -0.0406793 -0.0571993 0.141751 -0.00159719 0.643092 -0.280759 0.135445 -0.247301 0.485423 -0.283484 0.0444978 -0.26723
0.222484 0.0102348 -0.19152 0.289938 0.0672412 -0.171861 0.0280123 -0.155351 0.371011 -0.383413 -0.133128 0.402818 0.281062 -0.477916 -0.357409 -0.0199522 0.216953 -0.423735 -0.344212 -0.42172 -0.202439 0.0704508 0.781826 -0.690406 0.272371 0.0606588
0.679345 0.0401709 0.316395 -0.147616 -0.324044 -0.109692 0.158358 -0.0829903 0.422634 0.0479471 -0.0364368 0.337111 0.628579 0.018245 -0.169154 0.137137 -0.261429 0.139314 -0.263775 -0.170067 0.21511 0.0717346 0.610152 0.194192 0.00989169 0.17286
0.572066 -0.0758014 0.52399 -0.473607 -0.460226 -0.0367961 0.340559 -1.02992 0.0412344 0.0587586 -0.229808 0.948477 -0.0113884 0.238511 0.218888 -0.427432 -0.458546 0.174342 -0.431942 -0.330892 0.0266685 0.14009 0.367875 0.0619313 -0.179856 0.123143
0.127456 0.583988 0.0343554 0.314995 0.33153 0.342346 0.528399 -0.620359 -0.0580136 0.262087 0.192868 0.381354 0.136336 -0.0948278 0.817912 -0.245325 0.462022 0.107996 -0.386117 0.00487736 0.024338 -0.0466896 -0.434039 -0.119787 -0.151585 -0.310258 [ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.409844
[ Info: iteration 2, average log likelihood -1.409832
[ Info: iteration 3, average log likelihood -1.409821
[ Info: iteration 4, average log likelihood -1.409810
[ Info: iteration 5, average log likelihood -1.409799
[ Info: iteration 6, average log likelihood -1.409788
[ Info: iteration 7, average log likelihood -1.409778
[ Info: iteration 8, average log likelihood -1.409768
[ Info: iteration 9, average log likelihood -1.409758
kind full, method kmeans
[ Info: iteration 10, average log likelihood -1.409749
┌ Info: EM with 100000 data points 10 iterations avll -1.409749
└ 59.0 data points per parameter
[ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 9.778891e+05
1 7.058346e+05 -2.720546e+05 | 32
2 6.933066e+05 -1.252792e+04 | 32
3 6.882476e+05 -5.059015e+03 | 32
4 6.855857e+05 -2.661924e+03 | 32
5 6.837962e+05 -1.789526e+03 | 32
6 6.824679e+05 -1.328256e+03 | 32
7 6.814295e+05 -1.038382e+03 | 32
8 6.805557e+05 -8.738057e+02 | 32
9 6.797801e+05 -7.756667e+02 | 32
10 6.791563e+05 -6.237701e+02 | 32
11 6.786508e+05 -5.054859e+02 | 32
12 6.782197e+05 -4.311323e+02 | 32
13 6.778474e+05 -3.722878e+02 | 32
14 6.775223e+05 -3.250886e+02 | 32
15 6.772281e+05 -2.942247e+02 | 32
16 6.769817e+05 -2.463616e+02 | 32
17 6.767626e+05 -2.190790e+02 | 32
18 6.765674e+05 -1.952304e+02 | 32
19 6.764029e+05 -1.644823e+02 | 32
20 6.762509e+05 -1.519964e+02 | 32
21 6.761124e+05 -1.385427e+02 | 32
22 6.759791e+05 -1.333198e+02 | 32
23 6.758498e+05 -1.293071e+02 | 32
24 6.757332e+05 -1.165800e+02 | 32
25 6.756217e+05 -1.114263e+02 | 32
26 6.755230e+05 -9.877762e+01 | 32
27 6.754272e+05 -9.576538e+01 | 32
28 6.753227e+05 -1.045166e+02 | 32
29 6.752264e+05 -9.632382e+01 | 32
30 6.751255e+05 -1.008349e+02 | 32
31 6.750275e+05 -9.805879e+01 | 32
32 6.749302e+05 -9.723471e+01 | 32
33 6.748224e+05 -1.078030e+02 | 32
34 6.747141e+05 -1.083108e+02 | 32
35 6.746181e+05 -9.598959e+01 | 32
36 6.745368e+05 -8.132468e+01 | 32
37 6.744710e+05 -6.579681e+01 | 32
38 6.744170e+05 -5.405384e+01 | 32
39 6.743618e+05 -5.515528e+01 | 32
40 6.743077e+05 -5.411319e+01 | 32
41 6.742540e+05 -5.368987e+01 | 32
42 6.741956e+05 -5.835543e+01 | 32
43 6.741341e+05 -6.150538e+01 | 32
44 6.740697e+05 -6.438739e+01 | 32
45 6.740092e+05 -6.058855e+01 | 32
46 6.739528e+05 -5.632333e+01 | 32
47 6.738981e+05 -5.472760e+01 | 32
48 6.738448e+05 -5.332296e+01 | 32
49 6.737934e+05 -5.138437e+01 | 32
50 6.737464e+05 -4.698303e+01 | 32
K-means terminated without convergence after 50 iterations (objv = 673746.4189568642)
┌ Info: K-means with 32000 data points using 50 iterations
└ 37.0 data points per parameter
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.421672
[ Info: iteration 2, average log likelihood -1.416605
[ Info: iteration 3, average log likelihood -1.415118
[ Info: iteration 4, average log likelihood -1.413890
[ Info: iteration 5, average log likelihood -1.412635
[ Info: iteration 6, average log likelihood -1.411656
[ Info: iteration 7, average log likelihood -1.411107
[ Info: iteration 8, average log likelihood -1.410832
[ Info: iteration 9, average log likelihood -1.410677
[ Info: iteration 10, average log likelihood -1.410572
[ Info: iteration 11, average log likelihood -1.410491
[ Info: iteration 12, average log likelihood -1.410425
[ Info: iteration 13, average log likelihood -1.410368
[ Info: iteration 14, average log likelihood -1.410319
[ Info: iteration 15, average log likelihood -1.410274
[ Info: iteration 16, average log likelihood -1.410233
[ Info: iteration 17, average log likelihood -1.410195
[ Info: iteration 18, average log likelihood -1.410160
[ Info: iteration 19, average log likelihood -1.410127
[ Info: iteration 20, average log likelihood -1.410097
[ Info: iteration 21, average log likelihood -1.410068
[ Info: iteration 22, average log likelihood -1.410040
[ Info: iteration 23, average log likelihood -1.410014
[ Info: iteration 24, average log likelihood -1.409989
[ Info: iteration 25, average log likelihood -1.409965
[ Info: iteration 26, average log likelihood -1.409942
[ Info: iteration 27, average log likelihood -1.409921
[ Info: iteration 28, average log likelihood -1.409900
[ Info: iteration 29, average log likelihood -1.409880
[ Info: iteration 30, average log likelihood -1.409861
[ Info: iteration 31, average log likelihood -1.409843
[ Info: iteration 32, average log likelihood -1.409826
[ Info: iteration 33, average log likelihood -1.409809
[ Info: iteration 34, average log likelihood -1.409793
[ Info: iteration 35, average log likelihood -1.409777
[ Info: iteration 36, average log likelihood -1.409763
[ Info: iteration 37, average log likelihood -1.409748
[ Info: iteration 38, average log likelihood -1.409734
[ Info: iteration 39, average log likelihood -1.409721
[ Info: iteration 40, average log likelihood -1.409708
[ Info: iteration 41, average log likelihood -1.409696
[ Info: iteration 42, average log likelihood -1.409684
[ Info: iteration 43, average log likelihood -1.409672
[ Info: iteration 44, average log likelihood -1.409661
[ Info: iteration 45, average log likelihood -1.409649
[ Info: iteration 46, average log likelihood -1.409639
[ Info: iteration 47, average log likelihood -1.409628
[ Info: iteration 48, average log likelihood -1.409617
[ Info: iteration 49, average log likelihood -1.409607
[ Info: iteration 50, average log likelihood -1.409597
┌ Info: EM with 100000 data points 50 iterations avll -1.409597
└ 59.0 data points per parameter
32×26 Array{Float64,2}:
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0.357697 0.671573 0.185033 0.21929 0.205366 0.219968 0.0202843 -0.148933 0.275618 0.538844 0.216655 0.303713 -0.098394 -0.299417 0.731551 -1.08422 0.174887 0.268634 -0.0734383 0.0251148 0.314027 0.120608 0.233434 0.0996342 0.226408 -0.505289
0.207716 0.259738 0.0168752 -0.196299 -0.792911 -0.473216 -0.103246 0.219422 -0.654036 -0.388147 -0.092117 -0.808888 0.060909 -0.247651 0.0236153 0.172729 -0.285955 0.221428 -0.00792848 0.0250053 0.399641 -0.182532 0.225038 -0.486448 -0.0537754 0.588432
-0.122502 0.366728 -0.0121974 0.285912 0.129721 -0.0806301 0.214522 -0.405704 -0.542941 0.0439414 0.0237555 0.236132 -0.160186 0.162378 0.349953 -0.198574 0.370251 0.680387 -0.189982 0.223835 -0.470359 -0.0292346 -0.325539 -0.249853 0.22576 0.145221
0.104338 -0.502156 0.108919 -0.724147 -0.369555 -0.329292 -0.233459 0.194451 0.407725 0.24665 0.144436 -0.158067 0.0333343 -0.249045 -0.431797 0.177657 -0.186719 -0.900912 -0.0896696 -0.55879 0.455948 -0.283006 0.360424 0.298667 -0.484558 -0.302097
0.30368 -0.107148 0.4181 0.308582 0.0900382 1.24591 -0.0625449 -0.201873 1.12379 -0.0190343 -0.15494 0.87624 0.137404 -0.0726552 0.0399403 0.592237 0.970529 -0.140636 -0.0330316 0.136969 -0.512319 -0.231007 -0.675903 0.348615 0.344294 -0.116714
-0.493363 -0.286884 0.31152 0.217266 -0.252115 0.0228376 -0.683304 0.658329 -0.283427 0.00965869 0.0571038 0.385376 -0.698531 -0.196902 -0.176085 -0.314136 -0.142082 0.150976 0.218538 0.939531 0.411148 -0.221155 -0.561894 -0.0150734 -0.0875594 -0.117643
-0.0479854 0.237415 0.0323209 0.189457 0.235713 -0.228313 -0.203423 -0.338012 0.289379 -0.218299 -0.333687 0.0339476 0.237155 -0.244425 -0.290243 0.0073088 0.237612 -0.640278 -0.611272 -0.815776 -0.464299 0.272127 0.716017 -0.648156 -0.0146002 0.214327
0.141737 0.179823 -0.0361396 0.617282 0.193288 0.228794 -0.927226 0.360849 -0.432621 0.24297 0.262895 0.0402799 0.264788 -0.238683 0.30461 -0.486293 0.0182206 -0.140386 0.141214 0.0251201 0.0502269 0.418182 0.271378 0.0195747 -0.185873 0.127977
-0.440902 0.0755199 -0.722629 0.291318 0.459708 0.0153707 0.349011 0.183029 -0.378705 -0.226371 0.0247278 -0.54913 -0.278401 -0.319223 -0.110642 0.0334789 0.350542 -0.106258 0.318196 0.114272 0.224389 0.0398051 -0.325579 0.000718942 0.139021 -0.178206
-0.462908 0.733968 0.180355 -0.0517872 0.197389 0.604929 0.589637 0.165783 0.480367 -0.122494 -0.283607 0.0230803 -0.152671 0.137094 -1.21422 0.461818 0.24233 0.241495 -0.421172 -0.657918 0.136032 -0.488601 -0.377414 0.671332 -0.00346627 -0.144758
-0.0224551 0.198381 -0.0471393 0.229364 -0.242806 0.108548 0.0105255 0.062284 0.0905566 -0.137843 0.0953308 -0.196013 0.440959 -0.101833 -0.498322 0.713881 -0.377435 0.289817 0.067659 -0.179775 -0.14792 -0.0665043 0.197001 -0.0882855 0.24575 0.166117
-0.161627 -0.0449995 0.635993 -0.901874 -0.194603 -0.418839 0.407286 0.0752666 0.336898 0.176438 -0.327337 -0.210649 0.0279047 0.250185 -0.119387 -0.0491902 -0.559147 -0.0151527 0.259547 0.208526 0.61277 -0.670189 -0.00916694 0.235631 0.676275 -0.0190885 [ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.409587
[ Info: iteration 2, average log likelihood -1.409577
[ Info: iteration 3, average log likelihood -1.409568
[ Info: iteration 4, average log likelihood -1.409558
[ Info: iteration 5, average log likelihood -1.409549
[ Info: iteration 6, average log likelihood -1.409540
[ Info: iteration 7, average log likelihood -1.409531
[ Info: iteration 8, average log likelihood -1.409521
[ Info: iteration 9, average log likelihood -1.409513
[ Info: iteration 10, average log likelihood -1.409504
┌ Info: EM with 100000 data points 10 iterations avll -1.409504
└ 59.0 data points per parameter
[ Info: Initializing GMM, 2 Gaussians diag covariance 2 dimensions using 900 data points
┌ Info: K-means with 900 data points using 3 iterations
└ 150.0 data points per parameter
[ Info: Running 10 iterations EM on full cov GMM with 2 Gaussians in 2 dimensions
Iters objv objv-change | affected
-------------------------------------------------------------
0 1.678561e+05
1 2.230230e+04 -1.455538e+05 | 2
2 7.823675e+03 -1.447862e+04 | 0
3 7.823675e+03 0.000000e+00 | 0
K-means converged with 3 iterations (objv = 7823.67549422947)
[ Info: iteration 1, average log likelihood -2.043155
[ Info: iteration 2, average log likelihood -2.043154
[ Info: iteration 3, average log likelihood -2.043154
[ Info: iteration 4, average log likelihood -2.043154
[ Info: iteration 5, average log likelihood -2.043154
[ Info: iteration 6, average log likelihood -2.043154
[ Info: iteration 7, average log likelihood -2.043154
[ Info: iteration 8, average log likelihood -2.043154
[ Info: iteration 9, average log likelihood -2.043154
[ Info: iteration 10, average log likelihood -2.043154
┌ Info: EM with 900 data points 10 iterations avll -2.043154
└ 81.8 data points per parameter
Testing GaussianMixtures tests passed
Results with Julia v1.3.0
Testing was successful .
Last evaluation was ago and took 7 minutes, 58 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed URIParser ────────── v0.4.0
Installed GaussianMixtures ─── v0.3.0
Installed SortingAlgorithms ── v0.3.1
Installed JLD ──────────────── v0.9.1
Installed FileIO ───────────── v1.1.0
Installed Arpack ───────────── v0.3.1
Installed DataStructures ───── v0.17.6
Installed StaticArrays ─────── v0.12.1
Installed QuadGK ───────────── v2.1.1
Installed Compat ───────────── v2.2.0
Installed StatsFuns ────────── v0.9.0
Installed BinaryProvider ───── v0.5.8
Installed Rmath ────────────── v0.5.1
Installed Missings ─────────── v0.4.3
Installed NearestNeighbors ─── v0.4.4
Installed Distributions ────── v0.21.9
Installed LegacyStrings ────── v0.4.1
Installed CMakeWrapper ─────── v0.2.3
Installed OrderedCollections ─ v1.1.0
Installed Parameters ───────── v0.12.0
Installed SpecialFunctions ─── v0.8.0
Installed Distances ────────── v0.8.2
Installed BinDeps ──────────── v0.8.10
Installed DataAPI ──────────── v1.1.0
Installed Blosc ────────────── v0.5.1
Installed ScikitLearnBase ──── v0.5.0
Installed CMake ────────────── v1.1.2
Installed PDMats ───────────── v0.9.10
Installed StatsBase ────────── v0.32.0
Installed Clustering ───────── v0.13.3
Installed HDF5 ─────────────── v0.12.5
Updating `~/.julia/environments/v1.3/Project.toml`
[cc18c42c] + GaussianMixtures v0.3.0
Updating `~/.julia/environments/v1.3/Manifest.toml`
[7d9fca2a] + Arpack v0.3.1
[9e28174c] + BinDeps v0.8.10
[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 v0.17.6
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.21.9
[5789e2e9] + FileIO v1.1.0
[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
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[d96e819e] + Parameters v0.12.0
[1fd47b50] + QuadGK v2.1.1
[79098fc4] + Rmath v0.5.1
[6e75b9c4] + ScikitLearnBase v0.5.0
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.8.0
[90137ffa] + StaticArrays v0.12.1
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.9.0
[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 Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Building Rmath ───────────→ `~/.julia/packages/Rmath/4wt82/deps/build.log`
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Building CMake ───────────→ `~/.julia/packages/CMake/nSK2r/deps/build.log`
Building Blosc ───────────→ `~/.julia/packages/Blosc/lzFr0/deps/build.log`
Building HDF5 ────────────→ `~/.julia/packages/HDF5/Zh9on/deps/build.log`
Testing GaussianMixtures
Status `/tmp/jl_oLIhsP/Manifest.toml`
[7d9fca2a] Arpack v0.3.1
[9e28174c] BinDeps v0.8.10
[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 v0.17.6
[b4f34e82] Distances v0.8.2
[31c24e10] Distributions v0.21.9
[5789e2e9] FileIO v1.1.0
[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
[bac558e1] OrderedCollections v1.1.0
[90014a1f] PDMats v0.9.10
[d96e819e] Parameters v0.12.0
[1fd47b50] QuadGK v2.1.1
[79098fc4] Rmath v0.5.1
[6e75b9c4] ScikitLearnBase v0.5.0
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.8.0
[90137ffa] StaticArrays v0.12.1
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.9.0
[30578b45] URIParser v0.4.0
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[9abbd945] Profile [`@stdlib/Profile`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
[ Info: Testing Data
(100000, -1.3296494081103204e7, [94882.51209294474, 5117.487907055261], [-6763.862331130266 1053.5716545757464 -784.977133017846; 7441.029179691554 -1076.8770493687725 951.3450014159525], Array{Float64,2}[[87518.67333606655 408.6664351429119 596.1063722847459; 408.66643514291195 94880.2530762904 -158.09981021565625; 596.1063722847459 -158.0998102156562 94898.32243667853], [12634.607741102463 -749.0687387994843 -1056.835667108604; -749.0687387994843 4817.415018387235 351.03807470307066; -1056.835667108604 351.03807470307066 4566.936035357224]])
┌ Warning: rmprocs: process 1 not removed
└ @ Distributed /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.3/Distributed/src/cluster.jl:1015
[ Info: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 1.836751e+03
1 1.282650e+03 -5.541010e+02 | 7
2 1.168711e+03 -1.139389e+02 | 6
3 1.143076e+03 -2.563493e+01 | 0
4 1.143076e+03 0.000000e+00 | 0
K-means converged with 4 iterations (objv = 1143.0757529455818)
┌ Info: K-means with 272 data points using 4 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 -2.057410
└ 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 at broadcast.jl:630 [inlined]
└ @ Core ./broadcast.jl:630
┌ 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 at broadcast.jl:630 [inlined]
└ @ Core ./broadcast.jl:630
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = _broadcast_getindex_evalf at broadcast.jl:630 [inlined]
└ @ Core ./broadcast.jl:630
[ Info: iteration 1, lowerbound -3.699687
[ Info: iteration 2, lowerbound -3.592318
[ Info: iteration 3, lowerbound -3.471854
[ Info: iteration 4, lowerbound -3.322291
[ Info: iteration 5, lowerbound -3.146924
[ Info: iteration 6, lowerbound -2.964973
[ Info: iteration 7, lowerbound -2.808308
[ Info: dropping number of Gaussions to 7
[ Info: iteration 8, lowerbound -2.693055
[ Info: dropping number of Gaussions to 6
[ Info: iteration 9, lowerbound -2.611779
[ Info: dropping number of Gaussions to 5
[ Info: iteration 10, lowerbound -2.547222
[ Info: dropping number of Gaussions to 4
[ Info: iteration 11, lowerbound -2.496321
[ Info: dropping number of Gaussions to 3
[ Info: iteration 12, lowerbound -2.444720
[ Info: iteration 13, lowerbound -2.396533
[ Info: iteration 14, lowerbound -2.357996
[ Info: iteration 15, lowerbound -2.328416
[ Info: iteration 16, lowerbound -2.310896
[ Info: iteration 17, lowerbound -2.308144
[ Info: dropping number of Gaussions to 2
[ Info: iteration 18, lowerbound -2.302918
[ Info: iteration 19, lowerbound -2.299260
[ Info: iteration 20, lowerbound -2.299256
[ Info: iteration 21, lowerbound -2.299254
[ Info: iteration 22, lowerbound -2.299254
[ 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: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253
History[Tue Dec 3 01:03:41 2019: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points
, Tue Dec 3 01:03:48 2019: K-means with 272 data points using 4 iterations
11.3 data points per parameter
, Tue Dec 3 01:03:50 2019: EM with 272 data points 0 iterations avll -2.057410
5.8 data points per parameter
, Tue Dec 3 01:03:51 2019: GMM converted to Variational GMM
, Tue Dec 3 01:04:00 2019: iteration 1, lowerbound -3.699687
, Tue Dec 3 01:04:00 2019: iteration 2, lowerbound -3.592318
, Tue Dec 3 01:04:00 2019: iteration 3, lowerbound -3.471854
, Tue Dec 3 01:04:00 2019: iteration 4, lowerbound -3.322291
, Tue Dec 3 01:04:00 2019: iteration 5, lowerbound -3.146924
, Tue Dec 3 01:04:00 2019: iteration 6, lowerbound -2.964973
, Tue Dec 3 01:04:00 2019: iteration 7, lowerbound -2.808308
, Tue Dec 3 01:04:00 2019: dropping number of Gaussions to 7
, Tue Dec 3 01:04:00 2019: iteration 8, lowerbound -2.693055
, Tue Dec 3 01:04:00 2019: dropping number of Gaussions to 6
, Tue Dec 3 01:04:00 2019: iteration 9, lowerbound -2.611779
, Tue Dec 3 01:04:00 2019: dropping number of Gaussions to 5
, Tue Dec 3 01:04:00 2019: iteration 10, lowerbound -2.547222
, Tue Dec 3 01:04:00 2019: dropping number of Gaussions to 4
, Tue Dec 3 01:04:00 2019: iteration 11, lowerbound -2.496321
, Tue Dec 3 01:04:00 2019: dropping number of Gaussions to 3
, Tue Dec 3 01:04:00 2019: iteration 12, lowerbound -2.444720
, Tue Dec 3 01:04:00 2019: iteration 13, lowerbound -2.396533
, Tue Dec 3 01:04:00 2019: iteration 14, lowerbound -2.357996
, Tue Dec 3 01:04:00 2019: iteration 15, lowerbound -2.328416
, Tue Dec 3 01:04:00 2019: iteration 16, lowerbound -2.310896
, Tue Dec 3 01:04:00 2019: iteration 17, lowerbound -2.308144
, Tue Dec 3 01:04:01 2019: dropping number of Gaussions to 2
, Tue Dec 3 01:04:01 2019: iteration 18, lowerbound -2.302918
, Tue Dec 3 01:04:01 2019: iteration 19, lowerbound -2.299260
, Tue Dec 3 01:04:01 2019: iteration 20, lowerbound -2.299256
, Tue Dec 3 01:04:01 2019: iteration 21, lowerbound -2.299254
, Tue Dec 3 01:04:01 2019: iteration 22, lowerbound -2.299254
, Tue Dec 3 01:04:01 2019: iteration 23, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 24, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 25, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 26, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 27, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 28, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 29, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 30, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 31, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 32, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 33, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 34, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 35, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 36, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 37, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 38, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 39, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 40, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 41, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 42, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 43, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 44, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 45, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 46, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 47, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 48, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: iteration 49, lowerbound -2.299253
, Tue Dec 3 01:04:01 2019: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253
]
α = [178.04509222601396, 95.9549077739861]
β = [178.04509222601396, 95.9549077739861]
m = [4.250300733269908 79.2868669443618; 2.0002292577753695 53.851987172461286]
ν = [180.04509222601396, 97.9549077739861]
W = LinearAlgebra.UpperTriangular{Float64,Array{Float64,2}}[[0.18404155547484777 -0.007644049042327639; 0.0 0.008581705166333407], [0.3758763611948421 -0.008953123827346077; 0.0 0.012748664777409385]]
Kind: diag, size256
nx: 100000 sum(zeroth order stats): 100000.00000000003
avll from stats: -1.00544630893504
avll from llpg: -1.0054463089350327
avll direct: -1.0054463089350327
sum posterior: 100000.0
Kind: full, size16
nx: 100000 sum(zeroth order stats): 100000.00000000001
avll from stats: -0.9905420970932665
avll from llpg: -0.9905420970932662
avll direct: -0.9905420970932663
sum posterior: 100000.0
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0.0102288 -0.266087 -0.244016 -0.0275152 0.0900285 -0.0689679 0.124551 0.10644 0.0411886 0.225579 0.190086 0.12461 -0.0878042 0.0191646 0.0941818 0.0145861 -0.17007 -0.110665 -0.130307 -0.00605957 -0.0711008 -0.207987 -0.0977138 0.0811794 0.0894477 0.0426859
-0.0383466 0.103848 0.101137 -0.0387692 0.040001 0.0226064 -0.198142 0.104986 -0.000393984 -0.188436 -0.0926992 -0.000357096 -0.09586 0.02681 0.0451985 0.108505 0.0378757 -0.00674902 0.0878562 0.0107687 -0.100043 0.0803726 0.0542244 -0.0136984 -0.0567741 -0.00293511
-0.0688459 -0.0526684 0.0775315 0.0678664 0.10896 0.12327 -0.180657 0.00772293 0.17572 0.0848228 -0.0630842 0.00811555 -0.0326786 -0.0367944 -0.0409644 -0.0930666 -0.0157493 0.171481 -0.000923382 0.0616348 -0.00888036 0.0160192 0.0978523 -0.000372719 0.134524 0.0799563 kind diag, method split
┌ Info: 0: avll =
└ tll[1] = -1.3766914174710592
[ Info: Running 50 iterations EM on diag cov GMM with 2 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.376790
[ Info: iteration 2, average log likelihood -1.376678
[ Info: iteration 3, average log likelihood -1.375473
[ Info: iteration 4, average log likelihood -1.366140
[ Info: iteration 5, average log likelihood -1.350320
[ Info: iteration 6, average log likelihood -1.343975
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[ Info: iteration 8, average log likelihood -1.341306
[ Info: iteration 9, average log likelihood -1.340935
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[ Info: iteration 15, average log likelihood -1.339910
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[ Info: iteration 48, average log likelihood -1.335881
[ Info: iteration 49, average log likelihood -1.335880
[ Info: iteration 50, average log likelihood -1.335880
┌ Info: EM with 100000 data points 50 iterations avll -1.335880
└ 952.4 data points per parameter
┌ Info: 1
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.3767897895556815
│ -1.376677788877024
│ ⋮
└ -1.3358797372654245
[ Info: Running 50 iterations EM on diag cov GMM with 4 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.335996
[ Info: iteration 2, average log likelihood -1.335880
[ Info: iteration 3, average log likelihood -1.335128
[ Info: iteration 4, average log likelihood -1.329360
[ Info: iteration 5, average log likelihood -1.314013
[ Info: iteration 6, average log likelihood -1.300967
[ Info: iteration 7, average log likelihood -1.295775
[ Info: iteration 8, average log likelihood -1.293508
[ Info: iteration 9, average log likelihood -1.292221
[ Info: iteration 10, average log likelihood -1.291356
[ Info: iteration 11, average log likelihood -1.290733
[ Info: iteration 12, average log likelihood -1.290269
[ Info: iteration 13, average log likelihood -1.289899
[ Info: iteration 14, average log likelihood -1.289588
[ Info: iteration 15, average log likelihood -1.289321
[ Info: iteration 16, average log likelihood -1.289090
[ Info: iteration 17, average log likelihood -1.288884
[ Info: iteration 18, average log likelihood -1.288689
[ Info: iteration 19, average log likelihood -1.288489
[ Info: iteration 20, average log likelihood -1.288275
[ Info: iteration 21, average log likelihood -1.288043
[ Info: iteration 22, average log likelihood -1.287797
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[ Info: iteration 48, average log likelihood -1.286170
[ Info: iteration 49, average log likelihood -1.286169
[ Info: iteration 50, average log likelihood -1.286169
┌ Info: EM with 100000 data points 50 iterations avll -1.286169
└ 473.9 data points per parameter
┌ Info: 2
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.3359960480778206
│ -1.3358799995625312
│ ⋮
└ -1.2861686186159613
[ Info: Running 50 iterations EM on diag cov GMM with 8 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.286369
[ Info: iteration 2, average log likelihood -1.286167
[ Info: iteration 3, average log likelihood -1.285489
[ Info: iteration 4, average log likelihood -1.279749
[ Info: iteration 5, average log likelihood -1.262154
[ Info: iteration 6, average log likelihood -1.249327
[ Info: iteration 7, average log likelihood -1.245420
[ Info: iteration 8, average log likelihood -1.243603
[ Info: iteration 9, average log likelihood -1.241915
[ Info: iteration 10, average log likelihood -1.240025
[ Info: iteration 11, average log likelihood -1.238673
[ Info: iteration 12, average log likelihood -1.237865
[ Info: iteration 13, average log likelihood -1.237328
[ Info: iteration 14, average log likelihood -1.236959
[ Info: iteration 15, average log likelihood -1.236716
[ Info: iteration 16, average log likelihood -1.236548
[ Info: iteration 17, average log likelihood -1.236405
[ Info: iteration 18, average log likelihood -1.236245
[ Info: iteration 19, average log likelihood -1.236044
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[ Info: iteration 21, average log likelihood -1.235551
[ Info: iteration 22, average log likelihood -1.235343
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[ Info: iteration 35, average log likelihood -1.234822
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[ Info: iteration 45, average log likelihood -1.234402
[ Info: iteration 46, average log likelihood -1.234383
[ Info: iteration 47, average log likelihood -1.234364
[ Info: iteration 48, average log likelihood -1.234344
[ Info: iteration 49, average log likelihood -1.234322
[ Info: iteration 50, average log likelihood -1.234297
┌ Info: EM with 100000 data points 50 iterations avll -1.234297
└ 236.4 data points per parameter
┌ Info: 3
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.2863689323883747
│ -1.2861669895164447
│ ⋮
└ -1.2342970141721927
[ Info: Running 50 iterations EM on diag cov GMM with 16 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.234484
[ Info: iteration 2, average log likelihood -1.234146
[ Info: iteration 3, average log likelihood -1.232373
[ Info: iteration 4, average log likelihood -1.216354
[ Info: iteration 5, average log likelihood -1.179919
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -1.157193
[ Info: iteration 7, average log likelihood -1.160416
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -1.148936
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -1.149409
[ Info: iteration 10, average log likelihood -1.154787
[ Info: iteration 11, average log likelihood -1.145949
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 12, average log likelihood -1.139590
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 13, average log likelihood -1.149315
[ Info: iteration 14, average log likelihood -1.150207
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 15, average log likelihood -1.140737
[ Info: iteration 16, average log likelihood -1.149958
[ Info: iteration 17, average log likelihood -1.142428
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 5
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 18, average log likelihood -1.136643
[ Info: iteration 19, average log likelihood -1.155186
[ Info: iteration 20, average log likelihood -1.143808
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 5
│ 13
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 21, average log likelihood -1.135884
[ Info: iteration 22, average log likelihood -1.158997
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 23, average log likelihood -1.144417
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 24, average log likelihood -1.145056
[ Info: iteration 25, average log likelihood -1.151124
[ Info: iteration 26, average log likelihood -1.143312
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 27, average log likelihood -1.137568
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 28, average log likelihood -1.147727
[ Info: iteration 29, average log likelihood -1.149358
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 30, average log likelihood -1.139813
[ Info: iteration 31, average log likelihood -1.148181
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 13
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 32, average log likelihood -1.140104
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 5
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 33, average log likelihood -1.147536
[ Info: iteration 34, average log likelihood -1.158008
[ Info: iteration 35, average log likelihood -1.145357
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 36, average log likelihood -1.138686
[ Info: iteration 37, average log likelihood -1.148327
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 38, average log likelihood -1.141814
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 39, average log likelihood -1.144872
[ Info: iteration 40, average log likelihood -1.150726
[ Info: iteration 41, average log likelihood -1.143068
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 42, average log likelihood -1.137346
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 43, average log likelihood -1.146695
[ Info: iteration 44, average log likelihood -1.147457
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 5
│ 13
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 45, average log likelihood -1.136869
[ Info: iteration 46, average log likelihood -1.159562
[ Info: iteration 47, average log likelihood -1.145020
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 5
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 48, average log likelihood -1.137600
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 16
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 49, average log likelihood -1.147172
[ Info: iteration 50, average log likelihood -1.149373
┌ Info: EM with 100000 data points 50 iterations avll -1.149373
└ 118.1 data points per parameter
┌ Info: 4
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.234484458513118
│ -1.2341456820710413
│ ⋮
└ -1.1493734218233285
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 9
│ 10
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 1, average log likelihood -1.140353
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 9
│ 10
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 2, average log likelihood -1.138056
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 9
│ 10
│ 26
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 3, average log likelihood -1.133289
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 9
│ 10
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 4, average log likelihood -1.107692
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 5
│ 6
│ 9
│ 10
│ 26
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -1.055836
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 2
│ 8
│ 9
│ 10
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -1.056514
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 9
│ 10
│ 26
│ 29
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.053906
┌ Warning: Variances had to be floored
│ ind =
│ 8-element Array{Int64,1}:
│ 2
│ 5
│ 6
│ 9
│ 10
│ 28
│ 30
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -1.027048
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
│ 8
│ 9
│ 10
│ 24
│ 26
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -1.050185
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 9
│ 10
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.048896
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 2
│ 5
│ 6
│ 9
│ ⋮
│ 28
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 11, average log likelihood -1.010996
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 8
│ 9
│ 10
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 12, average log likelihood -1.048120
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 2
│ 9
│ 10
│ 24
│ 26
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 13, average log likelihood -1.021552
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 5
│ 6
│ 8
│ 9
│ 10
│ 28
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 14, average log likelihood -1.022800
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 2
│ 9
│ 10
│ 26
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 15, average log likelihood -1.042138
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 8
│ 9
│ 10
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 16, average log likelihood -1.026441
┌ Warning: Variances had to be floored
│ ind =
│ 10-element Array{Int64,1}:
│ 2
│ 5
│ 6
│ 9
│ ⋮
│ 28
│ 29
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 17, average log likelihood -0.996071
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 8
│ 9
│ 10
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 18, average log likelihood -1.059352
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 9
│ 10
│ 26
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 19, average log likelihood -1.024389
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 2
│ 5
│ 6
│ 8
│ ⋮
│ 26
│ 28
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 20, average log likelihood -1.001069
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 9
│ 10
│ 24
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 21, average log likelihood -1.046403
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
│ 2
│ 8
│ 9
│ 10
│ 26
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 22, average log likelihood -1.024765
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 5
│ 6
│ 9
│ 10
│ 28
│ 29
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 23, average log likelihood -1.013343
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 2
│ 8
│ 9
│ 10
│ 26
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 24, average log likelihood -1.039275
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 9
│ 10
│ 24
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 25, average log likelihood -1.028209
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 2
│ 5
│ 6
│ 8
│ ⋮
│ 26
│ 28
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 26, average log likelihood -1.011012
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 9
│ 10
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 27, average log likelihood -1.053304
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 8
│ 9
│ 10
│ 26
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 28, average log likelihood -1.016092
┌ Warning: Variances had to be floored
│ ind =
│ 10-element Array{Int64,1}:
│ 2
│ 5
│ 6
│ 9
│ ⋮
│ 28
│ 29
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 29, average log likelihood -0.995764
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 8
│ 9
│ 10
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 30, average log likelihood -1.059349
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
│ 2
│ 9
│ 10
│ 26
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 31, average log likelihood -1.024302
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 5
│ 6
│ 8
│ 9
│ 10
│ 28
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 32, average log likelihood -1.012407
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
│ 2
│ 9
│ 10
│ 24
│ 26
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 33, average log likelihood -1.035250
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 8
│ 9
│ 10
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 34, average log likelihood -1.035232
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 2
│ 5
│ 6
│ 9
│ ⋮
│ 28
│ 29
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 35, average log likelihood -1.002510
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 8
│ 9
│ 10
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 36, average log likelihood -1.050263
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
│ 9
│ 10
│ 24
│ 26
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 37, average log likelihood -1.017532
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 2
│ 5
│ 6
│ 8
│ ⋮
│ 26
│ 28
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 38, average log likelihood -1.010285
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 9
│ 10
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 39, average log likelihood -1.053279
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
│ 2
│ 8
│ 9
│ 10
│ 26
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 40, average log likelihood -1.016006
┌ Warning: Variances had to be floored
│ ind =
│ 8-element Array{Int64,1}:
│ 5
│ 6
│ 9
│ 10
│ 24
│ 28
│ 29
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 41, average log likelihood -1.007023
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 2
│ 8
│ 9
│ 10
│ 26
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 42, average log likelihood -1.048321
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 9
│ 10
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 43, average log likelihood -1.034963
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 2
│ 5
│ 6
│ 8
│ ⋮
│ 26
│ 28
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 44, average log likelihood -1.001841
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 9
│ 10
│ 24
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 45, average log likelihood -1.046432
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 8
│ 9
│ 10
│ 26
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 46, average log likelihood -1.024804
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 2
│ 5
│ 6
│ 9
│ ⋮
│ 28
│ 29
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 47, average log likelihood -1.002169
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 8
│ 9
│ 10
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 48, average log likelihood -1.050306
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 2
│ 9
│ 10
│ 24
│ 26
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 49, average log likelihood -1.017501
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 5
│ 6
│ 8
│ 9
│ 10
│ 28
│ 30
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 50, average log likelihood -1.021595
┌ Info: EM with 100000 data points 50 iterations avll -1.021595
└ 59.0 data points per parameter
┌ Info: 5
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.140353179092833
│ -1.1380561355322671
│ ⋮
└ -1.0215949071697195
┌ Info: Total log likelihood:
│ tll =
│ 251-element Array{Float64,1}:
│ -1.3766914174710592
│ -1.3767897895556815
│ -1.376677788877024
│ -1.3754732951560151
│ ⋮
│ -1.0503060758402902
│ -1.0175014888899832
└ -1.0215949071697195
32×26 Array{Float64,2}:
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0.05913 0.0976489 0.0651927 -0.170995 -0.14743 0.14495 -0.253594 -0.0132605 0.0132687 -0.124692 0.183852 0.168213 0.0770745 -0.00612685 -0.156455 0.029363 0.0519382 0.182868 -0.0631098 0.0852725 0.173322 -0.00247389 -0.120057 -0.00924121 -0.09588 -0.0960915
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0.121815 0.0529874 -0.102833 0.13457 -0.13415 0.0456844 0.126821 0.082572 -0.0672119 -0.0891891 0.134567 -0.029954 0.131401 -0.172243 -0.0713965 0.0320508 -0.0342152 0.030093 0.14398 0.093216 0.0132385 0.133671 -0.19935 -0.239159 0.059658 -0.0828405
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0.176187 0.0166974 -0.085567 0.142161 -0.0616481 -0.119512 -0.0883623 0.152159 -0.0309592 -0.0425422 0.00639768 0.154008 0.118474 -0.000706032 0.0170725 -0.120091 -0.0146135 -0.00665359 -0.00389559 -0.16112 -0.0536421 0.0112877 0.00968432 -0.0450114 0.114979 -0.207984
-0.138894 -0.128724 -0.00288934 0.172261 -0.136401 -0.05015 0.137169 -0.0718203 -0.127289 0.182251 -0.0373488 -0.0245366 -0.0729169 0.117441 0.0465543 -0.0633465 -0.144193 0.000152225 0.188634 -0.161748 0.109725 -0.145595 0.0355611 -0.0164255 -0.158822 0.0072571
-0.0199828 -0.25451 -0.228487 -0.0284986 0.0836262 -0.06894 0.0831981 0.117569 -0.00683111 0.202067 0.193552 0.118698 -0.0917065 0.0265187 0.131182 0.0525887 -0.170176 -0.107711 -0.129677 -0.0190324 -0.0581738 -0.207872 -0.096822 0.0907019 0.0903069 0.023225
0.0186284 -0.102351 -0.0984365 0.0122093 -0.0547539 0.00626387 -0.0410139 -0.0862266 0.00447871 0.0417248 -0.0424763 -0.0413244 -0.0952954 -0.118093 -0.00577039 0.0400589 0.0764504 0.150212 0.127579 0.052385 -0.159389 0.0433713 -0.119968 -0.00465344 -0.13438 -0.108195
0.0564682 0.110977 -0.0322963 -0.1012 0.0159186 0.119359 -0.11657 -0.0157516 0.111495 0.20952 0.0729138 0.0709276 -0.0644565 -0.035393 0.0364543 -0.00785054 -0.0821396 0.153391 0.0712066 -0.0781595 -0.0437265 0.168413 -0.0117218 -0.155389 -0.0618925 -0.188238
-0.110855 0.0758264 0.0157954 0.021199 0.0105014 -0.0320887 -0.0409727 0.0702985 -0.0496601 -0.0258468 -0.12497 -0.179745 0.147156 0.0591578 -0.228596 0.0717054 0.0819506 0.197231 0.148665 -0.0957757 0.0360997 0.0439438 0.0800455 -0.0625836 -0.0173948 -0.0104972
0.11148 -0.00303705 0.0345374 -0.040024 -0.0321226 0.0753986 -0.0435069 0.0750151 -0.0632652 0.00139389 -0.0831247 0.025604 0.0189056 0.0461604 -0.246439 -0.0454177 0.0128652 -0.0965168 0.0413081 0.149972 0.0714701 -0.00855091 -0.0516729 -0.326546 0.0113757 0.0600239
0.0134826 0.000604056 0.0851955 -0.122095 -0.131325 0.145362 0.0359987 -0.0347084 0.0716071 0.0795 -0.164822 0.0817974 0.0728177 0.0479927 0.102796 0.0546123 -0.0553159 0.0884979 0.0791276 0.107779 0.0473769 -0.0851153 0.0085588 0.109736 0.131299 0.309689
-0.0162782 -0.0121213 -0.135383 0.0888747 -0.0390296 -0.125822 -0.110908 0.036342 -0.034242 0.162088 -0.106975 0.0902955 -0.126173 0.0140759 -0.0490532 -0.00175418 -0.120752 -0.0353159 0.158185 0.0328858 -0.067366 -0.0380004 0.104852 -0.140501 0.105421 0.120997 [ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 2
│ 9
│ 10
│ 26
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 1, average log likelihood -1.042134
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 2, average log likelihood -1.006282
┌ Warning: Variances had to be floored
│ ind =
│ 11-element Array{Int64,1}:
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│ ⋮
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 3, average log likelihood -0.992800
┌ Warning: Variances had to be floored
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│ 6-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 4, average log likelihood -1.034512
┌ Warning: Variances had to be floored
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│ 7-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -1.012494
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -0.995200
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.033708
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -1.013223
┌ Warning: Variances had to be floored
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│ 11-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -0.994134
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.034415
┌ Info: EM with 100000 data points 10 iterations avll -1.034415
└ 59.0 data points per parameter
kind diag, method kmeans
[ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 7.643327e+05
1 6.349898e+05 -1.293429e+05 | 32
2 6.085645e+05 -2.642531e+04 | 32
3 5.963521e+05 -1.221231e+04 | 32
4 5.880497e+05 -8.302482e+03 | 32
5 5.818977e+05 -6.151997e+03 | 32
6 5.782540e+05 -3.643678e+03 | 32
7 5.761436e+05 -2.110390e+03 | 32
8 5.748331e+05 -1.310488e+03 | 32
9 5.737848e+05 -1.048347e+03 | 32
10 5.728577e+05 -9.270266e+02 | 32
11 5.720778e+05 -7.799724e+02 | 32
12 5.713609e+05 -7.168417e+02 | 32
13 5.706217e+05 -7.391992e+02 | 32
14 5.698844e+05 -7.373064e+02 | 32
15 5.689303e+05 -9.541012e+02 | 32
16 5.678352e+05 -1.095145e+03 | 32
17 5.668385e+05 -9.966670e+02 | 32
18 5.659910e+05 -8.475055e+02 | 32
19 5.654279e+05 -5.630800e+02 | 32
20 5.651585e+05 -2.694602e+02 | 32
21 5.650539e+05 -1.045604e+02 | 32
22 5.650172e+05 -3.666532e+01 | 31
23 5.649994e+05 -1.782776e+01 | 32
24 5.649901e+05 -9.290169e+00 | 27
25 5.649849e+05 -5.230347e+00 | 26
26 5.649816e+05 -3.297444e+00 | 25
27 5.649795e+05 -2.052584e+00 | 24
28 5.649781e+05 -1.418179e+00 | 16
29 5.649771e+05 -1.029868e+00 | 16
30 5.649765e+05 -5.880788e-01 | 19
31 5.649757e+05 -7.594005e-01 | 12
32 5.649753e+05 -4.649768e-01 | 18
33 5.649746e+05 -7.032001e-01 | 11
34 5.649742e+05 -3.727495e-01 | 12
35 5.649734e+05 -7.905714e-01 | 11
36 5.649729e+05 -5.538075e-01 | 11
37 5.649724e+05 -4.404562e-01 | 8
38 5.649719e+05 -4.905889e-01 | 9
39 5.649713e+05 -6.382396e-01 | 14
40 5.649705e+05 -7.490822e-01 | 16
41 5.649700e+05 -4.917147e-01 | 12
42 5.649696e+05 -4.121678e-01 | 9
43 5.649692e+05 -4.366672e-01 | 11
44 5.649688e+05 -3.545265e-01 | 5
45 5.649688e+05 -8.163655e-02 | 3
46 5.649687e+05 -4.610355e-02 | 5
47 5.649685e+05 -1.761953e-01 | 9
48 5.649683e+05 -2.608942e-01 | 10
49 5.649680e+05 -3.203133e-01 | 6
50 5.649678e+05 -1.133350e-01 | 5
K-means terminated without convergence after 50 iterations (objv = 564967.8443567804)
┌ Info: K-means with 32000 data points using 50 iterations
└ 37.0 data points per parameter
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.280479
[ Info: iteration 2, average log likelihood -1.245430
[ Info: iteration 3, average log likelihood -1.214836
[ Info: iteration 4, average log likelihood -1.176504
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -1.122014
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -1.095831
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.093009
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -1.075266
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -1.055867
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.033008
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 11, average log likelihood -1.038106
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 12, average log likelihood -1.045772
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 13, average log likelihood -1.058680
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 14, average log likelihood -1.012520
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 15, average log likelihood -1.040694
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 16, average log likelihood -1.029093
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 17, average log likelihood -1.036071
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 18, average log likelihood -1.043424
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 19, average log likelihood -1.027126
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 20, average log likelihood -1.007588
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 21, average log likelihood -1.064105
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 22, average log likelihood -1.036473
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 23, average log likelihood -1.002890
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 24, average log likelihood -1.027820
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 25, average log likelihood -1.057196
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 26, average log likelihood -1.025401
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 27, average log likelihood -1.056218
┌ Warning: Variances had to be floored
│ ind =
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│ 3
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 28, average log likelihood -1.028909
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 29, average log likelihood -1.001164
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 30, average log likelihood -1.050766
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 31, average log likelihood -1.023268
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 32, average log likelihood -1.042694
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 33, average log likelihood -1.037067
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 34, average log likelihood -1.025825
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 35, average log likelihood -1.003754
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 36, average log likelihood -1.056125
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 37, average log likelihood -1.041394
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 38, average log likelihood -1.055727
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 39, average log likelihood -1.003444
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 40, average log likelihood -1.036982
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 41, average log likelihood -1.053213
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 42, average log likelihood -1.018803
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 43, average log likelihood -1.022430
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 44, average log likelihood -1.042841
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 15
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 45, average log likelihood -1.044473
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 46, average log likelihood -1.025443
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 3
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 47, average log likelihood -1.020134
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 48, average log likelihood -1.023195
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 49, average log likelihood -1.044317
┌ Warning: Variances had to be floored
│ ind =
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 50, average log likelihood -1.030117
┌ Info: EM with 100000 data points 50 iterations avll -1.030117
└ 59.0 data points per parameter
32×26 Array{Float64,2}:
0.00032608 -0.122544 0.148789 -0.0764437 0.10576 -0.0562767 -0.0335326 -0.184063 -0.00604365 0.129861 -0.00921363 0.00197687 -0.0116991 0.0342154 -0.0687814 -0.0462902 -0.0518295 0.0218814 -0.197739 0.0885818 -0.188587 -0.117178 0.00682891 -0.144138 -0.110354 -0.0800895
0.104207 -0.00405564 0.045138 -0.117584 -0.15692 -0.103955 -0.155162 -0.00322744 0.178064 0.161963 -0.049946 0.0292773 0.138035 -0.096188 0.10415 0.0567627 0.0903749 -0.0131274 0.0994942 -0.0499685 -0.0804352 -0.0892638 0.102008 -0.0864976 0.21414 0.0463676
0.0573279 0.110749 -0.0331134 -0.101137 0.0152878 0.119678 -0.113348 -0.0156644 0.113693 0.209227 0.0713932 0.0707893 -0.0626659 -0.0352421 0.0363895 -0.00740207 -0.0807698 0.152752 0.0705034 -0.0786915 -0.0438926 0.167894 -0.0118787 -0.15429 -0.0610526 -0.186544
0.109386 0.077 -0.0822439 -0.637589 -0.0655712 0.0406796 -0.156539 -0.0123658 -0.0499306 0.125345 0.0552556 0.0583042 -0.090331 0.0355483 -0.0406782 -0.119575 -0.014914 0.151328 0.000960676 -0.0718575 -0.120882 -0.0227788 -0.249837 0.00214743 0.146444 -0.0984399
0.1208 0.0544936 -0.101953 0.13407 -0.135171 0.0468233 0.126231 0.0822826 -0.0667118 -0.0886549 0.133744 -0.0300482 0.129384 -0.171802 -0.0725423 0.0324399 -0.0348465 0.0295896 0.145429 0.0949782 0.0161267 0.132766 -0.199289 -0.237883 0.0596822 -0.0819421
0.018909 -0.101296 -0.0975285 0.0117306 -0.0537675 0.0102278 -0.0407252 -0.0887625 0.00418108 0.0388202 -0.0426094 -0.0413982 -0.10565 -0.119785 -0.00532395 0.0399074 0.0759766 0.151312 0.12889 0.055232 -0.158433 0.0416698 -0.12179 -0.00445713 -0.13361 -0.106575
-0.0429997 0.0326342 0.0485306 -0.048932 -0.0657457 0.0622999 -0.00233812 0.0164364 0.00893467 0.0291104 -0.150976 -0.0458522 0.102257 0.0599496 -0.060041 0.068006 0.00398544 0.139809 0.11437 0.0177002 0.0450555 -0.0232868 0.0447132 0.020979 0.0668278 0.166394
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0.0405061 -0.0658113 -0.0329263 0.0132592 0.298079 0.12882 -0.0214125 -0.00290625 0.354228 0.126477 0.0767648 0.0675163 -0.356739 0.084624 0.0724175 0.0230677 -0.0889786 -0.0163929 0.12089 -0.0189581 0.0768505 -0.0879372 0.0248721 0.060482 0.353595 -0.027105
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0.180493 0.00306496 -0.0843323 0.163521 -0.046178 -0.123145 -0.0900763 0.141735 -0.0368123 -0.0488997 0.00415529 0.156703 0.167977 -0.0196174 0.0394076 -0.0932046 -0.0282583 -0.0261181 -0.00250045 -0.15907 -0.0353168 -0.00182233 0.00537518 -0.010603 0.12847 -0.219422
0.108504 0.0667969 -0.00611863 0.00175792 0.0306853 -0.118292 -0.0313178 0.0450027 0.00686515 0.0243458 0.128545 -0.0274675 0.0338681 -0.0545039 -0.105556 -0.0383566 0.0230535 0.125069 -0.165667 0.0586892 -0.107782 0.169741 -0.158881 -0.054213 0.220348 0.132531
0.0797119 -0.0197481 -0.0341508 0.0743793 0.104875 -0.0831172 0.227738 0.0979544 0.0956272 0.00289147 0.0387849 -0.0256165 -0.0875271 -0.114534 -0.0688329 -0.0632731 -0.00588875 0.174459 -0.0999701 -0.118171 0.0463854 0.0992726 0.0655825 0.18354 0.0109867 0.094369
-0.0429472 -0.0519198 0.0866128 0.029529 0.108496 0.12317 -0.178917 -0.00173543 0.176505 0.148571 -0.0644955 0.0165103 -0.043231 -0.0625554 -0.0421478 -0.091692 -0.0278002 0.177139 -0.00317262 0.0521224 -0.0129365 0.0251414 0.0980146 -0.000908895 0.10801 0.0810906
0.145889 0.143478 0.0738142 -0.0305591 0.0870588 0.0743343 -0.00437983 0.0977113 0.0440051 -0.0794541 0.00563207 0.024575 -0.0950493 0.0837209 0.000437941 -0.00294071 0.126019 0.121214 -0.0493918 -0.157655 -0.129231 -0.0109116 -0.00183061 -0.110466 0.00474722 0.0962049
-0.0328191 0.149727 0.0710101 -0.043109 0.0141938 0.0226678 -0.202261 0.114752 0.0119134 -0.168429 -0.0769226 -0.00491338 -0.0941226 0.0228609 0.0291871 0.0808028 -0.00452439 -0.0361274 0.0944875 0.0135186 -0.110437 0.0817035 0.0731193 -0.00860333 -0.05399 0.00459047
0.174597 0.123405 -0.0788863 0.0426157 -0.175297 0.0447178 0.0194583 0.136456 -0.0562783 0.0582376 0.0102163 0.0395818 -0.0297859 -0.126553 -0.0867508 -0.144759 -0.143266 -0.0249165 -0.0372262 0.0047239 -0.261646 -0.142782 0.074693 0.0908031 0.204228 0.0441274[ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
0.102352 -0.050943 0.0404071 -0.0257112 0.0673829 0.0126466 0.0103373 -0.0330572 -0.0751348 0.063418 -0.108753 0.00518643 -0.0947047 -0.120909 0.101229 0.11234 0.13743 -0.0507422 -0.0330972 -0.13303 0.220291 0.0286255 0.0872158 0.017388 -0.0840677 -0.149306
0.107113 0.0201819 0.00994874 0.0475837 0.0158196 -0.210246 -0.0698214 -0.0763164 -0.0552062 -0.153672 0.222971 0.0982642 -9.90151e-5 0.10989 0.0109952 0.00949068 0.00424048 0.0193521 0.140593 0.0963827 0.0206476 0.129693 0.12737 -0.0997932 -0.12203 -0.0859676
-0.153973 0.00434505 -0.0263323 -0.0713893 -0.051576 -0.0309981 -0.0282685 0.0880608 0.0260455 0.0956863 0.126269 -0.0267211 -0.0169221 -0.0607859 0.0337132 0.0518473 0.0892173 0.0583146 -0.0259875 -0.115211 -0.0479479 -0.037276 0.0891235 -0.107971 0.146827 0.13173
-0.0238351 -0.0292419 0.142281 0.193315 0.0645299 0.00926925 0.0722027 0.0882527 0.151501 -0.00897596 -0.0559217 -0.0444604 -0.0210928 -0.0192755 0.0753622 0.114418 0.0124162 0.0183458 0.0515842 -0.170251 0.0959809 0.139069 -0.112274 -0.104296 0.0254739 -0.110367
0.236837 0.0895418 0.0205046 -0.135278 -0.184483 0.0400695 -0.0544114 -0.142615 0.155304 0.0966577 0.162112 0.00132318 0.0382435 -0.0937203 -0.0387851 -0.0866864 -0.00127152 0.0196063 -0.124356 -0.309137 -0.0182554 0.033193 -0.0618122 0.160174 -0.0580583 -0.016537 ┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 8
│ 21
│ 26
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 1, average log likelihood -1.031017
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 8
│ 14
│ 15
│ 17
│ ⋮
│ 26
│ 27
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 2, average log likelihood -0.977287
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
│ 3
│ 8
│ 18
│ 21
│ 26
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 3, average log likelihood -0.983148
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 2
│ 4
│ 8
│ 17
│ ⋮
│ 26
│ 27
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 4, average log likelihood -0.996713
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 8
│ 9
│ 14
│ 15
│ 21
│ 23
│ 26
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -0.979393
┌ Warning: Variances had to be floored
│ ind =
│ 10-element Array{Int64,1}:
│ 2
│ 3
│ 8
│ 17
│ ⋮
│ 27
│ 29
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -0.969689
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 8
│ 15
│ 21
│ 23
│ 26
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.010354
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 4
│ 8
│ 14
│ 17
│ ⋮
│ 26
│ 27
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -0.974127
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 2
│ 3
│ 8
│ 18
│ 21
│ 26
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -0.974731
┌ Warning: Variances had to be floored
│ ind =
│ 8-element Array{Int64,1}:
│ 8
│ 15
│ 17
│ 21
│ 23
│ 26
│ 27
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -0.998765
┌ Info: EM with 100000 data points 10 iterations avll -0.998765
└ 59.0 data points per parameter
32×26 Array{Float64,2}:
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-0.0648417 -0.0258743 0.0939791 -0.175859 -0.0864582 -0.0723552 -0.0628418 0.0296257 0.0999538 0.0397134 -0.222824 -0.107431 -0.0715391 -0.128309 0.103757 0.0112017 0.0175063 -0.0182455 -0.146151 0.0748589 -0.119271 -0.149026 -0.195466 -0.0469369 0.0832662 -0.0528636
0.046449 0.246296 0.00305049 0.0468048 0.0996465 0.0340392 -0.0067838 -0.191041 0.0477703 -0.112588 0.00995943 -0.0333757 0.00394723 -0.119993 0.0223585 0.213001 -0.105589 0.0626051 -0.150354 0.118739 -0.221601 -0.0541642 -0.0782957 -0.0600922 0.014101 0.0329024
-0.0498571 0.0549628 -0.0732454 -0.0198949 0.0869048 -0.187993 0.0786841 0.0205452 0.199476 0.0674429 0.0587214 0.132386 -0.183069 -0.00250749 -0.0264932 -0.0717157 -0.0522459 0.0568798 0.0663323 0.106921 0.0653199 0.0857165 0.0674274 0.0417338 -0.105724 0.0647898
-0.0367965 -0.0550635 -0.192387 0.121342 -0.054397 -0.110152 0.167271 0.142035 -0.0263415 -0.0198855 0.0109679 -0.122142 0.102781 0.193545 0.038703 0.0121604 0.120086 -0.213137 0.0148614 0.0705179 -0.0688152 -0.0604443 -0.118323 0.0358597 -0.00779315 0.122018
-0.0498771 -0.0453623 0.186613 0.00696929 -0.00203634 0.00316487 -0.0242328 -0.00437263 -0.015646 0.0313335 0.103842 0.14679 0.093726 0.213556 0.0665475 -0.0746969 0.186065 -0.00364038 -0.104196 0.0791206 0.00606819 0.0907493 0.00629904 -0.0492161 -0.0597244 -0.273005
-0.0890505 -0.0689047 0.0407078 -0.0851543 0.0492383 0.149577 -0.0877433 0.0324143 0.174006 0.0518355 0.0985556 0.00971924 0.308027 -0.0754425 0.0431594 -0.0362068 -0.214811 -0.0676003 0.236559 -0.0216364 0.0787101 0.0671592 -0.138635 0.100109 -0.0421792 0.00478289
0.161304 0.0267798 0.0113676 0.236178 0.00676074 0.0270075 -0.212293 -0.13721 0.0767577 0.130357 0.0639232 -0.0549165 0.0549203 0.125372 -0.0814673 -0.0171559 0.17571 -0.0918659 0.00296359 0.106939 0.127996 0.220827 0.0115184 0.0737591 0.0352041 0.204624
0.182636 0.0846836 0.211439 -0.0358971 0.112339 -0.11454 0.0610618 -0.0758182 0.123367 -0.000361988 -0.0852482 0.246891 0.0294113 0.0556433 -0.0596593 0.0298162 0.0464746 -0.0264076 -0.0457111 0.135761 0.0183087 0.157019 0.108776 -0.0236494 0.0206431 -0.0230893
-0.121493 -0.0291264 0.158104 -0.0428787 0.00799102 0.156695 0.152996 0.0340328 0.000577572 0.00549784 -0.00189646 0.154266 -0.219087 -0.100959 -0.0654498 -0.147139 -0.0139908 0.0738628 0.0240458 0.078378 -0.0947098 -0.0863192 0.0510631 -0.0963267 0.0396545 0.237955
0.0656424 0.025386 -0.0327781 0.131853 0.108374 0.0242399 -0.159831 -0.140226 -0.0351789 0.134166 -0.154974 0.157948 0.0219379 -0.116739 0.017436 0.184359 -0.0409514 0.0548861 -0.0111504 -0.157337 -0.117117 0.0801298 -0.0996376 -0.0833404 0.154631 -0.0193188
-0.16582 0.115237 0.0256507 0.00613059 -0.00793514 0.0232949 -0.0401057 0.101573 -0.0926465 0.0415843 -0.257662 -0.0457759 0.178822 0.00238534 -0.0342671 -0.214552 -0.0109335 0.16318 -0.0455268 0.125208 -0.0755188 0.00348887 -0.116656 0.117548 0.00173403 -0.0208508
-0.0884033 0.0504409 0.196228 0.0466031 -0.123031 0.214542 0.00802521 0.0104415 0.187785 -0.0770438 0.118502 0.0972176 0.0878752 0.0342786 -0.158809 0.0807457 0.125293 -0.0501847 0.0339569 0.0239635 -0.11608 -0.155141 0.136667 0.0772604 0.143522 0.167861 kind full, method split
┌ Info: 0: avll =
└ tll[1] = -1.4238831715345055
[ Info: Running 50 iterations EM on diag cov GMM with 2 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.423902
[ Info: iteration 2, average log likelihood -1.423836
[ Info: iteration 3, average log likelihood -1.423781
[ Info: iteration 4, average log likelihood -1.423714
[ Info: iteration 5, average log likelihood -1.423630
[ Info: iteration 6, average log likelihood -1.423531
[ Info: iteration 7, average log likelihood -1.423418
[ Info: iteration 8, average log likelihood -1.423288
[ Info: iteration 9, average log likelihood -1.423113
[ Info: iteration 10, average log likelihood -1.422832
[ Info: iteration 11, average log likelihood -1.422340
[ Info: iteration 12, average log likelihood -1.421549
[ Info: iteration 13, average log likelihood -1.420530
[ Info: iteration 14, average log likelihood -1.419589
[ Info: iteration 15, average log likelihood -1.418980
[ Info: iteration 16, average log likelihood -1.418682
[ Info: iteration 17, average log likelihood -1.418555
[ Info: iteration 18, average log likelihood -1.418504
[ Info: iteration 19, average log likelihood -1.418483
[ Info: iteration 20, average log likelihood -1.418475
[ Info: iteration 21, average log likelihood -1.418471
[ Info: iteration 22, average log likelihood -1.418469
[ Info: iteration 23, average log likelihood -1.418469
[ Info: iteration 24, average log likelihood -1.418468
[ Info: iteration 25, average log likelihood -1.418468
[ Info: iteration 26, average log likelihood -1.418468
[ Info: iteration 27, average log likelihood -1.418467
[ Info: iteration 28, average log likelihood -1.418467
[ Info: iteration 29, average log likelihood -1.418467
[ Info: iteration 30, average log likelihood -1.418467
[ Info: iteration 31, average log likelihood -1.418467
[ Info: iteration 32, average log likelihood -1.418467
[ Info: iteration 33, average log likelihood -1.418466
[ Info: iteration 34, average log likelihood -1.418466
[ Info: iteration 35, average log likelihood -1.418466
[ Info: iteration 36, average log likelihood -1.418466
[ Info: iteration 37, average log likelihood -1.418466
[ Info: iteration 38, average log likelihood -1.418466
[ Info: iteration 39, average log likelihood -1.418466
[ Info: iteration 40, average log likelihood -1.418466
[ Info: iteration 41, average log likelihood -1.418466
[ Info: iteration 42, average log likelihood -1.418466
[ Info: iteration 43, average log likelihood -1.418466
[ Info: iteration 44, average log likelihood -1.418466
[ Info: iteration 45, average log likelihood -1.418466
[ Info: iteration 46, average log likelihood -1.418466
[ Info: iteration 47, average log likelihood -1.418466
[ Info: iteration 48, average log likelihood -1.418466
[ Info: iteration 49, average log likelihood -1.418466
[ Info: iteration 50, average log likelihood -1.418466
┌ Info: EM with 100000 data points 50 iterations avll -1.418466
└ 952.4 data points per parameter
┌ Info: 1
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4239024618318172
│ -1.4238355412401373
│ ⋮
└ -1.4184656325373775
[ Info: Running 50 iterations EM on diag cov GMM with 4 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.418485
[ Info: iteration 2, average log likelihood -1.418414
[ Info: iteration 3, average log likelihood -1.418356
[ Info: iteration 4, average log likelihood -1.418284
[ Info: iteration 5, average log likelihood -1.418195
[ Info: iteration 6, average log likelihood -1.418097
[ Info: iteration 7, average log likelihood -1.417999
[ Info: iteration 8, average log likelihood -1.417916
[ Info: iteration 9, average log likelihood -1.417854
[ Info: iteration 10, average log likelihood -1.417809
[ Info: iteration 11, average log likelihood -1.417778
[ Info: iteration 12, average log likelihood -1.417756
[ Info: iteration 13, average log likelihood -1.417738
[ Info: iteration 14, average log likelihood -1.417722
[ Info: iteration 15, average log likelihood -1.417708
[ Info: iteration 16, average log likelihood -1.417696
[ Info: iteration 17, average log likelihood -1.417684
[ Info: iteration 18, average log likelihood -1.417672
[ Info: iteration 19, average log likelihood -1.417661
[ Info: iteration 20, average log likelihood -1.417649
[ Info: iteration 21, average log likelihood -1.417638
[ Info: iteration 22, average log likelihood -1.417626
[ Info: iteration 23, average log likelihood -1.417615
[ Info: iteration 24, average log likelihood -1.417603
[ Info: iteration 25, average log likelihood -1.417592
[ Info: iteration 26, average log likelihood -1.417580
[ Info: iteration 27, average log likelihood -1.417569
[ Info: iteration 28, average log likelihood -1.417559
[ Info: iteration 29, average log likelihood -1.417549
[ Info: iteration 30, average log likelihood -1.417539
[ Info: iteration 31, average log likelihood -1.417530
[ Info: iteration 32, average log likelihood -1.417522
[ Info: iteration 33, average log likelihood -1.417514
[ Info: iteration 34, average log likelihood -1.417507
[ Info: iteration 35, average log likelihood -1.417501
[ Info: iteration 36, average log likelihood -1.417495
[ Info: iteration 37, average log likelihood -1.417489
[ Info: iteration 38, average log likelihood -1.417484
[ Info: iteration 39, average log likelihood -1.417479
[ Info: iteration 40, average log likelihood -1.417475
[ Info: iteration 41, average log likelihood -1.417471
[ Info: iteration 42, average log likelihood -1.417467
[ Info: iteration 43, average log likelihood -1.417463
[ Info: iteration 44, average log likelihood -1.417460
[ Info: iteration 45, average log likelihood -1.417457
[ Info: iteration 46, average log likelihood -1.417454
[ Info: iteration 47, average log likelihood -1.417451
[ Info: iteration 48, average log likelihood -1.417449
[ Info: iteration 49, average log likelihood -1.417446
[ Info: iteration 50, average log likelihood -1.417444
┌ Info: EM with 100000 data points 50 iterations avll -1.417444
└ 473.9 data points per parameter
┌ Info: 2
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4184846738719863
│ -1.4184144970926815
│ ⋮
└ -1.4174438711725235
[ Info: Running 50 iterations EM on diag cov GMM with 8 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.417452
[ Info: iteration 2, average log likelihood -1.417392
[ Info: iteration 3, average log likelihood -1.417336
[ Info: iteration 4, average log likelihood -1.417268
[ Info: iteration 5, average log likelihood -1.417182
[ Info: iteration 6, average log likelihood -1.417074
[ Info: iteration 7, average log likelihood -1.416945
[ Info: iteration 8, average log likelihood -1.416803
[ Info: iteration 9, average log likelihood -1.416658
[ Info: iteration 10, average log likelihood -1.416525
[ Info: iteration 11, average log likelihood -1.416413
[ Info: iteration 12, average log likelihood -1.416325
[ Info: iteration 13, average log likelihood -1.416261
[ Info: iteration 14, average log likelihood -1.416214
[ Info: iteration 15, average log likelihood -1.416178
[ Info: iteration 16, average log likelihood -1.416151
[ Info: iteration 17, average log likelihood -1.416128
[ Info: iteration 18, average log likelihood -1.416108
[ Info: iteration 19, average log likelihood -1.416090
[ Info: iteration 20, average log likelihood -1.416074
[ Info: iteration 21, average log likelihood -1.416059
[ Info: iteration 22, average log likelihood -1.416045
[ Info: iteration 23, average log likelihood -1.416032
[ Info: iteration 24, average log likelihood -1.416019
[ Info: iteration 25, average log likelihood -1.416008
[ Info: iteration 26, average log likelihood -1.415997
[ Info: iteration 27, average log likelihood -1.415986
[ Info: iteration 28, average log likelihood -1.415977
[ Info: iteration 29, average log likelihood -1.415968
[ Info: iteration 30, average log likelihood -1.415959
[ Info: iteration 31, average log likelihood -1.415951
[ Info: iteration 32, average log likelihood -1.415943
[ Info: iteration 33, average log likelihood -1.415936
[ Info: iteration 34, average log likelihood -1.415929
[ Info: iteration 35, average log likelihood -1.415923
[ Info: iteration 36, average log likelihood -1.415917
[ Info: iteration 37, average log likelihood -1.415911
[ Info: iteration 38, average log likelihood -1.415905
[ Info: iteration 39, average log likelihood -1.415900
[ Info: iteration 40, average log likelihood -1.415895
[ Info: iteration 41, average log likelihood -1.415890
[ Info: iteration 42, average log likelihood -1.415886
[ Info: iteration 43, average log likelihood -1.415881
[ Info: iteration 44, average log likelihood -1.415877
[ Info: iteration 45, average log likelihood -1.415873
[ Info: iteration 46, average log likelihood -1.415869
[ Info: iteration 47, average log likelihood -1.415865
[ Info: iteration 48, average log likelihood -1.415861
[ Info: iteration 49, average log likelihood -1.415857
[ Info: iteration 50, average log likelihood -1.415854
┌ Info: EM with 100000 data points 50 iterations avll -1.415854
└ 236.4 data points per parameter
┌ Info: 3
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4174515802751309
│ -1.4173917873887416
│ ⋮
└ -1.4158535188834591
[ Info: Running 50 iterations EM on diag cov GMM with 16 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.415858
[ Info: iteration 2, average log likelihood -1.415791
[ Info: iteration 3, average log likelihood -1.415727
[ Info: iteration 4, average log likelihood -1.415650
[ Info: iteration 5, average log likelihood -1.415553
[ Info: iteration 6, average log likelihood -1.415435
[ Info: iteration 7, average log likelihood -1.415301
[ Info: iteration 8, average log likelihood -1.415160
[ Info: iteration 9, average log likelihood -1.415021
[ Info: iteration 10, average log likelihood -1.414892
[ Info: iteration 11, average log likelihood -1.414774
[ Info: iteration 12, average log likelihood -1.414670
[ Info: iteration 13, average log likelihood -1.414578
[ Info: iteration 14, average log likelihood -1.414498
[ Info: iteration 15, average log likelihood -1.414427
[ Info: iteration 16, average log likelihood -1.414366
[ Info: iteration 17, average log likelihood -1.414311
[ Info: iteration 18, average log likelihood -1.414263
[ Info: iteration 19, average log likelihood -1.414219
[ Info: iteration 20, average log likelihood -1.414181
[ Info: iteration 21, average log likelihood -1.414146
[ Info: iteration 22, average log likelihood -1.414114
[ Info: iteration 23, average log likelihood -1.414085
[ Info: iteration 24, average log likelihood -1.414059
[ Info: iteration 25, average log likelihood -1.414035
[ Info: iteration 26, average log likelihood -1.414012
[ Info: iteration 27, average log likelihood -1.413992
[ Info: iteration 28, average log likelihood -1.413974
[ Info: iteration 29, average log likelihood -1.413956
[ Info: iteration 30, average log likelihood -1.413941
[ Info: iteration 31, average log likelihood -1.413926
[ Info: iteration 32, average log likelihood -1.413912
[ Info: iteration 33, average log likelihood -1.413899
[ Info: iteration 34, average log likelihood -1.413888
[ Info: iteration 35, average log likelihood -1.413876
[ Info: iteration 36, average log likelihood -1.413866
[ Info: iteration 37, average log likelihood -1.413855
[ Info: iteration 38, average log likelihood -1.413846
[ Info: iteration 39, average log likelihood -1.413836
[ Info: iteration 40, average log likelihood -1.413828
[ Info: iteration 41, average log likelihood -1.413819
[ Info: iteration 42, average log likelihood -1.413811
[ Info: iteration 43, average log likelihood -1.413802
[ Info: iteration 44, average log likelihood -1.413795
[ Info: iteration 45, average log likelihood -1.413787
[ Info: iteration 46, average log likelihood -1.413779
[ Info: iteration 47, average log likelihood -1.413772
[ Info: iteration 48, average log likelihood -1.413764
[ Info: iteration 49, average log likelihood -1.413757
[ Info: iteration 50, average log likelihood -1.413750
┌ Info: EM with 100000 data points 50 iterations avll -1.413750
└ 118.1 data points per parameter
┌ Info: 4
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4158583008838521
│ -1.4157911939805077
│ ⋮
└ -1.413750008028605
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.413752
[ Info: iteration 2, average log likelihood -1.413681
[ Info: iteration 3, average log likelihood -1.413612
[ Info: iteration 4, average log likelihood -1.413529
[ Info: iteration 5, average log likelihood -1.413421
[ Info: iteration 6, average log likelihood -1.413286
[ Info: iteration 7, average log likelihood -1.413127
[ Info: iteration 8, average log likelihood -1.412953
[ Info: iteration 9, average log likelihood -1.412775
[ Info: iteration 10, average log likelihood -1.412602
[ Info: iteration 11, average log likelihood -1.412440
[ Info: iteration 12, average log likelihood -1.412293
[ Info: iteration 13, average log likelihood -1.412159
[ Info: iteration 14, average log likelihood -1.412039
[ Info: iteration 15, average log likelihood -1.411932
[ Info: iteration 16, average log likelihood -1.411836
[ Info: iteration 17, average log likelihood -1.411751
[ Info: iteration 18, average log likelihood -1.411676
[ Info: iteration 19, average log likelihood -1.411609
[ Info: iteration 20, average log likelihood -1.411550
[ Info: iteration 21, average log likelihood -1.411497
[ Info: iteration 22, average log likelihood -1.411451
[ Info: iteration 23, average log likelihood -1.411410
[ Info: iteration 24, average log likelihood -1.411372
[ Info: iteration 25, average log likelihood -1.411339
[ Info: iteration 26, average log likelihood -1.411308
[ Info: iteration 27, average log likelihood -1.411280
[ Info: iteration 28, average log likelihood -1.411254
[ Info: iteration 29, average log likelihood -1.411230
[ Info: iteration 30, average log likelihood -1.411207
[ Info: iteration 31, average log likelihood -1.411185
[ Info: iteration 32, average log likelihood -1.411164
[ Info: iteration 33, average log likelihood -1.411145
[ Info: iteration 34, average log likelihood -1.411126
[ Info: iteration 35, average log likelihood -1.411108
[ Info: iteration 36, average log likelihood -1.411090
[ Info: iteration 37, average log likelihood -1.411073
[ Info: iteration 38, average log likelihood -1.411057
[ Info: iteration 39, average log likelihood -1.411041
[ Info: iteration 40, average log likelihood -1.411026
[ Info: iteration 41, average log likelihood -1.411010
[ Info: iteration 42, average log likelihood -1.410996
[ Info: iteration 43, average log likelihood -1.410981
[ Info: iteration 44, average log likelihood -1.410966
[ Info: iteration 45, average log likelihood -1.410952
[ Info: iteration 46, average log likelihood -1.410938
[ Info: iteration 47, average log likelihood -1.410924
[ Info: iteration 48, average log likelihood -1.410910
[ Info: iteration 49, average log likelihood -1.410896
[ Info: iteration 50, average log likelihood -1.410882
┌ Info: EM with 100000 data points 50 iterations avll -1.410882
└ 59.0 data points per parameter
┌ Info: 5
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4137521819091907
│ -1.4136813900470042
│ ⋮
└ -1.4108823694227164
┌ Info: Total log likelihood:
│ tll =
│ 251-element Array{Float64,1}:
│ -1.4238831715345055
│ -1.4239024618318172
│ -1.4238355412401373
│ -1.4237808510824543
│ ⋮
│ -1.4109101026428537
│ -1.4108962055024425
└ -1.4108823694227164
32×26 Array{Float64,2}:
-0.553984 0.22446 0.307891 0.749996 -0.506771 0.270779 0.0589894 0.234063 -0.117905 -0.0650462 0.376062 -0.130712 0.083545 -0.573585 -0.349229 -0.172751 -0.241845 0.0794482 -0.35029 0.307334 0.212853 0.195629 -0.146018 -0.246876 -0.492512 0.166469
-0.190652 0.247904 0.212829 0.56118 -0.445695 -0.0851792 -0.258313 0.124281 0.158512 -0.148089 0.435015 0.179141 -0.149008 0.603381 -0.370773 -0.47816 -0.0275157 -0.237638 0.21717 -0.0158913 -0.00464738 -0.547123 -0.0996559 -0.00772091 -0.0600922 -0.024727
-0.00934553 -0.128251 -0.418182 0.16152 -0.111596 0.34577 0.0829477 0.0305031 -0.0764512 0.191657 -0.28071 -0.0251153 -0.512323 -0.557371 -0.170412 -0.0920771 -0.260742 -0.602872 0.455444 0.356949 0.0692826 0.108983 -0.222576 0.438048 -0.119943 -0.357926
-0.0277845 -0.529875 0.568722 0.0418424 -0.333912 0.067524 -0.0181221 -0.337174 0.218697 -0.131251 0.376301 0.0804913 0.472218 -0.250977 -0.0660723 -0.514735 -0.257573 -0.426579 0.15857 0.166873 -0.210588 -0.0998601 0.326787 0.44739 0.0203724 -0.0935825
0.195717 -0.684919 -0.403422 -0.299992 0.751869 -0.339014 -0.193269 -0.100535 0.151268 0.196401 -0.208217 0.337848 0.167397 0.313852 -0.0167023 0.378556 0.270246 -0.172709 0.242029 -0.0831016 -0.845433 0.276625 -0.213512 0.287682 -0.0362846 -0.464403
-0.241425 0.136341 -0.559046 0.00760764 0.429551 -0.350168 0.220544 -0.45788 0.508196 -0.0117865 -0.32068 -0.0249189 -0.417321 0.39972 -0.200864 -0.0699775 0.449638 -0.242809 0.770001 0.0767994 -0.15761 -0.248313 0.0605867 -0.0851447 0.443382 -0.353697
0.00786407 0.590221 -0.00299337 0.0340515 -0.371203 -0.0127042 0.0271585 0.716538 -0.301146 0.286234 -0.480384 -0.32919 -0.901366 0.317471 -0.334288 -0.0220635 0.0028411 0.368885 0.267687 0.120891 0.0727473 -0.130018 -0.644888 -0.150221 -0.262795 -0.137028
0.183336 0.525339 -0.365803 0.0435217 0.784547 0.0416424 -0.0358611 0.447918 -0.00145536 0.446738 0.233988 -0.144668 -0.409354 0.36721 0.262679 0.208015 0.616412 0.212571 0.063347 -0.287143 0.149287 0.18082 -0.314776 -0.291771 -0.203149 -0.202105
-0.864914 0.0948946 -0.618243 -0.891282 0.162044 0.174194 -0.317481 -0.567016 -0.250568 -0.341629 -0.166593 0.516 -0.253648 -0.439629 -0.279118 0.230005 0.148052 0.159424 -0.139311 -0.0245377 0.0618777 0.291332 -0.415648 0.125156 -0.0227572 0.4601
-0.158334 -0.185799 -0.0907807 -0.219135 0.0726378 0.296661 0.232548 -0.463535 -0.00994249 0.0881685 -0.210725 0.126207 0.151182 -0.500504 -0.0980981 0.298632 -0.637453 0.377499 -0.02144 0.123632 0.113111 -0.0676782 -0.148128 -0.321323 0.460527 0.0521532
0.189283 -0.262346 0.0480651 0.195951 -0.263141 -0.136773 -0.714357 0.284798 0.460998 0.215301 -0.710154 -0.191323 0.291937 -0.280062 0.417419 0.629838 -0.595425 0.188056 0.275899 0.201113 0.133238 -0.246457 0.233275 0.0865887 0.311694 -0.258836
0.0683009 -0.241259 -0.267327 -0.22583 -0.143133 0.270173 -0.0907697 0.422857 0.492336 0.429562 -0.417079 0.448533 0.304361 -0.251702 0.564623 0.202822 0.694466 0.125624 -0.114282 -0.17598 -0.15467 0.157992 0.376609 0.158849 0.267665 0.29286
0.253173 0.15445 0.122432 0.0676081 -0.0177418 -0.238966 -0.368018 0.175887 -0.0709944 0.0347837 0.347614 0.0162546 0.263144 -0.02777 0.182391 0.173777 0.0749511 -0.00784902 -0.0239132 0.256897 0.193111 0.28125 0.0574872 -0.0140509 -0.415478 -0.0983669
-0.0120481 0.139711 -0.0362922 -0.104767 -0.0332919 -0.0615415 -0.139626 0.016435 0.0357722 -0.151481 -0.108553 0.0141516 -0.0804902 0.191684 -0.12336 0.109653 -0.00687407 -0.0358907 -0.00778728 -0.0732009 -0.0783578 -0.0610743 -0.031403 -0.0209979 0.124936 -0.0481491
-0.0643453 -0.294445 0.00107326 0.285238 0.0649214 0.0678202 0.609156 0.0319043 0.049219 0.0730021 0.0563022 -0.147163 -0.0497954 -0.140469 0.00893829 -0.288289 0.101406 0.281081 0.152826 -0.0388318 -0.157119 -0.0598017 -0.0217003 0.0790038 -0.0712317 0.185438
0.0638029 0.612748 0.250209 -0.253452 -0.0414713 0.225698 0.613829 -0.0765463 -0.136776 -0.134259 0.146593 0.249744 -0.210752 0.191703 0.0541103 -0.352199 0.141446 -0.0828631 -0.146924 -0.177479 0.174152 0.0749451 0.298948 -0.0878891 0.129381 0.621272
-0.158314 -0.442306 0.174772 -0.212903 -0.0578335 0.199434 0.110314 -0.97801 0.048439 -0.131493 0.0255383 -0.393764 -0.39024 -0.0733475 -0.52676 -0.0928385 -0.709149 -0.578571 0.505991 0.123298 -0.200221 -0.188747 -0.247956 0.206156 0.299881 -0.860089
0.260535 0.4764 0.328148 -0.892801 0.16913 -0.172298 0.15247 -0.339399 -0.531199 -0.459388 -0.0224522 -0.369069 0.0601415 0.284272 0.188904 -0.259265 0.493994 -0.0444044 0.0381523 0.6552 0.0217813 0.446123 -0.53337 0.126016 -0.265999 -0.604257
0.0944908 0.131534 0.345397 -0.0499203 -0.0831502 -0.400456 -0.227669 -0.726039 0.169265 -0.968685 0.0125606 0.449422 0.19302 -0.230469 -0.0831897 0.0132364 -0.548037 -0.260017 -0.223657 0.101191 -0.722429 -0.162864 0.32538 0.233339 0.336943 0.481907
0.346982 0.208716 0.268968 -0.963649 0.142691 0.229471 0.096269 -0.768236 0.156375 -0.194812 -0.244894 0.307718 -0.172946 0.439099 0.407202 0.202773 -0.00967906 -0.185332 -0.354428 -0.300609 0.416808 -0.043629 0.381796 0.000175954 0.407162 0.176406
-0.319275 -0.271141 -0.0916594 -0.0934053 -0.231082 -0.056832 -1.21978 -0.179975 0.0594293 0.124423 0.67167 0.211425 -0.122122 0.241591 -0.316096 0.575735 0.0167738 -0.413983 0.074519 -0.343173 0.604247 -0.341176 0.191445 -0.279418 -0.216315 -0.274642
-0.623454 -0.135768 -0.584012 0.0305285 0.66167 0.0967846 -0.183002 -0.105317 -0.021334 0.252379 0.0160198 0.490782 0.200922 -0.442656 -0.380368 0.406268 -0.471207 0.752287 -0.0966547 0.00804234 0.173998 -0.281033 -0.26637 -0.958699 0.784086 -0.045222
-0.429643 -0.496946 0.216964 0.370692 0.283772 0.2632 0.674894 -0.215712 0.251919 -0.0223373 -0.372513 0.16001 -0.559946 0.28405 -0.26483 0.336816 0.307844 -0.38651 -1.05487 0.395591 0.0204349 -0.823927 0.234409 0.081933 -0.488683 0.175261
-0.370925 -0.271044 -0.00137704 0.283739 -0.0795371 0.58781 1.27333 -0.313951 0.0888195 0.141718 -0.141625 -0.116865 -0.0851466 -0.315256 0.114152 -0.303717 0.248617 0.0455978 0.258565 -0.143524 -0.0310993 -0.144106 0.17335 0.259233 0.163075 0.519128
0.400004 -0.615391 0.0048349 -0.554955 -0.0791971 0.560414 -0.170389 0.50732 -0.719607 0.100085 0.188298 -0.256199 0.75215 -0.352848 -0.251015 -0.0428335 -0.532506 -0.394643 -0.537288 -0.363079 0.172392 0.370988 0.221591 0.340281 -0.161904 0.322225
0.203885 0.774666 0.136058 0.14939 -0.0641312 -0.35554 -0.483036 0.668676 -0.347673 -0.044227 0.0107949 0.587392 0.331549 -0.0596098 -0.0402523 0.456071 0.334757 0.152095 -0.637353 0.0998303 0.119597 0.156503 0.237922 0.0109875 -0.381633 0.497184
0.46668 0.145585 0.0706786 -0.0245689 0.194683 0.150791 0.844401 0.320407 -0.181043 -0.168161 -0.177856 -0.302524 0.319728 -0.398316 0.552432 -0.248445 -0.0279912 0.901195 -0.0100677 0.245767 -0.172473 0.683123 -0.312113 0.122507 0.157416 0.336673
0.234287 0.28543 0.10513 -0.554415 -0.166075 -0.184855 -0.60768 0.1975 -0.246204 -0.0715261 0.35864 0.048365 0.700252 -0.204457 0.125709 -0.300848 -0.192718 0.647305 0.523453 -0.506272 -0.253286 0.437615 0.0853814 -0.105168 0.533111 -0.0504219
0.0964268 0.238628 0.00581067 -0.33677 0.159341 -0.0823109 -0.150858 -0.0480864 -0.0325094 0.060909 0.0432009 0.0789057 -0.0859432 0.192532 0.10789 0.132495 0.158933 0.0161898 -0.0357033 -0.0495546 0.0129288 0.0942389 -0.0197253 -0.049208 -0.0274956 -0.100568
0.185469 -0.161374 -0.0796741 0.871186 -0.245409 -0.231652 -0.381659 0.662634 0.561258 0.254774 0.301977 0.0517313 0.0117337 -0.0547185 0.0370724 -0.19915 0.169309 -0.185979 0.528462 0.279928 -0.245844 -0.095277 -0.154181 0.245884 -0.350864 -0.171289
0.804702 -0.165152 0.516178 0.759611 0.331792 -0.00319524 1.00482 0.549184 0.300555 0.764778 0.628879 -0.189559 0.348138 0.713991 0.0921804 -0.16347 -0.0240873 -0.629042 0.0919628 0.23751 0.304981 0.204303 0.880169 -0.498271 0.141893 -0.325372
0.893778 -0.0937053 0.901549 -0.179074 -0.151939 -0.260099 -0.0180602 0.265683 0.405333 -0.429413 -0.487273 -0.764567 -0.154992 0.988064 0.476117 -0.0917891 0.52134 -0.225399 0.11327 0.418983 -0.0129152 0.113833 0.324728 1.06905 -1.0814 0.304531 [ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.410869
[ Info: iteration 2, average log likelihood -1.410855
[ Info: iteration 3, average log likelihood -1.410841
[ Info: iteration 4, average log likelihood -1.410828
[ Info: iteration 5, average log likelihood -1.410815
[ Info: iteration 6, average log likelihood -1.410801
[ Info: iteration 7, average log likelihood -1.410789
[ Info: iteration 8, average log likelihood -1.410776
[ Info: iteration 9, average log likelihood -1.410764
[ Info: iteration 10, average log likelihood -1.410752
┌ Info: EM with 100000 data points 10 iterations avll -1.410752
└ 59.0 data points per parameter
kind full, method kmeans
[ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 8.934372e+05
1 7.076575e+05 -1.857797e+05 | 32
2 6.906369e+05 -1.702054e+04 | 32
3 6.850506e+05 -5.586327e+03 | 32
4 6.824238e+05 -2.626767e+03 | 32
5 6.808218e+05 -1.602018e+03 | 32
6 6.797506e+05 -1.071212e+03 | 32
7 6.788977e+05 -8.529079e+02 | 32
8 6.782006e+05 -6.970840e+02 | 32
9 6.776427e+05 -5.579261e+02 | 32
10 6.771819e+05 -4.607936e+02 | 32
11 6.768123e+05 -3.695541e+02 | 32
12 6.765042e+05 -3.081581e+02 | 32
13 6.762436e+05 -2.605784e+02 | 32
14 6.760438e+05 -1.997895e+02 | 32
15 6.758707e+05 -1.730912e+02 | 32
16 6.757224e+05 -1.483742e+02 | 32
17 6.755740e+05 -1.483939e+02 | 32
18 6.754322e+05 -1.417710e+02 | 32
19 6.752952e+05 -1.370130e+02 | 32
20 6.751707e+05 -1.244922e+02 | 32
21 6.750537e+05 -1.170376e+02 | 32
22 6.749423e+05 -1.113305e+02 | 32
23 6.748432e+05 -9.914293e+01 | 32
24 6.747563e+05 -8.691177e+01 | 32
25 6.746727e+05 -8.359410e+01 | 32
26 6.745933e+05 -7.935121e+01 | 32
27 6.745119e+05 -8.137017e+01 | 32
28 6.744299e+05 -8.209260e+01 | 32
29 6.743459e+05 -8.396473e+01 | 32
30 6.742609e+05 -8.500216e+01 | 32
31 6.741757e+05 -8.517629e+01 | 32
32 6.740866e+05 -8.908127e+01 | 32
33 6.740059e+05 -8.077904e+01 | 32
34 6.739321e+05 -7.378912e+01 | 32
35 6.738557e+05 -7.639585e+01 | 32
36 6.737763e+05 -7.939497e+01 | 32
37 6.736874e+05 -8.885867e+01 | 32
38 6.735972e+05 -9.025032e+01 | 32
39 6.735115e+05 -8.563602e+01 | 32
40 6.734217e+05 -8.987480e+01 | 32
41 6.733362e+05 -8.541669e+01 | 32
42 6.732579e+05 -7.830275e+01 | 32
43 6.731830e+05 -7.492126e+01 | 32
44 6.731114e+05 -7.163212e+01 | 32
45 6.730477e+05 -6.367326e+01 | 32
46 6.729856e+05 -6.210934e+01 | 32
47 6.729212e+05 -6.441503e+01 | 32
48 6.728634e+05 -5.773408e+01 | 32
49 6.728136e+05 -4.980996e+01 | 32
50 6.727684e+05 -4.525456e+01 | 32
K-means terminated without convergence after 50 iterations (objv = 672768.3845149358)
┌ Info: K-means with 32000 data points using 50 iterations
└ 37.0 data points per parameter
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.422954
[ Info: iteration 2, average log likelihood -1.417835
[ Info: iteration 3, average log likelihood -1.416450
[ Info: iteration 4, average log likelihood -1.415439
[ Info: iteration 5, average log likelihood -1.414438
[ Info: iteration 6, average log likelihood -1.413559
[ Info: iteration 7, average log likelihood -1.412941
[ Info: iteration 8, average log likelihood -1.412563
[ Info: iteration 9, average log likelihood -1.412329
[ Info: iteration 10, average log likelihood -1.412168
[ Info: iteration 11, average log likelihood -1.412046
[ Info: iteration 12, average log likelihood -1.411946
[ Info: iteration 13, average log likelihood -1.411861
[ Info: iteration 14, average log likelihood -1.411787
[ Info: iteration 15, average log likelihood -1.411721
[ Info: iteration 16, average log likelihood -1.411663
[ Info: iteration 17, average log likelihood -1.411610
[ Info: iteration 18, average log likelihood -1.411562
[ Info: iteration 19, average log likelihood -1.411518
[ Info: iteration 20, average log likelihood -1.411478
[ Info: iteration 21, average log likelihood -1.411441
[ Info: iteration 22, average log likelihood -1.411407
[ Info: iteration 23, average log likelihood -1.411375
[ Info: iteration 24, average log likelihood -1.411345
[ Info: iteration 25, average log likelihood -1.411318
[ Info: iteration 26, average log likelihood -1.411291
[ Info: iteration 27, average log likelihood -1.411267
[ Info: iteration 28, average log likelihood -1.411243
[ Info: iteration 29, average log likelihood -1.411221
[ Info: iteration 30, average log likelihood -1.411199
[ Info: iteration 31, average log likelihood -1.411178
[ Info: iteration 32, average log likelihood -1.411158
[ Info: iteration 33, average log likelihood -1.411138
[ Info: iteration 34, average log likelihood -1.411119
[ Info: iteration 35, average log likelihood -1.411101
[ Info: iteration 36, average log likelihood -1.411082
[ Info: iteration 37, average log likelihood -1.411065
[ Info: iteration 38, average log likelihood -1.411047
[ Info: iteration 39, average log likelihood -1.411030
[ Info: iteration 40, average log likelihood -1.411013
[ Info: iteration 41, average log likelihood -1.410997
[ Info: iteration 42, average log likelihood -1.410981
[ Info: iteration 43, average log likelihood -1.410965
[ Info: iteration 44, average log likelihood -1.410950
[ Info: iteration 45, average log likelihood -1.410935
[ Info: iteration 46, average log likelihood -1.410920
[ Info: iteration 47, average log likelihood -1.410906
[ Info: iteration 48, average log likelihood -1.410893
[ Info: iteration 49, average log likelihood -1.410879
[ Info: iteration 50, average log likelihood -1.410866
┌ Info: EM with 100000 data points 50 iterations avll -1.410866
└ 59.0 data points per parameter
32×26 Array{Float64,2}:
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0.334078 -0.252569 0.0503901 0.723895 -0.0585464 -0.416083 -0.359282 0.609245 0.602099 -0.114818 0.319703 0.386735 -0.387522 0.458248 0.0735178 -0.0180704 0.28044 -0.110784 0.331745 -0.107566 -0.326405 -0.572368 0.119188 0.711862 -0.217892 0.232179
0.257097 -0.0333044 -0.501857 0.334447 -0.27387 0.44821 0.165516 0.340401 0.0659099 0.383151 -0.326357 -0.119775 -0.36539 -0.618645 0.149116 -0.0998119 -0.0750454 -0.394514 0.502086 0.582796 0.124724 0.124799 -0.495726 0.447387 -0.297969 -0.201768
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0.396803 -0.568088 -0.00660363 0.0547828 0.64744 -0.81574 -0.185096 0.162821 0.0806759 -0.0866874 0.116848 -0.732335 0.367449 0.333579 -0.232987 0.257343 0.0227074 0.206582 0.197699 0.780253 -0.41648 0.51838 -0.260673 -0.0311738 -0.574408 -0.414943
0.422633 0.707817 -0.188827 -0.461711 0.150425 -0.650251 -0.845556 0.136707 -0.429813 -0.160962 0.146495 0.58988 0.35028 0.128732 -0.18558 0.116719 -0.054992 0.30502 0.147441 -0.324797 -0.299427 0.206175 0.110513 -0.102787 0.225104 -0.0287601
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0.27208 -0.423498 -0.196882 -0.626646 0.518021 -0.0835401 -0.222446 -0.174382 0.382096 0.260605 -0.212716 0.655348 0.253368 0.374022 0.470163 0.218428 0.681547 -0.435094 0.0318081 -0.0174114 -0.37198 0.0946975 0.131004 0.495114 0.100441 -0.274426
0.0419876 0.233237 -0.314145 0.0869355 0.554843 0.0686471 0.191761 0.502279 0.025127 0.689273 0.0574321 -0.197462 -0.273926 0.0067172 0.362792 0.196559 0.602872 0.357655 0.146054 -0.164493 0.150938 0.0959651 -0.282524 -0.214317 -0.300299 -0.182545
0.00820706 0.0300666 0.0510119 -0.00113629 -0.0732952 -0.0311108 0.0119669 -0.0605635 0.0814398 -0.0937089 0.00919535 0.0881225 0.053767 0.0252015 -0.0724961 -0.0277235 -0.0270807 -0.0675365 0.0137625 0.0476473 -0.0275862 -0.06316 0.0215673 0.0464826 0.034743 0.0372903
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0.228523 0.478484 -0.337321 -0.243836 0.574281 -0.144668 0.201152 0.130796 -0.137285 0.0585899 0.10665 -0.137873 -0.254142 0.553796 0.0495272 0.11445 0.497069 0.274347 0.184322 -0.234632 0.0052801 0.213723 -0.0799384 -0.148803 0.0352874 0.0475052
0.491313 0.09602 0.892656 0.387524 -0.355945 -0.206189 0.227472 0.0450992 0.0242604 -0.0562403 0.363638 -0.282544 0.331022 0.192379 0.41474 -0.320401 -0.12299 -0.0832533 0.100285 -0.0886337 -0.00752938 0.0119617 0.451155 0.169458 -0.269065 0.119933
-0.384983 -0.39054 0.18786 0.511034 0.00500331 0.311471 1.20141 -0.38132 0.249786 -0.0658895 -0.220661 -0.278396 -0.397083 0.0172025 -0.0464185 -0.218766 0.397188 -0.0968606 -0.0985717 0.100731 -0.0534532 -0.438166 0.138672 0.234262 -0.19963 0.379945
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0.806071 -0.0524753 0.137985 -0.331713 -0.261758 -0.0558232 -0.493931 0.21338 0.321043 0.105971 -0.969503 -0.359367 0.0885286 0.0140346 0.701992 0.81336 -0.297991 0.12052 0.188988 0.333218 0.191219 -0.0162284 0.38335 0.187226 0.260039 -0.0160056
0.354438 -0.209011 0.149427 0.704509 0.454647 -0.0284102 0.728853 0.424202 0.327028 0.814624 0.417556 0.26613 0.0965146 0.276238 -0.00412379 -0.100054 0.121612 -0.504654 -0.0475547 0.275527 0.437766 0.0306934 0.802438 -0.421139 0.0611919 -0.0768758
-0.195624 -0.411644 0.0188767 -0.111551 -0.142145 0.69235 0.0129829 0.340858 0.00879218 0.524259 0.0553922 0.042094 -0.416576 0.390533 -0.504935 0.589325 0.201875 -0.0142269 -0.424834 -0.60856 0.160042 -0.718058 -0.235519 -0.815119 -0.0394219 0.238144
-0.00144641 -0.054576 -0.0043048 -0.285025 0.0669237 0.235242 0.403842 -0.0602374 -0.137116 0.0156563 -0.2001 -0.171309 0.0446659 -0.392397 0.177558 -0.00749387 -0.230188 0.574746 -0.0138515 0.139854 0.0352033 0.302417 -0.164406 -0.114014 0.238914 0.0688556
0.120807 -0.0794015 0.892426 -0.686703 0.219239 -0.205418 -0.33145 -0.756391 0.48489 -0.3171 0.236385 -0.336131 0.113644 -0.131832 0.29905 -0.260053 -0.608585 0.271644 0.011469 -0.788794 -0.64861 -0.0944365 0.166046 -0.183526 1.06034 0.0434442
0.368071 -0.693373 0.17206 -0.750377 -0.00367436 0.527977 0.143732 0.410243 -0.846913 0.139516 0.0957971 -0.350731 0.623962 -0.304504 -0.148369 -0.17579 -0.455812 -0.406212 -0.339927 -0.484037 -0.051551 0.476546 0.24425 0.507339 -0.0755182 0.229634
-0.295403 -0.166012 -0.481455 -0.185961 0.290489 -0.180997 0.051595 -0.527551 0.373294 0.00752357 -0.526842 -0.0650036 -0.505143 0.246584 -0.245786 0.0532703 0.0584421 -0.343163 0.604974 -0.0729462 -0.407981 -0.152501 -0.0965786 0.00439873 0.447005 -0.600313
-0.0910405 -0.553848 0.0472491 -0.0180938 -0.136494 0.159582 -0.313561 -0.644691 -0.0926053 -0.176307 0.663084 -0.0752782 0.0163297 -0.12831 -0.55431 -0.00549735 -0.465135 -0.633698 0.273379 0.0917397 0.357309 -0.243157 0.0904489 0.21934 -0.056063 -0.4706
0.0387234 0.120427 0.160889 -0.330758 -0.290896 -0.211635 -0.901078 0.269644 0.0712789 -0.00894491 0.332939 0.283194 0.392008 -0.0219568 0.166723 0.287181 0.278645 0.018604 -0.25566 -0.0672782 0.27283 0.288705 0.267101 -0.0720179 -0.373133 0.0481894
-0.162706 0.698167 0.0799768 -0.214041 -0.393777 0.577808 0.469289 -0.110151 0.0421668 -0.116373 0.0520705 0.543693 -0.259044 -0.131505 0.0771823 -0.572769 -0.0558014 -0.10013 -0.0828528 -0.392111 0.61087 -0.266571 0.484691 -0.216878 0.442742 0.777907
0.330382 0.721705 0.556596 -0.988548 -0.0468779 -0.0818173 0.252775 -0.327781 -0.434814 -0.404035 -0.0645718 -0.170342 -0.265065 0.419707 0.162483 -0.31543 0.531256 -0.206539 -0.00205755 0.362003 0.109583 0.325132 -0.271043 0.345776 -0.321912 -0.157306
-0.342767 0.429532 -0.0891022 0.41106 -0.0803252 -0.0462622 -0.328522 0.616739 -0.287513 0.250764 -0.101581 -0.119792 -0.585735 0.210232 -0.243727 0.111335 0.125827 -0.153375 0.0602257 -0.138495 -0.0781774 -0.010263 -0.0788611 -0.0529263 -0.355552 -0.270865
0.493244 0.254929 0.215722 -0.0569194 -0.0431264 0.147325 0.826232 0.491499 0.335307 -0.169039 -0.578855 -0.126617 0.23097 -0.220805 0.507128 -0.626401 0.205153 0.878621 -0.029247 0.30339 -0.489736 0.599573 -0.138377 0.351096 0.199657 0.450123
-0.0456156 0.637224 -0.0885662 0.458675 -0.170301 -0.174741 -0.555301 0.0129448 0.463088 -0.160851 0.126522 0.130063 -0.375052 0.36416 -0.10212 0.0858657 0.0589636 -0.0326376 0.0587566 0.553001 0.303962 -0.286574 -0.618419 -0.46624 -0.211201 -0.287794 [ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.410854
[ Info: iteration 2, average log likelihood -1.410842
[ Info: iteration 3, average log likelihood -1.410830
[ Info: iteration 4, average log likelihood -1.410819
[ Info: iteration 5, average log likelihood -1.410808
[ Info: iteration 6, average log likelihood -1.410797
[ Info: iteration 7, average log likelihood -1.410786
[ Info: iteration 8, average log likelihood -1.410776
[ Info: iteration 9, average log likelihood -1.410766
[ Info: iteration 10, average log likelihood -1.410757
┌ Info: EM with 100000 data points 10 iterations avll -1.410757
└ 59.0 data points per parameter
[ Info: Initializing GMM, 2 Gaussians diag covariance 2 dimensions using 900 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 1.678561e+05
1 2.230230e+04 -1.455538e+05 | 2
2 7.823675e+03 -1.447862e+04 | 0
3 7.823675e+03 0.000000e+00 | 0
K-means converged with 3 iterations (objv = 7823.67549422947)
┌ Info: K-means with 900 data points using 3 iterations
└ 150.0 data points per parameter
[ Info: Running 10 iterations EM on full cov GMM with 2 Gaussians in 2 dimensions
[ Info: iteration 1, average log likelihood -2.043155
[ Info: iteration 2, average log likelihood -2.043154
[ Info: iteration 3, average log likelihood -2.043154
[ Info: iteration 4, average log likelihood -2.043154
[ Info: iteration 5, average log likelihood -2.043154
[ Info: iteration 6, average log likelihood -2.043154
[ Info: iteration 7, average log likelihood -2.043154
[ Info: iteration 8, average log likelihood -2.043154
[ Info: iteration 9, average log likelihood -2.043154
[ Info: iteration 10, average log likelihood -2.043154
┌ Info: EM with 900 data points 10 iterations avll -2.043154
└ 81.8 data points per parameter
Testing GaussianMixtures tests passed
Results with Julia v1.3.1-pre-7704df0a5a
Testing was successful .
Last evaluation was ago and took 10 minutes, 2 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed GaussianMixtures ─── v0.3.0
Installed LegacyStrings ────── v0.4.1
Installed Compat ───────────── v2.2.0
Installed CMake ────────────── v1.1.2
Installed HDF5 ─────────────── v0.12.5
Installed QuadGK ───────────── v2.1.1
Installed DataStructures ───── v0.17.6
Installed SpecialFunctions ─── v0.8.0
Installed Blosc ────────────── v0.5.1
Installed BinDeps ──────────── v0.8.10
Installed URIParser ────────── v0.4.0
Installed StaticArrays ─────── v0.12.1
Installed CMakeWrapper ─────── v0.2.3
Installed FileIO ───────────── v1.1.0
Installed Arpack ───────────── v0.3.1
Installed StatsBase ────────── v0.32.0
Installed JLD ──────────────── v0.9.1
Installed StatsFuns ────────── v0.9.0
Installed Parameters ───────── v0.12.0
Installed Missings ─────────── v0.4.3
Installed BinaryProvider ───── v0.5.8
Installed ScikitLearnBase ──── v0.5.0
Installed Distances ────────── v0.8.2
Installed Rmath ────────────── v0.5.1
Installed DataAPI ──────────── v1.1.0
Installed Distributions ────── v0.21.9
Installed SortingAlgorithms ── v0.3.1
Installed OrderedCollections ─ v1.1.0
Installed PDMats ───────────── v0.9.10
Installed NearestNeighbors ─── v0.4.4
Installed Clustering ───────── v0.13.3
Updating `~/.julia/environments/v1.3/Project.toml`
[cc18c42c] + GaussianMixtures v0.3.0
Updating `~/.julia/environments/v1.3/Manifest.toml`
[7d9fca2a] + Arpack v0.3.1
[9e28174c] + BinDeps v0.8.10
[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 v0.17.6
[b4f34e82] + Distances v0.8.2
[31c24e10] + Distributions v0.21.9
[5789e2e9] + FileIO v1.1.0
[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
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[d96e819e] + Parameters v0.12.0
[1fd47b50] + QuadGK v2.1.1
[79098fc4] + Rmath v0.5.1
[6e75b9c4] + ScikitLearnBase v0.5.0
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.8.0
[90137ffa] + StaticArrays v0.12.1
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.9.0
[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`
Building Blosc ───────────→ `~/.julia/packages/Blosc/lzFr0/deps/build.log`
Building HDF5 ────────────→ `~/.julia/packages/HDF5/Zh9on/deps/build.log`
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Building Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Building Rmath ───────────→ `~/.julia/packages/Rmath/4wt82/deps/build.log`
Testing GaussianMixtures
Status `/tmp/jl_4FwmOW/Manifest.toml`
[7d9fca2a] Arpack v0.3.1
[9e28174c] BinDeps v0.8.10
[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 v0.17.6
[b4f34e82] Distances v0.8.2
[31c24e10] Distributions v0.21.9
[5789e2e9] FileIO v1.1.0
[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
[bac558e1] OrderedCollections v1.1.0
[90014a1f] PDMats v0.9.10
[d96e819e] Parameters v0.12.0
[1fd47b50] QuadGK v2.1.1
[79098fc4] Rmath v0.5.1
[6e75b9c4] ScikitLearnBase v0.5.0
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.8.0
[90137ffa] StaticArrays v0.12.1
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.9.0
[30578b45] URIParser v0.4.0
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[9abbd945] Profile [`@stdlib/Profile`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
[ Info: Testing Data
(100000, -5.970719954888905e6, [99994.99999999997, 5.0000000000339755], [73.82271095773883 -305.70576188444784 -75.27582508717067; -10.194109054918508 14.270863806457285 -9.464526679160944], Array{Float64,2}[[99768.48326272935 909.617767462877 -236.20319685724775; 909.617767462877 99396.46789822711 -91.55082067146917; -236.20319685724775 -91.55082067146917 100553.89942937375], [25.904192640286105 -26.397757452243066 17.153498475987465; -26.397757452243066 42.30014826785995 -27.995817504143634; 17.153498475987465 -27.995817504143634 19.17860852065914]])
┌ Warning: rmprocs: process 1 not removed
└ @ Distributed /workspace/srcdir/julia/usr/share/julia/stdlib/v1.3/Distributed/src/cluster.jl:1015
[ Info: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 1.471549e+03
1 9.764717e+02 -4.950776e+02 | 8
2 9.166793e+02 -5.979231e+01 | 2
3 9.148495e+02 -1.829846e+00 | 0
4 9.148495e+02 0.000000e+00 | 0
K-means converged with 4 iterations (objv = 914.8494978374374)
┌ Info: K-means with 272 data points using 4 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 -2.076381
└ 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 at broadcast.jl:630 [inlined]
└ @ Core ./broadcast.jl:630
┌ 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 at broadcast.jl:630 [inlined]
└ @ Core ./broadcast.jl:630
┌ Warning: `lgamma(x::Real)` is deprecated, use `(logabsgamma(x))[1]` instead.
│ caller = _broadcast_getindex_evalf at broadcast.jl:630 [inlined]
└ @ Core ./broadcast.jl:630
[ Info: iteration 1, lowerbound -3.775676
[ Info: iteration 2, lowerbound -3.604697
[ Info: iteration 3, lowerbound -3.416887
[ Info: iteration 4, lowerbound -3.214269
[ Info: iteration 5, lowerbound -3.035162
[ Info: dropping number of Gaussions to 7
[ Info: iteration 6, lowerbound -2.908865
[ Info: iteration 7, lowerbound -2.840379
[ Info: dropping number of Gaussions to 5
[ Info: iteration 8, lowerbound -2.809155
[ Info: dropping number of Gaussions to 4
[ Info: iteration 9, lowerbound -2.790394
[ Info: dropping number of Gaussions to 3
[ Info: iteration 10, lowerbound -2.780716
[ Info: iteration 11, lowerbound -2.773586
[ Info: iteration 12, lowerbound -2.768563
[ Info: iteration 13, lowerbound -2.761301
[ Info: iteration 14, lowerbound -2.750919
[ Info: iteration 15, lowerbound -2.736395
[ Info: iteration 16, lowerbound -2.716696
[ Info: iteration 17, lowerbound -2.691011
[ Info: iteration 18, lowerbound -2.659049
[ Info: iteration 19, lowerbound -2.621316
[ Info: iteration 20, lowerbound -2.579256
[ Info: iteration 21, lowerbound -2.535166
[ Info: iteration 22, lowerbound -2.491713
[ Info: iteration 23, lowerbound -2.451058
[ Info: iteration 24, lowerbound -2.414083
[ Info: iteration 25, lowerbound -2.380453
[ Info: iteration 26, lowerbound -2.349961
[ Info: iteration 27, lowerbound -2.324738
[ Info: iteration 28, lowerbound -2.309763
[ Info: iteration 29, lowerbound -2.308474
[ Info: dropping number of Gaussions to 2
[ Info: iteration 30, lowerbound -2.302914
[ Info: iteration 31, lowerbound -2.299258
[ Info: iteration 32, lowerbound -2.299255
[ Info: iteration 33, lowerbound -2.299254
[ Info: iteration 34, lowerbound -2.299254
[ 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 Dec 3 04:04:56 2019: Initializing GMM, 8 Gaussians diag covariance 2 dimensions using 272 data points
, Tue Dec 3 04:05:05 2019: K-means with 272 data points using 4 iterations
11.3 data points per parameter
, Tue Dec 3 04:05:07 2019: EM with 272 data points 0 iterations avll -2.076381
5.8 data points per parameter
, Tue Dec 3 04:05:08 2019: GMM converted to Variational GMM
, Tue Dec 3 04:05:17 2019: iteration 1, lowerbound -3.775676
, Tue Dec 3 04:05:17 2019: iteration 2, lowerbound -3.604697
, Tue Dec 3 04:05:17 2019: iteration 3, lowerbound -3.416887
, Tue Dec 3 04:05:17 2019: iteration 4, lowerbound -3.214269
, Tue Dec 3 04:05:17 2019: iteration 5, lowerbound -3.035162
, Tue Dec 3 04:05:18 2019: dropping number of Gaussions to 7
, Tue Dec 3 04:05:18 2019: iteration 6, lowerbound -2.908865
, Tue Dec 3 04:05:18 2019: iteration 7, lowerbound -2.840379
, Tue Dec 3 04:05:18 2019: dropping number of Gaussions to 5
, Tue Dec 3 04:05:18 2019: iteration 8, lowerbound -2.809155
, Tue Dec 3 04:05:18 2019: dropping number of Gaussions to 4
, Tue Dec 3 04:05:18 2019: iteration 9, lowerbound -2.790394
, Tue Dec 3 04:05:18 2019: dropping number of Gaussions to 3
, Tue Dec 3 04:05:18 2019: iteration 10, lowerbound -2.780716
, Tue Dec 3 04:05:18 2019: iteration 11, lowerbound -2.773586
, Tue Dec 3 04:05:18 2019: iteration 12, lowerbound -2.768563
, Tue Dec 3 04:05:18 2019: iteration 13, lowerbound -2.761301
, Tue Dec 3 04:05:18 2019: iteration 14, lowerbound -2.750919
, Tue Dec 3 04:05:18 2019: iteration 15, lowerbound -2.736395
, Tue Dec 3 04:05:18 2019: iteration 16, lowerbound -2.716696
, Tue Dec 3 04:05:18 2019: iteration 17, lowerbound -2.691011
, Tue Dec 3 04:05:18 2019: iteration 18, lowerbound -2.659049
, Tue Dec 3 04:05:18 2019: iteration 19, lowerbound -2.621316
, Tue Dec 3 04:05:18 2019: iteration 20, lowerbound -2.579256
, Tue Dec 3 04:05:18 2019: iteration 21, lowerbound -2.535166
, Tue Dec 3 04:05:18 2019: iteration 22, lowerbound -2.491713
, Tue Dec 3 04:05:18 2019: iteration 23, lowerbound -2.451058
, Tue Dec 3 04:05:18 2019: iteration 24, lowerbound -2.414083
, Tue Dec 3 04:05:18 2019: iteration 25, lowerbound -2.380453
, Tue Dec 3 04:05:18 2019: iteration 26, lowerbound -2.349961
, Tue Dec 3 04:05:18 2019: iteration 27, lowerbound -2.324738
, Tue Dec 3 04:05:18 2019: iteration 28, lowerbound -2.309763
, Tue Dec 3 04:05:18 2019: iteration 29, lowerbound -2.308474
, Tue Dec 3 04:05:18 2019: dropping number of Gaussions to 2
, Tue Dec 3 04:05:18 2019: iteration 30, lowerbound -2.302914
, Tue Dec 3 04:05:18 2019: iteration 31, lowerbound -2.299258
, Tue Dec 3 04:05:18 2019: iteration 32, lowerbound -2.299255
, Tue Dec 3 04:05:18 2019: iteration 33, lowerbound -2.299254
, Tue Dec 3 04:05:18 2019: iteration 34, lowerbound -2.299254
, Tue Dec 3 04:05:18 2019: iteration 35, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 36, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 37, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 38, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 39, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 40, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 41, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 42, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 43, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 44, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 45, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 46, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 47, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 48, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 49, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: iteration 50, lowerbound -2.299253
, Tue Dec 3 04:05:18 2019: 50 variational Bayes EM-like iterations using 272 data points, final lowerbound -2.299253
]
α = [178.04509222738287, 95.95490777261709]
β = [178.04509222738287, 95.95490777261709]
m = [4.250300733258808 79.28686694419856; 2.0002292577638707 53.85198717240141]
ν = [180.04509222738287, 97.95490777261709]
W = LinearAlgebra.UpperTriangular{Float64,Array{Float64,2}}[[0.1840415554733207 -0.007644049042473372; 0.0 0.008581705166128297], [0.3758763612139578 -0.008953123827573172; 0.0 0.012748664777467464]]
Kind: diag, size256
nx: 100000 sum(zeroth order stats): 99999.99999999994
avll from stats: -0.9844978570835627
avll from llpg: -0.9844978570835633
avll direct: -0.9844978570835632
sum posterior: 100000.0
Kind: full, size16
nx: 100000 sum(zeroth order stats): 100000.00000000001
avll from stats: -0.9919551691976011
avll from llpg: -0.9919551691976009
avll direct: -0.9919551691976009
sum posterior: 100000.0
32×26 Array{Float64,2}:
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0.136706 0.099552 -0.0958756 -0.17552 -0.0927527 -0.0418364 0.0124186 -0.00758594 0.25915 -0.0467911 -0.107454 -0.238943 0.00951154 -0.00606447 0.0185125 0.047366 0.101493 -0.120967 0.122011 -0.0335992 -0.0955589 0.0533677 -0.00947546 0.145488 -0.202309 -0.0888935
0.0260712 0.148846 -0.0688362 0.0277725 0.204451 0.163552 0.130827 0.0880678 0.135398 -0.0376501 -0.135495 -0.199971 0.155273 0.0404535 0.104477 0.086286 -0.0473718 0.0246413 0.0174585 0.141186 0.0238227 -0.0366976 -0.144304 0.0245568 -0.00134947 -0.0578128
0.0390766 0.177545 0.00691069 0.0609433 -0.00143553 0.114084 -0.118306 0.0514687 0.00332628 -0.176566 -0.0745408 0.0892653 0.207189 0.106873 0.00664001 -0.174612 -0.0839131 0.0625777 0.00320227 0.0476375 0.0180044 0.0253405 -0.172598 0.107765 0.0612276 -0.051078
0.0882217 -0.0586789 0.094771 -0.0597275 0.0701274 -0.00095389 -0.142243 0.0352959 0.109665 -0.0658925 0.145287 0.167986 0.00151717 0.201214 0.0263995 0.0824008 -0.0190125 -0.0259461 -0.046218 0.0766248 0.0363755 -0.238262 -0.00379776 0.0245633 0.0830537 -0.0670119
0.29645 -0.0726284 -0.0446135 0.046203 0.0539788 -0.0628599 -0.16386 -0.132033 -0.00119498 0.0951066 -0.102786 -0.0314005 -0.0578883 -0.0486272 0.00765634 -0.0440233 0.0339534 0.0321591 -0.0371919 0.0495886 -0.100357 0.0341203 0.0243681 -0.0680407 0.134315 -0.0173043
0.0101451 0.164649 -0.0398943 -0.00312673 -0.177279 -0.0627119 -0.00419405 0.0347572 -0.0568637 0.181995 -0.0985063 -0.109149 0.032635 0.0286685 0.100798 0.001616 0.110164 -0.0867253 -0.0488685 -0.00414336 -0.0402632 0.180006 -0.0299768 0.0116772 0.0538116 0.084745
-0.00969742 -0.154143 -0.0536923 0.100437 -0.042879 -0.0732417 0.0987409 0.0514522 -0.153077 0.104255 0.0515144 -0.174051 0.17172 -0.0205082 0.103056 0.124292 0.1006 -0.0637551 0.123607 -0.11237 -0.0626607 0.10095 -0.0254954 -0.156713 -0.128191 0.188311
-0.234241 -0.0303729 -0.11807 0.114027 0.10051 -0.0106531 0.0772728 0.110375 0.0366831 -0.0311529 0.0777938 -0.0670209 0.17209 -0.0551384 0.0725076 0.0722786 -0.0705286 -0.113823 -0.0985188 0.101736 0.0902259 -0.246227 -0.0808345 0.101568 0.0849896 -0.168123
0.0439658 0.00632998 -0.167129 -0.0727304 0.00959414 0.0748401 0.0660813 -0.0118195 -0.139012 -0.157734 -0.192161 -0.0374417 0.0958423 0.0145454 -0.05279 -0.0105496 0.171546 0.0146169 -0.0726966 0.0260507 -0.0928631 -0.0443981 0.0115945 -0.124303 -0.137955 -0.000627315
0.121815 -0.1806 -0.0256023 -0.191861 -0.314095 -0.0440446 0.0244324 0.00801728 0.0098476 -0.0266724 -0.0944872 0.0492038 -0.0684004 0.111881 0.066162 0.0654148 0.01767 0.0205153 -0.0134129 0.0164789 -0.186055 0.0247111 -0.0778523 -0.259268 -0.00274108 0.195974
0.277199 -0.114951 0.0243181 0.0698104 0.0871288 0.0763358 -0.0544468 0.194922 0.028894 -0.0263337 -0.0456215 -0.153068 0.120419 0.0494176 0.0977727 -0.00615099 0.0152128 0.172743 0.0697227 0.0451108 0.0960361 0.0345536 0.047346 0.0281117 -0.00863617 0.196761
0.0255344 0.0866191 0.0639781 0.11314 -0.0712622 -0.043278 -0.191418 -0.0857547 -0.0565558 -0.0375944 -0.00648278 0.0463083 -0.0586902 -0.142965 -0.0938569 -0.131738 0.106256 0.00694993 0.0436041 0.0229313 -0.0296121 0.0126414 -0.0588736 -0.252874 0.299177 0.0347029
-0.121361 -0.0395638 -0.0192214 0.0850204 -0.149764 0.0523481 0.0357082 0.10621 0.0257468 0.0934016 0.0092514 -0.1699 -0.032999 -0.0510472 -0.0732543 -0.0837233 0.116746 -0.0704393 -0.00681486 -0.0509841 0.0414033 0.0407719 -0.0119199 0.0254369 0.0362223 0.0837653
-0.163448 0.0245663 -0.123994 -0.0844763 0.0146593 0.0519263 -0.122198 -0.0477849 0.0134166 0.172378 0.208423 -0.194274 0.0987968 -0.0213956 -0.0596304 -0.145111 -0.00468066 -0.0957186 0.0158702 0.121074 0.0926532 0.0230698 -0.0347769 -0.0006848 0.108911 0.194039
-0.16031 0.0122995 -0.0176206 0.0482598 -0.0747356 -0.210418 0.118941 0.0863376 0.272807 0.102867 -0.047248 0.0407097 0.0369108 -0.016381 0.0157019 -0.0575999 -0.0241631 0.0177212 0.0976913 -0.166516 0.0900719 0.00932491 -0.0106109 -0.0640423 0.0978533 0.131167
0.245321 0.0998751 -0.0680446 0.0381963 0.00633168 -0.28785 -0.0148128 -0.0665932 -0.0166107 0.0782369 0.0862163 -0.0167719 0.0503212 -0.229174 -0.0503592 -0.0133253 0.0998986 -0.0137927 0.0918432 -0.126407 0.105432 -0.092363 0.103592 0.0419802 -0.119566 -0.0283992 kind diag, method split
┌ Info: 0: avll =
└ tll[1] = -1.4282238221885204
[ Info: Running 50 iterations EM on diag cov GMM with 2 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.428307
[ Info: iteration 2, average log likelihood -1.428228
[ Info: iteration 3, average log likelihood -1.427595
[ Info: iteration 4, average log likelihood -1.420081
[ Info: iteration 5, average log likelihood -1.401128
[ Info: iteration 6, average log likelihood -1.393019
[ Info: iteration 7, average log likelihood -1.391440
[ Info: iteration 8, average log likelihood -1.390459
[ Info: iteration 9, average log likelihood -1.389784
[ Info: iteration 10, average log likelihood -1.389404
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[ Info: iteration 50, average log likelihood -1.388886
┌ Info: EM with 100000 data points 50 iterations avll -1.388886
└ 952.4 data points per parameter
┌ Info: 1
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.428307250421799
│ -1.428227900281631
│ ⋮
└ -1.3888859022471014
[ Info: Running 50 iterations EM on diag cov GMM with 4 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.389011
[ Info: iteration 2, average log likelihood -1.388913
[ Info: iteration 3, average log likelihood -1.388714
[ Info: iteration 4, average log likelihood -1.386834
[ Info: iteration 5, average log likelihood -1.376960
[ Info: iteration 6, average log likelihood -1.362444
[ Info: iteration 7, average log likelihood -1.355247
[ Info: iteration 8, average log likelihood -1.351007
[ Info: iteration 9, average log likelihood -1.348022
[ Info: iteration 10, average log likelihood -1.346361
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[ Info: iteration 13, average log likelihood -1.344838
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[ Info: iteration 15, average log likelihood -1.344578
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[ Info: iteration 49, average log likelihood -1.344281
[ Info: iteration 50, average log likelihood -1.344281
┌ Info: EM with 100000 data points 50 iterations avll -1.344281
└ 473.9 data points per parameter
┌ Info: 2
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.3890114154843485
│ -1.3889128289743886
│ ⋮
└ -1.3442811999159112
[ Info: Running 50 iterations EM on diag cov GMM with 8 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.344447
[ Info: iteration 2, average log likelihood -1.344262
[ Info: iteration 3, average log likelihood -1.343606
[ Info: iteration 4, average log likelihood -1.337651
[ Info: iteration 5, average log likelihood -1.321277
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[ Info: iteration 9, average log likelihood -1.301237
[ Info: iteration 10, average log likelihood -1.299604
[ Info: iteration 11, average log likelihood -1.298222
[ Info: iteration 12, average log likelihood -1.297000
[ Info: iteration 13, average log likelihood -1.295909
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[ Info: iteration 16, average log likelihood -1.293543
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[ Info: iteration 49, average log likelihood -1.288910
[ Info: iteration 50, average log likelihood -1.288908
┌ Info: EM with 100000 data points 50 iterations avll -1.288908
└ 236.4 data points per parameter
┌ Info: 3
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.3444470609855683
│ -1.3442620649624728
│ ⋮
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[ Info: Running 50 iterations EM on diag cov GMM with 16 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.289101
[ Info: iteration 2, average log likelihood -1.288866
[ Info: iteration 3, average log likelihood -1.287825
[ Info: iteration 4, average log likelihood -1.277055
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -1.244432
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[ Info: iteration 6, average log likelihood -1.225582
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.215642
[ Info: iteration 8, average log likelihood -1.221535
[ Info: iteration 9, average log likelihood -1.208152
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.191178
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 11, average log likelihood -1.205001
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[ Info: iteration 12, average log likelihood -1.211748
[ Info: iteration 13, average log likelihood -1.213887
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[ Info: iteration 14, average log likelihood -1.201262
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 15, average log likelihood -1.203337
[ Info: iteration 16, average log likelihood -1.205237
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 17, average log likelihood -1.190254
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[ Info: iteration 18, average log likelihood -1.196012
[ Info: iteration 19, average log likelihood -1.202839
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 20, average log likelihood -1.190991
[ Info: iteration 21, average log likelihood -1.188769
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[ Info: iteration 22, average log likelihood -1.176581
[ Info: iteration 23, average log likelihood -1.203476
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 25, average log likelihood -1.184246
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[ Info: iteration 26, average log likelihood -1.183296
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[ Info: iteration 27, average log likelihood -1.189101
[ Info: iteration 28, average log likelihood -1.202245
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[ Info: iteration 30, average log likelihood -1.178832
[ Info: iteration 31, average log likelihood -1.195762
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[ Info: iteration 32, average log likelihood -1.187964
[ Info: iteration 33, average log likelihood -1.196182
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[ Info: iteration 34, average log likelihood -1.187463
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 35, average log likelihood -1.191225
[ Info: iteration 36, average log likelihood -1.194636
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 37, average log likelihood -1.181946
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[ Info: iteration 38, average log likelihood -1.190863
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[ Info: iteration 40, average log likelihood -1.190165
[ Info: iteration 41, average log likelihood -1.188619
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[ Info: iteration 42, average log likelihood -1.176657
[ Info: iteration 43, average log likelihood -1.203220
[ Info: iteration 44, average log likelihood -1.198726
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 45, average log likelihood -1.184175
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 46, average log likelihood -1.183346
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 47, average log likelihood -1.188948
[ Info: iteration 48, average log likelihood -1.202138
[ Info: iteration 49, average log likelihood -1.192736
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 50, average log likelihood -1.178874
┌ Info: EM with 100000 data points 50 iterations avll -1.178874
└ 118.1 data points per parameter
┌ Info: 4
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.2891006464443098
│ -1.288866068883419
│ ⋮
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[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.195967
┌ Warning: Variances had to be floored
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 2, average log likelihood -1.187842
[ Info: iteration 3, average log likelihood -1.187887
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[ Info: iteration 4, average log likelihood -1.165609
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -1.142508
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[ Info: iteration 6, average log likelihood -1.099503
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[ Info: iteration 7, average log likelihood -1.104767
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[ Info: iteration 8, average log likelihood -1.109059
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[ Info: iteration 9, average log likelihood -1.103648
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[ Info: iteration 10, average log likelihood -1.112288
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 11, average log likelihood -1.091299
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 12, average log likelihood -1.098236
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 13, average log likelihood -1.117630
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 14, average log likelihood -1.074097
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[ Info: iteration 15, average log likelihood -1.095216
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[ Info: iteration 16, average log likelihood -1.110390
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[ Info: iteration 17, average log likelihood -1.087988
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[ Info: iteration 18, average log likelihood -1.092493
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[ Info: iteration 19, average log likelihood -1.109142
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[ Info: iteration 21, average log likelihood -1.090343
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[ Info: iteration 24, average log likelihood -1.115103
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[ Info: iteration 25, average log likelihood -1.096348
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[ Info: iteration 26, average log likelihood -1.079592
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[ Info: iteration 27, average log likelihood -1.112078
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[ Info: iteration 28, average log likelihood -1.104330
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[ Info: iteration 29, average log likelihood -1.088487
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[ Info: iteration 30, average log likelihood -1.094049
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[ Info: iteration 32, average log likelihood -1.106043
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[ Info: iteration 33, average log likelihood -1.112623
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[ Info: iteration 34, average log likelihood -1.085733
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[ Info: iteration 35, average log likelihood -1.078957
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[ Info: iteration 36, average log likelihood -1.113130
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[ Info: iteration 37, average log likelihood -1.095936
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[ Info: iteration 38, average log likelihood -1.084813
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[ Info: iteration 39, average log likelihood -1.100915
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[ Info: iteration 40, average log likelihood -1.103600
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[ Info: iteration 41, average log likelihood -1.093589
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[ Info: iteration 42, average log likelihood -1.104405
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[ Info: iteration 43, average log likelihood -1.090128
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[ Info: iteration 45, average log likelihood -1.111173
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│ 3-element Array{Int64,1}:
│ 5
│ 8
│ 13
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 48, average log likelihood -1.120874
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 6
│ 14
│ 26
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 49, average log likelihood -1.112140
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 4
│ 5
│ 7
│ 8
│ 27
│ 28
│ 29
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 50, average log likelihood -1.090482
┌ Info: EM with 100000 data points 50 iterations avll -1.090482
└ 59.0 data points per parameter
┌ Info: 5
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.1959674460267538
│ -1.1878423780942702
│ ⋮
└ -1.090482138891555
┌ Info: Total log likelihood:
│ tll =
│ 251-element Array{Float64,1}:
│ -1.4282238221885204
│ -1.428307250421799
│ -1.428227900281631
│ -1.4275946850168753
│ ⋮
│ -1.120873692257318
│ -1.1121404894677431
└ -1.090482138891555
32×26 Array{Float64,2}:
0.0935887 0.0096613 0.0471572 0.0758701 0.17098 0.167956 0.0900172 -0.0925154 0.0437667 0.000194932 0.234457 0.0499366 -0.125867 0.0438763 -0.0648049 0.0647418 0.01461 0.121272 -0.090428 -0.0168502 -0.0392905 0.19719 -0.14648 -0.148253 0.0604917 -0.0575704
0.0112427 0.0687238 0.0662274 0.114691 -0.0735755 -0.0527795 -0.230632 -0.061156 -0.0486781 -0.0595635 -0.00723772 0.0392023 -0.0556051 -0.145193 -0.0977665 -0.12892 0.133407 -0.00395249 0.0334018 0.0243476 -0.0273458 0.00278187 -0.00754118 -0.250214 0.299697 0.0139961
0.255135 0.0976892 -0.0240604 0.0385197 0.00599376 -0.35474 -0.030868 -0.0694439 -0.0198738 0.118937 0.142665 -0.0112281 0.0548539 -0.235307 -0.0427943 0.0145011 0.078914 -0.00775336 0.0933589 -0.171833 0.096839 -0.0926029 0.111244 0.0488209 -0.107415 -0.0211551
0.0431529 -0.141205 -0.15377 0.134578 0.042474 0.0927835 0.0445471 -0.134541 -0.107612 0.0874148 0.0247225 -0.0960996 0.0196917 -0.0211564 0.0089157 -0.118895 -0.00574533 -0.00761152 0.098366 -0.0552501 0.117266 -0.100831 0.129863 0.0143881 -0.0687505 -0.0433055
0.0313107 0.0437098 -0.130416 -0.0754274 0.160136 0.122921 0.111096 0.169401 -0.149647 -0.320433 -0.256016 -0.0510409 0.136172 0.00320246 0.00981362 -0.050867 0.041645 0.0152195 0.0194197 -0.0142238 -0.0943205 -0.0981818 -0.374569 -0.132875 -0.148454 -0.00184858
0.0522029 -0.0485169 -0.260032 -0.107807 -0.141832 0.0403298 0.0562458 -0.333712 -0.134554 -0.105293 -0.0952571 -0.0235933 0.0758987 0.0211991 -0.105876 0.106186 0.36145 0.0160323 -0.200699 0.0620118 -0.0928702 -0.0288021 0.54507 -0.112902 -0.120887 0.004085
0.619457 0.108517 -0.147206 -0.0755477 -0.0987385 -1.19445 -0.0557293 -0.0632069 0.00191474 0.0818713 -0.0498412 -0.0644182 0.172122 0.185833 0.0342633 0.357493 -0.284548 1.08797 0.0823971 -0.0297545 -0.325313 0.160431 0.0478295 -0.121161 -0.00654302 -0.00473364
0.0508296 -0.164435 -0.0719347 0.0202097 -0.0672529 0.123273 -0.0521934 -0.0112784 0.0123542 -0.00106655 -0.0694328 -0.0659941 0.0336102 0.182488 0.0330748 -0.282191 -0.0798456 -0.157989 0.0743738 -0.0144641 0.0599996 0.00107988 0.0358303 -0.100327 0.00889458 -0.0370926
-0.0620006 -0.0350821 -0.0169906 0.127202 0.0841684 0.0484862 0.0210366 -0.0578798 0.00601483 -0.0353778 0.166058 0.000299065 -0.0139711 0.076273 -0.0457769 0.0265198 0.000552361 0.105661 -0.034187 -0.011706 -0.074187 0.152442 0.0156945 -0.0506663 0.0489383 0.00371815
-0.162287 -0.173859 -0.13534 -0.0304463 0.0621659 -0.222596 0.0684721 0.131585 -0.0919789 -0.0016287 0.181805 -0.0653511 -0.0364316 -0.0580835 -0.0032914 -0.0752016 -0.101694 0.00536596 0.0967731 0.0444459 -0.0902317 -0.181861 0.145002 -0.0259581 0.247637 0.0620727
0.150055 -0.0818245 0.0530786 0.163752 -0.0550196 0.0660223 -0.00397245 -0.0415942 -0.0728204 -0.0905936 0.0346586 -0.180665 0.0165275 0.0457221 -0.127561 0.143092 -0.210191 0.0230087 -0.0619954 0.160922 -0.0642502 -0.0342097 0.150719 0.0853127 0.0376349 0.148917
0.0420092 -0.0114307 0.0934167 -0.0568543 -0.0106561 0.0613528 -0.0118814 -0.00223235 0.0216281 -0.0928068 -0.00310339 -0.0386965 0.0452518 0.044955 -0.00545212 0.0638944 -0.0523392 -0.0345205 -0.10783 0.127198 -0.0213529 0.0859088 0.0374763 -0.124432 -0.0532919 0.0672285
-0.0376943 -0.184313 0.047561 -0.181657 -0.0351501 0.12734 -0.1244 -0.0726728 0.00168491 0.0766803 0.0517789 0.0567417 -0.0184513 -0.146787 0.0978384 -0.0894827 0.0258533 0.0233131 0.00139924 0.280118 0.112632 0.0111189 0.0988288 0.142906 -0.0138908 0.0492659
-0.152645 0.0658274 0.0229224 0.00267343 0.000547934 -0.127776 0.0243676 -0.0904017 -0.0828487 0.0429811 0.0717892 0.0801113 0.0185621 -0.0286459 -0.001589 -0.00348914 -0.0519841 -0.224325 0.00945656 0.0388786 0.00762961 0.0338639 -0.0455992 -0.059178 0.0875999 -0.119856
0.0140412 -0.012208 0.0934781 -0.0501678 -0.0693747 -0.0375133 0.10893 0.0355651 -0.127463 0.0247058 -0.220838 -0.0389943 0.00147571 -0.0273838 -0.0295497 0.110119 0.0676454 0.262956 -0.0343392 0.0123552 -0.182845 -0.0738401 0.107603 0.0828579 -0.102329 -0.0193924
-0.138433 0.000623485 0.0437968 -0.025818 -0.00658039 0.0762761 0.0208853 0.0838729 0.0231965 0.0511195 -0.00172873 -0.0441313 0.0283544 0.0151502 -0.104687 -0.0205073 -0.188247 -0.00373885 -0.100022 -0.108474 0.0351514 0.0750257 -0.0125444 -0.0680491 -0.0680431 0.147171
0.0359701 -0.142704 -0.044028 0.00666516 0.029989 -0.127743 0.0371221 -0.23204 -0.069714 -0.044047 0.118861 -0.0651277 -0.0525157 -0.057992 0.155339 -0.0250588 -0.0489099 0.103804 -0.227091 -0.0544436 -0.119484 -0.093765 0.0981462 0.0448523 0.108914 -0.0558237
0.104015 -0.210094 -0.0162479 -0.0443639 -0.0546863 -0.0497789 0.0214768 -0.00724975 -0.0493159 -0.00242789 -0.0557161 -0.0531286 0.0372799 0.049684 0.0635949 0.0293245 -0.0396607 -0.00928059 0.0194783 -0.00734436 -0.122515 -0.019314 -0.0568362 -0.0867919 -0.0218652 0.125177
0.0451758 0.191292 -0.155964 0.088036 0.203624 0.164451 0.133432 -0.484326 0.069536 -0.0384025 -0.0974899 -0.240258 0.196342 0.0469348 0.161043 0.0397421 -0.0300308 -0.0472079 0.0207125 0.141341 -0.0246016 -0.00958346 -0.14513 -0.00194723 -0.00050874 -0.042593
0.00495125 0.146372 0.0131521 -0.0325219 0.206553 0.154127 0.127883 0.674217 0.215438 -0.0403247 -0.186277 -0.159307 0.149991 0.0338625 0.059409 0.152535 -0.0430863 0.0743399 0.0172644 0.139046 0.0520867 -0.0583078 -0.141846 0.033324 -0.00022271 -0.0611079
-0.116315 0.0352568 -0.12616 -0.0817433 -0.00254898 0.0455696 -0.123467 -0.0405158 0.00970987 0.196213 0.188824 -0.202351 0.105137 -0.0217913 -0.0558953 -0.149355 -0.00720124 -0.117341 0.00396471 0.118708 0.120158 0.00628897 -0.0212755 -0.00388375 0.010829 0.191394
0.0664639 -0.0427368 -0.0111 0.0595565 -0.181182 0.0548786 0.0362632 0.0287166 0.156026 0.0381319 -0.0300408 -0.0350494 0.09535 -0.0412327 -0.119269 0.0407118 0.0300392 -0.0563447 -0.155869 0.0342728 -0.134258 0.159936 0.00496751 -0.185649 0.0905668 0.0962407
0.139193 0.0526403 0.0128063 -0.191781 0.0729559 0.0447255 -0.0333265 -0.0417694 0.11737 0.0547831 0.0146894 -0.00655414 -0.044137 0.13245 0.19161 0.0669612 0.0428944 -0.00169285 0.178511 0.124602 -0.0913601 0.160262 -0.0611161 0.107008 -0.0841788 -0.0790234
0.0389208 0.194076 -0.00588889 0.0663444 -0.00231804 0.114282 -0.113214 0.0435368 0.0159935 -0.176742 -0.074732 0.0986991 0.205571 0.128931 0.0148701 -0.179878 -0.0592989 0.0376131 -0.00936217 0.048775 0.032527 0.0251112 -0.174189 0.108026 0.0508208 -0.0704338
-0.547755 0.0409679 -0.171178 0.158379 -0.150857 0.0594994 0.0172468 0.116126 0.0251258 0.054154 0.01246 -0.0889519 -0.0522306 -0.0529052 -0.113227 -0.0838343 0.151338 -0.0983415 -0.0316687 0.0865188 0.136132 0.0489902 -0.0104842 0.179032 0.0311013 0.0713657
0.276512 -0.0444139 -0.0690309 0.0454528 0.0217151 -0.0444145 -0.221348 -0.0822664 0.00817787 0.0766443 -0.103014 -0.0467212 -0.0505553 -0.135454 -0.0391867 -0.0548648 0.0126438 0.0440266 -0.0204114 0.025703 -0.0975989 0.0318566 0.00890207 -0.0136774 0.0914025 -0.00657811
0.0402229 -0.0905579 0.0044872 0.0606163 -0.149054 0.0539601 0.0988195 0.0687602 0.0222199 0.0924054 0.00980122 -0.20031 -0.0392417 -0.0169607 -0.076527 -0.0750682 0.107864 -0.0761908 -0.0372573 -0.0850451 0.0118615 0.0302198 -0.00500427 -0.048435 0.0568227 0.0450862
-0.230733 -0.0202409 -0.11211 0.11324 0.113287 -0.0154608 0.0773826 0.105643 0.0342484 -0.0379665 0.0660745 -0.0655964 0.169867 -0.0534554 0.0754881 0.0606065 -0.00997373 -0.100233 -0.0728204 0.108987 0.061911 -0.23606 -0.072986 0.105026 0.058321 -0.132469
0.147817 0.0870071 -0.068963 -0.175481 -0.0810596 -0.0502986 0.0109692 -0.0103719 0.249386 -0.0495963 -0.115391 -0.232654 0.00130511 -0.0105617 0.0294529 0.0476696 0.0930539 -0.136232 0.127695 -0.0204597 -0.0897393 0.0490508 -0.00952562 0.153784 -0.182251 -0.0723265
0.00821747 -0.00591802 0.1055 -0.0593748 -0.0204068 -0.0273963 -0.127505 0.0803305 0.174123 -0.0297758 0.0782863 0.0267953 -0.0318936 0.0616711 0.0159756 0.0156193 -0.0128629 -0.0302604 -0.00116341 0.0360947 0.00655866 -0.176436 0.000543974 -0.0103185 -0.0177002 -0.0373112
-0.153783 0.0151941 -0.0263542 0.0446434 -0.0765289 -0.203794 0.11867 0.0862431 0.253746 0.132482 -0.0762155 0.0384675 0.0437179 -0.0137567 0.0254686 -0.0582872 0.000692542 -0.0114491 0.104476 -0.167571 0.0864104 0.0398719 -0.00559504 -0.095039 0.113569 0.134639
0.142439 0.0160134 -0.00707784 0.0724836 -0.0471103 0.0158652 -0.0342688 0.114504 -0.0205116 0.0758213 -0.0654649 -0.128766 0.109897 0.0356689 0.0976765 -0.00140076 0.0575556 0.0659125 0.0157964 0.0308822 0.0269695 0.0911692 0.0118085 0.00501678 0.0252888 0.150034 [ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 1
│ 6
│ 7
│ 13
│ 25
│ 26
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 1, average log likelihood -1.097263
┌ Warning: Variances had to be floored
│ ind =
│ 12-element Array{Int64,1}:
│ 1
│ 5
│ 6
│ 8
│ ⋮
│ 28
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 2, average log likelihood -1.064573
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 1
│ 4
│ 6
│ 7
│ ⋮
│ 26
│ 27
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 3, average log likelihood -1.079961
┌ Warning: Variances had to be floored
│ ind =
│ 10-element Array{Int64,1}:
│ 1
│ 5
│ 6
│ 8
│ ⋮
│ 25
│ 26
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 4, average log likelihood -1.074967
┌ Warning: Variances had to be floored
│ ind =
│ 12-element Array{Int64,1}:
│ 1
│ 4
│ 6
│ 7
│ ⋮
│ 28
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -1.067566
┌ Warning: Variances had to be floored
│ ind =
│ 12-element Array{Int64,1}:
│ 1
│ 5
│ 6
│ 7
│ ⋮
│ 25
│ 26
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -1.074838
┌ Warning: Variances had to be floored
│ ind =
│ 9-element Array{Int64,1}:
│ 1
│ 6
│ 13
│ 25
│ ⋮
│ 28
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.078984
┌ Warning: Variances had to be floored
│ ind =
│ 11-element Array{Int64,1}:
│ 1
│ 4
│ 6
│ 7
│ ⋮
│ 25
│ 26
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -1.071714
┌ Warning: Variances had to be floored
│ ind =
│ 11-element Array{Int64,1}:
│ 1
│ 5
│ 6
│ 7
│ ⋮
│ 27
│ 28
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -1.080610
┌ Warning: Variances had to be floored
│ ind =
│ 13-element Array{Int64,1}:
│ 1
│ 4
│ 6
│ 7
│ ⋮
│ 26
│ 29
│ 31
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.061954
┌ Info: EM with 100000 data points 10 iterations avll -1.061954
└ 59.0 data points per parameter
kind diag, method kmeans
[ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 9.250572e+05
1 7.074572e+05 -2.176000e+05 | 32
2 6.816787e+05 -2.577854e+04 | 32
3 6.666914e+05 -1.498727e+04 | 32
4 6.546977e+05 -1.199371e+04 | 32
5 6.456823e+05 -9.015345e+03 | 32
6 6.404132e+05 -5.269108e+03 | 32
7 6.368676e+05 -3.545630e+03 | 32
8 6.342247e+05 -2.642854e+03 | 32
9 6.325831e+05 -1.641651e+03 | 32
10 6.317468e+05 -8.363213e+02 | 32
11 6.312605e+05 -4.862358e+02 | 32
12 6.307293e+05 -5.312613e+02 | 32
13 6.297279e+05 -1.001374e+03 | 32
14 6.285553e+05 -1.172634e+03 | 32
15 6.281439e+05 -4.113590e+02 | 32
16 6.279238e+05 -2.200780e+02 | 32
17 6.277624e+05 -1.614301e+02 | 32
18 6.276286e+05 -1.337501e+02 | 32
19 6.274908e+05 -1.378688e+02 | 31
20 6.273642e+05 -1.265437e+02 | 31
21 6.272788e+05 -8.542139e+01 | 32
22 6.272123e+05 -6.655595e+01 | 32
23 6.271734e+05 -3.884878e+01 | 32
24 6.271526e+05 -2.079772e+01 | 31
25 6.271402e+05 -1.241801e+01 | 31
26 6.271307e+05 -9.438870e+00 | 27
27 6.271249e+05 -5.799365e+00 | 28
28 6.271207e+05 -4.239965e+00 | 24
29 6.271178e+05 -2.942089e+00 | 23
30 6.271161e+05 -1.699672e+00 | 22
31 6.271145e+05 -1.587639e+00 | 20
32 6.271129e+05 -1.541910e+00 | 20
33 6.271114e+05 -1.510324e+00 | 21
34 6.271103e+05 -1.151808e+00 | 18
35 6.271093e+05 -9.691479e-01 | 20
36 6.271080e+05 -1.305303e+00 | 17
37 6.271073e+05 -6.539141e-01 | 12
38 6.271068e+05 -5.069148e-01 | 10
39 6.271064e+05 -4.042021e-01 | 5
40 6.271063e+05 -1.425464e-01 | 7
41 6.271062e+05 -1.267578e-01 | 0
42 6.271062e+05 0.000000e+00 | 0
K-means converged with 42 iterations (objv = 627106.1666352667)
┌ Info: K-means with 32000 data points using 42 iterations
└ 37.0 data points per parameter
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.341226
[ Info: iteration 2, average log likelihood -1.313420
[ Info: iteration 3, average log likelihood -1.287073
[ Info: iteration 4, average log likelihood -1.259385
[ Info: iteration 5, average log likelihood -1.220993
[ Info: iteration 6, average log likelihood -1.173161
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 10
│ 18
│ 19
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.127728
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 6
│ 20
│ 21
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -1.135730
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 28
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -1.107755
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 12
│ 14
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.087473
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 1
│ 19
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 11, average log likelihood -1.092458
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 6
│ 10
│ 24
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 12, average log likelihood -1.080294
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 20
│ 21
│ 28
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 13, average log likelihood -1.081801
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 18
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 14, average log likelihood -1.116663
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 12
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 15, average log likelihood -1.078730
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 1
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│ 14
│ 28
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 16, average log likelihood -1.080520
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 17
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 17, average log likelihood -1.104372
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 6
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│ 20
│ 24
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 18, average log likelihood -1.074358
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 12
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 19, average log likelihood -1.105279
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 1
│ 14
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 20, average log likelihood -1.076530
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 10
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 21, average log likelihood -1.063138
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 17
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 22, average log likelihood -1.079305
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 6
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│ 19
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 23, average log likelihood -1.076684
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 1
│ 12
│ 14
│ 28
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 24, average log likelihood -1.074709
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 10
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 25, average log likelihood -1.102929
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 16
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└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 26, average log likelihood -1.080835
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 6
│ 19
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 27, average log likelihood -1.069105
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 12
│ 14
│ 18
│ 21
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 28, average log likelihood -1.067507
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 1
│ 10
│ 28
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 29, average log likelihood -1.096147
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 20
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 30, average log likelihood -1.092871
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 6
│ 16
│ 17
│ 19
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 31, average log likelihood -1.057182
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 1
│ 12
│ 14
│ 21
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 32, average log likelihood -1.077928
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 10
│ 18
│ 20
│ 28
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 33, average log likelihood -1.100534
[ Info: iteration 34, average log likelihood -1.128266
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 6
│ 19
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 35, average log likelihood -1.064774
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 11
│ 12
│ 14
│ 20
│ 21
│ 28
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 36, average log likelihood -1.056492
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 10
│ 16
│ 18
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 37, average log likelihood -1.108259
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 1
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 38, average log likelihood -1.100392
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 6
│ 17
│ 19
│ 28
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 39, average log likelihood -1.057978
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 12
│ 20
│ 21
│ 25
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 40, average log likelihood -1.059412
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 1
│ 10
│ 14
│ 16
│ 18
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 41, average log likelihood -1.089518
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 11
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 42, average log likelihood -1.113744
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 6
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 43, average log likelihood -1.080199
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
│ 12
│ 19
│ 20
│ 21
│ 28
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 44, average log likelihood -1.032787
┌ Warning: Variances had to be floored
│ ind =
│ 5-element Array{Int64,1}:
│ 1
│ 10
│ 16
│ 17
│ 18
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 45, average log likelihood -1.091916
┌ Warning: Variances had to be floored
│ ind =
│ 2-element Array{Int64,1}:
│ 14
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 46, average log likelihood -1.108529
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 6
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 47, average log likelihood -1.080713
┌ Warning: Variances had to be floored
│ ind =
│ 6-element Array{Int64,1}:
│ 11
│ 12
│ 19
│ 20
│ 21
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 48, average log likelihood -1.046653
┌ Warning: Variances had to be floored
│ ind =
│ 1-element Array{Int64,1}:
│ 10
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 49, average log likelihood -1.111293
┌ Warning: Variances had to be floored
│ ind =
│ 4-element Array{Int64,1}:
│ 1
│ 14
│ 18
│ 28
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 50, average log likelihood -1.062169
┌ Info: EM with 100000 data points 50 iterations avll -1.062169
└ 59.0 data points per parameter
32×26 Array{Float64,2}:
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0.276069 -0.108571 0.024492 0.0631671 0.0629326 0.0762663 -0.059685 0.194252 0.0288793 -0.028569 -0.0482349 -0.153909 0.141599 0.0585426 0.0958359 -0.0116315 0.00191144 0.173064 0.0645616 0.0446279 0.096794 0.0317463 0.0453747 0.00786393 0.0175964 0.199326
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-0.0181384 -0.145684 0.130825 0.0272435 -0.0643799 0.0753649 -0.00111213 0.000512766 0.0630627 -0.00145365 0.0382654 -0.0499025 0.0258806 0.0776693 0.170417 0.0861686 -0.0557639 -0.0316464 -0.0766678 0.0422348 -0.0453484 0.0313238 0.130582 -0.306572 -0.0201847 0.145591
0.041729 -0.309394 0.0453607 -0.0304084 0.153564 -0.111902 0.0649949 -0.000826309 -0.036995 -0.0867085 -0.0637286 -0.093288 0.0418493 0.0562169 -0.0173075 -0.102563 -0.25303 0.0117752 -0.00895419 0.0543788 -0.116848 -0.136556 -0.0896939 0.130837 -0.0123872 0.0508972
0.0404311 -0.141074 -0.151714 0.136931 0.03479 0.0897087 0.0451776 -0.133722 -0.101948 0.0857098 0.0228937 -0.0983691 0.0204327 -0.0200925 0.00914278 -0.100553 -0.0059029 -0.00708894 0.103275 -0.062024 0.107818 -0.0857233 0.129131 0.0157957 -0.0681113 -0.0431912
-0.0984316 0.0585849 0.0678993 -0.0899103 -0.14443 -0.0763639 -0.0779013 0.138281 0.214126 0.000989318 -0.00703916 -0.0968017 -0.0624998 -0.00862245 0.0163983 -0.0525553 -0.000767921 -0.0414221 0.0389027 -0.00358146 -0.0237891 -0.106447 0.0112586 -0.06539 -0.155604 -0.01515
-0.123521 0.0345351 -0.13599 -0.0879938 0.00148357 0.0471076 -0.124422 -0.0365434 0.00969422 0.205235 0.191979 -0.202018 0.109833 -0.0255918 -0.0569028 -0.158869 -0.00690585 -0.112384 -9.9469e-5 0.12332 0.128571 -0.00250869 -0.025175 -0.000935983 0.0214647 0.196146
0.0643029 -0.0429821 -0.0112558 0.0602468 -0.181859 0.0556891 0.0364639 0.03138 0.156317 0.0374101 -0.030575 -0.0352694 0.0944736 -0.0412716 -0.118872 0.0435283 0.0298695 -0.0583011 -0.154803 0.0349012 -0.134551 0.156423 0.00484811 -0.186419 0.0904472 0.0945928
0.0278275 -0.130041 -0.0849508 0.0351244 -0.0602126 0.0377488 -0.0213508 0.0170728 0.0154605 -0.0047695 -0.0466833 -0.0664281 0.0700103 0.14274 0.0447378 -0.214016 -0.147878 -0.0963537 0.0630301 -0.000334999 0.0444575 -0.0266737 0.0262041 -0.071027 0.0134745 -0.0448079 [ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 6
│ 16
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 1, average log likelihood -1.070685
┌ Warning: Variances had to be floored
│ ind =
│ 10-element Array{Int64,1}:
│ 6
│ 11
│ 12
│ 16
│ ⋮
│ 21
│ 25
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 2, average log likelihood -1.020850
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 6
│ 10
│ 12
│ 16
│ 21
│ 25
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 3, average log likelihood -1.021838
┌ Warning: Variances had to be floored
│ ind =
│ 14-element Array{Int64,1}:
│ 1
│ 6
│ 11
│ 12
│ ⋮
│ 25
│ 28
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 4, average log likelihood -1.004903
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 6
│ 16
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 5, average log likelihood -1.065642
┌ Warning: Variances had to be floored
│ ind =
│ 11-element Array{Int64,1}:
│ 6
│ 10
│ 11
│ 12
│ ⋮
│ 21
│ 25
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 6, average log likelihood -1.017419
┌ Warning: Variances had to be floored
│ ind =
│ 7-element Array{Int64,1}:
│ 6
│ 12
│ 16
│ 21
│ 25
│ 28
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 7, average log likelihood -1.026108
┌ Warning: Variances had to be floored
│ ind =
│ 14-element Array{Int64,1}:
│ 1
│ 6
│ 10
│ 11
│ ⋮
│ 21
│ 25
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 8, average log likelihood -1.006346
┌ Warning: Variances had to be floored
│ ind =
│ 3-element Array{Int64,1}:
│ 6
│ 16
│ 25
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 9, average log likelihood -1.070101
┌ Warning: Variances had to be floored
│ ind =
│ 10-element Array{Int64,1}:
│ 6
│ 11
│ 12
│ 16
│ ⋮
│ 21
│ 25
│ 32
└ @ GaussianMixtures ~/.julia/packages/GaussianMixtures/RGtTJ/src/train.jl:255
[ Info: iteration 10, average log likelihood -1.021320
┌ Info: EM with 100000 data points 10 iterations avll -1.021320
└ 59.0 data points per parameter
32×26 Array{Float64,2}:
-0.101981 -0.139924 -0.0147637 -0.0575284 -0.0672938 0.000708168 -0.053624 -0.00733809 -0.115464 -0.0251965 0.134273 0.0138385 -0.142555 -0.144584 -0.0439331 -0.161359 -0.103304 0.0661239 -0.153971 -0.0910677 -0.0123652 -0.0667176 -0.0544731 -0.0121765 -0.251042 0.14649
0.144268 0.0440032 0.0256317 0.14737 0.0601981 0.0754644 -0.167729 -0.0420771 -0.0286954 0.107933 -0.103196 0.139149 0.0822638 -0.0296125 -0.0883451 -0.0489403 -0.133943 -0.00529243 -0.0384083 0.0187477 0.0622672 -0.0281678 0.128504 -0.026662 -0.151253 -0.0966717
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-0.100811 -0.0469469 -0.0985642 -0.0661619 0.160384 0.200829 -0.0148269 -0.00208999 -0.0772033 0.0531565 -0.0576449 -0.0188938 0.0134666 -0.0870041 -0.145697 0.0395728 0.169789 0.047822 0.00618118 0.154163 0.0056083 0.0340771 -0.0102604 0.0814454 0.0318696 0.0429718
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0.00905316 -0.142016 0.114431 -0.0897602 0.112206 -0.0921294 -0.0372736 -0.20778 0.0498223 -0.0491767 -0.0945398 0.18465 0.135844 0.0378514 0.015096 0.0420473 0.13465 0.0153763 0.0293192 -0.129936 -0.107596 -0.134131 0.0262168 0.0189297 -0.139638 0.0538881
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-0.144066 -0.0990649 0.093234 -0.104168 0.0799919 -0.017736 -0.089898 -0.1088 -0.070524 -0.0666028 0.050233 -0.130542 -0.0931789 -0.187394 0.244445 0.0197798 -0.0670337 -0.12079 -0.128595 -0.0524639 -0.0233553 -0.0433993 0.152667 0.0270579 0.0627029 -0.0267708
0.114289 -0.0546515 0.0750101 0.0635498 0.00473003 -0.102602 -0.0395925 -0.01532 0.0306031 0.0225875 0.102549 0.210177 -0.117598 0.213087 0.0674532 -0.173621 0.0322789 0.075254 -0.0354197 0.0183175 -0.0397242 0.0615104 0.12678 -0.103513 -0.0855066 -0.00567505
-0.0567507 -0.0200557 -0.159726 0.0448762 0.0144968 0.0018435 0.11342 -0.0761147 0.159226 -0.0730318 -0.138881 -0.0558674 -0.10907 0.0204649 -0.135118 -0.0631976 0.0308114 0.0807088 0.0950313 0.00456123 -0.0685664 0.04428 0.103933 -0.135744 -0.0406341 0.0195798
0.091382 -0.201548 0.0427969 0.111463 0.070161 -0.117246 0.157541 -0.200459 -0.093475 0.107533 -0.100712 0.1243 0.0355704 -0.1428 -0.0398259 0.10161 -0.0468989 -0.240055 -0.0372865 0.0243237 0.0252749 0.0395427 0.0309651 0.0684364 0.125149 0.0333584 kind full, method split
┌ Info: 0: avll =
└ tll[1] = -1.4306630159785543
[ Info: Running 50 iterations EM on diag cov GMM with 2 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.430683
[ Info: iteration 2, average log likelihood -1.430615
[ Info: iteration 3, average log likelihood -1.430563
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[ Info: iteration 50, average log likelihood -1.425287
┌ Info: EM with 100000 data points 50 iterations avll -1.425287
└ 952.4 data points per parameter
┌ Info: 1
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.4306831660090746
│ -1.4306151506930622
│ ⋮
└ -1.4252866806625486
[ Info: Running 50 iterations EM on diag cov GMM with 4 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.425307
[ Info: iteration 2, average log likelihood -1.425236
[ Info: iteration 3, average log likelihood -1.425181
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┌ Info: EM with 100000 data points 50 iterations avll -1.423810
└ 473.9 data points per parameter
┌ Info: 2
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.425306581944389
│ -1.4252359257826868
│ ⋮
└ -1.42381029590753
[ Info: Running 50 iterations EM on diag cov GMM with 8 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.423821
[ Info: iteration 2, average log likelihood -1.423769
[ Info: iteration 3, average log likelihood -1.423726
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[ Info: iteration 33, average log likelihood -1.422523
[ Info: iteration 34, average log likelihood -1.422511
[ Info: iteration 35, average log likelihood -1.422500
[ Info: iteration 36, average log likelihood -1.422489
[ Info: iteration 37, average log likelihood -1.422479
[ Info: iteration 38, average log likelihood -1.422469
[ Info: iteration 39, average log likelihood -1.422460
[ Info: iteration 40, average log likelihood -1.422452
[ Info: iteration 41, average log likelihood -1.422444
[ Info: iteration 42, average log likelihood -1.422436
[ Info: iteration 43, average log likelihood -1.422429
[ Info: iteration 44, average log likelihood -1.422422
[ Info: iteration 45, average log likelihood -1.422416
[ Info: iteration 46, average log likelihood -1.422410
[ Info: iteration 47, average log likelihood -1.422404
[ Info: iteration 48, average log likelihood -1.422398
[ Info: iteration 49, average log likelihood -1.422392
[ Info: iteration 50, average log likelihood -1.422387
┌ Info: EM with 100000 data points 50 iterations avll -1.422387
└ 236.4 data points per parameter
┌ Info: 3
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.423821260693816
│ -1.423769159493944
│ ⋮
└ -1.4223870713948619
[ Info: Running 50 iterations EM on diag cov GMM with 16 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.422391
[ Info: iteration 2, average log likelihood -1.422338
[ Info: iteration 3, average log likelihood -1.422290
[ Info: iteration 4, average log likelihood -1.422237
[ Info: iteration 5, average log likelihood -1.422175
[ Info: iteration 6, average log likelihood -1.422102
[ Info: iteration 7, average log likelihood -1.422018
[ Info: iteration 8, average log likelihood -1.421922
[ Info: iteration 9, average log likelihood -1.421819
[ Info: iteration 10, average log likelihood -1.421713
[ Info: iteration 11, average log likelihood -1.421606
[ Info: iteration 12, average log likelihood -1.421502
[ Info: iteration 13, average log likelihood -1.421405
[ Info: iteration 14, average log likelihood -1.421316
[ Info: iteration 15, average log likelihood -1.421238
[ Info: iteration 16, average log likelihood -1.421170
[ Info: iteration 17, average log likelihood -1.421113
[ Info: iteration 18, average log likelihood -1.421065
[ Info: iteration 19, average log likelihood -1.421024
[ Info: iteration 20, average log likelihood -1.420989
[ Info: iteration 21, average log likelihood -1.420959
[ Info: iteration 22, average log likelihood -1.420933
[ Info: iteration 23, average log likelihood -1.420910
[ Info: iteration 24, average log likelihood -1.420889
[ Info: iteration 25, average log likelihood -1.420870
[ Info: iteration 26, average log likelihood -1.420852
[ Info: iteration 27, average log likelihood -1.420835
[ Info: iteration 28, average log likelihood -1.420819
[ Info: iteration 29, average log likelihood -1.420804
[ Info: iteration 30, average log likelihood -1.420790
[ Info: iteration 31, average log likelihood -1.420776
[ Info: iteration 32, average log likelihood -1.420763
[ Info: iteration 33, average log likelihood -1.420750
[ Info: iteration 34, average log likelihood -1.420738
[ Info: iteration 35, average log likelihood -1.420726
[ Info: iteration 36, average log likelihood -1.420715
[ Info: iteration 37, average log likelihood -1.420704
[ Info: iteration 38, average log likelihood -1.420694
[ Info: iteration 39, average log likelihood -1.420684
[ Info: iteration 40, average log likelihood -1.420674
[ Info: iteration 41, average log likelihood -1.420665
[ Info: iteration 42, average log likelihood -1.420656
[ Info: iteration 43, average log likelihood -1.420648
[ Info: iteration 44, average log likelihood -1.420640
[ Info: iteration 45, average log likelihood -1.420632
[ Info: iteration 46, average log likelihood -1.420624
[ Info: iteration 47, average log likelihood -1.420617
[ Info: iteration 48, average log likelihood -1.420610
[ Info: iteration 49, average log likelihood -1.420604
[ Info: iteration 50, average log likelihood -1.420598
┌ Info: EM with 100000 data points 50 iterations avll -1.420598
└ 118.1 data points per parameter
┌ Info: 4
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.422391260690002
│ -1.4223375631247595
│ ⋮
└ -1.4205975638959212
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.420600
[ Info: iteration 2, average log likelihood -1.420540
[ Info: iteration 3, average log likelihood -1.420486
[ Info: iteration 4, average log likelihood -1.420423
[ Info: iteration 5, average log likelihood -1.420347
[ Info: iteration 6, average log likelihood -1.420251
[ Info: iteration 7, average log likelihood -1.420136
[ Info: iteration 8, average log likelihood -1.420002
[ Info: iteration 9, average log likelihood -1.419855
[ Info: iteration 10, average log likelihood -1.419704
[ Info: iteration 11, average log likelihood -1.419556
[ Info: iteration 12, average log likelihood -1.419418
[ Info: iteration 13, average log likelihood -1.419293
[ Info: iteration 14, average log likelihood -1.419182
[ Info: iteration 15, average log likelihood -1.419086
[ Info: iteration 16, average log likelihood -1.419002
[ Info: iteration 17, average log likelihood -1.418927
[ Info: iteration 18, average log likelihood -1.418862
[ Info: iteration 19, average log likelihood -1.418804
[ Info: iteration 20, average log likelihood -1.418752
[ Info: iteration 21, average log likelihood -1.418705
[ Info: iteration 22, average log likelihood -1.418663
[ Info: iteration 23, average log likelihood -1.418624
[ Info: iteration 24, average log likelihood -1.418589
[ Info: iteration 25, average log likelihood -1.418557
[ Info: iteration 26, average log likelihood -1.418527
[ Info: iteration 27, average log likelihood -1.418499
[ Info: iteration 28, average log likelihood -1.418473
[ Info: iteration 29, average log likelihood -1.418448
[ Info: iteration 30, average log likelihood -1.418425
[ Info: iteration 31, average log likelihood -1.418403
[ Info: iteration 32, average log likelihood -1.418383
[ Info: iteration 33, average log likelihood -1.418363
[ Info: iteration 34, average log likelihood -1.418344
[ Info: iteration 35, average log likelihood -1.418327
[ Info: iteration 36, average log likelihood -1.418310
[ Info: iteration 37, average log likelihood -1.418293
[ Info: iteration 38, average log likelihood -1.418278
[ Info: iteration 39, average log likelihood -1.418263
[ Info: iteration 40, average log likelihood -1.418248
[ Info: iteration 41, average log likelihood -1.418234
[ Info: iteration 42, average log likelihood -1.418221
[ Info: iteration 43, average log likelihood -1.418208
[ Info: iteration 44, average log likelihood -1.418195
[ Info: iteration 45, average log likelihood -1.418183
[ Info: iteration 46, average log likelihood -1.418170
[ Info: iteration 47, average log likelihood -1.418159
[ Info: iteration 48, average log likelihood -1.418147
[ Info: iteration 49, average log likelihood -1.418136
[ Info: iteration 50, average log likelihood -1.418124
┌ Info: EM with 100000 data points 50 iterations avll -1.418124
└ 59.0 data points per parameter
┌ Info: 5
│ : avll = = ": avll = "
│ avll =
│ 50-element Array{Float64,1}:
│ -1.420599947917332
│ -1.4205404875267773
│ ⋮
└ -1.4181244446971544
┌ Info: Total log likelihood:
│ tll =
│ 251-element Array{Float64,1}:
│ -1.4306630159785543
│ -1.4306831660090746
│ -1.4306151506930622
│ -1.4305629227447
│ ⋮
│ -1.418146906175143
│ -1.4181355581634971
└ -1.4181244446971544
32×26 Array{Float64,2}:
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0.222145 0.140431 -0.359759 -0.392482 -0.628127 -0.121562 0.464939 -0.102455 0.276919 0.341689 -0.133961 0.0533401 -0.243887 0.0168762 0.260544 0.142051 -0.0475632 -0.701849 0.12964 -0.165757 -0.46024 -0.118772 0.0918808 0.142911 -0.0599526 0.129543
-0.156916 0.0217758 -0.499665 -0.769991 -0.557102 0.075957 -0.158969 0.0937732 -0.343299 -0.0412322 0.267124 -0.352151 -0.107941 -0.0562884 -0.687714 0.129521 -0.0910253 -0.672927 0.0471499 0.321784 -0.404482 -0.329345 0.246599 0.225794 -0.483054 -0.347171
-0.238273 0.124342 0.39366 -0.463865 -0.196695 0.069092 -0.468911 -0.0450144 -0.510865 0.0326231 0.597111 -0.394455 0.136836 0.171449 -0.003716 0.291637 -0.359557 -0.456357 -0.287119 0.0196799 0.031128 -0.144618 -0.301991 0.300436 0.0495015 0.279525
-0.296366 0.122269 -0.160042 0.228338 -0.204418 -0.418094 0.0621958 -0.0696043 -0.145726 0.157441 0.00162187 -0.120695 0.213744 0.0148714 0.146077 -0.0470249 -0.00186814 -0.270721 -0.361304 -0.275305 0.0342199 -0.403728 -1.00337 -0.0750569 -0.224572 -0.115136
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0.100028 -0.268241 0.100916 -0.00724168 0.464035 0.000375231 0.232527 -0.203009 -0.0577291 0.0109932 -0.0509378 0.180792 -0.052657 -0.0917001 -0.120387 -0.312426 0.0476759 0.313197 0.00995449 0.0224344 0.11708 -0.139455 0.160846 0.13519 0.0153712 0.11214
0.400703 0.110982 0.46674 0.634091 0.0270471 -0.115272 -0.327698 -0.0714377 -0.0697822 0.371523 -0.3038 0.0881606 0.0278942 -0.47188 -0.278635 -0.280314 0.801037 0.276508 -0.43131 -0.415942 0.0375403 0.14465 -0.0288253 -0.213994 0.440795 -0.335054
-0.264372 0.110166 0.630268 0.127705 0.214653 0.0231696 0.119567 0.00602268 -0.348915 -0.104773 0.883392 -0.0465617 -0.100052 -0.417116 0.456202 -0.110936 0.805426 0.702226 -0.399596 0.171809 -0.30285 0.473722 -0.100583 -0.175933 0.388759 0.053653
0.0744451 -0.730239 0.172041 -0.725468 -0.368057 0.132481 0.0962818 0.15291 0.0379135 0.284288 0.0312134 -0.156888 0.351291 0.105924 -0.392192 0.413942 0.521734 0.4153 -1.17907 0.145302 -0.0586008 0.201326 -0.0683591 0.465831 0.752882 -0.473569
-0.41678 -0.399506 0.0539485 0.134589 -0.128146 0.102272 -0.167699 0.212639 -0.180615 0.25899 -0.333789 0.261717 0.221812 -0.25043 -0.00892259 0.370052 0.257131 0.19069 0.412909 0.0852041 -0.201363 0.152438 0.482606 0.72454 1.00057 0.26153
-0.451288 -0.2859 -0.171085 -0.0284535 -0.225319 0.169503 -0.0523349 0.0520683 0.139142 -0.627628 0.0387755 -0.101447 0.146491 0.155945 -0.113036 0.468969 0.429333 0.397139 0.359093 -0.204704 -0.268414 0.328532 0.116763 -0.228593 -0.0266188 -0.406495
-0.0338115 -0.742062 -0.591908 0.034397 -0.110436 0.28188 -0.303476 0.161977 0.490455 0.416466 -0.28106 0.2225 -0.121815 0.303473 0.153064 -0.0852153 -0.124035 0.419441 0.152811 -0.0256574 0.135813 0.256531 0.00842036 0.154434 0.148743 -0.0961892
-0.46646 0.0345048 -0.140148 -0.447913 0.136482 0.294002 0.414924 -0.178072 -0.447445 -0.240212 -0.104253 -0.436889 -0.358809 -0.362228 -0.613567 0.160152 -0.0542501 0.559688 0.335199 0.43635 0.372184 0.0011751 0.0624153 0.191725 0.0390111 0.108645
0.0929954 -0.321414 -0.0775946 -0.2627 -0.00174003 0.105097 0.558992 -0.278143 0.0311836 0.100791 0.113249 0.750703 0.236239 -0.56383 -0.364916 -0.284873 -0.0199918 0.307044 0.699828 0.313545 0.77971 0.587819 0.110362 -0.165689 -0.745218 -0.152504
-0.468403 -0.0705398 -0.163173 -0.139362 -0.161684 -0.271355 -0.0273425 0.187012 -0.0454441 -0.325863 0.373566 -0.0114182 -0.224892 0.235683 -0.0930042 0.134804 -0.0115612 -0.078297 0.0982805 -0.585686 0.00084255 0.0307877 -0.851458 0.0304091 -0.431603 -0.394597
-0.425467 -0.0431272 -0.6441 -0.342241 -0.525356 0.288106 -0.111862 -0.194494 0.314866 0.103252 0.250363 -0.00310173 0.30331 0.308118 0.702854 0.628047 -0.452995 -0.00258673 0.257471 0.485312 0.118562 0.0120234 -0.332634 0.200879 -0.299119 0.261498
-0.0175342 -0.423084 0.0227022 -0.331703 -0.0579178 0.235506 -0.0815269 0.0408747 -0.325797 -0.109312 -0.235303 0.11875 0.0538645 -0.424523 -0.394281 -0.11642 0.473786 0.242495 0.309607 0.25187 -0.117353 0.184905 0.354244 -0.0537985 0.0928699 -0.301471
-0.172819 0.399799 0.0475645 0.267987 -0.236813 -0.0806297 -0.192989 0.195481 -0.138112 0.0516114 0.0404916 -0.59199 -0.161361 0.193705 0.171162 0.288588 0.108741 -0.440763 -0.385527 -0.216685 -0.611789 -0.391766 -0.0253561 0.0875275 0.440951 0.0154534
0.220875 0.620156 0.739806 0.0480584 -0.477843 -0.855942 0.705119 -0.165319 -0.313794 -0.265791 -0.0322984 0.16878 -0.821038 -0.105907 -0.106209 -0.370962 0.113807 -0.0773302 0.486177 -0.0216453 -0.374594 0.123347 -0.406713 -0.0726421 -0.691246 0.13191
0.621272 0.42857 0.517979 -0.34541 0.266647 -0.189275 0.16893 -0.357716 -0.09123 -0.389946 0.34915 -0.460874 -0.483514 0.0239774 0.510128 0.419623 -0.100974 -0.505448 -0.264774 -0.141866 -0.145151 -0.175526 -0.346581 -0.370446 -0.374917 0.0590786
-0.243589 0.512287 0.270969 -0.502378 -0.00664037 -0.015572 -0.362734 -0.628056 0.361947 0.397042 0.417706 -0.116825 0.494181 0.103485 -0.246596 0.347391 0.0724152 0.193067 0.329208 0.274691 0.228501 0.615483 -0.325478 -0.487712 -0.20513 0.302605
0.136973 0.457792 0.880334 -0.392261 0.168213 -0.282695 0.691411 -0.166724 -0.347566 -0.357554 0.169233 -0.197138 0.865157 -0.358219 -0.497427 -0.137432 0.65978 -0.270829 -0.319531 0.355795 0.165502 -0.209172 0.484744 -0.418909 -0.173559 0.115955
0.318755 -0.000725616 -0.190124 0.8984 0.752475 -0.00911092 0.590279 -0.64134 0.968649 0.351766 -0.0215153 0.131041 -0.0163988 0.381322 -0.111986 -0.219557 0.155884 0.486662 -0.169678 -0.323897 0.261437 0.228752 0.300513 -0.108067 -0.0113979 0.330237
-0.17361 -0.773533 0.629722 0.36254 0.868395 -0.271054 -0.0845716 -0.429275 -0.302702 -0.141922 -0.532603 0.427807 0.192239 0.0260127 0.39969 -0.3428 -0.147347 0.519936 0.206186 -0.246351 0.703618 0.182817 -0.425307 -0.0971029 0.125272 0.757275
1.14936 0.0179955 -0.226807 -0.420365 -0.338228 0.013918 0.162146 -0.145165 -0.263823 0.490862 -0.0730365 0.430686 0.327316 -0.315448 -0.0588874 0.0485277 -0.490735 -0.324885 0.0626969 0.00687684 0.578423 -0.0597072 0.0123023 -0.00662611 0.0883684 0.720625
0.894602 -0.224552 0.320379 0.23725 0.278041 -0.168237 -0.0844479 0.101649 0.340196 0.47195 -0.0334984 0.144602 0.0363192 0.50705 0.472324 -0.172233 -0.0840769 -0.0834858 -0.364924 0.191569 0.234573 -0.292821 0.350804 -0.279116 0.0633605 0.226402
-0.178083 -0.0553835 -0.136238 0.669905 0.531748 0.125492 -0.592425 0.0749655 0.268862 -0.0855529 0.12536 -0.123362 0.4505 0.225774 0.184794 -0.747048 -0.692548 0.0737011 -0.181488 0.336458 0.288722 -0.207173 0.48571 -0.203089 0.0251807 -0.164206
-0.575389 0.0355628 0.0567941 0.772984 0.331586 -0.228381 -0.15812 0.295859 0.0651193 0.760767 0.104422 -0.458735 0.315076 -0.283618 -0.456476 -0.529277 0.249117 0.225771 0.3736 0.234034 0.105404 0.617043 0.243118 0.685097 0.179908 0.0704874
-0.0139958 -0.474481 -0.031834 0.425367 0.187652 0.10363 0.0395369 0.427835 -0.677045 -0.327472 -0.445825 0.053885 -0.321528 0.0540926 -0.246883 -0.689532 -0.0879879 0.0184557 -0.18781 -0.24495 -0.253773 -0.678517 0.333089 0.397992 0.277885 -0.354688
0.673846 0.0170556 -0.259704 0.307561 0.58581 0.178499 0.21258 -0.129237 0.532686 -0.110508 -0.600586 0.195756 -0.590868 -0.00713218 -0.726674 -0.723206 -0.777036 -0.0827696 -0.196573 -0.569785 0.122156 -0.0995672 0.319477 -0.156997 -0.443267 -0.456942 [ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.418114
[ Info: iteration 2, average log likelihood -1.418103
[ Info: iteration 3, average log likelihood -1.418092
[ Info: iteration 4, average log likelihood -1.418082
[ Info: iteration 5, average log likelihood -1.418072
[ Info: iteration 6, average log likelihood -1.418062
[ Info: iteration 7, average log likelihood -1.418052
[ Info: iteration 8, average log likelihood -1.418043
[ Info: iteration 9, average log likelihood -1.418033
[ Info: iteration 10, average log likelihood -1.418024
┌ Info: EM with 100000 data points 10 iterations avll -1.418024
└ 59.0 data points per parameter
kind full, method kmeans
[ Info: Initializing GMM, 32 Gaussians diag covariance 26 dimensions using 100000 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 9.239531e+05
1 7.140519e+05 -2.099012e+05 | 32
2 7.024738e+05 -1.157818e+04 | 32
3 6.978570e+05 -4.616719e+03 | 32
4 6.952014e+05 -2.655639e+03 | 32
5 6.934409e+05 -1.760478e+03 | 32
6 6.921476e+05 -1.293367e+03 | 32
7 6.911120e+05 -1.035567e+03 | 32
8 6.902545e+05 -8.575032e+02 | 32
9 6.895304e+05 -7.240769e+02 | 32
10 6.889266e+05 -6.038487e+02 | 32
11 6.884274e+05 -4.992028e+02 | 32
12 6.880098e+05 -4.175156e+02 | 32
13 6.876529e+05 -3.569716e+02 | 32
14 6.873567e+05 -2.961349e+02 | 32
15 6.870989e+05 -2.578882e+02 | 32
16 6.868712e+05 -2.276737e+02 | 32
17 6.866426e+05 -2.286092e+02 | 32
18 6.864316e+05 -2.109415e+02 | 32
19 6.862282e+05 -2.034651e+02 | 32
20 6.860304e+05 -1.977910e+02 | 32
21 6.858705e+05 -1.598606e+02 | 32
22 6.857233e+05 -1.471649e+02 | 32
23 6.855898e+05 -1.335458e+02 | 32
24 6.854744e+05 -1.154185e+02 | 32
25 6.853709e+05 -1.034884e+02 | 32
26 6.852694e+05 -1.015059e+02 | 32
27 6.851757e+05 -9.364132e+01 | 32
28 6.850870e+05 -8.871261e+01 | 32
29 6.849998e+05 -8.720706e+01 | 32
30 6.849134e+05 -8.640256e+01 | 32
31 6.848395e+05 -7.389791e+01 | 32
32 6.847737e+05 -6.580777e+01 | 32
33 6.847125e+05 -6.120471e+01 | 32
34 6.846538e+05 -5.870168e+01 | 32
35 6.846004e+05 -5.340094e+01 | 32
36 6.845425e+05 -5.794227e+01 | 32
37 6.844864e+05 -5.605828e+01 | 32
38 6.844320e+05 -5.438416e+01 | 32
39 6.843784e+05 -5.361520e+01 | 32
40 6.843339e+05 -4.451139e+01 | 32
41 6.842910e+05 -4.285459e+01 | 32
42 6.842530e+05 -3.801141e+01 | 32
43 6.842219e+05 -3.116286e+01 | 32
44 6.841917e+05 -3.018329e+01 | 32
45 6.841591e+05 -3.261570e+01 | 32
46 6.841236e+05 -3.546322e+01 | 32
47 6.840880e+05 -3.563064e+01 | 32
48 6.840589e+05 -2.904433e+01 | 32
49 6.840290e+05 -2.994957e+01 | 32
50 6.839997e+05 -2.927499e+01 | 32
K-means terminated without convergence after 50 iterations (objv = 683999.7083942282)
┌ Info: K-means with 32000 data points using 50 iterations
└ 37.0 data points per parameter
[ Info: Running 50 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.429737
[ Info: iteration 2, average log likelihood -1.424787
[ Info: iteration 3, average log likelihood -1.423512
[ Info: iteration 4, average log likelihood -1.422598
[ Info: iteration 5, average log likelihood -1.421598
[ Info: iteration 6, average log likelihood -1.420581
[ Info: iteration 7, average log likelihood -1.419793
[ Info: iteration 8, average log likelihood -1.419308
[ Info: iteration 9, average log likelihood -1.419024
[ Info: iteration 10, average log likelihood -1.418841
[ Info: iteration 11, average log likelihood -1.418710
[ Info: iteration 12, average log likelihood -1.418608
[ Info: iteration 13, average log likelihood -1.418525
[ Info: iteration 14, average log likelihood -1.418457
[ Info: iteration 15, average log likelihood -1.418400
[ Info: iteration 16, average log likelihood -1.418351
[ Info: iteration 17, average log likelihood -1.418308
[ Info: iteration 18, average log likelihood -1.418271
[ Info: iteration 19, average log likelihood -1.418238
[ Info: iteration 20, average log likelihood -1.418208
[ Info: iteration 21, average log likelihood -1.418182
[ Info: iteration 22, average log likelihood -1.418158
[ Info: iteration 23, average log likelihood -1.418136
[ Info: iteration 24, average log likelihood -1.418116
[ Info: iteration 25, average log likelihood -1.418097
[ Info: iteration 26, average log likelihood -1.418080
[ Info: iteration 27, average log likelihood -1.418064
[ Info: iteration 28, average log likelihood -1.418049
[ Info: iteration 29, average log likelihood -1.418035
[ Info: iteration 30, average log likelihood -1.418021
[ Info: iteration 31, average log likelihood -1.418009
[ Info: iteration 32, average log likelihood -1.417997
[ Info: iteration 33, average log likelihood -1.417986
[ Info: iteration 34, average log likelihood -1.417975
[ Info: iteration 35, average log likelihood -1.417964
[ Info: iteration 36, average log likelihood -1.417954
[ Info: iteration 37, average log likelihood -1.417945
[ Info: iteration 38, average log likelihood -1.417935
[ Info: iteration 39, average log likelihood -1.417926
[ Info: iteration 40, average log likelihood -1.417916
[ Info: iteration 41, average log likelihood -1.417907
[ Info: iteration 42, average log likelihood -1.417898
[ Info: iteration 43, average log likelihood -1.417889
[ Info: iteration 44, average log likelihood -1.417880
[ Info: iteration 45, average log likelihood -1.417871
[ Info: iteration 46, average log likelihood -1.417862
[ Info: iteration 47, average log likelihood -1.417852
[ Info: iteration 48, average log likelihood -1.417843
[ Info: iteration 49, average log likelihood -1.417834
[ Info: iteration 50, average log likelihood -1.417825
┌ Info: EM with 100000 data points 50 iterations avll -1.417825
└ 59.0 data points per parameter
32×26 Array{Float64,2}:
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-0.395795 0.0102254 0.715452 -0.704622 -0.1296 -0.0418258 0.322781 -0.0759441 -0.496997 -0.246783 0.78483 -0.223875 -0.680056 -0.135692 -0.196665 0.371613 0.478078 0.0578943 -0.0532327 -0.159898 -0.34635 0.281783 -0.287746 0.0745894 0.107196 -0.137515
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0.422029 -0.424554 -0.1201 -0.322095 0.0547622 0.0749615 0.407743 -0.429156 -0.0676894 0.0496185 0.112911 0.839466 0.171704 -0.300051 -0.118242 -0.170559 -0.297117 0.241252 0.460859 0.00217709 0.862923 0.543566 -0.135616 -0.278879 -0.721089 0.0573498
-0.0181202 -0.015391 -0.0433725 -0.455823 -0.355855 0.0380893 -0.0996998 0.123972 -0.333935 -0.0980678 -0.012069 0.0233823 -0.0721533 -0.274648 -0.107773 0.118005 0.176615 -0.160563 0.0185813 0.143822 -0.227193 -0.0184808 -0.0157701 0.00820062 0.0237001 -0.189538
0.277888 0.349974 0.281042 -0.160515 -0.150695 -0.494847 0.407765 -0.424914 -0.0780521 0.25531 0.0449432 -0.0645835 -0.263589 0.0856795 0.297997 -0.0696243 -0.139034 -0.568374 -0.12173 -0.101392 -0.401767 -0.200263 -0.351038 -0.0583192 -0.220931 0.426575
-0.442411 -0.0949013 0.0134368 0.791726 0.460045 -0.106991 0.129031 -0.0953696 0.213062 -0.258117 -0.248546 0.0274251 -0.0556954 0.236829 0.268813 -0.067356 -0.124702 0.190597 0.0150071 -0.456247 -0.107744 0.0922344 -0.568512 -0.147146 -0.0501601 -0.18776
-0.571059 0.282766 -0.0479701 -0.316736 -0.51928 0.225849 -0.0149706 -0.523949 -0.0865061 -0.484462 0.0199882 0.0709306 0.509111 -0.179538 -0.497731 0.340336 0.344971 0.31016 0.563279 -0.307757 -0.241691 0.477531 -0.280147 -0.476285 -0.368378 -0.350284
0.0603309 0.916563 0.405278 0.0775303 -0.16978 -0.757067 0.335563 0.142214 -0.187465 -0.373872 0.0543332 -0.19617 -0.555335 -0.259293 -0.0562508 -0.201297 -0.0124009 0.0238893 0.578919 0.23754 0.0608045 0.0523201 -0.733632 -0.57989 -0.842221 -0.0871921
0.467482 -0.108993 -0.357449 -0.569962 -0.232468 0.112044 -0.240636 -0.0388786 -0.0376128 -0.156552 -0.29353 0.0273573 -0.856163 -0.160386 0.0484554 0.1274 -0.188596 -0.678341 0.0818432 -0.137962 -0.27686 0.189554 0.176012 -0.263227 -0.473307 -0.383638
0.154324 0.0518264 0.268467 0.123943 -0.197086 -0.227373 -0.529617 0.101213 0.0152783 0.226024 0.227196 -0.237176 0.22592 0.052449 0.0555874 0.254214 0.252956 -0.227248 -0.327535 -0.219994 0.00191115 0.245768 -0.254258 -0.334079 0.105201 -0.0810407[ Info: Running 10 iterations EM on diag cov GMM with 32 Gaussians in 26 dimensions
[ Info: iteration 1, average log likelihood -1.417816
[ Info: iteration 2, average log likelihood -1.417807
[ Info: iteration 3, average log likelihood -1.417798
[ Info: iteration 4, average log likelihood -1.417789
[ Info: iteration 5, average log likelihood -1.417780
[ Info: iteration 6, average log likelihood -1.417771
[ Info: iteration 7, average log likelihood -1.417763
[ Info: iteration 8, average log likelihood -1.417754
[ Info: iteration 9, average log likelihood -1.417746
[ Info: iteration 10, average log likelihood -1.417738
┌ Info: EM with 100000 data points 10 iterations avll -1.417738
└ 59.0 data points per parameter
[ Info: Initializing GMM, 2 Gaussians diag covariance 2 dimensions using 900 data points
Iters objv objv-change | affected
-------------------------------------------------------------
0 1.678561e+05
1 2.230230e+04 -1.455538e+05 | 2
2 7.823675e+03 -1.447862e+04 | 0
3 7.823675e+03 0.000000e+00 | 0
K-means converged with 3 iterations (objv = 7823.67549422947)
┌ Info: K-means with 900 data points using 3 iterations
└ 150.0 data points per parameter
[ Info: Running 10 iterations EM on full cov GMM with 2 Gaussians in 2 dimensions
[ Info: iteration 1, average log likelihood -2.043155
[ Info: iteration 2, average log likelihood -2.043154
[ Info: iteration 3, average log likelihood -2.043154
[ Info: iteration 4, average log likelihood -2.043154
[ Info: iteration 5, average log likelihood -2.043154
[ Info: iteration 6, average log likelihood -2.043154
[ Info: iteration 7, average log likelihood -2.043154
[ Info: iteration 8, average log likelihood -2.043154
[ Info: iteration 9, average log likelihood -2.043154
[ Info: iteration 10, average log likelihood -2.043154
┌ Info: EM with 900 data points 10 iterations avll -2.043154
└ 81.8 data points per parameter
Testing GaussianMixtures tests passed