Package evaluation of KNearestCenters on Julia 1.10.8 (92f03a4775*) started at 2025-02-25T13:35:10.945 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 5.1s ################################################################################ # Installation # Installing KNearestCenters... Resolving package versions... Updating `~/.julia/environments/v1.10/Project.toml` [4dca28ae] + KNearestCenters v0.7.7 Updating `~/.julia/environments/v1.10/Manifest.toml` [7d9f7c33] + Accessors v0.1.41 [79e6a3ab] + Adapt v4.2.0 [dce04be8] + ArgCheck v2.4.0 [4fba245c] + ArrayInterface v7.18.0 [198e06fe] + BangBang v0.4.3 [9718e550] + Baselet v0.1.1 [62783981] + BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] + CPUSummary v0.2.6 [324d7699] + CategoricalArrays v0.10.8 [d360d2e6] + ChainRulesCore v1.25.1 [fb6a15b2] + CloseOpenIntervals v0.1.13 [f70d9fcc] + CommonWorldInvalidations v1.0.0 [34da2185] + Compat v4.16.0 [a33af91c] + CompositionsBase v0.1.2 [187b0558] + ConstructionBase v1.5.8 [6add18c4] + ContextVariablesX v0.1.3 [adafc99b] + CpuId v0.3.1 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.7.0 [864edb3b] + DataStructures v0.18.20 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [b4f34e82] + Distances v0.10.12 [ffbed154] + DocStringExtensions v0.9.3 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [5789e2e9] + FileIO v1.16.6 [9c68100b] + FoldsThreads v0.1.2 [069b7b12] + FunctionWrappers v1.1.3 [3e5b6fbb] + HostCPUFeatures v0.1.17 [615f187c] + IfElse v0.1.1 [22cec73e] + InitialValues v0.3.1 [842dd82b] + InlineStrings v1.4.3 [3587e190] + InverseFunctions v0.1.17 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.4 [82899510] + IteratorInterfaceExtensions v1.0.0 ⌅ [033835bb] + JLD2 v0.4.54 [b14d175d] + JuliaVariables v0.2.4 [5d8de97f] + KCenters v0.9.0 [4dca28ae] + KNearestCenters v0.7.7 [b964fa9f] + LaTeXStrings v1.4.0 [10f19ff3] + LayoutPointers v0.1.17 ⌅ [7f8f8fb0] + LearnBase v0.3.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [bdcacae8] + LoopVectorization v0.12.171 ⌅ [30fc2ffe] + LossFunctions v0.8.1 ⌃ [9920b226] + MLDataPattern v0.5.4 [cc2ba9b6] + MLDataUtils v0.5.4 [66a33bbf] + MLLabelUtils v0.5.7 [d8e11817] + MLStyle v0.4.17 ⌅ [f1d291b0] + MLUtils v0.2.11 [1914dd2f] + MacroTools v0.5.15 [d125e4d3] + ManualMemory v0.1.8 [dbb5928d] + MappedArrays v0.4.2 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [71a1bf82] + NameResolution v0.1.5 [6fe1bfb0] + OffsetArrays v1.15.0 [bac558e1] + OrderedCollections v1.8.0 [d96e819e] + Parameters v0.12.3 [f517fe37] + Polyester v0.7.16 [1d0040c9] + PolyesterWeave v0.2.2 [2dfb63ee] + PooledArrays v1.4.3 [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [8162dcfd] + PrettyPrint v0.2.0 [08abe8d2] + PrettyTables v2.4.0 [3cdcf5f2] + RecipesBase v1.3.4 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.0 [94e857df] + SIMDTypes v0.1.0 [476501e8] + SLEEFPirates v0.6.43 ⌅ [0e966ebe] + SearchModels v0.3.3 [91c51154] + SentinelArrays v1.4.8 [efcf1570] + Setfield v1.1.1 [605ecd9f] + ShowCases v0.1.0 [053f045d] + SimilaritySearch v0.11.10 [a2af1166] + SortingAlgorithms v1.2.1 [171d559e] + SplittablesBase v0.1.15 [aedffcd0] + Static v1.1.1 [0d7ed370] + StaticArrayInterface v1.8.0 [1e83bf80] + StaticArraysCore v1.4.3 [82ae8749] + StatsAPI v1.7.0 ⌅ [2913bbd2] + StatsBase v0.33.21 [7792a7ef] + StrideArraysCore v0.5.7 [892a3eda] + StringManipulation v0.4.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.0 [8290d209] + ThreadingUtilities v0.5.2 [3bb67fe8] + TranscodingStreams v0.11.3 [28d57a85] + Transducers v0.4.84 [3a884ed6] + UnPack v1.0.2 [3d5dd08c] + VectorizationBase v0.21.71 [0dad84c5] + ArgTools v1.1.1 [56f22d72] + Artifacts [2a0f44e3] + Base64 [ade2ca70] + Dates [8ba89e20] + Distributed [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching [9fa8497b] + Future [b77e0a4c] + InteractiveUtils [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 [8f399da3] + Libdl [37e2e46d] + LinearAlgebra [56ddb016] + Logging [d6f4376e] + Markdown [a63ad114] + Mmap [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.10.0 [de0858da] + Printf [3fa0cd96] + REPL [9a3f8284] + Random [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization [6462fe0b] + Sockets [2f01184e] + SparseArrays v1.10.0 [10745b16] + Statistics v1.10.0 [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test [cf7118a7] + UUIDs [4ec0a83e] + Unicode [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.4.0+0 [e37daf67] + LibGit2_jll v1.6.4+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.2+1 [14a3606d] + MozillaCACerts_jll v2023.1.10 [4536629a] + OpenBLAS_jll v0.3.23+4 [bea87d4a] + SuiteSparse_jll v7.2.1+1 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.52.0+1 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 5.61s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 203.56s ################################################################################ # Testing # Testing KNearestCenters Status `/tmp/jl_lLTTYU/Project.toml` ⌅ [4c88cf16] Aqua v0.6.7 [336ed68f] CSV v0.10.15 [324d7699] CategoricalArrays v0.10.8 [5d8de97f] KCenters v0.9.0 [4dca28ae] KNearestCenters v0.7.7 ⌅ [30fc2ffe] LossFunctions v0.8.1 ⌅ [f1d291b0] MLUtils v0.2.11 ⌅ [0e966ebe] SearchModels v0.3.3 [053f045d] SimilaritySearch v0.11.10 [82ae8749] StatsAPI v1.7.0 ⌅ [2913bbd2] StatsBase v0.33.21 [37e2e46d] LinearAlgebra [9a3f8284] Random [2f01184e] SparseArrays v1.10.0 [8dfed614] Test Status `/tmp/jl_lLTTYU/Manifest.toml` [7d9f7c33] Accessors v0.1.41 [79e6a3ab] Adapt v4.2.0 ⌅ [4c88cf16] Aqua v0.6.7 [dce04be8] ArgCheck v2.4.0 [4fba245c] ArrayInterface v7.18.0 [198e06fe] BangBang v0.4.3 [9718e550] Baselet v0.1.1 [62783981] BitTwiddlingConvenienceFunctions v0.1.6 [2a0fbf3d] CPUSummary v0.2.6 [336ed68f] CSV v0.10.15 [324d7699] CategoricalArrays v0.10.8 [d360d2e6] ChainRulesCore v1.25.1 [fb6a15b2] CloseOpenIntervals v0.1.13 [944b1d66] CodecZlib v0.7.8 [f70d9fcc] CommonWorldInvalidations v1.0.0 [34da2185] Compat v4.16.0 [a33af91c] CompositionsBase v0.1.2 [187b0558] ConstructionBase v1.5.8 [6add18c4] ContextVariablesX v0.1.3 [adafc99b] CpuId v0.3.1 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.0 [864edb3b] DataStructures v0.18.20 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [b4f34e82] Distances v0.10.12 [ffbed154] DocStringExtensions v0.9.3 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [5789e2e9] FileIO v1.16.6 [48062228] FilePathsBase v0.9.23 [9c68100b] FoldsThreads v0.1.2 [069b7b12] FunctionWrappers v1.1.3 [3e5b6fbb] HostCPUFeatures v0.1.17 [615f187c] IfElse v0.1.1 [22cec73e] InitialValues v0.3.1 [842dd82b] InlineStrings v1.4.3 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [82899510] IteratorInterfaceExtensions v1.0.0 ⌅ [033835bb] JLD2 v0.4.54 [b14d175d] JuliaVariables v0.2.4 [5d8de97f] KCenters v0.9.0 [4dca28ae] KNearestCenters v0.7.7 [b964fa9f] LaTeXStrings v1.4.0 [10f19ff3] LayoutPointers v0.1.17 ⌅ [7f8f8fb0] LearnBase v0.3.0 [2ab3a3ac] LogExpFunctions v0.3.29 [bdcacae8] LoopVectorization v0.12.171 ⌅ [30fc2ffe] LossFunctions v0.8.1 ⌃ [9920b226] MLDataPattern v0.5.4 [cc2ba9b6] MLDataUtils v0.5.4 [66a33bbf] MLLabelUtils v0.5.7 [d8e11817] MLStyle v0.4.17 ⌅ [f1d291b0] MLUtils v0.2.11 [1914dd2f] MacroTools v0.5.15 [d125e4d3] ManualMemory v0.1.8 [dbb5928d] MappedArrays v0.4.2 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [71a1bf82] NameResolution v0.1.5 [6fe1bfb0] OffsetArrays v1.15.0 [bac558e1] OrderedCollections v1.8.0 [d96e819e] Parameters v0.12.3 [69de0a69] Parsers v2.8.1 [f517fe37] Polyester v0.7.16 [1d0040c9] PolyesterWeave v0.2.2 [2dfb63ee] PooledArrays v1.4.3 [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [8162dcfd] PrettyPrint v0.2.0 [08abe8d2] PrettyTables v2.4.0 [3cdcf5f2] RecipesBase v1.3.4 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.0 [94e857df] SIMDTypes v0.1.0 [476501e8] SLEEFPirates v0.6.43 ⌅ [0e966ebe] SearchModels v0.3.3 [91c51154] SentinelArrays v1.4.8 [efcf1570] Setfield v1.1.1 [605ecd9f] ShowCases v0.1.0 [053f045d] SimilaritySearch v0.11.10 [a2af1166] SortingAlgorithms v1.2.1 [171d559e] SplittablesBase v0.1.15 [aedffcd0] Static v1.1.1 [0d7ed370] StaticArrayInterface v1.8.0 [1e83bf80] StaticArraysCore v1.4.3 [82ae8749] StatsAPI v1.7.0 ⌅ [2913bbd2] StatsBase v0.33.21 [7792a7ef] StrideArraysCore v0.5.7 [892a3eda] StringManipulation v0.4.1 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.0 [8290d209] ThreadingUtilities v0.5.2 [3bb67fe8] TranscodingStreams v0.11.3 [28d57a85] Transducers v0.4.84 [3a884ed6] UnPack v1.0.2 [3d5dd08c] VectorizationBase v0.21.71 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 [0dad84c5] ArgTools v1.1.1 [56f22d72] Artifacts [2a0f44e3] Base64 [ade2ca70] Dates [8ba89e20] Distributed [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching [9fa8497b] Future [b77e0a4c] InteractiveUtils [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 [8f399da3] Libdl [37e2e46d] LinearAlgebra [56ddb016] Logging [d6f4376e] Markdown [a63ad114] Mmap [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.10.0 [de0858da] Printf [3fa0cd96] REPL [9a3f8284] Random [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization [6462fe0b] Sockets [2f01184e] SparseArrays v1.10.0 [10745b16] Statistics v1.10.0 [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test [cf7118a7] UUIDs [4ec0a83e] Unicode [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.4.0+0 [e37daf67] LibGit2_jll v1.6.4+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.2+1 [14a3606d] MozillaCACerts_jll v2023.1.10 [4536629a] OpenBLAS_jll v0.3.23+4 [bea87d4a] SuiteSparse_jll v7.2.1+1 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.52.0+1 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... Test Summary: |Time Method ambiguity | None 0.1s Test Summary: | Pass Total Time Unbound type parameters | 1 1 0.4s Test Summary: | Pass Total Time Undefined exports | 1 1 0.0s Test Summary: | Pass Total Time Compare Project.toml and test/Project.toml | 1 1 0.0s Test Summary: | Pass Total Time Stale dependencies | 1 1 15.8s Test Summary: | Pass Total Time Compat bounds | 1 1 0.5s Test Summary: | Pass Total Time Project.toml formatting | 2 2 0.7s Test Summary: | Pass Total Time Piracy | 1 1 0.2s Test Summary: | Pass Total Time Scores | 9 9 2.4s ┌ Warning: Base.download is deprecated; use Downloads.download instead │ caller = loadiris(; at::Float64, shuffle::Bool) at loaddata.jl:9 └ @ Main ~/.julia/packages/KNearestCenters/2glmo/test/loaddata.jl:9 SearchModels> search params iter=0, tol=-1.0, initialpopulation=16, maxpopulation=16, bsize=8, mutbsize=4, crossbsize=8 SearchModels iteration 1> population: 11, bsize: 8, queue: 6, observed: 17, best-error: 0.014285714285714235 worst-error: 0.1785714285714286 SearchModels iteration 2> population: 16, bsize: 8, queue: 1, observed: 18, best-error: 0.014285714285714235 worst-error: 0.18571428571428572 SearchModels iteration 3> population: 16, bsize: 8, queue: 3, observed: 21, best-error: 0.014285714285714235 worst-error: 0.1785714285714286 SearchModels iteration 4> population: 16, bsize: 8, queue: 2, observed: 23, best-error: 0.014285714285714235 worst-error: 0.15000000000000002 SearchModels iteration 5> population: 16, bsize: 8, queue: 1, observed: 24, best-error: 0.014285714285714235 worst-error: 0.12857142857142856 SearchModels iteration 6> population: 16, bsize: 8, queue: 1, observed: 25, best-error: 0.014285714285714235 worst-error: 0.12857142857142856 SearchModels iteration 7> population: 16, bsize: 8, queue: 1, observed: 26, best-error: 0.014285714285714235 worst-error: 0.12142857142857144 SearchModels iteration 8> population: 16, bsize: 8, queue: 1, observed: 27, best-error: 0.014285714285714235 worst-error: 0.12142857142857144 SearchModels iteration 9> population: 16, bsize: 8, queue: 0, observed: 27, best-error: 0.014285714285714235 worst-error: 0.12142857142857144 SearchModels iteration 10> population: 16, bsize: 8, queue: 2, observed: 29, best-error: 0.014285714285714235 worst-error: 0.12142857142857144 SearchModels iteration 11> population: 16, bsize: 8, queue: 1, observed: 30, best-error: 0.014285714285714235 worst-error: 0.11428571428571432 SearchModels iteration 12> population: 16, bsize: 8, queue: 0, observed: 30, best-error: 0.014285714285714235 worst-error: 0.11428571428571432 SearchModels iteration 13> population: 16, bsize: 8, queue: 0, observed: 30, best-error: 0.014285714285714235 worst-error: 0.11428571428571432 SearchModels iteration 14> population: 16, bsize: 8, queue: 0, observed: 30, best-error: 0.014285714285714235 worst-error: 0.11428571428571432 SearchModels iteration 15> population: 16, bsize: 8, queue: 1, observed: 31, best-error: 0.014285714285714235 worst-error: 0.11428571428571432 SearchModels> reached maximum number of iterations 16 === BEST MODEL:KncConfig{DirectKernel, CosineDistance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(DirectKernel(), CosineDistance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0)) => 0.014285714285714235 Test Summary: | Pass Total Time NearestCenter search_models | 1 1 1m28.2s WARNING: Method definition loadiris() in module Main at /home/pkgeval/.julia/packages/KNearestCenters/2glmo/test/loaddata.jl:5 overwritten on the same line (check for duplicate calls to `include`). WARNING: Method definition kwcall(NamedTuple{names, T} where T<:Tuple where names, typeof(Main.loadiris)) in module Main at /home/pkgeval/.julia/packages/KNearestCenters/2glmo/test/loaddata.jl:5 overwritten on the same line (check for duplicate calls to `include`). WARNING: Method definition loadlinearreg() in module Main at /home/pkgeval/.julia/packages/KNearestCenters/2glmo/test/loaddata.jl:21 overwritten on the same line (check for duplicate calls to `include`). KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, CentroidSelection}(L2Distance(), CentroidSelection(), 1, 7, 1, :rand, 0.5, 3) *** starting iteration: 1; err: Float32[Inf, 0.5932875] *** *** computing centroids *** *** computing 7 nearest references *** *** new score with 7 references: Float32[Inf, 0.5932875, 0.43724436] *** *** finished computation of 7 references, err: Float32[Inf, 0.5932875, 0.43724436] *** *** center 1: selecting labels [2] (freq >= 3) [from [0, 3, 2]] *** center 2: selecting labels [3] (freq >= 3) [from [0, 0, 11]] *** center 3: selecting labels [3] (freq >= 3) [from [0, 1, 11]] *** center 4: selecting labels [2] (freq >= 3) [from [0, 11, 0]] *** center 5: selecting labels [1] (freq >= 3) [from [13, 0, 0]] *** center 6: selecting labels [1] (freq >= 3) [from [14, 0, 0]] *** center 7: selecting labels [2] (freq >= 3) [from [0, 9, 0]] finished with 7 centers; started with 7 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, CentroidSelection}(L2Distance(), CentroidSelection(), 1, 17, 1, :rand, 0.5, 3) *** starting iteration: 1; err: Float32[Inf, 0.4244394] *** *** computing centroids *** *** computing 17 nearest references *** *** new score with 17 references: Float32[Inf, 0.4244394, 0.32385972] *** *** finished computation of 17 references, err: Float32[Inf, 0.4244394, 0.32385972] *** *** center 1: selecting labels [1] (freq >= 3) [from [3, 0, 0]] *** center 2: selecting labels [3] (freq >= 3) [from [0, 0, 11]] *** center 3: selecting labels [3] (freq >= 3) [from [0, 0, 6]] *** center 4: selecting labels [1] (freq >= 3) [from [3, 0, 0]] *** center 5: selecting labels [2] (freq >= 3) [from [0, 3, 0]] *** center 6: selecting labels [3] (freq >= 3) [from [0, 2, 7]] *** center 7: selecting labels [1] (freq >= 3) [from [8, 0, 0]] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 2, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 10: selecting labels [2] (freq >= 3) [from [0, 6, 0]] *** center 11: selecting labels [1] (freq >= 3) [from [10, 0, 0]] *** center 12: selecting labels [2] (freq >= 3) [from [0, 5, 0]] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [2, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [1, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 2, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 17: selecting labels [2] (freq >= 3) [from [0, 4, 0]] finished with 11 centers; started with 17 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 (acc, acc12) = (0.92, 0.9733333333333334) Test Summary: | Pass Total Time KncProto | 2 2 23.6s SearchModels> search params iter=0, tol=-1.0, initialpopulation=32, maxpopulation=16, bsize=8, mutbsize=8, crossbsize=8 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(CosineDistance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 3, 24, 10, :rand, 0.8, 28) *** starting iteration: 1; err: Float32[Inf, 0.00035139956] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.00035139956, 0.0002943913] *** *** finished computation of 24 references, err: Float32[Inf, 0.00035139956, 0.0002943913] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [1, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 1, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [1, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [2, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [1, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 2, 2] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 0, 1] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [5, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [2, 0, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 6, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [2, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 5, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 0, 4] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [1, 0, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 0, 2] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 0, 3] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [2, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 0, 2] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [3, 0, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [0, 1, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 28, freqs: [1, 0, 0] finished with 0 centers; started with 24 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(CosineDistance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 3, 19, 7, :rand, 0.9, 16) *** starting iteration: 1; err: Float32[Inf, 0.0005342484] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.0005342484, 0.0003587743] *** *** finished computation of 19 references, err: Float32[Inf, 0.0005342484, 0.0003587743] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 2, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [6, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [6, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [4, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 3, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 2, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 3, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 3] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [2, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 2] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 2] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 2] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 5] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [3, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] finished with 0 centers; started with 19 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, CentroidSelection}(L1Distance(), CentroidSelection(), 3, 20, 4, :rand, 0.5, 19) *** starting iteration: 1; err: Float32[Inf, 0.62000006] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.62000006, 0.47113338] *** *** starting iteration: 2; err: Float32[Inf, 0.62000006, 0.47113338] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.62000006, 0.47113338, 0.42655003] *** *** starting iteration: 3; err: Float32[Inf, 0.62000006, 0.47113338, 0.42655003] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.62000006, 0.47113338, 0.42655003, 0.41472855] *** *** starting iteration: 4; err: Float32[Inf, 0.62000006, 0.47113338, 0.42655003, 0.41472855] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.62000006, 0.47113338, 0.42655003, 0.41472855, 0.4132286] *** *** finished computation of 20 references, err: Float32[Inf, 0.62000006, 0.47113338, 0.42655003, 0.41472855, 0.4132286] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 3, 1] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 4, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 2] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [3, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 1] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 1] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 3] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 5] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 1] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [2, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [7, 0, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [7, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 3, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [2, 0, 0] finished with 0 centers; started with 20 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, CentroidSelection}(L1Distance(), CentroidSelection(), 5, 23, 7, :rand, 0.4, 19) *** starting iteration: 1; err: Float32[Inf, 0.52] *** *** computing centroids *** *** computing 23 nearest references *** *** new score with 23 references: Float32[Inf, 0.52, 0.4292143] *** *** starting iteration: 2; err: Float32[Inf, 0.52, 0.4292143] *** *** computing centroids *** *** computing 23 nearest references *** *** new score with 23 references: Float32[Inf, 0.52, 0.4292143, 0.4006] *** *** starting iteration: 3; err: Float32[Inf, 0.52, 0.4292143, 0.4006] *** *** computing centroids *** *** computing 23 nearest references *** *** new score with 23 references: Float32[Inf, 0.52, 0.4292143, 0.4006, 0.36793336] *** *** starting iteration: 4; err: Float32[Inf, 0.52, 0.4292143, 0.4006, 0.36793336] *** *** computing centroids *** *** computing 23 nearest references *** *** new score with 23 references: Float32[Inf, 0.52, 0.4292143, 0.4006, 0.36793336, 0.36793336] *** *** finished computation of 23 references, err: Float32[Inf, 0.52, 0.4292143, 0.4006, 0.36793336, 0.36793336] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [5, 0, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 1] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 4, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [3, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [2, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 2] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [1, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 2, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 1] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 5] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 3] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 1] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 3, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [1, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 1] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 2, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [3, 0, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [2, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [4, 0, 0] finished with 0 centers; started with 23 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L2Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 4, 30, 10, :rand, 0.8, 22) *** starting iteration: 1; err: Float32[Inf, 0.24630634] *** *** computing centroids *** *** computing 30 nearest references *** *** new score with 30 references: Float32[Inf, 0.24630634, 0.20044164] *** *** starting iteration: 2; err: Float32[Inf, 0.24630634, 0.20044164] *** *** computing centroids *** *** computing 30 nearest references *** *** new score with 30 references: Float32[Inf, 0.24630634, 0.20044164, 0.19188166] *** *** starting iteration: 3; err: Float32[Inf, 0.24630634, 0.20044164, 0.19188166] *** *** computing centroids *** *** computing 30 nearest references *** *** new score with 30 references: Float32[Inf, 0.24630634, 0.20044164, 0.19188166, 0.19182102] *** *** finished computation of 30 references, err: Float32[Inf, 0.24630634, 0.20044164, 0.19188166, 0.19182102] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [1, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [1, 0, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 6, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [1, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 3] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [3, 0, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [1, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 3] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 1] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [5, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 1] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [3, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [1, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 1] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 1] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [2, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 0] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 0] *** center 26: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 0] *** center 27: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 0] *** center 28: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 4] *** center 29: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [3, 0, 0] *** center 30: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 0] finished with 0 centers; started with 30 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 25, 13, :rand, 0.1, 13) *** starting iteration: 1; err: Float32[Inf, 0.52599996] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992] *** *** starting iteration: 2; err: Float32[Inf, 0.52599996, 0.44799992] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342] *** *** starting iteration: 3; err: Float32[Inf, 0.52599996, 0.44799992, 0.342] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998] *** *** starting iteration: 4; err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003] *** *** starting iteration: 5; err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998] *** *** starting iteration: 6; err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996] *** *** starting iteration: 7; err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308] *** *** starting iteration: 8; err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996] *** *** starting iteration: 9; err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996, 0.36399996] *** *** starting iteration: 10; err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996, 0.36399996] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996, 0.36399996, 0.34999996] *** *** starting iteration: 11; err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996, 0.36399996, 0.34999996] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996, 0.36399996, 0.34999996, 0.33] *** *** starting iteration: 12; err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996, 0.36399996, 0.34999996, 0.33] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996, 0.36399996, 0.34999996, 0.33, 0.316] *** *** starting iteration: 13; err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996, 0.36399996, 0.34999996, 0.33, 0.316] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996, 0.36399996, 0.34999996, 0.33, 0.316, 0.296] *** *** finished computation of 25 references, err: Float32[Inf, 0.52599996, 0.44799992, 0.342, 0.34399998, 0.31000003, 0.35799998, 0.34599996, 0.308, 0.36199996, 0.36399996, 0.34999996, 0.33, 0.316, 0.296] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 4, 4] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 2, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 1] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 1] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [5, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 3, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [4, 0, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 2] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 1] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [3, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 1] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 1, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 4, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 1, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 3] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 1] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] finished with 0 centers; started with 25 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, CentroidSelection}(L1Distance(), CentroidSelection(), 1, 2, 10, :rand, 0.3, 1) *** starting iteration: 1; err: Float32[Inf, 1.7100003] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.7100003, 1.3306624] *** *** starting iteration: 2; err: Float32[Inf, 1.7100003, 1.3306624] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.7100003, 1.3306624, 1.3306624] *** *** finished computation of 2 references, err: Float32[Inf, 1.7100003, 1.3306624, 1.3306624] *** *** center 1: selecting labels [1, 2] (freq >= 1) [from [21, 1, 0]] ** center: 1, normalized-entropy: 0.2667649878030262, [(1, 21), (2, 1)] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 14, 14]] ** center: 2, normalized-entropy: 1.0, [(2, 14), (3, 14)] finished with 3 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, CentroidSelection}(L1Distance(), CentroidSelection(), 1, 2, 10, :rand, 0.3, 1) *** starting iteration: 1; err: Float32[Inf, 3.1720002] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.1720002, 1.9166155] *** *** starting iteration: 2; err: Float32[Inf, 3.1720002, 1.9166155] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.1720002, 1.9166155, 1.8821816] *** *** starting iteration: 3; err: Float32[Inf, 3.1720002, 1.9166155, 1.8821816] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.1720002, 1.9166155, 1.8821816, 1.8362668] *** *** starting iteration: 4; err: Float32[Inf, 3.1720002, 1.9166155, 1.8821816, 1.8362668] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.1720002, 1.9166155, 1.8821816, 1.8362668, 1.679353] *** *** starting iteration: 5; err: Float32[Inf, 3.1720002, 1.9166155, 1.8821816, 1.8362668, 1.679353] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.1720002, 1.9166155, 1.8821816, 1.8362668, 1.679353, 1.4827368] *** *** starting iteration: 6; err: Float32[Inf, 3.1720002, 1.9166155, 1.8821816, 1.8362668, 1.679353, 1.4827368] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.1720002, 1.9166155, 1.8821816, 1.8362668, 1.679353, 1.4827368, 1.4827368] *** *** finished computation of 2 references, err: Float32[Inf, 3.1720002, 1.9166155, 1.8821816, 1.8362668, 1.679353, 1.4827368, 1.4827368] *** *** center 1: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 9, 10]] ** center: 2, normalized-entropy: 0.9980008838722993, [(2, 9), (3, 10)] finished with 3 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, RandomCenterSelection}(L2Distance(), RandomCenterSelection(), 5, 2, 1, :rand, 0.3, 13) *** starting iteration: 1; err: Float32[Inf, 1.8729193] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.8729193, 1.8956641] *** *** finished computation of 2 references, err: Float32[Inf, 1.8729193, 1.8956641] *** *** center 1: selecting labels [1, 2] (freq >= 13) [from [21, 14, 4]] ** center: 1, normalized-entropy: 0.9709505944546688, [(1, 21), (2, 14)] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 1, 10] finished with 2 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, RandomCenterSelection}(L2Distance(), RandomCenterSelection(), 5, 2, 1, :rand, 0.3, 13) *** starting iteration: 1; err: Float32[Inf, 3.096819] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.096819, 1.110465] *** *** finished computation of 2 references, err: Float32[Inf, 3.096819, 1.110465] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [6, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 9, 10] finished with 0 centers; started with 2 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L2Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 1, 30, 1, :rand, 0.2, 16) *** starting iteration: 1; err: Float32[Inf, 0.22776264] *** *** computing centroids *** *** computing 30 nearest references *** *** new score with 30 references: Float32[Inf, 0.22776264, 0.18569808] *** *** finished computation of 30 references, err: Float32[Inf, 0.22776264, 0.18569808] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 1] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 2] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 3] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [2, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [2, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 6, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 1] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [6, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 1] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 2, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 2] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [2, 0, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [3, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 1] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 0] *** center 26: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 3] *** center 27: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 0] *** center 28: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 0] *** center 29: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 3, 0] *** center 30: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] finished with 0 centers; started with 30 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 7, 2, 4, :rand, 0.4, 16) *** starting iteration: 1; err: Float32[Inf, 1.6235694] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.6235694, 1.1985388] *** *** starting iteration: 2; err: Float32[Inf, 1.6235694, 1.1985388] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.6235694, 1.1985388, 1.1985388] *** *** finished computation of 2 references, err: Float32[Inf, 1.6235694, 1.1985388, 1.1985388] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 15, 14] *** center 2: selecting labels [1] (freq >= 16) [from [21, 0, 0]] finished with 1 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 7, 2, 4, :rand, 0.4, 16) *** starting iteration: 1; err: Float32[Inf, 1.7294354] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.7294354, 1.0323678] *** *** starting iteration: 2; err: Float32[Inf, 1.7294354, 1.0323678] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.7294354, 1.0323678, 0.8579118] *** *** starting iteration: 3; err: Float32[Inf, 1.7294354, 1.0323678, 0.8579118] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.7294354, 1.0323678, 0.8579118, 1.0323678] *** *** starting iteration: 4; err: Float32[Inf, 1.7294354, 1.0323678, 0.8579118, 1.0323678] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.7294354, 1.0323678, 0.8579118, 1.0323678, 1.0323678] *** *** finished computation of 2 references, err: Float32[Inf, 1.7294354, 1.0323678, 0.8579118, 1.0323678, 1.0323678] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [6, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 9, 10] finished with 0 centers; started with 2 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, CentroidSelection}(L2Distance(), CentroidSelection(), 3, 17, 10, :rand, 0.8, 7) *** starting iteration: 1; err: Float32[Inf, 0.3907546] *** *** computing centroids *** *** computing 17 nearest references *** *** new score with 17 references: Float32[Inf, 0.3907546, 0.2963324] *** *** starting iteration: 2; err: Float32[Inf, 0.3907546, 0.2963324] *** *** computing centroids *** *** computing 17 nearest references *** *** new score with 17 references: Float32[Inf, 0.3907546, 0.2963324, 0.27851138] *** *** starting iteration: 3; err: Float32[Inf, 0.3907546, 0.2963324, 0.27851138] *** *** computing centroids *** *** computing 17 nearest references *** *** new score with 17 references: Float32[Inf, 0.3907546, 0.2963324, 0.27851138, 0.27154377] *** *** starting iteration: 4; err: Float32[Inf, 0.3907546, 0.2963324, 0.27851138, 0.27154377] *** *** computing centroids *** *** computing 17 nearest references *** *** new score with 17 references: Float32[Inf, 0.3907546, 0.2963324, 0.27851138, 0.27154377, 0.26654643] *** *** starting iteration: 5; err: Float32[Inf, 0.3907546, 0.2963324, 0.27851138, 0.27154377, 0.26654643] *** *** computing centroids *** *** computing 17 nearest references *** *** new score with 17 references: Float32[Inf, 0.3907546, 0.2963324, 0.27851138, 0.27154377, 0.26654643, 0.26654643] *** *** finished computation of 17 references, err: Float32[Inf, 0.3907546, 0.2963324, 0.27851138, 0.27154377, 0.26654643, 0.26654643] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [5, 0, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 6] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 3, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 4] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 3, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 3, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [2, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 4, 2] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [2, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [6, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [3, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] finished with 0 centers; started with 17 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 3, 21, 10, :rand, 0.7, 22) *** starting iteration: 1; err: Float32[Inf, 0.49600005] *** *** computing centroids *** *** computing 21 nearest references *** *** new score with 21 references: Float32[Inf, 0.49600005, 0.43666673] *** *** starting iteration: 2; err: Float32[Inf, 0.49600005, 0.43666673] *** *** computing centroids *** *** computing 21 nearest references *** *** new score with 21 references: Float32[Inf, 0.49600005, 0.43666673, 0.41333336] *** *** starting iteration: 3; err: Float32[Inf, 0.49600005, 0.43666673, 0.41333336] *** *** computing centroids *** *** computing 21 nearest references *** *** new score with 21 references: Float32[Inf, 0.49600005, 0.43666673, 0.41333336, 0.4166667] *** *** starting iteration: 4; err: Float32[Inf, 0.49600005, 0.43666673, 0.41333336, 0.4166667] *** *** computing centroids *** *** computing 21 nearest references *** *** new score with 21 references: Float32[Inf, 0.49600005, 0.43666673, 0.41333336, 0.4166667, 0.4166667] *** *** finished computation of 21 references, err: Float32[Inf, 0.49600005, 0.43666673, 0.41333336, 0.4166667, 0.4166667] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [5, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [2, 0, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 2] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [5, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [1, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 4, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [2, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [1, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 3, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 2, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 6] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 4] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 0, 2] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 3, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [1, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [0, 1, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [3, 0, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 22, freqs: [1, 0, 0] finished with 0 centers; started with 21 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 5, 7, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.7699999] *** *** computing centroids *** *** computing 7 nearest references *** *** new score with 7 references: Float32[Inf, 0.7699999, 0.7012001] *** *** finished computation of 7 references, err: Float32[Inf, 0.7699999, 0.7012001] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 2, 4]] ** center: 1, normalized-entropy: 0.9182958340544894, [(2, 2), (3, 4)] *** center 2: selecting labels [1] (freq >= 1) [from [5, 0, 0]] *** center 3: selecting labels [3] (freq >= 1) [from [0, 0, 4]] *** center 4: selecting labels [3] (freq >= 1) [from [0, 0, 6]] *** center 5: selecting labels [2] (freq >= 1) [from [0, 5, 0]] *** center 6: selecting labels [2] (freq >= 1) [from [0, 8, 0]] *** center 7: selecting labels [1] (freq >= 1) [from [16, 0, 0]] finished with 8 centers; started with 7 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 5, 7, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.65200007] *** *** computing centroids *** *** computing 7 nearest references *** *** new score with 7 references: Float32[Inf, 0.65200007, 0.616] *** *** finished computation of 7 references, err: Float32[Inf, 0.65200007, 0.616] *** *** center 1: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 2: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 3: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 6, 0]] *** center 5: selecting labels [2, 3] (freq >= 1) [from [0, 1, 2]] ** center: 5, normalized-entropy: 0.9182958340544894, [(2, 1), (3, 2)] *** center 6: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 7: selecting labels [3] (freq >= 1) [from [0, 0, 6]] finished with 8 centers; started with 7 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 17, 4, :rand, 0.4, 19) *** starting iteration: 1; err: Float32[Inf, 0.6659999] *** *** computing centroids *** *** computing 17 nearest references *** *** new score with 17 references: Float32[Inf, 0.6659999, 0.54599994] *** *** starting iteration: 2; err: Float32[Inf, 0.6659999, 0.54599994] *** *** computing centroids *** *** computing 17 nearest references *** *** new score with 17 references: Float32[Inf, 0.6659999, 0.54599994, 0.51399994] *** *** starting iteration: 3; err: Float32[Inf, 0.6659999, 0.54599994, 0.51399994] *** *** computing centroids *** *** computing 17 nearest references *** *** new score with 17 references: Float32[Inf, 0.6659999, 0.54599994, 0.51399994, 0.5099999] *** *** starting iteration: 4; err: Float32[Inf, 0.6659999, 0.54599994, 0.51399994, 0.5099999] *** *** computing centroids *** *** computing 17 nearest references *** *** new score with 17 references: Float32[Inf, 0.6659999, 0.54599994, 0.51399994, 0.5099999, 0.52199996] *** *** finished computation of 17 references, err: Float32[Inf, 0.6659999, 0.54599994, 0.51399994, 0.5099999, 0.52199996] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [3, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 4, 1] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [5, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [1, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 3, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [3, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [2, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 3, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 4, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 1] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 4] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [5, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 3] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 4] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [2, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 1] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 0] finished with 0 centers; started with 17 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 24, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.00041642156] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.00041642156, 0.00035936563] *** *** finished computation of 24 references, err: Float32[Inf, 0.00041642156, 0.00035936563] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 3]] *** center 2: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 5: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 6: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 7: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 8: selecting labels [2, 3] (freq >= 1) [from [0, 2, 4]] ** center: 8, normalized-entropy: 0.9182958340544894, [(2, 2), (3, 4)] *** center 9: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 10: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 11: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 12: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 14: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 15: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 17: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 18: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 19: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 20: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 21: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 22: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 23: selecting labels [3] (freq >= 1) [from [0, 0, 6]] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] finished with 22 centers; started with 24 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 24, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.00022942154] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.00022942154, 0.00018256792] *** *** finished computation of 24 references, err: Float32[Inf, 0.00022942154, 0.00018256792] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 2: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 3: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 5: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 8: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 9: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 10: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 12: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 14: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 15: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 16: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 17: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 18: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 22: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 24: selecting labels [2] (freq >= 1) [from [0, 1, 0]] finished with 17 centers; started with 24 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 16, 4, :rand, 0.1, 7) *** starting iteration: 1; err: Float32[Inf, 0.4231319] *** *** computing centroids *** *** computing 16 nearest references *** *** new score with 16 references: Float32[Inf, 0.4231319, 0.35731205] *** *** starting iteration: 2; err: Float32[Inf, 0.4231319, 0.35731205] *** *** computing centroids *** *** computing 16 nearest references *** *** new score with 16 references: Float32[Inf, 0.4231319, 0.35731205, 0.34784764] *** *** starting iteration: 3; err: Float32[Inf, 0.4231319, 0.35731205, 0.34784764] *** *** computing centroids *** *** computing 16 nearest references *** *** new score with 16 references: Float32[Inf, 0.4231319, 0.35731205, 0.34784764, 0.35894975] *** *** starting iteration: 4; err: Float32[Inf, 0.4231319, 0.35731205, 0.34784764, 0.35894975] *** *** computing centroids *** *** computing 16 nearest references *** *** new score with 16 references: Float32[Inf, 0.4231319, 0.35731205, 0.34784764, 0.35894975, 0.34103367] *** *** finished computation of 16 references, err: Float32[Inf, 0.4231319, 0.35731205, 0.34784764, 0.35894975, 0.34103367] *** *** center 1: selecting labels [3] (freq >= 7) [from [0, 0, 8]] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] *** center 3: selecting labels [2] (freq >= 7) [from [0, 7, 2]] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [2, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [3, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [2, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 3, 0] *** center 10: selecting labels [1] (freq >= 7) [from [7, 0, 0]] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 4, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [3, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] finished with 3 centers; started with 16 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 16, 4, :rand, 0.1, 7) *** starting iteration: 1; err: Float32[Inf, 0.27805707] *** *** computing centroids *** *** computing 16 nearest references *** *** new score with 16 references: Float32[Inf, 0.27805707, 0.25580817] *** *** starting iteration: 2; err: Float32[Inf, 0.27805707, 0.25580817] *** *** computing centroids *** *** computing 16 nearest references *** *** new score with 16 references: Float32[Inf, 0.27805707, 0.25580817, 0.24995342] *** *** starting iteration: 3; err: Float32[Inf, 0.27805707, 0.25580817, 0.24995342] *** *** computing centroids *** *** computing 16 nearest references *** *** new score with 16 references: Float32[Inf, 0.27805707, 0.25580817, 0.24995342, 0.18468219] *** *** starting iteration: 4; err: Float32[Inf, 0.27805707, 0.25580817, 0.24995342, 0.18468219] *** *** computing centroids *** *** computing 16 nearest references *** *** new score with 16 references: Float32[Inf, 0.27805707, 0.25580817, 0.24995342, 0.18468219, 0.1860992] *** *** finished computation of 16 references, err: Float32[Inf, 0.27805707, 0.25580817, 0.24995342, 0.18468219, 0.1860992] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [2, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 1] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 3] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 2, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 2, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 2, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 2, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [2, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] finished with 0 centers; started with 16 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L2Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 5, 27, 4, :rand, 0.5, 7) *** starting iteration: 1; err: Float32[Inf, 0.22996649] *** *** computing centroids *** *** computing 27 nearest references *** *** new score with 27 references: Float32[Inf, 0.22996649, 0.19892125] *** *** starting iteration: 2; err: Float32[Inf, 0.22996649, 0.19892125] *** *** computing centroids *** *** computing 27 nearest references *** *** new score with 27 references: Float32[Inf, 0.22996649, 0.19892125, 0.1859582] *** *** starting iteration: 3; err: Float32[Inf, 0.22996649, 0.19892125, 0.1859582] *** *** computing centroids *** *** computing 27 nearest references *** *** new score with 27 references: Float32[Inf, 0.22996649, 0.19892125, 0.1859582, 0.1848009] *** *** starting iteration: 4; err: Float32[Inf, 0.22996649, 0.19892125, 0.1859582, 0.1848009] *** *** computing centroids *** *** computing 27 nearest references *** *** new score with 27 references: Float32[Inf, 0.22996649, 0.19892125, 0.1859582, 0.1848009, 0.1848009] *** *** finished computation of 27 references, err: Float32[Inf, 0.22996649, 0.19892125, 0.1859582, 0.1848009, 0.1848009] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 3, 0] *** center 2: selecting labels [1] (freq >= 7) [from [7, 0, 0]] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 2, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [3, 0, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [2, 0, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [2, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 1] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 2, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [2, 0, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 4] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 3] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 2] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [2, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 2, 0] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 26: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] *** center 27: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] finished with 1 centers; started with 27 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L2Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 5, 27, 4, :rand, 0.5, 7) *** starting iteration: 1; err: Float32[Inf, 0.11519714] *** *** computing centroids *** *** computing 27 nearest references *** *** new score with 27 references: Float32[Inf, 0.11519714, 0.08299367] *** *** starting iteration: 2; err: Float32[Inf, 0.11519714, 0.08299367] *** *** computing centroids *** *** computing 27 nearest references *** *** new score with 27 references: Float32[Inf, 0.11519714, 0.08299367, 0.07398015] *** *** starting iteration: 3; err: Float32[Inf, 0.11519714, 0.08299367, 0.07398015] *** *** computing centroids *** *** computing 27 nearest references *** *** new score with 27 references: Float32[Inf, 0.11519714, 0.08299367, 0.07398015, 0.07382104] *** *** finished computation of 27 references, err: Float32[Inf, 0.11519714, 0.08299367, 0.07398015, 0.07382104] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 3] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 1] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 1] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 1] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [3, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 1] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 2, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 1] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 1, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 1] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 1] *** center 26: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] *** center 27: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 0] finished with 0 centers; started with 27 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 7, 20, 4, :rand, 0.6, 13) *** starting iteration: 1; err: Float32[Inf, 0.38146743] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.38146743, 0.29404762] *** *** starting iteration: 2; err: Float32[Inf, 0.38146743, 0.29404762] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.38146743, 0.29404762, 0.31433007] *** *** starting iteration: 3; err: Float32[Inf, 0.38146743, 0.29404762, 0.31433007] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.38146743, 0.29404762, 0.31433007, 0.30577907] *** *** starting iteration: 4; err: Float32[Inf, 0.38146743, 0.29404762, 0.31433007, 0.30577907] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.38146743, 0.29404762, 0.31433007, 0.30577907, 0.29274774] *** *** finished computation of 20 references, err: Float32[Inf, 0.38146743, 0.29404762, 0.31433007, 0.30577907, 0.29274774] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 1] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 4, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 1] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 5, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 2] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [3, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 3, 4] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 3, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [3, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 3] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 3] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [7, 0, 0] finished with 0 centers; started with 20 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 13, :rand, 0.9, 7) *** starting iteration: 1; err: Float32[Inf, 0.77436006] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.77436006, 0.73000836] *** *** starting iteration: 2; err: Float32[Inf, 0.77436006, 0.73000836] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.77436006, 0.73000836, 0.7034676] *** *** starting iteration: 3; err: Float32[Inf, 0.77436006, 0.73000836, 0.7034676] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.77436006, 0.73000836, 0.7034676, 0.72314066] *** *** starting iteration: 4; err: Float32[Inf, 0.77436006, 0.73000836, 0.7034676, 0.72314066] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.77436006, 0.73000836, 0.7034676, 0.72314066, 0.8301756] *** *** starting iteration: 5; err: Float32[Inf, 0.77436006, 0.73000836, 0.7034676, 0.72314066, 0.8301756] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.77436006, 0.73000836, 0.7034676, 0.72314066, 0.8301756, 0.7034676] *** *** starting iteration: 6; err: Float32[Inf, 0.77436006, 0.73000836, 0.7034676, 0.72314066, 0.8301756, 0.7034676] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.77436006, 0.73000836, 0.7034676, 0.72314066, 0.8301756, 0.7034676, 0.70385283] *** *** finished computation of 4 references, err: Float32[Inf, 0.77436006, 0.73000836, 0.7034676, 0.72314066, 0.8301756, 0.7034676, 0.70385283] *** *** center 1: selecting labels [2, 3] (freq >= 7) [from [0, 9, 14]] ** center: 1, normalized-entropy: 0.9656361333706099, [(2, 9), (3, 14)] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 6, 0] *** center 3: selecting labels [1] (freq >= 7) [from [10, 0, 0]] *** center 4: selecting labels [1] (freq >= 7) [from [11, 0, 0]] finished with 4 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 13, :rand, 0.9, 7) *** starting iteration: 1; err: Float32[Inf, 0.70821625] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.70821625, 0.6272775] *** *** starting iteration: 2; err: Float32[Inf, 0.70821625, 0.6272775] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.70821625, 0.6272775, 0.5651787] *** *** starting iteration: 3; err: Float32[Inf, 0.70821625, 0.6272775, 0.5651787] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644] *** *** starting iteration: 4; err: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644, 0.57490575] *** *** starting iteration: 5; err: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644, 0.57490575] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644, 0.57490575, 0.5508639] *** *** starting iteration: 6; err: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644, 0.57490575, 0.5508639] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644, 0.57490575, 0.5508639, 0.6100042] *** *** starting iteration: 7; err: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644, 0.57490575, 0.5508639, 0.6100042] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644, 0.57490575, 0.5508639, 0.6100042, 0.52403307] *** *** starting iteration: 8; err: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644, 0.57490575, 0.5508639, 0.6100042, 0.52403307] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644, 0.57490575, 0.5508639, 0.6100042, 0.52403307, 0.52403307] *** *** finished computation of 4 references, err: Float32[Inf, 0.70821625, 0.6272775, 0.5651787, 0.6543644, 0.57490575, 0.5508639, 0.6100042, 0.52403307, 0.52403307] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] *** center 2: selecting labels [2] (freq >= 7) [from [0, 8, 0]] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [6, 0, 0] *** center 4: selecting labels [3] (freq >= 7) [from [0, 1, 8]] finished with 2 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 1, 20, 10, :rand, 0.9, 19) *** starting iteration: 1; err: Float32[Inf, 0.5379999] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.5379999, 0.51199996] *** *** starting iteration: 2; err: Float32[Inf, 0.5379999, 0.51199996] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.5379999, 0.51199996, 0.426] *** *** starting iteration: 3; err: Float32[Inf, 0.5379999, 0.51199996, 0.426] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.5379999, 0.51199996, 0.426, 0.41199997] *** *** starting iteration: 4; err: Float32[Inf, 0.5379999, 0.51199996, 0.426, 0.41199997] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.5379999, 0.51199996, 0.426, 0.41199997, 0.40799996] *** *** starting iteration: 5; err: Float32[Inf, 0.5379999, 0.51199996, 0.426, 0.41199997, 0.40799996] *** *** computing centroids *** *** computing 20 nearest references *** *** new score with 20 references: Float32[Inf, 0.5379999, 0.51199996, 0.426, 0.41199997, 0.40799996, 0.408] *** *** finished computation of 20 references, err: Float32[Inf, 0.5379999, 0.51199996, 0.426, 0.41199997, 0.40799996, 0.408] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [6, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [1, 0, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 2] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [1, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 2] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 2] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 5, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 5, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 5] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [2, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 1, 1] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [2, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [2, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 2, 2] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [3, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [1, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [1, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [2, 0, 0] finished with 0 centers; started with 20 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L2Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 2, 25, 10, :rand, 0.2, 4) *** starting iteration: 1; err: Float32[Inf, 0.32315636] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.32315636, 0.245816] *** *** starting iteration: 2; err: Float32[Inf, 0.32315636, 0.245816] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.32315636, 0.245816, 0.23097073] *** *** starting iteration: 3; err: Float32[Inf, 0.32315636, 0.245816, 0.23097073] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.32315636, 0.245816, 0.23097073, 0.23260744] *** *** starting iteration: 4; err: Float32[Inf, 0.32315636, 0.245816, 0.23097073, 0.23260744] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.32315636, 0.245816, 0.23097073, 0.23260744, 0.23260744] *** *** finished computation of 25 references, err: Float32[Inf, 0.32315636, 0.245816, 0.23097073, 0.23260744, 0.23260744] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 2] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 2] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 3, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [2, 0, 0] *** center 6: selecting labels [1] (freq >= 4) [from [6, 0, 0]] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 2] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 3] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 11: selecting labels [2] (freq >= 4) [from [0, 6, 0]] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [2, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [2, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 1] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 3, 0] *** center 21: selecting labels [1] (freq >= 4) [from [5, 0, 0]] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 2] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] finished with 3 centers; started with 25 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L2Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 2, 25, 10, :rand, 0.2, 4) *** starting iteration: 1; err: Float32[Inf, 0.31555653] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.31555653, 0.25964335] *** *** starting iteration: 2; err: Float32[Inf, 0.31555653, 0.25964335] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.31555653, 0.25964335, 0.25964335] *** *** finished computation of 25 references, err: Float32[Inf, 0.31555653, 0.25964335, 0.25964335] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 2] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 6: selecting labels [3] (freq >= 4) [from [0, 0, 5]] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 3, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 1] finished with 1 centers; started with 25 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, RandomCenterSelection}(L2Distance(), RandomCenterSelection(), 2, 9, 10, :rand, 0.9, 19) *** starting iteration: 1; err: Float32[Inf, 0.43141773] *** *** computing centroids *** *** computing 9 nearest references *** *** new score with 9 references: Float32[Inf, 0.43141773, 0.41686475] *** *** starting iteration: 2; err: Float32[Inf, 0.43141773, 0.41686475] *** *** computing centroids *** *** computing 9 nearest references *** *** new score with 9 references: Float32[Inf, 0.43141773, 0.41686475, 0.44308385] *** *** starting iteration: 3; err: Float32[Inf, 0.43141773, 0.41686475, 0.44308385] *** *** computing centroids *** *** computing 9 nearest references *** *** new score with 9 references: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301] *** *** starting iteration: 4; err: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301] *** *** computing centroids *** *** computing 9 nearest references *** *** new score with 9 references: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444] *** *** starting iteration: 5; err: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444] *** *** computing centroids *** *** computing 9 nearest references *** *** new score with 9 references: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878] *** *** starting iteration: 6; err: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878] *** *** computing centroids *** *** computing 9 nearest references *** *** new score with 9 references: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878, 0.44053215] *** *** starting iteration: 7; err: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878, 0.44053215] *** *** computing centroids *** *** computing 9 nearest references *** *** new score with 9 references: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878, 0.44053215, 0.44411433] *** *** starting iteration: 8; err: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878, 0.44053215, 0.44411433] *** *** computing centroids *** *** computing 9 nearest references *** *** new score with 9 references: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878, 0.44053215, 0.44411433, 0.44789246] *** *** starting iteration: 9; err: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878, 0.44053215, 0.44411433, 0.44789246] *** *** computing centroids *** *** computing 9 nearest references *** *** new score with 9 references: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878, 0.44053215, 0.44411433, 0.44789246, 0.4407061] *** *** starting iteration: 10; err: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878, 0.44053215, 0.44411433, 0.44789246, 0.4407061] *** *** computing centroids *** *** computing 9 nearest references *** *** new score with 9 references: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878, 0.44053215, 0.44411433, 0.44789246, 0.4407061, 0.45600158] *** *** finished computation of 9 references, err: Float32[Inf, 0.43141773, 0.41686475, 0.44308385, 0.4250301, 0.40691444, 0.43146878, 0.44053215, 0.44411433, 0.44789246, 0.4407061, 0.45600158] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 3, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 2] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 2] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 4] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 7, 1] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [15, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 0, 2] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 5, 3] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [6, 0, 0] finished with 0 centers; started with 9 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 5, 12, 13, :rand, 0.1, 10) *** starting iteration: 1; err: Float32[Inf, 0.7819999] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006] *** *** starting iteration: 2; err: Float32[Inf, 0.7819999, 0.61100006] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001] *** *** starting iteration: 3; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001] *** *** starting iteration: 4; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667] *** *** starting iteration: 5; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676] *** *** starting iteration: 6; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667] *** *** starting iteration: 7; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676] *** *** starting iteration: 8; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667] *** *** starting iteration: 9; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676] *** *** starting iteration: 10; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667] *** *** starting iteration: 11; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676] *** *** starting iteration: 12; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667] *** *** starting iteration: 13; err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676] *** *** finished computation of 12 references, err: Float32[Inf, 0.7819999, 0.61100006, 0.5810001, 0.5740001, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676, 0.5651667, 0.56616676] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 2] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 3, 3] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 1, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 2, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [1, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [5, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 4, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [1, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 5, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 9] *** center 11: selecting labels [1] (freq >= 10) [from [10, 0, 0]] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [4, 0, 0] finished with 1 centers; started with 12 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 5, 12, 13, :rand, 0.1, 10) *** starting iteration: 1; err: Float32[Inf, 0.56000006] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.56000006, 0.42400002] *** *** starting iteration: 2; err: Float32[Inf, 0.56000006, 0.42400002] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.56000006, 0.42400002, 0.39200002] *** *** starting iteration: 3; err: Float32[Inf, 0.56000006, 0.42400002, 0.39200002] *** *** computing centroids *** *** computing 12 nearest references *** *** new score with 12 references: Float32[Inf, 0.56000006, 0.42400002, 0.39200002, 0.39199996] *** *** finished computation of 12 references, err: Float32[Inf, 0.56000006, 0.42400002, 0.39200002, 0.39199996] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 3] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 2] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [2, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 2, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 1] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [1, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 1, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 4] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 1, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [2, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 5, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [1, 0, 0] finished with 0 centers; started with 12 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 3, 13, 4, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 0.74199986] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.74199986, 0.55866677] *** *** starting iteration: 2; err: Float32[Inf, 0.74199986, 0.55866677] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.74199986, 0.55866677, 0.54200006] *** *** starting iteration: 3; err: Float32[Inf, 0.74199986, 0.55866677, 0.54200006] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.74199986, 0.55866677, 0.54200006, 0.54266673] *** *** finished computation of 13 references, err: Float32[Inf, 0.74199986, 0.55866677, 0.54200006, 0.54266673] *** *** center 1: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 2: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 3: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 4, 0]] *** center 5: selecting labels [3] (freq >= 1) [from [0, 0, 7]] *** center 6: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 7: selecting labels [2] (freq >= 1) [from [0, 4, 0]] *** center 8: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 9: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 10: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 11: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 12: selecting labels [2, 3] (freq >= 1) [from [0, 3, 5]] ** center: 12, normalized-entropy: 0.954434002924965, [(2, 3), (3, 5)] *** center 13: selecting labels [2] (freq >= 1) [from [0, 1, 0]] finished with 14 centers; started with 13 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 3, 13, 4, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 0.512] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.512, 0.39] *** *** starting iteration: 2; err: Float32[Inf, 0.512, 0.39] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.512, 0.39, 0.38799998] *** *** starting iteration: 3; err: Float32[Inf, 0.512, 0.39, 0.38799998] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.512, 0.39, 0.38799998, 0.38799998] *** *** finished computation of 13 references, err: Float32[Inf, 0.512, 0.39, 0.38799998, 0.38799998] *** *** center 1: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 2: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 3: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 5: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 7: selecting labels [3] (freq >= 1) [from [0, 0, 3]] *** center 8: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 9: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 10: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 11: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 13: selecting labels [2] (freq >= 1) [from [0, 5, 0]] finished with 12 centers; started with 13 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 2, 7, :rand, 0.7, 4) *** starting iteration: 1; err: Float32[Inf, 0.031477638] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.031477638, 0.042058352] *** *** starting iteration: 2; err: Float32[Inf, 0.031477638, 0.042058352] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.031477638, 0.042058352, 0.04203986] *** *** finished computation of 2 references, err: Float32[Inf, 0.031477638, 0.042058352, 0.04203986] *** *** center 1: selecting labels [3] (freq >= 4) [from [0, 1, 7]] *** center 2: selecting labels [1, 2, 3] (freq >= 4) [from [21, 14, 7]] ** center: 2, normalized-entropy: 0.9206198357143047, [(1, 21), (2, 14), (3, 7)] finished with 4 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 2, 7, :rand, 0.7, 4) *** starting iteration: 1; err: Float32[Inf, 0.0033309886] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.0033309886, 0.0028389299] *** *** finished computation of 2 references, err: Float32[Inf, 0.0033309886, 0.0028389299] *** *** center 1: selecting labels [2, 3] (freq >= 4) [from [0, 9, 10]] ** center: 1, normalized-entropy: 0.9980008838722993, [(2, 9), (3, 10)] *** center 2: selecting labels [1] (freq >= 4) [from [6, 0, 0]] finished with 3 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 7, 5, 1, :rand, 0.1, 19) *** starting iteration: 1; err: Float32[Inf, 1.1290386] *** *** computing centroids *** *** computing 5 nearest references *** *** new score with 5 references: Float32[Inf, 1.1290386, 0.6728704] *** *** finished computation of 5 references, err: Float32[Inf, 1.1290386, 0.6728704] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 9, 14] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [6, 0, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [5, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [10, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 19, freqs: [0, 6, 0] finished with 0 centers; started with 5 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, CentroidSelection}(L1Distance(), CentroidSelection(), 2, 11, 7, :rand, 0.5, 25) *** starting iteration: 1; err: Float32[Inf, 0.75999993] *** *** computing centroids *** *** computing 11 nearest references *** *** new score with 11 references: Float32[Inf, 0.75999993, 0.63065565] *** *** starting iteration: 2; err: Float32[Inf, 0.75999993, 0.63065565] *** *** computing centroids *** *** computing 11 nearest references *** *** new score with 11 references: Float32[Inf, 0.75999993, 0.63065565, 0.6072333] *** *** starting iteration: 3; err: Float32[Inf, 0.75999993, 0.63065565, 0.6072333] *** *** computing centroids *** *** computing 11 nearest references *** *** new score with 11 references: Float32[Inf, 0.75999993, 0.63065565, 0.6072333, 0.58315545] *** *** starting iteration: 4; err: Float32[Inf, 0.75999993, 0.63065565, 0.6072333, 0.58315545] *** *** computing centroids *** *** computing 11 nearest references *** *** new score with 11 references: Float32[Inf, 0.75999993, 0.63065565, 0.6072333, 0.58315545, 0.5744333] *** *** starting iteration: 5; err: Float32[Inf, 0.75999993, 0.63065565, 0.6072333, 0.58315545, 0.5744333] *** *** computing centroids *** *** computing 11 nearest references *** *** new score with 11 references: Float32[Inf, 0.75999993, 0.63065565, 0.6072333, 0.58315545, 0.5744333, 0.5729333] *** *** starting iteration: 6; err: Float32[Inf, 0.75999993, 0.63065565, 0.6072333, 0.58315545, 0.5744333, 0.5729333] *** *** computing centroids *** *** computing 11 nearest references *** *** new score with 11 references: Float32[Inf, 0.75999993, 0.63065565, 0.6072333, 0.58315545, 0.5744333, 0.5729333, 0.5703778] *** *** starting iteration: 7; err: Float32[Inf, 0.75999993, 0.63065565, 0.6072333, 0.58315545, 0.5744333, 0.5729333, 0.5703778] *** *** computing centroids *** *** computing 11 nearest references *** *** new score with 11 references: Float32[Inf, 0.75999993, 0.63065565, 0.6072333, 0.58315545, 0.5744333, 0.5729333, 0.5703778, 0.56976664] *** *** finished computation of 11 references, err: Float32[Inf, 0.75999993, 0.63065565, 0.6072333, 0.58315545, 0.5744333, 0.5729333, 0.5703778, 0.56976664] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [2, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [0, 7, 2] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [7, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [0, 0, 8] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [0, 1, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [1, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [0, 0, 4] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [5, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [6, 0, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [0, 2, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 25, freqs: [0, 5, 0] finished with 0 centers; started with 11 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 2, 29, 1, :rand, 0.4, 10) *** starting iteration: 1; err: Float32[Inf, 0.23738529] *** *** computing centroids *** *** computing 29 nearest references *** *** new score with 29 references: Float32[Inf, 0.23738529, 0.19330215] *** *** finished computation of 29 references, err: Float32[Inf, 0.23738529, 0.19330215] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [5, 0, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 2] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [4, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [1, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [2, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 3, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [1, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 2, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [1, 0, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 4] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 2] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 1, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [1, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 1, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 1, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 3, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 1, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [2, 0, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [1, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 1] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 1] *** center 26: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [3, 0, 0] *** center 27: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 1, 0] *** center 28: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 0, 3] *** center 29: ignoring all elements because minimum-frequency restrictions were not met, freq >= 10, freqs: [0, 2, 1] finished with 0 centers; started with 29 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 19, 10, :rand, 0.9, 13) *** starting iteration: 1; err: Float32[Inf, 0.48799995] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.48799995, 0.47] *** *** starting iteration: 2; err: Float32[Inf, 0.48799995, 0.47] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.48799995, 0.47, 0.47599998] *** *** starting iteration: 3; err: Float32[Inf, 0.48799995, 0.47, 0.47599998] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993] *** *** starting iteration: 4; err: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994] *** *** starting iteration: 5; err: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996] *** *** starting iteration: 6; err: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996, 0.472] *** *** starting iteration: 7; err: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996, 0.472] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996, 0.472, 0.47999993] *** *** starting iteration: 8; err: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996, 0.472, 0.47999993] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996, 0.472, 0.47999993, 0.478] *** *** starting iteration: 9; err: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996, 0.472, 0.47999993, 0.478] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996, 0.472, 0.47999993, 0.478, 0.47] *** *** starting iteration: 10; err: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996, 0.472, 0.47999993, 0.478, 0.47] *** *** computing centroids *** *** computing 19 nearest references *** *** new score with 19 references: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996, 0.472, 0.47999993, 0.478, 0.47, 0.468] *** *** finished computation of 19 references, err: Float32[Inf, 0.48799995, 0.47, 0.47599998, 0.44999993, 0.45799994, 0.46599996, 0.472, 0.47999993, 0.478, 0.47, 0.468] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 3] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 2, 1] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 5, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 2] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [5, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 3, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 4, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 3] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 1] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [5, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 4] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [1, 0, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [3, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 1, 0] finished with 0 centers; started with 19 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(CosineDistance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 6, 33, 4, :rand, 0.9, 16) *** starting iteration: 1; err: Float32[Inf, 0.00026005256] *** *** computing centroids *** *** computing 33 nearest references *** *** new score with 33 references: Float32[Inf, 0.00026005256, 0.00012719314] *** *** finished computation of 33 references, err: Float32[Inf, 0.00026005256, 0.00012719314] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 2] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 1] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [3, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 3, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 2, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 1] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 3, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [3, 0, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [3, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 2] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 1] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 1] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 2] *** center 26: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 1] *** center 27: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 1] *** center 28: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 0] *** center 29: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [3, 0, 0] *** center 30: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 0, 2] *** center 31: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [2, 0, 0] *** center 32: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [0, 1, 0] *** center 33: ignoring all elements because minimum-frequency restrictions were not met, freq >= 16, freqs: [1, 0, 0] finished with 0 centers; started with 33 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 3, 32, 4, :rand, 0.3, 4) *** starting iteration: 1; err: Float32[Inf, 0.20891497] *** *** computing centroids *** *** computing 32 nearest references *** *** new score with 32 references: Float32[Inf, 0.20891497, 0.1988223] *** *** starting iteration: 2; err: Float32[Inf, 0.20891497, 0.1988223] *** *** computing centroids *** *** computing 32 nearest references *** *** new score with 32 references: Float32[Inf, 0.20891497, 0.1988223, 0.14756359] *** *** starting iteration: 3; err: Float32[Inf, 0.20891497, 0.1988223, 0.14756359] *** *** computing centroids *** *** computing 32 nearest references *** *** new score with 32 references: Float32[Inf, 0.20891497, 0.1988223, 0.14756359, 0.13577162] *** *** starting iteration: 4; err: Float32[Inf, 0.20891497, 0.1988223, 0.14756359, 0.13577162] *** *** computing centroids *** *** computing 32 nearest references *** *** new score with 32 references: Float32[Inf, 0.20891497, 0.1988223, 0.14756359, 0.13577162, 0.13960692] *** *** finished computation of 32 references, err: Float32[Inf, 0.20891497, 0.1988223, 0.14756359, 0.13577162, 0.13960692] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 2] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [2, 0, 0] *** center 5: selecting labels [1] (freq >= 4) [from [6, 0, 0]] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [3, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 2] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 2] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 2, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [2, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 3] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 3, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 3, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [2, 0, 0] *** center 26: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 27: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 28: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 29: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 30: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 31: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 32: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] finished with 1 centers; started with 32 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 3, 32, 4, :rand, 0.3, 4) *** starting iteration: 1; err: Float32[Inf, 0.117975056] *** *** computing centroids *** *** computing 32 nearest references *** *** new score with 32 references: Float32[Inf, 0.117975056, 0.068643525] *** *** starting iteration: 2; err: Float32[Inf, 0.117975056, 0.068643525] *** *** computing centroids *** *** computing 32 nearest references *** *** new score with 32 references: Float32[Inf, 0.117975056, 0.068643525, 0.068643525] *** *** finished computation of 32 references, err: Float32[Inf, 0.117975056, 0.068643525, 0.068643525] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 2, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 2] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [2, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 2, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 1] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 26: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 27: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 2] *** center 28: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [1, 0, 0] *** center 29: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 30: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] *** center 31: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 1, 0] *** center 32: ignoring all elements because minimum-frequency restrictions were not met, freq >= 4, freqs: [0, 0, 0] finished with 0 centers; started with 32 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, CentroidSelection}(L2Distance(), CentroidSelection(), 1, 18, 1, :rand, 0.3, 13) *** starting iteration: 1; err: Float32[Inf, 0.3624679] *** *** computing centroids *** *** computing 18 nearest references *** *** new score with 18 references: Float32[Inf, 0.3624679, 0.2871978] *** *** finished computation of 18 references, err: Float32[Inf, 0.3624679, 0.2871978] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 1] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 1, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 4, 3] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 3, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 4] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [4, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 2, 1] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 3, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [5, 0, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [4, 0, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 2] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 2] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 0, 1] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [2, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 13, freqs: [0, 2, 0] finished with 0 centers; started with 18 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingSearchModels iteration 1> population: 7, bsize: 8, queue: 14, observed: 46, best-error: 0.030000000000000027 worst-error: 0.59 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, RandomCenterSelection}(L2Distance(), RandomCenterSelection(), 2, 25, 7, :rand, 0.2, 3) *** starting iteration: 1; err: Float32[Inf, 0.30120873] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.30120873, 0.25162837] *** *** starting iteration: 2; err: Float32[Inf, 0.30120873, 0.25162837] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.30120873, 0.25162837, 0.21418577] *** *** starting iteration: 3; err: Float32[Inf, 0.30120873, 0.25162837, 0.21418577] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.30120873, 0.25162837, 0.21418577, 0.20005] *** *** starting iteration: 4; err: Float32[Inf, 0.30120873, 0.25162837, 0.21418577, 0.20005] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.30120873, 0.25162837, 0.21418577, 0.20005, 0.20444912] *** *** starting iteration: 5; err: Float32[Inf, 0.30120873, 0.25162837, 0.21418577, 0.20005, 0.20444912] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.30120873, 0.25162837, 0.21418577, 0.20005, 0.20444912, 0.19629864] *** *** starting iteration: 6; err: Float32[Inf, 0.30120873, 0.25162837, 0.21418577, 0.20005, 0.20444912, 0.19629864] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.30120873, 0.25162837, 0.21418577, 0.20005, 0.20444912, 0.19629864, 0.18665539] *** *** starting iteration: 7; err: Float32[Inf, 0.30120873, 0.25162837, 0.21418577, 0.20005, 0.20444912, 0.19629864, 0.18665539] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.30120873, 0.25162837, 0.21418577, 0.20005, 0.20444912, 0.19629864, 0.18665539, 0.19422702] *** *** finished computation of 25 references, err: Float32[Inf, 0.30120873, 0.25162837, 0.21418577, 0.20005, 0.20444912, 0.19629864, 0.18665539, 0.19422702] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 2: selecting labels [3] (freq >= 3) [from [0, 0, 4]] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 1] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 2] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 6: selecting labels [3] (freq >= 3) [from [0, 0, 3]] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 2, 2] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [2, 0, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 10: selecting labels [1] (freq >= 3) [from [5, 0, 0]] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 1] *** center 12: selecting labels [1] (freq >= 3) [from [3, 0, 0]] *** center 13: selecting labels [1] (freq >= 3) [from [6, 0, 0]] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 2, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [1, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [2, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 2, 0] *** center 19: selecting labels [2] (freq >= 3) [from [0, 3, 0]] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 1] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [1, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [1, 0, 0] finished with 6 centers; started with 25 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, RandomCenterSelection}(L2Distance(), RandomCenterSelection(), 2, 25, 7, :rand, 0.2, 3) *** starting iteration: 1; err: Float32[Inf, 0.24591666] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.24591666, 0.16032656] *** *** starting iteration: 2; err: Float32[Inf, 0.24591666, 0.16032656] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.24591666, 0.16032656, 0.14250372] *** *** starting iteration: 3; err: Float32[Inf, 0.24591666, 0.16032656, 0.14250372] *** *** computing centroids *** *** computing 25 nearest references *** *** new score with 25 references: Float32[Inf, 0.24591666, 0.16032656, 0.14250372, 0.14250372] *** *** finished computation of 25 references, err: Float32[Inf, 0.24591666, 0.16032656, 0.14250372, 0.14250372] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 2, 0] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 2] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 2] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 2, 0] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [2, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 13: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 1] *** center 14: selecting labels [1] (freq >= 3) [from [3, 0, 0]] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 2] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [1, 0, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 1] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 2] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 1, 0] *** center 25: ignoring all elements because minimum-frequency restrictions were not met, freq >= 3, freqs: [0, 0, 0] finished with 1 centers; started with 25 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 9, :rand, 0.9, 7) *** starting iteration: 1; err: Float32[Inf, 0.8136683] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.8136683, 0.7911087] *** *** starting iteration: 2; err: Float32[Inf, 0.8136683, 0.7911087] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.8136683, 0.7911087, 0.77189463] *** *** starting iteration: 3; err: Float32[Inf, 0.8136683, 0.7911087, 0.77189463] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774] *** *** starting iteration: 4; err: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774, 0.6921038] *** *** starting iteration: 5; err: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774, 0.6921038] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774, 0.6921038, 0.677141] *** *** starting iteration: 6; err: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774, 0.6921038, 0.677141] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774, 0.6921038, 0.677141, 0.71366584] *** *** starting iteration: 7; err: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774, 0.6921038, 0.677141, 0.71366584] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774, 0.6921038, 0.677141, 0.71366584, 0.70159006] *** *** starting iteration: 8; err: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774, 0.6921038, 0.677141, 0.71366584, 0.70159006] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774, 0.6921038, 0.677141, 0.71366584, 0.70159006, 0.7008296] *** *** finished computation of 4 references, err: Float32[Inf, 0.8136683, 0.7911087, 0.77189463, 0.71387774, 0.6921038, 0.677141, 0.71366584, 0.70159006, 0.7008296] *** *** center 1: selecting labels [2, 3] (freq >= 7) [from [0, 7, 14]] ** center: 1, normalized-entropy: 0.9182958340544894, [(2, 7), (3, 14)] *** center 2: selecting labels [1] (freq >= 7) [from [12, 0, 0]] *** center 3: selecting labels [2] (freq >= 7) [from [0, 8, 0]] *** center 4: selecting labels [1] (freq >= 7) [from [9, 0, 0]] finished with 5 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 9, :rand, 0.9, 7) *** starting iteration: 1; err: Float32[Inf, 0.83429426] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.83429426, 0.84652376] *** *** starting iteration: 2; err: Float32[Inf, 0.83429426, 0.84652376] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.83429426, 0.84652376, 0.84652376] *** *** finished computation of 4 references, err: Float32[Inf, 0.83429426, 0.84652376, 0.84652376] *** *** center 1: selecting labels [3] (freq >= 7) [from [0, 1, 10]] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [5, 0, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [1, 0, 0] *** center 4: selecting labels [2] (freq >= 7) [from [0, 8, 0]] finished with 2 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 4, 9, :rand, 0.9, 11) *** starting iteration: 1; err: Float32[Inf, 3.1180003] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1180003, 3.1499996] *** *** starting iteration: 2; err: Float32[Inf, 3.1180003, 3.1499996] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1180003, 3.1499996, 2.582] *** *** starting iteration: 3; err: Float32[Inf, 3.1180003, 3.1499996, 2.582] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001] *** *** starting iteration: 4; err: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001] *** *** starting iteration: 5; err: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001, 1.0320002] *** *** starting iteration: 6; err: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001, 1.0320002] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001, 1.0320002, 1.0380001] *** *** starting iteration: 7; err: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001, 1.0320002, 1.0380001] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001, 1.0320002, 1.0380001, 1.118] *** *** starting iteration: 8; err: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001, 1.0320002, 1.0380001, 1.118] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001, 1.0320002, 1.0380001, 1.118, 1.0240002] *** *** starting iteration: 9; err: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001, 1.0320002, 1.0380001, 1.118, 1.0240002] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001, 1.0320002, 1.0380001, 1.118, 1.0240002, 1.2540001] *** *** finished computation of 4 references, err: Float32[Inf, 3.1180003, 3.1499996, 2.582, 1.1960001, 1.0740001, 1.0320002, 1.0380001, 1.118, 1.0240002, 1.2540001] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 11, freqs: [0, 5, 9] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 11, freqs: [0, 10, 0] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 11, freqs: [0, 0, 5] *** center 4: selecting labels [1] (freq >= 11) [from [21, 0, 0]] finished with 1 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 4, 9, :rand, 0.9, 11) *** starting iteration: 1; err: Float32[Inf, 1.156] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.156, 1.212] *** *** starting iteration: 2; err: Float32[Inf, 1.156, 1.212] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.156, 1.212, 1.204] *** *** starting iteration: 3; err: Float32[Inf, 1.156, 1.212, 1.204] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994] *** *** starting iteration: 4; err: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985] *** *** starting iteration: 5; err: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985, 1.18] *** *** starting iteration: 6; err: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985, 1.18] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985, 1.18, 1.212] *** *** starting iteration: 7; err: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985, 1.18, 1.212] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985, 1.18, 1.212, 1.18] *** *** starting iteration: 8; err: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985, 1.18, 1.212, 1.18] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985, 1.18, 1.212, 1.18, 0.83999985] *** *** starting iteration: 9; err: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985, 1.18, 1.212, 1.18, 0.83999985] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985, 1.18, 1.212, 1.18, 0.83999985, 1.06] *** *** finished computation of 4 references, err: Float32[Inf, 1.156, 1.212, 1.204, 0.82399994, 0.83999985, 1.18, 1.212, 1.18, 0.83999985, 1.06] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 11, freqs: [0, 9, 2] *** center 2: ignoring all elements because minimum-frequency restrictions were not met, freq >= 11, freqs: [0, 0, 6] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 11, freqs: [6, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 11, freqs: [0, 0, 2] finished with 0 centers; started with 4 [ Info: ignoring configuration due to exception invalid setup on optimize!, n=0nothingKncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, RandomCenterSelection}(L2Distance(), RandomCenterSelection(), 4, 4, 13, :rand, 0.9, 5) *** starting iteration: 1; err: Float32[Inf, 0.6448015] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674] *** *** starting iteration: 2; err: Float32[Inf, 0.6448015, 0.7297674] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733] *** *** starting iteration: 3; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443] *** *** starting iteration: 4; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813] *** *** starting iteration: 5; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726] *** *** starting iteration: 6; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977] *** *** starting iteration: 7; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987] *** *** starting iteration: 8; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055] *** *** starting iteration: 9; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055, 0.7268766] *** *** starting iteration: 10; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055, 0.7268766] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055, 0.7268766, 0.8563893] *** *** starting iteration: 11; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055, 0.7268766, 0.8563893] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055, 0.7268766, 0.8563893, 0.66958153] *** *** starting iteration: 12; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055, 0.7268766, 0.8563893, 0.66958153] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055, 0.7268766, 0.8563893, 0.66958153, 0.6512748] *** *** starting iteration: 13; err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055, 0.7268766, 0.8563893, 0.66958153, 0.6512748] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055, 0.7268766, 0.8563893, 0.66958153, 0.6512748, 0.6363214] *** *** finished computation of 4 references, err: Float32[Inf, 0.6448015, 0.7297674, 0.72042733, 0.74128443, 0.6275813, 0.71093726, 0.73428977, 0.78088987, 0.69932055, 0.7268766, 0.8563893, 0.66958153, 0.6512748, 0.6363214] *** *** center 1: selecting labels [2, 3] (freq >= 5) [from [0, 12, 7]] ** center: 1, normalized-entropy: 0.9494520153879484, [(2, 12), (3, 7)] *** center 2: selecting labels [1] (freq >= 5) [from [21, 0, 0]] *** center 3: selecting labels [3] (freq >= 5) [from [0, 0, 7]] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 5, freqs: [0, 3, 0] finished with 4 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, RandomCenterSelection}(L2Distance(), RandomCenterSelection(), 4, 4, 13, :rand, 0.9, 5) *** starting iteration: 1; err: Float32[Inf, 0.85308295] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795] *** *** starting iteration: 2; err: Float32[Inf, 0.85308295, 0.7652795] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795] *** *** starting iteration: 3; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293] *** *** starting iteration: 4; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739] *** *** starting iteration: 5; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291] *** *** starting iteration: 6; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997] *** *** starting iteration: 7; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599] *** *** starting iteration: 8; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203] *** *** starting iteration: 9; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203, 0.7766862] *** *** starting iteration: 10; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203, 0.7766862] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203, 0.7766862, 0.7803765] *** *** starting iteration: 11; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203, 0.7766862, 0.7803765] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203, 0.7766862, 0.7803765, 1.1360288] *** *** starting iteration: 12; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203, 0.7766862, 0.7803765, 1.1360288] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203, 0.7766862, 0.7803765, 1.1360288, 0.797909] *** *** starting iteration: 13; err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203, 0.7766862, 0.7803765, 1.1360288, 0.797909] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203, 0.7766862, 0.7803765, 1.1360288, 0.797909, 0.66872] *** *** finished computation of 4 references, err: Float32[Inf, 0.85308295, 0.7652795, 0.7702795, 0.82888293, 0.7523739, 0.8032291, 0.88379997, 0.746599, 0.94071203, 0.7766862, 0.7803765, 1.1360288, 0.797909, 0.66872] *** *** center 1: selecting labels [2] (freq >= 5) [from [0, 6, 0]] *** center 2: selecting labels [1] (freq >= 5) [from [6, 0, 0]] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 5, freqs: [0, 0, 3] *** center 4: selecting labels [3] (freq >= 5) [from [0, 3, 7]] finished with 3 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, RandomCenterSelection}(L2Distance(), RandomCenterSelection(), 4, 4, 13, :rand, 1.0, 7) *** starting iteration: 1; err: Float32[Inf, 0.66250056] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301] *** *** starting iteration: 2; err: Float32[Inf, 0.66250056, 0.786301] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404] *** *** starting iteration: 3; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755] *** *** starting iteration: 4; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738] *** *** starting iteration: 5; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646] *** *** starting iteration: 6; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919] *** *** starting iteration: 7; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452] *** *** starting iteration: 8; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262] *** *** starting iteration: 9; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262, 0.6002894] *** *** starting iteration: 10; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262, 0.6002894] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262, 0.6002894, 1.0228809] *** *** starting iteration: 11; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262, 0.6002894, 1.0228809] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262, 0.6002894, 1.0228809, 0.7439028] *** *** starting iteration: 12; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262, 0.6002894, 1.0228809, 0.7439028] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262, 0.6002894, 1.0228809, 0.7439028, 0.745234] *** *** starting iteration: 13; err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262, 0.6002894, 1.0228809, 0.7439028, 0.745234] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262, 0.6002894, 1.0228809, 0.7439028, 0.745234, 0.66547745] *** *** finished computation of 4 references, err: Float32[Inf, 0.66250056, 0.786301, 0.70263404, 0.80656755, 0.7980738, 0.6887646, 0.6079919, 0.7100452, 0.8871262, 0.6002894, 1.0228809, 0.7439028, 0.745234, 0.66547745] *** *** center 1: selecting labels [3] (freq >= 7) [from [0, 0, 10]] *** center 2: selecting labels [2] (freq >= 7) [from [0, 15, 4]] *** center 3: selecting labels [1] (freq >= 7) [from [7, 0, 0]] *** center 4: selecting labels [1] (freq >= 7) [from [14, 0, 0]] finished with 4 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, RandomCenterSelection}(L2Distance(), RandomCenterSelection(), 4, 4, 13, :rand, 1.0, 7) *** starting iteration: 1; err: Float32[Inf, 0.95253503] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711] *** *** starting iteration: 2; err: Float32[Inf, 0.95253503, 0.8034711] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875] *** *** starting iteration: 3; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528] *** *** starting iteration: 4; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276] *** *** starting iteration: 5; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536] *** *** starting iteration: 6; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921] *** *** starting iteration: 7; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247] *** *** starting iteration: 8; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154] *** *** starting iteration: 9; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154, 0.5619577] *** *** starting iteration: 10; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154, 0.5619577] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154, 0.5619577, 0.5293787] *** *** starting iteration: 11; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154, 0.5619577, 0.5293787] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154, 0.5619577, 0.5293787, 0.7139634] *** *** starting iteration: 12; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154, 0.5619577, 0.5293787, 0.7139634] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154, 0.5619577, 0.5293787, 0.7139634, 0.67427635] *** *** starting iteration: 13; err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154, 0.5619577, 0.5293787, 0.7139634, 0.67427635] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154, 0.5619577, 0.5293787, 0.7139634, 0.67427635, 0.4894722] *** *** finished computation of 4 references, err: Float32[Inf, 0.95253503, 0.8034711, 1.0280875, 0.7126528, 0.67132276, 0.7662536, 0.7511921, 0.8318247, 0.6903154, 0.5619577, 0.5293787, 0.7139634, 0.67427635, 0.4894722] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 6, 0] *** center 2: selecting labels [3] (freq >= 7) [from [0, 3, 8]] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [6, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] finished with 1 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, CentroidSelection}(L1Distance(), CentroidSelection(), 4, 4, 13, :rand, 0.9, 5) *** starting iteration: 1; err: Float32[Inf, 1.2780001] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.2780001, 1.0488034] *** *** starting iteration: 2; err: Float32[Inf, 1.2780001, 1.0488034] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.2780001, 1.0488034, 0.9894392] *** *** starting iteration: 3; err: Float32[Inf, 1.2780001, 1.0488034, 0.9894392] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.2780001, 1.0488034, 0.9894392, 0.9551] *** *** starting iteration: 4; err: Float32[Inf, 1.2780001, 1.0488034, 0.9894392, 0.9551] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.2780001, 1.0488034, 0.9894392, 0.9551, 0.94593996] *** *** starting iteration: 5; err: Float32[Inf, 1.2780001, 1.0488034, 0.9894392, 0.9551, 0.94593996] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.2780001, 1.0488034, 0.9894392, 0.9551, 0.94593996, 0.9031639] *** *** starting iteration: 6; err: Float32[Inf, 1.2780001, 1.0488034, 0.9894392, 0.9551, 0.94593996, 0.9031639] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.2780001, 1.0488034, 0.9894392, 0.9551, 0.94593996, 0.9031639, 0.9031639] *** *** finished computation of 4 references, err: Float32[Inf, 1.2780001, 1.0488034, 0.9894392, 0.9551, 0.94593996, 0.9031639, 0.9031639] *** *** center 1: selecting labels [3] (freq >= 5) [from [0, 0, 10]] *** center 2: selecting labels [2] (freq >= 5) [from [0, 15, 4]] *** center 3: selecting labels [1] (freq >= 5) [from [7, 0, 0]] *** center 4: selecting labels [1] (freq >= 5) [from [14, 0, 0]] finished with 4 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, CentroidSelection}(L1Distance(), CentroidSelection(), 4, 4, 13, :rand, 0.9, 5) *** starting iteration: 1; err: Float32[Inf, 3.1000004] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1000004, 1.7270386] *** *** starting iteration: 2; err: Float32[Inf, 3.1000004, 1.7270386] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1000004, 1.7270386, 1.0777619] *** *** starting iteration: 3; err: Float32[Inf, 3.1000004, 1.7270386, 1.0777619] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1000004, 1.7270386, 1.0777619, 0.86139685] *** *** starting iteration: 4; err: Float32[Inf, 3.1000004, 1.7270386, 1.0777619, 0.86139685] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 3.1000004, 1.7270386, 1.0777619, 0.86139685, 0.86139685] *** *** finished computation of 4 references, err: Float32[Inf, 3.1000004, 1.7270386, 1.0777619, 0.86139685, 0.86139685] *** *** center 1: selecting labels [2] (freq >= 5) [from [0, 9, 0]] *** center 2: selecting labels [3] (freq >= 5) [from [0, 0, 7]] *** center 3: selecting labels [1] (freq >= 5) [from [6, 0, 0]] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 5, freqs: [0, 0, 3] finished with 3 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 4, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.68190557] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.68190557, 0.7008616] *** *** finished computation of 4 references, err: Float32[Inf, 0.68190557, 0.7008616] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 15, 2]] ** center: 1, normalized-entropy: 0.5225593745369407, [(2, 15), (3, 2)] *** center 2: selecting labels [1] (freq >= 1) [from [9, 0, 0]] *** center 3: selecting labels [3] (freq >= 1) [from [0, 0, 12]] *** center 4: selecting labels [1] (freq >= 1) [from [12, 0, 0]] finished with 4 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 4, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 1.69224] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.69224, 0.721948] *** *** finished computation of 4 references, err: Float32[Inf, 1.69224, 0.721948] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 9, 3]] ** center: 1, normalized-entropy: 0.8112781244591328, [(2, 9), (3, 3)] *** center 2: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 3: selecting labels [3] (freq >= 1) [from [0, 0, 5]] *** center 4: selecting labels [1] (freq >= 1) [from [6, 0, 0]] finished with 5 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 2, 1, :rand, 0.7, 4) *** starting iteration: 1; err: Float32[Inf, 0.0033725714] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.0033725714, 0.0029344324] *** *** finished computation of 2 references, err: Float32[Inf, 0.0033725714, 0.0029344324] *** *** center 1: selecting labels [1] (freq >= 4) [from [21, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 4) [from [0, 15, 14]] ** center: 2, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 2, 1, :rand, 0.7, 4) *** starting iteration: 1; err: Float32[Inf, 0.03029942] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.03029942, 0.017252935] *** *** finished computation of 2 references, err: Float32[Inf, 0.03029942, 0.017252935] *** *** center 1: selecting labels [3] (freq >= 4) [from [0, 2, 10]] *** center 2: selecting labels [1, 2] (freq >= 4) [from [6, 7, 0]] ** center: 2, normalized-entropy: 0.9957274520849256, [(1, 6), (2, 7)] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(CosineDistance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 6, 24, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.00045105207] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.00045105207, 0.00027027875] *** *** finished computation of 24 references, err: Float32[Inf, 0.00045105207, 0.00027027875] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 2: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 4: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 5: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 6: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 7: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 8: selecting labels [3] (freq >= 1) [from [0, 0, 3]] *** center 9: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 10: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 11: selecting labels [3] (freq >= 1) [from [0, 0, 3]] *** center 12: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 13: selecting labels [1] (freq >= 1) [from [5, 0, 0]] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 16: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 17: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 18: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 19: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 21: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 22: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 23: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 24: selecting labels [2] (freq >= 1) [from [0, 3, 0]] finished with 20 centers; started with 24 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(CosineDistance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 6, 24, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.000114208546] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.000114208546, 6.0080663f-5] *** *** finished computation of 24 references, err: Float32[Inf, 0.000114208546, 6.0080663f-5] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 2: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 4: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 5: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 7: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 8: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 9: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 10: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 11: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 12: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 13: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 14: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 15: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 16: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 17: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 21: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 22: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] finished with 18 centers; started with 24 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 2, 7, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 3.446] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.986] *** *** starting iteration: 2; err: Float32[Inf, 3.446, 2.986] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.986, 2.8759995] *** *** starting iteration: 3; err: Float32[Inf, 3.446, 2.986, 2.8759995] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.986, 2.8759995, 2.9019997] *** *** starting iteration: 4; err: Float32[Inf, 3.446, 2.986, 2.8759995, 2.9019997] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.986, 2.8759995, 2.9019997, 1.596] *** *** starting iteration: 5; err: Float32[Inf, 3.446, 2.986, 2.8759995, 2.9019997, 1.596] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.986, 2.8759995, 2.9019997, 1.596, 1.596] *** *** finished computation of 2 references, err: Float32[Inf, 3.446, 2.986, 2.8759995, 2.9019997, 1.596, 1.596] *** *** center 1: selecting labels [1] (freq >= 1) [from [21, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 15, 14]] ** center: 2, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 2, 7, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 5.3] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 5.3, 1.8000002] *** *** starting iteration: 2; err: Float32[Inf, 5.3, 1.8000002] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 5.3, 1.8000002, 1.776] *** *** starting iteration: 3; err: Float32[Inf, 5.3, 1.8000002, 1.776] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 5.3, 1.8000002, 1.776, 1.644] *** *** starting iteration: 4; err: Float32[Inf, 5.3, 1.8000002, 1.776, 1.644] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 5.3, 1.8000002, 1.776, 1.644, 1.716] *** *** starting iteration: 5; err: Float32[Inf, 5.3, 1.8000002, 1.776, 1.644, 1.716] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 5.3, 1.8000002, 1.776, 1.644, 1.716, 1.5560001] *** *** starting iteration: 6; err: Float32[Inf, 5.3, 1.8000002, 1.776, 1.644, 1.716, 1.5560001] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 5.3, 1.8000002, 1.776, 1.644, 1.716, 1.5560001, 1.9] *** *** starting iteration: 7; err: Float32[Inf, 5.3, 1.8000002, 1.776, 1.644, 1.716, 1.5560001, 1.9] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 5.3, 1.8000002, 1.776, 1.644, 1.716, 1.5560001, 1.9, 1.776] *** *** finished computation of 2 references, err: Float32[Inf, 5.3, 1.8000002, 1.776, 1.644, 1.716, 1.5560001, 1.9, 1.776] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 9, 10]] ** center: 1, normalized-entropy: 0.9980008838722993, [(2, 9), (3, 10)] *** center 2: selecting labels [1] (freq >= 1) [from [6, 0, 0]] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 2, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.0031209823] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.0031209823, 0.0029344324] *** *** finished computation of 2 references, err: Float32[Inf, 0.0031209823, 0.0029344324] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 15, 14]] ** center: 1, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] *** center 2: selecting labels [1] (freq >= 1) [from [21, 0, 0]] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 2, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.017105274] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.017105274, 0.0038962634] *** *** finished computation of 2 references, err: Float32[Inf, 0.017105274, 0.0038962634] *** *** center 1: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 9, 10]] ** center: 2, normalized-entropy: 0.9980008838722993, [(2, 9), (3, 10)] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 4, :rand, 0.9, 7) *** starting iteration: 1; err: Float32[Inf, 0.9399998] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.9399998, 1.1460001] *** *** starting iteration: 2; err: Float32[Inf, 0.9399998, 1.1460001] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.9399998, 1.1460001, 1.162] *** *** starting iteration: 3; err: Float32[Inf, 0.9399998, 1.1460001, 1.162] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.9399998, 1.1460001, 1.162, 1.1220001] *** *** starting iteration: 4; err: Float32[Inf, 0.9399998, 1.1460001, 1.162, 1.1220001] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.9399998, 1.1460001, 1.162, 1.1220001, 1.1740001] *** *** finished computation of 4 references, err: Float32[Inf, 0.9399998, 1.1460001, 1.162, 1.1220001, 1.1740001] *** *** center 1: selecting labels [2] (freq >= 7) [from [0, 8, 0]] *** center 2: selecting labels [1] (freq >= 7) [from [21, 0, 0]] *** center 3: selecting labels [3] (freq >= 7) [from [0, 0, 13]] *** center 4: selecting labels [2] (freq >= 7) [from [0, 7, 1]] finished with 4 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 4, :rand, 0.9, 7) *** starting iteration: 1; err: Float32[Inf, 1.872] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.872, 1.3] *** *** starting iteration: 2; err: Float32[Inf, 1.872, 1.3] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.872, 1.3, 1.212] *** *** starting iteration: 3; err: Float32[Inf, 1.872, 1.3, 1.212] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.872, 1.3, 1.212, 1.1880001] *** *** starting iteration: 4; err: Float32[Inf, 1.872, 1.3, 1.212, 1.1880001] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.872, 1.3, 1.212, 1.1880001, 1.212] *** *** finished computation of 4 references, err: Float32[Inf, 1.872, 1.3, 1.212, 1.1880001, 1.212] *** *** center 1: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 0, 2] *** center 2: selecting labels [3] (freq >= 7) [from [0, 4, 8]] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [6, 0, 0] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 7, freqs: [0, 5, 0] finished with 1 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 2, 2, 7, :rand, 0.2, 4) *** starting iteration: 1; err: Float32[Inf, 0.003906644] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.003906644, 0.0029344324] *** *** finished computation of 2 references, err: Float32[Inf, 0.003906644, 0.0029344324] *** *** center 1: selecting labels [2, 3] (freq >= 4) [from [0, 15, 14]] ** center: 1, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] *** center 2: selecting labels [1] (freq >= 4) [from [21, 0, 0]] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 2, 2, 7, :rand, 0.2, 4) *** starting iteration: 1; err: Float32[Inf, 0.017380029] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.017380029, 0.01734083] *** *** finished computation of 2 references, err: Float32[Inf, 0.017380029, 0.01734083] *** *** center 1: selecting labels [3] (freq >= 4) [from [0, 2, 10]] *** center 2: selecting labels [1, 2] (freq >= 4) [from [6, 7, 0]] ** center: 2, normalized-entropy: 0.9957274520849256, [(1, 6), (2, 7)] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 3, 24, 4, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 0.0003573234] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.0003573234, 0.00030744754] *** *** finished computation of 24 references, err: Float32[Inf, 0.0003573234, 0.00030744754] *** *** center 1: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 2: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 4: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 5: selecting labels [2, 3] (freq >= 1) [from [0, 1, 1]] ** center: 5, normalized-entropy: 1.0, [(2, 1), (3, 1)] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 7: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 8: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 9: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 10: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 11: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 12: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 13: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 14: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 15: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 16: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 17: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 18: selecting labels [3] (freq >= 1) [from [0, 0, 6]] *** center 19: selecting labels [2, 3] (freq >= 1) [from [0, 1, 1]] ** center: 19, normalized-entropy: 1.0, [(2, 1), (3, 1)] *** center 20: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 21: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 23: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 24: selecting labels [1] (freq >= 1) [from [5, 0, 0]] finished with 25 centers; started with 24 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 3, 24, 4, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 0.00012943578] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.00012943578, 0.000115925046] *** *** finished computation of 24 references, err: Float32[Inf, 0.00012943578, 0.000115925046] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 2: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 3: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 4: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 5: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 8: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 9: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 10: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 11: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 13: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 14: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 15: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 16: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 17: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 18: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 19: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 21: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] finished with 18 centers; started with 24 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 SearchModels iteration 2> population: 16, bsize: 8, queue: 12, observed: 58, best-error: 0.030000000000000027 worst-error: 0.30000000000000004 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 1, 2, 15, :rand, 0.44999999999999996, 1) *** starting iteration: 1; err: Float32[Inf, 0.99549943] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.99549943, 1.1905556] *** *** starting iteration: 2; err: Float32[Inf, 0.99549943, 1.1905556] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.99549943, 1.1905556, 1.1896296] *** *** finished computation of 2 references, err: Float32[Inf, 0.99549943, 1.1905556, 1.1896296] *** *** center 1: selecting labels [1] (freq >= 1) [from [21, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 15, 14]] ** center: 2, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 1, 2, 15, :rand, 0.44999999999999996, 1) *** starting iteration: 1; err: Float32[Inf, 1.9281725] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284] *** *** starting iteration: 2; err: Float32[Inf, 1.9281725, 1.6934284] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644] *** *** starting iteration: 3; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096] *** *** starting iteration: 4; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678] *** *** starting iteration: 5; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537] *** *** starting iteration: 6; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956] *** *** starting iteration: 7; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546] *** *** starting iteration: 8; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905] *** *** starting iteration: 9; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678] *** *** starting iteration: 10; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096] *** *** starting iteration: 11; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096, 1.1182814] *** *** starting iteration: 12; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096, 1.1182814] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096, 1.1182814, 0.8579118] *** *** starting iteration: 13; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096, 1.1182814, 0.8579118] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096, 1.1182814, 0.8579118, 0.9053537] *** *** starting iteration: 14; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096, 1.1182814, 0.8579118, 0.9053537] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096, 1.1182814, 0.8579118, 0.9053537, 1.0323678] *** *** starting iteration: 15; err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096, 1.1182814, 0.8579118, 0.9053537, 1.0323678] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096, 1.1182814, 0.8579118, 0.9053537, 1.0323678, 1.0564096] *** *** finished computation of 2 references, err: Float32[Inf, 1.9281725, 1.6934284, 0.92969644, 1.0564096, 1.0323678, 0.9053537, 0.9293956, 0.9056546, 1.0818905, 1.0323678, 1.0564096, 1.1182814, 0.8579118, 0.9053537, 1.0323678, 1.0564096] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 9, 10]] ** center: 1, normalized-entropy: 0.9980008838722993, [(2, 9), (3, 10)] *** center 2: selecting labels [1] (freq >= 1) [from [6, 0, 0]] finished with 3 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 8, 4, 1, :rand, 0.8, 2) *** starting iteration: 1; err: Float32[Inf, 1.078] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.078, 1.0780001] *** *** finished computation of 4 references, err: Float32[Inf, 1.078, 1.0780001] *** *** center 1: selecting labels [3] (freq >= 2) [from [0, 0, 9]] *** center 2: selecting labels [2, 3] (freq >= 2) [from [0, 9, 5]] ** center: 2, normalized-entropy: 0.940285958670631, [(2, 9), (3, 5)] *** center 3: selecting labels [1] (freq >= 2) [from [21, 0, 0]] *** center 4: selecting labels [2] (freq >= 2) [from [0, 6, 0]] finished with 5 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 8, 4, 1, :rand, 0.8, 2) *** starting iteration: 1; err: Float32[Inf, 1.3] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.3, 1.136] *** *** finished computation of 4 references, err: Float32[Inf, 1.3, 1.136] *** *** center 1: selecting labels [2] (freq >= 2) [from [0, 2, 0]] *** center 2: selecting labels [1] (freq >= 2) [from [6, 0, 0]] *** center 3: selecting labels [3] (freq >= 2) [from [0, 0, 7]] *** center 4: selecting labels [2, 3] (freq >= 2) [from [0, 7, 3]] ** center: 4, normalized-entropy: 0.8812908992306927, [(2, 7), (3, 3)] finished with 5 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 4, 1, :rand, 0.5333333333333333, 1) *** starting iteration: 1; err: Float32[Inf, 1.2419999] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.2419999, 1.8959998] *** *** finished computation of 4 references, err: Float32[Inf, 1.2419999, 1.8959998] *** *** center 1: selecting labels [1] (freq >= 1) [from [10, 0, 0]] *** center 2: selecting labels [1] (freq >= 1) [from [5, 0, 0]] *** center 3: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 4: selecting labels [2, 3] (freq >= 1) [from [0, 15, 14]] ** center: 4, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] finished with 5 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 4, 1, :rand, 0.5333333333333333, 1) *** starting iteration: 1; err: Float32[Inf, 1.3279998] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.3279998, 1.18] *** *** finished computation of 4 references, err: Float32[Inf, 1.3279998, 1.18] *** *** center 1: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 2: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 3: selecting labels [2, 3] (freq >= 1) [from [0, 5, 1]] ** center: 3, normalized-entropy: 0.6500224216483541, [(2, 5), (3, 1)] *** center 4: selecting labels [2, 3] (freq >= 1) [from [0, 4, 7]] ** center: 4, normalized-entropy: 0.9456603046006401, [(2, 4), (3, 7)] finished with 6 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 5, 2, 7, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 1.42] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.42, 1.2983999] *** *** starting iteration: 2; err: Float32[Inf, 1.42, 1.2983999] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.42, 1.2983999, 1.2983999] *** *** finished computation of 2 references, err: Float32[Inf, 1.42, 1.2983999, 1.2983999] *** *** center 1: selecting labels [1] (freq >= 1) [from [21, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 15, 14]] ** center: 2, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] finished with 3 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 5, 2, 7, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 2.6] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.6, 2.0709999] *** *** starting iteration: 2; err: Float32[Inf, 2.6, 2.0709999] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.6, 2.0709999, 1.9310001] *** *** starting iteration: 3; err: Float32[Inf, 2.6, 2.0709999, 1.9310001] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.6, 2.0709999, 1.9310001, 1.825] *** *** starting iteration: 4; err: Float32[Inf, 2.6, 2.0709999, 1.9310001, 1.825] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.6, 2.0709999, 1.9310001, 1.825, 1.4962666] *** *** starting iteration: 5; err: Float32[Inf, 2.6, 2.0709999, 1.9310001, 1.825, 1.4962666] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.6, 2.0709999, 1.9310001, 1.825, 1.4962666, 1.4909335] *** *** starting iteration: 6; err: Float32[Inf, 2.6, 2.0709999, 1.9310001, 1.825, 1.4962666, 1.4909335] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.6, 2.0709999, 1.9310001, 1.825, 1.4962666, 1.4909335, 1.4909335] *** *** finished computation of 2 references, err: Float32[Inf, 2.6, 2.0709999, 1.9310001, 1.825, 1.4962666, 1.4909335, 1.4909335] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 9, 10]] ** center: 1, normalized-entropy: 0.9980008838722993, [(2, 9), (3, 10)] *** center 2: selecting labels [1] (freq >= 1) [from [6, 0, 0]] finished with 3 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 8, 2, 7, :rand, 1.0, 1) *** starting iteration: 1; err: Float32[Inf, 1.514] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.514, 2.1239996] *** *** starting iteration: 2; err: Float32[Inf, 1.514, 2.1239996] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.514, 2.1239996, 2.068] *** *** starting iteration: 3; err: Float32[Inf, 1.514, 2.1239996, 2.068] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.514, 2.1239996, 2.068, 1.596] *** *** starting iteration: 4; err: Float32[Inf, 1.514, 2.1239996, 2.068, 1.596] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.514, 2.1239996, 2.068, 1.596, 2.1239996] *** *** starting iteration: 5; err: Float32[Inf, 1.514, 2.1239996, 2.068, 1.596, 2.1239996] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.514, 2.1239996, 2.068, 1.596, 2.1239996, 1.4980004] *** *** starting iteration: 6; err: Float32[Inf, 1.514, 2.1239996, 2.068, 1.596, 2.1239996, 1.4980004] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.514, 2.1239996, 2.068, 1.596, 2.1239996, 1.4980004, 2.038] *** *** starting iteration: 7; err: Float32[Inf, 1.514, 2.1239996, 2.068, 1.596, 2.1239996, 1.4980004, 2.038] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.514, 2.1239996, 2.068, 1.596, 2.1239996, 1.4980004, 2.038, 2.082] *** *** finished computation of 2 references, err: Float32[Inf, 1.514, 2.1239996, 2.068, 1.596, 2.1239996, 1.4980004, 2.038, 2.082] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 15, 14]] ** center: 1, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] *** center 2: selecting labels [1] (freq >= 1) [from [21, 0, 0]] finished with 2 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 8, 2, 7, :rand, 1.0, 1) *** starting iteration: 1; err: Float32[Inf, 3.2199998] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.2199998, 1.8000002] *** *** starting iteration: 2; err: Float32[Inf, 3.2199998, 1.8000002] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.2199998, 1.8000002, 1.776] *** *** starting iteration: 3; err: Float32[Inf, 3.2199998, 1.8000002, 1.776] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.2199998, 1.8000002, 1.776, 1.8640002] *** *** starting iteration: 4; err: Float32[Inf, 3.2199998, 1.8000002, 1.776, 1.8640002] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.2199998, 1.8000002, 1.776, 1.8640002, 1.776] *** *** starting iteration: 5; err: Float32[Inf, 3.2199998, 1.8000002, 1.776, 1.8640002, 1.776] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.2199998, 1.8000002, 1.776, 1.8640002, 1.776, 1.9] *** *** starting iteration: 6; err: Float32[Inf, 3.2199998, 1.8000002, 1.776, 1.8640002, 1.776, 1.9] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.2199998, 1.8000002, 1.776, 1.8640002, 1.776, 1.9, 1.9] *** *** finished computation of 2 references, err: Float32[Inf, 3.2199998, 1.8000002, 1.776, 1.8640002, 1.776, 1.9, 1.9] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 9, 10]] ** center: 1, normalized-entropy: 0.9980008838722993, [(2, 9), (3, 10)] *** center 2: selecting labels [1] (freq >= 1) [from [6, 0, 0]] finished with 2 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, CentroidSelection}(L2Distance(), CentroidSelection(), 5, 2, 10, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.9037389] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.9037389, 0.7677013] *** *** starting iteration: 2; err: Float32[Inf, 0.9037389, 0.7677013] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 0.9037389, 0.7677013, 0.7677013] *** *** finished computation of 2 references, err: Float32[Inf, 0.9037389, 0.7677013, 0.7677013] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 15, 14]] ** center: 1, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] *** center 2: selecting labels [1] (freq >= 1) [from [21, 0, 0]] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, CentroidSelection}(L2Distance(), CentroidSelection(), 5, 2, 10, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 1.2698694] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.2698694, 0.85716444] *** *** starting iteration: 2; err: Float32[Inf, 1.2698694, 0.85716444] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.2698694, 0.85716444, 0.85716444] *** *** finished computation of 2 references, err: Float32[Inf, 1.2698694, 0.85716444, 0.85716444] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 9, 10]] ** center: 1, normalized-entropy: 0.9980008838722993, [(2, 9), (3, 10)] *** center 2: selecting labels [1] (freq >= 1) [from [6, 0, 0]] finished with 3 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 13, 4, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 0.0007600404] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.0007600404, 0.00058656925] *** *** finished computation of 13 references, err: Float32[Inf, 0.0007600404, 0.00058656925] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 1, 2]] ** center: 1, normalized-entropy: 0.9182958340544894, [(2, 1), (3, 2)] *** center 2: selecting labels [2] (freq >= 1) [from [0, 10, 0]] *** center 3: selecting labels [2, 3] (freq >= 1) [from [0, 2, 2]] ** center: 3, normalized-entropy: 1.0, [(2, 2), (3, 2)] *** center 4: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 5: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 6: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 7: selecting labels [2, 3] (freq >= 1) [from [0, 1, 3]] ** center: 7, normalized-entropy: 0.8112781244591328, [(2, 1), (3, 3)] *** center 8: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 9: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 10: selecting labels [1] (freq >= 1) [from [7, 0, 0]] *** center 11: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 12: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 13: selecting labels [2, 3] (freq >= 1) [from [0, 1, 5]] ** center: 13, normalized-entropy: 0.6500224216483541, [(2, 1), (3, 5)] finished with 16 centers; started with 13 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 13, 4, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 0.00039178328] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.00039178328, 0.0002560669] *** *** finished computation of 13 references, err: Float32[Inf, 0.00039178328, 0.0002560669] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 4]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 2, 1]] ** center: 2, normalized-entropy: 0.9182958340544894, [(2, 2), (3, 1)] *** center 3: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 5: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 7: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 9: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 10: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 11: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 13: selecting labels [3] (freq >= 1) [from [0, 0, 4]] finished with 12 centers; started with 13 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 5, 2, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 1.5819998] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.5819998, 1.2983999] *** *** finished computation of 2 references, err: Float32[Inf, 1.5819998, 1.2983999] *** *** center 1: selecting labels [1] (freq >= 1) [from [21, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 15, 14]] ** center: 2, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 5, 2, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 4.152] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 4.152, 2.6456003] *** *** finished computation of 2 references, err: Float32[Inf, 4.152, 2.6456003] *** *** center 1: selecting labels [1, 2, 3] (freq >= 1) [from [6, 9, 5]] ** center: 1, normalized-entropy: 0.9713107216099229, [(1, 6), (2, 9), (3, 5)] *** center 2: selecting labels [3] (freq >= 1) [from [0, 0, 5]] finished with 4 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 4, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 1.062] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.062, 1.178] *** *** finished computation of 4 references, err: Float32[Inf, 1.062, 1.178] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 4]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 5, 10]] ** center: 2, normalized-entropy: 0.9182958340544894, [(2, 5), (3, 10)] *** center 3: selecting labels [1] (freq >= 1) [from [21, 0, 0]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 10, 0]] finished with 5 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 4, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.98] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.98, 0.932] *** *** finished computation of 4 references, err: Float32[Inf, 0.98, 0.932] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 3, 4]] ** center: 1, normalized-entropy: 0.9852281360342514, [(2, 3), (3, 4)] *** center 2: selecting labels [3] (freq >= 1) [from [0, 0, 6]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 6, 0]] *** center 4: selecting labels [1] (freq >= 1) [from [6, 0, 0]] finished with 5 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 1, 2, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 2.4125516] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.4125516, 1.1985388] *** *** finished computation of 2 references, err: Float32[Inf, 2.4125516, 1.1985388] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 15, 14]] ** center: 1, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] *** center 2: selecting labels [1] (freq >= 1) [from [21, 0, 0]] finished with 3 centers; started with 2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 1, 2, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 1.1372565] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 1.1372565, 1.0323678] *** *** finished computation of 2 references, err: Float32[Inf, 1.1372565, 1.0323678] *** *** center 1: selecting labels [2, 3] (freq >= 1) [from [0, 9, 10]] ** center: 1, normalized-entropy: 0.9980008838722993, [(2, 9), (3, 10)] *** center 2: selecting labels [1] (freq >= 1) [from [6, 0, 0]] finished with 3 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 6, 24, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.48] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.48, 0.42966664] *** *** finished computation of 24 references, err: Float32[Inf, 0.48, 0.42966664] *** *** center 1: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 2: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 3: selecting labels [2, 3] (freq >= 1) [from [0, 4, 3]] ** center: 3, normalized-entropy: 0.9852281360342514, [(2, 4), (3, 3)] *** center 4: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 5: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 7: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 8: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 9: selecting labels [3] (freq >= 1) [from [0, 0, 4]] *** center 10: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 11: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 13: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 14: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 15: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 16: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 18: selecting labels [2, 3] (freq >= 1) [from [0, 1, 1]] ** center: 18, normalized-entropy: 1.0, [(2, 1), (3, 1)] *** center 19: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 20: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 23: selecting labels [3] (freq >= 1) [from [0, 0, 5]] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] finished with 21 centers; started with 24 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 6, 24, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.432] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.432, 0.244] *** *** finished computation of 24 references, err: Float32[Inf, 0.432, 0.244] *** *** center 1: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 2: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 3: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 4: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 5: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 3]] *** center 7: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 8: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 9: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 11: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 13: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 16: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 17: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 18: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 19: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 21: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] finished with 16 centers; started with 24 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 3, 2, 7, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 3.446] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.8746667] *** *** starting iteration: 2; err: Float32[Inf, 3.446, 2.8746667] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.8746667, 1.878] *** *** starting iteration: 3; err: Float32[Inf, 3.446, 2.8746667, 1.878] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.8746667, 1.878, 1.3403997] *** *** starting iteration: 4; err: Float32[Inf, 3.446, 2.8746667, 1.878, 1.3403997] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.8746667, 1.878, 1.3403997, 1.3044] *** *** starting iteration: 5; err: Float32[Inf, 3.446, 2.8746667, 1.878, 1.3403997, 1.3044] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.8746667, 1.878, 1.3403997, 1.3044, 1.2983999] *** *** starting iteration: 6; err: Float32[Inf, 3.446, 2.8746667, 1.878, 1.3403997, 1.3044, 1.2983999] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 3.446, 2.8746667, 1.878, 1.3403997, 1.3044, 1.2983999, 1.2983999] *** *** finished computation of 2 references, err: Float32[Inf, 3.446, 2.8746667, 1.878, 1.3403997, 1.3044, 1.2983999, 1.2983999] *** *** center 1: selecting labels [1] (freq >= 1) [from [21, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 15, 14]] ** center: 2, normalized-entropy: 0.999142103991909, [(2, 15), (3, 14)] finished with 3 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 3, 2, 7, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 2.908] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.908, 2.4304001] *** *** starting iteration: 2; err: Float32[Inf, 2.908, 2.4304001] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.908, 2.4304001, 2.1986666] *** *** starting iteration: 3; err: Float32[Inf, 2.908, 2.4304001, 2.1986666] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.908, 2.4304001, 2.1986666, 2.0709999] *** *** starting iteration: 4; err: Float32[Inf, 2.908, 2.4304001, 2.1986666, 2.0709999] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.908, 2.4304001, 2.1986666, 2.0709999, 1.9310001] *** *** starting iteration: 5; err: Float32[Inf, 2.908, 2.4304001, 2.1986666, 2.0709999, 1.9310001] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.908, 2.4304001, 2.1986666, 2.0709999, 1.9310001, 1.825] *** *** starting iteration: 6; err: Float32[Inf, 2.908, 2.4304001, 2.1986666, 2.0709999, 1.9310001, 1.825] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.908, 2.4304001, 2.1986666, 2.0709999, 1.9310001, 1.825, 1.4962666] *** *** starting iteration: 7; err: Float32[Inf, 2.908, 2.4304001, 2.1986666, 2.0709999, 1.9310001, 1.825, 1.4962666] *** *** computing centroids *** *** computing 2 nearest references *** *** new score with 2 references: Float32[Inf, 2.908, 2.4304001, 2.1986666, 2.0709999, 1.9310001, 1.825, 1.4962666, 1.4909335] *** *** finished computation of 2 references, err: Float32[Inf, 2.908, 2.4304001, 2.1986666, 2.0709999, 1.9310001, 1.825, 1.4962666, 1.4909335] *** *** center 1: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 9, 10]] ** center: 2, normalized-entropy: 0.9980008838722993, [(2, 9), (3, 10)] finished with 3 centers; started with 2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 SearchModels iteration 3> population: 16, bsize: 8, queue: 10, observed: 68, best-error: 0.030000000000000027 worst-error: 0.08000000000000007 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 2, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 1.5080001] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.5080001, 1.3220003] *** *** starting iteration: 2; err: Float32[Inf, 1.5080001, 1.3220003] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.5080001, 1.3220003, 1.1980001] *** *** finished computation of 4 references, err: Float32[Inf, 1.5080001, 1.3220003, 1.1980001] *** *** center 1: selecting labels [2] (freq >= 1) [from [0, 10, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 5, 12]] ** center: 2, normalized-entropy: 0.8739810481273581, [(2, 5), (3, 12)] *** center 3: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 4: selecting labels [1] (freq >= 1) [from [21, 0, 0]] finished with 5 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 2, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 1.8280001] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.8280001, 1.18] *** *** starting iteration: 2; err: Float32[Inf, 1.8280001, 1.18] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.8280001, 1.18, 1.212] *** *** finished computation of 4 references, err: Float32[Inf, 1.8280001, 1.18, 1.212] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 2: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 3: selecting labels [3] (freq >= 1) [from [0, 0, 8]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 9, 0]] finished with 4 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 24, 3, :rand, 1.0, 1) *** starting iteration: 1; err: Float32[Inf, 0.00038612497] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.00038612497, 0.0003909346] *** *** finished computation of 24 references, err: Float32[Inf, 0.00038612497, 0.0003909346] *** *** center 1: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 2: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 3: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 4: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 5: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 7: selecting labels [1] (freq >= 1) [from [8, 0, 0]] *** center 8: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 9: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 10: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 11: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 12: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 13: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 14: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 15: selecting labels [2, 3] (freq >= 1) [from [0, 1, 2]] ** center: 15, normalized-entropy: 0.9182958340544894, [(2, 1), (3, 2)] *** center 16: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 17: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 18: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 19: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 20: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 21: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 22: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 23: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 24: selecting labels [2, 3] (freq >= 1) [from [0, 1, 1]] ** center: 24, normalized-entropy: 1.0, [(2, 1), (3, 1)] finished with 23 centers; started with 24 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 24, 3, :rand, 1.0, 1) *** starting iteration: 1; err: Float32[Inf, 0.00031257526] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.00031257526, 0.00033858194] *** *** finished computation of 24 references, err: Float32[Inf, 0.00031257526, 0.00033858194] *** *** center 1: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 2: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 4: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 5: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 5]] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 8: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 9: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 10: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 11: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 12: selecting labels [2, 3] (freq >= 1) [from [0, 2, 2]] ** center: 12, normalized-entropy: 1.0, [(2, 2), (3, 2)] *** center 13: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 18: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 20: selecting labels [2, 3] (freq >= 1) [from [0, 1, 1]] ** center: 20, normalized-entropy: 1.0, [(2, 1), (3, 1)] *** center 21: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 22: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] finished with 14 centers; started with 24 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L2Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 4, 4, 2, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 1.687524] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.687524, 0.62398577] *** *** starting iteration: 2; err: Float32[Inf, 1.687524, 0.62398577] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.687524, 0.62398577, 0.54303163] *** *** finished computation of 4 references, err: Float32[Inf, 1.687524, 0.62398577, 0.54303163] *** *** center 1: selecting labels [2] (freq >= 1) [from [0, 9, 0]] *** center 2: selecting labels [3] (freq >= 1) [from [0, 0, 10]] *** center 3: selecting labels [1] (freq >= 1) [from [21, 0, 0]] *** center 4: selecting labels [2, 3] (freq >= 1) [from [0, 6, 4]] ** center: 4, normalized-entropy: 0.9709505944546688, [(2, 6), (3, 4)] finished with 5 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L2Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 4, 4, 2, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.6370754] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6370754, 0.5791125] *** *** starting iteration: 2; err: Float32[Inf, 0.6370754, 0.5791125] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.6370754, 0.5791125, 0.5791125] *** *** finished computation of 4 references, err: Float32[Inf, 0.6370754, 0.5791125, 0.5791125] *** *** center 1: selecting labels [2] (freq >= 1) [from [0, 6, 0]] *** center 2: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 3: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 4: selecting labels [2, 3] (freq >= 1) [from [0, 3, 10]] ** center: 4, normalized-entropy: 0.7793498372920851, [(2, 3), (3, 10)] finished with 4 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.80873924] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.80873924, 0.9586141] *** *** finished computation of 4 references, err: Float32[Inf, 0.80873924, 0.9586141] *** *** center 1: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 14, 14]] ** center: 2, normalized-entropy: 1.0, [(2, 14), (3, 14)] *** center 3: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 4: selecting labels [1, 2] (freq >= 1) [from [12, 1, 0]] ** center: 4, normalized-entropy: 0.3912435636292556, [(1, 12), (2, 1)] finished with 5 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L2Distance, MedoidSelection{SqL2Distance}}(L2Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 1.1195328] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.1195328, 1.0708287] *** *** finished computation of 4 references, err: Float32[Inf, 1.1195328, 1.0708287] *** *** center 1: selecting labels [1, 2] (freq >= 1) [from [6, 6, 0]] ** center: 1, normalized-entropy: 1.0, [(1, 6), (2, 6)] *** center 2: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 3: selecting labels [3] (freq >= 1) [from [0, 0, 7]] *** center 4: selecting labels [2, 3] (freq >= 1) [from [0, 1, 3]] ** center: 4, normalized-entropy: 0.8112781244591328, [(2, 1), (3, 3)] finished with 6 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, RandomCenterSelection}(CosineDistance(), RandomCenterSelection(), 3, 24, 3, :rand, 0.4666666666666666, 1) *** starting iteration: 1; err: Float32[Inf, 0.0004305717] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.0004305717, 0.00030964546] *** *** finished computation of 24 references, err: Float32[Inf, 0.0004305717, 0.00030964546] *** *** center 1: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 2: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 4: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 5: selecting labels [2, 3] (freq >= 1) [from [0, 1, 4]] ** center: 5, normalized-entropy: 0.7219280948873623, [(2, 1), (3, 4)] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 6]] *** center 7: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 8: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 9: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 10: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 11: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 12: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 13: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 14: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 15: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 16: selecting labels [2, 3] (freq >= 1) [from [0, 1, 1]] ** center: 16, normalized-entropy: 1.0, [(2, 1), (3, 1)] *** center 17: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 18: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 19: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 20: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 21: selecting labels [3] (freq >= 1) [from [0, 0, 3]] *** center 22: selecting labels [1] (freq >= 1) [from [4, 0, 0]] *** center 23: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 24: selecting labels [2] (freq >= 1) [from [0, 1, 0]] finished with 26 centers; started with 24 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, RandomCenterSelection}(CosineDistance(), RandomCenterSelection(), 3, 24, 3, :rand, 0.4666666666666666, 1) *** starting iteration: 1; err: Float32[Inf, 0.00024856918] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.00024856918, 0.00024803093] *** *** finished computation of 24 references, err: Float32[Inf, 0.00024856918, 0.00024803093] *** *** center 1: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 2: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 5: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 6: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 7: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 8: selecting labels [2, 3] (freq >= 1) [from [0, 1, 2]] ** center: 8, normalized-entropy: 0.9182958340544894, [(2, 1), (3, 2)] *** center 9: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 10: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 12: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 13: selecting labels [3] (freq >= 1) [from [0, 0, 5]] *** center 14: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 15: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 16: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 17: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 18: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 19: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 20: selecting labels [2, 3] (freq >= 1) [from [0, 2, 1]] ** center: 20, normalized-entropy: 0.9182958340544894, [(2, 2), (3, 1)] *** center 21: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 22: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 23: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 24: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] finished with 16 centers; started with 24 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, RandomCenterSelection}(L1Distance(), RandomCenterSelection(), 4, 4, 1, :rand, 1.0, 2) *** starting iteration: 1; err: Float32[Inf, 1.1459998] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.1459998, 1.7040002] *** *** finished computation of 4 references, err: Float32[Inf, 1.1459998, 1.7040002] *** *** center 1: selecting labels [1] (freq >= 2) [from [13, 1, 0]] *** center 2: selecting labels [2, 3] (freq >= 2) [from [0, 6, 5]] ** center: 2, normalized-entropy: 0.9940302114769566, [(2, 6), (3, 5)] *** center 3: selecting labels [2, 3] (freq >= 2) [from [0, 8, 9]] ** center: 3, normalized-entropy: 0.9975025463691153, [(2, 8), (3, 9)] *** center 4: selecting labels [1] (freq >= 2) [from [8, 0, 0]] finished with 4 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, RandomCenterSelection}(L1Distance(), RandomCenterSelection(), 4, 4, 1, :rand, 1.0, 2) *** starting iteration: 1; err: Float32[Inf, 1.256] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.256, 0.85200006] *** *** finished computation of 4 references, err: Float32[Inf, 1.256, 0.85200006] *** *** center 1: selecting labels [1] (freq >= 2) [from [6, 0, 0]] *** center 2: selecting labels [3] (freq >= 2) [from [0, 0, 2]] *** center 3: selecting labels [2, 3] (freq >= 2) [from [0, 3, 8]] ** center: 3, normalized-entropy: 0.8453509366224364, [(2, 3), (3, 8)] *** center 4: selecting labels [2] (freq >= 2) [from [0, 6, 0]] finished with 4 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 1, :rand, 0.5333333333333333, 1) *** starting iteration: 1; err: Float32[Inf, 1.264] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.264, 1.04] *** *** finished computation of 4 references, err: Float32[Inf, 1.264, 1.04] *** *** center 1: selecting labels [1] (freq >= 1) [from [21, 0, 0]] *** center 2: selecting labels [2, 3] (freq >= 1) [from [0, 10, 1]] ** center: 2, normalized-entropy: 0.43949698692151346, [(2, 10), (3, 1)] *** center 3: selecting labels [3] (freq >= 1) [from [0, 0, 10]] *** center 4: selecting labels [2, 3] (freq >= 1) [from [0, 5, 3]] ** center: 4, normalized-entropy: 0.954434002924965, [(2, 5), (3, 3)] finished with 5 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, MedoidSelection{SqL2Distance}}(L1Distance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 4, 4, 1, :rand, 0.5333333333333333, 1) *** starting iteration: 1; err: Float32[Inf, 1.9480001] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 1.9480001, 0.928] *** *** finished computation of 4 references, err: Float32[Inf, 1.9480001, 0.928] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 10]] *** center 2: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 6, 0]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 3, 0]] finished with 4 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 3, 13, 4, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.616] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.616, 0.5183334] *** *** starting iteration: 2; err: Float32[Inf, 0.616, 0.5183334] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.616, 0.5183334, 0.5050001] *** *** starting iteration: 3; err: Float32[Inf, 0.616, 0.5183334, 0.5050001] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.616, 0.5183334, 0.5050001, 0.50600016] *** *** starting iteration: 4; err: Float32[Inf, 0.616, 0.5183334, 0.5050001, 0.50600016] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.616, 0.5183334, 0.5050001, 0.50600016, 0.5050001] *** *** finished computation of 13 references, err: Float32[Inf, 0.616, 0.5183334, 0.5050001, 0.50600016, 0.5050001] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 4]] *** center 2: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 3: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 4: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 5: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 6: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 7: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 8: selecting labels [2] (freq >= 1) [from [0, 4, 0]] *** center 9: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 10: selecting labels [2, 3] (freq >= 1) [from [0, 5, 4]] ** center: 10, normalized-entropy: 0.9910760598382222, [(2, 5), (3, 4)] *** center 11: selecting labels [1] (freq >= 1) [from [9, 0, 0]] *** center 12: selecting labels [1] (freq >= 1) [from [5, 0, 0]] *** center 13: selecting labels [3] (freq >= 1) [from [0, 0, 5]] finished with 14 centers; started with 13 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{L1Distance, KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}}(L1Distance(), KnnCentroidSelection{CentroidSelection, CentroidSelection, SqL2Distance}(CentroidSelection(), CentroidSelection(), SqL2Distance(), 0), 3, 13, 4, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.7] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.7, 0.49733335] *** *** starting iteration: 2; err: Float32[Inf, 0.7, 0.49733335] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.7, 0.49733335, 0.45066667] *** *** starting iteration: 3; err: Float32[Inf, 0.7, 0.49733335, 0.45066667] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.7, 0.49733335, 0.45066667, 0.45066667] *** *** finished computation of 13 references, err: Float32[Inf, 0.7, 0.49733335, 0.45066667, 0.45066667] *** *** center 1: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 2: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 3: selecting labels [3] (freq >= 1) [from [0, 0, 7]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 5: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 6: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 7: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 8: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 9: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 10: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 11: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 12: selecting labels [2, 3] (freq >= 1) [from [0, 1, 1]] ** center: 12, normalized-entropy: 1.0, [(2, 1), (3, 1)] *** center 13: selecting labels [3] (freq >= 1) [from [0, 0, 2]] finished with 12 centers; started with 13 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 3, 4, 4, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 0.0012163904] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.0012163904, 0.0019856198] *** *** finished computation of 4 references, err: Float32[Inf, 0.0012163904, 0.0019856198] *** *** center 1: selecting labels [2] (freq >= 1) [from [0, 13, 0]] *** center 2: selecting labels [1] (freq >= 1) [from [21, 0, 0]] *** center 3: selecting labels [2, 3] (freq >= 1) [from [0, 1, 13]] ** center: 3, normalized-entropy: 0.3712323266408756, [(2, 1), (3, 13)] *** center 4: selecting labels [2, 3] (freq >= 1) [from [0, 1, 1]] ** center: 4, normalized-entropy: 1.0, [(2, 1), (3, 1)] finished with 5 centers; started with 4 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 3, 4, 4, :rand, 0.7, 1) *** starting iteration: 1; err: Float32[Inf, 0.0034353677] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.0034353677, 0.0011990891] *** *** starting iteration: 2; err: Float32[Inf, 0.0034353677, 0.0011990891] *** *** computing centroids *** *** computing 4 nearest references *** *** new score with 4 references: Float32[Inf, 0.0034353677, 0.0011990891, 0.000702987] *** *** finished computation of 4 references, err: Float32[Inf, 0.0034353677, 0.0011990891, 0.000702987] *** *** center 1: selecting labels [1] (freq >= 1) [from [6, 0, 0]] *** center 2: selecting labels [3] (freq >= 1) [from [0, 0, 10]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 5, 0]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 4, 0]] finished with 4 centers; started with 4 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '1.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 13, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.0006372083] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.0006372083, 0.00042336283] *** *** finished computation of 13 references, err: Float32[Inf, 0.0006372083, 0.00042336283] *** *** center 1: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 2: selecting labels [2] (freq >= 1) [from [0, 5, 0]] *** center 3: selecting labels [1] (freq >= 1) [from [10, 0, 0]] *** center 4: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 5: selecting labels [2] (freq >= 1) [from [0, 5, 0]] *** center 6: selecting labels [3] (freq >= 1) [from [0, 0, 3]] *** center 7: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 8: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 9: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 10: selecting labels [1] (freq >= 1) [from [5, 0, 0]] *** center 11: selecting labels [3] (freq >= 1) [from [0, 0, 5]] *** center 12: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 13: selecting labels [2, 3] (freq >= 1) [from [0, 3, 2]] ** center: 13, normalized-entropy: 0.9709505944546688, [(2, 3), (3, 2)] finished with 13 centers; started with 13 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=1 [ Info: precision is zero for label '0.0'; #classes=1 KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 6, 13, 1, :rand, 0.8, 1) *** starting iteration: 1; err: Float32[Inf, 0.00024916482] *** *** computing centroids *** *** computing 13 nearest references *** *** new score with 13 references: Float32[Inf, 0.00024916482, 0.00028255166] *** *** finished computation of 13 references, err: Float32[Inf, 0.00024916482, 0.00028255166] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 3]] *** center 2: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 4: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 5: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 6: selecting labels [1] (freq >= 1) [from [5, 0, 0]] *** center 7: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 8: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 9: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 10: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 11: selecting labels [3] (freq >= 1) [from [0, 0, 5]] *** center 12: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 13: selecting labels [2] (freq >= 1) [from [0, 1, 0]] finished with 12 centers; started with 13 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 SearchModels> reached maximum number of iterations 4 [ Info: ("========== BEST MODEL ==========", KncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 24, 3, :rand, 1.0, 1) => 0.010000000000000009) KncProto> clustering data without knowing labelsKncProtoConfig{CosineDistance, MedoidSelection{SqL2Distance}}(CosineDistance(), MedoidSelection{SqL2Distance}(SqL2Distance(), 0.5f0), 5, 24, 3, :rand, 1.0, 1) *** starting iteration: 1; err: Float32[Inf, 0.00034470088] *** *** computing centroids *** *** computing 24 nearest references *** *** new score with 24 references: Float32[Inf, 0.00034470088, 0.00031504768] *** *** finished computation of 24 references, err: Float32[Inf, 0.00034470088, 0.00031504768] *** *** center 1: selecting labels [3] (freq >= 1) [from [0, 0, 1]] *** center 2: selecting labels [2] (freq >= 1) [from [0, 3, 0]] *** center 3: selecting labels [2] (freq >= 1) [from [0, 1, 0]] *** center 4: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 5: selecting labels [1] (freq >= 1) [from [2, 0, 0]] *** center 6: selecting labels [1] (freq >= 1) [from [1, 0, 0]] *** center 7: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 8: selecting labels [3] (freq >= 1) [from [0, 0, 3]] *** center 9: selecting labels [2] (freq >= 1) [from [0, 4, 0]] *** center 10: selecting labels [3] (freq >= 1) [from [0, 0, 2]] *** center 11: selecting labels [3] (freq >= 1) [from [0, 0, 6]] *** center 12: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 13: selecting labels [2] (freq >= 1) [from [0, 5, 0]] *** center 14: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 15: selecting labels [3] (freq >= 1) [from [0, 0, 6]] *** center 16: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 17: selecting labels [1] (freq >= 1) [from [12, 0, 0]] *** center 18: selecting labels [3] (freq >= 1) [from [0, 0, 4]] *** center 19: selecting labels [1] (freq >= 1) [from [3, 0, 0]] *** center 20: ignoring all elements because minimum-frequency restrictions were not met, freq >= 1, freqs: [0, 0, 0] *** center 21: selecting labels [2] (freq >= 1) [from [0, 2, 0]] *** center 22: selecting labels [2] (freq >= 1) [from [0, 6, 0]] *** center 23: selecting labels [2, 3] (freq >= 1) [from [0, 1, 2]] ** center: 23, normalized-entropy: 0.9182958340544894, [(2, 1), (3, 2)] *** center 24: selecting labels [1] (freq >= 1) [from [3, 0, 0]] finished with 21 centers; started with 24 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 [ Info: precision is zero for label '0.0'; #classes=2 [ Info: precision is zero for label '1.0'; #classes=2 acc = 0.9066666666666666 Test Summary: | Pass Total Time KncProto search_models | 2 2 1m20.1s WARNING: Method definition loadiris() in module Main at /home/pkgeval/.julia/packages/KNearestCenters/2glmo/test/loaddata.jl:5 overwritten on the same line (check for duplicate calls to `include`). WARNING: Method definition kwcall(NamedTuple{names, T} where T<:Tuple where names, typeof(Main.loadiris)) in module Main at /home/pkgeval/.julia/packages/KNearestCenters/2glmo/test/loaddata.jl:5 overwritten on the same line (check for duplicate calls to `include`). WARNING: Method definition loadlinearreg() in module Main at /home/pkgeval/.julia/packages/KNearestCenters/2glmo/test/loaddata.jl:21 overwritten on the same line (check for duplicate calls to `include`). [ Info: precision is zero for label 'Iris-virginica'; #classes=3 Test Summary: | Pass Total Time NearestCenter search_models | 1 1 1.4s Testing KNearestCenters tests passed Testing completed after 272.38s PkgEval succeeded after 494.79s