Last updated on 2019-08-20 05:47:03 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.0-3 | 73.34 | 30.60 | 103.94 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 0.0-3 | 58.36 | 23.23 | 81.59 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 0.0-3 | 181.89 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 0.0-3 | 148.13 | NOTE | |||
| r-devel-windows-ix86+x86_64 | 0.0-3 | 233.00 | 104.00 | 337.00 | NOTE | |
| r-patched-linux-x86_64 | 0.0-3 | 66.04 | 28.88 | 94.92 | NOTE | |
| r-patched-solaris-x86 | 0.0-3 | 143.80 | ERROR | |||
| r-release-linux-x86_64 | 0.0-3 | 64.27 | 28.26 | 92.53 | NOTE | |
| r-release-windows-ix86+x86_64 | 0.0-3 | 171.00 | 62.00 | 233.00 | NOTE | |
| r-release-osx-x86_64 | 0.0-3 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 0.0-3 | 221.00 | 78.00 | 299.00 | NOTE | |
| r-oldrel-osx-x86_64 | 0.0-3 | NOTE |
Version: 0.0-3
Check: for GNU extensions in Makefiles
Result: NOTE
GNU make is a SystemRequirements.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64
Version: 0.0-3
Check: installed package size
Result: NOTE
installed size is 6.7Mb
sub-directories of 1Mb or more:
libs 6.5Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-release-osx-x86_64
Version: 0.0-3
Check: tests
Result: ERROR
Running ‘testthat.R’ [0m/180m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(clusternor)
Loading required package: Rcpp
>
> test_check("clusternor")
***Running test for fcmeans***
Data ==> memory, centroids ==> memory
Data ==> memory, centroids ==> memory
Data ==> memory, centroids ==> compute
$nrow
[1] 50
$ncol
[1] 5
$iters
[1] 17
$k
[1] 8
$centers
[,1] [,2] [,3] [,4] [,5]
[1,] 1.944158 1.873933 1.811169 1.829127 1.705401
[2,] 1.944158 1.873933 1.811169 1.829127 1.705401
[3,] 1.944158 1.873933 1.811169 1.829127 1.705401
[4,] 1.944158 1.873933 1.811169 1.829127 1.705401
[5,] 1.944158 1.873933 1.811169 1.829127 1.705401
[6,] 1.944158 1.873933 1.811169 1.829127 1.705401
[7,] 1.944158 1.873933 1.811169 1.829127 1.705402
[8,] 1.944158 1.873933 1.811169 1.829127 1.705401
$cluster
[1] 7 3 7 7 3 7 7 7 7 3 7 3 7 3 3 7 3 7 3 7 3 7 7 3 3 7 7 3 3 3 7 3 3 7 3 7 3 3
[39] 7 3 3 3 3 3 7 3 3 3 3 3
$size
[1] 0 0 29 0 0 0 21 0
Data ==> memory, centroids ==> compute
$nrow
[1] 50
$ncol
[1] 5
$iters
[1] 17
$k
[1] 8
$centers
[,1] [,2] [,3] [,4] [,5]
[1,] 1.944158 1.873933 1.811169 1.829127 1.705401
[2,] 1.944158 1.873933 1.811169 1.829127 1.705401
[3,] 1.944158 1.873933 1.811169 1.829127 1.705401
[4,] 1.944158 1.873933 1.811169 1.829127 1.705401
[5,] 1.944158 1.873933 1.811169 1.829127 1.705401
[6,] 1.944158 1.873933 1.811169 1.829127 1.705401
[7,] 1.944158 1.873933 1.811169 1.829127 1.705402
[8,] 1.944158 1.873933 1.811169 1.829127 1.705401
$cluster
[1] 7 3 7 7 3 7 7 7 7 3 7 3 7 3 3 7 3 7 3 7 3 7 7 3 3 7 7 3 3 3 7 3 3 7 3 7 3 3
[39] 7 3 3 3 3 3 7 3 3 3 3 3
$size
[1] 0 0 29 0 0 0 21 0
Data ==> disk, centroids ==> compute
$nrow
[1] 50
$ncol
[1] 5
$iters
[1] 21
$k
[1] 8
$centers
[,1] [,2] [,3] [,4] [,5]
[1,] 1.639221 1.559834 1.690042 1.786238 1.736279
[2,] 1.639077 1.559679 1.690646 1.783806 1.738808
[3,] 1.639072 1.559676 1.690663 1.783734 1.738881
[4,] 1.639089 1.559687 1.690603 1.783979 1.738629
[5,] 1.639088 1.559687 1.690607 1.783966 1.738643
[6,] 1.639089 1.559687 1.690603 1.783981 1.738629
[7,] 1.639078 1.559680 1.690641 1.783825 1.738789
[8,] 1.639115 1.559710 1.690507 1.784370 1.738225
$cluster
[1] 3 3 3 3 1 1 3 3 1 3 3 1 1 3 3 1 3 3 1 3 1 1 1 3 3 1 3 3 1 3 3 3 3 1 1 1 3 3
[39] 1 3 1 1 3 3 1 3 1 1 3 3
$size
[1] 21 0 29 0 0 0 0 0
Data ==> disk, centroids ==> compute
$nrow
[1] 50
$ncol
[1] 5
$iters
[1] 21
$k
[1] 8
$centers
[,1] [,2] [,3] [,4] [,5]
[1,] 1.548574 1.487166 2.445550 1.085034 2.042715
[2,] 1.640020 1.547824 1.702029 1.790906 1.724251
[3,] 1.639560 1.549367 1.703571 1.785795 1.728055
[4,] 1.641571 1.540286 1.696512 1.803413 1.718341
[5,] 1.639806 1.550726 1.701379 1.794499 1.718218
[6,] 1.639806 1.550727 1.701374 1.794516 1.718200
[7,] 1.639807 1.550704 1.701433 1.794300 1.718434
[8,] 1.639716 1.550007 1.703228 1.787435 1.725812
$cluster
[1] 4 3 3 3 4 4 3 1 6 3 4 4 6 3 4 4 4 3 4 3 4 6 8 3 4 6 8 3 6 3 3 4 3 6 4 4 3 3
[39] 6 8 6 4 3 3 6 8 4 4 3 3
$size
[1] 1 0 19 17 0 9 0 4
── 1. Failure: data EM compared to same (@test-fuzzy-cmeans.R#76) ─────────────
`ret1` not identical to `ret2`.
Component "centers": Mean relative difference: 0.03371783
Component "cluster": Mean relative difference: 2.117647
Component "size": Mean relative difference: 1.2
***Running test for hmeans***
Data ==> ,centroids ==> memory
$nrow
[1] 50
$ncol
[1] 5
$iters
[1] 20
$k
[1] 8
$centers
[,1] [,2] [,3] [,4] [,5]
[1,] 2.426766 2.953499 4.043335 2.244559 3.710492
[2,] 4.122332 2.465384 3.589322 2.166748 3.575502
[3,] 2.626000 3.584148 2.022466 2.295955 4.134119
[4,] 1.943198 1.376050 2.189751 3.513497 4.592882
[5,] 4.307359 2.781234 3.196755 4.311270 3.415023
[6,] 2.795000 3.228041 4.430282 4.288907 1.526741
[7,] 1.580627 3.095462 1.962946 4.057300 1.642651
[8,] 2.477416 2.417114 2.200970 4.737974 2.761689
$cluster
[1] 5 1 3 2 7 8 1 1 8 1 4 7 6 1 5 5 5 1 5 2 6 5 2 3 4 5 1 2 7 3 3 4 3 7 5 8 1 1
[39] 6 5 5 6 2 3 7 3 5 7 2 3
$size
[1] 9 6 8 3 11 4 6 3
Data ==> memory, centroids ==> memory
$nrow
[1] 50
$ncol
[1] 5
$iters
[1] 20
$k
[1] 8
$centers
[,1] [,2] [,3] [,4] [,5]
[1,] 2.888524 2.603497 2.066828 2.375178 4.408128
[2,] 2.221906 3.679545 2.487253 2.001356 4.078200
[3,] 3.363949 1.514688 3.809288 1.941361 3.547053
[4,] 2.513951 3.081818 4.215258 1.409905 3.593325
[5,] 1.676915 3.191770 2.080428 4.447292 2.429021
[6,] 3.672408 1.905373 2.369369 4.077653 2.867364
[7,] 3.815225 4.217887 1.862281 2.514782 2.702868
[8,] 3.703079 4.045515 3.700746 3.861883 2.418166
$cluster
[1] 1 6 3 4 7 4 3 3 2 6 3 8 2 7 6 7 6 7 5 4 5 1 4 8 8 4 4 8 8 5 1 6 8 2 7 8 6 8
[39] 1 8 7 8 5 8 2 8 6 8 5 8
$size
[1] 4 4 4 6 5 7 6 14
Data ==> memory, centroids ==> memory
$nrow
[1] 50
$ncol
[1] 5
$iters
[1] 20
$k
[1] 8
$centers
[,1] [,2] [,3] [,4] [,5]
[1,] 2.888524 2.603497 2.066828 2.375178 4.408128
[2,] 2.221906 3.679545 2.487253 2.001356 4.078200
[3,] 3.363949 1.514688 3.809288 1.941361 3.547053
[4,] 2.513951 3.081818 4.215258 1.409905 3.593325
[5,] 1.676915 3.191770 2.080428 4.447292 2.429021
[6,] 3.672408 1.905373 2.369369 4.077653 2.867364
[7,] 3.815225 4.217887 1.862281 2.514782 2.702868
[8,] 3.703079 4.045515 3.700746 3.861883 2.418166
$cluster
[1] 1 6 3 4 7 4 3 3 2 6 3 8 2 7 6 7 6 7 5 4 5 1 4 8 8 4 4 8 8 5 1 6 8 2 7 8 6 8
[39] 1 8 7 8 5 8 2 8 6 8 5 8
$size
[1] 4 4 4 6 5 7 6 14
Data ==> memory, centroids ==> compute
$nrow
[1] 50
$ncol
[1] 5
$iters
[1] 20
$k
[1] 8
$centers
[,1] [,2] [,3] [,4] [,5]
[1,] 4.318027 3.274617 3.602438 4.262944 3.290726
[2,] 4.269881 4.268848 2.425307 3.481196 2.341417
[3,] 4.132397 1.768855 4.801009 1.373922 4.613465
[4,] 4.193760 3.652908 4.680513 1.539610 4.934383
[5,] 2.270267 3.412114 2.075016 3.820038 3.503621
[6,] 2.111149 2.999140 1.868376 3.905545 1.677061
[7,] 2.279295 3.155427 3.458205 1.953112 3.614739
[8,] 3.067549 2.376654 2.715911 1.486537 2.405939
$cluster
[1] 1 5 3 4 2 7 8 8 7 6 7 2 7 8 1 2 1 8 6 7 6 8 7 1 7 7 7 6 1 5 7 1 1 5 2 8 6 1
[39] 7 1 5 1 5 2 7 6 1 2 5 1
$size
[1] 12 6 1 1 6 6 12 6
Data ==> memory, centroids ==> compute
$nrow
[1] 50
$ncol
[1] 5
$iters
[1] 20
$k
[1] 8
$centers
[,1] [,2] [,3] [,4] [,5]
[1,] 4.318027 3.274617 3.602438 4.262944 3.290726
[2,] 4.269881 4.268848 2.425307 3.481196 2.341417
[3,] 4.132397 1.768855 4.801009 1.373922 4.613465
[4,] 4.193760 3.652908 4.680513 1.539610 4.934383
[5,] 2.270267 3.412114 2.075016 3.820038 3.503621
[6,] 2.111149 2.999140 1.868376 3.905545 1.677061
[7,] 2.279295 3.155427 3.458205 1.953112 3.614739
[8,] 3.067549 2.376654 2.715911 1.486537 2.405939
$cluster
[1] 1 5 3 4 2 7 8 8 7 6 7 2 7 8 1 2 1 8 6 7 6 8 7 1 7 7 7 6 1 5 7 1 1 5 2 8 6 1
[39] 7 1 5 1 5 2 7 6 1 2 5 1
$size
[1] 12 6 1 1 6 6 12 6
Data ==> test.data.bin , centroids ==> compute
$nrow
[1] 50
$ncol
[1] 5
$iters
[1] 20
$k
[1] 8
$centers
[,1] [,2] [,3] [,4] [,5]
[1,] 4.558290 3.366808 4.267130 4.358562 4.008078
[2,] 4.168830 2.707612 3.295831 3.355779 2.900720
[3,] 2.840925 1.679445 3.518764 4.744002 1.590549
[4,] 2.933031 1.435231 2.307501 4.381139 4.365722
[5,] 2.483710 2.725254 3.572734 2.464197 3.162331
[6,] 1.592081 2.303133 2.266422 4.066587 1.733709
[7,] 2.449150 4.140533 1.550568 3.287508 2.548211
[8,] 3.012037 3.752096 3.148389 2.154862 4.366184
Execution halted
Flavor: r-patched-solaris-x86