##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:HPiP.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings HPiP_1.12.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/HPiP.Rcheck’
* using R version 4.4.2 (2024-10-31)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.12.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
    29 | then the Kronecker product is the code{(pm × qn)} block matrix
       |                                       ^
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
corr_plot     50.728  1.789  53.484
var_imp       50.438  1.806  56.596
FSmethod      50.424  1.749  53.421
pred_ensembel 24.848  0.395  22.598
calculateTC    4.682  0.465   5.205
enrichfindP    0.884  0.078  13.472
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 3 NOTEs
See
  ‘/Users/biocbuild/bbs-3.20-bioc/meat/HPiP.Rcheck/00check.log’
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL HPiP
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library’
* installing *source* package ‘HPiP’ ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HPiP)

R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> BiocGenerics:::testPackage('HPiP')
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
avNNet
Loading required package: ggplot2
Loading required package: lattice
Fitting Repeat 1 

# weights:  103
initial  value 116.395420 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.382504 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.525450 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.960283 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.238841 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.800952 
iter  10 value 94.484211
iter  10 value 94.484211
iter  10 value 94.484211
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.162827 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.773831 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.280273 
final  value 93.109890 
converged
Fitting Repeat 5 

# weights:  305
initial  value 98.239575 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 94.676265 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 98.881195 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.779088 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 96.701936 
iter  10 value 86.749280
final  value 86.733916 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.659366 
iter  10 value 93.210840
iter  20 value 91.889294
iter  30 value 86.981343
iter  40 value 86.931896
iter  50 value 86.925942
iter  60 value 86.924305
final  value 86.924254 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.560467 
iter  10 value 92.267465
iter  20 value 90.940814
iter  30 value 84.966310
iter  40 value 82.336257
iter  50 value 81.481300
iter  60 value 81.028239
iter  70 value 80.982454
iter  80 value 79.988701
iter  90 value 79.856495
final  value 79.855773 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.481727 
iter  10 value 94.503622
iter  20 value 94.069687
iter  30 value 91.651292
iter  40 value 87.042718
iter  50 value 86.328503
iter  60 value 82.124682
iter  70 value 81.189552
iter  80 value 81.008234
iter  90 value 80.141312
iter 100 value 79.903445
final  value 79.903445 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 106.091866 
iter  10 value 94.426586
iter  20 value 93.348246
iter  30 value 92.491411
iter  40 value 88.054415
iter  50 value 86.412977
iter  60 value 85.055076
iter  70 value 82.768716
iter  80 value 81.717850
iter  90 value 80.607673
iter 100 value 80.072861
final  value 80.072861 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 96.778112 
iter  10 value 94.488342
iter  20 value 90.983693
iter  30 value 86.171251
iter  40 value 84.698970
iter  50 value 84.501961
iter  60 value 81.993826
iter  70 value 81.907932
iter  80 value 81.906612
final  value 81.906595 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.166426 
iter  10 value 93.729143
iter  20 value 85.692680
iter  30 value 84.515146
iter  40 value 82.733021
iter  50 value 82.658271
iter  60 value 82.042664
iter  70 value 81.981987
iter  80 value 81.981097
final  value 81.981089 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.020781 
iter  10 value 93.748574
iter  20 value 93.268624
iter  30 value 84.410356
iter  40 value 83.907457
iter  50 value 81.884676
iter  60 value 81.134162
iter  70 value 80.765108
iter  80 value 80.576989
iter  90 value 80.414441
iter 100 value 80.000000
final  value 80.000000 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 108.802719 
iter  10 value 94.503365
iter  20 value 94.409607
iter  30 value 94.222067
iter  40 value 94.038363
iter  50 value 86.246785
iter  60 value 85.558881
iter  70 value 82.735046
iter  80 value 80.528157
iter  90 value 80.047392
iter 100 value 79.511450
final  value 79.511450 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 115.375909 
iter  10 value 94.016567
iter  20 value 93.445055
iter  30 value 89.113073
iter  40 value 86.208102
iter  50 value 83.746579
iter  60 value 81.263541
iter  70 value 79.359040
iter  80 value 78.868323
iter  90 value 78.808467
iter 100 value 78.608265
final  value 78.608265 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 105.600941 
iter  10 value 95.987909
iter  20 value 94.121490
iter  30 value 90.291808
iter  40 value 86.917312
iter  50 value 86.169867
iter  60 value 83.148908
iter  70 value 79.729939
iter  80 value 78.978185
iter  90 value 78.689821
iter 100 value 78.445841
final  value 78.445841 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 110.038404 
iter  10 value 95.570962
iter  20 value 89.721231
iter  30 value 88.754968
iter  40 value 85.189561
iter  50 value 84.706804
iter  60 value 82.340585
iter  70 value 80.795265
iter  80 value 79.847190
iter  90 value 79.058033
iter 100 value 78.301287
final  value 78.301287 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 105.898168 
iter  10 value 94.569185
iter  20 value 88.697493
iter  30 value 83.931195
iter  40 value 82.373855
iter  50 value 80.554567
iter  60 value 79.743384
iter  70 value 79.451356
iter  80 value 79.312482
iter  90 value 79.207563
iter 100 value 79.122885
final  value 79.122885 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 109.588537 
iter  10 value 94.435559
iter  20 value 91.244154
iter  30 value 89.679868
iter  40 value 88.679589
iter  50 value 84.100667
iter  60 value 80.098259
iter  70 value 79.015089
iter  80 value 78.884691
iter  90 value 78.786302
iter 100 value 78.773500
final  value 78.773500 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 109.507534 
iter  10 value 94.331623
iter  20 value 85.316210
iter  30 value 83.220387
iter  40 value 81.379559
iter  50 value 80.597151
iter  60 value 80.074835
iter  70 value 79.212429
iter  80 value 78.640774
iter  90 value 78.227362
iter 100 value 78.065783
final  value 78.065783 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 107.494934 
iter  10 value 95.680464
iter  20 value 94.563782
iter  30 value 85.299276
iter  40 value 83.055379
iter  50 value 82.048932
iter  60 value 79.898956
iter  70 value 79.037773
iter  80 value 78.507758
iter  90 value 78.157227
iter 100 value 78.099719
final  value 78.099719 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 119.664589 
iter  10 value 94.360638
iter  20 value 88.084205
iter  30 value 86.069438
iter  40 value 81.031064
iter  50 value 80.123443
iter  60 value 79.923748
iter  70 value 79.468443
iter  80 value 78.861678
iter  90 value 78.445415
iter 100 value 78.191920
final  value 78.191920 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.787554 
final  value 94.485765 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.791054 
final  value 94.485960 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.107676 
final  value 94.485848 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.604032 
final  value 94.485901 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.477274 
iter  10 value 94.485843
iter  20 value 94.447240
iter  30 value 83.456528
iter  40 value 80.968885
iter  50 value 80.964077
final  value 80.964013 
converged
Fitting Repeat 1 

# weights:  305
initial  value 106.070215 
iter  10 value 94.031526
iter  20 value 94.027693
final  value 94.027333 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.478382 
iter  10 value 92.774762
iter  20 value 90.908268
final  value 90.908166 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.180035 
iter  10 value 94.488403
iter  20 value 94.018878
final  value 93.294828 
converged
Fitting Repeat 4 

# weights:  305
initial  value 111.428744 
iter  10 value 94.489444
iter  20 value 94.484253
iter  30 value 89.148899
iter  40 value 87.897032
iter  50 value 85.010077
iter  60 value 83.424922
iter  70 value 83.410956
iter  80 value 81.021705
iter  90 value 80.495416
iter 100 value 80.434215
final  value 80.434215 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 97.566068 
iter  10 value 94.031701
iter  20 value 93.991327
iter  30 value 93.978694
final  value 93.977394 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.966974 
iter  10 value 94.492447
iter  20 value 94.407636
iter  30 value 91.793031
iter  40 value 91.546980
iter  50 value 91.546638
iter  60 value 91.471717
iter  70 value 82.849277
iter  80 value 81.203807
iter  90 value 80.959815
iter 100 value 80.600963
final  value 80.600963 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 137.356957 
iter  10 value 94.492822
iter  20 value 94.269235
iter  30 value 91.270096
iter  40 value 91.257609
iter  50 value 91.256198
iter  60 value 90.680823
iter  70 value 89.438537
iter  80 value 89.436055
iter  90 value 89.305749
iter 100 value 89.225768
final  value 89.225768 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 100.351843 
iter  10 value 87.933950
iter  20 value 84.785465
iter  30 value 84.764954
iter  40 value 84.763976
iter  50 value 84.724394
iter  60 value 82.956157
iter  70 value 82.287652
iter  80 value 82.280527
iter  90 value 82.280383
iter 100 value 82.229241
final  value 82.229241 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 123.664203 
iter  10 value 94.492262
iter  20 value 94.483992
iter  30 value 93.567030
iter  40 value 93.293318
final  value 93.290648 
converged
Fitting Repeat 5 

# weights:  507
initial  value 98.093776 
iter  10 value 92.137366
iter  20 value 92.064379
iter  30 value 92.049472
iter  40 value 92.046012
iter  50 value 92.045458
final  value 92.045422 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.130673 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.564467 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.136256 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.519839 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.959374 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.789736 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.105874 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 106.144020 
iter  10 value 94.484207
iter  10 value 94.484207
iter  10 value 94.484207
final  value 94.484207 
converged
Fitting Repeat 4 

# weights:  305
initial  value 107.285034 
iter  10 value 94.484160
final  value 94.484137 
converged
Fitting Repeat 5 

# weights:  305
initial  value 110.278060 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.451243 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 103.072315 
iter  10 value 94.484137
iter  10 value 94.484137
iter  10 value 94.484137
final  value 94.484137 
converged
Fitting Repeat 3 

# weights:  507
initial  value 108.140235 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 102.942614 
final  value 94.354396 
converged
Fitting Repeat 5 

# weights:  507
initial  value 110.264080 
iter  10 value 93.072333
iter  20 value 92.647772
iter  30 value 92.647361
iter  40 value 92.646290
final  value 92.646203 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.804008 
iter  10 value 94.488066
iter  20 value 93.799588
iter  30 value 93.602545
iter  40 value 86.033243
iter  50 value 84.366089
iter  60 value 84.185106
iter  70 value 83.250352
iter  80 value 82.486685
iter  90 value 82.148172
iter 100 value 81.842161
final  value 81.842161 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 98.583509 
iter  10 value 94.467824
iter  20 value 94.095779
iter  30 value 88.090103
iter  40 value 85.575899
iter  50 value 84.321476
iter  60 value 83.875723
iter  70 value 83.484866
iter  80 value 83.435272
iter  90 value 83.433433
final  value 83.432853 
converged
Fitting Repeat 3 

# weights:  103
initial  value 120.932321 
iter  10 value 94.442811
iter  20 value 88.133553
iter  30 value 84.016272
iter  40 value 83.743879
iter  50 value 83.523577
iter  60 value 83.485886
iter  70 value 81.980445
iter  80 value 80.776001
iter  90 value 80.130696
iter 100 value 79.616591
final  value 79.616591 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 96.742743 
iter  10 value 94.488546
iter  20 value 85.967179
iter  30 value 84.421595
iter  40 value 84.216084
iter  50 value 82.743249
iter  60 value 82.049269
iter  70 value 81.957213
iter  80 value 81.890891
iter  90 value 81.840666
iter  90 value 81.840665
iter  90 value 81.840665
final  value 81.840665 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.379662 
iter  10 value 88.410718
iter  20 value 87.358579
iter  30 value 84.301246
iter  40 value 84.152373
iter  50 value 83.689492
iter  60 value 83.491015
iter  70 value 83.434033
final  value 83.433280 
converged
Fitting Repeat 1 

# weights:  305
initial  value 108.789591 
iter  10 value 94.675158
iter  20 value 92.224730
iter  30 value 84.746157
iter  40 value 84.543508
iter  50 value 83.830077
iter  60 value 80.809115
iter  70 value 79.092459
iter  80 value 78.755821
iter  90 value 78.530307
iter 100 value 78.363731
final  value 78.363731 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.470643 
iter  10 value 95.328259
iter  20 value 94.447063
iter  30 value 88.765311
iter  40 value 87.260250
iter  50 value 86.831093
iter  60 value 85.165491
iter  70 value 82.976067
iter  80 value 81.413113
iter  90 value 80.642730
iter 100 value 79.133371
final  value 79.133371 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.495332 
iter  10 value 94.408323
iter  20 value 88.343553
iter  30 value 83.360427
iter  40 value 81.258179
iter  50 value 79.717303
iter  60 value 79.016468
iter  70 value 78.717865
iter  80 value 78.159201
iter  90 value 78.113446
iter 100 value 78.103959
final  value 78.103959 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 108.222893 
iter  10 value 93.792685
iter  20 value 89.602612
iter  30 value 84.981399
iter  40 value 83.064689
iter  50 value 81.907191
iter  60 value 81.277074
iter  70 value 80.975570
iter  80 value 80.370527
iter  90 value 79.999437
iter 100 value 79.987308
final  value 79.987308 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 102.573436 
iter  10 value 94.257108
iter  20 value 87.628676
iter  30 value 86.204033
iter  40 value 82.929325
iter  50 value 81.864378
iter  60 value 81.166997
iter  70 value 80.979272
iter  80 value 80.688156
iter  90 value 80.636605
iter 100 value 80.342576
final  value 80.342576 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 111.696937 
iter  10 value 95.263175
iter  20 value 87.267094
iter  30 value 85.023540
iter  40 value 83.917986
iter  50 value 79.371886
iter  60 value 78.709891
iter  70 value 78.155999
iter  80 value 77.885269
iter  90 value 77.739957
iter 100 value 77.561410
final  value 77.561410 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 104.764519 
iter  10 value 94.800492
iter  20 value 88.976408
iter  30 value 83.253323
iter  40 value 80.389677
iter  50 value 78.605735
iter  60 value 78.200899
iter  70 value 77.954593
iter  80 value 77.839111
iter  90 value 77.736547
iter 100 value 77.700840
final  value 77.700840 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 136.876301 
iter  10 value 94.269570
iter  20 value 87.462980
iter  30 value 86.704976
iter  40 value 84.654812
iter  50 value 84.004278
iter  60 value 82.746001
iter  70 value 81.339456
iter  80 value 80.666544
iter  90 value 78.539458
iter 100 value 78.308272
final  value 78.308272 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 106.390941 
iter  10 value 94.709354
iter  20 value 94.108078
iter  30 value 89.005702
iter  40 value 83.823402
iter  50 value 82.940365
iter  60 value 81.500961
iter  70 value 80.857208
iter  80 value 80.466097
iter  90 value 80.378946
iter 100 value 79.424024
final  value 79.424024 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 107.395095 
iter  10 value 94.765752
iter  20 value 93.725523
iter  30 value 85.910000
iter  40 value 81.710441
iter  50 value 80.463972
iter  60 value 78.537494
iter  70 value 78.363871
iter  80 value 78.259118
iter  90 value 78.115140
iter 100 value 77.716407
final  value 77.716407 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 103.021373 
final  value 94.485921 
converged
Fitting Repeat 2 

# weights:  103
initial  value 103.416441 
iter  10 value 94.485817
final  value 94.485459 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.800330 
final  value 94.485905 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.566852 
final  value 94.485521 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.004991 
iter  10 value 94.330457
iter  20 value 94.206666
final  value 94.047398 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.106124 
iter  10 value 94.488499
iter  20 value 89.975388
iter  30 value 84.861407
iter  40 value 84.824294
iter  40 value 84.824293
iter  40 value 84.824293
final  value 84.824293 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.907599 
iter  10 value 94.489091
iter  20 value 94.484266
iter  30 value 84.892426
iter  40 value 84.865608
iter  50 value 83.337735
iter  60 value 83.065419
iter  70 value 83.060553
final  value 83.060211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.915524 
iter  10 value 94.489170
iter  20 value 94.473457
iter  30 value 83.844279
iter  40 value 83.377591
iter  50 value 83.188587
iter  60 value 83.096733
iter  70 value 83.079996
iter  80 value 83.075911
iter  90 value 83.067817
iter 100 value 83.065981
final  value 83.065981 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.507503 
iter  10 value 94.012990
iter  20 value 93.899562
iter  30 value 92.945983
iter  40 value 92.633712
iter  50 value 92.633246
iter  60 value 91.955522
iter  70 value 91.953508
iter  80 value 91.952949
iter  90 value 91.926068
iter 100 value 91.890303
final  value 91.890303 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 94.953096 
iter  10 value 94.486502
iter  20 value 94.454973
iter  30 value 89.441141
iter  40 value 89.427986
iter  50 value 88.857658
iter  60 value 88.852251
iter  70 value 88.824841
iter  80 value 88.813579
iter  90 value 88.688701
iter 100 value 88.687232
final  value 88.687232 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 104.905534 
iter  10 value 94.363141
iter  20 value 94.264040
iter  30 value 88.545143
iter  40 value 86.711616
iter  50 value 82.634085
iter  60 value 77.212858
iter  70 value 76.089628
iter  80 value 75.994998
iter  90 value 75.974263
iter 100 value 75.964126
final  value 75.964126 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 97.537292 
iter  10 value 94.325593
iter  20 value 93.701912
iter  30 value 91.892928
iter  40 value 91.681225
final  value 91.680111 
converged
Fitting Repeat 3 

# weights:  507
initial  value 116.641395 
iter  10 value 94.492672
iter  20 value 94.381714
iter  30 value 93.507104
iter  40 value 93.035218
final  value 93.029215 
converged
Fitting Repeat 4 

# weights:  507
initial  value 101.266129 
iter  10 value 94.438682
iter  20 value 94.303682
iter  30 value 83.913504
iter  40 value 83.191583
iter  50 value 83.143416
iter  60 value 83.118495
iter  70 value 83.117044
final  value 83.117013 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.297971 
iter  10 value 94.362532
iter  20 value 94.355236
iter  30 value 94.221756
iter  40 value 92.936569
iter  50 value 84.993146
iter  60 value 79.944662
iter  70 value 79.754571
iter  80 value 79.746021
final  value 79.746019 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.782446 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.845526 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.697055 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.702202 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.075470 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.739378 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 110.257339 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 115.019007 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 118.168388 
iter  10 value 94.028252
final  value 94.008696 
converged
Fitting Repeat 5 

# weights:  305
initial  value 124.727140 
iter  10 value 93.678713
final  value 93.678687 
converged
Fitting Repeat 1 

# weights:  507
initial  value 100.788821 
iter  10 value 93.768812
iter  20 value 93.641276
iter  30 value 86.829405
final  value 86.806626 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.095125 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 104.061663 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 106.548624 
iter  10 value 94.052910
iter  10 value 94.052910
iter  10 value 94.052910
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 114.738351 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 101.816480 
iter  10 value 94.057193
iter  20 value 94.041999
iter  30 value 92.663800
iter  40 value 91.605463
iter  50 value 91.446638
iter  60 value 88.461782
iter  70 value 85.868002
iter  80 value 84.989955
iter  90 value 84.532774
iter 100 value 84.229208
final  value 84.229208 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.818401 
iter  10 value 93.451317
iter  20 value 86.631131
iter  30 value 86.151788
iter  40 value 85.001417
iter  50 value 84.658003
iter  60 value 84.578830
iter  70 value 84.550972
iter  80 value 84.548341
final  value 84.548340 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.458675 
iter  10 value 94.056792
iter  20 value 92.957935
iter  30 value 86.210192
iter  40 value 85.937374
iter  50 value 85.558573
iter  60 value 85.363272
final  value 85.356748 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.369526 
iter  10 value 95.059113
iter  20 value 94.054900
iter  30 value 90.999820
iter  40 value 85.896377
iter  50 value 85.262931
iter  60 value 84.499284
iter  70 value 84.210189
iter  80 value 83.855951
iter  90 value 83.598464
iter 100 value 83.111099
final  value 83.111099 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 103.968225 
iter  10 value 94.055642
iter  20 value 94.001281
iter  30 value 90.010737
iter  40 value 88.663696
iter  50 value 88.240444
iter  60 value 85.946153
iter  70 value 85.242533
iter  80 value 84.583627
iter  90 value 84.571787
iter 100 value 84.566981
final  value 84.566981 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 109.550211 
iter  10 value 93.062236
iter  20 value 86.287221
iter  30 value 85.552367
iter  40 value 85.248118
iter  50 value 85.141388
iter  60 value 84.314742
iter  70 value 83.155710
iter  80 value 82.158886
iter  90 value 81.956111
iter 100 value 81.838310
final  value 81.838310 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 112.400724 
iter  10 value 94.043983
iter  20 value 86.835941
iter  30 value 86.065730
iter  40 value 85.749088
iter  50 value 85.573329
iter  60 value 85.130893
iter  70 value 84.730742
iter  80 value 84.415940
iter  90 value 84.318667
iter 100 value 84.140344
final  value 84.140344 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.941331 
iter  10 value 94.075228
iter  20 value 90.774948
iter  30 value 89.319221
iter  40 value 85.888161
iter  50 value 85.127930
iter  60 value 84.737754
iter  70 value 84.389670
iter  80 value 84.156072
iter  90 value 84.132203
iter 100 value 83.878562
final  value 83.878562 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 105.683240 
iter  10 value 93.971001
iter  20 value 91.698578
iter  30 value 86.198124
iter  40 value 84.868173
iter  50 value 84.755845
iter  60 value 84.543724
iter  70 value 84.396623
iter  80 value 84.385264
iter  90 value 84.358278
iter 100 value 83.800271
final  value 83.800271 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 107.106142 
iter  10 value 93.935695
iter  20 value 92.350394
iter  30 value 89.020867
iter  40 value 87.763059
iter  50 value 87.462364
iter  60 value 85.717912
iter  70 value 84.449427
iter  80 value 83.421152
iter  90 value 82.591024
iter 100 value 82.310420
final  value 82.310420 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 112.543716 
iter  10 value 97.799729
iter  20 value 86.811945
iter  30 value 85.981891
iter  40 value 84.983272
iter  50 value 84.290663
iter  60 value 84.171303
iter  70 value 83.876452
iter  80 value 83.321763
iter  90 value 82.027870
iter 100 value 81.851450
final  value 81.851450 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 102.155864 
iter  10 value 94.447635
iter  20 value 92.491020
iter  30 value 92.373854
iter  40 value 92.087157
iter  50 value 86.282668
iter  60 value 85.609868
iter  70 value 85.300103
iter  80 value 85.074738
iter  90 value 83.286847
iter 100 value 82.621218
final  value 82.621218 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 120.605656 
iter  10 value 94.819593
iter  20 value 89.613148
iter  30 value 86.150646
iter  40 value 85.814786
iter  50 value 84.367169
iter  60 value 83.270020
iter  70 value 82.726899
iter  80 value 82.285579
iter  90 value 82.054827
iter 100 value 82.034246
final  value 82.034246 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 109.763606 
iter  10 value 94.327099
iter  20 value 92.398888
iter  30 value 86.970476
iter  40 value 85.141282
iter  50 value 84.919884
iter  60 value 83.194815
iter  70 value 82.578513
iter  80 value 82.211634
iter  90 value 81.985988
iter 100 value 81.747094
final  value 81.747094 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.379539 
iter  10 value 93.379360
iter  20 value 89.240222
iter  30 value 88.284529
iter  40 value 88.136569
iter  50 value 85.373406
iter  60 value 84.100687
iter  70 value 82.664492
iter  80 value 82.235402
iter  90 value 81.901633
iter 100 value 81.756425
final  value 81.756425 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.075863 
final  value 94.054539 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.496046 
final  value 94.054758 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.869191 
final  value 94.054992 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.272827 
iter  10 value 94.054596
iter  20 value 94.052467
iter  30 value 85.848794
final  value 85.847159 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.286003 
final  value 94.054488 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.837842 
iter  10 value 88.078609
iter  20 value 87.471099
iter  30 value 87.414937
final  value 87.414427 
converged
Fitting Repeat 2 

# weights:  305
initial  value 94.537157 
iter  10 value 94.054982
iter  20 value 94.001927
iter  30 value 86.372870
iter  40 value 85.789665
iter  50 value 85.788697
iter  60 value 84.072106
iter  70 value 83.486763
iter  80 value 82.934840
iter  90 value 82.934275
iter 100 value 82.931844
final  value 82.931844 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 104.511819 
iter  10 value 93.926606
iter  20 value 93.880742
iter  30 value 93.808920
iter  40 value 92.203718
iter  50 value 84.397094
iter  60 value 84.150412
iter  70 value 84.150005
iter  80 value 84.121254
iter  90 value 82.986422
iter 100 value 82.460959
final  value 82.460959 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 96.472530 
iter  10 value 94.057836
iter  20 value 93.957454
iter  30 value 91.999197
iter  40 value 86.984531
iter  50 value 84.425849
iter  60 value 83.463790
final  value 83.436939 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.380255 
iter  10 value 93.916594
iter  20 value 93.728654
iter  30 value 93.727679
iter  40 value 93.722832
iter  50 value 89.568192
final  value 88.213842 
converged
Fitting Repeat 1 

# weights:  507
initial  value 106.149549 
iter  10 value 94.016210
iter  20 value 94.014287
iter  30 value 93.853294
iter  40 value 90.574070
iter  50 value 88.553371
iter  60 value 87.711687
iter  70 value 86.649914
iter  80 value 86.512477
iter  90 value 86.511978
iter 100 value 86.511891
final  value 86.511891 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 100.607328 
iter  10 value 94.061210
iter  20 value 94.052947
iter  30 value 85.517795
iter  40 value 85.387363
final  value 85.387360 
converged
Fitting Repeat 3 

# weights:  507
initial  value 107.656606 
iter  10 value 93.819117
iter  20 value 93.816189
iter  30 value 93.813505
iter  40 value 93.807343
iter  50 value 93.801006
iter  60 value 92.552595
iter  70 value 87.650382
iter  80 value 87.298051
iter  90 value 85.219825
iter 100 value 84.190362
final  value 84.190362 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 104.498988 
iter  10 value 94.016646
iter  20 value 93.882962
iter  30 value 87.625875
iter  40 value 85.620292
iter  50 value 84.386618
iter  60 value 83.974344
iter  70 value 83.776201
iter  80 value 83.684721
final  value 83.684656 
converged
Fitting Repeat 5 

# weights:  507
initial  value 106.940632 
iter  10 value 94.062327
iter  20 value 94.050185
iter  30 value 93.687978
iter  40 value 89.880632
iter  50 value 88.212570
iter  60 value 87.342918
iter  70 value 86.499409
iter  80 value 86.149654
iter  90 value 86.148936
final  value 86.148926 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.066310 
iter  10 value 92.748755
iter  20 value 92.686764
iter  30 value 92.686713
iter  30 value 92.686712
iter  30 value 92.686712
final  value 92.686712 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.477506 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.285227 
iter  10 value 86.496940
iter  20 value 86.496700
iter  30 value 86.312632
iter  40 value 85.232806
iter  50 value 85.196639
final  value 85.196216 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.655089 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.161767 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.189143 
final  value 94.008696 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.121915 
iter  10 value 93.292468
final  value 93.288889 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.711522 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.121353 
final  value 94.008695 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.878418 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.424533 
final  value 93.257143 
converged
Fitting Repeat 2 

# weights:  507
initial  value 104.625020 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 109.683080 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 116.912108 
iter  10 value 93.518803
iter  20 value 91.380207
iter  30 value 91.378271
final  value 91.378268 
converged
Fitting Repeat 5 

# weights:  507
initial  value 99.264198 
iter  10 value 93.978716
final  value 93.912644 
converged
Fitting Repeat 1 

# weights:  103
initial  value 108.847164 
iter  10 value 93.755784
iter  20 value 85.810232
iter  30 value 85.598920
iter  40 value 85.490984
iter  50 value 85.137943
iter  60 value 84.435744
final  value 84.429369 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.194587 
iter  10 value 94.204204
iter  20 value 94.031454
iter  30 value 93.831609
iter  40 value 93.157151
iter  50 value 85.860697
iter  60 value 82.432971
iter  70 value 82.211120
iter  80 value 81.278760
iter  90 value 81.066728
iter 100 value 80.960461
final  value 80.960461 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 103.921604 
iter  10 value 94.055046
iter  20 value 90.984665
iter  30 value 85.512444
iter  40 value 83.698532
iter  50 value 83.240523
iter  60 value 81.645886
iter  70 value 81.250541
iter  80 value 81.165506
iter  90 value 81.137799
final  value 81.137297 
converged
Fitting Repeat 4 

# weights:  103
initial  value 105.094413 
iter  10 value 93.889868
iter  20 value 90.753673
iter  30 value 89.489336
iter  40 value 83.260864
iter  50 value 82.196169
iter  60 value 81.408509
iter  70 value 81.361687
iter  80 value 81.273332
iter  90 value 81.155990
iter 100 value 81.137297
final  value 81.137297 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 99.079240 
iter  10 value 88.649459
iter  20 value 86.413067
iter  30 value 85.637031
iter  40 value 85.215740
iter  50 value 83.987139
iter  60 value 83.239401
iter  70 value 83.194133
iter  80 value 83.190833
iter  90 value 83.189825
final  value 83.189822 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.715312 
iter  10 value 94.079030
iter  20 value 86.436275
iter  30 value 85.271705
iter  40 value 84.551841
iter  50 value 84.335762
iter  60 value 81.877243
iter  70 value 81.462362
iter  80 value 81.279625
iter  90 value 81.252430
iter 100 value 81.157499
final  value 81.157499 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 111.057277 
iter  10 value 92.649087
iter  20 value 92.042498
iter  30 value 88.940867
iter  40 value 86.984328
iter  50 value 84.240621
iter  60 value 82.405759
iter  70 value 81.729130
iter  80 value 81.563888
iter  90 value 81.461825
iter 100 value 81.301133
final  value 81.301133 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 107.749525 
iter  10 value 94.086207
iter  20 value 93.297729
iter  30 value 91.483644
iter  40 value 90.986837
iter  50 value 90.882028
iter  60 value 90.736884
iter  70 value 84.813574
iter  80 value 82.650390
iter  90 value 82.606052
iter 100 value 82.576540
final  value 82.576540 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 98.870841 
iter  10 value 93.933566
iter  20 value 86.947542
iter  30 value 86.275917
iter  40 value 85.842381
iter  50 value 83.913187
iter  60 value 83.192728
iter  70 value 82.330256
iter  80 value 81.878295
iter  90 value 81.768952
iter 100 value 81.680865
final  value 81.680865 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 104.910052 
iter  10 value 93.978807
iter  20 value 84.925558
iter  30 value 82.247044
iter  40 value 81.904426
iter  50 value 80.933284
iter  60 value 80.425167
iter  70 value 80.198473
iter  80 value 80.068124
iter  90 value 79.894990
iter 100 value 79.642571
final  value 79.642571 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 108.023486 
iter  10 value 94.097582
iter  20 value 91.689922
iter  30 value 85.329876
iter  40 value 84.758895
iter  50 value 83.556840
iter  60 value 81.592058
iter  70 value 81.336291
iter  80 value 81.089098
iter  90 value 81.047820
iter 100 value 80.979083
final  value 80.979083 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.715213 
iter  10 value 94.471846
iter  20 value 88.354616
iter  30 value 85.755823
iter  40 value 83.711699
iter  50 value 83.365104
iter  60 value 83.296378
iter  70 value 82.473926
iter  80 value 81.761516
iter  90 value 81.551790
iter 100 value 80.573552
final  value 80.573552 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 112.311874 
iter  10 value 94.813939
iter  20 value 92.926079
iter  30 value 86.694908
iter  40 value 82.647294
iter  50 value 81.844177
iter  60 value 81.059481
iter  70 value 80.253316
iter  80 value 80.079359
iter  90 value 79.627502
iter 100 value 79.342619
final  value 79.342619 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 102.816780 
iter  10 value 94.735294
iter  20 value 94.003228
iter  30 value 93.212971
iter  40 value 88.801052
iter  50 value 84.467332
iter  60 value 82.517396
iter  70 value 81.983863
iter  80 value 81.807070
iter  90 value 81.696121
iter 100 value 81.274890
final  value 81.274890 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 131.852991 
iter  10 value 94.898072
iter  20 value 93.426246
iter  30 value 85.199410
iter  40 value 84.143741
iter  50 value 82.337699
iter  60 value 81.882422
iter  70 value 81.746017
iter  80 value 80.614270
iter  90 value 79.661877
iter 100 value 79.353546
final  value 79.353546 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 100.981771 
final  value 94.054414 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.527837 
final  value 94.010467 
converged
Fitting Repeat 3 

# weights:  103
initial  value 108.356843 
iter  10 value 94.010295
iter  10 value 94.010294
iter  10 value 94.010294
final  value 94.010294 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.602029 
final  value 94.054518 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.898772 
final  value 94.054462 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.326755 
iter  10 value 93.865327
iter  20 value 93.817367
iter  30 value 83.805904
final  value 83.660552 
converged
Fitting Repeat 2 

# weights:  305
initial  value 100.072976 
iter  10 value 94.057852
iter  20 value 86.650276
iter  30 value 85.471638
iter  40 value 84.917446
final  value 84.917444 
converged
Fitting Repeat 3 

# weights:  305
initial  value 108.370425 
iter  10 value 93.719088
iter  20 value 85.432731
iter  30 value 84.313803
iter  40 value 82.838109
iter  50 value 82.767669
iter  60 value 82.763067
final  value 82.762489 
converged
Fitting Repeat 4 

# weights:  305
initial  value 103.426118 
iter  10 value 94.057722
iter  20 value 93.786080
iter  30 value 85.500810
iter  40 value 84.545998
iter  50 value 82.614207
iter  60 value 82.611000
iter  70 value 82.609227
iter  70 value 82.609226
final  value 82.609226 
converged
Fitting Repeat 5 

# weights:  305
initial  value 98.940974 
iter  10 value 94.057511
iter  20 value 94.052941
iter  20 value 94.052940
iter  20 value 94.052940
final  value 94.052940 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.027833 
iter  10 value 94.053705
final  value 94.052932 
converged
Fitting Repeat 2 

# weights:  507
initial  value 99.507460 
iter  10 value 93.813153
iter  20 value 93.143305
iter  30 value 85.355193
iter  40 value 85.325535
iter  50 value 83.513896
iter  60 value 83.049786
iter  70 value 82.438174
iter  80 value 82.435810
final  value 82.434429 
converged
Fitting Repeat 3 

# weights:  507
initial  value 100.100413 
iter  10 value 94.016500
iter  20 value 94.009342
iter  30 value 93.915808
iter  40 value 88.518696
iter  50 value 86.326600
iter  60 value 83.470579
iter  70 value 82.810418
iter  80 value 82.697280
iter  90 value 82.667094
iter 100 value 82.586650
final  value 82.586650 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 110.630333 
iter  10 value 94.017208
iter  20 value 94.011313
iter  30 value 93.393079
iter  40 value 89.901291
iter  50 value 89.900662
iter  60 value 89.508119
iter  70 value 88.656620
iter  80 value 88.656234
iter  90 value 87.987282
iter 100 value 85.188202
final  value 85.188202 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 120.043202 
iter  10 value 94.017677
iter  20 value 94.009126
iter  30 value 93.633416
iter  40 value 85.440184
iter  50 value 85.415151
iter  60 value 85.414628
iter  70 value 85.412173
iter  80 value 85.405431
iter  90 value 85.405367
final  value 85.405276 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.547475 
final  value 94.446632 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.432748 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.621615 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.238752 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.779227 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.893852 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 99.660946 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.322000 
iter  10 value 94.416667
iter  10 value 94.416667
iter  10 value 94.416667
final  value 94.416667 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.445040 
iter  10 value 91.343429
iter  20 value 89.989395
iter  30 value 89.981749
final  value 89.981741 
converged
Fitting Repeat 5 

# weights:  305
initial  value 104.133451 
final  value 94.088889 
converged
Fitting Repeat 1 

# weights:  507
initial  value 115.511311 
final  value 94.466823 
converged
Fitting Repeat 2 

# weights:  507
initial  value 106.746902 
iter  10 value 94.484211
iter  10 value 94.484211
iter  10 value 94.484211
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 111.560090 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.697482 
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  507
initial  value 106.741785 
final  value 94.466823 
converged
Fitting Repeat 1 

# weights:  103
initial  value 108.667813 
iter  10 value 94.480669
iter  20 value 90.544021
iter  30 value 87.189938
iter  40 value 86.486665
iter  50 value 84.737948
iter  60 value 83.835946
iter  70 value 83.665701
iter  80 value 83.646600
iter  90 value 83.387442
final  value 83.365314 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.813612 
iter  10 value 94.486417
iter  20 value 91.461454
iter  30 value 90.701323
iter  40 value 90.404647
iter  50 value 89.754631
iter  60 value 89.455591
iter  70 value 89.453159
final  value 89.453129 
converged
Fitting Repeat 3 

# weights:  103
initial  value 105.502928 
iter  10 value 94.400370
iter  20 value 87.472927
iter  30 value 85.415560
iter  40 value 85.345286
iter  50 value 84.824005
iter  60 value 84.366341
iter  70 value 83.283856
iter  80 value 83.146047
iter  90 value 82.992013
iter 100 value 82.827681
final  value 82.827681 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 99.007984 
iter  10 value 94.492832
iter  20 value 94.477395
iter  30 value 91.329836
iter  40 value 90.546985
iter  50 value 90.477306
iter  60 value 89.934554
iter  70 value 89.455722
iter  80 value 89.453152
final  value 89.453129 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.886422 
iter  10 value 94.470808
iter  20 value 93.936910
iter  30 value 90.393004
iter  40 value 89.895305
iter  50 value 86.909755
iter  60 value 86.029429
final  value 86.029111 
converged
Fitting Repeat 1 

# weights:  305
initial  value 110.112429 
iter  10 value 94.884376
iter  20 value 92.207329
iter  30 value 90.925293
iter  40 value 86.309370
iter  50 value 84.978819
iter  60 value 84.590153
iter  70 value 84.041017
iter  80 value 83.663563
iter  90 value 83.405476
iter 100 value 83.215211
final  value 83.215211 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 107.027530 
iter  10 value 94.491079
iter  20 value 93.650546
iter  30 value 93.243038
iter  40 value 92.597560
iter  50 value 87.738546
iter  60 value 86.042146
iter  70 value 83.869416
iter  80 value 83.626767
iter  90 value 83.445381
iter 100 value 83.340319
final  value 83.340319 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.448799 
iter  10 value 91.136538
iter  20 value 84.442221
iter  30 value 84.029983
iter  40 value 83.841060
iter  50 value 83.726446
iter  60 value 83.497405
iter  70 value 83.247644
iter  80 value 83.205141
iter  90 value 83.027589
iter 100 value 82.260609
final  value 82.260609 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 115.033466 
iter  10 value 94.379294
iter  20 value 86.733866
iter  30 value 85.563200
iter  40 value 85.338392
iter  50 value 84.926396
iter  60 value 83.692719
iter  70 value 83.259001
iter  80 value 82.910697
iter  90 value 82.520907
iter 100 value 81.780951
final  value 81.780951 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 106.509371 
iter  10 value 94.576903
iter  20 value 85.387005
iter  30 value 85.286303
iter  40 value 85.164204
iter  50 value 85.056725
iter  60 value 84.602688
iter  70 value 83.351048
iter  80 value 82.449814
iter  90 value 82.126693
iter 100 value 82.057864
final  value 82.057864 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 123.659246 
iter  10 value 102.285698
iter  20 value 90.781729
iter  30 value 90.228037
iter  40 value 88.366402
iter  50 value 85.281454
iter  60 value 83.513694
iter  70 value 83.322405
iter  80 value 83.186684
iter  90 value 82.427211
iter 100 value 81.749045
final  value 81.749045 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 104.379547 
iter  10 value 93.825377
iter  20 value 90.344501
iter  30 value 90.134401
iter  40 value 88.088685
iter  50 value 86.113892
iter  60 value 85.665709
iter  70 value 84.889687
iter  80 value 84.671047
iter  90 value 83.201229
iter 100 value 82.815441
final  value 82.815441 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 114.728874 
iter  10 value 94.780445
iter  20 value 92.738168
iter  30 value 91.827566
iter  40 value 89.704303
iter  50 value 88.889760
iter  60 value 86.264570
iter  70 value 85.040051
iter  80 value 84.349413
iter  90 value 83.125676
iter 100 value 82.863834
final  value 82.863834 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 106.406955 
iter  10 value 93.902398
iter  20 value 93.730492
iter  30 value 90.889676
iter  40 value 89.637129
iter  50 value 84.913023
iter  60 value 83.591163
iter  70 value 83.370676
iter  80 value 83.223283
iter  90 value 83.166172
iter 100 value 83.082428
final  value 83.082428 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 111.511884 
iter  10 value 94.924442
iter  20 value 91.820759
iter  30 value 86.784891
iter  40 value 85.889834
iter  50 value 85.044778
iter  60 value 84.914732
iter  70 value 83.596448
iter  80 value 83.289375
iter  90 value 83.006477
iter 100 value 82.472262
final  value 82.472262 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 97.786233 
final  value 94.485991 
converged
Fitting Repeat 2 

# weights:  103
initial  value 118.055778 
iter  10 value 94.484647
final  value 94.484215 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.524077 
final  value 94.468285 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.847848 
final  value 94.485648 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.666814 
final  value 94.485824 
converged
Fitting Repeat 1 

# weights:  305
initial  value 106.098110 
iter  10 value 92.462680
iter  20 value 84.966169
iter  30 value 84.597318
iter  40 value 84.503270
iter  50 value 84.500561
iter  60 value 84.500150
iter  70 value 84.499708
iter  80 value 84.497194
iter  90 value 84.496901
final  value 84.496470 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.144413 
iter  10 value 94.488945
iter  20 value 93.024233
iter  30 value 87.433514
iter  40 value 85.544882
iter  50 value 84.568550
iter  60 value 83.179252
iter  70 value 82.431157
iter  80 value 81.673846
iter  90 value 81.670142
iter 100 value 81.559057
final  value 81.559057 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.796813 
iter  10 value 94.488410
iter  20 value 94.484222
final  value 94.484220 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.344403 
iter  10 value 94.488898
iter  20 value 93.437102
iter  30 value 92.455054
iter  40 value 86.152969
iter  50 value 86.151509
iter  60 value 86.140459
iter  70 value 86.139674
final  value 86.139532 
converged
Fitting Repeat 5 

# weights:  305
initial  value 106.469596 
iter  10 value 94.325117
iter  20 value 94.089590
iter  30 value 94.078797
final  value 94.078757 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.090238 
iter  10 value 94.479928
iter  20 value 94.473956
iter  30 value 94.466937
iter  40 value 94.359735
iter  50 value 90.634821
iter  60 value 90.262042
final  value 90.261902 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.068746 
iter  10 value 94.474803
iter  20 value 94.195158
iter  30 value 90.149864
iter  40 value 90.137762
iter  50 value 90.055866
iter  60 value 89.973769
iter  70 value 89.955328
iter  80 value 86.385550
iter  90 value 85.938310
iter 100 value 85.926723
final  value 85.926723 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 97.059589 
iter  10 value 94.491522
iter  20 value 93.697605
iter  30 value 93.196438
iter  30 value 93.196438
final  value 93.196430 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.461506 
iter  10 value 94.475027
iter  20 value 94.467738
final  value 94.467246 
converged
Fitting Repeat 5 

# weights:  507
initial  value 103.987038 
iter  10 value 92.519061
iter  20 value 89.197491
iter  30 value 89.005069
iter  40 value 88.301119
iter  50 value 84.753274
iter  60 value 84.685187
iter  70 value 83.247401
iter  80 value 83.239473
iter  90 value 83.236338
iter 100 value 83.234554
final  value 83.234554 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 123.898562 
iter  10 value 117.893226
iter  20 value 117.774426
final  value 109.491524 
converged
Fitting Repeat 2 

# weights:  305
initial  value 118.998142 
iter  10 value 117.871082
iter  20 value 117.843613
iter  30 value 117.518358
iter  40 value 105.445644
iter  50 value 105.364766
iter  60 value 105.358401
final  value 105.358389 
converged
Fitting Repeat 3 

# weights:  305
initial  value 124.567351 
iter  10 value 117.895413
iter  20 value 117.869692
iter  30 value 117.549947
final  value 117.549835 
converged
Fitting Repeat 4 

# weights:  305
initial  value 134.566186 
iter  10 value 117.895309
iter  20 value 117.890460
iter  30 value 117.545263
iter  40 value 109.388895
iter  50 value 107.840861
iter  60 value 107.828650
iter  70 value 107.141798
iter  80 value 107.050467
iter  90 value 107.048768
iter 100 value 107.047528
final  value 107.047528 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 137.366185 
iter  10 value 117.894860
iter  20 value 117.890299
iter  30 value 117.745496
iter  40 value 117.608143
iter  50 value 117.511748
final  value 117.511685 
converged
svmRadial
ranger
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases


RUNIT TEST PROTOCOL -- Fri Dec 20 04:50:37 2024 
*********************************************** 
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
HPiP RUnit Tests - 7 test functions, 0 errors, 0 failures
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: `repeats` has no meaning for this resampling method. 
2: executing %dopar% sequentially: no parallel backend registered 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
 74.416   2.066  85.767