##############################################################################
##############################################################################
###
### 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_1.15.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/HPiP.Rcheck’
* using R version 4.5.1 (2025-06-13)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 14.2.0
* 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 ‘1.15.0’
* 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 ... INFO
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
var_imp       31.702  1.755  33.746
FSmethod      29.132  1.559  30.907
corr_plot     27.842  1.577  29.659
pred_ensembel 11.212  0.413   9.954
enrichfindP    0.382  0.047  17.618
getFASTA       0.052  0.011   7.068
* 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: 2 NOTEs
See
  ‘/Users/biocbuild/bbs-3.22-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/4.5-x86_64/Resources/library’
* installing *source* package ‘HPiP’ ...
** this is package ‘HPiP’ version ‘1.15.0’
** 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 4.5.1 (2025-06-13) -- "Great Square Root"
Copyright (C) 2025 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 97.682616 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.655793 
iter  10 value 89.599437
final  value 88.555221 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.080875 
iter  10 value 94.026542
iter  10 value 94.026542
iter  10 value 94.026542
final  value 94.026542 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.815662 
final  value 94.026542 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.070014 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.644021 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.308918 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.641164 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.131014 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 102.624473 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.642447 
iter  10 value 93.088569
final  value 93.088180 
converged
Fitting Repeat 2 

# weights:  507
initial  value 95.668755 
final  value 92.874891 
converged
Fitting Repeat 3 

# weights:  507
initial  value 107.541861 
iter  10 value 93.718950
final  value 93.630893 
converged
Fitting Repeat 4 

# weights:  507
initial  value 110.054180 
final  value 94.026542 
converged
Fitting Repeat 5 

# weights:  507
initial  value 104.200097 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.638444 
iter  10 value 94.489011
iter  20 value 94.254705
iter  30 value 87.206809
iter  40 value 84.549532
iter  50 value 84.320998
iter  60 value 84.192899
iter  70 value 83.397495
iter  80 value 82.793209
iter  90 value 82.761084
final  value 82.759543 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.765493 
iter  10 value 94.501959
iter  20 value 89.935019
iter  30 value 86.813963
iter  40 value 85.872798
iter  50 value 85.634113
iter  60 value 85.571510
final  value 85.569479 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.554095 
iter  10 value 94.504713
iter  20 value 89.051858
iter  30 value 87.187205
iter  40 value 86.504893
iter  50 value 85.913550
iter  60 value 85.607342
final  value 85.569479 
converged
Fitting Repeat 4 

# weights:  103
initial  value 105.672247 
iter  10 value 94.349452
iter  20 value 87.924612
iter  30 value 87.120155
iter  40 value 86.901688
iter  50 value 86.406475
iter  60 value 84.796472
iter  70 value 81.894368
iter  80 value 81.732082
iter  90 value 81.720110
iter 100 value 81.712716
final  value 81.712716 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 99.497353 
iter  10 value 89.822076
iter  20 value 88.892285
iter  30 value 88.054756
iter  40 value 87.897841
iter  50 value 85.452758
iter  60 value 85.260443
iter  70 value 85.213127
final  value 85.211769 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.702458 
iter  10 value 93.898698
iter  20 value 91.241816
iter  30 value 90.776192
iter  40 value 86.145810
iter  50 value 84.601130
iter  60 value 82.675351
iter  70 value 81.669084
iter  80 value 80.827688
iter  90 value 80.546619
iter 100 value 80.330767
final  value 80.330767 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 107.894120 
iter  10 value 92.869955
iter  20 value 84.856617
iter  30 value 82.728764
iter  40 value 81.268872
iter  50 value 81.150697
iter  60 value 80.968442
iter  70 value 80.734026
iter  80 value 80.603530
iter  90 value 80.549812
iter 100 value 80.533524
final  value 80.533524 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 115.409215 
iter  10 value 95.809479
iter  20 value 90.103166
iter  30 value 84.256642
iter  40 value 83.433523
iter  50 value 82.859266
iter  60 value 81.279178
iter  70 value 80.799466
iter  80 value 80.603645
iter  90 value 80.557544
iter 100 value 80.399684
final  value 80.399684 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.436065 
iter  10 value 94.782172
iter  20 value 94.356690
iter  30 value 86.324854
iter  40 value 84.658754
iter  50 value 84.445531
iter  60 value 84.045922
iter  70 value 82.623758
iter  80 value 82.184302
iter  90 value 82.126063
iter 100 value 82.070422
final  value 82.070422 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 104.266487 
iter  10 value 94.418919
iter  20 value 90.842295
iter  30 value 86.578131
iter  40 value 84.861826
iter  50 value 83.800047
iter  60 value 82.363176
iter  70 value 81.564341
iter  80 value 81.096305
iter  90 value 80.915249
iter 100 value 80.591833
final  value 80.591833 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 115.965329 
iter  10 value 93.742511
iter  20 value 88.505513
iter  30 value 87.488400
iter  40 value 84.178261
iter  50 value 81.770204
iter  60 value 81.191788
iter  70 value 81.017021
iter  80 value 80.745800
iter  90 value 80.695251
iter 100 value 80.615628
final  value 80.615628 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 120.271769 
iter  10 value 95.495564
iter  20 value 88.833777
iter  30 value 84.573762
iter  40 value 84.018309
iter  50 value 83.573869
iter  60 value 82.999235
iter  70 value 82.547075
iter  80 value 81.314475
iter  90 value 81.027655
iter 100 value 80.691687
final  value 80.691687 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 105.330278 
iter  10 value 95.077121
iter  20 value 91.549824
iter  30 value 90.915487
iter  40 value 90.669263
iter  50 value 86.465775
iter  60 value 84.731992
iter  70 value 84.317239
iter  80 value 82.509180
iter  90 value 82.006117
iter 100 value 81.803015
final  value 81.803015 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 111.147093 
iter  10 value 93.753021
iter  20 value 85.659330
iter  30 value 84.162783
iter  40 value 82.300339
iter  50 value 81.523810
iter  60 value 81.040559
iter  70 value 80.491526
iter  80 value 80.248539
iter  90 value 80.115042
iter 100 value 79.968871
final  value 79.968871 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.742685 
iter  10 value 95.295710
iter  20 value 88.070556
iter  30 value 87.066195
iter  40 value 85.784408
iter  50 value 84.925684
iter  60 value 84.203208
iter  70 value 82.639313
iter  80 value 82.461992
iter  90 value 82.174679
iter 100 value 81.576572
final  value 81.576572 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.300787 
final  value 94.485702 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.253496 
final  value 94.485698 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.106031 
final  value 94.485811 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.831935 
final  value 94.485675 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.830877 
final  value 94.485745 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.380883 
iter  10 value 94.489446
iter  20 value 94.484723
iter  30 value 94.027361
iter  30 value 94.027361
iter  30 value 94.027360
final  value 94.027360 
converged
Fitting Repeat 2 

# weights:  305
initial  value 104.454954 
iter  10 value 94.311659
iter  20 value 94.185222
iter  30 value 94.115182
final  value 94.114844 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.509098 
iter  10 value 93.673782
iter  20 value 93.670535
iter  30 value 90.011618
iter  40 value 88.539513
iter  50 value 88.511811
iter  60 value 88.429140
iter  70 value 86.307752
iter  80 value 83.320591
iter  90 value 80.288030
iter 100 value 80.174046
final  value 80.174046 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 94.864506 
iter  10 value 94.486260
iter  20 value 94.484210
iter  30 value 93.657513
iter  40 value 92.841472
iter  50 value 91.420766
iter  60 value 90.725853
iter  70 value 90.562048
iter  80 value 90.561410
iter  90 value 90.442672
final  value 90.384555 
converged
Fitting Repeat 5 

# weights:  305
initial  value 115.239400 
iter  10 value 94.489294
iter  20 value 94.469816
iter  30 value 84.223854
iter  40 value 81.957035
iter  50 value 81.931297
iter  60 value 81.807176
iter  70 value 81.159238
iter  80 value 81.037500
iter  90 value 81.037272
iter 100 value 81.036640
final  value 81.036640 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 103.460646 
iter  10 value 94.491914
iter  20 value 94.332337
iter  30 value 92.461580
final  value 92.457360 
converged
Fitting Repeat 2 

# weights:  507
initial  value 109.427162 
iter  10 value 94.492591
iter  20 value 94.442560
iter  30 value 92.177021
iter  40 value 92.124979
iter  50 value 92.124462
iter  60 value 92.124281
final  value 92.124053 
converged
Fitting Repeat 3 

# weights:  507
initial  value 125.134044 
iter  10 value 94.035156
iter  20 value 94.028068
final  value 94.027835 
converged
Fitting Repeat 4 

# weights:  507
initial  value 104.814874 
iter  10 value 94.491207
iter  20 value 92.003926
iter  30 value 84.217416
iter  40 value 84.112277
iter  50 value 83.905954
iter  60 value 83.444147
iter  70 value 83.443909
iter  80 value 83.433792
iter  90 value 82.637169
iter 100 value 80.818113
final  value 80.818113 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 95.843737 
iter  10 value 87.355649
iter  20 value 84.028760
iter  30 value 83.997916
iter  40 value 83.854172
final  value 83.843818 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.654107 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.141939 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.252938 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 94.853897 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.330656 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 132.100649 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.325486 
iter  10 value 93.646889
iter  20 value 93.081407
final  value 93.080990 
converged
Fitting Repeat 3 

# weights:  305
initial  value 102.977305 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 100.018044 
final  value 93.860355 
converged
Fitting Repeat 5 

# weights:  305
initial  value 98.752959 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 117.624617 
iter  10 value 93.920788
iter  20 value 93.842774
final  value 93.842773 
converged
Fitting Repeat 2 

# weights:  507
initial  value 103.389492 
iter  10 value 93.758204
iter  20 value 93.704699
final  value 93.704357 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.269987 
iter  10 value 94.008021
iter  20 value 93.991530
final  value 93.991526 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.899831 
final  value 94.032967 
converged
Fitting Repeat 5 

# weights:  507
initial  value 99.526489 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 101.550007 
iter  10 value 94.056320
iter  20 value 93.854590
iter  30 value 93.828841
iter  40 value 93.781033
iter  50 value 91.677501
iter  60 value 91.443911
iter  70 value 87.981366
iter  80 value 85.792671
iter  90 value 85.096925
iter 100 value 84.426220
final  value 84.426220 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 108.272973 
iter  10 value 93.813823
iter  20 value 86.432083
iter  30 value 85.251914
iter  40 value 85.184400
iter  50 value 85.090036
final  value 85.089187 
converged
Fitting Repeat 3 

# weights:  103
initial  value 111.761580 
iter  10 value 94.809229
iter  20 value 94.005837
iter  30 value 93.836060
iter  40 value 93.827949
iter  50 value 93.827127
iter  60 value 93.818487
iter  70 value 92.770247
iter  80 value 88.220846
iter  90 value 88.038212
iter 100 value 85.588710
final  value 85.588710 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 108.126120 
iter  10 value 93.959446
iter  20 value 88.682253
iter  30 value 86.829671
iter  40 value 85.686348
iter  50 value 85.210665
iter  60 value 85.091777
final  value 85.089187 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.789422 
iter  10 value 94.093791
iter  20 value 93.830861
iter  30 value 93.316329
iter  40 value 92.627354
iter  50 value 89.193063
iter  60 value 87.855170
iter  70 value 87.780256
iter  80 value 87.346616
iter  90 value 86.584602
iter 100 value 86.118473
final  value 86.118473 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 99.504782 
iter  10 value 90.328431
iter  20 value 87.647963
iter  30 value 85.544507
iter  40 value 84.805141
iter  50 value 84.756887
iter  60 value 84.612996
iter  70 value 84.559468
iter  80 value 84.459089
iter  90 value 82.945972
iter 100 value 82.541625
final  value 82.541625 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 105.733495 
iter  10 value 93.656141
iter  20 value 90.768551
iter  30 value 86.358360
iter  40 value 85.686869
iter  50 value 85.044116
iter  60 value 84.565807
iter  70 value 84.540105
iter  80 value 84.485463
iter  90 value 83.326775
iter 100 value 83.016341
final  value 83.016341 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.064240 
iter  10 value 93.470603
iter  20 value 86.825987
iter  30 value 86.434793
iter  40 value 83.993759
iter  50 value 83.186118
iter  60 value 83.071386
iter  70 value 82.676237
iter  80 value 82.579023
iter  90 value 82.345416
iter 100 value 82.198803
final  value 82.198803 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 99.752055 
iter  10 value 91.352076
iter  20 value 85.094582
iter  30 value 84.924966
iter  40 value 84.128069
iter  50 value 83.246216
iter  60 value 82.536472
iter  70 value 82.487941
iter  80 value 82.411226
iter  90 value 82.394431
iter 100 value 82.197717
final  value 82.197717 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 112.092087 
iter  10 value 93.995068
iter  20 value 93.281420
iter  30 value 92.073041
iter  40 value 86.038280
iter  50 value 85.778833
iter  60 value 85.174683
iter  70 value 84.095719
iter  80 value 83.368793
iter  90 value 82.988462
iter 100 value 82.838376
final  value 82.838376 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 104.487763 
iter  10 value 94.167398
iter  20 value 94.057845
iter  30 value 93.838158
iter  40 value 93.826244
iter  50 value 93.773892
iter  60 value 92.334765
iter  70 value 88.100821
iter  80 value 85.463759
iter  90 value 84.227988
iter 100 value 83.408778
final  value 83.408778 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.727275 
iter  10 value 94.042494
iter  20 value 93.836933
iter  30 value 93.567182
iter  40 value 88.487471
iter  50 value 84.380343
iter  60 value 83.453579
iter  70 value 83.249141
iter  80 value 83.196078
iter  90 value 83.188489
iter 100 value 83.122383
final  value 83.122383 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 110.501187 
iter  10 value 94.259267
iter  20 value 94.025491
iter  30 value 90.431443
iter  40 value 87.301030
iter  50 value 85.775620
iter  60 value 85.582531
iter  70 value 85.303219
iter  80 value 84.696078
iter  90 value 83.908158
iter 100 value 83.006786
final  value 83.006786 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 143.292839 
iter  10 value 98.578728
iter  20 value 90.129188
iter  30 value 86.110205
iter  40 value 85.596442
iter  50 value 85.518192
iter  60 value 85.176259
iter  70 value 84.246441
iter  80 value 83.960410
iter  90 value 83.815549
iter 100 value 83.757857
final  value 83.757857 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 106.780293 
iter  10 value 90.281822
iter  20 value 86.206961
iter  30 value 85.210781
iter  40 value 84.967913
iter  50 value 84.422627
iter  60 value 83.385139
iter  70 value 82.482687
iter  80 value 82.345409
iter  90 value 82.310075
iter 100 value 82.194949
final  value 82.194949 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.024188 
iter  10 value 94.054618
iter  20 value 94.049307
iter  30 value 92.408505
iter  40 value 92.407434
iter  50 value 92.406598
iter  60 value 92.406082
iter  70 value 92.405946
final  value 92.405935 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.622977 
final  value 94.054701 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.530873 
iter  10 value 90.977413
iter  20 value 87.068561
iter  30 value 85.221696
iter  40 value 84.785730
iter  50 value 84.785574
iter  60 value 84.782033
iter  70 value 84.017309
iter  80 value 84.013262
final  value 84.013130 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.498947 
final  value 94.054801 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.350100 
final  value 94.054554 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.953754 
iter  10 value 92.705752
iter  20 value 92.213286
iter  30 value 91.967258
iter  40 value 86.811187
iter  50 value 85.697178
final  value 85.695527 
converged
Fitting Repeat 2 

# weights:  305
initial  value 115.006099 
iter  10 value 94.057720
iter  20 value 94.052928
iter  30 value 93.939811
iter  40 value 93.705544
iter  50 value 91.702039
iter  60 value 91.653655
iter  70 value 90.830419
iter  80 value 90.828342
iter  90 value 90.828064
iter 100 value 90.828006
final  value 90.828006 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 97.722583 
iter  10 value 93.906233
iter  20 value 93.834897
iter  30 value 93.821478
iter  40 value 93.818966
iter  50 value 93.818060
final  value 93.817955 
converged
Fitting Repeat 4 

# weights:  305
initial  value 99.230007 
iter  10 value 94.058005
iter  20 value 94.036605
iter  30 value 87.992999
iter  40 value 84.073132
iter  50 value 84.071056
iter  60 value 83.983168
iter  70 value 83.950915
iter  80 value 83.949658
iter  90 value 83.947229
final  value 83.946392 
converged
Fitting Repeat 5 

# weights:  305
initial  value 102.297754 
iter  10 value 94.057916
iter  20 value 93.995811
iter  30 value 90.513764
iter  40 value 86.202132
iter  50 value 86.200872
iter  60 value 86.200684
iter  70 value 86.200468
iter  80 value 86.074990
iter  90 value 85.958365
final  value 85.957776 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.678203 
iter  10 value 94.041248
iter  20 value 94.033078
iter  30 value 87.739925
iter  40 value 84.154851
iter  50 value 81.932751
iter  60 value 81.258028
iter  70 value 80.877742
iter  80 value 80.737429
iter  90 value 80.726625
iter 100 value 80.719956
final  value 80.719956 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 94.444307 
iter  10 value 94.060717
iter  20 value 86.594503
final  value 86.200691 
converged
Fitting Repeat 3 

# weights:  507
initial  value 110.320231 
iter  10 value 89.139208
iter  20 value 86.055558
iter  30 value 85.860762
iter  40 value 85.552681
iter  50 value 85.549753
iter  60 value 83.850342
iter  70 value 83.157942
iter  80 value 83.156213
final  value 83.155345 
converged
Fitting Repeat 4 

# weights:  507
initial  value 102.204207 
iter  10 value 93.813536
iter  20 value 93.811216
iter  30 value 93.388390
iter  40 value 93.196512
iter  50 value 93.196078
iter  60 value 93.189405
final  value 93.178391 
converged
Fitting Repeat 5 

# weights:  507
initial  value 99.756604 
iter  10 value 93.834619
iter  20 value 93.797160
iter  30 value 93.794700
final  value 93.794505 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.158373 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.465030 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.373166 
final  value 94.409357 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.923572 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.983117 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.007382 
iter  10 value 89.679001
iter  20 value 82.968462
iter  30 value 82.950017
final  value 82.949496 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.232821 
iter  10 value 92.136501
iter  20 value 87.328756
iter  30 value 86.877287
iter  40 value 86.875929
iter  40 value 86.875928
iter  40 value 86.875928
final  value 86.875928 
converged
Fitting Repeat 3 

# weights:  305
initial  value 106.727193 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.652342 
final  value 94.452425 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.828173 
iter  10 value 86.970870
iter  20 value 86.510758
final  value 86.507629 
converged
Fitting Repeat 1 

# weights:  507
initial  value 115.011580 
final  value 94.467391 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.053348 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 103.711572 
final  value 94.467391 
converged
Fitting Repeat 4 

# weights:  507
initial  value 94.903768 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.146582 
iter  10 value 88.349337
iter  20 value 84.562239
final  value 84.561431 
converged
Fitting Repeat 1 

# weights:  103
initial  value 103.070281 
iter  10 value 94.524946
iter  20 value 94.479039
iter  30 value 94.250110
iter  40 value 94.154440
iter  50 value 94.142378
iter  60 value 86.319856
iter  70 value 84.467730
iter  80 value 84.430613
iter  90 value 83.398854
iter 100 value 83.303402
final  value 83.303402 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 105.683263 
iter  10 value 94.453319
iter  20 value 91.671098
iter  30 value 89.647502
iter  40 value 85.615371
iter  50 value 80.577597
iter  60 value 79.720224
iter  70 value 79.025439
iter  80 value 78.993246
final  value 78.993238 
converged
Fitting Repeat 3 

# weights:  103
initial  value 117.437107 
iter  10 value 94.449393
iter  20 value 92.854115
iter  30 value 90.901286
iter  40 value 90.726385
iter  50 value 90.524828
final  value 90.509379 
converged
Fitting Repeat 4 

# weights:  103
initial  value 107.025466 
iter  10 value 94.488458
iter  20 value 94.417285
iter  30 value 90.007047
iter  40 value 83.598762
iter  50 value 83.165877
iter  60 value 83.123062
final  value 83.120862 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.798574 
iter  10 value 94.286726
iter  20 value 86.821382
iter  30 value 83.871064
iter  40 value 83.308764
iter  50 value 83.301597
final  value 83.300944 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.295749 
iter  10 value 89.860003
iter  20 value 87.349247
iter  30 value 86.351533
iter  40 value 85.728158
iter  50 value 80.482371
iter  60 value 79.233639
iter  70 value 77.910746
iter  80 value 77.442021
iter  90 value 77.316840
iter 100 value 77.292886
final  value 77.292886 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.955803 
iter  10 value 94.693165
iter  20 value 84.268982
iter  30 value 83.691088
iter  40 value 83.466997
iter  50 value 81.433577
iter  60 value 81.253325
iter  70 value 80.382415
iter  80 value 79.737440
iter  90 value 79.539699
iter 100 value 79.414379
final  value 79.414379 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.427251 
iter  10 value 94.515834
iter  20 value 91.580838
iter  30 value 85.037607
iter  40 value 83.052782
iter  50 value 83.014673
iter  60 value 82.920411
iter  70 value 81.902772
iter  80 value 80.570637
iter  90 value 79.863081
iter 100 value 79.714410
final  value 79.714410 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 106.072690 
iter  10 value 94.503347
iter  20 value 91.195307
iter  30 value 88.104445
iter  40 value 84.442930
iter  50 value 83.414667
iter  60 value 83.010036
iter  70 value 82.561223
iter  80 value 82.137011
iter  90 value 80.107958
iter 100 value 78.821534
final  value 78.821534 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 99.466150 
iter  10 value 94.310497
iter  20 value 92.897223
iter  30 value 85.373616
iter  40 value 85.093768
iter  50 value 84.870582
iter  60 value 83.692648
iter  70 value 81.515952
iter  80 value 80.327934
iter  90 value 80.232361
iter 100 value 80.080878
final  value 80.080878 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 123.957467 
iter  10 value 94.623611
iter  20 value 94.144189
iter  30 value 88.595776
iter  40 value 84.585664
iter  50 value 83.815708
iter  60 value 83.085904
iter  70 value 82.147211
iter  80 value 80.598569
iter  90 value 79.787701
iter 100 value 79.226634
final  value 79.226634 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 121.633646 
iter  10 value 94.543812
iter  20 value 94.451222
iter  30 value 86.146770
iter  40 value 82.846205
iter  50 value 80.921994
iter  60 value 79.251596
iter  70 value 78.669946
iter  80 value 78.253520
iter  90 value 77.715247
iter 100 value 77.486287
final  value 77.486287 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 110.012417 
iter  10 value 98.290774
iter  20 value 87.998731
iter  30 value 85.565458
iter  40 value 81.320557
iter  50 value 79.280730
iter  60 value 78.772671
iter  70 value 78.098514
iter  80 value 77.883130
iter  90 value 77.762421
iter 100 value 77.739862
final  value 77.739862 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 118.786468 
iter  10 value 94.400727
iter  20 value 85.638068
iter  30 value 83.914059
iter  40 value 83.456871
iter  50 value 82.494800
iter  60 value 81.531693
iter  70 value 79.946324
iter  80 value 79.478542
iter  90 value 78.647365
iter 100 value 77.975976
final  value 77.975976 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 113.523934 
iter  10 value 94.042363
iter  20 value 86.535074
iter  30 value 83.994857
iter  40 value 81.330021
iter  50 value 80.832851
iter  60 value 79.777009
iter  70 value 78.293413
iter  80 value 78.040682
iter  90 value 77.784270
iter 100 value 77.739299
final  value 77.739299 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.691834 
final  value 94.485895 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.852718 
iter  10 value 94.486129
final  value 94.485621 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.678912 
final  value 94.485930 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.483204 
final  value 94.485711 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.994792 
final  value 94.469087 
converged
Fitting Repeat 1 

# weights:  305
initial  value 106.183357 
iter  10 value 94.488529
iter  20 value 94.121155
iter  30 value 85.630587
iter  40 value 85.263617
iter  50 value 85.193259
final  value 85.193257 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.734469 
iter  10 value 94.486108
iter  20 value 88.706485
iter  30 value 86.715556
iter  40 value 86.707721
iter  50 value 85.290273
iter  60 value 83.852270
iter  70 value 80.586719
iter  80 value 80.264229
iter  90 value 80.148417
iter 100 value 79.071583
final  value 79.071583 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.136321 
iter  10 value 94.489003
iter  20 value 94.484462
iter  30 value 88.164375
final  value 87.955695 
converged
Fitting Repeat 4 

# weights:  305
initial  value 99.876365 
iter  10 value 94.472340
iter  20 value 94.468694
iter  30 value 94.008797
iter  40 value 86.601529
iter  50 value 85.784072
iter  60 value 85.783057
iter  60 value 85.783057
final  value 85.783057 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.729848 
iter  10 value 94.487846
iter  20 value 90.435033
iter  30 value 84.565955
iter  40 value 83.956397
final  value 83.956374 
converged
Fitting Repeat 1 

# weights:  507
initial  value 106.088959 
iter  10 value 92.765797
iter  20 value 92.764394
iter  30 value 87.303102
iter  40 value 85.985700
iter  40 value 85.985700
iter  40 value 85.985699
final  value 85.985699 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.298555 
iter  10 value 94.312519
iter  20 value 94.306380
iter  30 value 94.079231
iter  40 value 88.971640
iter  50 value 88.673077
iter  60 value 88.672989
iter  70 value 88.671684
iter  80 value 88.558986
iter  90 value 88.547155
iter 100 value 80.830599
final  value 80.830599 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 130.170033 
iter  10 value 94.476268
iter  20 value 92.174085
iter  30 value 87.318710
iter  40 value 85.273475
iter  50 value 85.101025
final  value 85.099926 
converged
Fitting Repeat 4 

# weights:  507
initial  value 100.453397 
iter  10 value 94.475424
iter  20 value 94.468847
final  value 94.468779 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.131304 
iter  10 value 94.153287
iter  20 value 94.145792
iter  30 value 93.429577
iter  40 value 93.425981
iter  50 value 87.314761
iter  60 value 87.035045
iter  70 value 85.549137
iter  80 value 85.279926
iter  90 value 85.278634
iter 100 value 85.043679
final  value 85.043679 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 106.097373 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.337638 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.923361 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.725502 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.493588 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 132.665108 
iter  10 value 94.052910
iter  10 value 94.052910
iter  10 value 94.052910
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.386340 
iter  10 value 93.885927
final  value 93.756805 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.779734 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 112.467738 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.113832 
final  value 93.988095 
converged
Fitting Repeat 1 

# weights:  507
initial  value 99.971373 
final  value 94.032967 
converged
Fitting Repeat 2 

# weights:  507
initial  value 122.332425 
iter  10 value 92.615964
iter  20 value 92.571897
iter  30 value 92.571474
final  value 92.571430 
converged
Fitting Repeat 3 

# weights:  507
initial  value 104.935601 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 110.423877 
final  value 94.032967 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.017248 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 106.969177 
iter  10 value 94.023918
iter  20 value 93.624053
iter  30 value 85.454661
iter  40 value 83.771021
iter  50 value 82.842284
iter  60 value 82.783424
final  value 82.783314 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.266057 
iter  10 value 94.032504
iter  20 value 90.639369
iter  30 value 87.730705
iter  40 value 87.446944
iter  50 value 87.292678
iter  60 value 80.946911
iter  70 value 80.545966
iter  80 value 80.181398
iter  90 value 80.135504
final  value 80.135501 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.116630 
iter  10 value 94.056155
iter  20 value 87.385387
iter  30 value 84.605637
iter  40 value 82.513847
iter  50 value 82.219671
iter  60 value 82.196006
iter  70 value 82.184874
final  value 82.184861 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.751048 
iter  10 value 94.000129
iter  20 value 87.105205
iter  30 value 86.548617
iter  40 value 85.096929
iter  50 value 84.193007
iter  60 value 84.093184
iter  60 value 84.093183
iter  60 value 84.093183
final  value 84.093183 
converged
Fitting Repeat 5 

# weights:  103
initial  value 106.120006 
iter  10 value 93.995242
iter  20 value 89.824049
iter  30 value 87.991829
iter  40 value 84.990888
iter  50 value 83.706533
iter  60 value 83.598692
iter  70 value 83.592017
final  value 83.591856 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.014353 
iter  10 value 93.981991
iter  20 value 89.893689
iter  30 value 87.114755
iter  40 value 83.474968
iter  50 value 83.147471
iter  60 value 81.741586
iter  70 value 80.296403
iter  80 value 79.612552
iter  90 value 78.998214
iter 100 value 78.932884
final  value 78.932884 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.517150 
iter  10 value 94.088011
iter  20 value 93.961146
iter  30 value 85.115893
iter  40 value 84.035717
iter  50 value 83.477426
iter  60 value 81.482619
iter  70 value 80.967566
iter  80 value 80.363850
iter  90 value 80.019370
iter 100 value 79.898295
final  value 79.898295 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.890834 
iter  10 value 92.990075
iter  20 value 85.081633
iter  30 value 83.809547
iter  40 value 83.347889
iter  50 value 83.228809
iter  60 value 83.196516
iter  70 value 82.851426
iter  80 value 82.270767
iter  90 value 81.816262
iter 100 value 81.477726
final  value 81.477726 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 114.231137 
iter  10 value 88.682755
iter  20 value 85.775479
iter  30 value 83.123911
iter  40 value 82.101851
iter  50 value 81.746069
iter  60 value 81.683264
iter  70 value 81.567120
iter  80 value 81.088614
iter  90 value 80.376044
iter 100 value 79.504199
final  value 79.504199 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 106.242593 
iter  10 value 94.055491
iter  20 value 90.860882
iter  30 value 89.669859
iter  40 value 83.758590
iter  50 value 80.066751
iter  60 value 79.505233
iter  70 value 79.458472
iter  80 value 79.401393
iter  90 value 79.183822
iter 100 value 78.787653
final  value 78.787653 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 104.369227 
iter  10 value 94.145129
iter  20 value 92.523217
iter  30 value 90.667982
iter  40 value 86.411807
iter  50 value 85.278582
iter  60 value 81.987087
iter  70 value 81.059922
iter  80 value 80.101186
iter  90 value 79.978584
iter 100 value 79.365638
final  value 79.365638 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 115.535788 
iter  10 value 93.606650
iter  20 value 84.905509
iter  30 value 83.518997
iter  40 value 82.766454
iter  50 value 81.629954
iter  60 value 80.985088
iter  70 value 80.778434
iter  80 value 80.490844
iter  90 value 79.844039
iter 100 value 79.445085
final  value 79.445085 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 110.600521 
iter  10 value 94.025435
iter  20 value 91.648345
iter  30 value 82.563790
iter  40 value 81.901616
iter  50 value 81.326967
iter  60 value 81.025608
iter  70 value 80.793685
iter  80 value 80.353726
iter  90 value 80.042223
iter 100 value 79.828388
final  value 79.828388 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 104.109141 
iter  10 value 93.032028
iter  20 value 84.048218
iter  30 value 82.978396
iter  40 value 82.879467
iter  50 value 82.618649
iter  60 value 81.121472
iter  70 value 79.715791
iter  80 value 79.250461
iter  90 value 79.071678
iter 100 value 78.886318
final  value 78.886318 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 103.687720 
iter  10 value 94.180342
iter  20 value 88.638396
iter  30 value 84.477531
iter  40 value 82.344791
iter  50 value 81.876041
iter  60 value 81.832654
iter  70 value 81.742778
iter  80 value 81.250664
iter  90 value 80.535615
iter 100 value 79.397068
final  value 79.397068 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 101.566029 
final  value 94.054760 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.066055 
final  value 94.054604 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.485897 
final  value 94.054246 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.741882 
iter  10 value 94.054821
iter  20 value 92.396081
iter  30 value 89.545214
iter  40 value 84.758119
iter  50 value 84.631761
iter  60 value 84.467624
iter  70 value 84.460660
iter  80 value 84.455388
final  value 84.455166 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.984337 
final  value 94.054702 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.614021 
iter  10 value 94.057704
iter  20 value 94.052956
iter  30 value 94.052914
final  value 94.052911 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.706705 
iter  10 value 86.001229
iter  20 value 83.611427
iter  30 value 83.576116
iter  40 value 81.090842
iter  50 value 80.950605
iter  60 value 80.945833
iter  70 value 80.572124
iter  80 value 80.304701
iter  90 value 78.980348
iter 100 value 78.754718
final  value 78.754718 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 96.850642 
iter  10 value 94.057771
iter  20 value 94.052932
iter  30 value 93.780250
iter  40 value 91.778805
iter  50 value 91.773622
iter  60 value 91.773251
iter  70 value 91.726809
iter  80 value 91.514940
iter  90 value 91.498199
iter 100 value 91.262939
final  value 91.262939 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.162582 
iter  10 value 93.993132
iter  20 value 93.985491
iter  30 value 88.302700
final  value 88.247369 
converged
Fitting Repeat 5 

# weights:  305
initial  value 105.634219 
iter  10 value 94.057808
iter  20 value 93.783914
iter  30 value 84.986679
iter  40 value 84.276461
final  value 84.275268 
converged
Fitting Repeat 1 

# weights:  507
initial  value 94.274210 
iter  10 value 85.812096
iter  20 value 85.511378
iter  30 value 85.495909
iter  40 value 84.331186
iter  50 value 78.420155
iter  60 value 77.867527
iter  70 value 77.853313
iter  80 value 77.847302
iter  90 value 77.846076
iter 100 value 77.827088
final  value 77.827088 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 110.604145 
iter  10 value 94.056528
iter  20 value 93.980653
iter  30 value 91.201952
iter  40 value 91.175111
iter  50 value 87.695481
iter  60 value 81.565706
iter  70 value 81.467297
iter  80 value 80.257729
iter  90 value 80.248817
iter 100 value 79.833127
final  value 79.833127 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 101.712713 
iter  10 value 94.040362
iter  20 value 93.232121
final  value 87.238025 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.578395 
iter  10 value 94.059189
iter  20 value 93.167868
iter  30 value 88.415109
final  value 82.583890 
converged
Fitting Repeat 5 

# weights:  507
initial  value 94.952197 
iter  10 value 94.058497
iter  20 value 94.012407
iter  30 value 93.811418
iter  40 value 93.556064
iter  50 value 85.581971
iter  60 value 80.911022
iter  70 value 78.619462
iter  80 value 78.314654
iter  90 value 78.156879
iter 100 value 78.116501
final  value 78.116501 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 105.556308 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.904094 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.238633 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.956197 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.338323 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.351530 
iter  10 value 92.266277
iter  20 value 92.193007
final  value 92.193002 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.091785 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 107.549513 
iter  10 value 93.772978
final  value 93.772973 
converged
Fitting Repeat 4 

# weights:  305
initial  value 118.248038 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.796025 
iter  10 value 93.772973
iter  10 value 93.772973
iter  10 value 93.772973
final  value 93.772973 
converged
Fitting Repeat 1 

# weights:  507
initial  value 106.750012 
iter  10 value 94.450224
iter  20 value 93.793230
final  value 93.772973 
converged
Fitting Repeat 2 

# weights:  507
initial  value 102.067935 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 122.580416 
iter  10 value 91.466406
iter  20 value 86.884571
final  value 86.884478 
converged
Fitting Repeat 4 

# weights:  507
initial  value 96.736212 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 121.603521 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.871575 
iter  10 value 94.174384
iter  20 value 86.032820
iter  30 value 85.105708
iter  40 value 84.570196
iter  50 value 83.987188
iter  60 value 83.804564
final  value 83.802146 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.259861 
iter  10 value 94.486656
iter  20 value 94.042969
iter  30 value 93.976993
iter  40 value 93.976816
iter  50 value 93.329170
iter  60 value 87.149762
iter  70 value 85.222803
iter  80 value 84.677731
iter  90 value 84.534888
iter 100 value 84.507455
final  value 84.507455 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 96.507732 
iter  10 value 94.463275
iter  20 value 88.475341
iter  30 value 87.141776
iter  40 value 86.642752
iter  50 value 83.549122
iter  60 value 83.374907
iter  70 value 83.372120
iter  70 value 83.372120
iter  70 value 83.372120
final  value 83.372120 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.388055 
iter  10 value 92.594762
iter  20 value 86.189095
iter  30 value 85.693369
iter  40 value 85.615017
iter  50 value 84.522951
iter  60 value 83.720426
iter  70 value 83.675832
iter  80 value 83.447575
iter  90 value 83.364277
final  value 83.363764 
converged
Fitting Repeat 5 

# weights:  103
initial  value 106.656159 
iter  10 value 94.001014
iter  20 value 91.812309
iter  30 value 87.324091
iter  40 value 86.722625
iter  50 value 85.746584
iter  60 value 84.464604
iter  70 value 82.992172
iter  80 value 82.734109
iter  90 value 82.686479
iter  90 value 82.686479
iter  90 value 82.686479
final  value 82.686479 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.576053 
iter  10 value 94.754002
iter  20 value 94.455480
iter  30 value 93.784808
iter  40 value 91.066088
iter  50 value 86.458317
iter  60 value 84.105372
iter  70 value 82.255419
iter  80 value 81.589770
iter  90 value 81.189237
iter 100 value 81.168935
final  value 81.168935 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.465954 
iter  10 value 94.405518
iter  20 value 90.495663
iter  30 value 86.459725
iter  40 value 85.304578
iter  50 value 84.739847
iter  60 value 83.993933
iter  70 value 83.511475
iter  80 value 83.370493
iter  90 value 83.244453
iter 100 value 83.208433
final  value 83.208433 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.994867 
iter  10 value 94.463684
iter  20 value 86.111708
iter  30 value 85.684917
iter  40 value 84.008262
iter  50 value 82.462755
iter  60 value 82.174014
iter  70 value 81.541218
iter  80 value 81.359879
iter  90 value 81.345325
iter 100 value 81.332412
final  value 81.332412 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.314766 
iter  10 value 94.513585
iter  20 value 90.033937
iter  30 value 85.333844
iter  40 value 83.741080
iter  50 value 83.231833
iter  60 value 82.885037
iter  70 value 82.015935
iter  80 value 81.749295
iter  90 value 81.511437
iter 100 value 81.503290
final  value 81.503290 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 111.337779 
iter  10 value 94.434545
iter  20 value 89.998008
iter  30 value 85.390237
iter  40 value 84.946007
iter  50 value 84.688965
iter  60 value 83.390995
iter  70 value 82.683271
iter  80 value 82.081025
iter  90 value 81.558256
iter 100 value 81.471896
final  value 81.471896 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 139.714113 
iter  10 value 94.546408
iter  20 value 91.449009
iter  30 value 85.387652
iter  40 value 82.916989
iter  50 value 82.324094
iter  60 value 81.791390
iter  70 value 81.610853
iter  80 value 80.990188
iter  90 value 80.706218
iter 100 value 80.641036
final  value 80.641036 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 112.130025 
iter  10 value 94.922296
iter  20 value 90.907692
iter  30 value 88.121057
iter  40 value 83.677172
iter  50 value 82.486640
iter  60 value 81.571164
iter  70 value 81.029057
iter  80 value 80.831127
iter  90 value 80.769950
iter 100 value 80.711017
final  value 80.711017 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 109.825933 
iter  10 value 92.394628
iter  20 value 87.355205
iter  30 value 85.848510
iter  40 value 85.685774
iter  50 value 84.356883
iter  60 value 82.606234
iter  70 value 81.760519
iter  80 value 81.474146
iter  90 value 81.320803
iter 100 value 81.221399
final  value 81.221399 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 104.240027 
iter  10 value 94.857544
iter  20 value 94.060546
iter  30 value 93.171632
iter  40 value 87.340318
iter  50 value 86.320540
iter  60 value 85.925687
iter  70 value 83.857729
iter  80 value 83.672670
iter  90 value 83.275872
iter 100 value 81.791235
final  value 81.791235 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 118.380498 
iter  10 value 94.942135
iter  20 value 93.785290
iter  30 value 93.579813
iter  40 value 89.530757
iter  50 value 85.803633
iter  60 value 84.390545
iter  70 value 83.728474
iter  80 value 82.859448
iter  90 value 81.445238
iter 100 value 81.064221
final  value 81.064221 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.253246 
final  value 94.485774 
converged
Fitting Repeat 2 

# weights:  103
initial  value 104.735830 
final  value 94.485873 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.365061 
final  value 94.485743 
converged
Fitting Repeat 4 

# weights:  103
initial  value 108.666199 
iter  10 value 94.486031
iter  20 value 94.484281
iter  30 value 94.179841
final  value 93.773483 
converged
Fitting Repeat 5 

# weights:  103
initial  value 106.384501 
iter  10 value 94.485716
final  value 94.484561 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.031492 
iter  10 value 91.574930
iter  20 value 86.502183
iter  30 value 86.396381
iter  40 value 86.393190
iter  50 value 86.151665
iter  60 value 85.578926
iter  70 value 85.572708
iter  80 value 85.042570
iter  90 value 84.679275
iter 100 value 83.863042
final  value 83.863042 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 106.564643 
iter  10 value 94.487902
iter  20 value 94.484683
final  value 94.484678 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.351916 
iter  10 value 94.520778
iter  20 value 94.488427
iter  30 value 94.119813
iter  40 value 94.095064
iter  50 value 84.375758
iter  60 value 84.328794
iter  70 value 83.621629
iter  80 value 83.621384
iter  90 value 83.620981
final  value 83.620102 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.655854 
iter  10 value 94.489388
iter  20 value 92.970753
iter  30 value 92.919162
iter  40 value 92.917947
final  value 92.917935 
converged
Fitting Repeat 5 

# weights:  305
initial  value 103.788476 
iter  10 value 94.488986
iter  20 value 94.200801
iter  30 value 88.292815
iter  40 value 86.670413
final  value 86.527768 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.265092 
iter  10 value 93.781258
iter  20 value 93.778410
iter  30 value 88.550679
iter  40 value 87.334712
iter  50 value 85.279808
iter  60 value 85.173879
final  value 85.173145 
converged
Fitting Repeat 2 

# weights:  507
initial  value 99.346568 
iter  10 value 94.491937
iter  20 value 94.310348
iter  30 value 90.600610
iter  40 value 89.786007
iter  50 value 85.550530
iter  60 value 83.340941
iter  70 value 82.910268
iter  80 value 82.701477
iter  90 value 82.630621
iter 100 value 82.630066
final  value 82.630066 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 95.163144 
iter  10 value 89.202822
iter  20 value 89.083634
iter  30 value 87.345457
iter  40 value 87.339339
iter  50 value 86.895642
iter  60 value 85.935099
iter  70 value 85.902938
iter  80 value 85.486356
iter  90 value 85.483779
iter 100 value 85.483664
final  value 85.483664 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 96.322488 
iter  10 value 93.730974
iter  20 value 93.730524
iter  30 value 93.729856
iter  40 value 93.726091
iter  50 value 93.716312
final  value 93.715409 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.359065 
iter  10 value 94.492470
iter  20 value 94.484538
iter  30 value 92.783460
iter  40 value 85.957980
iter  50 value 85.363096
iter  60 value 84.930107
iter  70 value 82.932467
iter  80 value 82.922798
iter  90 value 82.919721
iter 100 value 82.874838
final  value 82.874838 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 129.466989 
iter  10 value 117.986745
iter  20 value 115.302614
iter  30 value 110.292342
iter  40 value 109.727306
iter  50 value 106.571647
iter  60 value 105.125196
iter  70 value 104.471457
iter  80 value 104.298119
iter  90 value 103.373299
iter 100 value 101.823437
final  value 101.823437 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 138.422864 
iter  10 value 118.671851
iter  20 value 116.732700
iter  30 value 110.162753
iter  40 value 109.486123
iter  50 value 105.929572
iter  60 value 103.595151
iter  70 value 102.134534
iter  80 value 101.619177
iter  90 value 101.221632
iter 100 value 101.043928
final  value 101.043928 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 132.456362 
iter  10 value 117.909812
iter  20 value 113.660327
iter  30 value 111.861745
iter  40 value 105.989196
iter  50 value 103.206530
iter  60 value 102.151019
iter  70 value 101.581403
iter  80 value 101.225519
iter  90 value 101.077854
iter 100 value 101.044002
final  value 101.044002 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 155.930271 
iter  10 value 122.019406
iter  20 value 115.166088
iter  30 value 114.255279
iter  40 value 113.976540
iter  50 value 113.832317
iter  60 value 112.762589
iter  70 value 108.410848
iter  80 value 104.794737
iter  90 value 103.328249
iter 100 value 102.424487
final  value 102.424487 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 147.924078 
iter  10 value 119.191416
iter  20 value 118.653416
iter  30 value 111.748713
iter  40 value 110.277814
iter  50 value 107.706081
iter  60 value 107.445708
iter  70 value 107.006089
iter  80 value 105.022580
iter  90 value 103.006860
iter 100 value 101.824089
final  value 101.824089 
stopped after 100 iterations
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 -- Wed Sep  3 21:33:38 2025 
*********************************************** 
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 
 38.574   1.528 139.922