##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:HPiP.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings HPiP_1.16.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/HPiP.Rcheck’
* using R version 4.5.2 (2025-10-31)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
    GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
* running under: Ubuntu 24.04.4 LTS
* using session charset: UTF-8
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.16.1’
* 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 loading without being on the library search path ... 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
corr_plot     34.855  0.515  35.397
FSmethod      33.908  0.542  34.450
var_imp       33.606  0.555  34.162
pred_ensembel 12.758  0.084  11.589
enrichfindP    0.569  0.033   9.822
* 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 re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 2 NOTEs
See
  ‘/home/biocbuild/bbs-3.22-bioc/meat/HPiP.Rcheck/00check.log’
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD INSTALL HPiP
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.22-bioc/R/site-library’
* installing *source* package ‘HPiP’ ...
** this is package ‘HPiP’ version ‘1.16.1’
** 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 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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 100.180357 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 104.805336 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.707298 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.902815 
final  value 93.836066 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.488462 
final  value 94.011429 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.105838 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.657787 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.743465 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.105179 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 110.845919 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.067322 
iter  10 value 93.206256
iter  20 value 90.091410
iter  30 value 90.051215
iter  40 value 90.003399
iter  50 value 89.965730
final  value 89.963089 
converged
Fitting Repeat 2 

# weights:  507
initial  value 99.556749 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.863978 
iter  10 value 91.034530
iter  20 value 88.171391
iter  30 value 88.094468
iter  40 value 88.042756
final  value 88.042754 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.474778 
iter  10 value 93.507822
final  value 93.478227 
converged
Fitting Repeat 5 

# weights:  507
initial  value 122.506463 
iter  10 value 93.836699
final  value 93.836066 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.564049 
iter  10 value 94.053740
iter  20 value 92.367267
iter  30 value 90.369526
iter  40 value 88.206209
iter  50 value 87.904840
iter  60 value 87.500801
iter  70 value 87.232816
iter  80 value 87.124434
iter  90 value 87.071052
iter 100 value 86.851613
final  value 86.851613 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 99.835974 
iter  10 value 94.056592
iter  20 value 91.711377
iter  30 value 91.075347
iter  40 value 90.851960
iter  50 value 90.389373
iter  60 value 87.079568
iter  70 value 86.617590
iter  80 value 86.085756
iter  90 value 85.953535
iter 100 value 85.623532
final  value 85.623532 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 96.045309 
iter  10 value 94.055351
iter  20 value 88.614597
iter  30 value 88.408020
iter  40 value 87.765595
iter  50 value 87.143384
iter  60 value 87.083277
final  value 87.083260 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.364632 
iter  10 value 94.057514
iter  20 value 93.979742
iter  30 value 93.167952
iter  40 value 93.094910
iter  50 value 91.909094
iter  60 value 91.899151
final  value 91.899117 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.510473 
iter  10 value 93.894736
iter  20 value 88.083391
iter  30 value 86.671258
iter  40 value 85.983759
iter  50 value 85.572451
iter  60 value 85.317541
iter  70 value 85.089313
iter  80 value 85.054168
iter  90 value 85.041954
iter 100 value 85.023988
final  value 85.023988 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 107.343917 
iter  10 value 94.056473
iter  20 value 93.195782
iter  30 value 87.901874
iter  40 value 87.351185
iter  50 value 86.863763
iter  60 value 86.198494
iter  70 value 84.775967
iter  80 value 83.883753
iter  90 value 83.682674
iter 100 value 83.494626
final  value 83.494626 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 104.086050 
iter  10 value 94.176503
iter  20 value 93.858043
iter  30 value 93.555357
iter  40 value 92.845239
iter  50 value 89.183124
iter  60 value 87.349476
iter  70 value 86.401480
iter  80 value 85.989437
iter  90 value 85.562374
iter 100 value 85.200207
final  value 85.200207 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.848679 
iter  10 value 94.054265
iter  20 value 93.831887
iter  30 value 92.841644
iter  40 value 87.465673
iter  50 value 86.276927
iter  60 value 85.488747
iter  70 value 85.346335
iter  80 value 85.024083
iter  90 value 84.938240
iter 100 value 84.602669
final  value 84.602669 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 132.637161 
iter  10 value 93.904198
iter  20 value 93.787078
iter  30 value 92.626819
iter  40 value 92.356504
iter  50 value 92.268131
iter  60 value 91.943485
iter  70 value 87.836943
iter  80 value 87.551636
iter  90 value 86.942464
iter 100 value 85.394944
final  value 85.394944 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 107.659406 
iter  10 value 95.318361
iter  20 value 94.960345
iter  30 value 91.696944
iter  40 value 89.068133
iter  50 value 88.564870
iter  60 value 88.272455
iter  70 value 87.679775
iter  80 value 86.939440
iter  90 value 85.656758
iter 100 value 85.446197
final  value 85.446197 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 123.231735 
iter  10 value 100.018855
iter  20 value 89.707551
iter  30 value 88.900840
iter  40 value 88.049605
iter  50 value 86.647407
iter  60 value 85.866382
iter  70 value 85.675120
iter  80 value 85.112113
iter  90 value 84.154782
iter 100 value 83.629511
final  value 83.629511 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 133.807770 
iter  10 value 95.090971
iter  20 value 93.348318
iter  30 value 92.329512
iter  40 value 90.600551
iter  50 value 86.060148
iter  60 value 85.478088
iter  70 value 84.084688
iter  80 value 83.333162
iter  90 value 83.201867
iter 100 value 83.170730
final  value 83.170730 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.154484 
iter  10 value 94.638768
iter  20 value 91.670945
iter  30 value 88.766631
iter  40 value 88.385962
iter  50 value 87.984211
iter  60 value 85.427921
iter  70 value 84.730747
iter  80 value 84.091828
iter  90 value 83.862417
iter 100 value 83.732738
final  value 83.732738 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 103.220873 
iter  10 value 94.050343
iter  20 value 89.719272
iter  30 value 89.469841
iter  40 value 89.239456
iter  50 value 88.078024
iter  60 value 87.724232
iter  70 value 87.600033
iter  80 value 87.469836
iter  90 value 86.680552
iter 100 value 85.299735
final  value 85.299735 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 115.432590 
iter  10 value 94.091219
iter  20 value 93.275595
iter  30 value 90.067232
iter  40 value 87.195566
iter  50 value 86.998809
iter  60 value 86.660063
iter  70 value 85.697549
iter  80 value 84.541292
iter  90 value 84.093245
iter 100 value 83.482374
final  value 83.482374 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 103.434919 
final  value 94.054617 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.320510 
final  value 94.054619 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.583537 
final  value 94.054521 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.374959 
iter  10 value 94.054435
iter  20 value 93.985713
iter  30 value 92.957617
iter  40 value 92.841886
final  value 92.841740 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.939390 
iter  10 value 94.054688
iter  20 value 94.052931
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.455611 
iter  10 value 93.840657
iter  20 value 93.836847
final  value 93.836445 
converged
Fitting Repeat 2 

# weights:  305
initial  value 94.226192 
iter  10 value 94.053768
iter  20 value 90.172612
iter  30 value 88.642325
iter  40 value 88.641475
iter  50 value 88.599716
iter  60 value 88.581419
iter  60 value 88.581419
final  value 88.581419 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.713541 
iter  10 value 93.841292
iter  20 value 93.836934
iter  30 value 92.277408
iter  40 value 91.586648
iter  50 value 89.468911
iter  60 value 88.976424
iter  70 value 88.972786
iter  80 value 88.972491
iter  90 value 88.349859
iter 100 value 87.602311
final  value 87.602311 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.162718 
iter  10 value 94.057095
iter  20 value 94.021394
iter  30 value 93.804962
final  value 93.804953 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.723412 
iter  10 value 94.057848
iter  20 value 94.052973
iter  20 value 94.052972
iter  20 value 94.052972
final  value 94.052972 
converged
Fitting Repeat 1 

# weights:  507
initial  value 96.844258 
iter  10 value 94.057923
iter  20 value 93.947113
final  value 93.836236 
converged
Fitting Repeat 2 

# weights:  507
initial  value 103.857280 
iter  10 value 94.061033
iter  20 value 93.777686
iter  30 value 89.175538
iter  40 value 87.623768
iter  50 value 87.621349
iter  60 value 87.620797
iter  70 value 86.708747
iter  80 value 86.258936
iter  90 value 86.143169
iter 100 value 85.155510
final  value 85.155510 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 112.089703 
iter  10 value 94.064154
iter  20 value 94.013797
iter  30 value 89.042018
iter  40 value 88.033046
iter  50 value 87.103995
iter  60 value 87.012873
iter  70 value 86.980330
iter  80 value 86.950672
iter  90 value 86.676507
iter 100 value 86.432432
final  value 86.432432 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 106.188067 
iter  10 value 94.020308
iter  20 value 94.017084
iter  30 value 92.991061
iter  40 value 90.462267
iter  50 value 88.464019
iter  60 value 86.175742
iter  70 value 85.821895
iter  80 value 84.795400
iter  90 value 82.560107
iter 100 value 82.238475
final  value 82.238475 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 101.130480 
iter  10 value 93.679158
iter  20 value 88.980346
iter  30 value 87.806851
iter  40 value 87.185846
iter  50 value 87.161762
final  value 87.161739 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.930376 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.783921 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 109.441329 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 104.226776 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.577015 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 115.760723 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 110.383634 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 102.179230 
iter  10 value 94.088482
iter  20 value 92.634917
iter  30 value 92.631434
final  value 92.631431 
converged
Fitting Repeat 4 

# weights:  305
initial  value 122.838644 
iter  10 value 94.484877
final  value 94.483810 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.273272 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.160100 
iter  10 value 90.347809
iter  20 value 90.342215
iter  30 value 90.336771
iter  40 value 90.306903
iter  50 value 89.665123
final  value 89.663580 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.798489 
iter  10 value 94.275365
final  value 94.275362 
converged
Fitting Repeat 3 

# weights:  507
initial  value 94.429894 
iter  10 value 94.273924
iter  20 value 94.230017
final  value 94.228678 
converged
Fitting Repeat 4 

# weights:  507
initial  value 114.228084 
iter  10 value 94.310095
iter  20 value 94.275352
iter  20 value 94.275351
iter  20 value 94.275351
final  value 94.275351 
converged
Fitting Repeat 5 

# weights:  507
initial  value 98.842093 
iter  10 value 82.961142
iter  20 value 80.076814
iter  30 value 80.009727
final  value 79.966561 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.755926 
iter  10 value 93.661285
iter  20 value 82.360631
iter  30 value 81.134352
iter  40 value 80.838337
iter  50 value 80.828078
final  value 80.827879 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.451502 
iter  10 value 87.320783
iter  20 value 81.321512
iter  30 value 81.168005
iter  40 value 80.884763
iter  50 value 80.828400
final  value 80.827879 
converged
Fitting Repeat 3 

# weights:  103
initial  value 105.186525 
iter  10 value 94.484273
iter  20 value 91.960779
iter  30 value 90.296980
iter  40 value 90.026521
iter  50 value 82.110847
iter  60 value 81.888974
iter  70 value 81.639925
iter  80 value 80.997255
iter  90 value 80.528138
iter 100 value 80.510554
final  value 80.510554 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 99.884728 
iter  10 value 91.772705
iter  20 value 82.517738
iter  30 value 81.230691
iter  40 value 80.989134
iter  50 value 80.828562
final  value 80.827879 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.416092 
iter  10 value 94.492424
iter  20 value 94.366016
iter  30 value 93.778366
iter  40 value 84.417356
iter  50 value 81.315539
iter  60 value 80.859862
iter  70 value 80.827892
final  value 80.827879 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.607855 
iter  10 value 94.483482
iter  20 value 89.123218
iter  30 value 86.521812
iter  40 value 86.087067
iter  50 value 82.893513
iter  60 value 81.545864
iter  70 value 80.049328
iter  80 value 78.878911
iter  90 value 78.648767
iter 100 value 78.600840
final  value 78.600840 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.948165 
iter  10 value 93.245953
iter  20 value 88.355009
iter  30 value 87.582699
iter  40 value 85.782692
iter  50 value 82.310739
iter  60 value 81.349052
iter  70 value 79.788881
iter  80 value 76.950487
iter  90 value 76.303848
iter 100 value 76.255131
final  value 76.255131 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.564605 
iter  10 value 89.946789
iter  20 value 82.183653
iter  30 value 80.832340
iter  40 value 79.385131
iter  50 value 77.712005
iter  60 value 77.469088
iter  70 value 77.093819
iter  80 value 76.530430
iter  90 value 76.340472
iter 100 value 76.164635
final  value 76.164635 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 111.645229 
iter  10 value 94.358123
iter  20 value 83.274184
iter  30 value 82.355280
iter  40 value 81.749184
iter  50 value 79.693605
iter  60 value 78.917802
iter  70 value 78.167743
iter  80 value 77.967490
iter  90 value 77.231858
iter 100 value 76.914438
final  value 76.914438 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 104.073207 
iter  10 value 94.535965
iter  20 value 84.983665
iter  30 value 82.144071
iter  40 value 81.164407
iter  50 value 80.437920
iter  60 value 78.776020
iter  70 value 78.202288
iter  80 value 78.022951
iter  90 value 77.909114
iter 100 value 77.867160
final  value 77.867160 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 115.314148 
iter  10 value 96.501862
iter  20 value 87.291115
iter  30 value 83.470027
iter  40 value 81.970541
iter  50 value 79.665712
iter  60 value 76.784219
iter  70 value 76.095101
iter  80 value 75.823083
iter  90 value 75.679079
iter 100 value 75.567459
final  value 75.567459 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 108.907777 
iter  10 value 94.350237
iter  20 value 91.634219
iter  30 value 84.745083
iter  40 value 81.058372
iter  50 value 79.057711
iter  60 value 78.039883
iter  70 value 76.518861
iter  80 value 76.425757
iter  90 value 76.240350
iter 100 value 76.196680
final  value 76.196680 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 120.342508 
iter  10 value 94.457982
iter  20 value 83.142062
iter  30 value 82.255516
iter  40 value 79.058155
iter  50 value 78.479115
iter  60 value 78.071226
iter  70 value 77.906326
iter  80 value 77.722448
iter  90 value 77.581333
iter 100 value 77.567647
final  value 77.567647 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 116.202982 
iter  10 value 94.643385
iter  20 value 89.219681
iter  30 value 83.406105
iter  40 value 81.934587
iter  50 value 80.759177
iter  60 value 80.115325
iter  70 value 78.666878
iter  80 value 76.892069
iter  90 value 76.011164
iter 100 value 75.791429
final  value 75.791429 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 111.891504 
iter  10 value 97.647886
iter  20 value 89.531506
iter  30 value 87.159904
iter  40 value 82.888562
iter  50 value 77.992549
iter  60 value 77.220641
iter  70 value 76.477035
iter  80 value 76.303971
iter  90 value 76.017102
iter 100 value 75.910813
final  value 75.910813 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.434118 
final  value 94.485925 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.659250 
iter  10 value 94.277145
iter  20 value 94.275455
iter  30 value 84.461660
iter  40 value 83.196591
iter  50 value 83.194906
iter  60 value 83.174970
iter  70 value 83.151183
iter  80 value 83.148097
iter  90 value 83.147025
final  value 83.146610 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.996818 
iter  10 value 94.485676
iter  20 value 94.484225
final  value 94.275470 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.792689 
final  value 94.485605 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.040474 
final  value 94.486560 
converged
Fitting Repeat 1 

# weights:  305
initial  value 113.812876 
iter  10 value 94.489676
iter  20 value 94.484972
iter  30 value 94.469199
iter  40 value 91.964654
iter  50 value 87.218320
iter  60 value 78.975155
iter  70 value 77.681362
iter  80 value 77.494902
iter  90 value 77.437248
iter 100 value 77.433545
final  value 77.433545 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.574317 
iter  10 value 94.489655
iter  20 value 94.484330
iter  30 value 88.489127
iter  40 value 82.407335
iter  50 value 77.275047
iter  60 value 76.454898
iter  70 value 76.161438
iter  80 value 76.153626
iter  90 value 76.153533
iter 100 value 76.153250
final  value 76.153250 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 107.172857 
iter  10 value 94.489322
iter  20 value 94.421112
iter  30 value 91.473945
iter  40 value 90.276364
iter  50 value 90.235631
iter  60 value 82.123258
iter  70 value 77.670355
iter  80 value 77.639724
iter  90 value 77.635622
iter 100 value 77.590920
final  value 77.590920 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.936373 
iter  10 value 94.489436
iter  20 value 94.448270
iter  30 value 94.243188
final  value 94.229111 
converged
Fitting Repeat 5 

# weights:  305
initial  value 102.220773 
iter  10 value 94.281001
iter  20 value 93.614281
iter  30 value 86.626244
iter  30 value 86.626243
iter  30 value 86.626243
final  value 86.626243 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.098716 
iter  10 value 94.283615
iter  20 value 94.277615
iter  30 value 94.275717
iter  30 value 94.275717
iter  30 value 94.275717
final  value 94.275717 
converged
Fitting Repeat 2 

# weights:  507
initial  value 103.155353 
iter  10 value 94.470649
iter  20 value 94.443243
iter  30 value 84.990040
iter  40 value 84.601719
iter  50 value 84.554166
iter  60 value 77.838229
iter  70 value 77.033726
iter  80 value 76.730046
iter  90 value 76.652041
iter 100 value 76.528681
final  value 76.528681 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 95.135841 
iter  10 value 90.527892
iter  20 value 84.334966
iter  30 value 84.329548
iter  40 value 84.225445
iter  50 value 81.968307
iter  60 value 80.072569
iter  70 value 79.810644
iter  80 value 79.623027
iter  90 value 79.622295
iter 100 value 79.621747
final  value 79.621747 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 108.092219 
iter  10 value 94.492107
iter  20 value 94.428854
iter  30 value 91.189617
iter  40 value 81.322140
iter  50 value 81.008863
iter  60 value 80.976342
iter  70 value 80.924895
iter  80 value 80.917578
iter  90 value 80.876912
iter 100 value 80.805524
final  value 80.805524 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 100.399545 
iter  10 value 92.795095
iter  20 value 92.791360
iter  30 value 92.787119
iter  40 value 92.408578
iter  50 value 83.749900
iter  60 value 83.727479
final  value 83.727462 
converged
Fitting Repeat 1 

# weights:  103
initial  value 107.486977 
final  value 94.354396 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.873127 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.929241 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 104.013913 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 109.441329 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.557856 
iter  10 value 93.270649
iter  20 value 93.258413
final  value 93.258385 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.449382 
final  value 94.354396 
converged
Fitting Repeat 3 

# weights:  305
initial  value 96.895983 
iter  10 value 92.633876
iter  20 value 92.631439
final  value 92.631429 
converged
Fitting Repeat 4 

# weights:  305
initial  value 98.171187 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.782789 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 109.637089 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 121.646403 
final  value 94.354396 
converged
Fitting Repeat 3 

# weights:  507
initial  value 97.825796 
final  value 94.354396 
converged
Fitting Repeat 4 

# weights:  507
initial  value 118.895469 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 130.272277 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.077765 
iter  10 value 94.451968
iter  20 value 87.994793
iter  30 value 85.789641
iter  40 value 85.698342
iter  50 value 85.625722
iter  60 value 85.307111
iter  70 value 85.220979
final  value 85.220704 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.864591 
iter  10 value 94.488889
iter  20 value 92.812954
iter  30 value 84.984562
iter  40 value 84.488799
iter  50 value 84.099813
iter  60 value 83.571280
iter  70 value 83.004853
final  value 83.002660 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.888572 
iter  10 value 93.614062
iter  20 value 86.435231
iter  30 value 85.248878
iter  40 value 84.364001
iter  50 value 84.103407
iter  60 value 81.762955
iter  70 value 81.460306
iter  80 value 81.253095
final  value 81.250597 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.063776 
iter  10 value 94.495364
iter  20 value 94.487454
iter  30 value 94.384840
iter  40 value 94.382566
iter  50 value 93.857319
iter  60 value 93.587394
iter  70 value 93.224283
iter  80 value 91.715756
iter  90 value 86.865737
iter 100 value 82.648319
final  value 82.648319 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 100.390246 
iter  10 value 94.488438
iter  20 value 89.622584
iter  30 value 87.607622
iter  40 value 87.286232
iter  50 value 85.366588
iter  60 value 85.222789
iter  70 value 85.220993
final  value 85.220704 
converged
Fitting Repeat 1 

# weights:  305
initial  value 109.793383 
iter  10 value 94.078719
iter  20 value 85.605723
iter  30 value 85.222701
iter  40 value 83.091287
iter  50 value 81.274448
iter  60 value 80.322510
iter  70 value 79.892279
iter  80 value 79.823330
iter  90 value 79.771836
iter 100 value 79.752916
final  value 79.752916 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 108.870450 
iter  10 value 94.493274
iter  20 value 92.442577
iter  30 value 84.710187
iter  40 value 83.890246
iter  50 value 82.372163
iter  60 value 80.361921
iter  70 value 79.980556
iter  80 value 79.792411
iter  90 value 79.741749
iter 100 value 79.638955
final  value 79.638955 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 105.892293 
iter  10 value 94.389228
iter  20 value 93.399172
iter  30 value 90.590078
iter  40 value 88.389924
iter  50 value 88.027778
iter  60 value 86.776728
iter  70 value 84.374283
iter  80 value 81.541438
iter  90 value 80.062786
iter 100 value 79.745085
final  value 79.745085 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 111.348277 
iter  10 value 94.504522
iter  20 value 93.991475
iter  30 value 89.613107
iter  40 value 83.622768
iter  50 value 82.051849
iter  60 value 80.458423
iter  70 value 80.152074
iter  80 value 79.962309
iter  90 value 79.885607
iter 100 value 79.706799
final  value 79.706799 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 108.689087 
iter  10 value 94.512195
iter  20 value 93.524111
iter  30 value 93.493902
iter  40 value 93.472104
iter  50 value 92.312623
iter  60 value 88.201305
iter  70 value 86.004317
iter  80 value 84.555581
iter  90 value 83.959605
iter 100 value 83.733046
final  value 83.733046 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 107.317558 
iter  10 value 94.792887
iter  20 value 94.668276
iter  30 value 94.573557
iter  40 value 93.566972
iter  50 value 89.847143
iter  60 value 86.365538
iter  70 value 83.895789
iter  80 value 82.153778
iter  90 value 81.645968
iter 100 value 81.350463
final  value 81.350463 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 104.201437 
iter  10 value 97.627595
iter  20 value 86.357039
iter  30 value 82.199725
iter  40 value 81.422678
iter  50 value 80.224266
iter  60 value 80.074186
iter  70 value 79.963743
iter  80 value 79.850517
iter  90 value 79.720113
iter 100 value 79.400117
final  value 79.400117 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 103.944207 
iter  10 value 94.701351
iter  20 value 94.547704
iter  30 value 87.247770
iter  40 value 87.159766
iter  50 value 85.081129
iter  60 value 82.423030
iter  70 value 81.562318
iter  80 value 80.575705
iter  90 value 80.163532
iter 100 value 79.811669
final  value 79.811669 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 103.904608 
iter  10 value 97.476312
iter  20 value 93.367978
iter  30 value 86.082018
iter  40 value 85.485450
iter  50 value 83.492158
iter  60 value 82.438659
iter  70 value 81.648970
iter  80 value 80.597698
iter  90 value 80.223896
iter 100 value 80.100090
final  value 80.100090 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 116.563987 
iter  10 value 99.065890
iter  20 value 94.432072
iter  30 value 85.980721
iter  40 value 84.259810
iter  50 value 82.375266
iter  60 value 81.857550
iter  70 value 81.627018
iter  80 value 80.969699
iter  90 value 80.592210
iter 100 value 79.951401
final  value 79.951401 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.141749 
final  value 94.486196 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.876299 
iter  10 value 94.486064
final  value 94.484350 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.862041 
final  value 94.356001 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.710485 
final  value 94.485887 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.677218 
iter  10 value 84.717181
iter  20 value 84.637311
iter  30 value 84.635841
final  value 84.635830 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.939877 
iter  10 value 94.325706
iter  20 value 92.943007
iter  30 value 88.217090
iter  40 value 88.130724
iter  50 value 88.128285
iter  60 value 88.020306
iter  70 value 87.439629
iter  80 value 87.389724
final  value 87.389707 
converged
Fitting Repeat 2 

# weights:  305
initial  value 111.532612 
iter  10 value 94.359550
iter  20 value 94.354792
final  value 94.354503 
converged
Fitting Repeat 3 

# weights:  305
initial  value 112.675303 
iter  10 value 94.489045
iter  20 value 94.431539
iter  30 value 93.324889
final  value 93.320988 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.391749 
iter  10 value 94.358896
iter  20 value 94.355039
final  value 94.354517 
converged
Fitting Repeat 5 

# weights:  305
initial  value 94.768554 
iter  10 value 92.728268
iter  20 value 85.016081
iter  30 value 84.385176
iter  40 value 83.924576
iter  50 value 83.907951
iter  60 value 83.903289
iter  70 value 83.898143
iter  80 value 83.824194
iter  90 value 83.743088
iter 100 value 83.631901
final  value 83.631901 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 102.087572 
iter  10 value 94.493213
iter  20 value 93.079421
iter  30 value 88.481985
iter  40 value 88.365929
iter  50 value 87.980196
iter  60 value 83.587137
iter  70 value 83.548854
final  value 83.548493 
converged
Fitting Repeat 2 

# weights:  507
initial  value 97.719710 
iter  10 value 94.491508
iter  20 value 93.843358
iter  30 value 93.320638
iter  40 value 84.964379
iter  50 value 83.232825
iter  60 value 81.330793
iter  70 value 81.298531
iter  80 value 81.283082
iter  90 value 81.210740
iter 100 value 81.151488
final  value 81.151488 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 111.167923 
iter  10 value 92.008508
iter  20 value 86.949471
iter  30 value 86.948298
iter  40 value 85.592492
iter  50 value 81.814452
iter  60 value 79.392778
iter  70 value 78.443629
iter  80 value 78.081020
iter  90 value 78.047262
iter 100 value 78.046924
final  value 78.046924 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 118.043816 
iter  10 value 93.168533
iter  20 value 92.586828
iter  30 value 92.582129
iter  40 value 92.579563
iter  50 value 92.579202
iter  60 value 92.578733
final  value 92.578673 
converged
Fitting Repeat 5 

# weights:  507
initial  value 110.846263 
iter  10 value 94.492530
iter  20 value 94.484515
iter  30 value 93.060618
iter  40 value 83.927063
iter  50 value 83.918530
iter  60 value 83.915693
iter  70 value 83.915037
iter  80 value 83.559638
iter  90 value 83.528709
final  value 83.528546 
converged
Fitting Repeat 1 

# weights:  103
initial  value 101.733915 
iter  10 value 88.818483
iter  20 value 85.190671
iter  30 value 85.132746
iter  40 value 85.131041
final  value 85.131033 
converged
Fitting Repeat 2 

# weights:  103
initial  value 93.857490 
iter  10 value 88.022307
iter  20 value 84.897530
final  value 84.488790 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.081992 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.171188 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.948940 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.702296 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 101.899139 
iter  10 value 93.631111
iter  20 value 93.629653
final  value 93.629627 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.252622 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 115.227146 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 101.554360 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.106875 
final  value 93.836066 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.932562 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 100.866485 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 96.344194 
iter  10 value 84.468177
iter  20 value 83.231365
iter  30 value 82.965371
iter  40 value 82.530739
iter  50 value 82.400367
iter  60 value 82.397824
iter  70 value 82.397651
final  value 82.397630 
converged
Fitting Repeat 5 

# weights:  507
initial  value 103.474625 
final  value 93.818713 
converged
Fitting Repeat 1 

# weights:  103
initial  value 101.803006 
iter  10 value 93.731883
iter  20 value 93.619152
iter  30 value 92.131577
iter  40 value 87.754584
iter  50 value 87.288326
iter  60 value 87.057246
iter  70 value 83.162690
iter  80 value 82.951960
iter  90 value 82.895735
iter 100 value 82.859150
final  value 82.859150 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 100.477813 
iter  10 value 94.265489
iter  20 value 94.052523
iter  30 value 93.806157
iter  40 value 93.689392
iter  50 value 91.967352
iter  60 value 84.980897
iter  70 value 83.766911
iter  80 value 83.515000
iter  90 value 83.181598
iter 100 value 82.260800
final  value 82.260800 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 102.190537 
iter  10 value 94.049188
iter  20 value 93.927085
iter  30 value 93.629055
iter  40 value 90.325163
iter  50 value 87.557506
iter  60 value 84.865123
iter  70 value 83.775371
iter  80 value 83.488052
iter  90 value 83.408589
iter 100 value 83.332244
final  value 83.332244 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 105.926499 
iter  10 value 93.649237
iter  20 value 84.821766
iter  30 value 84.508252
iter  40 value 84.450739
iter  50 value 84.448386
iter  60 value 84.429630
iter  70 value 84.164965
iter  80 value 82.522997
iter  90 value 82.414893
final  value 82.413993 
converged
Fitting Repeat 5 

# weights:  103
initial  value 104.200689 
iter  10 value 94.055796
iter  20 value 92.913951
iter  30 value 91.953764
iter  40 value 89.569149
iter  50 value 87.296922
iter  60 value 85.154760
iter  70 value 84.217357
iter  80 value 84.136675
iter  90 value 83.322917
final  value 83.322340 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.799020 
iter  10 value 91.688903
iter  20 value 85.209381
iter  30 value 84.952338
iter  40 value 84.053488
iter  50 value 81.374842
iter  60 value 80.954466
iter  70 value 80.369270
iter  80 value 80.175306
iter  90 value 80.041536
iter 100 value 79.795306
final  value 79.795306 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 107.153634 
iter  10 value 94.057678
iter  20 value 93.695426
iter  30 value 90.230809
iter  40 value 84.138432
iter  50 value 83.927524
iter  60 value 83.384282
iter  70 value 83.317450
iter  80 value 83.289052
iter  90 value 83.126330
iter 100 value 82.205372
final  value 82.205372 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 132.197042 
iter  10 value 93.567537
iter  20 value 89.046017
iter  30 value 85.122302
iter  40 value 83.354373
iter  50 value 81.862597
iter  60 value 81.531922
iter  70 value 81.292394
iter  80 value 81.091145
iter  90 value 80.368317
iter 100 value 80.068885
final  value 80.068885 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 109.183249 
iter  10 value 93.987101
iter  20 value 90.278227
iter  30 value 86.902633
iter  40 value 86.564981
iter  50 value 82.541515
iter  60 value 81.044961
iter  70 value 79.928439
iter  80 value 79.570488
iter  90 value 79.487969
iter 100 value 79.264587
final  value 79.264587 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 100.776927 
iter  10 value 94.029733
iter  20 value 89.735637
iter  30 value 83.577535
iter  40 value 82.161647
iter  50 value 81.417640
iter  60 value 81.065033
iter  70 value 80.392776
iter  80 value 80.149023
iter  90 value 80.046979
iter 100 value 79.889881
final  value 79.889881 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 115.158119 
iter  10 value 94.094865
iter  20 value 93.809955
iter  30 value 84.401854
iter  40 value 83.255351
iter  50 value 83.077837
iter  60 value 82.794949
iter  70 value 81.582143
iter  80 value 80.403637
iter  90 value 80.018324
iter 100 value 79.650781
final  value 79.650781 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 117.457609 
iter  10 value 94.154150
iter  20 value 88.049918
iter  30 value 86.736366
iter  40 value 81.042800
iter  50 value 80.604796
iter  60 value 79.802990
iter  70 value 79.583571
iter  80 value 79.469745
iter  90 value 79.395965
iter 100 value 79.295136
final  value 79.295136 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.682840 
iter  10 value 95.721371
iter  20 value 93.667487
iter  30 value 93.537529
iter  40 value 88.928273
iter  50 value 83.357183
iter  60 value 82.875919
iter  70 value 82.547420
iter  80 value 80.875933
iter  90 value 80.085723
iter 100 value 79.473772
final  value 79.473772 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 114.412254 
iter  10 value 94.072487
iter  20 value 93.689201
iter  30 value 90.053098
iter  40 value 86.813112
iter  50 value 84.651172
iter  60 value 83.234984
iter  70 value 80.849314
iter  80 value 79.476180
iter  90 value 79.293718
iter 100 value 79.211299
final  value 79.211299 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 107.908974 
iter  10 value 94.376893
iter  20 value 85.927993
iter  30 value 84.525837
iter  40 value 83.602856
iter  50 value 83.065124
iter  60 value 81.962198
iter  70 value 80.270962
iter  80 value 80.042534
iter  90 value 79.671647
iter 100 value 79.319925
final  value 79.319925 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.433480 
final  value 94.054388 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.366238 
final  value 94.054570 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.701725 
final  value 94.054690 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.436609 
iter  10 value 94.056164
final  value 94.054188 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.037163 
final  value 94.054561 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.923146 
iter  10 value 93.853806
iter  20 value 93.841115
iter  30 value 93.837304
final  value 93.836432 
converged
Fitting Repeat 2 

# weights:  305
initial  value 100.716843 
iter  10 value 94.057113
iter  20 value 94.050581
iter  30 value 86.560872
iter  40 value 82.915792
iter  50 value 82.915254
iter  60 value 82.648001
iter  70 value 82.350369
final  value 82.350129 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.357460 
iter  10 value 89.974035
iter  20 value 86.987326
iter  30 value 86.978484
iter  40 value 86.788510
iter  50 value 86.675012
iter  60 value 85.965139
iter  70 value 85.936600
iter  80 value 85.927074
iter  90 value 85.921499
iter 100 value 85.920963
final  value 85.920963 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 94.196976 
iter  10 value 94.057713
iter  20 value 93.605368
iter  30 value 93.601953
iter  40 value 93.593148
iter  50 value 85.698766
iter  60 value 85.601218
iter  70 value 85.591986
iter  80 value 85.559887
iter  90 value 85.550828
final  value 85.549797 
converged
Fitting Repeat 5 

# weights:  305
initial  value 111.071456 
iter  10 value 93.841845
iter  20 value 93.837443
iter  30 value 91.177287
iter  40 value 83.669523
iter  50 value 80.788127
iter  60 value 79.175893
iter  70 value 78.220377
iter  80 value 78.083685
iter  90 value 78.074677
iter 100 value 77.844146
final  value 77.844146 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 107.458920 
iter  10 value 94.060208
iter  20 value 93.818275
iter  30 value 83.914390
final  value 83.911934 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.099441 
iter  10 value 85.773214
iter  20 value 84.289204
iter  30 value 84.284659
iter  40 value 84.281409
iter  50 value 84.069390
iter  60 value 83.808820
iter  70 value 83.806544
iter  80 value 83.636673
iter  90 value 81.848316
iter 100 value 78.604722
final  value 78.604722 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 101.091169 
iter  10 value 94.061420
iter  20 value 94.014700
iter  30 value 88.208086
iter  40 value 88.159972
iter  50 value 88.109952
iter  60 value 88.106040
iter  70 value 88.068080
iter  80 value 82.697741
iter  90 value 81.631737
iter 100 value 81.573190
final  value 81.573190 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 105.882518 
iter  10 value 94.061270
iter  20 value 94.052563
iter  30 value 83.913407
final  value 83.911298 
converged
Fitting Repeat 5 

# weights:  507
initial  value 107.051402 
iter  10 value 93.771599
iter  20 value 93.610334
iter  30 value 93.581524
iter  40 value 93.578215
iter  50 value 93.537504
iter  60 value 93.522627
iter  70 value 93.362031
iter  80 value 82.853834
iter  90 value 81.368483
iter 100 value 81.130780
final  value 81.130780 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.448798 
iter  10 value 87.605298
iter  20 value 85.787265
final  value 85.769539 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.727987 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.002378 
final  value 94.470285 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.155140 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.655347 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.899729 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.296105 
iter  10 value 94.467349
final  value 94.443190 
converged
Fitting Repeat 3 

# weights:  305
initial  value 102.475597 
iter  10 value 94.454588
iter  20 value 94.450858
final  value 94.450827 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.850823 
iter  10 value 83.209681
iter  20 value 81.613640
iter  30 value 81.610863
final  value 81.610860 
converged
Fitting Repeat 5 

# weights:  305
initial  value 99.128078 
iter  10 value 83.930365
iter  20 value 83.592845
iter  30 value 83.588976
final  value 83.587879 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.380998 
iter  10 value 94.455556
final  value 94.455163 
converged
Fitting Repeat 2 

# weights:  507
initial  value 101.737220 
final  value 94.467389 
converged
Fitting Repeat 3 

# weights:  507
initial  value 102.299295 
iter  10 value 94.480520
iter  10 value 94.480520
iter  10 value 94.480520
final  value 94.480520 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.954542 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 94.951612 
iter  10 value 94.467391
iter  10 value 94.467391
iter  10 value 94.467391
final  value 94.467391 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.219954 
iter  10 value 94.568697
iter  20 value 94.450626
iter  30 value 91.491082
iter  40 value 86.907396
iter  50 value 83.856893
iter  60 value 82.902775
iter  70 value 82.410470
iter  80 value 82.170302
iter  90 value 82.052621
iter 100 value 81.943873
final  value 81.943873 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 105.438358 
iter  10 value 93.274813
iter  20 value 88.154836
iter  30 value 85.440432
iter  40 value 84.966128
iter  50 value 84.875551
iter  60 value 84.824855
iter  70 value 84.818820
iter  80 value 82.169601
iter  90 value 81.949323
iter 100 value 81.934681
final  value 81.934681 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 111.004985 
iter  10 value 94.486375
iter  20 value 92.736458
iter  30 value 87.830429
iter  40 value 86.199059
iter  50 value 82.891853
iter  60 value 82.460050
iter  70 value 82.172512
iter  80 value 82.012548
iter  90 value 81.713025
iter 100 value 81.619769
final  value 81.619769 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 97.771297 
iter  10 value 94.504114
iter  20 value 94.465665
iter  30 value 88.960124
iter  40 value 85.005051
iter  50 value 84.587755
iter  60 value 84.421878
iter  70 value 82.275114
iter  80 value 81.987447
iter  90 value 81.545853
iter 100 value 81.194413
final  value 81.194413 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 100.401020 
iter  10 value 94.510119
iter  20 value 94.484487
iter  30 value 87.622047
iter  40 value 86.423819
iter  50 value 84.575340
iter  60 value 84.305040
iter  70 value 84.236496
iter  80 value 84.226508
iter  90 value 84.222237
iter 100 value 82.943431
final  value 82.943431 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 99.270595 
iter  10 value 94.586322
iter  20 value 90.351126
iter  30 value 83.689447
iter  40 value 83.278334
iter  50 value 82.232265
iter  60 value 82.061796
iter  70 value 81.847503
iter  80 value 81.679246
iter  90 value 81.556773
iter 100 value 81.542590
final  value 81.542590 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 104.127100 
iter  10 value 95.023652
iter  20 value 89.094941
iter  30 value 84.138812
iter  40 value 83.431038
iter  50 value 82.997190
iter  60 value 81.633772
iter  70 value 80.595443
iter  80 value 80.044512
iter  90 value 79.763536
iter 100 value 79.646222
final  value 79.646222 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.478169 
iter  10 value 95.526049
iter  20 value 85.934491
iter  30 value 84.891249
iter  40 value 84.447138
iter  50 value 84.260063
iter  60 value 83.583840
iter  70 value 82.592697
iter  80 value 82.324545
iter  90 value 81.421036
iter 100 value 80.983767
final  value 80.983767 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 112.719150 
iter  10 value 97.893189
iter  20 value 90.883750
iter  30 value 88.830463
iter  40 value 86.452988
iter  50 value 85.802049
iter  60 value 84.307012
iter  70 value 84.210291
iter  80 value 83.817631
iter  90 value 83.198666
iter 100 value 81.064802
final  value 81.064802 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 105.724457 
iter  10 value 94.304053
iter  20 value 88.967733
iter  30 value 86.901289
iter  40 value 81.349315
iter  50 value 80.513392
iter  60 value 80.316542
iter  70 value 80.131611
iter  80 value 80.098669
iter  90 value 80.089257
iter 100 value 80.058209
final  value 80.058209 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 103.466775 
iter  10 value 94.533582
iter  20 value 88.415708
iter  30 value 87.479396
iter  40 value 86.874241
iter  50 value 84.196553
iter  60 value 83.372085
iter  70 value 82.839705
iter  80 value 81.289892
iter  90 value 80.862120
iter 100 value 80.293310
final  value 80.293310 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 111.493338 
iter  10 value 94.324360
iter  20 value 87.430926
iter  30 value 86.118597
iter  40 value 84.689428
iter  50 value 82.521574
iter  60 value 82.357809
iter  70 value 81.694246
iter  80 value 80.691453
iter  90 value 80.078143
iter 100 value 79.510405
final  value 79.510405 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.108677 
iter  10 value 92.195191
iter  20 value 84.330229
iter  30 value 83.358678
iter  40 value 83.075143
iter  50 value 82.412707
iter  60 value 81.630646
iter  70 value 81.292303
iter  80 value 80.959401
iter  90 value 80.731004
iter 100 value 80.044861
final  value 80.044861 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 110.644336 
iter  10 value 93.763815
iter  20 value 87.846524
iter  30 value 87.015216
iter  40 value 81.263653
iter  50 value 80.652728
iter  60 value 79.935989
iter  70 value 79.872121
iter  80 value 79.780464
iter  90 value 79.610720
iter 100 value 79.348856
final  value 79.348856 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 118.875936 
iter  10 value 94.584240
iter  20 value 93.148664
iter  30 value 89.061703
iter  40 value 87.724005
iter  50 value 84.742963
iter  60 value 82.936808
iter  70 value 82.464304
iter  80 value 81.770700
iter  90 value 81.179051
iter 100 value 80.396467
final  value 80.396467 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.874424 
final  value 94.485656 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.834966 
final  value 94.485733 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.930710 
final  value 94.485812 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.915151 
iter  10 value 94.485857
iter  20 value 94.484271
iter  30 value 92.765111
iter  40 value 90.347049
iter  50 value 90.346073
iter  60 value 85.850253
iter  70 value 85.847447
iter  80 value 85.763075
iter  90 value 85.697909
final  value 85.697467 
converged
Fitting Repeat 5 

# weights:  103
initial  value 111.374670 
final  value 94.485711 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.173456 
iter  10 value 94.472621
iter  20 value 93.842200
iter  30 value 87.604206
iter  40 value 86.827790
iter  50 value 86.369293
iter  60 value 86.100366
iter  70 value 86.098986
iter  80 value 85.411877
iter  90 value 84.952108
iter 100 value 84.951973
final  value 84.951973 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.492012 
iter  10 value 94.481605
iter  20 value 94.472276
iter  30 value 94.467548
iter  40 value 90.604010
iter  50 value 85.524432
iter  60 value 85.400759
final  value 85.400363 
converged
Fitting Repeat 3 

# weights:  305
initial  value 98.479711 
iter  10 value 94.472432
iter  20 value 94.441757
iter  30 value 94.145736
iter  40 value 94.129630
iter  50 value 94.091874
iter  60 value 94.053846
final  value 94.053677 
converged
Fitting Repeat 4 

# weights:  305
initial  value 111.602751 
iter  10 value 94.489067
iter  20 value 94.482543
iter  30 value 94.127117
iter  40 value 86.640986
final  value 86.640473 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.090531 
iter  10 value 94.485359
iter  20 value 94.434391
iter  30 value 94.429232
iter  40 value 94.390162
iter  50 value 82.994162
iter  60 value 80.899636
iter  70 value 80.093816
iter  80 value 79.774942
iter  90 value 79.485854
iter 100 value 79.401288
final  value 79.401288 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 114.228790 
iter  10 value 94.436995
iter  20 value 94.428967
iter  30 value 87.197072
iter  40 value 86.314026
iter  50 value 86.210001
final  value 86.201139 
converged
Fitting Repeat 2 

# weights:  507
initial  value 95.289907 
iter  10 value 92.841807
iter  20 value 92.810122
iter  30 value 92.807741
iter  40 value 92.561479
iter  50 value 92.540343
final  value 92.540256 
converged
Fitting Repeat 3 

# weights:  507
initial  value 123.001099 
iter  10 value 94.492792
iter  20 value 94.485060
iter  30 value 94.355179
iter  40 value 83.759007
iter  50 value 83.600955
iter  60 value 83.275473
iter  70 value 81.001571
iter  80 value 80.101663
iter  90 value 79.987810
iter 100 value 79.250668
final  value 79.250668 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 103.632212 
iter  10 value 94.492176
iter  20 value 94.484143
iter  30 value 87.035451
iter  40 value 84.525348
iter  50 value 84.454918
iter  60 value 83.475741
iter  70 value 82.199322
iter  80 value 80.803501
iter  90 value 77.854057
iter 100 value 77.475496
final  value 77.475496 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 119.582499 
iter  10 value 83.803656
iter  20 value 83.746028
iter  30 value 83.736761
iter  40 value 83.735789
iter  50 value 83.735177
final  value 83.735153 
converged
Fitting Repeat 1 

# weights:  507
initial  value 144.288814 
iter  10 value 118.380195
iter  20 value 115.446222
iter  30 value 108.158518
iter  40 value 107.908679
iter  50 value 106.218097
iter  60 value 103.431086
iter  70 value 102.191707
iter  80 value 101.647903
iter  90 value 101.359854
iter 100 value 100.905808
final  value 100.905808 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 137.396301 
iter  10 value 118.103619
iter  20 value 108.813071
iter  30 value 107.578083
iter  40 value 106.808711
iter  50 value 105.458993
iter  60 value 102.408189
iter  70 value 101.611153
iter  80 value 101.318436
iter  90 value 101.242282
iter 100 value 101.148731
final  value 101.148731 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 130.046915 
iter  10 value 118.030885
iter  20 value 117.180734
iter  30 value 107.728085
iter  40 value 105.887509
iter  50 value 105.055455
iter  60 value 103.353677
iter  70 value 102.392568
iter  80 value 102.207754
iter  90 value 102.184465
iter 100 value 102.017743
final  value 102.017743 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 133.324053 
iter  10 value 118.557502
iter  20 value 105.470680
iter  30 value 104.911883
iter  40 value 103.798337
iter  50 value 103.105902
iter  60 value 101.862060
iter  70 value 101.375465
iter  80 value 100.863692
iter  90 value 100.492139
iter 100 value 100.416739
final  value 100.416739 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 130.328839 
iter  10 value 117.828413
iter  20 value 113.086327
iter  30 value 108.459198
iter  40 value 104.275312
iter  50 value 103.160372
iter  60 value 102.742791
iter  70 value 102.292063
iter  80 value 102.045844
iter  90 value 101.960279
iter 100 value 101.677530
final  value 101.677530 
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 Apr  8 00:31:24 2026 
*********************************************** 
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 
 40.559   0.795  95.956