##############################################################################
##############################################################################
###
### 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     33.806  0.516  34.322
var_imp       33.125  0.451  33.575
FSmethod      32.819  0.421  33.241
pred_ensembel 12.731  0.090  11.508
enrichfindP    0.608  0.037  13.260
* 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 104.215839 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.203892 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 115.938825 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.588226 
iter  10 value 93.286816
iter  20 value 93.286561
final  value 93.286556 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.580230 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 122.843997 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.751271 
final  value 94.354396 
converged
Fitting Repeat 3 

# weights:  305
initial  value 112.353932 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 108.394534 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 102.030793 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 101.904898 
iter  10 value 94.543241
iter  20 value 94.324036
iter  30 value 92.589489
iter  40 value 90.775462
iter  50 value 87.851982
iter  60 value 85.461485
iter  70 value 85.455062
iter  70 value 85.455061
iter  70 value 85.455061
final  value 85.455061 
converged
Fitting Repeat 2 

# weights:  507
initial  value 134.014934 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 105.325845 
iter  10 value 94.354396
iter  10 value 94.354396
iter  10 value 94.354396
final  value 94.354396 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.880323 
iter  10 value 91.172692
iter  20 value 91.017772
iter  30 value 91.017327
final  value 91.017312 
converged
Fitting Repeat 5 

# weights:  507
initial  value 107.280682 
final  value 94.096667 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.726543 
iter  10 value 94.687892
iter  20 value 94.391724
iter  30 value 94.382224
iter  40 value 94.330664
iter  50 value 92.349990
iter  60 value 89.339069
iter  70 value 88.652720
iter  80 value 87.017325
iter  90 value 86.064407
iter 100 value 85.401250
final  value 85.401250 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 101.955169 
iter  10 value 94.702578
iter  20 value 94.487246
iter  30 value 86.567484
iter  40 value 85.387011
iter  50 value 85.191694
iter  60 value 84.957774
iter  70 value 84.419764
iter  80 value 84.078790
iter  90 value 84.054209
final  value 84.054060 
converged
Fitting Repeat 3 

# weights:  103
initial  value 112.175031 
iter  10 value 94.404584
iter  20 value 89.859474
iter  30 value 89.617703
iter  40 value 89.149582
iter  50 value 88.917692
iter  60 value 86.683186
iter  70 value 86.216438
iter  80 value 86.178568
iter  90 value 86.176806
final  value 86.176777 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.068367 
iter  10 value 95.202414
iter  20 value 94.489147
iter  30 value 94.446501
iter  40 value 94.417327
iter  50 value 94.412822
iter  60 value 94.411665
iter  70 value 94.411604
iter  80 value 89.837004
iter  90 value 85.896691
iter 100 value 85.786638
final  value 85.786638 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 112.857142 
iter  10 value 94.480017
iter  20 value 92.757846
iter  30 value 92.172518
iter  40 value 90.368808
iter  50 value 86.033509
iter  60 value 85.509755
iter  70 value 85.127648
iter  80 value 84.871355
iter  90 value 84.844629
iter 100 value 84.373031
final  value 84.373031 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 99.684987 
iter  10 value 90.756577
iter  20 value 86.490673
iter  30 value 83.893362
iter  40 value 83.766340
iter  50 value 83.310463
iter  60 value 83.138034
iter  70 value 82.835312
iter  80 value 82.727650
iter  90 value 82.668106
iter 100 value 82.603298
final  value 82.603298 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 108.088943 
iter  10 value 94.453811
iter  20 value 93.009087
iter  30 value 87.563416
iter  40 value 84.619143
iter  50 value 84.381302
iter  60 value 84.072077
iter  70 value 83.967382
iter  80 value 83.950294
iter  90 value 83.923938
iter 100 value 83.642255
final  value 83.642255 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 105.251810 
iter  10 value 94.385959
iter  20 value 94.359584
iter  30 value 88.047926
iter  40 value 85.724025
iter  50 value 84.335714
iter  60 value 83.492608
iter  70 value 83.128920
iter  80 value 82.836648
iter  90 value 82.781358
iter 100 value 82.764674
final  value 82.764674 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 111.402570 
iter  10 value 95.327812
iter  20 value 90.337283
iter  30 value 89.561353
iter  40 value 88.923082
iter  50 value 88.772036
iter  60 value 87.498146
iter  70 value 85.768872
iter  80 value 84.777708
iter  90 value 84.368767
iter 100 value 84.203054
final  value 84.203054 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 111.617652 
iter  10 value 94.341051
iter  20 value 86.996699
iter  30 value 85.067200
iter  40 value 83.923536
iter  50 value 83.320201
iter  60 value 83.089186
iter  70 value 82.966760
iter  80 value 82.859010
iter  90 value 82.785078
iter 100 value 82.754488
final  value 82.754488 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 106.442681 
iter  10 value 93.919718
iter  20 value 91.744932
iter  30 value 87.244845
iter  40 value 86.525704
iter  50 value 84.178203
iter  60 value 83.857713
iter  70 value 83.549754
iter  80 value 82.956808
iter  90 value 82.833358
iter 100 value 82.811012
final  value 82.811012 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 106.649832 
iter  10 value 94.611529
iter  20 value 89.631173
iter  30 value 86.033937
iter  40 value 85.117148
iter  50 value 84.948528
iter  60 value 84.062254
iter  70 value 83.365742
iter  80 value 83.183745
iter  90 value 82.984006
iter 100 value 82.840435
final  value 82.840435 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 116.278741 
iter  10 value 95.657015
iter  20 value 92.360616
iter  30 value 88.959004
iter  40 value 87.255016
iter  50 value 86.913397
iter  60 value 85.357468
iter  70 value 84.096148
iter  80 value 83.845704
iter  90 value 83.585456
iter 100 value 83.202950
final  value 83.202950 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 120.365859 
iter  10 value 94.593995
iter  20 value 94.490018
iter  30 value 92.953358
iter  40 value 91.808683
iter  50 value 91.667737
iter  60 value 87.273872
iter  70 value 85.807283
iter  80 value 85.535594
iter  90 value 84.829016
iter 100 value 83.316228
final  value 83.316228 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 113.171564 
iter  10 value 94.476149
iter  20 value 92.946796
iter  30 value 91.046753
iter  40 value 89.776623
iter  50 value 89.206488
iter  60 value 88.284845
iter  70 value 85.332704
iter  80 value 84.134584
iter  90 value 83.746196
iter 100 value 83.364853
final  value 83.364853 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.630654 
iter  10 value 94.485941
iter  20 value 90.495814
iter  30 value 87.871918
iter  40 value 86.520582
final  value 86.520555 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.654628 
final  value 94.485772 
converged
Fitting Repeat 3 

# weights:  103
initial  value 107.363995 
final  value 94.486238 
converged
Fitting Repeat 4 

# weights:  103
initial  value 104.194341 
final  value 94.486016 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.466453 
final  value 94.485906 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.122452 
iter  10 value 94.488617
iter  20 value 93.650896
iter  30 value 93.483486
final  value 93.483145 
converged
Fitting Repeat 2 

# weights:  305
initial  value 123.767209 
iter  10 value 94.488853
iter  20 value 94.384867
iter  30 value 93.471155
iter  40 value 93.085804
iter  50 value 92.980263
iter  60 value 92.482286
iter  70 value 86.743731
final  value 86.743535 
converged
Fitting Repeat 3 

# weights:  305
initial  value 102.598143 
iter  10 value 94.150747
iter  20 value 89.038018
iter  30 value 89.022354
iter  40 value 88.649801
iter  50 value 85.395801
iter  60 value 85.292376
iter  70 value 84.445833
iter  80 value 83.449074
iter  90 value 82.694905
iter 100 value 82.603869
final  value 82.603869 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 113.547888 
iter  10 value 94.093591
iter  20 value 93.747625
final  value 91.886225 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.959762 
iter  10 value 94.433845
iter  20 value 94.357369
iter  30 value 94.357185
iter  40 value 94.355995
iter  50 value 94.354833
iter  60 value 94.267147
iter  70 value 89.878483
iter  80 value 89.118540
final  value 89.118530 
converged
Fitting Repeat 1 

# weights:  507
initial  value 113.404184 
iter  10 value 91.250330
iter  20 value 89.320757
iter  30 value 89.222396
iter  40 value 89.146964
iter  50 value 89.146617
iter  60 value 88.885660
iter  70 value 87.215291
iter  80 value 86.562271
final  value 86.473370 
converged
Fitting Repeat 2 

# weights:  507
initial  value 111.426571 
iter  10 value 94.362977
iter  20 value 94.354859
iter  30 value 88.037663
iter  40 value 86.626241
iter  50 value 83.874287
iter  60 value 82.792481
iter  70 value 82.660779
iter  80 value 82.639318
final  value 82.639189 
converged
Fitting Repeat 3 

# weights:  507
initial  value 102.939411 
iter  10 value 94.437147
iter  20 value 94.362233
final  value 94.362229 
converged
Fitting Repeat 4 

# weights:  507
initial  value 101.723891 
iter  10 value 94.097066
iter  20 value 92.008277
iter  30 value 87.929453
iter  40 value 87.789332
iter  50 value 86.026853
iter  60 value 86.001374
iter  70 value 86.000383
final  value 85.999875 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.171658 
iter  10 value 93.988461
iter  20 value 93.983271
iter  30 value 85.835302
iter  40 value 85.458726
iter  50 value 84.994898
iter  60 value 84.189337
iter  70 value 84.112296
iter  80 value 83.933821
final  value 83.933709 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.164226 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.014861 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 106.750755 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.054847 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.935589 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.270310 
iter  10 value 93.673277
iter  20 value 93.672973
iter  20 value 93.672973
iter  20 value 93.672973
final  value 93.672973 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.070277 
iter  10 value 84.019801
iter  20 value 83.575975
final  value 83.546560 
converged
Fitting Repeat 3 

# weights:  305
initial  value 107.664198 
iter  10 value 94.044450
final  value 94.044444 
converged
Fitting Repeat 4 

# weights:  305
initial  value 111.454757 
iter  10 value 93.836061
iter  10 value 93.836061
final  value 93.836061 
converged
Fitting Repeat 5 

# weights:  305
initial  value 105.734583 
iter  10 value 94.052914
iter  10 value 94.052914
iter  10 value 94.052914
final  value 94.052914 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.605028 
iter  10 value 93.734703
iter  10 value 93.734703
iter  10 value 93.734703
final  value 93.734703 
converged
Fitting Repeat 2 

# weights:  507
initial  value 124.864581 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 106.852521 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 108.729141 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 107.276581 
iter  10 value 93.836066
iter  10 value 93.836066
iter  10 value 93.836066
final  value 93.836066 
converged
Fitting Repeat 1 

# weights:  103
initial  value 121.385786 
iter  10 value 93.900203
iter  20 value 89.910841
iter  30 value 89.615675
iter  40 value 84.542757
iter  50 value 83.543203
iter  60 value 83.481569
iter  70 value 83.481035
final  value 83.481024 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.895369 
iter  10 value 94.032875
iter  20 value 86.579622
iter  30 value 84.527643
iter  40 value 83.729058
iter  50 value 83.660562
iter  60 value 83.484277
final  value 83.481024 
converged
Fitting Repeat 3 

# weights:  103
initial  value 107.993882 
iter  10 value 94.055173
iter  20 value 94.012402
iter  30 value 91.472735
iter  40 value 86.138697
iter  50 value 81.949540
iter  60 value 80.654034
iter  70 value 79.930263
iter  80 value 79.877036
final  value 79.877026 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.398252 
iter  10 value 94.057999
iter  20 value 93.949097
iter  30 value 90.754639
iter  40 value 87.157202
iter  50 value 84.984613
iter  60 value 84.084786
iter  70 value 83.813047
iter  80 value 83.575985
iter  90 value 83.537509
iter 100 value 83.492607
final  value 83.492607 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 105.647927 
iter  10 value 94.056958
iter  20 value 93.463376
iter  30 value 86.748011
iter  40 value 82.682290
iter  50 value 81.380965
iter  60 value 80.936136
iter  70 value 80.829095
final  value 80.798125 
converged
Fitting Repeat 1 

# weights:  305
initial  value 109.051310 
iter  10 value 94.207121
iter  20 value 94.069985
iter  30 value 93.001798
iter  40 value 87.954424
iter  50 value 84.243382
iter  60 value 83.421437
iter  70 value 81.192040
iter  80 value 80.171441
iter  90 value 79.084538
iter 100 value 78.966984
final  value 78.966984 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 102.560020 
iter  10 value 94.038746
iter  20 value 92.107473
iter  30 value 88.341850
iter  40 value 84.438567
iter  50 value 83.154973
iter  60 value 81.117757
iter  70 value 80.592579
iter  80 value 79.489997
iter  90 value 79.244148
iter 100 value 79.152175
final  value 79.152175 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 104.421981 
iter  10 value 94.101372
iter  20 value 92.781522
iter  30 value 88.696596
iter  40 value 81.612851
iter  50 value 80.448108
iter  60 value 79.482457
iter  70 value 78.933312
iter  80 value 78.688190
iter  90 value 78.324904
iter 100 value 78.304328
final  value 78.304328 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 99.342076 
iter  10 value 93.396281
iter  20 value 88.268201
iter  30 value 85.881603
iter  40 value 82.694679
iter  50 value 79.462050
iter  60 value 79.220970
iter  70 value 78.881776
iter  80 value 78.765101
iter  90 value 78.715226
iter 100 value 78.697831
final  value 78.697831 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.980542 
iter  10 value 93.838248
iter  20 value 86.326561
iter  30 value 84.464362
iter  40 value 83.091146
iter  50 value 82.832541
iter  60 value 81.154713
iter  70 value 78.934704
iter  80 value 78.476218
iter  90 value 78.410134
iter 100 value 78.299578
final  value 78.299578 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 111.489491 
iter  10 value 94.132216
iter  20 value 92.385609
iter  30 value 84.023301
iter  40 value 80.537973
iter  50 value 79.738704
iter  60 value 78.828172
iter  70 value 78.562210
iter  80 value 78.409518
iter  90 value 78.387910
iter 100 value 78.375836
final  value 78.375836 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 108.666841 
iter  10 value 93.860201
iter  20 value 86.717148
iter  30 value 83.490295
iter  40 value 79.899943
iter  50 value 79.435083
iter  60 value 79.229880
iter  70 value 78.792172
iter  80 value 78.529181
iter  90 value 78.187416
iter 100 value 77.837516
final  value 77.837516 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 130.871916 
iter  10 value 94.011878
iter  20 value 92.710364
iter  30 value 90.748292
iter  40 value 87.750071
iter  50 value 86.945400
iter  60 value 84.571823
iter  70 value 82.518885
iter  80 value 79.817690
iter  90 value 79.095418
iter 100 value 78.596648
final  value 78.596648 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 112.507157 
iter  10 value 94.038231
iter  20 value 93.360215
iter  30 value 91.417336
iter  40 value 87.903852
iter  50 value 83.007819
iter  60 value 81.691314
iter  70 value 81.267640
iter  80 value 79.866574
iter  90 value 79.397684
iter 100 value 79.235079
final  value 79.235079 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 110.916012 
iter  10 value 94.212479
iter  20 value 93.028954
iter  30 value 87.908570
iter  40 value 85.714314
iter  50 value 81.859821
iter  60 value 81.430091
iter  70 value 81.052255
iter  80 value 79.926715
iter  90 value 78.618092
iter 100 value 78.406480
final  value 78.406480 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 102.272584 
final  value 94.054710 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.097648 
final  value 94.054452 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.855964 
iter  10 value 93.837836
iter  20 value 93.836803
final  value 93.836229 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.580343 
iter  10 value 94.054665
iter  20 value 94.052835
iter  30 value 87.678985
final  value 85.855665 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.618853 
iter  10 value 89.729390
iter  20 value 89.302595
iter  30 value 89.297531
iter  40 value 85.909384
iter  50 value 85.907182
iter  60 value 85.906429
iter  70 value 85.136622
iter  80 value 84.611530
iter  90 value 84.608999
final  value 84.608700 
converged
Fitting Repeat 1 

# weights:  305
initial  value 119.855339 
iter  10 value 94.057462
iter  20 value 94.009307
iter  30 value 93.484502
iter  40 value 83.770262
iter  50 value 83.401474
iter  60 value 82.084448
iter  70 value 80.035642
iter  80 value 79.731593
iter  90 value 79.700848
final  value 79.697647 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.378770 
iter  10 value 94.058002
iter  20 value 94.017804
final  value 93.837248 
converged
Fitting Repeat 3 

# weights:  305
initial  value 101.794824 
iter  10 value 94.057319
iter  20 value 89.123010
iter  30 value 86.392089
iter  40 value 86.290816
iter  50 value 86.271670
iter  60 value 85.932429
final  value 85.932213 
converged
Fitting Repeat 4 

# weights:  305
initial  value 118.257912 
iter  10 value 94.058642
iter  20 value 93.929078
final  value 93.837000 
converged
Fitting Repeat 5 

# weights:  305
initial  value 106.432859 
iter  10 value 93.841132
iter  20 value 93.838200
final  value 93.837428 
converged
Fitting Repeat 1 

# weights:  507
initial  value 116.609996 
iter  10 value 93.845701
iter  20 value 93.813075
iter  30 value 90.237753
iter  40 value 89.242154
iter  50 value 88.627235
iter  60 value 88.379832
iter  70 value 88.124120
iter  80 value 88.099320
iter  90 value 88.099271
final  value 88.099232 
converged
Fitting Repeat 2 

# weights:  507
initial  value 99.978229 
iter  10 value 93.481940
iter  20 value 93.456132
iter  30 value 92.369803
iter  40 value 84.699228
iter  50 value 82.338001
iter  60 value 80.504221
iter  70 value 80.503311
final  value 80.503192 
converged
Fitting Repeat 3 

# weights:  507
initial  value 97.701565 
iter  10 value 94.053207
iter  20 value 93.883657
iter  30 value 89.626787
iter  40 value 88.648158
final  value 88.562551 
converged
Fitting Repeat 4 

# weights:  507
initial  value 109.697345 
iter  10 value 93.812932
iter  20 value 93.475702
iter  30 value 93.448437
final  value 93.439574 
converged
Fitting Repeat 5 

# weights:  507
initial  value 106.326252 
iter  10 value 93.843710
iter  20 value 93.843026
iter  30 value 93.833764
iter  40 value 92.731136
iter  50 value 85.296687
iter  60 value 85.290084
final  value 85.290074 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.738067 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.111490 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.911313 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.680596 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.204757 
iter  10 value 94.113141
final  value 94.112903 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.726555 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.743809 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 111.759861 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 109.586963 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 104.780981 
iter  10 value 94.112918
final  value 94.112903 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.458315 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 107.612869 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 97.564070 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 100.791772 
iter  10 value 90.656435
iter  20 value 89.770635
iter  30 value 85.523120
iter  40 value 84.311016
iter  50 value 84.272340
final  value 84.216851 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.608707 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.747648 
iter  10 value 93.824869
iter  20 value 86.961181
iter  30 value 85.528472
iter  40 value 85.447403
iter  50 value 83.459137
iter  60 value 82.655995
iter  70 value 82.609376
final  value 82.597907 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.059502 
iter  10 value 94.436267
iter  20 value 87.627268
iter  30 value 85.070447
iter  40 value 83.413946
iter  50 value 83.324166
iter  60 value 83.270813
iter  70 value 82.243568
iter  80 value 82.188536
final  value 82.188383 
converged
Fitting Repeat 3 

# weights:  103
initial  value 118.867070 
iter  10 value 94.283333
iter  20 value 88.702995
iter  30 value 85.296692
iter  40 value 83.881149
iter  50 value 83.691690
iter  60 value 81.631680
iter  70 value 80.320338
iter  80 value 79.374134
iter  90 value 79.352382
iter 100 value 79.351717
final  value 79.351717 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 103.348614 
iter  10 value 94.451673
iter  20 value 92.688272
iter  30 value 89.133984
iter  40 value 85.971297
iter  50 value 85.748247
iter  60 value 85.340403
iter  70 value 83.775397
iter  80 value 82.614385
iter  90 value 82.598071
final  value 82.597906 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.697377 
iter  10 value 94.486119
iter  20 value 92.360887
iter  30 value 89.119099
iter  40 value 88.776280
iter  50 value 86.123194
iter  60 value 82.576954
iter  70 value 82.355028
iter  80 value 82.226586
iter  90 value 79.636510
iter 100 value 79.206643
final  value 79.206643 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 122.546816 
iter  10 value 94.437577
iter  20 value 86.513259
iter  30 value 83.622552
iter  40 value 80.226353
iter  50 value 79.374541
iter  60 value 78.801704
iter  70 value 78.505360
iter  80 value 78.440529
iter  90 value 78.261937
iter 100 value 78.152965
final  value 78.152965 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.349366 
iter  10 value 94.507792
iter  20 value 87.292979
iter  30 value 84.530184
iter  40 value 84.153340
iter  50 value 82.929280
iter  60 value 81.810925
iter  70 value 79.782599
iter  80 value 78.923872
iter  90 value 78.773626
iter 100 value 78.494646
final  value 78.494646 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.292385 
iter  10 value 93.521986
iter  20 value 88.470760
iter  30 value 87.502523
iter  40 value 83.923757
iter  50 value 82.672795
iter  60 value 82.482177
iter  70 value 81.468409
iter  80 value 80.830308
iter  90 value 80.507198
iter 100 value 80.225530
final  value 80.225530 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 102.773550 
iter  10 value 94.270674
iter  20 value 93.278263
iter  30 value 92.354147
iter  40 value 86.863485
iter  50 value 80.979659
iter  60 value 79.583693
iter  70 value 79.033187
iter  80 value 78.372428
iter  90 value 78.286489
iter 100 value 78.199260
final  value 78.199260 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.197747 
iter  10 value 94.425971
iter  20 value 92.223766
iter  30 value 81.967768
iter  40 value 81.028544
iter  50 value 80.472353
iter  60 value 79.926811
iter  70 value 79.531018
iter  80 value 78.532726
iter  90 value 77.791014
iter 100 value 77.708296
final  value 77.708296 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 131.125349 
iter  10 value 92.245499
iter  20 value 85.962129
iter  30 value 84.800662
iter  40 value 80.731808
iter  50 value 79.584292
iter  60 value 78.902837
iter  70 value 78.448585
iter  80 value 78.160651
iter  90 value 78.100768
iter 100 value 78.015276
final  value 78.015276 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 114.601457 
iter  10 value 96.188446
iter  20 value 94.679225
iter  30 value 88.195105
iter  40 value 83.281930
iter  50 value 82.640255
iter  60 value 82.280098
iter  70 value 81.026308
iter  80 value 80.526350
iter  90 value 79.704079
iter 100 value 78.927173
final  value 78.927173 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 110.722262 
iter  10 value 96.715565
iter  20 value 94.544970
iter  30 value 91.863024
iter  40 value 90.088540
iter  50 value 82.118924
iter  60 value 80.287558
iter  70 value 78.924359
iter  80 value 78.488791
iter  90 value 78.337347
iter 100 value 78.247260
final  value 78.247260 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 107.484343 
iter  10 value 95.011984
iter  20 value 88.949315
iter  30 value 87.576046
iter  40 value 84.484818
iter  50 value 83.524933
iter  60 value 80.611186
iter  70 value 79.889744
iter  80 value 79.512712
iter  90 value 79.161256
iter 100 value 78.932014
final  value 78.932014 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 115.364650 
iter  10 value 94.155949
iter  20 value 85.532014
iter  30 value 84.356606
iter  40 value 84.208254
iter  50 value 80.476638
iter  60 value 79.332727
iter  70 value 78.966669
iter  80 value 78.791125
iter  90 value 78.395941
iter 100 value 78.046430
final  value 78.046430 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 102.650409 
iter  10 value 94.115031
iter  20 value 93.693065
iter  30 value 93.486968
iter  40 value 93.474845
iter  50 value 93.359977
iter  60 value 93.342241
iter  70 value 93.341140
iter  80 value 93.340993
iter  90 value 93.340834
iter  90 value 93.340834
iter  90 value 93.340834
final  value 93.340834 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.562897 
final  value 94.486087 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.484193 
iter  10 value 94.485874
iter  20 value 94.483716
iter  30 value 94.348707
iter  40 value 92.624142
iter  50 value 92.606156
iter  60 value 92.605906
final  value 92.605894 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.317855 
final  value 94.485547 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.587522 
final  value 94.485963 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.185927 
iter  10 value 94.405137
iter  20 value 94.217531
iter  30 value 93.378420
iter  40 value 93.340545
final  value 93.340327 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.803415 
iter  10 value 94.489031
iter  20 value 94.479224
iter  30 value 84.219344
iter  40 value 84.011670
iter  50 value 84.010418
final  value 84.010411 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.542742 
iter  10 value 93.817359
iter  20 value 93.807694
iter  30 value 93.804610
iter  40 value 93.801519
final  value 93.801085 
converged
Fitting Repeat 4 

# weights:  305
initial  value 108.180516 
iter  10 value 94.489341
iter  20 value 94.484645
iter  30 value 94.056687
iter  40 value 93.811704
final  value 93.779034 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.695443 
iter  10 value 94.488713
iter  20 value 92.207312
iter  30 value 84.702023
iter  40 value 81.967783
iter  50 value 81.947979
final  value 81.945457 
converged
Fitting Repeat 1 

# weights:  507
initial  value 96.473927 
iter  10 value 93.515255
iter  20 value 93.513897
iter  30 value 92.874962
iter  40 value 85.280346
iter  50 value 81.213741
iter  60 value 81.176087
iter  70 value 81.175622
iter  80 value 81.175067
iter  90 value 80.897188
iter 100 value 80.454366
final  value 80.454366 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 109.632394 
iter  10 value 86.559305
iter  20 value 84.216034
iter  30 value 84.172268
iter  40 value 83.948045
iter  50 value 82.525940
iter  60 value 82.114014
iter  70 value 82.111397
iter  80 value 82.108115
iter  90 value 78.822239
iter 100 value 78.119383
final  value 78.119383 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 103.119279 
iter  10 value 94.492729
iter  20 value 94.370411
iter  30 value 89.368825
iter  40 value 87.030108
iter  50 value 86.618282
iter  60 value 84.884340
iter  70 value 84.199719
iter  80 value 84.159484
iter  90 value 84.151013
iter 100 value 84.076372
final  value 84.076372 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 101.943151 
iter  10 value 94.491076
iter  20 value 94.479469
iter  30 value 93.877514
iter  40 value 93.386501
iter  50 value 88.585774
iter  60 value 86.296551
iter  70 value 81.499688
iter  80 value 78.050032
iter  90 value 77.769612
iter 100 value 77.769097
final  value 77.769097 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 102.612495 
iter  10 value 94.491981
iter  20 value 94.420625
iter  30 value 89.939851
iter  40 value 85.332882
iter  50 value 78.160483
iter  60 value 77.352483
iter  70 value 77.245737
iter  80 value 77.243783
iter  80 value 77.243783
final  value 77.243783 
converged
Fitting Repeat 1 

# weights:  103
initial  value 95.064442 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.046359 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 106.055095 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.310004 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 104.056494 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.519592 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 104.857807 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 117.498687 
final  value 94.400000 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.935401 
iter  10 value 94.336566
iter  20 value 94.306772
iter  30 value 94.304937
final  value 94.304908 
converged
Fitting Repeat 5 

# weights:  305
initial  value 107.388434 
final  value 94.467391 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.860944 
final  value 93.701657 
converged
Fitting Repeat 2 

# weights:  507
initial  value 113.015273 
iter  10 value 94.480508
final  value 94.467387 
converged
Fitting Repeat 3 

# weights:  507
initial  value 100.108910 
final  value 94.467391 
converged
Fitting Repeat 4 

# weights:  507
initial  value 108.008606 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.098921 
iter  10 value 94.484274
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.444502 
iter  10 value 94.488333
iter  20 value 87.483269
iter  30 value 85.194444
iter  40 value 84.701497
iter  50 value 84.512223
iter  60 value 84.037612
iter  70 value 83.883038
iter  80 value 83.820663
iter  90 value 83.742660
final  value 83.742636 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.667111 
iter  10 value 94.494725
iter  20 value 93.665380
iter  30 value 88.931895
iter  40 value 85.304526
iter  50 value 84.448281
iter  60 value 84.233890
iter  70 value 84.141930
iter  80 value 83.887274
iter  90 value 83.785846
iter 100 value 83.742641
final  value 83.742641 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 96.177839 
iter  10 value 94.488597
iter  20 value 94.224377
iter  30 value 93.862601
iter  40 value 93.856780
iter  50 value 93.336245
iter  60 value 88.157202
iter  70 value 86.651463
iter  80 value 86.416547
iter  90 value 85.427205
iter 100 value 84.563085
final  value 84.563085 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 104.415984 
iter  10 value 93.391309
iter  20 value 87.529932
iter  30 value 85.219948
iter  40 value 84.758016
iter  50 value 84.288075
iter  60 value 84.084140
iter  70 value 83.908506
iter  80 value 83.805221
iter  90 value 83.745101
final  value 83.742637 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.798071 
iter  10 value 94.488498
iter  20 value 94.397957
iter  30 value 90.204544
iter  40 value 86.277711
iter  50 value 85.306871
iter  60 value 84.136650
iter  70 value 83.475788
iter  80 value 83.311286
iter  90 value 82.553594
final  value 82.438926 
converged
Fitting Repeat 1 

# weights:  305
initial  value 106.912978 
iter  10 value 96.883033
iter  20 value 95.066392
iter  30 value 91.310568
iter  40 value 86.094561
iter  50 value 84.553205
iter  60 value 84.062172
iter  70 value 82.668927
iter  80 value 82.010496
iter  90 value 81.363806
iter 100 value 80.568577
final  value 80.568577 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 106.365004 
iter  10 value 94.480754
iter  20 value 93.029648
iter  30 value 87.632858
iter  40 value 87.010172
iter  50 value 84.745728
iter  60 value 83.965817
iter  70 value 83.892226
iter  80 value 83.746809
iter  90 value 83.318444
iter 100 value 82.116196
final  value 82.116196 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 120.244286 
iter  10 value 94.524408
iter  20 value 94.140559
iter  30 value 93.834710
iter  40 value 91.573192
iter  50 value 83.324177
iter  60 value 82.341467
iter  70 value 81.270915
iter  80 value 80.972887
iter  90 value 80.560569
iter 100 value 80.496480
final  value 80.496480 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 104.112083 
iter  10 value 94.515847
iter  20 value 93.793670
iter  30 value 90.253621
iter  40 value 89.307071
iter  50 value 87.519366
iter  60 value 83.404297
iter  70 value 82.318021
iter  80 value 81.426298
iter  90 value 81.340043
iter 100 value 81.327691
final  value 81.327691 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 110.544982 
iter  10 value 94.541222
iter  20 value 85.266460
iter  30 value 84.806681
iter  40 value 84.356380
iter  50 value 83.995238
iter  60 value 83.791065
iter  70 value 83.707876
iter  80 value 83.480195
iter  90 value 82.680873
iter 100 value 82.594103
final  value 82.594103 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 106.089916 
iter  10 value 97.468339
iter  20 value 87.463464
iter  30 value 83.143312
iter  40 value 81.437452
iter  50 value 81.049268
iter  60 value 80.726276
iter  70 value 80.373121
iter  80 value 80.348527
iter  90 value 80.319468
iter 100 value 80.275871
final  value 80.275871 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 118.047780 
iter  10 value 94.129980
iter  20 value 88.703453
iter  30 value 85.200376
iter  40 value 84.315903
iter  50 value 82.611979
iter  60 value 82.271667
iter  70 value 81.636427
iter  80 value 81.457434
iter  90 value 81.421826
iter 100 value 81.325231
final  value 81.325231 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.297106 
iter  10 value 96.402009
iter  20 value 94.981966
iter  30 value 93.869296
iter  40 value 91.534110
iter  50 value 84.518098
iter  60 value 83.177492
iter  70 value 81.989990
iter  80 value 81.321483
iter  90 value 80.830824
iter 100 value 80.745042
final  value 80.745042 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 117.912140 
iter  10 value 93.459927
iter  20 value 88.991007
iter  30 value 87.163167
iter  40 value 86.028597
iter  50 value 85.361274
iter  60 value 84.624962
iter  70 value 84.153232
iter  80 value 82.326195
iter  90 value 80.877698
iter 100 value 80.572903
final  value 80.572903 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 112.847704 
iter  10 value 96.620326
iter  20 value 93.845753
iter  30 value 85.593289
iter  40 value 83.844656
iter  50 value 83.751205
iter  60 value 83.232092
iter  70 value 82.660210
iter  80 value 82.214827
iter  90 value 82.082367
iter 100 value 82.046679
final  value 82.046679 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.202124 
final  value 94.485903 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.812884 
final  value 94.485782 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.566095 
final  value 94.485576 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.406011 
final  value 94.485738 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.017663 
final  value 94.485825 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.495833 
iter  10 value 94.489363
iter  20 value 94.453271
iter  30 value 90.382478
iter  40 value 85.126786
iter  50 value 84.974429
final  value 84.974360 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.832334 
iter  10 value 94.489178
iter  20 value 92.434970
final  value 92.029967 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.222307 
iter  10 value 94.506551
iter  20 value 94.493916
iter  30 value 90.253461
iter  40 value 90.213222
iter  50 value 89.726929
iter  60 value 89.271446
iter  70 value 89.214817
iter  80 value 88.070982
iter  90 value 87.863861
iter 100 value 85.373943
final  value 85.373943 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 108.051521 
iter  10 value 94.489217
iter  20 value 94.484572
iter  30 value 94.373938
iter  40 value 84.448125
iter  50 value 84.343160
iter  60 value 84.342647
iter  70 value 84.342104
iter  80 value 84.308258
iter  90 value 84.307497
iter 100 value 84.005933
final  value 84.005933 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 100.431310 
iter  10 value 94.471600
iter  20 value 94.467495
final  value 94.467398 
converged
Fitting Repeat 1 

# weights:  507
initial  value 109.456672 
iter  10 value 94.475235
iter  20 value 94.468170
final  value 94.467655 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.350523 
iter  10 value 93.999517
iter  20 value 93.980704
iter  30 value 93.946880
iter  40 value 93.941504
final  value 93.941131 
converged
Fitting Repeat 3 

# weights:  507
initial  value 102.331087 
iter  10 value 94.141556
iter  20 value 93.734046
iter  30 value 93.730103
iter  40 value 93.724625
iter  50 value 93.481020
iter  60 value 93.478489
iter  70 value 93.442798
final  value 93.442703 
converged
Fitting Repeat 4 

# weights:  507
initial  value 134.565131 
iter  10 value 94.475611
iter  20 value 94.468255
final  value 94.467606 
converged
Fitting Repeat 5 

# weights:  507
initial  value 106.803989 
iter  10 value 94.475379
iter  20 value 94.392025
iter  30 value 93.392511
final  value 93.291531 
converged
Fitting Repeat 1 

# weights:  103
initial  value 95.655344 
iter  10 value 93.021623
iter  20 value 83.564358
iter  30 value 83.432626
iter  40 value 83.235190
iter  50 value 83.144605
iter  60 value 83.144463
final  value 83.144462 
converged
Fitting Repeat 2 

# weights:  103
initial  value 107.692542 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.758697 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.834293 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.813716 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 93.368640 
iter  10 value 84.854739
iter  20 value 84.717614
iter  30 value 83.324897
iter  40 value 83.317595
iter  40 value 83.317595
iter  40 value 83.317595
final  value 83.317595 
converged
Fitting Repeat 2 

# weights:  305
initial  value 104.284574 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.370280 
final  value 93.697143 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.401259 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.753572 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.174707 
iter  10 value 93.496583
final  value 93.491422 
converged
Fitting Repeat 2 

# weights:  507
initial  value 97.802908 
iter  10 value 93.326854
final  value 93.180233 
converged
Fitting Repeat 3 

# weights:  507
initial  value 96.100998 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 102.397810 
final  value 93.288889 
converged
Fitting Repeat 5 

# weights:  507
initial  value 123.959023 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.050211 
iter  10 value 93.996690
iter  20 value 86.361264
iter  30 value 85.573310
iter  40 value 85.315437
iter  50 value 83.935535
iter  60 value 83.442345
iter  70 value 83.381186
final  value 83.380508 
converged
Fitting Repeat 2 

# weights:  103
initial  value 113.072701 
iter  10 value 93.063624
iter  20 value 87.826070
iter  30 value 85.378897
iter  40 value 84.025529
iter  50 value 83.539048
iter  60 value 83.174052
iter  70 value 83.011154
iter  80 value 82.959847
final  value 82.958889 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.835267 
iter  10 value 89.286200
iter  20 value 85.611632
iter  30 value 84.260883
iter  40 value 83.386170
iter  50 value 83.379391
final  value 83.379198 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.729931 
iter  10 value 93.552126
iter  20 value 93.261570
iter  30 value 93.258268
iter  40 value 93.005887
iter  50 value 89.268663
iter  60 value 86.948278
iter  70 value 84.508055
iter  80 value 84.125366
iter  90 value 83.438287
iter 100 value 83.380584
final  value 83.380584 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 98.910757 
iter  10 value 93.936591
iter  20 value 86.796748
iter  30 value 86.443052
iter  40 value 86.254795
iter  50 value 85.138249
iter  60 value 83.744204
iter  70 value 83.682199
iter  80 value 83.659723
iter  90 value 83.658339
final  value 83.658328 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.688851 
iter  10 value 91.701180
iter  20 value 84.178722
iter  30 value 83.955052
iter  40 value 82.396107
iter  50 value 81.822019
iter  60 value 81.560042
iter  70 value 81.151797
iter  80 value 80.971879
iter  90 value 80.963309
iter 100 value 80.872940
final  value 80.872940 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.143875 
iter  10 value 96.041808
iter  20 value 85.972737
iter  30 value 84.087978
iter  40 value 83.929018
iter  50 value 83.240378
iter  60 value 82.452470
iter  70 value 81.531488
iter  80 value 81.376591
iter  90 value 81.145620
iter 100 value 80.604003
final  value 80.604003 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.497178 
iter  10 value 94.241079
iter  20 value 87.241001
iter  30 value 85.410591
iter  40 value 84.474706
iter  50 value 83.399671
iter  60 value 82.039418
iter  70 value 81.286499
iter  80 value 81.199127
iter  90 value 81.092979
iter 100 value 81.047930
final  value 81.047930 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 112.073678 
iter  10 value 95.726166
iter  20 value 91.345493
iter  30 value 85.824716
iter  40 value 84.454022
iter  50 value 82.781605
iter  60 value 82.451070
iter  70 value 81.920809
iter  80 value 81.571939
iter  90 value 81.331375
iter 100 value 80.912276
final  value 80.912276 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 109.730039 
iter  10 value 94.084136
iter  20 value 92.586491
iter  30 value 85.784345
iter  40 value 84.032854
iter  50 value 83.585680
iter  60 value 82.875374
iter  70 value 81.822630
iter  80 value 81.597610
iter  90 value 81.540270
iter 100 value 81.495890
final  value 81.495890 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 103.849158 
iter  10 value 94.483397
iter  20 value 84.825100
iter  30 value 84.157692
iter  40 value 83.988864
iter  50 value 82.642860
iter  60 value 81.283644
iter  70 value 80.897818
iter  80 value 80.781979
iter  90 value 80.770891
iter 100 value 80.582307
final  value 80.582307 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.859882 
iter  10 value 93.386896
iter  20 value 87.367350
iter  30 value 84.678689
iter  40 value 82.125386
iter  50 value 81.211284
iter  60 value 80.804979
iter  70 value 80.757425
iter  80 value 80.687311
iter  90 value 80.470968
iter 100 value 80.298506
final  value 80.298506 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 121.339077 
iter  10 value 94.034446
iter  20 value 87.448901
iter  30 value 84.211227
iter  40 value 83.164193
iter  50 value 82.173553
iter  60 value 81.372790
iter  70 value 81.192315
iter  80 value 81.068695
iter  90 value 80.934038
iter 100 value 80.862368
final  value 80.862368 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 126.523535 
iter  10 value 93.877459
iter  20 value 87.868923
iter  30 value 85.533536
iter  40 value 85.283541
iter  50 value 84.155483
iter  60 value 82.022626
iter  70 value 81.239573
iter  80 value 80.813269
iter  90 value 80.698865
iter 100 value 80.679681
final  value 80.679681 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 123.167315 
iter  10 value 94.031692
iter  20 value 91.103804
iter  30 value 85.824629
iter  40 value 82.651393
iter  50 value 81.307493
iter  60 value 80.977626
iter  70 value 80.699003
iter  80 value 80.335323
iter  90 value 80.240245
iter 100 value 80.129595
final  value 80.129595 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.257423 
final  value 94.054527 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.653716 
final  value 94.054738 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.900331 
iter  10 value 93.479036
iter  20 value 93.446526
final  value 93.439856 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.093435 
final  value 94.054701 
converged
Fitting Repeat 5 

# weights:  103
initial  value 93.736999 
iter  10 value 85.410681
iter  20 value 84.467899
iter  30 value 84.439772
iter  40 value 84.431286
iter  50 value 84.429989
final  value 84.429949 
converged
Fitting Repeat 1 

# weights:  305
initial  value 125.530055 
iter  10 value 93.609145
iter  20 value 93.444269
iter  30 value 93.442585
iter  40 value 93.442122
iter  50 value 93.441921
iter  60 value 93.438595
iter  70 value 92.920116
iter  80 value 89.273035
iter  90 value 86.673634
iter 100 value 86.003849
final  value 86.003849 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 98.594626 
iter  10 value 93.920897
iter  20 value 93.275473
final  value 93.192815 
converged
Fitting Repeat 3 

# weights:  305
initial  value 116.958854 
iter  10 value 93.198532
iter  20 value 93.197124
iter  30 value 93.193795
final  value 93.193771 
converged
Fitting Repeat 4 

# weights:  305
initial  value 99.533573 
iter  10 value 94.055536
iter  20 value 93.788492
final  value 93.192812 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.085903 
iter  10 value 94.058197
iter  20 value 91.239514
iter  30 value 87.621658
iter  40 value 86.136144
iter  50 value 86.135460
iter  60 value 85.401314
iter  70 value 84.088352
iter  80 value 84.071023
iter  90 value 84.070735
iter 100 value 84.028112
final  value 84.028112 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 143.346304 
iter  10 value 93.924258
iter  20 value 93.842734
iter  30 value 93.193073
iter  40 value 86.205156
iter  50 value 84.715940
iter  60 value 84.707802
iter  70 value 84.703038
iter  80 value 84.700301
iter  90 value 84.700132
iter  90 value 84.700132
final  value 84.700132 
converged
Fitting Repeat 2 

# weights:  507
initial  value 108.887674 
iter  10 value 93.923424
iter  20 value 92.717691
iter  30 value 88.010990
iter  40 value 87.504788
iter  50 value 87.503413
iter  60 value 84.790326
iter  70 value 82.793380
final  value 82.789610 
converged
Fitting Repeat 3 

# weights:  507
initial  value 110.486581 
iter  10 value 94.060805
iter  20 value 94.052919
iter  30 value 93.194502
iter  40 value 93.192705
iter  40 value 93.192705
iter  40 value 93.192704
final  value 93.192704 
converged
Fitting Repeat 4 

# weights:  507
initial  value 100.730750 
iter  10 value 93.705597
iter  20 value 93.167609
iter  30 value 93.146977
iter  40 value 93.146854
final  value 93.146828 
converged
Fitting Repeat 5 

# weights:  507
initial  value 117.951068 
iter  10 value 94.061634
iter  20 value 93.440105
iter  30 value 93.193253
iter  40 value 93.185206
iter  50 value 87.804039
iter  60 value 84.252210
iter  70 value 82.277768
iter  80 value 80.460836
iter  90 value 80.072989
iter 100 value 80.036041
final  value 80.036041 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 127.536090 
iter  10 value 117.221201
iter  20 value 115.225332
iter  30 value 109.112131
iter  40 value 107.256259
iter  50 value 105.994830
iter  60 value 105.521655
iter  70 value 103.242043
iter  80 value 102.324222
iter  90 value 102.030682
iter 100 value 101.501519
final  value 101.501519 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 130.942568 
iter  10 value 117.918506
iter  20 value 117.865943
iter  30 value 115.595284
iter  40 value 108.511075
iter  50 value 105.989514
iter  60 value 104.575712
iter  70 value 103.353844
iter  80 value 102.717416
iter  90 value 101.640612
iter 100 value 100.988972
final  value 100.988972 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 134.100925 
iter  10 value 117.122479
iter  20 value 107.643673
iter  30 value 106.193374
iter  40 value 102.653145
iter  50 value 101.963344
iter  60 value 101.648337
iter  70 value 101.426548
iter  80 value 101.380963
iter  90 value 101.137396
iter 100 value 101.005560
final  value 101.005560 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 151.485706 
iter  10 value 120.803430
iter  20 value 110.275365
iter  30 value 104.812012
iter  40 value 101.633558
iter  50 value 100.879374
iter  60 value 100.713408
iter  70 value 100.627999
iter  80 value 100.547975
iter  90 value 100.438413
iter 100 value 100.274921
final  value 100.274921 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 133.364391 
iter  10 value 114.990171
iter  20 value 107.507510
iter  30 value 107.205874
iter  40 value 105.632945
iter  50 value 103.379651
iter  60 value 102.312106
iter  70 value 101.938468
iter  80 value 101.339636
iter  90 value 101.132854
iter 100 value 100.969925
final  value 100.969925 
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  1 00:30:29 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 
 40.117   1.112  99.108