##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:HPiP.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings HPiP_1.13.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.21-bioc/meat/HPiP.Rcheck’
* using R Under development (unstable) (2025-03-02 r87868)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.13.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
    29 | then the Kronecker product is the code{(pm × qn)} block matrix
       |                                       ^
* checking Rd metadata ... OK
* checking Rd cross-references ... INFO
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
var_imp       35.980  1.867  38.314
FSmethod      34.067  1.664  36.207
corr_plot     33.382  1.613  35.216
pred_ensembel 13.602  0.417  12.081
enrichfindP    0.465  0.054   8.843
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 2 NOTEs
See
  ‘/Users/biocbuild/bbs-3.21-bioc/meat/HPiP.Rcheck/00check.log’
for details.

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


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library’
* installing *source* package ‘HPiP’ ...
** this is package ‘HPiP’ version ‘1.13.0’
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HPiP)

R Under development (unstable) (2025-03-02 r87868) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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

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

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

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

# weights:  103
initial  value 100.913280 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.177725 
iter  10 value 92.318409
iter  20 value 92.265842
iter  20 value 92.265842
iter  20 value 92.265842
final  value 92.265842 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.102696 
iter  10 value 94.275363
iter  10 value 94.275362
iter  10 value 94.275362
final  value 94.275362 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.148240 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.383917 
iter  10 value 90.010720
final  value 89.850383 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.998054 
final  value 94.275362 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.948236 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.746391 
final  value 94.466823 
converged
Fitting Repeat 4 

# weights:  305
initial  value 101.468609 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.852921 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 118.090011 
final  value 94.275362 
converged
Fitting Repeat 2 

# weights:  507
initial  value 97.176705 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 120.504925 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.721999 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.677805 
iter  10 value 94.305882
iter  10 value 94.305882
iter  10 value 94.305882
final  value 94.305882 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.869541 
iter  10 value 95.026324
iter  20 value 94.487770
iter  30 value 91.592764
iter  40 value 88.780988
iter  50 value 88.427620
iter  60 value 87.799011
iter  70 value 86.816881
iter  80 value 86.487775
final  value 86.484702 
converged
Fitting Repeat 2 

# weights:  103
initial  value 120.107600 
iter  10 value 94.452144
iter  20 value 89.649828
iter  30 value 89.357547
iter  40 value 85.807070
iter  50 value 84.840934
iter  60 value 84.080884
iter  70 value 84.000249
final  value 83.995779 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.421896 
iter  10 value 93.970004
iter  20 value 87.893691
iter  30 value 86.980051
iter  40 value 86.620202
iter  50 value 85.312013
iter  60 value 84.388849
iter  70 value 84.251533
iter  80 value 84.201253
iter  90 value 84.052893
iter 100 value 83.995907
final  value 83.995907 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 103.351236 
iter  10 value 94.282790
iter  20 value 91.767946
iter  30 value 90.072251
iter  40 value 88.769109
iter  50 value 87.039725
iter  60 value 86.344897
iter  70 value 86.200609
iter  80 value 86.114044
final  value 86.112090 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.782430 
iter  10 value 95.133053
iter  20 value 91.756111
iter  30 value 91.659197
iter  40 value 90.391912
iter  50 value 85.745839
iter  60 value 84.879389
iter  70 value 84.604974
iter  80 value 84.070909
iter  90 value 83.881851
final  value 83.881841 
converged
Fitting Repeat 1 

# weights:  305
initial  value 113.496329 
iter  10 value 94.616980
iter  20 value 91.373138
iter  30 value 87.316349
iter  40 value 87.049381
iter  50 value 86.830048
iter  60 value 85.339457
iter  70 value 84.286747
iter  80 value 83.312400
iter  90 value 82.964602
iter 100 value 82.901000
final  value 82.901000 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 111.634436 
iter  10 value 94.734858
iter  20 value 93.259070
iter  30 value 88.217177
iter  40 value 88.029225
iter  50 value 85.092074
iter  60 value 82.985618
iter  70 value 82.650738
iter  80 value 82.580302
iter  90 value 82.440080
iter 100 value 82.291642
final  value 82.291642 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.592415 
iter  10 value 94.325992
iter  20 value 92.287209
iter  30 value 85.037027
iter  40 value 83.435701
iter  50 value 82.925652
iter  60 value 82.657978
iter  70 value 82.556484
iter  80 value 82.452217
iter  90 value 82.434700
iter 100 value 82.410344
final  value 82.410344 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 106.470680 
iter  10 value 93.946561
iter  20 value 89.538573
iter  30 value 87.504300
iter  40 value 87.002527
iter  50 value 86.213092
iter  60 value 85.497681
iter  70 value 83.633128
iter  80 value 83.119748
iter  90 value 83.016341
iter 100 value 82.827521
final  value 82.827521 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.741872 
iter  10 value 94.469475
iter  20 value 94.309569
iter  30 value 93.925856
iter  40 value 88.465389
iter  50 value 87.860188
iter  60 value 87.196855
iter  70 value 86.175906
iter  80 value 86.007084
iter  90 value 85.966498
iter 100 value 84.720850
final  value 84.720850 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 126.005428 
iter  10 value 97.495492
iter  20 value 90.555017
iter  30 value 90.408237
iter  40 value 90.271780
iter  50 value 90.227798
iter  60 value 90.097427
iter  70 value 87.533599
iter  80 value 85.279077
iter  90 value 84.676331
iter 100 value 84.475834
final  value 84.475834 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.527523 
iter  10 value 94.702017
iter  20 value 94.472499
iter  30 value 94.324389
iter  40 value 91.388396
iter  50 value 88.252004
iter  60 value 87.439331
iter  70 value 86.981599
iter  80 value 86.027360
iter  90 value 85.684011
iter 100 value 84.607260
final  value 84.607260 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 126.022535 
iter  10 value 100.281203
iter  20 value 91.442468
iter  30 value 87.801483
iter  40 value 85.299644
iter  50 value 84.785415
iter  60 value 83.800864
iter  70 value 83.785851
iter  80 value 83.737180
iter  90 value 83.450682
iter 100 value 83.183704
final  value 83.183704 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 118.792760 
iter  10 value 94.676199
iter  20 value 94.551986
iter  30 value 94.308735
iter  40 value 90.893647
iter  50 value 86.270819
iter  60 value 84.433729
iter  70 value 84.019428
iter  80 value 83.834134
iter  90 value 82.732276
iter 100 value 82.385201
final  value 82.385201 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.652071 
iter  10 value 94.217740
iter  20 value 91.550009
iter  30 value 89.211210
iter  40 value 87.733205
iter  50 value 84.126307
iter  60 value 83.182755
iter  70 value 83.118782
iter  80 value 82.777354
iter  90 value 82.742876
iter 100 value 82.504829
final  value 82.504829 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 105.034435 
iter  10 value 94.485972
iter  20 value 92.975084
iter  30 value 88.407714
iter  40 value 88.399971
iter  50 value 88.399352
final  value 88.398191 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.759896 
final  value 94.486096 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.331650 
iter  10 value 94.277111
iter  20 value 94.275580
final  value 94.275489 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.429119 
final  value 94.485643 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.864441 
iter  10 value 94.485793
iter  20 value 94.484222
final  value 94.484214 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.071379 
iter  10 value 94.454460
final  value 94.454292 
converged
Fitting Repeat 2 

# weights:  305
initial  value 112.952524 
iter  10 value 94.483367
iter  20 value 94.279998
iter  30 value 94.275789
iter  40 value 93.895012
iter  50 value 84.781253
iter  60 value 82.530231
iter  70 value 82.526349
iter  80 value 82.524212
iter  90 value 82.521307
iter 100 value 82.499280
final  value 82.499280 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.352893 
iter  10 value 94.488936
iter  20 value 94.382357
iter  30 value 91.993345
iter  40 value 90.936183
iter  50 value 88.203692
iter  60 value 87.862435
iter  70 value 87.372632
iter  80 value 87.312524
iter  90 value 87.312190
final  value 87.312111 
converged
Fitting Repeat 4 

# weights:  305
initial  value 100.248193 
iter  10 value 88.964394
iter  20 value 87.089079
iter  30 value 87.087718
iter  40 value 86.950624
final  value 86.946068 
converged
Fitting Repeat 5 

# weights:  305
initial  value 111.071736 
iter  10 value 94.488777
iter  20 value 94.479293
iter  30 value 94.448015
iter  40 value 94.318478
final  value 94.280219 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.746918 
iter  10 value 94.492654
iter  20 value 94.440802
iter  30 value 89.085673
iter  40 value 88.407786
iter  50 value 87.220563
iter  60 value 86.847954
iter  70 value 86.847165
iter  80 value 86.812964
final  value 86.812884 
converged
Fitting Repeat 2 

# weights:  507
initial  value 104.617401 
iter  10 value 94.283931
iter  20 value 94.276483
iter  30 value 94.264492
iter  40 value 90.848778
iter  50 value 90.504415
iter  60 value 90.379891
final  value 90.379826 
converged
Fitting Repeat 3 

# weights:  507
initial  value 105.163995 
iter  10 value 94.314517
iter  20 value 89.210909
iter  30 value 85.909946
iter  40 value 85.609860
final  value 85.607474 
converged
Fitting Repeat 4 

# weights:  507
initial  value 104.010522 
iter  10 value 94.492440
iter  20 value 94.458936
iter  30 value 91.095878
iter  40 value 91.056738
iter  50 value 85.337947
iter  60 value 84.536843
iter  70 value 84.531104
iter  80 value 84.490746
iter  90 value 84.439622
iter 100 value 83.450203
final  value 83.450203 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 115.708303 
iter  10 value 92.682619
iter  20 value 92.145218
iter  30 value 85.391051
iter  40 value 85.039879
iter  50 value 85.035873
iter  60 value 85.035097
iter  70 value 85.000749
iter  80 value 84.993768
iter  90 value 84.993070
iter 100 value 84.766640
final  value 84.766640 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 107.721637 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.859143 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.460431 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.115824 
iter  10 value 81.902926
final  value 81.866476 
converged
Fitting Repeat 5 

# weights:  103
initial  value 111.190870 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.459803 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 94.823949 
final  value 93.915746 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.156907 
final  value 93.915746 
converged
Fitting Repeat 4 

# weights:  305
initial  value 100.243335 
final  value 93.915746 
converged
Fitting Repeat 5 

# weights:  305
initial  value 109.094365 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 101.258153 
final  value 93.915746 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.908697 
final  value 93.915746 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.864951 
iter  10 value 93.894436
iter  20 value 93.869758
final  value 93.869756 
converged
Fitting Repeat 4 

# weights:  507
initial  value 115.200520 
final  value 93.915746 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.272272 
iter  10 value 91.165000
iter  20 value 90.487720
iter  30 value 90.239604
iter  40 value 90.226532
iter  50 value 90.225688
iter  50 value 90.225688
iter  50 value 90.225688
final  value 90.225688 
converged
Fitting Repeat 1 

# weights:  103
initial  value 105.637087 
iter  10 value 93.992510
iter  20 value 93.728138
iter  30 value 93.724128
iter  40 value 89.841241
iter  50 value 88.771788
iter  60 value 88.761713
final  value 88.761634 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.695622 
iter  10 value 94.060419
iter  20 value 93.959839
iter  30 value 93.697728
iter  40 value 86.432057
iter  50 value 85.778153
iter  60 value 85.686796
iter  70 value 84.105307
iter  80 value 83.181316
iter  90 value 83.079820
iter 100 value 83.076485
final  value 83.076485 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 98.729695 
iter  10 value 88.375304
iter  20 value 85.337887
iter  30 value 84.615257
iter  40 value 82.826954
iter  50 value 82.771437
iter  60 value 82.710488
iter  70 value 82.620493
iter  80 value 82.556743
final  value 82.556684 
converged
Fitting Repeat 4 

# weights:  103
initial  value 104.260873 
iter  10 value 94.040319
iter  20 value 92.707679
iter  30 value 86.113596
iter  40 value 82.426593
iter  50 value 80.775886
iter  60 value 80.112861
iter  70 value 80.011849
iter  80 value 79.882380
iter  90 value 79.806182
final  value 79.805839 
converged
Fitting Repeat 5 

# weights:  103
initial  value 115.071417 
iter  10 value 94.054847
iter  20 value 93.345228
iter  30 value 88.209284
iter  40 value 85.818609
iter  50 value 83.240476
iter  60 value 83.057057
final  value 83.052771 
converged
Fitting Repeat 1 

# weights:  305
initial  value 106.659014 
iter  10 value 86.900599
iter  20 value 82.241939
iter  30 value 80.410963
iter  40 value 79.636737
iter  50 value 79.419386
iter  60 value 79.280070
iter  70 value 79.022295
iter  80 value 78.671044
iter  90 value 78.661934
iter 100 value 78.659720
final  value 78.659720 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 109.888181 
iter  10 value 94.684721
iter  20 value 90.418371
iter  30 value 87.528545
iter  40 value 86.237399
iter  50 value 82.578025
iter  60 value 80.718807
iter  70 value 79.971648
iter  80 value 79.144498
iter  90 value 78.916243
iter 100 value 78.774832
final  value 78.774832 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.751851 
iter  10 value 94.626093
iter  20 value 94.454408
iter  30 value 93.916284
iter  40 value 92.026629
iter  50 value 86.393311
iter  60 value 81.741027
iter  70 value 81.351859
iter  80 value 81.227690
iter  90 value 81.073065
iter 100 value 80.629500
final  value 80.629500 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 107.850774 
iter  10 value 93.984365
iter  20 value 88.631791
iter  30 value 86.242358
iter  40 value 84.272309
iter  50 value 84.064572
iter  60 value 81.175332
iter  70 value 80.670550
iter  80 value 80.331473
iter  90 value 80.008606
iter 100 value 79.694222
final  value 79.694222 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 100.830509 
iter  10 value 93.995481
iter  20 value 87.950880
iter  30 value 83.809156
iter  40 value 80.793619
iter  50 value 79.369259
iter  60 value 78.913816
iter  70 value 78.563241
iter  80 value 78.396584
iter  90 value 78.331237
iter 100 value 78.316469
final  value 78.316469 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.505511 
iter  10 value 93.205441
iter  20 value 90.576306
iter  30 value 89.156422
iter  40 value 84.262289
iter  50 value 83.116871
iter  60 value 82.572857
iter  70 value 81.088336
iter  80 value 80.234940
iter  90 value 80.102559
iter 100 value 80.075868
final  value 80.075868 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 119.700526 
iter  10 value 95.720563
iter  20 value 89.386796
iter  30 value 85.386531
iter  40 value 82.512360
iter  50 value 81.655572
iter  60 value 81.141473
iter  70 value 80.450611
iter  80 value 79.270152
iter  90 value 79.026640
iter 100 value 78.845236
final  value 78.845236 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 109.102646 
iter  10 value 93.076421
iter  20 value 84.916473
iter  30 value 82.098336
iter  40 value 80.678562
iter  50 value 79.748893
iter  60 value 79.637389
iter  70 value 79.552647
iter  80 value 79.551722
iter  90 value 79.537665
iter 100 value 78.997765
final  value 78.997765 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 116.405622 
iter  10 value 95.525642
iter  20 value 93.952254
iter  30 value 85.477311
iter  40 value 84.411169
iter  50 value 83.795240
iter  60 value 80.691251
iter  70 value 79.551615
iter  80 value 79.414797
iter  90 value 79.364289
iter 100 value 78.738931
final  value 78.738931 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 108.103985 
iter  10 value 94.045381
iter  20 value 93.733267
iter  30 value 93.556284
iter  40 value 90.021082
iter  50 value 86.516542
iter  60 value 82.307752
iter  70 value 81.533509
iter  80 value 80.585371
iter  90 value 80.273768
iter 100 value 80.159647
final  value 80.159647 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.837147 
final  value 94.054236 
converged
Fitting Repeat 2 

# weights:  103
initial  value 107.259601 
iter  10 value 94.054561
iter  20 value 88.509127
iter  30 value 84.145377
iter  40 value 84.077884
iter  50 value 84.077766
final  value 84.077735 
converged
Fitting Repeat 3 

# weights:  103
initial  value 106.077129 
final  value 94.054558 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.236460 
final  value 94.054703 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.997871 
iter  10 value 94.056575
final  value 94.054753 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.502811 
iter  10 value 94.057547
iter  20 value 91.186098
iter  30 value 85.862860
iter  40 value 85.375769
iter  50 value 83.522950
iter  60 value 83.473014
iter  70 value 83.469012
iter  80 value 83.468264
iter  90 value 83.467742
final  value 83.466783 
converged
Fitting Repeat 2 

# weights:  305
initial  value 94.393034 
iter  10 value 94.053998
iter  20 value 94.049126
iter  30 value 92.213162
iter  40 value 92.212343
iter  50 value 92.212007
iter  60 value 92.211880
iter  70 value 92.200324
iter  80 value 84.310030
iter  90 value 84.073568
iter 100 value 84.073041
final  value 84.073041 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 115.880526 
iter  10 value 93.874896
iter  20 value 93.403781
iter  30 value 84.867132
iter  40 value 84.638031
iter  50 value 84.637080
iter  60 value 84.634685
iter  70 value 84.264877
iter  80 value 84.249401
final  value 84.249304 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.611674 
iter  10 value 93.680173
iter  20 value 93.499537
final  value 93.392851 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.427270 
iter  10 value 94.056415
iter  20 value 93.694342
final  value 93.653720 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.597646 
iter  10 value 92.931799
iter  20 value 91.993009
iter  30 value 91.987955
final  value 91.987851 
converged
Fitting Repeat 2 

# weights:  507
initial  value 99.501992 
iter  10 value 93.720842
iter  20 value 93.718031
iter  30 value 93.716903
iter  40 value 93.713957
iter  50 value 93.695963
iter  60 value 88.124123
iter  70 value 84.252279
iter  80 value 84.249800
iter  90 value 84.249409
iter 100 value 81.257224
final  value 81.257224 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 118.193450 
iter  10 value 93.931608
iter  20 value 93.923939
final  value 93.923443 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.164779 
iter  10 value 94.061000
iter  20 value 93.877389
iter  30 value 93.861154
iter  40 value 93.625360
iter  50 value 93.622039
final  value 93.622007 
converged
Fitting Repeat 5 

# weights:  507
initial  value 114.002211 
iter  10 value 93.665766
iter  20 value 93.657239
iter  30 value 93.517262
iter  40 value 87.637198
iter  50 value 84.092765
iter  60 value 84.090955
iter  70 value 84.070466
iter  80 value 81.989892
iter  90 value 81.875100
iter 100 value 81.874891
final  value 81.874891 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.808707 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.872506 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.970880 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 107.103623 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.656799 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.630437 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 107.816322 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 97.525664 
iter  10 value 94.026542
iter  10 value 94.026542
iter  10 value 94.026542
final  value 94.026542 
converged
Fitting Repeat 4 

# weights:  305
initial  value 106.086148 
iter  10 value 93.617777
final  value 93.300000 
converged
Fitting Repeat 5 

# weights:  305
initial  value 93.041286 
iter  10 value 86.107890
iter  20 value 84.572588
iter  30 value 84.537748
iter  40 value 84.532078
iter  50 value 84.464684
final  value 84.464474 
converged
Fitting Repeat 1 

# weights:  507
initial  value 99.951802 
iter  10 value 94.026544
final  value 94.026542 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.288308 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.239911 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 117.336996 
iter  10 value 91.735474
iter  20 value 88.394219
iter  30 value 86.905687
iter  40 value 86.709763
final  value 86.709642 
converged
Fitting Repeat 5 

# weights:  507
initial  value 98.289153 
final  value 94.482478 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.331309 
iter  10 value 89.122869
iter  20 value 85.279374
iter  30 value 85.086702
iter  40 value 84.564996
iter  50 value 84.129600
iter  60 value 82.649836
iter  70 value 82.376509
iter  80 value 82.178270
final  value 82.175724 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.367877 
iter  10 value 94.438765
iter  20 value 87.586569
iter  30 value 86.574874
iter  40 value 85.209748
iter  50 value 84.662398
iter  60 value 84.287575
iter  70 value 83.221202
iter  80 value 82.797040
iter  90 value 82.426041
iter 100 value 82.372860
final  value 82.372860 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 102.839336 
iter  10 value 94.488784
iter  20 value 94.445069
iter  30 value 94.225597
iter  40 value 94.216396
iter  50 value 94.155711
iter  60 value 85.940348
iter  70 value 85.161529
iter  80 value 85.026737
iter  90 value 83.834819
iter 100 value 83.105912
final  value 83.105912 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 103.991620 
iter  10 value 94.516810
iter  20 value 94.473114
iter  30 value 94.144321
iter  40 value 93.815202
iter  50 value 84.752434
iter  60 value 84.053369
iter  70 value 83.767342
iter  80 value 83.621659
iter  90 value 83.444890
iter 100 value 83.395407
final  value 83.395407 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 104.018411 
iter  10 value 94.483410
iter  20 value 85.344690
iter  30 value 84.745840
iter  40 value 84.196175
iter  50 value 83.594910
iter  60 value 83.443090
iter  70 value 83.374739
iter  80 value 83.371729
final  value 83.371715 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.298657 
iter  10 value 94.573540
iter  20 value 94.135545
iter  30 value 94.087531
iter  40 value 93.987049
iter  50 value 88.438838
iter  60 value 86.202379
iter  70 value 84.640493
iter  80 value 84.434815
iter  90 value 83.665734
iter 100 value 83.242382
final  value 83.242382 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.527863 
iter  10 value 88.893916
iter  20 value 86.307444
iter  30 value 85.005184
iter  40 value 84.591892
iter  50 value 83.497153
iter  60 value 82.876007
iter  70 value 82.557958
iter  80 value 82.287211
iter  90 value 82.132013
iter 100 value 81.926514
final  value 81.926514 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 102.699787 
iter  10 value 95.050225
iter  20 value 92.815268
iter  30 value 84.556763
iter  40 value 83.459547
iter  50 value 83.195078
iter  60 value 82.978244
iter  70 value 82.610777
iter  80 value 82.480325
iter  90 value 82.442496
iter 100 value 82.295612
final  value 82.295612 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 103.246518 
iter  10 value 94.381706
iter  20 value 93.604174
iter  30 value 85.727762
iter  40 value 85.287312
iter  50 value 83.917789
iter  60 value 83.191512
iter  70 value 82.911871
iter  80 value 82.252382
iter  90 value 82.014096
iter 100 value 81.784735
final  value 81.784735 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 128.114292 
iter  10 value 94.375415
iter  20 value 86.637838
iter  30 value 85.167101
iter  40 value 84.737023
iter  50 value 83.581095
iter  60 value 82.582518
iter  70 value 82.396180
iter  80 value 82.139079
iter  90 value 82.085844
iter 100 value 81.848884
final  value 81.848884 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 109.413049 
iter  10 value 94.489989
iter  20 value 94.203731
iter  30 value 92.486484
iter  40 value 85.951561
iter  50 value 84.260505
iter  60 value 82.852406
iter  70 value 82.611894
iter  80 value 82.231155
iter  90 value 81.611767
iter 100 value 81.046262
final  value 81.046262 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.395349 
iter  10 value 90.068695
iter  20 value 85.888083
iter  30 value 85.499001
iter  40 value 85.353930
iter  50 value 85.292535
iter  60 value 84.993454
iter  70 value 83.232444
iter  80 value 82.540654
iter  90 value 82.319442
iter 100 value 82.014564
final  value 82.014564 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 114.539646 
iter  10 value 99.136765
iter  20 value 90.279243
iter  30 value 86.047463
iter  40 value 85.605079
iter  50 value 85.227468
iter  60 value 83.682303
iter  70 value 81.965275
iter  80 value 81.286465
iter  90 value 81.130820
iter 100 value 80.933590
final  value 80.933590 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 111.753896 
iter  10 value 94.225085
iter  20 value 87.728881
iter  30 value 87.137496
iter  40 value 86.020306
iter  50 value 85.546499
iter  60 value 84.832668
iter  70 value 83.686361
iter  80 value 82.059546
iter  90 value 81.311993
iter 100 value 81.184157
final  value 81.184157 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 114.433878 
iter  10 value 94.305442
iter  20 value 93.945445
iter  30 value 88.477841
iter  40 value 85.747841
iter  50 value 85.205669
iter  60 value 84.863738
iter  70 value 84.238927
iter  80 value 83.435199
iter  90 value 82.973690
iter 100 value 82.624829
final  value 82.624829 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.731258 
final  value 94.485982 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.393617 
final  value 94.485911 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.962927 
final  value 94.485878 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.830023 
final  value 94.485823 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.425399 
final  value 94.485834 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.473669 
iter  10 value 93.305281
iter  20 value 93.302106
iter  30 value 92.721936
iter  40 value 83.842781
iter  50 value 83.630231
iter  60 value 83.441564
iter  70 value 83.398078
iter  80 value 82.288586
iter  90 value 82.262104
final  value 82.262022 
converged
Fitting Repeat 2 

# weights:  305
initial  value 116.749310 
iter  10 value 94.487978
iter  20 value 94.426118
iter  30 value 94.028180
iter  40 value 94.026887
final  value 94.026862 
converged
Fitting Repeat 3 

# weights:  305
initial  value 107.108654 
iter  10 value 94.031312
iter  20 value 94.027638
iter  30 value 90.322711
iter  40 value 85.849541
iter  50 value 84.924202
iter  60 value 84.922811
iter  70 value 84.922461
iter  80 value 84.053378
iter  90 value 84.035086
final  value 84.034798 
converged
Fitting Repeat 4 

# weights:  305
initial  value 98.671932 
iter  10 value 94.031354
iter  20 value 94.028021
iter  30 value 93.948347
iter  40 value 85.066066
iter  50 value 83.406418
iter  60 value 82.274314
iter  70 value 81.034678
iter  80 value 80.496354
iter  90 value 80.430110
iter 100 value 80.419423
final  value 80.419423 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 121.822217 
iter  10 value 93.981281
iter  20 value 93.975254
iter  30 value 93.973616
iter  40 value 92.350325
iter  50 value 84.585078
iter  60 value 84.244893
iter  70 value 83.996085
iter  80 value 83.808599
iter  90 value 83.808409
iter 100 value 83.808233
final  value 83.808233 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 100.079610 
iter  10 value 89.761392
iter  20 value 86.769307
iter  30 value 85.243723
iter  40 value 84.993463
iter  50 value 84.844197
iter  60 value 84.843383
iter  70 value 83.456670
iter  80 value 82.258567
iter  90 value 81.032903
iter 100 value 80.469001
final  value 80.469001 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.288625 
iter  10 value 94.331719
iter  20 value 94.328314
iter  30 value 88.238113
iter  40 value 87.065309
iter  50 value 86.990705
iter  60 value 86.982742
iter  70 value 86.075081
iter  80 value 85.875171
iter  90 value 85.872994
iter 100 value 85.868481
final  value 85.868481 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 116.478962 
iter  10 value 94.492421
iter  20 value 94.428510
iter  30 value 86.938429
iter  40 value 84.712937
iter  50 value 84.665330
iter  60 value 84.661263
iter  70 value 84.659030
iter  80 value 84.506086
iter  90 value 83.471852
iter 100 value 81.082350
final  value 81.082350 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 98.203762 
iter  10 value 94.492372
iter  20 value 94.258969
iter  30 value 83.757349
iter  40 value 83.729976
final  value 83.729972 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.400690 
iter  10 value 94.272618
iter  20 value 94.169078
final  value 94.027057 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.203717 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.601238 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.342300 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.021473 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.952985 
iter  10 value 93.601829
iter  20 value 93.321052
iter  30 value 92.324755
final  value 92.321846 
converged
Fitting Repeat 1 

# weights:  305
initial  value 128.173845 
iter  10 value 94.038251
iter  10 value 94.038251
iter  10 value 94.038251
final  value 94.038251 
converged
Fitting Repeat 2 

# weights:  305
initial  value 100.189994 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 105.771133 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 100.188650 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.487998 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 101.602910 
iter  10 value 93.271104
final  value 93.271095 
converged
Fitting Repeat 2 

# weights:  507
initial  value 104.076365 
final  value 94.038251 
converged
Fitting Repeat 3 

# weights:  507
initial  value 103.638645 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 101.409810 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 94.197458 
iter  10 value 84.127767
iter  20 value 83.452784
iter  30 value 83.452396
iter  40 value 83.396215
iter  50 value 83.314755
final  value 83.314670 
converged
Fitting Repeat 1 

# weights:  103
initial  value 106.831381 
iter  10 value 94.056698
iter  20 value 83.816238
iter  30 value 82.581103
iter  40 value 82.283372
iter  50 value 82.186490
iter  60 value 81.708723
iter  70 value 81.454529
iter  80 value 81.383623
final  value 81.383558 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.033865 
iter  10 value 93.988947
iter  20 value 84.024317
iter  30 value 82.471863
iter  40 value 82.243530
iter  50 value 81.976249
iter  60 value 81.850488
iter  70 value 81.842215
final  value 81.842059 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.430512 
iter  10 value 87.210318
iter  20 value 85.810907
iter  30 value 85.698843
iter  40 value 85.004958
iter  50 value 84.646222
iter  60 value 81.950841
iter  70 value 81.455162
iter  80 value 81.401231
iter  90 value 81.383559
iter  90 value 81.383558
iter  90 value 81.383558
final  value 81.383558 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.423231 
iter  10 value 94.008848
iter  20 value 93.491963
iter  30 value 88.793124
iter  40 value 86.671419
iter  50 value 86.088637
iter  60 value 83.633124
iter  70 value 82.641143
iter  80 value 82.341446
iter  90 value 81.867814
final  value 81.842059 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.796422 
iter  10 value 94.043662
iter  20 value 93.729749
iter  30 value 93.237317
iter  40 value 86.265096
iter  50 value 85.885934
iter  60 value 85.441308
iter  70 value 85.120912
iter  80 value 83.031921
iter  90 value 81.512133
iter 100 value 81.491793
final  value 81.491793 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 108.166394 
iter  10 value 93.903935
iter  20 value 86.643710
iter  30 value 86.415879
iter  40 value 85.231217
iter  50 value 82.629244
iter  60 value 81.764806
iter  70 value 81.558206
iter  80 value 81.512840
iter  90 value 81.484401
iter 100 value 81.377508
final  value 81.377508 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 107.102615 
iter  10 value 93.944756
iter  20 value 88.465627
iter  30 value 87.474311
iter  40 value 86.648668
iter  50 value 84.993166
iter  60 value 81.785793
iter  70 value 81.554609
iter  80 value 81.511131
iter  90 value 81.487423
iter 100 value 81.444909
final  value 81.444909 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.072420 
iter  10 value 95.484445
iter  20 value 93.390476
iter  30 value 88.986487
iter  40 value 85.445625
iter  50 value 81.246612
iter  60 value 80.516610
iter  70 value 79.831387
iter  80 value 79.337644
iter  90 value 79.040599
iter 100 value 78.517017
final  value 78.517017 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 108.881037 
iter  10 value 94.059049
iter  20 value 89.038844
iter  30 value 86.654944
iter  40 value 82.149090
iter  50 value 81.676681
iter  60 value 79.200189
iter  70 value 78.149986
iter  80 value 77.634973
iter  90 value 77.598331
iter 100 value 77.459039
final  value 77.459039 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 107.181804 
iter  10 value 94.013777
iter  20 value 88.698517
iter  30 value 81.957467
iter  40 value 80.113709
iter  50 value 79.303884
iter  60 value 78.535462
iter  70 value 77.539175
iter  80 value 77.489812
iter  90 value 77.391775
iter 100 value 77.377698
final  value 77.377698 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 106.721982 
iter  10 value 94.095793
iter  20 value 86.339955
iter  30 value 83.918276
iter  40 value 83.161684
iter  50 value 81.921032
iter  60 value 79.757855
iter  70 value 78.336857
iter  80 value 78.205974
iter  90 value 78.093525
iter 100 value 77.565851
final  value 77.565851 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 104.785766 
iter  10 value 88.195424
iter  20 value 83.213986
iter  30 value 79.472703
iter  40 value 77.991773
iter  50 value 77.934840
iter  60 value 77.583599
iter  70 value 77.382558
iter  80 value 77.346890
iter  90 value 77.323387
iter 100 value 77.229646
final  value 77.229646 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 102.902091 
iter  10 value 94.092739
iter  20 value 88.812267
iter  30 value 80.804519
iter  40 value 79.148367
iter  50 value 78.423086
iter  60 value 78.341737
iter  70 value 78.121990
iter  80 value 77.709516
iter  90 value 77.573051
iter 100 value 77.446514
final  value 77.446514 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 110.446997 
iter  10 value 94.134349
iter  20 value 93.193989
iter  30 value 84.143551
iter  40 value 81.930592
iter  50 value 79.396375
iter  60 value 78.741715
iter  70 value 77.837099
iter  80 value 77.394731
iter  90 value 77.101640
iter 100 value 76.941805
final  value 76.941805 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 110.131382 
iter  10 value 93.667928
iter  20 value 85.943493
iter  30 value 81.525836
iter  40 value 80.453799
iter  50 value 79.520139
iter  60 value 79.320257
iter  70 value 79.014328
iter  80 value 78.160630
iter  90 value 77.828771
iter 100 value 77.731669
final  value 77.731669 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 97.030727 
final  value 94.054475 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.236877 
iter  10 value 94.039902
iter  20 value 93.736665
iter  30 value 89.697905
iter  40 value 85.675893
iter  50 value 85.674088
iter  60 value 85.673910
iter  70 value 83.982699
iter  80 value 83.618054
iter  90 value 83.388393
iter 100 value 82.330159
final  value 82.330159 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 94.725768 
iter  10 value 94.043729
iter  20 value 94.042095
iter  30 value 93.866418
iter  40 value 81.698013
iter  50 value 80.818439
iter  60 value 80.661942
final  value 80.645562 
converged
Fitting Repeat 4 

# weights:  103
initial  value 94.820567 
iter  10 value 94.039869
iter  20 value 94.038281
iter  30 value 93.756640
iter  40 value 91.448527
iter  50 value 91.438669
iter  60 value 91.435421
iter  70 value 91.358322
iter  80 value 91.355365
iter  90 value 91.354545
iter 100 value 91.169956
final  value 91.169956 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 101.055442 
iter  10 value 94.029556
iter  20 value 94.029035
iter  30 value 94.028034
iter  40 value 91.123161
iter  50 value 90.598844
iter  50 value 90.598844
iter  50 value 90.598844
final  value 90.598844 
converged
Fitting Repeat 1 

# weights:  305
initial  value 121.472463 
iter  10 value 94.057850
iter  20 value 94.038608
iter  30 value 94.038409
iter  40 value 84.141319
iter  50 value 83.453410
iter  60 value 83.444796
iter  60 value 83.444795
iter  60 value 83.444795
final  value 83.444795 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.539529 
iter  10 value 94.056237
iter  20 value 93.810190
final  value 93.810169 
converged
Fitting Repeat 3 

# weights:  305
initial  value 107.443281 
iter  10 value 94.053289
iter  20 value 93.850899
iter  30 value 93.765133
iter  40 value 91.346514
iter  50 value 83.934034
iter  60 value 83.932593
iter  70 value 82.245773
iter  80 value 79.864773
iter  90 value 78.231323
iter 100 value 77.653882
final  value 77.653882 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 95.841248 
iter  10 value 85.219657
iter  20 value 84.966988
iter  30 value 82.843485
iter  40 value 81.850258
iter  50 value 81.845328
iter  60 value 81.505468
iter  70 value 81.505370
iter  80 value 81.505190
final  value 81.505151 
converged
Fitting Repeat 5 

# weights:  305
initial  value 102.268119 
iter  10 value 94.057324
iter  20 value 93.819673
iter  30 value 90.379283
final  value 83.454962 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.342181 
iter  10 value 94.060127
iter  20 value 94.036779
iter  30 value 83.718090
iter  40 value 83.071204
iter  50 value 81.272849
final  value 81.270280 
converged
Fitting Repeat 2 

# weights:  507
initial  value 98.907485 
iter  10 value 94.046589
iter  20 value 93.586597
iter  30 value 85.534784
iter  40 value 83.634626
iter  50 value 83.493254
iter  60 value 81.035784
iter  70 value 79.928217
iter  80 value 79.182232
iter  90 value 78.471256
iter 100 value 78.248313
final  value 78.248313 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 134.814596 
iter  10 value 94.061278
iter  20 value 94.049682
iter  30 value 91.338271
iter  40 value 84.656490
iter  50 value 84.621094
iter  60 value 83.547427
iter  70 value 83.454114
final  value 83.444926 
converged
Fitting Repeat 4 

# weights:  507
initial  value 100.714661 
iter  10 value 94.046459
iter  20 value 93.743382
iter  30 value 83.669484
iter  40 value 82.756360
iter  50 value 82.465341
iter  60 value 81.889260
iter  70 value 81.857340
final  value 81.856044 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.297571 
iter  10 value 94.046085
iter  20 value 94.039603
final  value 94.039583 
converged
Fitting Repeat 1 

# weights:  103
initial  value 102.834052 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.493220 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.633325 
iter  10 value 92.606829
iter  20 value 84.099511
iter  30 value 84.025707
iter  40 value 83.000887
iter  50 value 82.668407
final  value 82.668387 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.077599 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.340818 
final  value 94.313817 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.840232 
iter  10 value 94.466830
iter  20 value 94.443663
final  value 94.443243 
converged
Fitting Repeat 2 

# weights:  305
initial  value 101.748623 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 101.847510 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.928315 
iter  10 value 94.165746
iter  10 value 94.165746
iter  10 value 94.165746
final  value 94.165746 
converged
Fitting Repeat 5 

# weights:  305
initial  value 101.529374 
final  value 94.443243 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.316237 
iter  10 value 94.065750
final  value 94.065747 
converged
Fitting Repeat 2 

# weights:  507
initial  value 110.313430 
final  value 94.238210 
converged
Fitting Repeat 3 

# weights:  507
initial  value 121.190025 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.754423 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 107.454864 
iter  10 value 93.759198
final  value 93.756370 
converged
Fitting Repeat 1 

# weights:  103
initial  value 106.594850 
iter  10 value 93.820530
iter  20 value 92.326404
iter  30 value 92.162666
iter  40 value 92.146492
final  value 92.144871 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.353148 
iter  10 value 94.552507
iter  20 value 91.522006
iter  30 value 86.721974
iter  40 value 86.536372
iter  50 value 84.360355
iter  60 value 84.091546
iter  70 value 83.663890
iter  80 value 83.581849
final  value 83.581815 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.434207 
iter  10 value 94.265816
iter  20 value 92.746146
iter  30 value 92.445116
final  value 92.440463 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.449617 
iter  10 value 94.507117
iter  20 value 92.845287
iter  30 value 92.365590
iter  40 value 87.185047
iter  50 value 84.841963
iter  60 value 84.330092
iter  70 value 83.846082
iter  80 value 83.626155
iter  90 value 83.573979
final  value 83.573977 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.519627 
iter  10 value 94.485948
iter  20 value 91.376978
iter  30 value 88.530289
iter  40 value 88.154852
iter  50 value 85.859099
iter  60 value 85.416411
iter  70 value 85.117321
iter  80 value 84.687244
iter  90 value 84.103585
iter 100 value 84.033863
final  value 84.033863 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 103.292680 
iter  10 value 94.620953
iter  20 value 94.478315
iter  30 value 94.215295
iter  40 value 91.060575
iter  50 value 86.535650
iter  60 value 84.492546
iter  70 value 82.636126
iter  80 value 81.082069
iter  90 value 80.273286
iter 100 value 80.262110
final  value 80.262110 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 106.943018 
iter  10 value 94.550347
iter  20 value 93.473996
iter  30 value 88.855528
iter  40 value 84.792053
iter  50 value 84.322542
iter  60 value 82.775897
iter  70 value 82.340524
iter  80 value 81.498192
iter  90 value 80.780504
iter 100 value 80.181904
final  value 80.181904 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 113.132783 
iter  10 value 94.297727
iter  20 value 85.617685
iter  30 value 84.340688
iter  40 value 83.962294
iter  50 value 83.537633
iter  60 value 82.411532
iter  70 value 81.942059
iter  80 value 81.600938
iter  90 value 81.495164
iter 100 value 81.165205
final  value 81.165205 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.185431 
iter  10 value 94.378841
iter  20 value 87.359987
iter  30 value 86.436523
iter  40 value 84.062505
iter  50 value 83.536854
iter  60 value 83.445857
iter  70 value 82.729963
iter  80 value 81.901808
iter  90 value 80.760098
iter 100 value 80.520669
final  value 80.520669 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.019263 
iter  10 value 92.163521
iter  20 value 85.202139
iter  30 value 83.348233
iter  40 value 82.935434
iter  50 value 80.777227
iter  60 value 80.266693
iter  70 value 80.205821
iter  80 value 80.093843
iter  90 value 79.950001
iter 100 value 79.872147
final  value 79.872147 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 123.537751 
iter  10 value 94.610953
iter  20 value 87.097265
iter  30 value 86.243967
iter  40 value 85.487571
iter  50 value 84.614584
iter  60 value 84.495887
iter  70 value 84.489594
iter  80 value 84.421929
iter  90 value 83.153612
iter 100 value 81.367754
final  value 81.367754 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 111.506905 
iter  10 value 94.501773
iter  20 value 88.819888
iter  30 value 85.530751
iter  40 value 85.276658
iter  50 value 84.546824
iter  60 value 82.973150
iter  70 value 82.321842
iter  80 value 80.842612
iter  90 value 80.474512
iter 100 value 80.150093
final  value 80.150093 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.788258 
iter  10 value 93.388759
iter  20 value 84.548863
iter  30 value 83.269819
iter  40 value 82.248934
iter  50 value 80.923637
iter  60 value 80.560455
iter  70 value 80.497789
iter  80 value 80.301696
iter  90 value 80.275847
iter 100 value 80.236164
final  value 80.236164 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 119.285390 
iter  10 value 94.767783
iter  20 value 94.497194
iter  30 value 94.442266
iter  40 value 86.534562
iter  50 value 84.980353
iter  60 value 83.064378
iter  70 value 81.912861
iter  80 value 80.753978
iter  90 value 80.235650
iter 100 value 79.913884
final  value 79.913884 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 113.605591 
iter  10 value 98.534862
iter  20 value 92.898653
iter  30 value 91.605951
iter  40 value 88.110830
iter  50 value 83.762325
iter  60 value 81.858912
iter  70 value 80.740591
iter  80 value 79.955292
iter  90 value 79.707806
iter 100 value 79.591828
final  value 79.591828 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.454000 
iter  10 value 94.485995
iter  20 value 94.475515
iter  30 value 94.107000
iter  40 value 84.812479
iter  50 value 83.842270
iter  60 value 83.518511
iter  70 value 83.078279
iter  80 value 83.077802
iter  90 value 82.784419
iter 100 value 82.475635
final  value 82.475635 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 104.517110 
final  value 94.485847 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.797643 
final  value 94.485388 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.390335 
iter  10 value 94.320525
iter  20 value 94.115277
iter  30 value 94.070948
final  value 94.067253 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.994208 
final  value 94.485686 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.507864 
iter  10 value 94.489505
iter  20 value 94.484463
iter  30 value 89.452723
iter  40 value 88.892405
final  value 88.811851 
converged
Fitting Repeat 2 

# weights:  305
initial  value 101.230603 
iter  10 value 94.489390
iter  20 value 94.484273
final  value 94.484250 
converged
Fitting Repeat 3 

# weights:  305
initial  value 97.047492 
iter  10 value 94.448044
iter  20 value 94.331372
iter  30 value 90.519504
iter  40 value 90.105796
iter  50 value 90.099873
iter  60 value 90.095011
iter  70 value 90.087041
iter  80 value 89.672406
iter  90 value 89.667494
iter 100 value 88.549034
final  value 88.549034 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 105.928733 
iter  10 value 94.488189
iter  20 value 93.002731
iter  30 value 90.497947
iter  40 value 89.280684
iter  50 value 89.272088
final  value 89.271561 
converged
Fitting Repeat 5 

# weights:  305
initial  value 110.332094 
iter  10 value 94.448007
iter  20 value 94.444190
iter  30 value 93.538948
iter  40 value 93.538752
iter  50 value 93.348295
iter  60 value 93.189364
iter  70 value 93.093617
final  value 93.093549 
converged
Fitting Repeat 1 

# weights:  507
initial  value 111.226417 
iter  10 value 94.457182
iter  20 value 93.560183
iter  30 value 91.996370
iter  40 value 91.995322
final  value 91.995309 
converged
Fitting Repeat 2 

# weights:  507
initial  value 103.771480 
iter  10 value 94.492312
iter  20 value 87.876413
iter  30 value 84.461945
iter  40 value 84.437885
iter  50 value 84.420473
iter  60 value 84.369314
final  value 84.369245 
converged
Fitting Repeat 3 

# weights:  507
initial  value 106.032931 
iter  10 value 94.492030
iter  20 value 94.408015
iter  30 value 87.521906
iter  40 value 84.851494
iter  50 value 83.938768
iter  60 value 83.897015
iter  70 value 81.954702
iter  80 value 80.341228
iter  90 value 80.282038
iter 100 value 80.279393
final  value 80.279393 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 95.752883 
iter  10 value 92.298453
iter  20 value 92.290663
iter  30 value 92.280346
final  value 92.280157 
converged
Fitting Repeat 5 

# weights:  507
initial  value 103.148710 
iter  10 value 94.451367
iter  20 value 94.443511
iter  30 value 94.293743
iter  40 value 91.818657
iter  50 value 84.543318
iter  60 value 84.474786
iter  70 value 84.474398
final  value 84.474392 
converged
Fitting Repeat 1 

# weights:  507
initial  value 134.167418 
iter  10 value 119.302045
iter  20 value 118.303648
iter  30 value 115.901910
iter  40 value 106.672814
iter  50 value 105.483736
iter  60 value 104.183647
iter  70 value 101.216434
iter  80 value 100.706234
iter  90 value 100.526772
iter 100 value 100.237140
final  value 100.237140 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 136.967891 
iter  10 value 118.138045
iter  20 value 110.906142
iter  30 value 105.194706
iter  40 value 103.005963
iter  50 value 101.889856
iter  60 value 101.551197
iter  70 value 101.196389
iter  80 value 100.938919
iter  90 value 100.548790
iter 100 value 100.337309
final  value 100.337309 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 154.218844 
iter  10 value 118.492682
iter  20 value 117.816443
iter  30 value 116.197288
iter  40 value 113.543000
iter  50 value 107.777368
iter  60 value 106.303827
iter  70 value 105.604429
iter  80 value 105.247382
iter  90 value 104.883453
iter 100 value 103.413464
final  value 103.413464 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 133.556620 
iter  10 value 117.920532
iter  20 value 117.047308
iter  30 value 115.162323
iter  40 value 110.475956
iter  50 value 108.289403
iter  60 value 107.560644
iter  70 value 106.507367
iter  80 value 103.509011
iter  90 value 102.193889
iter 100 value 101.009991
final  value 101.009991 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 139.603522 
iter  10 value 117.920382
iter  20 value 116.861243
iter  30 value 109.667706
iter  40 value 107.241235
iter  50 value 105.535491
iter  60 value 103.181749
iter  70 value 101.800959
iter  80 value 101.568143
iter  90 value 101.523043
iter 100 value 101.119244
final  value 101.119244 
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 -- Fri Mar 21 21:49:22 2025 
*********************************************** 
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
HPiP RUnit Tests - 7 test functions, 0 errors, 0 failures
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: `repeats` has no meaning for this resampling method. 
2: executing %dopar% sequentially: no parallel backend registered 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
 40.921   1.644 114.225