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


* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/HPiP.Rcheck’
* using R version 4.4.3 (2025-02-28)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Ventura 13.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.12.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
    29 | then the Kronecker product is the code{(pm × qn)} block matrix
       |                                       ^
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
corr_plot     51.831  2.299  54.351
FSmethod      52.080  2.029  54.169
var_imp       51.395  2.298  53.922
pred_ensembel 16.321  0.551  14.810
enrichfindP    0.509  0.077   7.184
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

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

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


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library’
* installing *source* package ‘HPiP’ ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HPiP)

R version 4.4.3 (2025-02-28) -- "Trophy Case"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-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 106.406750 
final  value 94.448052 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.671467 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 105.532859 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.422538 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.260860 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.453595 
iter  10 value 94.346954
final  value 94.346668 
converged
Fitting Repeat 2 

# weights:  305
initial  value 100.197674 
final  value 94.448052 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.251659 
final  value 94.313817 
converged
Fitting Repeat 4 

# weights:  305
initial  value 105.915125 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 112.102179 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 112.327435 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.346738 
final  value 94.467391 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.096323 
iter  10 value 94.467391
iter  10 value 94.467391
iter  10 value 94.467391
final  value 94.467391 
converged
Fitting Repeat 4 

# weights:  507
initial  value 94.675825 
iter  10 value 93.330717
iter  20 value 93.051064
iter  30 value 93.049983
final  value 93.049968 
converged
Fitting Repeat 5 

# weights:  507
initial  value 124.627934 
final  value 94.313817 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.112266 
iter  10 value 94.329459
iter  20 value 87.966514
iter  30 value 87.637263
iter  40 value 87.323767
iter  50 value 85.976942
iter  60 value 85.081867
iter  70 value 84.282674
iter  80 value 84.119590
iter  90 value 83.909250
iter 100 value 83.888287
final  value 83.888287 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 97.347067 
iter  10 value 94.488976
iter  20 value 92.312780
iter  30 value 87.205068
iter  40 value 84.264932
iter  50 value 83.764262
iter  60 value 83.262081
iter  70 value 83.132926
iter  80 value 82.858330
final  value 82.857910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 107.817303 
iter  10 value 94.504900
iter  20 value 94.121476
iter  30 value 86.257231
iter  40 value 85.506818
iter  50 value 84.658061
iter  60 value 84.414441
iter  70 value 82.412036
final  value 82.398409 
converged
Fitting Repeat 4 

# weights:  103
initial  value 107.690452 
iter  10 value 94.446700
iter  20 value 93.478458
iter  30 value 93.221715
iter  40 value 88.948325
iter  50 value 88.359435
iter  60 value 86.936091
iter  70 value 84.260705
iter  80 value 83.462758
iter  90 value 83.347181
iter 100 value 82.971808
final  value 82.971808 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 115.804984 
iter  10 value 94.058901
iter  20 value 86.348208
iter  30 value 85.130721
iter  40 value 83.688786
iter  50 value 82.134613
iter  60 value 81.516276
iter  70 value 81.299419
iter  80 value 81.023780
iter  90 value 80.949356
iter 100 value 80.919823
final  value 80.919823 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 99.970194 
iter  10 value 94.476961
iter  20 value 88.972926
iter  30 value 85.530594
iter  40 value 84.955924
iter  50 value 84.651703
iter  60 value 84.531849
iter  70 value 84.417119
iter  80 value 83.078781
iter  90 value 82.578429
iter 100 value 82.253895
final  value 82.253895 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 117.809394 
iter  10 value 92.652046
iter  20 value 86.261965
iter  30 value 84.202987
iter  40 value 82.740280
iter  50 value 82.346028
iter  60 value 82.221539
iter  70 value 82.132548
iter  80 value 82.076694
iter  90 value 81.915742
iter 100 value 81.812259
final  value 81.812259 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.221401 
iter  10 value 93.011780
iter  20 value 85.935446
iter  30 value 85.308224
iter  40 value 84.455987
iter  50 value 84.396441
iter  60 value 83.536507
iter  70 value 82.634299
iter  80 value 82.526018
iter  90 value 82.520223
iter 100 value 82.494436
final  value 82.494436 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.491289 
iter  10 value 94.501269
iter  20 value 94.410475
iter  30 value 91.393388
iter  40 value 88.138961
iter  50 value 83.329641
iter  60 value 81.185918
iter  70 value 80.232495
iter  80 value 79.809571
iter  90 value 79.577745
iter 100 value 79.486565
final  value 79.486565 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 109.397555 
iter  10 value 94.267461
iter  20 value 88.400257
iter  30 value 85.728590
iter  40 value 84.113788
iter  50 value 83.341943
iter  60 value 80.681732
iter  70 value 80.072151
iter  80 value 79.955804
iter  90 value 79.765667
iter 100 value 79.736898
final  value 79.736898 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 104.346391 
iter  10 value 87.057737
iter  20 value 86.325366
iter  30 value 85.519949
iter  40 value 85.260624
iter  50 value 84.111562
iter  60 value 83.158895
iter  70 value 82.143351
iter  80 value 81.357561
iter  90 value 81.144320
iter 100 value 80.831314
final  value 80.831314 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 110.139979 
iter  10 value 95.230579
iter  20 value 86.405766
iter  30 value 82.689728
iter  40 value 81.295488
iter  50 value 80.475304
iter  60 value 80.302756
iter  70 value 80.233269
iter  80 value 80.079495
iter  90 value 79.795055
iter 100 value 79.589677
final  value 79.589677 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 127.449205 
iter  10 value 95.159507
iter  20 value 94.589719
iter  30 value 87.739622
iter  40 value 86.440274
iter  50 value 84.728960
iter  60 value 84.602002
iter  70 value 84.238997
iter  80 value 82.036405
iter  90 value 81.284223
iter 100 value 81.215552
final  value 81.215552 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 102.598936 
iter  10 value 94.135315
iter  20 value 86.751325
iter  30 value 85.813221
iter  40 value 82.781719
iter  50 value 82.538819
iter  60 value 81.931471
iter  70 value 81.656396
iter  80 value 81.503742
iter  90 value 80.898172
iter 100 value 80.459794
final  value 80.459794 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 112.598896 
iter  10 value 94.474702
iter  20 value 93.902608
iter  30 value 85.318488
iter  40 value 84.006905
iter  50 value 83.746950
iter  60 value 83.478854
iter  70 value 83.310956
iter  80 value 81.254092
iter  90 value 81.038681
iter 100 value 80.473938
final  value 80.473938 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 103.403920 
final  value 94.485666 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.750944 
iter  10 value 94.449789
iter  20 value 94.449113
final  value 94.448076 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.576146 
final  value 94.468835 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.812339 
final  value 94.485844 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.635642 
final  value 94.485839 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.474967 
iter  10 value 94.472274
iter  20 value 94.468086
final  value 94.467662 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.915519 
iter  10 value 94.487948
iter  20 value 94.482687
iter  30 value 94.448290
final  value 94.448221 
converged
Fitting Repeat 3 

# weights:  305
initial  value 112.982550 
iter  10 value 94.471927
iter  20 value 94.467599
final  value 94.467590 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.992177 
iter  10 value 94.438097
iter  20 value 94.427709
iter  30 value 94.424757
final  value 94.424307 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.785475 
iter  10 value 88.340974
iter  20 value 87.774907
iter  30 value 87.770843
iter  40 value 87.039032
iter  50 value 86.457335
iter  60 value 86.435989
final  value 86.435986 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.418701 
iter  10 value 94.488510
iter  20 value 94.135100
iter  30 value 86.477322
iter  40 value 84.406889
iter  50 value 84.264860
iter  60 value 81.648820
iter  70 value 79.090187
iter  80 value 78.906319
iter  90 value 78.904979
final  value 78.904967 
converged
Fitting Repeat 2 

# weights:  507
initial  value 117.012466 
iter  10 value 94.475483
iter  20 value 94.473610
iter  30 value 94.468624
iter  40 value 93.306378
iter  50 value 83.118091
final  value 83.097236 
converged
Fitting Repeat 3 

# weights:  507
initial  value 96.044427 
iter  10 value 94.475902
iter  20 value 94.442224
iter  30 value 94.438649
iter  40 value 94.434098
iter  50 value 90.501180
iter  60 value 86.721714
iter  70 value 86.714235
final  value 86.714155 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.116150 
iter  10 value 94.475782
iter  20 value 94.307865
iter  30 value 86.992271
iter  40 value 82.303582
iter  50 value 81.627134
iter  60 value 81.505167
iter  70 value 81.192213
iter  80 value 79.352361
iter  90 value 78.751723
iter 100 value 78.586445
final  value 78.586445 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 103.780926 
iter  10 value 94.485054
iter  20 value 94.478233
iter  30 value 92.549545
iter  40 value 85.648184
iter  50 value 84.418819
iter  60 value 82.708242
iter  70 value 82.418444
iter  80 value 82.416355
iter  90 value 82.414420
iter 100 value 82.055081
final  value 82.055081 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.711810 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.445259 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.883779 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.782308 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.831133 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.840707 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 94.788104 
final  value 93.836066 
converged
Fitting Repeat 3 

# weights:  305
initial  value 98.528669 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 101.565597 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 111.463091 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 105.979023 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 98.296367 
iter  10 value 93.519193
iter  20 value 93.478616
iter  30 value 93.478228
iter  30 value 93.478228
iter  30 value 93.478228
final  value 93.478228 
converged
Fitting Repeat 3 

# weights:  507
initial  value 104.300329 
iter  10 value 93.756214
iter  20 value 93.676377
final  value 93.676191 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.332485 
final  value 93.671508 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.865978 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.151731 
iter  10 value 93.855237
iter  20 value 89.132015
iter  30 value 87.481520
iter  40 value 86.759632
iter  50 value 83.140600
iter  60 value 81.973183
iter  70 value 81.851300
iter  80 value 81.737457
iter  90 value 81.613902
iter 100 value 81.526332
final  value 81.526332 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 99.459907 
iter  10 value 90.568987
iter  20 value 84.754345
iter  30 value 84.211284
iter  40 value 84.043009
iter  50 value 83.959676
iter  60 value 83.004404
iter  70 value 81.880410
iter  80 value 81.666135
iter  90 value 81.665466
iter  90 value 81.665466
iter  90 value 81.665466
final  value 81.665466 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.826702 
iter  10 value 94.109907
iter  20 value 94.054640
iter  30 value 93.713305
iter  40 value 90.983516
iter  50 value 89.648629
iter  60 value 88.079575
iter  70 value 86.555858
iter  80 value 85.455437
iter  90 value 85.188089
iter 100 value 85.010420
final  value 85.010420 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 100.033527 
iter  10 value 93.989074
iter  20 value 87.320917
iter  30 value 85.544097
iter  40 value 85.477947
iter  50 value 85.460041
iter  60 value 85.387959
iter  70 value 85.238160
iter  80 value 85.099714
iter  90 value 84.977066
iter 100 value 84.894209
final  value 84.894209 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 98.815111 
iter  10 value 94.055402
iter  20 value 90.174703
iter  30 value 87.457043
iter  40 value 86.683168
iter  50 value 86.139065
iter  60 value 84.863633
iter  70 value 84.613089
iter  80 value 84.558216
final  value 84.558033 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.922213 
iter  10 value 94.552826
iter  20 value 94.139475
iter  30 value 92.705749
iter  40 value 87.156628
iter  50 value 85.644775
iter  60 value 83.540965
iter  70 value 81.426216
iter  80 value 80.787749
iter  90 value 80.440880
iter 100 value 80.384139
final  value 80.384139 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.571458 
iter  10 value 94.051949
iter  20 value 89.428263
iter  30 value 85.248128
iter  40 value 83.926137
iter  50 value 82.180088
iter  60 value 81.856471
iter  70 value 81.501933
iter  80 value 80.304432
iter  90 value 80.194056
iter 100 value 80.138321
final  value 80.138321 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 108.230916 
iter  10 value 93.656072
iter  20 value 90.417888
iter  30 value 87.607564
iter  40 value 85.931689
iter  50 value 85.686520
iter  60 value 83.160747
iter  70 value 81.149974
iter  80 value 80.568087
iter  90 value 80.472290
iter 100 value 80.262253
final  value 80.262253 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.464222 
iter  10 value 90.903834
iter  20 value 85.802918
iter  30 value 85.597253
iter  40 value 85.131453
iter  50 value 83.325193
iter  60 value 81.362472
iter  70 value 81.033326
iter  80 value 80.470625
iter  90 value 80.391920
iter 100 value 80.365225
final  value 80.365225 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.164781 
iter  10 value 93.899628
iter  20 value 87.023311
iter  30 value 85.824934
iter  40 value 85.764390
iter  50 value 84.504023
iter  60 value 83.441012
iter  70 value 81.822886
iter  80 value 80.901257
iter  90 value 80.700004
iter 100 value 80.490560
final  value 80.490560 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 108.416538 
iter  10 value 94.122250
iter  20 value 88.676841
iter  30 value 87.599731
iter  40 value 86.470600
iter  50 value 83.826707
iter  60 value 82.378010
iter  70 value 81.963591
iter  80 value 81.696144
iter  90 value 81.412699
iter 100 value 80.868232
final  value 80.868232 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 119.586446 
iter  10 value 93.483679
iter  20 value 88.912479
iter  30 value 86.378946
iter  40 value 83.290948
iter  50 value 81.411130
iter  60 value 80.780425
iter  70 value 80.644674
iter  80 value 80.546303
iter  90 value 80.180096
iter 100 value 80.028337
final  value 80.028337 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 119.398178 
iter  10 value 93.835331
iter  20 value 86.435267
iter  30 value 85.880871
iter  40 value 83.537610
iter  50 value 82.300528
iter  60 value 81.516514
iter  70 value 80.626082
iter  80 value 80.323915
iter  90 value 80.184000
iter 100 value 80.141374
final  value 80.141374 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 122.095986 
iter  10 value 95.107604
iter  20 value 90.195459
iter  30 value 87.272438
iter  40 value 85.373444
iter  50 value 84.095940
iter  60 value 82.863213
iter  70 value 82.757749
iter  80 value 82.323947
iter  90 value 81.129550
iter 100 value 80.710446
final  value 80.710446 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 109.780743 
iter  10 value 93.988604
iter  20 value 91.568743
iter  30 value 87.213658
iter  40 value 84.024019
iter  50 value 83.341488
iter  60 value 82.478986
iter  70 value 82.359761
iter  80 value 82.243597
iter  90 value 80.849265
iter 100 value 80.676198
final  value 80.676198 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.945755 
final  value 94.054488 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.243990 
final  value 94.054455 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.731350 
final  value 94.054456 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.588096 
iter  10 value 94.054559
iter  20 value 94.015855
iter  30 value 93.838606
final  value 93.838033 
converged
Fitting Repeat 5 

# weights:  103
initial  value 113.070378 
final  value 94.054754 
converged
Fitting Repeat 1 

# weights:  305
initial  value 116.938879 
iter  10 value 94.058124
iter  20 value 94.052927
iter  30 value 86.760675
iter  40 value 84.540799
iter  50 value 80.524732
iter  60 value 79.505823
iter  70 value 79.440637
iter  80 value 78.913774
iter  90 value 78.516245
iter 100 value 78.183907
final  value 78.183907 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 106.092735 
iter  10 value 94.055466
iter  20 value 91.274103
iter  30 value 86.038653
iter  40 value 85.826817
iter  50 value 85.824489
iter  60 value 85.775817
final  value 85.762432 
converged
Fitting Repeat 3 

# weights:  305
initial  value 104.305289 
iter  10 value 93.769138
iter  20 value 93.486251
iter  30 value 93.484477
iter  40 value 93.465917
iter  50 value 90.038344
iter  60 value 85.807849
iter  70 value 84.746301
iter  80 value 84.562707
iter  90 value 84.427738
final  value 84.427378 
converged
Fitting Repeat 4 

# weights:  305
initial  value 101.608841 
iter  10 value 94.057982
iter  20 value 93.205776
iter  30 value 83.796991
iter  40 value 83.708722
iter  50 value 83.707015
iter  50 value 83.707015
iter  50 value 83.707015
final  value 83.707015 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.332858 
iter  10 value 90.624750
iter  20 value 90.561718
iter  30 value 90.408386
iter  40 value 90.343929
iter  50 value 90.343430
final  value 90.341564 
converged
Fitting Repeat 1 

# weights:  507
initial  value 94.476888 
iter  10 value 94.054319
iter  20 value 93.182354
iter  30 value 84.784684
iter  40 value 84.643052
iter  50 value 82.970718
iter  60 value 82.868630
iter  70 value 82.867264
iter  80 value 82.700435
iter  90 value 82.613617
iter 100 value 82.422480
final  value 82.422480 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 101.444800 
iter  10 value 94.061468
iter  20 value 94.009501
iter  30 value 93.733090
iter  40 value 87.019859
iter  50 value 86.927531
iter  60 value 86.926152
iter  70 value 86.926025
iter  80 value 86.198474
iter  90 value 86.155708
final  value 86.155698 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.408742 
iter  10 value 94.059340
iter  20 value 94.020767
iter  30 value 93.500326
iter  40 value 93.439020
final  value 93.422450 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.532068 
iter  10 value 94.059046
iter  20 value 94.052934
final  value 94.052912 
converged
Fitting Repeat 5 

# weights:  507
initial  value 120.319398 
iter  10 value 94.041949
iter  20 value 92.405189
final  value 92.359476 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.869949 
final  value 94.466823 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.807671 
final  value 94.484210 
converged
Fitting Repeat 3 

# weights:  103
initial  value 109.445170 
final  value 94.466823 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.437668 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.780249 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.426489 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 100.244545 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 127.682617 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.371533 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.963331 
final  value 94.466823 
converged
Fitting Repeat 1 

# weights:  507
initial  value 130.780304 
iter  10 value 94.484671
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 111.840213 
final  value 94.484209 
converged
Fitting Repeat 3 

# weights:  507
initial  value 99.057627 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 105.547151 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 99.144603 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.900211 
iter  10 value 94.451289
iter  20 value 88.637531
iter  30 value 86.201006
iter  40 value 85.740101
iter  50 value 85.623794
iter  60 value 85.575430
iter  70 value 82.824315
iter  80 value 82.691243
iter  90 value 82.660745
final  value 82.658100 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.936560 
iter  10 value 94.455615
iter  20 value 87.334334
iter  30 value 86.494978
iter  40 value 85.769952
iter  50 value 85.615900
iter  60 value 83.429741
iter  70 value 82.773726
iter  80 value 82.678501
iter  90 value 82.658105
final  value 82.658100 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.125936 
iter  10 value 94.223537
iter  20 value 85.480976
iter  30 value 83.976979
iter  40 value 83.867134
iter  50 value 83.608772
iter  60 value 83.389191
iter  70 value 81.587807
iter  80 value 80.993443
iter  90 value 80.864583
iter 100 value 80.651001
final  value 80.651001 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 99.985469 
iter  10 value 94.487434
iter  20 value 94.140498
iter  30 value 86.828536
iter  40 value 85.951040
iter  50 value 84.713940
iter  60 value 83.157494
iter  70 value 82.801293
iter  80 value 82.658113
final  value 82.658100 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.697669 
iter  10 value 94.447796
iter  20 value 89.265906
iter  30 value 87.087740
iter  40 value 86.479341
iter  50 value 85.262799
iter  60 value 84.392019
iter  70 value 83.847352
iter  80 value 83.651967
iter  90 value 82.028436
iter 100 value 81.066497
final  value 81.066497 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 100.226698 
iter  10 value 94.533817
iter  20 value 94.248697
iter  30 value 92.170209
iter  40 value 88.778787
iter  50 value 84.542201
iter  60 value 84.270419
iter  70 value 83.657571
iter  80 value 82.617841
iter  90 value 80.995470
iter 100 value 80.343882
final  value 80.343882 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 107.219284 
iter  10 value 94.493271
iter  20 value 86.060624
iter  30 value 85.442719
iter  40 value 85.226915
iter  50 value 84.964740
iter  60 value 84.884935
iter  70 value 84.776167
iter  80 value 81.765142
iter  90 value 80.959756
iter 100 value 80.197090
final  value 80.197090 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.079013 
iter  10 value 94.491950
iter  20 value 93.962443
iter  30 value 85.701519
iter  40 value 83.741859
iter  50 value 83.477156
iter  60 value 82.944511
iter  70 value 81.831546
iter  80 value 79.370908
iter  90 value 79.186043
iter 100 value 79.056540
final  value 79.056540 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 99.970709 
iter  10 value 94.438293
iter  20 value 93.457749
iter  30 value 89.129318
iter  40 value 87.663493
iter  50 value 87.127712
iter  60 value 83.968129
iter  70 value 80.635277
iter  80 value 79.442237
iter  90 value 79.023535
iter 100 value 78.722830
final  value 78.722830 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 110.985962 
iter  10 value 94.558729
iter  20 value 94.325919
iter  30 value 91.340126
iter  40 value 84.322332
iter  50 value 83.257750
iter  60 value 82.968483
iter  70 value 82.874577
iter  80 value 82.280444
iter  90 value 82.123531
iter 100 value 81.702763
final  value 81.702763 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 102.625697 
iter  10 value 96.653421
iter  20 value 87.218019
iter  30 value 85.417740
iter  40 value 84.757194
iter  50 value 83.316108
iter  60 value 80.302217
iter  70 value 79.689857
iter  80 value 79.175387
iter  90 value 78.828250
iter 100 value 78.685570
final  value 78.685570 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 109.367818 
iter  10 value 99.385596
iter  20 value 94.832926
iter  30 value 86.493115
iter  40 value 85.801275
iter  50 value 83.539926
iter  60 value 83.031199
iter  70 value 82.183551
iter  80 value 81.005592
iter  90 value 79.767923
iter 100 value 79.551040
final  value 79.551040 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 118.199243 
iter  10 value 96.327552
iter  20 value 90.294553
iter  30 value 84.237944
iter  40 value 83.509306
iter  50 value 83.174671
iter  60 value 82.525741
iter  70 value 81.653864
iter  80 value 81.526571
iter  90 value 81.448769
iter 100 value 80.599056
final  value 80.599056 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 108.938281 
iter  10 value 95.813168
iter  20 value 94.256429
iter  30 value 87.472747
iter  40 value 83.522066
iter  50 value 83.167143
iter  60 value 81.217986
iter  70 value 80.456360
iter  80 value 80.397806
iter  90 value 79.659669
iter 100 value 79.361366
final  value 79.361366 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 116.779108 
iter  10 value 94.144456
iter  20 value 85.719454
iter  30 value 83.531838
iter  40 value 83.349238
iter  50 value 82.914745
iter  60 value 81.807893
iter  70 value 81.355338
iter  80 value 81.273916
iter  90 value 81.193543
iter 100 value 80.149657
final  value 80.149657 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.435277 
final  value 94.485653 
converged
Fitting Repeat 2 

# weights:  103
initial  value 104.475228 
final  value 94.485797 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.049145 
final  value 94.485915 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.106964 
iter  10 value 94.485643
final  value 94.484660 
converged
Fitting Repeat 5 

# weights:  103
initial  value 112.013883 
iter  10 value 94.468588
iter  20 value 94.467906
final  value 94.467597 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.161652 
iter  10 value 94.482754
iter  20 value 94.439085
iter  30 value 94.433115
iter  40 value 92.382153
iter  50 value 84.330918
iter  60 value 84.297289
iter  70 value 83.855534
iter  80 value 81.506520
iter  90 value 80.719016
iter 100 value 79.606877
final  value 79.606877 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 109.458578 
iter  10 value 94.489150
final  value 94.484388 
converged
Fitting Repeat 3 

# weights:  305
initial  value 104.617229 
iter  10 value 94.471394
iter  20 value 94.467043
iter  30 value 88.698081
iter  40 value 86.710729
iter  50 value 86.709395
iter  60 value 85.678826
iter  70 value 82.975802
iter  80 value 82.630890
iter  90 value 82.615871
iter 100 value 82.595707
final  value 82.595707 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.384129 
iter  10 value 94.471838
iter  20 value 94.276843
iter  30 value 87.641401
iter  40 value 82.505198
iter  50 value 82.484159
iter  60 value 82.097707
iter  70 value 82.094205
iter  80 value 82.092124
iter  90 value 82.088989
iter  90 value 82.088989
final  value 82.088989 
converged
Fitting Repeat 5 

# weights:  305
initial  value 116.932358 
iter  10 value 94.488647
iter  20 value 94.484568
iter  30 value 94.178306
iter  40 value 85.089466
iter  50 value 85.066869
iter  60 value 85.066179
iter  70 value 82.720755
iter  80 value 82.627600
iter  90 value 82.546726
iter 100 value 82.503573
final  value 82.503573 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 101.920776 
iter  10 value 94.318821
iter  20 value 94.068985
iter  30 value 92.112368
iter  40 value 85.412115
iter  50 value 85.170405
iter  60 value 84.784386
iter  70 value 84.771425
final  value 84.771076 
converged
Fitting Repeat 2 

# weights:  507
initial  value 112.933024 
iter  10 value 94.474576
iter  20 value 94.468572
iter  30 value 91.819766
iter  40 value 91.782296
iter  50 value 91.772774
iter  60 value 91.738858
iter  70 value 91.411208
iter  80 value 91.377230
iter  90 value 84.372840
iter 100 value 83.475693
final  value 83.475693 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 100.812906 
iter  10 value 94.492219
iter  20 value 94.418231
iter  30 value 91.815294
final  value 91.808963 
converged
Fitting Repeat 4 

# weights:  507
initial  value 105.787145 
iter  10 value 92.836611
iter  20 value 86.385559
iter  30 value 81.068003
iter  40 value 79.463650
iter  50 value 79.246718
iter  60 value 79.243729
iter  70 value 79.242416
final  value 79.239718 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.655966 
iter  10 value 94.475167
iter  20 value 94.256005
iter  30 value 94.207064
iter  40 value 94.206148
final  value 94.206143 
converged
Fitting Repeat 1 

# weights:  103
initial  value 101.023231 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.650237 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.587411 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.697199 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.789466 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.321465 
final  value 94.354396 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.090612 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.643623 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.710557 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 111.319325 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 111.280304 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.450568 
final  value 94.354396 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.834201 
final  value 94.354396 
converged
Fitting Repeat 4 

# weights:  507
initial  value 110.942896 
iter  10 value 91.975111
final  value 91.967755 
converged
Fitting Repeat 5 

# weights:  507
initial  value 113.907231 
iter  10 value 93.493107
final  value 93.482424 
converged
Fitting Repeat 1 

# weights:  103
initial  value 102.727163 
iter  10 value 94.485752
iter  20 value 94.110044
iter  30 value 94.088651
iter  40 value 89.748350
iter  50 value 89.315912
iter  60 value 89.312751
iter  70 value 89.312140
iter  80 value 86.766037
iter  90 value 86.519321
iter 100 value 86.356025
final  value 86.356025 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 104.295808 
iter  10 value 94.480449
iter  20 value 94.309413
iter  30 value 92.356058
iter  40 value 90.234092
iter  50 value 86.787170
iter  60 value 86.488554
iter  70 value 86.480387
iter  80 value 86.477956
iter  80 value 86.477955
iter  80 value 86.477955
final  value 86.477955 
converged
Fitting Repeat 3 

# weights:  103
initial  value 105.874924 
iter  10 value 94.488849
iter  20 value 94.315510
iter  30 value 92.999707
iter  40 value 92.187389
iter  50 value 92.051004
iter  60 value 90.824178
iter  70 value 88.843421
iter  80 value 88.194257
iter  90 value 87.391488
iter 100 value 86.438033
final  value 86.438033 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 110.371948 
iter  10 value 94.486876
iter  20 value 93.833836
iter  30 value 90.698867
iter  40 value 88.849905
iter  50 value 87.516002
iter  60 value 86.975399
iter  70 value 86.904522
iter  80 value 86.731598
iter  90 value 86.705407
final  value 86.705380 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.513152 
iter  10 value 94.370447
iter  20 value 94.119504
iter  30 value 88.799256
iter  40 value 87.159545
iter  50 value 86.883752
iter  60 value 86.729877
iter  70 value 86.705385
final  value 86.705380 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.545373 
iter  10 value 94.676937
iter  20 value 86.990062
iter  30 value 84.748151
iter  40 value 83.446179
iter  50 value 82.623475
iter  60 value 82.387811
iter  70 value 82.286462
iter  80 value 82.282772
iter  90 value 82.242664
iter 100 value 81.986872
final  value 81.986872 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 118.876205 
iter  10 value 94.641702
iter  20 value 87.242716
iter  30 value 86.319072
iter  40 value 85.773749
iter  50 value 85.281140
iter  60 value 84.230286
iter  70 value 82.583556
iter  80 value 82.225434
iter  90 value 82.099847
iter 100 value 81.932310
final  value 81.932310 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 108.101334 
iter  10 value 94.340717
iter  20 value 89.319666
iter  30 value 86.652745
iter  40 value 85.868479
iter  50 value 84.916260
iter  60 value 83.995555
iter  70 value 83.477760
iter  80 value 82.759150
iter  90 value 82.735922
iter 100 value 82.709573
final  value 82.709573 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 106.685070 
iter  10 value 94.437874
iter  20 value 93.753346
iter  30 value 89.613296
iter  40 value 85.708547
iter  50 value 83.649183
iter  60 value 82.749159
iter  70 value 82.406409
iter  80 value 82.226429
iter  90 value 82.202256
iter 100 value 82.154242
final  value 82.154242 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 118.728066 
iter  10 value 92.997156
iter  20 value 87.615444
iter  30 value 86.679520
iter  40 value 86.093657
iter  50 value 85.973255
iter  60 value 85.924270
iter  70 value 85.149974
iter  80 value 84.161797
iter  90 value 83.913657
iter 100 value 83.679221
final  value 83.679221 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 107.231125 
iter  10 value 95.118840
iter  20 value 94.261254
iter  30 value 87.276261
iter  40 value 85.011369
iter  50 value 84.028554
iter  60 value 83.468794
iter  70 value 82.425020
iter  80 value 82.032117
iter  90 value 81.745536
iter 100 value 81.531448
final  value 81.531448 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 124.828555 
iter  10 value 94.494438
iter  20 value 94.190442
iter  30 value 92.431965
iter  40 value 91.162468
iter  50 value 85.833651
iter  60 value 84.155495
iter  70 value 83.340450
iter  80 value 82.615621
iter  90 value 82.443932
iter 100 value 82.171158
final  value 82.171158 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 113.926943 
iter  10 value 94.478964
iter  20 value 87.665747
iter  30 value 86.898595
iter  40 value 86.592064
iter  50 value 85.085076
iter  60 value 83.665268
iter  70 value 83.034785
iter  80 value 82.444435
iter  90 value 82.351750
iter 100 value 82.091797
final  value 82.091797 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 122.210302 
iter  10 value 94.442497
iter  20 value 89.154200
iter  30 value 86.815233
iter  40 value 85.738341
iter  50 value 84.688682
iter  60 value 84.345742
iter  70 value 83.954493
iter  80 value 83.242293
iter  90 value 83.078154
iter 100 value 82.968315
final  value 82.968315 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 137.254681 
iter  10 value 94.229295
iter  20 value 90.338847
iter  30 value 86.897478
iter  40 value 86.178228
iter  50 value 85.716435
iter  60 value 85.024707
iter  70 value 84.883547
iter  80 value 83.440715
iter  90 value 83.135025
iter 100 value 82.705556
final  value 82.705556 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.758968 
final  value 94.485988 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.211427 
final  value 94.485928 
converged
Fitting Repeat 3 

# weights:  103
initial  value 105.703969 
final  value 94.486013 
converged
Fitting Repeat 4 

# weights:  103
initial  value 94.814889 
final  value 94.485825 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.480813 
final  value 94.485876 
converged
Fitting Repeat 1 

# weights:  305
initial  value 118.607938 
iter  10 value 94.486106
iter  20 value 94.384005
iter  30 value 94.368812
iter  40 value 94.224884
iter  50 value 94.097802
iter  60 value 94.094132
iter  70 value 92.713842
iter  80 value 92.632546
iter  90 value 92.632458
iter 100 value 92.632307
final  value 92.632307 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 114.648716 
iter  10 value 94.489141
iter  20 value 94.321335
final  value 94.052738 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.527181 
iter  10 value 93.813965
iter  20 value 90.082517
iter  30 value 88.857792
iter  40 value 88.829721
final  value 88.828913 
converged
Fitting Repeat 4 

# weights:  305
initial  value 100.078085 
iter  10 value 94.359607
iter  20 value 94.354870
iter  30 value 94.277358
iter  40 value 92.634032
iter  50 value 92.632009
final  value 92.631970 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.065021 
iter  10 value 94.489185
iter  20 value 94.484578
iter  30 value 94.053088
iter  30 value 94.053088
iter  30 value 94.053088
final  value 94.053088 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.830072 
iter  10 value 94.471963
iter  20 value 93.740723
iter  30 value 88.572598
iter  40 value 88.184908
iter  50 value 88.107232
iter  60 value 87.259446
iter  70 value 84.150201
iter  80 value 83.910789
iter  90 value 83.867038
iter 100 value 83.779337
final  value 83.779337 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 108.509852 
iter  10 value 93.708588
iter  20 value 93.707227
iter  30 value 92.516758
iter  40 value 87.749418
iter  50 value 84.600620
iter  60 value 83.923414
iter  70 value 83.243957
iter  80 value 82.720989
iter  90 value 82.309863
iter 100 value 81.904451
final  value 81.904451 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 108.921692 
iter  10 value 94.491906
iter  20 value 94.313528
iter  30 value 93.347560
iter  40 value 93.284134
iter  50 value 89.740233
iter  60 value 87.732011
iter  70 value 87.728231
final  value 87.728098 
converged
Fitting Repeat 4 

# weights:  507
initial  value 96.043621 
iter  10 value 94.489742
iter  20 value 94.453554
iter  30 value 94.215068
iter  40 value 94.214875
iter  50 value 94.147654
iter  60 value 88.593372
iter  70 value 87.808738
iter  80 value 86.083652
iter  90 value 84.848709
iter 100 value 81.530488
final  value 81.530488 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 114.779500 
iter  10 value 88.705877
iter  20 value 87.972489
final  value 87.967828 
converged
Fitting Repeat 1 

# weights:  103
initial  value 95.923633 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.416674 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.632309 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.917827 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.045193 
iter  10 value 89.575624
iter  20 value 83.178839
iter  30 value 83.152925
final  value 83.152920 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.112849 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 100.501042 
iter  10 value 93.328261
iter  10 value 93.328261
iter  10 value 93.328261
final  value 93.328261 
converged
Fitting Repeat 3 

# weights:  305
initial  value 102.982784 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 117.101285 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.126309 
iter  10 value 93.226524
final  value 93.215984 
converged
Fitting Repeat 1 

# weights:  507
initial  value 113.937351 
iter  10 value 93.482435
final  value 93.328261 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.972114 
iter  10 value 92.775065
iter  20 value 83.168945
iter  30 value 82.694059
iter  40 value 82.643365
final  value 82.643363 
converged
Fitting Repeat 3 

# weights:  507
initial  value 95.566817 
iter  10 value 93.328261
iter  10 value 93.328261
iter  10 value 93.328261
final  value 93.328261 
converged
Fitting Repeat 4 

# weights:  507
initial  value 132.211738 
iter  10 value 93.907743
final  value 93.903448 
converged
Fitting Repeat 5 

# weights:  507
initial  value 99.963553 
iter  10 value 93.183825
final  value 93.183802 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.076550 
iter  10 value 93.970079
iter  20 value 93.331540
iter  30 value 93.298076
iter  40 value 92.922552
iter  50 value 86.840665
iter  60 value 85.721349
iter  70 value 85.463547
iter  80 value 85.037624
iter  90 value 83.578380
iter 100 value 83.170137
final  value 83.170137 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.999884 
iter  10 value 92.529667
iter  20 value 85.702126
iter  30 value 85.352197
iter  40 value 84.935006
iter  50 value 83.372133
iter  60 value 83.089825
iter  70 value 83.010305
final  value 83.009674 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.886918 
iter  10 value 94.061027
iter  20 value 89.645676
iter  30 value 88.375775
iter  40 value 85.312663
iter  50 value 83.450716
iter  60 value 82.969743
iter  70 value 82.873882
iter  80 value 82.706419
iter  90 value 82.662588
iter  90 value 82.662588
iter  90 value 82.662588
final  value 82.662588 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.355196 
iter  10 value 94.061573
iter  20 value 93.360713
iter  30 value 83.698151
iter  40 value 82.923859
iter  50 value 82.205494
iter  60 value 82.038358
final  value 82.037613 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.706595 
iter  10 value 93.941563
iter  20 value 88.286109
iter  30 value 87.466671
iter  40 value 86.975042
iter  50 value 86.652907
iter  60 value 83.646884
iter  70 value 83.258845
iter  80 value 83.011167
final  value 83.009673 
converged
Fitting Repeat 1 

# weights:  305
initial  value 116.750026 
iter  10 value 94.047831
iter  20 value 91.950410
iter  30 value 91.582116
iter  40 value 91.043045
iter  50 value 84.470486
iter  60 value 82.716847
iter  70 value 80.305315
iter  80 value 79.994890
iter  90 value 79.757158
iter 100 value 79.553637
final  value 79.553637 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.001062 
iter  10 value 94.055911
iter  20 value 87.952791
iter  30 value 85.464240
iter  40 value 83.873419
iter  50 value 83.262568
iter  60 value 82.875151
iter  70 value 81.290040
iter  80 value 80.071852
iter  90 value 79.956246
iter 100 value 79.795227
final  value 79.795227 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 138.772658 
iter  10 value 93.807372
iter  20 value 92.677843
iter  30 value 87.051346
iter  40 value 84.431627
iter  50 value 83.394437
iter  60 value 80.665404
iter  70 value 80.162922
iter  80 value 80.065988
iter  90 value 79.866641
iter 100 value 79.379659
final  value 79.379659 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 114.887805 
iter  10 value 91.185346
iter  20 value 84.586109
iter  30 value 83.920411
iter  40 value 83.376766
iter  50 value 81.641421
iter  60 value 81.169107
iter  70 value 80.050471
iter  80 value 79.610860
iter  90 value 79.360415
iter 100 value 79.310846
final  value 79.310846 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 111.307645 
iter  10 value 93.454334
iter  20 value 90.782627
iter  30 value 83.694221
iter  40 value 82.799399
iter  50 value 81.665776
iter  60 value 80.284518
iter  70 value 80.074972
iter  80 value 79.667385
iter  90 value 79.574348
iter 100 value 79.532538
final  value 79.532538 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 124.062381 
iter  10 value 94.090913
iter  20 value 85.424064
iter  30 value 83.405800
iter  40 value 83.147631
iter  50 value 80.896386
iter  60 value 80.505443
iter  70 value 79.852524
iter  80 value 79.621967
iter  90 value 79.391655
iter 100 value 79.353291
final  value 79.353291 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 111.819973 
iter  10 value 94.587727
iter  20 value 91.687240
iter  30 value 84.800027
iter  40 value 83.598436
iter  50 value 81.840350
iter  60 value 80.131715
iter  70 value 79.562580
iter  80 value 79.418300
iter  90 value 79.056969
iter 100 value 78.756922
final  value 78.756922 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 127.178286 
iter  10 value 94.971444
iter  20 value 93.441778
iter  30 value 85.352123
iter  40 value 84.236889
iter  50 value 82.591764
iter  60 value 81.952816
iter  70 value 81.821540
iter  80 value 81.300829
iter  90 value 79.764837
iter 100 value 79.243904
final  value 79.243904 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 108.821157 
iter  10 value 94.663468
iter  20 value 87.242099
iter  30 value 86.713811
iter  40 value 84.853599
iter  50 value 81.038444
iter  60 value 80.239727
iter  70 value 80.082263
iter  80 value 79.963630
iter  90 value 79.725693
iter 100 value 79.672590
final  value 79.672590 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 147.752895 
iter  10 value 88.634981
iter  20 value 87.493136
iter  30 value 86.985957
iter  40 value 85.149864
iter  50 value 84.361311
iter  60 value 82.911846
iter  70 value 82.002414
iter  80 value 80.351896
iter  90 value 79.842547
iter 100 value 79.339246
final  value 79.339246 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.873614 
final  value 94.054507 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.615572 
iter  10 value 94.054663
iter  20 value 94.052952
iter  30 value 93.184858
final  value 93.184364 
converged
Fitting Repeat 3 

# weights:  103
initial  value 109.599797 
final  value 94.054429 
converged
Fitting Repeat 4 

# weights:  103
initial  value 105.388062 
final  value 94.054486 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.084678 
final  value 94.054639 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.610896 
iter  10 value 93.908292
iter  20 value 93.438869
iter  30 value 89.502158
iter  40 value 89.400192
iter  50 value 89.394641
iter  60 value 89.394459
iter  70 value 89.085860
iter  80 value 85.753242
iter  90 value 85.423361
iter 100 value 83.588173
final  value 83.588173 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 104.689009 
iter  10 value 94.058284
iter  20 value 94.036761
iter  30 value 88.906142
iter  40 value 88.651276
iter  50 value 88.155879
iter  60 value 87.300388
iter  70 value 83.961730
iter  80 value 83.899906
iter  90 value 83.765995
iter 100 value 83.105404
final  value 83.105404 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 110.166748 
iter  10 value 94.058060
iter  20 value 94.032024
iter  30 value 93.328746
iter  30 value 93.328746
iter  30 value 93.328746
final  value 93.328746 
converged
Fitting Repeat 4 

# weights:  305
initial  value 93.416016 
iter  10 value 87.203307
iter  20 value 86.938424
iter  30 value 86.879925
iter  40 value 86.876702
iter  50 value 86.740677
iter  60 value 86.536932
final  value 86.536919 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.100091 
iter  10 value 94.058314
iter  20 value 93.904118
iter  30 value 93.184090
iter  30 value 93.184090
iter  30 value 93.184090
final  value 93.184090 
converged
Fitting Repeat 1 

# weights:  507
initial  value 96.714438 
iter  10 value 88.490133
iter  20 value 88.151146
iter  30 value 87.791916
iter  40 value 87.785830
iter  50 value 86.463552
iter  60 value 86.453348
final  value 86.452334 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.646345 
iter  10 value 94.060334
iter  20 value 93.361899
iter  30 value 93.087385
final  value 93.087323 
converged
Fitting Repeat 3 

# weights:  507
initial  value 107.006610 
iter  10 value 93.336855
iter  20 value 93.332092
iter  30 value 93.090933
iter  40 value 83.375363
iter  50 value 79.488579
iter  60 value 78.338052
iter  70 value 78.206401
iter  80 value 78.079314
iter  90 value 78.035836
iter 100 value 78.029564
final  value 78.029564 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 101.319266 
iter  10 value 93.336644
iter  20 value 93.331898
iter  30 value 93.248850
iter  40 value 91.826448
iter  50 value 88.148223
iter  60 value 87.983516
iter  70 value 87.982962
final  value 87.982499 
converged
Fitting Repeat 5 

# weights:  507
initial  value 106.033836 
iter  10 value 94.061185
iter  20 value 94.038736
iter  30 value 93.193045
iter  40 value 87.878148
iter  50 value 87.783709
iter  60 value 84.390196
iter  70 value 84.341328
final  value 84.340903 
converged
Fitting Repeat 1 

# weights:  305
initial  value 122.674036 
iter  10 value 117.834865
iter  20 value 111.916261
iter  30 value 107.548582
iter  40 value 105.708835
iter  50 value 105.194673
iter  60 value 104.857185
iter  70 value 103.201547
iter  80 value 102.338660
iter  90 value 102.038004
iter 100 value 101.632651
final  value 101.632651 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 130.420829 
iter  10 value 120.465538
iter  20 value 115.288842
iter  30 value 113.758935
iter  40 value 112.349445
iter  50 value 111.021457
iter  60 value 107.412015
iter  70 value 104.689075
iter  80 value 104.464792
iter  90 value 104.240945
iter 100 value 103.195561
final  value 103.195561 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 140.454843 
iter  10 value 121.554085
iter  20 value 118.005472
iter  30 value 113.701424
iter  40 value 109.152729
iter  50 value 108.812090
iter  60 value 106.294588
iter  70 value 104.759448
iter  80 value 102.285798
iter  90 value 101.731993
iter 100 value 101.516502
final  value 101.516502 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 129.933375 
iter  10 value 117.147690
iter  20 value 111.439503
iter  30 value 107.587433
iter  40 value 105.486171
iter  50 value 104.683577
iter  60 value 103.517948
iter  70 value 102.308340
iter  80 value 101.213741
iter  90 value 101.026741
iter 100 value 100.900292
final  value 100.900292 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 127.140761 
iter  10 value 117.629192
iter  20 value 108.894492
iter  30 value 104.989324
iter  40 value 104.253563
iter  50 value 103.694868
iter  60 value 101.952145
iter  70 value 101.095993
iter  80 value 100.904478
iter  90 value 100.811541
iter 100 value 100.800040
final  value 100.800040 
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 -- Tue Apr  1 22:04:48 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 
 50.150   1.708  67.905