##############################################################################
##############################################################################
###
### 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 4.4.2 (2024-10-31)
* using platform: x86_64-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 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 ‘1.12.0’
* 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
var_imp       51.373  1.861  58.932
corr_plot     50.941  1.843  55.496
FSmethod      50.548  1.816  54.207
pred_ensembel 24.977  0.406  23.808
calculateTC    4.668  0.443   5.362
enrichfindP    0.882  0.082  13.657
* 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-x86_64/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 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 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 106.555657 
iter  10 value 92.945386
final  value 92.945355 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.322053 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.010043 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 94.618772 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.741344 
iter  10 value 88.155744
iter  20 value 82.871988
iter  30 value 82.738264
final  value 82.737855 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.859680 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 131.035381 
iter  10 value 92.945357
final  value 92.945355 
converged
Fitting Repeat 3 

# weights:  305
initial  value 108.436333 
iter  10 value 92.915115
iter  10 value 92.915115
iter  10 value 92.915115
final  value 92.915115 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.397824 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 110.932685 
iter  10 value 92.945372
final  value 92.945355 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.271185 
iter  10 value 93.522920
iter  20 value 92.908947
final  value 92.908838 
converged
Fitting Repeat 2 

# weights:  507
initial  value 115.931174 
final  value 94.025289 
converged
Fitting Repeat 3 

# weights:  507
initial  value 111.438079 
final  value 94.052911 
converged
Fitting Repeat 4 

# weights:  507
initial  value 118.813758 
iter  10 value 93.731345
final  value 92.945357 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.864577 
iter  10 value 92.945357
iter  10 value 92.945356
iter  10 value 92.945356
final  value 92.945356 
converged
Fitting Repeat 1 

# weights:  103
initial  value 103.142364 
iter  10 value 93.695589
iter  20 value 85.834541
iter  30 value 85.356414
iter  40 value 84.597074
iter  50 value 80.975560
iter  60 value 79.894635
iter  70 value 78.680368
final  value 78.670022 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.669872 
iter  10 value 93.217647
iter  20 value 92.446689
iter  30 value 89.403632
iter  40 value 88.300687
iter  50 value 85.415510
iter  60 value 84.907802
iter  70 value 83.188970
iter  80 value 82.876283
iter  90 value 82.873625
final  value 82.873623 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.996053 
iter  10 value 92.646656
iter  20 value 92.449834
iter  30 value 86.573148
iter  40 value 85.590907
iter  50 value 85.538574
iter  60 value 84.278089
iter  70 value 84.222610
iter  80 value 80.284866
iter  90 value 80.008615
iter 100 value 78.644820
final  value 78.644820 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 99.640361 
iter  10 value 93.430471
iter  20 value 90.424633
iter  30 value 83.818420
iter  40 value 83.112761
iter  50 value 82.944241
iter  60 value 82.875667
final  value 82.873627 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.759056 
iter  10 value 93.331798
iter  20 value 88.098551
iter  30 value 85.516353
iter  40 value 84.188161
iter  50 value 83.822670
iter  60 value 82.491624
iter  70 value 82.110194
iter  80 value 82.081642
final  value 82.073735 
converged
Fitting Repeat 1 

# weights:  305
initial  value 111.794255 
iter  10 value 93.966129
iter  20 value 85.002031
iter  30 value 84.679337
iter  40 value 84.513334
iter  50 value 83.236090
iter  60 value 78.731497
iter  70 value 77.199524
iter  80 value 76.813319
iter  90 value 76.682942
iter 100 value 76.610085
final  value 76.610085 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 103.440579 
iter  10 value 94.152717
iter  20 value 93.918714
iter  30 value 91.050076
iter  40 value 87.228473
iter  50 value 81.868635
iter  60 value 81.234852
iter  70 value 80.840842
iter  80 value 80.556038
iter  90 value 78.426755
iter 100 value 77.986978
final  value 77.986978 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.194858 
iter  10 value 93.224927
iter  20 value 84.166892
iter  30 value 81.770030
iter  40 value 81.651222
iter  50 value 81.279502
iter  60 value 81.157801
iter  70 value 80.562236
iter  80 value 78.020205
iter  90 value 77.396887
iter 100 value 77.173044
final  value 77.173044 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 103.697656 
iter  10 value 97.770077
iter  20 value 95.916119
iter  30 value 91.691218
iter  40 value 90.713901
iter  50 value 90.068573
iter  60 value 86.557643
iter  70 value 82.206466
iter  80 value 81.390231
iter  90 value 80.890613
iter 100 value 79.751581
final  value 79.751581 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 114.200238 
iter  10 value 94.975296
iter  20 value 93.766830
iter  30 value 92.849801
iter  40 value 91.757568
iter  50 value 91.407333
iter  60 value 84.128657
iter  70 value 82.985265
iter  80 value 80.693112
iter  90 value 79.610860
iter 100 value 77.331491
final  value 77.331491 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 104.893603 
iter  10 value 94.137090
iter  20 value 86.762875
iter  30 value 85.271301
iter  40 value 83.544376
iter  50 value 81.798496
iter  60 value 79.201288
iter  70 value 78.725019
iter  80 value 78.513828
iter  90 value 77.987235
iter 100 value 77.548094
final  value 77.548094 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 104.424130 
iter  10 value 93.181167
iter  20 value 87.314382
iter  30 value 82.905740
iter  40 value 81.206670
iter  50 value 80.272459
iter  60 value 79.484495
iter  70 value 79.129413
iter  80 value 78.456836
iter  90 value 77.712944
iter 100 value 77.603209
final  value 77.603209 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 114.282032 
iter  10 value 92.634348
iter  20 value 91.366624
iter  30 value 85.981860
iter  40 value 83.085663
iter  50 value 81.777916
iter  60 value 80.900355
iter  70 value 78.948992
iter  80 value 77.725982
iter  90 value 77.125472
iter 100 value 76.851446
final  value 76.851446 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 122.537194 
iter  10 value 94.225257
iter  20 value 87.987737
iter  30 value 84.350734
iter  40 value 83.335445
iter  50 value 83.101357
iter  60 value 82.609615
iter  70 value 78.589614
iter  80 value 77.893460
iter  90 value 77.509840
iter 100 value 77.347691
final  value 77.347691 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 107.295129 
iter  10 value 92.472010
iter  20 value 84.009739
iter  30 value 82.915898
iter  40 value 80.814522
iter  50 value 79.335509
iter  60 value 78.928746
iter  70 value 78.460569
iter  80 value 77.713940
iter  90 value 77.618961
iter 100 value 77.575304
final  value 77.575304 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 105.837406 
final  value 94.054569 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.337543 
final  value 94.054316 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.319192 
iter  10 value 94.054642
iter  20 value 94.052977
final  value 94.052915 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.142574 
final  value 94.054683 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.339218 
iter  10 value 94.054526
iter  20 value 94.053020
iter  30 value 89.052897
iter  40 value 83.296730
iter  50 value 80.804535
iter  60 value 80.716047
iter  70 value 80.649632
iter  80 value 80.644530
iter  90 value 80.644236
final  value 80.644219 
converged
Fitting Repeat 1 

# weights:  305
initial  value 113.153072 
iter  10 value 92.950634
iter  20 value 92.274041
iter  30 value 92.170735
iter  40 value 92.168445
iter  50 value 92.167240
iter  60 value 92.166810
iter  70 value 92.053229
final  value 92.052841 
converged
Fitting Repeat 2 

# weights:  305
initial  value 113.520289 
iter  10 value 92.184529
iter  20 value 92.163250
final  value 92.059196 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.691263 
iter  10 value 92.950527
iter  20 value 92.947871
iter  30 value 84.961818
iter  40 value 82.623020
final  value 82.602337 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.795197 
iter  10 value 92.218397
iter  20 value 92.170889
iter  30 value 92.170332
iter  40 value 88.319732
iter  50 value 82.734710
iter  60 value 78.442717
iter  70 value 76.792735
iter  80 value 75.212307
iter  90 value 75.001157
iter 100 value 74.622428
final  value 74.622428 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 104.394526 
iter  10 value 92.950445
iter  20 value 92.946518
final  value 92.945787 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.096662 
iter  10 value 92.231592
iter  20 value 92.175757
iter  30 value 92.157065
iter  40 value 92.022424
iter  50 value 92.018379
iter  60 value 92.018278
iter  70 value 92.017975
iter  80 value 92.017723
final  value 92.017700 
converged
Fitting Repeat 2 

# weights:  507
initial  value 98.528433 
iter  10 value 92.954214
iter  20 value 92.950034
iter  30 value 92.294547
iter  40 value 91.918957
iter  50 value 90.155764
iter  60 value 81.311837
iter  70 value 80.792866
iter  80 value 80.788978
iter  90 value 80.788709
iter 100 value 80.719772
final  value 80.719772 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 97.597477 
iter  10 value 92.180616
iter  20 value 92.173446
iter  30 value 92.167229
iter  40 value 92.165967
iter  40 value 92.165967
final  value 92.165967 
converged
Fitting Repeat 4 

# weights:  507
initial  value 105.412829 
iter  10 value 94.061066
iter  20 value 94.052991
iter  30 value 91.502378
iter  40 value 85.712444
iter  50 value 82.808610
iter  60 value 79.205891
iter  70 value 78.432864
iter  80 value 77.814179
iter  90 value 77.462182
iter 100 value 77.446480
final  value 77.446480 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.853178 
iter  10 value 94.060792
iter  20 value 93.998633
iter  30 value 92.947152
iter  40 value 84.036536
iter  50 value 83.722841
iter  60 value 83.717224
iter  70 value 83.707568
iter  80 value 83.672681
iter  90 value 81.442117
iter 100 value 81.440781
final  value 81.440781 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.959974 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 118.878612 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.301392 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 104.505543 
final  value 94.467391 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.375843 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.060306 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 101.457856 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 96.289775 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.513118 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.496193 
iter  10 value 84.575504
iter  20 value 83.148723
iter  30 value 83.006341
iter  40 value 83.005707
iter  40 value 83.005707
iter  40 value 83.005707
final  value 83.005707 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.295962 
final  value 94.484210 
converged
Fitting Repeat 2 

# weights:  507
initial  value 105.757896 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 114.601213 
iter  10 value 94.484486
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 118.924854 
iter  10 value 91.343908
iter  20 value 91.321665
final  value 91.321637 
converged
Fitting Repeat 5 

# weights:  507
initial  value 99.801438 
final  value 94.467391 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.209688 
iter  10 value 94.399248
iter  20 value 86.312602
iter  30 value 84.035441
iter  40 value 83.725735
iter  50 value 83.556903
iter  60 value 83.466039
final  value 83.462210 
converged
Fitting Repeat 2 

# weights:  103
initial  value 106.938393 
iter  10 value 94.393619
iter  20 value 90.232660
iter  30 value 88.607111
iter  40 value 88.253717
iter  50 value 85.486428
iter  60 value 84.255487
iter  70 value 84.096151
iter  80 value 83.397557
iter  90 value 83.346029
iter 100 value 83.293787
final  value 83.293787 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 98.503857 
iter  10 value 94.442112
iter  20 value 84.478968
iter  30 value 83.722067
iter  40 value 83.404844
iter  50 value 83.133151
final  value 83.114469 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.800060 
iter  10 value 94.454477
iter  20 value 88.284209
iter  30 value 87.361457
iter  40 value 83.865355
iter  50 value 83.558665
iter  60 value 83.338059
iter  70 value 83.231731
iter  80 value 83.208514
final  value 83.206288 
converged
Fitting Repeat 5 

# weights:  103
initial  value 104.464318 
iter  10 value 94.486124
iter  20 value 91.338341
iter  30 value 84.275429
iter  40 value 83.985767
iter  50 value 83.437854
iter  60 value 83.311172
final  value 83.310730 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.903118 
iter  10 value 94.385596
iter  20 value 85.705797
iter  30 value 83.538969
iter  40 value 82.226145
iter  50 value 81.392078
iter  60 value 81.069143
iter  70 value 80.747756
iter  80 value 80.630535
iter  90 value 80.558577
iter 100 value 80.198030
final  value 80.198030 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 108.440973 
iter  10 value 95.846250
iter  20 value 87.569953
iter  30 value 85.341149
iter  40 value 83.784510
iter  50 value 81.317824
iter  60 value 80.107828
iter  70 value 79.950428
iter  80 value 79.825713
iter  90 value 79.734013
iter 100 value 79.689743
final  value 79.689743 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.539245 
iter  10 value 94.192802
iter  20 value 87.394499
iter  30 value 86.548591
iter  40 value 86.361086
iter  50 value 85.551796
iter  60 value 84.312190
iter  70 value 83.015291
iter  80 value 81.681457
iter  90 value 81.404224
iter 100 value 80.625621
final  value 80.625621 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 122.793603 
iter  10 value 94.513185
iter  20 value 88.536980
iter  30 value 84.233180
iter  40 value 83.738095
iter  50 value 83.103761
iter  60 value 82.956243
iter  70 value 81.838666
iter  80 value 81.302863
iter  90 value 80.595000
iter 100 value 80.218219
final  value 80.218219 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 106.182515 
iter  10 value 94.117769
iter  20 value 87.014078
iter  30 value 85.554756
iter  40 value 83.140363
iter  50 value 81.735895
iter  60 value 81.156676
iter  70 value 80.084269
iter  80 value 79.931384
iter  90 value 79.822511
iter 100 value 79.780300
final  value 79.780300 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.874374 
iter  10 value 93.276908
iter  20 value 90.552303
iter  30 value 86.585166
iter  40 value 85.080414
iter  50 value 83.563831
iter  60 value 83.186315
iter  70 value 82.817111
iter  80 value 81.481171
iter  90 value 80.908016
iter 100 value 80.528945
final  value 80.528945 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 111.152339 
iter  10 value 94.641284
iter  20 value 88.433684
iter  30 value 85.889430
iter  40 value 84.304495
iter  50 value 83.708294
iter  60 value 81.450812
iter  70 value 81.109832
iter  80 value 79.894363
iter  90 value 79.753241
iter 100 value 79.624169
final  value 79.624169 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 112.013599 
iter  10 value 94.956606
iter  20 value 89.660309
iter  30 value 84.428905
iter  40 value 83.631744
iter  50 value 82.767383
iter  60 value 82.504253
iter  70 value 81.553037
iter  80 value 80.472100
iter  90 value 80.194905
iter 100 value 79.877901
final  value 79.877901 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 129.734477 
iter  10 value 94.573463
iter  20 value 89.432868
iter  30 value 85.859459
iter  40 value 85.401668
iter  50 value 84.308482
iter  60 value 82.623105
iter  70 value 82.378034
iter  80 value 82.046935
iter  90 value 81.727975
iter 100 value 81.449586
final  value 81.449586 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 112.111575 
iter  10 value 94.469944
iter  20 value 93.268195
iter  30 value 85.045279
iter  40 value 84.121777
iter  50 value 83.324249
iter  60 value 81.125380
iter  70 value 80.547779
iter  80 value 80.265168
iter  90 value 80.184896
iter 100 value 80.099219
final  value 80.099219 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.405666 
final  value 94.485713 
converged
Fitting Repeat 2 

# weights:  103
initial  value 92.093510 
iter  10 value 89.915682
iter  20 value 89.908196
final  value 89.908065 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.591506 
final  value 94.485985 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.627842 
final  value 94.484474 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.349151 
final  value 94.468945 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.558992 
iter  10 value 94.489629
iter  20 value 94.441145
iter  30 value 91.989215
iter  40 value 91.952169
iter  50 value 90.794423
iter  60 value 90.792279
iter  70 value 90.791863
iter  80 value 90.271221
iter  90 value 89.872277
iter 100 value 85.299596
final  value 85.299596 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 96.578754 
iter  10 value 94.472348
iter  20 value 94.467512
final  value 94.467408 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.698726 
iter  10 value 94.488831
iter  20 value 94.484379
iter  30 value 91.077242
iter  40 value 85.244557
iter  50 value 84.887172
iter  60 value 83.580742
iter  70 value 83.345695
iter  80 value 83.344455
iter  90 value 83.049003
iter 100 value 81.417829
final  value 81.417829 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.796195 
iter  10 value 94.472755
iter  20 value 94.468725
iter  30 value 94.467439
iter  40 value 90.658114
iter  50 value 90.652087
final  value 90.652085 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.613137 
iter  10 value 94.488483
iter  20 value 88.393554
iter  30 value 83.533491
iter  40 value 83.507765
iter  50 value 83.506418
iter  60 value 83.095288
final  value 83.088876 
converged
Fitting Repeat 1 

# weights:  507
initial  value 111.920494 
iter  10 value 94.156614
iter  20 value 94.153048
iter  30 value 93.784046
iter  40 value 91.187354
iter  50 value 90.864507
iter  60 value 90.712671
iter  70 value 90.623126
iter  80 value 90.622232
iter  90 value 90.613097
iter 100 value 90.573896
final  value 90.573896 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 101.932773 
iter  10 value 94.492218
final  value 94.492043 
converged
Fitting Repeat 3 

# weights:  507
initial  value 95.036569 
iter  10 value 94.491159
iter  20 value 94.468863
iter  30 value 83.350552
iter  40 value 82.060139
iter  50 value 81.102626
iter  60 value 80.353899
iter  70 value 79.861041
iter  80 value 79.860036
iter  90 value 79.661499
iter 100 value 79.554167
final  value 79.554167 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 113.457598 
iter  10 value 94.492293
iter  20 value 94.260880
iter  30 value 87.081674
iter  40 value 85.150038
iter  50 value 85.085107
final  value 85.084308 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.133308 
iter  10 value 93.693797
iter  20 value 93.680319
iter  30 value 93.675877
iter  40 value 93.432053
iter  50 value 90.135808
iter  60 value 90.012678
iter  70 value 87.940355
final  value 87.927792 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.376244 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.642706 
final  value 94.275362 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.498815 
iter  10 value 93.944413
iter  20 value 92.534535
iter  30 value 90.966498
iter  40 value 90.954002
iter  50 value 90.953730
final  value 90.953716 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.062683 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.731035 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 116.959004 
iter  10 value 94.266823
iter  20 value 83.713518
iter  30 value 82.543298
iter  40 value 82.541218
iter  40 value 82.541218
iter  40 value 82.541218
final  value 82.541218 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.130498 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 97.847775 
final  value 94.275362 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.474725 
final  value 94.275362 
converged
Fitting Repeat 5 

# weights:  305
initial  value 117.381194 
final  value 94.428839 
converged
Fitting Repeat 1 

# weights:  507
initial  value 99.702067 
final  value 94.275362 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.555799 
final  value 94.470285 
converged
Fitting Repeat 3 

# weights:  507
initial  value 106.228813 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.238598 
final  value 94.484212 
converged
Fitting Repeat 5 

# weights:  507
initial  value 108.425745 
iter  10 value 94.263888
final  value 94.248062 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.269397 
iter  10 value 94.474847
iter  20 value 92.880999
iter  30 value 86.506393
iter  40 value 85.394720
iter  50 value 83.810949
iter  60 value 83.510500
final  value 83.495949 
converged
Fitting Repeat 2 

# weights:  103
initial  value 104.475363 
iter  10 value 91.491309
iter  20 value 90.756645
iter  30 value 90.213071
iter  40 value 90.148002
iter  50 value 87.090674
iter  60 value 81.373713
iter  70 value 79.860836
iter  80 value 79.537538
iter  90 value 79.386824
iter 100 value 79.179198
final  value 79.179198 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 103.298586 
iter  10 value 94.456912
iter  20 value 92.658505
iter  30 value 84.943956
iter  40 value 84.003130
iter  50 value 83.058783
iter  60 value 82.513395
iter  70 value 81.729067
iter  80 value 81.514428
final  value 81.514380 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.238739 
iter  10 value 94.488597
iter  20 value 93.732541
iter  30 value 83.964431
iter  40 value 82.779137
iter  50 value 81.896903
iter  60 value 81.515492
final  value 81.514380 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.979381 
iter  10 value 94.469092
iter  20 value 87.211052
iter  30 value 83.991057
iter  40 value 83.333340
iter  50 value 82.850858
iter  60 value 80.185500
iter  70 value 79.476568
iter  80 value 79.279943
iter  90 value 78.986427
iter 100 value 78.851038
final  value 78.851038 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 105.311321 
iter  10 value 90.954736
iter  20 value 83.713859
iter  30 value 82.210436
iter  40 value 82.178812
iter  50 value 81.656262
iter  60 value 80.404726
iter  70 value 79.513182
iter  80 value 78.372809
iter  90 value 78.129637
iter 100 value 78.022535
final  value 78.022535 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 103.131055 
iter  10 value 94.550671
iter  20 value 94.239388
iter  30 value 89.934306
iter  40 value 87.795463
iter  50 value 81.812819
iter  60 value 81.090606
iter  70 value 79.479253
iter  80 value 78.092442
iter  90 value 77.781022
iter 100 value 77.649013
final  value 77.649013 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 112.030966 
iter  10 value 94.503717
iter  20 value 89.348147
iter  30 value 83.236077
iter  40 value 81.589086
iter  50 value 81.475553
iter  60 value 81.147979
iter  70 value 79.768745
iter  80 value 78.994799
iter  90 value 78.117307
iter 100 value 77.847823
final  value 77.847823 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 109.854697 
iter  10 value 94.642286
iter  20 value 94.116158
iter  30 value 84.119436
iter  40 value 82.816749
iter  50 value 81.772341
iter  60 value 81.575058
iter  70 value 81.527289
iter  80 value 81.460581
iter  90 value 80.599716
iter 100 value 79.780533
final  value 79.780533 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.364157 
iter  10 value 95.908481
iter  20 value 93.671749
iter  30 value 82.843324
iter  40 value 81.392086
iter  50 value 79.915090
iter  60 value 79.491657
iter  70 value 79.152499
iter  80 value 79.075354
iter  90 value 78.951739
iter 100 value 78.515470
final  value 78.515470 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 109.643254 
iter  10 value 94.526329
iter  20 value 85.790889
iter  30 value 81.243468
iter  40 value 79.619198
iter  50 value 78.715946
iter  60 value 78.237770
iter  70 value 78.120706
iter  80 value 78.087620
iter  90 value 77.876716
iter 100 value 77.567247
final  value 77.567247 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 111.534579 
iter  10 value 94.371332
iter  20 value 85.197165
iter  30 value 79.720799
iter  40 value 79.300473
iter  50 value 78.533366
iter  60 value 78.207755
iter  70 value 77.953297
iter  80 value 77.807196
iter  90 value 77.746946
iter 100 value 77.696220
final  value 77.696220 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 103.346011 
iter  10 value 94.557684
iter  20 value 91.179410
iter  30 value 83.542953
iter  40 value 83.061689
iter  50 value 79.606779
iter  60 value 78.682221
iter  70 value 78.436175
iter  80 value 77.980000
iter  90 value 77.780932
iter 100 value 77.459848
final  value 77.459848 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 119.905584 
iter  10 value 94.488674
iter  20 value 88.407755
iter  30 value 83.939384
iter  40 value 81.790626
iter  50 value 80.865365
iter  60 value 78.842163
iter  70 value 77.775808
iter  80 value 77.476976
iter  90 value 77.379721
iter 100 value 77.341802
final  value 77.341802 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 125.065900 
iter  10 value 94.354721
iter  20 value 91.140798
iter  30 value 89.292538
iter  40 value 81.658606
iter  50 value 80.565327
iter  60 value 79.640080
iter  70 value 79.374715
iter  80 value 78.931981
iter  90 value 78.778217
iter 100 value 78.405576
final  value 78.405576 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 103.852466 
final  value 94.485975 
converged
Fitting Repeat 2 

# weights:  103
initial  value 117.715988 
iter  10 value 94.486098
iter  20 value 94.484254
iter  30 value 93.919171
iter  40 value 86.737352
iter  50 value 86.315283
iter  60 value 84.667784
iter  70 value 82.879332
iter  80 value 82.860824
iter  90 value 82.859127
iter 100 value 82.857217
final  value 82.857217 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 98.086072 
final  value 94.485978 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.889285 
final  value 94.485635 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.020856 
iter  10 value 93.923952
iter  20 value 93.922757
iter  30 value 85.936765
iter  40 value 85.934247
iter  50 value 84.180362
iter  60 value 84.174316
iter  70 value 84.172252
iter  80 value 82.267315
final  value 82.267306 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.046138 
iter  10 value 94.280415
iter  20 value 93.904098
iter  30 value 88.388569
iter  40 value 85.232813
iter  50 value 83.175908
iter  60 value 83.120297
iter  70 value 83.118357
final  value 83.118049 
converged
Fitting Repeat 2 

# weights:  305
initial  value 101.389142 
iter  10 value 84.672187
iter  20 value 83.147134
iter  30 value 83.034887
final  value 83.034414 
converged
Fitting Repeat 3 

# weights:  305
initial  value 122.967228 
iter  10 value 94.489474
iter  20 value 94.447295
iter  30 value 93.207889
iter  40 value 86.114099
iter  50 value 86.109904
iter  60 value 86.109798
iter  70 value 84.268907
iter  80 value 77.528867
iter  90 value 76.715575
iter 100 value 76.711889
final  value 76.711889 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.340687 
iter  10 value 94.472210
iter  20 value 93.424457
iter  30 value 82.870592
iter  40 value 82.559873
iter  50 value 82.558652
iter  60 value 82.558209
iter  70 value 82.557672
iter  80 value 82.067356
iter  90 value 78.714823
iter 100 value 78.281531
final  value 78.281531 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 97.979310 
iter  10 value 94.293971
iter  20 value 94.280233
iter  30 value 94.276246
iter  40 value 94.275621
iter  50 value 91.102702
iter  60 value 90.974260
iter  70 value 90.939900
iter  80 value 90.755566
final  value 90.715691 
converged
Fitting Repeat 1 

# weights:  507
initial  value 110.155207 
iter  10 value 94.283984
iter  20 value 94.275982
iter  30 value 93.954162
iter  40 value 90.232614
iter  50 value 85.191765
iter  60 value 84.793470
iter  70 value 84.792632
iter  80 value 84.792484
final  value 84.792474 
converged
Fitting Repeat 2 

# weights:  507
initial  value 107.822407 
iter  10 value 94.492916
iter  20 value 93.664411
final  value 87.592676 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.972873 
iter  10 value 94.283655
iter  20 value 93.929167
iter  30 value 93.923751
final  value 93.923673 
converged
Fitting Repeat 4 

# weights:  507
initial  value 106.292301 
iter  10 value 94.485807
iter  20 value 85.069785
iter  30 value 82.393666
iter  40 value 82.029511
iter  50 value 82.028433
final  value 82.028074 
converged
Fitting Repeat 5 

# weights:  507
initial  value 110.021057 
iter  10 value 94.283080
iter  20 value 94.051197
iter  30 value 88.522793
iter  40 value 85.873814
iter  50 value 85.873542
final  value 85.870152 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.258394 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.084504 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.719919 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.213635 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.467663 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.262566 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 108.156436 
final  value 94.443243 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.886850 
iter  10 value 94.430344
iter  20 value 94.427937
final  value 94.427934 
converged
Fitting Repeat 4 

# weights:  305
initial  value 109.442183 
iter  10 value 94.457434
iter  20 value 92.263929
iter  30 value 91.830692
iter  40 value 91.829031
final  value 91.828930 
converged
Fitting Repeat 5 

# weights:  305
initial  value 98.017985 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 110.713661 
final  value 94.467391 
converged
Fitting Repeat 2 

# weights:  507
initial  value 99.739188 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.091412 
iter  10 value 94.467397
final  value 94.467391 
converged
Fitting Repeat 4 

# weights:  507
initial  value 100.565774 
final  value 94.467391 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.416913 
final  value 94.342012 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.394487 
iter  10 value 94.214113
iter  20 value 91.012925
iter  30 value 90.579132
iter  40 value 90.119684
iter  50 value 89.754295
iter  60 value 89.552817
iter  70 value 89.550756
final  value 89.550749 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.255505 
iter  10 value 94.487732
iter  20 value 94.433507
iter  30 value 92.854478
iter  40 value 92.099132
iter  50 value 90.770741
iter  60 value 88.761592
iter  70 value 88.471448
iter  80 value 88.272636
iter  90 value 87.944125
iter 100 value 87.409284
final  value 87.409284 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 99.168697 
iter  10 value 94.352608
iter  20 value 91.809768
iter  30 value 91.117813
iter  40 value 90.343413
iter  50 value 90.080480
iter  60 value 89.728784
iter  70 value 89.621119
iter  80 value 89.538355
final  value 89.537737 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.083525 
iter  10 value 94.593418
iter  20 value 94.031272
iter  30 value 93.683939
iter  40 value 88.523521
iter  50 value 88.308237
iter  60 value 88.248469
iter  70 value 88.128921
iter  80 value 87.617853
iter  90 value 87.367059
iter 100 value 87.365933
final  value 87.365933 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 107.051380 
iter  10 value 94.486436
iter  20 value 94.399454
iter  30 value 89.507335
iter  40 value 88.452527
iter  50 value 88.398212
iter  60 value 88.071074
iter  70 value 87.794267
iter  80 value 87.755492
final  value 87.755489 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.594782 
iter  10 value 94.492104
iter  20 value 92.811557
iter  30 value 89.640311
iter  40 value 88.727453
iter  50 value 88.074187
iter  60 value 86.769658
iter  70 value 86.174588
iter  80 value 86.067484
iter  90 value 85.920004
iter 100 value 85.726609
final  value 85.726609 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 111.584095 
iter  10 value 94.555793
iter  20 value 94.475990
iter  30 value 89.213403
iter  40 value 88.521771
iter  50 value 88.399066
iter  60 value 87.530948
iter  70 value 86.819726
iter  80 value 86.498337
iter  90 value 86.392993
iter 100 value 85.977871
final  value 85.977871 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 112.798357 
iter  10 value 94.728494
iter  20 value 89.443507
iter  30 value 89.089912
iter  40 value 87.523995
iter  50 value 87.293795
iter  60 value 87.209399
iter  70 value 87.200947
iter  80 value 87.147975
iter  90 value 87.002343
iter 100 value 86.192809
final  value 86.192809 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.295297 
iter  10 value 94.289651
iter  20 value 92.859168
iter  30 value 88.558409
iter  40 value 88.450683
iter  50 value 88.355672
iter  60 value 87.834604
iter  70 value 87.654810
iter  80 value 86.958391
iter  90 value 86.195635
iter 100 value 86.038484
final  value 86.038484 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 100.525527 
iter  10 value 94.524028
iter  20 value 93.307596
iter  30 value 89.856251
iter  40 value 87.882017
iter  50 value 86.507839
iter  60 value 86.077265
iter  70 value 85.878669
iter  80 value 85.857277
iter  90 value 85.847264
iter 100 value 85.804801
final  value 85.804801 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 106.902671 
iter  10 value 100.336684
iter  20 value 94.123819
iter  30 value 88.021514
iter  40 value 87.012355
iter  50 value 86.524361
iter  60 value 86.164061
iter  70 value 86.097548
iter  80 value 86.017981
iter  90 value 85.825906
iter 100 value 85.750716
final  value 85.750716 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 112.991996 
iter  10 value 94.497593
iter  20 value 93.205992
iter  30 value 89.353740
iter  40 value 88.576711
iter  50 value 86.960618
iter  60 value 86.408557
iter  70 value 85.821698
iter  80 value 85.651243
iter  90 value 85.592720
iter 100 value 85.420128
final  value 85.420128 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.436657 
iter  10 value 94.823556
iter  20 value 88.922933
iter  30 value 86.938028
iter  40 value 86.584509
iter  50 value 86.479004
iter  60 value 86.357565
iter  70 value 86.120144
iter  80 value 86.004822
iter  90 value 85.782508
iter 100 value 85.655537
final  value 85.655537 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 119.005182 
iter  10 value 94.705720
iter  20 value 93.567962
iter  30 value 89.166234
iter  40 value 87.965476
iter  50 value 86.619024
iter  60 value 86.223632
iter  70 value 86.108953
iter  80 value 86.046049
iter  90 value 86.030989
iter 100 value 85.914177
final  value 85.914177 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 119.221108 
iter  10 value 95.794097
iter  20 value 94.868013
iter  30 value 94.041992
iter  40 value 92.109081
iter  50 value 89.630906
iter  60 value 87.268602
iter  70 value 86.922514
iter  80 value 86.854222
iter  90 value 86.619326
iter 100 value 86.241731
final  value 86.241731 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.809339 
final  value 94.469105 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.891603 
final  value 94.468878 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.899040 
final  value 94.486061 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.770255 
final  value 94.486090 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.412789 
final  value 94.487364 
converged
Fitting Repeat 1 

# weights:  305
initial  value 106.940250 
iter  10 value 94.489126
iter  20 value 94.079674
iter  30 value 90.913331
iter  40 value 89.017773
iter  50 value 88.892578
iter  60 value 88.780536
iter  70 value 88.740151
iter  80 value 88.738085
final  value 88.738074 
converged
Fitting Repeat 2 

# weights:  305
initial  value 99.995103 
iter  10 value 94.489881
iter  20 value 94.484065
iter  30 value 94.470016
iter  40 value 89.294610
iter  50 value 89.277179
iter  60 value 89.221449
final  value 89.221447 
converged
Fitting Repeat 3 

# weights:  305
initial  value 106.965887 
iter  10 value 94.488947
iter  20 value 94.484332
iter  30 value 93.974272
iter  40 value 89.552780
iter  50 value 87.921800
iter  60 value 87.813505
iter  70 value 87.812105
final  value 87.811981 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.007551 
iter  10 value 94.327736
iter  20 value 94.308082
iter  30 value 93.920422
iter  40 value 93.851313
iter  50 value 93.849856
iter  60 value 93.848316
iter  70 value 93.847071
iter  80 value 93.844832
final  value 93.844421 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.473478 
iter  10 value 94.488867
iter  20 value 94.434436
final  value 94.354324 
converged
Fitting Repeat 1 

# weights:  507
initial  value 111.330034 
iter  10 value 94.474973
iter  20 value 94.467666
iter  30 value 94.024463
iter  40 value 89.283307
iter  50 value 87.902250
iter  60 value 87.247524
iter  70 value 87.246141
iter  80 value 87.221206
iter  90 value 86.405122
iter 100 value 86.298421
final  value 86.298421 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 97.121257 
iter  10 value 94.475610
iter  20 value 94.467541
iter  30 value 90.453759
iter  40 value 87.875767
iter  50 value 87.869140
iter  60 value 87.581684
iter  70 value 87.036656
iter  80 value 87.035439
iter  90 value 87.034785
final  value 87.034512 
converged
Fitting Repeat 3 

# weights:  507
initial  value 112.229205 
iter  10 value 94.492425
iter  20 value 94.458641
iter  30 value 93.934452
iter  40 value 93.786619
final  value 93.786576 
converged
Fitting Repeat 4 

# weights:  507
initial  value 128.833493 
iter  10 value 94.492759
iter  20 value 94.474892
final  value 94.467495 
converged
Fitting Repeat 5 

# weights:  507
initial  value 114.943170 
iter  10 value 94.492842
iter  20 value 94.346141
iter  30 value 93.541054
iter  40 value 90.265050
iter  50 value 90.078754
final  value 90.078426 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.145904 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 115.618793 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.585943 
final  value 93.915746 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.583756 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.277772 
final  value 93.869755 
converged
Fitting Repeat 1 

# weights:  305
initial  value 110.900880 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 106.306209 
final  value 93.915746 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.980644 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 101.366001 
iter  10 value 92.701774
final  value 92.701662 
converged
Fitting Repeat 5 

# weights:  305
initial  value 103.679758 
iter  10 value 92.907224
final  value 92.838591 
converged
Fitting Repeat 1 

# weights:  507
initial  value 107.484513 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 97.030397 
final  value 93.915746 
converged
Fitting Repeat 3 

# weights:  507
initial  value 103.346921 
iter  10 value 92.129798
iter  20 value 90.183386
iter  30 value 90.116487
iter  40 value 89.837346
iter  50 value 89.829438
final  value 89.829297 
converged
Fitting Repeat 4 

# weights:  507
initial  value 103.405272 
iter  10 value 93.870855
iter  20 value 93.853603
iter  30 value 93.793770
iter  40 value 93.792215
iter  50 value 93.788221
final  value 93.788212 
converged
Fitting Repeat 5 

# weights:  507
initial  value 133.681408 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.962994 
iter  10 value 94.111699
iter  20 value 94.056167
iter  30 value 89.644880
iter  40 value 86.945711
iter  50 value 86.908587
iter  60 value 86.449021
iter  70 value 86.325088
iter  80 value 86.000006
iter  90 value 85.916025
final  value 85.915764 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.970407 
iter  10 value 90.791692
iter  20 value 88.513918
iter  30 value 86.304107
iter  40 value 85.915981
final  value 85.915764 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.844451 
iter  10 value 94.056577
iter  20 value 94.002647
iter  30 value 93.878209
iter  40 value 93.869050
iter  50 value 93.785680
iter  60 value 91.150053
iter  70 value 86.555070
iter  80 value 85.980912
iter  90 value 85.915798
final  value 85.915764 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.264333 
iter  10 value 94.055222
iter  20 value 94.002826
iter  30 value 93.871517
iter  40 value 93.531354
iter  50 value 88.371249
iter  60 value 87.310287
iter  70 value 80.721086
iter  80 value 80.337018
iter  90 value 79.618530
iter 100 value 79.446052
final  value 79.446052 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 102.858741 
iter  10 value 93.857074
iter  20 value 93.265523
iter  30 value 89.219071
iter  40 value 85.827917
iter  50 value 85.124668
iter  60 value 84.826303
iter  70 value 82.350950
iter  80 value 82.151631
iter  90 value 82.116518
final  value 82.116513 
converged
Fitting Repeat 1 

# weights:  305
initial  value 109.299956 
iter  10 value 94.183516
iter  20 value 89.692655
iter  30 value 89.173472
iter  40 value 86.879211
iter  50 value 84.002743
iter  60 value 81.889122
iter  70 value 80.359197
iter  80 value 79.209676
iter  90 value 78.878992
iter 100 value 78.800849
final  value 78.800849 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.133284 
iter  10 value 93.887707
iter  20 value 90.634575
iter  30 value 82.630093
iter  40 value 80.939766
iter  50 value 79.224582
iter  60 value 79.055047
iter  70 value 78.809822
iter  80 value 78.353303
iter  90 value 77.913225
iter 100 value 77.804031
final  value 77.804031 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.487401 
iter  10 value 94.121083
iter  20 value 87.383706
iter  30 value 84.181059
iter  40 value 83.444787
iter  50 value 81.390723
iter  60 value 80.301643
iter  70 value 80.013820
iter  80 value 79.974616
iter  90 value 79.919825
iter 100 value 79.615376
final  value 79.615376 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 105.845346 
iter  10 value 93.720249
iter  20 value 87.550173
iter  30 value 84.309531
iter  40 value 83.998926
iter  50 value 82.306499
iter  60 value 81.356535
iter  70 value 81.089456
iter  80 value 80.656213
iter  90 value 79.284314
iter 100 value 78.801576
final  value 78.801576 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 107.332841 
iter  10 value 94.053594
iter  20 value 90.251304
iter  30 value 87.498103
iter  40 value 84.492194
iter  50 value 83.471157
iter  60 value 83.297546
iter  70 value 82.998630
iter  80 value 82.948705
iter  90 value 82.884388
iter 100 value 82.197168
final  value 82.197168 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 113.270159 
iter  10 value 92.578657
iter  20 value 88.275788
iter  30 value 87.634010
iter  40 value 83.718790
iter  50 value 83.410382
iter  60 value 83.292069
iter  70 value 82.871958
iter  80 value 81.484195
iter  90 value 80.709836
iter 100 value 80.106961
final  value 80.106961 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 118.118418 
iter  10 value 94.911801
iter  20 value 93.675011
iter  30 value 92.956958
iter  40 value 86.509534
iter  50 value 81.952572
iter  60 value 80.632635
iter  70 value 79.414855
iter  80 value 78.965861
iter  90 value 78.915856
iter 100 value 78.843319
final  value 78.843319 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 106.194678 
iter  10 value 93.526928
iter  20 value 88.178299
iter  30 value 82.719414
iter  40 value 79.510825
iter  50 value 79.061073
iter  60 value 78.810368
iter  70 value 78.598878
iter  80 value 78.310000
iter  90 value 78.221012
iter 100 value 78.073992
final  value 78.073992 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 118.631685 
iter  10 value 94.384102
iter  20 value 93.851240
iter  30 value 89.672893
iter  40 value 88.187858
iter  50 value 83.258998
iter  60 value 82.226019
iter  70 value 82.094711
iter  80 value 79.902758
iter  90 value 79.411921
iter 100 value 78.845287
final  value 78.845287 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.357065 
iter  10 value 93.926132
iter  20 value 87.256776
iter  30 value 84.293179
iter  40 value 81.167064
iter  50 value 80.438968
iter  60 value 79.660012
iter  70 value 79.168770
iter  80 value 78.866472
iter  90 value 78.734354
iter 100 value 78.621727
final  value 78.621727 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.649745 
final  value 93.917194 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.603384 
final  value 93.917491 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.005218 
final  value 94.054653 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.495780 
final  value 94.054575 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.632271 
final  value 94.054388 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.450526 
iter  10 value 93.374637
iter  20 value 88.597625
iter  30 value 88.595061
iter  40 value 86.164565
final  value 85.126234 
converged
Fitting Repeat 2 

# weights:  305
initial  value 99.146813 
iter  10 value 94.054206
iter  20 value 94.052577
iter  30 value 91.826362
iter  40 value 90.474968
iter  50 value 90.474645
iter  50 value 90.474645
iter  50 value 90.474645
final  value 90.474645 
converged
Fitting Repeat 3 

# weights:  305
initial  value 116.423038 
iter  10 value 94.057742
iter  20 value 94.052920
iter  20 value 94.052919
final  value 94.052919 
converged
Fitting Repeat 4 

# weights:  305
initial  value 123.473236 
iter  10 value 94.057495
iter  20 value 93.719648
iter  30 value 92.706129
iter  40 value 92.703306
final  value 92.703303 
converged
Fitting Repeat 5 

# weights:  305
initial  value 116.822045 
iter  10 value 94.057455
iter  20 value 94.052844
iter  30 value 89.765213
iter  40 value 85.834273
iter  50 value 80.143295
iter  60 value 78.587871
iter  70 value 78.477584
iter  80 value 78.369936
iter  90 value 78.211630
iter 100 value 78.177528
final  value 78.177528 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 151.473069 
iter  10 value 94.062563
iter  20 value 93.540402
iter  30 value 89.833044
iter  40 value 82.934873
iter  50 value 82.432502
iter  60 value 80.536657
iter  70 value 79.461065
iter  80 value 79.134686
iter  90 value 79.024658
iter 100 value 78.996213
final  value 78.996213 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 98.457710 
iter  10 value 93.923889
iter  20 value 93.915581
iter  30 value 93.464331
iter  40 value 85.691772
iter  50 value 80.405990
iter  60 value 80.265297
iter  70 value 80.256497
iter  80 value 80.013505
iter  90 value 78.901137
iter 100 value 78.252543
final  value 78.252543 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 98.708685 
iter  10 value 94.061394
iter  20 value 94.004246
iter  30 value 86.452183
iter  40 value 86.144515
iter  50 value 86.138325
iter  60 value 86.137339
iter  70 value 84.205315
iter  80 value 83.567255
final  value 83.564739 
converged
Fitting Repeat 4 

# weights:  507
initial  value 105.924758 
iter  10 value 93.925790
iter  20 value 93.923712
iter  30 value 93.919792
iter  40 value 93.919508
iter  50 value 93.915896
iter  60 value 92.364227
iter  70 value 86.526111
iter  80 value 86.190465
final  value 86.165578 
converged
Fitting Repeat 5 

# weights:  507
initial  value 94.313955 
iter  10 value 94.053990
iter  20 value 84.676072
iter  30 value 84.638343
iter  40 value 84.482199
iter  50 value 84.481070
iter  60 value 83.955763
iter  70 value 80.494597
iter  80 value 79.467058
iter  90 value 79.407483
final  value 79.406691 
converged
Fitting Repeat 1 

# weights:  507
initial  value 123.590177 
iter  10 value 105.354039
iter  20 value 105.347185
iter  30 value 105.151639
final  value 105.141656 
converged
Fitting Repeat 2 

# weights:  507
initial  value 125.293405 
iter  10 value 117.767023
iter  20 value 117.617235
iter  30 value 117.219110
iter  40 value 107.995068
iter  50 value 104.773900
iter  60 value 100.770372
iter  70 value 99.854379
iter  80 value 99.442219
iter  90 value 99.293769
iter 100 value 99.248911
final  value 99.248911 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 130.289040 
iter  10 value 114.822610
iter  20 value 114.537570
iter  30 value 114.534569
iter  40 value 114.532019
iter  50 value 114.272944
iter  60 value 114.246120
final  value 114.245274 
converged
Fitting Repeat 4 

# weights:  507
initial  value 119.345799 
iter  10 value 117.874041
iter  20 value 117.866166
iter  30 value 117.865491
iter  40 value 116.479099
iter  50 value 103.747267
iter  60 value 103.126140
iter  70 value 102.946072
iter  80 value 102.512484
iter  90 value 101.603789
iter 100 value 101.558365
final  value 101.558365 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 122.642388 
iter  10 value 117.897958
iter  20 value 117.760472
final  value 117.759094 
converged
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 Jan 21 05:23:51 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 
 75.577   2.035 146.732