##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD check --install=check:HPiP.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings HPiP_1.12.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.20-bioc/meat/HPiP.Rcheck’
* using R version 4.4.3 (2025-02-28)
* using platform: aarch64-unknown-linux-gnu
* R was compiled by
    aarch64-unknown-linux-gnu-gcc (GCC) 14.2.0
    GNU Fortran (GCC) 14.2.0
* running under: openEuler 24.03 (LTS)
* 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 loading without being on the library search path ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
    29 | then the Kronecker product is the code{(pm × qn)} block matrix
       |                                       ^
* checking Rd metadata ... OK
* checking Rd cross-references ... 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       34.302  0.420  34.770
corr_plot     33.933  0.235  34.227
FSmethod      33.668  0.351  34.085
pred_ensembel 17.478  0.633  16.902
enrichfindP    0.466  0.048  20.251
getFASTA       0.130  0.004   5.829
* 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
  ‘/home/biocbuild/bbs-3.20-bioc/meat/HPiP.Rcheck/00check.log’
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD INSTALL HPiP
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/R/R-4.4.3/site-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-unknown-linux-gnu

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

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

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

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

# weights:  103
initial  value 94.658152 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.900503 
final  value 94.466823 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.964828 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 106.724659 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 104.557261 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 120.795888 
iter  10 value 93.216798
final  value 93.216667 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.396639 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 98.105474 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 110.800163 
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.559368 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 108.999708 
iter  10 value 94.478287
iter  10 value 94.478287
iter  10 value 94.478287
final  value 94.478287 
converged
Fitting Repeat 2 

# weights:  507
initial  value 115.891845 
final  value 94.476191 
converged
Fitting Repeat 3 

# weights:  507
initial  value 112.633583 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 96.514774 
final  value 94.409363 
converged
Fitting Repeat 5 

# weights:  507
initial  value 106.626449 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.015973 
iter  10 value 94.481863
iter  20 value 94.385333
iter  30 value 94.379535
iter  40 value 90.971830
iter  50 value 90.865141
iter  60 value 90.152736
iter  70 value 90.074660
iter  80 value 86.539127
iter  90 value 86.167476
iter 100 value 86.050263
final  value 86.050263 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.898522 
iter  10 value 93.586302
iter  20 value 88.116501
iter  30 value 87.909212
iter  40 value 86.899281
iter  50 value 86.601415
iter  60 value 86.568892
final  value 86.560299 
converged
Fitting Repeat 3 

# weights:  103
initial  value 112.414701 
iter  10 value 94.331660
iter  20 value 87.870672
iter  30 value 86.558883
iter  40 value 85.991829
iter  50 value 85.773285
iter  60 value 85.612620
iter  70 value 85.570332
final  value 85.558613 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.044707 
iter  10 value 94.486769
iter  20 value 92.959461
iter  30 value 91.563102
iter  40 value 91.171160
iter  50 value 91.160050
final  value 91.160046 
converged
Fitting Repeat 5 

# weights:  103
initial  value 105.543970 
iter  10 value 94.488529
iter  20 value 94.430441
iter  30 value 94.397421
iter  40 value 89.155588
iter  50 value 86.913758
iter  60 value 85.622368
iter  70 value 85.338837
iter  80 value 85.175221
iter  90 value 85.098662
iter 100 value 85.071500
final  value 85.071500 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 115.564836 
iter  10 value 94.496587
iter  20 value 94.476282
iter  30 value 93.172594
iter  40 value 88.905850
iter  50 value 88.094722
iter  60 value 87.632651
iter  70 value 85.588679
iter  80 value 83.555455
iter  90 value 83.340779
iter 100 value 83.241633
final  value 83.241633 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 121.393076 
iter  10 value 94.480094
iter  20 value 93.391147
iter  30 value 91.109155
iter  40 value 89.243189
iter  50 value 87.680710
iter  60 value 87.045677
iter  70 value 86.524065
iter  80 value 84.452317
iter  90 value 83.013864
iter 100 value 82.835422
final  value 82.835422 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.322664 
iter  10 value 94.295576
iter  20 value 88.782104
iter  30 value 88.133335
iter  40 value 88.013859
iter  50 value 87.885873
iter  60 value 86.969612
iter  70 value 85.452244
iter  80 value 84.549955
iter  90 value 84.175791
iter 100 value 83.730442
final  value 83.730442 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 106.780176 
iter  10 value 96.631439
iter  20 value 90.339163
iter  30 value 88.964829
iter  40 value 88.181854
iter  50 value 86.512795
iter  60 value 84.085669
iter  70 value 83.878421
iter  80 value 83.636269
iter  90 value 83.599122
iter 100 value 83.474100
final  value 83.474100 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 102.533631 
iter  10 value 94.535653
iter  20 value 90.676662
iter  30 value 88.240535
iter  40 value 87.913344
iter  50 value 87.802322
iter  60 value 86.771819
iter  70 value 85.473990
iter  80 value 84.558170
iter  90 value 83.153668
iter 100 value 82.614368
final  value 82.614368 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 127.685195 
iter  10 value 94.915704
iter  20 value 94.602911
iter  30 value 92.714091
iter  40 value 89.479962
iter  50 value 86.834647
iter  60 value 85.607474
iter  70 value 84.888814
iter  80 value 84.355936
iter  90 value 84.226810
iter 100 value 83.871784
final  value 83.871784 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 122.132583 
iter  10 value 94.385720
iter  20 value 91.866531
iter  30 value 91.059604
iter  40 value 90.011621
iter  50 value 89.635006
iter  60 value 85.559815
iter  70 value 84.986423
iter  80 value 84.719775
iter  90 value 84.242266
iter 100 value 83.973160
final  value 83.973160 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.280855 
iter  10 value 94.095110
iter  20 value 88.925371
iter  30 value 85.202450
iter  40 value 83.849412
iter  50 value 83.296175
iter  60 value 82.943276
iter  70 value 82.721129
iter  80 value 82.525680
iter  90 value 82.253484
iter 100 value 82.115748
final  value 82.115748 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 111.133163 
iter  10 value 94.524833
iter  20 value 93.822579
iter  30 value 86.294770
iter  40 value 84.852585
iter  50 value 83.822187
iter  60 value 82.472047
iter  70 value 82.297864
iter  80 value 82.258491
iter  90 value 82.119934
iter 100 value 81.957703
final  value 81.957703 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 114.955525 
iter  10 value 95.239918
iter  20 value 85.948321
iter  30 value 85.242602
iter  40 value 84.736383
iter  50 value 84.133556
iter  60 value 83.027336
iter  70 value 82.623948
iter  80 value 82.458000
iter  90 value 82.213718
iter 100 value 82.063734
final  value 82.063734 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 103.760289 
final  value 94.485782 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.494319 
final  value 94.485978 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.992724 
final  value 94.485794 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.312641 
final  value 94.468340 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.664779 
final  value 94.485741 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.426571 
iter  10 value 94.487428
iter  20 value 94.459986
iter  30 value 91.857166
iter  40 value 91.570942
iter  50 value 91.569574
final  value 91.569572 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.205665 
iter  10 value 94.488855
iter  20 value 94.484232
final  value 94.484215 
converged
Fitting Repeat 3 

# weights:  305
initial  value 97.709699 
iter  10 value 94.471734
iter  20 value 94.467693
final  value 94.467003 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.232845 
iter  10 value 94.098454
iter  20 value 93.440724
iter  30 value 93.439996
iter  40 value 93.439737
final  value 93.439606 
converged
Fitting Repeat 5 

# weights:  305
initial  value 94.735397 
iter  10 value 93.127588
iter  20 value 93.039395
iter  30 value 93.026888
iter  40 value 92.163744
iter  50 value 90.225772
final  value 90.217431 
converged
Fitting Repeat 1 

# weights:  507
initial  value 110.915988 
iter  10 value 93.224337
iter  20 value 93.108920
iter  30 value 90.304379
final  value 90.302197 
converged
Fitting Repeat 2 

# weights:  507
initial  value 94.841024 
iter  10 value 93.267266
iter  20 value 88.780840
iter  30 value 87.646297
iter  40 value 87.277744
iter  50 value 85.059533
iter  60 value 84.094774
iter  70 value 84.012737
final  value 84.012234 
converged
Fitting Repeat 3 

# weights:  507
initial  value 96.288905 
iter  10 value 94.475059
iter  20 value 94.389707
iter  30 value 90.700474
iter  40 value 88.131655
iter  50 value 87.878993
iter  60 value 87.878364
final  value 87.878271 
converged
Fitting Repeat 4 

# weights:  507
initial  value 104.663554 
iter  10 value 94.492495
iter  20 value 94.484280
iter  30 value 94.355308
iter  40 value 87.510987
iter  50 value 87.395250
final  value 87.395133 
converged
Fitting Repeat 5 

# weights:  507
initial  value 113.893340 
iter  10 value 94.475051
iter  20 value 90.566286
iter  30 value 84.599794
iter  40 value 84.596686
iter  50 value 84.338427
iter  60 value 82.547749
iter  70 value 82.518224
iter  80 value 82.516132
iter  90 value 82.508788
iter 100 value 82.499374
final  value 82.499374 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.610729 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.132089 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.220644 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.684031 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.292200 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.336132 
iter  10 value 94.034464
final  value 94.032967 
converged
Fitting Repeat 2 

# weights:  305
initial  value 117.521085 
final  value 94.032967 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.486401 
iter  10 value 93.332537
final  value 93.332520 
converged
Fitting Repeat 4 

# weights:  305
initial  value 105.627740 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.271294 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.716567 
final  value 94.042012 
converged
Fitting Repeat 2 

# weights:  507
initial  value 102.397919 
iter  10 value 88.072077
iter  20 value 87.986630
iter  30 value 87.978544
final  value 87.976152 
converged
Fitting Repeat 3 

# weights:  507
initial  value 97.388397 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.436932 
iter  10 value 93.273022
iter  20 value 92.843022
iter  30 value 92.478943
iter  40 value 92.474744
final  value 92.474741 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.097131 
iter  10 value 94.081537
iter  20 value 87.385808
iter  30 value 86.910900
iter  40 value 86.886765
iter  50 value 86.875054
final  value 86.874985 
converged
Fitting Repeat 1 

# weights:  103
initial  value 105.464241 
iter  10 value 93.896934
iter  20 value 93.737346
iter  30 value 93.574667
iter  40 value 93.512688
iter  50 value 93.265125
iter  60 value 85.304672
iter  70 value 84.465771
iter  80 value 82.233133
iter  90 value 81.605007
iter 100 value 81.528135
final  value 81.528135 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 102.527054 
iter  10 value 93.577339
iter  20 value 85.873017
iter  30 value 85.538034
iter  40 value 83.833863
iter  50 value 83.429805
iter  60 value 83.271898
final  value 83.267263 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.677645 
iter  10 value 94.124575
iter  20 value 94.055766
iter  30 value 93.884831
iter  40 value 93.665085
iter  50 value 89.888975
iter  60 value 86.958857
iter  70 value 84.966162
iter  80 value 84.176821
iter  90 value 83.412234
iter 100 value 83.131784
final  value 83.131784 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 96.581241 
iter  10 value 94.056651
iter  20 value 84.902096
iter  30 value 84.594810
iter  40 value 84.203379
iter  50 value 83.939174
iter  60 value 83.789951
iter  70 value 83.731255
final  value 83.726948 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.711595 
iter  10 value 94.056727
iter  20 value 89.274371
iter  30 value 85.276895
iter  40 value 83.889860
iter  50 value 83.629801
iter  60 value 83.462586
iter  70 value 83.141947
iter  80 value 83.028724
iter  80 value 83.028724
iter  80 value 83.028724
final  value 83.028724 
converged
Fitting Repeat 1 

# weights:  305
initial  value 143.313684 
iter  10 value 93.983536
iter  20 value 88.139983
iter  30 value 84.743147
iter  40 value 83.966198
iter  50 value 81.205226
iter  60 value 80.755611
iter  70 value 80.511917
iter  80 value 80.359879
iter  90 value 79.951232
iter 100 value 79.703525
final  value 79.703525 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 113.240111 
iter  10 value 94.237678
iter  20 value 92.747339
iter  30 value 91.081757
iter  40 value 86.946475
iter  50 value 85.896517
iter  60 value 83.598115
iter  70 value 81.525178
iter  80 value 81.039819
iter  90 value 80.426054
iter 100 value 80.281980
final  value 80.281980 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 99.185921 
iter  10 value 93.552718
iter  20 value 86.991374
iter  30 value 85.282806
iter  40 value 83.014412
iter  50 value 82.030668
iter  60 value 81.977640
iter  70 value 81.136582
iter  80 value 80.183675
iter  90 value 79.752908
iter 100 value 79.574636
final  value 79.574636 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 130.236794 
iter  10 value 94.055289
iter  20 value 89.329873
iter  30 value 84.521880
iter  40 value 83.017553
iter  50 value 81.770140
iter  60 value 81.442253
iter  70 value 80.929421
iter  80 value 80.480580
iter  90 value 80.321153
iter 100 value 80.118521
final  value 80.118521 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 106.097834 
iter  10 value 94.080324
iter  20 value 93.935357
iter  30 value 93.553428
iter  40 value 93.437581
iter  50 value 90.816760
iter  60 value 83.473511
iter  70 value 82.544622
iter  80 value 80.373821
iter  90 value 80.242209
iter 100 value 80.192820
final  value 80.192820 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 118.587605 
iter  10 value 94.188779
iter  20 value 93.483775
iter  30 value 88.043131
iter  40 value 86.288803
iter  50 value 85.721258
iter  60 value 85.317989
iter  70 value 84.037137
iter  80 value 82.498330
iter  90 value 81.129432
iter 100 value 80.610746
final  value 80.610746 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 104.863679 
iter  10 value 86.814598
iter  20 value 84.070473
iter  30 value 83.647434
iter  40 value 82.438254
iter  50 value 82.111036
iter  60 value 81.730962
iter  70 value 81.580958
iter  80 value 81.559960
iter  90 value 81.468508
iter 100 value 81.274390
final  value 81.274390 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 108.077555 
iter  10 value 93.976608
iter  20 value 93.225738
iter  30 value 86.434257
iter  40 value 84.942721
iter  50 value 83.436949
iter  60 value 81.598550
iter  70 value 80.100188
iter  80 value 79.664876
iter  90 value 79.332318
iter 100 value 79.146149
final  value 79.146149 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 114.632009 
iter  10 value 93.914972
iter  20 value 90.418308
iter  30 value 85.011895
iter  40 value 83.264864
iter  50 value 81.551299
iter  60 value 79.945047
iter  70 value 79.412307
iter  80 value 79.357895
iter  90 value 79.184122
iter 100 value 79.055406
final  value 79.055406 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 105.139273 
iter  10 value 93.871044
iter  20 value 90.659516
iter  30 value 84.663117
iter  40 value 83.743650
iter  50 value 82.279487
iter  60 value 81.987406
iter  70 value 81.579572
iter  80 value 81.361985
iter  90 value 81.318807
iter 100 value 81.290165
final  value 81.290165 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 100.375599 
iter  10 value 94.054746
iter  20 value 94.051747
iter  30 value 93.749987
final  value 93.747063 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.048805 
final  value 94.054256 
converged
Fitting Repeat 3 

# weights:  103
initial  value 103.467264 
final  value 94.054513 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.376905 
iter  10 value 92.334091
iter  20 value 92.333505
iter  30 value 92.332990
iter  40 value 92.332767
iter  50 value 92.212498
final  value 92.208027 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.430194 
final  value 94.054565 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.477248 
iter  10 value 94.054348
iter  20 value 93.295902
iter  30 value 90.073948
iter  40 value 84.368910
iter  50 value 83.462182
iter  60 value 83.241410
iter  70 value 83.240989
iter  80 value 83.150489
iter  90 value 80.650298
iter 100 value 80.648790
final  value 80.648790 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.252761 
iter  10 value 92.472407
iter  20 value 92.393806
iter  30 value 92.393511
iter  40 value 91.868584
iter  50 value 91.783067
iter  60 value 91.782602
iter  70 value 91.782006
iter  80 value 90.384832
iter  90 value 85.121742
iter 100 value 80.632985
final  value 80.632985 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.979003 
iter  10 value 89.799170
iter  20 value 82.061247
iter  30 value 81.834649
iter  40 value 81.834310
iter  50 value 81.335776
iter  60 value 81.292744
iter  70 value 81.211621
iter  80 value 81.122808
iter  90 value 81.071309
iter 100 value 81.037403
final  value 81.037403 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 97.348212 
iter  10 value 93.584720
iter  20 value 91.340462
iter  30 value 85.170016
iter  40 value 83.885810
iter  50 value 83.723739
iter  60 value 83.694582
iter  70 value 83.693750
iter  80 value 83.693682
iter  90 value 83.673511
iter 100 value 83.604142
final  value 83.604142 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 100.739288 
iter  10 value 94.058118
iter  20 value 85.361620
iter  30 value 85.323311
iter  40 value 85.285713
iter  50 value 85.121442
iter  60 value 81.260450
iter  70 value 80.619294
iter  80 value 79.961666
iter  90 value 79.960401
iter 100 value 79.960036
final  value 79.960036 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 99.922194 
iter  10 value 93.553800
iter  20 value 93.456683
iter  30 value 93.338108
iter  40 value 92.642719
iter  50 value 91.580924
iter  60 value 91.536557
iter  70 value 91.530437
iter  80 value 91.529120
final  value 91.528797 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.515116 
iter  10 value 94.061013
iter  20 value 93.470155
iter  30 value 92.845407
final  value 92.844011 
converged
Fitting Repeat 3 

# weights:  507
initial  value 129.930866 
iter  10 value 94.042320
iter  20 value 93.831780
iter  30 value 90.741272
iter  40 value 87.620772
iter  50 value 87.617319
iter  60 value 87.615655
iter  70 value 87.604374
iter  80 value 86.918625
iter  90 value 83.071724
iter 100 value 82.312651
final  value 82.312651 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 94.061690 
iter  10 value 85.352533
iter  20 value 85.232891
iter  30 value 85.227474
iter  40 value 84.085595
iter  50 value 83.967097
iter  60 value 83.838550
iter  70 value 83.463409
iter  80 value 83.046551
iter  90 value 83.044776
iter 100 value 83.043307
final  value 83.043307 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 110.544610 
iter  10 value 94.041609
iter  20 value 94.012184
iter  30 value 89.767812
iter  40 value 88.417046
iter  50 value 88.392948
iter  60 value 86.848505
iter  70 value 81.798036
iter  80 value 80.689170
iter  90 value 79.039622
iter 100 value 78.639308
final  value 78.639308 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 101.171661 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 103.932448 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.580876 
iter  10 value 92.211113
final  value 92.211111 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.117488 
final  value 93.915746 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.113340 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.296775 
iter  10 value 93.919667
final  value 93.915746 
converged
Fitting Repeat 2 

# weights:  305
initial  value 112.252064 
final  value 93.915746 
converged
Fitting Repeat 3 

# weights:  305
initial  value 116.444600 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 91.144059 
iter  10 value 86.163970
final  value 86.163858 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.329906 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 101.386592 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 105.380583 
iter  10 value 93.360911
iter  20 value 93.328540
final  value 93.328497 
converged
Fitting Repeat 3 

# weights:  507
initial  value 97.106535 
iter  10 value 93.937327
final  value 93.937249 
converged
Fitting Repeat 4 

# weights:  507
initial  value 110.236631 
final  value 93.915746 
converged
Fitting Repeat 5 

# weights:  507
initial  value 99.572897 
iter  10 value 93.963056
iter  20 value 93.943842
iter  20 value 93.943841
iter  20 value 93.943841
final  value 93.943841 
converged
Fitting Repeat 1 

# weights:  103
initial  value 101.442950 
iter  10 value 94.013787
iter  20 value 88.491039
iter  30 value 85.276654
iter  40 value 84.762770
iter  50 value 83.877417
iter  60 value 83.749791
iter  70 value 83.748808
final  value 83.748766 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.841974 
iter  10 value 94.075491
iter  20 value 87.069380
iter  30 value 86.386272
iter  40 value 85.135180
iter  50 value 83.779001
iter  60 value 83.753436
iter  70 value 83.749126
iter  80 value 83.748766
iter  80 value 83.748766
iter  80 value 83.748766
final  value 83.748766 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.147828 
iter  10 value 94.217231
iter  20 value 94.056503
iter  30 value 93.824879
iter  40 value 91.634406
iter  50 value 90.710334
iter  60 value 85.676917
iter  70 value 84.967986
iter  80 value 83.656483
iter  90 value 83.106210
iter 100 value 82.747717
final  value 82.747717 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 103.218572 
iter  10 value 94.054936
iter  20 value 93.885942
iter  30 value 85.268133
iter  40 value 83.688953
iter  50 value 83.580143
iter  60 value 83.509075
iter  70 value 83.092633
iter  80 value 82.866232
iter  90 value 82.745230
final  value 82.745216 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.835132 
iter  10 value 93.962606
iter  20 value 93.801693
iter  30 value 87.266113
iter  40 value 84.951202
iter  50 value 84.933632
iter  60 value 84.925753
iter  70 value 84.147909
iter  80 value 83.911115
iter  90 value 83.767315
iter 100 value 83.757003
final  value 83.757003 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 118.890476 
iter  10 value 93.314854
iter  20 value 84.023990
iter  30 value 82.848667
iter  40 value 82.021424
iter  50 value 81.788146
iter  60 value 81.743216
iter  70 value 81.700405
iter  80 value 81.678976
iter  90 value 81.568453
iter 100 value 81.516645
final  value 81.516645 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.298345 
iter  10 value 93.299232
iter  20 value 85.972805
iter  30 value 84.794002
iter  40 value 82.859892
iter  50 value 82.682609
iter  60 value 81.987882
iter  70 value 81.789202
iter  80 value 81.745585
iter  90 value 81.737017
iter 100 value 81.727189
final  value 81.727189 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.985258 
iter  10 value 94.019678
iter  20 value 84.465737
iter  30 value 84.167998
iter  40 value 83.639323
iter  50 value 83.520244
iter  60 value 83.512521
iter  60 value 83.512520
final  value 83.512520 
converged
Fitting Repeat 4 

# weights:  305
initial  value 105.092943 
iter  10 value 94.127706
iter  20 value 92.904179
iter  30 value 89.283268
iter  40 value 83.593429
iter  50 value 83.469041
iter  60 value 83.385901
iter  70 value 83.293075
iter  80 value 83.080672
iter  90 value 82.629360
iter 100 value 81.927744
final  value 81.927744 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 120.858444 
iter  10 value 93.923934
iter  20 value 87.912890
iter  30 value 84.010935
iter  40 value 83.348744
iter  50 value 82.585626
iter  60 value 81.867713
iter  70 value 81.758005
iter  80 value 81.721327
iter  90 value 81.713234
iter 100 value 81.709660
final  value 81.709660 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.803564 
iter  10 value 94.056207
iter  20 value 90.251625
iter  30 value 87.008897
iter  40 value 85.258648
iter  50 value 83.908539
iter  60 value 83.739803
iter  70 value 83.045208
iter  80 value 82.121123
iter  90 value 81.634293
iter 100 value 81.270137
final  value 81.270137 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.895400 
iter  10 value 94.153339
iter  20 value 93.164925
iter  30 value 90.370624
iter  40 value 89.750150
iter  50 value 84.567533
iter  60 value 84.160642
iter  70 value 83.665198
iter  80 value 83.540539
iter  90 value 82.501975
iter 100 value 81.981044
final  value 81.981044 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 112.664294 
iter  10 value 94.098313
iter  20 value 91.114861
iter  30 value 85.043565
iter  40 value 84.575612
iter  50 value 83.028940
iter  60 value 82.643205
iter  70 value 81.872337
iter  80 value 81.571262
iter  90 value 81.406478
iter 100 value 81.150574
final  value 81.150574 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 120.626972 
iter  10 value 94.161961
iter  20 value 88.357551
iter  30 value 85.012802
iter  40 value 83.919071
iter  50 value 83.754070
iter  60 value 83.117808
iter  70 value 82.047886
iter  80 value 81.655983
iter  90 value 81.327804
iter 100 value 81.199481
final  value 81.199481 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 129.395266 
iter  10 value 94.219585
iter  20 value 85.652520
iter  30 value 83.999387
iter  40 value 83.771066
iter  50 value 83.639245
iter  60 value 82.333005
iter  70 value 81.929752
iter  80 value 81.718397
iter  90 value 81.350429
iter 100 value 81.161158
final  value 81.161158 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 112.482144 
final  value 94.054435 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.490975 
final  value 94.054481 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.088152 
iter  10 value 93.917406
iter  20 value 93.915882
iter  20 value 93.915881
iter  20 value 93.915881
final  value 93.915881 
converged
Fitting Repeat 4 

# weights:  103
initial  value 109.494774 
iter  10 value 94.054510
iter  20 value 93.873451
iter  30 value 89.775705
iter  40 value 89.772724
final  value 89.772717 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.548287 
iter  10 value 94.054468
iter  20 value 94.052912
iter  30 value 93.546602
iter  40 value 92.217776
iter  50 value 92.214900
final  value 92.213682 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.234457 
iter  10 value 94.057854
iter  20 value 94.053150
iter  30 value 92.621727
iter  40 value 83.748061
iter  50 value 83.673426
iter  60 value 83.672855
iter  70 value 83.672635
iter  80 value 83.672029
iter  90 value 83.671002
iter 100 value 83.183725
final  value 83.183725 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 107.263002 
iter  10 value 94.058120
iter  20 value 93.923741
iter  30 value 90.235855
final  value 89.496184 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.454012 
iter  10 value 94.056044
iter  20 value 94.031761
final  value 93.869879 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.439390 
iter  10 value 93.920644
iter  20 value 93.890926
iter  30 value 93.570113
iter  40 value 88.686278
iter  50 value 88.613847
iter  60 value 88.599738
iter  70 value 88.583970
iter  80 value 88.519688
iter  90 value 88.329452
iter 100 value 88.326837
final  value 88.326837 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.196430 
iter  10 value 94.058018
iter  20 value 92.784600
iter  30 value 91.865063
iter  40 value 85.487264
iter  50 value 84.017834
iter  60 value 83.672299
iter  70 value 83.477807
final  value 83.474574 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.092813 
iter  10 value 93.923911
iter  20 value 93.910262
iter  30 value 85.623190
iter  40 value 85.483616
iter  50 value 85.433871
iter  60 value 85.432416
iter  70 value 85.425594
iter  80 value 83.829311
iter  90 value 82.619044
iter 100 value 82.558241
final  value 82.558241 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 99.310551 
iter  10 value 94.061160
iter  20 value 93.978040
iter  30 value 87.514157
iter  40 value 86.991155
iter  50 value 84.221663
iter  60 value 83.479743
iter  70 value 83.477721
iter  80 value 83.475551
iter  90 value 83.110997
iter 100 value 82.443183
final  value 82.443183 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 97.195728 
iter  10 value 94.061485
final  value 94.053546 
converged
Fitting Repeat 4 

# weights:  507
initial  value 111.220291 
iter  10 value 93.924082
iter  20 value 93.915977
final  value 93.915852 
converged
Fitting Repeat 5 

# weights:  507
initial  value 132.856139 
iter  10 value 94.060605
iter  20 value 91.462227
iter  30 value 83.511703
iter  40 value 83.477842
final  value 83.477751 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.299056 
iter  10 value 94.279665
final  value 94.275362 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.884921 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 122.973569 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.251565 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 116.514092 
final  value 94.052434 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.368421 
final  value 94.484210 
converged
Fitting Repeat 2 

# weights:  305
initial  value 108.857579 
iter  10 value 93.851665
iter  20 value 93.814237
final  value 93.813954 
converged
Fitting Repeat 3 

# weights:  305
initial  value 105.282798 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.781567 
final  value 94.275362 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.110409 
iter  10 value 93.179400
iter  20 value 93.170780
final  value 93.170767 
converged
Fitting Repeat 1 

# weights:  507
initial  value 109.741234 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 109.118772 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 99.440086 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 107.381679 
final  value 94.275362 
converged
Fitting Repeat 5 

# weights:  507
initial  value 105.343885 
iter  10 value 94.275392
final  value 94.275365 
converged
Fitting Repeat 1 

# weights:  103
initial  value 109.945580 
iter  10 value 93.884848
iter  20 value 93.820962
iter  30 value 90.846495
iter  40 value 87.148758
iter  50 value 87.000223
iter  60 value 86.381879
iter  70 value 85.816444
iter  80 value 85.706608
iter  90 value 85.659583
iter 100 value 85.404366
final  value 85.404366 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 106.185999 
iter  10 value 94.487234
iter  20 value 94.464721
iter  30 value 93.884115
iter  40 value 93.829781
iter  50 value 93.823118
iter  60 value 93.822978
final  value 93.822924 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.150245 
iter  10 value 94.488091
iter  20 value 93.966465
iter  30 value 93.845023
iter  40 value 93.822557
iter  50 value 88.313253
iter  60 value 87.078835
iter  70 value 86.862301
iter  80 value 86.779770
iter  90 value 85.420923
iter 100 value 84.764246
final  value 84.764246 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 99.224774 
iter  10 value 94.346249
iter  20 value 86.924978
iter  30 value 85.756183
iter  40 value 85.257876
iter  50 value 84.881754
iter  60 value 84.698265
final  value 84.697962 
converged
Fitting Repeat 5 

# weights:  103
initial  value 104.897445 
iter  10 value 94.486308
iter  20 value 93.853288
iter  30 value 93.824529
iter  40 value 89.325416
iter  50 value 86.904678
iter  60 value 86.902338
iter  70 value 86.868825
iter  80 value 86.189013
iter  90 value 85.178427
iter 100 value 84.728051
final  value 84.728051 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 110.910185 
iter  10 value 94.663413
iter  20 value 92.147019
iter  30 value 86.872504
iter  40 value 86.094050
iter  50 value 83.029422
iter  60 value 82.268362
iter  70 value 81.024417
iter  80 value 80.582406
iter  90 value 80.142403
iter 100 value 79.943275
final  value 79.943275 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 106.091256 
iter  10 value 88.675841
iter  20 value 86.451713
iter  30 value 86.073048
iter  40 value 85.140130
iter  50 value 84.194170
iter  60 value 81.820817
iter  70 value 81.177021
iter  80 value 80.994329
iter  90 value 80.884527
iter 100 value 80.794647
final  value 80.794647 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.146543 
iter  10 value 94.121939
iter  20 value 93.867329
iter  30 value 89.393184
iter  40 value 86.854203
iter  50 value 86.763524
iter  60 value 85.688735
iter  70 value 84.830990
iter  80 value 83.846986
iter  90 value 83.513996
iter 100 value 81.807008
final  value 81.807008 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.494772 
iter  10 value 94.393599
iter  20 value 92.735600
iter  30 value 92.206825
iter  40 value 90.786215
iter  50 value 89.246037
iter  60 value 88.435403
iter  70 value 85.601997
iter  80 value 83.313533
iter  90 value 81.408600
iter 100 value 81.119401
final  value 81.119401 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.259655 
iter  10 value 94.509786
iter  20 value 88.156006
iter  30 value 86.542328
iter  40 value 85.531567
iter  50 value 84.980538
iter  60 value 84.801217
iter  70 value 84.699845
iter  80 value 84.642148
iter  90 value 84.585150
iter 100 value 83.714776
final  value 83.714776 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 116.985350 
iter  10 value 93.933704
iter  20 value 91.123050
iter  30 value 85.949345
iter  40 value 84.098554
iter  50 value 83.410360
iter  60 value 82.986074
iter  70 value 81.577453
iter  80 value 81.241355
iter  90 value 80.892177
iter 100 value 80.156253
final  value 80.156253 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 110.049748 
iter  10 value 94.247253
iter  20 value 87.284372
iter  30 value 83.151693
iter  40 value 81.925608
iter  50 value 80.729451
iter  60 value 80.117342
iter  70 value 79.898891
iter  80 value 79.689897
iter  90 value 79.548848
iter 100 value 79.498961
final  value 79.498961 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 106.518375 
iter  10 value 95.062023
iter  20 value 94.497715
iter  30 value 94.235638
iter  40 value 86.015209
iter  50 value 84.813197
iter  60 value 84.510032
iter  70 value 83.998287
iter  80 value 83.061904
iter  90 value 82.013427
iter 100 value 80.873793
final  value 80.873793 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 113.136803 
iter  10 value 94.512101
iter  20 value 87.432058
iter  30 value 86.021275
iter  40 value 85.279443
iter  50 value 84.441390
iter  60 value 84.356270
iter  70 value 84.334410
iter  80 value 83.999927
iter  90 value 82.654574
iter 100 value 81.846935
final  value 81.846935 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 103.500455 
iter  10 value 92.729491
iter  20 value 87.299817
iter  30 value 83.051706
iter  40 value 81.674765
iter  50 value 80.745279
iter  60 value 80.693333
iter  70 value 80.413407
iter  80 value 80.288641
iter  90 value 80.267498
iter 100 value 80.261317
final  value 80.261317 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 101.484966 
final  value 94.485813 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.291990 
final  value 94.485850 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.455778 
final  value 94.485984 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.265148 
iter  10 value 94.485965
iter  20 value 94.484257
final  value 94.484219 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.445367 
iter  10 value 94.485892
iter  20 value 94.436737
final  value 93.814258 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.939077 
iter  10 value 94.489575
iter  20 value 94.484228
iter  30 value 93.867409
iter  40 value 93.003293
iter  50 value 92.999842
iter  60 value 92.999776
final  value 92.999761 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.068646 
iter  10 value 94.280578
iter  20 value 93.985137
iter  30 value 84.921811
iter  40 value 84.760771
iter  50 value 84.752302
iter  60 value 84.644193
iter  70 value 84.359577
iter  80 value 84.358594
iter  90 value 84.334191
iter 100 value 84.240171
final  value 84.240171 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 99.608640 
iter  10 value 94.489009
iter  20 value 94.484400
final  value 94.484383 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.816407 
iter  10 value 94.488988
iter  20 value 94.416097
iter  30 value 93.753433
final  value 93.753414 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.455862 
iter  10 value 94.488561
iter  20 value 94.473382
iter  30 value 87.935240
iter  40 value 86.751806
iter  50 value 86.623867
iter  60 value 86.550565
final  value 86.549752 
converged
Fitting Repeat 1 

# weights:  507
initial  value 108.846316 
iter  10 value 93.822441
iter  20 value 93.815639
iter  30 value 93.814546
iter  40 value 93.798520
iter  50 value 93.754840
iter  60 value 93.753904
final  value 93.753809 
converged
Fitting Repeat 2 

# weights:  507
initial  value 110.922412 
iter  10 value 94.491774
iter  20 value 94.427582
iter  30 value 90.692439
iter  40 value 88.655114
iter  50 value 85.498104
iter  60 value 83.728028
iter  70 value 83.504983
iter  80 value 83.361484
iter  90 value 82.545408
iter 100 value 82.454231
final  value 82.454231 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 96.658460 
iter  10 value 94.492193
iter  20 value 94.464646
iter  30 value 93.814612
iter  30 value 93.814611
iter  30 value 93.814611
final  value 93.814611 
converged
Fitting Repeat 4 

# weights:  507
initial  value 101.987543 
iter  10 value 94.283830
iter  20 value 94.141846
iter  30 value 93.785028
final  value 93.784859 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.012580 
iter  10 value 90.850705
iter  20 value 89.342076
iter  30 value 86.958665
iter  40 value 86.944852
iter  50 value 86.941957
iter  60 value 86.393445
iter  70 value 85.870194
iter  80 value 85.248691
iter  90 value 84.766121
iter 100 value 84.495452
final  value 84.495452 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 107.069069 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.493563 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.365383 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.550902 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.911655 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.445399 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 106.785454 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.057478 
iter  10 value 92.062290
final  value 92.059162 
converged
Fitting Repeat 4 

# weights:  305
initial  value 111.993072 
iter  10 value 92.078703
iter  20 value 84.480077
iter  30 value 84.320789
iter  40 value 84.319769
iter  50 value 83.639086
iter  60 value 83.611887
final  value 83.611639 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.125373 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 99.923476 
iter  10 value 92.550480
iter  20 value 91.929300
final  value 91.929293 
converged
Fitting Repeat 2 

# weights:  507
initial  value 98.763844 
iter  10 value 93.657047
iter  20 value 92.823244
iter  30 value 92.822869
iter  40 value 92.820801
final  value 92.820789 
converged
Fitting Repeat 3 

# weights:  507
initial  value 107.782340 
iter  10 value 94.112911
final  value 94.112903 
converged
Fitting Repeat 4 

# weights:  507
initial  value 106.666595 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.913598 
final  value 93.634731 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.279869 
iter  10 value 94.488404
iter  20 value 94.417536
iter  30 value 93.783183
iter  40 value 92.734993
iter  50 value 90.542552
iter  60 value 90.444938
iter  70 value 90.439587
final  value 90.439577 
converged
Fitting Repeat 2 

# weights:  103
initial  value 113.419260 
iter  10 value 94.194910
iter  20 value 85.238556
iter  30 value 81.660687
iter  40 value 80.258529
iter  50 value 78.579456
iter  60 value 78.009857
iter  70 value 77.967945
iter  80 value 77.965595
final  value 77.963392 
converged
Fitting Repeat 3 

# weights:  103
initial  value 107.893430 
iter  10 value 94.363157
iter  20 value 88.739037
iter  30 value 86.562222
iter  40 value 82.209617
iter  50 value 78.811426
iter  60 value 78.293504
iter  70 value 78.071855
iter  80 value 77.925582
iter  90 value 77.654713
final  value 77.651588 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.275786 
iter  10 value 93.132872
iter  20 value 84.928731
iter  30 value 84.374077
iter  40 value 83.070200
iter  50 value 82.887398
iter  60 value 82.356011
iter  70 value 81.935793
final  value 81.927165 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.282195 
iter  10 value 94.488843
iter  20 value 94.126546
iter  30 value 93.851641
iter  40 value 93.817530
iter  50 value 88.796501
iter  60 value 86.767048
iter  70 value 83.638735
iter  80 value 83.099394
iter  90 value 82.683555
iter 100 value 82.345079
final  value 82.345079 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 117.636591 
iter  10 value 94.203000
iter  20 value 85.613596
iter  30 value 83.393382
iter  40 value 82.406103
iter  50 value 81.949587
iter  60 value 81.750881
iter  70 value 80.298656
iter  80 value 78.295111
iter  90 value 77.545305
iter 100 value 77.182481
final  value 77.182481 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 114.359490 
iter  10 value 94.726469
iter  20 value 94.503853
iter  30 value 86.056172
iter  40 value 84.224138
iter  50 value 83.809227
iter  60 value 81.266107
iter  70 value 79.403086
iter  80 value 78.627618
iter  90 value 77.806922
iter 100 value 77.455078
final  value 77.455078 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.778156 
iter  10 value 94.380909
iter  20 value 87.842912
iter  30 value 82.161306
iter  40 value 80.208897
iter  50 value 78.922423
iter  60 value 78.221055
iter  70 value 77.579151
iter  80 value 77.028259
iter  90 value 76.964507
iter 100 value 76.920719
final  value 76.920719 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 116.564648 
iter  10 value 94.850786
iter  20 value 94.528690
iter  30 value 92.421217
iter  40 value 85.243370
iter  50 value 85.018306
iter  60 value 84.417300
iter  70 value 80.187597
iter  80 value 78.363805
iter  90 value 77.410140
iter 100 value 77.206306
final  value 77.206306 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 129.810129 
iter  10 value 94.077830
iter  20 value 92.574810
iter  30 value 84.491558
iter  40 value 83.364043
iter  50 value 79.288101
iter  60 value 77.803951
iter  70 value 76.984395
iter  80 value 76.586025
iter  90 value 76.303067
iter 100 value 76.151632
final  value 76.151632 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 123.935633 
iter  10 value 94.525631
iter  20 value 90.753225
iter  30 value 87.625407
iter  40 value 84.871858
iter  50 value 84.099942
iter  60 value 80.996678
iter  70 value 77.843795
iter  80 value 77.204151
iter  90 value 76.619858
iter 100 value 76.438612
final  value 76.438612 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 109.794654 
iter  10 value 94.490907
iter  20 value 86.951850
iter  30 value 82.130969
iter  40 value 80.437206
iter  50 value 77.998881
iter  60 value 76.981465
iter  70 value 76.375768
iter  80 value 76.150810
iter  90 value 76.060224
iter 100 value 75.935721
final  value 75.935721 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 105.687381 
iter  10 value 95.236094
iter  20 value 94.602943
iter  30 value 86.317296
iter  40 value 84.992996
iter  50 value 82.275026
iter  60 value 80.249294
iter  70 value 79.670263
iter  80 value 77.880661
iter  90 value 77.247634
iter 100 value 77.074152
final  value 77.074152 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 105.284226 
iter  10 value 96.403238
iter  20 value 89.748582
iter  30 value 86.955252
iter  40 value 84.097478
iter  50 value 83.108726
iter  60 value 82.594430
iter  70 value 79.155515
iter  80 value 78.119581
iter  90 value 77.398594
iter 100 value 77.355208
final  value 77.355208 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 107.206821 
iter  10 value 94.457370
iter  20 value 94.135659
iter  30 value 89.079446
iter  40 value 83.229228
iter  50 value 82.169881
iter  60 value 80.272695
iter  70 value 79.827524
iter  80 value 78.755390
iter  90 value 78.464791
iter 100 value 78.209037
final  value 78.209037 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.536979 
iter  10 value 94.485829
final  value 94.484216 
converged
Fitting Repeat 2 

# weights:  103
initial  value 111.338366 
final  value 94.485828 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.763386 
final  value 94.485828 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.935526 
final  value 94.486134 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.279651 
final  value 94.486045 
converged
Fitting Repeat 1 

# weights:  305
initial  value 125.505927 
iter  10 value 94.489261
iter  20 value 94.484303
iter  30 value 93.944589
iter  40 value 88.375324
iter  50 value 87.762926
iter  60 value 87.542130
iter  70 value 86.825281
iter  80 value 86.800729
iter  90 value 86.800494
iter 100 value 85.252273
final  value 85.252273 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.754580 
iter  10 value 94.487217
iter  20 value 94.116814
iter  30 value 94.113175
final  value 94.113167 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.482229 
iter  10 value 93.944268
iter  20 value 93.882088
iter  30 value 93.477489
iter  40 value 92.986029
iter  50 value 92.794040
iter  60 value 92.615748
iter  70 value 88.074490
iter  80 value 81.831535
iter  90 value 81.825313
iter 100 value 81.817648
final  value 81.817648 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 96.629225 
iter  10 value 94.118370
iter  20 value 94.114284
iter  30 value 93.501076
iter  40 value 83.913684
final  value 83.787448 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.330383 
iter  10 value 94.489063
iter  20 value 94.465349
iter  30 value 93.729566
iter  40 value 93.728691
iter  50 value 93.728077
iter  60 value 93.727143
final  value 93.727137 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.776539 
iter  10 value 94.492061
iter  20 value 94.469231
iter  30 value 93.725523
final  value 93.725508 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.326917 
iter  10 value 93.665617
iter  20 value 93.643054
iter  30 value 90.327264
iter  40 value 83.786107
iter  40 value 83.786107
iter  40 value 83.786107
final  value 83.786107 
converged
Fitting Repeat 3 

# weights:  507
initial  value 100.612955 
iter  10 value 93.191147
iter  20 value 92.033896
iter  30 value 91.919372
iter  40 value 91.215673
iter  50 value 91.008575
iter  60 value 90.978144
iter  70 value 90.962263
iter  80 value 90.958055
iter  90 value 90.956231
iter 100 value 90.951982
final  value 90.951982 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 109.700697 
iter  10 value 93.480951
iter  20 value 93.469611
iter  30 value 89.232323
iter  40 value 85.414449
iter  50 value 83.785778
iter  60 value 83.608356
final  value 83.604413 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.175254 
iter  10 value 93.466626
iter  20 value 81.132402
iter  30 value 81.094980
iter  40 value 80.671610
iter  50 value 80.540512
iter  60 value 80.537838
iter  70 value 78.717987
iter  80 value 77.459271
iter  90 value 76.227949
iter 100 value 76.173303
final  value 76.173303 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 159.033109 
iter  10 value 118.657184
iter  20 value 116.711297
iter  30 value 106.295955
iter  40 value 105.812798
iter  50 value 104.590660
iter  60 value 104.333207
iter  70 value 104.247850
iter  80 value 103.971436
iter  90 value 103.739875
iter 100 value 103.529215
final  value 103.529215 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 138.214401 
iter  10 value 118.342796
iter  20 value 117.726648
iter  30 value 115.283945
iter  40 value 108.822628
iter  50 value 104.353142
iter  60 value 103.511068
iter  70 value 102.370554
iter  80 value 102.251957
iter  90 value 101.782678
iter 100 value 101.221293
final  value 101.221293 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 128.317796 
iter  10 value 116.731123
iter  20 value 106.635894
iter  30 value 102.905569
iter  40 value 102.247422
iter  50 value 101.989341
iter  60 value 101.431685
iter  70 value 101.074333
iter  80 value 101.042765
iter  90 value 101.022235
iter 100 value 100.925748
final  value 100.925748 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 138.657907 
iter  10 value 121.470556
iter  20 value 117.839362
iter  30 value 115.105186
iter  40 value 107.669941
iter  50 value 105.772884
iter  60 value 105.697078
iter  70 value 104.912360
iter  80 value 103.087725
iter  90 value 102.934996
iter 100 value 102.404605
final  value 102.404605 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 140.545015 
iter  10 value 117.723808
iter  20 value 111.634025
iter  30 value 109.525062
iter  40 value 108.746369
iter  50 value 104.883831
iter  60 value 104.488306
iter  70 value 103.768208
iter  80 value 102.866169
iter  90 value 101.781479
iter 100 value 101.218748
final  value 101.218748 
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 07:32:01 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 
 52.384   1.345 206.722