##############################################################################
##############################################################################
###
### 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_1.17.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.23-bioc/meat/HPiP.Rcheck’
* using R Under development (unstable) (2025-11-04 r88984)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 16.0.0 (clang-1600.0.26.6)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Ventura 13.7.8
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.17.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking 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
Unknown package ‘ftrCOOL’ in Rd xrefs
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... WARNING
Codoc mismatches from Rd file 'pred_ensembel.Rd':
pred_ensembel
  Code: function(features, gold_standard, classifier = c("avNNet",
                 "svmRadial", "ranger"), resampling.method = "cv",
                 ncross = 2, repeats = 2, verboseIter = TRUE, plots =
                 FALSE, filename = "plots.pdf")
  Docs: function(features, gold_standard, classifier = c("avNNet",
                 "svmRadial", "ranger"), resampling.method = "cv",
                 ncross = 2, repeats = 2, verboseIter = TRUE, plots =
                 TRUE, filename = "plots.pdf")
  Mismatches in argument default values:
    Name: 'plots' Code: FALSE Docs: TRUE

* 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
FSmethod      18.231  0.688  18.991
var_imp       18.108  0.752  18.880
corr_plot     18.026  0.650  18.733
pred_ensembel  6.007  0.121   5.448
enrichfindP    0.184  0.037  23.507
getFASTA       0.029  0.005   7.402
* 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: 1 WARNING, 3 NOTEs
See
  ‘/Users/biocbuild/bbs-3.23-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/4.6-arm64/Resources/library’
* installing *source* package ‘HPiP’ ...
** this is package ‘HPiP’ version ‘1.17.1’
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HPiP)

R Under development (unstable) (2025-11-04 r88984) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

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

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

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

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

# weights:  103
initial  value 113.167189 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.506648 
iter  10 value 94.053388
iter  20 value 94.052436
iter  20 value 94.052435
iter  20 value 94.052435
final  value 94.052435 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.158639 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.147572 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.068750 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.670724 
final  value 94.479540 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.327548 
final  value 94.052434 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.155247 
final  value 94.395062 
converged
Fitting Repeat 4 

# weights:  305
initial  value 126.863809 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 104.335039 
iter  10 value 93.571597
iter  20 value 91.841933
iter  30 value 91.814749
final  value 91.814654 
converged
Fitting Repeat 1 

# weights:  507
initial  value 96.586247 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.960881 
iter  10 value 94.484212
iter  10 value 94.484211
iter  10 value 94.484211
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 95.273183 
final  value 94.275362 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.613918 
final  value 94.275362 
converged
Fitting Repeat 5 

# weights:  507
initial  value 145.460746 
iter  10 value 94.275363
iter  10 value 94.275362
iter  10 value 94.275362
final  value 94.275362 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.074875 
iter  10 value 94.441138
iter  20 value 92.277792
iter  30 value 92.112958
iter  40 value 92.080274
iter  50 value 91.976921
iter  60 value 91.908544
iter  70 value 91.782113
iter  80 value 91.683984
final  value 91.683254 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.430797 
iter  10 value 94.502770
iter  20 value 94.464258
iter  30 value 90.614670
iter  40 value 84.393495
iter  50 value 83.987172
iter  60 value 82.645236
iter  70 value 82.213378
iter  80 value 82.144482
iter  90 value 82.086609
final  value 82.086598 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.225984 
iter  10 value 94.367459
iter  20 value 85.094177
iter  30 value 84.573196
iter  40 value 83.763687
iter  50 value 83.238473
iter  60 value 82.638918
iter  70 value 82.543633
final  value 82.543611 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.642877 
iter  10 value 94.525981
iter  20 value 94.125306
iter  30 value 89.257608
iter  40 value 86.489557
iter  50 value 85.690225
iter  60 value 83.542811
iter  70 value 82.443198
iter  80 value 81.478893
iter  90 value 81.377270
iter 100 value 81.356503
final  value 81.356503 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 98.937882 
iter  10 value 94.458019
iter  20 value 92.540420
iter  30 value 92.414881
iter  40 value 92.218550
iter  50 value 90.386880
iter  60 value 87.061145
iter  70 value 85.773818
iter  80 value 85.465175
iter  90 value 85.436129
final  value 85.432192 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.144555 
iter  10 value 92.611785
iter  20 value 92.095292
iter  30 value 91.683158
iter  40 value 89.325410
iter  50 value 86.420593
iter  60 value 85.317450
iter  70 value 83.079987
iter  80 value 81.150386
iter  90 value 80.734500
iter 100 value 80.571933
final  value 80.571933 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 108.715185 
iter  10 value 94.666397
iter  20 value 94.299358
iter  30 value 84.956069
iter  40 value 83.953391
iter  50 value 83.171696
iter  60 value 82.532829
iter  70 value 82.417830
iter  80 value 82.391739
iter  90 value 81.826011
iter 100 value 80.952240
final  value 80.952240 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 105.769163 
iter  10 value 94.327491
iter  20 value 88.709865
iter  30 value 87.441159
iter  40 value 87.000657
iter  50 value 84.126456
iter  60 value 83.030404
iter  70 value 81.778384
iter  80 value 81.683418
iter  90 value 81.563031
iter 100 value 81.539072
final  value 81.539072 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 111.618409 
iter  10 value 94.448882
iter  20 value 93.732451
iter  30 value 86.700087
iter  40 value 85.913375
iter  50 value 85.259261
iter  60 value 82.405769
iter  70 value 81.945065
iter  80 value 81.455294
iter  90 value 81.051056
iter 100 value 80.896565
final  value 80.896565 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 123.002060 
iter  10 value 94.526484
iter  20 value 93.618185
iter  30 value 89.287220
iter  40 value 88.200145
iter  50 value 84.194551
iter  60 value 82.430712
iter  70 value 81.586452
iter  80 value 81.426122
iter  90 value 81.224541
iter 100 value 81.103106
final  value 81.103106 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.294160 
iter  10 value 95.680704
iter  20 value 93.948732
iter  30 value 85.655948
iter  40 value 84.269623
iter  50 value 83.765617
iter  60 value 83.472601
iter  70 value 83.079389
iter  80 value 80.654762
iter  90 value 79.580854
iter 100 value 79.353281
final  value 79.353281 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 121.977910 
iter  10 value 94.691304
iter  20 value 94.284550
iter  30 value 92.867848
iter  40 value 89.917756
iter  50 value 85.778078
iter  60 value 81.866185
iter  70 value 80.698516
iter  80 value 80.370121
iter  90 value 79.885467
iter 100 value 79.520845
final  value 79.520845 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 106.645359 
iter  10 value 94.480952
iter  20 value 93.793558
iter  30 value 93.248328
iter  40 value 92.941216
iter  50 value 92.205958
iter  60 value 88.540112
iter  70 value 83.573527
iter  80 value 80.936563
iter  90 value 80.648489
iter 100 value 80.266542
final  value 80.266542 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 127.971935 
iter  10 value 94.921589
iter  20 value 87.618314
iter  30 value 86.313719
iter  40 value 85.587496
iter  50 value 84.675393
iter  60 value 84.332953
iter  70 value 82.254961
iter  80 value 80.845862
iter  90 value 80.167059
iter 100 value 80.052156
final  value 80.052156 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 118.879504 
iter  10 value 96.402843
iter  20 value 91.661689
iter  30 value 89.808714
iter  40 value 87.799965
iter  50 value 86.439048
iter  60 value 85.232565
iter  70 value 85.007323
iter  80 value 84.509404
iter  90 value 83.007285
iter 100 value 81.650490
final  value 81.650490 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 100.152730 
iter  10 value 94.481210
iter  20 value 94.327677
final  value 94.327608 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.070924 
final  value 94.485870 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.575869 
final  value 94.485932 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.767058 
final  value 94.486041 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.272479 
iter  10 value 94.386709
final  value 94.327332 
converged
Fitting Repeat 1 

# weights:  305
initial  value 108.053993 
iter  10 value 94.494481
iter  20 value 94.477749
iter  30 value 91.279056
iter  40 value 90.374742
iter  50 value 85.924283
iter  60 value 84.090301
iter  70 value 84.089299
iter  80 value 84.085076
iter  90 value 84.084603
iter 100 value 84.084482
final  value 84.084482 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 103.138481 
iter  10 value 94.280877
iter  20 value 94.275824
final  value 94.275553 
converged
Fitting Repeat 3 

# weights:  305
initial  value 107.637347 
iter  10 value 93.948162
iter  20 value 89.229548
iter  30 value 85.959021
iter  40 value 85.667375
iter  50 value 85.590273
iter  60 value 85.587676
iter  70 value 85.585602
iter  80 value 85.326669
iter  90 value 85.271351
iter 100 value 84.276243
final  value 84.276243 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.781626 
iter  10 value 94.330785
iter  20 value 92.127703
iter  30 value 86.420899
iter  40 value 86.334757
iter  50 value 86.332319
iter  60 value 85.170010
iter  70 value 82.900857
iter  80 value 82.749634
iter  90 value 82.749287
final  value 82.749150 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.298074 
iter  10 value 94.489177
iter  20 value 94.484244
iter  30 value 93.344490
iter  40 value 85.333268
iter  50 value 80.483620
iter  60 value 80.204081
iter  70 value 79.909309
iter  80 value 79.657202
iter  90 value 79.504088
final  value 79.502871 
converged
Fitting Repeat 1 

# weights:  507
initial  value 107.515388 
iter  10 value 94.491943
iter  20 value 94.343712
iter  30 value 84.886787
final  value 84.885568 
converged
Fitting Repeat 2 

# weights:  507
initial  value 122.601495 
iter  10 value 94.283637
iter  20 value 94.279114
iter  30 value 89.481579
iter  40 value 85.637663
iter  50 value 82.742257
iter  60 value 79.758735
iter  70 value 79.717168
iter  80 value 79.457509
iter  90 value 79.352480
iter 100 value 79.180747
final  value 79.180747 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 114.131227 
iter  10 value 94.283515
iter  20 value 94.265057
iter  30 value 84.270072
iter  40 value 83.674319
iter  50 value 82.168430
iter  60 value 82.095102
iter  70 value 82.092161
final  value 82.092076 
converged
Fitting Repeat 4 

# weights:  507
initial  value 121.708520 
iter  10 value 94.492371
iter  20 value 94.015025
iter  30 value 87.141539
iter  40 value 82.347723
iter  50 value 82.068810
iter  60 value 81.869914
final  value 81.868658 
converged
Fitting Repeat 5 

# weights:  507
initial  value 115.101397 
iter  10 value 94.492367
iter  20 value 94.483812
iter  30 value 91.505134
iter  40 value 87.976537
iter  50 value 83.015620
iter  60 value 82.744305
iter  70 value 82.739775
final  value 82.739756 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.361660 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.101399 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.796839 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 94.582527 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.497614 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.236137 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.302984 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 124.455457 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 111.434450 
iter  10 value 92.716611
final  value 92.716378 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.976011 
iter  10 value 92.301960
iter  20 value 92.220089
iter  30 value 91.728568
iter  40 value 91.714294
final  value 91.714286 
converged
Fitting Repeat 1 

# weights:  507
initial  value 116.024434 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 94.550702 
final  value 94.052909 
converged
Fitting Repeat 3 

# weights:  507
initial  value 107.683805 
iter  10 value 94.062261
iter  20 value 92.892795
final  value 92.892737 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.460232 
iter  10 value 93.368593
final  value 93.356725 
converged
Fitting Repeat 5 

# weights:  507
initial  value 105.662410 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 102.663572 
iter  10 value 93.120054
iter  20 value 88.739276
iter  30 value 87.838445
iter  40 value 87.365980
iter  50 value 87.283468
iter  60 value 87.139973
iter  70 value 85.955830
iter  80 value 85.257117
iter  90 value 85.198667
iter 100 value 85.156115
final  value 85.156115 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.690496 
iter  10 value 94.072097
iter  20 value 94.018630
iter  30 value 93.428522
iter  40 value 93.230076
iter  50 value 89.569838
iter  60 value 87.776119
iter  70 value 87.535189
iter  80 value 86.697916
iter  90 value 86.165227
iter 100 value 85.589837
final  value 85.589837 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 105.069780 
iter  10 value 94.054497
iter  20 value 88.508512
iter  30 value 86.339926
iter  40 value 83.832886
iter  50 value 83.590706
iter  60 value 83.358835
iter  70 value 83.305026
final  value 83.284630 
converged
Fitting Repeat 4 

# weights:  103
initial  value 113.065586 
iter  10 value 94.008183
iter  20 value 93.430730
iter  30 value 93.428124
iter  40 value 93.426912
iter  50 value 93.312483
iter  60 value 90.083690
iter  70 value 89.627900
iter  80 value 87.197368
iter  90 value 84.357956
iter 100 value 83.747711
final  value 83.747711 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 120.227554 
iter  10 value 94.054614
iter  20 value 93.784604
iter  30 value 88.882959
iter  40 value 87.403406
iter  50 value 84.671528
iter  60 value 83.653870
iter  70 value 83.392563
iter  80 value 83.313323
iter  90 value 83.295746
iter 100 value 83.284438
final  value 83.284438 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 116.420532 
iter  10 value 94.061456
iter  20 value 93.252621
iter  30 value 89.776293
iter  40 value 87.573013
iter  50 value 87.125752
iter  60 value 85.027625
iter  70 value 84.803871
iter  80 value 84.778154
iter  90 value 84.706285
iter 100 value 84.623151
final  value 84.623151 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 102.015952 
iter  10 value 93.332341
iter  20 value 89.457549
iter  30 value 87.010727
iter  40 value 85.776547
iter  50 value 85.625595
iter  60 value 84.947895
iter  70 value 83.928126
iter  80 value 83.545377
iter  90 value 82.972345
iter 100 value 82.291490
final  value 82.291490 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.911946 
iter  10 value 93.461916
iter  20 value 93.427415
iter  30 value 93.405656
iter  40 value 90.317309
iter  50 value 87.236138
iter  60 value 85.926938
iter  70 value 85.599158
iter  80 value 84.985994
iter  90 value 83.884849
iter 100 value 83.596010
final  value 83.596010 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 99.992654 
iter  10 value 94.156836
iter  20 value 93.493581
iter  30 value 93.415359
iter  40 value 89.194188
iter  50 value 87.257507
iter  60 value 86.359832
iter  70 value 85.675959
iter  80 value 85.474633
iter  90 value 85.324340
iter 100 value 85.298906
final  value 85.298906 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 121.501667 
iter  10 value 93.359289
iter  20 value 91.063840
iter  30 value 86.287189
iter  40 value 85.710957
iter  50 value 85.439561
iter  60 value 85.219068
iter  70 value 84.912745
iter  80 value 84.208181
iter  90 value 83.993455
iter 100 value 83.904794
final  value 83.904794 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 105.838169 
iter  10 value 94.745062
iter  20 value 90.995215
iter  30 value 86.131966
iter  40 value 85.078179
iter  50 value 84.945343
iter  60 value 84.038978
iter  70 value 83.836615
iter  80 value 83.663410
iter  90 value 83.361752
iter 100 value 82.752962
final  value 82.752962 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 118.445935 
iter  10 value 94.310214
iter  20 value 92.936588
iter  30 value 87.907471
iter  40 value 85.235448
iter  50 value 83.345061
iter  60 value 82.611935
iter  70 value 82.339072
iter  80 value 81.932177
iter  90 value 81.856718
iter 100 value 81.788590
final  value 81.788590 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 113.808570 
iter  10 value 95.947084
iter  20 value 89.343116
iter  30 value 87.708837
iter  40 value 85.451533
iter  50 value 85.224773
iter  60 value 85.180138
iter  70 value 85.136568
iter  80 value 84.727456
iter  90 value 83.447994
iter 100 value 83.070269
final  value 83.070269 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 106.074725 
iter  10 value 94.632327
iter  20 value 86.576902
iter  30 value 85.954284
iter  40 value 84.960659
iter  50 value 83.366903
iter  60 value 83.236908
iter  70 value 83.125580
iter  80 value 83.097426
iter  90 value 82.974287
iter 100 value 82.713520
final  value 82.713520 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 102.618265 
iter  10 value 95.741715
iter  20 value 89.199651
iter  30 value 87.737639
iter  40 value 85.421844
iter  50 value 84.564904
iter  60 value 83.833122
iter  70 value 83.358430
iter  80 value 82.158930
iter  90 value 81.920968
iter 100 value 81.700005
final  value 81.700005 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.317930 
iter  10 value 94.054432
iter  20 value 94.052715
iter  30 value 92.897836
iter  40 value 92.894728
iter  50 value 85.228582
iter  60 value 85.155736
final  value 85.150804 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.621561 
iter  10 value 87.252959
iter  20 value 87.250602
iter  30 value 87.238278
iter  40 value 85.931745
iter  50 value 85.370819
final  value 85.369765 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.661241 
final  value 94.054465 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.333798 
iter  10 value 94.029354
iter  20 value 92.476506
iter  30 value 89.108453
final  value 89.108442 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.317890 
iter  10 value 91.592290
final  value 91.435057 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.920989 
iter  10 value 94.057361
iter  20 value 93.981994
final  value 93.357155 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.179042 
iter  10 value 94.057608
iter  20 value 94.053101
iter  30 value 91.700621
iter  40 value 91.377680
iter  50 value 88.024370
iter  60 value 84.938540
iter  70 value 84.500115
iter  80 value 84.371209
final  value 84.371157 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.782310 
iter  10 value 94.059682
iter  20 value 94.055920
iter  30 value 90.402806
iter  40 value 85.676114
iter  50 value 85.370523
final  value 85.370517 
converged
Fitting Repeat 4 

# weights:  305
initial  value 103.694497 
iter  10 value 93.796484
iter  20 value 93.790316
iter  30 value 89.065465
iter  40 value 86.646630
final  value 86.603299 
converged
Fitting Repeat 5 

# weights:  305
initial  value 94.970431 
iter  10 value 94.057577
iter  20 value 93.367751
iter  30 value 87.697938
iter  40 value 87.498132
iter  50 value 87.495297
iter  60 value 87.492516
iter  70 value 87.474626
iter  80 value 87.444232
iter  90 value 87.442272
iter 100 value 87.441837
final  value 87.441837 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 98.805836 
iter  10 value 94.060573
iter  20 value 92.865611
iter  30 value 90.572685
iter  40 value 88.985232
final  value 88.985127 
converged
Fitting Repeat 2 

# weights:  507
initial  value 107.860086 
iter  10 value 93.844336
iter  20 value 93.837207
final  value 93.836348 
converged
Fitting Repeat 3 

# weights:  507
initial  value 95.256215 
iter  10 value 93.807892
iter  20 value 93.793754
iter  30 value 93.792806
iter  40 value 93.790186
iter  50 value 91.142706
iter  60 value 88.027214
iter  70 value 86.926518
iter  80 value 86.908505
iter  90 value 86.907689
iter 100 value 86.689706
final  value 86.689706 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 98.522874 
iter  10 value 88.723522
iter  20 value 87.436212
iter  30 value 87.419519
iter  40 value 87.418214
iter  50 value 87.414654
iter  60 value 87.413870
iter  70 value 85.079430
iter  80 value 84.590561
iter  90 value 84.590429
final  value 84.589115 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.549462 
iter  10 value 92.381647
iter  20 value 92.238184
iter  30 value 92.234037
iter  40 value 91.974116
iter  50 value 91.939955
iter  60 value 91.932008
iter  70 value 91.895977
final  value 91.895652 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.010736 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.300331 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.385805 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.810866 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.475817 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.540037 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.022733 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 108.484780 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.237882 
iter  10 value 88.967639
iter  20 value 85.082739
iter  30 value 84.775174
iter  40 value 84.732227
final  value 84.711713 
converged
Fitting Repeat 5 

# weights:  305
initial  value 130.162565 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.126565 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 113.091824 
final  value 94.466823 
converged
Fitting Repeat 3 

# weights:  507
initial  value 104.318826 
iter  10 value 93.699869
final  value 93.692939 
converged
Fitting Repeat 4 

# weights:  507
initial  value 99.272295 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 99.130571 
iter  10 value 94.139368
iter  10 value 94.139368
iter  10 value 94.139368
final  value 94.139368 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.217078 
iter  10 value 94.467303
iter  20 value 94.298693
iter  30 value 93.975400
iter  40 value 93.864835
iter  50 value 93.750840
iter  60 value 89.166694
iter  70 value 87.366889
iter  80 value 85.305402
iter  90 value 84.963190
iter 100 value 84.624729
final  value 84.624729 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.572220 
iter  10 value 94.498867
iter  20 value 91.604151
iter  30 value 86.946954
iter  40 value 86.671282
iter  50 value 86.535349
iter  60 value 86.151342
iter  70 value 83.276402
iter  80 value 83.050273
iter  90 value 83.040091
final  value 83.039983 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.781785 
iter  10 value 94.084668
iter  20 value 92.591303
iter  30 value 87.790381
iter  40 value 86.738278
iter  50 value 83.268927
iter  60 value 81.969303
iter  70 value 81.558452
iter  80 value 81.547327
final  value 81.547325 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.496872 
iter  10 value 94.488938
iter  20 value 94.480649
iter  30 value 93.998242
iter  40 value 93.930373
iter  50 value 93.901904
iter  60 value 89.179078
iter  70 value 87.909824
iter  80 value 86.807308
iter  90 value 84.576683
iter 100 value 82.138674
final  value 82.138674 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 101.968695 
iter  10 value 94.385897
iter  20 value 85.056669
iter  30 value 84.398808
iter  40 value 83.981442
iter  50 value 83.690768
iter  60 value 83.521788
final  value 83.521214 
converged
Fitting Repeat 1 

# weights:  305
initial  value 109.892728 
iter  10 value 94.279825
iter  20 value 88.071954
iter  30 value 84.780966
iter  40 value 82.648545
iter  50 value 80.625146
iter  60 value 80.365886
iter  70 value 79.968595
iter  80 value 79.770619
iter  90 value 79.736618
iter 100 value 79.719497
final  value 79.719497 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.502543 
iter  10 value 94.661132
iter  20 value 93.869300
iter  30 value 86.083600
iter  40 value 85.335295
iter  50 value 83.928097
iter  60 value 81.548506
iter  70 value 80.131905
iter  80 value 80.024669
iter  90 value 80.012223
iter 100 value 80.006742
final  value 80.006742 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 102.979612 
iter  10 value 94.421086
iter  20 value 91.240369
iter  30 value 83.620626
iter  40 value 82.532536
iter  50 value 82.153725
iter  60 value 81.761015
iter  70 value 81.647025
iter  80 value 81.473429
iter  90 value 80.989171
iter 100 value 80.366501
final  value 80.366501 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 119.095255 
iter  10 value 93.540039
iter  20 value 89.122457
iter  30 value 82.851745
iter  40 value 81.841563
iter  50 value 81.194717
iter  60 value 80.868968
iter  70 value 80.545238
iter  80 value 80.303676
iter  90 value 80.197859
iter 100 value 80.154399
final  value 80.154399 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 136.245018 
iter  10 value 93.266775
iter  20 value 87.970878
iter  30 value 86.318878
iter  40 value 86.206186
iter  50 value 85.853392
iter  60 value 82.936724
iter  70 value 81.883338
iter  80 value 80.968803
iter  90 value 80.453748
iter 100 value 80.288224
final  value 80.288224 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 111.718309 
iter  10 value 96.038134
iter  20 value 92.725077
iter  30 value 87.381237
iter  40 value 86.039605
iter  50 value 83.555428
iter  60 value 81.899772
iter  70 value 81.052338
iter  80 value 80.341407
iter  90 value 80.081853
iter 100 value 79.993332
final  value 79.993332 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 106.401168 
iter  10 value 95.075695
iter  20 value 93.900778
iter  30 value 85.861000
iter  40 value 82.961396
iter  50 value 82.824695
iter  60 value 82.113420
iter  70 value 80.917719
iter  80 value 80.306218
iter  90 value 80.118909
iter 100 value 80.061835
final  value 80.061835 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 116.001883 
iter  10 value 94.697807
iter  20 value 94.124611
iter  30 value 85.343280
iter  40 value 82.875058
iter  50 value 81.830010
iter  60 value 81.525055
iter  70 value 80.759449
iter  80 value 80.507122
iter  90 value 80.228093
iter 100 value 80.042566
final  value 80.042566 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 107.427611 
iter  10 value 94.285749
iter  20 value 91.375388
iter  30 value 89.718907
iter  40 value 88.274776
iter  50 value 87.436479
iter  60 value 84.337705
iter  70 value 82.644825
iter  80 value 82.324146
iter  90 value 81.805418
iter 100 value 81.352532
final  value 81.352532 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 107.157490 
iter  10 value 94.433120
iter  20 value 91.436021
iter  30 value 87.186190
iter  40 value 85.906480
iter  50 value 84.814691
iter  60 value 83.712723
iter  70 value 83.201708
iter  80 value 82.675352
iter  90 value 80.477583
iter 100 value 79.838157
final  value 79.838157 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.332563 
iter  10 value 91.743079
iter  20 value 84.588313
iter  30 value 84.587674
iter  40 value 83.957639
iter  50 value 83.956969
iter  60 value 83.915334
iter  70 value 83.753011
final  value 83.752742 
converged
Fitting Repeat 2 

# weights:  103
initial  value 103.531922 
final  value 94.485723 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.812844 
final  value 94.485774 
converged
Fitting Repeat 4 

# weights:  103
initial  value 105.606173 
final  value 94.485875 
converged
Fitting Repeat 5 

# weights:  103
initial  value 106.492121 
final  value 94.486164 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.790742 
iter  10 value 94.488913
iter  20 value 94.477335
iter  30 value 93.871835
final  value 93.871749 
converged
Fitting Repeat 2 

# weights:  305
initial  value 112.200760 
iter  10 value 93.467050
iter  20 value 93.455937
iter  30 value 87.665318
iter  40 value 82.833209
iter  50 value 81.321508
iter  60 value 80.530498
iter  70 value 80.516667
iter  80 value 80.497333
iter  90 value 80.199882
iter 100 value 80.159225
final  value 80.159225 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.629038 
iter  10 value 89.299935
iter  20 value 84.587954
iter  30 value 84.579877
iter  40 value 83.783371
iter  50 value 82.847448
iter  60 value 82.590138
iter  70 value 82.588548
iter  80 value 82.587049
iter  90 value 82.503469
iter 100 value 82.009005
final  value 82.009005 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 104.302618 
iter  10 value 94.489101
iter  20 value 88.999666
iter  30 value 83.532701
iter  40 value 82.235380
iter  50 value 80.715818
iter  60 value 80.320724
iter  70 value 80.164641
iter  80 value 80.162610
final  value 80.162302 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.921572 
iter  10 value 93.534067
iter  20 value 93.530523
iter  30 value 93.072049
iter  40 value 85.520851
iter  50 value 85.478777
iter  60 value 82.702204
iter  70 value 82.609040
iter  80 value 82.419833
iter  90 value 82.295778
iter 100 value 82.295476
final  value 82.295476 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 119.281916 
iter  10 value 90.895264
iter  20 value 83.607097
iter  30 value 82.607892
iter  40 value 82.601778
iter  50 value 82.600608
iter  60 value 82.597593
final  value 82.594088 
converged
Fitting Repeat 2 

# weights:  507
initial  value 113.334865 
iter  10 value 94.492219
iter  20 value 94.484206
iter  30 value 93.843016
iter  40 value 93.700707
iter  50 value 84.661218
iter  60 value 82.789774
iter  70 value 81.211118
iter  80 value 80.336932
iter  90 value 78.988252
iter 100 value 78.851053
final  value 78.851053 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.563182 
iter  10 value 94.492239
iter  20 value 94.480188
iter  30 value 93.929199
iter  40 value 84.587787
iter  50 value 84.569469
iter  60 value 83.770262
final  value 83.764083 
converged
Fitting Repeat 4 

# weights:  507
initial  value 127.384802 
iter  10 value 93.790099
iter  20 value 93.786591
iter  30 value 92.339079
iter  40 value 87.138132
iter  50 value 84.825199
iter  60 value 80.810855
iter  70 value 79.850054
iter  80 value 79.610399
iter  90 value 79.073856
iter 100 value 78.838370
final  value 78.838370 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 98.676118 
iter  10 value 94.491123
iter  20 value 93.874512
iter  30 value 93.734019
iter  40 value 93.535238
final  value 93.535234 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.596092 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 112.699631 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.615732 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 106.735419 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.461116 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 113.862342 
final  value 94.467391 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.261938 
iter  10 value 93.513128
final  value 92.221661 
converged
Fitting Repeat 3 

# weights:  305
initial  value 117.607993 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.696432 
final  value 94.467391 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.096867 
final  value 94.467391 
converged
Fitting Repeat 1 

# weights:  507
initial  value 133.243562 
final  value 94.467386 
converged
Fitting Repeat 2 

# weights:  507
initial  value 97.804316 
final  value 94.467391 
converged
Fitting Repeat 3 

# weights:  507
initial  value 99.063354 
final  value 94.467391 
converged
Fitting Repeat 4 

# weights:  507
initial  value 109.630877 
iter  10 value 94.461515
iter  20 value 93.916424
iter  30 value 93.913328
final  value 93.913318 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.293795 
iter  10 value 86.041153
iter  20 value 85.349470
iter  30 value 85.348932
iter  40 value 85.348020
iter  50 value 84.586117
final  value 84.578587 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.824948 
iter  10 value 89.627554
iter  20 value 87.391419
iter  30 value 86.884888
iter  40 value 86.680063
iter  50 value 86.627306
final  value 86.620931 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.818019 
iter  10 value 89.768509
iter  20 value 87.249958
iter  30 value 86.357190
iter  40 value 86.245150
iter  50 value 86.083323
iter  60 value 86.009013
final  value 85.988003 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.912553 
iter  10 value 94.430879
iter  20 value 93.390564
iter  30 value 88.473188
iter  40 value 87.597276
iter  50 value 86.735327
iter  60 value 86.588649
iter  70 value 86.522529
iter  80 value 84.657555
iter  90 value 83.759093
iter 100 value 83.709586
final  value 83.709586 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 100.152799 
iter  10 value 94.490201
iter  20 value 92.685469
iter  30 value 89.020220
iter  40 value 87.270823
iter  50 value 86.945492
iter  60 value 86.286162
iter  70 value 85.988561
iter  80 value 83.585773
iter  90 value 83.218263
final  value 83.216717 
converged
Fitting Repeat 5 

# weights:  103
initial  value 107.747322 
iter  10 value 94.573899
iter  20 value 94.486594
iter  30 value 94.195975
iter  40 value 90.963513
iter  50 value 87.168222
iter  60 value 86.709213
iter  70 value 86.569719
iter  80 value 86.256603
iter  90 value 84.301222
iter 100 value 83.853674
final  value 83.853674 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 119.285262 
iter  10 value 94.514155
iter  20 value 94.383943
iter  30 value 87.821508
iter  40 value 86.216341
iter  50 value 84.707941
iter  60 value 81.864250
iter  70 value 80.885683
iter  80 value 80.762293
iter  90 value 80.662785
iter 100 value 80.477282
final  value 80.477282 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 108.164727 
iter  10 value 94.472496
iter  20 value 94.100070
iter  30 value 89.360011
iter  40 value 87.426815
iter  50 value 87.283396
iter  60 value 86.870691
iter  70 value 86.514641
iter  80 value 85.580064
iter  90 value 83.371677
iter 100 value 81.278639
final  value 81.278639 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 104.886954 
iter  10 value 94.359880
iter  20 value 92.937646
iter  30 value 88.966927
iter  40 value 87.083001
iter  50 value 82.755221
iter  60 value 81.946706
iter  70 value 81.386349
iter  80 value 80.902525
iter  90 value 80.649011
iter 100 value 80.601096
final  value 80.601096 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 119.089532 
iter  10 value 94.437557
iter  20 value 85.459521
iter  30 value 84.264520
iter  40 value 83.692672
iter  50 value 83.307316
iter  60 value 82.213892
iter  70 value 81.904808
iter  80 value 81.748233
iter  90 value 81.276506
iter 100 value 81.181561
final  value 81.181561 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 106.587650 
iter  10 value 94.504013
iter  20 value 88.448958
iter  30 value 87.158790
iter  40 value 87.060653
iter  50 value 86.374853
iter  60 value 84.368625
iter  70 value 82.056503
iter  80 value 81.501459
iter  90 value 81.250344
iter 100 value 80.771233
final  value 80.771233 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 105.482475 
iter  10 value 95.166517
iter  20 value 90.490801
iter  30 value 86.461120
iter  40 value 84.171493
iter  50 value 82.948722
iter  60 value 82.587703
iter  70 value 82.335345
iter  80 value 82.175492
iter  90 value 81.899168
iter 100 value 81.337357
final  value 81.337357 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.739194 
iter  10 value 94.512284
iter  20 value 93.598383
iter  30 value 87.293021
iter  40 value 86.469905
iter  50 value 85.010951
iter  60 value 83.437762
iter  70 value 82.742953
iter  80 value 82.184125
iter  90 value 80.956465
iter 100 value 80.627742
final  value 80.627742 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 116.729701 
iter  10 value 95.815397
iter  20 value 93.580089
iter  30 value 92.503958
iter  40 value 84.806414
iter  50 value 82.352356
iter  60 value 82.187958
iter  70 value 81.290542
iter  80 value 80.266446
iter  90 value 79.796801
iter 100 value 79.591997
final  value 79.591997 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 113.528870 
iter  10 value 94.475309
iter  20 value 88.744132
iter  30 value 85.246804
iter  40 value 84.388011
iter  50 value 83.615245
iter  60 value 83.326410
iter  70 value 83.283977
iter  80 value 83.271285
iter  90 value 82.877709
iter 100 value 82.029129
final  value 82.029129 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 111.995767 
iter  10 value 94.497306
iter  20 value 90.609874
iter  30 value 88.062781
iter  40 value 87.377861
iter  50 value 85.863940
iter  60 value 84.244563
iter  70 value 82.707968
iter  80 value 81.900944
iter  90 value 81.749183
iter 100 value 81.698125
final  value 81.698125 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 100.414385 
final  value 94.486060 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.347296 
iter  10 value 94.486100
iter  20 value 94.473919
iter  30 value 89.910435
iter  40 value 89.358750
iter  50 value 89.358373
iter  60 value 88.897061
iter  70 value 88.860145
iter  70 value 88.860144
iter  70 value 88.860144
final  value 88.860144 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.220836 
iter  10 value 94.481143
iter  20 value 94.306852
iter  30 value 93.247541
iter  40 value 93.138148
iter  50 value 93.137242
final  value 93.137120 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.555388 
final  value 94.485792 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.725274 
final  value 94.485836 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.280960 
iter  10 value 94.488875
iter  20 value 94.483853
iter  30 value 94.276426
final  value 94.276425 
converged
Fitting Repeat 2 

# weights:  305
initial  value 112.555440 
iter  10 value 94.149775
iter  20 value 94.125185
iter  30 value 94.113942
iter  40 value 94.112664
iter  50 value 86.604091
iter  60 value 86.187469
iter  70 value 84.906214
final  value 84.405910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.623019 
iter  10 value 94.488736
iter  20 value 94.313250
iter  30 value 87.835128
final  value 87.821459 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.723346 
iter  10 value 94.488931
iter  20 value 94.453828
iter  30 value 86.769434
iter  40 value 85.893175
iter  50 value 85.834264
iter  60 value 82.844451
iter  70 value 82.700500
iter  80 value 82.576137
iter  90 value 82.567553
iter 100 value 82.563819
final  value 82.563819 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 97.787105 
iter  10 value 94.471602
iter  20 value 94.467545
final  value 94.467530 
converged
Fitting Repeat 1 

# weights:  507
initial  value 142.721892 
iter  10 value 94.475286
iter  20 value 94.470327
iter  30 value 93.225324
iter  40 value 92.251410
iter  50 value 92.087805
iter  60 value 92.073528
iter  60 value 92.073527
iter  60 value 92.073527
final  value 92.073527 
converged
Fitting Repeat 2 

# weights:  507
initial  value 116.470459 
iter  10 value 94.475400
iter  20 value 94.468169
iter  30 value 88.493288
iter  40 value 87.363796
final  value 87.361300 
converged
Fitting Repeat 3 

# weights:  507
initial  value 105.795038 
iter  10 value 89.955394
iter  20 value 86.471934
iter  30 value 85.429929
iter  40 value 85.199899
iter  50 value 85.197004
final  value 85.194954 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.425262 
iter  10 value 94.036105
iter  20 value 93.135811
iter  30 value 92.925279
iter  40 value 92.609357
iter  50 value 92.575462
iter  60 value 92.573739
iter  60 value 92.573739
final  value 92.573739 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.974040 
iter  10 value 94.477654
iter  20 value 94.475251
iter  30 value 94.127253
iter  40 value 89.728274
iter  50 value 84.679095
iter  60 value 84.040501
iter  70 value 83.945390
iter  80 value 83.333219
iter  90 value 83.010892
iter 100 value 81.789339
final  value 81.789339 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.438636 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.289291 
final  value 93.860355 
converged
Fitting Repeat 3 

# weights:  103
initial  value 118.511314 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.445246 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.735679 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.581545 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.839235 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.639106 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.597082 
final  value 93.582418 
converged
Fitting Repeat 5 

# weights:  305
initial  value 98.089770 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 128.465309 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 103.948043 
final  value 93.628453 
converged
Fitting Repeat 3 

# weights:  507
initial  value 99.921266 
final  value 93.582418 
converged
Fitting Repeat 4 

# weights:  507
initial  value 99.958619 
final  value 93.582418 
converged
Fitting Repeat 5 

# weights:  507
initial  value 108.399771 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.651078 
iter  10 value 94.086507
iter  20 value 93.756325
iter  30 value 93.704406
iter  40 value 93.688199
iter  50 value 93.034431
iter  60 value 92.996447
iter  70 value 87.069917
iter  80 value 85.979523
iter  90 value 83.961309
iter 100 value 83.750126
final  value 83.750126 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 101.385884 
iter  10 value 93.999746
iter  20 value 93.708249
iter  30 value 93.689785
iter  40 value 93.155935
iter  50 value 93.033364
iter  60 value 87.203706
iter  70 value 86.552819
iter  80 value 86.176173
iter  90 value 85.822411
iter 100 value 82.442721
final  value 82.442721 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 99.080988 
iter  10 value 94.049116
iter  20 value 93.776798
iter  30 value 92.599166
iter  40 value 88.013621
iter  50 value 87.268061
iter  60 value 85.329945
iter  70 value 81.068664
iter  80 value 80.980343
iter  90 value 80.662103
iter 100 value 79.198804
final  value 79.198804 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 98.026917 
iter  10 value 93.956928
iter  20 value 83.326390
iter  30 value 82.489553
iter  40 value 81.227291
iter  50 value 80.841578
iter  60 value 79.352875
iter  70 value 78.965777
iter  80 value 78.960705
final  value 78.919512 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.749745 
iter  10 value 86.503250
iter  20 value 84.576165
iter  30 value 84.123888
iter  40 value 83.217458
iter  50 value 82.197647
iter  60 value 82.183511
iter  70 value 81.591948
iter  80 value 81.176232
iter  90 value 81.167643
final  value 81.167627 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.005292 
iter  10 value 92.220064
iter  20 value 82.190789
iter  30 value 81.094571
iter  40 value 79.972471
iter  50 value 79.389308
iter  60 value 78.862988
iter  70 value 78.218394
iter  80 value 77.810464
iter  90 value 77.759107
iter 100 value 77.657787
final  value 77.657787 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 103.305513 
iter  10 value 93.690600
iter  20 value 91.613167
iter  30 value 88.970164
iter  40 value 85.181028
iter  50 value 84.117208
iter  60 value 81.986572
iter  70 value 79.085518
iter  80 value 77.830821
iter  90 value 77.692984
iter 100 value 77.612724
final  value 77.612724 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 102.118971 
iter  10 value 94.114109
iter  20 value 93.690504
iter  30 value 84.115652
iter  40 value 82.598703
iter  50 value 82.285018
iter  60 value 81.820972
iter  70 value 80.688573
iter  80 value 79.332902
iter  90 value 78.761448
iter 100 value 78.577074
final  value 78.577074 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.324380 
iter  10 value 93.228833
iter  20 value 93.049841
iter  30 value 93.008834
iter  40 value 83.941578
iter  50 value 81.879842
iter  60 value 79.303100
iter  70 value 78.812438
iter  80 value 78.431541
iter  90 value 78.186021
iter 100 value 77.952822
final  value 77.952822 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.871924 
iter  10 value 93.868478
iter  20 value 93.698674
iter  30 value 85.019720
iter  40 value 81.921427
iter  50 value 79.266565
iter  60 value 78.352852
iter  70 value 77.847656
iter  80 value 77.632222
iter  90 value 77.566125
iter 100 value 77.526301
final  value 77.526301 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 113.586598 
iter  10 value 93.878697
iter  20 value 89.633513
iter  30 value 82.525790
iter  40 value 81.646552
iter  50 value 81.514617
iter  60 value 80.840491
iter  70 value 80.327486
iter  80 value 79.742983
iter  90 value 78.323457
iter 100 value 77.784607
final  value 77.784607 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 107.895311 
iter  10 value 93.639135
iter  20 value 83.484909
iter  30 value 81.433249
iter  40 value 79.059593
iter  50 value 78.405262
iter  60 value 78.029185
iter  70 value 77.839757
iter  80 value 77.780642
iter  90 value 77.694077
iter 100 value 77.384601
final  value 77.384601 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 110.978359 
iter  10 value 94.125729
iter  20 value 93.960603
iter  30 value 92.345091
iter  40 value 84.505734
iter  50 value 80.558777
iter  60 value 79.273071
iter  70 value 78.835105
iter  80 value 78.164607
iter  90 value 78.083925
iter 100 value 77.831973
final  value 77.831973 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 103.146669 
iter  10 value 93.949328
iter  20 value 90.320247
iter  30 value 82.819637
iter  40 value 82.336713
iter  50 value 81.802937
iter  60 value 79.508463
iter  70 value 78.529395
iter  80 value 77.733244
iter  90 value 77.644294
iter 100 value 77.586366
final  value 77.586366 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 103.562436 
iter  10 value 94.195146
iter  20 value 93.300174
iter  30 value 88.782969
iter  40 value 84.769836
iter  50 value 82.680275
iter  60 value 81.936710
iter  70 value 81.077195
iter  80 value 80.809718
iter  90 value 79.985957
iter 100 value 78.758619
final  value 78.758619 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 107.980651 
final  value 94.054497 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.140532 
final  value 94.054621 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.794371 
final  value 94.054514 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.970970 
iter  10 value 93.330118
final  value 93.330115 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.384060 
final  value 94.054484 
converged
Fitting Repeat 1 

# weights:  305
initial  value 113.180782 
iter  10 value 94.057664
iter  20 value 94.051232
iter  30 value 89.273101
iter  40 value 86.472025
iter  50 value 86.463690
iter  60 value 86.330872
iter  70 value 86.329097
iter  80 value 83.362687
iter  90 value 81.981443
final  value 81.977057 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.360030 
iter  10 value 93.587836
iter  20 value 93.133944
iter  30 value 92.820664
iter  40 value 92.820229
iter  50 value 92.819489
iter  60 value 85.645487
iter  70 value 81.626082
iter  80 value 81.602283
iter  90 value 80.928619
iter 100 value 80.927241
final  value 80.927241 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 97.305175 
iter  10 value 94.058163
iter  20 value 94.053582
final  value 94.053413 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.361572 
iter  10 value 94.057063
iter  20 value 93.992619
iter  30 value 91.475003
iter  40 value 89.693550
iter  50 value 89.692950
iter  60 value 89.001435
iter  70 value 88.894169
iter  80 value 88.893726
iter  90 value 88.126685
iter 100 value 87.959291
final  value 87.959291 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 99.028572 
iter  10 value 94.058134
iter  20 value 93.977078
iter  30 value 89.058686
iter  40 value 83.363233
iter  50 value 82.121239
iter  60 value 82.002368
iter  70 value 81.896343
iter  80 value 81.752598
iter  90 value 81.645524
iter 100 value 80.983846
final  value 80.983846 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 99.379295 
iter  10 value 88.552773
iter  20 value 80.847716
iter  30 value 78.578281
iter  40 value 77.927872
iter  50 value 77.925038
final  value 77.921114 
converged
Fitting Repeat 2 

# weights:  507
initial  value 116.165101 
iter  10 value 93.986132
iter  20 value 93.418936
iter  30 value 93.414431
iter  40 value 92.862204
final  value 92.862134 
converged
Fitting Repeat 3 

# weights:  507
initial  value 114.030742 
iter  10 value 94.060794
iter  20 value 94.031776
iter  30 value 93.183395
iter  40 value 89.121841
iter  50 value 83.120199
iter  60 value 82.711584
iter  70 value 82.698878
final  value 82.698783 
converged
Fitting Repeat 4 

# weights:  507
initial  value 94.576104 
iter  10 value 85.918356
iter  20 value 82.515985
iter  30 value 82.140714
iter  40 value 80.748920
iter  50 value 79.395109
iter  60 value 79.360422
iter  70 value 79.297527
iter  80 value 79.295798
iter  90 value 79.294467
iter 100 value 78.865998
final  value 78.865998 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 100.421840 
iter  10 value 93.605088
iter  20 value 93.586536
iter  30 value 93.580428
final  value 93.580071 
converged
Fitting Repeat 1 

# weights:  305
initial  value 124.772354 
iter  10 value 117.895058
iter  20 value 117.642790
iter  30 value 114.604434
iter  40 value 114.603698
final  value 114.603433 
converged
Fitting Repeat 2 

# weights:  305
initial  value 128.500172 
iter  10 value 117.763778
iter  20 value 117.572342
iter  30 value 110.554786
iter  40 value 105.154051
iter  50 value 104.417510
final  value 104.417227 
converged
Fitting Repeat 3 

# weights:  305
initial  value 118.130209 
iter  10 value 117.895114
iter  20 value 117.883348
iter  30 value 108.669363
iter  40 value 107.259326
iter  50 value 107.252693
final  value 107.252678 
converged
Fitting Repeat 4 

# weights:  305
initial  value 141.007859 
iter  10 value 117.895239
iter  20 value 117.890326
final  value 117.890303 
converged
Fitting Repeat 5 

# weights:  305
initial  value 118.608144 
iter  10 value 117.525446
iter  20 value 117.504686
iter  30 value 117.499035
iter  40 value 106.355988
iter  50 value 103.226565
iter  60 value 103.208376
final  value 103.208267 
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 -- Wed Dec  3 23:43:56 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 
 18.742   0.459  73.482