##############################################################################
##############################################################################
###
### 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_1.10.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.19-bioc/meat/HPiP.Rcheck’
* using R version 4.4.0 (2024-04-24)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Ventura 13.6.5
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.10.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
    29 | then the Kronecker product is the code{(pm × qn)} block matrix
       |                                       ^
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
var_imp       41.905  1.825  64.093
corr_plot     39.491  1.760  60.890
FSmethod      39.330  1.647  61.633
pred_ensembel 13.616  0.382  17.826
enrichfindP    0.535  0.115  15.866
getFASTA       0.076  0.020   5.175
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 3 NOTEs
See
  ‘/Users/biocbuild/bbs-3.19-bioc/meat/HPiP.Rcheck/00check.log’
for details.

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


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

R version 4.4.0 (2024-04-24) -- "Puppy Cup"
Copyright (C) 2024 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 101.892845 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.129033 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.688912 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.976505 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.336568 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 115.408499 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 106.060102 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 96.950535 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.182853 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 102.787371 
final  value 94.443243 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.743760 
final  value 94.484210 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.756441 
iter  10 value 93.624515
iter  20 value 91.988225
iter  30 value 91.937748
final  value 91.937593 
converged
Fitting Repeat 3 

# weights:  507
initial  value 99.250648 
iter  10 value 93.922222
iter  10 value 93.922222
iter  10 value 93.922222
final  value 93.922222 
converged
Fitting Repeat 4 

# weights:  507
initial  value 109.470992 
final  value 94.443243 
converged
Fitting Repeat 5 

# weights:  507
initial  value 107.436871 
final  value 94.443243 
converged
Fitting Repeat 1 

# weights:  103
initial  value 103.702050 
iter  10 value 94.434607
iter  20 value 87.632980
iter  30 value 86.556315
iter  40 value 84.806419
iter  50 value 84.710967
final  value 84.710850 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.845082 
iter  10 value 94.486521
iter  20 value 88.920890
iter  30 value 85.627718
iter  40 value 85.146923
iter  50 value 85.125260
final  value 85.121580 
converged
Fitting Repeat 3 

# weights:  103
initial  value 108.709807 
iter  10 value 94.486659
iter  20 value 94.486443
iter  30 value 93.873960
iter  40 value 93.679027
iter  50 value 93.639440
iter  60 value 93.531603
iter  70 value 88.103192
iter  80 value 87.293656
iter  90 value 85.350368
iter 100 value 82.668403
final  value 82.668403 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 103.718060 
iter  10 value 94.340560
iter  20 value 93.397276
iter  30 value 84.990022
iter  40 value 84.819516
iter  50 value 84.719663
final  value 84.710850 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.191699 
iter  10 value 94.488524
iter  10 value 94.488524
iter  20 value 85.256008
iter  30 value 84.767267
iter  40 value 84.710886
final  value 84.710850 
converged
Fitting Repeat 1 

# weights:  305
initial  value 108.122253 
iter  10 value 94.433204
iter  20 value 87.765081
iter  30 value 86.033590
iter  40 value 83.441845
iter  50 value 82.546941
iter  60 value 82.199804
iter  70 value 81.929724
iter  80 value 81.899530
iter  90 value 81.173114
iter 100 value 80.752754
final  value 80.752754 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 108.978214 
iter  10 value 92.535698
iter  20 value 89.421602
iter  30 value 86.912893
iter  40 value 83.751242
iter  50 value 82.550518
iter  60 value 81.737779
iter  70 value 81.240869
iter  80 value 81.024015
iter  90 value 80.981349
iter 100 value 80.935487
final  value 80.935487 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 113.287133 
iter  10 value 94.399361
iter  20 value 88.670384
iter  30 value 84.860961
iter  40 value 83.796460
iter  50 value 82.236387
iter  60 value 82.170473
iter  70 value 81.928189
iter  80 value 81.760736
iter  90 value 81.483636
iter 100 value 81.291370
final  value 81.291370 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.159204 
iter  10 value 91.837633
iter  20 value 85.084655
iter  30 value 84.759017
iter  40 value 84.511886
iter  50 value 83.567529
iter  60 value 83.241648
iter  70 value 83.132718
iter  80 value 83.074957
iter  90 value 82.658863
iter 100 value 82.520215
final  value 82.520215 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 113.415882 
iter  10 value 93.354357
iter  20 value 88.725806
iter  30 value 88.388855
iter  40 value 87.025137
iter  50 value 85.678341
iter  60 value 81.796746
iter  70 value 81.061438
iter  80 value 80.743163
iter  90 value 80.630747
iter 100 value 80.616602
final  value 80.616602 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 111.948165 
iter  10 value 94.195542
iter  20 value 87.142600
iter  30 value 86.865950
iter  40 value 85.220041
iter  50 value 84.025362
iter  60 value 82.644498
iter  70 value 82.183280
iter  80 value 82.068645
iter  90 value 81.777416
iter 100 value 81.476502
final  value 81.476502 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 111.499430 
iter  10 value 94.411335
iter  20 value 89.345010
iter  30 value 85.483078
iter  40 value 83.022235
iter  50 value 82.196078
iter  60 value 81.737873
iter  70 value 81.196410
iter  80 value 81.112629
iter  90 value 81.079563
iter 100 value 80.997767
final  value 80.997767 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 108.224371 
iter  10 value 94.669884
iter  20 value 94.427874
iter  30 value 87.636642
iter  40 value 86.154032
iter  50 value 85.519824
iter  60 value 84.617312
iter  70 value 83.334967
iter  80 value 83.026622
iter  90 value 82.653470
iter 100 value 82.476272
final  value 82.476272 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 103.363794 
iter  10 value 93.465571
iter  20 value 87.613945
iter  30 value 84.722848
iter  40 value 84.283105
iter  50 value 82.600655
iter  60 value 81.733097
iter  70 value 81.020996
iter  80 value 80.545996
iter  90 value 80.242041
iter 100 value 80.137617
final  value 80.137617 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 121.033481 
iter  10 value 94.397821
iter  20 value 88.907300
iter  30 value 86.682761
iter  40 value 86.441604
iter  50 value 84.812362
iter  60 value 82.206796
iter  70 value 81.382909
iter  80 value 80.784236
iter  90 value 80.562305
iter 100 value 80.488282
final  value 80.488282 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.706358 
iter  10 value 94.485756
iter  20 value 92.004873
iter  30 value 84.036040
iter  30 value 84.036040
iter  30 value 84.036040
final  value 84.036040 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.681536 
final  value 94.485699 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.289331 
final  value 94.485495 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.280914 
final  value 94.485644 
converged
Fitting Repeat 5 

# weights:  103
initial  value 104.210271 
final  value 94.486306 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.217307 
iter  10 value 92.316097
iter  20 value 92.298660
iter  30 value 92.296688
iter  40 value 91.483579
iter  50 value 91.476563
iter  60 value 91.474176
final  value 91.474097 
converged
Fitting Repeat 2 

# weights:  305
initial  value 100.648938 
iter  10 value 94.488714
iter  20 value 94.356507
iter  30 value 92.587697
iter  40 value 92.532329
iter  50 value 92.532084
iter  60 value 92.531338
iter  70 value 91.611519
iter  80 value 91.611383
iter  90 value 91.609987
iter  90 value 91.609987
final  value 91.609987 
converged
Fitting Repeat 3 

# weights:  305
initial  value 123.394534 
iter  10 value 94.448919
iter  20 value 94.447845
iter  30 value 94.443897
final  value 94.443493 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.586739 
iter  10 value 88.783477
iter  20 value 88.648474
iter  30 value 88.647164
iter  40 value 87.815975
iter  50 value 87.813281
iter  60 value 84.959922
iter  70 value 84.042237
iter  80 value 84.036571
iter  90 value 84.035837
iter 100 value 83.926013
final  value 83.926013 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 124.251421 
iter  10 value 94.489188
iter  20 value 94.297190
iter  30 value 90.511372
iter  40 value 88.974763
iter  50 value 85.511077
final  value 85.492460 
converged
Fitting Repeat 1 

# weights:  507
initial  value 113.541970 
iter  10 value 94.491771
iter  20 value 93.910278
iter  30 value 93.742201
iter  40 value 90.800305
iter  50 value 83.787892
iter  60 value 82.496791
iter  70 value 82.459806
iter  80 value 82.457212
iter  90 value 82.456717
iter 100 value 82.410631
final  value 82.410631 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 112.809771 
iter  10 value 94.492926
iter  20 value 94.298877
iter  30 value 93.791185
iter  40 value 93.789330
iter  50 value 93.788702
iter  60 value 93.779793
iter  70 value 92.342241
iter  80 value 90.920408
iter  90 value 90.859404
iter 100 value 90.834778
final  value 90.834778 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 95.534991 
iter  10 value 94.490440
iter  20 value 92.506546
iter  30 value 86.708889
iter  40 value 86.708632
final  value 86.708629 
converged
Fitting Repeat 4 

# weights:  507
initial  value 121.418055 
iter  10 value 94.451442
iter  20 value 94.192583
iter  30 value 89.757375
iter  40 value 81.967826
iter  50 value 81.457548
iter  60 value 81.284531
iter  70 value 81.263894
iter  80 value 81.262508
iter  90 value 80.635644
iter 100 value 80.453879
final  value 80.453879 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 97.936211 
iter  10 value 94.271129
iter  20 value 93.901259
iter  30 value 91.499737
iter  40 value 91.499181
iter  50 value 91.449119
iter  60 value 82.546113
iter  70 value 82.214969
iter  80 value 81.219187
iter  90 value 81.209818
iter 100 value 81.207972
final  value 81.207972 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 97.050615 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.468677 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.158030 
final  value 93.765896 
converged
Fitting Repeat 4 

# weights:  103
initial  value 110.673090 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.581286 
iter  10 value 91.362122
iter  20 value 88.068256
iter  30 value 88.057354
final  value 88.057344 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.566404 
final  value 94.052911 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.462418 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.969798 
iter  10 value 93.582418
iter  10 value 93.582418
iter  10 value 93.582418
final  value 93.582418 
converged
Fitting Repeat 4 

# weights:  305
initial  value 101.896162 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 107.226203 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 122.683019 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 108.962280 
final  value 93.582418 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.853995 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 100.560727 
iter  10 value 93.599645
final  value 93.582418 
converged
Fitting Repeat 5 

# weights:  507
initial  value 101.135503 
iter  10 value 93.106566
final  value 93.063302 
converged
Fitting Repeat 1 

# weights:  103
initial  value 103.791539 
iter  10 value 94.057771
iter  20 value 93.728530
iter  30 value 86.140567
iter  40 value 84.795920
iter  50 value 84.504073
iter  60 value 83.937713
iter  70 value 83.192902
iter  80 value 82.530462
iter  90 value 82.503172
final  value 82.503170 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.467408 
iter  10 value 94.045829
iter  20 value 93.698697
iter  30 value 93.683537
iter  40 value 93.628237
iter  50 value 87.807858
iter  60 value 84.345158
iter  70 value 83.965084
iter  80 value 83.529880
iter  90 value 82.688949
iter 100 value 82.521700
final  value 82.521700 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 101.603367 
iter  10 value 94.601532
iter  20 value 94.021939
iter  30 value 93.420844
iter  40 value 87.129629
iter  50 value 83.807452
iter  60 value 83.503569
iter  70 value 83.467818
iter  80 value 83.267467
iter  90 value 83.215023
final  value 83.214640 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.658137 
iter  10 value 93.982351
iter  20 value 93.322601
iter  30 value 93.145886
iter  40 value 91.267490
iter  50 value 82.487219
iter  60 value 81.139660
iter  70 value 80.592349
iter  80 value 80.490066
final  value 80.489084 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.008905 
iter  10 value 93.987808
iter  20 value 90.715260
iter  30 value 87.756650
iter  40 value 85.927563
iter  50 value 85.577608
iter  60 value 81.382286
iter  70 value 80.798720
iter  80 value 80.500682
iter  90 value 79.997131
iter 100 value 79.922608
final  value 79.922608 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 99.875582 
iter  10 value 94.095082
iter  20 value 92.459976
iter  30 value 84.678904
iter  40 value 84.332310
iter  50 value 84.219475
iter  60 value 83.123247
iter  70 value 83.048223
iter  80 value 82.834875
iter  90 value 82.712089
iter 100 value 82.627878
final  value 82.627878 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 111.370897 
iter  10 value 94.185724
iter  20 value 90.418725
iter  30 value 85.415253
iter  40 value 83.883225
iter  50 value 82.857193
iter  60 value 80.919364
iter  70 value 80.417067
iter  80 value 79.935243
iter  90 value 79.670238
iter 100 value 79.432560
final  value 79.432560 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.310041 
iter  10 value 93.956640
iter  20 value 93.466274
iter  30 value 90.148118
iter  40 value 85.703916
iter  50 value 84.204054
iter  60 value 79.858724
iter  70 value 79.418510
iter  80 value 79.233377
iter  90 value 79.134880
iter 100 value 78.930616
final  value 78.930616 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.216937 
iter  10 value 93.286676
iter  20 value 85.967051
iter  30 value 85.192970
iter  40 value 84.900661
iter  50 value 84.386374
iter  60 value 83.739105
iter  70 value 81.971299
iter  80 value 80.310976
iter  90 value 79.442186
iter 100 value 79.331673
final  value 79.331673 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 98.793137 
iter  10 value 94.116434
iter  20 value 93.971706
iter  30 value 85.257987
iter  40 value 84.754592
iter  50 value 83.097259
iter  60 value 82.567085
iter  70 value 81.917835
iter  80 value 80.451336
iter  90 value 79.356336
iter 100 value 79.090231
final  value 79.090231 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 103.389738 
iter  10 value 94.020078
iter  20 value 93.504209
iter  30 value 92.292432
iter  40 value 88.915380
iter  50 value 83.429010
iter  60 value 81.292428
iter  70 value 80.756608
iter  80 value 79.770997
iter  90 value 79.563824
iter 100 value 79.523326
final  value 79.523326 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 104.271116 
iter  10 value 94.401034
iter  20 value 86.810188
iter  30 value 83.039400
iter  40 value 80.793560
iter  50 value 79.777537
iter  60 value 79.177438
iter  70 value 78.785276
iter  80 value 78.500350
iter  90 value 78.163800
iter 100 value 78.054050
final  value 78.054050 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.713353 
iter  10 value 94.094664
iter  20 value 88.974720
iter  30 value 81.952161
iter  40 value 80.986160
iter  50 value 80.896577
iter  60 value 80.551821
iter  70 value 80.447496
iter  80 value 80.001618
iter  90 value 79.435793
iter 100 value 78.877383
final  value 78.877383 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 111.441143 
iter  10 value 94.101689
iter  20 value 85.217586
iter  30 value 84.806434
iter  40 value 83.120111
iter  50 value 81.915735
iter  60 value 80.170992
iter  70 value 79.771866
iter  80 value 79.539041
iter  90 value 79.511952
iter 100 value 79.503927
final  value 79.503927 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 128.451750 
iter  10 value 94.293452
iter  20 value 92.781517
iter  30 value 89.875381
iter  40 value 87.285944
iter  50 value 86.439353
iter  60 value 83.064860
iter  70 value 82.135878
iter  80 value 81.487966
iter  90 value 80.538403
iter 100 value 79.734301
final  value 79.734301 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.013055 
iter  10 value 94.054394
final  value 94.053286 
converged
Fitting Repeat 2 

# weights:  103
initial  value 103.833313 
final  value 93.811518 
converged
Fitting Repeat 3 

# weights:  103
initial  value 109.105881 
final  value 94.054675 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.140217 
final  value 94.054549 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.123870 
final  value 94.054763 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.159082 
iter  10 value 94.057629
iter  20 value 94.052952
iter  30 value 93.585774
final  value 93.582558 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.252416 
iter  10 value 93.587637
iter  20 value 93.583011
iter  30 value 84.015168
iter  40 value 83.914695
iter  50 value 81.961210
iter  60 value 81.059707
iter  70 value 79.417086
iter  80 value 79.346323
iter  90 value 79.329314
iter 100 value 79.280780
final  value 79.280780 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.765867 
iter  10 value 90.304245
iter  20 value 84.814186
iter  30 value 84.168846
iter  40 value 84.101782
iter  50 value 83.598877
iter  60 value 83.579049
iter  70 value 83.578163
iter  80 value 82.595432
iter  90 value 82.171299
iter 100 value 82.170662
final  value 82.170662 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 104.869078 
iter  10 value 93.557806
iter  20 value 93.534196
iter  30 value 86.462591
iter  40 value 86.441038
iter  50 value 85.227932
iter  60 value 84.633579
iter  70 value 84.011674
iter  80 value 83.718322
iter  90 value 83.466643
iter 100 value 83.422126
final  value 83.422126 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 105.424054 
iter  10 value 94.057655
iter  20 value 94.041696
iter  30 value 84.618587
iter  40 value 84.004455
iter  50 value 83.183610
iter  60 value 79.982080
iter  70 value 79.927102
iter  80 value 79.865474
final  value 79.864862 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.235643 
iter  10 value 94.060993
iter  20 value 94.052496
iter  30 value 91.633280
iter  40 value 84.999289
iter  50 value 80.964204
iter  60 value 80.683261
iter  70 value 79.719881
final  value 79.717924 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.241164 
iter  10 value 93.590735
iter  20 value 93.585648
iter  30 value 93.394253
iter  40 value 85.453978
iter  50 value 82.049035
iter  60 value 81.895879
iter  70 value 80.358228
iter  80 value 78.034474
iter  90 value 77.315399
iter 100 value 77.277892
final  value 77.277892 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 105.435260 
iter  10 value 94.060640
iter  20 value 92.337215
final  value 92.238719 
converged
Fitting Repeat 4 

# weights:  507
initial  value 108.450028 
iter  10 value 94.060920
iter  20 value 87.604113
iter  30 value 82.555751
iter  40 value 82.294457
iter  50 value 82.293742
iter  60 value 82.284721
final  value 82.284109 
converged
Fitting Repeat 5 

# weights:  507
initial  value 121.940872 
iter  10 value 93.589693
iter  20 value 93.582118
final  value 93.524477 
converged
Fitting Repeat 1 

# weights:  103
initial  value 101.606358 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.954519 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.397961 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.193848 
iter  10 value 93.279366
iter  20 value 93.271931
final  value 93.271928 
converged
Fitting Repeat 5 

# weights:  103
initial  value 123.551519 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.203993 
iter  10 value 94.038252
iter  10 value 94.038251
iter  10 value 94.038251
final  value 94.038251 
converged
Fitting Repeat 2 

# weights:  305
initial  value 115.052443 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.226297 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.318314 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 112.199504 
final  value 94.038251 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.169548 
final  value 94.038251 
converged
Fitting Repeat 2 

# weights:  507
initial  value 119.000746 
iter  10 value 94.038250
iter  10 value 94.038250
iter  10 value 94.038250
final  value 94.038250 
converged
Fitting Repeat 3 

# weights:  507
initial  value 99.149509 
iter  10 value 93.649711
iter  10 value 93.649711
iter  10 value 93.649711
final  value 93.649711 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.685456 
iter  10 value 91.596438
iter  20 value 91.098485
iter  30 value 91.092707
final  value 91.092705 
converged
Fitting Repeat 5 

# weights:  507
initial  value 119.721448 
iter  10 value 94.052910
iter  10 value 94.052910
iter  10 value 94.052910
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.061124 
iter  10 value 94.049277
iter  20 value 91.791622
iter  30 value 88.097436
iter  40 value 86.482629
iter  50 value 84.500713
iter  60 value 83.098317
iter  70 value 81.892445
iter  80 value 80.480881
iter  90 value 80.365451
iter 100 value 79.812048
final  value 79.812048 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 98.701393 
iter  10 value 93.378975
iter  20 value 83.662072
iter  30 value 83.280384
iter  40 value 82.756041
iter  50 value 82.086343
iter  60 value 82.043223
iter  70 value 81.887719
iter  80 value 81.852758
iter  90 value 80.810203
iter 100 value 79.705511
final  value 79.705511 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 97.308143 
iter  10 value 94.056666
iter  20 value 87.502025
iter  30 value 83.998327
iter  40 value 82.976929
iter  50 value 81.566629
iter  60 value 80.876871
iter  70 value 79.710771
iter  80 value 79.400675
final  value 79.362614 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.616565 
iter  10 value 94.150535
iter  20 value 94.056867
iter  30 value 87.981017
iter  40 value 85.828548
iter  50 value 85.570838
iter  60 value 85.475281
iter  70 value 85.110256
iter  80 value 82.873253
iter  90 value 82.752816
iter 100 value 82.751671
final  value 82.751671 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 99.218303 
iter  10 value 94.093703
iter  20 value 94.052357
iter  30 value 84.281543
iter  40 value 83.390489
iter  50 value 83.350597
iter  60 value 82.969230
iter  70 value 82.309724
iter  80 value 81.893385
iter  90 value 81.864975
iter 100 value 81.849650
final  value 81.849650 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 114.721660 
iter  10 value 94.096173
iter  20 value 90.058516
iter  30 value 86.727817
iter  40 value 83.658923
iter  50 value 83.051447
iter  60 value 82.621655
iter  70 value 81.944813
iter  80 value 80.305110
iter  90 value 78.961445
iter 100 value 78.616061
final  value 78.616061 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.987104 
iter  10 value 93.871535
iter  20 value 89.744385
iter  30 value 86.494232
iter  40 value 84.958517
iter  50 value 84.120673
iter  60 value 83.501349
iter  70 value 83.133246
iter  80 value 82.754829
iter  90 value 80.363636
iter 100 value 79.101245
final  value 79.101245 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.729153 
iter  10 value 94.096531
iter  20 value 84.810011
iter  30 value 83.951816
iter  40 value 83.208378
iter  50 value 83.090541
iter  60 value 82.914061
iter  70 value 82.102682
iter  80 value 80.705146
iter  90 value 79.518895
iter 100 value 78.826645
final  value 78.826645 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 111.663859 
iter  10 value 94.062378
iter  20 value 84.097442
iter  30 value 82.784777
iter  40 value 82.144058
iter  50 value 81.640811
iter  60 value 80.468082
iter  70 value 79.864583
iter  80 value 79.391666
iter  90 value 78.636893
iter 100 value 77.949300
final  value 77.949300 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 104.561207 
iter  10 value 93.584213
iter  20 value 87.581893
iter  30 value 86.203194
iter  40 value 83.869691
iter  50 value 83.360375
iter  60 value 82.260190
iter  70 value 82.191633
iter  80 value 81.774479
iter  90 value 80.024300
iter 100 value 78.546236
final  value 78.546236 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 115.884455 
iter  10 value 94.175680
iter  20 value 91.789476
iter  30 value 85.322130
iter  40 value 84.499491
iter  50 value 83.082727
iter  60 value 81.696499
iter  70 value 79.921458
iter  80 value 79.353551
iter  90 value 79.036772
iter 100 value 78.860445
final  value 78.860445 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 104.143143 
iter  10 value 94.155826
iter  20 value 87.350690
iter  30 value 85.282692
iter  40 value 83.430398
iter  50 value 81.399339
iter  60 value 79.013956
iter  70 value 78.240025
iter  80 value 78.143876
iter  90 value 78.018244
iter 100 value 77.808176
final  value 77.808176 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 114.923710 
iter  10 value 93.982772
iter  20 value 92.733855
iter  30 value 82.566490
iter  40 value 81.723342
iter  50 value 80.701480
iter  60 value 80.419659
iter  70 value 79.488596
iter  80 value 78.627055
iter  90 value 78.217585
iter 100 value 78.071329
final  value 78.071329 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 126.188577 
iter  10 value 95.400680
iter  20 value 89.349141
iter  30 value 83.844716
iter  40 value 82.164637
iter  50 value 80.236732
iter  60 value 79.314549
iter  70 value 78.888101
iter  80 value 78.784349
iter  90 value 78.693334
iter 100 value 78.428297
final  value 78.428297 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 128.228177 
iter  10 value 94.083310
iter  20 value 91.668713
iter  30 value 85.847181
iter  40 value 85.009882
iter  50 value 83.746848
iter  60 value 83.343139
iter  70 value 82.433133
iter  80 value 80.841044
iter  90 value 79.720078
iter 100 value 79.365994
final  value 79.365994 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.932563 
final  value 94.054473 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.679586 
final  value 94.054406 
converged
Fitting Repeat 3 

# weights:  103
initial  value 116.954394 
iter  10 value 94.054447
iter  20 value 93.782641
iter  30 value 89.826721
iter  40 value 88.158976
iter  50 value 83.699075
iter  60 value 83.241799
final  value 83.233957 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.848674 
final  value 94.054765 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.190352 
iter  10 value 94.054747
iter  20 value 94.050845
iter  30 value 90.775386
final  value 90.726312 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.760903 
iter  10 value 93.966618
iter  20 value 93.846135
iter  30 value 93.674238
iter  40 value 93.673572
iter  50 value 85.134278
final  value 84.573122 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.897790 
iter  10 value 94.043218
iter  20 value 94.038354
iter  30 value 91.912099
iter  40 value 83.139350
iter  50 value 79.313501
iter  60 value 79.028569
iter  70 value 78.923073
iter  80 value 78.903895
iter  90 value 78.903651
iter 100 value 78.895320
final  value 78.895320 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 96.507609 
iter  10 value 94.056092
iter  20 value 89.365308
iter  30 value 86.370896
iter  30 value 86.370895
final  value 86.178815 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.672853 
iter  10 value 94.057663
iter  20 value 93.976200
iter  30 value 90.761405
iter  40 value 90.757235
iter  50 value 90.690554
iter  60 value 90.685832
iter  70 value 90.685118
iter  80 value 90.562997
iter  90 value 90.557099
iter 100 value 90.556724
final  value 90.556724 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 94.804946 
iter  10 value 94.057044
iter  20 value 93.813056
iter  30 value 91.890597
iter  40 value 91.260930
iter  50 value 91.259694
iter  60 value 91.257900
iter  70 value 91.154301
iter  80 value 91.152367
final  value 91.152325 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.121025 
iter  10 value 93.109637
iter  20 value 93.108548
iter  30 value 93.108258
iter  40 value 93.078672
iter  50 value 93.050532
iter  60 value 92.662402
iter  70 value 90.816328
iter  80 value 90.755791
iter  90 value 90.749720
iter 100 value 90.717115
final  value 90.717115 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 117.452454 
iter  10 value 94.127176
iter  20 value 94.114361
iter  30 value 93.174838
iter  40 value 87.591008
iter  50 value 87.571846
iter  60 value 87.326149
iter  70 value 87.283631
iter  80 value 87.034043
iter  90 value 81.821216
iter 100 value 77.087206
final  value 77.087206 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 94.762112 
iter  10 value 94.046384
iter  20 value 93.999774
iter  30 value 89.606210
iter  40 value 84.571425
iter  50 value 84.415096
iter  60 value 84.413936
iter  70 value 84.413719
iter  80 value 84.413228
iter  90 value 84.412717
final  value 84.412709 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.281389 
iter  10 value 86.979458
iter  20 value 82.710749
iter  30 value 82.563566
final  value 82.557463 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.189332 
iter  10 value 94.046632
iter  20 value 94.045792
iter  30 value 94.044826
iter  40 value 94.042330
iter  50 value 94.041845
iter  60 value 94.017834
iter  70 value 85.582519
iter  80 value 84.344892
iter  90 value 83.241820
iter 100 value 81.899940
final  value 81.899940 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 97.551936 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.260925 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.852732 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.060219 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.567432 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.964596 
iter  10 value 94.275370
final  value 94.275363 
converged
Fitting Repeat 2 

# weights:  305
initial  value 107.428721 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 104.613245 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 113.292842 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.902778 
final  value 94.275362 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.895838 
iter  10 value 94.275363
iter  10 value 94.275362
iter  10 value 94.275362
final  value 94.275362 
converged
Fitting Repeat 2 

# weights:  507
initial  value 113.133218 
final  value 94.275362 
converged
Fitting Repeat 3 

# weights:  507
initial  value 103.970925 
final  value 94.312038 
converged
Fitting Repeat 4 

# weights:  507
initial  value 115.605968 
final  value 94.275362 
converged
Fitting Repeat 5 

# weights:  507
initial  value 106.676806 
iter  10 value 94.349765
final  value 94.313817 
converged
Fitting Repeat 1 

# weights:  103
initial  value 116.547402 
iter  10 value 94.429483
iter  20 value 93.535830
iter  30 value 93.497153
iter  40 value 92.568387
iter  50 value 84.554181
iter  60 value 83.861181
iter  70 value 83.050500
iter  80 value 82.277146
iter  90 value 81.781071
final  value 81.774378 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.584539 
iter  10 value 93.513213
iter  20 value 92.321333
iter  30 value 92.061494
iter  40 value 91.966135
iter  50 value 91.715854
iter  60 value 91.709553
final  value 91.709470 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.988040 
iter  10 value 94.368753
iter  20 value 92.914708
iter  30 value 90.120867
iter  40 value 83.823607
iter  50 value 83.539870
iter  60 value 82.670077
iter  70 value 82.600367
iter  80 value 82.555355
final  value 82.553262 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.437583 
iter  10 value 94.491669
iter  20 value 94.486620
iter  30 value 94.102907
iter  40 value 90.008215
iter  50 value 85.083465
iter  60 value 82.667599
iter  70 value 82.149787
iter  80 value 81.904822
iter  90 value 81.774380
final  value 81.774378 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.342341 
iter  10 value 94.409325
iter  20 value 94.081464
iter  30 value 91.910726
iter  40 value 85.421004
iter  50 value 84.001186
iter  60 value 83.958749
iter  70 value 83.597044
iter  80 value 83.289611
final  value 83.287469 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.216370 
iter  10 value 94.526055
iter  20 value 94.007079
iter  30 value 89.539325
iter  40 value 86.490362
iter  50 value 85.901412
iter  60 value 85.269507
iter  70 value 83.723983
iter  80 value 82.203658
iter  90 value 80.950392
iter 100 value 80.642673
final  value 80.642673 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 104.239480 
iter  10 value 94.312206
iter  20 value 86.357038
iter  30 value 83.807038
iter  40 value 82.704061
iter  50 value 82.275828
iter  60 value 80.898153
iter  70 value 80.494743
iter  80 value 80.098265
iter  90 value 79.869587
iter 100 value 79.768572
final  value 79.768572 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 109.103972 
iter  10 value 94.549400
iter  20 value 86.971099
iter  30 value 83.640188
iter  40 value 82.061401
iter  50 value 80.847398
iter  60 value 80.467717
iter  70 value 80.219064
iter  80 value 79.647993
iter  90 value 79.616339
iter 100 value 79.539527
final  value 79.539527 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 102.897178 
iter  10 value 94.478000
iter  20 value 94.108107
iter  30 value 93.755404
iter  40 value 87.351413
iter  50 value 84.763934
iter  60 value 84.026847
iter  70 value 82.988043
iter  80 value 82.410973
iter  90 value 82.321532
iter 100 value 81.835911
final  value 81.835911 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 128.882521 
iter  10 value 94.579498
iter  20 value 94.202625
iter  30 value 88.583422
iter  40 value 86.627388
iter  50 value 82.431410
iter  60 value 82.259227
iter  70 value 82.165959
iter  80 value 81.292716
iter  90 value 80.667339
iter 100 value 80.519618
final  value 80.519618 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 111.435520 
iter  10 value 88.317318
iter  20 value 87.449608
iter  30 value 85.796775
iter  40 value 84.953254
iter  50 value 83.864112
iter  60 value 81.613877
iter  70 value 80.809400
iter  80 value 80.590405
iter  90 value 80.482166
iter 100 value 80.431750
final  value 80.431750 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 109.898452 
iter  10 value 94.191995
iter  20 value 88.818189
iter  30 value 86.657353
iter  40 value 86.398433
iter  50 value 83.126247
iter  60 value 81.314511
iter  70 value 80.439320
iter  80 value 80.148460
iter  90 value 80.018131
iter 100 value 79.974753
final  value 79.974753 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 103.650382 
iter  10 value 94.515610
iter  20 value 93.226317
iter  30 value 85.811800
iter  40 value 85.153482
iter  50 value 84.068298
iter  60 value 83.683801
iter  70 value 82.947008
iter  80 value 82.585626
iter  90 value 82.312988
iter 100 value 82.216512
final  value 82.216512 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 118.869071 
iter  10 value 94.621830
iter  20 value 90.005905
iter  30 value 87.936450
iter  40 value 87.126988
iter  50 value 86.343120
iter  60 value 85.157166
iter  70 value 83.310460
iter  80 value 82.460005
iter  90 value 81.453809
iter 100 value 80.564074
final  value 80.564074 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 110.959598 
iter  10 value 94.322437
iter  20 value 93.299768
iter  30 value 84.451916
iter  40 value 83.412600
iter  50 value 82.996395
iter  60 value 81.696480
iter  70 value 80.932182
iter  80 value 79.918765
iter  90 value 79.458418
iter 100 value 79.316545
final  value 79.316545 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 101.302124 
final  value 94.485813 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.468891 
final  value 94.485882 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.190985 
final  value 94.486200 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.227887 
iter  10 value 94.485849
iter  20 value 94.484226
final  value 94.484215 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.760550 
iter  10 value 94.276925
iter  20 value 93.266159
iter  30 value 86.960129
final  value 86.960123 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.892134 
iter  10 value 89.996666
iter  20 value 83.342615
iter  30 value 82.878159
iter  40 value 82.737333
iter  50 value 82.718993
iter  60 value 82.708757
iter  70 value 82.704491
iter  80 value 82.700976
iter  90 value 81.977161
iter 100 value 81.903451
final  value 81.903451 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 96.354301 
iter  10 value 94.488684
iter  20 value 94.484223
final  value 94.484214 
converged
Fitting Repeat 3 

# weights:  305
initial  value 98.175066 
iter  10 value 94.280400
iter  20 value 94.130592
iter  30 value 94.038211
iter  40 value 94.028121
final  value 94.027966 
converged
Fitting Repeat 4 

# weights:  305
initial  value 105.651906 
iter  10 value 90.660556
iter  20 value 87.304849
iter  30 value 86.218367
iter  40 value 85.614419
iter  50 value 85.607112
iter  60 value 85.539258
iter  70 value 84.914947
iter  80 value 84.617591
iter  90 value 84.589168
iter 100 value 84.582064
final  value 84.582064 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.263117 
iter  10 value 94.269373
final  value 94.267667 
converged
Fitting Repeat 1 

# weights:  507
initial  value 101.751654 
iter  10 value 94.284364
iter  20 value 94.281586
iter  30 value 94.275524
iter  40 value 92.228271
iter  50 value 88.503238
iter  60 value 86.510757
iter  70 value 86.028504
final  value 86.027589 
converged
Fitting Repeat 2 

# weights:  507
initial  value 102.543277 
iter  10 value 94.092045
iter  20 value 94.088501
iter  30 value 94.057394
iter  40 value 92.621983
iter  50 value 87.514335
iter  60 value 85.827897
iter  70 value 82.810641
iter  80 value 82.588446
iter  90 value 82.588140
iter 100 value 82.587949
final  value 82.587949 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 102.747051 
iter  10 value 94.236900
iter  20 value 94.233257
final  value 94.230216 
converged
Fitting Repeat 4 

# weights:  507
initial  value 101.509470 
iter  10 value 94.491307
iter  20 value 94.243193
iter  30 value 91.125664
iter  40 value 91.030417
iter  50 value 91.027895
iter  60 value 91.027308
iter  70 value 87.896408
iter  80 value 86.476510
iter  90 value 83.784149
iter 100 value 81.654798
final  value 81.654798 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.009970 
iter  10 value 94.091643
iter  20 value 94.086142
iter  30 value 94.084752
iter  40 value 89.350792
iter  50 value 84.077268
iter  60 value 84.076360
iter  70 value 83.613591
iter  80 value 83.609749
iter  90 value 83.544678
iter 100 value 81.716808
final  value 81.716808 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 110.085099 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.605095 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 103.123510 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.568605 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.797997 
final  value 94.354396 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.298806 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 122.012720 
iter  10 value 92.752039
iter  20 value 89.037975
final  value 88.780974 
converged
Fitting Repeat 3 

# weights:  305
initial  value 106.914481 
iter  10 value 94.386587
final  value 94.354396 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.106506 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 110.180316 
final  value 94.479532 
converged
Fitting Repeat 1 

# weights:  507
initial  value 96.288276 
iter  10 value 92.478359
iter  20 value 92.068481
final  value 92.068451 
converged
Fitting Repeat 2 

# weights:  507
initial  value 97.601032 
iter  10 value 94.132626
final  value 94.132577 
converged
Fitting Repeat 3 

# weights:  507
initial  value 94.498399 
final  value 94.484213 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.425764 
final  value 94.354396 
converged
Fitting Repeat 5 

# weights:  507
initial  value 100.587169 
final  value 94.479533 
converged
Fitting Repeat 1 

# weights:  103
initial  value 101.194720 
iter  10 value 91.766781
iter  20 value 88.002359
iter  30 value 86.904864
iter  40 value 86.278227
iter  50 value 85.743008
final  value 85.737149 
converged
Fitting Repeat 2 

# weights:  103
initial  value 103.110661 
iter  10 value 94.546299
iter  20 value 94.436408
iter  30 value 94.343064
iter  40 value 94.157086
iter  50 value 89.818706
iter  60 value 88.522856
iter  70 value 87.649534
iter  80 value 87.385226
iter  90 value 86.198417
iter 100 value 85.739984
final  value 85.739984 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 101.864791 
iter  10 value 94.058717
iter  20 value 88.699283
iter  30 value 88.189608
iter  40 value 86.769027
iter  50 value 86.037242
iter  60 value 85.492606
iter  70 value 85.257181
iter  80 value 85.189688
iter  90 value 85.106467
final  value 85.095471 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.841339 
iter  10 value 94.349829
iter  20 value 93.337416
iter  30 value 87.915718
iter  40 value 87.549239
iter  50 value 87.152700
iter  60 value 86.599739
iter  70 value 86.246157
iter  80 value 85.738788
final  value 85.737149 
converged
Fitting Repeat 5 

# weights:  103
initial  value 104.802817 
iter  10 value 93.430271
iter  20 value 87.295846
iter  30 value 87.022541
iter  40 value 86.877026
iter  50 value 86.088656
iter  60 value 85.330558
iter  70 value 85.201125
iter  80 value 85.178653
iter  90 value 85.159657
iter  90 value 85.159657
iter  90 value 85.159657
final  value 85.159657 
converged
Fitting Repeat 1 

# weights:  305
initial  value 115.056388 
iter  10 value 94.597262
iter  20 value 93.773786
iter  30 value 87.848568
iter  40 value 87.069065
iter  50 value 86.809234
iter  60 value 86.400445
iter  70 value 85.630210
iter  80 value 85.550231
iter  90 value 85.058576
iter 100 value 83.770977
final  value 83.770977 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 104.792721 
iter  10 value 94.666350
iter  20 value 91.125198
iter  30 value 86.548645
iter  40 value 85.590685
iter  50 value 85.223163
iter  60 value 84.343619
iter  70 value 83.579367
iter  80 value 83.411083
iter  90 value 83.343697
iter 100 value 83.304113
final  value 83.304113 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 108.250164 
iter  10 value 94.400914
iter  20 value 93.215354
iter  30 value 92.765388
iter  40 value 86.504248
iter  50 value 85.535581
iter  60 value 84.224827
iter  70 value 83.350483
iter  80 value 83.264640
iter  90 value 83.192868
iter 100 value 83.033164
final  value 83.033164 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 107.444605 
iter  10 value 95.614736
iter  20 value 93.611006
iter  30 value 87.778051
iter  40 value 84.948567
iter  50 value 83.832160
iter  60 value 83.483845
iter  70 value 83.115906
iter  80 value 83.002204
iter  90 value 82.989161
iter 100 value 82.988103
final  value 82.988103 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 119.002852 
iter  10 value 94.126540
iter  20 value 89.321014
iter  30 value 87.908681
iter  40 value 85.383337
iter  50 value 83.639313
iter  60 value 83.493261
iter  70 value 83.390187
iter  80 value 83.251100
iter  90 value 82.977411
iter 100 value 82.779798
final  value 82.779798 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 114.058353 
iter  10 value 94.487188
iter  20 value 94.194605
iter  30 value 90.176644
iter  40 value 87.081318
iter  50 value 85.072988
iter  60 value 84.304643
iter  70 value 84.105571
iter  80 value 83.889055
iter  90 value 83.686380
iter 100 value 83.399072
final  value 83.399072 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 136.828653 
iter  10 value 94.512396
iter  20 value 93.720843
iter  30 value 91.852255
iter  40 value 87.117238
iter  50 value 84.795707
iter  60 value 83.661944
iter  70 value 83.269198
iter  80 value 83.067474
iter  90 value 82.981991
iter 100 value 82.977774
final  value 82.977774 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.827124 
iter  10 value 94.374497
iter  20 value 90.009030
iter  30 value 88.518227
iter  40 value 88.088637
iter  50 value 86.692251
iter  60 value 85.560845
iter  70 value 84.554913
iter  80 value 83.637917
iter  90 value 83.099676
iter 100 value 83.019206
final  value 83.019206 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 127.913513 
iter  10 value 91.580867
iter  20 value 88.928271
iter  30 value 87.916149
iter  40 value 86.574255
iter  50 value 85.042275
iter  60 value 83.670106
iter  70 value 83.524808
iter  80 value 83.472014
iter  90 value 83.267174
iter 100 value 82.968022
final  value 82.968022 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 114.732950 
iter  10 value 94.520328
iter  20 value 94.444988
iter  30 value 93.989455
iter  40 value 89.161734
iter  50 value 87.664284
iter  60 value 85.518080
iter  70 value 84.207580
iter  80 value 83.501250
iter  90 value 83.274246
iter 100 value 83.160008
final  value 83.160008 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.965172 
final  value 94.485839 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.441723 
final  value 94.485926 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.727276 
final  value 94.485810 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.231830 
iter  10 value 94.356207
iter  20 value 93.315809
iter  30 value 92.612590
final  value 92.612308 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.074884 
final  value 94.485893 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.773490 
iter  10 value 94.488738
iter  20 value 94.370762
iter  30 value 94.133024
final  value 94.133006 
converged
Fitting Repeat 2 

# weights:  305
initial  value 114.313685 
iter  10 value 94.489239
iter  20 value 94.483839
iter  30 value 89.208854
iter  40 value 87.148221
iter  50 value 86.557838
iter  60 value 86.160577
iter  70 value 86.064642
iter  80 value 85.378375
iter  90 value 83.617482
iter 100 value 82.361783
final  value 82.361783 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 110.655311 
iter  10 value 92.792176
iter  20 value 92.789782
iter  30 value 92.610551
iter  40 value 85.984462
iter  50 value 85.887781
iter  60 value 85.525914
final  value 85.486401 
converged
Fitting Repeat 4 

# weights:  305
initial  value 114.200162 
iter  10 value 94.359874
iter  20 value 94.354833
iter  30 value 92.289808
iter  40 value 89.426057
iter  50 value 89.360813
final  value 89.359842 
converged
Fitting Repeat 5 

# weights:  305
initial  value 115.232309 
iter  10 value 94.488861
iter  20 value 94.356343
final  value 94.354712 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.543337 
iter  10 value 94.331194
iter  20 value 94.275394
iter  30 value 92.150734
iter  40 value 88.964620
iter  50 value 88.883038
iter  60 value 88.380538
iter  70 value 88.293208
final  value 88.293146 
converged
Fitting Repeat 2 

# weights:  507
initial  value 136.729968 
iter  10 value 94.492108
iter  20 value 94.483338
iter  30 value 94.282832
iter  40 value 91.789770
iter  50 value 90.151983
iter  60 value 86.449477
iter  70 value 84.512927
iter  80 value 84.332366
iter  90 value 84.159647
iter 100 value 84.100328
final  value 84.100328 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 103.065055 
iter  10 value 94.362025
iter  20 value 94.361382
iter  30 value 94.354503
iter  40 value 91.747815
iter  50 value 85.684965
iter  60 value 85.463754
iter  70 value 85.462805
final  value 85.462700 
converged
Fitting Repeat 4 

# weights:  507
initial  value 144.641602 
iter  10 value 94.492410
iter  20 value 94.484326
iter  30 value 92.826358
iter  40 value 89.631614
final  value 89.629160 
converged
Fitting Repeat 5 

# weights:  507
initial  value 115.008126 
iter  10 value 94.330780
iter  20 value 94.284779
iter  30 value 88.209045
iter  40 value 87.344581
iter  50 value 85.190736
iter  60 value 83.514432
iter  70 value 82.695104
iter  80 value 82.627850
iter  90 value 82.627582
iter 100 value 82.626765
final  value 82.626765 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 117.989129 
iter  10 value 117.891488
iter  20 value 117.852664
iter  30 value 110.377588
iter  40 value 108.166941
iter  50 value 108.001897
iter  60 value 107.979656
final  value 107.979652 
converged
Fitting Repeat 2 

# weights:  305
initial  value 134.701359 
iter  10 value 117.763663
iter  20 value 117.645938
iter  30 value 105.358212
iter  40 value 105.342792
iter  50 value 105.341685
final  value 105.341660 
converged
Fitting Repeat 3 

# weights:  305
initial  value 126.037504 
iter  10 value 117.763784
iter  20 value 117.733687
iter  30 value 117.731402
final  value 117.729959 
converged
Fitting Repeat 4 

# weights:  305
initial  value 119.365309 
iter  10 value 117.894616
iter  20 value 117.850109
iter  30 value 115.358488
iter  40 value 109.326477
iter  50 value 107.715141
iter  60 value 107.692287
iter  70 value 106.695029
iter  80 value 106.678748
iter  90 value 104.305639
iter 100 value 104.270647
final  value 104.270647 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 123.330763 
iter  10 value 117.894729
iter  20 value 117.762071
iter  30 value 107.263788
iter  40 value 107.166250
iter  50 value 106.860185
iter  60 value 106.730781
iter  70 value 106.084579
iter  80 value 106.066537
final  value 106.064389 
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 -- Mon Jun 10 18:52:46 2024 
*********************************************** 
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
HPiP RUnit Tests - 7 test functions, 0 errors, 0 failures
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: `repeats` has no meaning for this resampling method. 
2: executing %dopar% sequentially: no parallel backend registered 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
 40.066   1.470  67.700