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


* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/HPiP.Rcheck’
* using R version 4.4.2 (2024-10-31)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.12.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
    29 | then the Kronecker product is the code{(pm × qn)} block matrix
       |                                       ^
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
var_imp       52.400  2.093  59.069
FSmethod      50.879  1.865  53.510
corr_plot     50.509  1.869  53.148
pred_ensembel 25.024  0.433  22.682
calculateTC    4.652  0.465   5.354
enrichfindP    0.894  0.081  14.276
getFASTA       0.121  0.016   7.552
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

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

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


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library’
* installing *source* package ‘HPiP’ ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HPiP)

R version 4.4.2 (2024-10-31) -- "Pile of Leaves"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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

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

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

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

# weights:  103
initial  value 104.683971 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.817959 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.685960 
iter  10 value 88.692145
iter  20 value 87.033745
iter  30 value 86.045389
iter  40 value 85.530843
iter  50 value 84.844845
final  value 84.840914 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.021881 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.989908 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 112.169356 
iter  10 value 92.436367
iter  20 value 92.405237
final  value 92.405177 
converged
Fitting Repeat 2 

# weights:  305
initial  value 104.434271 
final  value 94.052928 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.214778 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.106752 
final  value 94.032967 
converged
Fitting Repeat 5 

# weights:  305
initial  value 107.442772 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.068896 
iter  10 value 93.968390
final  value 93.968388 
converged
Fitting Repeat 2 

# weights:  507
initial  value 110.098468 
iter  10 value 94.052910
iter  10 value 94.052910
iter  10 value 94.052910
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.713393 
iter  10 value 94.041077
iter  20 value 94.032264
final  value 94.032190 
converged
Fitting Repeat 4 

# weights:  507
initial  value 108.476112 
iter  10 value 93.886126
final  value 93.884577 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.700090 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 102.386077 
iter  10 value 94.026866
iter  20 value 93.569764
iter  30 value 89.190882
iter  40 value 88.297635
iter  50 value 86.936104
iter  60 value 85.681542
iter  70 value 85.593556
iter  80 value 84.908095
iter  90 value 84.739946
final  value 84.738305 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.597406 
iter  10 value 94.067094
iter  20 value 94.053142
iter  30 value 93.819939
iter  40 value 92.650018
iter  50 value 92.026027
iter  60 value 91.971235
iter  70 value 91.940319
iter  80 value 86.323286
iter  90 value 85.019300
iter 100 value 84.362840
final  value 84.362840 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 97.459806 
iter  10 value 94.067414
iter  20 value 93.841338
iter  30 value 89.438740
iter  40 value 86.065497
iter  50 value 85.923024
iter  60 value 85.366512
iter  70 value 85.135928
iter  80 value 85.121468
iter  90 value 85.007178
final  value 85.006300 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.854212 
iter  10 value 94.061895
iter  20 value 90.446827
iter  30 value 87.784313
iter  40 value 87.427455
iter  50 value 87.271997
iter  60 value 86.923587
iter  70 value 86.729728
iter  80 value 86.660855
iter  90 value 84.603943
final  value 84.592550 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.970804 
iter  10 value 94.031373
iter  20 value 92.595391
iter  30 value 92.150907
iter  40 value 91.961231
final  value 91.960295 
converged
Fitting Repeat 1 

# weights:  305
initial  value 106.640246 
iter  10 value 94.071956
iter  20 value 94.012311
iter  30 value 88.496337
iter  40 value 84.307463
iter  50 value 83.634523
iter  60 value 83.237108
iter  70 value 82.908237
iter  80 value 82.465742
iter  90 value 82.186353
iter 100 value 81.608808
final  value 81.608808 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 103.111202 
iter  10 value 94.055861
iter  20 value 87.955667
iter  30 value 87.175697
iter  40 value 86.497372
iter  50 value 85.647734
iter  60 value 85.196669
iter  70 value 83.513732
iter  80 value 82.490836
iter  90 value 82.090574
iter 100 value 81.822103
final  value 81.822103 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.435288 
iter  10 value 93.987789
iter  20 value 87.996037
iter  30 value 86.374181
iter  40 value 85.690061
iter  50 value 84.523132
iter  60 value 84.278386
iter  70 value 84.001826
iter  80 value 83.793828
iter  90 value 82.985805
iter 100 value 82.405323
final  value 82.405323 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.435860 
iter  10 value 93.334764
iter  20 value 89.272634
iter  30 value 88.222623
iter  40 value 87.970789
iter  50 value 87.861770
iter  60 value 87.716021
iter  70 value 84.588163
iter  80 value 84.020230
iter  90 value 82.779603
iter 100 value 81.878963
final  value 81.878963 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.885752 
iter  10 value 94.652523
iter  20 value 89.979218
iter  30 value 87.233394
iter  40 value 86.778012
iter  50 value 85.595867
iter  60 value 84.812380
iter  70 value 84.151795
iter  80 value 83.292251
iter  90 value 83.159695
iter 100 value 83.020574
final  value 83.020574 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 103.238220 
iter  10 value 93.991930
iter  20 value 89.564917
iter  30 value 87.091622
iter  40 value 86.322967
iter  50 value 86.116229
iter  60 value 84.693158
iter  70 value 83.502707
iter  80 value 83.299218
iter  90 value 83.109562
iter 100 value 82.640485
final  value 82.640485 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 130.888885 
iter  10 value 94.059140
iter  20 value 89.383853
iter  30 value 87.322349
iter  40 value 85.038318
iter  50 value 84.323698
iter  60 value 84.072629
iter  70 value 83.680163
iter  80 value 83.523819
iter  90 value 83.495700
iter 100 value 83.408576
final  value 83.408576 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 116.579962 
iter  10 value 94.332313
iter  20 value 88.106912
iter  30 value 86.846895
iter  40 value 85.189585
iter  50 value 83.642232
iter  60 value 83.072425
iter  70 value 82.083156
iter  80 value 81.455138
iter  90 value 81.229497
iter 100 value 81.106531
final  value 81.106531 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 115.908021 
iter  10 value 94.023305
iter  20 value 90.461526
iter  30 value 86.550988
iter  40 value 85.104319
iter  50 value 83.945544
iter  60 value 82.790819
iter  70 value 82.374478
iter  80 value 82.122863
iter  90 value 82.006158
iter 100 value 81.725213
final  value 81.725213 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 106.184885 
iter  10 value 94.862594
iter  20 value 88.608778
iter  30 value 86.331089
iter  40 value 85.006334
iter  50 value 84.772171
iter  60 value 83.459128
iter  70 value 83.052388
iter  80 value 82.475368
iter  90 value 82.260974
iter 100 value 81.959001
final  value 81.959001 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.139351 
iter  10 value 94.054246
final  value 94.053415 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.739915 
final  value 94.054654 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.235599 
final  value 94.054424 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.279648 
final  value 94.054583 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.618652 
final  value 94.054445 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.807299 
iter  10 value 94.055366
iter  20 value 94.052923
iter  30 value 93.741285
iter  40 value 87.065362
iter  50 value 86.638910
iter  60 value 82.816914
iter  70 value 81.301267
iter  80 value 81.210953
iter  90 value 81.111266
iter 100 value 81.094257
final  value 81.094257 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 105.451276 
iter  10 value 94.046819
iter  20 value 94.034057
final  value 94.033006 
converged
Fitting Repeat 3 

# weights:  305
initial  value 113.347800 
iter  10 value 94.057307
iter  20 value 94.052562
iter  30 value 92.813316
iter  40 value 87.908666
iter  50 value 86.166442
iter  60 value 85.672200
final  value 85.671402 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.677172 
iter  10 value 94.057405
iter  20 value 92.324100
iter  30 value 88.057977
iter  40 value 86.640085
iter  50 value 86.367699
iter  60 value 86.358344
iter  70 value 86.334502
iter  80 value 86.216572
iter  90 value 86.204580
iter 100 value 86.000636
final  value 86.000636 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 113.052250 
iter  10 value 94.057992
iter  20 value 94.037953
iter  30 value 86.588198
iter  40 value 85.213282
iter  50 value 85.189530
iter  60 value 85.187792
iter  70 value 83.955568
iter  80 value 83.570253
iter  90 value 82.684820
iter 100 value 82.148562
final  value 82.148562 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 94.323341 
iter  10 value 92.866990
iter  20 value 92.815577
iter  30 value 92.798861
iter  40 value 92.710286
iter  50 value 92.704588
iter  60 value 92.703390
final  value 92.702911 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.971264 
iter  10 value 93.972986
iter  20 value 93.930425
iter  30 value 93.924714
iter  40 value 87.284957
iter  50 value 86.397240
iter  60 value 85.428284
iter  70 value 83.009853
iter  80 value 81.375158
iter  90 value 80.355633
iter 100 value 80.258886
final  value 80.258886 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 96.185964 
iter  10 value 94.059723
iter  20 value 94.052925
iter  20 value 94.052925
final  value 94.052925 
converged
Fitting Repeat 4 

# weights:  507
initial  value 122.052590 
iter  10 value 94.057911
iter  20 value 94.039465
iter  30 value 94.036597
iter  40 value 93.970544
iter  50 value 92.211245
iter  60 value 92.113233
final  value 92.112767 
converged
Fitting Repeat 5 

# weights:  507
initial  value 118.110858 
iter  10 value 94.041177
iter  20 value 94.031031
iter  30 value 93.603375
iter  40 value 86.911640
iter  50 value 86.402811
iter  60 value 86.395439
iter  70 value 86.395199
iter  80 value 86.297420
iter  90 value 86.293979
iter 100 value 86.293877
final  value 86.293877 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 106.579484 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.167609 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.471096 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.913856 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 117.960990 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.461499 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 106.205260 
iter  10 value 94.484211
iter  10 value 94.484211
iter  10 value 94.484211
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.213425 
final  value 94.443243 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.461385 
final  value 93.991342 
converged
Fitting Repeat 5 

# weights:  305
initial  value 102.105389 
iter  10 value 94.402439
iter  10 value 94.402439
iter  10 value 94.402439
final  value 94.402439 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.132245 
iter  10 value 94.443247
final  value 94.443244 
converged
Fitting Repeat 2 

# weights:  507
initial  value 105.147484 
iter  10 value 93.300000
iter  10 value 93.300000
iter  10 value 93.300000
final  value 93.300000 
converged
Fitting Repeat 3 

# weights:  507
initial  value 96.475520 
final  value 94.443243 
converged
Fitting Repeat 4 

# weights:  507
initial  value 103.466728 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.007123 
iter  10 value 94.443278
final  value 94.443243 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.650633 
iter  10 value 94.133301
iter  20 value 94.022802
iter  30 value 88.453941
iter  40 value 86.080860
iter  50 value 85.621220
iter  60 value 85.447167
iter  70 value 83.370257
iter  80 value 82.952783
iter  90 value 82.843463
iter 100 value 82.814394
final  value 82.814394 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 98.776812 
iter  10 value 94.400398
iter  20 value 92.817479
iter  30 value 87.206902
iter  40 value 84.367981
iter  50 value 83.857578
iter  60 value 83.482208
iter  70 value 83.028602
iter  80 value 82.959674
iter  90 value 82.901323
final  value 82.896321 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.332226 
iter  10 value 94.254062
iter  20 value 89.030440
iter  30 value 86.982106
iter  40 value 86.575059
iter  50 value 85.579368
iter  60 value 83.622682
iter  70 value 83.043100
iter  80 value 82.905846
iter  90 value 82.871346
iter 100 value 82.786465
final  value 82.786465 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 96.559290 
iter  10 value 94.428462
iter  20 value 90.056720
iter  30 value 88.079517
iter  40 value 87.386120
iter  50 value 87.161424
iter  60 value 86.258385
iter  70 value 85.912951
iter  80 value 85.411424
iter  90 value 85.319074
final  value 85.317618 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.878585 
iter  10 value 88.519149
iter  20 value 84.118358
iter  30 value 83.943564
iter  40 value 83.276310
iter  50 value 82.909587
final  value 82.896321 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.685815 
iter  10 value 94.616607
iter  20 value 94.233532
iter  30 value 93.375208
iter  40 value 84.794917
iter  50 value 84.282748
iter  60 value 83.406593
iter  70 value 82.214058
iter  80 value 81.906130
iter  90 value 81.749875
iter 100 value 81.596747
final  value 81.596747 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 109.118899 
iter  10 value 94.542724
iter  20 value 94.301671
iter  30 value 89.142657
iter  40 value 84.124588
iter  50 value 83.632963
iter  60 value 83.022648
iter  70 value 82.148196
iter  80 value 81.518203
iter  90 value 81.239134
iter 100 value 81.160525
final  value 81.160525 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 113.520721 
iter  10 value 94.498172
iter  20 value 90.586655
iter  30 value 88.082554
iter  40 value 87.599376
iter  50 value 86.647676
iter  60 value 86.363913
iter  70 value 85.261614
iter  80 value 84.195180
iter  90 value 83.113163
iter 100 value 82.967455
final  value 82.967455 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 103.854160 
iter  10 value 94.424623
iter  20 value 86.285752
iter  30 value 86.011447
iter  40 value 85.583272
iter  50 value 85.404078
iter  60 value 85.265621
iter  70 value 85.117334
iter  80 value 85.048756
iter  90 value 84.995822
iter 100 value 83.912621
final  value 83.912621 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 100.307258 
iter  10 value 94.490343
iter  20 value 93.638200
iter  30 value 93.054488
iter  40 value 87.989433
iter  50 value 85.535136
iter  60 value 85.324351
iter  70 value 83.644090
iter  80 value 82.701425
iter  90 value 82.293772
iter 100 value 81.954527
final  value 81.954527 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 106.504167 
iter  10 value 94.594163
iter  20 value 92.518721
iter  30 value 87.352129
iter  40 value 86.335029
iter  50 value 82.564642
iter  60 value 82.191159
iter  70 value 81.972256
iter  80 value 81.865322
iter  90 value 81.836431
iter 100 value 81.680470
final  value 81.680470 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.259207 
iter  10 value 94.373691
iter  20 value 93.650415
iter  30 value 92.769558
iter  40 value 86.692442
iter  50 value 85.397134
iter  60 value 83.721908
iter  70 value 82.936358
iter  80 value 81.816901
iter  90 value 81.437546
iter 100 value 81.232052
final  value 81.232052 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 115.810895 
iter  10 value 94.161948
iter  20 value 86.747941
iter  30 value 84.531782
iter  40 value 83.687036
iter  50 value 83.352355
iter  60 value 83.074427
iter  70 value 82.962162
iter  80 value 82.951751
iter  90 value 82.640761
iter 100 value 82.025583
final  value 82.025583 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 122.249217 
iter  10 value 100.755709
iter  20 value 90.479058
iter  30 value 87.574940
iter  40 value 83.582800
iter  50 value 82.181540
iter  60 value 81.749543
iter  70 value 81.271879
iter  80 value 81.209874
iter  90 value 81.162557
iter 100 value 81.036883
final  value 81.036883 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 113.878261 
iter  10 value 94.838077
iter  20 value 87.469859
iter  30 value 85.501590
iter  40 value 85.028450
iter  50 value 84.901425
iter  60 value 84.629721
iter  70 value 83.509725
iter  80 value 83.267087
iter  90 value 82.993959
iter 100 value 82.628816
final  value 82.628816 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 102.366580 
final  value 94.485860 
converged
Fitting Repeat 2 

# weights:  103
initial  value 104.869083 
iter  10 value 94.485720
iter  20 value 94.484230
iter  30 value 87.990025
iter  40 value 86.699814
final  value 86.699761 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.577535 
final  value 94.486092 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.713850 
final  value 94.444993 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.994722 
final  value 94.485829 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.980865 
iter  10 value 94.430457
iter  20 value 94.425224
iter  30 value 89.921404
iter  40 value 89.771319
iter  50 value 88.810599
iter  60 value 88.794855
iter  60 value 88.794854
iter  60 value 88.794854
final  value 88.794854 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.345815 
iter  10 value 94.488700
iter  20 value 94.484279
iter  30 value 88.365615
iter  40 value 83.249304
iter  50 value 83.225009
final  value 83.224988 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.768091 
iter  10 value 94.488583
iter  20 value 94.418641
iter  30 value 85.796072
iter  40 value 85.041149
final  value 84.997171 
converged
Fitting Repeat 4 

# weights:  305
initial  value 104.020381 
iter  10 value 92.327795
iter  20 value 91.480735
iter  30 value 89.793646
iter  40 value 89.399068
iter  50 value 89.058570
iter  60 value 88.997444
iter  70 value 88.621104
iter  80 value 88.590033
iter  90 value 88.589155
iter 100 value 88.587841
final  value 88.587841 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 98.610963 
iter  10 value 94.456474
iter  20 value 94.448183
iter  30 value 94.060627
iter  40 value 93.559671
iter  50 value 91.742697
iter  60 value 90.165063
iter  70 value 90.163127
final  value 90.163116 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.879808 
iter  10 value 94.492253
iter  20 value 94.207320
iter  30 value 86.720080
iter  40 value 84.480829
iter  50 value 84.451628
iter  60 value 84.446688
iter  70 value 83.974175
iter  80 value 83.872863
iter  90 value 83.516758
iter 100 value 82.045727
final  value 82.045727 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 98.417189 
iter  10 value 90.219236
iter  20 value 86.847473
iter  30 value 86.288425
iter  40 value 86.194272
iter  50 value 86.182802
iter  60 value 85.825306
iter  70 value 83.226295
iter  80 value 83.043164
iter  90 value 82.128257
iter 100 value 82.019656
final  value 82.019656 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 97.433244 
iter  10 value 94.492060
iter  20 value 94.484094
iter  30 value 93.301625
iter  40 value 92.884383
iter  50 value 90.932478
iter  60 value 85.666829
iter  70 value 85.258613
iter  80 value 85.258534
final  value 85.258196 
converged
Fitting Repeat 4 

# weights:  507
initial  value 103.813268 
iter  10 value 94.451421
iter  20 value 94.347481
iter  30 value 93.304527
iter  40 value 86.315167
iter  50 value 86.311848
iter  60 value 86.309401
iter  70 value 86.272673
iter  80 value 85.657368
final  value 85.657342 
converged
Fitting Repeat 5 

# weights:  507
initial  value 94.974237 
iter  10 value 93.308250
iter  20 value 93.274619
iter  30 value 91.898399
iter  40 value 90.844212
iter  50 value 90.814055
iter  60 value 90.812015
final  value 90.812003 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.035378 
iter  10 value 93.328261
iter  10 value 93.328261
iter  10 value 93.328261
final  value 93.328261 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.793287 
final  value 94.052911 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.879101 
iter  10 value 93.328279
final  value 93.328261 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.053877 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.907778 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.617476 
iter  10 value 93.328262
final  value 93.328261 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.231358 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.335038 
iter  10 value 88.663390
iter  20 value 85.979499
iter  30 value 81.868471
iter  40 value 81.844214
final  value 81.844156 
converged
Fitting Repeat 4 

# weights:  305
initial  value 109.922732 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 105.736777 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.890007 
iter  10 value 86.696730
iter  20 value 86.326959
iter  30 value 86.325704
final  value 86.325680 
converged
Fitting Repeat 2 

# weights:  507
initial  value 108.452141 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 94.663684 
iter  10 value 85.507049
iter  20 value 81.659228
iter  30 value 81.644536
iter  40 value 81.643706
final  value 81.643691 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.886494 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.515203 
iter  10 value 90.174865
iter  20 value 88.673788
final  value 88.673670 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.906255 
iter  10 value 94.018137
iter  20 value 93.178980
iter  30 value 93.145309
iter  40 value 93.142673
iter  50 value 93.142032
iter  60 value 87.853135
iter  70 value 84.253160
iter  80 value 82.513693
iter  90 value 81.836920
iter 100 value 81.737932
final  value 81.737932 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.413207 
iter  10 value 94.105188
iter  20 value 92.145859
iter  30 value 86.658443
iter  40 value 83.674141
iter  50 value 82.609240
iter  60 value 81.668424
iter  70 value 80.646508
iter  80 value 79.150379
iter  90 value 78.899213
final  value 78.886207 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.428301 
iter  10 value 93.947060
iter  20 value 93.545064
iter  30 value 93.018339
iter  40 value 90.486609
iter  50 value 86.835282
iter  60 value 86.560004
iter  70 value 86.425824
iter  80 value 86.327122
iter  90 value 86.213966
iter 100 value 82.892163
final  value 82.892163 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 98.071686 
iter  10 value 88.992020
iter  20 value 83.787873
iter  30 value 82.513503
iter  40 value 81.671638
iter  50 value 81.398808
iter  60 value 81.320058
iter  70 value 81.255041
iter  80 value 80.009370
iter  90 value 78.771028
iter 100 value 78.738387
final  value 78.738387 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 99.561744 
iter  10 value 94.055028
iter  20 value 93.966984
iter  30 value 93.164707
iter  40 value 93.147206
iter  50 value 93.146457
iter  60 value 93.145295
iter  70 value 93.143226
iter  80 value 84.660828
iter  90 value 83.458320
iter 100 value 81.296234
final  value 81.296234 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 107.861672 
iter  10 value 93.925201
iter  20 value 91.577161
iter  30 value 91.167016
iter  40 value 90.736177
iter  50 value 90.047624
iter  60 value 89.669840
iter  70 value 88.100518
iter  80 value 79.587398
iter  90 value 78.440148
iter 100 value 78.179797
final  value 78.179797 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.211382 
iter  10 value 93.997101
iter  20 value 89.206610
iter  30 value 83.789945
iter  40 value 81.943954
iter  50 value 80.031583
iter  60 value 79.724580
iter  70 value 79.707490
iter  80 value 79.676946
iter  90 value 79.646719
iter 100 value 79.451903
final  value 79.451903 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 98.950637 
iter  10 value 84.830787
iter  20 value 83.219723
iter  30 value 81.949702
iter  40 value 81.204211
iter  50 value 80.475374
iter  60 value 79.405315
iter  70 value 78.378953
iter  80 value 77.779248
iter  90 value 77.574959
iter 100 value 77.397348
final  value 77.397348 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.668904 
iter  10 value 94.032300
iter  20 value 90.006619
iter  30 value 82.966380
iter  40 value 82.346584
iter  50 value 81.010518
iter  60 value 79.243858
iter  70 value 78.185780
iter  80 value 77.950311
iter  90 value 77.815374
iter 100 value 77.783926
final  value 77.783926 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.233898 
iter  10 value 93.641783
iter  20 value 92.754406
iter  30 value 85.665952
iter  40 value 83.515753
iter  50 value 78.976616
iter  60 value 78.257037
iter  70 value 78.091321
iter  80 value 77.951593
iter  90 value 77.768071
iter 100 value 77.492286
final  value 77.492286 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 106.437899 
iter  10 value 92.239835
iter  20 value 86.616732
iter  30 value 84.431765
iter  40 value 82.029442
iter  50 value 80.479403
iter  60 value 78.502320
iter  70 value 78.158272
iter  80 value 77.922975
iter  90 value 77.668450
iter 100 value 77.433685
final  value 77.433685 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 107.910596 
iter  10 value 94.456857
iter  20 value 93.941149
iter  30 value 91.449960
iter  40 value 89.658742
iter  50 value 86.701793
iter  60 value 85.711937
iter  70 value 82.125460
iter  80 value 81.668984
iter  90 value 80.854989
iter 100 value 80.178909
final  value 80.178909 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 115.191363 
iter  10 value 93.958188
iter  20 value 91.174773
iter  30 value 88.775600
iter  40 value 83.443285
iter  50 value 80.985669
iter  60 value 79.203627
iter  70 value 78.216200
iter  80 value 77.861696
iter  90 value 77.812810
iter 100 value 77.632918
final  value 77.632918 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 103.641781 
iter  10 value 93.399948
iter  20 value 88.067805
iter  30 value 87.404675
iter  40 value 86.648080
iter  50 value 86.480370
iter  60 value 82.405496
iter  70 value 81.106614
iter  80 value 80.883362
iter  90 value 80.843130
iter 100 value 80.297977
final  value 80.297977 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 109.572728 
iter  10 value 96.367632
iter  20 value 93.542612
iter  30 value 90.702301
iter  40 value 87.143513
iter  50 value 80.070199
iter  60 value 78.199678
iter  70 value 77.645814
iter  80 value 77.416436
iter  90 value 77.311014
iter 100 value 77.254502
final  value 77.254502 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.943556 
final  value 94.054963 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.721942 
final  value 94.054206 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.773744 
final  value 94.054708 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.504986 
final  value 94.054388 
converged
Fitting Repeat 5 

# weights:  103
initial  value 107.569616 
iter  10 value 94.054525
iter  20 value 94.052975
final  value 94.052920 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.251815 
iter  10 value 93.333367
iter  20 value 93.329721
iter  30 value 93.128075
iter  40 value 92.923084
iter  50 value 92.922773
final  value 92.922681 
converged
Fitting Repeat 2 

# weights:  305
initial  value 98.889137 
iter  10 value 93.335764
iter  20 value 93.334881
iter  30 value 93.041812
iter  40 value 88.980962
iter  50 value 81.896656
iter  60 value 81.717836
final  value 81.714242 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.672255 
iter  10 value 94.057400
iter  20 value 94.028413
final  value 92.934256 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.469623 
iter  10 value 92.688466
iter  20 value 92.607710
iter  30 value 89.992360
iter  40 value 89.976623
iter  50 value 89.828564
iter  60 value 83.762678
iter  70 value 83.733067
iter  80 value 81.687664
iter  90 value 81.342505
iter 100 value 81.295654
final  value 81.295654 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.042481 
iter  10 value 94.055008
iter  20 value 94.052943
iter  20 value 94.052942
iter  20 value 94.052942
final  value 94.052942 
converged
Fitting Repeat 1 

# weights:  507
initial  value 107.005380 
iter  10 value 93.912349
iter  20 value 93.858818
iter  30 value 83.754579
iter  40 value 80.890686
iter  50 value 79.564849
iter  60 value 77.666123
iter  70 value 77.275478
final  value 77.275271 
converged
Fitting Repeat 2 

# weights:  507
initial  value 115.729956 
iter  10 value 93.336884
iter  20 value 93.332360
iter  30 value 91.930610
iter  40 value 91.896153
iter  50 value 91.896023
iter  60 value 89.966708
iter  70 value 88.785031
iter  80 value 88.768982
iter  90 value 88.768754
iter  90 value 88.768754
final  value 88.768754 
converged
Fitting Repeat 3 

# weights:  507
initial  value 113.380823 
iter  10 value 94.061052
iter  20 value 94.037459
iter  30 value 91.193841
iter  40 value 90.181566
final  value 90.176908 
converged
Fitting Repeat 4 

# weights:  507
initial  value 118.381228 
iter  10 value 93.336170
iter  20 value 93.327927
iter  30 value 88.212758
iter  40 value 82.575382
iter  50 value 78.969406
iter  60 value 78.503917
iter  70 value 78.202016
iter  80 value 77.924582
iter  90 value 76.378788
iter 100 value 76.293684
final  value 76.293684 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 98.072675 
iter  10 value 90.829621
iter  20 value 79.611639
iter  30 value 77.855174
iter  40 value 76.975888
iter  50 value 76.896918
iter  60 value 76.895961
iter  70 value 76.859137
iter  80 value 76.761606
iter  90 value 76.747715
iter 100 value 76.744216
final  value 76.744216 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.922984 
iter  10 value 93.249256
final  value 93.247059 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.205455 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 103.094139 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.884948 
final  value 94.354396 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.376685 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.652015 
final  value 94.354396 
converged
Fitting Repeat 2 

# weights:  305
initial  value 118.103807 
final  value 94.354396 
converged
Fitting Repeat 3 

# weights:  305
initial  value 105.501227 
final  value 94.354396 
converged
Fitting Repeat 4 

# weights:  305
initial  value 103.147333 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 103.215308 
iter  10 value 93.543951
iter  20 value 93.300003
final  value 93.300000 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.046627 
iter  10 value 92.796084
iter  20 value 91.743818
final  value 91.730488 
converged
Fitting Repeat 2 

# weights:  507
initial  value 120.400135 
iter  10 value 94.484213
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 100.720023 
iter  10 value 93.629411
iter  20 value 90.593342
iter  30 value 88.930404
iter  40 value 87.367623
iter  50 value 87.290085
final  value 87.290072 
converged
Fitting Repeat 4 

# weights:  507
initial  value 111.358321 
iter  10 value 93.940728
iter  20 value 93.720838
final  value 93.720833 
converged
Fitting Repeat 5 

# weights:  507
initial  value 120.905865 
iter  10 value 88.963080
iter  20 value 85.800617
iter  30 value 85.604307
iter  40 value 85.586513
iter  50 value 85.585135
iter  60 value 85.559499
iter  70 value 85.464037
iter  80 value 85.139653
iter  90 value 84.865077
final  value 84.865035 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.928420 
iter  10 value 94.431935
iter  20 value 89.297071
iter  30 value 86.615637
iter  40 value 85.015361
iter  50 value 84.282886
iter  60 value 84.100880
iter  70 value 83.942704
iter  80 value 83.592294
iter  90 value 83.483926
iter 100 value 83.479896
final  value 83.479896 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 98.082012 
iter  10 value 94.479144
iter  20 value 93.985131
iter  30 value 93.819290
iter  40 value 93.753213
iter  50 value 85.796222
iter  60 value 84.560240
iter  70 value 84.342386
iter  80 value 84.242042
iter  90 value 83.880928
iter 100 value 83.547468
final  value 83.547468 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 98.244032 
iter  10 value 94.491446
iter  20 value 94.117766
iter  30 value 93.630745
iter  40 value 88.883314
iter  50 value 84.426058
iter  60 value 83.966050
iter  70 value 83.699022
iter  80 value 83.540633
iter  90 value 83.412267
final  value 83.412117 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.386847 
iter  10 value 94.495886
iter  20 value 93.593507
iter  30 value 88.574287
iter  40 value 86.853351
iter  50 value 86.547952
iter  60 value 84.617061
iter  70 value 84.224580
iter  80 value 83.982949
iter  90 value 83.848071
iter 100 value 83.757896
final  value 83.757896 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 99.993014 
iter  10 value 94.853208
iter  20 value 94.486901
iter  30 value 94.459828
iter  40 value 94.452906
iter  50 value 94.396140
iter  60 value 93.703800
iter  70 value 91.953937
iter  80 value 91.418137
iter  90 value 90.831849
iter 100 value 90.648397
final  value 90.648397 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 108.002877 
iter  10 value 94.720895
iter  20 value 90.410487
iter  30 value 85.818660
iter  40 value 85.303165
iter  50 value 83.474692
iter  60 value 83.329775
iter  70 value 83.060701
iter  80 value 82.964311
iter  90 value 82.913385
iter 100 value 82.859112
final  value 82.859112 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 114.732392 
iter  10 value 94.007088
iter  20 value 92.139099
iter  30 value 89.459106
iter  40 value 86.564234
iter  50 value 84.778134
iter  60 value 84.132672
iter  70 value 83.769261
iter  80 value 82.988436
iter  90 value 82.190286
iter 100 value 82.094345
final  value 82.094345 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 108.107551 
iter  10 value 93.392916
iter  20 value 87.515325
iter  30 value 86.696471
iter  40 value 85.515782
iter  50 value 84.520327
iter  60 value 83.853311
iter  70 value 83.621961
iter  80 value 83.387126
iter  90 value 83.192590
iter 100 value 82.731716
final  value 82.731716 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 107.888203 
iter  10 value 94.435774
iter  20 value 93.777026
iter  30 value 88.489751
iter  40 value 88.080684
iter  50 value 88.035444
iter  60 value 87.907086
iter  70 value 85.858787
iter  80 value 85.563634
iter  90 value 84.692316
iter 100 value 84.217094
final  value 84.217094 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 108.040137 
iter  10 value 94.551570
iter  20 value 92.250660
iter  30 value 92.084156
iter  40 value 91.752624
iter  50 value 91.388652
iter  60 value 85.483880
iter  70 value 85.072802
iter  80 value 85.020515
iter  90 value 84.847135
iter 100 value 84.595869
final  value 84.595869 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 106.135288 
iter  10 value 89.943148
iter  20 value 87.631123
iter  30 value 87.526306
iter  40 value 87.399525
iter  50 value 87.154325
iter  60 value 84.442163
iter  70 value 83.929887
iter  80 value 83.603893
iter  90 value 83.535804
iter 100 value 83.343712
final  value 83.343712 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 107.560724 
iter  10 value 94.571904
iter  20 value 94.255069
iter  30 value 88.755866
iter  40 value 87.463520
iter  50 value 85.808393
iter  60 value 85.458575
iter  70 value 84.367164
iter  80 value 83.980591
iter  90 value 83.347069
iter 100 value 83.093840
final  value 83.093840 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 126.838504 
iter  10 value 94.991084
iter  20 value 92.355271
iter  30 value 87.354315
iter  40 value 86.651595
iter  50 value 86.415791
iter  60 value 85.980049
iter  70 value 85.324887
iter  80 value 83.914818
iter  90 value 82.878335
iter 100 value 82.668007
final  value 82.668007 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 109.248712 
iter  10 value 94.787311
iter  20 value 92.996233
iter  30 value 86.449038
iter  40 value 85.619744
iter  50 value 85.029356
iter  60 value 83.799719
iter  70 value 83.217058
iter  80 value 82.762012
iter  90 value 82.513465
iter 100 value 82.455406
final  value 82.455406 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 124.882042 
iter  10 value 94.717961
iter  20 value 94.021223
iter  30 value 93.916706
iter  40 value 91.572717
iter  50 value 88.524029
iter  60 value 85.472377
iter  70 value 84.125708
iter  80 value 83.612320
iter  90 value 82.906165
iter 100 value 82.755741
final  value 82.755741 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.883241 
final  value 94.485600 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.576413 
final  value 94.485926 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.323175 
final  value 94.457094 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.264584 
final  value 94.485847 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.477783 
final  value 94.485821 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.045839 
iter  10 value 93.211637
iter  20 value 93.206818
iter  30 value 92.854868
iter  40 value 92.203783
iter  50 value 91.430447
iter  60 value 91.339748
iter  70 value 91.251731
final  value 91.251454 
converged
Fitting Repeat 2 

# weights:  305
initial  value 99.581434 
iter  10 value 94.359444
iter  20 value 93.961867
iter  30 value 90.906870
iter  40 value 90.880326
iter  50 value 90.879788
iter  60 value 90.879687
iter  70 value 90.879470
iter  80 value 88.686544
iter  90 value 87.325426
iter 100 value 87.311398
final  value 87.311398 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 107.075584 
iter  10 value 94.488736
final  value 94.484228 
converged
Fitting Repeat 4 

# weights:  305
initial  value 101.703220 
iter  10 value 94.035416
iter  20 value 94.010981
iter  30 value 92.145642
iter  40 value 85.756984
iter  50 value 85.749903
iter  60 value 85.681192
iter  70 value 85.678660
iter  80 value 85.204563
final  value 85.124664 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.853763 
iter  10 value 94.433617
iter  20 value 94.271344
iter  30 value 86.953343
iter  40 value 84.936613
iter  50 value 84.889723
iter  60 value 84.877396
iter  70 value 84.863827
iter  80 value 84.714562
iter  90 value 84.047619
iter 100 value 83.919807
final  value 83.919807 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.260693 
iter  10 value 89.684790
iter  20 value 87.805500
iter  30 value 86.775160
iter  40 value 86.711514
iter  50 value 86.705167
iter  60 value 84.901384
iter  70 value 84.894159
iter  70 value 84.894159
iter  70 value 84.894159
final  value 84.894159 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.382704 
iter  10 value 94.362734
iter  20 value 93.828533
iter  30 value 93.624683
iter  40 value 93.623768
iter  50 value 90.892028
iter  60 value 88.078777
iter  70 value 87.954119
iter  80 value 86.321858
iter  90 value 85.328156
iter 100 value 85.262797
final  value 85.262797 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 116.233905 
iter  10 value 94.492443
iter  20 value 94.477614
iter  30 value 94.143165
iter  40 value 91.881774
iter  50 value 88.761458
iter  60 value 87.159023
iter  70 value 87.126689
iter  80 value 86.860709
iter  90 value 86.860449
iter 100 value 86.852939
final  value 86.852939 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 103.115889 
iter  10 value 94.362297
iter  20 value 94.244945
iter  30 value 89.086755
iter  40 value 86.918504
iter  50 value 84.069204
iter  60 value 83.370445
iter  70 value 83.356379
iter  80 value 83.018146
iter  90 value 82.863084
iter 100 value 82.366351
final  value 82.366351 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 107.841873 
iter  10 value 93.649060
iter  20 value 93.071628
iter  30 value 93.065956
iter  40 value 91.851435
iter  50 value 85.701592
iter  60 value 85.532754
iter  70 value 85.434696
iter  80 value 85.432481
iter  80 value 85.432480
final  value 85.432480 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.659092 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.016749 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.375859 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.781896 
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.481247 
iter  10 value 91.249393
iter  20 value 87.537325
iter  30 value 87.536203
iter  40 value 82.774385
iter  50 value 82.467490
iter  60 value 82.466356
final  value 82.466340 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.029929 
final  value 94.448052 
converged
Fitting Repeat 2 

# weights:  305
initial  value 101.737745 
final  value 94.466823 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.180321 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.830795 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.929898 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.903912 
final  value 94.305882 
converged
Fitting Repeat 2 

# weights:  507
initial  value 95.693953 
iter  10 value 93.937686
iter  20 value 93.901432
iter  30 value 93.885325
final  value 93.885053 
converged
Fitting Repeat 3 

# weights:  507
initial  value 115.455632 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.108352 
final  value 94.448052 
converged
Fitting Repeat 5 

# weights:  507
initial  value 101.841074 
iter  10 value 94.432791
iter  20 value 94.427734
final  value 94.427727 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.450587 
iter  10 value 94.503442
iter  20 value 94.344644
iter  30 value 92.074516
iter  40 value 90.002224
iter  50 value 89.464343
iter  60 value 84.549590
iter  70 value 82.647743
iter  80 value 80.705503
iter  90 value 79.349649
iter 100 value 79.226059
final  value 79.226059 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.665924 
iter  10 value 94.348497
iter  20 value 86.611178
iter  30 value 85.853947
iter  40 value 84.764166
iter  50 value 84.492630
iter  60 value 83.549639
iter  70 value 82.614057
iter  80 value 82.464669
iter  90 value 82.348805
final  value 82.322496 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.455910 
iter  10 value 94.491895
iter  20 value 94.482839
iter  30 value 94.038740
iter  40 value 93.956959
iter  50 value 87.113790
iter  60 value 86.887977
iter  70 value 84.823617
iter  80 value 84.733571
iter  90 value 80.799808
iter 100 value 80.684446
final  value 80.684446 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 98.256160 
iter  10 value 94.486876
iter  20 value 94.148877
iter  30 value 86.011276
iter  40 value 82.075094
iter  50 value 81.574896
iter  60 value 81.411272
iter  70 value 81.027036
iter  80 value 80.757861
iter  90 value 80.684618
final  value 80.684270 
converged
Fitting Repeat 5 

# weights:  103
initial  value 112.414701 
iter  10 value 94.375150
iter  20 value 86.939007
iter  30 value 84.526650
iter  40 value 84.237996
iter  50 value 81.911317
iter  60 value 81.321842
iter  70 value 80.771639
iter  80 value 80.684295
final  value 80.684270 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.726440 
iter  10 value 94.511444
iter  20 value 94.423402
iter  30 value 87.279145
iter  40 value 84.848120
iter  50 value 83.198541
iter  60 value 80.228754
iter  70 value 79.949125
iter  80 value 79.532600
iter  90 value 79.440771
iter 100 value 79.424852
final  value 79.424852 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 112.282992 
iter  10 value 94.598057
iter  20 value 85.795398
iter  30 value 84.688860
iter  40 value 83.817495
iter  50 value 82.281316
iter  60 value 80.450476
iter  70 value 80.269025
iter  80 value 80.264858
iter  90 value 80.238691
iter 100 value 80.187527
final  value 80.187527 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.419357 
iter  10 value 91.744918
iter  20 value 88.393489
iter  30 value 87.805556
iter  40 value 85.430779
iter  50 value 83.167529
iter  60 value 82.400953
iter  70 value 80.903332
iter  80 value 80.607348
iter  90 value 79.600604
iter 100 value 78.109140
final  value 78.109140 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 119.848138 
iter  10 value 94.369475
iter  20 value 86.908587
iter  30 value 85.387934
iter  40 value 84.613058
iter  50 value 83.063276
iter  60 value 80.799969
iter  70 value 80.614487
iter  80 value 80.213852
iter  90 value 78.766080
iter 100 value 78.246392
final  value 78.246392 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 108.499145 
iter  10 value 94.369488
iter  20 value 85.915942
iter  30 value 84.594825
iter  40 value 82.782601
iter  50 value 82.360575
iter  60 value 81.824917
iter  70 value 79.178644
iter  80 value 78.210421
iter  90 value 77.561561
iter 100 value 77.532098
final  value 77.532098 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 107.566065 
iter  10 value 94.490925
iter  20 value 92.927826
iter  30 value 86.449583
iter  40 value 85.993967
iter  50 value 81.089297
iter  60 value 80.251707
iter  70 value 78.780732
iter  80 value 78.241002
iter  90 value 77.669640
iter 100 value 77.289245
final  value 77.289245 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.792812 
iter  10 value 94.844827
iter  20 value 92.117595
iter  30 value 83.484725
iter  40 value 81.197043
iter  50 value 79.178196
iter  60 value 78.519383
iter  70 value 78.263657
iter  80 value 77.935476
iter  90 value 77.567457
iter 100 value 77.561683
final  value 77.561683 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.222252 
iter  10 value 93.350613
iter  20 value 85.311289
iter  30 value 81.782029
iter  40 value 80.578214
iter  50 value 80.350081
iter  60 value 80.278319
iter  70 value 80.079736
iter  80 value 79.853612
iter  90 value 78.717021
iter 100 value 77.791510
final  value 77.791510 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 111.511718 
iter  10 value 94.491359
iter  20 value 86.836918
iter  30 value 86.274993
iter  40 value 86.106633
iter  50 value 82.497691
iter  60 value 81.500090
iter  70 value 77.790024
iter  80 value 77.460250
iter  90 value 77.327659
iter 100 value 77.224250
final  value 77.224250 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.029585 
iter  10 value 93.245038
iter  20 value 86.438596
iter  30 value 85.183411
iter  40 value 82.638345
iter  50 value 81.025339
iter  60 value 79.532349
iter  70 value 79.120973
iter  80 value 78.417443
iter  90 value 78.347926
iter 100 value 78.027233
final  value 78.027233 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 103.347300 
final  value 94.485864 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.957997 
iter  10 value 94.485856
iter  20 value 94.484274
final  value 94.484217 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.679400 
final  value 94.485938 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.559109 
iter  10 value 94.486260
iter  20 value 94.485754
iter  30 value 94.484356
iter  40 value 92.078766
iter  50 value 91.664048
iter  60 value 91.653976
iter  70 value 91.653767
iter  80 value 79.548615
iter  90 value 79.240982
iter 100 value 79.236688
final  value 79.236688 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 99.100705 
iter  10 value 94.486036
iter  20 value 94.482980
iter  30 value 90.771733
iter  40 value 82.798087
iter  50 value 82.793297
iter  60 value 79.493438
iter  70 value 79.460308
iter  80 value 79.345184
iter  90 value 79.298312
iter 100 value 79.293424
final  value 79.293424 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 109.713089 
iter  10 value 94.489333
iter  20 value 94.484386
iter  30 value 94.380491
iter  40 value 85.874630
iter  50 value 82.675325
iter  60 value 82.619986
iter  70 value 82.135535
iter  80 value 80.665762
iter  90 value 80.615011
iter 100 value 80.612401
final  value 80.612401 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 96.474711 
iter  10 value 94.489013
iter  20 value 91.258218
iter  30 value 87.541522
iter  40 value 87.538050
iter  50 value 86.197594
iter  60 value 83.036903
final  value 83.036468 
converged
Fitting Repeat 3 

# weights:  305
initial  value 98.160419 
iter  10 value 94.471294
iter  20 value 94.466608
iter  30 value 91.158203
iter  40 value 91.075485
iter  50 value 91.056866
iter  60 value 86.969432
iter  70 value 86.485674
iter  80 value 86.039074
iter  90 value 84.509121
final  value 84.508365 
converged
Fitting Repeat 4 

# weights:  305
initial  value 101.774159 
iter  10 value 94.488951
iter  20 value 94.481104
iter  30 value 93.316049
iter  40 value 87.359660
iter  50 value 87.349451
final  value 87.341072 
converged
Fitting Repeat 5 

# weights:  305
initial  value 101.065293 
iter  10 value 94.489189
iter  20 value 94.484232
iter  30 value 94.324266
iter  40 value 85.977824
final  value 85.652761 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.436326 
iter  10 value 94.435275
iter  20 value 94.430122
iter  30 value 94.416817
final  value 94.410586 
converged
Fitting Repeat 2 

# weights:  507
initial  value 107.583019 
iter  10 value 94.493108
iter  20 value 92.345242
iter  30 value 79.479339
iter  40 value 78.428824
iter  50 value 77.754588
iter  60 value 77.626397
iter  70 value 77.618204
iter  80 value 77.615983
iter  90 value 77.614859
iter 100 value 77.489778
final  value 77.489778 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 96.050441 
iter  10 value 94.490097
iter  20 value 94.375384
iter  30 value 94.174789
iter  40 value 86.349561
iter  50 value 85.733089
iter  60 value 85.697709
iter  70 value 85.697604
iter  80 value 84.277537
iter  90 value 81.903865
final  value 81.903592 
converged
Fitting Repeat 4 

# weights:  507
initial  value 105.987410 
iter  10 value 94.474821
iter  20 value 93.729552
iter  30 value 85.337250
iter  40 value 84.752434
iter  50 value 84.751563
iter  60 value 84.730031
iter  70 value 84.626951
iter  80 value 84.625613
iter  90 value 84.625110
iter 100 value 82.912050
final  value 82.912050 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 95.579856 
iter  10 value 86.494904
iter  20 value 85.722506
iter  30 value 85.715949
iter  40 value 85.709709
iter  50 value 85.660438
iter  60 value 85.587573
iter  70 value 81.881002
iter  80 value 81.761389
iter  90 value 81.688860
iter 100 value 81.688790
final  value 81.688790 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 137.647954 
iter  10 value 117.899078
iter  20 value 117.874707
iter  30 value 107.276308
iter  40 value 107.261916
final  value 106.837099 
converged
Fitting Repeat 2 

# weights:  507
initial  value 128.404022 
iter  10 value 113.434461
iter  20 value 111.168868
iter  30 value 111.167461
iter  40 value 111.036913
final  value 111.031046 
converged
Fitting Repeat 3 

# weights:  507
initial  value 129.244453 
iter  10 value 116.407074
iter  20 value 108.516625
iter  30 value 108.515866
iter  40 value 108.232143
final  value 107.830113 
converged
Fitting Repeat 4 

# weights:  507
initial  value 138.254242 
iter  10 value 117.427412
iter  20 value 117.377112
iter  30 value 117.210598
iter  40 value 117.206619
iter  50 value 117.157878
final  value 117.156723 
converged
Fitting Repeat 5 

# weights:  507
initial  value 128.399472 
iter  10 value 117.766974
iter  20 value 117.602144
iter  30 value 108.655136
iter  40 value 107.257935
iter  50 value 107.033629
iter  60 value 106.833536
iter  70 value 105.514820
iter  80 value 103.750982
iter  90 value 103.106196
iter 100 value 102.827637
final  value 102.827637 
stopped after 100 iterations
svmRadial
ranger
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases


RUNIT TEST PROTOCOL -- Tue Feb  4 05:11:13 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 
 76.155   2.084 120.943