##############################################################################
##############################################################################
###
### Running command:
###
###   E:\biocbuild\bbs-3.21-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:HPiP.install-out.txt --library=E:\biocbuild\bbs-3.21-bioc\R\library --no-vignettes --timings HPiP_1.13.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'E:/biocbuild/bbs-3.21-bioc/meat/HPiP.Rcheck'
* using R Under development (unstable) (2025-01-21 r87610 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 13.3.0
    GNU Fortran (GCC) 13.3.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* using option '--no-vignettes'
* checking for file 'HPiP/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'HPiP' version '1.13.0'
* 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 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 ... INFO
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       36.11   1.41   37.53
FSmethod      35.55   1.72   37.43
corr_plot     32.89   1.55   34.43
pred_ensembel 14.48   0.39   13.50
enrichfindP    0.73   0.06   14.74
* 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: 2 NOTEs
See
  'E:/biocbuild/bbs-3.21-bioc/meat/HPiP.Rcheck/00check.log'
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   E:\biocbuild\bbs-3.21-bioc\R\bin\R.exe CMD INSTALL HPiP
###
##############################################################################
##############################################################################


* installing to library 'E:/biocbuild/bbs-3.21-bioc/R/library'
* installing *source* package 'HPiP' ...
** this is package 'HPiP' version '1.13.0'
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HPiP)

R Under development (unstable) (2025-01-21 r87610 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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 95.413804 
final  value 94.448052 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.913784 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.722338 
final  value 94.448052 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.619280 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.008580 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 119.469124 
final  value 93.922222 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.234780 
iter  10 value 94.409357
iter  10 value 94.409357
iter  10 value 94.409357
final  value 94.409357 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.850996 
final  value 94.443243 
converged
Fitting Repeat 4 

# weights:  305
initial  value 106.017576 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 102.488536 
iter  10 value 87.684455
iter  20 value 84.399455
iter  30 value 83.947843
iter  40 value 83.842900
iter  50 value 83.153451
iter  60 value 83.152731
final  value 83.152730 
converged
Fitting Repeat 1 

# weights:  507
initial  value 112.348618 
iter  10 value 94.112906
final  value 94.112903 
converged
Fitting Repeat 2 

# weights:  507
initial  value 104.492403 
final  value 94.443243 
converged
Fitting Repeat 3 

# weights:  507
initial  value 100.606189 
final  value 94.448052 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.782476 
iter  10 value 91.449637
final  value 91.427076 
converged
Fitting Repeat 5 

# weights:  507
initial  value 114.570231 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.838225 
iter  10 value 94.488894
iter  20 value 94.437216
iter  30 value 93.562680
iter  40 value 85.347885
iter  50 value 84.501848
iter  60 value 83.984982
iter  70 value 83.127536
iter  80 value 82.426540
iter  90 value 82.413880
iter 100 value 81.938731
final  value 81.938731 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 101.363033 
iter  10 value 94.488462
iter  20 value 92.412550
iter  30 value 85.936128
iter  40 value 85.320093
iter  50 value 84.954914
iter  60 value 84.323121
iter  70 value 84.261580
iter  80 value 84.242805
final  value 84.242247 
converged
Fitting Repeat 3 

# weights:  103
initial  value 107.784846 
iter  10 value 94.494541
iter  20 value 94.234581
iter  30 value 93.740390
iter  40 value 87.404746
iter  50 value 85.032659
iter  60 value 84.259027
iter  70 value 83.438770
iter  80 value 82.448989
iter  90 value 81.883853
iter 100 value 81.597840
final  value 81.597840 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 98.126582 
iter  10 value 94.486740
iter  20 value 86.562563
iter  30 value 85.332479
final  value 85.244431 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.761915 
iter  10 value 94.402885
iter  20 value 91.974901
iter  30 value 91.451763
iter  40 value 91.443048
iter  50 value 91.442977
iter  50 value 91.442977
iter  50 value 91.442977
final  value 91.442977 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.617718 
iter  10 value 94.428206
iter  20 value 92.240606
iter  30 value 91.761157
iter  40 value 90.399135
iter  50 value 87.728617
iter  60 value 86.801004
iter  70 value 82.288375
iter  80 value 81.039181
iter  90 value 80.609777
iter 100 value 80.411991
final  value 80.411991 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 110.262188 
iter  10 value 94.625014
iter  20 value 93.875986
iter  30 value 86.664205
iter  40 value 85.283823
iter  50 value 83.062377
iter  60 value 82.597020
iter  70 value 81.701006
iter  80 value 81.464245
iter  90 value 81.309862
iter 100 value 80.897853
final  value 80.897853 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 109.332890 
iter  10 value 93.272219
iter  20 value 92.061977
iter  30 value 88.801037
iter  40 value 86.884261
iter  50 value 84.231147
iter  60 value 83.176806
iter  70 value 81.804435
iter  80 value 81.040642
iter  90 value 80.703184
iter 100 value 80.396798
final  value 80.396798 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 111.325195 
iter  10 value 94.321951
iter  20 value 86.233651
iter  30 value 85.329059
iter  40 value 84.314183
iter  50 value 81.230261
iter  60 value 80.519600
iter  70 value 80.312131
iter  80 value 80.272565
iter  90 value 80.230092
iter 100 value 80.164949
final  value 80.164949 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 118.599098 
iter  10 value 97.549149
iter  20 value 94.527000
iter  30 value 94.408438
iter  40 value 94.158619
iter  50 value 93.887135
iter  60 value 90.907983
iter  70 value 85.343991
iter  80 value 83.334775
iter  90 value 82.782165
iter 100 value 82.419885
final  value 82.419885 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 136.123654 
iter  10 value 95.851327
iter  20 value 91.606774
iter  30 value 89.781705
iter  40 value 86.052094
iter  50 value 84.141473
iter  60 value 83.397365
iter  70 value 82.906610
iter  80 value 81.606291
iter  90 value 80.850212
iter 100 value 80.480637
final  value 80.480637 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 111.067379 
iter  10 value 94.129448
iter  20 value 87.341217
iter  30 value 81.990944
iter  40 value 81.190073
iter  50 value 80.907813
iter  60 value 80.857833
iter  70 value 80.836846
iter  80 value 80.808230
iter  90 value 80.572772
iter 100 value 80.122606
final  value 80.122606 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 103.482721 
iter  10 value 94.981871
iter  20 value 91.626318
iter  30 value 91.279749
iter  40 value 90.694919
iter  50 value 87.841831
iter  60 value 86.960392
iter  70 value 84.896185
iter  80 value 82.774933
iter  90 value 80.809657
iter 100 value 80.708853
final  value 80.708853 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 121.713245 
iter  10 value 94.535246
iter  20 value 94.081133
iter  30 value 94.009759
iter  40 value 93.672395
iter  50 value 91.845293
iter  60 value 88.534575
iter  70 value 84.474570
iter  80 value 83.293477
iter  90 value 82.052701
iter 100 value 81.606247
final  value 81.606247 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 109.372797 
iter  10 value 95.633996
iter  20 value 91.801551
iter  30 value 88.039346
iter  40 value 84.795077
iter  50 value 83.714416
iter  60 value 82.914272
iter  70 value 81.408508
iter  80 value 80.561989
iter  90 value 80.062957
iter 100 value 79.903518
final  value 79.903518 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.032859 
final  value 94.486020 
converged
Fitting Repeat 2 

# weights:  103
initial  value 107.258415 
iter  10 value 93.775229
iter  20 value 93.774413
iter  30 value 92.652556
iter  40 value 84.957798
iter  50 value 83.380018
final  value 83.212214 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.767475 
iter  10 value 94.444954
iter  20 value 94.443722
iter  30 value 94.443306
iter  40 value 92.473837
final  value 92.422232 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.688043 
final  value 94.485529 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.645226 
final  value 94.485658 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.049572 
iter  10 value 94.489165
iter  20 value 94.484547
iter  20 value 94.484546
iter  20 value 94.484546
final  value 94.484546 
converged
Fitting Repeat 2 

# weights:  305
initial  value 98.085719 
iter  10 value 94.448300
iter  20 value 94.316001
iter  30 value 91.791983
iter  40 value 88.394346
iter  50 value 88.374160
iter  60 value 87.941678
iter  70 value 87.858454
iter  80 value 87.858223
iter  90 value 87.839004
iter 100 value 87.838651
final  value 87.838651 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.844597 
iter  10 value 94.482872
iter  20 value 94.138378
iter  30 value 87.486007
iter  40 value 86.789453
iter  50 value 86.767219
final  value 86.767170 
converged
Fitting Repeat 4 

# weights:  305
initial  value 100.720289 
iter  10 value 94.489301
iter  20 value 94.456517
final  value 93.922567 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.051268 
iter  10 value 93.927576
iter  20 value 93.924283
iter  30 value 93.869822
iter  40 value 90.539214
iter  50 value 83.709332
iter  60 value 82.856032
iter  70 value 82.799088
final  value 82.798387 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.052595 
iter  10 value 94.491973
final  value 94.484802 
converged
Fitting Repeat 2 

# weights:  507
initial  value 97.300054 
iter  10 value 92.990558
iter  20 value 92.981315
iter  30 value 92.974573
iter  40 value 92.969799
iter  50 value 92.939460
iter  60 value 90.580477
iter  70 value 90.547415
iter  80 value 90.289084
iter  90 value 90.026158
iter 100 value 90.011815
final  value 90.011815 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 99.839612 
iter  10 value 94.119328
iter  20 value 93.049201
iter  30 value 91.946238
iter  40 value 91.845973
iter  50 value 91.838330
iter  60 value 87.883034
iter  70 value 85.573825
iter  80 value 85.489359
iter  90 value 85.488776
final  value 85.488737 
converged
Fitting Repeat 4 

# weights:  507
initial  value 101.999146 
iter  10 value 94.451589
iter  20 value 93.794126
iter  30 value 84.477612
final  value 84.474977 
converged
Fitting Repeat 5 

# weights:  507
initial  value 117.558443 
iter  10 value 94.452624
iter  20 value 94.445507
iter  30 value 86.649312
iter  40 value 86.135104
iter  50 value 85.824573
iter  60 value 85.620355
iter  70 value 85.614574
final  value 85.614418 
converged
Fitting Repeat 1 

# weights:  103
initial  value 106.977242 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.645733 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.669363 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.999628 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.494070 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 112.909966 
final  value 94.275362 
converged
Fitting Repeat 2 

# weights:  305
initial  value 101.108484 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 111.459378 
final  value 94.449438 
converged
Fitting Repeat 4 

# weights:  305
initial  value 107.063881 
iter  10 value 93.066104
iter  10 value 93.066104
iter  10 value 93.066104
final  value 93.066104 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.086695 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.602750 
final  value 94.275362 
converged
Fitting Repeat 2 

# weights:  507
initial  value 97.740573 
final  value 94.275362 
converged
Fitting Repeat 3 

# weights:  507
initial  value 123.878088 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 106.502665 
final  value 94.399989 
converged
Fitting Repeat 5 

# weights:  507
initial  value 106.223314 
iter  10 value 94.262839
iter  20 value 93.662632
iter  20 value 93.662632
iter  20 value 93.662632
final  value 93.662632 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.986388 
iter  10 value 94.500488
iter  20 value 87.728387
iter  30 value 86.190085
iter  40 value 86.125238
iter  50 value 85.966665
iter  60 value 85.753511
iter  70 value 85.280205
iter  80 value 84.233535
iter  90 value 84.212000
iter 100 value 83.368535
final  value 83.368535 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 100.439361 
iter  10 value 94.511089
iter  20 value 94.487300
iter  30 value 94.487014
iter  40 value 94.433587
iter  50 value 92.853993
iter  60 value 87.580317
iter  70 value 86.896546
iter  80 value 86.308922
iter  90 value 85.541022
iter 100 value 84.936588
final  value 84.936588 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 99.996822 
iter  10 value 94.474821
iter  20 value 94.093317
iter  30 value 92.196676
iter  40 value 88.450282
iter  50 value 84.901091
iter  60 value 84.426728
iter  70 value 83.590789
iter  80 value 83.244986
iter  90 value 82.894699
iter 100 value 82.662843
final  value 82.662843 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 97.576611 
iter  10 value 94.416126
iter  20 value 88.767876
iter  30 value 87.086074
iter  40 value 86.304704
iter  50 value 85.197883
iter  60 value 84.896830
iter  70 value 84.409081
iter  80 value 84.318138
final  value 84.317826 
converged
Fitting Repeat 5 

# weights:  103
initial  value 123.701305 
iter  10 value 94.719588
iter  20 value 94.440175
iter  30 value 94.164174
iter  40 value 87.289214
iter  50 value 85.637059
iter  60 value 85.279574
iter  70 value 83.161884
iter  80 value 81.809226
iter  90 value 80.906717
iter 100 value 80.880596
final  value 80.880596 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 108.421454 
iter  10 value 94.777644
iter  20 value 89.519804
iter  30 value 85.470670
iter  40 value 84.850983
iter  50 value 83.376317
iter  60 value 82.732770
iter  70 value 82.634490
iter  80 value 82.402094
iter  90 value 81.103706
iter 100 value 80.986256
final  value 80.986256 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.815492 
iter  10 value 93.584663
iter  20 value 87.341771
iter  30 value 83.180569
iter  40 value 82.842802
iter  50 value 82.794372
iter  60 value 82.728426
iter  70 value 82.463611
iter  80 value 80.899040
iter  90 value 80.101402
iter 100 value 79.904497
final  value 79.904497 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 108.420147 
iter  10 value 94.494276
iter  20 value 93.977772
iter  30 value 93.124440
iter  40 value 88.836805
iter  50 value 86.990109
iter  60 value 86.642928
iter  70 value 83.297817
iter  80 value 80.790099
iter  90 value 80.192805
iter 100 value 79.861133
final  value 79.861133 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 99.942137 
iter  10 value 94.252194
iter  20 value 87.849356
iter  30 value 83.971043
iter  40 value 81.980436
iter  50 value 81.483901
iter  60 value 81.242305
iter  70 value 80.472938
iter  80 value 80.147292
iter  90 value 79.519274
iter 100 value 79.073969
final  value 79.073969 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 122.710191 
iter  10 value 88.122662
iter  20 value 84.635776
iter  30 value 84.092416
iter  40 value 83.485608
iter  50 value 81.871871
iter  60 value 81.568433
iter  70 value 81.247972
iter  80 value 81.130148
iter  90 value 80.823923
iter 100 value 80.428352
final  value 80.428352 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 120.044045 
iter  10 value 94.439674
iter  20 value 91.403282
iter  30 value 85.987287
iter  40 value 84.967506
iter  50 value 82.445857
iter  60 value 81.266771
iter  70 value 80.254168
iter  80 value 79.937160
iter  90 value 79.679203
iter 100 value 79.644130
final  value 79.644130 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.793947 
iter  10 value 94.815306
iter  20 value 94.452878
iter  30 value 94.084132
iter  40 value 85.895066
iter  50 value 84.143050
iter  60 value 80.089010
iter  70 value 79.723680
iter  80 value 79.606439
iter  90 value 79.465852
iter 100 value 79.372859
final  value 79.372859 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 120.855091 
iter  10 value 94.691992
iter  20 value 88.678055
iter  30 value 83.956026
iter  40 value 80.945276
iter  50 value 80.554967
iter  60 value 80.397720
iter  70 value 80.130295
iter  80 value 79.937680
iter  90 value 79.762026
iter 100 value 79.431044
final  value 79.431044 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 104.986193 
iter  10 value 94.247527
iter  20 value 90.621652
iter  30 value 82.809545
iter  40 value 81.822734
iter  50 value 80.399881
iter  60 value 79.836430
iter  70 value 79.739176
iter  80 value 79.634742
iter  90 value 79.558835
iter 100 value 79.546703
final  value 79.546703 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 105.081287 
iter  10 value 94.590789
iter  20 value 94.126256
iter  30 value 92.020275
iter  40 value 88.135199
iter  50 value 83.491270
iter  60 value 82.121285
iter  70 value 81.947206
iter  80 value 81.070640
iter  90 value 80.305405
iter 100 value 80.135341
final  value 80.135341 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 105.614049 
final  value 94.486010 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.643605 
iter  10 value 94.401841
iter  20 value 94.326595
iter  30 value 94.275963
final  value 94.275843 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.939041 
iter  10 value 94.103171
final  value 94.073374 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.492320 
iter  10 value 94.486105
final  value 94.484220 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.565125 
final  value 94.485757 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.596056 
iter  10 value 94.489010
iter  20 value 94.484229
iter  30 value 94.334036
final  value 94.275443 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.778532 
iter  10 value 94.488938
iter  20 value 94.443354
iter  30 value 89.890556
iter  40 value 86.057222
iter  50 value 86.054109
iter  60 value 85.928114
iter  70 value 85.303702
iter  80 value 85.277475
iter  90 value 84.703636
iter 100 value 84.641877
final  value 84.641877 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.872082 
iter  10 value 92.070322
iter  20 value 92.068705
iter  30 value 92.066331
iter  40 value 90.155228
iter  50 value 84.319778
iter  60 value 83.976449
iter  70 value 83.851163
iter  80 value 83.735118
iter  90 value 83.734045
iter 100 value 83.733179
final  value 83.733179 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 98.802980 
iter  10 value 94.280509
iter  20 value 94.275960
final  value 94.275688 
converged
Fitting Repeat 5 

# weights:  305
initial  value 94.178412 
iter  10 value 90.732065
iter  20 value 89.863702
iter  30 value 88.330738
iter  40 value 87.941452
iter  50 value 87.281154
iter  60 value 87.279564
iter  70 value 87.278963
final  value 87.278567 
converged
Fitting Repeat 1 

# weights:  507
initial  value 115.460845 
iter  10 value 94.283378
iter  20 value 94.276365
iter  30 value 94.275475
iter  40 value 94.229382
iter  50 value 92.486687
iter  60 value 81.536775
iter  70 value 80.284042
iter  80 value 80.037469
final  value 80.037424 
converged
Fitting Repeat 2 

# weights:  507
initial  value 106.858506 
iter  10 value 94.256014
iter  20 value 90.589177
iter  30 value 84.020972
iter  40 value 82.887002
iter  50 value 82.734600
iter  60 value 82.090180
iter  70 value 80.165372
iter  80 value 79.737850
iter  90 value 79.702642
iter 100 value 79.702590
final  value 79.702590 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 98.279969 
iter  10 value 93.868026
iter  20 value 93.861763
iter  30 value 93.800777
iter  40 value 85.498190
iter  50 value 85.333846
iter  60 value 84.050127
iter  70 value 83.858876
iter  80 value 83.837220
iter  90 value 83.837011
final  value 83.836618 
converged
Fitting Repeat 4 

# weights:  507
initial  value 116.351882 
iter  10 value 94.491648
iter  20 value 94.487374
iter  30 value 94.278395
iter  40 value 94.275730
final  value 94.275699 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.532077 
iter  10 value 90.711549
iter  20 value 84.765512
iter  30 value 83.595190
iter  40 value 83.319409
iter  50 value 83.276137
iter  60 value 83.275184
iter  70 value 83.271603
final  value 83.270208 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.558573 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 106.089291 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.828836 
iter  10 value 94.035095
final  value 94.035091 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.508002 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.765322 
final  value 94.022599 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.250549 
iter  10 value 86.688769
iter  20 value 86.638414
iter  30 value 86.611924
iter  40 value 86.607910
final  value 86.607906 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.339314 
final  value 94.032967 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.677246 
final  value 94.032967 
converged
Fitting Repeat 4 

# weights:  305
initial  value 105.561769 
final  value 94.032967 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.522015 
iter  10 value 93.806086
final  value 93.804329 
converged
Fitting Repeat 1 

# weights:  507
initial  value 111.545759 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 118.876890 
iter  10 value 93.921280
final  value 93.921213 
converged
Fitting Repeat 3 

# weights:  507
initial  value 97.664072 
iter  10 value 93.875057
iter  20 value 93.864168
final  value 93.862937 
converged
Fitting Repeat 4 

# weights:  507
initial  value 125.732400 
iter  10 value 87.510264
iter  20 value 86.998265
iter  30 value 86.997170
final  value 86.997168 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.570080 
final  value 94.050051 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.589535 
iter  10 value 94.017680
iter  20 value 90.760162
iter  30 value 89.592913
iter  40 value 88.312496
iter  50 value 86.294685
iter  60 value 85.163636
iter  70 value 85.126337
iter  80 value 84.946941
final  value 84.939489 
converged
Fitting Repeat 2 

# weights:  103
initial  value 106.040901 
iter  10 value 94.038209
iter  20 value 89.310300
iter  30 value 87.476956
iter  40 value 85.089583
iter  50 value 84.960571
iter  60 value 84.949024
iter  70 value 84.854568
final  value 84.849966 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.447320 
iter  10 value 93.435998
iter  20 value 89.139850
iter  30 value 87.715297
iter  40 value 86.656949
iter  50 value 85.196547
iter  60 value 85.180522
final  value 85.180514 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.592596 
iter  10 value 94.055891
iter  20 value 88.897728
iter  30 value 87.837963
iter  40 value 86.484447
iter  50 value 85.662627
iter  60 value 85.556172
iter  70 value 85.454037
iter  80 value 85.355654
final  value 85.355607 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.963177 
iter  10 value 89.848857
iter  20 value 85.766870
iter  30 value 84.999727
iter  40 value 84.952513
iter  50 value 84.942544
iter  60 value 84.881656
final  value 84.849966 
converged
Fitting Repeat 1 

# weights:  305
initial  value 112.341375 
iter  10 value 94.332764
iter  20 value 93.750254
iter  30 value 85.762408
iter  40 value 84.988049
iter  50 value 84.253914
iter  60 value 83.368457
iter  70 value 83.120776
iter  80 value 83.071707
iter  90 value 82.959206
iter 100 value 82.902595
final  value 82.902595 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 105.070102 
iter  10 value 93.745937
iter  20 value 93.489255
iter  30 value 86.316085
iter  40 value 85.658473
iter  50 value 85.337354
iter  60 value 85.308754
iter  70 value 85.196289
iter  80 value 84.998355
iter  90 value 84.626041
iter 100 value 83.444132
final  value 83.444132 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.091765 
iter  10 value 94.068889
iter  20 value 93.478152
iter  30 value 87.463423
iter  40 value 87.109958
iter  50 value 86.017401
iter  60 value 85.661180
iter  70 value 85.257233
iter  80 value 83.214379
iter  90 value 82.845670
iter 100 value 82.706145
final  value 82.706145 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 97.268636 
iter  10 value 93.399803
iter  20 value 87.118345
iter  30 value 85.331327
iter  40 value 84.649449
iter  50 value 84.341671
iter  60 value 82.934831
iter  70 value 82.324149
iter  80 value 82.208336
iter  90 value 82.075759
iter 100 value 81.958062
final  value 81.958062 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 105.890133 
iter  10 value 93.968729
iter  20 value 89.579468
iter  30 value 88.874322
iter  40 value 87.010332
iter  50 value 86.982618
iter  60 value 86.856951
iter  70 value 86.300571
iter  80 value 83.997566
iter  90 value 82.741884
iter 100 value 82.158075
final  value 82.158075 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 107.044800 
iter  10 value 94.040826
iter  20 value 87.001595
iter  30 value 85.917270
iter  40 value 84.317460
iter  50 value 83.787344
iter  60 value 83.326732
iter  70 value 82.974913
iter  80 value 82.380973
iter  90 value 81.853516
iter 100 value 81.708751
final  value 81.708751 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 118.755674 
iter  10 value 93.722059
iter  20 value 88.927212
iter  30 value 87.354427
iter  40 value 85.204006
iter  50 value 82.499790
iter  60 value 82.285318
iter  70 value 81.993382
iter  80 value 81.908439
iter  90 value 81.828975
iter 100 value 81.603845
final  value 81.603845 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 109.918586 
iter  10 value 94.029050
iter  20 value 92.218705
iter  30 value 88.370857
iter  40 value 87.899792
iter  50 value 86.684914
iter  60 value 85.921398
iter  70 value 84.572473
iter  80 value 83.566550
iter  90 value 83.319247
iter 100 value 83.254417
final  value 83.254417 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 105.165371 
iter  10 value 94.174258
iter  20 value 90.586477
iter  30 value 87.358333
iter  40 value 87.139104
iter  50 value 84.588578
iter  60 value 83.102759
iter  70 value 82.850916
iter  80 value 81.932789
iter  90 value 81.633115
iter 100 value 81.581853
final  value 81.581853 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 114.363561 
iter  10 value 93.313916
iter  20 value 89.147914
iter  30 value 86.621067
iter  40 value 85.649285
iter  50 value 85.133194
iter  60 value 84.634015
iter  70 value 84.314901
iter  80 value 84.202501
iter  90 value 84.127696
iter 100 value 83.848332
final  value 83.848332 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.168024 
final  value 94.054409 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.233969 
iter  10 value 94.034924
iter  20 value 94.033110
iter  30 value 88.348773
final  value 88.348048 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.766006 
iter  10 value 94.054568
iter  20 value 94.033456
final  value 94.033028 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.096789 
final  value 94.054763 
converged
Fitting Repeat 5 

# weights:  103
initial  value 110.206072 
final  value 94.054487 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.217051 
iter  10 value 94.054830
iter  20 value 93.948554
iter  30 value 93.905176
iter  40 value 93.805022
final  value 93.804920 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.085353 
iter  10 value 94.057303
iter  20 value 94.035529
iter  30 value 94.033226
iter  40 value 89.302327
iter  50 value 85.107408
iter  60 value 85.106122
iter  70 value 85.089725
final  value 85.089230 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.961183 
iter  10 value 94.057748
iter  20 value 94.047965
iter  30 value 90.615609
iter  40 value 85.234759
iter  50 value 84.488366
iter  60 value 84.452774
iter  70 value 84.399990
iter  80 value 84.394706
iter  90 value 84.388618
iter 100 value 84.217432
final  value 84.217432 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 105.706704 
iter  10 value 94.057810
iter  20 value 93.995798
iter  30 value 93.863052
final  value 93.863050 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.667611 
iter  10 value 94.188479
iter  20 value 94.126587
iter  30 value 90.436844
iter  40 value 87.085097
iter  50 value 86.818859
iter  60 value 86.406125
iter  70 value 82.930193
iter  80 value 82.632390
iter  90 value 82.608722
iter 100 value 82.587497
final  value 82.587497 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 106.311585 
iter  10 value 86.564456
iter  20 value 86.278867
iter  30 value 86.272809
iter  40 value 85.622358
iter  50 value 85.593546
iter  60 value 85.593197
final  value 85.593190 
converged
Fitting Repeat 2 

# weights:  507
initial  value 104.334520 
iter  10 value 94.044470
iter  20 value 94.039520
iter  30 value 93.962087
iter  40 value 90.882237
iter  50 value 86.270117
iter  60 value 84.841564
iter  70 value 84.840345
final  value 84.840330 
converged
Fitting Repeat 3 

# weights:  507
initial  value 109.730170 
iter  10 value 94.051792
iter  20 value 94.041238
iter  30 value 94.033550
iter  40 value 93.650921
iter  50 value 92.758967
final  value 92.758749 
converged
Fitting Repeat 4 

# weights:  507
initial  value 106.219890 
iter  10 value 94.057668
iter  20 value 94.020022
iter  30 value 85.037005
final  value 84.811807 
converged
Fitting Repeat 5 

# weights:  507
initial  value 99.032249 
iter  10 value 94.060063
iter  20 value 93.976567
iter  30 value 93.862997
iter  40 value 85.584920
iter  50 value 85.076557
iter  60 value 84.241422
iter  70 value 84.079358
final  value 84.078513 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.394866 
final  value 93.915746 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.169786 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.404851 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.143409 
iter  10 value 93.697143
iter  10 value 93.697143
iter  10 value 93.697143
final  value 93.697143 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.525393 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.780538 
final  value 94.052911 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.712395 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.382092 
iter  10 value 92.599715
final  value 92.599711 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.566694 
iter  10 value 92.360872
final  value 92.360695 
converged
Fitting Repeat 5 

# weights:  305
initial  value 103.648033 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 104.424073 
final  value 93.915746 
converged
Fitting Repeat 2 

# weights:  507
initial  value 115.796956 
final  value 93.915746 
converged
Fitting Repeat 3 

# weights:  507
initial  value 104.046130 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 104.111897 
iter  10 value 93.901246
iter  20 value 93.866107
iter  30 value 93.863102
final  value 93.863073 
converged
Fitting Repeat 5 

# weights:  507
initial  value 109.731424 
final  value 93.915746 
converged
Fitting Repeat 1 

# weights:  103
initial  value 116.055513 
iter  10 value 93.553961
iter  20 value 86.768836
iter  30 value 84.651471
iter  40 value 83.452838
iter  50 value 82.918324
iter  60 value 82.850770
final  value 82.842773 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.864626 
iter  10 value 92.334219
iter  20 value 83.820928
iter  30 value 83.478979
iter  40 value 83.110918
iter  50 value 83.053042
iter  60 value 82.845572
final  value 82.842773 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.946739 
iter  10 value 93.239444
iter  20 value 90.145420
iter  30 value 85.126767
iter  40 value 83.138557
iter  50 value 82.715879
iter  60 value 82.615027
iter  70 value 82.437545
iter  80 value 82.416987
final  value 82.416397 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.555703 
iter  10 value 93.983710
iter  20 value 88.323611
iter  30 value 85.261570
iter  40 value 81.616868
iter  50 value 79.909179
iter  60 value 79.434133
iter  70 value 79.421092
iter  80 value 79.391352
final  value 79.391213 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.365006 
iter  10 value 93.926728
iter  20 value 91.945178
iter  30 value 87.419981
iter  40 value 86.808755
iter  50 value 86.684537
iter  60 value 86.669665
iter  70 value 86.667738
iter  80 value 86.633900
iter  90 value 82.909035
iter 100 value 82.523575
final  value 82.523575 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 110.632539 
iter  10 value 94.064464
iter  20 value 93.751990
iter  30 value 84.783352
iter  40 value 83.675266
iter  50 value 82.937253
iter  60 value 81.138495
iter  70 value 80.619680
iter  80 value 80.293154
iter  90 value 79.908181
iter 100 value 78.927471
final  value 78.927471 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 116.750974 
iter  10 value 93.055991
iter  20 value 90.926449
iter  30 value 85.836667
iter  40 value 83.324319
iter  50 value 81.439650
iter  60 value 80.111236
iter  70 value 79.443874
iter  80 value 79.083079
iter  90 value 78.864595
iter 100 value 78.483653
final  value 78.483653 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.834895 
iter  10 value 93.365877
iter  20 value 84.759061
iter  30 value 83.364743
iter  40 value 83.173134
iter  50 value 82.096603
iter  60 value 80.451002
iter  70 value 80.047790
iter  80 value 79.502497
iter  90 value 79.275764
iter 100 value 78.498098
final  value 78.498098 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 105.671731 
iter  10 value 94.061474
iter  20 value 92.446467
iter  30 value 88.364821
iter  40 value 80.910022
iter  50 value 80.509765
iter  60 value 80.012091
iter  70 value 78.945714
iter  80 value 78.392904
iter  90 value 78.044049
iter 100 value 77.816634
final  value 77.816634 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 106.182390 
iter  10 value 93.977365
iter  20 value 93.945869
iter  30 value 93.816676
iter  40 value 85.506276
iter  50 value 82.800844
iter  60 value 82.613401
iter  70 value 82.555121
iter  80 value 82.362394
iter  90 value 81.535174
iter 100 value 81.425746
final  value 81.425746 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 108.205102 
iter  10 value 91.998849
iter  20 value 84.797128
iter  30 value 81.431176
iter  40 value 80.085158
iter  50 value 79.523308
iter  60 value 79.210816
iter  70 value 78.851583
iter  80 value 78.291705
iter  90 value 77.965604
iter 100 value 77.835924
final  value 77.835924 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 127.210007 
iter  10 value 93.960230
iter  20 value 90.863856
iter  30 value 87.029549
iter  40 value 83.693202
iter  50 value 80.144072
iter  60 value 80.007898
iter  70 value 79.884388
iter  80 value 79.232258
iter  90 value 78.483195
iter 100 value 78.116907
final  value 78.116907 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 115.462367 
iter  10 value 93.920849
iter  20 value 89.780678
iter  30 value 85.596357
iter  40 value 82.147991
iter  50 value 81.510757
iter  60 value 81.025714
iter  70 value 79.690329
iter  80 value 79.588622
iter  90 value 79.545216
iter 100 value 79.146110
final  value 79.146110 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 135.567265 
iter  10 value 94.651655
iter  20 value 94.006927
iter  30 value 88.604807
iter  40 value 87.539185
iter  50 value 86.136502
iter  60 value 86.006116
iter  70 value 84.454314
iter  80 value 80.728602
iter  90 value 78.329843
iter 100 value 77.807799
final  value 77.807799 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 118.411074 
iter  10 value 94.222021
iter  20 value 86.880859
iter  30 value 84.605546
iter  40 value 82.787314
iter  50 value 79.979683
iter  60 value 79.290587
iter  70 value 78.936728
iter  80 value 78.552468
iter  90 value 78.412692
iter 100 value 78.208329
final  value 78.208329 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 105.865170 
final  value 94.054274 
converged
Fitting Repeat 2 

# weights:  103
initial  value 104.418783 
final  value 94.054631 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.959105 
iter  10 value 94.054605
iter  20 value 94.052914
iter  30 value 93.839626
iter  40 value 93.697357
final  value 93.697306 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.574975 
iter  10 value 94.054673
iter  20 value 93.993850
iter  30 value 84.475500
iter  40 value 84.475291
iter  50 value 83.700961
iter  60 value 81.944964
final  value 81.904762 
converged
Fitting Repeat 5 

# weights:  103
initial  value 110.656873 
final  value 94.054255 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.833880 
iter  10 value 84.607157
iter  20 value 82.409217
iter  30 value 81.807829
iter  40 value 81.649712
iter  50 value 81.647809
iter  60 value 81.644395
final  value 81.644286 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.362195 
iter  10 value 94.057844
iter  20 value 93.754455
final  value 93.697255 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.236886 
iter  10 value 94.057666
iter  20 value 94.045066
iter  30 value 85.033521
iter  40 value 84.000753
iter  50 value 84.000346
final  value 84.000240 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.026264 
iter  10 value 94.057815
iter  20 value 92.879218
iter  30 value 92.703374
iter  40 value 87.894581
iter  50 value 83.503745
iter  60 value 83.194859
iter  70 value 83.170977
iter  80 value 82.105825
iter  90 value 81.448114
iter 100 value 81.420655
final  value 81.420655 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 94.357830 
iter  10 value 93.920426
iter  20 value 93.915903
final  value 93.915882 
converged
Fitting Repeat 1 

# weights:  507
initial  value 101.193519 
iter  10 value 94.061049
iter  20 value 94.052931
iter  30 value 88.875986
iter  40 value 82.625667
iter  50 value 82.006943
iter  60 value 80.180525
iter  70 value 78.325794
iter  80 value 78.106447
iter  90 value 78.087009
final  value 78.086831 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.419381 
iter  10 value 93.924955
iter  20 value 93.845569
iter  30 value 83.490029
iter  40 value 80.432349
iter  50 value 80.406429
iter  60 value 80.097791
final  value 80.096926 
converged
Fitting Repeat 3 

# weights:  507
initial  value 108.204444 
iter  10 value 94.060688
iter  20 value 94.019421
iter  30 value 93.305604
iter  40 value 89.567914
iter  50 value 88.798691
final  value 88.798575 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.898211 
iter  10 value 93.923494
iter  20 value 93.836407
iter  30 value 86.496773
iter  40 value 85.572050
iter  40 value 85.572050
iter  40 value 85.572050
final  value 85.572050 
converged
Fitting Repeat 5 

# weights:  507
initial  value 114.648334 
iter  10 value 88.965380
iter  20 value 81.895528
iter  30 value 77.766359
iter  40 value 77.145239
iter  50 value 77.142761
iter  60 value 77.134055
iter  70 value 77.086416
iter  80 value 76.913916
iter  90 value 76.501130
iter 100 value 76.360976
final  value 76.360976 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.407200 
final  value 94.275362 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.648425 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.735513 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.723980 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.509579 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.072125 
final  value 94.275362 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.060469 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.040729 
iter  10 value 93.920382
final  value 93.907003 
converged
Fitting Repeat 4 

# weights:  305
initial  value 109.425549 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.555273 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 115.719767 
final  value 93.701657 
converged
Fitting Repeat 2 

# weights:  507
initial  value 110.195226 
final  value 94.165746 
converged
Fitting Repeat 3 

# weights:  507
initial  value 102.537953 
final  value 93.701657 
converged
Fitting Repeat 4 

# weights:  507
initial  value 113.283128 
iter  10 value 93.804409
iter  20 value 93.781865
iter  20 value 93.781865
iter  20 value 93.781865
final  value 93.781865 
converged
Fitting Repeat 5 

# weights:  507
initial  value 114.359240 
iter  10 value 91.631883
iter  20 value 91.576564
final  value 91.576471 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.566322 
iter  10 value 93.603442
iter  20 value 86.997729
iter  30 value 86.831628
iter  40 value 85.983906
iter  50 value 85.722251
iter  60 value 85.581533
iter  70 value 85.478468
final  value 85.465284 
converged
Fitting Repeat 2 

# weights:  103
initial  value 103.584894 
iter  10 value 94.487296
iter  20 value 94.435108
iter  30 value 93.861884
iter  40 value 93.846519
iter  50 value 93.846362
iter  60 value 93.549056
iter  70 value 91.811243
iter  80 value 89.730718
iter  90 value 88.529711
iter 100 value 86.141697
final  value 86.141697 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 102.316261 
iter  10 value 94.488998
iter  20 value 91.530729
iter  30 value 88.066292
iter  40 value 87.443900
iter  50 value 87.074420
iter  60 value 87.010499
iter  70 value 86.754098
iter  80 value 86.387673
iter  90 value 86.349141
final  value 86.349001 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.102899 
iter  10 value 94.318845
iter  20 value 89.363099
iter  30 value 88.242702
iter  40 value 86.659397
iter  50 value 85.469802
iter  60 value 84.567635
iter  70 value 84.075226
iter  80 value 83.180964
iter  90 value 82.969509
iter 100 value 82.702301
final  value 82.702301 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 100.346843 
iter  10 value 93.919356
iter  20 value 87.089050
iter  30 value 86.568513
iter  40 value 86.307830
iter  50 value 86.005470
iter  60 value 85.649189
iter  70 value 85.466019
final  value 85.465284 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.457355 
iter  10 value 95.243112
iter  20 value 94.582845
iter  30 value 89.908088
iter  40 value 87.186734
iter  50 value 86.746255
iter  60 value 85.977411
iter  70 value 85.697085
iter  80 value 85.161254
iter  90 value 84.636964
iter 100 value 83.323788
final  value 83.323788 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 106.509103 
iter  10 value 94.561132
iter  20 value 93.293161
iter  30 value 87.614310
iter  40 value 84.342713
iter  50 value 82.276587
iter  60 value 81.854080
iter  70 value 81.729800
iter  80 value 81.696848
iter  90 value 81.687617
iter 100 value 81.674046
final  value 81.674046 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 110.280876 
iter  10 value 94.427430
iter  20 value 93.859791
iter  30 value 93.429957
iter  40 value 89.996734
iter  50 value 86.393381
iter  60 value 85.534903
iter  70 value 84.996787
iter  80 value 84.241421
iter  90 value 83.702073
iter 100 value 83.437151
final  value 83.437151 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 99.932227 
iter  10 value 94.405110
iter  20 value 88.978887
iter  30 value 85.723624
iter  40 value 85.235086
iter  50 value 84.758318
iter  60 value 84.658285
iter  70 value 84.619073
iter  80 value 84.553743
iter  90 value 84.459036
iter 100 value 83.123107
final  value 83.123107 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 104.431507 
iter  10 value 93.870950
iter  20 value 88.619508
iter  30 value 88.155659
iter  40 value 87.554078
iter  50 value 85.360296
iter  60 value 84.671659
iter  70 value 84.375772
iter  80 value 83.738798
iter  90 value 83.208823
iter 100 value 82.957887
final  value 82.957887 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 111.271898 
iter  10 value 94.491228
iter  20 value 93.834134
iter  30 value 89.799156
iter  40 value 86.859468
iter  50 value 86.619600
iter  60 value 85.497419
iter  70 value 83.431517
iter  80 value 82.379429
iter  90 value 81.712600
iter 100 value 81.510642
final  value 81.510642 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 107.815897 
iter  10 value 91.162137
iter  20 value 87.750617
iter  30 value 84.261525
iter  40 value 83.393646
iter  50 value 83.018630
iter  60 value 82.451591
iter  70 value 82.067219
iter  80 value 81.957525
iter  90 value 81.878701
iter 100 value 81.814600
final  value 81.814600 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 113.321652 
iter  10 value 94.386404
iter  20 value 89.468073
iter  30 value 86.540613
iter  40 value 86.326270
iter  50 value 85.644346
iter  60 value 83.062384
iter  70 value 82.327812
iter  80 value 82.168873
iter  90 value 82.115131
iter 100 value 81.986889
final  value 81.986889 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 115.264112 
iter  10 value 94.219209
iter  20 value 93.577815
iter  30 value 87.523778
iter  40 value 85.555379
iter  50 value 85.023507
iter  60 value 84.352439
iter  70 value 83.705035
iter  80 value 83.222712
iter  90 value 82.669281
iter 100 value 82.170709
final  value 82.170709 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 113.552561 
iter  10 value 94.805045
iter  20 value 87.756050
iter  30 value 84.947396
iter  40 value 84.287002
iter  50 value 83.896393
iter  60 value 83.786411
iter  70 value 83.671441
iter  80 value 83.326957
iter  90 value 83.211154
iter 100 value 82.784283
final  value 82.784283 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 113.251805 
final  value 94.485804 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.565593 
iter  10 value 85.717600
iter  20 value 85.393485
iter  30 value 85.319839
final  value 85.309433 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.214735 
iter  10 value 94.486025
iter  20 value 94.484073
iter  30 value 86.861635
iter  40 value 86.497459
iter  50 value 86.497430
iter  60 value 86.093691
iter  70 value 86.092290
final  value 86.092193 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.268271 
iter  10 value 93.775878
iter  20 value 93.695360
iter  30 value 88.392128
iter  40 value 88.356051
iter  50 value 88.355457
iter  60 value 88.354469
iter  70 value 85.364119
iter  80 value 85.349888
iter  90 value 85.316192
iter 100 value 84.938144
final  value 84.938144 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 96.171355 
final  value 94.485922 
converged
Fitting Repeat 1 

# weights:  305
initial  value 111.257263 
iter  10 value 94.488801
iter  20 value 92.792719
iter  30 value 88.222569
iter  40 value 88.215291
iter  50 value 88.214439
final  value 88.213672 
converged
Fitting Repeat 2 

# weights:  305
initial  value 110.597211 
iter  10 value 94.280547
iter  20 value 94.276140
iter  30 value 88.738200
iter  40 value 85.368134
iter  50 value 84.739516
iter  60 value 84.736843
iter  70 value 84.543859
iter  80 value 84.518148
iter  90 value 84.129579
iter 100 value 83.509040
final  value 83.509040 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 99.981379 
iter  10 value 94.488885
iter  20 value 94.484267
iter  30 value 94.275531
iter  30 value 94.275531
iter  30 value 94.275531
final  value 94.275531 
converged
Fitting Repeat 4 

# weights:  305
initial  value 103.750769 
iter  10 value 94.489245
iter  20 value 94.484200
iter  30 value 92.896471
iter  40 value 85.414271
iter  50 value 85.397816
iter  60 value 85.397107
iter  70 value 85.396596
iter  80 value 85.395017
iter  90 value 85.218234
iter 100 value 85.218184
final  value 85.218184 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 99.604029 
iter  10 value 93.378244
iter  20 value 93.219291
iter  30 value 93.214155
iter  40 value 93.146834
iter  50 value 93.114282
iter  60 value 93.113479
final  value 93.113224 
converged
Fitting Repeat 1 

# weights:  507
initial  value 99.195089 
iter  10 value 91.906349
iter  20 value 90.728985
iter  30 value 90.703673
iter  40 value 90.700036
final  value 90.698492 
converged
Fitting Repeat 2 

# weights:  507
initial  value 101.395045 
iter  10 value 93.795903
iter  20 value 93.788692
iter  30 value 93.786231
iter  40 value 93.784323
iter  50 value 87.614965
iter  60 value 85.393167
iter  70 value 84.791482
iter  80 value 84.746598
final  value 84.746455 
converged
Fitting Repeat 3 

# weights:  507
initial  value 104.031879 
iter  10 value 94.492804
iter  20 value 94.399939
iter  30 value 87.510496
iter  40 value 85.585303
iter  50 value 85.317505
final  value 85.316429 
converged
Fitting Repeat 4 

# weights:  507
initial  value 96.854188 
iter  10 value 94.276871
iter  20 value 93.730923
iter  30 value 91.446372
iter  40 value 91.422635
iter  50 value 91.288698
iter  60 value 91.283356
iter  70 value 88.144533
iter  80 value 88.015290
iter  90 value 88.011631
iter 100 value 87.958639
final  value 87.958639 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 100.334595 
iter  10 value 93.916299
iter  20 value 93.792961
iter  30 value 93.785141
iter  40 value 93.784232
iter  50 value 92.825391
iter  60 value 89.289896
iter  70 value 84.522447
iter  80 value 83.389671
iter  90 value 83.020265
iter 100 value 82.862573
final  value 82.862573 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 133.707379 
iter  10 value 117.894966
iter  20 value 117.776074
iter  30 value 113.640928
iter  40 value 112.300295
iter  50 value 104.754785
iter  60 value 103.741605
iter  70 value 103.630703
iter  80 value 103.625939
iter  90 value 103.625636
final  value 103.625547 
converged
Fitting Repeat 2 

# weights:  305
initial  value 122.167893 
iter  10 value 117.895453
iter  20 value 115.699730
iter  30 value 115.304363
iter  40 value 115.229724
iter  50 value 115.227016
final  value 115.225970 
converged
Fitting Repeat 3 

# weights:  305
initial  value 124.523147 
iter  10 value 117.870789
iter  20 value 117.866336
iter  30 value 117.753220
iter  40 value 108.843567
iter  50 value 104.880938
iter  60 value 103.921639
iter  70 value 103.918328
iter  80 value 103.887778
iter  90 value 103.803829
iter 100 value 103.801951
final  value 103.801951 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 135.042891 
iter  10 value 117.776052
iter  20 value 117.734489
iter  30 value 117.731538
iter  40 value 117.516644
iter  50 value 117.511331
iter  50 value 117.511331
final  value 117.511331 
converged
Fitting Repeat 5 

# weights:  305
initial  value 120.968098 
iter  10 value 117.894942
iter  20 value 117.647060
iter  30 value 115.616419
iter  40 value 114.915750
final  value 114.915351 
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 Feb  3 01:59:08 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 
  42.96    1.37  126.04