##############################################################################
##############################################################################
###
### 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_1.14.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'E:/biocbuild/bbs-3.21-bioc/meat/HPiP.Rcheck'
* using R version 4.5.0 RC (2025-04-04 r88126 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 14.2.0
    GNU Fortran (GCC) 14.2.0
* 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 '1.14.0'
* 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
corr_plot     35.64   1.37   37.05
var_imp       35.28   1.23   36.53
FSmethod      34.20   1.95   36.30
pred_ensembel 14.34   0.38   13.31
enrichfindP    0.67   0.10   14.16
* 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 '1.14.0'
** 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 version 4.5.0 RC (2025-04-04 r88126 ucrt) -- "How About a Twenty-Six"
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 95.076972 
final  value 94.473118 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.743662 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.014187 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.512644 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.168050 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.935777 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.569227 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.037948 
iter  10 value 85.107886
iter  20 value 84.929134
final  value 84.928944 
converged
Fitting Repeat 4 

# weights:  305
initial  value 101.289816 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 104.416919 
iter  10 value 94.196332
iter  20 value 93.775051
final  value 93.772974 
converged
Fitting Repeat 1 

# weights:  507
initial  value 106.289422 
final  value 94.484210 
converged
Fitting Repeat 2 

# weights:  507
initial  value 105.961415 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.444024 
iter  10 value 93.772976
final  value 93.772973 
converged
Fitting Repeat 4 

# weights:  507
initial  value 99.137112 
iter  10 value 93.701669
final  value 93.701657 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.520063 
final  value 94.473118 
converged
Fitting Repeat 1 

# weights:  103
initial  value 101.194408 
iter  10 value 94.178366
iter  20 value 86.863633
iter  30 value 83.639919
iter  40 value 83.114890
iter  50 value 82.020173
iter  60 value 81.240908
iter  70 value 80.896063
iter  80 value 80.613463
iter  90 value 80.115131
final  value 80.104723 
converged
Fitting Repeat 2 

# weights:  103
initial  value 110.484101 
iter  10 value 94.435213
iter  20 value 93.764005
iter  30 value 85.372742
iter  40 value 85.158041
iter  50 value 83.852593
iter  60 value 82.144990
iter  70 value 81.934339
iter  80 value 81.432976
iter  90 value 81.289479
iter 100 value 80.880979
final  value 80.880979 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 100.079983 
iter  10 value 94.479015
iter  20 value 90.051659
iter  30 value 85.710805
iter  40 value 84.796960
iter  50 value 82.372852
iter  60 value 82.184453
iter  70 value 81.898053
iter  80 value 81.584715
iter  90 value 81.520820
final  value 81.520551 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.699399 
iter  10 value 94.526702
iter  20 value 94.486629
iter  30 value 93.890031
iter  40 value 93.884163
iter  50 value 91.054655
iter  60 value 86.088340
iter  70 value 85.169151
iter  80 value 82.671439
iter  90 value 81.564474
iter 100 value 81.374873
final  value 81.374873 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 101.746723 
iter  10 value 94.478493
iter  20 value 94.290311
iter  30 value 85.208270
iter  40 value 82.793925
iter  50 value 82.162480
iter  60 value 81.834613
iter  70 value 81.738292
iter  80 value 81.640800
iter  90 value 81.520562
final  value 81.520551 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.446972 
iter  10 value 94.357027
iter  20 value 88.643009
iter  30 value 82.434706
iter  40 value 81.959617
iter  50 value 81.791686
iter  60 value 81.424527
iter  70 value 81.209391
iter  80 value 80.315739
iter  90 value 79.238394
iter 100 value 78.787840
final  value 78.787840 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.167086 
iter  10 value 90.802729
iter  20 value 84.525700
iter  30 value 82.688058
iter  40 value 82.474690
iter  50 value 82.166841
iter  60 value 82.100502
iter  70 value 81.962893
iter  80 value 81.466396
iter  90 value 81.164550
iter 100 value 81.001389
final  value 81.001389 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.254930 
iter  10 value 94.313843
iter  20 value 88.698298
iter  30 value 83.408523
iter  40 value 80.840372
iter  50 value 80.178481
iter  60 value 79.489908
iter  70 value 79.145887
iter  80 value 78.974809
iter  90 value 78.671607
iter 100 value 78.525777
final  value 78.525777 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.848299 
iter  10 value 94.224988
iter  20 value 87.222549
iter  30 value 86.262576
iter  40 value 84.679699
iter  50 value 84.303852
iter  60 value 83.892954
iter  70 value 82.965731
iter  80 value 80.665341
iter  90 value 79.684417
iter 100 value 79.369006
final  value 79.369006 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.763309 
iter  10 value 94.440173
iter  20 value 85.443890
iter  30 value 84.633641
iter  40 value 84.311106
iter  50 value 82.356578
iter  60 value 81.785783
iter  70 value 81.703463
iter  80 value 81.614071
iter  90 value 81.312346
iter 100 value 81.251452
final  value 81.251452 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 109.978249 
iter  10 value 94.449400
iter  20 value 93.130172
iter  30 value 85.732994
iter  40 value 83.273974
iter  50 value 82.575760
iter  60 value 81.663045
iter  70 value 81.295923
iter  80 value 80.934396
iter  90 value 80.814472
iter 100 value 80.241781
final  value 80.241781 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 104.253311 
iter  10 value 94.413929
iter  20 value 89.478982
iter  30 value 88.047976
iter  40 value 83.223752
iter  50 value 81.072576
iter  60 value 80.287562
iter  70 value 79.900525
iter  80 value 79.428731
iter  90 value 78.817450
iter 100 value 78.546737
final  value 78.546737 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 128.309289 
iter  10 value 94.531799
iter  20 value 87.768593
iter  30 value 86.880150
iter  40 value 82.634040
iter  50 value 81.416042
iter  60 value 81.320713
iter  70 value 80.998771
iter  80 value 80.355853
iter  90 value 79.735391
iter 100 value 79.253269
final  value 79.253269 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 105.636180 
iter  10 value 94.470432
iter  20 value 93.333380
iter  30 value 86.708858
iter  40 value 84.505874
iter  50 value 82.075807
iter  60 value 80.731621
iter  70 value 78.761803
iter  80 value 78.407248
iter  90 value 78.184958
iter 100 value 77.910213
final  value 77.910213 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 132.811603 
iter  10 value 94.975424
iter  20 value 93.655588
iter  30 value 85.510600
iter  40 value 83.202729
iter  50 value 82.067166
iter  60 value 81.888605
iter  70 value 80.602436
iter  80 value 78.834547
iter  90 value 78.770457
iter 100 value 78.651243
final  value 78.651243 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.034348 
final  value 94.485935 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.698590 
final  value 94.313616 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.520911 
final  value 94.485740 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.898430 
final  value 94.475003 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.340603 
iter  10 value 94.485751
iter  20 value 94.484250
iter  30 value 94.338110
iter  40 value 90.031974
iter  50 value 89.901139
iter  60 value 89.900101
iter  70 value 89.896858
iter  80 value 89.896047
iter  90 value 89.895115
final  value 89.894518 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.811513 
iter  10 value 84.908988
iter  20 value 82.501085
iter  30 value 82.490344
iter  40 value 82.487244
iter  50 value 82.485542
iter  60 value 82.400677
iter  70 value 82.341917
iter  80 value 81.893287
iter  90 value 80.053378
iter 100 value 78.029701
final  value 78.029701 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 112.386926 
iter  10 value 94.489176
iter  20 value 94.484487
iter  30 value 94.407029
iter  40 value 88.010373
iter  50 value 80.783904
iter  60 value 79.656326
iter  70 value 79.033138
iter  80 value 78.991412
iter  90 value 78.990380
iter 100 value 78.965890
final  value 78.965890 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 96.169638 
iter  10 value 94.491322
iter  20 value 93.303531
iter  30 value 83.787041
iter  40 value 83.785313
iter  50 value 83.763459
iter  60 value 82.699959
iter  70 value 82.084822
iter  80 value 81.802638
iter  90 value 80.771963
iter 100 value 79.436800
final  value 79.436800 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.810168 
iter  10 value 93.688644
iter  20 value 93.651544
iter  30 value 83.832022
iter  40 value 81.153826
final  value 81.015491 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.350285 
iter  10 value 94.489477
iter  20 value 94.473324
iter  30 value 94.473248
final  value 94.473242 
converged
Fitting Repeat 1 

# weights:  507
initial  value 131.606494 
iter  10 value 94.375627
iter  20 value 94.320767
iter  30 value 94.312568
iter  40 value 93.881060
iter  50 value 91.815782
iter  60 value 91.721863
iter  60 value 91.721863
final  value 91.721862 
converged
Fitting Repeat 2 

# weights:  507
initial  value 98.551743 
iter  10 value 94.491540
iter  20 value 94.170149
iter  30 value 91.873918
iter  40 value 86.745251
iter  50 value 85.755624
iter  60 value 85.072445
iter  70 value 84.867643
iter  80 value 83.434285
iter  90 value 83.350096
iter 100 value 83.323274
final  value 83.323274 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 119.643381 
iter  10 value 94.481968
iter  20 value 94.475069
iter  30 value 93.142167
iter  40 value 92.183318
iter  50 value 92.105961
iter  60 value 84.196650
iter  70 value 83.729925
iter  80 value 82.445093
iter  90 value 82.419170
iter 100 value 82.397567
final  value 82.397567 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 97.197382 
iter  10 value 94.417410
iter  20 value 94.380718
iter  30 value 94.379492
iter  40 value 94.377670
iter  50 value 85.039641
iter  60 value 84.916122
iter  70 value 84.901098
iter  80 value 84.880954
iter  90 value 84.028570
iter 100 value 81.429140
final  value 81.429140 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 99.347390 
iter  10 value 93.079615
iter  20 value 93.035982
iter  30 value 93.025234
iter  40 value 92.953408
iter  50 value 92.879328
iter  60 value 92.871764
final  value 92.871734 
converged
Fitting Repeat 1 

# weights:  103
initial  value 95.101624 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.468969 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 105.974209 
iter  10 value 93.804515
iter  20 value 91.919445
iter  20 value 91.919444
iter  20 value 91.919444
final  value 91.919444 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.897924 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.571778 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.924441 
iter  10 value 93.999030
final  value 93.582418 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.206840 
final  value 93.582418 
converged
Fitting Repeat 3 

# weights:  305
initial  value 97.228958 
final  value 93.582418 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.994455 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 99.966787 
iter  10 value 93.582450
final  value 93.582418 
converged
Fitting Repeat 1 

# weights:  507
initial  value 106.305800 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.406103 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 105.716989 
final  value 94.052874 
converged
Fitting Repeat 4 

# weights:  507
initial  value 117.852737 
iter  10 value 94.053222
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 133.050952 
final  value 93.582418 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.097645 
iter  10 value 94.193998
iter  20 value 93.728567
iter  30 value 91.482182
iter  40 value 91.011309
iter  50 value 89.477653
iter  60 value 83.844270
iter  70 value 82.178637
iter  80 value 82.086123
iter  90 value 82.076311
final  value 82.075733 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.519726 
iter  10 value 93.844615
iter  20 value 86.283722
iter  30 value 84.551490
iter  40 value 84.349536
iter  50 value 83.634751
final  value 83.599494 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.852626 
iter  10 value 93.627003
iter  20 value 86.429740
iter  30 value 83.499494
iter  40 value 83.248475
iter  50 value 83.195313
iter  60 value 83.171109
final  value 83.171106 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.945378 
iter  10 value 94.054883
iter  20 value 93.186126
iter  30 value 89.803471
iter  40 value 84.787481
iter  50 value 84.255029
iter  60 value 83.822759
iter  70 value 82.996633
iter  80 value 82.321777
iter  90 value 82.079861
final  value 82.076648 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.342461 
iter  10 value 94.055122
iter  20 value 93.831449
iter  30 value 88.141930
iter  40 value 85.215228
iter  50 value 84.126396
iter  60 value 83.364435
iter  70 value 80.621852
iter  80 value 80.225817
iter  90 value 80.092559
iter 100 value 80.045297
final  value 80.045297 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 101.371243 
iter  10 value 93.979995
iter  20 value 88.843394
iter  30 value 86.586432
iter  40 value 84.730218
iter  50 value 81.132304
iter  60 value 80.533614
iter  70 value 80.223788
iter  80 value 79.897970
iter  90 value 79.796534
iter 100 value 79.427771
final  value 79.427771 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 110.388900 
iter  10 value 94.040125
iter  20 value 91.810419
iter  30 value 89.662901
iter  40 value 85.629877
iter  50 value 82.312417
iter  60 value 81.646912
iter  70 value 80.915734
iter  80 value 80.296641
iter  90 value 80.175423
iter 100 value 80.127206
final  value 80.127206 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 107.574793 
iter  10 value 94.270428
iter  20 value 93.629563
iter  30 value 87.713329
iter  40 value 84.514385
iter  50 value 84.259740
iter  60 value 82.680817
iter  70 value 81.280485
iter  80 value 81.093151
iter  90 value 80.846203
iter 100 value 80.224177
final  value 80.224177 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 103.352648 
iter  10 value 94.302739
iter  20 value 93.150067
iter  30 value 93.015235
iter  40 value 90.215165
iter  50 value 83.674427
iter  60 value 80.800465
iter  70 value 80.376069
iter  80 value 80.054200
iter  90 value 79.611127
iter 100 value 79.355219
final  value 79.355219 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.268783 
iter  10 value 94.240835
iter  20 value 85.989610
iter  30 value 84.056595
iter  40 value 83.134438
iter  50 value 81.419004
iter  60 value 80.242619
iter  70 value 79.663060
iter  80 value 79.541456
iter  90 value 79.516622
iter 100 value 79.455523
final  value 79.455523 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 128.756186 
iter  10 value 94.102333
iter  20 value 91.765134
iter  30 value 86.447964
iter  40 value 84.675440
iter  50 value 83.956946
iter  60 value 83.377528
iter  70 value 83.255257
iter  80 value 83.087984
iter  90 value 81.649534
iter 100 value 80.329521
final  value 80.329521 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 124.519121 
iter  10 value 94.884997
iter  20 value 93.880753
iter  30 value 91.480033
iter  40 value 84.312540
iter  50 value 83.010086
iter  60 value 81.541598
iter  70 value 81.318414
iter  80 value 80.501509
iter  90 value 79.857555
iter 100 value 79.274738
final  value 79.274738 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 105.159425 
iter  10 value 94.193299
iter  20 value 91.270946
iter  30 value 87.666692
iter  40 value 87.242440
iter  50 value 83.831929
iter  60 value 81.679484
iter  70 value 81.332465
iter  80 value 81.068888
iter  90 value 80.937949
iter 100 value 80.279543
final  value 80.279543 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 128.731578 
iter  10 value 93.637678
iter  20 value 85.037984
iter  30 value 82.611885
iter  40 value 82.234141
iter  50 value 81.169838
iter  60 value 80.417852
iter  70 value 80.316605
iter  80 value 80.155081
iter  90 value 79.493400
iter 100 value 78.728806
final  value 78.728806 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 113.487695 
iter  10 value 94.838632
iter  20 value 85.741653
iter  30 value 85.000811
iter  40 value 84.598359
iter  50 value 84.154846
iter  60 value 81.700962
iter  70 value 80.006171
iter  80 value 79.196595
iter  90 value 78.859438
iter 100 value 78.498169
final  value 78.498169 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.834983 
iter  10 value 91.787090
iter  20 value 90.000691
iter  30 value 89.991943
iter  40 value 89.991574
iter  50 value 89.903538
final  value 89.902454 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.702112 
final  value 94.054652 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.657906 
iter  10 value 94.054523
iter  20 value 94.052712
iter  30 value 93.583325
final  value 93.583139 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.349816 
iter  10 value 94.053361
iter  20 value 92.904586
iter  30 value 82.219763
iter  40 value 81.839904
iter  50 value 81.626451
iter  60 value 81.584592
iter  70 value 81.563137
iter  80 value 81.562958
iter  90 value 81.188293
iter 100 value 81.089848
final  value 81.089848 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 97.564940 
iter  10 value 93.584291
iter  20 value 93.583068
iter  30 value 93.261635
iter  40 value 92.072527
iter  50 value 92.072318
iter  60 value 87.719575
iter  70 value 83.041060
iter  80 value 81.203541
iter  90 value 81.013361
iter 100 value 81.006321
final  value 81.006321 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 94.795319 
iter  10 value 94.057576
iter  20 value 93.923479
iter  30 value 93.583214
iter  30 value 93.583214
iter  30 value 93.583214
final  value 93.583214 
converged
Fitting Repeat 2 

# weights:  305
initial  value 106.377562 
iter  10 value 93.341822
iter  20 value 93.333553
iter  30 value 93.330835
iter  40 value 93.330079
iter  50 value 93.274348
iter  60 value 93.273549
iter  70 value 92.840356
iter  80 value 92.753114
iter  90 value 92.744750
iter 100 value 84.806695
final  value 84.806695 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 96.603674 
iter  10 value 91.032139
iter  20 value 82.320003
iter  30 value 81.432054
iter  40 value 80.897233
final  value 80.896640 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.538062 
iter  10 value 93.587516
iter  20 value 93.584401
iter  30 value 93.583658
iter  40 value 93.582148
final  value 93.582078 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.561101 
iter  10 value 93.159144
iter  20 value 93.156007
final  value 93.154880 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.618681 
iter  10 value 94.061037
iter  20 value 93.055047
iter  30 value 92.583372
iter  40 value 89.057474
iter  50 value 83.720482
iter  60 value 83.708443
iter  70 value 83.706869
iter  80 value 82.094753
iter  90 value 81.479165
iter 100 value 78.462105
final  value 78.462105 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 115.226637 
iter  10 value 94.060990
iter  20 value 94.052947
final  value 94.052935 
converged
Fitting Repeat 3 

# weights:  507
initial  value 109.131254 
iter  10 value 93.591854
iter  20 value 93.585638
iter  30 value 93.582083
iter  40 value 87.332827
iter  50 value 84.139717
final  value 84.063913 
converged
Fitting Repeat 4 

# weights:  507
initial  value 107.773537 
iter  10 value 94.061014
iter  20 value 93.964688
iter  30 value 93.117714
iter  40 value 86.160804
iter  50 value 82.762720
iter  60 value 81.279615
iter  70 value 81.136334
iter  80 value 79.950977
iter  90 value 79.335675
iter 100 value 79.068933
final  value 79.068933 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 113.191963 
iter  10 value 94.062176
iter  20 value 93.235769
iter  30 value 84.129439
iter  40 value 83.911138
iter  50 value 82.369327
iter  60 value 81.062210
iter  70 value 80.452164
iter  80 value 80.276206
iter  90 value 80.190865
iter 100 value 80.190750
final  value 80.190750 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.643352 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.092772 
final  value 94.461207 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.466902 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.634170 
final  value 94.026542 
converged
Fitting Repeat 5 

# weights:  103
initial  value 109.658367 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.928573 
iter  10 value 94.482326
iter  20 value 94.015120
iter  30 value 93.508177
final  value 93.508117 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.698736 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 105.617541 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 112.646429 
iter  10 value 94.026542
iter  10 value 94.026542
iter  10 value 94.026542
final  value 94.026542 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.578513 
iter  10 value 88.514857
iter  20 value 85.369048
iter  20 value 85.369048
iter  20 value 85.369048
final  value 85.369048 
converged
Fitting Repeat 1 

# weights:  507
initial  value 104.755771 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 99.142008 
iter  10 value 94.026561
final  value 94.026542 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.984024 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 99.176026 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 117.357571 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.413593 
iter  10 value 94.423865
iter  20 value 86.526760
iter  30 value 84.834781
iter  40 value 83.858632
iter  50 value 83.023614
iter  60 value 81.532398
iter  70 value 81.501242
iter  80 value 81.431798
iter  90 value 81.321215
iter 100 value 81.305310
final  value 81.305310 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 102.081282 
iter  10 value 93.329505
iter  20 value 88.590681
iter  30 value 85.764548
iter  40 value 84.977126
iter  50 value 83.594311
iter  60 value 83.549119
final  value 83.549116 
converged
Fitting Repeat 3 

# weights:  103
initial  value 112.310866 
iter  10 value 94.486502
iter  20 value 94.150259
iter  30 value 94.127233
iter  40 value 94.126340
iter  50 value 93.860176
iter  60 value 91.840379
iter  70 value 90.192508
iter  80 value 87.976763
iter  90 value 84.867789
iter 100 value 83.755477
final  value 83.755477 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 98.081852 
iter  10 value 94.550124
iter  20 value 94.216877
iter  30 value 94.126360
iter  40 value 87.346309
iter  50 value 84.966347
iter  60 value 84.076790
iter  70 value 82.870042
iter  80 value 82.074054
iter  90 value 81.740604
iter 100 value 81.684686
final  value 81.684686 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 98.979029 
iter  10 value 91.638548
iter  20 value 89.087397
iter  30 value 88.508557
iter  40 value 87.791382
iter  50 value 85.494977
iter  60 value 84.931541
iter  70 value 83.990838
iter  80 value 83.957617
final  value 83.957615 
converged
Fitting Repeat 1 

# weights:  305
initial  value 111.158196 
iter  10 value 94.306333
iter  20 value 92.241580
iter  30 value 90.547593
iter  40 value 89.410984
iter  50 value 82.947473
iter  60 value 82.227502
iter  70 value 81.588163
iter  80 value 80.966270
iter  90 value 80.368907
iter 100 value 79.959997
final  value 79.959997 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 118.740972 
iter  10 value 94.542490
iter  20 value 92.274440
iter  30 value 89.336243
iter  40 value 86.602641
iter  50 value 82.085977
iter  60 value 81.606203
iter  70 value 81.268807
iter  80 value 80.918917
iter  90 value 80.141297
iter 100 value 80.045358
final  value 80.045358 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 104.556560 
iter  10 value 94.498644
iter  20 value 92.270500
iter  30 value 88.318740
iter  40 value 83.899562
iter  50 value 82.509326
iter  60 value 81.595146
iter  70 value 80.876646
iter  80 value 80.485903
iter  90 value 80.441297
iter 100 value 80.432820
final  value 80.432820 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 106.386637 
iter  10 value 93.753061
iter  20 value 83.869676
iter  30 value 83.507522
iter  40 value 82.792896
iter  50 value 82.246649
iter  60 value 80.736039
iter  70 value 80.471048
iter  80 value 80.301239
iter  90 value 80.104513
iter 100 value 80.037546
final  value 80.037546 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 105.885670 
iter  10 value 93.448772
iter  20 value 91.473945
iter  30 value 86.740864
iter  40 value 86.018625
iter  50 value 85.689575
iter  60 value 84.924906
iter  70 value 84.009753
iter  80 value 82.940852
iter  90 value 82.098621
iter 100 value 81.596316
final  value 81.596316 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 107.945502 
iter  10 value 94.510109
iter  20 value 92.851252
iter  30 value 89.845333
iter  40 value 89.431204
iter  50 value 88.302396
iter  60 value 88.176597
iter  70 value 87.952343
iter  80 value 85.813924
iter  90 value 81.696802
iter 100 value 80.994618
final  value 80.994618 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 109.297597 
iter  10 value 93.815393
iter  20 value 89.614165
iter  30 value 87.765379
iter  40 value 86.620533
iter  50 value 85.386690
iter  60 value 83.926625
iter  70 value 82.672912
iter  80 value 82.047312
iter  90 value 81.855519
iter 100 value 81.761499
final  value 81.761499 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 135.994686 
iter  10 value 94.297534
iter  20 value 93.298228
iter  30 value 88.101984
iter  40 value 86.524900
iter  50 value 85.422314
iter  60 value 83.876758
iter  70 value 83.461405
iter  80 value 83.176036
iter  90 value 82.947641
iter 100 value 82.336952
final  value 82.336952 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 110.398547 
iter  10 value 94.513286
iter  20 value 94.229133
iter  30 value 91.584066
iter  40 value 84.167608
iter  50 value 83.666940
iter  60 value 82.248300
iter  70 value 81.665914
iter  80 value 80.895705
iter  90 value 80.840159
iter 100 value 80.779582
final  value 80.779582 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.577422 
iter  10 value 94.561116
iter  20 value 94.304831
iter  30 value 94.048950
iter  40 value 92.915682
iter  50 value 89.351197
iter  60 value 84.746129
iter  70 value 83.541220
iter  80 value 82.205316
iter  90 value 81.523398
iter 100 value 81.449068
final  value 81.449068 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.411162 
final  value 94.485862 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.519147 
final  value 94.485975 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.034365 
final  value 94.486167 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.730564 
final  value 94.485704 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.016789 
final  value 94.486110 
converged
Fitting Repeat 1 

# weights:  305
initial  value 112.279794 
iter  10 value 94.489325
iter  20 value 94.469167
iter  30 value 88.184888
iter  40 value 83.544035
iter  50 value 82.475301
iter  60 value 82.466474
iter  60 value 82.466474
iter  60 value 82.466474
final  value 82.466474 
converged
Fitting Repeat 2 

# weights:  305
initial  value 109.627559 
iter  10 value 89.842727
iter  20 value 84.583631
iter  30 value 83.084781
iter  40 value 83.002143
iter  50 value 82.999649
iter  60 value 82.838987
final  value 82.837291 
converged
Fitting Repeat 3 

# weights:  305
initial  value 112.871953 
iter  10 value 94.488659
iter  20 value 92.153986
iter  30 value 86.206268
iter  40 value 85.608426
final  value 85.605451 
converged
Fitting Repeat 4 

# weights:  305
initial  value 103.586338 
iter  10 value 94.496418
iter  20 value 89.588630
iter  30 value 88.567339
iter  40 value 88.559342
iter  50 value 88.186837
iter  60 value 88.181734
iter  70 value 87.603504
final  value 87.602510 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.925004 
iter  10 value 94.485342
final  value 94.026841 
converged
Fitting Repeat 1 

# weights:  507
initial  value 98.280508 
iter  10 value 93.895494
iter  20 value 93.891649
iter  30 value 93.890862
iter  40 value 87.752957
iter  50 value 85.501974
iter  60 value 85.473560
iter  70 value 85.418098
iter  80 value 84.822374
iter  90 value 84.821324
iter 100 value 84.184007
final  value 84.184007 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 122.275749 
iter  10 value 94.492051
iter  20 value 94.243111
iter  30 value 83.637238
iter  40 value 83.450899
iter  50 value 83.447938
iter  50 value 83.447938
final  value 83.447938 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.481416 
iter  10 value 94.261152
iter  20 value 94.128394
final  value 94.027979 
converged
Fitting Repeat 4 

# weights:  507
initial  value 101.311753 
iter  10 value 93.897898
iter  20 value 93.892864
iter  30 value 88.495905
iter  40 value 80.842293
iter  50 value 80.316207
iter  60 value 80.199522
iter  70 value 79.994224
iter  80 value 78.693646
iter  90 value 78.668220
iter 100 value 78.611907
final  value 78.611907 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 115.592041 
iter  10 value 94.037782
iter  20 value 94.030942
iter  30 value 94.029485
iter  40 value 90.251315
iter  50 value 84.922589
iter  60 value 84.282283
iter  70 value 83.600057
iter  80 value 81.111418
iter  90 value 79.983790
iter 100 value 79.283384
final  value 79.283384 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.583416 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.028211 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 111.964947 
final  value 94.484209 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.713419 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.377327 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.536265 
final  value 94.467391 
converged
Fitting Repeat 2 

# weights:  305
initial  value 108.130144 
final  value 94.467391 
converged
Fitting Repeat 3 

# weights:  305
initial  value 111.852468 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 99.637892 
final  value 94.448052 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.180976 
final  value 94.467391 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.529115 
iter  10 value 92.954604
iter  20 value 92.837801
iter  30 value 92.837084
iter  30 value 92.837083
iter  30 value 92.837083
final  value 92.837083 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.576860 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 107.754095 
iter  10 value 93.842899
iter  20 value 87.927328
iter  30 value 86.640118
iter  40 value 86.525413
iter  50 value 86.242042
iter  60 value 86.090917
iter  70 value 86.090158
final  value 86.090149 
converged
Fitting Repeat 4 

# weights:  507
initial  value 101.525580 
final  value 94.467391 
converged
Fitting Repeat 5 

# weights:  507
initial  value 113.395612 
final  value 94.467391 
converged
Fitting Repeat 1 

# weights:  103
initial  value 101.846058 
iter  10 value 94.504549
iter  20 value 88.920744
iter  30 value 87.236418
iter  40 value 86.929559
iter  50 value 86.441204
iter  60 value 86.092180
iter  70 value 85.682405
iter  80 value 85.673606
iter  90 value 85.596944
iter 100 value 85.577771
final  value 85.577771 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 98.425263 
iter  10 value 93.983718
iter  20 value 87.413753
iter  30 value 87.141100
iter  40 value 86.310577
iter  50 value 85.643679
iter  60 value 85.359156
iter  70 value 85.351454
final  value 85.351234 
converged
Fitting Repeat 3 

# weights:  103
initial  value 109.170910 
iter  10 value 94.326705
iter  20 value 91.582140
iter  30 value 91.352491
iter  40 value 90.801432
iter  50 value 88.815104
iter  60 value 86.695085
iter  70 value 86.569812
iter  80 value 86.390051
iter  90 value 84.667439
iter 100 value 83.147305
final  value 83.147305 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 103.141348 
iter  10 value 93.926162
iter  20 value 85.481979
iter  30 value 85.298697
iter  40 value 84.347648
iter  50 value 83.551906
iter  60 value 83.345638
iter  70 value 83.237411
iter  80 value 82.934724
iter  90 value 82.670235
final  value 82.666393 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.003657 
iter  10 value 94.697222
iter  20 value 94.488146
iter  30 value 90.899589
iter  40 value 88.802866
iter  50 value 88.195125
iter  60 value 86.798740
iter  70 value 86.492671
iter  80 value 86.469432
iter  90 value 86.458665
final  value 86.455096 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.502667 
iter  10 value 94.595197
iter  20 value 90.891705
iter  30 value 87.947921
iter  40 value 86.791220
iter  50 value 86.404411
iter  60 value 85.390490
iter  70 value 84.357892
iter  80 value 83.965917
iter  90 value 82.965652
iter 100 value 82.144102
final  value 82.144102 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 106.585660 
iter  10 value 94.404018
iter  20 value 93.735045
iter  30 value 91.480057
iter  40 value 89.010137
iter  50 value 87.432917
iter  60 value 87.032065
iter  70 value 84.401377
iter  80 value 83.298123
iter  90 value 83.112276
iter 100 value 83.052167
final  value 83.052167 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 108.633670 
iter  10 value 95.650803
iter  20 value 94.497738
iter  30 value 89.839586
iter  40 value 88.625635
iter  50 value 88.462727
iter  60 value 86.433535
iter  70 value 86.286206
iter  80 value 86.153762
iter  90 value 85.963415
iter 100 value 83.746357
final  value 83.746357 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 122.655208 
iter  10 value 94.551263
iter  20 value 91.951989
iter  30 value 87.363049
iter  40 value 85.966791
iter  50 value 85.721290
iter  60 value 85.665012
iter  70 value 85.468807
iter  80 value 84.940845
iter  90 value 83.712038
iter 100 value 83.101913
final  value 83.101913 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 111.464537 
iter  10 value 94.770447
iter  20 value 89.492992
iter  30 value 88.747218
iter  40 value 88.400627
iter  50 value 87.313787
iter  60 value 85.119192
iter  70 value 84.599189
iter  80 value 83.968561
iter  90 value 83.818667
iter 100 value 83.800635
final  value 83.800635 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 105.631872 
iter  10 value 94.596501
iter  20 value 94.453686
iter  30 value 90.033068
iter  40 value 87.270232
iter  50 value 85.524805
iter  60 value 84.340217
iter  70 value 83.243356
iter  80 value 82.874756
iter  90 value 82.613029
iter 100 value 82.557488
final  value 82.557488 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 117.466275 
iter  10 value 95.016462
iter  20 value 94.287160
iter  30 value 94.157216
iter  40 value 91.441511
iter  50 value 87.625690
iter  60 value 87.280153
iter  70 value 86.956779
iter  80 value 85.272386
iter  90 value 83.635971
iter 100 value 82.944464
final  value 82.944464 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 122.401267 
iter  10 value 94.203761
iter  20 value 89.757961
iter  30 value 85.418003
iter  40 value 85.019465
iter  50 value 84.127529
iter  60 value 82.089751
iter  70 value 81.291801
iter  80 value 81.161244
iter  90 value 80.956994
iter 100 value 80.873354
final  value 80.873354 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 124.099032 
iter  10 value 94.174271
iter  20 value 91.336728
iter  30 value 87.783613
iter  40 value 85.230262
iter  50 value 83.712791
iter  60 value 83.163238
iter  70 value 83.018056
iter  80 value 82.920538
iter  90 value 82.072659
iter 100 value 81.454599
final  value 81.454599 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 103.729571 
iter  10 value 94.573797
iter  20 value 91.516376
iter  30 value 89.874892
iter  40 value 86.173389
iter  50 value 84.753034
iter  60 value 83.889311
iter  70 value 82.129327
iter  80 value 81.682842
iter  90 value 81.632430
iter 100 value 81.484974
final  value 81.484974 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 101.095797 
iter  10 value 94.485979
iter  20 value 94.475191
iter  30 value 94.467472
final  value 94.467405 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.620379 
final  value 94.442326 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.556735 
final  value 94.485633 
converged
Fitting Repeat 4 

# weights:  103
initial  value 106.910657 
final  value 94.485684 
converged
Fitting Repeat 5 

# weights:  103
initial  value 104.970419 
final  value 94.485359 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.645671 
iter  10 value 94.472184
iter  20 value 94.468223
iter  30 value 90.851283
iter  40 value 84.646364
iter  50 value 84.525146
iter  60 value 84.525003
final  value 84.524960 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.884511 
iter  10 value 94.162255
iter  20 value 94.093070
iter  30 value 92.728597
iter  40 value 89.056491
iter  50 value 88.557165
iter  60 value 87.314330
final  value 87.298081 
converged
Fitting Repeat 3 

# weights:  305
initial  value 102.127153 
iter  10 value 94.489054
final  value 94.484218 
converged
Fitting Repeat 4 

# weights:  305
initial  value 104.084030 
iter  10 value 93.808393
iter  20 value 93.729933
iter  30 value 92.583634
iter  40 value 92.565181
iter  50 value 92.564940
iter  60 value 92.562616
iter  70 value 92.442077
iter  80 value 91.868002
iter  90 value 91.844647
iter 100 value 91.842431
final  value 91.842431 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 97.765756 
iter  10 value 94.093687
iter  20 value 90.707580
iter  30 value 88.555740
final  value 88.554280 
converged
Fitting Repeat 1 

# weights:  507
initial  value 144.618612 
iter  10 value 95.658061
iter  20 value 94.488566
iter  30 value 94.486745
iter  40 value 94.457826
iter  50 value 94.450719
iter  60 value 92.275327
iter  70 value 87.983422
iter  80 value 84.002746
iter  90 value 83.286615
iter 100 value 82.983711
final  value 82.983711 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 125.120179 
iter  10 value 94.475906
iter  20 value 94.468002
final  value 94.467595 
converged
Fitting Repeat 3 

# weights:  507
initial  value 96.038272 
iter  10 value 94.480711
iter  20 value 94.456960
iter  30 value 94.448177
final  value 94.448072 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.153238 
iter  10 value 94.451123
iter  20 value 94.402278
iter  30 value 91.527260
iter  40 value 90.488423
iter  50 value 90.488236
iter  50 value 90.488235
iter  50 value 90.488235
final  value 90.488235 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.220421 
iter  10 value 93.626976
iter  20 value 92.715354
iter  30 value 92.346414
iter  40 value 92.313387
iter  50 value 83.975112
iter  60 value 83.406786
iter  70 value 83.001228
iter  80 value 82.787437
iter  90 value 82.232568
iter 100 value 81.328941
final  value 81.328941 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.568180 
final  value 93.915746 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.409222 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.564123 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 108.447727 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.412655 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 111.920998 
iter  10 value 91.119537
iter  20 value 88.639544
iter  30 value 84.818222
iter  40 value 84.595260
iter  50 value 84.592204
final  value 84.592200 
converged
Fitting Repeat 2 

# weights:  305
initial  value 94.633828 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.439319 
iter  10 value 93.866992
final  value 93.865909 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.096635 
final  value 93.865909 
converged
Fitting Repeat 5 

# weights:  305
initial  value 99.285678 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 96.863691 
final  value 93.869755 
converged
Fitting Repeat 2 

# weights:  507
initial  value 108.884568 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.536600 
final  value 93.915746 
converged
Fitting Repeat 4 

# weights:  507
initial  value 107.057380 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 93.633482 
iter  10 value 86.666156
iter  20 value 84.165736
iter  30 value 83.764913
final  value 83.764688 
converged
Fitting Repeat 1 

# weights:  103
initial  value 102.469563 
iter  10 value 91.180927
iter  20 value 87.282258
iter  30 value 86.951862
iter  40 value 85.455695
iter  50 value 84.980207
iter  60 value 84.960101
final  value 84.959664 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.186750 
iter  10 value 93.930521
iter  20 value 90.139286
iter  30 value 89.252362
iter  40 value 86.181242
iter  50 value 85.214249
iter  60 value 85.168902
iter  70 value 84.938005
iter  80 value 84.076224
iter  90 value 83.812401
iter 100 value 83.528321
final  value 83.528321 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 102.678065 
iter  10 value 94.394790
iter  20 value 94.056785
iter  30 value 93.734743
iter  40 value 93.690660
iter  50 value 89.833072
iter  60 value 86.511461
iter  70 value 85.488782
iter  80 value 85.341421
iter  90 value 85.058513
iter 100 value 84.959660
final  value 84.959660 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 106.751192 
iter  10 value 93.177487
iter  20 value 87.086843
iter  30 value 86.520090
iter  40 value 84.720666
iter  50 value 83.983891
iter  60 value 83.860131
iter  70 value 83.729094
iter  80 value 83.706457
iter  90 value 83.635729
iter 100 value 83.595305
final  value 83.595305 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 107.665498 
iter  10 value 94.056065
iter  20 value 88.456453
iter  30 value 87.177962
iter  40 value 87.056571
iter  50 value 85.210973
iter  60 value 84.959657
final  value 84.959653 
converged
Fitting Repeat 1 

# weights:  305
initial  value 110.283419 
iter  10 value 93.878562
iter  20 value 89.062383
iter  30 value 86.463862
iter  40 value 85.478830
iter  50 value 84.941634
iter  60 value 84.873901
iter  70 value 84.699075
iter  80 value 83.977351
iter  90 value 83.156240
iter 100 value 82.947977
final  value 82.947977 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 109.071623 
iter  10 value 94.100841
iter  20 value 88.258268
iter  30 value 87.460805
iter  40 value 84.616890
iter  50 value 84.038501
iter  60 value 83.323374
iter  70 value 82.496098
iter  80 value 81.991627
iter  90 value 81.947809
iter 100 value 81.865586
final  value 81.865586 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.141846 
iter  10 value 93.995736
iter  20 value 91.316680
iter  30 value 87.024654
iter  40 value 86.816985
iter  50 value 86.569865
iter  60 value 86.184521
iter  70 value 85.998175
iter  80 value 85.749619
iter  90 value 83.501487
iter 100 value 82.942050
final  value 82.942050 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 103.345215 
iter  10 value 94.228864
iter  20 value 90.382274
iter  30 value 86.392112
iter  40 value 83.458894
iter  50 value 83.267146
iter  60 value 83.036454
iter  70 value 82.886657
iter  80 value 82.754374
iter  90 value 82.563267
iter 100 value 82.136509
final  value 82.136509 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 105.414207 
iter  10 value 94.252500
iter  20 value 92.818077
iter  30 value 90.420117
iter  40 value 87.755924
iter  50 value 86.653461
iter  60 value 84.552610
iter  70 value 83.337446
iter  80 value 82.667383
iter  90 value 82.217738
iter 100 value 81.974685
final  value 81.974685 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 113.679945 
iter  10 value 93.729713
iter  20 value 87.099803
iter  30 value 86.850009
iter  40 value 84.894566
iter  50 value 83.454137
iter  60 value 82.929482
iter  70 value 82.425640
iter  80 value 82.087799
iter  90 value 81.959405
iter 100 value 81.837509
final  value 81.837509 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 108.298023 
iter  10 value 95.311339
iter  20 value 88.599340
iter  30 value 86.770138
iter  40 value 86.410305
iter  50 value 85.893919
iter  60 value 84.527573
iter  70 value 83.948739
iter  80 value 83.529072
iter  90 value 82.914059
iter 100 value 82.098016
final  value 82.098016 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.829246 
iter  10 value 94.185228
iter  20 value 89.084117
iter  30 value 85.904789
iter  40 value 83.857487
iter  50 value 83.230927
iter  60 value 82.739120
iter  70 value 82.272842
iter  80 value 82.049435
iter  90 value 81.945509
iter 100 value 81.813785
final  value 81.813785 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 118.434484 
iter  10 value 93.906826
iter  20 value 87.194650
iter  30 value 85.351940
iter  40 value 85.284320
iter  50 value 84.699200
iter  60 value 84.070426
iter  70 value 83.649331
iter  80 value 83.532879
iter  90 value 82.888124
iter 100 value 82.415524
final  value 82.415524 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 112.878726 
iter  10 value 94.273407
iter  20 value 93.261591
iter  30 value 87.219388
iter  40 value 84.881014
iter  50 value 84.277142
iter  60 value 83.848712
iter  70 value 83.443623
iter  80 value 83.039585
iter  90 value 82.299271
iter 100 value 82.086946
final  value 82.086946 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 104.813451 
iter  10 value 94.055606
final  value 94.052900 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.889807 
final  value 94.054417 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.858528 
iter  10 value 94.054604
final  value 94.052912 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.257978 
iter  10 value 94.054806
iter  20 value 93.992725
final  value 93.657655 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.358802 
final  value 93.871417 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.370783 
iter  10 value 93.920774
iter  20 value 93.915925
final  value 93.915915 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.656562 
iter  10 value 94.055503
iter  10 value 94.055503
iter  10 value 94.055503
final  value 94.055503 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.662463 
iter  10 value 90.224837
iter  20 value 89.611029
iter  30 value 88.659930
iter  40 value 87.445289
iter  50 value 87.437152
iter  60 value 87.029723
iter  70 value 86.500041
iter  80 value 86.492476
iter  90 value 84.903240
iter 100 value 84.636498
final  value 84.636498 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.121279 
iter  10 value 93.920559
iter  20 value 93.869588
iter  30 value 93.662720
iter  40 value 93.654272
iter  50 value 93.631186
iter  60 value 92.276184
iter  70 value 89.125688
iter  80 value 87.079533
iter  90 value 86.531871
iter 100 value 86.430972
final  value 86.430972 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 98.386371 
iter  10 value 93.814984
iter  20 value 93.771115
iter  30 value 93.758728
iter  40 value 93.758081
iter  50 value 90.384572
iter  60 value 90.363961
iter  70 value 90.203120
final  value 90.202933 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.383010 
iter  10 value 93.663115
iter  20 value 93.661260
iter  30 value 93.654088
iter  40 value 90.626483
iter  50 value 86.484726
iter  60 value 83.368814
iter  70 value 82.647492
iter  80 value 82.555154
iter  90 value 82.389314
iter 100 value 82.228006
final  value 82.228006 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 102.386855 
iter  10 value 94.060525
iter  20 value 94.050991
iter  30 value 93.655730
final  value 93.654568 
converged
Fitting Repeat 3 

# weights:  507
initial  value 121.046166 
iter  10 value 93.722703
iter  20 value 93.649759
iter  30 value 88.564543
iter  40 value 85.006849
iter  50 value 83.316318
iter  60 value 82.970179
iter  70 value 82.762663
iter  80 value 82.745911
iter  90 value 82.658586
iter 100 value 81.854274
final  value 81.854274 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 113.160991 
iter  10 value 94.270837
iter  20 value 89.364098
iter  30 value 85.856293
iter  40 value 83.001178
iter  50 value 82.468627
iter  60 value 82.374577
iter  70 value 82.339903
iter  80 value 82.335021
final  value 82.334829 
converged
Fitting Repeat 5 

# weights:  507
initial  value 116.194254 
iter  10 value 93.722437
iter  20 value 93.718275
iter  30 value 93.713883
iter  40 value 93.713452
iter  50 value 92.685531
iter  60 value 86.831978
iter  70 value 83.886809
iter  80 value 81.309376
iter  90 value 80.949958
iter 100 value 80.915050
final  value 80.915050 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 124.878025 
iter  10 value 117.894743
iter  20 value 117.890303
iter  30 value 117.019395
iter  40 value 116.110848
iter  50 value 116.011180
final  value 116.011033 
converged
Fitting Repeat 2 

# weights:  305
initial  value 118.796759 
iter  10 value 117.103018
iter  20 value 117.099472
final  value 117.099307 
converged
Fitting Repeat 3 

# weights:  305
initial  value 120.115452 
iter  10 value 117.561417
iter  20 value 117.551470
iter  30 value 112.279113
iter  40 value 108.790009
iter  50 value 108.788930
iter  60 value 107.910119
iter  70 value 107.781528
iter  80 value 107.718753
iter  90 value 107.718372
iter 100 value 107.716268
final  value 107.716268 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 121.331573 
iter  10 value 117.763822
iter  20 value 117.390498
iter  30 value 114.792891
iter  40 value 113.922193
iter  50 value 113.615617
iter  60 value 113.377070
final  value 113.351941 
converged
Fitting Repeat 5 

# weights:  305
initial  value 120.867913 
iter  10 value 117.894721
iter  20 value 117.778176
iter  30 value 108.162006
iter  40 value 107.984111
iter  50 value 107.982236
iter  60 value 105.603006
iter  70 value 105.275211
iter  80 value 105.251174
iter  90 value 103.306668
iter 100 value 102.165082
final  value 102.165082 
stopped after 100 iterations
svmRadial
ranger
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases


RUNIT TEST PROTOCOL -- Tue Apr 29 06:28:01 2025 
*********************************************** 
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
HPiP RUnit Tests - 7 test functions, 0 errors, 0 failures
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: `repeats` has no meaning for this resampling method. 
2: executing %dopar% sequentially: no parallel backend registered 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  44.42    1.32  107.37