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


* using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/HPiP.Rcheck’
* using R version 4.4.3 (2025-02-28)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Ventura 13.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.12.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
    29 | then the Kronecker product is the code{(pm × qn)} block matrix
       |                                       ^
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
corr_plot     51.714  2.207  54.092
var_imp       51.413  2.108  53.696
FSmethod      51.417  2.002  53.600
pred_ensembel 16.919  0.535  15.409
enrichfindP    0.495  0.075  10.976
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

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

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


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

R version 4.4.3 (2025-02-28) -- "Trophy Case"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

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

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

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

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

# weights:  103
initial  value 97.290610 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.606151 
final  value 94.021262 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.036323 
final  value 93.653870 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.049645 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.659943 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.144527 
iter  10 value 93.391909
final  value 93.391892 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.018574 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.372812 
final  value 94.052911 
converged
Fitting Repeat 4 

# weights:  305
initial  value 113.233048 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.181472 
final  value 93.391892 
converged
Fitting Repeat 1 

# weights:  507
initial  value 107.015984 
final  value 94.052911 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.203337 
final  value 92.974286 
converged
Fitting Repeat 3 

# weights:  507
initial  value 100.562831 
iter  10 value 94.055882
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 102.571516 
iter  10 value 94.032328
iter  10 value 94.032328
iter  10 value 94.032328
final  value 94.032328 
converged
Fitting Repeat 5 

# weights:  507
initial  value 111.150445 
iter  10 value 93.391917
final  value 93.391892 
converged
Fitting Repeat 1 

# weights:  103
initial  value 105.674589 
iter  10 value 94.162045
iter  20 value 94.056814
iter  30 value 93.597898
iter  40 value 93.279211
iter  50 value 93.229492
iter  60 value 93.189112
iter  70 value 89.964339
iter  80 value 88.904151
iter  90 value 88.798869
iter 100 value 86.700837
final  value 86.700837 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 115.727120 
iter  10 value 94.026704
iter  20 value 89.363385
iter  30 value 85.825059
iter  40 value 85.220597
iter  50 value 85.081856
iter  60 value 85.060357
final  value 85.060320 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.473436 
iter  10 value 93.995357
iter  20 value 93.459332
iter  30 value 88.110975
iter  40 value 86.391865
iter  50 value 86.349792
iter  60 value 86.272655
iter  70 value 85.281708
iter  80 value 85.166858
iter  90 value 85.065367
iter 100 value 85.060321
final  value 85.060321 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 101.552945 
iter  10 value 93.871410
iter  20 value 89.619983
iter  30 value 88.386718
iter  40 value 87.925135
iter  50 value 86.594914
iter  60 value 86.401311
iter  70 value 86.393955
final  value 86.393946 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.780562 
iter  10 value 94.056566
iter  20 value 88.153570
iter  30 value 85.561634
iter  40 value 85.240179
iter  50 value 85.145094
iter  60 value 85.062290
iter  70 value 85.060321
iter  70 value 85.060320
iter  70 value 85.060320
final  value 85.060320 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.699598 
iter  10 value 94.058428
iter  20 value 93.600214
iter  30 value 93.376860
iter  40 value 88.257344
iter  50 value 86.698411
iter  60 value 85.344294
iter  70 value 83.735320
iter  80 value 83.409247
iter  90 value 82.736162
iter 100 value 82.102322
final  value 82.102322 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.200193 
iter  10 value 89.300284
iter  20 value 88.115300
iter  30 value 88.007842
iter  40 value 87.297001
iter  50 value 84.282987
iter  60 value 82.255956
iter  70 value 81.591912
iter  80 value 81.545784
iter  90 value 81.476454
iter 100 value 81.296328
final  value 81.296328 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 101.494563 
iter  10 value 93.682292
iter  20 value 85.855510
iter  30 value 85.257930
iter  40 value 85.013947
iter  50 value 84.845645
iter  60 value 84.677483
iter  70 value 84.185728
iter  80 value 82.905940
iter  90 value 81.749767
iter 100 value 81.308099
final  value 81.308099 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 106.112360 
iter  10 value 89.164843
iter  20 value 87.450508
iter  30 value 87.073348
iter  40 value 86.317618
iter  50 value 85.525673
iter  60 value 83.497224
iter  70 value 82.301625
iter  80 value 81.480912
iter  90 value 81.074216
iter 100 value 80.957855
final  value 80.957855 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 102.615194 
iter  10 value 94.104098
iter  20 value 88.616180
iter  30 value 85.306989
iter  40 value 84.571405
iter  50 value 84.131020
iter  60 value 83.978385
iter  70 value 82.317232
iter  80 value 81.801121
iter  90 value 81.679320
iter 100 value 81.532618
final  value 81.532618 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 134.856521 
iter  10 value 94.868239
iter  20 value 93.414976
iter  30 value 91.146877
iter  40 value 85.995500
iter  50 value 83.623051
iter  60 value 82.891380
iter  70 value 82.093149
iter  80 value 81.379078
iter  90 value 81.247333
iter 100 value 81.203628
final  value 81.203628 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 112.702902 
iter  10 value 95.332618
iter  20 value 92.471469
iter  30 value 84.036992
iter  40 value 82.357302
iter  50 value 81.692216
iter  60 value 81.536254
iter  70 value 81.315786
iter  80 value 80.989468
iter  90 value 80.794400
iter 100 value 80.527739
final  value 80.527739 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 108.876965 
iter  10 value 97.034833
iter  20 value 90.800621
iter  30 value 88.028453
iter  40 value 86.275469
iter  50 value 82.224452
iter  60 value 81.272205
iter  70 value 81.174065
iter  80 value 80.981995
iter  90 value 80.885824
iter 100 value 80.848824
final  value 80.848824 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 130.562756 
iter  10 value 92.432105
iter  20 value 83.782782
iter  30 value 82.703400
iter  40 value 81.762254
iter  50 value 81.381943
iter  60 value 81.129227
iter  70 value 80.958719
iter  80 value 80.823904
iter  90 value 80.567785
iter 100 value 80.442256
final  value 80.442256 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 111.329478 
iter  10 value 94.539008
iter  20 value 92.323318
iter  30 value 87.163828
iter  40 value 84.027407
iter  50 value 82.604364
iter  60 value 82.216984
iter  70 value 82.124134
iter  80 value 82.049627
iter  90 value 81.962674
iter 100 value 81.512206
final  value 81.512206 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.716240 
final  value 94.054597 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.292272 
final  value 94.054331 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.244205 
final  value 94.054273 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.102186 
iter  10 value 94.054546
iter  20 value 94.037403
iter  30 value 93.392268
iter  30 value 93.392267
iter  30 value 93.392267
final  value 93.392267 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.799145 
final  value 94.054807 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.071327 
iter  10 value 93.397540
iter  20 value 93.394764
final  value 93.392237 
converged
Fitting Repeat 2 

# weights:  305
initial  value 99.106049 
iter  10 value 94.057556
iter  20 value 93.899640
iter  30 value 91.759998
iter  40 value 91.714507
final  value 91.703684 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.796940 
iter  10 value 89.236390
iter  20 value 89.134171
iter  30 value 89.093175
iter  40 value 89.091847
final  value 89.091084 
converged
Fitting Repeat 4 

# weights:  305
initial  value 106.701123 
iter  10 value 93.108174
iter  20 value 93.023293
final  value 92.976146 
converged
Fitting Repeat 5 

# weights:  305
initial  value 109.433172 
iter  10 value 89.322488
iter  20 value 88.212732
iter  30 value 88.211742
iter  40 value 88.210605
iter  50 value 88.206919
iter  60 value 86.953733
final  value 86.949033 
converged
Fitting Repeat 1 

# weights:  507
initial  value 111.206483 
iter  10 value 92.485665
iter  20 value 92.192279
iter  30 value 92.188383
iter  40 value 92.182251
iter  50 value 92.180843
iter  60 value 92.179165
iter  70 value 92.177566
iter  80 value 92.177438
iter  90 value 92.024370
iter 100 value 87.283473
final  value 87.283473 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 97.317213 
iter  10 value 92.188210
iter  20 value 92.184331
iter  30 value 87.183984
iter  40 value 86.800400
iter  50 value 85.795667
iter  60 value 83.683543
iter  70 value 82.906394
iter  80 value 82.719448
iter  90 value 82.593038
iter 100 value 82.404072
final  value 82.404072 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 100.427044 
iter  10 value 91.059539
iter  20 value 88.588896
iter  30 value 87.764324
iter  40 value 86.678559
final  value 86.676904 
converged
Fitting Repeat 4 

# weights:  507
initial  value 121.556688 
iter  10 value 94.061095
iter  20 value 92.443057
iter  30 value 91.142647
iter  40 value 91.137378
iter  50 value 91.137026
iter  60 value 91.134307
iter  70 value 91.130526
iter  80 value 91.130228
iter  90 value 88.536722
iter 100 value 84.517862
final  value 84.517862 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 127.231950 
iter  10 value 93.999884
iter  20 value 93.724091
iter  30 value 92.060625
iter  40 value 90.297105
iter  50 value 89.994126
iter  60 value 89.900306
iter  70 value 89.484530
iter  80 value 88.708667
iter  90 value 88.577602
iter 100 value 87.908896
final  value 87.908896 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 103.713474 
final  value 94.354396 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.865179 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.443692 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.345465 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.508676 
iter  10 value 93.991593
final  value 93.976398 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.560195 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.271841 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 114.282170 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.614518 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 99.981272 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.887381 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 120.276757 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 107.166569 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 117.583697 
final  value 94.354396 
converged
Fitting Repeat 5 

# weights:  507
initial  value 117.231781 
final  value 94.461538 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.124620 
iter  10 value 94.096056
iter  20 value 85.332051
iter  30 value 83.778769
iter  40 value 82.168671
iter  50 value 81.817623
iter  60 value 81.639159
final  value 81.638082 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.460001 
iter  10 value 94.489370
iter  20 value 94.142533
iter  30 value 94.047319
iter  40 value 94.017458
iter  50 value 86.914064
iter  60 value 86.359584
iter  70 value 85.360808
iter  80 value 85.264328
iter  90 value 85.095506
iter 100 value 85.081056
final  value 85.081056 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 98.295279 
iter  10 value 93.757471
iter  20 value 86.961937
iter  30 value 86.301987
iter  40 value 85.413012
iter  50 value 85.098138
iter  60 value 85.081053
final  value 85.081049 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.572491 
iter  10 value 94.723591
iter  20 value 94.489257
final  value 94.486427 
converged
Fitting Repeat 5 

# weights:  103
initial  value 105.169955 
iter  10 value 94.490947
iter  20 value 86.771308
iter  30 value 85.735915
iter  40 value 85.440287
iter  50 value 85.145254
iter  60 value 85.091525
iter  70 value 85.081590
final  value 85.081049 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.072879 
iter  10 value 94.522625
iter  20 value 93.314383
iter  30 value 87.741056
iter  40 value 86.077249
iter  50 value 84.300416
iter  60 value 82.857715
iter  70 value 82.155940
iter  80 value 81.959421
iter  90 value 81.900009
iter 100 value 81.853248
final  value 81.853248 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 115.171906 
iter  10 value 93.975426
iter  20 value 88.279339
iter  30 value 85.624027
iter  40 value 85.290979
iter  50 value 84.782451
iter  60 value 84.636347
iter  70 value 84.515436
iter  80 value 83.313288
iter  90 value 82.942407
iter 100 value 82.572073
final  value 82.572073 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 104.490099 
iter  10 value 96.580132
iter  20 value 94.609894
iter  30 value 87.414881
iter  40 value 85.549739
iter  50 value 85.281544
iter  60 value 84.819727
iter  70 value 84.688715
iter  80 value 83.543219
iter  90 value 82.622578
iter 100 value 80.303876
final  value 80.303876 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 104.797809 
iter  10 value 94.425943
iter  20 value 89.281797
iter  30 value 87.784422
iter  40 value 87.310545
iter  50 value 86.815142
iter  60 value 84.010616
iter  70 value 81.674076
iter  80 value 81.129014
iter  90 value 80.964993
iter 100 value 80.797543
final  value 80.797543 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 99.909584 
iter  10 value 94.307755
iter  20 value 88.674681
iter  30 value 87.066412
iter  40 value 84.097575
iter  50 value 83.030959
iter  60 value 82.335596
iter  70 value 82.056082
iter  80 value 81.680917
iter  90 value 81.408405
iter 100 value 81.242377
final  value 81.242377 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.820788 
iter  10 value 94.240438
iter  20 value 85.198101
iter  30 value 83.890113
iter  40 value 82.044684
iter  50 value 81.560821
iter  60 value 81.142812
iter  70 value 80.880035
iter  80 value 80.417834
iter  90 value 80.290248
iter 100 value 80.181989
final  value 80.181989 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 110.603457 
iter  10 value 94.628700
iter  20 value 91.132910
iter  30 value 86.957784
iter  40 value 84.416333
iter  50 value 82.336419
iter  60 value 82.074059
iter  70 value 81.907542
iter  80 value 81.176290
iter  90 value 80.804769
iter 100 value 80.620916
final  value 80.620916 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 126.831772 
iter  10 value 95.315526
iter  20 value 92.223410
iter  30 value 86.785448
iter  40 value 85.132921
iter  50 value 84.940170
iter  60 value 84.836318
iter  70 value 84.729063
iter  80 value 84.263997
iter  90 value 81.575892
iter 100 value 80.472195
final  value 80.472195 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 129.965527 
iter  10 value 94.787629
iter  20 value 92.834228
iter  30 value 84.903083
iter  40 value 82.753634
iter  50 value 82.261210
iter  60 value 82.130786
iter  70 value 81.845420
iter  80 value 80.978166
iter  90 value 80.254572
iter 100 value 79.665535
final  value 79.665535 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 105.499769 
iter  10 value 94.500823
iter  20 value 93.897652
iter  30 value 87.307430
iter  40 value 84.063815
iter  50 value 82.535625
iter  60 value 80.904363
iter  70 value 80.825752
iter  80 value 80.310569
iter  90 value 79.917476
iter 100 value 79.861532
final  value 79.861532 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 97.744027 
iter  10 value 94.485988
final  value 94.484215 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.876421 
iter  10 value 94.485984
iter  20 value 94.484295
iter  30 value 91.769553
iter  40 value 91.086404
iter  50 value 91.085994
iter  60 value 90.722126
iter  70 value 90.721758
iter  80 value 83.435321
iter  90 value 81.518047
final  value 81.484505 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.301060 
final  value 94.486123 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.381962 
final  value 94.485545 
converged
Fitting Repeat 5 

# weights:  103
initial  value 109.865530 
iter  10 value 94.507833
iter  20 value 94.501473
final  value 94.484511 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.651485 
iter  10 value 94.359284
iter  20 value 94.356473
iter  30 value 94.035497
iter  40 value 93.941468
iter  50 value 93.939710
iter  60 value 93.919628
final  value 93.913416 
converged
Fitting Repeat 2 

# weights:  305
initial  value 98.989592 
iter  10 value 94.219274
iter  20 value 94.211521
iter  30 value 94.209291
final  value 94.208857 
converged
Fitting Repeat 3 

# weights:  305
initial  value 98.324304 
iter  10 value 94.488644
iter  20 value 94.084799
iter  30 value 91.786300
iter  40 value 89.679492
iter  50 value 85.916981
iter  60 value 82.051205
iter  70 value 81.472966
iter  80 value 81.435305
iter  90 value 81.434294
iter 100 value 81.433792
final  value 81.433792 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 107.091485 
iter  10 value 94.360708
iter  20 value 94.198444
iter  30 value 84.702437
iter  40 value 83.837098
iter  50 value 83.785544
iter  60 value 83.781560
iter  70 value 83.781289
iter  80 value 83.780745
iter  90 value 83.780313
iter 100 value 83.779831
final  value 83.779831 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 99.291278 
iter  10 value 84.195455
iter  20 value 81.345250
iter  30 value 80.923918
iter  40 value 80.907810
iter  50 value 80.586789
iter  60 value 80.586332
iter  70 value 80.575688
iter  80 value 80.501090
iter  90 value 79.618277
iter 100 value 79.271615
final  value 79.271615 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 100.220798 
iter  10 value 94.362678
iter  20 value 94.057902
iter  30 value 88.848221
iter  40 value 87.223515
iter  50 value 86.935275
final  value 86.933456 
converged
Fitting Repeat 2 

# weights:  507
initial  value 103.718434 
iter  10 value 94.491991
iter  20 value 94.247127
iter  30 value 86.083300
iter  40 value 86.068076
iter  50 value 86.066453
final  value 86.066401 
converged
Fitting Repeat 3 

# weights:  507
initial  value 100.474480 
iter  10 value 94.491385
iter  20 value 94.482720
iter  30 value 94.361498
iter  40 value 94.043253
iter  50 value 94.004226
iter  60 value 94.003715
iter  70 value 93.987697
final  value 93.977030 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.665859 
iter  10 value 94.010312
iter  20 value 93.992522
iter  30 value 92.917369
iter  40 value 81.728024
iter  50 value 81.604317
iter  60 value 80.249424
iter  70 value 79.854530
iter  80 value 79.702066
iter  90 value 79.680910
iter 100 value 79.655412
final  value 79.655412 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 96.247184 
iter  10 value 94.492424
iter  20 value 94.409426
iter  30 value 93.994539
iter  40 value 84.706161
iter  50 value 81.295603
iter  60 value 81.139548
iter  70 value 81.073403
iter  80 value 78.861368
iter  90 value 78.069797
iter 100 value 78.031539
final  value 78.031539 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.261852 
iter  10 value 92.945420
final  value 92.945355 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.275962 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.011769 
iter  10 value 92.945356
iter  10 value 92.945355
iter  10 value 92.945355
final  value 92.945355 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.889664 
iter  10 value 92.945363
final  value 92.945355 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.690842 
iter  10 value 92.954142
final  value 92.945355 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.293651 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 114.347356 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.960698 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 125.952023 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 119.803711 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.161392 
iter  10 value 90.092737
iter  20 value 87.821658
final  value 87.821657 
converged
Fitting Repeat 2 

# weights:  507
initial  value 116.370621 
iter  10 value 94.042023
final  value 94.042012 
converged
Fitting Repeat 3 

# weights:  507
initial  value 121.530469 
iter  10 value 92.823305
final  value 92.694915 
converged
Fitting Repeat 4 

# weights:  507
initial  value 99.359708 
final  value 92.945356 
converged
Fitting Repeat 5 

# weights:  507
initial  value 101.337539 
iter  10 value 94.023994
iter  20 value 94.022600
iter  20 value 94.022599
iter  20 value 94.022599
final  value 94.022599 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.414895 
iter  10 value 94.056718
iter  20 value 93.099388
iter  30 value 92.995735
iter  40 value 91.138528
iter  50 value 81.685965
iter  60 value 81.117653
iter  70 value 80.983583
iter  80 value 80.609720
iter  90 value 80.493063
final  value 80.493000 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.178419 
iter  10 value 93.888528
iter  20 value 89.151197
iter  30 value 87.376776
iter  40 value 86.916174
iter  50 value 84.582986
iter  60 value 81.318140
iter  70 value 80.382862
iter  80 value 79.955095
iter  90 value 79.393160
iter 100 value 77.882629
final  value 77.882629 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 98.242658 
iter  10 value 94.132861
iter  20 value 94.044778
iter  30 value 93.968342
iter  40 value 93.802324
iter  50 value 93.744300
iter  60 value 93.315328
iter  70 value 92.989902
iter  80 value 91.956589
iter  90 value 88.078820
iter 100 value 86.354007
final  value 86.354007 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 103.204123 
iter  10 value 94.063299
iter  20 value 93.758124
iter  30 value 93.386390
iter  40 value 93.229118
iter  50 value 87.937142
iter  60 value 87.234570
iter  70 value 86.714125
iter  80 value 82.706425
iter  90 value 80.760499
iter 100 value 80.560133
final  value 80.560133 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 100.072494 
iter  10 value 86.326857
iter  20 value 81.942145
iter  30 value 81.232477
iter  40 value 81.042598
iter  50 value 78.567214
iter  60 value 77.343804
iter  70 value 77.159437
iter  80 value 77.153228
final  value 77.153184 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.771519 
iter  10 value 93.775040
iter  20 value 90.208165
iter  30 value 87.567286
iter  40 value 86.779986
iter  50 value 82.011241
iter  60 value 80.245433
iter  70 value 80.052000
iter  80 value 77.669764
iter  90 value 76.710465
iter 100 value 76.556910
final  value 76.556910 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 107.870628 
iter  10 value 93.625526
iter  20 value 85.323399
iter  30 value 83.090807
iter  40 value 82.377967
iter  50 value 81.225012
iter  60 value 79.222440
iter  70 value 78.169390
iter  80 value 77.277594
iter  90 value 76.817321
iter 100 value 76.612337
final  value 76.612337 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 98.922395 
iter  10 value 90.789524
iter  20 value 79.407422
iter  30 value 77.660938
iter  40 value 76.480615
iter  50 value 76.307138
iter  60 value 76.126115
iter  70 value 75.962116
iter  80 value 75.931719
iter  90 value 75.912804
iter 100 value 75.898363
final  value 75.898363 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 108.641067 
iter  10 value 94.386818
iter  20 value 93.954802
iter  30 value 92.020671
iter  40 value 80.547221
iter  50 value 80.370426
iter  60 value 80.325391
iter  70 value 77.796632
iter  80 value 76.728849
iter  90 value 76.499593
iter 100 value 76.428815
final  value 76.428815 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 110.390606 
iter  10 value 94.286874
iter  20 value 93.704858
iter  30 value 83.442974
iter  40 value 82.871343
iter  50 value 79.267691
iter  60 value 77.802033
iter  70 value 77.493457
iter  80 value 77.094856
iter  90 value 76.189574
iter 100 value 76.055242
final  value 76.055242 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 132.924645 
iter  10 value 94.412882
iter  20 value 84.479464
iter  30 value 83.148732
iter  40 value 80.924971
iter  50 value 80.089213
iter  60 value 78.046358
iter  70 value 77.056379
iter  80 value 76.327913
iter  90 value 76.134671
iter 100 value 75.942682
final  value 75.942682 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 111.053472 
iter  10 value 94.968078
iter  20 value 92.413166
iter  30 value 82.507036
iter  40 value 81.213420
iter  50 value 77.988991
iter  60 value 77.040788
iter  70 value 76.882962
iter  80 value 76.766842
iter  90 value 76.381932
iter 100 value 76.103163
final  value 76.103163 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 108.549456 
iter  10 value 94.568658
iter  20 value 93.106786
iter  30 value 81.034253
iter  40 value 78.237246
iter  50 value 77.402943
iter  60 value 77.179917
iter  70 value 76.581665
iter  80 value 76.020494
iter  90 value 75.739487
iter 100 value 75.329640
final  value 75.329640 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 133.140418 
iter  10 value 92.275243
iter  20 value 87.145641
iter  30 value 86.323453
iter  40 value 81.234304
iter  50 value 77.843196
iter  60 value 77.311915
iter  70 value 77.113508
iter  80 value 76.480771
iter  90 value 76.296105
iter 100 value 76.002833
final  value 76.002833 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.811264 
iter  10 value 91.272127
iter  20 value 81.418343
iter  30 value 80.294555
iter  40 value 79.298178
iter  50 value 78.377989
iter  60 value 76.904382
iter  70 value 76.007978
iter  80 value 75.760585
iter  90 value 75.522552
iter 100 value 75.489137
final  value 75.489137 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.260780 
iter  10 value 92.947673
iter  20 value 92.946153
iter  30 value 92.328147
iter  40 value 84.937912
final  value 84.934200 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.552723 
iter  10 value 93.062085
final  value 92.947618 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.074044 
iter  10 value 83.337483
iter  20 value 80.796891
iter  30 value 80.312479
iter  40 value 79.226086
iter  50 value 79.219441
iter  60 value 79.185711
iter  70 value 79.166820
iter  80 value 79.166148
iter  90 value 79.165550
iter 100 value 79.164531
final  value 79.164531 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 96.942983 
final  value 94.054599 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.549244 
iter  10 value 91.296955
iter  20 value 91.283301
iter  30 value 91.281858
iter  40 value 89.736966
iter  50 value 89.723499
iter  60 value 89.723271
iter  70 value 89.722995
iter  80 value 89.722817
iter  90 value 89.722400
final  value 89.722221 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.350152 
iter  10 value 89.912645
iter  20 value 89.753072
iter  30 value 89.728494
iter  40 value 89.601275
iter  50 value 88.146810
iter  60 value 87.558891
iter  70 value 77.799392
iter  80 value 75.490274
iter  90 value 75.061989
iter 100 value 75.007950
final  value 75.007950 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.947642 
iter  10 value 94.056781
iter  20 value 93.420352
iter  30 value 82.441919
iter  40 value 82.331016
iter  50 value 82.327850
iter  60 value 82.324632
iter  70 value 80.891611
iter  80 value 79.435769
iter  90 value 76.714416
iter 100 value 76.153118
final  value 76.153118 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 95.467497 
iter  10 value 94.057273
iter  20 value 94.038006
iter  30 value 93.314778
iter  40 value 92.607754
iter  50 value 92.272997
final  value 92.272754 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.328278 
iter  10 value 93.936335
iter  20 value 91.560275
iter  30 value 83.787895
iter  40 value 83.508679
iter  50 value 83.473861
iter  60 value 82.809541
final  value 82.798093 
converged
Fitting Repeat 5 

# weights:  305
initial  value 102.175363 
iter  10 value 94.058025
iter  20 value 92.235564
iter  30 value 80.578774
iter  40 value 79.826420
iter  50 value 79.420831
iter  60 value 79.418890
final  value 79.418373 
converged
Fitting Repeat 1 

# weights:  507
initial  value 106.723019 
iter  10 value 94.061221
iter  20 value 94.052958
iter  30 value 92.946365
iter  30 value 92.946364
final  value 92.946364 
converged
Fitting Repeat 2 

# weights:  507
initial  value 95.085096 
iter  10 value 91.263573
iter  20 value 91.258546
iter  30 value 91.254478
iter  40 value 91.254335
iter  50 value 91.225754
iter  60 value 90.037940
iter  70 value 89.973584
final  value 89.973533 
converged
Fitting Repeat 3 

# weights:  507
initial  value 97.969426 
iter  10 value 94.060749
iter  20 value 94.040351
iter  30 value 88.247206
iter  40 value 80.942961
iter  50 value 79.075943
iter  60 value 79.072193
final  value 79.072190 
converged
Fitting Repeat 4 

# weights:  507
initial  value 109.451650 
iter  10 value 86.234235
iter  20 value 85.623038
iter  30 value 85.622532
iter  40 value 85.619045
iter  50 value 85.614914
iter  50 value 85.614913
final  value 85.614913 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.090000 
iter  10 value 92.953853
iter  20 value 92.913291
iter  30 value 92.703971
iter  40 value 92.659509
iter  50 value 91.558218
iter  60 value 91.497075
iter  70 value 91.496661
iter  80 value 91.486128
iter  90 value 91.387376
iter 100 value 91.382165
final  value 91.382165 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 97.710267 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.278450 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.599793 
final  value 94.275362 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.199870 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.842393 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 108.156700 
final  value 94.305882 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.884703 
iter  10 value 86.044663
iter  20 value 84.636741
iter  30 value 84.636304
iter  40 value 84.627582
iter  50 value 84.118391
iter  60 value 83.497862
final  value 83.491441 
converged
Fitting Repeat 3 

# weights:  305
initial  value 102.792264 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 105.433331 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 101.989822 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.125524 
iter  10 value 91.996507
iter  20 value 91.684165
iter  30 value 91.676675
final  value 91.676672 
converged
Fitting Repeat 2 

# weights:  507
initial  value 114.195883 
iter  10 value 93.189466
iter  20 value 84.588796
final  value 84.588757 
converged
Fitting Repeat 3 

# weights:  507
initial  value 100.575073 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.109498 
final  value 94.275362 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.212595 
iter  10 value 90.095549
iter  20 value 84.205309
final  value 84.182582 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.817557 
iter  10 value 94.505829
iter  20 value 92.748241
iter  30 value 90.692655
iter  40 value 85.930243
iter  50 value 84.950052
iter  60 value 84.735933
iter  70 value 82.582056
iter  80 value 81.835971
iter  90 value 81.715737
iter 100 value 81.663336
final  value 81.663336 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 101.911958 
iter  10 value 94.484927
iter  20 value 84.410729
iter  30 value 83.377610
iter  40 value 83.162444
iter  50 value 82.175902
iter  60 value 82.151354
iter  70 value 82.135207
final  value 82.131317 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.368339 
iter  10 value 94.555015
iter  20 value 94.472917
iter  30 value 83.991557
iter  40 value 82.773499
iter  50 value 82.353229
iter  60 value 82.312443
iter  70 value 82.258895
iter  80 value 82.142126
final  value 82.131317 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.042036 
iter  10 value 94.488622
iter  20 value 83.868302
iter  30 value 83.279865
iter  40 value 83.034976
iter  50 value 82.385823
iter  60 value 81.761517
iter  70 value 81.662028
iter  80 value 81.654944
final  value 81.648530 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.041483 
iter  10 value 89.869866
iter  20 value 83.317412
iter  30 value 82.773177
iter  40 value 82.039263
iter  50 value 81.769150
iter  60 value 81.701921
iter  70 value 81.664799
iter  80 value 81.650559
iter  90 value 81.648530
iter  90 value 81.648530
iter  90 value 81.648530
final  value 81.648530 
converged
Fitting Repeat 1 

# weights:  305
initial  value 104.704852 
iter  10 value 94.545580
iter  20 value 92.199490
iter  30 value 92.097552
iter  40 value 90.007765
iter  50 value 85.780137
iter  60 value 83.501008
iter  70 value 82.286300
iter  80 value 81.388965
iter  90 value 80.983408
iter 100 value 80.809657
final  value 80.809657 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.478516 
iter  10 value 94.670237
iter  20 value 94.237876
iter  30 value 92.732075
iter  40 value 92.213779
iter  50 value 87.226447
iter  60 value 84.835721
iter  70 value 82.892232
iter  80 value 80.922400
iter  90 value 80.603838
iter 100 value 80.106318
final  value 80.106318 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 99.589164 
iter  10 value 94.546541
iter  20 value 93.499927
iter  30 value 91.133685
iter  40 value 91.041647
iter  50 value 90.542208
iter  60 value 83.959551
iter  70 value 82.882652
iter  80 value 82.149994
iter  90 value 81.873507
iter 100 value 81.828629
final  value 81.828629 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 112.448582 
iter  10 value 93.770576
iter  20 value 88.955135
iter  30 value 85.560520
iter  40 value 85.307471
iter  50 value 84.850801
iter  60 value 83.357763
iter  70 value 81.685181
iter  80 value 80.395597
iter  90 value 79.332568
iter 100 value 79.095755
final  value 79.095755 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 121.498910 
iter  10 value 96.282359
iter  20 value 95.248336
iter  30 value 86.021866
iter  40 value 84.368073
iter  50 value 83.031051
iter  60 value 82.627603
iter  70 value 82.474250
iter  80 value 82.317957
iter  90 value 81.931842
iter 100 value 80.860656
final  value 80.860656 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 126.274196 
iter  10 value 94.512824
iter  20 value 88.223616
iter  30 value 83.934079
iter  40 value 82.595197
iter  50 value 80.489399
iter  60 value 80.110296
iter  70 value 79.839733
iter  80 value 79.455751
iter  90 value 79.271219
iter 100 value 79.020402
final  value 79.020402 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.397619 
iter  10 value 93.327464
iter  20 value 92.432477
iter  30 value 91.709782
iter  40 value 82.733185
iter  50 value 81.314711
iter  60 value 80.658081
iter  70 value 79.876530
iter  80 value 79.468456
iter  90 value 79.379422
iter 100 value 79.121483
final  value 79.121483 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.554698 
iter  10 value 95.719334
iter  20 value 94.530194
iter  30 value 94.231362
iter  40 value 86.180721
iter  50 value 85.176982
iter  60 value 84.267890
iter  70 value 80.934267
iter  80 value 80.627753
iter  90 value 80.017151
iter 100 value 79.642148
final  value 79.642148 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 105.194712 
iter  10 value 96.231120
iter  20 value 94.682017
iter  30 value 85.679472
iter  40 value 82.649181
iter  50 value 80.697342
iter  60 value 80.288187
iter  70 value 80.061854
iter  80 value 79.558767
iter  90 value 79.537118
iter 100 value 79.472326
final  value 79.472326 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.747391 
iter  10 value 94.485109
iter  20 value 94.208937
iter  30 value 91.095065
iter  40 value 90.289492
iter  50 value 86.777807
iter  60 value 82.506585
iter  70 value 80.947981
iter  80 value 79.943289
iter  90 value 79.721021
iter 100 value 79.627268
final  value 79.627268 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 101.535635 
final  value 94.485859 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.529518 
iter  10 value 94.277292
iter  20 value 94.275565
iter  30 value 94.229283
final  value 94.228877 
converged
Fitting Repeat 3 

# weights:  103
initial  value 105.942588 
iter  10 value 90.345140
iter  20 value 90.342761
iter  30 value 90.305104
iter  40 value 90.303903
iter  50 value 88.864276
iter  60 value 88.777479
iter  70 value 88.581320
iter  80 value 88.408847
iter  90 value 88.408548
final  value 88.408544 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.911311 
final  value 94.485763 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.178109 
iter  10 value 94.485628
iter  20 value 94.484216
iter  20 value 94.484215
iter  20 value 94.484215
final  value 94.484215 
converged
Fitting Repeat 1 

# weights:  305
initial  value 114.377696 
iter  10 value 94.453116
iter  20 value 87.354653
iter  30 value 81.294078
iter  40 value 79.303387
iter  50 value 78.863033
iter  60 value 78.725481
iter  70 value 78.725030
iter  80 value 78.724288
iter  90 value 78.478547
iter 100 value 78.143599
final  value 78.143599 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 102.466346 
iter  10 value 94.488175
iter  20 value 86.925824
iter  30 value 85.360376
iter  40 value 85.360167
iter  50 value 85.358700
iter  60 value 85.179139
iter  70 value 81.556621
iter  80 value 78.998432
iter  90 value 78.208065
iter 100 value 77.789117
final  value 77.789117 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 109.586931 
iter  10 value 94.488881
iter  20 value 94.484124
final  value 94.275490 
converged
Fitting Repeat 4 

# weights:  305
initial  value 99.813069 
iter  10 value 94.488487
iter  20 value 94.331116
iter  30 value 89.196286
iter  40 value 82.680448
iter  50 value 81.249648
iter  60 value 81.052245
iter  70 value 81.052114
final  value 81.052104 
converged
Fitting Repeat 5 

# weights:  305
initial  value 106.115933 
iter  10 value 94.488829
iter  20 value 94.481541
iter  30 value 86.051531
iter  40 value 83.038061
iter  50 value 83.034845
iter  60 value 82.431549
final  value 82.401008 
converged
Fitting Repeat 1 

# weights:  507
initial  value 114.524284 
iter  10 value 94.284430
iter  20 value 94.280131
iter  30 value 94.232853
iter  40 value 94.229939
iter  50 value 86.192992
iter  60 value 84.796284
iter  70 value 83.963541
iter  80 value 83.962640
iter  90 value 83.961935
iter 100 value 83.960119
final  value 83.960119 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 138.340693 
iter  10 value 95.004545
iter  20 value 94.237705
iter  30 value 94.231020
iter  40 value 89.343913
iter  50 value 82.277572
iter  60 value 82.274978
iter  70 value 82.117860
iter  80 value 82.105195
iter  90 value 81.363769
iter 100 value 80.732507
final  value 80.732507 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.340962 
iter  10 value 94.456457
iter  20 value 94.448260
iter  30 value 82.709727
final  value 82.413550 
converged
Fitting Repeat 4 

# weights:  507
initial  value 108.294247 
iter  10 value 92.451602
iter  20 value 88.683438
iter  30 value 88.672075
iter  40 value 88.178115
iter  50 value 88.175332
final  value 88.175131 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.398669 
iter  10 value 94.491608
iter  20 value 94.472902
iter  30 value 89.243192
iter  40 value 87.904044
iter  50 value 83.247399
iter  60 value 82.412635
final  value 82.412516 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.311307 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.622121 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.373206 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 106.924451 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.070872 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 110.314381 
final  value 94.354396 
converged
Fitting Repeat 2 

# weights:  305
initial  value 115.539915 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 96.909369 
iter  10 value 91.802961
iter  20 value 86.971332
final  value 86.756876 
converged
Fitting Repeat 4 

# weights:  305
initial  value 103.920819 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 106.368253 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 94.745055 
iter  10 value 94.354396
iter  10 value 94.354396
iter  10 value 94.354396
final  value 94.354396 
converged
Fitting Repeat 2 

# weights:  507
initial  value 111.616703 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 112.071831 
iter  10 value 89.505178
final  value 88.182700 
converged
Fitting Repeat 4 

# weights:  507
initial  value 128.447370 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 127.748444 
iter  10 value 94.484555
final  value 94.252920 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.285939 
iter  10 value 94.489835
iter  20 value 94.452488
iter  30 value 89.896580
iter  40 value 88.181418
iter  50 value 87.134197
iter  60 value 86.158571
iter  70 value 86.045689
final  value 86.044918 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.678388 
iter  10 value 93.260017
iter  20 value 89.955802
iter  30 value 88.166667
iter  40 value 87.652754
iter  50 value 87.005317
iter  60 value 85.402485
final  value 85.373496 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.281227 
final  value 94.488599 
converged
Fitting Repeat 4 

# weights:  103
initial  value 104.093582 
iter  10 value 94.398293
iter  20 value 89.517368
iter  30 value 88.126864
iter  40 value 87.700437
iter  50 value 87.341445
iter  60 value 86.553974
iter  70 value 86.045784
final  value 86.042350 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.612415 
iter  10 value 94.542151
iter  20 value 94.474390
iter  30 value 87.420743
iter  40 value 86.817046
iter  50 value 86.681405
iter  60 value 86.601788
iter  70 value 86.124129
iter  80 value 85.227665
iter  90 value 85.075282
iter 100 value 85.021946
final  value 85.021946 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 109.067694 
iter  10 value 94.751900
iter  20 value 94.551467
iter  30 value 94.219734
iter  40 value 94.051646
iter  50 value 93.213271
iter  60 value 90.124776
iter  70 value 89.348336
iter  80 value 88.502127
iter  90 value 88.289553
iter 100 value 88.019596
final  value 88.019596 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.291021 
iter  10 value 93.439292
iter  20 value 88.495498
iter  30 value 86.587133
iter  40 value 86.179396
iter  50 value 85.910320
iter  60 value 85.624812
iter  70 value 84.697039
iter  80 value 83.682475
iter  90 value 83.385696
iter 100 value 83.027012
final  value 83.027012 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 102.909357 
iter  10 value 94.499662
iter  20 value 89.501710
iter  30 value 88.137023
iter  40 value 87.162714
iter  50 value 86.798792
iter  60 value 84.738529
iter  70 value 84.373413
iter  80 value 84.223567
iter  90 value 84.037104
iter 100 value 83.665606
final  value 83.665606 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 103.354272 
iter  10 value 94.326219
iter  20 value 92.985330
iter  30 value 92.716643
iter  40 value 89.766396
iter  50 value 88.673688
iter  60 value 86.598144
iter  70 value 85.676272
iter  80 value 84.551871
iter  90 value 83.490270
iter 100 value 83.342566
final  value 83.342566 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 115.150438 
iter  10 value 95.211900
iter  20 value 94.542602
iter  30 value 94.429550
iter  40 value 90.977084
iter  50 value 88.506503
iter  60 value 86.882709
iter  70 value 86.433412
iter  80 value 86.417970
iter  90 value 85.324098
iter 100 value 84.451686
final  value 84.451686 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 122.476384 
iter  10 value 96.040039
iter  20 value 91.394678
iter  30 value 87.605630
iter  40 value 85.644979
iter  50 value 84.718731
iter  60 value 84.421076
iter  70 value 84.300399
iter  80 value 84.158303
iter  90 value 83.819920
iter 100 value 83.294527
final  value 83.294527 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 115.239188 
iter  10 value 94.982488
iter  20 value 91.447171
iter  30 value 88.565882
iter  40 value 86.204221
iter  50 value 84.533770
iter  60 value 83.581300
iter  70 value 83.183063
iter  80 value 83.044354
iter  90 value 82.996824
iter 100 value 82.892060
final  value 82.892060 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 130.456724 
iter  10 value 94.832845
iter  20 value 94.124731
iter  30 value 90.257395
iter  40 value 86.755132
iter  50 value 85.968552
iter  60 value 84.257173
iter  70 value 83.544786
iter  80 value 83.144537
iter  90 value 82.969022
iter 100 value 82.833316
final  value 82.833316 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 111.298453 
iter  10 value 94.460008
iter  20 value 94.368790
iter  30 value 92.179954
iter  40 value 89.625322
iter  50 value 87.421229
iter  60 value 86.831004
iter  70 value 84.954833
iter  80 value 84.518111
iter  90 value 84.318141
iter 100 value 83.872313
final  value 83.872313 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 126.313920 
iter  10 value 94.411805
iter  20 value 88.380292
iter  30 value 88.009392
iter  40 value 87.519485
iter  50 value 87.143941
iter  60 value 86.357617
iter  70 value 85.775731
iter  80 value 84.725417
iter  90 value 83.952900
iter 100 value 83.496060
final  value 83.496060 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 97.643810 
final  value 94.356100 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.388046 
final  value 94.485749 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.209698 
final  value 94.485868 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.329368 
final  value 94.485957 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.617621 
iter  10 value 94.485725
iter  20 value 94.482992
iter  30 value 93.570533
iter  40 value 93.569123
iter  50 value 93.536340
iter  50 value 93.536339
iter  50 value 93.536339
final  value 93.536339 
converged
Fitting Repeat 1 

# weights:  305
initial  value 117.890426 
iter  10 value 94.648981
iter  20 value 93.031365
iter  30 value 86.907154
iter  40 value 86.877004
iter  50 value 86.519406
iter  60 value 86.423239
iter  70 value 86.323954
iter  80 value 86.285425
iter  90 value 86.274681
iter 100 value 86.173897
final  value 86.173897 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 97.522653 
iter  10 value 94.359245
iter  20 value 92.347683
iter  30 value 87.958877
iter  40 value 87.579687
iter  50 value 86.620325
final  value 86.620264 
converged
Fitting Repeat 3 

# weights:  305
initial  value 104.441362 
iter  10 value 94.359586
iter  20 value 93.490909
iter  30 value 92.529307
iter  40 value 92.196543
iter  50 value 84.605251
iter  60 value 84.599070
iter  70 value 83.585567
final  value 83.583918 
converged
Fitting Repeat 4 

# weights:  305
initial  value 105.306552 
iter  10 value 94.489339
iter  20 value 94.472502
iter  30 value 93.928502
iter  40 value 91.075983
iter  50 value 90.713411
iter  60 value 90.442485
iter  70 value 90.420920
iter  80 value 90.419740
iter  90 value 87.522130
iter 100 value 85.379352
final  value 85.379352 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 104.650951 
iter  10 value 94.488915
iter  20 value 94.257821
iter  30 value 94.255477
iter  40 value 94.248020
iter  50 value 94.245602
iter  60 value 94.245468
final  value 94.245438 
converged
Fitting Repeat 1 

# weights:  507
initial  value 112.718322 
iter  10 value 94.362711
iter  20 value 94.356266
iter  30 value 94.246313
iter  40 value 92.499883
iter  50 value 87.879191
iter  60 value 87.800826
iter  70 value 87.779642
iter  80 value 87.779370
final  value 87.779356 
converged
Fitting Repeat 2 

# weights:  507
initial  value 114.657820 
iter  10 value 94.097304
iter  20 value 94.056072
iter  30 value 93.980780
final  value 93.980687 
converged
Fitting Repeat 3 

# weights:  507
initial  value 96.565930 
iter  10 value 94.487299
iter  20 value 91.285550
iter  30 value 88.010779
iter  40 value 85.775498
iter  50 value 84.268407
iter  60 value 84.021221
iter  70 value 83.826123
iter  80 value 83.817195
iter  90 value 83.778716
iter 100 value 82.913234
final  value 82.913234 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 97.367578 
iter  10 value 94.253771
iter  20 value 94.248570
iter  30 value 94.247012
iter  40 value 93.857858
iter  50 value 92.932870
iter  60 value 92.837843
iter  70 value 92.816805
iter  80 value 92.815804
iter  90 value 92.815511
iter 100 value 92.815171
final  value 92.815171 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 111.040333 
iter  10 value 94.492009
iter  20 value 92.366836
iter  30 value 87.431157
final  value 87.431077 
converged
Fitting Repeat 1 

# weights:  305
initial  value 129.179114 
final  value 117.959265 
converged
Fitting Repeat 2 

# weights:  305
initial  value 131.218960 
iter  10 value 117.894613
iter  20 value 117.872703
iter  30 value 114.821102
iter  40 value 114.398609
iter  50 value 113.829068
iter  60 value 113.828364
iter  70 value 112.598069
iter  80 value 107.005510
iter  90 value 107.004617
iter 100 value 107.002570
final  value 107.002570 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 121.350551 
iter  10 value 117.895139
iter  20 value 116.986989
iter  30 value 106.857528
final  value 106.834117 
converged
Fitting Repeat 4 

# weights:  305
initial  value 151.931412 
iter  10 value 117.895164
iter  20 value 117.855061
iter  30 value 117.536075
iter  40 value 109.774377
iter  50 value 109.394898
final  value 109.382362 
converged
Fitting Repeat 5 

# weights:  305
initial  value 121.449549 
iter  10 value 117.895276
iter  20 value 117.886950
iter  30 value 115.219339
iter  40 value 113.635479
iter  50 value 113.512552
final  value 113.353361 
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 -- Fri Mar 14 21:44:54 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 
 48.793   1.730 121.168