##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.20-bioc/R/bin/R CMD check --install=check:HPiP.install-out.txt --library=/home/biocbuild/bbs-3.20-bioc/R/site-library --timings HPiP_1.12.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.20-bioc/meat/HPiP.Rcheck’
* using R version 4.4.2 (2024-10-31)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.2.0-23ubuntu4) 13.2.0
    GNU Fortran (Ubuntu 13.2.0-23ubuntu4) 13.2.0
* running under: Ubuntu 24.04.1 LTS
* using session charset: UTF-8
* 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 ...Warning: unable to access index for repository https://CRAN.R-project.org/src/contrib:
  cannot open URL 'https://CRAN.R-project.org/src/contrib/PACKAGES'
 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 loading without being on the library search path ... 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
Unknown package ‘ftrCOOL’ in Rd xrefs
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
var_imp       32.936  0.443  33.380
FSmethod      32.267  0.394  32.661
corr_plot     31.918  0.073  31.994
pred_ensembel 12.342  0.160  11.250
enrichfindP    0.512  0.027   8.563
* 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 re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

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

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.20-bioc/R/bin/R CMD INSTALL HPiP
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.20-bioc/R/site-library’
* installing *source* package ‘HPiP’ ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HPiP)

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

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 96.577515 
final  value 94.427577 
converged
Fitting Repeat 2 

# weights:  103
initial  value 110.256965 
final  value 94.484210 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.102398 
iter  10 value 94.467532
final  value 94.466823 
converged
Fitting Repeat 4 

# weights:  103
initial  value 106.632448 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.318471 
final  value 94.052435 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.947638 
iter  10 value 87.334204
iter  20 value 84.470718
iter  30 value 84.468070
final  value 84.468069 
converged
Fitting Repeat 2 

# weights:  305
initial  value 98.287095 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 98.114475 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.233339 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.875600 
iter  10 value 92.608651
final  value 92.608648 
converged
Fitting Repeat 1 

# weights:  507
initial  value 111.603477 
iter  10 value 94.484211
iter  10 value 94.484211
iter  10 value 94.484211
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 103.754328 
iter  10 value 93.468468
iter  20 value 92.315417
iter  30 value 91.483269
iter  40 value 90.482046
iter  50 value 90.464958
final  value 90.464816 
converged
Fitting Repeat 3 

# weights:  507
initial  value 120.828315 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 113.779018 
iter  10 value 94.466823
iter  10 value 94.466823
iter  10 value 94.466823
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  507
initial  value 107.146443 
iter  10 value 94.387430
iter  10 value 94.387430
iter  10 value 94.387430
final  value 94.387430 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.371197 
iter  10 value 94.488061
iter  20 value 94.457524
iter  30 value 85.537867
iter  40 value 83.918812
iter  50 value 83.783031
iter  60 value 83.281702
iter  70 value 83.194385
final  value 83.194297 
converged
Fitting Repeat 2 

# weights:  103
initial  value 110.248059 
iter  10 value 94.389088
iter  20 value 93.034660
iter  30 value 90.377131
iter  40 value 89.831353
iter  50 value 89.101393
iter  60 value 85.295764
iter  70 value 84.027467
iter  80 value 83.810740
iter  90 value 83.449246
iter 100 value 82.701139
final  value 82.701139 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 104.231769 
iter  10 value 94.429724
iter  20 value 89.458027
iter  30 value 85.907465
iter  40 value 85.785837
iter  50 value 85.050220
iter  60 value 82.108393
iter  70 value 81.444440
iter  80 value 80.901977
iter  90 value 80.793285
iter 100 value 80.791653
final  value 80.791653 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 101.294817 
iter  10 value 94.487126
iter  20 value 94.168326
iter  30 value 93.508195
iter  40 value 84.081898
iter  50 value 83.443867
iter  60 value 81.747215
iter  70 value 81.233187
iter  80 value 80.859340
final  value 80.791648 
converged
Fitting Repeat 5 

# weights:  103
initial  value 111.198484 
iter  10 value 94.486943
final  value 94.486432 
converged
Fitting Repeat 1 

# weights:  305
initial  value 121.986559 
iter  10 value 93.370585
iter  20 value 86.702067
iter  30 value 85.955034
iter  40 value 85.194274
iter  50 value 84.962585
iter  60 value 83.105309
iter  70 value 82.890074
iter  80 value 81.552766
iter  90 value 80.187709
iter 100 value 79.979086
final  value 79.979086 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 107.643197 
iter  10 value 94.477463
iter  20 value 93.235489
iter  30 value 89.573097
iter  40 value 87.291717
iter  50 value 84.081626
iter  60 value 83.228075
iter  70 value 82.950124
iter  80 value 82.536386
iter  90 value 81.547908
iter 100 value 80.474228
final  value 80.474228 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 113.912013 
iter  10 value 94.579556
iter  20 value 94.382024
iter  30 value 91.553433
iter  40 value 91.337488
iter  50 value 89.025565
iter  60 value 83.805324
iter  70 value 81.703058
iter  80 value 80.678819
iter  90 value 79.948247
iter 100 value 79.762288
final  value 79.762288 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 111.010926 
iter  10 value 94.601478
iter  20 value 94.490595
iter  30 value 94.445910
iter  40 value 91.321942
iter  50 value 84.600552
iter  60 value 82.314131
iter  70 value 81.752050
iter  80 value 81.081197
iter  90 value 80.218334
iter 100 value 79.900931
final  value 79.900931 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 106.176210 
iter  10 value 94.690427
iter  20 value 94.632504
iter  30 value 94.490234
iter  40 value 87.567695
iter  50 value 87.017522
iter  60 value 84.737113
iter  70 value 84.360012
iter  80 value 84.170439
iter  90 value 84.035263
iter 100 value 83.950544
final  value 83.950544 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 125.571273 
iter  10 value 93.484786
iter  20 value 85.969004
iter  30 value 83.547306
iter  40 value 81.990464
iter  50 value 80.048187
iter  60 value 79.712779
iter  70 value 79.631808
iter  80 value 79.536034
iter  90 value 79.363099
iter 100 value 79.276165
final  value 79.276165 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 108.954573 
iter  10 value 90.880092
iter  20 value 87.205356
iter  30 value 84.608545
iter  40 value 82.985399
iter  50 value 82.621952
iter  60 value 81.656044
iter  70 value 81.470207
iter  80 value 80.836333
iter  90 value 80.256393
iter 100 value 79.775732
final  value 79.775732 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 106.130666 
iter  10 value 94.614587
iter  20 value 91.710649
iter  30 value 85.653528
iter  40 value 84.434969
iter  50 value 83.089636
iter  60 value 81.919282
iter  70 value 81.581487
iter  80 value 81.175493
iter  90 value 80.029749
iter 100 value 79.707038
final  value 79.707038 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 122.259713 
iter  10 value 94.513657
iter  20 value 93.522439
iter  30 value 92.523037
iter  40 value 92.275733
iter  50 value 91.586039
iter  60 value 90.356933
iter  70 value 87.669678
iter  80 value 83.840374
iter  90 value 83.290678
iter 100 value 81.881558
final  value 81.881558 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 112.037365 
iter  10 value 94.395383
iter  20 value 85.568825
iter  30 value 85.198294
iter  40 value 84.787984
iter  50 value 83.348777
iter  60 value 82.956347
iter  70 value 82.933957
iter  80 value 82.175300
iter  90 value 81.230948
iter 100 value 80.430894
final  value 80.430894 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 109.261804 
iter  10 value 94.485804
iter  20 value 94.484226
iter  30 value 91.187364
iter  40 value 91.182999
iter  50 value 85.005473
iter  60 value 83.213903
iter  70 value 82.599169
iter  80 value 82.208626
iter  90 value 81.590640
iter 100 value 81.567196
final  value 81.567196 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 100.565659 
final  value 94.485879 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.723031 
final  value 94.485888 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.842052 
final  value 94.485718 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.171085 
final  value 94.485820 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.003348 
iter  10 value 94.488735
iter  20 value 94.484357
iter  30 value 88.953230
iter  40 value 86.390574
iter  50 value 86.142082
iter  60 value 85.810790
iter  70 value 85.808185
final  value 85.808151 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.221320 
iter  10 value 94.471410
iter  20 value 94.198213
iter  30 value 88.516068
iter  40 value 87.155859
iter  50 value 87.116713
iter  60 value 87.115092
iter  70 value 86.965481
iter  80 value 86.135121
iter  90 value 86.130519
iter 100 value 86.127980
final  value 86.127980 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 113.523058 
iter  10 value 94.472089
iter  20 value 94.423256
final  value 92.615006 
converged
Fitting Repeat 4 

# weights:  305
initial  value 98.461637 
iter  10 value 94.492643
iter  20 value 94.464525
iter  30 value 91.020020
iter  40 value 90.971913
iter  50 value 90.963534
iter  60 value 86.479770
iter  70 value 84.181788
iter  80 value 83.994044
iter  90 value 83.981377
iter 100 value 83.303870
final  value 83.303870 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 98.448633 
iter  10 value 94.471907
iter  20 value 93.162555
iter  30 value 91.183710
iter  40 value 91.181866
iter  50 value 90.797929
iter  60 value 81.395258
iter  70 value 80.503721
iter  80 value 80.256950
iter  90 value 79.680143
iter 100 value 79.494259
final  value 79.494259 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 102.944630 
iter  10 value 94.474749
iter  20 value 91.400459
iter  30 value 85.990470
iter  40 value 85.965708
final  value 85.965623 
converged
Fitting Repeat 2 

# weights:  507
initial  value 112.142790 
iter  10 value 94.439776
iter  20 value 94.436360
iter  30 value 86.295669
iter  40 value 83.633275
iter  50 value 83.506572
iter  60 value 83.373433
iter  70 value 83.348204
final  value 83.347988 
converged
Fitting Repeat 3 

# weights:  507
initial  value 104.550296 
iter  10 value 94.491347
iter  20 value 94.250746
iter  30 value 92.136533
iter  40 value 84.791749
iter  50 value 84.198636
iter  60 value 84.198294
iter  70 value 84.159717
iter  80 value 83.717658
iter  90 value 83.032263
iter 100 value 80.843484
final  value 80.843484 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 94.696460 
iter  10 value 92.617258
iter  20 value 92.609873
iter  30 value 90.748076
iter  40 value 84.527055
iter  50 value 82.665046
iter  60 value 82.336007
iter  70 value 79.597179
iter  80 value 79.467304
iter  90 value 79.298045
iter 100 value 79.214178
final  value 79.214178 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 95.683581 
iter  10 value 94.451001
iter  20 value 94.437265
final  value 94.428679 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.119975 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.128393 
final  value 94.448052 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.935091 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.981078 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.771040 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.281186 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.328758 
iter  10 value 84.675887
final  value 83.509340 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.360936 
iter  10 value 94.484211
iter  10 value 94.484211
iter  10 value 94.484211
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 109.968711 
iter  10 value 94.218811
final  value 93.864373 
converged
Fitting Repeat 5 

# weights:  305
initial  value 111.025298 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 129.037982 
iter  10 value 94.277902
final  value 94.229692 
converged
Fitting Repeat 2 

# weights:  507
initial  value 107.091093 
iter  10 value 94.112923
iter  20 value 94.070346
final  value 94.066446 
converged
Fitting Repeat 3 

# weights:  507
initial  value 96.981775 
iter  10 value 91.225177
iter  20 value 83.004963
iter  30 value 82.634060
iter  40 value 82.426323
final  value 82.425399 
converged
Fitting Repeat 4 

# weights:  507
initial  value 130.029089 
iter  10 value 94.029675
final  value 94.029451 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.184571 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.118647 
iter  10 value 94.486890
iter  20 value 94.292028
iter  30 value 93.970066
iter  40 value 93.961310
iter  50 value 93.961130
iter  60 value 93.957032
iter  70 value 92.307981
iter  80 value 84.034842
iter  90 value 83.590620
iter 100 value 83.506767
final  value 83.506767 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 101.386220 
iter  10 value 94.464240
iter  20 value 93.442309
iter  30 value 86.300635
iter  40 value 84.004929
iter  50 value 83.828357
iter  60 value 83.511175
iter  70 value 82.729130
iter  80 value 82.723568
final  value 82.723564 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.840055 
iter  10 value 94.486817
iter  20 value 94.053202
iter  30 value 86.725187
iter  40 value 84.954215
iter  50 value 84.435451
iter  60 value 81.951819
iter  70 value 81.249439
iter  80 value 81.006329
iter  90 value 80.958515
iter 100 value 80.893365
final  value 80.893365 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 96.455054 
iter  10 value 94.236567
iter  20 value 93.710050
iter  30 value 89.779848
iter  40 value 86.112053
iter  50 value 83.310397
iter  60 value 82.688091
iter  70 value 81.858847
iter  80 value 81.551876
iter  90 value 81.517525
iter 100 value 81.317617
final  value 81.317617 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 100.714912 
iter  10 value 94.486759
iter  20 value 94.353993
iter  30 value 87.840701
iter  40 value 85.898898
iter  50 value 83.594134
iter  60 value 82.818236
iter  70 value 82.026170
iter  80 value 81.516155
iter  90 value 80.866428
final  value 80.855876 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.835179 
iter  10 value 94.492996
iter  20 value 88.798346
iter  30 value 84.073254
iter  40 value 83.995094
iter  50 value 82.629247
iter  60 value 82.402065
iter  70 value 82.246338
iter  80 value 81.931092
iter  90 value 80.535725
iter 100 value 80.127006
final  value 80.127006 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 115.747674 
iter  10 value 94.491865
iter  20 value 94.457511
iter  30 value 93.726135
iter  40 value 86.532782
iter  50 value 85.630794
iter  60 value 84.681343
iter  70 value 84.075909
iter  80 value 83.936587
iter  90 value 83.897478
iter 100 value 83.798894
final  value 83.798894 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 102.790510 
iter  10 value 91.157993
iter  20 value 84.084321
iter  30 value 83.445158
iter  40 value 83.261838
iter  50 value 82.739806
iter  60 value 81.234376
iter  70 value 80.529566
iter  80 value 79.800294
iter  90 value 79.636231
iter 100 value 79.594485
final  value 79.594485 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 109.213559 
iter  10 value 94.423166
iter  20 value 93.822564
iter  30 value 90.689310
iter  40 value 86.360800
iter  50 value 85.753220
iter  60 value 84.678158
iter  70 value 84.330604
iter  80 value 83.951804
iter  90 value 82.813986
iter 100 value 81.624956
final  value 81.624956 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 112.502159 
iter  10 value 94.351530
iter  20 value 84.586628
iter  30 value 83.426899
iter  40 value 80.960533
iter  50 value 80.720783
iter  60 value 80.418438
iter  70 value 80.032089
iter  80 value 79.665724
iter  90 value 79.574975
iter 100 value 79.559284
final  value 79.559284 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 114.640875 
iter  10 value 95.193343
iter  20 value 94.350048
iter  30 value 86.196893
iter  40 value 85.212622
iter  50 value 84.599997
iter  60 value 83.729892
iter  70 value 83.120375
iter  80 value 81.128221
iter  90 value 80.784049
iter 100 value 80.165473
final  value 80.165473 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 120.182523 
iter  10 value 94.318956
iter  20 value 87.322503
iter  30 value 83.119782
iter  40 value 82.327621
iter  50 value 81.237505
iter  60 value 80.621817
iter  70 value 80.496236
iter  80 value 80.071591
iter  90 value 79.875145
iter 100 value 79.831088
final  value 79.831088 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 105.197997 
iter  10 value 97.453094
iter  20 value 92.031030
iter  30 value 91.543456
iter  40 value 90.712130
iter  50 value 90.478003
iter  60 value 88.847280
iter  70 value 86.410937
iter  80 value 85.494996
iter  90 value 83.073544
iter 100 value 82.124015
final  value 82.124015 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 114.106920 
iter  10 value 94.749000
iter  20 value 93.998822
iter  30 value 90.979328
iter  40 value 88.830166
iter  50 value 87.366231
iter  60 value 86.882146
iter  70 value 85.524478
iter  80 value 82.814813
iter  90 value 82.159989
iter 100 value 81.412711
final  value 81.412711 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 105.754613 
iter  10 value 95.277716
iter  20 value 94.149186
iter  30 value 84.038201
iter  40 value 83.728592
iter  50 value 83.011360
iter  60 value 82.277113
iter  70 value 80.671338
iter  80 value 79.501262
iter  90 value 79.303562
iter 100 value 79.215366
final  value 79.215366 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.463378 
final  value 94.485899 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.706179 
final  value 94.486011 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.184733 
final  value 94.486122 
converged
Fitting Repeat 4 

# weights:  103
initial  value 107.890399 
iter  10 value 92.938948
iter  20 value 92.768667
final  value 92.766877 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.199110 
iter  10 value 94.486038
iter  20 value 94.484228
final  value 94.484213 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.640601 
iter  10 value 94.280305
iter  20 value 94.275641
final  value 94.275505 
converged
Fitting Repeat 2 

# weights:  305
initial  value 108.677297 
iter  10 value 94.489947
iter  20 value 94.452173
iter  30 value 92.914856
iter  40 value 91.795301
iter  50 value 91.702844
final  value 91.702601 
converged
Fitting Repeat 3 

# weights:  305
initial  value 103.887885 
iter  10 value 94.489235
iter  20 value 93.868053
iter  30 value 91.965834
iter  40 value 91.941718
iter  50 value 82.870290
final  value 82.762922 
converged
Fitting Repeat 4 

# weights:  305
initial  value 110.069249 
iter  10 value 94.327520
iter  20 value 94.319918
iter  30 value 93.787866
iter  40 value 93.783945
iter  50 value 86.192321
iter  60 value 85.678841
iter  70 value 85.649780
iter  80 value 84.436242
iter  90 value 83.708684
iter 100 value 83.690636
final  value 83.690636 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 120.752553 
iter  10 value 93.946068
iter  20 value 93.928761
iter  30 value 93.867621
iter  40 value 93.864711
iter  40 value 93.864710
iter  40 value 93.864710
final  value 93.864710 
converged
Fitting Repeat 1 

# weights:  507
initial  value 96.938941 
iter  10 value 94.491489
iter  20 value 94.472132
iter  30 value 84.006954
iter  40 value 83.431020
iter  50 value 82.426897
iter  60 value 81.952064
iter  70 value 81.947029
iter  80 value 81.275290
iter  90 value 79.800619
iter 100 value 79.659570
final  value 79.659570 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 100.942271 
iter  10 value 94.456412
iter  20 value 85.125747
iter  30 value 82.838874
iter  40 value 81.791062
iter  50 value 81.780279
iter  60 value 81.596165
iter  70 value 81.595171
iter  80 value 81.592809
final  value 81.591085 
converged
Fitting Repeat 3 

# weights:  507
initial  value 124.821925 
iter  10 value 94.283952
iter  20 value 94.269901
iter  30 value 89.208832
iter  40 value 83.510365
iter  50 value 82.445997
final  value 82.443395 
converged
Fitting Repeat 4 

# weights:  507
initial  value 105.876598 
iter  10 value 94.492028
iter  20 value 94.470096
iter  30 value 83.171494
iter  40 value 82.362538
iter  50 value 82.338824
iter  60 value 82.248593
iter  70 value 82.132855
iter  80 value 81.893293
iter  90 value 80.217713
iter 100 value 78.145916
final  value 78.145916 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 100.960225 
iter  10 value 94.283698
iter  20 value 93.862437
iter  30 value 84.305348
iter  40 value 83.220054
iter  50 value 83.162979
iter  60 value 82.783161
iter  70 value 82.448658
iter  80 value 82.427022
final  value 82.426973 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.314400 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.305938 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.230115 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.165387 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.433996 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.205344 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 100.549334 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 125.352763 
final  value 94.035088 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.111116 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.565462 
final  value 94.032967 
converged
Fitting Repeat 1 

# weights:  507
initial  value 111.375627 
iter  10 value 94.034835
final  value 94.032967 
converged
Fitting Repeat 2 

# weights:  507
initial  value 95.526525 
iter  10 value 93.330419
final  value 93.330380 
converged
Fitting Repeat 3 

# weights:  507
initial  value 111.486477 
final  value 94.035088 
converged
Fitting Repeat 4 

# weights:  507
initial  value 123.947904 
final  value 93.851170 
converged
Fitting Repeat 5 

# weights:  507
initial  value 105.820002 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.313976 
iter  10 value 94.048286
iter  20 value 91.885962
iter  30 value 87.789403
iter  40 value 85.809005
iter  50 value 85.226929
iter  60 value 84.936481
iter  60 value 84.936480
final  value 84.936480 
converged
Fitting Repeat 2 

# weights:  103
initial  value 106.575308 
iter  10 value 94.051257
iter  20 value 92.944727
iter  30 value 85.762510
iter  40 value 84.694037
iter  50 value 84.602759
iter  60 value 84.553510
iter  70 value 83.764688
iter  80 value 82.993072
iter  90 value 82.707397
iter 100 value 82.503720
final  value 82.503720 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 96.118030 
iter  10 value 93.871831
iter  20 value 86.253628
iter  30 value 84.754567
iter  40 value 84.586942
iter  50 value 84.543435
iter  60 value 84.477855
iter  70 value 82.627876
iter  80 value 82.477954
iter  90 value 82.477200
iter 100 value 82.469568
final  value 82.469568 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 98.197689 
iter  10 value 94.056857
iter  20 value 91.811176
iter  30 value 87.195423
iter  40 value 86.911433
iter  50 value 85.363453
iter  60 value 84.961501
iter  70 value 84.800918
iter  80 value 84.781749
final  value 84.778831 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.052860 
iter  10 value 94.056734
iter  20 value 89.577373
iter  30 value 85.929948
iter  40 value 84.957098
iter  50 value 84.543955
iter  60 value 83.152540
iter  70 value 82.748150
iter  80 value 82.482846
iter  90 value 82.478566
iter 100 value 82.473763
final  value 82.473763 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 115.745825 
iter  10 value 94.226608
iter  20 value 94.024554
iter  30 value 91.524547
iter  40 value 89.964916
iter  50 value 89.293448
iter  60 value 84.769410
iter  70 value 83.564671
iter  80 value 82.388752
iter  90 value 81.893495
iter 100 value 81.520423
final  value 81.520423 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.258864 
iter  10 value 90.500492
iter  20 value 89.021549
iter  30 value 85.741752
iter  40 value 84.698683
iter  50 value 83.138146
iter  60 value 81.656709
iter  70 value 81.413106
iter  80 value 81.207113
iter  90 value 81.179978
iter 100 value 81.179367
final  value 81.179367 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 110.610798 
iter  10 value 94.050715
iter  20 value 92.926618
iter  30 value 88.063626
iter  40 value 86.588286
iter  50 value 85.896827
iter  60 value 85.012654
iter  70 value 84.182316
iter  80 value 83.239790
iter  90 value 82.878771
iter 100 value 82.425374
final  value 82.425374 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 105.605246 
iter  10 value 93.313012
iter  20 value 88.384012
iter  30 value 85.998245
iter  40 value 84.673689
iter  50 value 84.434315
iter  60 value 84.234307
iter  70 value 83.017451
iter  80 value 81.193425
iter  90 value 80.877276
iter 100 value 80.676372
final  value 80.676372 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 113.604415 
iter  10 value 94.074448
iter  20 value 90.018298
iter  30 value 88.440406
iter  40 value 86.781584
iter  50 value 85.481605
iter  60 value 85.176854
iter  70 value 85.014509
iter  80 value 84.679320
iter  90 value 84.535496
iter 100 value 84.443786
final  value 84.443786 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 112.287455 
iter  10 value 93.987937
iter  20 value 92.163713
iter  30 value 87.259040
iter  40 value 84.986359
iter  50 value 84.059382
iter  60 value 82.774691
iter  70 value 81.667996
iter  80 value 81.442122
iter  90 value 81.252991
iter 100 value 80.636139
final  value 80.636139 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 113.455469 
iter  10 value 93.278268
iter  20 value 89.242260
iter  30 value 85.921217
iter  40 value 84.490564
iter  50 value 83.231998
iter  60 value 81.402848
iter  70 value 80.945466
iter  80 value 80.772501
iter  90 value 80.725389
iter 100 value 80.499321
final  value 80.499321 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 115.807653 
iter  10 value 94.331214
iter  20 value 93.971424
iter  30 value 87.138058
iter  40 value 86.681850
iter  50 value 86.323365
iter  60 value 85.643956
iter  70 value 85.047929
iter  80 value 83.844501
iter  90 value 82.872410
iter 100 value 81.763283
final  value 81.763283 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 104.835374 
iter  10 value 94.240613
iter  20 value 92.763897
iter  30 value 92.313584
iter  40 value 92.237001
iter  50 value 90.406480
iter  60 value 83.806965
iter  70 value 82.599297
iter  80 value 82.115750
iter  90 value 82.022565
iter 100 value 81.837486
final  value 81.837486 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 111.811707 
iter  10 value 94.096176
iter  20 value 88.800172
iter  30 value 86.374401
iter  40 value 86.132865
iter  50 value 85.999211
iter  60 value 85.712014
iter  70 value 85.583656
iter  80 value 85.306424
iter  90 value 83.267961
iter 100 value 82.265563
final  value 82.265563 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 111.259504 
iter  10 value 94.054774
iter  20 value 94.052938
iter  30 value 93.992340
iter  40 value 92.293306
iter  50 value 92.278386
iter  60 value 92.171552
iter  70 value 92.167794
iter  80 value 89.498674
iter  90 value 87.427898
iter 100 value 86.927704
final  value 86.927704 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 94.279788 
final  value 94.054506 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.703926 
final  value 94.059875 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.618285 
final  value 94.054169 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.397364 
final  value 94.054803 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.152838 
iter  10 value 87.563933
iter  20 value 86.930445
iter  30 value 86.824998
iter  40 value 86.824448
iter  50 value 86.253355
iter  60 value 85.556398
iter  70 value 83.988192
iter  80 value 83.968248
iter  90 value 83.968176
final  value 83.967085 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.890014 
iter  10 value 93.864930
iter  20 value 92.216882
iter  30 value 92.214626
iter  40 value 92.212585
iter  40 value 92.212585
iter  40 value 92.212585
final  value 92.212585 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.463965 
iter  10 value 94.051619
iter  20 value 91.261742
final  value 89.802918 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.406916 
iter  10 value 94.038024
iter  20 value 94.034093
iter  30 value 94.029776
iter  40 value 91.265101
iter  50 value 86.922175
iter  60 value 86.213338
final  value 86.210255 
converged
Fitting Repeat 5 

# weights:  305
initial  value 112.036334 
iter  10 value 94.057436
iter  20 value 94.052917
iter  30 value 88.793443
iter  40 value 84.257917
final  value 84.254735 
converged
Fitting Repeat 1 

# weights:  507
initial  value 99.936958 
iter  10 value 93.875721
iter  20 value 93.556502
iter  30 value 88.397041
iter  40 value 85.731099
iter  50 value 85.497702
iter  60 value 83.624442
iter  70 value 83.393808
iter  80 value 83.291781
iter  90 value 82.569976
iter 100 value 81.880818
final  value 81.880818 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 98.157766 
iter  10 value 94.060601
iter  20 value 89.673212
iter  30 value 87.590977
iter  40 value 87.560809
final  value 87.560743 
converged
Fitting Repeat 3 

# weights:  507
initial  value 97.451588 
iter  10 value 94.060432
iter  20 value 92.067585
iter  30 value 90.750955
iter  40 value 90.727307
iter  50 value 90.727165
iter  60 value 90.727093
iter  70 value 90.726471
final  value 90.726464 
converged
Fitting Repeat 4 

# weights:  507
initial  value 94.389893 
iter  10 value 94.050720
iter  20 value 94.044466
final  value 94.043748 
converged
Fitting Repeat 5 

# weights:  507
initial  value 98.237915 
iter  10 value 94.060972
iter  20 value 94.032949
iter  30 value 92.520278
iter  40 value 92.480947
iter  50 value 90.559329
iter  60 value 88.399677
iter  70 value 88.095190
iter  80 value 85.973116
iter  90 value 85.876405
iter 100 value 85.868308
final  value 85.868308 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.951853 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.266536 
final  value 93.836066 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.490017 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.770475 
final  value 93.604520 
converged
Fitting Repeat 5 

# weights:  103
initial  value 105.203437 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.300676 
final  value 93.836066 
converged
Fitting Repeat 2 

# weights:  305
initial  value 115.929156 
final  value 94.025289 
converged
Fitting Repeat 3 

# weights:  305
initial  value 96.447895 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 120.858532 
final  value 93.836066 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.511315 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 94.585009 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 99.944757 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 127.289784 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 127.340553 
final  value 93.836066 
converged
Fitting Repeat 5 

# weights:  507
initial  value 103.759569 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.676098 
iter  10 value 94.054927
iter  20 value 87.665496
iter  30 value 86.894173
iter  40 value 86.776143
iter  50 value 86.382230
iter  60 value 86.343879
iter  60 value 86.343878
iter  60 value 86.343878
final  value 86.343878 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.847519 
iter  10 value 94.049606
iter  20 value 93.892616
iter  30 value 93.890068
iter  40 value 92.264329
iter  50 value 89.431417
iter  60 value 87.700823
iter  70 value 87.161511
iter  80 value 86.931766
iter  90 value 86.527866
iter 100 value 86.268178
final  value 86.268178 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 97.599277 
iter  10 value 94.067509
iter  20 value 88.189350
iter  30 value 87.381236
iter  40 value 87.030948
iter  50 value 86.438840
iter  60 value 86.382664
iter  70 value 86.352311
final  value 86.343878 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.921534 
iter  10 value 94.004397
iter  20 value 91.734351
iter  30 value 90.241688
iter  40 value 88.851313
iter  50 value 86.342072
iter  60 value 85.860620
iter  70 value 85.741897
iter  80 value 85.394995
iter  90 value 84.919238
iter 100 value 83.585982
final  value 83.585982 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 97.868087 
iter  10 value 93.810978
iter  20 value 88.030157
iter  30 value 87.160225
iter  40 value 86.608624
iter  50 value 86.150414
iter  60 value 85.730869
iter  70 value 85.685450
final  value 85.685357 
converged
Fitting Repeat 1 

# weights:  305
initial  value 128.267620 
iter  10 value 93.966009
iter  20 value 89.443101
iter  30 value 88.489180
iter  40 value 87.207724
iter  50 value 86.898843
iter  60 value 82.990105
iter  70 value 82.591667
iter  80 value 82.414712
iter  90 value 82.078189
iter 100 value 81.795385
final  value 81.795385 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 103.710597 
iter  10 value 94.055508
iter  20 value 93.238928
iter  30 value 88.444800
iter  40 value 87.510772
iter  50 value 86.005665
iter  60 value 85.301381
iter  70 value 83.419680
iter  80 value 82.707198
iter  90 value 82.512342
iter 100 value 82.297299
final  value 82.297299 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 115.369251 
iter  10 value 94.094225
iter  20 value 93.654408
iter  30 value 90.977178
iter  40 value 87.313773
iter  50 value 86.622577
iter  60 value 85.799653
iter  70 value 84.569157
iter  80 value 83.227681
iter  90 value 82.925786
iter 100 value 82.894462
final  value 82.894462 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 104.463124 
iter  10 value 93.946794
iter  20 value 89.358142
iter  30 value 88.294650
iter  40 value 88.016243
iter  50 value 87.061236
iter  60 value 85.639353
iter  70 value 85.408897
iter  80 value 83.493663
iter  90 value 82.793325
iter 100 value 82.505742
final  value 82.505742 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.487758 
iter  10 value 92.799069
iter  20 value 89.358648
iter  30 value 87.024261
iter  40 value 86.523337
iter  50 value 86.428850
iter  60 value 85.475307
iter  70 value 83.259082
iter  80 value 82.767317
iter  90 value 82.636768
iter 100 value 82.301828
final  value 82.301828 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 123.888439 
iter  10 value 94.396149
iter  20 value 93.438452
iter  30 value 87.597833
iter  40 value 86.893141
iter  50 value 86.550032
iter  60 value 86.246349
iter  70 value 85.808825
iter  80 value 84.744844
iter  90 value 84.491085
iter 100 value 83.477646
final  value 83.477646 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 102.634340 
iter  10 value 93.819512
iter  20 value 88.116500
iter  30 value 86.887878
iter  40 value 86.622562
iter  50 value 86.376497
iter  60 value 86.176324
iter  70 value 85.430077
iter  80 value 84.158850
iter  90 value 83.636113
iter 100 value 83.148348
final  value 83.148348 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.223879 
iter  10 value 93.775627
iter  20 value 90.818283
iter  30 value 90.190435
iter  40 value 88.428769
iter  50 value 84.651844
iter  60 value 83.130264
iter  70 value 82.586520
iter  80 value 82.169305
iter  90 value 82.105209
iter 100 value 82.002578
final  value 82.002578 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 107.206744 
iter  10 value 94.381284
iter  20 value 94.149406
iter  30 value 93.984286
iter  40 value 93.519096
iter  50 value 91.476488
iter  60 value 87.349023
iter  70 value 86.742461
iter  80 value 86.336373
iter  90 value 85.691458
iter 100 value 85.137757
final  value 85.137757 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 126.542436 
iter  10 value 94.033867
iter  20 value 93.664418
iter  30 value 88.604570
iter  40 value 87.061253
iter  50 value 85.752319
iter  60 value 83.439809
iter  70 value 83.062907
iter  80 value 82.980932
iter  90 value 82.829364
iter 100 value 82.640366
final  value 82.640366 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 104.558689 
final  value 94.054522 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.033335 
final  value 94.054736 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.817036 
final  value 94.054631 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.171464 
final  value 94.054633 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.568951 
iter  10 value 93.837849
iter  20 value 93.823605
iter  30 value 93.556897
iter  40 value 93.503168
iter  50 value 89.430486
iter  60 value 86.287720
iter  70 value 85.577462
iter  80 value 83.972409
iter  90 value 83.960954
final  value 83.960925 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.399977 
iter  10 value 93.805870
iter  20 value 93.803688
iter  30 value 93.724656
iter  40 value 93.506620
iter  50 value 93.504002
final  value 93.503976 
converged
Fitting Repeat 2 

# weights:  305
initial  value 94.184243 
iter  10 value 94.057992
iter  20 value 93.961652
iter  30 value 86.955466
iter  40 value 86.914186
iter  50 value 86.630839
iter  60 value 86.134647
final  value 86.134642 
converged
Fitting Repeat 3 

# weights:  305
initial  value 96.351478 
iter  10 value 93.841372
iter  20 value 93.836374
final  value 93.836242 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.676813 
iter  10 value 94.057715
iter  20 value 94.038064
iter  30 value 93.706076
final  value 93.705042 
converged
Fitting Repeat 5 

# weights:  305
initial  value 110.553747 
iter  10 value 94.058236
iter  20 value 94.020146
iter  30 value 88.439581
iter  40 value 86.710737
iter  50 value 86.669795
final  value 86.669760 
converged
Fitting Repeat 1 

# weights:  507
initial  value 99.341057 
iter  10 value 94.060634
iter  20 value 94.053516
iter  30 value 93.051128
iter  40 value 90.054794
iter  50 value 88.119308
iter  60 value 87.917567
iter  70 value 87.826802
final  value 87.823498 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.782939 
iter  10 value 93.810375
iter  20 value 93.806220
iter  30 value 93.662095
iter  40 value 87.240996
final  value 87.056904 
converged
Fitting Repeat 3 

# weights:  507
initial  value 117.550912 
iter  10 value 94.061602
iter  20 value 93.902180
iter  30 value 87.485815
iter  40 value 85.700908
iter  50 value 85.564565
iter  60 value 85.432739
iter  70 value 85.415210
iter  80 value 85.413723
iter  90 value 85.412971
final  value 85.412915 
converged
Fitting Repeat 4 

# weights:  507
initial  value 103.284783 
iter  10 value 93.715027
iter  20 value 93.713762
iter  30 value 93.642946
iter  40 value 93.642082
iter  50 value 88.578981
iter  60 value 87.863575
iter  70 value 87.862934
iter  80 value 87.862567
iter  80 value 87.862566
iter  80 value 87.862566
final  value 87.862566 
converged
Fitting Repeat 5 

# weights:  507
initial  value 107.000655 
iter  10 value 93.844333
iter  20 value 93.837417
iter  30 value 93.837249
iter  40 value 93.829958
iter  50 value 93.486395
iter  60 value 89.342646
iter  70 value 88.861337
iter  80 value 87.268048
final  value 87.267298 
converged
Fitting Repeat 1 

# weights:  103
initial  value 94.684443 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.699022 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 110.323049 
iter  10 value 93.943272
final  value 93.943263 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.935141 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.371333 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.138882 
final  value 94.088889 
converged
Fitting Repeat 2 

# weights:  305
initial  value 99.981261 
final  value 94.275362 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.654038 
final  value 94.088889 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.340704 
final  value 94.275362 
converged
Fitting Repeat 5 

# weights:  305
initial  value 109.125885 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 120.209097 
iter  10 value 94.470585
iter  20 value 91.006929
iter  30 value 86.139246
iter  40 value 86.130630
iter  50 value 86.050811
iter  60 value 85.011540
iter  70 value 84.717861
iter  80 value 84.694109
iter  90 value 84.693094
final  value 84.693015 
converged
Fitting Repeat 2 

# weights:  507
initial  value 101.366844 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 106.425459 
iter  10 value 94.377544
final  value 94.275362 
converged
Fitting Repeat 4 

# weights:  507
initial  value 103.199519 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 110.377161 
iter  10 value 93.841188
iter  20 value 93.558308
final  value 93.558233 
converged
Fitting Repeat 1 

# weights:  103
initial  value 105.305654 
iter  10 value 97.816142
iter  20 value 94.488440
iter  30 value 94.236938
iter  40 value 94.014058
iter  50 value 93.785805
iter  60 value 93.475610
iter  70 value 93.468571
iter  80 value 93.071312
iter  90 value 88.143896
iter 100 value 82.330509
final  value 82.330509 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 97.352903 
iter  10 value 94.474467
iter  20 value 92.430946
iter  30 value 88.482841
iter  40 value 84.148212
iter  50 value 83.546717
iter  60 value 82.803980
iter  70 value 82.740419
iter  70 value 82.740419
iter  70 value 82.740419
final  value 82.740419 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.346380 
iter  10 value 94.483689
iter  20 value 88.628891
iter  30 value 85.190771
iter  40 value 83.713386
iter  50 value 83.682787
iter  60 value 83.226176
iter  70 value 83.178622
final  value 83.178330 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.442627 
iter  10 value 94.174951
iter  20 value 94.021925
iter  30 value 93.925859
iter  40 value 84.348940
iter  50 value 83.337542
iter  60 value 83.237078
iter  70 value 82.805040
iter  80 value 82.740459
final  value 82.740419 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.453928 
iter  10 value 94.487152
iter  20 value 87.234133
iter  30 value 84.633083
iter  40 value 84.029166
iter  50 value 83.352335
iter  60 value 83.186095
iter  70 value 83.178330
iter  70 value 83.178330
iter  70 value 83.178330
final  value 83.178330 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.877274 
iter  10 value 94.797480
iter  20 value 86.799263
iter  30 value 83.879774
iter  40 value 81.398511
iter  50 value 80.545984
iter  60 value 80.475519
iter  70 value 79.930431
iter  80 value 79.638053
iter  90 value 79.519545
iter 100 value 79.080943
final  value 79.080943 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 116.044880 
iter  10 value 94.317442
iter  20 value 89.707074
iter  30 value 84.333270
iter  40 value 81.764212
iter  50 value 81.250127
iter  60 value 81.015865
iter  70 value 80.810892
iter  80 value 80.522074
iter  90 value 80.317373
iter 100 value 80.313438
final  value 80.313438 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 119.651655 
iter  10 value 94.267838
iter  20 value 89.760055
iter  30 value 84.405408
iter  40 value 83.224993
iter  50 value 82.221959
iter  60 value 81.344357
iter  70 value 81.151403
iter  80 value 80.336909
iter  90 value 79.999299
iter 100 value 79.549269
final  value 79.549269 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 106.581264 
iter  10 value 94.601848
iter  20 value 91.182348
iter  30 value 86.161525
iter  40 value 85.749480
iter  50 value 83.613822
iter  60 value 82.292252
iter  70 value 81.788869
iter  80 value 81.267421
iter  90 value 81.190281
iter 100 value 81.102239
final  value 81.102239 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 100.930085 
iter  10 value 94.517111
iter  20 value 86.837415
iter  30 value 86.208378
iter  40 value 85.969755
iter  50 value 85.153896
iter  60 value 80.947922
iter  70 value 79.038207
iter  80 value 78.679119
iter  90 value 78.541556
iter 100 value 78.404681
final  value 78.404681 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 105.296619 
iter  10 value 94.276510
iter  20 value 87.925999
iter  30 value 84.306381
iter  40 value 83.757175
iter  50 value 82.951859
iter  60 value 82.804457
iter  70 value 80.683299
iter  80 value 80.056451
iter  90 value 79.966321
iter 100 value 79.780997
final  value 79.780997 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 106.410206 
iter  10 value 94.461265
iter  20 value 93.165049
iter  30 value 87.220980
iter  40 value 84.316584
iter  50 value 80.929822
iter  60 value 79.809643
iter  70 value 79.510314
iter  80 value 78.885527
iter  90 value 78.631099
iter 100 value 78.547662
final  value 78.547662 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 109.205622 
iter  10 value 94.577751
iter  20 value 91.138033
iter  30 value 84.421182
iter  40 value 81.194339
iter  50 value 80.119058
iter  60 value 79.005699
iter  70 value 78.942535
iter  80 value 78.937921
iter  90 value 78.906026
iter 100 value 78.725710
final  value 78.725710 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 118.685605 
iter  10 value 96.732194
iter  20 value 95.382036
iter  30 value 91.164909
iter  40 value 87.447815
iter  50 value 86.984757
iter  60 value 86.514731
iter  70 value 86.272444
iter  80 value 84.006632
iter  90 value 82.090939
iter 100 value 81.409788
final  value 81.409788 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 123.032214 
iter  10 value 94.457703
iter  20 value 88.383957
iter  30 value 87.430447
iter  40 value 83.846073
iter  50 value 81.271443
iter  60 value 79.789451
iter  70 value 78.924703
iter  80 value 78.691930
iter  90 value 78.661942
iter 100 value 78.651469
final  value 78.651469 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.926133 
final  value 94.486025 
converged
Fitting Repeat 2 

# weights:  103
initial  value 111.050765 
final  value 94.485697 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.648915 
iter  10 value 94.277221
iter  20 value 94.275791
iter  30 value 93.881343
iter  40 value 93.722511
iter  50 value 93.378430
iter  60 value 93.368897
iter  60 value 93.368897
final  value 93.368897 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.246730 
final  value 94.486068 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.167544 
final  value 94.485829 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.536240 
iter  10 value 94.488967
iter  20 value 94.481426
iter  30 value 91.405227
iter  40 value 89.821494
iter  50 value 88.871761
iter  60 value 88.866009
iter  70 value 88.865887
iter  80 value 88.865730
iter  80 value 88.865729
final  value 88.865729 
converged
Fitting Repeat 2 

# weights:  305
initial  value 113.799932 
iter  10 value 92.725841
iter  20 value 89.335695
final  value 89.282411 
converged
Fitting Repeat 3 

# weights:  305
initial  value 117.600080 
iter  10 value 94.280855
iter  20 value 94.276398
iter  30 value 94.271608
iter  40 value 90.127171
iter  50 value 85.172730
iter  60 value 80.622989
iter  70 value 80.407808
final  value 80.401027 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.414029 
iter  10 value 94.488222
iter  20 value 94.472092
iter  30 value 93.914744
iter  40 value 93.912950
iter  50 value 91.122222
iter  60 value 90.009616
final  value 90.009615 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.300521 
iter  10 value 93.940076
iter  20 value 93.938705
iter  30 value 93.938167
iter  40 value 93.936078
iter  50 value 93.883647
iter  60 value 93.481505
iter  70 value 89.537126
iter  80 value 87.864717
iter  90 value 86.489640
iter 100 value 86.434906
final  value 86.434906 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 108.707901 
iter  10 value 94.492193
iter  20 value 94.483054
iter  30 value 93.909756
iter  40 value 92.661436
iter  50 value 86.947354
iter  60 value 81.179984
iter  70 value 79.685942
iter  80 value 78.909936
final  value 78.905862 
converged
Fitting Repeat 2 

# weights:  507
initial  value 98.985501 
iter  10 value 94.493296
iter  20 value 94.257030
iter  30 value 83.040337
final  value 82.998487 
converged
Fitting Repeat 3 

# weights:  507
initial  value 106.117732 
iter  10 value 94.097363
iter  20 value 88.028830
iter  30 value 83.383696
iter  40 value 82.841942
iter  50 value 82.824883
final  value 82.824107 
converged
Fitting Repeat 4 

# weights:  507
initial  value 104.648718 
iter  10 value 90.773295
iter  20 value 90.157733
iter  30 value 89.748636
iter  40 value 89.231192
iter  50 value 89.225679
iter  60 value 87.302513
iter  70 value 86.302803
iter  80 value 86.295764
final  value 86.295733 
converged
Fitting Repeat 5 

# weights:  507
initial  value 124.044103 
iter  10 value 94.236615
iter  20 value 93.202508
iter  30 value 93.022088
iter  40 value 92.897838
iter  50 value 92.575636
iter  60 value 92.570947
iter  70 value 92.570173
iter  80 value 92.568679
iter  90 value 92.567369
iter 100 value 90.429830
final  value 90.429830 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 125.902053 
iter  10 value 117.763831
iter  20 value 117.615987
iter  30 value 113.858755
iter  40 value 113.773582
final  value 113.773568 
converged
Fitting Repeat 2 

# weights:  305
initial  value 129.972029 
iter  10 value 117.894846
iter  20 value 117.879009
iter  30 value 108.822025
iter  40 value 107.006816
iter  50 value 107.006705
final  value 107.006652 
converged
Fitting Repeat 3 

# weights:  305
initial  value 132.262071 
iter  10 value 117.895349
iter  20 value 117.758861
iter  30 value 117.316438
iter  40 value 108.015250
iter  50 value 106.030920
iter  60 value 105.925149
final  value 105.924254 
converged
Fitting Repeat 4 

# weights:  305
initial  value 119.026495 
iter  10 value 117.763339
iter  20 value 117.550532
iter  30 value 113.352834
iter  40 value 106.872525
iter  50 value 102.614922
iter  60 value 101.709834
iter  70 value 101.527257
iter  80 value 100.960169
iter  90 value 99.939789
iter 100 value 99.778181
final  value 99.778181 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 128.563674 
iter  10 value 117.774934
iter  20 value 117.499517
iter  30 value 105.614823
iter  40 value 102.544102
iter  50 value 101.250881
iter  60 value 100.756768
iter  70 value 100.563361
iter  80 value 100.442847
iter  90 value 100.375299
iter 100 value 100.373438
final  value 100.373438 
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 -- Fri Nov 29 01:26:46 2024 
*********************************************** 
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
HPiP RUnit Tests - 7 test functions, 0 errors, 0 failures
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: `repeats` has no meaning for this resampling method. 
2: executing %dopar% sequentially: no parallel backend registered 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
 36.995   1.203  44.232